From 09494ea545738538f9db2dceeffe10d421060ee5 Mon Sep 17 00:00:00 2001 From: Vincent Quenneville-Belair Date: Thu, 16 Apr 2020 15:01:14 -0400 Subject: [PATCH 01/73] set version. --- .circleci/config.yml | 4 ++-- .circleci/config.yml.in | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index b4fd761c00..b62bb37c12 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -15,11 +15,11 @@ binary_common: &binary_common build_version: description: "version number of release binary; by default, build a nightly" type: string - default: "" + default: "0.5.0" pytorch_version: description: "PyTorch version to build against; by default, use a nightly" type: string - default: "" + default: "1.5.0" # Don't edit these python_version: description: "Python version to build against (e.g., 3.8)" diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index f5f985c982..fb3316969a 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -15,11 +15,11 @@ binary_common: &binary_common build_version: description: "version number of release binary; by default, build a nightly" type: string - default: "" + default: "0.5.0" pytorch_version: description: "PyTorch version to build against; by default, use a nightly" type: string - default: "" + default: "1.5.0" # Don't edit these python_version: description: "Python version to build against (e.g., 3.8)" From 4f76758408c346bf3f4b3f1ac606f2b75753cd7c Mon Sep 17 00:00:00 2001 From: cpuhrsch Date: Tue, 28 Apr 2020 16:58:21 -0400 Subject: [PATCH 02/73] Re-sync with internal repository (#592) --- .circleci/config.yml | 597 ------------------ .circleci/config.yml.in | 263 -------- .circleci/docker/Dockerfile | 36 -- .circleci/regenerate.py | 124 ---- .circleci/test/test_sort_yaml.py | 14 - .clang-format | 88 --- .clang-tidy | 33 - .flake8 | 4 - .github/ISSUE_TEMPLATE/bug-report.md | 53 -- .github/ISSUE_TEMPLATE/documentation.md | 9 - .github/ISSUE_TEMPLATE/feature-request.md | 24 - .../ISSUE_TEMPLATE/questions-help-support.md | 13 - .gitignore | 123 ---- .python3 | 0 .travis.yml | 36 -- 15 files changed, 1417 deletions(-) delete mode 100644 .circleci/config.yml delete mode 100644 .circleci/config.yml.in delete mode 100644 .circleci/docker/Dockerfile delete mode 100755 .circleci/regenerate.py delete mode 100755 .circleci/test/test_sort_yaml.py delete mode 100644 .clang-format delete mode 100644 .clang-tidy delete mode 100644 .flake8 delete mode 100644 .github/ISSUE_TEMPLATE/bug-report.md delete mode 100644 .github/ISSUE_TEMPLATE/documentation.md delete mode 100644 .github/ISSUE_TEMPLATE/feature-request.md delete mode 100644 .github/ISSUE_TEMPLATE/questions-help-support.md delete mode 100644 .gitignore create mode 100644 .python3 delete mode 100644 .travis.yml diff --git a/.circleci/config.yml b/.circleci/config.yml deleted file mode 100644 index b62bb37c12..0000000000 --- a/.circleci/config.yml +++ /dev/null @@ -1,597 +0,0 @@ -version: 2.1 - -# How to test the Linux jobs: -# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/ -# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.8 -# - Replace binary_linux_wheel_py3.8 with the name of the job you want to test. -# Job names are 'name:' key. - -orbs: - win: circleci/windows@2.0.0 - -binary_common: &binary_common - parameters: - # Edit these defaults to do a release - build_version: - description: "version number of release binary; by default, build a nightly" - type: string - default: "0.5.0" - pytorch_version: - description: "PyTorch version to build against; by default, use a nightly" - type: string - default: "1.5.0" - # Don't edit these - python_version: - description: "Python version to build against (e.g., 3.8)" - type: string - unicode_abi: - description: "Python 2.7 wheel only: whether or not we are cp27mu (default: no)" - type: string - default: "" - environment: - PYTHON_VERSION: << parameters.python_version >> - BUILD_VERSION: << parameters.build_version >> - PYTORCH_VERSION: << parameters.pytorch_version >> - UNICODE_ABI: << parameters.unicode_abi >> - CU_VERSION: cpu - -smoke_test_common: &smoke_test_common - <<: *binary_common - docker: - - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:d26af7d0-8458-face-book-84239b5c75c0 - aws_auth: - aws_access_key_id: ${ECR_AWS_ACCESS_KEY} - aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} - -jobs: - circleci_consistency: - docker: - - image: circleci/python:3.8 - steps: - - checkout - - run: - command: | - pip install --user --progress-bar off jinja2 pyyaml - python .circleci/regenerate.py - git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1) - - binary_linux_wheel: - <<: *binary_common - docker: - - image: "pytorch/manylinux-cuda100" - resource_class: 2xlarge+ - steps: - - checkout - - run: packaging/build_wheel.sh - - store_artifacts: - path: dist - - persist_to_workspace: - root: dist - paths: - - "*" - - binary_linux_conda: - <<: *binary_common - docker: - - image: "pytorch/conda-cuda" - resource_class: 2xlarge+ - steps: - - checkout - - run: packaging/build_conda.sh - - store_artifacts: - path: /opt/conda/conda-bld/linux-64 - - persist_to_workspace: - root: /opt/conda/conda-bld/linux-64 - paths: - - "*" - - binary_win_conda: - <<: *binary_common - executor: - name: win/default - shell: bash.exe - steps: - - checkout - - run: - command: | - choco install miniconda3 - (& "C:\tools\miniconda3\Scripts\conda.exe" "shell.powershell" "hook") | Out-String | Invoke-Expression - conda activate base - conda install -yq conda-build "conda-package-handling!=1.5.0" - bash packaging/build_conda.sh - shell: powershell.exe - - binary_macos_wheel: - <<: *binary_common - macos: - xcode: "9.0" - steps: - - checkout - - run: - # Cannot easily deduplicate this as source'ing activate - # will set environment variables which we need to propagate - # to build_wheel.sh - command: | - curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh - sh conda.sh -b - source $HOME/miniconda3/bin/activate - packaging/build_wheel.sh - - store_artifacts: - path: dist - - persist_to_workspace: - root: dist - paths: - - "*" - - binary_macos_conda: - <<: *binary_common - macos: - xcode: "9.0" - steps: - - checkout - - run: - command: | - curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh - sh conda.sh -b - source $HOME/miniconda3/bin/activate - conda install -yq conda-build - packaging/build_conda.sh - - store_artifacts: - path: /Users/distiller/miniconda3/conda-bld/osx-64 - - persist_to_workspace: - root: /Users/distiller/miniconda3/conda-bld/osx-64 - paths: - - "*" - - # Requires org-member context - binary_conda_upload: - docker: - - image: continuumio/miniconda - steps: - - attach_workspace: - at: ~/workspace - - run: - command: | - # Prevent credential from leaking - conda install -yq anaconda-client - set -x - anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u pytorch-nightly --label main --no-progress --force - - # Requires org-member context - binary_wheel_upload: - docker: - - image: circleci/python:3.8 - steps: - - attach_workspace: - at: ~/workspace - - checkout - - run: - command: | - pip install --user awscli - export PATH="$HOME/.local/bin:$PATH" - # Prevent credential from leaking - set +x - export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}" - export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}" - set -x - for pkg in ~/workspace/*.whl; do - aws s3 cp "$pkg" "s3://pytorch/whl/nightly/" --acl public-read - done - - smoke_test_linux_conda: - <<: *smoke_test_common - steps: - - attach_workspace: - at: ~/workspace - - run: - name: install binaries - command: | - set -x - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-nightly pytorch - conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - - run: - name: smoke test - command: | - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - python -c "import torchaudio" - - smoke_test_linux_pip: - <<: *smoke_test_common - steps: - - attach_workspace: - at: ~/workspace - - run: - name: install binaries - command: | - set -x - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html - - run: - name: smoke test - command: | - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - python -c "import torchaudio" - - docker_image_build: - machine: - image: ubuntu-1604:201903-01 - resource_class: large - environment: - image_name: torchaudio/smoke_test - steps: - - checkout - - run: - name: build_docker image - no_output_timeout: "1h" - command: | - cd .circleci/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} - - run: - name: upload docker image - no_output_timeout: "1h" - command: | - set +x - export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} - export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} - eval $(aws ecr get-login --region us-east-1 --no-include-email) - set -x - docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} - docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} - -workflows: - build: - jobs: - - circleci_consistency - - binary_linux_wheel: - name: binary_linux_wheel_py3.5 - python_version: '3.5' - - binary_linux_wheel: - name: binary_linux_wheel_py3.6 - python_version: '3.6' - - binary_linux_wheel: - name: binary_linux_wheel_py3.7 - python_version: '3.7' - - binary_linux_wheel: - name: binary_linux_wheel_py3.8 - python_version: '3.8' - - binary_macos_wheel: - name: binary_macos_wheel_py3.5 - python_version: '3.5' - - binary_macos_wheel: - name: binary_macos_wheel_py3.6 - python_version: '3.6' - - binary_macos_wheel: - name: binary_macos_wheel_py3.7 - python_version: '3.7' - - binary_macos_wheel: - name: binary_macos_wheel_py3.8 - python_version: '3.8' - - binary_linux_conda: - name: binary_linux_conda_py3.5 - python_version: '3.5' - - binary_linux_conda: - name: binary_linux_conda_py3.6 - python_version: '3.6' - - binary_linux_conda: - name: binary_linux_conda_py3.7 - python_version: '3.7' - - binary_linux_conda: - name: binary_linux_conda_py3.8 - python_version: '3.8' - - binary_macos_conda: - name: binary_macos_conda_py3.5 - python_version: '3.5' - - binary_macos_conda: - name: binary_macos_conda_py3.6 - python_version: '3.6' - - binary_macos_conda: - name: binary_macos_conda_py3.7 - python_version: '3.7' - - binary_macos_conda: - name: binary_macos_conda_py3.8 - python_version: '3.8' - - binary_win_conda: - name: torchaudio_win_py3.6 - python_version: "3.6" - - nightly: - jobs: - - circleci_consistency - - binary_linux_wheel: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.5 - python_version: '3.5' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.5_upload - requires: - - nightly_binary_linux_wheel_py3.5 - - smoke_test_linux_pip: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.5_smoke_test_pip - python_version: '3.5' - requires: - - nightly_binary_linux_wheel_py3.5_upload - - binary_linux_wheel: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.6 - python_version: '3.6' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.6_upload - requires: - - nightly_binary_linux_wheel_py3.6 - - smoke_test_linux_pip: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.6_smoke_test_pip - python_version: '3.6' - requires: - - nightly_binary_linux_wheel_py3.6_upload - - binary_linux_wheel: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.7 - python_version: '3.7' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.7_upload - requires: - - nightly_binary_linux_wheel_py3.7 - - smoke_test_linux_pip: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.7_smoke_test_pip - python_version: '3.7' - requires: - - nightly_binary_linux_wheel_py3.7_upload - - binary_linux_wheel: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.8 - python_version: '3.8' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.8_upload - requires: - - nightly_binary_linux_wheel_py3.8 - - smoke_test_linux_pip: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.8_smoke_test_pip - python_version: '3.8' - requires: - - nightly_binary_linux_wheel_py3.8_upload - - binary_macos_wheel: - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.5 - python_version: '3.5' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.5_upload - requires: - - nightly_binary_macos_wheel_py3.5 - - binary_macos_wheel: - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.6 - python_version: '3.6' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.6_upload - requires: - - nightly_binary_macos_wheel_py3.6 - - binary_macos_wheel: - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.7 - python_version: '3.7' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.7_upload - requires: - - nightly_binary_macos_wheel_py3.7 - - binary_macos_wheel: - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.8 - python_version: '3.8' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.8_upload - requires: - - nightly_binary_macos_wheel_py3.8 - - binary_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.5 - python_version: '3.5' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.5_upload - requires: - - nightly_binary_linux_conda_py3.5 - - smoke_test_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.5_smoke_test_conda - python_version: '3.5' - requires: - - nightly_binary_linux_conda_py3.5_upload - - binary_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.6 - python_version: '3.6' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.6_upload - requires: - - nightly_binary_linux_conda_py3.6 - - smoke_test_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.6_smoke_test_conda - python_version: '3.6' - requires: - - nightly_binary_linux_conda_py3.6_upload - - binary_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.7 - python_version: '3.7' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.7_upload - requires: - - nightly_binary_linux_conda_py3.7 - - smoke_test_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.7_smoke_test_conda - python_version: '3.7' - requires: - - nightly_binary_linux_conda_py3.7_upload - - binary_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.8 - python_version: '3.8' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.8_upload - requires: - - nightly_binary_linux_conda_py3.8 - - smoke_test_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.8_smoke_test_conda - python_version: '3.8' - requires: - - nightly_binary_linux_conda_py3.8_upload - - binary_macos_conda: - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.5 - python_version: '3.5' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.5_upload - requires: - - nightly_binary_macos_conda_py3.5 - - binary_macos_conda: - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.6 - python_version: '3.6' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.6_upload - requires: - - nightly_binary_macos_conda_py3.6 - - binary_macos_conda: - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.7 - python_version: '3.7' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.7_upload - requires: - - nightly_binary_macos_conda_py3.7 - - binary_macos_conda: - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.8 - python_version: '3.8' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.8_upload - requires: - - nightly_binary_macos_conda_py3.8 - docker_build: - triggers: - - schedule: - cron: "0 10 * * 0" - filters: - branches: - only: - - master - jobs: - - docker_image_build \ No newline at end of file diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in deleted file mode 100644 index fb3316969a..0000000000 --- a/.circleci/config.yml.in +++ /dev/null @@ -1,263 +0,0 @@ -version: 2.1 - -# How to test the Linux jobs: -# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/ -# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.8 -# - Replace binary_linux_wheel_py3.8 with the name of the job you want to test. -# Job names are 'name:' key. - -orbs: - win: circleci/windows@2.0.0 - -binary_common: &binary_common - parameters: - # Edit these defaults to do a release - build_version: - description: "version number of release binary; by default, build a nightly" - type: string - default: "0.5.0" - pytorch_version: - description: "PyTorch version to build against; by default, use a nightly" - type: string - default: "1.5.0" - # Don't edit these - python_version: - description: "Python version to build against (e.g., 3.8)" - type: string - unicode_abi: - description: "Python 2.7 wheel only: whether or not we are cp27mu (default: no)" - type: string - default: "" - environment: - PYTHON_VERSION: << parameters.python_version >> - BUILD_VERSION: << parameters.build_version >> - PYTORCH_VERSION: << parameters.pytorch_version >> - UNICODE_ABI: << parameters.unicode_abi >> - CU_VERSION: cpu - -smoke_test_common: &smoke_test_common - <<: *binary_common - docker: - - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:d26af7d0-8458-face-book-84239b5c75c0 - aws_auth: - aws_access_key_id: ${ECR_AWS_ACCESS_KEY} - aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} - -jobs: - circleci_consistency: - docker: - - image: circleci/python:3.8 - steps: - - checkout - - run: - command: | - pip install --user --progress-bar off jinja2 pyyaml - python .circleci/regenerate.py - git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1) - - binary_linux_wheel: - <<: *binary_common - docker: - - image: "pytorch/manylinux-cuda100" - resource_class: 2xlarge+ - steps: - - checkout - - run: packaging/build_wheel.sh - - store_artifacts: - path: dist - - persist_to_workspace: - root: dist - paths: - - "*" - - binary_linux_conda: - <<: *binary_common - docker: - - image: "pytorch/conda-cuda" - resource_class: 2xlarge+ - steps: - - checkout - - run: packaging/build_conda.sh - - store_artifacts: - path: /opt/conda/conda-bld/linux-64 - - persist_to_workspace: - root: /opt/conda/conda-bld/linux-64 - paths: - - "*" - - binary_win_conda: - <<: *binary_common - executor: - name: win/default - shell: bash.exe - steps: - - checkout - - run: - command: | - choco install miniconda3 - (& "C:\tools\miniconda3\Scripts\conda.exe" "shell.powershell" "hook") | Out-String | Invoke-Expression - conda activate base - conda install -yq conda-build "conda-package-handling!=1.5.0" - bash packaging/build_conda.sh - shell: powershell.exe - - binary_macos_wheel: - <<: *binary_common - macos: - xcode: "9.0" - steps: - - checkout - - run: - # Cannot easily deduplicate this as source'ing activate - # will set environment variables which we need to propagate - # to build_wheel.sh - command: | - curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh - sh conda.sh -b - source $HOME/miniconda3/bin/activate - packaging/build_wheel.sh - - store_artifacts: - path: dist - - persist_to_workspace: - root: dist - paths: - - "*" - - binary_macos_conda: - <<: *binary_common - macos: - xcode: "9.0" - steps: - - checkout - - run: - command: | - curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh - sh conda.sh -b - source $HOME/miniconda3/bin/activate - conda install -yq conda-build - packaging/build_conda.sh - - store_artifacts: - path: /Users/distiller/miniconda3/conda-bld/osx-64 - - persist_to_workspace: - root: /Users/distiller/miniconda3/conda-bld/osx-64 - paths: - - "*" - - # Requires org-member context - binary_conda_upload: - docker: - - image: continuumio/miniconda - steps: - - attach_workspace: - at: ~/workspace - - run: - command: | - # Prevent credential from leaking - conda install -yq anaconda-client - set -x - anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u pytorch-nightly --label main --no-progress --force - - # Requires org-member context - binary_wheel_upload: - docker: - - image: circleci/python:3.8 - steps: - - attach_workspace: - at: ~/workspace - - checkout - - run: - command: | - pip install --user awscli - export PATH="$HOME/.local/bin:$PATH" - # Prevent credential from leaking - set +x - export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}" - export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}" - set -x - for pkg in ~/workspace/*.whl; do - aws s3 cp "$pkg" "s3://pytorch/whl/nightly/" --acl public-read - done - - smoke_test_linux_conda: - <<: *smoke_test_common - steps: - - attach_workspace: - at: ~/workspace - - run: - name: install binaries - command: | - set -x - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-nightly pytorch - conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - - run: - name: smoke test - command: | - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - python -c "import torchaudio" - - smoke_test_linux_pip: - <<: *smoke_test_common - steps: - - attach_workspace: - at: ~/workspace - - run: - name: install binaries - command: | - set -x - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html - - run: - name: smoke test - command: | - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - python -c "import torchaudio" - - docker_image_build: - machine: - image: ubuntu-1604:201903-01 - resource_class: large - environment: - image_name: torchaudio/smoke_test - steps: - - checkout - - run: - name: build_docker image - no_output_timeout: "1h" - command: | - cd .circleci/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} - - run: - name: upload docker image - no_output_timeout: "1h" - command: | - set +x - export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} - export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} - eval $(aws ecr get-login --region us-east-1 --no-include-email) - set -x - docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} - docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} - -workflows: - build: - jobs: - - circleci_consistency - {{ workflows() }} - - binary_win_conda: - name: torchaudio_win_py3.6 - python_version: "3.6" - - nightly: - jobs: - - circleci_consistency - {{ workflows(prefix="nightly_", filter_branch="nightly", upload=True) }} - docker_build: - triggers: - - schedule: - cron: "0 10 * * 0" - filters: - branches: - only: - - master - jobs: - - docker_image_build diff --git a/.circleci/docker/Dockerfile b/.circleci/docker/Dockerfile deleted file mode 100644 index 6066668694..0000000000 --- a/.circleci/docker/Dockerfile +++ /dev/null @@ -1,36 +0,0 @@ -# this Dockerfile is for torchaudio smoke test, it will be created periodically via CI system -# if you need to do it locally, follow below steps once you have Docker installed -# assuming you're within the directory where this Dockerfile located -# $ docker build . -t torchaudio/smoketest - -# if you want to push to aws ecr, make sure you have the rights to write to ECR, then run -# $ eval $(aws ecr get-login --region us-east-1 --no-include-email) -# $ export MYTAG=localbuild ## you can choose whatever tag you like -# $ docker tag torchaudio/smoketest 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:${MYTAG} -# $ docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:${MYTAG} - -FROM ubuntu:latest - -RUN apt-get -qq update && apt-get -qq -y install curl bzip2 sox libsox-dev libsox-fmt-all \ - && curl -sSL https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda.sh \ - && bash /tmp/miniconda.sh -bfp /usr/local \ - && rm -rf /tmp/miniconda.sh \ - && conda install -y python=3 \ - && conda update conda \ - && apt-get -qq -y remove curl bzip2 \ - && apt-get -qq -y autoremove \ - && apt-get autoclean \ - && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log \ - && conda clean --all --yes - -ENV PATH /opt/conda/bin:$PATH - -RUN conda create -y --name python3.5 python=3.5 -RUN conda create -y --name python3.6 python=3.6 -RUN conda create -y --name python3.7 python=3.7 -SHELL [ "/bin/bash", "-c" ] -RUN echo "source /usr/local/etc/profile.d/conda.sh" >> ~/.bashrc -RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.5 && conda install -y -c conda-forge sox && conda install -y numpy -RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.6 && conda install -y -c conda-forge sox && conda install -y numpy -RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.7 && conda install -y -c conda-forge sox && conda install -y numpy -CMD [ "/bin/bash"] diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py deleted file mode 100755 index 19f52be756..0000000000 --- a/.circleci/regenerate.py +++ /dev/null @@ -1,124 +0,0 @@ -#!/usr/bin/env python3 - -""" -This script should use a very simple, functional programming style. -Avoid Jinja macros in favor of native Python functions. - -Don't go overboard on code generation; use Python only to generate -content that can't be easily declared statically using CircleCI's YAML API. - -Data declarations (e.g. the nested loops for defining the configuration matrix) -should be at the top of the file for easy updating. - -See this comment for design rationale: -https://github.com/pytorch/vision/pull/1321#issuecomment-531033978 -""" - -import jinja2 -import yaml -import os.path - - -def workflows(prefix='', upload=False, filter_branch=None, indentation=6): - w = [] - for btype in ["wheel", "conda"]: - for os_type in ["linux", "macos"]: - for python_version in ["3.5", "3.6", "3.7", "3.8"]: - for unicode in ([False, True] if btype == "wheel" and python_version == "2.7" else [False]): - w += workflow_pair(btype, os_type, python_version, unicode, filter_branch, prefix, upload) - - return indent(indentation, w) - - -def workflow_pair(btype, os_type, python_version, unicode, filter_branch, prefix='', upload=False): - - w = [] - unicode_suffix = "_unicode" if unicode else "" - base_workflow_name = "{prefix}binary_{os_type}_{btype}_py{python_version}{unicode_suffix}".format( - prefix=prefix, - os_type=os_type, - btype=btype, - python_version=python_version, - unicode_suffix=unicode_suffix) - - w.append(generate_base_workflow(base_workflow_name, python_version, unicode, filter_branch, os_type, btype)) - - if upload: - - is_py3_linux = os_type == 'linux' and not python_version.startswith("2.") - - w.append(generate_upload_workflow(base_workflow_name, filter_branch, btype)) - - if filter_branch == 'nightly' and is_py3_linux: - pydistro = 'pip' if btype == 'wheel' else 'conda' - w.append(generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version)) - - return w - - -def generate_base_workflow(base_workflow_name, python_version, unicode, filter_branch, os_type, btype): - - d = { - "name": base_workflow_name, - "python_version": python_version, - } - - if unicode: - d["unicode_abi"] = '1' - - if filter_branch: - d["filters"] = gen_filter_branch_tree(filter_branch) - - return {"binary_{os_type}_{btype}".format(os_type=os_type, btype=btype): d} - - -def gen_filter_branch_tree(branch_name): - return {"branches": {"only": branch_name}} - - -def generate_upload_workflow(base_workflow_name, filter_branch, btype): - d = { - "name": "{base_workflow_name}_upload".format(base_workflow_name=base_workflow_name), - "context": "org-member", - "requires": [base_workflow_name], - } - - if filter_branch: - d["filters"] = gen_filter_branch_tree(filter_branch) - - return {"binary_{btype}_upload".format(btype=btype): d} - - -def generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version): - - required_build_suffix = "_upload" - required_build_name = base_workflow_name + required_build_suffix - - smoke_suffix = "smoke_test_{pydistro}".format(pydistro=pydistro) - d = { - "name": "{base_workflow_name}_{smoke_suffix}".format( - base_workflow_name=base_workflow_name, smoke_suffix=smoke_suffix), - "requires": [required_build_name], - "python_version": python_version, - } - - if filter_branch: - d["filters"] = gen_filter_branch_tree(filter_branch) - - return {"smoke_test_linux_{pydistro}".format(pydistro=pydistro): d} - - -def indent(indentation, data_list): - return ("\n" + " " * indentation).join(yaml.dump(data_list).splitlines()) - - -if __name__ == "__main__": - d = os.path.dirname(__file__) - env = jinja2.Environment( - loader=jinja2.FileSystemLoader(d), - lstrip_blocks=True, - autoescape=False, - ) - - with open(os.path.join(d, 'config.yml'), 'w') as f: - f.write(env.get_template('config.yml.in').render(workflows=workflows)) diff --git a/.circleci/test/test_sort_yaml.py b/.circleci/test/test_sort_yaml.py deleted file mode 100755 index dc6db481dd..0000000000 --- a/.circleci/test/test_sort_yaml.py +++ /dev/null @@ -1,14 +0,0 @@ -#!/usr/bin/env python3 - -""" -To compare new version with previous: - - ./regenerate.sh - meld <(git show HEAD:./config.yml | ./sort-yaml.py) <(cat config.yml | ./sort-yaml.py) -""" - - -import sys -import yaml - -sys.stdout.write(yaml.dump(yaml.load(sys.stdin, Loader=yaml.FullLoader), sort_keys=True)) diff --git a/.clang-format b/.clang-format deleted file mode 100644 index 73304266bd..0000000000 --- a/.clang-format +++ /dev/null @@ -1,88 +0,0 @@ ---- -AccessModifierOffset: -1 -AlignAfterOpenBracket: AlwaysBreak -AlignConsecutiveAssignments: false -AlignConsecutiveDeclarations: false -AlignEscapedNewlinesLeft: true -AlignOperands: false -AlignTrailingComments: false -AllowAllParametersOfDeclarationOnNextLine: false -AllowShortBlocksOnASingleLine: false -AllowShortCaseLabelsOnASingleLine: false -AllowShortFunctionsOnASingleLine: Empty -AllowShortIfStatementsOnASingleLine: false -AllowShortLoopsOnASingleLine: false -AlwaysBreakAfterReturnType: None -AlwaysBreakBeforeMultilineStrings: true -AlwaysBreakTemplateDeclarations: true -BinPackArguments: false -BinPackParameters: false -BraceWrapping: - AfterClass: false - AfterControlStatement: false - AfterEnum: false - AfterFunction: false - AfterNamespace: false - AfterObjCDeclaration: false - AfterStruct: false - AfterUnion: false - BeforeCatch: false - BeforeElse: false - IndentBraces: false -BreakBeforeBinaryOperators: None -BreakBeforeBraces: Attach -BreakBeforeTernaryOperators: true -BreakConstructorInitializersBeforeComma: false -BreakAfterJavaFieldAnnotations: false -BreakStringLiterals: false -ColumnLimit: 80 -CommentPragmas: '^ IWYU pragma:' -CompactNamespaces: false -ConstructorInitializerAllOnOneLineOrOnePerLine: true -ConstructorInitializerIndentWidth: 4 -ContinuationIndentWidth: 4 -Cpp11BracedListStyle: true -DerivePointerAlignment: false -DisableFormat: false -ForEachMacros: [ FOR_EACH_RANGE, FOR_EACH, ] -IncludeCategories: - - Regex: '^<.*\.h(pp)?>' - Priority: 1 - - Regex: '^<.*' - Priority: 2 - - Regex: '.*' - Priority: 3 -IndentCaseLabels: true -IndentWidth: 2 -IndentWrappedFunctionNames: false -KeepEmptyLinesAtTheStartOfBlocks: false -MacroBlockBegin: '' -MacroBlockEnd: '' -MaxEmptyLinesToKeep: 1 -NamespaceIndentation: None -ObjCBlockIndentWidth: 2 -ObjCSpaceAfterProperty: false -ObjCSpaceBeforeProtocolList: false -PenaltyBreakBeforeFirstCallParameter: 1 -PenaltyBreakComment: 300 -PenaltyBreakFirstLessLess: 120 -PenaltyBreakString: 1000 -PenaltyExcessCharacter: 1000000 -PenaltyReturnTypeOnItsOwnLine: 2000000 -PointerAlignment: Left -ReflowComments: true -SortIncludes: true -SpaceAfterCStyleCast: false -SpaceBeforeAssignmentOperators: true -SpaceBeforeParens: ControlStatements -SpaceInEmptyParentheses: false -SpacesBeforeTrailingComments: 1 -SpacesInAngles: false -SpacesInContainerLiterals: true -SpacesInCStyleCastParentheses: false -SpacesInParentheses: false -SpacesInSquareBrackets: false -Standard: Cpp11 -TabWidth: 8 -UseTab: Never -... diff --git a/.clang-tidy b/.clang-tidy deleted file mode 100644 index a3a40b5907..0000000000 --- a/.clang-tidy +++ /dev/null @@ -1,33 +0,0 @@ ---- -# NOTE there must be no spaces before the '-', so put the comma first. -Checks: ' - -* - ,bugprone-* - ,-bugprone-forward-declaration-namespace - ,-bugprone-macro-parentheses - ,cppcoreguidelines-* - ,-cppcoreguidelines-interfaces-global-init - ,-cppcoreguidelines-owning-memory - ,-cppcoreguidelines-pro-bounds-array-to-pointer-decay - ,-cppcoreguidelines-pro-bounds-constant-array-index - ,-cppcoreguidelines-pro-bounds-pointer-arithmetic - ,-cppcoreguidelines-pro-type-cstyle-cast - ,-cppcoreguidelines-pro-type-reinterpret-cast - ,-cppcoreguidelines-pro-type-static-cast-downcast - ,-cppcoreguidelines-pro-type-union-access - ,-cppcoreguidelines-pro-type-vararg - ,-cppcoreguidelines-special-member-functions - ,hicpp-exception-baseclass - ,hicpp-avoid-goto - ,modernize-* - ,-modernize-return-braced-init-list - ,-modernize-use-auto - ,-modernize-use-default-member-init - ,-modernize-use-using - ,performance-unnecessary-value-param - ' -WarningsAsErrors: '*' -HeaderFilterRegex: 'torchaudio/.*' -AnalyzeTemporaryDtors: false -CheckOptions: -... diff --git a/.flake8 b/.flake8 deleted file mode 100644 index 388d81d723..0000000000 --- a/.flake8 +++ /dev/null @@ -1,4 +0,0 @@ -[flake8] -max-line-length = 120 -ignore = E305,E402,E721,E741,F401,F403,F405,F999,W503,W504 -exclude = build,docs/source,_ext diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md deleted file mode 100644 index fb50bd8504..0000000000 --- a/.github/ISSUE_TEMPLATE/bug-report.md +++ /dev/null @@ -1,53 +0,0 @@ ---- -name: "\U0001F41B Bug Report" -about: Submit a bug report to help us improve Torchaudio - ---- - -## ๐Ÿ› Bug - - - -## To Reproduce - -Steps to reproduce the behavior: - -1. -1. -1. - - - -## Expected behavior - - - -## Environment - - - What commands did you used to install torchaudio (conda/pip/build from source)? - - If you are building from source, which commit is it? - - What does `torchaudio.__version__` print? (If applicable) - -Please copy and paste the output from our -[environment collection script](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py) -(or fill out the checklist below manually). - -You can get the script and run it with: -``` -wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py -# For security purposes, please check the contents of collect_env.py before running it. -python collect_env.py -``` - - - PyTorch Version (e.g., 1.0): - - OS (e.g., Linux): - - How you installed PyTorch (`conda`, `pip`, source): - - Build command you used (if compiling from source): - - Python version: - - CUDA/cuDNN version: - - GPU models and configuration: - - Any other relevant information: - -## Additional context - - diff --git a/.github/ISSUE_TEMPLATE/documentation.md b/.github/ISSUE_TEMPLATE/documentation.md deleted file mode 100644 index ae745121c5..0000000000 --- a/.github/ISSUE_TEMPLATE/documentation.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -name: "\U0001F4DA Documentation" -about: Report an issue related to https://pytorch.org/audio - ---- - -## ๐Ÿ“š Documentation - - diff --git a/.github/ISSUE_TEMPLATE/feature-request.md b/.github/ISSUE_TEMPLATE/feature-request.md deleted file mode 100644 index f3896e5368..0000000000 --- a/.github/ISSUE_TEMPLATE/feature-request.md +++ /dev/null @@ -1,24 +0,0 @@ ---- -name: "\U0001F680Feature Request" -about: Submit a proposal/request for a new Torchaudio feature - ---- - -## ๐Ÿš€ Feature - - -## Motivation - - - -## Pitch - - - -## Alternatives - - - -## Additional context - - diff --git a/.github/ISSUE_TEMPLATE/questions-help-support.md b/.github/ISSUE_TEMPLATE/questions-help-support.md deleted file mode 100644 index 77bfb55b9a..0000000000 --- a/.github/ISSUE_TEMPLATE/questions-help-support.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -name: "โ“Questions/Help/Support" -about: Do you need support? We have resources. - ---- - -## โ“ Questions and Help - -### Please note that this issue tracker is not a help form and this issue will be closed. - -We have a set of [listed resources available on the website](https://pytorch.org/resources). Our primary means of support is our discussion forum: - -- [Discussion Forum](https://discuss.pytorch.org/) diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 41a434acb2..0000000000 --- a/.gitignore +++ /dev/null @@ -1,123 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# temp files -~* -*.swp - -# C extensions / folders -*.so -_ext/ - -# Distribution / packaging -.Python -env/ -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ -docs/src/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints/ - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# dotenv -.env - -# virtualenv -.venv -venv/ -ENV/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# PyCharm project settings -.idea - -# OSX dir files -.DS_Store - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ - -# Generated Files -test/assets/sinewave.wav -torchaudio/version.py -gen.yml - -# Examples -examples/interactive_asr/data/*.txt -examples/interactive_asr/data/*.model -examples/interactive_asr/data/*.pt diff --git a/.python3 b/.python3 new file mode 100644 index 0000000000..e69de29bb2 diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 1fd00c3e96..0000000000 --- a/.travis.yml +++ /dev/null @@ -1,36 +0,0 @@ -# note dist: 'trusty' does not work here -dist: xenial - -language: python - -# cache miniconda installer and similar files -cache: - directories: - - /home/travis/download - -# This matrix tests that the code works on Python 3.5, 3.6, 3.7, passes -# lint and example tests. -matrix: - fast_finish: true - include: - - env: PYTHON_VERSION="3.5" - - env: PYTHON_VERSION="3.6" - - env: PYTHON_VERSION="3.7" - - env: PYTHON_VERSION="3.5" RUN_FLAKE8="true" SKIP_INSTALL="true" SKIP_TESTS="true" - - env: PYTHON_VERSION="3.5" RUN_EXAMPLE_TESTS="true" SKIP_TESTS="true" - allow_failures: - - env: PYTHON_VERSION="3.5" RUN_EXAMPLE_TESTS="true" SKIP_TESTS="true" - -addons: - apt: - packages: - sox - libsox-dev - libsox-fmt-all - portaudio19-dev - -notifications: - email: false - -install: source build_tools/travis/install.sh -script: bash build_tools/travis/test_script.sh From e2430778611a4ea26a1913e327525901953c5d11 Mon Sep 17 00:00:00 2001 From: Christian Puhrsch Date: Wed, 29 Apr 2020 00:29:57 -0400 Subject: [PATCH 03/73] Set up ShipIt fbshipit-source-id: 4fb853c391900d3070b936e5a3e4609eb78a780d From 0d99b9d0a4e8d994ddd881feebff4857e3206efd Mon Sep 17 00:00:00 2001 From: Christian Puhrsch Date: Tue, 28 Apr 2020 19:16:39 -0700 Subject: [PATCH 04/73] 20200428 pytorch/audio import Summary: [10:30:47: cpuhrsch@devvm3140 pytorch]$ ./fb_build/import_audio.sh Reviewed By: vincentqb Differential Revision: D21282421 fbshipit-source-id: 9bde1455ca6a19defbf33dbbfc5f0d49a8e4dc6a --- CODE_OF_CONDUCT.md | 76 ++ README.md | 2 +- build_tools/travis/install.sh | 1 + build_tools/travis/test_script.sh | 1 + docs/source/datasets.rst | 36 +- docs/source/functional.rst | 99 ++- docs/source/transforms.rst | 16 +- mypy.ini | 3 + packaging/build_conda.sh | 2 +- packaging/build_wheel.sh | 2 +- requirements.txt | 5 +- setup.py | 2 +- test/assets/vad-hello-mono-32000.wav | Bin 0 -> 275106 bytes test/assets/vad-hello-stereo-44100.wav | Bin 0 -> 937862 bytes test/common_utils.py | 23 +- test/test_batch_consistency.py | 156 ++-- test/test_compliance_kaldi.py | 25 +- test/test_dataloader.py | 29 +- test/test_datasets.py | 4 +- test/test_functional.py | 70 +- test/{test.py => test_io.py} | 0 test/test_kaldi_io.py | 8 +- test/test_librosa_compatibility.py | 43 +- ...filtering.py => test_sox_compatibility.py} | 321 ++++----- test/test_sox_effects.py | 36 +- test/test_torchscript_consistency.py | 671 +++++++++++------- test/test_transforms.py | 25 +- torchaudio/__init__.py | 184 +++-- torchaudio/_backend.py | 22 +- torchaudio/_soundfile_backend.py | 52 +- torchaudio/_sox_backend.py | 32 +- torchaudio/common_utils.py | 2 +- torchaudio/datasets/commonvoice.py | 96 ++- torchaudio/datasets/librispeech.py | 44 +- torchaudio/datasets/ljspeech.py | 23 +- torchaudio/datasets/speechcommands.py | 29 +- torchaudio/datasets/utils.py | 105 +-- torchaudio/datasets/vctk.py | 53 +- torchaudio/datasets/yesno.py | 37 +- torchaudio/functional.py | 582 +++++++++++++-- torchaudio/kaldi_io.py | 38 +- torchaudio/sox_effects.py | 9 +- torchaudio/transforms.py | 166 ++++- 43 files changed, 2145 insertions(+), 985 deletions(-) create mode 100644 CODE_OF_CONDUCT.md create mode 100644 mypy.ini create mode 100644 test/assets/vad-hello-mono-32000.wav create mode 100644 test/assets/vad-hello-stereo-44100.wav rename test/{test.py => test_io.py} (100%) rename test/{test_functional_filtering.py => test_sox_compatibility.py} (54%) diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000..b91e23b17c --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or +advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic +address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a +professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/README.md b/README.md index f36f19db36..816fd75f6c 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,7 @@ build nightlies based on PyTorch nightlies by hand following the instructions in ``` pip install numpy -pip install torchaudio_nightly -f https://download.pytorch.org/whl/nightly/torch_nightly.html +pip install --pre torchaudio -f https://download.pytorch.org/whl/nightly/torch_nightly.html ``` To install the latest nightly via conda, run: diff --git a/build_tools/travis/install.sh b/build_tools/travis/install.sh index 61c564a5bf..3586d5bd08 100644 --- a/build_tools/travis/install.sh +++ b/build_tools/travis/install.sh @@ -48,6 +48,7 @@ popd source activate testenv # Install requirements via pip in our conda environment +conda install -y pytorch cpuonly -c pytorch-nightly pip install -r requirements.txt # Install the following only if running tests diff --git a/build_tools/travis/test_script.sh b/build_tools/travis/test_script.sh index 06d70956b8..e854a211b3 100755 --- a/build_tools/travis/test_script.sh +++ b/build_tools/travis/test_script.sh @@ -6,6 +6,7 @@ set -e python --version +python -c 'import torch;print("torch:", torch.__version__)' run_tests() { # find all the test files that match "test*.py" diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index eef25da616..27d4b20a34 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -25,15 +25,47 @@ All the datasets have almost similar API. They all have two common arguments: .. currentmodule:: torchaudio.datasets +COMMONVOICE +~~~~~~~~~~~ + +.. autoclass:: COMMONVOICE + :members: __getitem__ + :special-members: + + +LIBRISPEECH +~~~~~~~~~~~ + +.. autoclass:: LIBRISPEECH + :members: __getitem__ + :special-members: + + +LJSPEECH +~~~~~~~~ + +.. autoclass:: LJSPEECH + :members: __getitem__ + :special-members: + + +SPEECHCOMMANDS +~~~~~~~~~~~~~~ + +.. autoclass:: SPEECHCOMMANDS + :members: __getitem__ + :special-members: + + VCTK -~~~~~ +~~~~ .. autoclass:: VCTK :members: __getitem__ :special-members: YESNO -~~~~~~~~~~~~~ +~~~~~ .. autoclass:: YESNO :members: __getitem__ diff --git a/docs/source/functional.rst b/docs/source/functional.rst index a76bf4dc4c..d8f318ea18 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -2,113 +2,168 @@ :class: hidden-section torchaudio.functional -====================== +===================== .. currentmodule:: torchaudio.functional Functions to perform common audio operations. :hidden:`istft` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~ .. autofunction:: istft :hidden:`spectrogram` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: spectrogram :hidden:`amplitude_to_DB` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: amplitude_to_DB :hidden:`create_fb_matrix` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: create_fb_matrix :hidden:`create_dct` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~ .. autofunction:: create_dct :hidden:`mu_law_encoding` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: mu_law_encoding :hidden:`mu_law_decoding` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: mu_law_decoding :hidden:`complex_norm` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: complex_norm :hidden:`angle` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~ .. autofunction:: angle :hidden:`magphase` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~ .. autofunction:: magphase :hidden:`phase_vocoder` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: phase_vocoder :hidden:`lfilter` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~ .. autofunction:: lfilter :hidden:`biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~ .. autofunction:: biquad :hidden:`lowpass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: lowpass_biquad :hidden:`highpass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: highpass_biquad :hidden:`allpass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: allpass_biquad :hidden:`equalizer_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: equalizer_biquad -:hidden:`mask_along_axis` +:hidden:`bandpass_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: bandpass_biquad + +:hidden:`bandreject_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: bandreject_biquad + +:hidden:`band_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: band_biquad + +:hidden:`treble_biquad` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: treble_biquad + +:hidden:`deemph_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: deemph_biquad + +:hidden:`riaa_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: riaa_biquad + +:hidden:`contrast` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: contrast + +:hidden:`dcshift` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: dcshift + +:hidden:`overdrive` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: overdrive + +:hidden:`mask_along_axis` +~~~~~~~~~~~~~~~~~~~~~~~~~ + .. autofunction:: mask_along_axis :hidden:`mask_along_axis_iid` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: mask_along_axis_iid :hidden:`compute_deltas` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: compute_deltas :hidden:`detect_pitch_frequency` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: detect_pitch_frequency + +:hidden:`sliding_window_cmn` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: sliding_window_cmn + +:hidden:`vad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: vad diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst index aa50b33714..fc6a4d4a9f 100644 --- a/docs/source/transforms.rst +++ b/docs/source/transforms.rst @@ -19,7 +19,7 @@ Transforms are common audio transforms. They can be chained together using :clas :hidden:`GriffinLim` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. autoclass:: GriffinLim +.. autoclass:: GriffinLim .. automethod:: forward @@ -128,3 +128,17 @@ Transforms are common audio transforms. They can be chained together using :clas .. autoclass:: Vol .. automethod:: forward + +:hidden:`SlidingWindowCmn` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: SlidingWindowCmn + + .. automethod:: forward + +:hidden:`Vad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: Vad + + .. automethod:: forward diff --git a/mypy.ini b/mypy.ini new file mode 100644 index 0000000000..068f0918a3 --- /dev/null +++ b/mypy.ini @@ -0,0 +1,3 @@ +[mypy] +allow_redefinition = True +ignore_missing_imports = True diff --git a/packaging/build_conda.sh b/packaging/build_conda.sh index b75bbf8a13..3e011d312f 100755 --- a/packaging/build_conda.sh +++ b/packaging/build_conda.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="conda" export NO_CUDA_PACKAGE=1 -setup_env 0.5.0 +setup_env 0.6.0 export SOURCE_ROOT_DIR="$PWD" setup_conda_pytorch_constraint conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchaudio diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index 1e4f927ef5..fc0742ece1 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="wheel" export NO_CUDA_PACKAGE=1 -setup_env 0.5.0 +setup_env 0.6.0 setup_wheel_python "$script_dir/build_from_source.sh" "$(pwd)" # Build static dependencies pip_install numpy future diff --git a/requirements.txt b/requirements.txt index c67dd4627a..316bd5c7fd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -torch>=1.2.0 +torch>=1.4.0 # Required for Windows because it's the only available backend SoundFile; sys_platform == 'win32' @@ -18,6 +18,3 @@ scipy # Unit tests with pytest pytest - -# Testing only Py3 compat -backports.tempfile diff --git a/setup.py b/setup.py index 8382c1114c..0128a2fe61 100644 --- a/setup.py +++ b/setup.py @@ -55,7 +55,7 @@ def check_env_flag(name, default=''): # Creating the version file cwd = os.path.dirname(os.path.abspath(__file__)) -version = '0.5.0a0' +version = '0.6.0a0' sha = 'Unknown' try: diff --git a/test/assets/vad-hello-mono-32000.wav b/test/assets/vad-hello-mono-32000.wav new file mode 100644 index 0000000000000000000000000000000000000000..1822c3196c5e1b744d07c20bfe65035b76c4db7a GIT binary patch literal 275106 zcmZU+2b2`W_x{~sb~duWl3~fPX zBuLJZb52XxWz$TD_w#grIsX3VrO)il^mJEO-SFH{RhQekbnG~#dBhlS)9oD|82)s% z5{6-z9L5WLh#7`ulrV-3d3wlbuF)^yKhOW~b9hGggonOgz0NTF;GgGvyz3SI{qOtL zyZ?LFy}Ih^U;X0yCa>@sebXzv#xm^x-*c}%UGM6XuDyD{?!EfE&KpM9m$2>d+5h)R zy7T}3Q+xXN|Ni~&s;e#NI*m8{9`;u~4r!n#|KFZmeZqfFzItEyoR9+HRaehkegE&b zt7E)6qVNj+RlEPS{a-ubxsXO0)79_)_51%F>D6bc_g9~P`kDeh8hu{6+I`t~Tk-*b+=-mpg3hxeEQBOkIy4pXrrGAI)#r^pIo*IrIq~8B+ zLH$twis)fMa1?ST?4L_dbw}8fDB}u8o5UECgQsY`HnfQ`%FF&m##=-i`gFlR=b!SA z`p5h;e7?dJYQ+!680GF%G&aLX5AJsYN)__72)`6Fit|dMT{md0D4$O~ zRdUc?3_9oV+<4GapZ7wpy69*7XZ_Ruae8ow<2b#^;VDjVZz`=vcyg+d#aDVBBZiW^^<<7`Ga2Id0)-!?y>G=Zz=%{Z6Bw(bc%o zXlXR&nbnMnMy65Ks6=myL7Orh#i2$LuaL%ZMtO;mdTfsZK`P_WSGnt&CMP zKgn^1>vG^p0axpukQ-+LO65YEbDX&ZbuI3_0_7?jEsTan1;$j~C<~WQGsfNiVJLW- z7LWS}`FxIc&T(9#T}iWK!=sJ7fa}ti1bUkm^u9DLrSmyGXt6Zs(u|UfO5@QL(iq7= z6zP%#s+AmCzT}^WZ>NG$h<8VLouaQf!8k7PS9}(irA^X7X`ot?L?+M&JNTXw@K(=} z#HR2})5vBF$DoZ`7nP1NuG2hEv^m53rJx6KzXUueOCLpfjUWS#6z5DGqd7FH&aai> zQ%NJv^&uxLMp@IS!W~y=EuZ5A^xFkj_VITwBQoeogf?p!jRIa*;|_=O8c{YJNTmI; zJS9EQiV}Rv;Co3%kr>=*)03)31D;+cxI)rVicgZ{#9;iQa526|p@A?#qn5r(mTcn; z96ZhQcKhp*ll@TjFn!y@>(KvbavWNph6We7>WZI2|5Cvai?&6}kP0Csia~*EjO&rw zDoAW~qZT8|LUwGU06q#cT%^*2Ho3GUYUsQqwutZIWIp^8a>au)!Uo}~%e#;#&R-+o zYc$}d_?r@pBQ2mt8sBaDRL!UhO)47ojaG1`9vr9!XR2_Npe-RqA~f;f^lnCc8tD}# zNScoXa&?x`N;}TO{WHiwcF?o)@N1L50}5T>%o*-D%WtOxzMl8D`&;-Fp;bwXv_R4? zeF(>LiE)*OA{FUhDqJiD7b^yORWgvH4EQEai*w?RBr}Qkkjv5y%eaX2oDbw%6qNQ# ze=mTlm*`P8&&}cUWu7Z~iDDtuq!A`M5K4e#poo4SrvJjdJX*QL-vTHT@}n@|jmLG; zLGdFGS=h_S^$bxe&rhenX@MLFl}jK8rI3sg^trfE+i1c_%NykwUs=wU4m3Q|XbPg$ z=WI#tEzk9m=~9d|hjE>y%|iMvxfj5wsSN+Qievd(aAwG=VPote0fmDg|Hlw)0^K+rZ zI)9nJk@svyb26Z=#h8*9hwS|o`hNf#UEsGPymDz(s1<{+(v<4ZsV$Uhi50rpxFOIZ zAzW#mC>i$yYbhD1NY9he@5}TwgPu#TB%RWoZ2DOgX!0qfXD3|Q1wLh;IPILL4@o>B zN)IhYC=W>7l9y)lDLojqq@*%@$Yk8|0VKEbdZIit1Knx~+Lb}lrHvJli-sIE;7T#B z5MP80!tN+kN<)4uXe$KIfEsc7b%LHq!_RV^ZIfxV;!ZvK9zXtcX^jY$!Hj>d! 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""" tmp_dir = tempfile.TemporaryDirectory() - copytree(os.path.join(TEST_DIR_PATH, "assets"), + copytree(os.path.join(_TEST_DIR_PATH, "assets"), os.path.join(tmp_dir.name, "assets")) return tmp_dir.name, tmp_dir @@ -47,12 +52,6 @@ def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32): return torch.tensor(arr).float().view(size) / m -def random_int_tensor(seed, size, low=0, high=2 ** 32, a=22695477, c=1, m=2 ** 32): - """ Same as random_float_tensor but integers between [low, high) - """ - return torch.floor(random_float_tensor(seed, size, a, c, m) * (high - low)) + low - - @contextmanager def AudioBackendScope(new_backend): previous_backend = torchaudio.get_audio_backend() @@ -65,18 +64,14 @@ def AudioBackendScope(new_backend): def filter_backends_with_mp3(backends): # Filter out backends that do not support mp3 - - test_dirpath, _ = create_temp_assets_dir() - test_filepath = os.path.join( - test_dirpath, "assets", "steam-train-whistle-daniel_simon.mp3" - ) + test_filepath = get_asset_path('steam-train-whistle-daniel_simon.mp3') def supports_mp3(backend): try: with AudioBackendScope(backend): torchaudio.load(test_filepath) return True - except RuntimeError: + except (RuntimeError, ImportError): return False return [backend for backend in backends if supports_mp3(backend)] diff --git a/test/test_batch_consistency.py b/test/test_batch_consistency.py index 69c398bfb6..675a21e029 100644 --- a/test/test_batch_consistency.py +++ b/test/test_batch_consistency.py @@ -1,5 +1,4 @@ """Test numerical consistency among single input and batched input.""" -import os import unittest import torch @@ -7,67 +6,25 @@ import torchaudio.functional as F import common_utils -from common_utils import AudioBackendScope, BACKENDS -def _test_batch_shape(functional, tensor, *args, **kwargs): - - kwargs_compare = {} - if 'atol' in kwargs: - atol = kwargs['atol'] - del kwargs['atol'] - kwargs_compare['atol'] = atol - - if 'rtol' in kwargs: - rtol = kwargs['rtol'] - del kwargs['rtol'] - kwargs_compare['rtol'] = rtol - - # Single then transform then batch - +def _test_batch_consistency(functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, **kwargs): + # run then batch the result torch.random.manual_seed(42) expected = functional(tensor.clone(), *args, **kwargs) - expected = expected.unsqueeze(0).unsqueeze(0) - - # 1-Batch then transform - tensors = tensor.unsqueeze(0).unsqueeze(0) + expected = expected.repeat([batch_size] + [1] * expected.dim()) + # batch the input and run torch.random.manual_seed(42) - computed = functional(tensors.clone(), *args, **kwargs) - - assert expected.shape == computed.shape, (expected.shape, computed.shape) - assert torch.allclose(expected, computed, **kwargs_compare) - - return tensors, expected - - -def _test_batch(functional, tensor, *args, **kwargs): - tensors, expected = _test_batch_shape(functional, tensor, *args, **kwargs) - - kwargs_compare = {} - if 'atol' in kwargs: - atol = kwargs['atol'] - del kwargs['atol'] - kwargs_compare['atol'] = atol - - if 'rtol' in kwargs: - rtol = kwargs['rtol'] - del kwargs['rtol'] - kwargs_compare['rtol'] = rtol - - # 3-Batch then transform + pattern = [batch_size] + [1] * tensor.dim() + computed = functional(tensor.repeat(pattern), *args, **kwargs) - ind = [3] + [1] * (int(tensors.dim()) - 1) - tensors = tensor.repeat(*ind) + torch.testing.assert_allclose(computed, expected, rtol=rtol, atol=atol) - ind = [3] + [1] * (int(expected.dim()) - 1) - expected = expected.repeat(*ind) - torch.random.manual_seed(42) - computed = functional(tensors.clone(), *args, **kwargs) - - assert expected.shape == computed.shape, (expected.shape, computed.shape) - assert torch.allclose(expected, computed, **kwargs_compare) +def _test_batch(functional, tensor, *args, atol=1e-8, rtol=1e-5, **kwargs): + _test_batch_consistency(functional, tensor, *args, batch_size=1, atol=atol, rtol=rtol, **kwargs) + _test_batch_consistency(functional, tensor, *args, batch_size=3, atol=atol, rtol=rtol, **kwargs) class TestFunctional(unittest.TestCase): @@ -95,7 +52,7 @@ def test_detect_pitch_frequency(self): '440Hz_44100Hz_16bit_05sec.wav', # 1ch ] for filename in filenames: - filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', filename) + filepath = common_utils.get_asset_path(filename) waveform, sample_rate = torchaudio.load(filepath) _test_batch(F.detect_pitch_frequency, waveform, sample_rate) @@ -107,6 +64,30 @@ def test_istft(self): ]) _test_batch(F.istft, stft, n_fft=4, length=4) + def test_contrast(self): + waveform = torch.rand(2, 100) - 0.5 + _test_batch(F.contrast, waveform, enhancement_amount=80.) + + def test_dcshift(self): + waveform = torch.rand(2, 100) - 0.5 + _test_batch(F.dcshift, waveform, shift=0.5, limiter_gain=0.05) + + def test_overdrive(self): + waveform = torch.rand(2, 100) - 0.5 + _test_batch(F.overdrive, waveform, gain=45, colour=30) + + def test_sliding_window_cmn(self): + waveform = torch.randn(2, 1024) - 0.5 + _test_batch(F.sliding_window_cmn, waveform, center=True, norm_vars=True) + _test_batch(F.sliding_window_cmn, waveform, center=True, norm_vars=False) + _test_batch(F.sliding_window_cmn, waveform, center=False, norm_vars=True) + _test_batch(F.sliding_window_cmn, waveform, center=False, norm_vars=False) + + def test_vad(self): + filepath = common_utils.get_asset_path("vad-hello-mono-32000.wav") + waveform, sample_rate = torchaudio.load(filepath) + _test_batch(F.vad, waveform, sample_rate=sample_rate) + class TestTransforms(unittest.TestCase): """Test suite for classes defined in `transforms` module""" @@ -119,8 +100,7 @@ def test_batch_AmplitudeToDB(self): # Batch then transform computed = torchaudio.transforms.AmplitudeToDB()(spec.repeat(3, 1, 1)) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_Resample(self): waveform = torch.randn(2, 2786) @@ -131,8 +111,7 @@ def test_batch_Resample(self): # Batch then transform computed = torchaudio.transforms.Resample()(waveform.repeat(3, 1, 1)) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_MelScale(self): specgram = torch.randn(2, 31, 2786) @@ -144,8 +123,7 @@ def test_batch_MelScale(self): computed = torchaudio.transforms.MelScale()(specgram.repeat(3, 1, 1, 1)) # shape = (3, 2, 201, 1394) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_InverseMelScale(self): n_mels = 32 @@ -159,11 +137,10 @@ def test_batch_InverseMelScale(self): computed = torchaudio.transforms.InverseMelScale(n_stft, n_mels)(mel_spec.repeat(3, 1, 1, 1)) # shape = (3, 2, n_mels, 32) - assert computed.shape == expected.shape, (computed.shape, expected.shape) # Because InverseMelScale runs SGD on randomly initialized values so they do not yield # exactly same result. For this reason, tolerance is very relaxed here. - assert torch.allclose(computed, expected, atol=1.0) + torch.testing.assert_allclose(computed, expected, atol=1.0, rtol=1e-5) def test_batch_compute_deltas(self): specgram = torch.randn(2, 31, 2786) @@ -175,12 +152,10 @@ def test_batch_compute_deltas(self): computed = torchaudio.transforms.ComputeDeltas()(specgram.repeat(3, 1, 1, 1)) # shape = (3, 2, 201, 1394) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_mulaw(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 # Single then transform then batch @@ -192,8 +167,7 @@ def test_batch_mulaw(self): computed = torchaudio.transforms.MuLawEncoding()(waveform_batched) # shape = (3, 2, 201, 1394) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) # Single then transform then batch waveform_decoded = torchaudio.transforms.MuLawDecoding()(waveform_encoded) @@ -203,12 +177,10 @@ def test_batch_mulaw(self): computed = torchaudio.transforms.MuLawDecoding()(computed) # shape = (3, 2, 201, 1394) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_spectrogram(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 # Single then transform then batch @@ -216,13 +188,10 @@ def test_batch_spectrogram(self): # Batch then transform computed = torchaudio.transforms.Spectrogram()(waveform.repeat(3, 1, 1)) - - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_melspectrogram(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 # Single then transform then batch @@ -230,15 +199,10 @@ def test_batch_melspectrogram(self): # Batch then transform computed = torchaudio.transforms.MelSpectrogram()(waveform.repeat(3, 1, 1)) + torch.testing.assert_allclose(computed, expected) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) - - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_batch_mfcc(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.mp3') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # Single then transform then batch @@ -246,13 +210,10 @@ def test_batch_mfcc(self): # Batch then transform computed = torchaudio.transforms.MFCC()(waveform.repeat(3, 1, 1)) - - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected, atol=1e-5) + torch.testing.assert_allclose(computed, expected, atol=1e-5, rtol=1e-5) def test_batch_TimeStretch(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 kwargs = { @@ -283,12 +244,10 @@ def test_batch_TimeStretch(self): hop_length=512, )(complex_specgrams.repeat(3, 1, 1, 1, 1)) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected, atol=1e-5) + torch.testing.assert_allclose(computed, expected, atol=1e-5, rtol=1e-5) def test_batch_Fade(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 fade_in_len = 3000 fade_out_len = 3000 @@ -298,13 +257,10 @@ def test_batch_Fade(self): # Batch then transform computed = torchaudio.transforms.Fade(fade_in_len, fade_out_len)(waveform.repeat(3, 1, 1)) - - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_Vol(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 # Single then transform then batch @@ -312,9 +268,7 @@ def test_batch_Vol(self): # Batch then transform computed = torchaudio.transforms.Vol(gain=1.1)(waveform.repeat(3, 1, 1)) - - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) if __name__ == '__main__': diff --git a/test/test_compliance_kaldi.py b/test/test_compliance_kaldi.py index 83751e3e0e..6951ccf59b 100644 --- a/test/test_compliance_kaldi.py +++ b/test/test_compliance_kaldi.py @@ -5,7 +5,9 @@ import torchaudio import torchaudio.compliance.kaldi as kaldi import unittest -from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir + +import common_utils +from common_utils import AudioBackendScope, BACKENDS def extract_window(window, wave, f, frame_length, frame_shift, snip_edges): @@ -45,10 +47,9 @@ def first_sample_of_frame(frame, window_size, window_shift, snip_edges): class Test_Kaldi(unittest.TestCase): - test_dirpath, test_dir = create_temp_assets_dir() - test_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file.wav') - test_8000_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file_8000.wav') - kaldi_output_dir = os.path.join(test_dirpath, 'assets', 'kaldi') + test_filepath = common_utils.get_asset_path('kaldi_file.wav') + test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav') + kaldi_output_dir = common_utils.get_asset_path('kaldi') test_filepaths = {prefix: [] for prefix in compliance.utils.TEST_PREFIX} # separating test files by their types (e.g 'spec', 'fbank', etc.) @@ -77,7 +78,7 @@ def _test_get_strided_helper(self, num_samples, window_size, window_shift, snip_ for r in range(m): extract_window(window, waveform, r, window_size, window_shift, snip_edges) - self.assertTrue(torch.allclose(window, output)) + torch.testing.assert_allclose(window, output) def test_get_strided(self): # generate any combination where 0 < window_size <= num_samples and @@ -90,8 +91,7 @@ def test_get_strided(self): def _create_data_set(self): # used to generate the dataset to test on. this is not used in testing (offline procedure) - test_dirpath = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) - test_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file.wav') + test_filepath = common_utils.get_asset_path('kaldi_file.wav') sr = 16000 x = torch.arange(0, 20).float() # between [-6,6] @@ -104,7 +104,7 @@ def _create_data_set(self): sound, sample_rate = torchaudio.load(test_filepath, normalization=False) print(y >> 16) self.assertTrue(sample_rate == sr) - self.assertTrue(torch.allclose(y, sound)) + torch.testing.assert_allclose(y, sound) def _print_diagnostic(self, output, expect_output): # given an output and expected output, it will print the absolute/relative errors (max and mean squared) @@ -156,8 +156,7 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil output = get_output_fn(sound, args) self._print_diagnostic(output, kaldi_output) - self.assertTrue(output.shape, kaldi_output.shape) - self.assertTrue(torch.allclose(output, kaldi_output, atol=atol, rtol=rtol)) + torch.testing.assert_allclose(output, kaldi_output, atol=atol, rtol=rtol) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -299,7 +298,7 @@ def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_fact ground_truth = ground_truth[..., n_to_trim:-n_to_trim] estimate = estimate[..., n_to_trim:-n_to_trim] - self.assertTrue(torch.allclose(ground_truth, estimate, atol=atol, rtol=rtol)) + torch.testing.assert_allclose(estimate, ground_truth, atol=atol, rtol=rtol) def test_resample_waveform_downsample_accuracy(self): for i in range(1, 20): @@ -324,7 +323,7 @@ def test_resample_waveform_multi_channel(self): for i in range(num_channels): single_channel = sound * (i + 1) * 1.5 single_channel_sampled = kaldi.resample_waveform(single_channel, sample_rate, sample_rate // 2) - self.assertTrue(torch.allclose(multi_sound_sampled[i, :], single_channel_sampled, rtol=1e-4)) + torch.testing.assert_allclose(multi_sound_sampled[i, :], single_channel_sampled[0], rtol=1e-4, atol=1e-8) if __name__ == '__main__': diff --git a/test/test_dataloader.py b/test/test_dataloader.py index 10f861c8b8..602491ee21 100644 --- a/test/test_dataloader.py +++ b/test/test_dataloader.py @@ -1,23 +1,17 @@ import unittest -import torch -import torch.nn as nn -from torch.utils.data import Dataset, DataLoader -import torchaudio -import math -import os -from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir +import torchaudio +from torch.utils.data import Dataset, DataLoader -@unittest.skipIf("sox" not in BACKENDS, "sox not available") -class TORCHAUDIODS(Dataset): +import common_utils +from common_utils import AudioBackendScope, BACKENDS - test_dirpath, test_dir = create_temp_assets_dir() +class TORCHAUDIODS(Dataset): def __init__(self): - self.asset_dirpath = os.path.join(self.test_dirpath, "assets") sound_files = ["sinewave.wav", "steam-train-whistle-daniel_simon.mp3"] - self.data = [os.path.join(self.asset_dirpath, fn) for fn in sound_files] - self.si, self.ei = torchaudio.info(os.path.join(self.asset_dirpath, "sinewave.wav")) + self.data = [common_utils.get_asset_path(fn) for fn in sound_files] + self.si, self.ei = torchaudio.info(common_utils.get_asset_path("sinewave.wav")) self.si.precision = 16 self.E = torchaudio.sox_effects.SoxEffectsChain() self.E.append_effect_to_chain("rate", [self.si.rate]) # resample to 16000hz @@ -36,14 +30,6 @@ def __len__(self): @unittest.skipIf("sox" not in BACKENDS, "sox not available") class Test_DataLoader(unittest.TestCase): - @classmethod - def setUpClass(cls): - torchaudio.initialize_sox() - - @classmethod - def tearDownClass(cls): - torchaudio.shutdown_sox() - def test_1(self): expected_size = (2, 1, 16000) ds = TORCHAUDIODS() @@ -51,6 +37,7 @@ def test_1(self): for x in dl: self.assertTrue(x.size() == expected_size) + if __name__ == '__main__': with AudioBackendScope("sox"): unittest.main() diff --git a/test/test_datasets.py b/test/test_datasets.py index 4aa35b2063..e42e21e999 100644 --- a/test/test_datasets.py +++ b/test/test_datasets.py @@ -1,4 +1,3 @@ -import os import unittest from torchaudio.datasets.commonvoice import COMMONVOICE @@ -13,8 +12,7 @@ class TestDatasets(unittest.TestCase): - test_dirpath, test_dir = common_utils.create_temp_assets_dir() - path = os.path.join(test_dirpath, "assets") + path = common_utils.get_asset_path() def test_yesno(self): data = YESNO(self.path) diff --git a/test/test_functional.py b/test/test_functional.py index f779d8988d..a126bd9581 100644 --- a/test/test_functional.py +++ b/test/test_functional.py @@ -1,5 +1,4 @@ import math -import os import unittest import torch @@ -10,14 +9,49 @@ import common_utils +class _LfilterMixin: + device = None + dtype = None + + def test_simple(self): + """ + Create a very basic signal, + Then make a simple 4th order delay + The output should be same as the input but shifted + """ + + torch.random.manual_seed(42) + waveform = torch.rand(2, 44100 * 1, dtype=self.dtype, device=self.device) + b_coeffs = torch.tensor([0, 0, 0, 1], dtype=self.dtype, device=self.device) + a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device) + output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) + + torch.testing.assert_allclose(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) + + +class TestLfilterFloat32CPU(_LfilterMixin, unittest.TestCase): + device = torch.device('cpu') + dtype = torch.float32 + + +class TestLfilterFloat64CPU(_LfilterMixin, unittest.TestCase): + device = torch.device('cpu') + dtype = torch.float64 + + +@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") +class TestLfilterFloat32CUDA(_LfilterMixin, unittest.TestCase): + device = torch.device('cuda') + dtype = torch.float32 + + class TestComputeDeltas(unittest.TestCase): """Test suite for correctness of compute_deltas""" def test_one_channel(self): specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) computed = F.compute_deltas(specgram, win_length=3) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_two_channels(self): specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], @@ -25,16 +59,13 @@ def test_two_channels(self): expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], [0.5, 1.0, 1.0, 0.5]]]) computed = F.compute_deltas(specgram, win_length=3) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def _compare_estimate(sound, estimate, atol=1e-6, rtol=1e-8): # trim sound for case when constructed signal is shorter than original sound = sound[..., :estimate.size(-1)] - - assert sound.shape == estimate.shape, (sound.shape, estimate.shape) - assert torch.allclose(sound, estimate, atol=atol, rtol=rtol) + torch.testing.assert_allclose(estimate, sound, atol=atol, rtol=rtol) def _test_istft_is_inverse_of_stft(kwargs): @@ -269,10 +300,8 @@ def test_linearity_of_istft4(self): class TestDetectPitchFrequency(unittest.TestCase): def test_pitch(self): - test_filepath_100 = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', "100Hz_44100Hz_16bit_05sec.wav") - test_filepath_440 = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', "440Hz_44100Hz_16bit_05sec.wav") + test_filepath_100 = common_utils.get_asset_path("100Hz_44100Hz_16bit_05sec.wav") + test_filepath_440 = common_utils.get_asset_path("440Hz_44100Hz_16bit_05sec.wav") # Files from https://www.mediacollege.com/audio/tone/download/ tests = [ @@ -308,13 +337,13 @@ def test_DB_to_amplitude(self): db = F.amplitude_to_DB(torch.abs(x), multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - self.assertTrue(torch.allclose(torch.abs(x), x2, atol=5e-5)) + torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) # Spectrogram amplitude -> DB -> amplitude db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - self.assertTrue(torch.allclose(spec, x2, atol=5e-5)) + torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) # Waveform power -> DB -> power multiplier = 10. @@ -323,13 +352,13 @@ def test_DB_to_amplitude(self): db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - self.assertTrue(torch.allclose(torch.abs(x), x2, atol=5e-5)) + torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) # Spectrogram power -> DB -> power db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - self.assertTrue(torch.allclose(spec, x2, atol=5e-5)) + torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) @pytest.mark.parametrize('complex_tensor', [ @@ -341,7 +370,7 @@ def test_complex_norm(complex_tensor, power): expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) norm_tensor = F.complex_norm(complex_tensor, power) - assert torch.allclose(expected_norm_tensor, norm_tensor, atol=1e-5) + torch.testing.assert_allclose(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) @pytest.mark.parametrize('specgram', [ @@ -365,13 +394,12 @@ def test_mask_along_axis(specgram, mask_param, mask_value, axis): assert num_masked_columns < mask_param -@pytest.mark.parametrize('specgrams', [ - torch.randn(4, 2, 1025, 400), -]) @pytest.mark.parametrize('mask_param', [100]) @pytest.mark.parametrize('mask_value', [0., 30.]) @pytest.mark.parametrize('axis', [2, 3]) -def test_mask_along_axis_iid(specgrams, mask_param, mask_value, axis): +def test_mask_along_axis_iid(mask_param, mask_value, axis): + torch.random.manual_seed(42) + specgrams = torch.randn(4, 2, 1025, 400) mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) diff --git a/test/test.py b/test/test_io.py similarity index 100% rename from test/test.py rename to test/test_io.py diff --git a/test/test_kaldi_io.py b/test/test_kaldi_io.py index f4ff557e56..bc9903a3ef 100644 --- a/test/test_kaldi_io.py +++ b/test/test_kaldi_io.py @@ -1,21 +1,21 @@ -import os +import unittest + import torch import torchaudio.kaldi_io as kio -import unittest + import common_utils class Test_KaldiIO(unittest.TestCase): data1 = [[1, 2, 3], [11, 12, 13], [21, 22, 23]] data2 = [[31, 32, 33], [41, 42, 43], [51, 52, 53]] - test_dirpath, test_dir = common_utils.create_temp_assets_dir() def _test_helper(self, file_name, expected_data, fn, expected_dtype): """ Takes a file_name to the input data and a function fn to extract the data. It compares the extracted data to the expected_data. The expected_dtype will be used to check that the extracted data is of the right type. """ - test_filepath = os.path.join(self.test_dirpath, "assets", file_name) + test_filepath = common_utils.get_asset_path(file_name) expected_output = {'key' + str(idx + 1): torch.tensor(val, dtype=expected_dtype) for idx, val in enumerate(expected_data)} diff --git a/test/test_librosa_compatibility.py b/test/test_librosa_compatibility.py index 36bdb7a195..460f379ce9 100644 --- a/test/test_librosa_compatibility.py +++ b/test/test_librosa_compatibility.py @@ -51,7 +51,7 @@ def test_griffinlim(self): momentum=momentum, init=init, length=length) lr_out = torch.from_numpy(lr_out).unsqueeze(0) - assert torch.allclose(ta_out, lr_out, atol=5e-5) + torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0): librosa_fb = librosa.filters.mel(sr=sample_rate, @@ -68,7 +68,8 @@ def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fm n_freqs=(n_fft // 2 + 1)) for i_mel_bank in range(n_mels): - assert torch.allclose(fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4) + torch.testing.assert_allclose(fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), + atol=1e-4, rtol=1e-5) def test_create_fb(self): self._test_create_fb() @@ -91,18 +92,18 @@ def test_amplitude_to_DB(self): ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) lr_out = librosa.core.power_to_db(spec.numpy()) - lr_out = torch.from_numpy(lr_out).unsqueeze(0) + lr_out = torch.from_numpy(lr_out) - assert torch.allclose(ta_out, lr_out, atol=5e-5) + torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) # Amplitude to DB multiplier = 20.0 ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) lr_out = librosa.core.amplitude_to_db(spec.numpy()) - lr_out = torch.from_numpy(lr_out).unsqueeze(0) + lr_out = torch.from_numpy(lr_out) - assert torch.allclose(ta_out, lr_out, atol=5e-5) + torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) @pytest.mark.parametrize('complex_specgrams', [ @@ -148,7 +149,7 @@ def test_phase_vocoder(complex_specgrams, rate, hop_length): def _load_audio_asset(*asset_paths, **kwargs): - file_path = os.path.join(common_utils.TEST_DIR_PATH, 'assets', *asset_paths) + file_path = common_utils.get_asset_path(*asset_paths) sound, sample_rate = torchaudio.load(file_path, **kwargs) return sound, sample_rate @@ -164,7 +165,7 @@ def _test_compatibilities(n_fft, hop_length, power, n_mels, n_mfcc, sample_rate) y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) out_torch = spect_transform(sound).squeeze().cpu() - assert torch.allclose(out_torch, torch.from_numpy(out_librosa), atol=1e-5) + torch.testing.assert_allclose(out_torch, torch.from_numpy(out_librosa), atol=1e-5, rtol=1e-5) # test mel spectrogram melspect_transform = torchaudio.transforms.MelSpectrogram( @@ -175,25 +176,25 @@ def _test_compatibilities(n_fft, hop_length, power, n_mels, n_mfcc, sample_rate) hop_length=hop_length, n_mels=n_mels, htk=True, norm=None) librosa_mel_tensor = torch.from_numpy(librosa_mel) torch_mel = melspect_transform(sound).squeeze().cpu() - assert torch.allclose( - torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3) + torch.testing.assert_allclose( + torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3, rtol=1e-5) # test s2db power_to_db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.) power_to_db_torch = power_to_db_transform(spect_transform(sound)).squeeze().cpu() power_to_db_librosa = librosa.core.spectrum.power_to_db(out_librosa) - assert torch.allclose(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3) + torch.testing.assert_allclose(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3, rtol=1e-5) mag_to_db_transform = torchaudio.transforms.AmplitudeToDB('magnitude', 80.) mag_to_db_torch = mag_to_db_transform(torch.abs(sound)).squeeze().cpu() mag_to_db_librosa = librosa.core.spectrum.amplitude_to_db(sound_librosa) - assert torch.allclose(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3) + torch.testing.assert_allclose(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3, rtol=1e-5) power_to_db_torch = power_to_db_transform(melspect_transform(sound)).squeeze().cpu() db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) db_librosa_tensor = torch.from_numpy(db_librosa) - assert torch.allclose( - power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3) + torch.testing.assert_allclose( + power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3, rtol=1e-5) # test MFCC melkwargs = {'hop_length': hop_length, 'n_fft': n_fft} @@ -214,8 +215,8 @@ def _test_compatibilities(n_fft, hop_length, power, n_mels, n_mfcc, sample_rate) librosa_mfcc_tensor = torch.from_numpy(librosa_mfcc) torch_mfcc = mfcc_transform(sound).squeeze().cpu() - assert torch.allclose( - torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3) + torch.testing.assert_allclose( + torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3, rtol=1e-5) class TestTransforms(_LibrosaMixin, unittest.TestCase): @@ -242,10 +243,8 @@ def test_basics2(self): } _test_compatibilities(**kwargs) - # NOTE: Test passes offline, but fails on TravisCI, see #372. - @unittest.skipIf( - os.environ.get('CI') == 'true' and os.environ.get('TRAVIS') == 'true', - 'Test is known to fail on TravisCI') + # NOTE: Test passes offline, but fails on TravisCI (and CircleCI), see #372. + @unittest.skipIf('CI' in os.environ, 'Test is known to fail on CI') def test_basics3(self): kwargs = { 'n_fft': 200, @@ -289,7 +288,7 @@ def test_MelScale(self): S=spec_lr, sr=sample_rate, n_fft=n_fft, hop_length=hop_length, win_length=n_fft, center=True, window='hann', n_mels=n_mels, htk=True, norm=None) # Note: Using relaxed rtol instead of atol - assert torch.allclose(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), rtol=1e-3) + torch.testing.assert_allclose(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), atol=1e-8, rtol=1e-3) def test_InverseMelScale(self): """InverseMelScale transform is comparable to that of librosa""" @@ -332,7 +331,7 @@ def test_InverseMelScale(self): # https://github.com/pytorch/audio/pull/366 for the discussion of the choice of algorithm # https://github.com/pytorch/audio/pull/448/files#r385747021 for the distribution of P-inf # distance over frequencies. - assert torch.allclose(spec_ta, spec_lr, atol=threshold) + torch.testing.assert_allclose(spec_ta, spec_lr, atol=threshold, rtol=1e-5) threshold = 1700.0 # This threshold was choosen empirically, based on the following observations diff --git a/test/test_functional_filtering.py b/test/test_sox_compatibility.py similarity index 54% rename from test/test_functional_filtering.py rename to test/test_sox_compatibility.py index c02abbd8d2..508ec7abfa 100644 --- a/test/test_functional_filtering.py +++ b/test/test_sox_compatibility.py @@ -1,4 +1,3 @@ -import os import unittest import torch @@ -6,102 +5,15 @@ import torchaudio.functional as F import torchaudio.transforms as T -from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir +import common_utils +from common_utils import AudioBackendScope, BACKENDS class TestFunctionalFiltering(unittest.TestCase): - test_dirpath, test_dir = create_temp_assets_dir() - - def _test_lfilter_basic(self, dtype, device): - """ - Create a very basic signal, - Then make a simple 4th order delay - The output should be same as the input but shifted - """ - - torch.random.manual_seed(42) - waveform = torch.rand(2, 44100 * 1, dtype=dtype, device=device) - b_coeffs = torch.tensor([0, 0, 0, 1], dtype=dtype, device=device) - a_coeffs = torch.tensor([1, 0, 0, 0], dtype=dtype, device=device) - output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) - - assert torch.allclose(waveform[:, 0:-3], output_waveform[:, 3:], atol=1e-5) - - def test_lfilter_basic(self): - self._test_lfilter_basic(torch.float32, torch.device("cpu")) - - def test_lfilter_basic_double(self): - self._test_lfilter_basic(torch.float64, torch.device("cpu")) - - def test_lfilter_basic_gpu(self): - if torch.cuda.is_available(): - self._test_lfilter_basic(torch.float32, torch.device("cuda:0")) - else: - print("skipping GPU test for lfilter_basic because device not available") - pass - - def _test_lfilter(self, waveform, device): - """ - Design an IIR lowpass filter using scipy.signal filter design - https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirdesign.html#scipy.signal.iirdesign - - Example - >>> from scipy.signal import iirdesign - >>> b, a = iirdesign(0.2, 0.3, 1, 60) - """ - - b_coeffs = torch.tensor( - [ - 0.00299893, - -0.0051152, - 0.00841964, - -0.00747802, - 0.00841964, - -0.0051152, - 0.00299893, - ], - device=device, - ) - a_coeffs = torch.tensor( - [ - 1.0, - -4.8155751, - 10.2217618, - -12.14481273, - 8.49018171, - -3.3066882, - 0.56088705, - ], - device=device, - ) - - output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) - assert len(output_waveform.size()) == 2 - assert output_waveform.size(0) == waveform.size(0) - assert output_waveform.size(1) == waveform.size(1) - - def test_lfilter(self): - - filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) - - self._test_lfilter(waveform, torch.device("cpu")) - - def test_lfilter_gpu(self): - if torch.cuda.is_available(): - filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) - cuda0 = torch.device("cuda:0") - cuda_waveform = waveform.cuda(device=cuda0) - self._test_lfilter(cuda_waveform, cuda0) - else: - print("skipping GPU test for lfilter because device not available") - pass - @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_gain(self): - test_filepath = os.path.join(self.test_dirpath, "assets", "steam-train-whistle-daniel_simon.wav") + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) waveform_gain = F.gain(waveform, 3) @@ -112,12 +24,12 @@ def test_gain(self): E.append_effect_to_chain("gain", [3]) sox_gain_waveform = E.sox_build_flow_effects()[0] - assert torch.allclose(waveform_gain, sox_gain_waveform, atol=1e-04) + torch.testing.assert_allclose(waveform_gain, sox_gain_waveform, atol=1e-04, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_dither(self): - test_filepath = os.path.join(self.test_dirpath, "assets", "steam-train-whistle-daniel_simon.wav") + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) waveform_dithered = F.dither(waveform) @@ -128,18 +40,18 @@ def test_dither(self): E.append_effect_to_chain("dither", []) sox_dither_waveform = E.sox_build_flow_effects()[0] - assert torch.allclose(waveform_dithered, sox_dither_waveform, atol=1e-04) + torch.testing.assert_allclose(waveform_dithered, sox_dither_waveform, atol=1e-04, rtol=1e-5) E.clear_chain() E.append_effect_to_chain("dither", ["-s"]) sox_dither_waveform_ns = E.sox_build_flow_effects()[0] - assert torch.allclose(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02) + torch.testing.assert_allclose(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_vctk_transform_pipeline(self): - test_filepath_vctk = os.path.join(self.test_dirpath, "assets/VCTK-Corpus/wav48/p224/", "p224_002.wav") + test_filepath_vctk = common_utils.get_asset_path('VCTK-Corpus', 'wav48', 'p224', 'p224_002.wav') wf_vctk, sr_vctk = torchaudio.load(test_filepath_vctk) # rate @@ -157,28 +69,27 @@ def test_vctk_transform_pipeline(self): E.append_effect_to_chain("dither", ["-s"]) wf_vctk_sox = E.sox_build_flow_effects()[0] - assert torch.allclose(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) + torch.testing.assert_allclose(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_lowpass(self): - """ Test biquad lowpass filter, compare to SoX implementation """ - CUTOFF_FREQ = 3000 + cutoff_freq = 3000 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("lowpass", [CUTOFF_FREQ]) + E.append_effect_to_chain("lowpass", [cutoff_freq]) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.lowpass_biquad(waveform, sample_rate, CUTOFF_FREQ) + output_waveform = F.lowpass_biquad(waveform, sample_rate, cutoff_freq) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -187,19 +98,19 @@ def test_highpass(self): Test biquad highpass filter, compare to SoX implementation """ - CUTOFF_FREQ = 2000 + cutoff_freq = 2000 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("highpass", [CUTOFF_FREQ]) + E.append_effect_to_chain("highpass", [cutoff_freq]) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.highpass_biquad(waveform, sample_rate, CUTOFF_FREQ) + output_waveform = F.highpass_biquad(waveform, sample_rate, cutoff_freq) # TBD - this fails at the 1e-4 level, debug why - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-3) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -208,19 +119,19 @@ def test_allpass(self): Test biquad allpass filter, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 + central_freq = 1000 + q = 0.707 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("allpass", [CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("allpass", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.allpass_biquad(waveform, sample_rate, CENTRAL_FREQ, Q) + output_waveform = F.allpass_biquad(waveform, sample_rate, central_freq, q) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -229,20 +140,20 @@ def test_bandpass_with_csg(self): Test biquad bandpass filter, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 - CONST_SKIRT_GAIN = True + central_freq = 1000 + q = 0.707 + const_skirt_gain = True - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("bandpass", ["-c", CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("bandpass", ["-c", central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandpass_biquad(waveform, sample_rate, CENTRAL_FREQ, Q, CONST_SKIRT_GAIN) + output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -251,20 +162,20 @@ def test_bandpass_without_csg(self): Test biquad bandpass filter, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 - CONST_SKIRT_GAIN = False + central_freq = 1000 + q = 0.707 + const_skirt_gain = False - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("bandpass", [CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("bandpass", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandpass_biquad(waveform, sample_rate, CENTRAL_FREQ, Q, CONST_SKIRT_GAIN) + output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -273,19 +184,19 @@ def test_bandreject(self): Test biquad bandreject filter, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 + central_freq = 1000 + q = 0.707 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("bandreject", [CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("bandreject", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandreject_biquad(waveform, sample_rate, CENTRAL_FREQ, Q) + output_waveform = F.bandreject_biquad(waveform, sample_rate, central_freq, q) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -294,20 +205,20 @@ def test_band_with_noise(self): Test biquad band filter with noise mode, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 - NOISE = True + central_freq = 1000 + q = 0.707 + noise = True - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("band", ["-n", CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("band", ["-n", central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.band_biquad(waveform, sample_rate, CENTRAL_FREQ, Q, NOISE) + output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -316,20 +227,20 @@ def test_band_without_noise(self): Test biquad band filter without noise mode, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 - NOISE = False + central_freq = 1000 + q = 0.707 + noise = False - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("band", [CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("band", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.band_biquad(waveform, sample_rate, CENTRAL_FREQ, Q, NOISE) + output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -338,20 +249,20 @@ def test_treble(self): Test biquad treble filter, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 - GAIN = 40 + central_freq = 1000 + q = 0.707 + gain = 40 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("treble", [GAIN, CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("treble", [gain, central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.treble_biquad(waveform, sample_rate, GAIN, CENTRAL_FREQ, Q) + output_waveform = F.treble_biquad(waveform, sample_rate, gain, central_freq, q) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -360,7 +271,7 @@ def test_deemph(self): Test biquad deemph filter, compare to SoX implementation """ - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) E.append_effect_to_chain("deemph") @@ -369,7 +280,7 @@ def test_deemph(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.deemph_biquad(waveform, sample_rate) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -378,7 +289,7 @@ def test_riaa(self): Test biquad riaa filter, compare to SoX implementation """ - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) E.append_effect_to_chain("riaa") @@ -387,7 +298,81 @@ def test_riaa(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.riaa_biquad(waveform, sample_rate) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_contrast(self): + """ + Test contrast effect, compare to SoX implementation + """ + enhancement_amount = 80. + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("contrast", [enhancement_amount]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.contrast(waveform, enhancement_amount) + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_dcshift_with_limiter(self): + """ + Test dcshift effect, compare to SoX implementation + """ + shift = 0.5 + limiter_gain = 0.05 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("dcshift", [shift, limiter_gain]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, _ = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.dcshift(waveform, shift, limiter_gain) + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_dcshift_without_limiter(self): + """ + Test dcshift effect, compare to SoX implementation + """ + shift = 0.6 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("dcshift", [shift]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, _ = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.dcshift(waveform, shift) + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_overdrive(self): + """ + Test overdrive effect, compare to SoX implementation + """ + gain = 30 + colour = 40 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("overdrive", [gain, colour]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, _ = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.overdrive(waveform, gain, colour) + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -396,26 +381,26 @@ def test_equalizer(self): Test biquad peaking equalizer filter, compare to SoX implementation """ - CENTER_FREQ = 300 - Q = 0.707 - GAIN = 1 + center_freq = 300 + q = 0.707 + gain = 1 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("equalizer", [CENTER_FREQ, Q, GAIN]) + E.append_effect_to_chain("equalizer", [center_freq, q, gain]) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.equalizer_biquad(waveform, sample_rate, CENTER_FREQ, GAIN, Q) + output_waveform = F.equalizer_biquad(waveform, sample_rate, center_freq, gain, q) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_perf_biquad_filtering(self): - fn_sine = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + fn_sine = common_utils.get_asset_path('whitenoise.wav') b0 = 0.4 b1 = 0.2 @@ -435,7 +420,7 @@ def test_perf_biquad_filtering(self): waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2]) ) - assert torch.allclose(waveform_sox_out, waveform_lfilter_out, atol=1e-4) + torch.testing.assert_allclose(waveform_lfilter_out, waveform_sox_out, atol=1e-4, rtol=1e-5) if __name__ == "__main__": diff --git a/test/test_sox_effects.py b/test/test_sox_effects.py index 3445a5144b..487d73a53d 100644 --- a/test/test_sox_effects.py +++ b/test/test_sox_effects.py @@ -2,27 +2,17 @@ import torch import torchaudio import math -import os -from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir +import common_utils +from common_utils import AudioBackendScope, BACKENDS @unittest.skipIf("sox" not in BACKENDS, "sox not available") class Test_SoxEffectsChain(unittest.TestCase): - test_dirpath, test_dir = create_temp_assets_dir() - test_filepath = os.path.join(test_dirpath, "assets", - "steam-train-whistle-daniel_simon.mp3") - - @classmethod - def setUpClass(cls): - torchaudio.initialize_sox() - - @classmethod - def tearDownClass(cls): - torchaudio.shutdown_sox() + test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.mp3") def test_single_channel(self): - fn_sine = os.path.join(self.test_dirpath, "assets", "sinewave.wav") + fn_sine = common_utils.get_asset_path("sinewave.wav") E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(fn_sine) E.append_effect_to_chain("echos", [0.8, 0.7, 40, 0.25, 63, 0.3]) @@ -259,6 +249,24 @@ def test_vol(self): # check if effect worked self.assertTrue(x.allclose(z, rtol=1e-4, atol=1e-4)) + def test_vad(self): + sample_files = [ + common_utils.get_asset_path("vad-hello-stereo-44100.wav"), + common_utils.get_asset_path("vad-hello-mono-32000.wav") + ] + + for sample_file in sample_files: + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(sample_file) + E.append_effect_to_chain("vad") + x, _ = E.sox_build_flow_effects() + + x_orig, sample_rate = torchaudio.load(sample_file) + vad = torchaudio.transforms.Vad(sample_rate) + + y = vad(x_orig) + self.assertTrue(x.allclose(y, rtol=1e-4, atol=1e-4)) + if __name__ == '__main__': with AudioBackendScope("sox"): diff --git a/test/test_torchscript_consistency.py b/test/test_torchscript_consistency.py index fba009d29d..5f0b12c76b 100644 --- a/test/test_torchscript_consistency.py +++ b/test/test_torchscript_consistency.py @@ -1,413 +1,588 @@ """Test suites for jit-ability and its numerical compatibility""" -import os import unittest import torch import torchaudio import torchaudio.functional as F -import torchaudio.transforms +import torchaudio.transforms as T import common_utils -def _test_torchscript_functional_shape(py_method, *args, **kwargs): - jit_method = torch.jit.script(py_method) - - jit_out = jit_method(*args, **kwargs) - py_out = py_method(*args, **kwargs) - - assert jit_out.shape == py_out.shape - return jit_out, py_out - - -def _test_torchscript_functional(py_method, *args, **kwargs): - jit_out, py_out = _test_torchscript_functional_shape(py_method, *args, **kwargs) - assert torch.allclose(jit_out, py_out) - - -def _test_lfilter(waveform): - """ - Design an IIR lowpass filter using scipy.signal filter design - https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirdesign.html#scipy.signal.iirdesign - - Example - >>> from scipy.signal import iirdesign - >>> b, a = iirdesign(0.2, 0.3, 1, 60) - """ - b_coeffs = torch.tensor( - [ - 0.00299893, - -0.0051152, - 0.00841964, - -0.00747802, - 0.00841964, - -0.0051152, - 0.00299893, - ], - device=waveform.device, - ) - a_coeffs = torch.tensor( - [ - 1.0, - -4.8155751, - 10.2217618, - -12.14481273, - 8.49018171, - -3.3066882, - 0.56088705, - ], - device=waveform.device, - ) - _test_torchscript_functional(F.lfilter, waveform, a_coeffs, b_coeffs) - - -class TestFunctional(unittest.TestCase): - """Test functions in `functional` module.""" +def _assert_functional_consistency(func, tensor, device, shape_only=False): + tensor = tensor.to(device) + ts_func = torch.jit.script(func) + output = func(tensor) + ts_output = ts_func(tensor) + + if shape_only: + assert ts_output.shape == output.shape, (ts_output.shape, output.shape) + else: + torch.testing.assert_allclose(ts_output, output) + + +def _assert_transforms_consistency(transform, tensor, device): + tensor = tensor.to(device) + transform = transform.to(device) + ts_transform = torch.jit.script(transform) + output = transform(tensor) + ts_output = ts_transform(tensor) + torch.testing.assert_allclose(ts_output, output) + + +class _FunctionalTestMixin: + """Implements test for `functinoal` modul that are performed for different devices""" + device = None + + def _assert_consistency(self, func, tensor, shape_only=False): + return _assert_functional_consistency(func, tensor, self.device, shape_only=shape_only) + def test_spectrogram(self): + def func(tensor): + n_fft = 400 + ws = 400 + hop = 200 + pad = 0 + window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) + power = 2. + normalize = False + return F.spectrogram(tensor, pad, window, n_fft, hop, ws, power, normalize) + tensor = torch.rand((1, 1000)) - n_fft = 400 - ws = 400 - hop = 200 - pad = 0 - window = torch.hann_window(ws) - power = 2 - normalize = False - - _test_torchscript_functional( - F.spectrogram, tensor, pad, window, n_fft, hop, ws, power, normalize - ) + self._assert_consistency(func, tensor) def test_griffinlim(self): + def func(tensor): + n_fft = 400 + ws = 400 + hop = 200 + window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) + power = 2. + normalize = False + momentum = 0.99 + n_iter = 32 + length = 1000 + rand_int = False + return F.griffinlim(tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, rand_int) + tensor = torch.rand((1, 201, 6)) - n_fft = 400 - ws = 400 - hop = 200 - window = torch.hann_window(ws) - power = 2 - normalize = False - momentum = 0.99 - n_iter = 32 - length = 1000 - - _test_torchscript_functional( - F.griffinlim, tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, 0 - ) + self._assert_consistency(func, tensor) def test_compute_deltas(self): + def func(tensor): + win_length = 2 * 7 + 1 + return F.compute_deltas(tensor, win_length=win_length) + channel = 13 n_mfcc = channel * 3 time = 1021 - win_length = 2 * 7 + 1 - specgram = torch.randn(channel, n_mfcc, time) - - _test_torchscript_functional(F.compute_deltas, specgram, win_length=win_length) + tensor = torch.randn(channel, n_mfcc, time) + self._assert_consistency(func, tensor) def test_detect_pitch_frequency(self): - filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.mp3') - waveform, sample_rate = torchaudio.load(filepath) - _test_torchscript_functional(F.detect_pitch_frequency, waveform, sample_rate) + filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.mp3') + waveform, _ = torchaudio.load(filepath) + + def func(tensor): + sample_rate = 44100 + return F.detect_pitch_frequency(tensor, sample_rate) + + self._assert_consistency(func, waveform) def test_create_fb_matrix(self): - n_stft = 100 - f_min = 0.0 - f_max = 20.0 - n_mels = 10 - sample_rate = 16000 + if self.device != torch.device('cpu'): + raise unittest.SkipTest('No need to perform test on device other than CPU') + + def func(_): + n_stft = 100 + f_min = 0.0 + f_max = 20.0 + n_mels = 10 + sample_rate = 16000 + return F.create_fb_matrix(n_stft, f_min, f_max, n_mels, sample_rate) - _test_torchscript_functional(F.create_fb_matrix, n_stft, f_min, f_max, n_mels, sample_rate) + dummy = torch.zeros(1, 1) + self._assert_consistency(func, dummy) def test_amplitude_to_DB(self): - spec = torch.rand((6, 201)) - multiplier = 10.0 - amin = 1e-10 - db_multiplier = 0.0 - top_db = 80.0 + def func(tensor): + multiplier = 10.0 + amin = 1e-10 + db_multiplier = 0.0 + top_db = 80.0 + return F.amplitude_to_DB(tensor, multiplier, amin, db_multiplier, top_db) - _test_torchscript_functional(F.amplitude_to_DB, spec, multiplier, amin, db_multiplier, top_db) + tensor = torch.rand((6, 201)) + self._assert_consistency(func, tensor) def test_DB_to_amplitude(self): - x = torch.rand((1, 100)) - ref = 1. - power = 1. + def func(tensor): + ref = 1. + power = 1. + return F.DB_to_amplitude(tensor, ref, power) - _test_torchscript_functional(F.DB_to_amplitude, x, ref, power) + tensor = torch.rand((1, 100)) + self._assert_consistency(func, tensor) def test_create_dct(self): - n_mfcc = 40 - n_mels = 128 - norm = "ortho" + if self.device != torch.device('cpu'): + raise unittest.SkipTest('No need to perform test on device other than CPU') + + def func(_): + n_mfcc = 40 + n_mels = 128 + norm = "ortho" + return F.create_dct(n_mfcc, n_mels, norm) - _test_torchscript_functional(F.create_dct, n_mfcc, n_mels, norm) + dummy = torch.zeros(1, 1) + self._assert_consistency(func, dummy) def test_mu_law_encoding(self): - tensor = torch.rand((1, 10)) - qc = 256 + def func(tensor): + qc = 256 + return F.mu_law_encoding(tensor, qc) - _test_torchscript_functional(F.mu_law_encoding, tensor, qc) + tensor = torch.rand((1, 10)) + self._assert_consistency(func, tensor) def test_mu_law_decoding(self): - tensor = torch.rand((1, 10)) - qc = 256 + def func(tensor): + qc = 256 + return F.mu_law_decoding(tensor, qc) - _test_torchscript_functional(F.mu_law_decoding, tensor, qc) + tensor = torch.rand((1, 10)) + self._assert_consistency(func, tensor) def test_complex_norm(self): - complex_tensor = torch.randn(1, 2, 1025, 400, 2) - power = 2 + def func(tensor): + power = 2. + return F.complex_norm(tensor, power) - _test_torchscript_functional(F.complex_norm, complex_tensor, power) + tensor = torch.randn(1, 2, 1025, 400, 2) + _assert_functional_consistency(func, tensor, self.device) def test_mask_along_axis(self): - specgram = torch.randn(2, 1025, 400) - mask_param = 100 - mask_value = 30. - axis = 2 + def func(tensor): + mask_param = 100 + mask_value = 30. + axis = 2 + return F.mask_along_axis(tensor, mask_param, mask_value, axis) - _test_torchscript_functional(F.mask_along_axis, specgram, mask_param, mask_value, axis) + tensor = torch.randn(2, 1025, 400) + self._assert_consistency(func, tensor) def test_mask_along_axis_iid(self): - specgrams = torch.randn(4, 2, 1025, 400) - mask_param = 100 - mask_value = 30. - axis = 2 + def func(tensor): + mask_param = 100 + mask_value = 30. + axis = 2 + return F.mask_along_axis_iid(tensor, mask_param, mask_value, axis) - _test_torchscript_functional(F.mask_along_axis_iid, specgrams, mask_param, mask_value, axis) + tensor = torch.randn(4, 2, 1025, 400) + self._assert_consistency(func, tensor) def test_gain(self): + def func(tensor): + gainDB = 2.0 + return F.gain(tensor, gainDB) + tensor = torch.rand((1, 1000)) - gainDB = 2.0 + self._assert_consistency(func, tensor) - _test_torchscript_functional(F.gain, tensor, gainDB) + def test_dither_TPDF(self): + def func(tensor): + return F.dither(tensor, 'TPDF') - def test_dither(self): tensor = torch.rand((2, 1000)) + self._assert_consistency(func, tensor, shape_only=True) + + def test_dither_RPDF(self): + def func(tensor): + return F.dither(tensor, 'RPDF') + + tensor = torch.rand((2, 1000)) + self._assert_consistency(func, tensor, shape_only=True) + + def test_dither_GPDF(self): + def func(tensor): + return F.dither(tensor, 'GPDF') - _test_torchscript_functional_shape(F.dither, tensor) - _test_torchscript_functional_shape(F.dither, tensor, "RPDF") - _test_torchscript_functional_shape(F.dither, tensor, "GPDF") + tensor = torch.rand((2, 1000)) + self._assert_consistency(func, tensor, shape_only=True) def test_lfilter(self): - filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav') + filepath = common_utils.get_asset_path('whitenoise.wav') waveform, _ = torchaudio.load(filepath, normalization=True) - _test_lfilter(waveform) - @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") - def test_lfilter_cuda(self): - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) - _test_lfilter(waveform.cuda(device=torch.device("cuda:0"))) + def func(tensor): + # Design an IIR lowpass filter using scipy.signal filter design + # https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirdesign.html#scipy.signal.iirdesign + # + # Example + # >>> from scipy.signal import iirdesign + # >>> b, a = iirdesign(0.2, 0.3, 1, 60) + b_coeffs = torch.tensor( + [ + 0.00299893, + -0.0051152, + 0.00841964, + -0.00747802, + 0.00841964, + -0.0051152, + 0.00299893, + ], + device=tensor.device, + dtype=tensor.dtype, + ) + a_coeffs = torch.tensor( + [ + 1.0, + -4.8155751, + 10.2217618, + -12.14481273, + 8.49018171, + -3.3066882, + 0.56088705, + ], + device=tensor.device, + dtype=tensor.dtype, + ) + return F.lfilter(tensor, a_coeffs, b_coeffs) + + self._assert_consistency(func, waveform) def test_lowpass(self): - cutoff_freq = 3000 + filepath = common_utils.get_asset_path('whitenoise.wav') + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav') - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.lowpass_biquad, waveform, sample_rate, cutoff_freq) + def func(tensor): + sample_rate = 44100 + cutoff_freq = 3000. + return F.lowpass_biquad(tensor, sample_rate, cutoff_freq) + + self._assert_consistency(func, waveform) def test_highpass(self): - cutoff_freq = 2000 + filepath = common_utils.get_asset_path('whitenoise.wav') + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav') - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.highpass_biquad, waveform, sample_rate, cutoff_freq) + def func(tensor): + sample_rate = 44100 + cutoff_freq = 2000. + return F.highpass_biquad(tensor, sample_rate, cutoff_freq) + + self._assert_consistency(func, waveform) def test_allpass(self): - central_freq = 1000 - q = 0.707 + filepath = common_utils.get_asset_path('whitenoise.wav') + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav') - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.allpass_biquad, waveform, sample_rate, central_freq, q) + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + return F.allpass_biquad(tensor, sample_rate, central_freq, q) + + self._assert_consistency(func, waveform) def test_bandpass_with_csg(self): - central_freq = 1000 - q = 0.707 - const_skirt_gain = True + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional( - F.bandpass_biquad, waveform, sample_rate, central_freq, q, const_skirt_gain) + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + const_skirt_gain = True + return F.bandpass_biquad(tensor, sample_rate, central_freq, q, const_skirt_gain) + + self._assert_consistency(func, waveform) def test_bandpass_withou_csg(self): - central_freq = 1000 - q = 0.707 - const_skirt_gain = False + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + const_skirt_gain = True + return F.bandpass_biquad(tensor, sample_rate, central_freq, q, const_skirt_gain) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional( - F.bandpass_biquad, waveform, sample_rate, central_freq, q, const_skirt_gain) + self._assert_consistency(func, waveform) def test_bandreject(self): - central_freq = 1000 - q = 0.707 + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + return F.bandreject_biquad(tensor, sample_rate, central_freq, q) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional( - F.bandreject_biquad, waveform, sample_rate, central_freq, q) + self._assert_consistency(func, waveform) def test_band_with_noise(self): - central_freq = 1000 - q = 0.707 - noise = True + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + noise = True + return F.band_biquad(tensor, sample_rate, central_freq, q, noise) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.band_biquad, waveform, sample_rate, central_freq, q, noise) + self._assert_consistency(func, waveform) def test_band_without_noise(self): - central_freq = 1000 - q = 0.707 - noise = False + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.band_biquad, waveform, sample_rate, central_freq, q, noise) + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + noise = False + return F.band_biquad(tensor, sample_rate, central_freq, q, noise) + + self._assert_consistency(func, waveform) def test_treble(self): - gain = 40 - central_freq = 1000 - q = 0.707 + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.treble_biquad, waveform, sample_rate, gain, central_freq, q) + def func(tensor): + sample_rate = 44100 + gain = 40. + central_freq = 1000. + q = 0.707 + return F.treble_biquad(tensor, sample_rate, gain, central_freq, q) + + self._assert_consistency(func, waveform) def test_deemph(self): - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.deemph_biquad, waveform, sample_rate) + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + return F.deemph_biquad(tensor, sample_rate) + + self._assert_consistency(func, waveform) def test_riaa(self): - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.riaa_biquad, waveform, sample_rate) + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + return F.riaa_biquad(tensor, sample_rate) + + self._assert_consistency(func, waveform) def test_equalizer(self): - center_freq = 300 - gain = 1 - q = 0.707 + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + center_freq = 300. + gain = 1. + q = 0.707 + return F.equalizer_biquad(tensor, sample_rate, center_freq, gain, q) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional( - F.equalizer_biquad, waveform, sample_rate, center_freq, gain, q) + self._assert_consistency(func, waveform) def test_perf_biquad_filtering(self): - a = torch.tensor([0.7, 0.2, 0.6]) - b = torch.tensor([0.4, 0.2, 0.9]) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") + filepath = common_utils.get_asset_path("whitenoise.wav") waveform, _ = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.lfilter, waveform, a, b) + def func(tensor): + a = torch.tensor([0.7, 0.2, 0.6], device=tensor.device, dtype=tensor.dtype) + b = torch.tensor([0.4, 0.2, 0.9], device=tensor.device, dtype=tensor.dtype) + return F.lfilter(tensor, a, b) + + self._assert_consistency(func, waveform) + + def test_sliding_window_cmn(self): + def func(tensor): + cmn_window = 600 + min_cmn_window = 100 + center = False + norm_vars = False + a = torch.tensor( + [ + [ + -1.915875792503357, + 1.147700309753418 + ], + [ + 1.8242558240890503, + 1.3869990110397339 + ] + ], + device=tensor.device, + dtype=tensor.dtype + ) + return F.sliding_window_cmn(a, cmn_window, min_cmn_window, center, norm_vars) + b = torch.tensor( + [ + [ + -1.8701, + -0.1196 + ], + [ + 1.8701, + 0.1196 + ] + ] + ) + self._assert_consistency(func, b) -RUN_CUDA = torch.cuda.is_available() -print("Run test with cuda:", RUN_CUDA) + def test_contrast(self): + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + def func(tensor): + enhancement_amount = 80. + return F.contrast(tensor, enhancement_amount) -def _test_script_module(f, tensor, *args, **kwargs): + self._assert_consistency(func, waveform) - py_method = f(*args, **kwargs) - jit_method = torch.jit.script(py_method) + def test_dcshift(self): + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) - py_out = py_method(tensor) - jit_out = jit_method(tensor) + def func(tensor): + shift = 0.5 + limiter_gain = 0.05 + return F.dcshift(tensor, shift, limiter_gain) - assert torch.allclose(jit_out, py_out) + self._assert_consistency(func, waveform) - if RUN_CUDA: + def test_overdrive(self): + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) - tensor = tensor.to("cuda") + def func(tensor): + gain = 30. + colour = 50. + return F.overdrive(tensor, gain, colour) - py_method = py_method.cuda() - jit_method = torch.jit.script(py_method) + self._assert_consistency(func, waveform) - py_out = py_method(tensor) - jit_out = jit_method(tensor) - assert torch.allclose(jit_out, py_out) +class _TransformsTestMixin: + """Implements test for Transforms that are performed for different devices""" + device = None + def _assert_consistency(self, transform, tensor): + _assert_transforms_consistency(transform, tensor, self.device) -class TestTransforms(unittest.TestCase): def test_Spectrogram(self): tensor = torch.rand((1, 1000)) - _test_script_module(torchaudio.transforms.Spectrogram, tensor) + self._assert_consistency(T.Spectrogram(), tensor) def test_GriffinLim(self): tensor = torch.rand((1, 201, 6)) - _test_script_module(torchaudio.transforms.GriffinLim, tensor, length=1000, rand_init=False) + self._assert_consistency(T.GriffinLim(length=1000, rand_init=False), tensor) def test_AmplitudeToDB(self): spec = torch.rand((6, 201)) - _test_script_module(torchaudio.transforms.AmplitudeToDB, spec) + self._assert_consistency(T.AmplitudeToDB(), spec) def test_MelScale(self): spec_f = torch.rand((1, 6, 201)) - _test_script_module(torchaudio.transforms.MelScale, spec_f) + self._assert_consistency(T.MelScale(), spec_f) def test_MelSpectrogram(self): tensor = torch.rand((1, 1000)) - _test_script_module(torchaudio.transforms.MelSpectrogram, tensor) + self._assert_consistency(T.MelSpectrogram(), tensor) def test_MFCC(self): tensor = torch.rand((1, 1000)) - _test_script_module(torchaudio.transforms.MFCC, tensor) + self._assert_consistency(T.MFCC(), tensor) def test_Resample(self): tensor = torch.rand((2, 1000)) sample_rate = 100. sample_rate_2 = 50. - - _test_script_module(torchaudio.transforms.Resample, tensor, sample_rate, sample_rate_2) + self._assert_consistency(T.Resample(sample_rate, sample_rate_2), tensor) def test_ComplexNorm(self): tensor = torch.rand((1, 2, 201, 2)) - _test_script_module(torchaudio.transforms.ComplexNorm, tensor) + self._assert_consistency(T.ComplexNorm(), tensor) def test_MuLawEncoding(self): tensor = torch.rand((1, 10)) - _test_script_module(torchaudio.transforms.MuLawEncoding, tensor) + self._assert_consistency(T.MuLawEncoding(), tensor) def test_MuLawDecoding(self): tensor = torch.rand((1, 10)) - _test_script_module(torchaudio.transforms.MuLawDecoding, tensor) + self._assert_consistency(T.MuLawDecoding(), tensor) def test_TimeStretch(self): n_freq = 400 hop_length = 512 fixed_rate = 1.3 tensor = torch.rand((10, 2, n_freq, 10, 2)) - _test_script_module( - torchaudio.transforms.TimeStretch, - tensor, n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate) + self._assert_consistency( + T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate), + tensor, + ) def test_Fade(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) fade_in_len = 3000 fade_out_len = 3000 - - _test_script_module(torchaudio.transforms.Fade, waveform, fade_in_len, fade_out_len) + self._assert_consistency(T.Fade(fade_in_len, fade_out_len), waveform) def test_FrequencyMasking(self): tensor = torch.rand((10, 2, 50, 10, 2)) - _test_script_module( - torchaudio.transforms.FrequencyMasking, tensor, freq_mask_param=60, iid_masks=False) + self._assert_consistency(T.FrequencyMasking(freq_mask_param=60, iid_masks=False), tensor) def test_TimeMasking(self): tensor = torch.rand((10, 2, 50, 10, 2)) - _test_script_module( - torchaudio.transforms.TimeMasking, tensor, time_mask_param=30, iid_masks=False) + self._assert_consistency(T.TimeMasking(time_mask_param=30, iid_masks=False), tensor) def test_Vol(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) - _test_script_module(torchaudio.transforms.Vol, waveform, 1.1) + self._assert_consistency(T.Vol(1.1), waveform) + + def test_SlidingWindowCmn(self): + tensor = torch.rand((1000, 10)) + self._assert_consistency(T.SlidingWindowCmn(), tensor) + + def test_Vad(self): + filepath = common_utils.get_asset_path("vad-hello-mono-32000.wav") + waveform, sample_rate = torchaudio.load(filepath) + self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform) + + +class TestFunctionalCPU(_FunctionalTestMixin, unittest.TestCase): + """Test suite for Functional module on CPU""" + device = torch.device('cpu') + + +@unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') +class TestFunctionalCUDA(_FunctionalTestMixin, unittest.TestCase): + """Test suite for Functional module on GPU""" + device = torch.device('cuda') + + +class TestTransformsCPU(_TransformsTestMixin, unittest.TestCase): + """Test suite for Transforms module on CPU""" + device = torch.device('cpu') + + +@unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') +class TestTransformsCUDA(_TransformsTestMixin, unittest.TestCase): + """Test suite for Transforms module on GPU""" + device = torch.device('cuda') if __name__ == '__main__': diff --git a/test/test_transforms.py b/test/test_transforms.py index f8501e72e2..9c0313ba7c 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1,5 +1,4 @@ import math -import os import unittest import torch @@ -7,7 +6,7 @@ import torchaudio.transforms as transforms import torchaudio.functional as F -from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir +import common_utils class Tester(unittest.TestCase): @@ -19,10 +18,6 @@ class Tester(unittest.TestCase): waveform = (torch.cos(2 * math.pi * torch.arange(0, 4 * sample_rate).float() * freq / sample_rate)) waveform.unsqueeze_(0) # (1, 64000) waveform = (waveform * volume * 2**31).long() - # file for stereo stft test - test_dirpath, test_dir = create_temp_assets_dir() - test_filepath = os.path.join(test_dirpath, 'assets', - 'steam-train-whistle-daniel_simon.wav') def scale(self, waveform, factor=2.0**31): # scales a waveform by a factor @@ -45,7 +40,8 @@ def test_mu_law_companding(self): self.assertTrue(waveform_exp.min() >= -1. and waveform_exp.max() <= 1.) def test_AmplitudeToDB(self): - waveform, sample_rate = torchaudio.load(self.test_filepath) + filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') + waveform, sample_rate = torchaudio.load(filepath) mag_to_db_transform = transforms.AmplitudeToDB('magnitude', 80.) power_to_db_transform = transforms.AmplitudeToDB('power', 80.) @@ -53,7 +49,7 @@ def test_AmplitudeToDB(self): mag_to_db_torch = mag_to_db_transform(torch.abs(waveform)) power_to_db_torch = power_to_db_transform(torch.pow(waveform, 2)) - self.assertTrue(torch.allclose(mag_to_db_torch, power_to_db_torch)) + torch.testing.assert_allclose(mag_to_db_torch, power_to_db_torch) def test_melscale_load_save(self): specgram = torch.ones(1, 1000, 100) @@ -67,7 +63,7 @@ def test_melscale_load_save(self): fb_copy = melscale_transform_copy.fb self.assertEqual(fb_copy.size(), (1000, 128)) - self.assertTrue(torch.allclose(fb, fb_copy)) + torch.testing.assert_allclose(fb, fb_copy) def test_melspectrogram_load_save(self): waveform = self.waveform.float() @@ -83,10 +79,10 @@ def test_melspectrogram_load_save(self): fb = mel_spectrogram_transform.mel_scale.fb fb_copy = mel_spectrogram_transform_copy.mel_scale.fb - self.assertTrue(torch.allclose(window, window_copy)) + torch.testing.assert_allclose(window, window_copy) # the default for n_fft = 400 and n_mels = 128 self.assertEqual(fb_copy.size(), (201, 128)) - self.assertTrue(torch.allclose(fb, fb_copy)) + torch.testing.assert_allclose(fb, fb_copy) def test_mel2(self): top_db = 80. @@ -114,7 +110,8 @@ def test_mel2(self): self.assertTrue(mel_transform2.mel_scale.fb.sum(1).le(1.).all()) self.assertTrue(mel_transform2.mel_scale.fb.sum(1).ge(0.).all()) # check on multi-channel audio - x_stereo, sr_stereo = torchaudio.load(self.test_filepath) # (2, 278756), 44100 + filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') + x_stereo, sr_stereo = torchaudio.load(filepath) # (2, 278756), 44100 spectrogram_stereo = s2db(mel_transform(x_stereo)) # (2, 128, 1394) self.assertTrue(spectrogram_stereo.dim() == 3) self.assertTrue(spectrogram_stereo.size(0) == 2) @@ -164,7 +161,7 @@ def test_mfcc(self): self.assertTrue(torch_mfcc_norm_none.allclose(norm_check)) def test_resample_size(self): - input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') + input_path = common_utils.get_asset_path('sinewave.wav') waveform, sample_rate = torchaudio.load(input_path) upsample_rate = sample_rate * 2 @@ -217,7 +214,7 @@ def test_compute_deltas_twochannel(self): transform = transforms.ComputeDeltas(win_length=3) computed = transform(specgram) assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected, atol=1e-6, rtol=1e-8) + torch.testing.assert_allclose(computed, expected, atol=1e-6, rtol=1e-8) if __name__ == '__main__': diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index 2b04a2f28a..e748a34bc2 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -1,15 +1,17 @@ +import atexit import os.path +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union import torch - +from torch import Tensor from torchaudio import ( compliance, datasets, kaldi_io, sox_effects, - transforms, + transforms ) - from torchaudio._backend import ( check_input, _audio_backend_guard, @@ -17,6 +19,7 @@ get_audio_backend, set_audio_backend, ) +from torchaudio._soundfile_backend import SignalInfo, EncodingInfo try: from .version import __version__, git_version # noqa: F401 @@ -24,27 +27,27 @@ pass -def load(filepath, - out=None, - normalization=True, - channels_first=True, - num_frames=0, - offset=0, - signalinfo=None, - encodinginfo=None, - filetype=None): +def load(filepath: Union[str, Path], + out: Optional[Tensor] = None, + normalization: Union[bool, float, Callable] = True, + channels_first: bool = True, + num_frames: int = 0, + offset: int = 0, + signalinfo: Optional[SignalInfo] = None, + encodinginfo: Optional[EncodingInfo] = None, + filetype: Optional[str] = None) -> Tuple[Tensor, int]: r"""Loads an audio file from disk into a tensor Args: - filepath (str or pathlib.Path): Path to audio file - out (torch.Tensor, optional): An output tensor to use instead of creating one. (Default: ``None``) - normalization (bool, number, or callable, optional): If boolean `True`, then output is divided by `1 << 31` + filepath (str or Path): Path to audio file + out (Tensor, optional): An output tensor to use instead of creating one. (Default: ``None``) + normalization (bool, float, or callable, optional): If boolean `True`, then output is divided by `1 << 31` (assumes signed 32-bit audio), and normalizes to `[-1, 1]`. - If `number`, then output is divided by that number - If `callable`, then the output is passed as a parameter + If `float`, then output is divided by that number + If `Callable`, then the output is passed as a parameter to the given function, then the output is divided by the result. (Default: ``True``) - channels_first (bool): Set channels first or length first in result. (Default: ``True``) + channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) num_frames (int, optional): Number of frames to load. 0 to load everything after the offset. (Default: ``0``) offset (int, optional): Number of frames from the start of the file to begin data loading. @@ -57,7 +60,7 @@ def load(filepath, automatically. (Default: ``None``) Returns: - Tuple[torch.Tensor, int]: An output tensor of size `[C x L]` or `[L x C]` where L is the number + (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number of audio frames and C is the number of channels. An integer which is the sample rate of the audio (as listed in the metadata of the file) @@ -86,15 +89,15 @@ def load(filepath, ) -def load_wav(filepath, **kwargs): +def load_wav(filepath: Union[str, Path], **kwargs: Any) -> Tuple[Tensor, int]: r""" Loads a wave file. It assumes that the wav file uses 16 bit per sample that needs normalization by shifting the input right by 16 bits. Args: - filepath (str or pathlib.Path): Path to audio file + filepath (str or Path): Path to audio file Returns: - Tuple[torch.Tensor, int]: An output tensor of size `[C x L]` or `[L x C]` where L is the number + (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number of audio frames and C is the number of channels. An integer which is the sample rate of the audio (as listed in the metadata of the file) """ @@ -102,17 +105,17 @@ def load_wav(filepath, **kwargs): return load(filepath, **kwargs) -def save(filepath, src, sample_rate, precision=16, channels_first=True): +def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: r"""Convenience function for `save_encinfo`. Args: filepath (str): Path to audio file - src (torch.Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is + src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is the number of audio frames, C is the number of channels sample_rate (int): An integer which is the sample rate of the audio (as listed in the metadata of the file) - precision (int): Bit precision (Default: ``16``) - channels_first (bool): Set channels first or length first in result. ( + precision (int, optional): Bit precision (Default: ``16``) + channels_first (bool, optional): Set channels first or length first in result. ( Default: ``True``) """ @@ -122,23 +125,23 @@ def save(filepath, src, sample_rate, precision=16, channels_first=True): @_audio_backend_guard("sox") -def save_encinfo(filepath, - src, - channels_first=True, - signalinfo=None, - encodinginfo=None, - filetype=None): +def save_encinfo(filepath: str, + src: Tensor, + channels_first: bool = True, + signalinfo: Optional[SignalInfo] = None, + encodinginfo: Optional[EncodingInfo] = None, + filetype: Optional[str] = None) -> None: r"""Saves a tensor of an audio signal to disk as a standard format like mp3, wav, etc. Args: filepath (str): Path to audio file - src (torch.Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is + src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is the number of audio frames, C is the number of channels - channels_first (bool): Set channels first or length first in result. (Default: ``True``) - signalinfo (sox_signalinfo_t): A sox_signalinfo_t type, which could be helpful if the - audio type cannot be automatically determined. (Default: ``None``) + channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) + signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the + audio type cannot be automatically determined (Default: ``None``). encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the - audio type cannot be automatically determined. (Default: ``None``) + audio type cannot be automatically determined (Default: ``None``). filetype (str, optional): A filetype or extension to be set if sox cannot determine it automatically. (Default: ``None``) @@ -165,17 +168,18 @@ def save_encinfo(filepath, "Expected format where C < 16, but found {}".format(src.size())) # sox stores the sample rate as a float, though practically sample rates are almost always integers # convert integers to floats - if not isinstance(signalinfo.rate, float): - if float(signalinfo.rate) == signalinfo.rate: - signalinfo.rate = float(signalinfo.rate) - else: - raise TypeError('Sample rate should be a float or int') - # check if the bit precision (i.e. bits per sample) is an integer - if not isinstance(signalinfo.precision, int): - if int(signalinfo.precision) == signalinfo.precision: - signalinfo.precision = int(signalinfo.precision) - else: - raise TypeError('Bit precision should be an integer') + if signalinfo: + if signalinfo.rate and not isinstance(signalinfo.rate, float): + if float(signalinfo.rate) == signalinfo.rate: + signalinfo.rate = float(signalinfo.rate) + else: + raise TypeError('Sample rate should be a float or int') + # check if the bit precision (i.e. bits per sample) is an integer + if signalinfo.precision and not isinstance(signalinfo.precision, int): + if int(signalinfo.precision) == signalinfo.precision: + signalinfo.precision = int(signalinfo.precision) + else: + raise TypeError('Bit precision should be an integer') # programs such as librosa normalize the signal, unnormalize if detected if src.min() >= -1.0 and src.max() <= 1.0: src = src * (1 << 31) @@ -193,14 +197,14 @@ def save_encinfo(filepath, _torch_sox.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) -def info(filepath): +def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: r"""Gets metadata from an audio file without loading the signal. Args: filepath (str): Path to audio file Returns: - Tuple[sox_signalinfo_t, sox_encodinginfo_t]: A si (sox_signalinfo_t) signal + (sox_signalinfo_t, sox_encodinginfo_t): A si (sox_signalinfo_t) signal info as a python object. An ei (sox_encodinginfo_t) encoding info Example @@ -212,7 +216,7 @@ def info(filepath): @_audio_backend_guard("sox") -def sox_signalinfo_t(): +def sox_signalinfo_t() -> SignalInfo: r"""Create a sox_signalinfo_t object. This object can be used to set the sample rate, number of channels, length, bit precision and headroom multiplier primarily for effects @@ -237,7 +241,7 @@ def sox_signalinfo_t(): @_audio_backend_guard("sox") -def sox_encodinginfo_t(): +def sox_encodinginfo_t() -> EncodingInfo: r"""Create a sox_encodinginfo_t object. This object can be used to set the encoding type, bit precision, compression factor, reverse bytes, reverse nibbles, reverse bits and endianness. This can be used in an effects chain to encode the @@ -277,7 +281,7 @@ def sox_encodinginfo_t(): @_audio_backend_guard("sox") -def get_sox_encoding_t(i=None): +def get_sox_encoding_t(i: int = None) -> EncodingInfo: r"""Get enum of sox_encoding_t for sox encodings. Args: @@ -297,7 +301,7 @@ def get_sox_encoding_t(i=None): @_audio_backend_guard("sox") -def get_sox_option_t(i=2): +def get_sox_option_t(i: int = 2) -> Any: r"""Get enum of sox_option_t for sox encodinginfo options. Args: @@ -316,7 +320,7 @@ def get_sox_option_t(i=2): @_audio_backend_guard("sox") -def get_sox_bool(i=0): +def get_sox_bool(i: int = 0) -> Any: r"""Get enum of sox_bool for sox encodinginfo options. Args: @@ -335,28 +339,70 @@ def get_sox_bool(i=0): return _torch_sox.sox_bool(i) -@_audio_backend_guard("sox") -def initialize_sox(): - """Initialize sox for use with effects chains. This is not required for simple - loading. Importantly, only run `initialize_sox` once and do not shutdown - after each effect chain, but rather once you are finished with all effects chains. - """ +_SOX_INITIALIZED = False +# This variable has a micro lifecycle. (False -> True -> None) +# False: Not initialized +# True: Initialized +# None: Already shut down (should not be initialized again.) - import _torch_sox - return _torch_sox.initialize_sox() +_SOX_SUCCESS_CODE = 0 +# defined at +# https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93a9ef2b87ec303edfe40751d9a85fadeeb @_audio_backend_guard("sox") -def shutdown_sox(): - """Showdown sox for effects chain. Not required for simple loading. Importantly, - only call once. Attempting to re-initialize sox will result in seg faults. +def initialize_sox() -> int: + """Initialize sox for use with effects chains. + + You only need to call this function once to use SoX effects chains multiple times. + It is safe to call this function multiple times as long as ``shutdown_sox`` is not yet called. + Once ``shutdown_sox`` is called, you can no longer use SoX effects and calling this function + results in `RuntimeError`. + + Note: + This function is not required for simple loading. + + Returns: + int: Code corresponding to sox_error_t enum. See + https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 """ + global _SOX_INITIALIZED + if _SOX_INITIALIZED is None: + raise RuntimeError('SoX effects chain has been already shut down. Can not initialize again.') + if not _SOX_INITIALIZED: + import _torch_sox + code = _torch_sox.initialize_sox() + if code == _SOX_SUCCESS_CODE: + _SOX_INITIALIZED = True + atexit.register(shutdown_sox) + return code + return _SOX_SUCCESS_CODE - import _torch_sox - return _torch_sox.shutdown_sox() + +@_audio_backend_guard("sox") +def shutdown_sox() -> int: + """Showdown sox for effects chain. + + You do not need to call this function as it will be called automatically + at the end of program execution, if ``initialize_sox`` was called. + + It is safe to call this function multiple times. + + Returns: + int: Code corresponding to sox_error_t enum. See + https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + """ + global _SOX_INITIALIZED + if _SOX_INITIALIZED: + import _torch_sox + code = _torch_sox.shutdown_sox() + if code == _SOX_INITIALIZED: + _SOX_INITIALIZED = None + return code + return _SOX_SUCCESS_CODE -def _audio_normalization(signal, normalization): +def _audio_normalization(signal: Tensor, normalization: Union[bool, float, Callable]) -> None: """Audio normalization of a tensor in-place. The normalization can be a bool, a number, or a callable that takes the audio tensor as an input. SoX uses 32-bit signed integers internally, thus bool normalizes based on that assumption. diff --git a/torchaudio/_backend.py b/torchaudio/_backend.py index d95b8159ab..f5646d7970 100644 --- a/torchaudio/_backend.py +++ b/torchaudio/_backend.py @@ -1,20 +1,26 @@ from functools import wraps +from typing import Any, List, Union import platform import torch +from torch import Tensor from . import _soundfile_backend, _sox_backend -_audio_backend = "soundfile" if platform.system() == "Windows" else "sox" -_audio_backends = {"sox": _sox_backend, "soundfile": _soundfile_backend} +if platform.system() == "Windows": + _audio_backend = "soundfile" + _audio_backends = {"soundfile": _soundfile_backend} +else: + _audio_backend = "sox" + _audio_backends = {"sox": _sox_backend, "soundfile": _soundfile_backend} -def set_audio_backend(backend): +def set_audio_backend(backend: str) -> None: """ Specifies the package used to load. Args: - backend (string): Name of the backend. One of {}. + backend (str): Name of the backend. One of {}. """.format(_audio_backends.keys()) global _audio_backend if backend not in _audio_backends: @@ -24,14 +30,14 @@ def set_audio_backend(backend): _audio_backend = backend -def get_audio_backend(): +def get_audio_backend() -> str: """ Gets the name of the package used to load. """ return _audio_backend -def _get_audio_backend_module(): +def _get_audio_backend_module() -> Any: """ Gets the module backend to load. """ @@ -39,7 +45,7 @@ def _get_audio_backend_module(): return _audio_backends[backend] -def _audio_backend_guard(backends): +def _audio_backend_guard(backends: Union[str, List[str]]) -> Any: if isinstance(backends, str): backends = [backends] @@ -55,7 +61,7 @@ def wrapper(*args, **kwargs): return decorator -def check_input(src): +def check_input(src: Tensor) -> None: if not torch.is_tensor(src): raise TypeError('Expected a tensor, got %s' % type(src)) if src.is_cuda: diff --git a/torchaudio/_soundfile_backend.py b/torchaudio/_soundfile_backend.py index f3b72d071a..1a88ba5e80 100644 --- a/torchaudio/_soundfile_backend.py +++ b/torchaudio/_soundfile_backend.py @@ -1,6 +1,8 @@ import os +from typing import Any, Optional, Tuple, Union import torch +from torch import Tensor _subtype_to_precision = { 'PCM_S8': 8, @@ -12,7 +14,11 @@ class SignalInfo: - def __init__(self, channels=None, rate=None, precision=None, length=None): + def __init__(self, + channels: Optional[int] = None, + rate: Optional[float] = None, + precision: Optional[int] = None, + length: Optional[int] = None) -> None: self.channels = channels self.rate = rate self.precision = precision @@ -20,16 +26,14 @@ def __init__(self, channels=None, rate=None, precision=None, length=None): class EncodingInfo: - def __init__( - self, - encoding=None, - bits_per_sample=None, - compression=None, - reverse_bytes=None, - reverse_nibbles=None, - reverse_bits=None, - opposite_endian=None - ): + def __init__(self, + encoding: Any = None, + bits_per_sample: Optional[int] = None, + compression: Optional[float] = None, + reverse_bytes: Any = None, + reverse_nibbles: Any = None, + reverse_bits: Any = None, + opposite_endian: Optional[bool] = None) -> None: self.encoding = encoding self.bits_per_sample = bits_per_sample self.compression = compression @@ -39,24 +43,22 @@ def __init__( self.opposite_endian = opposite_endian -def check_input(src): +def check_input(src: Tensor) -> None: if not torch.is_tensor(src): raise TypeError("Expected a tensor, got %s" % type(src)) if src.is_cuda: raise TypeError("Expected a CPU based tensor, got %s" % type(src)) -def load( - filepath, - out=None, - normalization=True, - channels_first=True, - num_frames=0, - offset=0, - signalinfo=None, - encodinginfo=None, - filetype=None, -): +def load(filepath: str, + out: Optional[Tensor] = None, + normalization: Optional[bool] = True, + channels_first: Optional[bool] = True, + num_frames: int = 0, + offset: int = 0, + signalinfo: SignalInfo = None, + encodinginfo: EncodingInfo = None, + filetype: Optional[str] = None) -> Tuple[Tensor, int]: r"""See torchaudio.load""" assert out is None @@ -96,7 +98,7 @@ def load( return out, sample_rate -def save(filepath, src, sample_rate, precision=16, channels_first=True): +def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: r"""See torchaudio.save""" ch_idx, len_idx = (0, 1) if channels_first else (1, 0) @@ -129,7 +131,7 @@ def save(filepath, src, sample_rate, precision=16, channels_first=True): return soundfile.write(filepath, src, sample_rate, precision) -def info(filepath): +def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: r"""See torchaudio.info""" import soundfile diff --git a/torchaudio/_sox_backend.py b/torchaudio/_sox_backend.py index 3aa66d94d5..802bd0fe17 100644 --- a/torchaudio/_sox_backend.py +++ b/torchaudio/_sox_backend.py @@ -1,21 +1,21 @@ import os.path +from typing import Optional, Tuple import torch - +from torch import Tensor import torchaudio - - -def load( - filepath, - out=None, - normalization=True, - channels_first=True, - num_frames=0, - offset=0, - signalinfo=None, - encodinginfo=None, - filetype=None, -): +from torchaudio._soundfile_backend import SignalInfo, EncodingInfo + + +def load(filepath: str, + out: Optional[Tensor] = None, + normalization: bool = True, + channels_first: bool = True, + num_frames: int = 0, + offset: int = 0, + signalinfo: SignalInfo = None, + encodinginfo: EncodingInfo = None, + filetype: Optional[str] = None) -> Tuple[Tensor, int]: r"""See torchaudio.load""" # stringify if `pathlib.Path` (noop if already `str`) @@ -53,7 +53,7 @@ def load( return out, sample_rate -def save(filepath, src, sample_rate, precision=16, channels_first=True): +def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: r"""See torchaudio.save""" si = torchaudio.sox_signalinfo_t() @@ -65,7 +65,7 @@ def save(filepath, src, sample_rate, precision=16, channels_first=True): return torchaudio.save_encinfo(filepath, src, channels_first, si) -def info(filepath): +def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: r"""See torchaudio.info""" import _torch_sox diff --git a/torchaudio/common_utils.py b/torchaudio/common_utils.py index 2321ad26a6..28b77200a6 100644 --- a/torchaudio/common_utils.py +++ b/torchaudio/common_utils.py @@ -4,7 +4,7 @@ PY34 = sys.version_info >= (3, 4) -def _check_module_exists(name): +def _check_module_exists(name: str) -> bool: r"""Returns if a top-level module with :attr:`name` exists *without** importing it. This is generally safer than try-catch block around a `import X`. It avoids third party libraries breaking assumptions of some of diff --git a/torchaudio/datasets/commonvoice.py b/torchaudio/datasets/commonvoice.py index 7d8e57c5fc..a11b9dc1c8 100644 --- a/torchaudio/datasets/commonvoice.py +++ b/torchaudio/datasets/commonvoice.py @@ -1,9 +1,10 @@ import os - -from torch.utils.data import Dataset +from typing import List, Dict, Tuple import torchaudio from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader +from torch import Tensor +from torch.utils.data import Dataset # Default TSV should be one of # dev.tsv @@ -17,9 +18,72 @@ URL = "english" VERSION = "cv-corpus-4-2019-12-10" TSV = "train.tsv" - - -def load_commonvoice_item(line, header, path, folder_audio): +_CHECKSUMS = { + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/tt.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/en.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/de.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/fr.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/cy.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/br.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/cv.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/tr.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ky.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ga-IE.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/kab.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ca.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/zh-TW.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/sl.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/it.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/nl.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/cnh.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/eo.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/et.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/fa.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/eu.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/es.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/zh-CN.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/mn.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/sah.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/dv.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/rw.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/sv-SE.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ru.tar.gz": + None +} + + +def load_commonvoice_item(line: List[str], + header: List[str], + path: str, + folder_audio: str) -> Tuple[Tensor, int, Dict[str, str]]: # Each line as the following data: # client_id, path, sentence, up_votes, down_votes, age, gender, accent @@ -47,12 +111,13 @@ class COMMONVOICE(Dataset): _ext_audio = ".mp3" _folder_audio = "clips" - def __init__(self, root, - tsv=TSV, - url=URL, - folder_in_archive=FOLDER_IN_ARCHIVE, - version=VERSION, - download=False): + def __init__(self, + root: str, + tsv: str = TSV, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + version: str = VERSION, + download: bool = False) -> None: languages = { "tatar": "tt", @@ -115,8 +180,9 @@ def __init__(self, root, if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) - extract_archive(archive, self._path) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum) + extract_archive(archive) self._tsv = os.path.join(root, folder_in_archive, tsv) @@ -125,9 +191,9 @@ def __init__(self, root, self._header = next(walker) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, Dict[str, str]]: line = self._walker[n] return load_commonvoice_item(line, self._header, self._path, self._folder_audio) - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/librispeech.py b/torchaudio/datasets/librispeech.py index 8ca8c1af38..449ab744d3 100644 --- a/torchaudio/datasets/librispeech.py +++ b/torchaudio/datasets/librispeech.py @@ -1,20 +1,39 @@ import os +from typing import Tuple import torchaudio +from torch import Tensor from torch.utils.data import Dataset from torchaudio.datasets.utils import ( download_url, extract_archive, - unicode_csv_reader, walk_files, ) URL = "train-clean-100" FOLDER_IN_ARCHIVE = "LibriSpeech" - - -def load_librispeech_item(fileid, path, ext_audio, ext_txt): - +_CHECKSUMS = { + "http://www.openslr.org/resources/12/dev-clean.tar.gz": + "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3", + "http://www.openslr.org/resources/12/dev-other.tar.gz": + "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365", + "http://www.openslr.org/resources/12/test-clean.tar.gz": + "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23", + "http://www.openslr.org/resources/12/test-other.tar.gz": + "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29", + "http://www.openslr.org/resources/12/train-clean-100.tar.gz": + "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2", + "http://www.openslr.org/resources/12/train-clean-360.tar.gz": + "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf", + "http://www.openslr.org/resources/12/train-other-500.tar.gz": + "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2" +} + + +def load_librispeech_item(fileid: str, + path: str, + ext_audio: str, + ext_txt: str) -> Tuple[Tensor, int, str, int, int, int]: speaker_id, chapter_id, utterance_id = fileid.split("-") file_text = speaker_id + "-" + chapter_id + ext_txt @@ -56,9 +75,11 @@ class LIBRISPEECH(Dataset): _ext_txt = ".trans.txt" _ext_audio = ".flac" - def __init__( - self, root, url=URL, folder_in_archive=FOLDER_IN_ARCHIVE, download=False - ): + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False) -> None: if url in [ "dev-clean", @@ -86,7 +107,8 @@ def __init__( if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum) extract_archive(archive) walker = walk_files( @@ -94,9 +116,9 @@ def __init__( ) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]: fileid = self._walker[n] return load_librispeech_item(fileid, self._path, self._ext_audio, self._ext_txt) - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/ljspeech.py b/torchaudio/datasets/ljspeech.py index f08795277a..0b4ee86acc 100644 --- a/torchaudio/datasets/ljspeech.py +++ b/torchaudio/datasets/ljspeech.py @@ -1,15 +1,21 @@ import os import csv +from typing import List, Tuple import torchaudio from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader +from torch import Tensor from torch.utils.data import Dataset URL = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2" FOLDER_IN_ARCHIVE = "wavs" +_CHECKSUMS = { + "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2": + "be1a30453f28eb8dd26af4101ae40cbf2c50413b1bb21936cbcdc6fae3de8aa5" +} -def load_ljspeech_item(line, path, ext_audio): +def load_ljspeech_item(line: List[str], path: str, ext_audio: str) -> Tuple[Tensor, int, str, str]: assert len(line) == 3 fileid, transcript, normalized_transcript = line fileid_audio = fileid + ext_audio @@ -35,9 +41,11 @@ class LJSPEECH(Dataset): _ext_audio = ".wav" _ext_archive = '.tar.bz2' - def __init__( - self, root, url=URL, folder_in_archive=FOLDER_IN_ARCHIVE, download=False - ): + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False) -> None: basename = os.path.basename(url) archive = os.path.join(root, basename) @@ -51,16 +59,17 @@ def __init__( if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum) extract_archive(archive) with open(self._metadata_path, "r") as metadata: walker = unicode_csv_reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: line = self._walker[n] return load_ljspeech_item(line, self._path, self._ext_audio) - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/speechcommands.py b/torchaudio/datasets/speechcommands.py index 5c67eb060b..9943d5965b 100644 --- a/torchaudio/datasets/speechcommands.py +++ b/torchaudio/datasets/speechcommands.py @@ -1,7 +1,9 @@ import os +from typing import Tuple import torchaudio from torch.utils.data import Dataset +from torch import Tensor from torchaudio.datasets.utils import ( download_url, extract_archive, @@ -12,9 +14,15 @@ URL = "speech_commands_v0.02" HASH_DIVIDER = "_nohash_" EXCEPT_FOLDER = "_background_noise_" +_CHECKSUMS = { + "https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.01.tar.gz": + "3cd23799cb2bbdec517f1cc028f8d43c", + "https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.02.tar.gz": + "6b74f3901214cb2c2934e98196829835", +} -def load_speechcommands_item(filepath, path): +def load_speechcommands_item(filepath: str, path: str) -> Tuple[Tensor, int, str, str, int]: relpath = os.path.relpath(filepath, path) label, filename = os.path.split(relpath) speaker, _ = os.path.splitext(filename) @@ -33,13 +41,11 @@ class SPEECHCOMMANDS(Dataset): waveform, sample_rate, label, speaker_id, utterance_number """ - def __init__( - self, - root, - url=URL, - folder_in_archive=FOLDER_IN_ARCHIVE, - download=False - ): + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False) -> None: if url in [ "speech_commands_v0.01", "speech_commands_v0.02", @@ -60,16 +66,17 @@ def __init__( if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") extract_archive(archive, self._path) walker = walk_files(self._path, suffix=".wav", prefix=True) walker = filter(lambda w: HASH_DIVIDER in w and EXCEPT_FOLDER not in w, walker) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int]: fileid = self._walker[n] return load_speechcommands_item(fileid, self._path) - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/utils.py b/torchaudio/datasets/utils.py index f7d12baa2d..2840b43471 100644 --- a/torchaudio/datasets/utils.py +++ b/torchaudio/datasets/utils.py @@ -7,20 +7,24 @@ import tarfile import threading import zipfile +from _io import TextIOWrapper from queue import Queue +from typing import Any, Iterable, List, Optional, Tuple, Union import torch import urllib +import urllib.request from torch.utils.data import Dataset from torch.utils.model_zoo import tqdm -def unicode_csv_reader(unicode_csv_data, **kwargs): +def unicode_csv_reader(unicode_csv_data: TextIOWrapper, **kwargs: Any) -> Any: r"""Since the standard csv library does not handle unicode in Python 2, we need a wrapper. Borrowed and slightly modified from the Python docs: https://docs.python.org/2/library/csv.html#csv-examples - Arguments: - unicode_csv_data: unicode csv data (see example below) + Args: + unicode_csv_data (TextIOWrapper): unicode csv data (see example below) + Examples: >>> from torchaudio.datasets.utils import unicode_csv_reader >>> import io @@ -44,7 +48,7 @@ def unicode_csv_reader(unicode_csv_data, **kwargs): yield line -def makedir_exist_ok(dirpath): +def makedir_exist_ok(dirpath: str) -> None: """ Python2 support for os.makedirs(.., exist_ok=True) """ @@ -57,14 +61,17 @@ def makedir_exist_ok(dirpath): raise -def stream_url(url, start_byte=None, block_size=32 * 1024, progress_bar=True): +def stream_url(url: str, + start_byte: Optional[int] = None, + block_size: int = 32 * 1024, + progress_bar: bool = True) -> Iterable: """Stream url by chunk Args: url (str): Url. - start_byte (Optional[int]): Start streaming at that point. - block_size (int): Size of chunks to stream. - progress_bar (bool): Display a progress bar. + start_byte (int, optional): Start streaming at that point (Default: ``None``). + block_size (int, optional): Size of chunks to stream (Default: ``32 * 1024``). + progress_bar (bool, optional): Display a progress bar (Default: ``True``). """ # If we already have the whole file, there is no need to download it again @@ -95,25 +102,23 @@ def stream_url(url, start_byte=None, block_size=32 * 1024, progress_bar=True): pbar.update(len(chunk)) -def download_url( - url, - download_folder, - filename=None, - hash_value=None, - hash_type="sha256", - progress_bar=True, - resume=False, -): +def download_url(url: str, + download_folder: str, + filename: Optional[str] = None, + hash_value: Optional[str] = None, + hash_type: str = "sha256", + progress_bar: bool = True, + resume: bool = False) -> None: """Download file to disk. Args: url (str): Url. download_folder (str): Folder to download file. - filename (str): Name of downloaded file. If None, it is inferred from the url. - hash_value (str): Hash for url. - hash_type (str): Hash type, among "sha256" and "md5". - progress_bar (bool): Display a progress bar. - resume (bool): Enable resuming download. + filename (str, optional): Name of downloaded file. If None, it is inferred from the url (Default: ``None``). + hash_value (str, optional): Hash for url (Default: ``None``). + hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``). + progress_bar (bool, optional): Display a progress bar (Default: ``True``). + resume (bool, optional): Enable resuming download (Default: ``False``). """ req = urllib.request.Request(url, method="HEAD") @@ -122,10 +127,10 @@ def download_url( # Detect filename filename = filename or req_info.get_filename() or os.path.basename(url) filepath = os.path.join(download_folder, filename) - if resume and os.path.exists(filepath): mode = "ab" - local_size = os.path.getsize(filepath) + local_size: Optional[int] = os.path.getsize(filepath) + elif not resume and os.path.exists(filepath): raise RuntimeError( "{} already exists. Delete the file manually and retry.".format(filepath) @@ -157,13 +162,16 @@ def download_url( ) -def validate_file(file_obj, hash_value, hash_type="sha256"): +def validate_file(file_obj: Any, hash_value: str, hash_type: str = "sha256") -> bool: """Validate a given file object with its hash. Args: file_obj: File object to read from. hash_value (str): Hash for url. - hash_type (str): Hash type, among "sha256" and "md5". + hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``). + + Returns: + bool: return True if its a valid file, else False. """ if hash_type == "sha256": @@ -183,14 +191,16 @@ def validate_file(file_obj, hash_value, hash_type="sha256"): return hash_func.hexdigest() == hash_value -def extract_archive(from_path, to_path=None, overwrite=False): +def extract_archive(from_path: str, to_path: Optional[str] = None, overwrite: bool = False) -> List[str]: """Extract archive. - Arguments: - from_path: the path of the archive. - to_path: the root path of the extraced files (directory of from_path) - overwrite: overwrite existing files (False) + Args: + from_path (str): the path of the archive. + to_path (str, optional): the root path of the extraced files (directory of from_path) (Default: ``None``) + overwrite (bool, optional): overwrite existing files (Default: ``False``) + Returns: - List of paths to extracted files even if not overwritten. + list: List of paths to extracted files even if not overwritten. + Examples: >>> url = 'http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz' >>> from_path = './validation.tar.gz' @@ -206,7 +216,7 @@ def extract_archive(from_path, to_path=None, overwrite=False): with tarfile.open(from_path, "r") as tar: logging.info("Opened tar file {}.".format(from_path)) files = [] - for file_ in tar: + for file_ in tar: # type: Any file_path = os.path.join(to_path, file_.name) if file_.isfile(): files.append(file_path) @@ -237,7 +247,10 @@ def extract_archive(from_path, to_path=None, overwrite=False): raise NotImplementedError("We currently only support tar.gz, tgz, and zip achives.") -def walk_files(root, suffix, prefix=False, remove_suffix=False): +def walk_files(root: str, + suffix: Union[str, Tuple[str]], + prefix: bool = False, + remove_suffix: bool = False) -> Iterable[str]: """List recursively all files ending with a suffix at a given root Args: root (str): Path to directory whose folders need to be listed @@ -269,14 +282,14 @@ class _DiskCache(Dataset): Wrap a dataset so that, whenever a new item is returned, it is saved to disk. """ - def __init__(self, dataset, location=".cached"): + def __init__(self, dataset: Dataset, location: str = ".cached") -> None: self.dataset = dataset self.location = location self._id = id(self) - self._cache = [None] * len(dataset) + self._cache: List = [None] * len(dataset) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Any: if self._cache[n]: f = self._cache[n] return torch.load(f) @@ -291,11 +304,11 @@ def __getitem__(self, n): return item - def __len__(self): + def __len__(self) -> int: return len(self.dataset) -def diskcache_iterator(dataset, location=".cached"): +def diskcache_iterator(dataset: Dataset, location: str = ".cached") -> Dataset: return _DiskCache(dataset, location) @@ -311,31 +324,31 @@ class _ThreadedIterator(threading.Thread): class _End: pass - def __init__(self, generator, maxsize): + def __init__(self, generator: Iterable, maxsize: int) -> None: threading.Thread.__init__(self) - self.queue = Queue(maxsize) + self.queue: Queue = Queue(maxsize) self.generator = generator self.daemon = True self.start() - def run(self): + def run(self) -> None: for item in self.generator: self.queue.put(item) self.queue.put(self._End) - def __iter__(self): + def __iter__(self) -> Any: return self - def __next__(self): + def __next__(self) -> Any: next_item = self.queue.get() if next_item == self._End: raise StopIteration return next_item # Required for Python 2.7 compatibility - def next(self): + def next(self) -> Any: return self.__next__() -def bg_iterator(iterable, maxsize): +def bg_iterator(iterable: Iterable, maxsize: int) -> Any: return _ThreadedIterator(iterable, maxsize=maxsize) diff --git a/torchaudio/datasets/vctk.py b/torchaudio/datasets/vctk.py index 87f51d68a0..c84373713d 100644 --- a/torchaudio/datasets/vctk.py +++ b/torchaudio/datasets/vctk.py @@ -1,17 +1,31 @@ import os import warnings +from typing import Any, Tuple import torchaudio +from torch import Tensor from torch.utils.data import Dataset -from torchaudio.datasets.utils import download_url, extract_archive, walk_files +from torchaudio.datasets.utils import ( + download_url, + extract_archive, + walk_files +) URL = "http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz" FOLDER_IN_ARCHIVE = "VCTK-Corpus" - - -def load_vctk_item( - fileid, path, ext_audio, ext_txt, folder_audio, folder_txt, downsample=False -): +_CHECKSUMS = { + "http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz": + "45e8dede780278ef5541fde0b82ac292" +} + + +def load_vctk_item(fileid: str, + path: str, + ext_audio: str, + ext_txt: str, + folder_audio: str, + folder_txt: str, + downsample: bool = False) -> Tuple[Tensor, int, str, str, str]: speaker_id, utterance_id = fileid.split("_") # Read text @@ -50,21 +64,19 @@ class VCTK(Dataset): _ext_audio = ".wav" _except_folder = "p315" - def __init__( - self, - root, - url=URL, - folder_in_archive=FOLDER_IN_ARCHIVE, - download=False, - downsample=False, - transform=None, - target_transform=None, - ): + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False, + downsample: bool = False, + transform: Any = None, + target_transform: Any = None) -> None: if downsample: warnings.warn( "In the next version, transforms will not be part of the dataset. " - "Please use `downsample=False` to enable this behavior now, ", + "Please use `downsample=False` to enable this behavior now, " "and suppress this warning." ) @@ -86,7 +98,8 @@ def __init__( if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") extract_archive(archive) if not os.path.isdir(self._path): @@ -100,7 +113,7 @@ def __init__( walker = filter(lambda w: self._except_folder not in w, walker) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str]: fileid = self._walker[n] item = load_vctk_item( fileid, @@ -121,5 +134,5 @@ def __getitem__(self, n): utterance = self.target_transform(utterance) return waveform, sample_rate, utterance, speaker_id, utterance_id - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/yesno.py b/torchaudio/datasets/yesno.py index 7d0fe58f4c..e45b157c35 100644 --- a/torchaudio/datasets/yesno.py +++ b/torchaudio/datasets/yesno.py @@ -1,15 +1,25 @@ import os import warnings +from typing import Any, List, Tuple import torchaudio +from torch import Tensor from torch.utils.data import Dataset -from torchaudio.datasets.utils import download_url, extract_archive, walk_files +from torchaudio.datasets.utils import ( + download_url, + extract_archive, + walk_files +) URL = "http://www.openslr.org/resources/1/waves_yesno.tar.gz" FOLDER_IN_ARCHIVE = "waves_yesno" +_CHECKSUMS = { + "http://www.openslr.org/resources/1/waves_yesno.tar.gz": + "962ff6e904d2df1126132ecec6978786" +} -def load_yesno_item(fileid, path, ext_audio): +def load_yesno_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, int, List[int]]: # Read label labels = [int(c) for c in fileid.split("_")] @@ -28,15 +38,13 @@ class YESNO(Dataset): _ext_audio = ".wav" - def __init__( - self, - root, - url=URL, - folder_in_archive=FOLDER_IN_ARCHIVE, - download=False, - transform=None, - target_transform=None, - ): + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False, + transform: Any = None, + target_transform: Any = None) -> None: if transform is not None or target_transform is not None: warnings.warn( @@ -55,7 +63,8 @@ def __init__( if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") extract_archive(archive) if not os.path.isdir(self._path): @@ -68,7 +77,7 @@ def __init__( ) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, List[int]]: fileid = self._walker[n] item = load_yesno_item(fileid, self._path, self._ext_audio) @@ -82,5 +91,5 @@ def __getitem__(self, n): labels = self.target_transform(labels) return waveform, sample_rate, labels - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 282470fb4c..cb5156596d 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -31,37 +31,16 @@ "deemph_biquad", "riaa_biquad", "biquad", + "contrast", + "dcshift", + "overdrive", 'mask_along_axis', - 'mask_along_axis_iid' + 'mask_along_axis_iid', + 'sliding_window_cmn', + 'vad', ] -# TODO: remove this once https://github.com/pytorch/pytorch/issues/21478 gets solved -@torch.jit.ignore -def _stft( - waveform: Tensor, - n_fft: int, - hop_length: Optional[int], - win_length: Optional[int], - window: Optional[Tensor], - center: bool, - pad_mode: str, - normalized: bool, - onesided: bool -) -> Tensor: - return torch.stft( - waveform, - n_fft, - hop_length, - win_length, - window, - center, - pad_mode, - normalized, - onesided, - ) - - def istft( stft_matrix: Tensor, n_fft: int, @@ -265,7 +244,7 @@ def spectrogram( waveform = waveform.view(-1, shape[-1]) # default values are consistent with librosa.core.spectrum._spectrogram - spec_f = _stft( + spec_f = torch.stft( waveform, n_fft, hop_length, win_length, window, True, "reflect", False, True ) @@ -365,8 +344,8 @@ def griffinlim( length=length).float() # Rebuild the spectrogram - rebuilt = _stft(inverse, n_fft, hop_length, win_length, window, - True, 'reflect', False, True) + rebuilt = torch.stft(inverse, n_fft, hop_length, win_length, window, + True, 'reflect', False, True) # Update our phase estimates angles = rebuilt - tprev.mul_(momentum / (1 + momentum)) @@ -743,11 +722,11 @@ def lfilter( # (n_order, ) matmul (n_channel, n_order, n_sample) -> (n_channel, n_sample) input_signal_windows = torch.matmul(b_coeffs_flipped, torch.take(padded_waveform, window_idxs)) + input_signal_windows.div_(a_coeffs[0]) + a_coeffs_flipped.div_(a_coeffs[0]) for i_sample, o0 in enumerate(input_signal_windows.t()): windowed_output_signal = padded_output_waveform[:, i_sample:(i_sample + n_order)] - o0.sub_(torch.mv(windowed_output_signal, a_coeffs_flipped)) - o0.div_(a_coeffs[0]) - + o0.addmv_(windowed_output_signal, a_coeffs_flipped, alpha=-1) padded_output_waveform[:, i_sample + n_order - 1] = o0 output = torch.clamp(padded_output_waveform[:, (n_order - 1):], min=-1., max=1.) @@ -1186,6 +1165,137 @@ def riaa_biquad( return biquad(waveform, b0, b1, b2, a0, a1, a2) +def contrast( + waveform: Tensor, + enhancement_amount: float = 75. +) -> Tensor: + r"""Apply contrast effect. Similar to SoX implementation. + Comparable with compression, this effect modifies an audio signal to make it sound louder + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + enhancement_amount (float): controls the amount of the enhancement + Allowed range of values for enhancement_amount : 0-100 + Note that enhancement_amount = 0 still gives a significant contrast enhancement + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + """ + + if not 0 <= enhancement_amount <= 100: + raise ValueError("Allowed range of values for enhancement_amount : 0-100") + + contrast = enhancement_amount / 750. + + temp1 = waveform * (math.pi / 2) + temp2 = contrast * torch.sin(temp1 * 4) + output_waveform = torch.sin(temp1 + temp2) + + return output_waveform + + +def dcshift( + waveform: Tensor, + shift: float, + limiter_gain: Optional[float] = None +) -> Tensor: + r"""Apply a DC shift to the audio. Similar to SoX implementation. + This can be useful to remove a DC offset + (caused perhaps by a hardware problem in the recording chain) from the audio + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + shift (float): indicates the amount to shift the audio + Allowed range of values for shift : -2.0 to +2.0 + limiter_gain (float): It is used only on peaks to prevent clipping + It should have a value much less than 1 (e.g. 0.05 or 0.02) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + """ + output_waveform = waveform + limiter_threshold = 0. + + if limiter_gain is not None: + limiter_threshold = 1.0 - (abs(shift) - limiter_gain) + + if limiter_gain is not None and shift > 0: + mask = waveform > limiter_threshold + temp = (waveform[mask] - limiter_threshold) * limiter_gain / (1 - limiter_threshold) + output_waveform[mask] = (temp + limiter_threshold + shift).clamp(max=limiter_threshold) + output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1) + elif limiter_gain is not None and shift < 0: + mask = waveform < -limiter_threshold + temp = (waveform[mask] + limiter_threshold) * limiter_gain / (1 - limiter_threshold) + output_waveform[mask] = (temp - limiter_threshold + shift).clamp(min=-limiter_threshold) + output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1) + else: + output_waveform = (waveform + shift).clamp(min=-1, max=1) + + return output_waveform + + +def overdrive( + waveform: Tensor, + gain: float = 20, + colour: float = 20 +) -> Tensor: + r"""Apply a overdrive effect to the audio. Similar to SoX implementation. + This effect applies a non linear distortion to the audio signal. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + gain (float): desired gain at the boost (or attenuation) in dB + Allowed range of values are 0 to 100 + colour (float): controls the amount of even harmonic content in the over-driven output + Allowed range of values are 0 to 100 + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + """ + actual_shape = waveform.shape + device, dtype = waveform.device, waveform.dtype + + # convert to 2D (..,time) + waveform = waveform.view(-1, actual_shape[-1]) + + gain = _dB2Linear(gain) + colour = colour / 200 + last_in = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device) + last_out = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device) + + temp = waveform * gain + colour + + mask1 = temp < -1 + temp[mask1] = torch.tensor(-2.0 / 3.0, dtype=dtype, device=device) + # Wrapping the constant with Tensor is required for Torchscript + + mask2 = temp > 1 + temp[mask2] = torch.tensor(2.0 / 3.0, dtype=dtype, device=device) + + mask3 = (~mask1 & ~mask2) + temp[mask3] = temp[mask3] - (temp[mask3]**3) * (1. / 3) + + output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) + + # TODO: Implement a torch CPP extension + for i in range(waveform.shape[-1]): + last_out = temp[:, i] - last_in + 0.995 * last_out + last_in = temp[:, i] + output_waveform[:, i] = waveform[:, i] * 0.5 + last_out * 0.75 + + return output_waveform.clamp(min=-1, max=1).view(actual_shape) + + def mask_along_axis_iid( specgrams: Tensor, mask_param: int, @@ -1210,13 +1320,16 @@ def mask_along_axis_iid( if axis != 2 and axis != 3: raise ValueError('Only Frequency and Time masking are supported') - value = torch.rand(specgrams.shape[:2]) * mask_param - min_value = torch.rand(specgrams.shape[:2]) * (specgrams.size(axis) - value) + device = specgrams.device + dtype = specgrams.dtype + + value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * mask_param + min_value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * (specgrams.size(axis) - value) # Create broadcastable mask - mask_start = (min_value.long())[..., None, None].float() - mask_end = (min_value.long() + value.long())[..., None, None].float() - mask = torch.arange(0, specgrams.size(axis)).float() + mask_start = min_value[..., None, None] + mask_end = (min_value + value)[..., None, None] + mask = torch.arange(0, specgrams.size(axis), device=device, dtype=dtype) # Per batch example masking specgrams = specgrams.transpose(axis, -1) @@ -1298,6 +1411,8 @@ def compute_deltas( >>> delta = compute_deltas(specgram) >>> delta2 = compute_deltas(delta) """ + device = specgram.device + dtype = specgram.dtype # pack batch shape = specgram.size() @@ -1312,7 +1427,7 @@ def compute_deltas( specgram = torch.nn.functional.pad(specgram, (n, n), mode=mode) - kernel = (torch.arange(-n, n + 1, 1, device=specgram.device, dtype=specgram.dtype).repeat(specgram.shape[1], 1, 1)) + kernel = torch.arange(-n, n + 1, 1, device=device, dtype=dtype).repeat(specgram.shape[1], 1, 1) output = torch.nn.functional.conv1d(specgram, kernel, groups=specgram.shape[1]) / denom @@ -1431,7 +1546,7 @@ def _apply_probability_distribution( signal_scaled_dis = signal_scaled + gaussian else: # dtype needed for https://github.com/pytorch/pytorch/issues/32358 - TPDF = torch.bartlett_window(time_size + 1, dtype=torch.float) + TPDF = torch.bartlett_window(time_size + 1, dtype=signal_scaled.dtype, device=signal_scaled.device) TPDF = TPDF.repeat((channel_size + 1), 1) signal_scaled_dis = signal_scaled + TPDF @@ -1631,3 +1746,390 @@ def detect_pitch_frequency( freq = freq.view(shape[:-1] + list(freq.shape[-1:])) return freq + + +def sliding_window_cmn( + waveform: Tensor, + cmn_window: int = 600, + min_cmn_window: int = 100, + center: bool = False, + norm_vars: bool = False, +) -> Tensor: + r""" + Apply sliding-window cepstral mean (and optionally variance) normalization per utterance. + + Args: + waveform (Tensor): Tensor of audio of dimension (..., freq, time) + cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600) + min_cmn_window (int, optional): Minimum CMN window used at start of decoding (adds latency only at start). + Only applicable if center == false, ignored if center==true (int, default = 100) + center (bool, optional): If true, use a window centered on the current frame + (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false) + norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false) + + Returns: + Tensor: Tensor of freq of dimension (..., frame) + """ + input_shape = waveform.shape + num_frames, num_feats = input_shape[-2:] + waveform = waveform.view(-1, num_frames, num_feats) + num_channels = waveform.shape[0] + + dtype = waveform.dtype + device = waveform.device + last_window_start = last_window_end = -1 + cur_sum = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) + cur_sumsq = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) + cmn_waveform = torch.zeros( + num_channels, num_frames, num_feats, dtype=dtype, device=device) + for t in range(num_frames): + window_start = 0 + window_end = 0 + if center: + window_start = t - cmn_window // 2 + window_end = window_start + cmn_window + else: + window_start = t - cmn_window + window_end = t + 1 + if window_start < 0: + window_end -= window_start + window_start = 0 + if not center: + if window_end > t: + window_end = max(t + 1, min_cmn_window) + if window_end > num_frames: + window_start -= (window_end - num_frames) + window_end = num_frames + if window_start < 0: + window_start = 0 + if last_window_start == -1: + input_part = waveform[:, window_start: window_end - window_start, :] + cur_sum += torch.sum(input_part, 1) + if norm_vars: + cur_sumsq += torch.cumsum(input_part ** 2, 1)[:, -1, :] + else: + if window_start > last_window_start: + frame_to_remove = waveform[:, last_window_start, :] + cur_sum -= frame_to_remove + if norm_vars: + cur_sumsq -= (frame_to_remove ** 2) + if window_end > last_window_end: + frame_to_add = waveform[:, last_window_end, :] + cur_sum += frame_to_add + if norm_vars: + cur_sumsq += (frame_to_add ** 2) + window_frames = window_end - window_start + last_window_start = window_start + last_window_end = window_end + cmn_waveform[:, t, :] = waveform[:, t, :] - cur_sum / window_frames + if norm_vars: + if window_frames == 1: + cmn_waveform[:, t, :] = torch.zeros( + num_channels, num_feats, dtype=dtype, device=device) + else: + variance = cur_sumsq + variance = variance / window_frames + variance -= ((cur_sum ** 2) / (window_frames ** 2)) + variance = torch.pow(variance, -0.5) + cmn_waveform[:, t, :] *= variance + + cmn_waveform = cmn_waveform.view(input_shape[:-2] + (num_frames, num_feats)) + if len(input_shape) == 2: + cmn_waveform = cmn_waveform.squeeze(0) + return cmn_waveform + + +def _measure( + measure_len_ws: int, + samples: Tensor, + spectrum: Tensor, + noise_spectrum: Tensor, + spectrum_window: Tensor, + spectrum_start: int, + spectrum_end: int, + cepstrum_window: Tensor, + cepstrum_start: int, + cepstrum_end: int, + noise_reduction_amount: float, + measure_smooth_time_mult: float, + noise_up_time_mult: float, + noise_down_time_mult: float, + index_ns: int, + boot_count: int +) -> float: + + assert spectrum.size()[-1] == noise_spectrum.size()[-1] + + samplesLen_ns = samples.size()[-1] + dft_len_ws = spectrum.size()[-1] + + dftBuf = torch.zeros(dft_len_ws) + + _index_ns = torch.tensor([index_ns] + [ + (index_ns + i) % samplesLen_ns + for i in range(1, measure_len_ws) + ]) + dftBuf[:measure_len_ws] = \ + samples[_index_ns] * spectrum_window[:measure_len_ws] + + # memset(c->dftBuf + i, 0, (p->dft_len_ws - i) * sizeof(*c->dftBuf)); + dftBuf[measure_len_ws:dft_len_ws].zero_() + + # lsx_safe_rdft((int)p->dft_len_ws, 1, c->dftBuf); + _dftBuf = torch.rfft(dftBuf, 1) + + # memset(c->dftBuf, 0, p->spectrum_start * sizeof(*c->dftBuf)); + _dftBuf[:spectrum_start].zero_() + + mult: float = boot_count / (1. + boot_count) \ + if boot_count >= 0 \ + else measure_smooth_time_mult + + _d = complex_norm(_dftBuf[spectrum_start:spectrum_end]) + spectrum[spectrum_start:spectrum_end].mul_(mult).add_(_d * (1 - mult)) + _d = spectrum[spectrum_start:spectrum_end] ** 2 + + _zeros = torch.zeros(spectrum_end - spectrum_start) + _mult = _zeros \ + if boot_count >= 0 \ + else torch.where( + _d > noise_spectrum[spectrum_start:spectrum_end], + torch.tensor(noise_up_time_mult), # if + torch.tensor(noise_down_time_mult) # else + ) + + noise_spectrum[spectrum_start:spectrum_end].mul_(_mult).add_(_d * (1 - _mult)) + _d = torch.sqrt( + torch.max( + _zeros, + _d - noise_reduction_amount * noise_spectrum[spectrum_start:spectrum_end])) + + _cepstrum_Buf: Tensor = torch.zeros(dft_len_ws >> 1) + _cepstrum_Buf[spectrum_start:spectrum_end] = _d * cepstrum_window + _cepstrum_Buf[spectrum_end:dft_len_ws >> 1].zero_() + + # lsx_safe_rdft((int)p->dft_len_ws >> 1, 1, c->dftBuf); + _cepstrum_Buf = torch.rfft(_cepstrum_Buf, 1) + + result: float = float(torch.sum( + complex_norm( + _cepstrum_Buf[cepstrum_start:cepstrum_end], + power=2.0))) + result = \ + math.log(result / (cepstrum_end - cepstrum_start)) \ + if result > 0 \ + else -math.inf + return max(0, 21 + result) + + +def vad( + waveform: Tensor, + sample_rate: int, + trigger_level: float = 7.0, + trigger_time: float = 0.25, + search_time: float = 1.0, + allowed_gap: float = 0.25, + pre_trigger_time: float = 0.0, + # Fine-tuning parameters + boot_time: float = .35, + noise_up_time: float = .1, + noise_down_time: float = .01, + noise_reduction_amount: float = 1.35, + measure_freq: float = 20.0, + measure_duration: Optional[float] = None, + measure_smooth_time: float = .4, + hp_filter_freq: float = 50., + lp_filter_freq: float = 6000., + hp_lifter_freq: float = 150., + lp_lifter_freq: float = 2000., +) -> Tensor: + r"""Voice Activity Detector. Similar to SoX implementation. + Attempts to trim silence and quiet background sounds from the ends of recordings of speech. + The algorithm currently uses a simple cepstral power measurement to detect voice, + so may be fooled by other things, especially music. + + The effect can trim only from the front of the audio, + so in order to trim from the back, the reverse effect must also be used. + + Args: + waveform (Tensor): Tensor of audio of dimension `(..., time)` + sample_rate (int): Sample rate of audio signal. + trigger_level (float, optional): The measurement level used to trigger activity detection. + This may need to be cahnged depending on the noise level, signal level, + and other characteristics of the input audio. (Default: 7.0) + trigger_time (float, optional): The time constant (in seconds) + used to help ignore short bursts of sound. (Default: 0.25) + search_time (float, optional): The amount of audio (in seconds) + to search for quieter/shorter bursts of audio to include prior + to the detected trigger point. (Default: 1.0) + allowed_gap (float, optional): The allowed gap (in seconds) between + quiteter/shorter bursts of audio to include prior + to the detected trigger point. (Default: 0.25) + pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve + before the trigger point and any found quieter/shorter bursts. (Default: 0.0) + boot_time (float, optional) The algorithm (internally) uses adaptive noise + estimation/reduction in order to detect the start of the wanted audio. + This option sets the time for the initial noise estimate. (Default: 0.35) + noise_up_time (float, optional) Time constant used by the adaptive noise estimator + for when the noise level is increasing. (Default: 0.1) + noise_down_time (float, optional) Time constant used by the adaptive noise estimator + for when the noise level is decreasing. (Default: 0.01) + noise_reduction_amount (float, optional) Amount of noise reduction to use in + the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35) + measure_freq (float, optional) Frequency of the algorithmโ€™s + processing/measurements. (Default: 20.0) + measure_duration: (float, optional) Measurement duration. + (Default: Twice the measurement period; i.e. with overlap.) + measure_smooth_time (float, optional) Time constant used to smooth + spectral measurements. (Default: 0.4) + hp_filter_freq (float, optional) "Brick-wall" frequency of high-pass filter applied + at the input to the detector algorithm. (Default: 50.0) + lp_filter_freq (float, optional) "Brick-wall" frequency of low-pass filter applied + at the input to the detector algorithm. (Default: 6000.0) + hp_lifter_freq (float, optional) "Brick-wall" frequency of high-pass lifter used + in the detector algorithm. (Default: 150.0) + lp_lifter_freq (float, optional) "Brick-wall" frequency of low-pass lifter used + in the detector algorithm. (Default: 2000.0) + + Returns: + Tensor: Tensor of audio of dimension (..., time). + + References: + http://sox.sourceforge.net/sox.html + """ + + measure_duration: float = 2.0 / measure_freq \ + if measure_duration is None \ + else measure_duration + + measure_len_ws = int(sample_rate * measure_duration + .5) + measure_len_ns = measure_len_ws + # for (dft_len_ws = 16; dft_len_ws < measure_len_ws; dft_len_ws <<= 1); + dft_len_ws = 16 + while (dft_len_ws < measure_len_ws): + dft_len_ws *= 2 + + measure_period_ns = int(sample_rate / measure_freq + .5) + measures_len = math.ceil(search_time * measure_freq) + search_pre_trigger_len_ns = measures_len * measure_period_ns + gap_len = int(allowed_gap * measure_freq + .5) + + fixed_pre_trigger_len_ns = int(pre_trigger_time * sample_rate + .5) + samplesLen_ns = fixed_pre_trigger_len_ns + search_pre_trigger_len_ns + measure_len_ns + + spectrum_window = torch.zeros(measure_len_ws) + for i in range(measure_len_ws): + # sox.h:741 define SOX_SAMPLE_MIN (sox_sample_t)SOX_INT_MIN(32) + spectrum_window[i] = 2. / math.sqrt(float(measure_len_ws)) + # lsx_apply_hann(spectrum_window, (int)measure_len_ws); + spectrum_window *= torch.hann_window(measure_len_ws, dtype=torch.float) + + spectrum_start: int = int(hp_filter_freq / sample_rate * dft_len_ws + .5) + spectrum_start: int = max(spectrum_start, 1) + spectrum_end: int = int(lp_filter_freq / sample_rate * dft_len_ws + .5) + spectrum_end: int = min(spectrum_end, dft_len_ws // 2) + + cepstrum_window = torch.zeros(spectrum_end - spectrum_start) + for i in range(spectrum_end - spectrum_start): + cepstrum_window[i] = 2. / math.sqrt(float(spectrum_end) - spectrum_start) + # lsx_apply_hann(cepstrum_window,(int)(spectrum_end - spectrum_start)); + cepstrum_window *= torch.hann_window(spectrum_end - spectrum_start, dtype=torch.float) + + cepstrum_start = math.ceil(sample_rate * .5 / lp_lifter_freq) + cepstrum_end = math.floor(sample_rate * .5 / hp_lifter_freq) + cepstrum_end = min(cepstrum_end, dft_len_ws // 4) + + assert cepstrum_end > cepstrum_start + + noise_up_time_mult = math.exp(-1. / (noise_up_time * measure_freq)) + noise_down_time_mult = math.exp(-1. / (noise_down_time * measure_freq)) + measure_smooth_time_mult = math.exp(-1. / (measure_smooth_time * measure_freq)) + trigger_meas_time_mult = math.exp(-1. / (trigger_time * measure_freq)) + + boot_count_max = int(boot_time * measure_freq - .5) + measure_timer_ns = measure_len_ns + boot_count = measures_index = flushedLen_ns = samplesIndex_ns = 0 + + # pack batch + shape = waveform.size() + waveform = waveform.view(-1, shape[-1]) + + n_channels, ilen = waveform.size() + + mean_meas = torch.zeros(n_channels) + samples = torch.zeros(n_channels, samplesLen_ns) + spectrum = torch.zeros(n_channels, dft_len_ws) + noise_spectrum = torch.zeros(n_channels, dft_len_ws) + measures = torch.zeros(n_channels, measures_len) + + has_triggered: bool = False + num_measures_to_flush: int = 0 + pos: int = 0 + + while (pos < ilen and not has_triggered): + measure_timer_ns -= 1 + for i in range(n_channels): + samples[i, samplesIndex_ns] = waveform[i, pos] + # if (!p->measure_timer_ns) { + if (measure_timer_ns == 0): + index_ns: int = \ + (samplesIndex_ns + samplesLen_ns - measure_len_ns) % samplesLen_ns + meas: float = _measure( + measure_len_ws=measure_len_ws, + samples=samples[i], + spectrum=spectrum[i], + noise_spectrum=noise_spectrum[i], + spectrum_window=spectrum_window, + spectrum_start=spectrum_start, + spectrum_end=spectrum_end, + cepstrum_window=cepstrum_window, + cepstrum_start=cepstrum_start, + cepstrum_end=cepstrum_end, + noise_reduction_amount=noise_reduction_amount, + measure_smooth_time_mult=measure_smooth_time_mult, + noise_up_time_mult=noise_up_time_mult, + noise_down_time_mult=noise_down_time_mult, + index_ns=index_ns, + boot_count=boot_count) + measures[i, measures_index] = meas + mean_meas[i] = mean_meas[i] * trigger_meas_time_mult + meas * (1. - trigger_meas_time_mult) + + has_triggered = has_triggered or (mean_meas[i] >= trigger_level) + if has_triggered: + n: int = measures_len + k: int = measures_index + jTrigger: int = n + jZero: int = n + j: int = 0 + + for j in range(n): + if (measures[i, k] >= trigger_level) and (j <= jTrigger + gap_len): + jZero = jTrigger = j + elif (measures[i, k] == 0) and (jTrigger >= jZero): + jZero = j + k = (k + n - 1) % n + j = min(j, jZero) + # num_measures_to_flush = range_limit(j, num_measures_to_flush, n); + num_measures_to_flush = (min(max(num_measures_to_flush, j), n)) + # end if has_triggered + # end if (measure_timer_ns == 0): + # end for + samplesIndex_ns += 1 + pos += 1 + # end while + if samplesIndex_ns == samplesLen_ns: + samplesIndex_ns = 0 + if measure_timer_ns == 0: + measure_timer_ns = measure_period_ns + measures_index += 1 + measures_index = measures_index % measures_len + if boot_count >= 0: + boot_count = -1 if boot_count == boot_count_max else boot_count + 1 + + if has_triggered: + flushedLen_ns = (measures_len - num_measures_to_flush) * measure_period_ns + samplesIndex_ns = (samplesIndex_ns + flushedLen_ns) % samplesLen_ns + + res = waveform[:, pos - samplesLen_ns + flushedLen_ns:] + # unpack batch + return res.view(shape[:-1] + res.shape[-1:]) diff --git a/torchaudio/kaldi_io.py b/torchaudio/kaldi_io.py index 50b101db7a..d32aaed357 100644 --- a/torchaudio/kaldi_io.py +++ b/torchaudio/kaldi_io.py @@ -1,7 +1,10 @@ # To use this file, the dependency (https://github.com/vesis84/kaldi-io-for-python) # needs to be installed. This is a light wrapper around kaldi_io that returns # torch.Tensors. +from typing import Any, Callable, Iterable, Tuple, Union + import torch +from torch import Tensor from torchaudio.common_utils import IMPORT_KALDI_IO, IMPORT_NUMPY if IMPORT_NUMPY: @@ -20,19 +23,20 @@ ] -def _convert_method_output_to_tensor(file_or_fd, fn, convert_contiguous=False): +def _convert_method_output_to_tensor(file_or_fd: Any, + fn: Callable, + convert_contiguous: bool = False) -> Iterable[Tuple[str, Tensor]]: r"""Takes a method invokes it. The output is converted to a tensor. Args: file_or_fd (str/FileDescriptor): File name or file descriptor - fn (Callable[[...], Generator[str, numpy.ndarray]]): Function that has the signature ( - file name/descriptor) -> Generator(str, numpy.ndarray) and converts it to ( - file name/descriptor) -> Generator(str, torch.Tensor). - convert_contiguous (bool): Determines whether the array should be converted into a - contiguous layout. (Default: ``None``) + fn (Callable): Function that has the signature (file name/descriptor) and converts it to + Iterable[Tuple[str, Tensor]]. + convert_contiguous (bool, optional): Determines whether the array should be converted into a + contiguous layout. (Default: ``False``) Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is vec/mat + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is vec/mat """ if not IMPORT_KALDI_IO: raise ImportError('Could not import kaldi_io. Did you install it?') @@ -43,14 +47,14 @@ def _convert_method_output_to_tensor(file_or_fd, fn, convert_contiguous=False): yield key, torch.from_numpy(np_arr) -def read_vec_int_ark(file_or_fd): +def read_vec_int_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, which reads from the ark file/stream. Args: file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is the vector read from file + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the vector read from file Example >>> # read ark to a 'dictionary' @@ -62,14 +66,14 @@ def read_vec_int_ark(file_or_fd): return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_int_ark, convert_contiguous=True) -def read_vec_flt_scp(file_or_fd): +def read_vec_flt_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, read according to Kaldi scp. Args: file_or_fd (str/FileDescriptor): scp, gzipped scp, pipe or opened file descriptor Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is the vector read from file + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the vector read from file Example >>> # read scp to a 'dictionary' @@ -78,14 +82,14 @@ def read_vec_flt_scp(file_or_fd): return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_scp) -def read_vec_flt_ark(file_or_fd): +def read_vec_flt_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, which reads from the ark file/stream. Args: file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is the vector read from file + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the vector read from file Example >>> # read ark to a 'dictionary' @@ -94,14 +98,14 @@ def read_vec_flt_ark(file_or_fd): return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_ark) -def read_mat_scp(file_or_fd): +def read_mat_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,matrix) tuples, read according to Kaldi scp. Args: file_or_fd (str/FileDescriptor): scp, gzipped scp, pipe or opened file descriptor Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is the matrix read from file + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the matrix read from file Example >>> # read scp to a 'dictionary' @@ -110,14 +114,14 @@ def read_mat_scp(file_or_fd): return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_mat_scp) -def read_mat_ark(file_or_fd): +def read_mat_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,matrix) tuples, which reads from the ark file/stream. Args: file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is the matrix read from file + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the matrix read from file Example >>> # read ark to a 'dictionary' diff --git a/torchaudio/sox_effects.py b/torchaudio/sox_effects.py index 76591bd084..472d291902 100644 --- a/torchaudio/sox_effects.py +++ b/torchaudio/sox_effects.py @@ -86,8 +86,8 @@ def __init__(self, out_siginfo: Any = None, out_encinfo: Any = None, filetype: str = "raw") -> None: - self.input_file = None - self.chain = [] + self.input_file: Optional[str] = None + self.chain: List[str] = [] self.MAX_EFFECT_OPTS = 20 self.out_siginfo = out_siginfo self.out_encinfo = out_encinfo @@ -100,12 +100,12 @@ def __init__(self, def append_effect_to_chain(self, ename: str, - eargs: Optional[List[str]] = None) -> None: + eargs: Optional[Union[List[str], str]] = None) -> None: r"""Append effect to a sox effects chain. Args: ename (str): which is the name of effect - eargs (List[str], optional): which is a list of effect options. (Default: ``None``) + eargs (List[str] or str, optional): which is a list of effect options. (Default: ``None``) """ e = SoxEffect() # check if we have a valid effect @@ -149,6 +149,7 @@ def sox_build_flow_effects(self, # print("effect options:", [x.eopts for x in self.chain]) + torchaudio.initialize_sox() import _torch_sox sr = _torch_sox.build_flow_effects(self.input_file, out, diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 8775bbdb5b..29da721869 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -26,6 +26,8 @@ 'Fade', 'FrequencyMasking', 'TimeMasking', + 'SlidingWindowCmn', + 'Vad', ] @@ -453,7 +455,7 @@ def __init__(self, super(MFCC, self).__init__() supported_dct_types = [2] if dct_type not in supported_dct_types: - raise ValueError('DCT type not supported'.format(dct_type)) + raise ValueError('DCT type not supported: {}'.format(dct_type)) self.sample_rate = sample_rate self.n_mfcc = n_mfcc self.dct_type = dct_type @@ -837,10 +839,10 @@ class Vol(torch.nn.Module): Args: gain (float): Interpreted according to the given gain_type: - If `gain_typeโ€™ = โ€˜amplitudeโ€™, `gainโ€™ is a positive amplitude ratio. - If `gain_typeโ€™ = โ€˜powerโ€™, `gainโ€™ is a power (voltage squared). - If `gain_typeโ€™ = โ€˜dbโ€™, `gainโ€™ is in decibels. - gain_type (str, optional): Type of gain. One of: โ€˜amplitudeโ€™, โ€˜powerโ€™, โ€˜dbโ€™ (Default: ``"amplitude"``) + If ``gain_type`` = ``amplitude``, ``gain`` is a positive amplitude ratio. + If ``gain_type`` = ``power``, ``gain`` is a power (voltage squared). + If ``gain_type`` = ``db``, ``gain`` is in decibels. + gain_type (str, optional): Type of gain. One of: ``amplitude``, ``power``, ``db`` (Default: ``amplitude``) """ def __init__(self, gain: float, gain_type: str = 'amplitude'): @@ -869,3 +871,157 @@ def forward(self, waveform: Tensor) -> Tensor: waveform = F.gain(waveform, 10 * math.log10(self.gain)) return torch.clamp(waveform, -1, 1) + + +class SlidingWindowCmn(torch.nn.Module): + r""" + Apply sliding-window cepstral mean (and optionally variance) normalization per utterance. + + Args: + cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600) + min_cmn_window (int, optional): Minimum CMN window used at start of decoding (adds latency only at start). + Only applicable if center == false, ignored if center==true (int, default = 100) + center (bool, optional): If true, use a window centered on the current frame + (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false) + norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false) + """ + + def __init__(self, + cmn_window: int = 600, + min_cmn_window: int = 100, + center: bool = False, + norm_vars: bool = False) -> None: + super().__init__() + self.cmn_window = cmn_window + self.min_cmn_window = min_cmn_window + self.center = center + self.norm_vars = norm_vars + + def forward(self, waveform: Tensor) -> Tensor: + r""" + Args: + waveform (Tensor): Tensor of audio of dimension (..., time). + + Returns: + Tensor: Tensor of audio of dimension (..., time). + """ + cmn_waveform = F.sliding_window_cmn( + waveform, self.cmn_window, self.min_cmn_window, self.center, self.norm_vars) + return cmn_waveform + + +class Vad(torch.nn.Module): + r"""Voice Activity Detector. Similar to SoX implementation. + Attempts to trim silence and quiet background sounds from the ends of recordings of speech. + The algorithm currently uses a simple cepstral power measurement to detect voice, + so may be fooled by other things, especially music. + + The effect can trim only from the front of the audio, + so in order to trim from the back, the reverse effect must also be used. + + Args: + sample_rate (int): Sample rate of audio signal. + trigger_level (float, optional): The measurement level used to trigger activity detection. + This may need to be cahnged depending on the noise level, signal level, + and other characteristics of the input audio. (Default: 7.0) + trigger_time (float, optional): The time constant (in seconds) + used to help ignore short bursts of sound. (Default: 0.25) + search_time (float, optional): The amount of audio (in seconds) + to search for quieter/shorter bursts of audio to include prior + to the detected trigger point. (Default: 1.0) + allowed_gap (float, optional): The allowed gap (in seconds) between + quiteter/shorter bursts of audio to include prior + to the detected trigger point. (Default: 0.25) + pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve + before the trigger point and any found quieter/shorter bursts. (Default: 0.0) + boot_time (float, optional) The algorithm (internally) uses adaptive noise + estimation/reduction in order to detect the start of the wanted audio. + This option sets the time for the initial noise estimate. (Default: 0.35) + noise_up_time (float, optional) Time constant used by the adaptive noise estimator + for when the noise level is increasing. (Default: 0.1) + noise_down_time (float, optional) Time constant used by the adaptive noise estimator + for when the noise level is decreasing. (Default: 0.01) + noise_reduction_amount (float, optional) Amount of noise reduction to use in + the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35) + measure_freq (float, optional) Frequency of the algorithmโ€™s + processing/measurements. (Default: 20.0) + measure_duration: (float, optional) Measurement duration. + (Default: Twice the measurement period; i.e. with overlap.) + measure_smooth_time (float, optional) Time constant used to smooth + spectral measurements. (Default: 0.4) + hp_filter_freq (float, optional) "Brick-wall" frequency of high-pass filter applied + at the input to the detector algorithm. (Default: 50.0) + lp_filter_freq (float, optional) "Brick-wall" frequency of low-pass filter applied + at the input to the detector algorithm. (Default: 6000.0) + hp_lifter_freq (float, optional) "Brick-wall" frequency of high-pass lifter used + in the detector algorithm. (Default: 150.0) + lp_lifter_freq (float, optional) "Brick-wall" frequency of low-pass lifter used + in the detector algorithm. (Default: 2000.0) + + References: + http://sox.sourceforge.net/sox.html + """ + + def __init__(self, + sample_rate: int, + trigger_level: float = 7.0, + trigger_time: float = 0.25, + search_time: float = 1.0, + allowed_gap: float = 0.25, + pre_trigger_time: float = 0.0, + boot_time: float = .35, + noise_up_time: float = .1, + noise_down_time: float = .01, + noise_reduction_amount: float = 1.35, + measure_freq: float = 20.0, + measure_duration: Optional[float] = None, + measure_smooth_time: float = .4, + hp_filter_freq: float = 50., + lp_filter_freq: float = 6000., + hp_lifter_freq: float = 150., + lp_lifter_freq: float = 2000.) -> None: + super().__init__() + + self.sample_rate = sample_rate + self.trigger_level = trigger_level + self.trigger_time = trigger_time + self.search_time = search_time + self.allowed_gap = allowed_gap + self.pre_trigger_time = pre_trigger_time + self.boot_time = boot_time + self.noise_up_time = noise_up_time + self.noise_down_time = noise_up_time + self.noise_reduction_amount = noise_reduction_amount + self.measure_freq = measure_freq + self.measure_duration = measure_duration + self.measure_smooth_time = measure_smooth_time + self.hp_filter_freq = hp_filter_freq + self.lp_filter_freq = lp_filter_freq + self.hp_lifter_freq = hp_lifter_freq + self.lp_lifter_freq = lp_lifter_freq + + def forward(self, waveform: Tensor) -> Tensor: + r""" + Args: + waveform (Tensor): Tensor of audio of dimension `(..., time)` + """ + return F.vad( + waveform=waveform, + sample_rate=self.sample_rate, + trigger_level=self.trigger_level, + trigger_time=self.trigger_time, + search_time=self.search_time, + allowed_gap=self.allowed_gap, + pre_trigger_time=self.pre_trigger_time, + boot_time=self.boot_time, + noise_up_time=self.noise_up_time, + noise_down_time=self.noise_up_time, + noise_reduction_amount=self.noise_reduction_amount, + measure_freq=self.measure_freq, + measure_duration=self.measure_duration, + measure_smooth_time=self.measure_smooth_time, + hp_filter_freq=self.hp_filter_freq, + lp_filter_freq=self.lp_filter_freq, + hp_lifter_freq=self.hp_lifter_freq, + lp_lifter_freq=self.lp_lifter_freq, + ) From e065a0f4c49680b17e2cfd9e666962a8ee1cefb0 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Thu, 28 May 2020 12:18:22 -0700 Subject: [PATCH 05/73] Import torchaudio 20200528 Summary: Import Up to #664 Reviewed By: cpuhrsch Differential Revision: D21728204 fbshipit-source-id: 648dd622087fa762194ca5f89a310500e777263d --- .use_external_sox | 0 README.md | 25 +- build_tools/__init__.py | 0 build_tools/setup_helpers/__init__.py | 1 + .../setup_helpers/build_third_party.sh | 36 ++ .../setup_helpers/build_third_party_helper.sh | 173 +++++++++ build_tools/setup_helpers/extension.py | 111 ++++++ docs/source/functional.rst | 5 + docs/source/models.rst | 17 + packaging/build_from_source.sh | 62 ---- packaging/build_wheel.sh | 8 +- packaging/pkg_helpers.bash | 2 +- packaging/torchaudio/build.sh | 4 +- setup.py | 99 ++---- test/README.md | 51 +++ test/__init__.py | 10 + test/assets/vad-go-mono-32000.wav | Bin 0 -> 54660 bytes test/assets/vad-go-stereo-44100.wav | Bin 0 -> 173400 bytes test/assets/vad-hello-mono-32000.wav | Bin 275106 -> 0 bytes test/assets/vad-hello-stereo-44100.wav | Bin 937862 -> 0 bytes test/common_utils.py | 104 ++++++ test/kaldi_compatibility_cpu_test.py | 5 + test/kaldi_compatibility_cuda_test.py | 5 + test/kaldi_compatibility_impl.py | 104 ++++++ test/test_batch_consistency.py | 102 +++--- test/test_compliance_kaldi.py | 10 +- test/test_dataloader.py | 10 +- test/test_datasets.py | 5 +- test/test_functional.py | 39 +-- test/test_io.py | 4 +- test/test_kaldi_io.py | 2 +- test/test_librosa_compatibility.py | 184 +++++----- test/test_models.py | 30 ++ test/test_sox_compatibility.py | 93 +++-- test/test_sox_effects.py | 50 ++- test/test_transforms.py | 17 +- test/torchscript_consistency_cpu_test.py | 5 + test/torchscript_consistency_cuda_test.py | 5 + ...ncy.py => torchscript_consistency_impl.py} | 221 ++++++------ torchaudio/__init__.py | 40 +-- torchaudio/_sox_backend.py | 8 +- torchaudio/compliance/kaldi.py | 67 ++-- torchaudio/{torch_sox.cpp => csrc/sox.cpp} | 14 +- torchaudio/{torch_sox.h => csrc/sox.h} | 18 +- torchaudio/functional.py | 327 +++++++++++------- torchaudio/models/__init__.py | 1 + torchaudio/models/wav2letter.py | 74 ++++ torchaudio/sox_effects.py | 26 +- torchaudio/transforms.py | 30 +- 49 files changed, 1517 insertions(+), 687 deletions(-) create mode 100644 .use_external_sox create mode 100644 build_tools/__init__.py create mode 100644 build_tools/setup_helpers/__init__.py create mode 100755 build_tools/setup_helpers/build_third_party.sh create mode 100644 build_tools/setup_helpers/build_third_party_helper.sh create mode 100644 build_tools/setup_helpers/extension.py create mode 100644 docs/source/models.rst delete mode 100755 packaging/build_from_source.sh create mode 100644 test/README.md create mode 100644 test/__init__.py create mode 100644 test/assets/vad-go-mono-32000.wav create mode 100644 test/assets/vad-go-stereo-44100.wav delete mode 100644 test/assets/vad-hello-mono-32000.wav delete mode 100644 test/assets/vad-hello-stereo-44100.wav create mode 100644 test/kaldi_compatibility_cpu_test.py create mode 100644 test/kaldi_compatibility_cuda_test.py create mode 100644 test/kaldi_compatibility_impl.py create mode 100644 test/test_models.py create mode 100644 test/torchscript_consistency_cpu_test.py create mode 100644 test/torchscript_consistency_cuda_test.py rename test/{test_torchscript_consistency.py => torchscript_consistency_impl.py} (74%) rename torchaudio/{torch_sox.cpp => csrc/sox.cpp} (98%) rename torchaudio/{torch_sox.h => csrc/sox.h} (88%) create mode 100644 torchaudio/models/__init__.py create mode 100644 torchaudio/models/wav2letter.py diff --git a/.use_external_sox b/.use_external_sox new file mode 100644 index 0000000000..e69de29bb2 diff --git a/README.md b/README.md index 816fd75f6c..fc3326075a 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ having all the computations be through Pytorch operations which makes it easy to use and feel like a natural extension. - [Support audio I/O (Load files, Save files)](http://pytorch.org/audio/) - - Load the following formats into a torch Tensor using sox + - Load the following formats into a torch Tensor using SoX - mp3, wav, aac, ogg, flac, avr, cdda, cvs/vms, - aiff, au, amr, mp2, mp4, ac3, avi, wmv, - mpeg, ircam and any other format supported by libsox. @@ -98,10 +98,29 @@ Quick Usage ```python import torchaudio -waveform, sample_rate = torchaudio.load('foo.mp3') # load tensor from file -torchaudio.save('foo_save.mp3', waveform, sample_rate) # save tensor to file + +waveform, sample_rate = torchaudio.load('foo.wav') # load tensor from file +torchaudio.save('foo_save.wav', waveform, sample_rate) # save tensor to file ``` +Backend Dispatch +---------------- + +By default in OSX and Linux, torchaudio uses SoX as a backend to load and save files. +The backend can be changed to [SoundFile](https://pysoundfile.readthedocs.io/en/latest/) +using the following. See [SoundFile](https://pysoundfile.readthedocs.io/en/latest/) +for installation instructions. + +```python +import torchaudio +torchaudio.set_audio_backend("soundfile") # switch backend + +waveform, sample_rate = torchaudio.load('foo.wav') # load tensor from file, as usual +torchaudio.save('foo_save.wav', waveform, sample_rate) # save tensor to file, as usual +``` + +Unlike SoX, SoundFile does not currently support mp3. + API Reference ------------- diff --git a/build_tools/__init__.py b/build_tools/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/build_tools/setup_helpers/__init__.py b/build_tools/setup_helpers/__init__.py new file mode 100644 index 0000000000..7afa3f31cd --- /dev/null +++ b/build_tools/setup_helpers/__init__.py @@ -0,0 +1 @@ +from .extension import * # noqa diff --git a/build_tools/setup_helpers/build_third_party.sh b/build_tools/setup_helpers/build_third_party.sh new file mode 100755 index 0000000000..c5e8f8115d --- /dev/null +++ b/build_tools/setup_helpers/build_third_party.sh @@ -0,0 +1,36 @@ +#!/bin/bash +# Build third party libraries in `/third_party/build` or in `/third_party/build`. + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +root_dir="${this_dir}/../.." + +prefix="${1:-}" +if [ -z "${prefix}" ]; then + prefix="${root_dir}" +fi + +tp_dir="${prefix}/third_party" +tmp_dir="${tp_dir}/tmp" +build_dir="${tp_dir}/build" + +mkdir -p "${tmp_dir}" "${build_dir}" + +. "${this_dir}/build_third_party_helper.sh" + +if ! found_lame "${build_dir}" ; then + build_lame "${tmp_dir}" "${build_dir}" +fi + +if ! found_flac "${build_dir}" ; then + build_flac "${tmp_dir}" "${build_dir}" +fi + +if ! found_mad "${build_dir}" ; then + build_mad "${tmp_dir}" "${build_dir}" +fi + +if ! found_sox "${build_dir}" ; then + build_sox "${tmp_dir}" "${build_dir}" +fi diff --git a/build_tools/setup_helpers/build_third_party_helper.sh b/build_tools/setup_helpers/build_third_party_helper.sh new file mode 100644 index 0000000000..a31fafe718 --- /dev/null +++ b/build_tools/setup_helpers/build_third_party_helper.sh @@ -0,0 +1,173 @@ +#!/usr/bin/env bash + +set -euo pipefail + +# Global options +CURL_OPTS="-L --retry 10 --connect-timeout 5 --max-time 180" +MAKE_OPTS="-j" +CONFIG_OPTS="" + +if [ -z ${DEBUG+x} ]; then + CURL_OPTS="${CURL_OPTS} --silent --show-error" + MAKE_OPTS="${MAKE_OPTS} --quiet" + CONFIG_OPTS="${CONFIG_OPTS} --quiet" +fi + +all_found() { + dir="$1" + shift + while [ "$#" -gt 0 ]; do + if [ ! -f "${dir}/$1" ]; then + return 1 + fi + shift + done +} + + +found_lame() { + all_found "$1" 'include/lame/lame.h' 'lib/libmp3lame.a' +} + +found_flac() { + all_found "$1" \ + 'include/FLAC/format.h' \ + 'include/FLAC/stream_decoder.h' \ + 'include/FLAC/export.h' \ + 'include/FLAC/ordinals.h' \ + 'include/FLAC/all.h' \ + 'include/FLAC/assert.h' \ + 'include/FLAC/callback.h' \ + 'include/FLAC/metadata.h' \ + 'include/FLAC/stream_encoder.h' \ + 'include/FLAC++/export.h' \ + 'include/FLAC++/decoder.h' \ + 'include/FLAC++/all.h' \ + 'include/FLAC++/metadata.h' \ + 'include/FLAC++/encoder.h' \ + 'lib/libFLAC++.a' \ + 'lib/libFLAC.a' +} + +found_mad() { + all_found "$1" 'include/mad.h' 'lib/libmad.a' +} + +found_sox() { + all_found "$1" 'include/sox.h' 'lib/libsox.a' +} + +build_lame() { + work_dir="$1" + install_dir="$2" + package="lame-3.99.5" + url="https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz" + ( + cd "${work_dir}" + if [ ! -d "${package}" ]; then + if [ ! -f "${package}.tar.gz" ]; then + printf "Fetching liblame from %s\n" "${url}" + curl $CURL_OPTS -o "${package}.tar.gz" "${url}" + fi + tar xfp "${package}.tar.gz" + fi + # build statically + printf "Building liblame\n" + cd "${package}" + if [ ! -f Makefile ]; then + ./configure ${CONFIG_OPTS} \ + --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ + --with-pic --disable-debug --disable-dependency-tracking --enable-nasm + fi + make ${MAKE_OPTS} > make.log 2>&1 + make ${MAKE_OPTS} install + ) +} + +build_flac() { + work_dir="$1" + install_dir="$2" + package="flac-1.3.2" + url="https://downloads.sourceforge.net/project/flac/flac-src/flac-1.3.2.tar.xz" + ( + cd "${work_dir}" + if [ ! -d "${package}" ]; then + if [ ! -f "${package}.tar.xz" ]; then + printf "Fetching flac from %s\n" "${url}" + curl $CURL_OPTS -o "${package}.tar.xz" "${url}" + fi + tar xfp "${package}.tar.xz" + fi + # build statically + printf "Building flac\n" + cd "${package}" + if [ ! -f Makefile ]; then + ./configure ${CONFIG_OPTS} \ + --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ + --with-pic --disable-debug --disable-dependency-tracking + fi + make ${MAKE_OPTS} > make.log 2>&1 + make ${MAKE_OPTS} install + ) +} + +build_mad() { + work_dir="$1" + install_dir="$2" + package="libmad-0.15.1b" + url="https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/libmad-0.15.1b.tar.gz" + ( + cd "${work_dir}" + if [ ! -d "${package}" ]; then + if [ ! -f "${package}.tar.gz" ]; then + printf "Fetching mad from %s\n" "${url}" + curl $CURL_OPTS -o "${package}.tar.gz" "${url}" + fi + tar xfp "${package}.tar.gz" + fi + # build statically + printf "Building mad\n" + cd "${package}" + if [ ! -f Makefile ]; then + # See https://stackoverflow.com/a/12864879/23845 + sed -i.bak 's/-march=i486//' configure + ./configure ${CONFIG_OPTS} \ + --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ + --with-pic --disable-debug --disable-dependency-tracking + fi + make ${MAKE_OPTS} > make.log 2>&1 + make ${MAKE_OPTS} install + ) +} + +build_sox() { + work_dir="$1" + install_dir="$2" + package="sox-14.4.2" + url="https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2" + ( + cd "${work_dir}" + if [ ! -d "${package}" ]; then + if [ ! -f "${package}.tar.bz2" ]; then + printf "Fetching SoX from %s\n" "${url}" + curl $CURL_OPTS -o "${package}.tar.bz2" "${url}" + fi + tar xfp "${package}.tar.bz2" + fi + # build statically + printf "Building sox\n" + cd "${package}" + if [ ! -f Makefile ]; then + # --without-png makes OS X build less hazardous; somehow the build + # finds png and enables it. We don't want it; we'd need to package + # it statically if we do. + ./configure ${CONFIG_OPTS} --disable-shared --enable-static --prefix="${install_dir}" \ + LDFLAGS="-L${install_dir}/lib" CPPFLAGS="-I${install_dir}/include" \ + --with-lame --with-flac --with-mad --without-alsa --without-coreaudio \ + --without-png --without-oggvorbis --without-oss --without-sndfile \ + CFLAGS=-fPIC CXXFLAGS=-fPIC --with-pic --disable-debug --disable-dependency-tracking + fi + make ${MAKE_OPTS} > make.log 2>&1 + make ${MAKE_OPTS} install + ) +} diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py new file mode 100644 index 0000000000..65e48dfc10 --- /dev/null +++ b/build_tools/setup_helpers/extension.py @@ -0,0 +1,111 @@ +import platform +import subprocess +from pathlib import Path + +from torch.utils.cpp_extension import ( + CppExtension, + BuildExtension as TorchBuildExtension +) + +__all__ = [ + 'get_ext_modules', + 'BuildExtension', +] + +_THIS_DIR = Path(__file__).parent.resolve() +_ROOT_DIR = _THIS_DIR.parent.parent.resolve() +_CSRC_DIR = _ROOT_DIR / 'torchaudio' / 'csrc' +_TP_BASE_DIR = _ROOT_DIR / 'third_party' +_TP_INSTALL_DIR = _TP_BASE_DIR / 'build' + +# Temporary fix for building in fbcode +# at the moment, we have to use external sox in fbcode +_BUILD_DEPS = not (_ROOT_DIR / '.use_external_sox').exists() + + +def _get_eca(debug): + eca = [] + if debug: + eca += ["-O0", "-g"] + else: + eca += ["-O3"] + return eca + + +def _get_ela(debug): + ela = [] + if debug: + if platform.system() == "Windows": + ela += ["/DEBUG:FULL"] + else: + ela += ["-O0", "-g"] + else: + ela += ["-O3"] + return ela + + +def _get_srcs(): + return [str(p) for p in _CSRC_DIR.glob('**/*.cpp')] + + +def _get_include_dirs(): + dirs = [ + str(_ROOT_DIR), + ] + if _BUILD_DEPS: + dirs.append(str(_TP_INSTALL_DIR / 'include')) + return dirs + + +def _get_extra_objects(): + objs = [] + if _BUILD_DEPS: + # NOTE: The order of the library listed bellow matters. + # + # (the most important thing is that dependencies come after a library + # e.g., sox comes first) + libs = ['libsox.a', 'libmad.a', 'libFLAC.a', 'libmp3lame.a'] + for lib in libs: + objs.append(str(_TP_INSTALL_DIR / 'lib' / lib)) + return objs + + +def _get_libraries(): + return [] if _BUILD_DEPS else ['sox'] + + +def _build_codecs(): + subprocess.run( + args=[str(_THIS_DIR / 'build_third_party.sh')], + check=True, + ) + + +def _configure_third_party(): + _build_codecs() + + +_EXT_NAME = 'torchaudio._torchaudio' + + +def get_ext_modules(debug=False): + if platform.system() == 'Windows': + return None + return [ + CppExtension( + _EXT_NAME, + _get_srcs(), + libraries=_get_libraries(), + include_dirs=_get_include_dirs(), + extra_compile_args=_get_eca(debug), + extra_objects=_get_extra_objects(), + extra_link_args=_get_ela(debug), + ), + ] + + +class BuildExtension(TorchBuildExtension): + def build_extension(self, ext): + if ext.name == _EXT_NAME and _BUILD_DEPS: + _configure_third_party() + super().build_extension(ext) diff --git a/docs/source/functional.rst b/docs/source/functional.rst index d8f318ea18..5f68666539 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -138,6 +138,11 @@ Functions to perform common audio operations. .. autofunction:: overdrive +:hidden:`phaser` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: phaser + :hidden:`mask_along_axis` ~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/source/models.rst b/docs/source/models.rst new file mode 100644 index 0000000000..5b40482976 --- /dev/null +++ b/docs/source/models.rst @@ -0,0 +1,17 @@ +.. role:: hidden + :class: hidden-section + +torchaudio.models +====================== + +.. currentmodule:: torchaudio.models + +The models subpackage contains definitions of models for addressing common audio tasks. + + +:hidden:`Wav2Letter` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: Wav2Letter + + .. automethod:: forward diff --git a/packaging/build_from_source.sh b/packaging/build_from_source.sh deleted file mode 100755 index f5981f0a5e..0000000000 --- a/packaging/build_from_source.sh +++ /dev/null @@ -1,62 +0,0 @@ -#!/bin/bash - -set -ex - -# Arguments: PREFIX, specifying where to install dependencies into - -PREFIX="$1" - -rm -rf /tmp/torchaudio-deps -mkdir /tmp/torchaudio-deps -pushd /tmp/torchaudio-deps - - -# Curl Settings -CURL_OPTS="-L --retry 10 --connect-timeout 5 --max-time 180" - -curl $CURL_OPTS -o sox-14.4.2.tar.bz2 "https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2" -curl $CURL_OPTS -o lame-3.99.5.tar.gz "https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz" -curl $CURL_OPTS -o flac-1.3.2.tar.xz "https://downloads.sourceforge.net/project/flac/flac-src/flac-1.3.2.tar.xz" -curl $CURL_OPTS -o libmad-0.15.1b.tar.gz "https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/libmad-0.15.1b.tar.gz" - -# unpack the dependencies -tar xfp sox-14.4.2.tar.bz2 -tar xfp lame-3.99.5.tar.gz -tar xfp flac-1.3.2.tar.xz -tar xfp libmad-0.15.1b.tar.gz - -# build lame, statically -pushd lame-3.99.5 -./configure --disable-shared --enable-static --prefix="$PREFIX/third_party/lame" CFLAGS=-fPIC CXXFLAGS=-fPIC --with-pic --disable-debug --disable-dependency-tracking --enable-nasm -make -s -j && make install -popd - -# build flac, statically -pushd flac-1.3.2 -./configure --disable-shared --enable-static --prefix="$PREFIX/third_party/flac" CFLAGS=-fPIC CXXFLAGS=-fPIC \ - --with-pic --disable-debug --disable-dependency-tracking -make -s -j && make install -popd - -# build mad, statically -pushd libmad-0.15.1b -# See https://stackoverflow.com/a/12864879/23845 -sed -i.bak 's/-march=i486//' configure -./configure --disable-shared --enable-static --prefix="$PREFIX/third_party/mad" CFLAGS=-fPIC CXXFLAGS=-fPIC \ - --with-pic --disable-debug --disable-dependency-tracking -make -s -j && make install -popd - -# build sox, statically -# --without-png makes OS X build less hazardous; somehow the build -# finds png and enables it. We don't want it; we'd need to package -# it statically if we do. -pushd sox-14.4.2 -./configure --disable-shared --enable-static --prefix="$PREFIX/third_party/sox" \ - LDFLAGS="-L$PREFIX/third_party/lame/lib -L$PREFIX/third_party/flac/lib -L$PREFIX/third_party/mad/lib" \ - CPPFLAGS="-I$PREFIX/third_party/lame/include -I$PREFIX/third_party/flac/include -I$PREFIX/third_party/mad/include" \ - --with-lame --with-flac --with-mad --without-alsa --without-coreaudio --without-png --without-oggvorbis --without-oss --without-sndfile CFLAGS=-fPIC CXXFLAGS=-fPIC --with-pic --disable-debug --disable-dependency-tracking -make -s -j && make install -popd - -popd diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index fc0742ece1..73cec89ff5 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -8,8 +8,12 @@ export BUILD_TYPE="wheel" export NO_CUDA_PACKAGE=1 setup_env 0.6.0 setup_wheel_python -"$script_dir/build_from_source.sh" "$(pwd)" # Build static dependencies pip_install numpy future setup_pip_pytorch_version python setup.py clean -IS_WHEEL=1 python setup.py bdist_wheel +if [[ "$OSTYPE" == "msys" ]]; then + python_tag="$(echo "cp$PYTHON_VERSION" | tr -d '.')" + python setup.py bdist_wheel --plat-name win_amd64 --python-tag $python_tag +else + python setup.py bdist_wheel +fi diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index bc8ec997df..4030128a98 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -122,7 +122,7 @@ retry () { # # Precondition: If Linux, you are in a soumith/manylinux-cuda* Docker image setup_wheel_python() { - if [[ "$(uname)" == Darwin ]]; then + if [[ "$(uname)" == Darwin || "$OSTYPE" == "msys" ]]; then eval "$(conda shell.bash hook)" conda env remove -n "env$PYTHON_VERSION" || true conda create -yn "env$PYTHON_VERSION" python="$PYTHON_VERSION" diff --git a/packaging/torchaudio/build.sh b/packaging/torchaudio/build.sh index dff89cf31b..e2b53ddcf4 100644 --- a/packaging/torchaudio/build.sh +++ b/packaging/torchaudio/build.sh @@ -1,6 +1,4 @@ #!/usr/bin/env bash set -ex -packaging/build_from_source.sh "$(pwd)" - -IS_CONDA=1 python setup.py install --single-version-externally-managed --record=record.txt +python setup.py install --single-version-externally-managed --record=record.txt diff --git a/setup.py b/setup.py index 0128a2fe61..5cf1ad680a 100644 --- a/setup.py +++ b/setup.py @@ -1,65 +1,22 @@ #!/usr/bin/env python import os -import platform -import sys +import shutil import subprocess - +from pathlib import Path +import distutils.command.clean from setuptools import setup, find_packages -from torch.utils.cpp_extension import BuildExtension, CppExtension - - -def check_env_flag(name, default=''): - return os.getenv(name, default).upper() in set(['ON', '1', 'YES', 'TRUE', 'Y']) - -DEBUG = check_env_flag('DEBUG') -IS_WHEEL = check_env_flag('IS_WHEEL') -IS_CONDA = check_env_flag('IS_CONDA') - -print('DEBUG:', DEBUG, 'IS_WHEEL:', IS_WHEEL, 'IS_CONDA:', IS_CONDA) - -eca = [] -ela = [] -if DEBUG: - if platform.system() == 'Windows': - ela += ['/DEBUG:FULL'] - else: - eca += ['-O0', '-g'] - ela += ['-O0', '-g'] +from build_tools import setup_helpers -libraries = [] -include_dirs = [] -extra_objects = [] - -# Hypothetically, the conda distribution could rely on an external sox, -# but the library is pretty small and it is not available on the default -# anaconda channel. So we statically link it in, just as we do with wheels. -if IS_WHEEL or IS_CONDA: - audio_path = os.path.dirname(os.path.abspath(__file__)) - - include_dirs += [os.path.join(audio_path, 'third_party/flac/include')] - include_dirs += [os.path.join(audio_path, 'third_party/lame/include')] - include_dirs += [os.path.join(audio_path, 'third_party/sox/include')] - include_dirs += [os.path.join(audio_path, 'third_party/mad/include')] - - # proper link order (sox, mad, flac, lame) - # (the most important thing is that dependencies come after a libraryl - # e.g., sox comes first) - extra_objects += [os.path.join(audio_path, 'third_party/sox/lib/libsox.a')] - extra_objects += [os.path.join(audio_path, 'third_party/mad/lib/libmad.a')] - extra_objects += [os.path.join(audio_path, 'third_party/flac/lib/libFLAC.a')] - extra_objects += [os.path.join(audio_path, 'third_party/lame/lib/libmp3lame.a')] -else: - libraries += ['sox'] +ROOT_DIR = Path(__file__).parent.resolve() # Creating the version file -cwd = os.path.dirname(os.path.abspath(__file__)) version = '0.6.0a0' sha = 'Unknown' try: - sha = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=cwd).decode('ascii').strip() + sha = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=ROOT_DIR).decode('ascii').strip() except Exception: pass @@ -69,7 +26,7 @@ def check_env_flag(name, default=''): version += '+' + sha[:7] print('-- Building version ' + version) -version_path = os.path.join(cwd, 'torchaudio', 'version.py') +version_path = ROOT_DIR / 'torchaudio' / 'version.py' with open(version_path, 'w') as f: f.write("__version__ = '{}'\n".format(version)) f.write("git_version = {}\n".format(repr(sha))) @@ -80,19 +37,26 @@ def check_env_flag(name, default=''): if pytorch_package_version is not None: pytorch_package_dep += "==" + pytorch_package_version -if platform.system() == 'Windows': - ext_modules = None -else: - ext_modules = [ - CppExtension( - '_torch_sox', - ['torchaudio/torch_sox.cpp'], - libraries=libraries, - include_dirs=include_dirs, - extra_compile_args=eca, - extra_objects=extra_objects, - extra_link_args=ela), - ] + +class clean(distutils.command.clean.clean): + def run(self): + # Run default behavior first + distutils.command.clean.clean.run(self) + + # Remove torchaudio extension + for path in (ROOT_DIR / 'torchaudio').glob('**/*.so'): + print(f'removing \'{path}\'') + path.unlink() + # Remove build directory + build_dirs = [ + ROOT_DIR / 'build', + ROOT_DIR / 'third_party' / 'build', + ] + for path in build_dirs: + if path.exists(): + print(f'removing \'{path}\' (and everything under it)') + shutil.rmtree(str(path), ignore_errors=True) + setup( name="torchaudio", @@ -116,9 +80,10 @@ def check_env_flag(name, default=''): "Topic :: Multimedia :: Sound/Audio", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], - # Exclude the build files. - packages=find_packages(exclude=["build"]), - ext_modules=ext_modules, - cmdclass={'build_ext': BuildExtension}, + packages=find_packages(exclude=["build*", "test*", "torchaudio.csrc*", "third_party*", "build_tools*"]), + ext_modules=setup_helpers.get_ext_modules(), + cmdclass={ + 'build_ext': setup_helpers.BuildExtension.with_options(no_python_abi_suffix=True) + }, install_requires=[pytorch_package_dep] ) diff --git a/test/README.md b/test/README.md new file mode 100644 index 0000000000..a6686603af --- /dev/null +++ b/test/README.md @@ -0,0 +1,51 @@ +# Torchaudio Test Suite + +## Structure of tests + +The following is an overview of the tests and related modules for `torchaudio`. + +### Purpose specific test suites + +#### Numerical compatibility agains existing software +- [Librosa compatibility test](./test_librosa_compatibility.py) + Test suite for numerical compatibility against librosa. +- [SoX compatibility test](./test_sox_compatibility.py) + Test suite for numerical compatibility against SoX. +- [Kaldi compatibility test](./test_kaldi_compatibility.py) + Test suite for numerical compatibility against Kaldi. + +#### Result consistency with PyTorch framework +- [TorchScript consistency test](./test_torchscript_consistency.py) + Test suite to check 1. if an API is TorchScript-able, and 2. the results from Python and Torchscript match. +- [Batch consistency test](./test_batch_consistency.py) + Test suite to check if functionals/Transforms handle single sample input and batch input and return the same result. + +### Module specific test suites + +The following test modules are defined for corresponding `torchaudio` module/functions. + +- [`torchaudio.datasets`](./test_datasets.py) +- [`torchaudio.functional`](./test_functional.py) +- [`torchaudio.transforms`](./test_transforms.py) +- [`torchaudio.compliance.kaldi`](./test_compliance_kaldi.py) +- [`torchaudio.kaldi_io`](./test_kaldi_io.py) +- [`torchaudio.sox_effects`](test/test_sox_effects.py) +- [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](test/test_io.py) + +### Test modules that do not fall into the above categories +- [test_dataloader.py](./test_dataloader.py) + Simple test for loading data and applying preprocessing. + +### Support files +- [assets](./assets): Contain sample audio files. +- [assets/kaldi](./assets/kaldi): Contains Kaldi format matrix files used in [./test_compliance_kaldi.py](./test_compliance_kaldi.py). +- [compliance](./compliance): Scripts used to generate above Kaldi matrix files. + + +## Adding test + +When you add a new feature(functional/transform), consider the following + +1. When you add a new feature, please make it Torchscript-able and batch-consistent unless it degrades the performance. Please add the tests to see if the new feature meet these requirements. +1. If the feature should be numerical compatible against existing software (SoX, Librosa, Kaldi etc), add a corresponding test. +1. 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b/test/common_utils.py index d3ef58a51f..5d1dc8abe1 100644 --- a/test/common_utils.py +++ b/test/common_utils.py @@ -1,9 +1,12 @@ import os import tempfile +import unittest +from typing import Type, Iterable, Union from contextlib import contextmanager from shutil import copytree import torch +from torch.testing._internal.common_utils import TestCase import torchaudio _TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__)) @@ -78,3 +81,104 @@ def supports_mp3(backend): BACKENDS_MP3 = filter_backends_with_mp3(BACKENDS) + + +class TestBaseMixin: + dtype = None + device = None + + +_SKIP_IF_NO_CUDA = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') + + +def define_test_suite(testbase: Type[TestBaseMixin], dtype: str, device: str): + if dtype not in ['float32', 'float64']: + raise NotImplementedError(f'Unexpected dtype: {dtype}') + if device not in ['cpu', 'cuda']: + raise NotImplementedError(f'Unexpected device: {device}') + + name = f'Test{testbase.__name__}_{device.upper()}_{dtype.capitalize()}' + attrs = {'dtype': getattr(torch, dtype), 'device': torch.device(device)} + testsuite = type(name, (testbase, TestCase), attrs) + + if device == 'cuda': + testsuite = _SKIP_IF_NO_CUDA(testsuite) + return testsuite + + +def define_test_suites( + scope: dict, + testbases: Iterable[Type[TestBaseMixin]], + dtypes: Iterable[str] = ('float32', 'float64'), + devices: Iterable[str] = ('cpu', 'cuda'), +): + for suite in testbases: + for device in devices: + for dtype in dtypes: + t = define_test_suite(suite, dtype, device) + scope[t.__name__] = t + + +def get_whitenoise( + *, + sample_rate: int = 16000, + duration: float = 1, # seconds + n_channels: int = 1, + seed: int = 0, + dtype: Union[str, torch.dtype] = "float32", + device: Union[str, torch.device] = "cpu", +): + """Generate pseudo audio data with whitenoise + + Args: + sample_rate: Sampling rate + duration: Length of the resulting Tensor in seconds. + n_channels: Number of channels + seed: Seed value used for random number generation. + Note that this function does not modify global random generator state. + dtype: Torch dtype + device: device + Returns: + Tensor: shape of (n_channels, sample_rate * duration) + """ + if isinstance(dtype, str): + dtype = getattr(torch, dtype) + shape = [n_channels, sample_rate * duration] + # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices, + # so we only folk on CPU, generate values and move the data to the given device + with torch.random.fork_rng([]): + torch.random.manual_seed(seed) + tensor = torch.randn(shape, dtype=dtype, device='cpu') + tensor /= 2.0 + tensor.clamp_(-1.0, 1.0) + return tensor.to(device=device) + + +def get_sinusoid( + *, + frequency: float = 300, + sample_rate: int = 16000, + duration: float = 1, # seconds + n_channels: int = 1, + dtype: Union[str, torch.dtype] = "float32", + device: Union[str, torch.device] = "cpu", +): + """Generate pseudo audio data with sine wave. + + Args: + frequency: Frequency of sine wave + sample_rate: Sampling rate + duration: Length of the resulting Tensor in seconds. + n_channels: Number of channels + dtype: Torch dtype + device: device + + Returns: + Tensor: shape of (n_channels, sample_rate * duration) + """ + if isinstance(dtype, str): + dtype = getattr(torch, dtype) + pie2 = 2 * 3.141592653589793 + end = pie2 * frequency * duration + theta = torch.linspace(0, end, sample_rate * duration, dtype=dtype, device=device) + return torch.sin(theta, out=None).repeat([n_channels, 1]) diff --git a/test/kaldi_compatibility_cpu_test.py b/test/kaldi_compatibility_cpu_test.py new file mode 100644 index 0000000000..cd85074ac0 --- /dev/null +++ b/test/kaldi_compatibility_cpu_test.py @@ -0,0 +1,5 @@ +from . import common_utils +from .kaldi_compatibility_impl import Kaldi + + +common_utils.define_test_suites(globals(), [Kaldi], devices=['cpu']) diff --git a/test/kaldi_compatibility_cuda_test.py b/test/kaldi_compatibility_cuda_test.py new file mode 100644 index 0000000000..acaa2408f5 --- /dev/null +++ b/test/kaldi_compatibility_cuda_test.py @@ -0,0 +1,5 @@ +from . import common_utils +from .kaldi_compatibility_impl import Kaldi + + +common_utils.define_test_suites(globals(), [Kaldi], devices=['cuda']) diff --git a/test/kaldi_compatibility_impl.py b/test/kaldi_compatibility_impl.py new file mode 100644 index 0000000000..8fbdff75cb --- /dev/null +++ b/test/kaldi_compatibility_impl.py @@ -0,0 +1,104 @@ +"""Test suites for checking numerical compatibility against Kaldi""" +import shutil +import unittest +import subprocess + +import kaldi_io +import torch +import torchaudio.functional as F +import torchaudio.compliance.kaldi + +from . import common_utils + + +def _not_available(cmd): + return shutil.which(cmd) is None + + +def _convert_args(**kwargs): + args = [] + for key, value in kwargs.items(): + key = '--' + key.replace('_', '-') + value = str(value).lower() if value in [True, False] else str(value) + args.append('%s=%s' % (key, value)) + return args + + +def _run_kaldi(command, input_type, input_value): + """Run provided Kaldi command, pass a tensor and get the resulting tensor + + Arguments: + input_type: str + 'ark' or 'scp' + input_value: + Tensor for 'ark' + string for 'scp' (path to an audio file) + """ + key = 'foo' + process = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) + if input_type == 'ark': + kaldi_io.write_mat(process.stdin, input_value.cpu().numpy(), key=key) + elif input_type == 'scp': + process.stdin.write(f'{key} {input_value}'.encode('utf8')) + else: + raise NotImplementedError('Unexpected type') + process.stdin.close() + result = dict(kaldi_io.read_mat_ark(process.stdout))['foo'] + return torch.from_numpy(result.copy()) # copy supresses some torch warning + + +class Kaldi(common_utils.TestBaseMixin): + def assert_equal(self, output, *, expected, rtol=None, atol=None): + expected = expected.to(dtype=self.dtype, device=self.device) + self.assertEqual(output, expected, rtol=rtol, atol=atol) + + @unittest.skipIf(_not_available('apply-cmvn-sliding'), '`apply-cmvn-sliding` not available') + def test_sliding_window_cmn(self): + """sliding_window_cmn should be numerically compatible with apply-cmvn-sliding""" + kwargs = { + 'cmn_window': 600, + 'min_cmn_window': 100, + 'center': False, + 'norm_vars': False, + } + + tensor = torch.randn(40, 10, dtype=self.dtype, device=self.device) + result = F.sliding_window_cmn(tensor, **kwargs) + command = ['apply-cmvn-sliding'] + _convert_args(**kwargs) + ['ark:-', 'ark:-'] + kaldi_result = _run_kaldi(command, 'ark', tensor) + self.assert_equal(result, expected=kaldi_result) + + @unittest.skipIf(_not_available('compute-fbank-feats'), '`compute-fbank-feats` not available') + def test_fbank(self): + """fbank should be numerically compatible with compute-fbank-feats""" + kwargs = { + 'blackman_coeff': 4.3926, + 'dither': 0.0, + 'energy_floor': 2.0617, + 'frame_length': 0.5625, + 'frame_shift': 0.0625, + 'high_freq': 4253, + 'htk_compat': True, + 'low_freq': 1367, + 'num_mel_bins': 5, + 'preemphasis_coefficient': 0.84, + 'raw_energy': False, + 'remove_dc_offset': True, + 'round_to_power_of_two': True, + 'snip_edges': True, + 'subtract_mean': False, + 'use_energy': True, + 'use_log_fbank': True, + 'use_power': False, + 'vtln_high': 2112, + 'vtln_low': 1445, + 'vtln_warp': 1.0000, + 'window_type': 'hamming', + + } + wave_file = common_utils.get_asset_path('kaldi_file.wav') + waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + result = torchaudio.compliance.kaldi.fbank(waveform, **kwargs) + command = ['compute-fbank-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] + kaldi_result = _run_kaldi(command, 'scp', wave_file) + self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) diff --git a/test/test_batch_consistency.py b/test/test_batch_consistency.py index 675a21e029..c10652d9ca 100644 --- a/test/test_batch_consistency.py +++ b/test/test_batch_consistency.py @@ -2,33 +2,36 @@ import unittest import torch +from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F -import common_utils +from . import common_utils -def _test_batch_consistency(functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, **kwargs): - # run then batch the result - torch.random.manual_seed(42) - expected = functional(tensor.clone(), *args, **kwargs) - expected = expected.repeat([batch_size] + [1] * expected.dim()) - - # batch the input and run - torch.random.manual_seed(42) - pattern = [batch_size] + [1] * tensor.dim() - computed = functional(tensor.repeat(pattern), *args, **kwargs) - - torch.testing.assert_allclose(computed, expected, rtol=rtol, atol=atol) - - -def _test_batch(functional, tensor, *args, atol=1e-8, rtol=1e-5, **kwargs): - _test_batch_consistency(functional, tensor, *args, batch_size=1, atol=atol, rtol=rtol, **kwargs) - _test_batch_consistency(functional, tensor, *args, batch_size=3, atol=atol, rtol=rtol, **kwargs) - - -class TestFunctional(unittest.TestCase): +class TestFunctional(TestCase): """Test functions defined in `functional` module""" + def assert_batch_consistency( + self, functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, seed=42, **kwargs): + # run then batch the result + torch.random.manual_seed(seed) + expected = functional(tensor.clone(), *args, **kwargs) + expected = expected.repeat([batch_size] + [1] * expected.dim()) + + # batch the input and run + torch.random.manual_seed(seed) + pattern = [batch_size] + [1] * tensor.dim() + computed = functional(tensor.repeat(pattern), *args, **kwargs) + + self.assertEqual(computed, expected, rtol=rtol, atol=atol) + + def assert_batch_consistencies( + self, functional, tensor, *args, atol=1e-8, rtol=1e-5, seed=42, **kwargs): + self.assert_batch_consistency( + functional, tensor, *args, batch_size=1, atol=atol, rtol=rtol, seed=seed, **kwargs) + self.assert_batch_consistency( + functional, tensor, *args, batch_size=3, atol=atol, rtol=rtol, seed=seed, **kwargs) + def test_griffinlim(self): n_fft = 400 ws = 400 @@ -40,7 +43,7 @@ def test_griffinlim(self): n_iter = 32 length = 1000 tensor = torch.rand((1, 201, 6)) - _test_batch( + self.assert_batch_consistencies( F.griffinlim, tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, 0, atol=5e-5 ) @@ -54,7 +57,7 @@ def test_detect_pitch_frequency(self): for filename in filenames: filepath = common_utils.get_asset_path(filename) waveform, sample_rate = torchaudio.load(filepath) - _test_batch(F.detect_pitch_frequency, waveform, sample_rate) + self.assert_batch_consistencies(F.detect_pitch_frequency, waveform, sample_rate) def test_istft(self): stft = torch.tensor([ @@ -62,34 +65,39 @@ def test_istft(self): [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]], [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]] ]) - _test_batch(F.istft, stft, n_fft=4, length=4) + self.assert_batch_consistencies(F.istft, stft, n_fft=4, length=4) def test_contrast(self): waveform = torch.rand(2, 100) - 0.5 - _test_batch(F.contrast, waveform, enhancement_amount=80.) + self.assert_batch_consistencies(F.contrast, waveform, enhancement_amount=80.) def test_dcshift(self): waveform = torch.rand(2, 100) - 0.5 - _test_batch(F.dcshift, waveform, shift=0.5, limiter_gain=0.05) + self.assert_batch_consistencies(F.dcshift, waveform, shift=0.5, limiter_gain=0.05) def test_overdrive(self): waveform = torch.rand(2, 100) - 0.5 - _test_batch(F.overdrive, waveform, gain=45, colour=30) + self.assert_batch_consistencies(F.overdrive, waveform, gain=45, colour=30) + + def test_phaser(self): + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, sample_rate = torchaudio.load(filepath) + self.assert_batch_consistencies(F.phaser, waveform, sample_rate) def test_sliding_window_cmn(self): waveform = torch.randn(2, 1024) - 0.5 - _test_batch(F.sliding_window_cmn, waveform, center=True, norm_vars=True) - _test_batch(F.sliding_window_cmn, waveform, center=True, norm_vars=False) - _test_batch(F.sliding_window_cmn, waveform, center=False, norm_vars=True) - _test_batch(F.sliding_window_cmn, waveform, center=False, norm_vars=False) + self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=True, norm_vars=True) + self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=True, norm_vars=False) + self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=False, norm_vars=True) + self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=False, norm_vars=False) def test_vad(self): - filepath = common_utils.get_asset_path("vad-hello-mono-32000.wav") + filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") waveform, sample_rate = torchaudio.load(filepath) - _test_batch(F.vad, waveform, sample_rate=sample_rate) + self.assert_batch_consistencies(F.vad, waveform, sample_rate=sample_rate) -class TestTransforms(unittest.TestCase): +class TestTransforms(TestCase): """Test suite for classes defined in `transforms` module""" def test_batch_AmplitudeToDB(self): spec = torch.rand((6, 201)) @@ -100,7 +108,7 @@ def test_batch_AmplitudeToDB(self): # Batch then transform computed = torchaudio.transforms.AmplitudeToDB()(spec.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_Resample(self): waveform = torch.randn(2, 2786) @@ -111,7 +119,7 @@ def test_batch_Resample(self): # Batch then transform computed = torchaudio.transforms.Resample()(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_MelScale(self): specgram = torch.randn(2, 31, 2786) @@ -123,7 +131,7 @@ def test_batch_MelScale(self): computed = torchaudio.transforms.MelScale()(specgram.repeat(3, 1, 1, 1)) # shape = (3, 2, 201, 1394) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_InverseMelScale(self): n_mels = 32 @@ -140,7 +148,7 @@ def test_batch_InverseMelScale(self): # Because InverseMelScale runs SGD on randomly initialized values so they do not yield # exactly same result. For this reason, tolerance is very relaxed here. - torch.testing.assert_allclose(computed, expected, atol=1.0, rtol=1e-5) + self.assertEqual(computed, expected, atol=1.0, rtol=1e-5) def test_batch_compute_deltas(self): specgram = torch.randn(2, 31, 2786) @@ -152,7 +160,7 @@ def test_batch_compute_deltas(self): computed = torchaudio.transforms.ComputeDeltas()(specgram.repeat(3, 1, 1, 1)) # shape = (3, 2, 201, 1394) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_mulaw(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -167,7 +175,7 @@ def test_batch_mulaw(self): computed = torchaudio.transforms.MuLawEncoding()(waveform_batched) # shape = (3, 2, 201, 1394) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) # Single then transform then batch waveform_decoded = torchaudio.transforms.MuLawDecoding()(waveform_encoded) @@ -177,7 +185,7 @@ def test_batch_mulaw(self): computed = torchaudio.transforms.MuLawDecoding()(computed) # shape = (3, 2, 201, 1394) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_spectrogram(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -188,7 +196,7 @@ def test_batch_spectrogram(self): # Batch then transform computed = torchaudio.transforms.Spectrogram()(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_melspectrogram(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -199,7 +207,7 @@ def test_batch_melspectrogram(self): # Batch then transform computed = torchaudio.transforms.MelSpectrogram()(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_mfcc(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -210,7 +218,7 @@ def test_batch_mfcc(self): # Batch then transform computed = torchaudio.transforms.MFCC()(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected, atol=1e-5, rtol=1e-5) + self.assertEqual(computed, expected, atol=1e-4, rtol=1e-5) def test_batch_TimeStretch(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -244,7 +252,7 @@ def test_batch_TimeStretch(self): hop_length=512, )(complex_specgrams.repeat(3, 1, 1, 1, 1)) - torch.testing.assert_allclose(computed, expected, atol=1e-5, rtol=1e-5) + self.assertEqual(computed, expected, atol=1e-5, rtol=1e-5) def test_batch_Fade(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -257,7 +265,7 @@ def test_batch_Fade(self): # Batch then transform computed = torchaudio.transforms.Fade(fade_in_len, fade_out_len)(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_Vol(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -268,7 +276,7 @@ def test_batch_Vol(self): # Batch then transform computed = torchaudio.transforms.Vol(gain=1.1)(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) if __name__ == '__main__': diff --git a/test/test_compliance_kaldi.py b/test/test_compliance_kaldi.py index 6951ccf59b..2a255a5a4b 100644 --- a/test/test_compliance_kaldi.py +++ b/test/test_compliance_kaldi.py @@ -1,13 +1,13 @@ import math import os -import compliance.utils import torch import torchaudio import torchaudio.compliance.kaldi as kaldi import unittest -import common_utils -from common_utils import AudioBackendScope, BACKENDS +from . import common_utils +from .compliance import utils as compliance_utils +from .common_utils import AudioBackendScope, BACKENDS def extract_window(window, wave, f, frame_length, frame_shift, snip_edges): @@ -50,7 +50,7 @@ class Test_Kaldi(unittest.TestCase): test_filepath = common_utils.get_asset_path('kaldi_file.wav') test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav') kaldi_output_dir = common_utils.get_asset_path('kaldi') - test_filepaths = {prefix: [] for prefix in compliance.utils.TEST_PREFIX} + test_filepaths = {prefix: [] for prefix in compliance_utils.TEST_PREFIX} # separating test files by their types (e.g 'spec', 'fbank', etc.) for f in os.listdir(kaldi_output_dir): @@ -151,7 +151,7 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil args = f.split('-') args[-1] = os.path.splitext(args[-1])[0] assert len(args) == expected_num_args, 'invalid test kaldi file name' - args = [compliance.utils.parse(arg) for arg in args] + args = [compliance_utils.parse(arg) for arg in args] output = get_output_fn(sound, args) diff --git a/test/test_dataloader.py b/test/test_dataloader.py index 602491ee21..cfcc25e7c4 100644 --- a/test/test_dataloader.py +++ b/test/test_dataloader.py @@ -3,8 +3,8 @@ import torchaudio from torch.utils.data import Dataset, DataLoader -import common_utils -from common_utils import AudioBackendScope, BACKENDS +from . import common_utils +from .common_utils import AudioBackendScope, BACKENDS class TORCHAUDIODS(Dataset): @@ -28,8 +28,9 @@ def __len__(self): return len(self.data) -@unittest.skipIf("sox" not in BACKENDS, "sox not available") class Test_DataLoader(unittest.TestCase): + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_1(self): expected_size = (2, 1, 16000) ds = TORCHAUDIODS() @@ -39,5 +40,4 @@ def test_1(self): if __name__ == '__main__': - with AudioBackendScope("sox"): - unittest.main() + unittest.main() diff --git a/test/test_datasets.py b/test/test_datasets.py index e42e21e999..96c12884d6 100644 --- a/test/test_datasets.py +++ b/test/test_datasets.py @@ -8,7 +8,7 @@ from torchaudio.datasets.yesno import YESNO from torchaudio.datasets.ljspeech import LJSPEECH -import common_utils +from . import common_utils class TestDatasets(unittest.TestCase): @@ -26,10 +26,12 @@ def test_librispeech(self): data = LIBRISPEECH(self.path, "dev-clean") data[0] + @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") def test_commonvoice(self): data = COMMONVOICE(self.path, url="tatar") data[0] + @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") def test_commonvoice_diskcache(self): data = COMMONVOICE(self.path, url="tatar") data = diskcache_iterator(data) @@ -38,6 +40,7 @@ def test_commonvoice_diskcache(self): # Load data[0] + @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") def test_commonvoice_bg(self): data = COMMONVOICE(self.path, url="tatar") data = bg_iterator(data, 5) diff --git a/test/test_functional.py b/test/test_functional.py index a126bd9581..ac90b7bde5 100644 --- a/test/test_functional.py +++ b/test/test_functional.py @@ -6,13 +6,10 @@ import torchaudio.functional as F import pytest -import common_utils +from . import common_utils -class _LfilterMixin: - device = None - dtype = None - +class Lfilter(common_utils.TestBaseMixin): def test_simple(self): """ Create a very basic signal, @@ -26,23 +23,19 @@ def test_simple(self): a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device) output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) - torch.testing.assert_allclose(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) - - -class TestLfilterFloat32CPU(_LfilterMixin, unittest.TestCase): - device = torch.device('cpu') - dtype = torch.float32 - + self.assertEqual(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) -class TestLfilterFloat64CPU(_LfilterMixin, unittest.TestCase): - device = torch.device('cpu') - dtype = torch.float64 + def test_clamp(self): + input_signal = torch.ones(1, 44100 * 1, dtype=self.dtype, device=self.device) + b_coeffs = torch.tensor([1, 0], dtype=self.dtype, device=self.device) + a_coeffs = torch.tensor([1, -0.95], dtype=self.dtype, device=self.device) + output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=True) + assert output_signal.max() <= 1 + output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=False) + assert output_signal.max() > 1 -@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") -class TestLfilterFloat32CUDA(_LfilterMixin, unittest.TestCase): - device = torch.device('cuda') - dtype = torch.float32 +common_utils.define_test_suites(globals(), [Lfilter]) class TestComputeDeltas(unittest.TestCase): @@ -179,7 +172,7 @@ def test_istft_of_zeros(self): def test_istft_requires_overlap_windows(self): # the window is size 1 but it hops 20 so there is a gap which throw an error stft = torch.zeros((3, 5, 2)) - self.assertRaises(AssertionError, torchaudio.functional.istft, stft, n_fft=4, + self.assertRaises(RuntimeError, torchaudio.functional.istft, stft, n_fft=4, hop_length=20, win_length=1, window=torch.ones(1)) def test_istft_requires_nola(self): @@ -199,11 +192,11 @@ def test_istft_requires_nola(self): # A window of ones meets NOLA but a window of zeros does not. This should # throw an error. torchaudio.functional.istft(stft, **kwargs_ok) - self.assertRaises(AssertionError, torchaudio.functional.istft, stft, **kwargs_not_ok) + self.assertRaises(RuntimeError, torchaudio.functional.istft, stft, **kwargs_not_ok) def test_istft_requires_non_empty(self): - self.assertRaises(AssertionError, torchaudio.functional.istft, torch.zeros((3, 0, 2)), 2) - self.assertRaises(AssertionError, torchaudio.functional.istft, torch.zeros((0, 3, 2)), 2) + self.assertRaises(RuntimeError, torchaudio.functional.istft, torch.zeros((3, 0, 2)), 2) + self.assertRaises(RuntimeError, torchaudio.functional.istft, torch.zeros((0, 3, 2)), 2) def _test_istft_of_sine(self, amplitude, L, n): # stft of amplitude*sin(2*pi/L*n*x) with the hop length and window size equaling L diff --git a/test/test_io.py b/test/test_io.py index 80968e15ad..0295cb785e 100644 --- a/test/test_io.py +++ b/test/test_io.py @@ -3,7 +3,7 @@ import torchaudio import math import os -from common_utils import AudioBackendScope, BACKENDS, BACKENDS_MP3, create_temp_assets_dir +from .common_utils import AudioBackendScope, BACKENDS, BACKENDS_MP3, create_temp_assets_dir class Test_LoadSave(unittest.TestCase): @@ -171,7 +171,7 @@ def _test_3_load_and_save_is_identity(self): self.assertEqual(sample_rate, sample_rate2) os.unlink(output_path) - @unittest.skipIf(set(["sox", "soundfile"]) not in set(BACKENDS), "sox and soundfile are not available") + @unittest.skipIf(any(be not in BACKENDS for be in ["sox", "soundfile"]), "sox and soundfile are not available") def test_3_load_and_save_is_identity_across_backend(self): with self.subTest(): self._test_3_load_and_save_is_identity_across_backend("sox", "soundfile") diff --git a/test/test_kaldi_io.py b/test/test_kaldi_io.py index bc9903a3ef..942b11de35 100644 --- a/test/test_kaldi_io.py +++ b/test/test_kaldi_io.py @@ -3,7 +3,7 @@ import torch import torchaudio.kaldi_io as kio -import common_utils +from . import common_utils class Test_KaldiIO(unittest.TestCase): diff --git a/test/test_librosa_compatibility.py b/test/test_librosa_compatibility.py index 460f379ce9..665341d945 100644 --- a/test/test_librosa_compatibility.py +++ b/test/test_librosa_compatibility.py @@ -1,8 +1,10 @@ """Test suites for numerical compatibility with librosa""" import os import unittest +from distutils.version import StrictVersion import torch +from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F from torchaudio.common_utils import IMPORT_LIBROSA @@ -14,18 +16,11 @@ import pytest -import common_utils +from . import common_utils -class _LibrosaMixin: - """Automatically skip tests if librosa is not available""" - def setUp(self): - super().setUp() - if not IMPORT_LIBROSA: - raise unittest.SkipTest('Librosa not available') - - -class TestFunctional(_LibrosaMixin, unittest.TestCase): +@unittest.skipIf(not IMPORT_LIBROSA, "Librosa not available") +class TestFunctional(TestCase): """Test suite for functions in `functional` module.""" def test_griffinlim(self): # NOTE: This test is flaky without a fixed random seed @@ -51,25 +46,26 @@ def test_griffinlim(self): momentum=momentum, init=init, length=length) lr_out = torch.from_numpy(lr_out).unsqueeze(0) - torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) + self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) - def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0): + def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0, norm=None): librosa_fb = librosa.filters.mel(sr=sample_rate, n_fft=n_fft, n_mels=n_mels, fmax=fmax, fmin=fmin, htk=True, - norm=None) + norm=norm) fb = F.create_fb_matrix(sample_rate=sample_rate, n_mels=n_mels, f_max=fmax, f_min=fmin, - n_freqs=(n_fft // 2 + 1)) + n_freqs=(n_fft // 2 + 1), + norm=norm) for i_mel_bank in range(n_mels): - torch.testing.assert_allclose(fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), - atol=1e-4, rtol=1e-5) + self.assertEqual( + fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4, rtol=1e-5) def test_create_fb(self): self._test_create_fb() @@ -79,6 +75,14 @@ def test_create_fb(self): self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0) self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0) self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0) + if StrictVersion(librosa.__version__) < StrictVersion("0.7.2"): + return + self._test_create_fb(n_mels=128, sample_rate=44100, norm="slaney") + self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0, norm="slaney") + self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0, norm="slaney") + self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0, norm="slaney") + self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0, norm="slaney") + self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0, norm="slaney") def test_amplitude_to_DB(self): spec = torch.rand((6, 201)) @@ -94,7 +98,7 @@ def test_amplitude_to_DB(self): lr_out = librosa.core.power_to_db(spec.numpy()) lr_out = torch.from_numpy(lr_out) - torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) + self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) # Amplitude to DB multiplier = 20.0 @@ -103,7 +107,7 @@ def test_amplitude_to_DB(self): lr_out = librosa.core.amplitude_to_db(spec.numpy()) lr_out = torch.from_numpy(lr_out) - torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) + self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) @pytest.mark.parametrize('complex_specgrams', [ @@ -154,73 +158,73 @@ def _load_audio_asset(*asset_paths, **kwargs): return sound, sample_rate -def _test_compatibilities(n_fft, hop_length, power, n_mels, n_mfcc, sample_rate): - sound, sample_rate = _load_audio_asset('sinewave.wav') - sound_librosa = sound.cpu().numpy().squeeze() # (64000) - - # test core spectrogram - spect_transform = torchaudio.transforms.Spectrogram( - n_fft=n_fft, hop_length=hop_length, power=power) - out_librosa, _ = librosa.core.spectrum._spectrogram( - y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) - - out_torch = spect_transform(sound).squeeze().cpu() - torch.testing.assert_allclose(out_torch, torch.from_numpy(out_librosa), atol=1e-5, rtol=1e-5) - - # test mel spectrogram - melspect_transform = torchaudio.transforms.MelSpectrogram( - sample_rate=sample_rate, window_fn=torch.hann_window, - hop_length=hop_length, n_mels=n_mels, n_fft=n_fft) - librosa_mel = librosa.feature.melspectrogram( - y=sound_librosa, sr=sample_rate, n_fft=n_fft, - hop_length=hop_length, n_mels=n_mels, htk=True, norm=None) - librosa_mel_tensor = torch.from_numpy(librosa_mel) - torch_mel = melspect_transform(sound).squeeze().cpu() - torch.testing.assert_allclose( - torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3, rtol=1e-5) - - # test s2db - power_to_db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.) - power_to_db_torch = power_to_db_transform(spect_transform(sound)).squeeze().cpu() - power_to_db_librosa = librosa.core.spectrum.power_to_db(out_librosa) - torch.testing.assert_allclose(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3, rtol=1e-5) - - mag_to_db_transform = torchaudio.transforms.AmplitudeToDB('magnitude', 80.) - mag_to_db_torch = mag_to_db_transform(torch.abs(sound)).squeeze().cpu() - mag_to_db_librosa = librosa.core.spectrum.amplitude_to_db(sound_librosa) - torch.testing.assert_allclose(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3, rtol=1e-5) - - power_to_db_torch = power_to_db_transform(melspect_transform(sound)).squeeze().cpu() - db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) - db_librosa_tensor = torch.from_numpy(db_librosa) - torch.testing.assert_allclose( - power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3, rtol=1e-5) - - # test MFCC - melkwargs = {'hop_length': hop_length, 'n_fft': n_fft} - mfcc_transform = torchaudio.transforms.MFCC( - sample_rate=sample_rate, n_mfcc=n_mfcc, norm='ortho', melkwargs=melkwargs) - - # librosa.feature.mfcc doesn't pass kwargs properly since some of the - # kwargs for melspectrogram and mfcc are the same. We just follow the - # function body in - # https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram - # to mirror this function call with correct args: - # - # librosa_mfcc = librosa.feature.mfcc( - # y=sound_librosa, sr=sample_rate, n_mfcc = n_mfcc, - # hop_length=hop_length, n_fft=n_fft, htk=True, norm=None, n_mels=n_mels) - - librosa_mfcc = scipy.fftpack.dct(db_librosa, axis=0, type=2, norm='ortho')[:n_mfcc] - librosa_mfcc_tensor = torch.from_numpy(librosa_mfcc) - torch_mfcc = mfcc_transform(sound).squeeze().cpu() - - torch.testing.assert_allclose( - torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3, rtol=1e-5) - - -class TestTransforms(_LibrosaMixin, unittest.TestCase): +@unittest.skipIf(not IMPORT_LIBROSA, "Librosa not available") +class TestTransforms(TestCase): """Test suite for functions in `transforms` module.""" + def assert_compatibilities(self, n_fft, hop_length, power, n_mels, n_mfcc, sample_rate): + sound, sample_rate = _load_audio_asset('sinewave.wav') + sound_librosa = sound.cpu().numpy().squeeze() # (64000) + + # test core spectrogram + spect_transform = torchaudio.transforms.Spectrogram( + n_fft=n_fft, hop_length=hop_length, power=power) + out_librosa, _ = librosa.core.spectrum._spectrogram( + y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) + + out_torch = spect_transform(sound).squeeze().cpu() + self.assertEqual(out_torch, torch.from_numpy(out_librosa), atol=1e-5, rtol=1e-5) + + # test mel spectrogram + melspect_transform = torchaudio.transforms.MelSpectrogram( + sample_rate=sample_rate, window_fn=torch.hann_window, + hop_length=hop_length, n_mels=n_mels, n_fft=n_fft) + librosa_mel = librosa.feature.melspectrogram( + y=sound_librosa, sr=sample_rate, n_fft=n_fft, + hop_length=hop_length, n_mels=n_mels, htk=True, norm=None) + librosa_mel_tensor = torch.from_numpy(librosa_mel) + torch_mel = melspect_transform(sound).squeeze().cpu() + self.assertEqual( + torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3, rtol=1e-5) + + # test s2db + power_to_db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.) + power_to_db_torch = power_to_db_transform(spect_transform(sound)).squeeze().cpu() + power_to_db_librosa = librosa.core.spectrum.power_to_db(out_librosa) + self.assertEqual(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3, rtol=1e-5) + + mag_to_db_transform = torchaudio.transforms.AmplitudeToDB('magnitude', 80.) + mag_to_db_torch = mag_to_db_transform(torch.abs(sound)).squeeze().cpu() + mag_to_db_librosa = librosa.core.spectrum.amplitude_to_db(sound_librosa) + self.assertEqual(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3, rtol=1e-5) + + power_to_db_torch = power_to_db_transform(melspect_transform(sound)).squeeze().cpu() + db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) + db_librosa_tensor = torch.from_numpy(db_librosa) + self.assertEqual( + power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3, rtol=1e-5) + + # test MFCC + melkwargs = {'hop_length': hop_length, 'n_fft': n_fft} + mfcc_transform = torchaudio.transforms.MFCC( + sample_rate=sample_rate, n_mfcc=n_mfcc, norm='ortho', melkwargs=melkwargs) + + # librosa.feature.mfcc doesn't pass kwargs properly since some of the + # kwargs for melspectrogram and mfcc are the same. We just follow the + # function body in + # https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram + # to mirror this function call with correct args: + # + # librosa_mfcc = librosa.feature.mfcc( + # y=sound_librosa, sr=sample_rate, n_mfcc = n_mfcc, + # hop_length=hop_length, n_fft=n_fft, htk=True, norm=None, n_mels=n_mels) + + librosa_mfcc = scipy.fftpack.dct(db_librosa, axis=0, type=2, norm='ortho')[:n_mfcc] + librosa_mfcc_tensor = torch.from_numpy(librosa_mfcc) + torch_mfcc = mfcc_transform(sound).squeeze().cpu() + + self.assertEqual( + torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3, rtol=1e-5) + def test_basics1(self): kwargs = { 'n_fft': 400, @@ -230,7 +234,7 @@ def test_basics1(self): 'n_mfcc': 40, 'sample_rate': 16000 } - _test_compatibilities(**kwargs) + self.assert_compatibilities(**kwargs) def test_basics2(self): kwargs = { @@ -241,7 +245,7 @@ def test_basics2(self): 'n_mfcc': 20, 'sample_rate': 16000 } - _test_compatibilities(**kwargs) + self.assert_compatibilities(**kwargs) # NOTE: Test passes offline, but fails on TravisCI (and CircleCI), see #372. @unittest.skipIf('CI' in os.environ, 'Test is known to fail on CI') @@ -254,7 +258,7 @@ def test_basics3(self): 'n_mfcc': 50, 'sample_rate': 24000 } - _test_compatibilities(**kwargs) + self.assert_compatibilities(**kwargs) def test_basics4(self): kwargs = { @@ -265,7 +269,7 @@ def test_basics4(self): 'n_mfcc': 40, 'sample_rate': 16000 } - _test_compatibilities(**kwargs) + self.assert_compatibilities(**kwargs) @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") @common_utils.AudioBackendScope("sox") @@ -288,7 +292,7 @@ def test_MelScale(self): S=spec_lr, sr=sample_rate, n_fft=n_fft, hop_length=hop_length, win_length=n_fft, center=True, window='hann', n_mels=n_mels, htk=True, norm=None) # Note: Using relaxed rtol instead of atol - torch.testing.assert_allclose(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), atol=1e-8, rtol=1e-3) + self.assertEqual(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), atol=1e-8, rtol=1e-3) def test_InverseMelScale(self): """InverseMelScale transform is comparable to that of librosa""" @@ -331,7 +335,7 @@ def test_InverseMelScale(self): # https://github.com/pytorch/audio/pull/366 for the discussion of the choice of algorithm # https://github.com/pytorch/audio/pull/448/files#r385747021 for the distribution of P-inf # distance over frequencies. - torch.testing.assert_allclose(spec_ta, spec_lr, atol=threshold, rtol=1e-5) + self.assertEqual(spec_ta, spec_lr, atol=threshold, rtol=1e-5) threshold = 1700.0 # This threshold was choosen empirically, based on the following observations diff --git a/test/test_models.py b/test/test_models.py new file mode 100644 index 0000000000..fe8df9a167 --- /dev/null +++ b/test/test_models.py @@ -0,0 +1,30 @@ +import pytest + +import torch +from torchaudio.models import Wav2Letter + + +class TestWav2Letter: + @pytest.mark.parametrize('batch_size', [2]) + @pytest.mark.parametrize('num_features', [1]) + @pytest.mark.parametrize('num_classes', [40]) + @pytest.mark.parametrize('input_length', [320]) + def test_waveform(self, batch_size, num_features, num_classes, input_length): + model = Wav2Letter() + + x = torch.rand(batch_size, num_features, input_length) + out = model(x) + + assert out.size() == (batch_size, num_classes, 2) + + @pytest.mark.parametrize('batch_size', [2]) + @pytest.mark.parametrize('num_features', [13]) + @pytest.mark.parametrize('num_classes', [40]) + @pytest.mark.parametrize('input_length', [2]) + def test_mfcc(self, batch_size, num_features, num_classes, input_length): + model = Wav2Letter(input_type="mfcc", num_features=13) + + x = torch.rand(batch_size, num_features, input_length) + out = model(x) + + assert out.size() == (batch_size, num_classes, 2) diff --git a/test/test_sox_compatibility.py b/test/test_sox_compatibility.py index 508ec7abfa..dfa57cec47 100644 --- a/test/test_sox_compatibility.py +++ b/test/test_sox_compatibility.py @@ -1,15 +1,16 @@ import unittest import torch +from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F import torchaudio.transforms as T -import common_utils -from common_utils import AudioBackendScope, BACKENDS +from . import common_utils +from .common_utils import AudioBackendScope, BACKENDS -class TestFunctionalFiltering(unittest.TestCase): +class TestFunctionalFiltering(TestCase): @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_gain(self): @@ -24,7 +25,7 @@ def test_gain(self): E.append_effect_to_chain("gain", [3]) sox_gain_waveform = E.sox_build_flow_effects()[0] - torch.testing.assert_allclose(waveform_gain, sox_gain_waveform, atol=1e-04, rtol=1e-5) + self.assertEqual(waveform_gain, sox_gain_waveform, atol=1e-04, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -40,13 +41,13 @@ def test_dither(self): E.append_effect_to_chain("dither", []) sox_dither_waveform = E.sox_build_flow_effects()[0] - torch.testing.assert_allclose(waveform_dithered, sox_dither_waveform, atol=1e-04, rtol=1e-5) + self.assertEqual(waveform_dithered, sox_dither_waveform, atol=1e-04, rtol=1e-5) E.clear_chain() E.append_effect_to_chain("dither", ["-s"]) sox_dither_waveform_ns = E.sox_build_flow_effects()[0] - torch.testing.assert_allclose(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02, rtol=1e-5) + self.assertEqual(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -69,7 +70,7 @@ def test_vctk_transform_pipeline(self): E.append_effect_to_chain("dither", ["-s"]) wf_vctk_sox = E.sox_build_flow_effects()[0] - torch.testing.assert_allclose(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) + self.assertEqual(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -89,7 +90,7 @@ def test_lowpass(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.lowpass_biquad(waveform, sample_rate, cutoff_freq) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -110,7 +111,7 @@ def test_highpass(self): output_waveform = F.highpass_biquad(waveform, sample_rate, cutoff_freq) # TBD - this fails at the 1e-4 level, debug why - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -131,7 +132,7 @@ def test_allpass(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.allpass_biquad(waveform, sample_rate, central_freq, q) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -153,7 +154,7 @@ def test_bandpass_with_csg(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -175,7 +176,7 @@ def test_bandpass_without_csg(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -196,7 +197,7 @@ def test_bandreject(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.bandreject_biquad(waveform, sample_rate, central_freq, q) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -218,7 +219,7 @@ def test_band_with_noise(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -240,7 +241,7 @@ def test_band_without_noise(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -262,7 +263,7 @@ def test_treble(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.treble_biquad(waveform, sample_rate, gain, central_freq, q) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -280,7 +281,7 @@ def test_deemph(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.deemph_biquad(waveform, sample_rate) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -298,7 +299,7 @@ def test_riaa(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.riaa_biquad(waveform, sample_rate) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -316,7 +317,7 @@ def test_contrast(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.contrast(waveform, enhancement_amount) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -335,7 +336,7 @@ def test_dcshift_with_limiter(self): waveform, _ = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.dcshift(waveform, shift, limiter_gain) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -353,7 +354,7 @@ def test_dcshift_without_limiter(self): waveform, _ = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.dcshift(waveform, shift) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -372,7 +373,51 @@ def test_overdrive(self): waveform, _ = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.overdrive(waveform, gain, colour) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_phaser_sine(self): + """ + Test phaser effect with sine moduldation, compare to SoX implementation + """ + gain_in = 0.5 + gain_out = 0.8 + delay_ms = 2.0 + decay = 0.4 + speed = 0.5 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-s"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.phaser(waveform, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) + + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_phaser_triangle(self): + """ + Test phaser effect with triangle modulation, compare to SoX implementation + """ + gain_in = 0.5 + gain_out = 0.8 + delay_ms = 2.0 + decay = 0.4 + speed = 0.5 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-t"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.phaser(waveform, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=False) + + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -394,7 +439,7 @@ def test_equalizer(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.equalizer_biquad(waveform, sample_rate, center_freq, gain, q) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -420,7 +465,7 @@ def test_perf_biquad_filtering(self): waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2]) ) - torch.testing.assert_allclose(waveform_lfilter_out, waveform_sox_out, atol=1e-4, rtol=1e-5) + self.assertEqual(waveform_lfilter_out, waveform_sox_out, atol=1e-4, rtol=1e-5) if __name__ == "__main__": diff --git a/test/test_sox_effects.py b/test/test_sox_effects.py index 487d73a53d..213477537e 100644 --- a/test/test_sox_effects.py +++ b/test/test_sox_effects.py @@ -3,14 +3,15 @@ import torchaudio import math -import common_utils -from common_utils import AudioBackendScope, BACKENDS +from . import common_utils +from .common_utils import AudioBackendScope, BACKENDS -@unittest.skipIf("sox" not in BACKENDS, "sox not available") class Test_SoxEffectsChain(unittest.TestCase): test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.mp3") + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_single_channel(self): fn_sine = common_utils.get_asset_path("sinewave.wav") E = torchaudio.sox_effects.SoxEffectsChain() @@ -20,6 +21,8 @@ def test_single_channel(self): # check if effects worked # print(x.size()) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_rate_channels(self): target_rate = 16000 target_channels = 1 @@ -32,6 +35,8 @@ def test_rate_channels(self): self.assertEqual(sr, target_rate) self.assertEqual(x.size(0), target_channels) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_lowpass_speed(self): speed = .8 si, _ = torchaudio.info(self.test_filepath) @@ -44,6 +49,8 @@ def test_lowpass_speed(self): # check if effects worked self.assertEqual(x.size(1), int((si.length / si.channels) / speed)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_ulaw_and_siginfo(self): si_out = torchaudio.sox_signalinfo_t() ei_out = torchaudio.sox_encodinginfo_t() @@ -61,6 +68,8 @@ def test_ulaw_and_siginfo(self): self.assertLess(x.unique().size(0), 2**8 + 1) self.assertEqual(x.numel(), si_in.length) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_band_chorus(self): si_in, ei_in = torchaudio.info(self.test_filepath) ei_in.encoding = torchaudio.get_sox_encoding_t(1) @@ -75,6 +84,8 @@ def test_band_chorus(self): self.assertEqual(x.size(0), si_in.channels) self.assertGreaterEqual(x.size(1) * x.size(0), si_in.length) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_synth(self): si_in, ei_in = torchaudio.info(self.test_filepath) len_in_seconds = si_in.length / si_in.channels / si_in.rate @@ -88,6 +99,8 @@ def test_synth(self): self.assertEqual(x.size(0), si_in.channels) self.assertEqual(si_in.length, x.size(0) * x.size(1)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_gain(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -111,6 +124,8 @@ def test_gain(self): E.clear_chain() self.assertLess(x.abs().max().item(), 1.) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_tempo_or_speed(self): tempo = .8 si, _ = torchaudio.info(self.test_filepath) @@ -144,6 +159,8 @@ def test_tempo_or_speed(self): # check if effect worked self.assertAlmostEqual(x.size(1), math.ceil((si.length / si.channels) / speed), delta=1) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_trim(self): x_orig, _ = torchaudio.load(self.test_filepath) offset = "10000s" @@ -157,6 +174,8 @@ def test_trim(self): # check if effect worked self.assertTrue(x.allclose(x_orig[:, offset_int:(offset_int + num_frames_int)], rtol=1e-4, atol=1e-4)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_silence_contrast(self): si, _ = torchaudio.info(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -167,6 +186,8 @@ def test_silence_contrast(self): # check if effect worked self.assertLess(x.numel(), si.length) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_reverse(self): x_orig, _ = torchaudio.load(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -177,6 +198,8 @@ def test_reverse(self): rev_idx = torch.LongTensor(range(x_orig.size(1))[::-1]) self.assertTrue(x_orig.allclose(x_rev[:, rev_idx], rtol=1e-5, atol=2e-5)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_compand_fade(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -186,6 +209,8 @@ def test_compand_fade(self): # check if effect worked # print(x.size()) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_biquad_delay(self): si, _ = torchaudio.info(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -197,6 +222,8 @@ def test_biquad_delay(self): # check if effect worked self.assertTrue(x.size(1) == (si.length / si.channels) + 15000) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_invalid_effect_name(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -204,6 +231,8 @@ def test_invalid_effect_name(self): with self.assertRaises(LookupError): E.append_effect_to_chain("special", [""]) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_unimplemented_effect(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -211,6 +240,8 @@ def test_unimplemented_effect(self): with self.assertRaises(NotImplementedError): E.append_effect_to_chain("spectrogram", [""]) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_invalid_effect_options(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -219,6 +250,8 @@ def test_invalid_effect_options(self): with self.assertRaises(RuntimeError): E.sox_build_flow_effects() + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_fade(self): x_orig, _ = torchaudio.load(self.test_filepath) fade_in_len = 44100 @@ -235,6 +268,8 @@ def test_fade(self): # check if effect worked self.assertTrue(x.allclose(fade(x_orig), rtol=1e-4, atol=1e-4)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_vol(self): x_orig, _ = torchaudio.load(self.test_filepath) @@ -249,10 +284,12 @@ def test_vol(self): # check if effect worked self.assertTrue(x.allclose(z, rtol=1e-4, atol=1e-4)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_vad(self): sample_files = [ - common_utils.get_asset_path("vad-hello-stereo-44100.wav"), - common_utils.get_asset_path("vad-hello-mono-32000.wav") + common_utils.get_asset_path("vad-go-stereo-44100.wav"), + common_utils.get_asset_path("vad-go-mono-32000.wav") ] for sample_file in sample_files: @@ -269,5 +306,4 @@ def test_vad(self): if __name__ == '__main__': - with AudioBackendScope("sox"): - unittest.main() + unittest.main() diff --git a/test/test_transforms.py b/test/test_transforms.py index 9c0313ba7c..02faa68992 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -2,14 +2,15 @@ import unittest import torch +from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.transforms as transforms import torchaudio.functional as F -import common_utils +from . import common_utils -class Tester(unittest.TestCase): +class Tester(TestCase): # create a sinewave signal for testing sample_rate = 16000 @@ -49,7 +50,7 @@ def test_AmplitudeToDB(self): mag_to_db_torch = mag_to_db_transform(torch.abs(waveform)) power_to_db_torch = power_to_db_transform(torch.pow(waveform, 2)) - torch.testing.assert_allclose(mag_to_db_torch, power_to_db_torch) + self.assertEqual(mag_to_db_torch, power_to_db_torch) def test_melscale_load_save(self): specgram = torch.ones(1, 1000, 100) @@ -63,7 +64,7 @@ def test_melscale_load_save(self): fb_copy = melscale_transform_copy.fb self.assertEqual(fb_copy.size(), (1000, 128)) - torch.testing.assert_allclose(fb, fb_copy) + self.assertEqual(fb, fb_copy) def test_melspectrogram_load_save(self): waveform = self.waveform.float() @@ -79,10 +80,10 @@ def test_melspectrogram_load_save(self): fb = mel_spectrogram_transform.mel_scale.fb fb_copy = mel_spectrogram_transform_copy.mel_scale.fb - torch.testing.assert_allclose(window, window_copy) + self.assertEqual(window, window_copy) # the default for n_fft = 400 and n_mels = 128 self.assertEqual(fb_copy.size(), (201, 128)) - torch.testing.assert_allclose(fb, fb_copy) + self.assertEqual(fb, fb_copy) def test_mel2(self): top_db = 80. @@ -205,7 +206,7 @@ def test_compute_deltas_transform_same_as_functional(self, atol=1e-6, rtol=1e-8) computed_transform = transform(specgram) computed_functional = F.compute_deltas(specgram, win_length=win_length) - torch.testing.assert_allclose(computed_functional, computed_transform, atol=atol, rtol=rtol) + self.assertEqual(computed_functional, computed_transform, atol=atol, rtol=rtol) def test_compute_deltas_twochannel(self): specgram = torch.tensor([1., 2., 3., 4.]).repeat(1, 2, 1) @@ -214,7 +215,7 @@ def test_compute_deltas_twochannel(self): transform = transforms.ComputeDeltas(win_length=3) computed = transform(specgram) assert computed.shape == expected.shape, (computed.shape, expected.shape) - torch.testing.assert_allclose(computed, expected, atol=1e-6, rtol=1e-8) + self.assertEqual(computed, expected, atol=1e-6, rtol=1e-8) if __name__ == '__main__': diff --git a/test/torchscript_consistency_cpu_test.py b/test/torchscript_consistency_cpu_test.py new file mode 100644 index 0000000000..51a3a477f0 --- /dev/null +++ b/test/torchscript_consistency_cpu_test.py @@ -0,0 +1,5 @@ +from .common_utils import define_test_suites +from .torchscript_consistency_impl import Functional, Transforms + + +define_test_suites(globals(), [Functional, Transforms], devices=['cpu']) diff --git a/test/torchscript_consistency_cuda_test.py b/test/torchscript_consistency_cuda_test.py new file mode 100644 index 0000000000..0899ba2066 --- /dev/null +++ b/test/torchscript_consistency_cuda_test.py @@ -0,0 +1,5 @@ +from .common_utils import define_test_suites +from .torchscript_consistency_impl import Functional, Transforms + + +define_test_suites(globals(), [Functional, Transforms], devices=['cuda']) diff --git a/test/test_torchscript_consistency.py b/test/torchscript_consistency_impl.py similarity index 74% rename from test/test_torchscript_consistency.py rename to test/torchscript_consistency_impl.py index 5f0b12c76b..a4d3cc79cf 100644 --- a/test/test_torchscript_consistency.py +++ b/test/torchscript_consistency_impl.py @@ -6,36 +6,21 @@ import torchaudio.functional as F import torchaudio.transforms as T -import common_utils +from . import common_utils -def _assert_functional_consistency(func, tensor, device, shape_only=False): - tensor = tensor.to(device) - ts_func = torch.jit.script(func) - output = func(tensor) - ts_output = ts_func(tensor) - - if shape_only: - assert ts_output.shape == output.shape, (ts_output.shape, output.shape) - else: - torch.testing.assert_allclose(ts_output, output) - - -def _assert_transforms_consistency(transform, tensor, device): - tensor = tensor.to(device) - transform = transform.to(device) - ts_transform = torch.jit.script(transform) - output = transform(tensor) - ts_output = ts_transform(tensor) - torch.testing.assert_allclose(ts_output, output) - - -class _FunctionalTestMixin: +class Functional(common_utils.TestBaseMixin): """Implements test for `functinoal` modul that are performed for different devices""" - device = None - def _assert_consistency(self, func, tensor, shape_only=False): - return _assert_functional_consistency(func, tensor, self.device, shape_only=shape_only) + tensor = tensor.to(device=self.device, dtype=self.dtype) + + ts_func = torch.jit.script(func) + output = func(tensor) + ts_output = ts_func(tensor) + if shape_only: + ts_output = ts_output.shape + output = output.shape + self.assertEqual(ts_output, output) def test_spectrogram(self): def func(tensor): @@ -48,7 +33,7 @@ def func(tensor): normalize = False return F.spectrogram(tensor, pad, window, n_fft, hop, ws, power, normalize) - tensor = torch.rand((1, 1000)) + tensor = common_utils.get_whitenoise() self._assert_consistency(func, tensor) def test_griffinlim(self): @@ -80,8 +65,7 @@ def func(tensor): self._assert_consistency(func, tensor) def test_detect_pitch_frequency(self): - filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.mp3') - waveform, _ = torchaudio.load(filepath) + waveform = common_utils.get_sinusoid(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -99,7 +83,8 @@ def func(_): f_max = 20.0 n_mels = 10 sample_rate = 16000 - return F.create_fb_matrix(n_stft, f_min, f_max, n_mels, sample_rate) + norm = "slaney" + return F.create_fb_matrix(n_stft, f_min, f_max, n_mels, sample_rate, norm) dummy = torch.zeros(1, 1) self._assert_consistency(func, dummy) @@ -142,8 +127,8 @@ def func(tensor): qc = 256 return F.mu_law_encoding(tensor, qc) - tensor = torch.rand((1, 10)) - self._assert_consistency(func, tensor) + waveform = common_utils.get_whitenoise() + self._assert_consistency(func, waveform) def test_mu_law_decoding(self): def func(tensor): @@ -159,7 +144,7 @@ def func(tensor): return F.complex_norm(tensor, power) tensor = torch.randn(1, 2, 1025, 400, 2) - _assert_functional_consistency(func, tensor, self.device) + self._assert_consistency(func, tensor) def test_mask_along_axis(self): def func(tensor): @@ -193,26 +178,28 @@ def test_dither_TPDF(self): def func(tensor): return F.dither(tensor, 'TPDF') - tensor = torch.rand((2, 1000)) + tensor = common_utils.get_whitenoise(n_channels=2) self._assert_consistency(func, tensor, shape_only=True) def test_dither_RPDF(self): def func(tensor): return F.dither(tensor, 'RPDF') - tensor = torch.rand((2, 1000)) + tensor = common_utils.get_whitenoise(n_channels=2) self._assert_consistency(func, tensor, shape_only=True) def test_dither_GPDF(self): def func(tensor): return F.dither(tensor, 'GPDF') - tensor = torch.rand((2, 1000)) + tensor = common_utils.get_whitenoise(n_channels=2) self._assert_consistency(func, tensor, shape_only=True) def test_lfilter(self): - filepath = common_utils.get_asset_path('whitenoise.wav') - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise() def func(tensor): # Design an IIR lowpass filter using scipy.signal filter design @@ -252,8 +239,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_lowpass(self): - filepath = common_utils.get_asset_path('whitenoise.wav') - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -263,8 +252,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_highpass(self): - filepath = common_utils.get_asset_path('whitenoise.wav') - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -274,8 +265,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_allpass(self): - filepath = common_utils.get_asset_path('whitenoise.wav') - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -286,8 +279,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_bandpass_with_csg(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -298,9 +293,11 @@ def func(tensor): self._assert_consistency(func, waveform) - def test_bandpass_withou_csg(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + def test_bandpass_without_csg(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -312,8 +309,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_bandreject(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -324,8 +323,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_band_with_noise(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -337,8 +338,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_band_without_noise(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -350,8 +353,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_treble(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -363,8 +368,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_deemph(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -373,8 +380,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_riaa(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -383,8 +392,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_equalizer(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -396,8 +407,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_perf_biquad_filtering(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise() def func(tensor): a = torch.tensor([0.7, 0.2, 0.6], device=tensor.device, dtype=tensor.dtype) @@ -442,8 +455,7 @@ def func(tensor): self._assert_consistency(func, b) def test_contrast(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + waveform = common_utils.get_whitenoise() def func(tensor): enhancement_amount = 80. @@ -452,8 +464,7 @@ def func(tensor): self._assert_consistency(func, waveform) def test_dcshift(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + waveform = common_utils.get_whitenoise() def func(tensor): shift = 0.5 @@ -463,8 +474,7 @@ def func(tensor): self._assert_consistency(func, waveform) def test_overdrive(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + waveform = common_utils.get_whitenoise() def func(tensor): gain = 30. @@ -473,13 +483,31 @@ def func(tensor): self._assert_consistency(func, waveform) + def test_phaser(self): + waveform = common_utils.get_whitenoise(sample_rate=44100) -class _TransformsTestMixin: - """Implements test for Transforms that are performed for different devices""" - device = None + def func(tensor): + gain_in = 0.5 + gain_out = 0.8 + delay_ms = 2.0 + decay = 0.4 + speed = 0.5 + sample_rate = 44100 + return F.phaser(tensor, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) + self._assert_consistency(func, waveform) + + +class Transforms(common_utils.TestBaseMixin): + """Implements test for Transforms that are performed for different devices""" def _assert_consistency(self, transform, tensor): - _assert_transforms_consistency(transform, tensor, self.device) + tensor = tensor.to(device=self.device, dtype=self.dtype) + transform = transform.to(device=self.device, dtype=self.dtype) + + ts_transform = torch.jit.script(transform) + output = transform(tensor) + ts_output = ts_transform(tensor) + self.assertEqual(ts_output, output) def test_Spectrogram(self): tensor = torch.rand((1, 1000)) @@ -506,17 +534,16 @@ def test_MFCC(self): self._assert_consistency(T.MFCC(), tensor) def test_Resample(self): - tensor = torch.rand((2, 1000)) - sample_rate = 100. - sample_rate_2 = 50. - self._assert_consistency(T.Resample(sample_rate, sample_rate_2), tensor) + sr1, sr2 = 16000, 8000 + tensor = common_utils.get_whitenoise(sample_rate=sr1) + self._assert_consistency(T.Resample(float(sr1), float(sr2)), tensor) def test_ComplexNorm(self): tensor = torch.rand((1, 2, 201, 2)) self._assert_consistency(T.ComplexNorm(), tensor) def test_MuLawEncoding(self): - tensor = torch.rand((1, 10)) + tensor = common_utils.get_whitenoise() self._assert_consistency(T.MuLawEncoding(), tensor) def test_MuLawDecoding(self): @@ -534,8 +561,7 @@ def test_TimeStretch(self): ) def test_Fade(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) + waveform = common_utils.get_whitenoise() fade_in_len = 3000 fade_out_len = 3000 self._assert_consistency(T.Fade(fade_in_len, fade_out_len), waveform) @@ -549,8 +575,7 @@ def test_TimeMasking(self): self._assert_consistency(T.TimeMasking(time_mask_param=30, iid_masks=False), tensor) def test_Vol(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) + waveform = common_utils.get_whitenoise() self._assert_consistency(T.Vol(1.1), waveform) def test_SlidingWindowCmn(self): @@ -558,32 +583,6 @@ def test_SlidingWindowCmn(self): self._assert_consistency(T.SlidingWindowCmn(), tensor) def test_Vad(self): - filepath = common_utils.get_asset_path("vad-hello-mono-32000.wav") + filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") waveform, sample_rate = torchaudio.load(filepath) self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform) - - -class TestFunctionalCPU(_FunctionalTestMixin, unittest.TestCase): - """Test suite for Functional module on CPU""" - device = torch.device('cpu') - - -@unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') -class TestFunctionalCUDA(_FunctionalTestMixin, unittest.TestCase): - """Test suite for Functional module on GPU""" - device = torch.device('cuda') - - -class TestTransformsCPU(_TransformsTestMixin, unittest.TestCase): - """Test suite for Transforms module on CPU""" - device = torch.device('cpu') - - -@unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') -class TestTransformsCUDA(_TransformsTestMixin, unittest.TestCase): - """Test suite for Transforms module on GPU""" - device = torch.device('cuda') - - -if __name__ == '__main__': - unittest.main() diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index e748a34bc2..a08eaf6964 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -193,8 +193,8 @@ def save_encinfo(filepath: str, # save data to file src = src.contiguous() - import _torch_sox - _torch_sox.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) + from . import _torchaudio + _torchaudio.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: @@ -236,8 +236,8 @@ def sox_signalinfo_t() -> SignalInfo: >>> si.length = 0 """ - import _torch_sox - return _torch_sox.sox_signalinfo_t() + from . import _torchaudio + return _torchaudio.sox_signalinfo_t() @_audio_backend_guard("sox") @@ -271,8 +271,8 @@ def sox_encodinginfo_t() -> EncodingInfo: """ - import _torch_sox - ei = _torch_sox.sox_encodinginfo_t() + from . import _torchaudio + ei = _torchaudio.sox_encodinginfo_t() sdo = get_sox_option_t(2) # sox_default_option ei.reverse_bytes = sdo ei.reverse_nibbles = sdo @@ -292,12 +292,12 @@ def get_sox_encoding_t(i: int = None) -> EncodingInfo: sox_encoding_t: A sox_encoding_t type for output encoding """ - import _torch_sox + from . import _torchaudio if i is None: # one can see all possible values using the .__members__ attribute - return _torch_sox.sox_encoding_t + return _torchaudio.sox_encoding_t else: - return _torch_sox.sox_encoding_t(i) + return _torchaudio.sox_encoding_t(i) @_audio_backend_guard("sox") @@ -312,11 +312,11 @@ def get_sox_option_t(i: int = 2) -> Any: sox_option_t: A sox_option_t type """ - import _torch_sox + from . import _torchaudio if i is None: - return _torch_sox.sox_option_t + return _torchaudio.sox_option_t else: - return _torch_sox.sox_option_t(i) + return _torchaudio.sox_option_t(i) @_audio_backend_guard("sox") @@ -332,14 +332,14 @@ def get_sox_bool(i: int = 0) -> Any: sox_bool: A sox_bool type """ - import _torch_sox + from . import _torchaudio if i is None: - return _torch_sox.sox_bool + return _torchaudio.sox_bool else: - return _torch_sox.sox_bool(i) + return _torchaudio.sox_bool(i) -_SOX_INITIALIZED = False +_SOX_INITIALIZED: Optional[bool] = False # This variable has a micro lifecycle. (False -> True -> None) # False: Not initialized # True: Initialized @@ -370,8 +370,8 @@ def initialize_sox() -> int: if _SOX_INITIALIZED is None: raise RuntimeError('SoX effects chain has been already shut down. Can not initialize again.') if not _SOX_INITIALIZED: - import _torch_sox - code = _torch_sox.initialize_sox() + from . import _torchaudio + code = _torchaudio.initialize_sox() if code == _SOX_SUCCESS_CODE: _SOX_INITIALIZED = True atexit.register(shutdown_sox) @@ -394,8 +394,8 @@ def shutdown_sox() -> int: """ global _SOX_INITIALIZED if _SOX_INITIALIZED: - import _torch_sox - code = _torch_sox.shutdown_sox() + from . import _torchaudio + code = _torchaudio.shutdown_sox() if code == _SOX_INITIALIZED: _SOX_INITIALIZED = None return code diff --git a/torchaudio/_sox_backend.py b/torchaudio/_sox_backend.py index 802bd0fe17..3478d30d50 100644 --- a/torchaudio/_sox_backend.py +++ b/torchaudio/_sox_backend.py @@ -35,8 +35,8 @@ def load(filepath: str, if offset < 0: raise ValueError("Expected positive offset value") - import _torch_sox - sample_rate = _torch_sox.read_audio_file( + from . import _torchaudio + sample_rate = _torchaudio.read_audio_file( filepath, out, channels_first, @@ -68,5 +68,5 @@ def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, chan def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: r"""See torchaudio.info""" - import _torch_sox - return _torch_sox.get_info(filepath) + from . import _torchaudio + return _torchaudio.get_info(filepath) diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index e9189b2172..8115c3a25f 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -33,6 +33,10 @@ WINDOWS = [HAMMING, HANNING, POVEY, RECTANGULAR, BLACKMAN] +def _get_epsilon(device, dtype): + return EPSILON.to(device=device, dtype=dtype) + + def _next_power_of_2(x: int) -> int: r"""Returns the smallest power of 2 that is greater than x """ @@ -60,7 +64,7 @@ def _get_strided(waveform: Tensor, window_size: int, window_shift: int, snip_edg if snip_edges: if num_samples < window_size: - return torch.empty((0, 0)) + return torch.empty((0, 0), dtype=waveform.dtype, device=waveform.device) else: m = 1 + (num_samples - window_size) // window_shift else: @@ -83,24 +87,27 @@ def _get_strided(waveform: Tensor, window_size: int, window_shift: int, snip_edg def _feature_window_function(window_type: str, window_size: int, - blackman_coeff: float) -> Tensor: + blackman_coeff: float, + device: torch.device, + dtype: int, + ) -> Tensor: r"""Returns a window function with the given type and size """ if window_type == HANNING: - return torch.hann_window(window_size, periodic=False) + return torch.hann_window(window_size, periodic=False, device=device, dtype=dtype) elif window_type == HAMMING: - return torch.hamming_window(window_size, periodic=False, alpha=0.54, beta=0.46) + return torch.hamming_window(window_size, periodic=False, alpha=0.54, beta=0.46, device=device, dtype=dtype) elif window_type == POVEY: # like hanning but goes to zero at edges - return torch.hann_window(window_size, periodic=False).pow(0.85) + return torch.hann_window(window_size, periodic=False, device=device, dtype=dtype).pow(0.85) elif window_type == RECTANGULAR: - return torch.ones(window_size) + return torch.ones(window_size, device=device, dtype=dtype) elif window_type == BLACKMAN: a = 2 * math.pi / (window_size - 1) - window_function = torch.arange(window_size) + window_function = torch.arange(window_size, device=device, dtype=dtype) # can't use torch.blackman_window as they use different coefficients return (blackman_coeff - 0.5 * torch.cos(a * window_function) + - (0.5 - blackman_coeff) * torch.cos(2 * a * window_function)) + (0.5 - blackman_coeff) * torch.cos(2 * a * window_function)).to(device=device, dtype=dtype) else: raise Exception('Invalid window type ' + window_type) @@ -110,12 +117,12 @@ def _get_log_energy(strided_input: Tensor, energy_floor: float) -> Tensor: r"""Returns the log energy of size (m) for a strided_input (m,*) """ + device, dtype = strided_input.device, strided_input.dtype log_energy = torch.max(strided_input.pow(2).sum(1), epsilon).log() # size (m) if energy_floor == 0.0: return log_energy - else: - return torch.max(log_energy, - torch.tensor(math.log(energy_floor))) + return torch.max( + log_energy, torch.tensor(math.log(energy_floor), device=device, dtype=dtype)) def _get_waveform_and_window_properties(waveform: Tensor, @@ -160,12 +167,15 @@ def _get_window(waveform: Tensor, Returns: (Tensor, Tensor): strided_input of size (m, ``padded_window_size``) and signal_log_energy of size (m) """ + device, dtype = waveform.device, waveform.dtype + epsilon = _get_epsilon(device, dtype) + # size (m, window_size) strided_input = _get_strided(waveform, window_size, window_shift, snip_edges) if dither != 0.0: # Returns a random number strictly between 0 and 1 - x = torch.max(EPSILON, torch.rand(strided_input.shape)) + x = torch.max(epsilon, torch.rand(strided_input.shape, device=device, dtype=dtype)) rand_gauss = torch.sqrt(-2 * x.log()) * torch.cos(2 * math.pi * x) strided_input = strided_input + rand_gauss * dither @@ -177,7 +187,7 @@ def _get_window(waveform: Tensor, if raw_energy: # Compute the log energy of each row/frame before applying preemphasis and # window function - signal_log_energy = _get_log_energy(strided_input, EPSILON, energy_floor) # size (m) + signal_log_energy = _get_log_energy(strided_input, epsilon, energy_floor) # size (m) if preemphasis_coefficient != 0.0: # strided_input[i,j] -= preemphasis_coefficient * strided_input[i, max(0, j-1)] for all i,j @@ -187,7 +197,7 @@ def _get_window(waveform: Tensor, # Apply window_function to each row/frame window_function = _feature_window_function( - window_type, window_size, blackman_coeff).unsqueeze(0) # size (1, window_size) + window_type, window_size, blackman_coeff, device, dtype).unsqueeze(0) # size (1, window_size) strided_input = strided_input * window_function # size (m, window_size) # Pad columns with zero until we reach size (m, padded_window_size) @@ -198,7 +208,7 @@ def _get_window(waveform: Tensor, # Compute energy after window function (not the raw one) if not raw_energy: - signal_log_energy = _get_log_energy(strided_input, EPSILON, energy_floor) # size (m) + signal_log_energy = _get_log_energy(strided_input, epsilon, energy_floor) # size (m) return strided_input, signal_log_energy @@ -541,12 +551,14 @@ def fbank(waveform: Tensor, Tensor: A fbank identical to what Kaldi would output. The shape is (m, ``num_mel_bins + use_energy``) where m is calculated in _get_strided """ + device, dtype = waveform.device, waveform.dtype + waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties( waveform, channel, sample_frequency, frame_shift, frame_length, round_to_power_of_two, preemphasis_coefficient) if len(waveform) < min_duration * sample_frequency: # signal is too short - return torch.empty(0) + return torch.empty(0, device=device, dtype=dtype) # strided_input, size (m, padded_window_size) and signal_log_energy, size (m) strided_input, signal_log_energy = _get_window( @@ -563,6 +575,7 @@ def fbank(waveform: Tensor, # size (num_mel_bins, padded_window_size // 2) mel_energies, _ = get_mel_banks(num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp) + mel_energies = mel_energies.to(device=device, dtype=dtype) # pad right column with zeros and add dimension, size (1, num_mel_bins, padded_window_size // 2 + 1) mel_energies = torch.nn.functional.pad(mel_energies, (0, 1), mode='constant', value=0).unsqueeze(0) @@ -571,7 +584,7 @@ def fbank(waveform: Tensor, mel_energies = (power_spectrum * mel_energies).sum(dim=2) if use_log_fbank: # avoid log of zero (which should be prevented anyway by dithering) - mel_energies = torch.max(mel_energies, EPSILON).log() + mel_energies = torch.max(mel_energies, _get_epsilon(device, dtype)).log() # if use_energy then add it as the last column for htk_compat == true else first column if use_energy: @@ -737,7 +750,9 @@ def _get_LR_indices_and_weights(orig_freq: float, output_samples_in_unit: int, window_width: float, lowpass_cutoff: float, - lowpass_filter_width: int) -> Tuple[Tensor, Tensor]: + lowpass_filter_width: int, + device: torch.device, + dtype: int) -> Tuple[Tensor, Tensor]: r"""Based on LinearResample::SetIndexesAndWeights where it retrieves the weights for resampling as well as the indices in which they are valid. LinearResample (LR) means that the output signal is at linearly spaced intervals (i.e the output signal has a @@ -785,7 +800,7 @@ def _get_LR_indices_and_weights(orig_freq: float, which correspond with min_input_index, size (``output_samples_in_unit``, ``max_weight_width``)). """ assert lowpass_cutoff < min(orig_freq, new_freq) / 2 - output_t = torch.arange(0., output_samples_in_unit) / new_freq + output_t = torch.arange(0., output_samples_in_unit, device=device, dtype=dtype) / new_freq min_t = output_t - window_width max_t = output_t + window_width @@ -795,7 +810,7 @@ def _get_LR_indices_and_weights(orig_freq: float, max_weight_width = num_indices.max() # create a group of weights of size (output_samples_in_unit, max_weight_width) - j = torch.arange(max_weight_width).unsqueeze(0) + j = torch.arange(max_weight_width, device=device, dtype=dtype).unsqueeze(0) input_index = min_input_index.unsqueeze(1) + j delta_t = (input_index / orig_freq) - output_t.unsqueeze(1) @@ -890,6 +905,8 @@ def resample_waveform(waveform: Tensor, Returns: Tensor: The waveform at the new frequency """ + device, dtype = waveform.device, waveform.dtype + assert waveform.dim() == 2 assert orig_freq > 0.0 and new_freq > 0.0 @@ -903,9 +920,9 @@ def resample_waveform(waveform: Tensor, output_samples_in_unit = int(new_freq) // base_freq window_width = lowpass_filter_width / (2.0 * lowpass_cutoff) - first_indices, weights = _get_LR_indices_and_weights(orig_freq, new_freq, output_samples_in_unit, - window_width, lowpass_cutoff, lowpass_filter_width) - weights = weights.to(waveform.device) # TODO Create weights on device directly + first_indices, weights = _get_LR_indices_and_weights( + orig_freq, new_freq, output_samples_in_unit, + window_width, lowpass_cutoff, lowpass_filter_width, device, dtype) assert first_indices.dim() == 1 # TODO figure a better way to do this. conv1d reaches every element i*stride + padding @@ -918,9 +935,9 @@ def resample_waveform(waveform: Tensor, window_size = weights.size(1) tot_output_samp = _get_num_LR_output_samples(wave_len, orig_freq, new_freq) output = torch.zeros((num_channels, tot_output_samp), - device=waveform.device) + device=device, dtype=dtype) # eye size: (num_channels, num_channels, 1) - eye = torch.eye(num_channels, device=waveform.device).unsqueeze(2) + eye = torch.eye(num_channels, device=device, dtype=dtype).unsqueeze(2) for i in range(first_indices.size(0)): wave_to_conv = waveform first_index = int(first_indices[i].item()) diff --git a/torchaudio/torch_sox.cpp b/torchaudio/csrc/sox.cpp similarity index 98% rename from torchaudio/torch_sox.cpp rename to torchaudio/csrc/sox.cpp index 5f5a182ebc..3ae81bef19 100644 --- a/torchaudio/torch_sox.cpp +++ b/torchaudio/csrc/sox.cpp @@ -1,6 +1,4 @@ -#include - -#include +#include #include #include @@ -70,12 +68,6 @@ void read_audio( } } // namespace -struct SoxEffect { - SoxEffect() : ename(""), eopts({""}) { } - std::string ename; - std::vector eopts; -}; - std::tuple get_info( const std::string& file_name ) { @@ -195,7 +187,7 @@ void write_audio_file( } int initialize_sox() { - /* Initializion for sox effects. Only initialize once */ + /* Initialization for sox effects. Only initialize once */ return sox_init(); } @@ -389,7 +381,7 @@ int build_flow_effects(const std::string& file_name, } // namespace audio } // namespace torch -PYBIND11_MODULE(_torch_sox, m) { +PYBIND11_MODULE(_torchaudio, m) { py::class_(m, "SoxEffect") .def(py::init<>()) .def("__repr__", [](const torch::audio::SoxEffect &self) { diff --git a/torchaudio/torch_sox.h b/torchaudio/csrc/sox.h similarity index 88% rename from torchaudio/torch_sox.h rename to torchaudio/csrc/sox.h index 9714fc90ba..8d851c9b21 100644 --- a/torchaudio/torch_sox.h +++ b/torchaudio/csrc/sox.h @@ -1,4 +1,10 @@ +#include + #include +#include +#include + +#include namespace at { struct Tensor; @@ -9,7 +15,7 @@ namespace torch { namespace audio { /// Reads an audio file from the given `path` into the `output` `Tensor` and /// returns the sample rate of the audio file. /// Throws `std::runtime_error` if the audio file could not be opened, or an -/// error ocurred during reading of the audio data. +/// error occurred during reading of the audio data. int read_audio_file( const std::string& file_name, at::Tensor output, @@ -18,24 +24,24 @@ int read_audio_file( int64_t offset, sox_signalinfo_t* si, sox_encodinginfo_t* ei, - const char* ft) + const char* ft); /// Writes the data of a `Tensor` into an audio file at the given `path`, with /// a certain extension (e.g. `wav`or `mp3`) and sample rate. /// Throws `std::runtime_error` when the audio file could not be opened for -/// writing, or an error ocurred during writing of the audio data. +/// writing, or an error occurred during writing of the audio data. void write_audio_file( const std::string& file_name, - at::Tensor& tensor, + const at::Tensor& tensor, sox_signalinfo_t* si, sox_encodinginfo_t* ei, - const char* file_type) + const char* file_type); /// Reads an audio file from the given `path` and returns a tuple of /// sox_signalinfo_t and sox_encodinginfo_t, which contain information about /// the audio file such as sample rate, length, bit precision, encoding and more. /// Throws `std::runtime_error` if the audio file could not be opened, or an -/// error ocurred during reading of the audio data. +/// error occurred during reading of the audio data. std::tuple get_info( const std::string& file_name); diff --git a/torchaudio/functional.py b/torchaudio/functional.py index cb5156596d..f59c46eb14 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -2,6 +2,7 @@ import math from typing import Optional, Tuple +import warnings import torch from torch import Tensor @@ -34,6 +35,7 @@ "contrast", "dcshift", "overdrive", + "phaser", 'mask_along_axis', 'mask_along_axis_iid', 'sliding_window_cmn', @@ -48,7 +50,7 @@ def istft( win_length: Optional[int] = None, window: Optional[Tensor] = None, center: bool = True, - pad_mode: str = "reflect", + pad_mode: Optional[str] = None, normalized: bool = False, onesided: bool = True, length: Optional[int] = None, @@ -93,8 +95,7 @@ def istft( center (bool, optional): Whether ``input`` was padded on both sides so that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. (Default: ``True``) - pad_mode (str, optional): Controls the padding method used when ``center`` is True. (Default: - ``"reflect"``) + pad_mode: This argument was ignored and to be removed. normalized (bool, optional): Whether the STFT was normalized. (Default: ``False``) onesided (bool, optional): Whether the STFT is onesided. (Default: ``True``) length (int or None, optional): The amount to trim the signal by (i.e. the @@ -103,105 +104,16 @@ def istft( Returns: Tensor: Least squares estimation of the original signal of size (..., signal_length) """ - stft_matrix_dim = stft_matrix.dim() - assert 3 <= stft_matrix_dim, "Incorrect stft dimension: %d" % (stft_matrix_dim) - assert stft_matrix.numel() > 0 - - if stft_matrix_dim == 3: - # add a channel dimension - stft_matrix = stft_matrix.unsqueeze(0) - - # pack batch - shape = stft_matrix.size() - stft_matrix = stft_matrix.view(-1, shape[-3], shape[-2], shape[-1]) - - dtype = stft_matrix.dtype - device = stft_matrix.device - fft_size = stft_matrix.size(1) - assert (onesided and n_fft // 2 + 1 == fft_size) or ( - not onesided and n_fft == fft_size - ), ( - "one_sided implies that n_fft // 2 + 1 == fft_size and not one_sided implies n_fft == fft_size. " - + "Given values were onesided: %s, n_fft: %d, fft_size: %d" - % ("True" if onesided else False, n_fft, fft_size) - ) - - # use stft defaults for Optionals - if win_length is None: - win_length = n_fft - - if hop_length is None: - hop_length = int(win_length // 4) - - # There must be overlap - assert 0 < hop_length <= win_length - assert 0 < win_length <= n_fft - - if window is None: - window = torch.ones(win_length, device=device, dtype=dtype) - - assert window.dim() == 1 and window.size(0) == win_length - - if win_length != n_fft: - # center window with pad left and right zeros - left = (n_fft - win_length) // 2 - window = torch.nn.functional.pad(window, (left, n_fft - win_length - left)) - assert window.size(0) == n_fft - # win_length and n_fft are synonymous from here on - - stft_matrix = stft_matrix.transpose(1, 2) # size (channel, n_frame, fft_size, 2) - stft_matrix = torch.irfft( - stft_matrix, 1, normalized, onesided, signal_sizes=(n_fft,) - ) # size (channel, n_frame, n_fft) - - assert stft_matrix.size(2) == n_fft - n_frame = stft_matrix.size(1) - - ytmp = stft_matrix * window.view(1, 1, n_fft) # size (channel, n_frame, n_fft) - # each column of a channel is a frame which needs to be overlap added at the right place - ytmp = ytmp.transpose(1, 2) # size (channel, n_fft, n_frame) - - # this does overlap add where the frames of ytmp are added such that the i'th frame of - # ytmp is added starting at i*hop_length in the output - y = torch.nn.functional.fold( - ytmp, (1, (n_frame - 1) * hop_length + n_fft), (1, n_fft), stride=(1, hop_length) - ).squeeze(2) - - # do the same for the window function - window_sq = ( - window.pow(2).view(n_fft, 1).repeat((1, n_frame)).unsqueeze(0) - ) # size (1, n_fft, n_frame) - window_envelop = torch.nn.functional.fold( - window_sq, (1, (n_frame - 1) * hop_length + n_fft), (1, n_fft), stride=(1, hop_length) - ).squeeze(2) # size (1, 1, expected_signal_len) - - expected_signal_len = n_fft + hop_length * (n_frame - 1) - assert y.size(2) == expected_signal_len - assert window_envelop.size(2) == expected_signal_len - - half_n_fft = n_fft // 2 - # we need to trim the front padding away if center - start = half_n_fft if center else 0 - end = -half_n_fft if length is None else start + length - - y = y[:, :, start:end] - window_envelop = window_envelop[:, :, start:end] - - # check NOLA non-zero overlap condition - window_envelop_lowest = window_envelop.abs().min() - assert window_envelop_lowest > 1e-11, "window overlap add min: %f" % ( - window_envelop_lowest - ) - - y = (y / window_envelop).squeeze(1) # size (channel, expected_signal_len) - - # unpack batch - y = y.view(shape[:-3] + y.shape[-1:]) - - if stft_matrix_dim == 3: # remove the channel dimension - y = y.squeeze(0) - - return y + warnings.warn( + 'istft has been moved to PyTorch and will be removed from torchaudio, ' + 'please use torch.istft instead.') + if pad_mode is not None: + warnings.warn( + 'The parameter `pad_mode` was ignored in isftft, and is thus being deprecated. ' + 'Please set `pad_mode` to None to suppress this warning.') + return torch.istft( + input=stft_matrix, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, + center=center, normalized=normalized, onesided=onesided, length=length) def spectrogram( @@ -241,7 +153,7 @@ def spectrogram( # pack batch shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) + waveform = waveform.reshape(-1, shape[-1]) # default values are consistent with librosa.core.spectrum._spectrogram spec_f = torch.stft( @@ -249,7 +161,7 @@ def spectrogram( ) # unpack batch - spec_f = spec_f.view(shape[:-1] + spec_f.shape[-3:]) + spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-3:]) if normalized: spec_f /= window.pow(2.).sum().sqrt() @@ -310,11 +222,11 @@ def griffinlim( torch.Tensor: waveform of (..., time), where time equals the ``length`` parameter if given. """ assert momentum < 1, 'momentum=%s > 1 can be unstable' % momentum - assert momentum > 0, 'momentum=%s < 0' % momentum + assert momentum >= 0, 'momentum=%s < 0' % momentum # pack batch shape = specgram.size() - specgram = specgram.view([-1] + list(shape[-2:])) + specgram = specgram.reshape([-1] + list(shape[-2:])) specgram = specgram.pow(1 / power) @@ -348,7 +260,9 @@ def griffinlim( True, 'reflect', False, True) # Update our phase estimates - angles = rebuilt - tprev.mul_(momentum / (1 + momentum)) + angles = rebuilt + if momentum: + angles = angles - tprev.mul_(momentum / (1 + momentum)) angles = angles.div_(complex_norm(angles).add_(1e-16).unsqueeze(-1).expand_as(angles)) # Return the final phase estimates @@ -360,7 +274,7 @@ def griffinlim( length=length) # unpack batch - waveform = waveform.view(shape[:-2] + waveform.shape[-1:]) + waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:]) return waveform @@ -421,7 +335,8 @@ def create_fb_matrix( f_min: float, f_max: float, n_mels: int, - sample_rate: int + sample_rate: int, + norm: Optional[str] = None ) -> Tensor: r"""Create a frequency bin conversion matrix. @@ -431,6 +346,8 @@ def create_fb_matrix( f_max (float): Maximum frequency (Hz) n_mels (int): Number of mel filterbanks sample_rate (int): Sample rate of the audio waveform + norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band + (area normalization). (Default: ``None``) Returns: Tensor: Triangular filter banks (fb matrix) of size (``n_freqs``, ``n_mels``) @@ -439,6 +356,10 @@ def create_fb_matrix( size (..., ``n_freqs``), the applied result would be ``A * create_fb_matrix(A.size(-1), ...)``. """ + + if norm is not None and norm != "slaney": + raise ValueError("norm must be one of None or 'slaney'") + # freq bins # Equivalent filterbank construction by Librosa all_freqs = torch.linspace(0, sample_rate // 2, n_freqs) @@ -458,6 +379,12 @@ def create_fb_matrix( down_slopes = (-1.0 * slopes[:, :-2]) / f_diff[:-1] # (n_freqs, n_mels) up_slopes = slopes[:, 2:] / f_diff[1:] # (n_freqs, n_mels) fb = torch.max(zero, torch.min(down_slopes, up_slopes)) + + if norm is not None and norm == "slaney": + # Slaney-style mel is scaled to be approx constant energy per channel + enorm = 2.0 / (f_pts[2:n_mels + 2] - f_pts[:n_mels]) + fb *= enorm.unsqueeze(0) + return fb @@ -623,7 +550,7 @@ def phase_vocoder( # pack batch shape = complex_specgrams.size() - complex_specgrams = complex_specgrams.view([-1] + list(shape[-3:])) + complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-3:])) time_steps = torch.arange(0, complex_specgrams.size(-2), @@ -663,7 +590,7 @@ def phase_vocoder( complex_specgrams_stretch = torch.stack([real_stretch, imag_stretch], dim=-1) # unpack batch - complex_specgrams_stretch = complex_specgrams_stretch.view(shape[:-3] + complex_specgrams_stretch.shape[1:]) + complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-3] + complex_specgrams_stretch.shape[1:]) return complex_specgrams_stretch @@ -671,25 +598,27 @@ def phase_vocoder( def lfilter( waveform: Tensor, a_coeffs: Tensor, - b_coeffs: Tensor + b_coeffs: Tensor, + clamp: bool = True, ) -> Tensor: r"""Perform an IIR filter by evaluating difference equation. Args: - waveform (Tensor): audio waveform of dimension of `(..., time)`. Must be normalized to -1 to 1. - a_coeffs (Tensor): denominator coefficients of difference equation of dimension of `(n_order + 1)`. - Lower delays coefficients are first, e.g. `[a0, a1, a2, ...]`. + waveform (Tensor): audio waveform of dimension of ``(..., time)``. Must be normalized to -1 to 1. + a_coeffs (Tensor): denominator coefficients of difference equation of dimension of ``(n_order + 1)``. + Lower delays coefficients are first, e.g. ``[a0, a1, a2, ...]``. Must be same size as b_coeffs (pad with 0's as necessary). - b_coeffs (Tensor): numerator coefficients of difference equation of dimension of `(n_order + 1)`. - Lower delays coefficients are first, e.g. `[b0, b1, b2, ...]`. + b_coeffs (Tensor): numerator coefficients of difference equation of dimension of ``(n_order + 1)``. + Lower delays coefficients are first, e.g. ``[b0, b1, b2, ...]``. Must be same size as a_coeffs (pad with 0's as necessary). + clamp (bool, optional): If ``True``, clamp the output signal to be in the range [-1, 1] (Default: ``True``) Returns: - Tensor: Waveform with dimension of `(..., time)`. Output will be clipped to -1 to 1. + Tensor: Waveform with dimension of ``(..., time)``. """ # pack batch shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) + waveform = waveform.reshape(-1, shape[-1]) assert (a_coeffs.size(0) == b_coeffs.size(0)) assert (len(waveform.size()) == 2) @@ -729,10 +658,13 @@ def lfilter( o0.addmv_(windowed_output_signal, a_coeffs_flipped, alpha=-1) padded_output_waveform[:, i_sample + n_order - 1] = o0 - output = torch.clamp(padded_output_waveform[:, (n_order - 1):], min=-1., max=1.) + output = padded_output_waveform[:, (n_order - 1):] + + if clamp: + output = torch.clamp(output, min=-1., max=1.) # unpack batch - output = output.view(shape[:-1] + output.shape[-1:]) + output = output.reshape(shape[:-1] + output.shape[-1:]) return output @@ -1296,6 +1228,135 @@ def overdrive( return output_waveform.clamp(min=-1, max=1).view(actual_shape) +def phaser( + waveform: Tensor, + sample_rate: int, + gain_in: float = 0.4, + gain_out: float = 0.74, + delay_ms: float = 3.0, + decay: float = 0.4, + mod_speed: float = 0.5, + sinusoidal: bool = True +) -> Tensor: + r"""Apply a phasing effect to the audio. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + gain_in (float): desired input gain at the boost (or attenuation) in dB + Allowed range of values are 0 to 1 + gain_out (float): desired output gain at the boost (or attenuation) in dB + Allowed range of values are 0 to 1e9 + delay_ms (float): desired delay in milli seconds + Allowed range of values are 0 to 5.0 + decay (float): desired decay relative to gain-in + Allowed range of values are 0 to 0.99 + mod_speed (float): modulation speed in Hz + Allowed range of values are 0.1 to 2 + sinusoidal (bool): If ``True``, uses sinusoidal modulation (preferable for multiple instruments) + If ``False``, uses triangular modulation (gives single instruments a sharper phasing effect) + (Default: ``True``) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + Scott Lehman, Effects Explained, http://harmony-central.com/Effects/effects-explained.html + """ + actual_shape = waveform.shape + device, dtype = waveform.device, waveform.dtype + + # convert to 2D (channels,time) + waveform = waveform.view(-1, actual_shape[-1]) + + delay_buf_len = int((delay_ms * .001 * sample_rate) + .5) + delay_buf = torch.zeros(waveform.shape[0], delay_buf_len, dtype=dtype, device=device) + + mod_buf_len = int(sample_rate / mod_speed + .5) + mod_buf = torch.zeros(mod_buf_len, dtype=dtype, device=device) + + if sinusoidal: + wave_type = 'SINE' + else: + wave_type = 'TRIANGLE' + + mod_buf = _generate_wave_table(wave_type=wave_type, + data_type='INT', + table_size=mod_buf_len, + min=1., + max=float(delay_buf_len), + phase=math.pi / 2) + + delay_pos = 0 + mod_pos = 0 + + output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) + + for i in range(waveform.shape[-1]): + idx = int((delay_pos + mod_buf[mod_pos]) % delay_buf_len) + temp = (waveform[:, i] * gain_in) + (delay_buf[:, idx] * decay) + mod_pos = (mod_pos + 1) % mod_buf_len + delay_pos = (delay_pos + 1) % delay_buf_len + delay_buf[:, delay_pos] = temp + output_waveform[:, i] = temp * gain_out + + return output_waveform.clamp(min=-1, max=1).view(actual_shape) + + +def _generate_wave_table( + wave_type: str, + data_type: str, + table_size: int, + min: float, + max: float, + phase: float +) -> Tensor: + r"""A helper fucntion for phaser. Generates a table with given parameters + + Args: + wave_type (str): SINE or TRIANGULAR + data_type (str): desired data_type ( `INT` or `FLOAT` ) + table_size (int): desired table size + min (float): desired min value + max (float): desired max value + phase (float): desired phase + + Returns: + Tensor: A 1D tensor with wave table values + """ + + phase_offset = int(phase / math.pi / 2 * table_size + 0.5) + + t = torch.arange(table_size).to(torch.int32) + + point = (t + phase_offset) % table_size + + d = torch.zeros_like(point).to(torch.float64) + + if wave_type == 'SINE': + d = (torch.sin(point.to(torch.float64) / table_size * 2 * math.pi) + 1) / 2 + elif wave_type == 'TRIANGLE': + d = point.to(torch.float64) * 2 / table_size + value = 4 * point / table_size + d[value == 0] = d[value == 0] + 0.5 + d[value == 1] = 1.5 - d[value == 1] + d[value == 2] = 1.5 - d[value == 2] + d[value == 3] = d[value == 3] - 1.5 + + d = d * (max - min) + min + + if data_type == 'INT': + mask = d < 0 + d[mask] = d[mask] - 0.5 + d[~mask] = d[~mask] + 0.5 + d = d.to(torch.int32) + elif data_type == 'FLOAT': + d = d.to(torch.float32) + + return d + + def mask_along_axis_iid( specgrams: Tensor, mask_param: int, @@ -1305,7 +1366,6 @@ def mask_along_axis_iid( r""" Apply a mask along ``axis``. Mask will be applied from indices ``[v_0, v_0 + v)``, where ``v`` is sampled from ``uniform(0, mask_param)``, and ``v_0`` from ``uniform(0, max_v - v)``. - All examples will have the same mask interval. Args: specgrams (Tensor): Real spectrograms (batch, channel, freq, time) @@ -1362,7 +1422,7 @@ def mask_along_axis( # pack batch shape = specgram.size() - specgram = specgram.view([-1] + list(shape[-2:])) + specgram = specgram.reshape([-1] + list(shape[-2:])) value = torch.rand(1) * mask_param min_value = torch.rand(1) * (specgram.size(axis) - value) @@ -1379,7 +1439,7 @@ def mask_along_axis( raise ValueError('Only Frequency and Time masking are supported') # unpack batch - specgram = specgram.view(shape[:-2] + specgram.shape[-2:]) + specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:]) return specgram @@ -1416,7 +1476,7 @@ def compute_deltas( # pack batch shape = specgram.size() - specgram = specgram.view(1, -1, shape[-1]) + specgram = specgram.reshape(1, -1, shape[-1]) assert win_length >= 3 @@ -1432,7 +1492,7 @@ def compute_deltas( output = torch.nn.functional.conv1d(specgram, kernel, groups=specgram.shape[1]) / denom # unpack batch - output = output.view(shape) + output = output.reshape(shape) return output @@ -1466,10 +1526,11 @@ def _add_noise_shaping( error[n] = dithered[n] - original[n] noise_shaped_waveform[n] = dithered[n] + error[n-1] """ - waveform = waveform.view(-1, waveform.size()[-1]) + wf_shape = waveform.size() + waveform = waveform.reshape(-1, wf_shape[-1]) dithered_shape = dithered_waveform.size() - dithered_waveform = dithered_waveform.view(-1, dithered_shape[-1]) + dithered_waveform = dithered_waveform.reshape(-1, dithered_shape[-1]) error = dithered_waveform - waveform @@ -1480,7 +1541,7 @@ def _add_noise_shaping( error[index] = error_offset[:waveform.size()[1]] noise_shaped = dithered_waveform + error - return noise_shaped.view(dithered_shape[:-1] + noise_shaped.shape[-1:]) + return noise_shaped.reshape(dithered_shape[:-1] + noise_shaped.shape[-1:]) def _apply_probability_distribution( @@ -1513,7 +1574,7 @@ def _apply_probability_distribution( # pack batch shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) + waveform = waveform.reshape(-1, shape[-1]) channel_size = waveform.size()[0] - 1 time_size = waveform.size()[-1] - 1 @@ -1554,7 +1615,7 @@ def _apply_probability_distribution( quantised_signal = quantised_signal_scaled / down_scaling # unpack batch - return quantised_signal.view(shape[:-1] + quantised_signal.shape[-1:]) + return quantised_signal.reshape(shape[:-1] + quantised_signal.shape[-1:]) def dither( @@ -1732,7 +1793,7 @@ def detect_pitch_frequency( """ # pack batch shape = list(waveform.size()) - waveform = waveform.view([-1] + shape[-1:]) + waveform = waveform.reshape([-1] + shape[-1:]) nccf = _compute_nccf(waveform, sample_rate, frame_time, freq_low) indices = _find_max_per_frame(nccf, sample_rate, freq_high) @@ -1743,7 +1804,7 @@ def detect_pitch_frequency( freq = sample_rate / (EPSILON + indices.to(torch.float)) # unpack batch - freq = freq.view(shape[:-1] + list(freq.shape[-1:])) + freq = freq.reshape(shape[:-1] + list(freq.shape[-1:])) return freq diff --git a/torchaudio/models/__init__.py b/torchaudio/models/__init__.py new file mode 100644 index 0000000000..1abdac6271 --- /dev/null +++ b/torchaudio/models/__init__.py @@ -0,0 +1 @@ +from .wav2letter import * diff --git a/torchaudio/models/wav2letter.py b/torchaudio/models/wav2letter.py new file mode 100644 index 0000000000..3466e42dd2 --- /dev/null +++ b/torchaudio/models/wav2letter.py @@ -0,0 +1,74 @@ +from typing import Optional + +from torch import Tensor +from torch import nn + +__all__ = ["Wav2Letter"] + + +class Wav2Letter(nn.Module): + r"""Wav2Letter model architecture from the `"Wav2Letter: an End-to-End ConvNet-based Speech Recognition System" + `_ paper. + + :math:`\text{padding} = \frac{\text{ceil}(\text{kernel} - \text{stride})}{2}` + + Args: + num_classes (int, optional): Number of classes to be classified. (Default: ``40``) + input_type (str, optional): Wav2Letter can use as input: ``waveform``, ``power_spectrum`` + or ``mfcc`` (Default: ``waveform``). + num_features (int, optional): Number of input features that the network will receive (Default: ``1``). + """ + + def __init__(self, num_classes: int = 40, + input_type: str = "waveform", + num_features: int = 1) -> None: + super(Wav2Letter, self).__init__() + + acoustic_num_features = 250 if input_type == "waveform" else num_features + acoustic_model = nn.Sequential( + nn.Conv1d(in_channels=acoustic_num_features, out_channels=250, kernel_size=48, stride=2, padding=23), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=2000, kernel_size=32, stride=1, padding=16), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=2000, out_channels=2000, kernel_size=1, stride=1, padding=0), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=2000, out_channels=num_classes, kernel_size=1, stride=1, padding=0), + nn.ReLU(inplace=True) + ) + + if input_type == "waveform": + waveform_model = nn.Sequential( + nn.Conv1d(in_channels=num_features, out_channels=250, kernel_size=250, stride=160, padding=45), + nn.ReLU(inplace=True) + ) + self.acoustic_model = nn.Sequential(waveform_model, acoustic_model) + + if input_type in ["power_spectrum", "mfcc"]: + self.acoustic_model = acoustic_model + + def forward(self, x: Tensor) -> Tensor: + r""" + Args: + x (Tensor): Tensor of dimension (batch_size, num_features, input_length). + + Returns: + Tensor: Predictor tensor of dimension (batch_size, number_of_classes, input_length). + """ + + x = self.acoustic_model(x) + x = nn.functional.log_softmax(x, dim=1) + return x diff --git a/torchaudio/sox_effects.py b/torchaudio/sox_effects.py index 472d291902..d1eb04e53f 100644 --- a/torchaudio/sox_effects.py +++ b/torchaudio/sox_effects.py @@ -16,8 +16,8 @@ def effect_names() -> List[str]: >>> EFFECT_NAMES = torchaudio.sox_effects.effect_names() """ - import _torch_sox - return _torch_sox.get_effect_names() + from . import _torchaudio + return _torchaudio.get_effect_names() @_audio_backend_guard("sox") @@ -29,8 +29,8 @@ def SoxEffect(): name of effect, and eopts (List[str]) which is a list of effect options. """ - import _torch_sox - return _torch_sox.SoxEffect() + from . import _torchaudio + return _torchaudio.SoxEffect() class SoxEffectsChain(object): @@ -150,15 +150,15 @@ def sox_build_flow_effects(self, # print("effect options:", [x.eopts for x in self.chain]) torchaudio.initialize_sox() - import _torch_sox - sr = _torch_sox.build_flow_effects(self.input_file, - out, - self.channels_first, - self.out_siginfo, - self.out_encinfo, - self.filetype, - self.chain, - self.MAX_EFFECT_OPTS) + from . import _torchaudio + sr = _torchaudio.build_flow_effects(self.input_file, + out, + self.channels_first, + self.out_siginfo, + self.out_encinfo, + self.filetype, + self.chain, + self.MAX_EFFECT_OPTS) torchaudio._audio_normalization(out, self.normalization) diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 29da721869..f34f5c468e 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -247,7 +247,7 @@ def forward(self, specgram: Tensor) -> Tensor: # pack batch shape = specgram.size() - specgram = specgram.view(-1, shape[-2], shape[-1]) + specgram = specgram.reshape(-1, shape[-2], shape[-1]) if self.fb.numel() == 0: tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max, self.n_mels, self.sample_rate) @@ -260,7 +260,7 @@ def forward(self, specgram: Tensor) -> Tensor: mel_specgram = torch.matmul(specgram.transpose(1, 2), self.fb).transpose(1, 2) # unpack batch - mel_specgram = mel_specgram.view(shape[:-2] + mel_specgram.shape[-2:]) + mel_specgram = mel_specgram.reshape(shape[:-2] + mel_specgram.shape[-2:]) return mel_specgram @@ -395,6 +395,8 @@ def __init__(self, pad: int = 0, n_mels: int = 128, window_fn: Callable[..., Tensor] = torch.hann_window, + power: Optional[float] = 2., + normalized: bool = False, wkwargs: Optional[dict] = None) -> None: super(MelSpectrogram, self).__init__() self.sample_rate = sample_rate @@ -402,13 +404,15 @@ def __init__(self, self.win_length = win_length if win_length is not None else n_fft self.hop_length = hop_length if hop_length is not None else self.win_length // 2 self.pad = pad + self.power = power + self.normalized = normalized self.n_mels = n_mels # number of mel frequency bins self.f_max = f_max self.f_min = f_min self.spectrogram = Spectrogram(n_fft=self.n_fft, win_length=self.win_length, hop_length=self.hop_length, - pad=self.pad, window_fn=window_fn, power=2., - normalized=False, wkwargs=wkwargs) + pad=self.pad, window_fn=window_fn, power=self.power, + normalized=self.normalized, wkwargs=wkwargs) self.mel_scale = MelScale(self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1) def forward(self, waveform: Tensor) -> Tensor: @@ -485,7 +489,7 @@ def forward(self, waveform: Tensor) -> Tensor: # pack batch shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) + waveform = waveform.reshape(-1, shape[-1]) mel_specgram = self.MelSpectrogram(waveform) if self.log_mels: @@ -498,7 +502,7 @@ def forward(self, waveform: Tensor) -> Tensor: mfcc = torch.matmul(mel_specgram.transpose(1, 2), self.dct_mat).transpose(1, 2) # unpack batch - mfcc = mfcc.view(shape[:-1] + mfcc.shape[-2:]) + mfcc = mfcc.reshape(shape[:-1] + mfcc.shape[-2:]) return mfcc @@ -779,6 +783,7 @@ class _AxisMasking(torch.nn.Module): mask_param (int): Maximum possible length of the mask. axis (int): What dimension the mask is applied on. iid_masks (bool): Applies iid masks to each of the examples in the batch dimension. + This option is applicable only when the input tensor is 4D. """ __constants__ = ['mask_param', 'axis', 'iid_masks'] @@ -798,7 +803,6 @@ def forward(self, specgram: Tensor, mask_value: float = 0.) -> Tensor: Returns: Tensor: Masked spectrogram of dimensions (..., freq, time). """ - # if iid_masks flag marked and specgram has a batch dimension if self.iid_masks and specgram.dim() == 4: return F.mask_along_axis_iid(specgram, self.mask_param, mask_value, self.axis + 1) @@ -812,10 +816,10 @@ class FrequencyMasking(_AxisMasking): Args: freq_mask_param (int): maximum possible length of the mask. Indices uniformly sampled from [0, freq_mask_param). - iid_masks (bool, optional): whether to apply the same mask to all - the examples/channels in the batch. (Default: ``False``) + iid_masks (bool, optional): whether to apply different masks to each + example/channel in the batch. (Default: ``False``) + This option is applicable only when the input tensor is 4D. """ - def __init__(self, freq_mask_param: int, iid_masks: bool = False) -> None: super(FrequencyMasking, self).__init__(freq_mask_param, 1, iid_masks) @@ -826,10 +830,10 @@ class TimeMasking(_AxisMasking): Args: time_mask_param (int): maximum possible length of the mask. Indices uniformly sampled from [0, time_mask_param). - iid_masks (bool, optional): whether to apply the same mask to all - the examples/channels in the batch. (Default: ``False``) + iid_masks (bool, optional): whether to apply different masks to each + example/channel in the batch. (Default: ``False``) + This option is applicable only when the input tensor is 4D. """ - def __init__(self, time_mask_param: int, iid_masks: bool = False) -> None: super(TimeMasking, self).__init__(time_mask_param, 2, iid_masks) From ca14c17d107fc5aa8e13ccdcb181d8cf5eb23360 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Mon, 1 Jun 2020 09:23:36 -0700 Subject: [PATCH 06/73] Remove unnecessary config file from torchaudio Summary: Turned out .use_external_sox is not necessary for building torchaudio in fbcode. Reviewed By: vincentqb Differential Revision: D21792939 fbshipit-source-id: c0fb5173c6533e67114f50ddc8e9425bd129574f --- .use_external_sox | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .use_external_sox diff --git a/.use_external_sox b/.use_external_sox deleted file mode 100644 index e69de29bb2..0000000000 From 74bf3974d8385f45ba810d9fb04a17760f7218d4 Mon Sep 17 00:00:00 2001 From: Ji Chen Date: Fri, 5 Jun 2020 08:46:10 -0700 Subject: [PATCH 07/73] Import torchaudio 20200605 Summary: import torchaudio 0.5.0 in fbcode using import_audio.sh: Reviewed By: vincentqb Differential Revision: D21884426 fbshipit-source-id: b6f2cc308e597caef2dd767c315b167c09fb0d4c --- README.md | 108 +- .../setup_helpers/build_third_party.sh | 24 +- .../setup_helpers/build_third_party_helper.sh | 118 +- build_tools/setup_helpers/extension.py | 27 +- docs/source/datasets.rst | 8 + docs/source/functional.rst | 10 + packaging/build_wheel.sh | 2 +- packaging/torchaudio/build.sh | 2 +- test/assets/genres/noise/noise.0000.wav | Bin 0 -> 223532 bytes test/assets/kaldi_test_fbank_args.json | 88 ++ test/assets/kaldi_test_mfcc_args.json | 117 ++ test/kaldi_compatibility_impl.py | 46 +- test/test_batch_consistency.py | 6 + test/test_datasets.py | 5 + test/test_sox_compatibility.py | 118 ++ test/test_transforms.py | 3 + test/torchscript_consistency_impl.py | 32 + torchaudio/compliance/kaldi.py | 6 +- torchaudio/datasets/__init__.py | 2 + torchaudio/datasets/gtzan.py | 1054 +++++++++++++++++ torchaudio/functional.py | 193 +++ 21 files changed, 1867 insertions(+), 102 deletions(-) create mode 100644 test/assets/genres/noise/noise.0000.wav create mode 100644 test/assets/kaldi_test_fbank_args.json create mode 100644 test/assets/kaldi_test_mfcc_args.json create mode 100644 torchaudio/datasets/gtzan.py diff --git a/README.md b/README.md index fc3326075a..94f993ddaa 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,9 @@ torchaudio: an audio library for PyTorch ======================================== -[![Build Status](https://travis-ci.org/pytorch/audio.svg?branch=master)](https://travis-ci.org/pytorch/audio) +[![Build Status](https://circleci.com/gh/pytorch/audio.svg?style=svg)](https://app.circleci.com/pipelines/github/pytorch/audio) +[![Coverage](https://codecov.io/gh/pytorch/audio/branch/master/graph/badge.svg)](https://codecov.io/gh/pytorch/audio) +[![Documentation](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchaudio%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/audio/) The aim of torchaudio is to apply [PyTorch](https://github.com/pytorch/pytorch) to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy @@ -26,30 +28,26 @@ to use and feel like a natural extension. Dependencies ------------ -* pytorch (nightly version needed for development) -* libsox v14.3.2 or above +* PyTorch (See below for the compatible versions) +* libsox v14.3.2 or above (only required when building from source) * [optional] vesis84/kaldi-io-for-python commit cb46cb1f44318a5d04d4941cf39084c5b021241e or above -Quick install on -OSX (Homebrew): -```bash -brew install sox -``` -Linux (Ubuntu): -```bash -sudo apt-get install sox libsox-dev libsox-fmt-all -``` -Anaconda -```bash -conda install -c conda-forge sox -``` +The following is the corresponding ``torchaudio`` versions and supported Python versions. + +| ``torch`` | ``torchaudio`` | ``python`` | +| ------------------------ | ------------------------ | ------------------------------- | +| ``master`` / ``nightly`` | ``master`` / ``nightly`` | ``>=3.6`` | +| ``1.5.0`` | ``0.5.0`` | ``>=3.5`` | +| ``1.4.0`` | ``0.4.0`` | ``==2.7``, ``>=3.5``, ``<=3.8`` | + Installation ------------ -### Binaries +### Binary Distibutions To install the latest version using anaconda, run: + ``` conda install -c pytorch torchaudio ``` @@ -64,26 +62,48 @@ pip install torchaudio -f https://download.pytorch.org/whl/torch_stable.html torch from PyPI. If you need a different torch configuration, preinstall torch before running this command.) -At the moment, there is no automated nightly build process, but we occasionally -build nightlies based on PyTorch nightlies by hand following the instructions in -[packaging](packaging). To install the latest nightly via pip, run: +### Nightly build + +Note that nightly build is build on PyTorch's nightly build. Therefore, you need to install the latest PyTorch when you use nightly build of torchaudio. + +**pip** ``` pip install numpy pip install --pre torchaudio -f https://download.pytorch.org/whl/nightly/torch_nightly.html ``` -To install the latest nightly via conda, run: +**conda** ``` conda install -y -c pytorch-nightly torchaudio ``` - ### From Source If your system configuration is not among the supported configurations -above, you can build from source. +above, you can build torchaudio from source. + +This will require libsox v14.3.2 or above. + +
Click here for the examples on how to install SoX + +OSX (Homebrew): +```bash +brew install sox +``` + +Linux (Ubuntu): +```bash +sudo apt-get install sox libsox-dev libsox-fmt-all +``` + +Anaconda +```bash +conda install -c conda-forge sox +``` + +
```bash # Linux @@ -93,6 +113,48 @@ python setup.py install MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install ``` +Alternatively, the build process can build SoX (and codecs such as libmad, lame and flac) statically and torchaudio can link them, by setting environment variable `BUILD_SOX=1`. +The build process will fetch and build SoX, liblame, libmad, flac before building extension. + +```bash +# Linux +BUILD_SOX=1 python setup.py install + +# OSX +BUILD_SOX=1 MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install +``` + +This is known to work on linux and unix distributions such as Ubuntu and CentOS 7 and macOS. +If you try this on a new system and found a solution to make it work, feel free to share it by opening and issue. + +#### Troubleshooting + +
checking build system type... ./config.guess: unable to guess system type + +Since the configuration file for codecs are old, they cannot correctly detect the new environments, such as Jetson Aarch. You need to replace the `config.guess` file in `./third_party/tmp/lame-3.99.5/config.guess` and/or `./third_party/tmp/libmad-0.15.1b/config.guess` with [the latest one](https://github.com/gcc-mirror/gcc/blob/master/config.guess). + +See also: [#658](https://github.com/pytorch/audio/issues/658) + +
+ +
Undefined reference to `tgetnum' when using `BUILD_SOX` + +If while building from within an anaconda environment you come across errors similar to the following: + +``` +../bin/ld: console.c:(.text+0xc1): undefined reference to `tgetnum' +``` + +Install `ncurses` from `conda-forge` before running `python setup.py install`: + +``` +# Install ncurses from conda-forge +conda install -c conda-forge ncurses +``` + +
+ + Quick Usage ----------- diff --git a/build_tools/setup_helpers/build_third_party.sh b/build_tools/setup_helpers/build_third_party.sh index c5e8f8115d..9577776cc9 100755 --- a/build_tools/setup_helpers/build_third_party.sh +++ b/build_tools/setup_helpers/build_third_party.sh @@ -1,5 +1,6 @@ #!/bin/bash -# Build third party libraries in `/third_party/build` or in `/third_party/build`. +# Build third party libraries (SoX, lame, libmad, and flac) +# Usage: ./build_thid_parth.sh [prefix] [download_only?=false] set -e @@ -10,6 +11,7 @@ prefix="${1:-}" if [ -z "${prefix}" ]; then prefix="${root_dir}" fi +download_only="${2:-false}" tp_dir="${prefix}/third_party" tmp_dir="${tp_dir}/tmp" @@ -20,17 +22,29 @@ mkdir -p "${tmp_dir}" "${build_dir}" . "${this_dir}/build_third_party_helper.sh" if ! found_lame "${build_dir}" ; then - build_lame "${tmp_dir}" "${build_dir}" + get_lame "${tmp_dir}" + if [ "${download_only}" = "false" ]; then + build_lame "${tmp_dir}" "${build_dir}" + fi fi if ! found_flac "${build_dir}" ; then - build_flac "${tmp_dir}" "${build_dir}" + get_flac "${tmp_dir}" + if [ "${download_only}" = "false" ]; then + build_flac "${tmp_dir}" "${build_dir}" + fi fi if ! found_mad "${build_dir}" ; then - build_mad "${tmp_dir}" "${build_dir}" + get_mad "${tmp_dir}" + if [ "${download_only}" = "false" ]; then + build_mad "${tmp_dir}" "${build_dir}" + fi fi if ! found_sox "${build_dir}" ; then - build_sox "${tmp_dir}" "${build_dir}" + get_sox "${tmp_dir}" + if [ "${download_only}" = "false" ]; then + build_sox "${tmp_dir}" "${build_dir}" + fi fi diff --git a/build_tools/setup_helpers/build_third_party_helper.sh b/build_tools/setup_helpers/build_third_party_helper.sh index a31fafe718..7cca812409 100644 --- a/build_tools/setup_helpers/build_third_party_helper.sh +++ b/build_tools/setup_helpers/build_third_party_helper.sh @@ -57,23 +57,34 @@ found_sox() { all_found "$1" 'include/sox.h' 'lib/libsox.a' } -build_lame() { +LAME="lame-3.99.5" +LAME_ARCHIVE="${LAME}.tar.gz" + +get_lame() { work_dir="$1" - install_dir="$2" - package="lame-3.99.5" - url="https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz" + url="https://downloads.sourceforge.net/project/lame/lame/3.99/${LAME_ARCHIVE}" ( cd "${work_dir}" - if [ ! -d "${package}" ]; then - if [ ! -f "${package}.tar.gz" ]; then + if [ ! -d "${LAME}" ]; then + if [ ! -f "${LAME_ARCHIVE}" ]; then printf "Fetching liblame from %s\n" "${url}" - curl $CURL_OPTS -o "${package}.tar.gz" "${url}" + curl $CURL_OPTS -O "${url}" fi - tar xfp "${package}.tar.gz" fi + ) +} + +build_lame() { + work_dir="$1" + install_dir="$2" + ( + cd "${work_dir}" + if [ ! -d "${LAME}" ]; then + tar xfp "${LAME_ARCHIVE}" + fi + cd "${LAME}" # build statically printf "Building liblame\n" - cd "${package}" if [ ! -f Makefile ]; then ./configure ${CONFIG_OPTS} \ --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ @@ -84,23 +95,34 @@ build_lame() { ) } -build_flac() { +FLAC="flac-1.3.2" +FLAC_ARCHIVE="${FLAC}.tar.xz" + +get_flac() { work_dir="$1" - install_dir="$2" - package="flac-1.3.2" - url="https://downloads.sourceforge.net/project/flac/flac-src/flac-1.3.2.tar.xz" + url="https://downloads.sourceforge.net/project/flac/flac-src/${FLAC_ARCHIVE}" ( cd "${work_dir}" - if [ ! -d "${package}" ]; then - if [ ! -f "${package}.tar.xz" ]; then + if [ ! -d "${FLAC}" ]; then + if [ ! -f "${FLAC_ARCHIVE}" ]; then printf "Fetching flac from %s\n" "${url}" - curl $CURL_OPTS -o "${package}.tar.xz" "${url}" + curl $CURL_OPTS -O "${url}" fi - tar xfp "${package}.tar.xz" fi + ) +} + +build_flac() { + work_dir="$1" + install_dir="$2" + ( + cd "${work_dir}" + if [ ! -d "${FLAC}" ]; then + tar xfp "${FLAC_ARCHIVE}" + fi + cd "${FLAC}" # build statically printf "Building flac\n" - cd "${package}" if [ ! -f Makefile ]; then ./configure ${CONFIG_OPTS} \ --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ @@ -111,23 +133,34 @@ build_flac() { ) } -build_mad() { +LIBMAD="libmad-0.15.1b" +LIBMAD_ARCHIVE="${LIBMAD}.tar.gz" + +get_mad() { work_dir="$1" - install_dir="$2" - package="libmad-0.15.1b" - url="https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/libmad-0.15.1b.tar.gz" + url="https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/${LIBMAD_ARCHIVE}" ( cd "${work_dir}" - if [ ! -d "${package}" ]; then - if [ ! -f "${package}.tar.gz" ]; then + if [ ! -d "${LIBMAD}" ]; then + if [ ! -f "${LIBMAD_ARCHIVE}" ]; then printf "Fetching mad from %s\n" "${url}" - curl $CURL_OPTS -o "${package}.tar.gz" "${url}" + curl $CURL_OPTS -O "${url}" fi - tar xfp "${package}.tar.gz" fi + ) +} + +build_mad() { + work_dir="$1" + install_dir="$2" + ( + cd "${work_dir}" + if [ ! -d "${LIBMAD}" ]; then + tar xfp "${LIBMAD_ARCHIVE}" + fi + cd "${LIBMAD}" # build statically printf "Building mad\n" - cd "${package}" if [ ! -f Makefile ]; then # See https://stackoverflow.com/a/12864879/23845 sed -i.bak 's/-march=i486//' configure @@ -140,23 +173,34 @@ build_mad() { ) } -build_sox() { +SOX="sox-14.4.2" +SOX_ARCHIVE="${SOX}.tar.bz2" + +get_sox() { work_dir="$1" - install_dir="$2" - package="sox-14.4.2" - url="https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2" + url="https://downloads.sourceforge.net/project/sox/sox/14.4.2/${SOX_ARCHIVE}" ( cd "${work_dir}" - if [ ! -d "${package}" ]; then - if [ ! -f "${package}.tar.bz2" ]; then + if [ ! -d "${SOX}" ]; then + if [ ! -f "${SOX_ARCHIVE}" ]; then printf "Fetching SoX from %s\n" "${url}" - curl $CURL_OPTS -o "${package}.tar.bz2" "${url}" + curl $CURL_OPTS -O "${url}" fi - tar xfp "${package}.tar.bz2" fi + ) +} + +build_sox() { + work_dir="$1" + install_dir="$2" + ( + cd "${work_dir}" + if [ ! -d "${SOX}" ]; then + tar xfp "${SOX_ARCHIVE}" + fi + cd "${SOX}" # build statically - printf "Building sox\n" - cd "${package}" + printf "Building SoX\n" if [ ! -f Makefile ]; then # --without-png makes OS X build less hazardous; somehow the build # finds png and enables it. We don't want it; we'd need to package diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 65e48dfc10..dcf61ab259 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -1,3 +1,4 @@ +import os import platform import subprocess from pathlib import Path @@ -18,9 +19,21 @@ _TP_BASE_DIR = _ROOT_DIR / 'third_party' _TP_INSTALL_DIR = _TP_BASE_DIR / 'build' -# Temporary fix for building in fbcode -# at the moment, we have to use external sox in fbcode -_BUILD_DEPS = not (_ROOT_DIR / '.use_external_sox').exists() + +def _get_build_sox(): + val = os.environ.get('BUILD_SOX', '0') + trues = ['1', 'true', 'TRUE', 'on', 'ON', 'yes', 'YES'] + falses = ['0', 'false', 'FALSE', 'off', 'OFF', 'no', 'NO'] + if val in trues: + return True + if val not in falses: + print( + f'WARNING: Unexpected environment variable value `BUILD_SOX={val}`. ' + f'Expected one of {trues + falses}') + return False + + +_BUILD_SOX = _get_build_sox() def _get_eca(debug): @@ -52,14 +65,14 @@ def _get_include_dirs(): dirs = [ str(_ROOT_DIR), ] - if _BUILD_DEPS: + if _BUILD_SOX: dirs.append(str(_TP_INSTALL_DIR / 'include')) return dirs def _get_extra_objects(): objs = [] - if _BUILD_DEPS: + if _BUILD_SOX: # NOTE: The order of the library listed bellow matters. # # (the most important thing is that dependencies come after a library @@ -71,7 +84,7 @@ def _get_extra_objects(): def _get_libraries(): - return [] if _BUILD_DEPS else ['sox'] + return [] if _BUILD_SOX else ['sox'] def _build_codecs(): @@ -106,6 +119,6 @@ def get_ext_modules(debug=False): class BuildExtension(TorchBuildExtension): def build_extension(self, ext): - if ext.name == _EXT_NAME and _BUILD_DEPS: + if ext.name == _EXT_NAME and _BUILD_SOX: _configure_third_party() super().build_extension(ext) diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 27d4b20a34..8ba05dc670 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -33,6 +33,14 @@ COMMONVOICE :special-members: +GTZAN +~~~~~ + +.. autoclass:: GTZAN + :members: __getitem__ + :special-members: + + LIBRISPEECH ~~~~~~~~~~~ diff --git a/docs/source/functional.rst b/docs/source/functional.rst index 5f68666539..12ee165060 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -113,6 +113,11 @@ Functions to perform common audio operations. .. autofunction:: treble_biquad +:hidden:`bass_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: bass_biquad + :hidden:`deemph_biquad` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -143,6 +148,11 @@ Functions to perform common audio operations. .. autofunction:: phaser +:hidden:`flanger` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: flanger + :hidden:`mask_along_axis` ~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index 73cec89ff5..d08196cb20 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -15,5 +15,5 @@ if [[ "$OSTYPE" == "msys" ]]; then python_tag="$(echo "cp$PYTHON_VERSION" | tr -d '.')" python setup.py bdist_wheel --plat-name win_amd64 --python-tag $python_tag else - python setup.py bdist_wheel + BUILD_SOX=1 python setup.py bdist_wheel fi diff --git a/packaging/torchaudio/build.sh b/packaging/torchaudio/build.sh index e2b53ddcf4..99c17b6913 100644 --- a/packaging/torchaudio/build.sh +++ b/packaging/torchaudio/build.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash set -ex -python setup.py install --single-version-externally-managed --record=record.txt +BUILD_SOX=1 python setup.py install --single-version-externally-managed --record=record.txt diff --git a/test/assets/genres/noise/noise.0000.wav b/test/assets/genres/noise/noise.0000.wav new file mode 100644 index 0000000000000000000000000000000000000000..cf1cbcde22e41b56c4a02a8a5b13dbedd5dc3b3a GIT binary patch literal 223532 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b/test/assets/kaldi_test_mfcc_args.json @@ -0,0 +1,117 @@ +{"blackman_coeff": 0.0063, "energy_floor": 3.0323, "frame_length": 0.625, "frame_shift": 0.0625, "high_freq": 5842, "htk_compat": false, "low_freq": 3240, "num_mel_bins": 4, "preemphasis_coefficient": 0.29, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "use_energy": true, "num_ceps": 2, "cepstral_lifter": 73.5792, "vtln_high": 5749, "vtln_low": 4449, "vtln_warp": 1.0, "window_type": "blackman", "dither": 0.0} +{"blackman_coeff": 0.013, "energy_floor": 1.8509, "frame_length": 1.1875, "frame_shift": 0.625, "high_freq": 7999, "htk_compat": false, "low_freq": 4330, "num_mel_bins": 5, "preemphasis_coefficient": 0.38, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "use_energy": true, "num_ceps": 3, "cepstral_lifter": 8.1048, "vtln_high": 7497, "vtln_low": 7397, "vtln_warp": 1.0, 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"use_energy": true, "num_ceps": 4, "cepstral_lifter": 70.0635, "vtln_high": 7277, "vtln_low": 7265, "vtln_warp": 1.0, "window_type": "hamming", "dither": 0.0} +{"blackman_coeff": 4.5663, "energy_floor": 1.127, "frame_length": 1.125, "frame_shift": 0.8125, "high_freq": 6069, "htk_compat": true, "low_freq": 167, "num_mel_bins": 8, "preemphasis_coefficient": 0.68, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "use_energy": true, "num_ceps": 6, "cepstral_lifter": 1.624, "vtln_high": 2148, "vtln_low": 461, "vtln_warp": 1.0, "window_type": "rectangular", "dither": 0.0} +{"blackman_coeff": 4.5896, "energy_floor": 2.7617, "frame_length": 1.0625, "frame_shift": 0.8125, "high_freq": 3851, "htk_compat": true, "low_freq": 1115, "num_mel_bins": 4, "preemphasis_coefficient": 0.03, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "use_energy": false, "num_ceps": 4, "cepstral_lifter": 49.7637, "vtln_high": 2897, "vtln_low": 2701, "vtln_warp": 1.0, "window_type": "hanning", "dither": 0.0} +{"blackman_coeff": 4.6128, "energy_floor": 0.1203, "frame_length": 1.1875, "frame_shift": 0.9375, "high_freq": 6901, "htk_compat": false, "low_freq": 3577, "num_mel_bins": 6, "preemphasis_coefficient": 0.25, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "use_energy": false, "num_ceps": 3, "cepstral_lifter": 70.5509, "vtln_high": 5962, "vtln_low": 4190, "vtln_warp": 1.0, "window_type": "rectangular", "dither": 0.0} +{"blackman_coeff": 4.6262, "energy_floor": 4.1656, "frame_length": 1.1875, "frame_shift": 0.8125, "high_freq": 6147, "htk_compat": false, "low_freq": 1684, "num_mel_bins": 6, "preemphasis_coefficient": 0.58, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "use_energy": true, "num_ceps": 5, "cepstral_lifter": 54.2056, "vtln_high": 5259, "vtln_low": 2363, "vtln_warp": 1.0, "window_type": "rectangular", "dither": 0.0} +{"blackman_coeff": 4.6741, "energy_floor": 4.3867, "frame_length": 1.125, "frame_shift": 1.125, "high_freq": 6273, "htk_compat": false, "low_freq": 2481, "num_mel_bins": 4, "preemphasis_coefficient": 0.15, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": true, "subtract_mean": false, "use_energy": false, "num_ceps": 4, "cepstral_lifter": 75.6122, "vtln_high": 3701, "vtln_low": 2992, "vtln_warp": 1.0, "window_type": "hamming", "dither": 0.0} +{"blackman_coeff": 4.6765, "energy_floor": 1.2644, "frame_length": 1.125, "frame_shift": 0.75, "high_freq": 5204, "htk_compat": false, "low_freq": 276, "num_mel_bins": 4, "preemphasis_coefficient": 0.04, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": true, "subtract_mean": true, "use_energy": false, "num_ceps": 3, "cepstral_lifter": 96.116, "vtln_high": 5148, "vtln_low": 2541, "vtln_warp": 1.0, "window_type": "hamming", "dither": 0.0} +{"blackman_coeff": 4.7216, "energy_floor": 2.4818, "frame_length": 0.8125, "frame_shift": 0.375, "high_freq": 6723, "htk_compat": true, "low_freq": 2352, "num_mel_bins": 6, "preemphasis_coefficient": 0.14, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "use_energy": false, "num_ceps": 3, "cepstral_lifter": 32.0303, "vtln_high": 5598, "vtln_low": 2579, "vtln_warp": 1.0, "window_type": "hanning", "dither": 0.0} +{"blackman_coeff": 4.7919, "energy_floor": 2.6435, "frame_length": 0.625, "frame_shift": 0.5, "high_freq": 7971, "htk_compat": false, "low_freq": 1812, "num_mel_bins": 4, "preemphasis_coefficient": 0.65, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": false, "subtract_mean": true, "use_energy": false, "num_ceps": 4, "cepstral_lifter": 27.7648, "vtln_high": 7735, "vtln_low": 7419, "vtln_warp": 1.0, "window_type": "povey", "dither": 0.0} +{"blackman_coeff": 4.814, "energy_floor": 0.468, "frame_length": 1.0625, "frame_shift": 0.6875, "high_freq": 5252, "htk_compat": true, "low_freq": 569, "num_mel_bins": 6, "preemphasis_coefficient": 0.85, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "use_energy": false, "num_ceps": 6, "cepstral_lifter": 56.449, "vtln_high": 4397, "vtln_low": 4332, "vtln_warp": 1.0, "window_type": "hamming", "dither": 0.0} +{"blackman_coeff": 4.95, "energy_floor": 4.5916, "frame_length": 1.125, "frame_shift": 1.0625, "high_freq": 5044, "htk_compat": true, "low_freq": 617, "num_mel_bins": 8, "preemphasis_coefficient": 0.89, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": false, "subtract_mean": true, "use_energy": true, "num_ceps": 8, "cepstral_lifter": 23.3238, "vtln_high": 2732, "vtln_low": 2677, "vtln_warp": 1.0, "window_type": "hanning", "dither": 0.0} +{"blackman_coeff": 4.9663, "energy_floor": 4.7867, "frame_length": 1.1875, "frame_shift": 0.5, "high_freq": 2424, "htk_compat": false, "low_freq": 350, "num_mel_bins": 4, "preemphasis_coefficient": 0.39, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "use_energy": true, "num_ceps": 2, "cepstral_lifter": 59.4319, "vtln_high": 1202, "vtln_low": 1063, "vtln_warp": 1.0, "window_type": "hamming", "dither": 0.0} diff --git a/test/kaldi_compatibility_impl.py b/test/kaldi_compatibility_impl.py index 8fbdff75cb..2d3313d007 100644 --- a/test/kaldi_compatibility_impl.py +++ b/test/kaldi_compatibility_impl.py @@ -1,4 +1,5 @@ """Test suites for checking numerical compatibility against Kaldi""" +import json import shutil import unittest import subprocess @@ -9,6 +10,7 @@ import torchaudio.compliance.kaldi from . import common_utils +from parameterized import parameterized, param def _not_available(cmd): @@ -47,6 +49,11 @@ def _run_kaldi(command, input_type, input_value): return torch.from_numpy(result.copy()) # copy supresses some torch warning +def _load_params(path): + with open(path, 'r') as file: + return [param(json.loads(line)) for line in file] + + class Kaldi(common_utils.TestBaseMixin): def assert_equal(self, output, *, expected, rtol=None, atol=None): expected = expected.to(dtype=self.dtype, device=self.device) @@ -68,37 +75,24 @@ def test_sliding_window_cmn(self): kaldi_result = _run_kaldi(command, 'ark', tensor) self.assert_equal(result, expected=kaldi_result) + @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_fbank_args.json'))) @unittest.skipIf(_not_available('compute-fbank-feats'), '`compute-fbank-feats` not available') - def test_fbank(self): + def test_fbank(self, kwargs): """fbank should be numerically compatible with compute-fbank-feats""" - kwargs = { - 'blackman_coeff': 4.3926, - 'dither': 0.0, - 'energy_floor': 2.0617, - 'frame_length': 0.5625, - 'frame_shift': 0.0625, - 'high_freq': 4253, - 'htk_compat': True, - 'low_freq': 1367, - 'num_mel_bins': 5, - 'preemphasis_coefficient': 0.84, - 'raw_energy': False, - 'remove_dc_offset': True, - 'round_to_power_of_two': True, - 'snip_edges': True, - 'subtract_mean': False, - 'use_energy': True, - 'use_log_fbank': True, - 'use_power': False, - 'vtln_high': 2112, - 'vtln_low': 1445, - 'vtln_warp': 1.0000, - 'window_type': 'hamming', - - } wave_file = common_utils.get_asset_path('kaldi_file.wav') waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) result = torchaudio.compliance.kaldi.fbank(waveform, **kwargs) command = ['compute-fbank-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] kaldi_result = _run_kaldi(command, 'scp', wave_file) self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) + + @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_mfcc_args.json'))) + @unittest.skipIf(_not_available('compute-mfcc-feats'), '`compute-mfcc-feats` not available') + def test_mfcc(self, kwargs): + """mfcc should be numerically compatible with compute-mfcc-feats""" + wave_file = common_utils.get_asset_path('kaldi_file.wav') + waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + result = torchaudio.compliance.kaldi.mfcc(waveform, **kwargs) + command = ['compute-mfcc-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] + kaldi_result = _run_kaldi(command, 'scp', wave_file) + self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) diff --git a/test/test_batch_consistency.py b/test/test_batch_consistency.py index c10652d9ca..103d83bb84 100644 --- a/test/test_batch_consistency.py +++ b/test/test_batch_consistency.py @@ -84,6 +84,12 @@ def test_phaser(self): waveform, sample_rate = torchaudio.load(filepath) self.assert_batch_consistencies(F.phaser, waveform, sample_rate) + def test_flanger(self): + torch.random.manual_seed(40) + waveform = torch.rand(2, 100) - 0.5 + sample_rate = 44100 + self.assert_batch_consistencies(F.flanger, waveform, sample_rate) + def test_sliding_window_cmn(self): waveform = torch.randn(2, 1024) - 0.5 self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=True, norm_vars=True) diff --git a/test/test_datasets.py b/test/test_datasets.py index 96c12884d6..ab1e74e888 100644 --- a/test/test_datasets.py +++ b/test/test_datasets.py @@ -7,6 +7,7 @@ from torchaudio.datasets.vctk import VCTK from torchaudio.datasets.yesno import YESNO from torchaudio.datasets.ljspeech import LJSPEECH +from torchaudio.datasets.gtzan import GTZAN from . import common_utils @@ -55,6 +56,10 @@ def test_speechcommands(self): data = SPEECHCOMMANDS(self.path) data[0] + def test_gtzan(self): + data = GTZAN(self.path) + data[0] + if __name__ == "__main__": unittest.main() diff --git a/test/test_sox_compatibility.py b/test/test_sox_compatibility.py index dfa57cec47..b6e6dbaedd 100644 --- a/test/test_sox_compatibility.py +++ b/test/test_sox_compatibility.py @@ -265,6 +265,28 @@ def test_treble(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_bass(self): + """ + Test biquad bass filter, compare to SoX implementation + """ + + central_freq = 1000 + q = 0.707 + gain = 40 + + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("bass", [gain, central_freq, str(q) + 'q']) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.bass_biquad(waveform, sample_rate, gain, central_freq, q) + + self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-4, rtol=1e-5) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_deemph(self): @@ -419,6 +441,102 @@ def test_phaser_triangle(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_flanger_triangle_linear(self): + """ + Test flanger effect with triangle modulation and linear interpolation, compare to SoX implementation + """ + delay = 0.6 + depth = 0.87 + regen = 3.0 + width = 0.9 + speed = 0.5 + phase = 30 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "linear"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, + modulation='triangular', interpolation='linear') + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_flanger_triangle_quad(self): + """ + Test flanger effect with triangle modulation and quadratic interpolation, compare to SoX implementation + """ + delay = 0.8 + depth = 0.88 + regen = 3.0 + width = 0.4 + speed = 0.5 + phase = 40 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "quadratic"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, + modulation='triangular', interpolation='quadratic') + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_flanger_sine_linear(self): + """ + Test flanger effect with sine modulation and linear interpolation, compare to SoX implementation + """ + delay = 0.8 + depth = 0.88 + regen = 3.0 + width = 0.23 + speed = 1.3 + phase = 60 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "linear"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, + modulation='sinusoidal', interpolation='linear') + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_flanger_sine_quad(self): + """ + Test flanger effect with sine modulation and quadratic interpolation, compare to SoX implementation + """ + delay = 0.9 + depth = 0.9 + regen = 4.0 + width = 0.23 + speed = 1.3 + phase = 25 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "quadratic"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, + modulation='sinusoidal', interpolation='quadratic') + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_equalizer(self): diff --git a/test/test_transforms.py b/test/test_transforms.py index 02faa68992..ad8a55fb4b 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -31,7 +31,10 @@ def test_mu_law_companding(self): quantization_channels = 256 waveform = self.waveform.clone() + if not waveform.is_floating_point(): + waveform = waveform.to(torch.get_default_dtype()) waveform /= torch.abs(waveform).max() + self.assertTrue(waveform.min() >= -1. and waveform.max() <= 1.) waveform_mu = transforms.MuLawEncoding(quantization_channels)(waveform) diff --git a/test/torchscript_consistency_impl.py b/test/torchscript_consistency_impl.py index a4d3cc79cf..a84b629bd7 100644 --- a/test/torchscript_consistency_impl.py +++ b/test/torchscript_consistency_impl.py @@ -367,6 +367,21 @@ def func(tensor): self._assert_consistency(func, waveform) + def test_bass(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + gain = 40. + central_freq = 1000. + q = 0.707 + return F.bass_biquad(tensor, sample_rate, gain, central_freq, q) + + self._assert_consistency(func, waveform) + def test_deemph(self): if self.dtype == torch.float64: raise unittest.SkipTest("This test is known to fail for float64") @@ -497,6 +512,23 @@ def func(tensor): self._assert_consistency(func, waveform) + def test_flanger(self): + torch.random.manual_seed(40) + waveform = torch.rand(2, 100) - 0.5 + + def func(tensor): + delay = 0.8 + depth = 0.88 + regen = 3.0 + width = 0.23 + speed = 1.3 + phase = 60. + sample_rate = 44100 + return F.flanger(tensor, sample_rate, delay, depth, regen, width, speed, + phase, modulation='sinusoidal', interpolation='linear') + + self._assert_consistency(func, waveform) + class Transforms(common_utils.TestBaseMixin): """Implements test for Transforms that are performed for different devices""" diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index 8115c3a25f..bdf5f407ba 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -696,6 +696,8 @@ def mfcc( """ assert num_ceps <= num_mel_bins, 'num_ceps cannot be larger than num_mel_bins: %d vs %d' % (num_ceps, num_mel_bins) + device, dtype = waveform.device, waveform.dtype + # The mel_energies should not be squared (use_power=True), not have mean subtracted # (subtract_mean=False), and use log (use_log_fbank=True). # size (m, num_mel_bins + use_energy) @@ -717,7 +719,7 @@ def mfcc( feature = feature[:, mel_offset:(num_mel_bins + mel_offset)] # size (num_mel_bins, num_ceps) - dct_matrix = _get_dct_matrix(num_ceps, num_mel_bins) + dct_matrix = _get_dct_matrix(num_ceps, num_mel_bins).to(dtype=dtype, device=device) # size (m, num_ceps) feature = feature.matmul(dct_matrix) @@ -725,7 +727,7 @@ def mfcc( if cepstral_lifter != 0.0: # size (1, num_ceps) lifter_coeffs = _get_lifter_coeffs(num_ceps, cepstral_lifter).unsqueeze(0) - feature *= lifter_coeffs + feature *= lifter_coeffs.to(device=device, dtype=dtype) # if use_energy then replace the last column for htk_compat == true else first column if use_energy: diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index 26d8216114..1741889029 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -3,6 +3,7 @@ from .speechcommands import SPEECHCOMMANDS from .utils import bg_iterator, diskcache_iterator from .vctk import VCTK +from .gtzan import GTZAN from .yesno import YESNO from .ljspeech import LJSPEECH @@ -13,6 +14,7 @@ "VCTK", "YESNO", "LJSPEECH", + "GTZAN", "diskcache_iterator", "bg_iterator", ) diff --git a/torchaudio/datasets/gtzan.py b/torchaudio/datasets/gtzan.py new file mode 100644 index 0000000000..13d7e8b68b --- /dev/null +++ b/torchaudio/datasets/gtzan.py @@ -0,0 +1,1054 @@ +import os +import warnings +from typing import Any, Tuple + +import torchaudio +from torch import Tensor +from torch.utils.data import Dataset +from torchaudio.datasets.utils import ( + download_url, + extract_archive, + walk_files, +) + +# The following lists prefixed with `filtered_` provide a filtered split +# that: +# +# a. Mitigate a known issue with GTZAN (duplication) +# +# b. Provide a standard split for testing it against other +# methods (e.g. the one in jordipons/sklearn-audio-transfer-learning). +# +# Those are used when GTZAN is initialised with the `filtered` keyword. +# The split was taken from (github) jordipons/sklearn-audio-transfer-learning. + +filtered_test = [ + "blues.00012", + "blues.00013", + "blues.00014", + "blues.00015", + "blues.00016", + "blues.00017", + "blues.00018", + "blues.00019", + "blues.00020", + "blues.00021", + "blues.00022", + "blues.00023", + "blues.00024", + "blues.00025", + "blues.00026", + "blues.00027", + "blues.00028", + "blues.00061", + "blues.00062", + "blues.00063", + "blues.00064", + "blues.00065", + "blues.00066", + "blues.00067", + "blues.00068", + "blues.00069", + "blues.00070", + "blues.00071", + "blues.00072", + "blues.00098", + "blues.00099", + "classical.00011", + "classical.00012", + "classical.00013", + "classical.00014", + "classical.00015", + "classical.00016", + "classical.00017", + "classical.00018", + "classical.00019", + "classical.00020", + "classical.00021", + "classical.00022", + "classical.00023", + "classical.00024", + "classical.00025", + "classical.00026", + "classical.00027", + "classical.00028", + "classical.00029", + "classical.00034", + "classical.00035", + "classical.00036", + "classical.00037", + "classical.00038", + "classical.00039", + "classical.00040", + "classical.00041", + "classical.00049", + "classical.00077", + "classical.00078", + "classical.00079", + "country.00030", + "country.00031", + "country.00032", + "country.00033", + "country.00034", + "country.00035", + "country.00036", + "country.00037", + "country.00038", + "country.00039", + "country.00040", + "country.00043", + "country.00044", + "country.00046", + "country.00047", + "country.00048", + "country.00050", + "country.00051", + "country.00053", + "country.00054", + "country.00055", + "country.00056", + "country.00057", + "country.00058", + "country.00059", + "country.00060", + "country.00061", + "country.00062", + "country.00063", + "country.00064", + "disco.00001", + "disco.00021", + "disco.00058", + "disco.00062", + "disco.00063", + "disco.00064", + "disco.00065", + "disco.00066", + "disco.00069", + "disco.00076", + "disco.00077", + "disco.00078", + "disco.00079", + "disco.00080", + "disco.00081", + "disco.00082", + "disco.00083", + "disco.00084", + "disco.00085", + "disco.00086", + "disco.00087", + "disco.00088", + "disco.00091", + "disco.00092", + "disco.00093", + "disco.00094", + "disco.00096", + "disco.00097", + "disco.00099", + "hiphop.00000", + "hiphop.00026", + "hiphop.00027", + "hiphop.00030", + "hiphop.00040", + "hiphop.00043", + "hiphop.00044", + "hiphop.00045", + "hiphop.00051", + "hiphop.00052", + "hiphop.00053", + "hiphop.00054", + "hiphop.00062", + "hiphop.00063", + "hiphop.00064", + "hiphop.00065", + "hiphop.00066", + "hiphop.00067", + "hiphop.00068", + "hiphop.00069", + "hiphop.00070", + "hiphop.00071", + "hiphop.00072", + "hiphop.00073", + "hiphop.00074", + "hiphop.00075", + "hiphop.00099", + "jazz.00073", + "jazz.00074", + "jazz.00075", + "jazz.00076", + "jazz.00077", + "jazz.00078", + "jazz.00079", + "jazz.00080", + "jazz.00081", + "jazz.00082", + "jazz.00083", + "jazz.00084", + "jazz.00085", + "jazz.00086", + "jazz.00087", + "jazz.00088", + "jazz.00089", + "jazz.00090", + "jazz.00091", + "jazz.00092", + "jazz.00093", + "jazz.00094", + "jazz.00095", + "jazz.00096", + "jazz.00097", + "jazz.00098", + "jazz.00099", + "metal.00012", + "metal.00013", + "metal.00014", + "metal.00015", + "metal.00022", + "metal.00023", + "metal.00025", + "metal.00026", + "metal.00027", + "metal.00028", + "metal.00029", + "metal.00030", + "metal.00031", + "metal.00032", + "metal.00033", + "metal.00038", + "metal.00039", + "metal.00067", + "metal.00070", + "metal.00073", + "metal.00074", + "metal.00075", + "metal.00078", + "metal.00083", + "metal.00085", + "metal.00087", + "metal.00088", + "pop.00000", + "pop.00001", + "pop.00013", + "pop.00014", + "pop.00043", + "pop.00063", + "pop.00064", + "pop.00065", + "pop.00066", + "pop.00069", + "pop.00070", + "pop.00071", + "pop.00072", + "pop.00073", + "pop.00074", + "pop.00075", + "pop.00076", + "pop.00077", + "pop.00078", + "pop.00079", + "pop.00082", + "pop.00088", + "pop.00089", + "pop.00090", + "pop.00091", + "pop.00092", + "pop.00093", + "pop.00094", + "pop.00095", + "pop.00096", + "reggae.00034", + "reggae.00035", + "reggae.00036", + "reggae.00037", + "reggae.00038", + "reggae.00039", + "reggae.00040", + "reggae.00046", + "reggae.00047", + "reggae.00048", + "reggae.00052", + "reggae.00053", + "reggae.00064", + "reggae.00065", + "reggae.00066", + "reggae.00067", + "reggae.00068", + "reggae.00071", + "reggae.00079", + "reggae.00082", + "reggae.00083", + "reggae.00084", + "reggae.00087", + "reggae.00088", + "reggae.00089", + "reggae.00090", + "rock.00010", + "rock.00011", + "rock.00012", + "rock.00013", + "rock.00014", + "rock.00015", + "rock.00027", + "rock.00028", + "rock.00029", + "rock.00030", + "rock.00031", + "rock.00032", + "rock.00033", + "rock.00034", + "rock.00035", + "rock.00036", + "rock.00037", + "rock.00039", + "rock.00040", + "rock.00041", + "rock.00042", + "rock.00043", + "rock.00044", + "rock.00045", + "rock.00046", + "rock.00047", + "rock.00048", + "rock.00086", + "rock.00087", + "rock.00088", + "rock.00089", + "rock.00090", +] + +filtered_train = [ + "blues.00029", + "blues.00030", + "blues.00031", + "blues.00032", + "blues.00033", + "blues.00034", + "blues.00035", + "blues.00036", + "blues.00037", + "blues.00038", + "blues.00039", + "blues.00040", + "blues.00041", + "blues.00042", + "blues.00043", + "blues.00044", + "blues.00045", + "blues.00046", + "blues.00047", + "blues.00048", + "blues.00049", + "blues.00073", + "blues.00074", + "blues.00075", + "blues.00076", + "blues.00077", + "blues.00078", + "blues.00079", + "blues.00080", + "blues.00081", + "blues.00082", + "blues.00083", + "blues.00084", + "blues.00085", + "blues.00086", + "blues.00087", + "blues.00088", + "blues.00089", + "blues.00090", + "blues.00091", + "blues.00092", + "blues.00093", + "blues.00094", + "blues.00095", + "blues.00096", + "blues.00097", + "classical.00030", + "classical.00031", + "classical.00032", + "classical.00033", + "classical.00043", + "classical.00044", + "classical.00045", + "classical.00046", + "classical.00047", + "classical.00048", + "classical.00050", + "classical.00051", + "classical.00052", + "classical.00053", + "classical.00054", + "classical.00055", + "classical.00056", + "classical.00057", + "classical.00058", + "classical.00059", + "classical.00060", + "classical.00061", + "classical.00062", + "classical.00063", + "classical.00064", + "classical.00065", + "classical.00066", + "classical.00067", + "classical.00080", + "classical.00081", + "classical.00082", + "classical.00083", + "classical.00084", + "classical.00085", + "classical.00086", + "classical.00087", + "classical.00088", + "classical.00089", + "classical.00090", + "classical.00091", + "classical.00092", + "classical.00093", + "classical.00094", + "classical.00095", + "classical.00096", + "classical.00097", + "classical.00098", + "classical.00099", + "country.00019", + "country.00020", + "country.00021", + "country.00022", + "country.00023", + "country.00024", + "country.00025", + "country.00026", + "country.00028", + "country.00029", + "country.00065", + "country.00066", + "country.00067", + "country.00068", + "country.00069", + "country.00070", + "country.00071", + "country.00072", + "country.00073", + "country.00074", + "country.00075", + "country.00076", + "country.00077", + "country.00078", + "country.00079", + "country.00080", + "country.00081", + "country.00082", + "country.00083", + "country.00084", + "country.00085", + "country.00086", + "country.00087", + "country.00088", + "country.00089", + "country.00090", + "country.00091", + "country.00092", + "country.00093", + "country.00094", + "country.00095", + "country.00096", + "country.00097", + "country.00098", + "country.00099", + "disco.00005", + "disco.00015", + "disco.00016", + "disco.00017", + "disco.00018", + "disco.00019", + "disco.00020", + "disco.00022", + "disco.00023", + "disco.00024", + "disco.00025", + "disco.00026", + "disco.00027", + "disco.00028", + "disco.00029", + "disco.00030", + "disco.00031", + "disco.00032", + "disco.00033", + "disco.00034", + "disco.00035", + "disco.00036", + "disco.00037", + "disco.00039", + "disco.00040", + "disco.00041", + "disco.00042", + "disco.00043", + "disco.00044", + "disco.00045", + "disco.00047", + "disco.00049", + "disco.00053", + "disco.00054", + "disco.00056", + "disco.00057", + "disco.00059", + "disco.00061", + "disco.00070", + "disco.00073", + "disco.00074", + "disco.00089", + "hiphop.00002", + "hiphop.00003", + "hiphop.00004", + "hiphop.00005", + "hiphop.00006", + "hiphop.00007", + "hiphop.00008", + "hiphop.00009", + "hiphop.00010", + "hiphop.00011", + "hiphop.00012", + "hiphop.00013", + "hiphop.00014", + "hiphop.00015", + "hiphop.00016", + "hiphop.00017", + "hiphop.00018", + "hiphop.00019", + "hiphop.00020", + "hiphop.00021", + "hiphop.00022", + "hiphop.00023", + "hiphop.00024", + "hiphop.00025", + "hiphop.00028", + "hiphop.00029", + "hiphop.00031", + "hiphop.00032", + "hiphop.00033", + "hiphop.00034", + "hiphop.00035", + "hiphop.00036", + "hiphop.00037", + "hiphop.00038", + "hiphop.00041", + "hiphop.00042", + "hiphop.00055", + "hiphop.00056", + "hiphop.00057", + "hiphop.00058", + "hiphop.00059", + "hiphop.00060", + "hiphop.00061", + "hiphop.00077", + "hiphop.00078", + "hiphop.00079", + "hiphop.00080", + "jazz.00000", + "jazz.00001", + "jazz.00011", + "jazz.00012", + "jazz.00013", + "jazz.00014", + "jazz.00015", + "jazz.00016", + "jazz.00017", + "jazz.00018", + "jazz.00019", + "jazz.00020", + "jazz.00021", + "jazz.00022", + "jazz.00023", + "jazz.00024", + "jazz.00041", + "jazz.00047", + "jazz.00048", + "jazz.00049", + "jazz.00050", + "jazz.00051", + "jazz.00052", + "jazz.00053", + "jazz.00054", + "jazz.00055", + "jazz.00056", + "jazz.00057", + "jazz.00058", + "jazz.00059", + "jazz.00060", + "jazz.00061", + "jazz.00062", + "jazz.00063", + "jazz.00064", + "jazz.00065", + "jazz.00066", + "jazz.00067", + "jazz.00068", + "jazz.00069", + "jazz.00070", + "jazz.00071", + "jazz.00072", + "metal.00002", + "metal.00003", + "metal.00005", + "metal.00021", + "metal.00024", + "metal.00035", + "metal.00046", + "metal.00047", + "metal.00048", + "metal.00049", + "metal.00050", + "metal.00051", + "metal.00052", + "metal.00053", + "metal.00054", + "metal.00055", + "metal.00056", + "metal.00057", + "metal.00059", + "metal.00060", + "metal.00061", + "metal.00062", + "metal.00063", + "metal.00064", + "metal.00065", + "metal.00066", + "metal.00069", + "metal.00071", + "metal.00072", + "metal.00079", + "metal.00080", + "metal.00084", + "metal.00086", + "metal.00089", + "metal.00090", + "metal.00091", + "metal.00092", + "metal.00093", + "metal.00094", + "metal.00095", + "metal.00096", + "metal.00097", + "metal.00098", + "metal.00099", + "pop.00002", + "pop.00003", + "pop.00004", + "pop.00005", + "pop.00006", + "pop.00007", + "pop.00008", + "pop.00009", + "pop.00011", + "pop.00012", + "pop.00016", + "pop.00017", + "pop.00018", + "pop.00019", + "pop.00020", + "pop.00023", + "pop.00024", + "pop.00025", + "pop.00026", + "pop.00027", + "pop.00028", + "pop.00029", + "pop.00031", + "pop.00032", + "pop.00033", + "pop.00034", + "pop.00035", + "pop.00036", + "pop.00038", + "pop.00039", + "pop.00040", + "pop.00041", + "pop.00042", + "pop.00044", + "pop.00046", + "pop.00049", + "pop.00050", + "pop.00080", + "pop.00097", + "pop.00098", + "pop.00099", + "reggae.00000", + "reggae.00001", + "reggae.00002", + "reggae.00004", + "reggae.00006", + "reggae.00009", + "reggae.00011", + "reggae.00012", + "reggae.00014", + "reggae.00015", + "reggae.00016", + "reggae.00017", + "reggae.00018", + "reggae.00019", + "reggae.00020", + "reggae.00021", + "reggae.00022", + "reggae.00023", + "reggae.00024", + "reggae.00025", + "reggae.00026", + "reggae.00027", + "reggae.00028", + "reggae.00029", + "reggae.00030", + "reggae.00031", + "reggae.00032", + "reggae.00042", + "reggae.00043", + "reggae.00044", + "reggae.00045", + "reggae.00049", + "reggae.00050", + "reggae.00051", + "reggae.00054", + "reggae.00055", + "reggae.00056", + "reggae.00057", + "reggae.00058", + "reggae.00059", + "reggae.00060", + "reggae.00063", + "reggae.00069", + "rock.00000", + "rock.00001", + "rock.00002", + "rock.00003", + "rock.00004", + "rock.00005", + "rock.00006", + "rock.00007", + "rock.00008", + "rock.00009", + "rock.00016", + "rock.00017", + "rock.00018", + "rock.00019", + "rock.00020", + "rock.00021", + "rock.00022", + "rock.00023", + "rock.00024", + "rock.00025", + "rock.00026", + "rock.00057", + "rock.00058", + "rock.00059", + "rock.00060", + "rock.00061", + "rock.00062", + "rock.00063", + "rock.00064", + "rock.00065", + "rock.00066", + "rock.00067", + "rock.00068", + "rock.00069", + "rock.00070", + "rock.00091", + "rock.00092", + "rock.00093", + "rock.00094", + "rock.00095", + "rock.00096", + "rock.00097", + "rock.00098", + "rock.00099", +] + +filtered_valid = [ + "blues.00000", + "blues.00001", + "blues.00002", + "blues.00003", + "blues.00004", + "blues.00005", + "blues.00006", + "blues.00007", + "blues.00008", + "blues.00009", + "blues.00010", + "blues.00011", + "blues.00050", + "blues.00051", + "blues.00052", + "blues.00053", + "blues.00054", + "blues.00055", + "blues.00056", + "blues.00057", + "blues.00058", + "blues.00059", + "blues.00060", + "classical.00000", + "classical.00001", + "classical.00002", + "classical.00003", + "classical.00004", + "classical.00005", + "classical.00006", + "classical.00007", + "classical.00008", + "classical.00009", + "classical.00010", + "classical.00068", + "classical.00069", + "classical.00070", + "classical.00071", + "classical.00072", + "classical.00073", + "classical.00074", + "classical.00075", + "classical.00076", + "country.00000", + "country.00001", + "country.00002", + "country.00003", + "country.00004", + "country.00005", + "country.00006", + "country.00007", + "country.00009", + "country.00010", + "country.00011", + "country.00012", + "country.00013", + "country.00014", + "country.00015", + "country.00016", + "country.00017", + "country.00018", + "country.00027", + "country.00041", + "country.00042", + "country.00045", + "country.00049", + "disco.00000", + "disco.00002", + "disco.00003", + "disco.00004", + "disco.00006", + "disco.00007", + "disco.00008", + "disco.00009", + "disco.00010", + "disco.00011", + "disco.00012", + "disco.00013", + "disco.00014", + "disco.00046", + "disco.00048", + "disco.00052", + "disco.00067", + "disco.00068", + "disco.00072", + "disco.00075", + "disco.00090", + "disco.00095", + "hiphop.00081", + "hiphop.00082", + "hiphop.00083", + "hiphop.00084", + "hiphop.00085", + "hiphop.00086", + "hiphop.00087", + "hiphop.00088", + "hiphop.00089", + "hiphop.00090", + "hiphop.00091", + "hiphop.00092", + "hiphop.00093", + "hiphop.00094", + "hiphop.00095", + "hiphop.00096", + "hiphop.00097", + "hiphop.00098", + "jazz.00002", + "jazz.00003", + "jazz.00004", + "jazz.00005", + "jazz.00006", + "jazz.00007", + "jazz.00008", + "jazz.00009", + "jazz.00010", + "jazz.00025", + "jazz.00026", + "jazz.00027", + "jazz.00028", + "jazz.00029", + "jazz.00030", + "jazz.00031", + "jazz.00032", + "metal.00000", + "metal.00001", + "metal.00006", + "metal.00007", + "metal.00008", + "metal.00009", + "metal.00010", + "metal.00011", + "metal.00016", + "metal.00017", + "metal.00018", + "metal.00019", + "metal.00020", + "metal.00036", + "metal.00037", + "metal.00068", + "metal.00076", + "metal.00077", + "metal.00081", + "metal.00082", + "pop.00010", + "pop.00053", + "pop.00055", + "pop.00058", + "pop.00059", + "pop.00060", + "pop.00061", + "pop.00062", + "pop.00081", + "pop.00083", + "pop.00084", + "pop.00085", + "pop.00086", + "reggae.00061", + "reggae.00062", + "reggae.00070", + "reggae.00072", + "reggae.00074", + "reggae.00076", + "reggae.00077", + "reggae.00078", + "reggae.00085", + "reggae.00092", + "reggae.00093", + "reggae.00094", + "reggae.00095", + "reggae.00096", + "reggae.00097", + "reggae.00098", + "reggae.00099", + "rock.00038", + "rock.00049", + "rock.00050", + "rock.00051", + "rock.00052", + "rock.00053", + "rock.00054", + "rock.00055", + "rock.00056", + "rock.00071", + "rock.00072", + "rock.00073", + "rock.00074", + "rock.00075", + "rock.00076", + "rock.00077", + "rock.00078", + "rock.00079", + "rock.00080", + "rock.00081", + "rock.00082", + "rock.00083", + "rock.00084", + "rock.00085", +] + + +URL = "http://opihi.cs.uvic.ca/sound/genres.tar.gz" +FOLDER_IN_ARCHIVE = "genres" +_CHECKSUMS = {"http://opihi.cs.uvic.ca/sound/genres.tar.gz": "5b3d6dddb579ab49814ab86dba69e7c7"} + + +def load_gtzan_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, str]: + """ + Loads a file from the dataset and returns the raw waveform + as a Torch Tensor, its sample rate as an integer, and its + genre as a string. + """ + # Filenames are of the form label.id, e.g. blues.00078 + label, _ = fileid.split(".") + + # Read wav + file_audio = os.path.join(path, label, fileid + ext_audio) + waveform, sample_rate = torchaudio.load(file_audio) + + return waveform, sample_rate, label + + +class GTZAN(Dataset): + """ + Create a Dataset for GTZAN. Each item is a tuple of the form: + waveform, sample_rate, label. + + Please see http://marsyas.info/downloads/datasets.html + if you are planning to use this dataset to publish results. + """ + + _ext_audio = ".wav" + + def __init__( + self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False, + subset: Any = None, + ) -> None: + + # super(GTZAN, self).__init__() + self.root = root + self.url = url + self.folder_in_archive = folder_in_archive + self.download = download + self.subset = subset + + assert subset is None or subset in ["training", "validation", "testing"], ( + "When `subset` not None, it must take a value from " + + "{'training', 'validation', 'testing'}." + ) + + archive = os.path.basename(url) + archive = os.path.join(root, archive) + self._path = os.path.join(root, folder_in_archive) + + if download: + if not os.path.isdir(self._path): + if not os.path.isfile(archive): + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") + extract_archive(archive) + + if not os.path.isdir(self._path): + raise RuntimeError( + "Dataset not found. Please use `download=True` to download it." + ) + + if self.subset is None: + walker = walk_files( + self._path, suffix=self._ext_audio, prefix=False, remove_suffix=True + ) + self._walker = list(walker) + else: + if self.subset == "training": + self._walker = filtered_train + elif self.subset == "validation": + self._walker = filtered_valid + elif self.subset == "testing": + self._walker = filtered_test + + def __getitem__(self, n: int) -> Tuple[Tensor, int, str]: + fileid = self._walker[n] + item = load_gtzan_item(fileid, self._path, self._ext_audio) + waveform, sample_rate, label = item + return waveform, sample_rate, label + + def __len__(self) -> int: + return len(self._walker) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index f59c46eb14..92580e1d9e 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -29,6 +29,7 @@ "equalizer_biquad", "band_biquad", "treble_biquad", + "bass_biquad", "deemph_biquad", "riaa_biquad", "biquad", @@ -36,6 +37,7 @@ "dcshift", "overdrive", "phaser", + "flanger", 'mask_along_axis', 'mask_along_axis_iid', 'sliding_window_cmn', @@ -987,6 +989,47 @@ def treble_biquad( return biquad(waveform, b0, b1, b2, a0, a1, a2) +def bass_biquad( + waveform: Tensor, + sample_rate: int, + gain: float, + central_freq: float = 100, + Q: float = 0.707 +) -> Tensor: + r"""Design a bass tone-control effect. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + gain (float): desired gain at the boost (or attenuation) in dB. + central_freq (float, optional): central frequency (in Hz). (Default: ``100``) + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF + """ + w0 = 2 * math.pi * central_freq / sample_rate + alpha = math.sin(w0) / 2 / Q + A = math.exp(gain / 40 * math.log(10)) + + temp1 = 2 * math.sqrt(A) * alpha + temp2 = (A - 1) * math.cos(w0) + temp3 = (A + 1) * math.cos(w0) + + b0 = A * ((A + 1) - temp2 + temp1) + b1 = 2 * A * ((A - 1) - temp3) + b2 = A * ((A + 1) - temp2 - temp1) + a0 = (A + 1) + temp2 + temp1 + a1 = -2 * ((A - 1) + temp3) + a2 = (A + 1) + temp2 - temp1 + + return biquad(waveform, b0 / a0, b1 / a0, b2 / a0, a0 / a0, a1 / a0, a2 / a0) + + def deemph_biquad( waveform: Tensor, sample_rate: int @@ -1357,6 +1400,156 @@ def _generate_wave_table( return d +def flanger( + waveform: Tensor, + sample_rate: int, + delay: float = 0., + depth: float = 2., + regen: float = 0., + width: float = 71., + speed: float = 0.5, + phase: float = 25., + modulation: str = 'sinusoidal', + interpolation: str = 'linear' +) -> Tensor: + r"""Apply a flanger effect to the audio. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., channel, time)` . + Max 4 channels allowed + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + delay (float): desired delay in milliseconds(ms) + Allowed range of values are 0 to 30 + depth (float): desired delay depth in milliseconds(ms) + Allowed range of values are 0 to 10 + regen (float): desired regen(feeback gain) in dB + Allowed range of values are -95 to 95 + width (float): desired width(delay gain) in dB + Allowed range of values are 0 to 100 + speed (float): modulation speed in Hz + Allowed range of values are 0.1 to 10 + phase (float): percentage phase-shift for multi-channel + Allowed range of values are 0 to 100 + modulation (str): Use either "sinusoidal" or "triangular" modulation. (Default: ``sinusoidal``) + interpolation (str): Use either "linear" or "quadratic" for delay-line interpolation. (Default: ``linear``) + + Returns: + Tensor: Waveform of dimension of `(..., channel, time)` + + References: + http://sox.sourceforge.net/sox.html + + Scott Lehman, Effects Explained, + https://web.archive.org/web/20051125072557/http://www.harmony-central.com/Effects/effects-explained.html + """ + + if modulation not in ('sinusoidal', 'triangular'): + raise ValueError("Only 'sinusoidal' or 'triangular' modulation allowed") + + if interpolation not in ('linear', 'quadratic'): + raise ValueError("Only 'linear' or 'quadratic' interpolation allowed") + + actual_shape = waveform.shape + device, dtype = waveform.device, waveform.dtype + + if actual_shape[-2] > 4: + raise ValueError("Max 4 channels allowed") + + # convert to 3D (batch, channels, time) + waveform = waveform.view(-1, actual_shape[-2], actual_shape[-1]) + + # Scaling + feedback_gain = regen / 100 + delay_gain = width / 100 + channel_phase = phase / 100 + delay_min = delay / 1000 + delay_depth = depth / 1000 + + n_channels = waveform.shape[-2] + + if modulation == 'sinusoidal': + wave_type = 'SINE' + else: + wave_type = 'TRIANGLE' + + # Balance output: + in_gain = 1. / (1 + delay_gain) + delay_gain = delay_gain / (1 + delay_gain) + + # Balance feedback loop: + delay_gain = delay_gain * (1 - abs(feedback_gain)) + + delay_buf_length = int((delay_min + delay_depth) * sample_rate + 0.5) + delay_buf_length = delay_buf_length + 2 + + delay_bufs = torch.zeros(waveform.shape[0], n_channels, delay_buf_length, dtype=dtype, device=device) + delay_last = torch.zeros(waveform.shape[0], n_channels, dtype=dtype, device=device) + + lfo_length = int(sample_rate / speed) + + lfo = torch.zeros(lfo_length, dtype=dtype, device=device) + + table_min = math.floor(delay_min * sample_rate + 0.5) + table_max = delay_buf_length - 2. + + lfo = _generate_wave_table(wave_type=wave_type, + data_type='FLOAT', + table_size=lfo_length, + min=float(table_min), + max=float(table_max), + phase=3 * math.pi / 2) + + output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) + + delay_buf_pos = 0 + lfo_pos = 0 + channel_idxs = torch.arange(0, n_channels) + + for i in range(waveform.shape[-1]): + + delay_buf_pos = (delay_buf_pos + delay_buf_length - 1) % delay_buf_length + + cur_channel_phase = (channel_idxs * lfo_length * channel_phase + .5).to(torch.int64) + delay_tensor = lfo[(lfo_pos + cur_channel_phase) % lfo_length] + frac_delay = torch.frac(delay_tensor) + delay_tensor = torch.floor(delay_tensor) + + int_delay = delay_tensor.to(torch.int64) + + temp = waveform[:, :, i] + + delay_bufs[:, :, delay_buf_pos] = temp + delay_last * feedback_gain + + delayed_0 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length] + + int_delay = int_delay + 1 + + delayed_1 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length] + + int_delay = int_delay + 1 + + if interpolation == 'linear': + delayed = delayed_0 + (delayed_1 - delayed_0) * frac_delay + else: + delayed_2 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length] + + int_delay = int_delay + 1 + + delayed_2 = delayed_2 - delayed_0 + delayed_1 = delayed_1 - delayed_0 + a = delayed_2 * .5 - delayed_1 + b = delayed_1 * 2 - delayed_2 * .5 + + delayed = delayed_0 + (a * frac_delay + b) * frac_delay + + delay_last = delayed + output_waveform[:, :, i] = waveform[:, :, i] * in_gain + delayed * delay_gain + + lfo_pos = (lfo_pos + 1) % lfo_length + + return output_waveform.clamp(min=-1, max=1).view(actual_shape) + + def mask_along_axis_iid( specgrams: Tensor, mask_param: int, From 0af364402e4764a60e8da0874d3434925d5f1ca7 Mon Sep 17 00:00:00 2001 From: Ben Mehne Date: Wed, 10 Jun 2020 10:46:43 -0700 Subject: [PATCH 08/73] Change parameterized testing system to be compatible with unittest Summary: The previous implementation of parameterized testing worked by modifying test.common_utils inplace. This doesn't work in general because unittest's contract with test modules is such that it must be able to load the module and run the test itself. Because the previous implementation needed to load the module and modify it, it is incompatible. Reviewed By: mthrok Differential Revision: D21964676 fbshipit-source-id: 9bb71e8c3f9fab074239b22306f3bbddb0f3975b --- test/common_utils.py | 9 +++++++++ test/torchscript_consistency_cpu_test.py | 13 +++++++++++-- 2 files changed, 20 insertions(+), 2 deletions(-) diff --git a/test/common_utils.py b/test/common_utils.py index 5d1dc8abe1..00d10a6df1 100644 --- a/test/common_utils.py +++ b/test/common_utils.py @@ -119,6 +119,15 @@ def define_test_suites( scope[t.__name__] = t +def common_test_class_parameters( + dtypes: Iterable[str] = ("float32", "float64"), + devices: Iterable[str] = ("cpu", "cuda"), +): + for device in devices: + for dtype in dtypes: + yield {"device": torch.device(device), "dtype": getattr(torch, dtype)} + + def get_whitenoise( *, sample_rate: int = 16000, diff --git a/test/torchscript_consistency_cpu_test.py b/test/torchscript_consistency_cpu_test.py index 51a3a477f0..81e6616821 100644 --- a/test/torchscript_consistency_cpu_test.py +++ b/test/torchscript_consistency_cpu_test.py @@ -1,5 +1,14 @@ -from .common_utils import define_test_suites +from parameterized import parameterized_class + +from .common_utils import TestCase, common_test_class_parameters from .torchscript_consistency_impl import Functional, Transforms +parameters = list(common_test_class_parameters(devices=['cpu'])) +@parameterized_class(parameters) +class TestFunctional(Functional, TestCase): + pass + -define_test_suites(globals(), [Functional, Transforms], devices=['cpu']) +@parameterized_class(parameters) +class TestTransforms(Transforms, TestCase): + pass From 9a2d018f8356ea93542990079755ad64d2bde15c Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Mon, 22 Jun 2020 16:41:11 -0700 Subject: [PATCH 09/73] Import torchaudio 20200618 #718 Summary: Import torchaudio up to #719 Reviewed By: zhangguanheng66 Differential Revision: D22119491 fbshipit-source-id: e14842278a32c9373179fc132e8111a0ffe66d93 --- docs/source/datasets.rst | 8 + test/README.md | 10 + 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test/assets/kaldi_test_mfcc_args.json | 3 - test/assets/kaldi_test_spectrogram_args.json | 109 +++++ test/common_utils.py | 80 ++-- ...t_functional.py => functional_cpu_test.py} | 44 +- test/functional_cuda_test.py | 16 + test/functional_impl.py | 31 ++ test/kaldi_compatibility_cpu_test.py | 11 +- test/kaldi_compatibility_cuda_test.py | 13 +- test/kaldi_compatibility_impl.py | 13 + test/test_backend.py | 42 ++ test/test_batch_consistency.py | 9 +- test/test_compliance_kaldi.py | 99 +--- test/test_dataloader.py | 8 +- test/test_datasets.py | 41 +- test/test_io.py | 59 +-- test/test_kaldi_io.py | 2 +- test/test_librosa_compatibility.py | 27 +- test/test_models.py | 51 ++- test/test_sox_compatibility.py | 61 +-- test/test_sox_effects.py | 47 +- test/test_transforms.py | 4 +- test/torchscript_consistency_cpu_test.py | 28 +- test/torchscript_consistency_cuda_test.py | 27 +- test/torchscript_consistency_impl.py | 1 + torchaudio/__init__.py | 430 ++---------------- torchaudio/_backend.py | 68 --- torchaudio/_internal/__init__.py | 0 torchaudio/_internal/misc_ops.py | 30 ++ torchaudio/_internal/module_utils.py | 56 +++ torchaudio/_sox_backend.py | 72 --- torchaudio/backend/__init__.py | 21 + torchaudio/backend/common.py | 143 ++++++ torchaudio/backend/no_backend.py | 35 ++ .../soundfile_backend.py} | 69 ++- torchaudio/backend/sox_backend.py | 275 +++++++++++ torchaudio/backend/utils.py | 74 +++ torchaudio/common_utils.py | 38 -- torchaudio/compliance/kaldi.py | 5 +- torchaudio/csrc/register.cpp | 18 + torchaudio/csrc/typedefs.cpp | 23 + torchaudio/csrc/typedefs.h | 23 + torchaudio/datasets/__init__.py | 2 + torchaudio/datasets/cmuarctic.py | 150 ++++++ torchaudio/extension/__init__.py | 7 + torchaudio/extension/extension.py | 49 ++ torchaudio/functional.py | 24 +- torchaudio/kaldi_io.py | 16 +- torchaudio/models/__init__.py | 1 + torchaudio/models/_wavernn.py | 105 +++++ torchaudio/sox_effects/__init__.py | 12 + torchaudio/{ => sox_effects}/sox_effects.py | 93 +++- 428 files 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test/{test_functional.py => functional_cpu_test.py} (90%) create mode 100644 test/functional_cuda_test.py create mode 100644 test/functional_impl.py create mode 100644 test/test_backend.py delete mode 100644 torchaudio/_backend.py create mode 100644 torchaudio/_internal/__init__.py create mode 100644 torchaudio/_internal/misc_ops.py create mode 100644 torchaudio/_internal/module_utils.py delete mode 100644 torchaudio/_sox_backend.py create mode 100644 torchaudio/backend/__init__.py create mode 100644 torchaudio/backend/common.py create mode 100644 torchaudio/backend/no_backend.py rename torchaudio/{_soundfile_backend.py => backend/soundfile_backend.py} (67%) create mode 100644 torchaudio/backend/sox_backend.py create mode 100644 torchaudio/backend/utils.py delete mode 100644 torchaudio/common_utils.py create mode 100644 torchaudio/csrc/register.cpp create mode 100644 torchaudio/csrc/typedefs.cpp create mode 100644 torchaudio/csrc/typedefs.h create mode 100644 torchaudio/datasets/cmuarctic.py create mode 100644 torchaudio/extension/__init__.py create mode 100644 torchaudio/extension/extension.py create mode 100644 torchaudio/models/_wavernn.py create mode 100644 torchaudio/sox_effects/__init__.py rename torchaudio/{ => sox_effects}/sox_effects.py (71%) diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 8ba05dc670..2f844293f6 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -25,6 +25,14 @@ All the datasets have almost similar API. They all have two common arguments: .. currentmodule:: torchaudio.datasets +CMUARCTIC +~~~~~~~~~ + +.. autoclass:: CMUARCTIC + :members: __getitem__ + :special-members: + + COMMONVOICE ~~~~~~~~~~~ diff --git a/test/README.md b/test/README.md index a6686603af..35a926c120 100644 --- a/test/README.md +++ b/test/README.md @@ -44,6 +44,16 @@ The following test modules are defined for corresponding `torchaudio` module/fun ## Adding test +The following is the current practice of torchaudio test suite. + +1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./common_utils.py) has some data generator functions. +1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless you are writing tests that are common to CPU / CUDA. + - Set class memeber `dtype`, `device` and `backend` for the desired behavior. + - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail. + - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability. +1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchscript_consistency_impl.py) and test definitions for [CPU](./torchscript_consistency_cpu_test.py) and [CUDA](./torchscript_consistency_cuda_test.py) devices. +1. For numerically comparing Tensors, use `assertEqual` method from `common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. + When you add a new feature(functional/transform), consider the following 1. When you add a new feature, please make it Torchscript-able and batch-consistent unless it degrades the performance. Please add the tests to see if the new feature meet these requirements. diff --git a/test/assets/ARCTIC/cmu_us_aew_arctic/etc/txt.done.data b/test/assets/ARCTIC/cmu_us_aew_arctic/etc/txt.done.data new file mode 100644 index 0000000000..b6a7aa90ba --- /dev/null +++ b/test/assets/ARCTIC/cmu_us_aew_arctic/etc/txt.done.data @@ -0,0 +1 @@ +( arctic_a0024 "This is the test text." ) diff --git a/test/assets/ARCTIC/cmu_us_aew_arctic/wav/arctic_a0024.wav b/test/assets/ARCTIC/cmu_us_aew_arctic/wav/arctic_a0024.wav new file mode 100644 index 0000000000000000000000000000000000000000..b95ce95776fafd3e3cbaf670153f8d73167dfab3 GIT binary patch literal 441078 zcmWKXWmpqj6vqc_V;gLY9E@1dHM(JRcL_=eh=F2&9j{=EiHeA#0@96icXu}|=-9~7 zu-E5)x#xbkU+#0x`QP9DUl(f&3wk*i;9-2q{KC~+SR?=dVEH%9vH*bBo-6=X01{wl z?dpd9w{GoV;b`q@;fDA(xAVVqK|`CMszXpwRt5z6-||oUw~Gk<6BnU*CUeK6*Ivw8 z*ewZ7Lki>0D;STV`ps6vry6)dVVN$8`j(Y%V2NnhQo)=;R$#>R>XHSo;!`4z>YT<5 zrnLP%Y*NedQi;lRmlx4YsoliXpQT$g;X(NY%R#%ET84mNh=GL!>Z;|#ZDyK_T;O=x 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"num_ceps": 4, "cepstral_lifter": 3.5732, "vtln_high": 4583, "vtln_low": 3742, "vtln_warp": 1.0, "window_type": "povey", "dither": 0.0} {"blackman_coeff": 3.2416, "energy_floor": 1.0604, "frame_length": 0.875, "frame_shift": 0.5, "high_freq": 4730, "htk_compat": false, "low_freq": 968, "num_mel_bins": 4, "preemphasis_coefficient": 0.11, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "use_energy": true, "num_ceps": 3, "cepstral_lifter": 60.5751, "vtln_high": 3542, "vtln_low": 1943, "vtln_warp": 1.0, "window_type": "blackman", "dither": 0.0} {"blackman_coeff": 3.2698, "energy_floor": 3.0361, "frame_length": 1.0625, "frame_shift": 0.75, "high_freq": 4870, "htk_compat": true, "low_freq": 1281, "num_mel_bins": 7, "preemphasis_coefficient": 0.64, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "use_energy": true, "num_ceps": 3, "cepstral_lifter": 28.536, "vtln_high": 4401, "vtln_low": 3315, "vtln_warp": 1.0, "window_type": "blackman", "dither": 0.0} {"blackman_coeff": 3.3078, "energy_floor": 4.9217, "frame_length": 1.0, "frame_shift": 0.3125, "high_freq": 6758, "htk_compat": true, "low_freq": 760, "num_mel_bins": 5, "preemphasis_coefficient": 0.98, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "use_energy": true, "num_ceps": 3, "cepstral_lifter": 97.4694, "vtln_high": 6022, "vtln_low": 5650, "vtln_warp": 1.0, "window_type": "rectangular", "dither": 0.0} diff --git a/test/assets/kaldi_test_spectrogram_args.json b/test/assets/kaldi_test_spectrogram_args.json new file mode 100644 index 0000000000..c44245b6c0 --- /dev/null +++ b/test/assets/kaldi_test_spectrogram_args.json @@ -0,0 +1,109 @@ +{"blackman_coeff": 0.0016, "dither": 0, "energy_floor": 4.668, "frame_length": 0.625, "frame_shift": 0.25, "preemphasis_coefficient": 0.82, "raw_energy": false, 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0.5625, "preemphasis_coefficient": 0.27, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "rectangular"} +{"blackman_coeff": 3.7759, "dither": 0, "energy_floor": 1.7002, "frame_length": 1.0625, "frame_shift": 0.6875, "preemphasis_coefficient": 0.42, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "hamming"} +{"blackman_coeff": 3.7921, "dither": 0, "energy_floor": 3.4087, "frame_length": 0.25, "frame_shift": 1.0, "preemphasis_coefficient": 0.54, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "blackman"} +{"blackman_coeff": 3.7954, "dither": 0, "energy_floor": 3.5651, "frame_length": 0.5, "frame_shift": 0.8125, "preemphasis_coefficient": 0.06, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "window_type": "blackman"} +{"blackman_coeff": 3.799, "dither": 0, "energy_floor": 3.0026, "frame_length": 0.625, "frame_shift": 1.0, "preemphasis_coefficient": 0.82, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": false, "snip_edges": true, "subtract_mean": true, "window_type": "hamming"} +{"blackman_coeff": 3.8659, "dither": 0, "energy_floor": 1.7487, "frame_length": 1.1875, "frame_shift": 0.375, "preemphasis_coefficient": 1.0, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "window_type": "hanning"} +{"blackman_coeff": 3.951, "dither": 0, "energy_floor": 0.3903, "frame_length": 1.125, "frame_shift": 1.0, "preemphasis_coefficient": 0.41, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": true, "subtract_mean": true, "window_type": "rectangular"} +{"blackman_coeff": 4.0045, "dither": 0, "energy_floor": 3.061, "frame_length": 0.625, "frame_shift": 1.0625, "preemphasis_coefficient": 0.74, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "window_type": "hanning"} +{"blackman_coeff": 4.0187, "dither": 0, "energy_floor": 4.8148, "frame_length": 0.375, "frame_shift": 0.6875, "preemphasis_coefficient": 0.68, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": false, "snip_edges": false, "subtract_mean": false, "window_type": "hanning"} +{"blackman_coeff": 4.032, "dither": 0, "energy_floor": 2.2019, "frame_length": 1.125, "frame_shift": 0.25, "preemphasis_coefficient": 0.78, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "rectangular"} +{"blackman_coeff": 4.0627, "dither": 0, "energy_floor": 4.1729, "frame_length": 0.625, "frame_shift": 1.125, "preemphasis_coefficient": 0.89, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": false, "subtract_mean": false, "window_type": "povey"} +{"blackman_coeff": 4.0736, "dither": 0, "energy_floor": 0.9155, "frame_length": 1.0625, "frame_shift": 0.5625, "preemphasis_coefficient": 0.82, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "hamming"} +{"blackman_coeff": 4.1131, "dither": 0, "energy_floor": 3.9204, "frame_length": 0.5, "frame_shift": 0.125, "preemphasis_coefficient": 0.39, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "hanning"} +{"blackman_coeff": 4.1816, "dither": 0, "energy_floor": 1.665, "frame_length": 0.8125, "frame_shift": 0.375, "preemphasis_coefficient": 0.37, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "window_type": "rectangular"} +{"blackman_coeff": 4.1897, "dither": 0, "energy_floor": 1.2668, "frame_length": 0.1875, "frame_shift": 0.625, "preemphasis_coefficient": 0.74, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "hamming"} +{"blackman_coeff": 4.2217, "dither": 0, "energy_floor": 3.6775, "frame_length": 0.3125, "frame_shift": 0.125, "preemphasis_coefficient": 0.01, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "hamming"} +{"blackman_coeff": 4.2785, "dither": 0, "energy_floor": 0.7201, "frame_length": 0.8125, "frame_shift": 0.8125, "preemphasis_coefficient": 0.3, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "window_type": "hanning"} +{"blackman_coeff": 4.3304, "dither": 0, "energy_floor": 1.0538, "frame_length": 0.875, "frame_shift": 1.125, "preemphasis_coefficient": 0.92, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "povey"} +{"blackman_coeff": 4.3942, "dither": 0, "energy_floor": 3.9813, "frame_length": 0.75, "frame_shift": 0.6875, "preemphasis_coefficient": 0.27, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "blackman"} +{"blackman_coeff": 4.4432, "dither": 0, "energy_floor": 2.0441, "frame_length": 0.5, "frame_shift": 0.6875, "preemphasis_coefficient": 0.77, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "window_type": "hanning"} +{"blackman_coeff": 4.4459, "dither": 0, "energy_floor": 0.5135, "frame_length": 0.25, "frame_shift": 0.1875, "preemphasis_coefficient": 0.29, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "window_type": "hanning"} +{"blackman_coeff": 4.5486, "dither": 0, "energy_floor": 1.3248, "frame_length": 0.1875, "frame_shift": 1.125, "preemphasis_coefficient": 0.91, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "window_type": "hanning"} +{"blackman_coeff": 4.5535, "dither": 0, "energy_floor": 2.1772, "frame_length": 0.4375, "frame_shift": 0.875, "preemphasis_coefficient": 0.21, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "hanning"} +{"blackman_coeff": 4.5835, "dither": 0, "energy_floor": 0.3781, "frame_length": 0.875, "frame_shift": 0.875, "preemphasis_coefficient": 0.04, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "hamming"} +{"blackman_coeff": 4.6297, "dither": 0, "energy_floor": 2.49, "frame_length": 0.5, "frame_shift": 0.25, "preemphasis_coefficient": 0.03, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "window_type": "rectangular"} +{"blackman_coeff": 4.6749, "dither": 0, "energy_floor": 4.8853, "frame_length": 0.25, "frame_shift": 0.25, "preemphasis_coefficient": 0.48, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": false, "snip_edges": false, "subtract_mean": false, "window_type": "povey"} +{"blackman_coeff": 4.6971, "dither": 0, "energy_floor": 1.3632, "frame_length": 0.875, "frame_shift": 0.9375, "preemphasis_coefficient": 0.44, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": false, "snip_edges": true, "subtract_mean": false, "window_type": "blackman"} diff --git a/test/common_utils.py b/test/common_utils.py index 00d10a6df1..fe045d9db8 100644 --- a/test/common_utils.py +++ b/test/common_utils.py @@ -1,16 +1,15 @@ import os import tempfile import unittest -from typing import Type, Iterable, Union -from contextlib import contextmanager +from typing import Union from shutil import copytree import torch -from torch.testing._internal.common_utils import TestCase +from torch.testing._internal.common_utils import TestCase as PytorchTestCase import torchaudio _TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__)) -BACKENDS = torchaudio._backend._audio_backends +BACKENDS = torchaudio.list_audio_backends() def get_asset_path(*paths): @@ -55,24 +54,14 @@ def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32): return torch.tensor(arr).float().view(size) / m -@contextmanager -def AudioBackendScope(new_backend): - previous_backend = torchaudio.get_audio_backend() - try: - torchaudio.set_audio_backend(new_backend) - yield - finally: - torchaudio.set_audio_backend(previous_backend) - - def filter_backends_with_mp3(backends): # Filter out backends that do not support mp3 test_filepath = get_asset_path('steam-train-whistle-daniel_simon.mp3') def supports_mp3(backend): + torchaudio.set_audio_backend(backend) try: - with AudioBackendScope(backend): - torchaudio.load(test_filepath) + torchaudio.load(test_filepath) return True except (RuntimeError, ImportError): return False @@ -83,49 +72,38 @@ def supports_mp3(backend): BACKENDS_MP3 = filter_backends_with_mp3(BACKENDS) -class TestBaseMixin: - dtype = None - device = None - - -_SKIP_IF_NO_CUDA = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') +def set_audio_backend(backend): + """Allow additional backend value, 'default'""" + if backend == 'default': + if 'sox' in BACKENDS: + be = 'sox' + elif 'soundfile' in BACKENDS: + be = 'soundfile' + else: + raise unittest.SkipTest('No default backend available') + else: + be = backend + torchaudio.set_audio_backend(be) -def define_test_suite(testbase: Type[TestBaseMixin], dtype: str, device: str): - if dtype not in ['float32', 'float64']: - raise NotImplementedError(f'Unexpected dtype: {dtype}') - if device not in ['cpu', 'cuda']: - raise NotImplementedError(f'Unexpected device: {device}') - name = f'Test{testbase.__name__}_{device.upper()}_{dtype.capitalize()}' - attrs = {'dtype': getattr(torch, dtype), 'device': torch.device(device)} - testsuite = type(name, (testbase, TestCase), attrs) +class TestBaseMixin: + """Mixin to provide consistent way to define device/dtype/backend aware TestCase""" + dtype = None + device = None + backend = None - if device == 'cuda': - testsuite = _SKIP_IF_NO_CUDA(testsuite) - return testsuite + def setUp(self): + super().setUp() + set_audio_backend(self.backend) -def define_test_suites( - scope: dict, - testbases: Iterable[Type[TestBaseMixin]], - dtypes: Iterable[str] = ('float32', 'float64'), - devices: Iterable[str] = ('cpu', 'cuda'), -): - for suite in testbases: - for device in devices: - for dtype in dtypes: - t = define_test_suite(suite, dtype, device) - scope[t.__name__] = t +class TorchaudioTestCase(TestBaseMixin, PytorchTestCase): + pass -def common_test_class_parameters( - dtypes: Iterable[str] = ("float32", "float64"), - devices: Iterable[str] = ("cpu", "cuda"), -): - for device in devices: - for dtype in dtypes: - yield {"device": torch.device(device), "dtype": getattr(torch, dtype)} +skipIfNoSoxBackend = unittest.skipIf('sox' not in BACKENDS, 'Sox backend not available') +skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') def get_whitenoise( diff --git a/test/test_functional.py b/test/functional_cpu_test.py similarity index 90% rename from test/test_functional.py rename to test/functional_cpu_test.py index ac90b7bde5..470d6ab770 100644 --- a/test/test_functional.py +++ b/test/functional_cpu_test.py @@ -7,38 +7,20 @@ import pytest from . import common_utils +from .functional_impl import Lfilter -class Lfilter(common_utils.TestBaseMixin): - def test_simple(self): - """ - Create a very basic signal, - Then make a simple 4th order delay - The output should be same as the input but shifted - """ +class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') - torch.random.manual_seed(42) - waveform = torch.rand(2, 44100 * 1, dtype=self.dtype, device=self.device) - b_coeffs = torch.tensor([0, 0, 0, 1], dtype=self.dtype, device=self.device) - a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device) - output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) - self.assertEqual(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) +class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') - def test_clamp(self): - input_signal = torch.ones(1, 44100 * 1, dtype=self.dtype, device=self.device) - b_coeffs = torch.tensor([1, 0], dtype=self.dtype, device=self.device) - a_coeffs = torch.tensor([1, -0.95], dtype=self.dtype, device=self.device) - output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=True) - assert output_signal.max() <= 1 - output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=False) - assert output_signal.max() > 1 - -common_utils.define_test_suites(globals(), [Lfilter]) - - -class TestComputeDeltas(unittest.TestCase): +class TestComputeDeltas(common_utils.TorchaudioTestCase): """Test suite for correctness of compute_deltas""" def test_one_channel(self): specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) @@ -75,7 +57,7 @@ def _test_istft_is_inverse_of_stft(kwargs): _compare_estimate(sound, estimate) -class TestIstft(unittest.TestCase): +class TestIstft(common_utils.TorchaudioTestCase): """Test suite for correctness of istft with various input""" number_of_trials = 100 @@ -291,7 +273,9 @@ def test_linearity_of_istft4(self): self._test_linearity_of_istft(data_size, kwargs4, atol=1e-5, rtol=1e-8) -class TestDetectPitchFrequency(unittest.TestCase): +class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): + backend = 'default' + def test_pitch(self): test_filepath_100 = common_utils.get_asset_path("100Hz_44100Hz_16bit_05sec.wav") test_filepath_440 = common_utils.get_asset_path("440Hz_44100Hz_16bit_05sec.wav") @@ -312,7 +296,7 @@ def test_pitch(self): self.assertFalse(s) -class TestDB_to_amplitude(unittest.TestCase): +class TestDB_to_amplitude(common_utils.TorchaudioTestCase): def test_DB_to_amplitude(self): # Make some noise x = torch.rand(1000) @@ -381,7 +365,7 @@ def test_mask_along_axis(specgram, mask_param, mask_value, axis): masked_columns = (mask_specgram == mask_value).sum(other_axis) num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum() - num_masked_columns /= mask_specgram.size(0) + num_masked_columns //= mask_specgram.size(0) assert mask_specgram.size() == specgram.size() assert num_masked_columns < mask_param diff --git a/test/functional_cuda_test.py b/test/functional_cuda_test.py new file mode 100644 index 0000000000..9602dcf071 --- /dev/null +++ b/test/functional_cuda_test.py @@ -0,0 +1,16 @@ +import torch + +from . import common_utils +from .functional_impl import Lfilter + + +@common_utils.skipIfNoCuda +class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/functional_impl.py b/test/functional_impl.py new file mode 100644 index 0000000000..fb5e7dc7ac --- /dev/null +++ b/test/functional_impl.py @@ -0,0 +1,31 @@ +"""Test defintion common to CPU and CUDA""" +import torch +import torchaudio.functional as F + +from . import common_utils + + +class Lfilter(common_utils.TestBaseMixin): + def test_simple(self): + """ + Create a very basic signal, + Then make a simple 4th order delay + The output should be same as the input but shifted + """ + + torch.random.manual_seed(42) + waveform = torch.rand(2, 44100 * 1, dtype=self.dtype, device=self.device) + b_coeffs = torch.tensor([0, 0, 0, 1], dtype=self.dtype, device=self.device) + a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device) + output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) + + self.assertEqual(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) + + def test_clamp(self): + input_signal = torch.ones(1, 44100 * 1, dtype=self.dtype, device=self.device) + b_coeffs = torch.tensor([1, 0], dtype=self.dtype, device=self.device) + a_coeffs = torch.tensor([1, -0.95], dtype=self.dtype, device=self.device) + output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=True) + assert output_signal.max() <= 1 + output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=False) + assert output_signal.max() > 1 diff --git a/test/kaldi_compatibility_cpu_test.py b/test/kaldi_compatibility_cpu_test.py index cd85074ac0..3357123296 100644 --- a/test/kaldi_compatibility_cpu_test.py +++ b/test/kaldi_compatibility_cpu_test.py @@ -1,5 +1,14 @@ +import torch + from . import common_utils from .kaldi_compatibility_impl import Kaldi -common_utils.define_test_suites(globals(), [Kaldi], devices=['cpu']) +class TestKaldiFloat32(Kaldi, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') + + +class TestKaldiFloat64(Kaldi, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') diff --git a/test/kaldi_compatibility_cuda_test.py b/test/kaldi_compatibility_cuda_test.py index acaa2408f5..e23dbf3f40 100644 --- a/test/kaldi_compatibility_cuda_test.py +++ b/test/kaldi_compatibility_cuda_test.py @@ -1,5 +1,16 @@ +import torch + from . import common_utils from .kaldi_compatibility_impl import Kaldi -common_utils.define_test_suites(globals(), [Kaldi], devices=['cuda']) +@common_utils.skipIfNoCuda +class TestKaldiFloat32(Kaldi, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestKaldiFloat64(Kaldi, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/kaldi_compatibility_impl.py b/test/kaldi_compatibility_impl.py index 2d3313d007..ec58a3fa44 100644 --- a/test/kaldi_compatibility_impl.py +++ b/test/kaldi_compatibility_impl.py @@ -55,6 +55,8 @@ def _load_params(path): class Kaldi(common_utils.TestBaseMixin): + backend = 'sox' + def assert_equal(self, output, *, expected, rtol=None, atol=None): expected = expected.to(dtype=self.dtype, device=self.device) self.assertEqual(output, expected, rtol=rtol, atol=atol) @@ -86,6 +88,17 @@ def test_fbank(self, kwargs): kaldi_result = _run_kaldi(command, 'scp', wave_file) self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) + @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_spectrogram_args.json'))) + @unittest.skipIf(_not_available('compute-spectrogram-feats'), '`compute-spectrogram-feats` not available') + def test_spectrogram(self, kwargs): + """spectrogram should be numerically compatible with compute-spectrogram-feats""" + wave_file = common_utils.get_asset_path('kaldi_file.wav') + waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + result = torchaudio.compliance.kaldi.spectrogram(waveform, **kwargs) + command = ['compute-spectrogram-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] + kaldi_result = _run_kaldi(command, 'scp', wave_file) + self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) + @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_mfcc_args.json'))) @unittest.skipIf(_not_available('compute-mfcc-feats'), '`compute-mfcc-feats` not available') def test_mfcc(self, kwargs): diff --git a/test/test_backend.py b/test/test_backend.py new file mode 100644 index 0000000000..1e8f9e4fd6 --- /dev/null +++ b/test/test_backend.py @@ -0,0 +1,42 @@ +import unittest + +import torchaudio +from torchaudio._internal.module_utils import is_module_available + +from . import common_utils + + +class BackendSwitchMixin: + """Test set/get_audio_backend works""" + backend = None + backend_module = None + + def test_switch(self): + torchaudio.set_audio_backend(self.backend) + if self.backend is None: + assert torchaudio.get_audio_backend() is None + else: + assert torchaudio.get_audio_backend() == self.backend + assert torchaudio.load == self.backend_module.load + assert torchaudio.load_wav == self.backend_module.load_wav + assert torchaudio.save == self.backend_module.save + assert torchaudio.info == self.backend_module.info + + +class TestBackendSwitch_NoBackend(BackendSwitchMixin, common_utils.TorchaudioTestCase): + backend = None + backend_module = torchaudio.backend.no_backend + + +@unittest.skipIf( + not is_module_available('torchaudio._torchaudio'), + 'torchaudio C++ extension not available') +class TestBackendSwitch_SoX(BackendSwitchMixin, common_utils.TorchaudioTestCase): + backend = 'sox' + backend_module = torchaudio.backend.sox_backend + + +@unittest.skipIf(not is_module_available('soundfile'), '"soundfile" not available') +class TestBackendSwitch_soundfile(BackendSwitchMixin, common_utils.TorchaudioTestCase): + backend = 'soundfile' + backend_module = torchaudio.backend.soundfile_backend diff --git a/test/test_batch_consistency.py b/test/test_batch_consistency.py index 103d83bb84..2a88598f88 100644 --- a/test/test_batch_consistency.py +++ b/test/test_batch_consistency.py @@ -2,14 +2,14 @@ import unittest import torch -from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F from . import common_utils -class TestFunctional(TestCase): +class TestFunctional(common_utils.TorchaudioTestCase): + backend = 'default' """Test functions defined in `functional` module""" def assert_batch_consistency( self, functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, seed=42, **kwargs): @@ -98,12 +98,15 @@ def test_sliding_window_cmn(self): self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=False, norm_vars=False) def test_vad(self): + common_utils.set_audio_backend('default') filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") waveform, sample_rate = torchaudio.load(filepath) self.assert_batch_consistencies(F.vad, waveform, sample_rate=sample_rate) -class TestTransforms(TestCase): +class TestTransforms(common_utils.TorchaudioTestCase): + backend = 'default' + """Test suite for classes defined in `transforms` module""" def test_batch_AmplitudeToDB(self): spec = torch.rand((6, 201)) diff --git a/test/test_compliance_kaldi.py b/test/test_compliance_kaldi.py index 2a255a5a4b..f9e097e09c 100644 --- a/test/test_compliance_kaldi.py +++ b/test/test_compliance_kaldi.py @@ -1,13 +1,13 @@ -import math import os +import math +import unittest + import torch import torchaudio import torchaudio.compliance.kaldi as kaldi -import unittest from . import common_utils from .compliance import utils as compliance_utils -from .common_utils import AudioBackendScope, BACKENDS def extract_window(window, wave, f, frame_length, frame_shift, snip_edges): @@ -46,7 +46,10 @@ def first_sample_of_frame(frame, window_size, window_shift, snip_edges): window[f, s] = wave[s_in_wave] -class Test_Kaldi(unittest.TestCase): +@common_utils.skipIfNoSoxBackend +class Test_Kaldi(common_utils.TorchaudioTestCase): + backend = 'sox' + test_filepath = common_utils.get_asset_path('kaldi_file.wav') test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav') kaldi_output_dir = common_utils.get_asset_path('kaldi') @@ -158,98 +161,10 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil self._print_diagnostic(output, kaldi_output) torch.testing.assert_allclose(output, kaldi_output, atol=atol, rtol=rtol) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") - def test_spectrogram(self): - def get_output_fn(sound, args): - output = kaldi.spectrogram( - sound, - blackman_coeff=args[1], - dither=args[2], - energy_floor=args[3], - frame_length=args[4], - frame_shift=args[5], - preemphasis_coefficient=args[6], - raw_energy=args[7], - remove_dc_offset=args[8], - round_to_power_of_two=args[9], - snip_edges=args[10], - subtract_mean=args[11], - window_type=args[12]) - return output - - self._compliance_test_helper(self.test_filepath, 'spec', 131, 13, get_output_fn, atol=1e-3, rtol=0) - - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") - def test_fbank(self): - def get_output_fn(sound, args): - output = kaldi.fbank( - sound, - blackman_coeff=args[1], - dither=0.0, - energy_floor=args[2], - frame_length=args[3], - frame_shift=args[4], - high_freq=args[5], - htk_compat=args[6], - low_freq=args[7], - num_mel_bins=args[8], - preemphasis_coefficient=args[9], - raw_energy=args[10], - remove_dc_offset=args[11], - round_to_power_of_two=args[12], - snip_edges=args[13], - subtract_mean=args[14], - use_energy=args[15], - use_log_fbank=args[16], - use_power=args[17], - vtln_high=args[18], - vtln_low=args[19], - vtln_warp=args[20], - window_type=args[21]) - return output - - self._compliance_test_helper(self.test_filepath, 'fbank', 97, 22, get_output_fn, atol=1e-3, rtol=1e-1) - - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") - def test_mfcc(self): - def get_output_fn(sound, args): - output = kaldi.mfcc( - sound, - blackman_coeff=args[1], - dither=0.0, - energy_floor=args[2], - frame_length=args[3], - frame_shift=args[4], - high_freq=args[5], - htk_compat=args[6], - low_freq=args[7], - num_mel_bins=args[8], - preemphasis_coefficient=args[9], - raw_energy=args[10], - remove_dc_offset=args[11], - round_to_power_of_two=args[12], - snip_edges=args[13], - subtract_mean=args[14], - use_energy=args[15], - num_ceps=args[16], - cepstral_lifter=args[17], - vtln_high=args[18], - vtln_low=args[19], - vtln_warp=args[20], - window_type=args[21]) - return output - - self._compliance_test_helper(self.test_filepath, 'mfcc', 145, 22, get_output_fn, atol=1e-3) - def test_mfcc_empty(self): # Passing in an empty tensor should result in an error self.assertRaises(AssertionError, kaldi.mfcc, torch.empty(0)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_resample_waveform(self): def get_output_fn(sound, args): output = kaldi.resample_waveform(sound, args[1], args[2]) diff --git a/test/test_dataloader.py b/test/test_dataloader.py index cfcc25e7c4..1bf73ebb64 100644 --- a/test/test_dataloader.py +++ b/test/test_dataloader.py @@ -4,7 +4,6 @@ from torch.utils.data import Dataset, DataLoader from . import common_utils -from .common_utils import AudioBackendScope, BACKENDS class TORCHAUDIODS(Dataset): @@ -28,9 +27,10 @@ def __len__(self): return len(self.data) -class Test_DataLoader(unittest.TestCase): - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") +class Test_DataLoader(common_utils.TorchaudioTestCase): + backend = 'sox' + + @common_utils.skipIfNoSoxBackend def test_1(self): expected_size = (2, 1, 16000) ds = TORCHAUDIODS() diff --git a/test/test_datasets.py b/test/test_datasets.py index ab1e74e888..3ac7fae8ac 100644 --- a/test/test_datasets.py +++ b/test/test_datasets.py @@ -8,11 +8,13 @@ from torchaudio.datasets.yesno import YESNO from torchaudio.datasets.ljspeech import LJSPEECH from torchaudio.datasets.gtzan import GTZAN +from torchaudio.datasets.cmuarctic import CMUARCTIC from . import common_utils -class TestDatasets(unittest.TestCase): +class TestDatasets(common_utils.TorchaudioTestCase): + backend = 'default' path = common_utils.get_asset_path() def test_yesno(self): @@ -27,12 +29,32 @@ def test_librispeech(self): data = LIBRISPEECH(self.path, "dev-clean") data[0] - @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") + def test_ljspeech(self): + data = LJSPEECH(self.path) + data[0] + + def test_speechcommands(self): + data = SPEECHCOMMANDS(self.path) + data[0] + + def test_gtzan(self): + data = GTZAN(self.path) + data[0] + + def test_cmuarctic(self): + data = CMUARCTIC(self.path) + data[0] + + +@common_utils.skipIfNoSoxBackend +class TestCommonVoice(common_utils.TorchaudioTestCase): + backend = 'sox' + path = common_utils.get_asset_path() + def test_commonvoice(self): data = COMMONVOICE(self.path, url="tatar") data[0] - @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") def test_commonvoice_diskcache(self): data = COMMONVOICE(self.path, url="tatar") data = diskcache_iterator(data) @@ -41,25 +63,12 @@ def test_commonvoice_diskcache(self): # Load data[0] - @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") def test_commonvoice_bg(self): data = COMMONVOICE(self.path, url="tatar") data = bg_iterator(data, 5) for _ in data: pass - def test_ljspeech(self): - data = LJSPEECH(self.path) - data[0] - - def test_speechcommands(self): - data = SPEECHCOMMANDS(self.path) - data[0] - - def test_gtzan(self): - data = GTZAN(self.path) - data[0] - if __name__ == "__main__": unittest.main() diff --git a/test/test_io.py b/test/test_io.py index 0295cb785e..f58f66ed11 100644 --- a/test/test_io.py +++ b/test/test_io.py @@ -1,9 +1,11 @@ +import os +import math import unittest + import torch import torchaudio -import math -import os -from .common_utils import AudioBackendScope, BACKENDS, BACKENDS_MP3, create_temp_assets_dir + +from .common_utils import BACKENDS, BACKENDS_MP3, create_temp_assets_dir class Test_LoadSave(unittest.TestCase): @@ -16,13 +18,13 @@ class Test_LoadSave(unittest.TestCase): def test_1_save(self): for backend in BACKENDS_MP3: with self.subTest(): - with AudioBackendScope(backend): - self._test_1_save(self.test_filepath, False) + torchaudio.set_audio_backend(backend) + self._test_1_save(self.test_filepath, False) for backend in BACKENDS: with self.subTest(): - with AudioBackendScope(backend): - self._test_1_save(self.test_filepath_wav, True) + torchaudio.set_audio_backend(backend) + self._test_1_save(self.test_filepath_wav, True) def _test_1_save(self, test_filepath, normalization): # load signal @@ -67,8 +69,8 @@ def _test_1_save(self, test_filepath, normalization): def test_1_save_sine(self): for backend in BACKENDS: with self.subTest(): - with AudioBackendScope(backend): - self._test_1_save_sine() + torchaudio.set_audio_backend(backend) + self._test_1_save_sine() def _test_1_save_sine(self): @@ -100,13 +102,13 @@ def _test_1_save_sine(self): def test_2_load(self): for backend in BACKENDS_MP3: with self.subTest(): - with AudioBackendScope(backend): - self._test_2_load(self.test_filepath, 278756) + torchaudio.set_audio_backend(backend) + self._test_2_load(self.test_filepath, 278756) for backend in BACKENDS: with self.subTest(): - with AudioBackendScope(backend): - self._test_2_load(self.test_filepath_wav, 276858) + torchaudio.set_audio_backend(backend) + self._test_2_load(self.test_filepath_wav, 276858) def _test_2_load(self, test_filepath, length): # check normal loading @@ -141,8 +143,8 @@ def _test_2_load(self, test_filepath, length): def test_2_load_nonormalization(self): for backend in BACKENDS_MP3: with self.subTest(): - with AudioBackendScope(backend): - self._test_2_load_nonormalization(self.test_filepath, 278756) + torchaudio.set_audio_backend(backend) + self._test_2_load_nonormalization(self.test_filepath, 278756) def _test_2_load_nonormalization(self, test_filepath, length): @@ -158,8 +160,8 @@ def _test_2_load_nonormalization(self, test_filepath, length): def test_3_load_and_save_is_identity(self): for backend in BACKENDS: with self.subTest(): - with AudioBackendScope(backend): - self._test_3_load_and_save_is_identity() + torchaudio.set_audio_backend(backend) + self._test_3_load_and_save_is_identity() def _test_3_load_and_save_is_identity(self): input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') @@ -179,16 +181,15 @@ def test_3_load_and_save_is_identity_across_backend(self): self._test_3_load_and_save_is_identity_across_backend("soundfile", "sox") def _test_3_load_and_save_is_identity_across_backend(self, backend1, backend2): - with AudioBackendScope(backend1): - - input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') - tensor1, sample_rate1 = torchaudio.load(input_path) + torchaudio.set_audio_backend(backend1) + input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') + tensor1, sample_rate1 = torchaudio.load(input_path) - output_path = os.path.join(self.test_dirpath, 'test.wav') - torchaudio.save(output_path, tensor1, sample_rate1) + output_path = os.path.join(self.test_dirpath, 'test.wav') + torchaudio.save(output_path, tensor1, sample_rate1) - with AudioBackendScope(backend2): - tensor2, sample_rate2 = torchaudio.load(output_path) + torchaudio.set_audio_backend(backend2) + tensor2, sample_rate2 = torchaudio.load(output_path) self.assertTrue(tensor1.allclose(tensor2)) self.assertEqual(sample_rate1, sample_rate2) @@ -197,8 +198,8 @@ def _test_3_load_and_save_is_identity_across_backend(self, backend1, backend2): def test_4_load_partial(self): for backend in BACKENDS_MP3: with self.subTest(): - with AudioBackendScope(backend): - self._test_4_load_partial() + torchaudio.set_audio_backend(backend) + self._test_4_load_partial() def _test_4_load_partial(self): num_frames = 101 @@ -239,8 +240,8 @@ def _test_4_load_partial(self): def test_5_get_info(self): for backend in BACKENDS: with self.subTest(): - with AudioBackendScope(backend): - self._test_5_get_info() + torchaudio.set_audio_backend(backend) + self._test_5_get_info() def _test_5_get_info(self): input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') diff --git a/test/test_kaldi_io.py b/test/test_kaldi_io.py index 942b11de35..4bd0fd56a4 100644 --- a/test/test_kaldi_io.py +++ b/test/test_kaldi_io.py @@ -6,7 +6,7 @@ from . import common_utils -class Test_KaldiIO(unittest.TestCase): +class Test_KaldiIO(common_utils.TorchaudioTestCase): data1 = [[1, 2, 3], [11, 12, 13], [21, 22, 23]] data2 = [[31, 32, 33], [41, 42, 43], [51, 52, 53]] diff --git a/test/test_librosa_compatibility.py b/test/test_librosa_compatibility.py index 665341d945..62e9d3ca88 100644 --- a/test/test_librosa_compatibility.py +++ b/test/test_librosa_compatibility.py @@ -4,12 +4,13 @@ from distutils.version import StrictVersion import torch -from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F -from torchaudio.common_utils import IMPORT_LIBROSA +from torchaudio._internal.module_utils import is_module_available -if IMPORT_LIBROSA: +LIBROSA_AVAILABLE = is_module_available('librosa') + +if LIBROSA_AVAILABLE: import numpy as np import librosa import scipy @@ -19,8 +20,8 @@ from . import common_utils -@unittest.skipIf(not IMPORT_LIBROSA, "Librosa not available") -class TestFunctional(TestCase): +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") +class TestFunctional(common_utils.TorchaudioTestCase): """Test suite for functions in `functional` module.""" def test_griffinlim(self): # NOTE: This test is flaky without a fixed random seed @@ -115,12 +116,8 @@ def test_amplitude_to_DB(self): ]) @pytest.mark.parametrize('rate', [0.5, 1.01, 1.3]) @pytest.mark.parametrize('hop_length', [256]) +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") def test_phase_vocoder(complex_specgrams, rate, hop_length): - - # Using a decorator here causes parametrize to fail on Python 2 - if not IMPORT_LIBROSA: - raise unittest.SkipTest('Librosa is not available') - # Due to cummulative sum, numerical error in using torch.float32 will # result in bottom right values of the stretched sectrogram to not # match with librosa. @@ -158,10 +155,11 @@ def _load_audio_asset(*asset_paths, **kwargs): return sound, sample_rate -@unittest.skipIf(not IMPORT_LIBROSA, "Librosa not available") -class TestTransforms(TestCase): +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") +class TestTransforms(common_utils.TorchaudioTestCase): """Test suite for functions in `transforms` module.""" def assert_compatibilities(self, n_fft, hop_length, power, n_mels, n_mfcc, sample_rate): + common_utils.set_audio_backend('default') sound, sample_rate = _load_audio_asset('sinewave.wav') sound_librosa = sound.cpu().numpy().squeeze() # (64000) @@ -271,8 +269,7 @@ def test_basics4(self): } self.assert_compatibilities(**kwargs) - @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") - @common_utils.AudioBackendScope("sox") + @unittest.skipIf(not common_utils.BACKENDS_MP3, 'no backend to read mp3') def test_MelScale(self): """MelScale transform is comparable to that of librosa""" n_fft = 2048 @@ -280,6 +277,7 @@ def test_MelScale(self): hop_length = n_fft // 4 # Prepare spectrogram input. We use torchaudio to compute one. + common_utils.set_audio_backend('default') sound, sample_rate = _load_audio_asset('whitenoise_1min.mp3') sound = sound.mean(dim=0, keepdim=True) spec_ta = F.spectrogram( @@ -302,6 +300,7 @@ def test_InverseMelScale(self): hop_length = n_fft // 4 # Prepare mel spectrogram input. We use torchaudio to compute one. + common_utils.set_audio_backend('default') sound, sample_rate = _load_audio_asset( 'steam-train-whistle-daniel_simon.wav', offset=2**10, num_frames=2**14) sound = sound.mean(dim=0, keepdim=True) diff --git a/test/test_models.py b/test/test_models.py index fe8df9a167..7bd3f3819d 100644 --- a/test/test_models.py +++ b/test/test_models.py @@ -1,30 +1,51 @@ -import pytest - import torch -from torchaudio.models import Wav2Letter +from torchaudio.models import Wav2Letter, _MelResNet class TestWav2Letter: - @pytest.mark.parametrize('batch_size', [2]) - @pytest.mark.parametrize('num_features', [1]) - @pytest.mark.parametrize('num_classes', [40]) - @pytest.mark.parametrize('input_length', [320]) - def test_waveform(self, batch_size, num_features, num_classes, input_length): - model = Wav2Letter() + + def test_waveform(self): + batch_size = 2 + num_features = 1 + num_classes = 40 + input_length = 320 + + model = Wav2Letter(num_classes=num_classes, num_features=num_features) x = torch.rand(batch_size, num_features, input_length) out = model(x) assert out.size() == (batch_size, num_classes, 2) - @pytest.mark.parametrize('batch_size', [2]) - @pytest.mark.parametrize('num_features', [13]) - @pytest.mark.parametrize('num_classes', [40]) - @pytest.mark.parametrize('input_length', [2]) - def test_mfcc(self, batch_size, num_features, num_classes, input_length): - model = Wav2Letter(input_type="mfcc", num_features=13) + def test_mfcc(self): + batch_size = 2 + num_features = 13 + num_classes = 40 + input_length = 2 + + model = Wav2Letter(num_classes=num_classes, input_type="mfcc", num_features=num_features) x = torch.rand(batch_size, num_features, input_length) out = model(x) assert out.size() == (batch_size, num_classes, 2) + + +class TestMelResNet: + + def test_waveform(self): + + batch_size = 2 + num_features = 200 + input_dims = 100 + output_dims = 128 + res_blocks = 10 + hidden_dims = 128 + pad = 2 + + model = _MelResNet(res_blocks, input_dims, hidden_dims, output_dims, pad) + + x = torch.rand(batch_size, input_dims, num_features) + out = model(x) + + assert out.size() == (batch_size, output_dims, num_features - pad * 2) diff --git a/test/test_sox_compatibility.py b/test/test_sox_compatibility.py index b6e6dbaedd..23d58a3fb1 100644 --- a/test/test_sox_compatibility.py +++ b/test/test_sox_compatibility.py @@ -1,18 +1,17 @@ import unittest import torch -from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F import torchaudio.transforms as T from . import common_utils -from .common_utils import AudioBackendScope, BACKENDS -class TestFunctionalFiltering(TestCase): - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") +@common_utils.skipIfNoSoxBackend +class TestFunctionalFiltering(common_utils.TorchaudioTestCase): + backend = 'sox' + def test_gain(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) @@ -27,8 +26,6 @@ def test_gain(self): self.assertEqual(waveform_gain, sox_gain_waveform, atol=1e-04, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_dither(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) @@ -49,8 +46,6 @@ def test_dither(self): self.assertEqual(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_vctk_transform_pipeline(self): test_filepath_vctk = common_utils.get_asset_path('VCTK-Corpus', 'wav48', 'p224', 'p224_002.wav') wf_vctk, sr_vctk = torchaudio.load(test_filepath_vctk) @@ -72,8 +67,6 @@ def test_vctk_transform_pipeline(self): self.assertEqual(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_lowpass(self): """ Test biquad lowpass filter, compare to SoX implementation @@ -92,8 +85,6 @@ def test_lowpass(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_highpass(self): """ Test biquad highpass filter, compare to SoX implementation @@ -113,8 +104,6 @@ def test_highpass(self): # TBD - this fails at the 1e-4 level, debug why self.assertEqual(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_allpass(self): """ Test biquad allpass filter, compare to SoX implementation @@ -134,8 +123,6 @@ def test_allpass(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_bandpass_with_csg(self): """ Test biquad bandpass filter, compare to SoX implementation @@ -156,8 +143,6 @@ def test_bandpass_with_csg(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_bandpass_without_csg(self): """ Test biquad bandpass filter, compare to SoX implementation @@ -178,8 +163,6 @@ def test_bandpass_without_csg(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_bandreject(self): """ Test biquad bandreject filter, compare to SoX implementation @@ -199,8 +182,6 @@ def test_bandreject(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_band_with_noise(self): """ Test biquad band filter with noise mode, compare to SoX implementation @@ -221,8 +202,6 @@ def test_band_with_noise(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_band_without_noise(self): """ Test biquad band filter without noise mode, compare to SoX implementation @@ -243,8 +222,6 @@ def test_band_without_noise(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_treble(self): """ Test biquad treble filter, compare to SoX implementation @@ -265,8 +242,6 @@ def test_treble(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_bass(self): """ Test biquad bass filter, compare to SoX implementation @@ -287,8 +262,6 @@ def test_bass(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_deemph(self): """ Test biquad deemph filter, compare to SoX implementation @@ -305,8 +278,6 @@ def test_deemph(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_riaa(self): """ Test biquad riaa filter, compare to SoX implementation @@ -323,8 +294,6 @@ def test_riaa(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_contrast(self): """ Test contrast effect, compare to SoX implementation @@ -341,8 +310,6 @@ def test_contrast(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_dcshift_with_limiter(self): """ Test dcshift effect, compare to SoX implementation @@ -360,8 +327,6 @@ def test_dcshift_with_limiter(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_dcshift_without_limiter(self): """ Test dcshift effect, compare to SoX implementation @@ -378,8 +343,6 @@ def test_dcshift_without_limiter(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_overdrive(self): """ Test overdrive effect, compare to SoX implementation @@ -397,8 +360,6 @@ def test_overdrive(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_phaser_sine(self): """ Test phaser effect with sine moduldation, compare to SoX implementation @@ -419,8 +380,6 @@ def test_phaser_sine(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_phaser_triangle(self): """ Test phaser effect with triangle modulation, compare to SoX implementation @@ -441,8 +400,6 @@ def test_phaser_triangle(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_flanger_triangle_linear(self): """ Test flanger effect with triangle modulation and linear interpolation, compare to SoX implementation @@ -465,8 +422,6 @@ def test_flanger_triangle_linear(self): torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_flanger_triangle_quad(self): """ Test flanger effect with triangle modulation and quadratic interpolation, compare to SoX implementation @@ -489,8 +444,6 @@ def test_flanger_triangle_quad(self): torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_flanger_sine_linear(self): """ Test flanger effect with sine modulation and linear interpolation, compare to SoX implementation @@ -513,8 +466,6 @@ def test_flanger_sine_linear(self): torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_flanger_sine_quad(self): """ Test flanger effect with sine modulation and quadratic interpolation, compare to SoX implementation @@ -537,8 +488,6 @@ def test_flanger_sine_quad(self): torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_equalizer(self): """ Test biquad peaking equalizer filter, compare to SoX implementation @@ -559,8 +508,6 @@ def test_equalizer(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_perf_biquad_filtering(self): fn_sine = common_utils.get_asset_path('whitenoise.wav') diff --git a/test/test_sox_effects.py b/test/test_sox_effects.py index 213477537e..68440db8d2 100644 --- a/test/test_sox_effects.py +++ b/test/test_sox_effects.py @@ -1,17 +1,18 @@ +import math import unittest + import torch import torchaudio -import math from . import common_utils -from .common_utils import AudioBackendScope, BACKENDS -class Test_SoxEffectsChain(unittest.TestCase): +@common_utils.skipIfNoSoxBackend +class Test_SoxEffectsChain(common_utils.TorchaudioTestCase): + backend = 'sox' + test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.mp3") - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_single_channel(self): fn_sine = common_utils.get_asset_path("sinewave.wav") E = torchaudio.sox_effects.SoxEffectsChain() @@ -21,8 +22,6 @@ def test_single_channel(self): # check if effects worked # print(x.size()) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_rate_channels(self): target_rate = 16000 target_channels = 1 @@ -35,8 +34,6 @@ def test_rate_channels(self): self.assertEqual(sr, target_rate) self.assertEqual(x.size(0), target_channels) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_lowpass_speed(self): speed = .8 si, _ = torchaudio.info(self.test_filepath) @@ -49,8 +46,6 @@ def test_lowpass_speed(self): # check if effects worked self.assertEqual(x.size(1), int((si.length / si.channels) / speed)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_ulaw_and_siginfo(self): si_out = torchaudio.sox_signalinfo_t() ei_out = torchaudio.sox_encodinginfo_t() @@ -68,8 +63,6 @@ def test_ulaw_and_siginfo(self): self.assertLess(x.unique().size(0), 2**8 + 1) self.assertEqual(x.numel(), si_in.length) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_band_chorus(self): si_in, ei_in = torchaudio.info(self.test_filepath) ei_in.encoding = torchaudio.get_sox_encoding_t(1) @@ -84,8 +77,6 @@ def test_band_chorus(self): self.assertEqual(x.size(0), si_in.channels) self.assertGreaterEqual(x.size(1) * x.size(0), si_in.length) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_synth(self): si_in, ei_in = torchaudio.info(self.test_filepath) len_in_seconds = si_in.length / si_in.channels / si_in.rate @@ -99,8 +90,6 @@ def test_synth(self): self.assertEqual(x.size(0), si_in.channels) self.assertEqual(si_in.length, x.size(0) * x.size(1)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_gain(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -124,8 +113,6 @@ def test_gain(self): E.clear_chain() self.assertLess(x.abs().max().item(), 1.) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_tempo_or_speed(self): tempo = .8 si, _ = torchaudio.info(self.test_filepath) @@ -159,8 +146,6 @@ def test_tempo_or_speed(self): # check if effect worked self.assertAlmostEqual(x.size(1), math.ceil((si.length / si.channels) / speed), delta=1) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_trim(self): x_orig, _ = torchaudio.load(self.test_filepath) offset = "10000s" @@ -174,8 +159,6 @@ def test_trim(self): # check if effect worked self.assertTrue(x.allclose(x_orig[:, offset_int:(offset_int + num_frames_int)], rtol=1e-4, atol=1e-4)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_silence_contrast(self): si, _ = torchaudio.info(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -186,8 +169,6 @@ def test_silence_contrast(self): # check if effect worked self.assertLess(x.numel(), si.length) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_reverse(self): x_orig, _ = torchaudio.load(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -198,8 +179,6 @@ def test_reverse(self): rev_idx = torch.LongTensor(range(x_orig.size(1))[::-1]) self.assertTrue(x_orig.allclose(x_rev[:, rev_idx], rtol=1e-5, atol=2e-5)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_compand_fade(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -209,8 +188,6 @@ def test_compand_fade(self): # check if effect worked # print(x.size()) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_biquad_delay(self): si, _ = torchaudio.info(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -222,8 +199,6 @@ def test_biquad_delay(self): # check if effect worked self.assertTrue(x.size(1) == (si.length / si.channels) + 15000) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_invalid_effect_name(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -231,8 +206,6 @@ def test_invalid_effect_name(self): with self.assertRaises(LookupError): E.append_effect_to_chain("special", [""]) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_unimplemented_effect(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -240,8 +213,6 @@ def test_unimplemented_effect(self): with self.assertRaises(NotImplementedError): E.append_effect_to_chain("spectrogram", [""]) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_invalid_effect_options(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -250,8 +221,6 @@ def test_invalid_effect_options(self): with self.assertRaises(RuntimeError): E.sox_build_flow_effects() - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_fade(self): x_orig, _ = torchaudio.load(self.test_filepath) fade_in_len = 44100 @@ -268,8 +237,6 @@ def test_fade(self): # check if effect worked self.assertTrue(x.allclose(fade(x_orig), rtol=1e-4, atol=1e-4)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_vol(self): x_orig, _ = torchaudio.load(self.test_filepath) @@ -284,8 +251,6 @@ def test_vol(self): # check if effect worked self.assertTrue(x.allclose(z, rtol=1e-4, atol=1e-4)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_vad(self): sample_files = [ common_utils.get_asset_path("vad-go-stereo-44100.wav"), diff --git a/test/test_transforms.py b/test/test_transforms.py index ad8a55fb4b..af2b834fdb 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -2,7 +2,6 @@ import unittest import torch -from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.transforms as transforms import torchaudio.functional as F @@ -10,7 +9,8 @@ from . import common_utils -class Tester(TestCase): +class Tester(common_utils.TorchaudioTestCase): + backend = 'default' # create a sinewave signal for testing sample_rate = 16000 diff --git a/test/torchscript_consistency_cpu_test.py b/test/torchscript_consistency_cpu_test.py index 81e6616821..38fe7961fd 100644 --- a/test/torchscript_consistency_cpu_test.py +++ b/test/torchscript_consistency_cpu_test.py @@ -1,14 +1,24 @@ -from parameterized import parameterized_class +import torch -from .common_utils import TestCase, common_test_class_parameters +from . import common_utils from .torchscript_consistency_impl import Functional, Transforms -parameters = list(common_test_class_parameters(devices=['cpu'])) -@parameterized_class(parameters) -class TestFunctional(Functional, TestCase): - pass +class TestFunctionalFloat32(Functional, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') -@parameterized_class(parameters) -class TestTransforms(Transforms, TestCase): - pass + +class TestFunctionalFloat64(Functional, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') + + +class TestTransformsFloat32(Transforms, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') + + +class TestTransformsFloat64(Transforms, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') diff --git a/test/torchscript_consistency_cuda_test.py b/test/torchscript_consistency_cuda_test.py index 0899ba2066..b317334ce4 100644 --- a/test/torchscript_consistency_cuda_test.py +++ b/test/torchscript_consistency_cuda_test.py @@ -1,5 +1,28 @@ -from .common_utils import define_test_suites +import torch + +from . import common_utils from .torchscript_consistency_impl import Functional, Transforms -define_test_suites(globals(), [Functional, Transforms], devices=['cuda']) +@common_utils.skipIfNoCuda +class TestFunctionalFloat32(Functional, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestFunctionalFloat64(Functional, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestTransformsFloat32(Transforms, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestTransformsFloat64(Transforms, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/torchscript_consistency_impl.py b/test/torchscript_consistency_impl.py index a84b629bd7..2527cd5fbf 100644 --- a/test/torchscript_consistency_impl.py +++ b/test/torchscript_consistency_impl.py @@ -616,5 +616,6 @@ def test_SlidingWindowCmn(self): def test_Vad(self): filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") + common_utils.set_audio_backend('default') waveform, sample_rate = torchaudio.load(filepath) self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform) diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index a08eaf6964..16419ff3ff 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -1,10 +1,5 @@ -import atexit -import os.path -from pathlib import Path -from typing import Any, Callable, Optional, Tuple, Union - -import torch -from torch import Tensor +from . import extension +from torchaudio._internal import module_utils as _mod_utils from torchaudio import ( compliance, datasets, @@ -12,14 +7,23 @@ sox_effects, transforms ) -from torchaudio._backend import ( - check_input, - _audio_backend_guard, - _get_audio_backend_module, +from torchaudio.backend import ( + list_audio_backends, get_audio_backend, set_audio_backend, + save_encinfo, + sox_signalinfo_t, + sox_encodinginfo_t, + get_sox_option_t, + get_sox_encoding_t, + get_sox_bool, + SignalInfo, + EncodingInfo, +) +from torchaudio.sox_effects import ( + init_sox_effects as _init_sox_effects, + shutdown_sox_effects as _shutdown_sox_effects, ) -from torchaudio._soundfile_backend import SignalInfo, EncodingInfo try: from .version import __version__, git_version # noqa: F401 @@ -27,397 +31,25 @@ pass -def load(filepath: Union[str, Path], - out: Optional[Tensor] = None, - normalization: Union[bool, float, Callable] = True, - channels_first: bool = True, - num_frames: int = 0, - offset: int = 0, - signalinfo: Optional[SignalInfo] = None, - encodinginfo: Optional[EncodingInfo] = None, - filetype: Optional[str] = None) -> Tuple[Tensor, int]: - r"""Loads an audio file from disk into a tensor - - Args: - filepath (str or Path): Path to audio file - out (Tensor, optional): An output tensor to use instead of creating one. (Default: ``None``) - normalization (bool, float, or callable, optional): If boolean `True`, then output is divided by `1 << 31` - (assumes signed 32-bit audio), and normalizes to `[-1, 1]`. - If `float`, then output is divided by that number - If `Callable`, then the output is passed as a parameter - to the given function, then the output is divided by - the result. (Default: ``True``) - channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) - num_frames (int, optional): Number of frames to load. 0 to load everything after the offset. - (Default: ``0``) - offset (int, optional): Number of frames from the start of the file to begin data loading. - (Default: ``0``) - signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the - audio type cannot be automatically determined. (Default: ``None``) - encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the - audio type cannot be automatically determined. (Default: ``None``) - filetype (str, optional): A filetype or extension to be set if sox cannot determine it - automatically. (Default: ``None``) - - Returns: - (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number - of audio frames and C is the number of channels. An integer which is the sample rate of the - audio (as listed in the metadata of the file) - - Example - >>> data, sample_rate = torchaudio.load('foo.mp3') - >>> print(data.size()) - torch.Size([2, 278756]) - >>> print(sample_rate) - 44100 - >>> data_vol_normalized, _ = torchaudio.load('foo.mp3', normalization=lambda x: torch.abs(x).max()) - >>> print(data_vol_normalized.abs().max()) - 1. - - """ - - return _get_audio_backend_module().load( - filepath, - out=out, - normalization=normalization, - channels_first=channels_first, - num_frames=num_frames, - offset=offset, - signalinfo=signalinfo, - encodinginfo=encodinginfo, - filetype=filetype, - ) - - -def load_wav(filepath: Union[str, Path], **kwargs: Any) -> Tuple[Tensor, int]: - r""" Loads a wave file. It assumes that the wav file uses 16 bit per sample that needs normalization by shifting - the input right by 16 bits. - - Args: - filepath (str or Path): Path to audio file - - Returns: - (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number - of audio frames and C is the number of channels. An integer which is the sample rate of the - audio (as listed in the metadata of the file) - """ - kwargs['normalization'] = 1 << 16 - return load(filepath, **kwargs) - - -def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: - r"""Convenience function for `save_encinfo`. - - Args: - filepath (str): Path to audio file - src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is - the number of audio frames, C is the number of channels - sample_rate (int): An integer which is the sample rate of the - audio (as listed in the metadata of the file) - precision (int, optional): Bit precision (Default: ``16``) - channels_first (bool, optional): Set channels first or length first in result. ( - Default: ``True``) - """ - - return _get_audio_backend_module().save( - filepath, src, sample_rate, precision=precision, channels_first=channels_first - ) - - -@_audio_backend_guard("sox") -def save_encinfo(filepath: str, - src: Tensor, - channels_first: bool = True, - signalinfo: Optional[SignalInfo] = None, - encodinginfo: Optional[EncodingInfo] = None, - filetype: Optional[str] = None) -> None: - r"""Saves a tensor of an audio signal to disk as a standard format like mp3, wav, etc. - - Args: - filepath (str): Path to audio file - src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is - the number of audio frames, C is the number of channels - channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) - signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the - audio type cannot be automatically determined (Default: ``None``). - encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the - audio type cannot be automatically determined (Default: ``None``). - filetype (str, optional): A filetype or extension to be set if sox cannot determine it - automatically. (Default: ``None``) - - Example - >>> data, sample_rate = torchaudio.load('foo.mp3') - >>> torchaudio.save('foo.wav', data, sample_rate) - - """ - ch_idx, len_idx = (0, 1) if channels_first else (1, 0) - - # check if save directory exists - abs_dirpath = os.path.dirname(os.path.abspath(filepath)) - if not os.path.isdir(abs_dirpath): - raise OSError("Directory does not exist: {}".format(abs_dirpath)) - # check that src is a CPU tensor - check_input(src) - # Check/Fix shape of source data - if src.dim() == 1: - # 1d tensors as assumed to be mono signals - src.unsqueeze_(ch_idx) - elif src.dim() > 2 or src.size(ch_idx) > 16: - # assumes num_channels < 16 - raise ValueError( - "Expected format where C < 16, but found {}".format(src.size())) - # sox stores the sample rate as a float, though practically sample rates are almost always integers - # convert integers to floats - if signalinfo: - if signalinfo.rate and not isinstance(signalinfo.rate, float): - if float(signalinfo.rate) == signalinfo.rate: - signalinfo.rate = float(signalinfo.rate) - else: - raise TypeError('Sample rate should be a float or int') - # check if the bit precision (i.e. bits per sample) is an integer - if signalinfo.precision and not isinstance(signalinfo.precision, int): - if int(signalinfo.precision) == signalinfo.precision: - signalinfo.precision = int(signalinfo.precision) - else: - raise TypeError('Bit precision should be an integer') - # programs such as librosa normalize the signal, unnormalize if detected - if src.min() >= -1.0 and src.max() <= 1.0: - src = src * (1 << 31) - src = src.long() - # set filetype and allow for files with no extensions - extension = os.path.splitext(filepath)[1] - filetype = extension[1:] if len(extension) > 0 else filetype - # transpose from C x L -> L x C - if channels_first: - src = src.transpose(1, 0) - # save data to file - src = src.contiguous() - - from . import _torchaudio - _torchaudio.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) - - -def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: - r"""Gets metadata from an audio file without loading the signal. - - Args: - filepath (str): Path to audio file - - Returns: - (sox_signalinfo_t, sox_encodinginfo_t): A si (sox_signalinfo_t) signal - info as a python object. An ei (sox_encodinginfo_t) encoding info - - Example - >>> si, ei = torchaudio.info('foo.wav') - >>> rate, channels, encoding = si.rate, si.channels, ei.encoding - """ - - return _get_audio_backend_module().info(filepath) - - -@_audio_backend_guard("sox") -def sox_signalinfo_t() -> SignalInfo: - r"""Create a sox_signalinfo_t object. This object can be used to set the sample - rate, number of channels, length, bit precision and headroom multiplier - primarily for effects - - Returns: sox_signalinfo_t(object) - - rate (float), sample rate as a float, practically will likely be an integer float - - channel (int), number of audio channels - - precision (int), bit precision - - length (int), length of audio in samples * channels, 0 for unspecified and -1 for unknown - - mult (float, optional), headroom multiplier for effects and ``None`` for no multiplier - - Example - >>> si = torchaudio.sox_signalinfo_t() - >>> si.channels = 1 - >>> si.rate = 16000. - >>> si.precision = 16 - >>> si.length = 0 - """ - - from . import _torchaudio - return _torchaudio.sox_signalinfo_t() - - -@_audio_backend_guard("sox") -def sox_encodinginfo_t() -> EncodingInfo: - r"""Create a sox_encodinginfo_t object. This object can be used to set the encoding - type, bit precision, compression factor, reverse bytes, reverse nibbles, - reverse bits and endianness. This can be used in an effects chain to encode the - final output or to save a file with a specific encoding. For example, one could - use the sox ulaw encoding to do 8-bit ulaw encoding. Note in a tensor output - the result will be a 32-bit number, but number of unique values will be determined by - the bit precision. - - Returns: sox_encodinginfo_t(object) - - encoding (sox_encoding_t), output encoding - - bits_per_sample (int), bit precision, same as `precision` in sox_signalinfo_t - - compression (float), compression for lossy formats, 0.0 for default compression - - reverse_bytes (sox_option_t), reverse bytes, use sox_option_default - - reverse_nibbles (sox_option_t), reverse nibbles, use sox_option_default - - reverse_bits (sox_option_t), reverse bytes, use sox_option_default - - opposite_endian (sox_bool), change endianness, use sox_false - - Example - >>> ei = torchaudio.sox_encodinginfo_t() - >>> ei.encoding = torchaudio.get_sox_encoding_t(1) - >>> ei.bits_per_sample = 16 - >>> ei.compression = 0 - >>> ei.reverse_bytes = torchaudio.get_sox_option_t(2) - >>> ei.reverse_nibbles = torchaudio.get_sox_option_t(2) - >>> ei.reverse_bits = torchaudio.get_sox_option_t(2) - >>> ei.opposite_endian = torchaudio.get_sox_bool(0) - - """ - - from . import _torchaudio - ei = _torchaudio.sox_encodinginfo_t() - sdo = get_sox_option_t(2) # sox_default_option - ei.reverse_bytes = sdo - ei.reverse_nibbles = sdo - ei.reverse_bits = sdo - return ei - - -@_audio_backend_guard("sox") -def get_sox_encoding_t(i: int = None) -> EncodingInfo: - r"""Get enum of sox_encoding_t for sox encodings. - - Args: - i (int, optional): Choose type or get a dict with all possible options - use ``__members__`` to see all options when not specified. (Default: ``None``) - - Returns: - sox_encoding_t: A sox_encoding_t type for output encoding - """ - - from . import _torchaudio - if i is None: - # one can see all possible values using the .__members__ attribute - return _torchaudio.sox_encoding_t - else: - return _torchaudio.sox_encoding_t(i) - - -@_audio_backend_guard("sox") -def get_sox_option_t(i: int = 2) -> Any: - r"""Get enum of sox_option_t for sox encodinginfo options. - - Args: - i (int, optional): Choose type or get a dict with all possible options - use ``__members__`` to see all options when not specified. - (Default: ``sox_option_default`` or ``2``) - Returns: - sox_option_t: A sox_option_t type - """ - - from . import _torchaudio - if i is None: - return _torchaudio.sox_option_t - else: - return _torchaudio.sox_option_t(i) - - -@_audio_backend_guard("sox") -def get_sox_bool(i: int = 0) -> Any: - r"""Get enum of sox_bool for sox encodinginfo options. - - Args: - i (int, optional): Choose type or get a dict with all possible options - use ``__members__`` to see all options when not specified. (Default: - ``sox_false`` or ``0``) - - Returns: - sox_bool: A sox_bool type - """ - - from . import _torchaudio - if i is None: - return _torchaudio.sox_bool - else: - return _torchaudio.sox_bool(i) - - -_SOX_INITIALIZED: Optional[bool] = False -# This variable has a micro lifecycle. (False -> True -> None) -# False: Not initialized -# True: Initialized -# None: Already shut down (should not be initialized again.) - -_SOX_SUCCESS_CODE = 0 -# defined at -# https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93a9ef2b87ec303edfe40751d9a85fadeeb - - -@_audio_backend_guard("sox") +@_mod_utils.deprecated( + "Please remove the function call to initialize_sox. " + "Resource initialization is now automatically handled.") def initialize_sox() -> int: - """Initialize sox for use with effects chains. - - You only need to call this function once to use SoX effects chains multiple times. - It is safe to call this function multiple times as long as ``shutdown_sox`` is not yet called. - Once ``shutdown_sox`` is called, you can no longer use SoX effects and calling this function - results in `RuntimeError`. + """Initialize sox effects. - Note: - This function is not required for simple loading. - - Returns: - int: Code corresponding to sox_error_t enum. See - https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + This function is deprecated. See ``torchaudio.sox_effects.init_sox_effects`` """ - global _SOX_INITIALIZED - if _SOX_INITIALIZED is None: - raise RuntimeError('SoX effects chain has been already shut down. Can not initialize again.') - if not _SOX_INITIALIZED: - from . import _torchaudio - code = _torchaudio.initialize_sox() - if code == _SOX_SUCCESS_CODE: - _SOX_INITIALIZED = True - atexit.register(shutdown_sox) - return code - return _SOX_SUCCESS_CODE - - -@_audio_backend_guard("sox") -def shutdown_sox() -> int: - """Showdown sox for effects chain. + _init_sox_effects() - You do not need to call this function as it will be called automatically - at the end of program execution, if ``initialize_sox`` was called. - It is safe to call this function multiple times. +@_mod_utils.deprecated( + "Please remove the function call to torchaudio.shutdown_sox. " + "Resource clean up is now automatically handled. " + "In the unlikely event that you need to manually shutdown sox, " + "please use torchaudio.sox_effects.shutdown_sox_effects.") +def shutdown_sox(): + """Shutdown sox effects. - Returns: - int: Code corresponding to sox_error_t enum. See - https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + This function is deprecated. See ``torchaudio.sox_effects.shutdown_sox_effects`` """ - global _SOX_INITIALIZED - if _SOX_INITIALIZED: - from . import _torchaudio - code = _torchaudio.shutdown_sox() - if code == _SOX_INITIALIZED: - _SOX_INITIALIZED = None - return code - return _SOX_SUCCESS_CODE - - -def _audio_normalization(signal: Tensor, normalization: Union[bool, float, Callable]) -> None: - """Audio normalization of a tensor in-place. The normalization can be a bool, - a number, or a callable that takes the audio tensor as an input. SoX uses - 32-bit signed integers internally, thus bool normalizes based on that assumption. - """ - - if not normalization: - return - - if isinstance(normalization, bool): - normalization = 1 << 31 - - if isinstance(normalization, (float, int)): - # normalize with custom value - a = normalization - signal /= a - elif callable(normalization): - a = normalization(signal) - signal /= a + _shutdown_sox_effects() diff --git a/torchaudio/_backend.py b/torchaudio/_backend.py deleted file mode 100644 index f5646d7970..0000000000 --- a/torchaudio/_backend.py +++ /dev/null @@ -1,68 +0,0 @@ -from functools import wraps -from typing import Any, List, Union - -import platform -import torch -from torch import Tensor - -from . import _soundfile_backend, _sox_backend - - -if platform.system() == "Windows": - _audio_backend = "soundfile" - _audio_backends = {"soundfile": _soundfile_backend} -else: - _audio_backend = "sox" - _audio_backends = {"sox": _sox_backend, "soundfile": _soundfile_backend} - - -def set_audio_backend(backend: str) -> None: - """ - Specifies the package used to load. - Args: - backend (str): Name of the backend. One of {}. - """.format(_audio_backends.keys()) - global _audio_backend - if backend not in _audio_backends: - raise ValueError( - "Invalid backend '{}'. Options are {}.".format(backend, _audio_backends.keys()) - ) - _audio_backend = backend - - -def get_audio_backend() -> str: - """ - Gets the name of the package used to load. - """ - return _audio_backend - - -def _get_audio_backend_module() -> Any: - """ - Gets the module backend to load. - """ - backend = get_audio_backend() - return _audio_backends[backend] - - -def _audio_backend_guard(backends: Union[str, List[str]]) -> Any: - - if isinstance(backends, str): - backends = [backends] - - def decorator(func): - @wraps(func) - def wrapper(*args, **kwargs): - if get_audio_backend() not in backends: - raise RuntimeError("Function {} requires backend to be one of {}.".format(func.__name__, backends)) - return func(*args, **kwargs) - return wrapper - - return decorator - - -def check_input(src: Tensor) -> None: - if not torch.is_tensor(src): - raise TypeError('Expected a tensor, got %s' % type(src)) - if src.is_cuda: - raise TypeError('Expected a CPU based tensor, got %s' % type(src)) diff --git a/torchaudio/_internal/__init__.py b/torchaudio/_internal/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/torchaudio/_internal/misc_ops.py b/torchaudio/_internal/misc_ops.py new file mode 100644 index 0000000000..b67b8d1598 --- /dev/null +++ b/torchaudio/_internal/misc_ops.py @@ -0,0 +1,30 @@ +from typing import Union, Callable + +import torch +from torch import Tensor + + +def normalize_audio(signal: Tensor, normalization: Union[bool, float, Callable]) -> None: + """Audio normalization of a tensor in-place. The normalization can be a bool, + a number, or a callable that takes the audio tensor as an input. SoX uses + 32-bit signed integers internally, thus bool normalizes based on that assumption. + """ + + if not normalization: + return + + if isinstance(normalization, bool): + normalization = 1 << 31 + + if isinstance(normalization, (float, int)): + # normalize with custom value + signal /= normalization + elif callable(normalization): + signal /= normalization(signal) + + +def check_input(src: Tensor) -> None: + if not torch.is_tensor(src): + raise TypeError('Expected a tensor, got %s' % type(src)) + if src.is_cuda: + raise TypeError('Expected a CPU based tensor, got %s' % type(src)) diff --git a/torchaudio/_internal/module_utils.py b/torchaudio/_internal/module_utils.py new file mode 100644 index 0000000000..575a76c1ed --- /dev/null +++ b/torchaudio/_internal/module_utils.py @@ -0,0 +1,56 @@ +import warnings +import importlib.util +from typing import Optional +from functools import wraps + + +def is_module_available(*modules: str) -> bool: + r"""Returns if a top-level module with :attr:`name` exists *without** + importing it. This is generally safer than try-catch block around a + `import X`. It avoids third party libraries breaking assumptions of some of + our tests, e.g., setting multiprocessing start method when imported + (see librosa/#747, torchvision/#544). + """ + return all(importlib.util.find_spec(m) is not None for m in modules) + + +def requires_module(*modules: str): + """Decorate function to give error message if invoked without required optional modules. + + This decorator is to give better error message to users rather + than raising ``NameError: name 'module' is not defined`` at random places. + """ + missing = [m for m in modules if not is_module_available(m)] + + if not missing: + # fall through. If all the modules are available, no need to decorate + def decorator(func): + return func + else: + req = f'module: {missing[0]}' if len(missing) == 1 else f'modules: {missing}' + + def decorator(func): + @wraps(func) + def wrapped(*args, **kwargs): + raise RuntimeError(f'{func.__module__}.{func.__name__} requires {req}') + return wrapped + return decorator + + +def deprecated(direction: str, version: Optional[str] = None): + """Decorator to add deprecation message + + Args: + direction: Migration steps to be given to users. + """ + def decorator(func): + @wraps(func) + def wrapped(*args, **kwargs): + message = ( + f'{func.__module__}.{func.__name__} has been deprecated ' + f'and will be removed from {"future" if version is None else version} release.' + f'{direction}') + warnings.warn(message, stacklevel=2) + return func(*args, **kwargs) + return wrapped + return decorator diff --git a/torchaudio/_sox_backend.py b/torchaudio/_sox_backend.py deleted file mode 100644 index 3478d30d50..0000000000 --- a/torchaudio/_sox_backend.py +++ /dev/null @@ -1,72 +0,0 @@ -import os.path -from typing import Optional, Tuple - -import torch -from torch import Tensor -import torchaudio -from torchaudio._soundfile_backend import SignalInfo, EncodingInfo - - -def load(filepath: str, - out: Optional[Tensor] = None, - normalization: bool = True, - channels_first: bool = True, - num_frames: int = 0, - offset: int = 0, - signalinfo: SignalInfo = None, - encodinginfo: EncodingInfo = None, - filetype: Optional[str] = None) -> Tuple[Tensor, int]: - r"""See torchaudio.load""" - - # stringify if `pathlib.Path` (noop if already `str`) - filepath = str(filepath) - # check if valid file - if not os.path.isfile(filepath): - raise OSError("{} not found or is a directory".format(filepath)) - - # initialize output tensor - if out is not None: - torchaudio.check_input(out) - else: - out = torch.FloatTensor() - - if num_frames < -1: - raise ValueError("Expected value for num_samples -1 (entire file) or >=0") - if offset < 0: - raise ValueError("Expected positive offset value") - - from . import _torchaudio - sample_rate = _torchaudio.read_audio_file( - filepath, - out, - channels_first, - num_frames, - offset, - signalinfo, - encodinginfo, - filetype - ) - - # normalize if needed - torchaudio._audio_normalization(out, normalization) - - return out, sample_rate - - -def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: - r"""See torchaudio.save""" - - si = torchaudio.sox_signalinfo_t() - ch_idx = 0 if channels_first else 1 - si.rate = sample_rate - si.channels = 1 if src.dim() == 1 else src.size(ch_idx) - si.length = src.numel() - si.precision = precision - return torchaudio.save_encinfo(filepath, src, channels_first, si) - - -def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: - r"""See torchaudio.info""" - - from . import _torchaudio - return _torchaudio.get_info(filepath) diff --git a/torchaudio/backend/__init__.py b/torchaudio/backend/__init__.py new file mode 100644 index 0000000000..0812c09cf4 --- /dev/null +++ b/torchaudio/backend/__init__.py @@ -0,0 +1,21 @@ +from . import utils +from .utils import ( + list_audio_backends, + get_audio_backend, + set_audio_backend, +) +from .sox_backend import ( + save_encinfo, + sox_signalinfo_t, + sox_encodinginfo_t, + get_sox_option_t, + get_sox_encoding_t, + get_sox_bool, +) +from .common import ( + SignalInfo, + EncodingInfo, +) + + +utils._init_audio_backend() diff --git a/torchaudio/backend/common.py b/torchaudio/backend/common.py new file mode 100644 index 0000000000..0593c34bd5 --- /dev/null +++ b/torchaudio/backend/common.py @@ -0,0 +1,143 @@ +from typing import Any, Optional + + +class SignalInfo: + def __init__(self, + channels: Optional[int] = None, + rate: Optional[float] = None, + precision: Optional[int] = None, + length: Optional[int] = None) -> None: + self.channels = channels + self.rate = rate + self.precision = precision + self.length = length + + +class EncodingInfo: + def __init__(self, + encoding: Any = None, + bits_per_sample: Optional[int] = None, + compression: Optional[float] = None, + reverse_bytes: Any = None, + reverse_nibbles: Any = None, + reverse_bits: Any = None, + opposite_endian: Optional[bool] = None) -> None: + self.encoding = encoding + self.bits_per_sample = bits_per_sample + self.compression = compression + self.reverse_bytes = reverse_bytes + self.reverse_nibbles = reverse_nibbles + self.reverse_bits = reverse_bits + self.opposite_endian = opposite_endian + + +_LOAD_DOCSTRING = r"""Loads an audio file from disk into a tensor + +Args: + filepath: Path to audio file + + out: An optional output tensor to use instead of creating one. (Default: ``None``) + + normalization: Optional normalization. + If boolean `True`, then output is divided by `1 << 31`. + Assuming the input is signed 32-bit audio, this normalizes to `[-1, 1]`. + If `float`, then output is divided by that number. + If `Callable`, then the output is passed as a paramete to the given function, + then the output is divided by the result. (Default: ``True``) + + channels_first: Set channels first or length first in result. (Default: ``True``) + + num_frames: Number of frames to load. 0 to load everything after the offset. + (Default: ``0``) + + offset: Number of frames from the start of the file to begin data loading. + (Default: ``0``) + + signalinfo: A sox_signalinfo_t type, which could be helpful if the + audio type cannot be automatically determined. (Default: ``None``) + + encodinginfo: A sox_encodinginfo_t type, which could be set if the + audio type cannot be automatically determined. (Default: ``None``) + + filetype: A filetype or extension to be set if sox cannot determine it + automatically. (Default: ``None``) + +Returns: + (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where + L is the number of audio frames and + C is the number of channels. + An integer which is the sample rate of the audio (as listed in the metadata of the file) + +Example + >>> data, sample_rate = torchaudio.load('foo.mp3') + >>> print(data.size()) + torch.Size([2, 278756]) + >>> print(sample_rate) + 44100 + >>> data_vol_normalized, _ = torchaudio.load('foo.mp3', normalization=lambda x: torch.abs(x).max()) + >>> print(data_vol_normalized.abs().max()) + 1. +""" + + +_LOAD_WAV_DOCSTRING = r""" Loads a wave file. + +It assumes that the wav file uses 16 bit per sample that needs normalization by +shifting the input right by 16 bits. + +Args: + filepath: Path to audio file + +Returns: + (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number + of audio frames and C is the number of channels. An integer which is the sample rate of the + audio (as listed in the metadata of the file) +""" + +_SAVE_DOCSTRING = r"""Saves a Tensor on file as an audio file + +Args: + filepath: Path to audio file + src: An input 2D tensor of shape `[C x L]` or `[L x C]` where L is + the number of audio frames, C is the number of channels + sample_rate: An integer which is the sample rate of the + audio (as listed in the metadata of the file) + precision Bit precision (Default: ``16``) + channels_first (bool, optional): Set channels first or length first in result. ( + Default: ``True``) +""" + + +_INFO_DOCSTRING = r"""Gets metadata from an audio file without loading the signal. + +Args: + filepath: Path to audio file + +Returns: + (sox_signalinfo_t, sox_encodinginfo_t): A si (sox_signalinfo_t) signal + info as a python object. An ei (sox_encodinginfo_t) encoding info + +Example + >>> si, ei = torchaudio.info('foo.wav') + >>> rate, channels, encoding = si.rate, si.channels, ei.encoding +""" + + +def _impl_load(func): + setattr(func, '__doc__', _LOAD_DOCSTRING) + return func + + +def _impl_load_wav(func): + setattr(func, '__doc__', _LOAD_WAV_DOCSTRING) + return func + + +def _impl_save(func): + setattr(func, '__doc__', _SAVE_DOCSTRING) + return func + + +def _impl_info(func): + setattr(func, '__doc__', _INFO_DOCSTRING) + return func diff --git a/torchaudio/backend/no_backend.py b/torchaudio/backend/no_backend.py new file mode 100644 index 0000000000..60571c34a9 --- /dev/null +++ b/torchaudio/backend/no_backend.py @@ -0,0 +1,35 @@ +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +from torch import Tensor + +from . import common +from .common import SignalInfo, EncodingInfo + + +@common._impl_load +def load(filepath: Union[str, Path], + out: Optional[Tensor] = None, + normalization: Union[bool, float, Callable] = True, + channels_first: bool = True, + num_frames: int = 0, + offset: int = 0, + signalinfo: Optional[SignalInfo] = None, + encodinginfo: Optional[EncodingInfo] = None, + filetype: Optional[str] = None) -> Tuple[Tensor, int]: + raise RuntimeError('No audio I/O backend is available.') + + +@common._impl_load_wav +def load_wav(filepath: Union[str, Path], **kwargs: Any) -> Tuple[Tensor, int]: + raise RuntimeError('No audio I/O backend is available.') + + +@common._impl_save +def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: + raise RuntimeError('No audio I/O backend is available.') + + +@common._impl_info +def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: + raise RuntimeError('No audio I/O backend is available.') diff --git a/torchaudio/_soundfile_backend.py b/torchaudio/backend/soundfile_backend.py similarity index 67% rename from torchaudio/_soundfile_backend.py rename to torchaudio/backend/soundfile_backend.py index 1a88ba5e80..680264bcd6 100644 --- a/torchaudio/_soundfile_backend.py +++ b/torchaudio/backend/soundfile_backend.py @@ -1,9 +1,20 @@ import os -from typing import Any, Optional, Tuple, Union +from typing import Optional, Tuple import torch from torch import Tensor +from torchaudio._internal import ( + module_utils as _mod_utils, + misc_ops as _misc_ops, +) +from . import common +from .common import SignalInfo, EncodingInfo + +if _mod_utils.is_module_available('soundfile'): + import soundfile + + _subtype_to_precision = { 'PCM_S8': 8, 'PCM_16': 16, @@ -13,43 +24,8 @@ } -class SignalInfo: - def __init__(self, - channels: Optional[int] = None, - rate: Optional[float] = None, - precision: Optional[int] = None, - length: Optional[int] = None) -> None: - self.channels = channels - self.rate = rate - self.precision = precision - self.length = length - - -class EncodingInfo: - def __init__(self, - encoding: Any = None, - bits_per_sample: Optional[int] = None, - compression: Optional[float] = None, - reverse_bytes: Any = None, - reverse_nibbles: Any = None, - reverse_bits: Any = None, - opposite_endian: Optional[bool] = None) -> None: - self.encoding = encoding - self.bits_per_sample = bits_per_sample - self.compression = compression - self.reverse_bytes = reverse_bytes - self.reverse_nibbles = reverse_nibbles - self.reverse_bits = reverse_bits - self.opposite_endian = opposite_endian - - -def check_input(src: Tensor) -> None: - if not torch.is_tensor(src): - raise TypeError("Expected a tensor, got %s" % type(src)) - if src.is_cuda: - raise TypeError("Expected a CPU based tensor, got %s" % type(src)) - - +@_mod_utils.requires_module('soundfile') +@common._impl_load def load(filepath: str, out: Optional[Tensor] = None, normalization: Optional[bool] = True, @@ -80,8 +56,6 @@ def load(filepath: str, if offset < 0: raise ValueError("Expected positive offset value") - import soundfile - # initialize output tensor # TODO call libsoundfile directly to avoid numpy out, sample_rate = soundfile.read( @@ -98,6 +72,15 @@ def load(filepath: str, return out, sample_rate +@_mod_utils.requires_module('soundfile') +@common._impl_load_wav +def load_wav(filepath, **kwargs): + kwargs['normalization'] = 1 << 16 + return load(filepath, **kwargs) + + +@_mod_utils.requires_module('soundfile') +@common._impl_save def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: r"""See torchaudio.save""" @@ -108,7 +91,7 @@ def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, chan if not os.path.isdir(abs_dirpath): raise OSError("Directory does not exist: {}".format(abs_dirpath)) # check that src is a CPU tensor - check_input(src) + _misc_ops.check_input(src) # Check/Fix shape of source data if src.dim() == 1: # 1d tensors as assumed to be mono signals @@ -127,14 +110,14 @@ def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, chan precision = "PCM_S8" if precision == 8 else "PCM_" + str(precision) - import soundfile return soundfile.write(filepath, src, sample_rate, precision) +@_mod_utils.requires_module('soundfile') +@common._impl_info def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: r"""See torchaudio.info""" - import soundfile sfi = soundfile.info(filepath) precision = _subtype_to_precision[sfi.subtype] diff --git a/torchaudio/backend/sox_backend.py b/torchaudio/backend/sox_backend.py new file mode 100644 index 0000000000..4e2b118f8b --- /dev/null +++ b/torchaudio/backend/sox_backend.py @@ -0,0 +1,275 @@ +import os.path +from typing import Any, Optional, Tuple + +import torch +from torch import Tensor + +from torchaudio._internal import ( + module_utils as _mod_utils, + misc_ops as _misc_ops, +) +from . import common +from .common import SignalInfo, EncodingInfo + +if _mod_utils.is_module_available('torchaudio._torchaudio'): + from torchaudio import _torchaudio + + +@_mod_utils.requires_module('torchaudio._torchaudio') +@common._impl_load +def load(filepath: str, + out: Optional[Tensor] = None, + normalization: bool = True, + channels_first: bool = True, + num_frames: int = 0, + offset: int = 0, + signalinfo: SignalInfo = None, + encodinginfo: EncodingInfo = None, + filetype: Optional[str] = None) -> Tuple[Tensor, int]: + r"""See torchaudio.load""" + + # stringify if `pathlib.Path` (noop if already `str`) + filepath = str(filepath) + # check if valid file + if not os.path.isfile(filepath): + raise OSError("{} not found or is a directory".format(filepath)) + + # initialize output tensor + if out is not None: + _misc_ops.check_input(out) + else: + out = torch.FloatTensor() + + if num_frames < -1: + raise ValueError("Expected value for num_samples -1 (entire file) or >=0") + if offset < 0: + raise ValueError("Expected positive offset value") + + sample_rate = _torchaudio.read_audio_file( + filepath, + out, + channels_first, + num_frames, + offset, + signalinfo, + encodinginfo, + filetype + ) + + # normalize if needed + _misc_ops.normalize_audio(out, normalization) + + return out, sample_rate + + +@_mod_utils.requires_module('torchaudio._torchaudio') +@common._impl_load_wav +def load_wav(filepath, **kwargs): + kwargs['normalization'] = 1 << 16 + return load(filepath, **kwargs) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +@common._impl_save +def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: + r"""See torchaudio.save""" + + si = sox_signalinfo_t() + ch_idx = 0 if channels_first else 1 + si.rate = sample_rate + si.channels = 1 if src.dim() == 1 else src.size(ch_idx) + si.length = src.numel() + si.precision = precision + return save_encinfo(filepath, src, channels_first, si) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +@common._impl_info +def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: + r"""See torchaudio.info""" + return _torchaudio.get_info(filepath) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def save_encinfo(filepath: str, + src: Tensor, + channels_first: bool = True, + signalinfo: Optional[SignalInfo] = None, + encodinginfo: Optional[EncodingInfo] = None, + filetype: Optional[str] = None) -> None: + r"""Saves a tensor of an audio signal to disk as a standard format like mp3, wav, etc. + + Args: + filepath (str): Path to audio file + src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is + the number of audio frames, C is the number of channels + channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) + signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the + audio type cannot be automatically determined (Default: ``None``). + encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the + audio type cannot be automatically determined (Default: ``None``). + filetype (str, optional): A filetype or extension to be set if sox cannot determine it + automatically. (Default: ``None``) + + Example + >>> data, sample_rate = torchaudio.load('foo.mp3') + >>> torchaudio.save('foo.wav', data, sample_rate) + + """ + ch_idx, len_idx = (0, 1) if channels_first else (1, 0) + + # check if save directory exists + abs_dirpath = os.path.dirname(os.path.abspath(filepath)) + if not os.path.isdir(abs_dirpath): + raise OSError("Directory does not exist: {}".format(abs_dirpath)) + # check that src is a CPU tensor + _misc_ops.check_input(src) + # Check/Fix shape of source data + if src.dim() == 1: + # 1d tensors as assumed to be mono signals + src.unsqueeze_(ch_idx) + elif src.dim() > 2 or src.size(ch_idx) > 16: + # assumes num_channels < 16 + raise ValueError( + "Expected format where C < 16, but found {}".format(src.size())) + # sox stores the sample rate as a float, though practically sample rates are almost always integers + # convert integers to floats + if signalinfo: + if signalinfo.rate and not isinstance(signalinfo.rate, float): + if float(signalinfo.rate) == signalinfo.rate: + signalinfo.rate = float(signalinfo.rate) + else: + raise TypeError('Sample rate should be a float or int') + # check if the bit precision (i.e. bits per sample) is an integer + if signalinfo.precision and not isinstance(signalinfo.precision, int): + if int(signalinfo.precision) == signalinfo.precision: + signalinfo.precision = int(signalinfo.precision) + else: + raise TypeError('Bit precision should be an integer') + # programs such as librosa normalize the signal, unnormalize if detected + if src.min() >= -1.0 and src.max() <= 1.0: + src = src * (1 << 31) + src = src.long() + # set filetype and allow for files with no extensions + extension = os.path.splitext(filepath)[1] + filetype = extension[1:] if len(extension) > 0 else filetype + # transpose from C x L -> L x C + if channels_first: + src = src.transpose(1, 0) + # save data to file + src = src.contiguous() + _torchaudio.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def sox_signalinfo_t() -> SignalInfo: + r"""Create a sox_signalinfo_t object. This object can be used to set the sample + rate, number of channels, length, bit precision and headroom multiplier + primarily for effects + + Returns: sox_signalinfo_t(object) + - rate (float), sample rate as a float, practically will likely be an integer float + - channel (int), number of audio channels + - precision (int), bit precision + - length (int), length of audio in samples * channels, 0 for unspecified and -1 for unknown + - mult (float, optional), headroom multiplier for effects and ``None`` for no multiplier + + Example + >>> si = torchaudio.sox_signalinfo_t() + >>> si.channels = 1 + >>> si.rate = 16000. + >>> si.precision = 16 + >>> si.length = 0 + """ + return _torchaudio.sox_signalinfo_t() + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def sox_encodinginfo_t() -> EncodingInfo: + r"""Create a sox_encodinginfo_t object. This object can be used to set the encoding + type, bit precision, compression factor, reverse bytes, reverse nibbles, + reverse bits and endianness. This can be used in an effects chain to encode the + final output or to save a file with a specific encoding. For example, one could + use the sox ulaw encoding to do 8-bit ulaw encoding. Note in a tensor output + the result will be a 32-bit number, but number of unique values will be determined by + the bit precision. + + Returns: sox_encodinginfo_t(object) + - encoding (sox_encoding_t), output encoding + - bits_per_sample (int), bit precision, same as `precision` in sox_signalinfo_t + - compression (float), compression for lossy formats, 0.0 for default compression + - reverse_bytes (sox_option_t), reverse bytes, use sox_option_default + - reverse_nibbles (sox_option_t), reverse nibbles, use sox_option_default + - reverse_bits (sox_option_t), reverse bytes, use sox_option_default + - opposite_endian (sox_bool), change endianness, use sox_false + + Example + >>> ei = torchaudio.sox_encodinginfo_t() + >>> ei.encoding = torchaudio.get_sox_encoding_t(1) + >>> ei.bits_per_sample = 16 + >>> ei.compression = 0 + >>> ei.reverse_bytes = torchaudio.get_sox_option_t(2) + >>> ei.reverse_nibbles = torchaudio.get_sox_option_t(2) + >>> ei.reverse_bits = torchaudio.get_sox_option_t(2) + >>> ei.opposite_endian = torchaudio.get_sox_bool(0) + + """ + ei = _torchaudio.sox_encodinginfo_t() + sdo = get_sox_option_t(2) # sox_default_option + ei.reverse_bytes = sdo + ei.reverse_nibbles = sdo + ei.reverse_bits = sdo + return ei + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def get_sox_encoding_t(i: int = None) -> EncodingInfo: + r"""Get enum of sox_encoding_t for sox encodings. + + Args: + i (int, optional): Choose type or get a dict with all possible options + use ``__members__`` to see all options when not specified. (Default: ``None``) + + Returns: + sox_encoding_t: A sox_encoding_t type for output encoding + """ + if i is None: + # one can see all possible values using the .__members__ attribute + return _torchaudio.sox_encoding_t + else: + return _torchaudio.sox_encoding_t(i) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def get_sox_option_t(i: int = 2) -> Any: + r"""Get enum of sox_option_t for sox encodinginfo options. + + Args: + i (int, optional): Choose type or get a dict with all possible options + use ``__members__`` to see all options when not specified. + (Default: ``sox_option_default`` or ``2``) + Returns: + sox_option_t: A sox_option_t type + """ + if i is None: + return _torchaudio.sox_option_t + else: + return _torchaudio.sox_option_t(i) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def get_sox_bool(i: int = 0) -> Any: + r"""Get enum of sox_bool for sox encodinginfo options. + + Args: + i (int, optional): Choose type or get a dict with all possible options + use ``__members__`` to see all options when not specified. (Default: + ``sox_false`` or ``0``) + + Returns: + sox_bool: A sox_bool type + """ + if i is None: + return _torchaudio.sox_bool + else: + return _torchaudio.sox_bool(i) diff --git a/torchaudio/backend/utils.py b/torchaudio/backend/utils.py new file mode 100644 index 0000000000..d537f01daf --- /dev/null +++ b/torchaudio/backend/utils.py @@ -0,0 +1,74 @@ +"""Defines utilities for switching audio backends""" +import warnings +from typing import Optional, List + +import torchaudio +from torchaudio._internal.module_utils import is_module_available +from . import ( + no_backend, + sox_backend, + soundfile_backend, +) + +__all__ = [ + 'list_audio_backends', + 'get_audio_backend', + 'set_audio_backend', +] + + +def list_audio_backends() -> List[str]: + """List available backends""" + backends = [] + if is_module_available('soundfile'): + backends.append('soundfile') + if is_module_available('torchaudio._torchaudio'): + backends.append('sox') + return backends + + +def set_audio_backend(backend: Optional[str]) -> None: + """Set the backend for I/O operation + + Args: + backend (str): Name of the backend. One of "sox" or "soundfile", + based on availability of the system. + """ + if backend is not None and backend not in list_audio_backends(): + raise RuntimeError( + f'Backend "{backend}" is not one of ' + f'available backends: {list_audio_backends()}.') + + if backend is None: + module = no_backend + elif backend == 'sox': + module = sox_backend + elif backend == 'soundfile': + module = soundfile_backend + else: + raise NotImplementedError(f'Unexpected backend "{backend}"') + + for func in ['save', 'load', 'load_wav', 'info']: + setattr(torchaudio, func, getattr(module, func)) + + +def _init_audio_backend(): + backends = list_audio_backends() + if 'sox' in backends: + set_audio_backend('sox') + elif 'soundfile' in backends: + set_audio_backend('soundfile') + else: + warnings.warn('No audio backend is available.') + set_audio_backend(None) + + +def get_audio_backend() -> Optional[str]: + """Get the name of the current backend""" + if torchaudio.load == no_backend.load: + return None + if torchaudio.load == sox_backend.load: + return 'sox' + if torchaudio.load == soundfile_backend.load: + return 'soundfile' + raise ValueError('Unknown backend.') diff --git a/torchaudio/common_utils.py b/torchaudio/common_utils.py deleted file mode 100644 index 28b77200a6..0000000000 --- a/torchaudio/common_utils.py +++ /dev/null @@ -1,38 +0,0 @@ -import sys - -PY3 = sys.version_info > (3, 0) -PY34 = sys.version_info >= (3, 4) - - -def _check_module_exists(name: str) -> bool: - r"""Returns if a top-level module with :attr:`name` exists *without** - importing it. This is generally safer than try-catch block around a - `import X`. It avoids third party libraries breaking assumptions of some of - our tests, e.g., setting multiprocessing start method when imported - (see librosa/#747, torchvision/#544). - """ - if not PY3: # Python 2 - import imp - try: - imp.find_module(name) - return True - except ImportError: - return False - elif not PY34: # Python [3, 3.4) - import importlib - loader = importlib.find_loader(name) - return loader is not None - else: # Python >= 3.4 - import importlib - import importlib.util - spec = importlib.util.find_spec(name) - return spec is not None - -IMPORT_NUMPY = _check_module_exists('numpy') -IMPORT_KALDI_IO = _check_module_exists('kaldi_io') -IMPORT_SCIPY = _check_module_exists('scipy') - -# On Py2, importing librosa 0.6.1 triggers a TypeError (if using newest joblib) -# see librosa/librosa#729. -# TODO: allow Py2 when librosa 0.6.2 releases -IMPORT_LIBROSA = _check_module_exists('librosa') and PY3 diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index bdf5f407ba..6211b09f2d 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -272,6 +272,9 @@ def spectrogram(waveform: Tensor, Tensor: A spectrogram identical to what Kaldi would output. The shape is (m, ``padded_window_size // 2 + 1``) where m is calculated in _get_strided """ + device, dtype = waveform.device, waveform.dtype + epsilon = _get_epsilon(device, dtype) + waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties( waveform, channel, sample_frequency, frame_shift, frame_length, round_to_power_of_two, preemphasis_coefficient) @@ -287,7 +290,7 @@ def spectrogram(waveform: Tensor, fft = torch.rfft(strided_input, 1, normalized=False, onesided=True) # Convert the FFT into a power spectrum - power_spectrum = torch.max(fft.pow(2).sum(2), EPSILON).log() # size (m, padded_window_size // 2 + 1) + power_spectrum = torch.max(fft.pow(2).sum(2), epsilon).log() # size (m, padded_window_size // 2 + 1) power_spectrum[:, 0] = signal_log_energy power_spectrum = _subtract_column_mean(power_spectrum, subtract_mean) diff --git a/torchaudio/csrc/register.cpp b/torchaudio/csrc/register.cpp new file mode 100644 index 0000000000..81b1a84c96 --- /dev/null +++ b/torchaudio/csrc/register.cpp @@ -0,0 +1,18 @@ +#ifndef TORCHAUDIO_REGISTER_H +#define TORCHAUDIO_REGISTER_H + +#include + +namespace torchaudio { +namespace { + +static auto registerSignalInfo = + torch::class_("torchaudio", "SignalInfo") + .def(torch::init()) + .def("get_sample_rate", &SignalInfo::getSampleRate) + .def("get_num_channels", &SignalInfo::getNumChannels) + .def("get_num_samples", &SignalInfo::getNumSamples); + +} // namespace +} // namespace torchaudio +#endif diff --git a/torchaudio/csrc/typedefs.cpp b/torchaudio/csrc/typedefs.cpp new file mode 100644 index 0000000000..7b81d665dc --- /dev/null +++ b/torchaudio/csrc/typedefs.cpp @@ -0,0 +1,23 @@ +#include + +namespace torchaudio { +SignalInfo::SignalInfo( + const int64_t sample_rate_, + const int64_t num_channels_, + const int64_t num_samples_) + : sample_rate(sample_rate_), + num_channels(num_channels_), + num_samples(num_samples_){}; + +int64_t SignalInfo::getSampleRate() const { + return sample_rate; +} + +int64_t SignalInfo::getNumChannels() const { + return num_channels; +} + +int64_t SignalInfo::getNumSamples() const { + return num_samples; +} +} // namespace torchaudio diff --git a/torchaudio/csrc/typedefs.h b/torchaudio/csrc/typedefs.h new file mode 100644 index 0000000000..646ed09f3d --- /dev/null +++ b/torchaudio/csrc/typedefs.h @@ -0,0 +1,23 @@ +#ifndef TORCHAUDIO_TYPDEFS_H +#define TORCHAUDIO_TYPDEFS_H + +#include + +namespace torchaudio { +struct SignalInfo : torch::CustomClassHolder { + int64_t sample_rate; + int64_t num_channels; + int64_t num_samples; + + SignalInfo( + const int64_t sample_rate_, + const int64_t num_channels_, + const int64_t num_samples_); + int64_t getSampleRate() const; + int64_t getNumChannels() const; + int64_t getNumSamples() const; +}; + +} // namespace torchaudio + +#endif diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index 1741889029..942f2c83a1 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -6,6 +6,7 @@ from .gtzan import GTZAN from .yesno import YESNO from .ljspeech import LJSPEECH +from .cmuarctic import CMUARCTIC __all__ = ( "COMMONVOICE", @@ -15,6 +16,7 @@ "YESNO", "LJSPEECH", "GTZAN", + "CMUARCTIC", "diskcache_iterator", "bg_iterator", ) diff --git a/torchaudio/datasets/cmuarctic.py b/torchaudio/datasets/cmuarctic.py new file mode 100644 index 0000000000..7aa6d541e5 --- /dev/null +++ b/torchaudio/datasets/cmuarctic.py @@ -0,0 +1,150 @@ +import os +from typing import Tuple + +import torchaudio +from torch import Tensor +from torch.utils.data import Dataset +from torchaudio.datasets.utils import ( + download_url, + extract_archive, + unicode_csv_reader, +) + +URL = "aew" +FOLDER_IN_ARCHIVE = "ARCTIC" +_CHECKSUMS = { + "http://festvox.org/cmu_arctic/packed/cmu_us_aew_arctic.tar.bz2": + "4382b116efcc8339c37e01253cb56295", + "http://festvox.org/cmu_arctic/packed/cmu_us_ahw_arctic.tar.bz2": + "b072d6e961e3f36a2473042d097d6da9", + "http://festvox.org/cmu_arctic/packed/cmu_us_aup_arctic.tar.bz2": + "5301c7aee8919d2abd632e2667adfa7f", + "http://festvox.org/cmu_arctic/packed/cmu_us_awb_arctic.tar.bz2": + "280fdff1e9857119d9a2c57b50e12db7", + "http://festvox.org/cmu_arctic/packed/cmu_us_axb_arctic.tar.bz2": + "5e21cb26c6529c533df1d02ccde5a186", + "http://festvox.org/cmu_arctic/packed/cmu_us_bdl_arctic.tar.bz2": + "b2c3e558f656af2e0a65da0ac0c3377a", + "http://festvox.org/cmu_arctic/packed/cmu_us_clb_arctic.tar.bz2": + "3957c503748e3ce17a3b73c1b9861fb0", + "http://festvox.org/cmu_arctic/packed/cmu_us_eey_arctic.tar.bz2": + "59708e932d27664f9eda3e8e6859969b", + "http://festvox.org/cmu_arctic/packed/cmu_us_fem_arctic.tar.bz2": + "dba4f992ff023347c07c304bf72f4c73", + "http://festvox.org/cmu_arctic/packed/cmu_us_gka_arctic.tar.bz2": + "24a876ea7335c1b0ff21460e1241340f", + "http://festvox.org/cmu_arctic/packed/cmu_us_jmk_arctic.tar.bz2": + "afb69d95f02350537e8a28df5ab6004b", + "http://festvox.org/cmu_arctic/packed/cmu_us_ksp_arctic.tar.bz2": + "4ce5b3b91a0a54b6b685b1b05aa0b3be", + "http://festvox.org/cmu_arctic/packed/cmu_us_ljm_arctic.tar.bz2": + "6f45a3b2c86a4ed0465b353be291f77d", + "http://festvox.org/cmu_arctic/packed/cmu_us_lnh_arctic.tar.bz2": + "c6a15abad5c14d27f4ee856502f0232f", + "http://festvox.org/cmu_arctic/packed/cmu_us_rms_arctic.tar.bz2": + "71072c983df1e590d9e9519e2a621f6e", + "http://festvox.org/cmu_arctic/packed/cmu_us_rxr_arctic.tar.bz2": + "3771ff03a2f5b5c3b53aa0a68b9ad0d5", + "http://festvox.org/cmu_arctic/packed/cmu_us_slp_arctic.tar.bz2": + "9cbf984a832ea01b5058ba9a96862850", + "http://festvox.org/cmu_arctic/packed/cmu_us_slt_arctic.tar.bz2": + "959eecb2cbbc4ac304c6b92269380c81", +} + + +def load_cmuarctic_item(line: str, + path: str, + folder_audio: str, + ext_audio: str) -> Tuple[Tensor, int, str, str]: + + utterance_id, utterance = line[0].strip().split(" ", 2)[1:] + + # Remove space, double quote, and single parenthesis from utterance + utterance = utterance[1:-3] + + file_audio = os.path.join(path, folder_audio, utterance_id + ext_audio) + + # Load audio + waveform, sample_rate = torchaudio.load(file_audio) + + return ( + waveform, + sample_rate, + utterance, + utterance_id.split("_")[1] + ) + + +class CMUARCTIC(Dataset): + """ + Create a Dataset for CMU_arctic. Each item is a tuple of the form: + waveform, sample_rate, utterance, utterance_id + """ + + _file_text = "txt.done.data" + _folder_text = "etc" + _ext_audio = ".wav" + _folder_audio = "wav" + + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False) -> None: + + if url in [ + "aew", + "ahw", + "aup", + "awb", + "axb", + "bdl", + "clb", + "eey", + "fem", + "gka", + "jmk", + "ksp", + "ljm", + "lnh", + "rms", + "rxr", + "slp", + "slt" + ]: + + url = "cmu_us_" + url + "_arctic" + ext_archive = ".tar.bz2" + base_url = "http://www.festvox.org/cmu_arctic/packed/" + + url = os.path.join(base_url, url + ext_archive) + + basename = os.path.basename(url) + root = os.path.join(root, folder_in_archive) + if not os.path.isdir(root): + os.mkdir(root) + archive = os.path.join(root, basename) + + basename = basename.split(".")[0] + + self._path = os.path.join(root, basename) + + if download: + if not os.path.isdir(self._path): + if not os.path.isfile(archive): + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") + extract_archive(archive) + + self._text = os.path.join(self._path, self._folder_text, self._file_text) + + with open(self._text, "r") as text: + walker = unicode_csv_reader(text, delimiter="\n") + self._walker = list(walker) + + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: + line = self._walker[n] + return load_cmuarctic_item(line, self._path, self._folder_audio, self._ext_audio) + + def __len__(self) -> int: + return len(self._walker) diff --git a/torchaudio/extension/__init__.py b/torchaudio/extension/__init__.py new file mode 100644 index 0000000000..d9b6c76fac --- /dev/null +++ b/torchaudio/extension/__init__.py @@ -0,0 +1,7 @@ +from .extension import ( + _init_extension, +) + +_init_extension() + +del _init_extension diff --git a/torchaudio/extension/extension.py b/torchaudio/extension/extension.py new file mode 100644 index 0000000000..4a2ab82124 --- /dev/null +++ b/torchaudio/extension/extension.py @@ -0,0 +1,49 @@ +import warnings +import importlib +from collections import namedtuple + +import torch +from torchaudio._internal import module_utils as _mod_utils + + +def _init_extension(): + ext = 'torchaudio._torchaudio' + if _mod_utils.is_module_available(ext): + _init_script_module(ext) + else: + warnings.warn('torchaudio C++ extension is not available.') + _init_dummy_module() + + +def _init_script_module(module): + path = importlib.util.find_spec(module).origin + torch.classes.load_library(path) + torch.ops.load_library(path) + + +def _init_dummy_module(): + class SignalInfo: + """Data class for audio format information + + Used when torchaudio C++ extension is not available for annotating + sox_io backend functions so that torchaudio is still importable + without extension. + This class has to implement the same interface as C++ equivalent. + """ + def __init__(self, sample_rate: int, num_channels: int, num_samples: int): + self.sample_rate = sample_rate + self.num_channels = num_channels + self.num_samples = num_samples + + def get_sample_rate(self): + return self.sample_rate + + def get_num_channels(self): + return self.num_channels + + def get_num_samples(self): + return self.num_samples + + DummyModule = namedtuple('torchaudio', ['SignalInfo']) + module = DummyModule(SignalInfo) + setattr(torch.classes, 'torchaudio', module) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 92580e1d9e..28cb6a3fa2 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -265,7 +265,7 @@ def griffinlim( angles = rebuilt if momentum: angles = angles - tprev.mul_(momentum / (1 + momentum)) - angles = angles.div_(complex_norm(angles).add_(1e-16).unsqueeze(-1).expand_as(angles)) + angles = angles.div(complex_norm(angles).add(1e-16).unsqueeze(-1).expand_as(angles)) # Return the final phase estimates waveform = istft(specgram * angles, @@ -1317,7 +1317,6 @@ def phaser( delay_buf = torch.zeros(waveform.shape[0], delay_buf_len, dtype=dtype, device=device) mod_buf_len = int(sample_rate / mod_speed + .5) - mod_buf = torch.zeros(mod_buf_len, dtype=dtype, device=device) if sinusoidal: wave_type = 'SINE' @@ -1329,7 +1328,8 @@ def phaser( table_size=mod_buf_len, min=1., max=float(delay_buf_len), - phase=math.pi / 2) + phase=math.pi / 2, + device=device) delay_pos = 0 mod_pos = 0 @@ -1353,7 +1353,8 @@ def _generate_wave_table( table_size: int, min: float, max: float, - phase: float + phase: float, + device: torch.device ) -> Tensor: r"""A helper fucntion for phaser. Generates a table with given parameters @@ -1364,24 +1365,24 @@ def _generate_wave_table( min (float): desired min value max (float): desired max value phase (float): desired phase - + device (torch.device): Torch device on which table must be generated Returns: Tensor: A 1D tensor with wave table values """ phase_offset = int(phase / math.pi / 2 * table_size + 0.5) - t = torch.arange(table_size).to(torch.int32) + t = torch.arange(table_size, device=device, dtype=torch.int32) point = (t + phase_offset) % table_size - d = torch.zeros_like(point).to(torch.float64) + d = torch.zeros_like(point, device=device, dtype=torch.float64) if wave_type == 'SINE': d = (torch.sin(point.to(torch.float64) / table_size * 2 * math.pi) + 1) / 2 elif wave_type == 'TRIANGLE': d = point.to(torch.float64) * 2 / table_size - value = 4 * point / table_size + value = 4 * point // table_size d[value == 0] = d[value == 0] + 0.5 d[value == 1] = 1.5 - d[value == 1] d[value == 2] = 1.5 - d[value == 2] @@ -1487,8 +1488,6 @@ def flanger( lfo_length = int(sample_rate / speed) - lfo = torch.zeros(lfo_length, dtype=dtype, device=device) - table_min = math.floor(delay_min * sample_rate + 0.5) table_max = delay_buf_length - 2. @@ -1497,13 +1496,14 @@ def flanger( table_size=lfo_length, min=float(table_min), max=float(table_max), - phase=3 * math.pi / 2) + phase=3 * math.pi / 2, + device=device) output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) delay_buf_pos = 0 lfo_pos = 0 - channel_idxs = torch.arange(0, n_channels) + channel_idxs = torch.arange(0, n_channels, device=device) for i in range(waveform.shape[-1]): diff --git a/torchaudio/kaldi_io.py b/torchaudio/kaldi_io.py index d32aaed357..7f14f48e2c 100644 --- a/torchaudio/kaldi_io.py +++ b/torchaudio/kaldi_io.py @@ -1,16 +1,14 @@ # To use this file, the dependency (https://github.com/vesis84/kaldi-io-for-python) # needs to be installed. This is a light wrapper around kaldi_io that returns # torch.Tensors. -from typing import Any, Callable, Iterable, Tuple, Union +from typing import Any, Callable, Iterable, Tuple import torch from torch import Tensor -from torchaudio.common_utils import IMPORT_KALDI_IO, IMPORT_NUMPY +from torchaudio._internal import module_utils as _mod_utils -if IMPORT_NUMPY: +if _mod_utils.is_module_available('kaldi_io', 'numpy'): import numpy as np - -if IMPORT_KALDI_IO: import kaldi_io @@ -38,15 +36,13 @@ def _convert_method_output_to_tensor(file_or_fd: Any, Returns: Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is vec/mat """ - if not IMPORT_KALDI_IO: - raise ImportError('Could not import kaldi_io. Did you install it?') - for key, np_arr in fn(file_or_fd): if convert_contiguous: np_arr = np.ascontiguousarray(np_arr) yield key, torch.from_numpy(np_arr) +@_mod_utils.requires_module('kaldi_io', 'numpy') def read_vec_int_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, which reads from the ark file/stream. @@ -66,6 +62,7 @@ def read_vec_int_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_int_ark, convert_contiguous=True) +@_mod_utils.requires_module('kaldi_io', 'numpy') def read_vec_flt_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, read according to Kaldi scp. @@ -82,6 +79,7 @@ def read_vec_flt_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_scp) +@_mod_utils.requires_module('kaldi_io', 'numpy') def read_vec_flt_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, which reads from the ark file/stream. @@ -98,6 +96,7 @@ def read_vec_flt_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_ark) +@_mod_utils.requires_module('kaldi_io', 'numpy') def read_mat_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,matrix) tuples, read according to Kaldi scp. @@ -114,6 +113,7 @@ def read_mat_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_mat_scp) +@_mod_utils.requires_module('kaldi_io', 'numpy') def read_mat_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,matrix) tuples, which reads from the ark file/stream. diff --git a/torchaudio/models/__init__.py b/torchaudio/models/__init__.py index 1abdac6271..20c1bdf265 100644 --- a/torchaudio/models/__init__.py +++ b/torchaudio/models/__init__.py @@ -1 +1,2 @@ from .wav2letter import * +from ._wavernn import * diff --git a/torchaudio/models/_wavernn.py b/torchaudio/models/_wavernn.py new file mode 100644 index 0000000000..04155fb87c --- /dev/null +++ b/torchaudio/models/_wavernn.py @@ -0,0 +1,105 @@ +from torch import Tensor +from torch import nn + +__all__ = ["_ResBlock", "_MelResNet"] + + +class _ResBlock(nn.Module): + r"""This is a ResNet block layer. This layer is based on the paper "Deep Residual Learning + for Image Recognition". Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. CVPR, 2016. + It is a block used in WaveRNN. WaveRNN is based on the paper "Efficient Neural Audio Synthesis". + Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, + Florian Stimberg, Aaron van den Oord, Sander Dieleman, Koray Kavukcuoglu. arXiv:1802.08435, 2018. + + Args: + num_dims: the number of compute dimensions in the input (default=128). + + Examples:: + >>> resblock = _ResBlock(num_dims=128) + >>> input = torch.rand(10, 128, 512) + >>> output = resblock(input) + """ + + def __init__(self, num_dims: int = 128) -> None: + super().__init__() + + self.resblock_model = nn.Sequential( + nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False), + nn.BatchNorm1d(num_dims), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False), + nn.BatchNorm1d(num_dims) + ) + + def forward(self, x: Tensor) -> Tensor: + r"""Pass the input through the _ResBlock layer. + + Args: + x: the input sequence to the _ResBlock layer (required). + + Shape: + - x: :math:`(N, S, T)`. + - output: :math:`(N, S, T)`. + where N is the batch size, S is the number of input sequence, + T is the length of input sequence. + """ + + residual = x + return self.resblock_model(x) + residual + + +class _MelResNet(nn.Module): + r"""This is a MelResNet layer based on a stack of ResBlocks. It is a block used in WaveRNN. + WaveRNN is based on the paper "Efficient Neural Audio Synthesis". Nal Kalchbrenner, Erich Elsen, + Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron van den Oord, + Sander Dieleman, Koray Kavukcuoglu. arXiv:1802.08435, 2018. + + Args: + res_blocks: the number of ResBlock in stack (default=10). + input_dims: the number of input sequence (default=100). + hidden_dims: the number of compute dimensions (default=128). + output_dims: the number of output sequence (default=128). + pad: the number of kernal size (pad * 2 + 1) in the first Conv1d layer (default=2). + + Examples:: + >>> melresnet = _MelResNet(res_blocks=10, input_dims=100, + hidden_dims=128, output_dims=128, pad=2) + >>> input = torch.rand(10, 100, 512) + >>> output = melresnet(input) + """ + + def __init__(self, res_blocks: int = 10, + input_dims: int = 100, + hidden_dims: int = 128, + output_dims: int = 128, + pad: int = 2) -> None: + super().__init__() + + kernel_size = pad * 2 + 1 + ResBlocks = [] + + for i in range(res_blocks): + ResBlocks.append(_ResBlock(hidden_dims)) + + self.melresnet_model = nn.Sequential( + nn.Conv1d(in_channels=input_dims, out_channels=hidden_dims, kernel_size=kernel_size, bias=False), + nn.BatchNorm1d(hidden_dims), + nn.ReLU(inplace=True), + *ResBlocks, + nn.Conv1d(in_channels=hidden_dims, out_channels=output_dims, kernel_size=1) + ) + + def forward(self, x: Tensor) -> Tensor: + r"""Pass the input through the _MelResNet layer. + + Args: + x: the input sequence to the _MelResNet layer (required). + + Shape: + - x: :math:`(N, S, T)`. + - output: :math:`(N, P, T - 2 * pad)`. + where N is the batch size, S is the number of input sequence, + P is the number of output sequence, T is the length of input sequence. + """ + + return self.melresnet_model(x) diff --git a/torchaudio/sox_effects/__init__.py b/torchaudio/sox_effects/__init__.py new file mode 100644 index 0000000000..115b70c895 --- /dev/null +++ b/torchaudio/sox_effects/__init__.py @@ -0,0 +1,12 @@ +from torchaudio._internal import module_utils as _mod_utils +from .sox_effects import ( + init_sox_effects, + shutdown_sox_effects, + effect_names, + SoxEffect, + SoxEffectsChain, +) + + +if _mod_utils.is_module_available('torchaudio._torchaudio'): + init_sox_effects() diff --git a/torchaudio/sox_effects.py b/torchaudio/sox_effects/sox_effects.py similarity index 71% rename from torchaudio/sox_effects.py rename to torchaudio/sox_effects/sox_effects.py index d1eb04e53f..6a6b5be0a5 100644 --- a/torchaudio/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -1,12 +1,85 @@ +import atexit from typing import Any, Callable, List, Optional, Tuple, Union import torch -import torchaudio from torch import Tensor -from torchaudio._backend import _audio_backend_guard +from torchaudio._internal import ( + module_utils as _mod_utils, + misc_ops as _misc_ops, +) -@_audio_backend_guard("sox") +if _mod_utils.is_module_available('torchaudio._torchaudio'): + from torchaudio import _torchaudio + + +_SOX_INITIALIZED: Optional[bool] = False +# This variable has a micro lifecycle. (False -> True -> None) +# False: Not initialized +# True: Initialized +# None: Already shut down (should not be initialized again.) + +_SOX_SUCCESS_CODE = 0 +# defined at +# https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93a9ef2b87ec303edfe40751d9a85fadeeb + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def init_sox_effects() -> int: + """Initialize resources required to use ``SoxEffectsChain`` + + You do not need to call this function manually. It is called automatically. + + Once initialized, you do not need to call this function again across the multiple call of + ``SoxEffectsChain.sox_build_flow_effects``, though it is safe to do so as long as + ``shutdown_sox_effects`` is not called yet. + Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and calling + this function results in `RuntimeError`. + + Note: + This function is not required for simple loading. + + Returns: + int: Code corresponding to sox_error_t enum. See + https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + """ + global _SOX_INITIALIZED + if _SOX_INITIALIZED is None: + raise RuntimeError('SoX effects chain has been already shut down. Can not initialize again.') + if not _SOX_INITIALIZED: + code = _torchaudio.initialize_sox() + if code == _SOX_SUCCESS_CODE: + _SOX_INITIALIZED = True + atexit.register(shutdown_sox_effects) + return code + return _SOX_SUCCESS_CODE + + +@_mod_utils.requires_module("torchaudio._torchaudio") +def shutdown_sox_effects() -> int: + """Clean up resources required to use ``SoxEffectsChain`` + + You do not need to call this function manually. It is called automatically. + + It is safe to call this function multiple times. + Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and calling + this function results in `RuntimeError`. + + + Returns: + int: Code corresponding to sox_error_t enum. See + https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + """ + global _SOX_INITIALIZED + if _SOX_INITIALIZED: + code = _torchaudio.shutdown_sox() + if code == _SOX_INITIALIZED: + _SOX_INITIALIZED = None + return code + return _SOX_SUCCESS_CODE + + +@_mod_utils.requires_module('torchaudio._torchaudio') def effect_names() -> List[str]: """Gets list of valid sox effect names @@ -15,12 +88,10 @@ def effect_names() -> List[str]: Example >>> EFFECT_NAMES = torchaudio.sox_effects.effect_names() """ - - from . import _torchaudio return _torchaudio.get_effect_names() -@_audio_backend_guard("sox") +@_mod_utils.requires_module('torchaudio._torchaudio') def SoxEffect(): r"""Create an object for passing sox effect information between python and c++ @@ -28,8 +99,6 @@ def SoxEffect(): SoxEffect: An object with the following attributes: ename (str) which is the name of effect, and eopts (List[str]) which is a list of effect options. """ - - from . import _torchaudio return _torchaudio.SoxEffect() @@ -123,7 +192,7 @@ def append_effect_to_chain(self, e.eopts = eargs self.chain.append(e) - @_audio_backend_guard("sox") + @_mod_utils.requires_module('torchaudio._torchaudio') def sox_build_flow_effects(self, out: Optional[Tensor] = None) -> Tuple[Tensor, int]: r"""Build effects chain and flow effects from input file to output tensor @@ -138,7 +207,7 @@ def sox_build_flow_effects(self, """ # initialize output tensor if out is not None: - torchaudio.check_input(out) + _misc_ops.check_input(out) else: out = torch.FloatTensor() if not len(self.chain): @@ -149,8 +218,6 @@ def sox_build_flow_effects(self, # print("effect options:", [x.eopts for x in self.chain]) - torchaudio.initialize_sox() - from . import _torchaudio sr = _torchaudio.build_flow_effects(self.input_file, out, self.channels_first, @@ -160,7 +227,7 @@ def sox_build_flow_effects(self, self.chain, self.MAX_EFFECT_OPTS) - torchaudio._audio_normalization(out, self.normalization) + _misc_ops.normalize_audio(out, self.normalization) return out, sr From 36414bcc0d3b6bb575d0b5dda21735ed98317610 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Fri, 17 Jul 2020 09:28:17 -0700 Subject: [PATCH 10/73] Import torchaudio 20200714 #782 (#784) Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/784 - Import torchaudio. - Change test util module name from test_case_utils to case_utils Reviewed By: cpuhrsch Differential Revision: D22261638 fbshipit-source-id: eb4df500c1d7db0a60baa100dd22795a63851438 --- README.md | 4 +- .../setup_helpers/build_third_party.sh | 50 --- .../setup_helpers/build_third_party_helper.sh | 217 ----------- build_tools/setup_helpers/extension.py | 36 +- packaging/build_conda.sh | 2 +- packaging/build_wheel.sh | 2 +- packaging/pkg_helpers.bash | 3 +- setup.py | 2 +- test/README.md | 52 +++ .../tt/clips/common_voice_tt_00000000.mp3 | Bin 57136 -> 0 bytes .../tt/clips/common_voice_tt_00000000.wav | Bin 0 -> 80700 bytes .../cv-corpus-4-2019-12-10/tt/train.tsv | 4 +- test/assets/dtmf_30s_stereo.mp3 | Bin 228984 -> 0 bytes test/assets/io/96k_0_1ch.opus | Bin 0 -> 8259 bytes test/assets/io/96k_0_2ch.opus | Bin 0 -> 6740 bytes test/assets/io/96k_10_1ch.opus | Bin 0 -> 15033 bytes test/assets/io/96k_10_2ch.opus | Bin 0 -> 15160 bytes test/assets/io/96k_5_1ch.opus | Bin 0 -> 13074 bytes test/assets/io/96k_5_2ch.opus | Bin 0 -> 10618 bytes test/assets/io/generate_opus.py | 50 +++ test/common_utils.py | 171 --------- test/common_utils/__init__.py | 31 ++ test/common_utils/backend_utils.py | 41 ++ test/common_utils/case_utils.py | 75 ++++ test/common_utils/data_utils.py | 94 +++++ test/common_utils/parameterized_utils.py | 10 + test/common_utils/sox_utils.py | 79 ++++ test/common_utils/wav_utils.py | 86 +++++ test/functional_cpu_test.py | 35 +- test/kaldi_compatibility_impl.py | 52 ++- test/sox_io_backend/__init__.py | 0 test/sox_io_backend/common.py | 2 + test/sox_io_backend/test_info.py | 125 +++++++ test/sox_io_backend/test_load.py | 263 +++++++++++++ test/sox_io_backend/test_roundtrip.py | 52 +++ test/sox_io_backend/test_save.py | 304 +++++++++++++++ test/sox_io_backend/test_torchscript.py | 149 ++++++++ test/test_backend.py | 15 +- test/test_datasets.py | 3 +- test/test_io.py | 33 +- test/test_librosa_compatibility.py | 9 +- test/test_models.py | 117 +++++- test/test_sox_compatibility.py | 206 +++++------ test/test_sox_effects.py | 4 +- test/test_transforms.py | 6 +- test/torchscript_consistency_impl.py | 4 +- third_party/CMakeLists.txt | 80 ++++ third_party/build_codec_helper.sh | 13 + third_party/patch/libmad.patch | 86 +++++ torchaudio/backend/sox_io_backend.py | 134 +++++++ torchaudio/backend/utils.py | 6 + torchaudio/csrc/register.cpp | 59 ++- torchaudio/csrc/sox.cpp | 33 -- torchaudio/csrc/sox.h | 7 - torchaudio/csrc/sox_effects.cpp | 54 +++ torchaudio/csrc/sox_effects.h | 18 + torchaudio/csrc/sox_io.cpp | 170 +++++++++ torchaudio/csrc/sox_io.h | 41 ++ torchaudio/csrc/sox_utils.cpp | 245 ++++++++++++ torchaudio/csrc/sox_utils.h | 100 +++++ torchaudio/csrc/typedefs.cpp | 23 -- torchaudio/csrc/typedefs.h | 23 -- torchaudio/extension/extension.py | 29 -- torchaudio/functional.py | 7 +- torchaudio/models/_wavernn.py | 350 ++++++++++++++---- torchaudio/sox_effects/__init__.py | 2 + torchaudio/sox_effects/sox_effects.py | 54 +-- 67 files changed, 3049 insertions(+), 873 deletions(-) delete mode 100755 build_tools/setup_helpers/build_third_party.sh delete mode 100644 build_tools/setup_helpers/build_third_party_helper.sh delete mode 100644 test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.mp3 create mode 100644 test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav delete mode 100644 test/assets/dtmf_30s_stereo.mp3 create mode 100644 test/assets/io/96k_0_1ch.opus create mode 100644 test/assets/io/96k_0_2ch.opus create mode 100644 test/assets/io/96k_10_1ch.opus create mode 100644 test/assets/io/96k_10_2ch.opus create mode 100644 test/assets/io/96k_5_1ch.opus create mode 100644 test/assets/io/96k_5_2ch.opus create mode 100644 test/assets/io/generate_opus.py delete mode 100644 test/common_utils.py create mode 100644 test/common_utils/__init__.py create mode 100644 test/common_utils/backend_utils.py create mode 100644 test/common_utils/case_utils.py create mode 100644 test/common_utils/data_utils.py create mode 100644 test/common_utils/parameterized_utils.py create mode 100644 test/common_utils/sox_utils.py create mode 100644 test/common_utils/wav_utils.py create mode 100644 test/sox_io_backend/__init__.py create mode 100644 test/sox_io_backend/common.py create mode 100644 test/sox_io_backend/test_info.py create mode 100644 test/sox_io_backend/test_load.py create mode 100644 test/sox_io_backend/test_roundtrip.py create mode 100644 test/sox_io_backend/test_save.py create mode 100644 test/sox_io_backend/test_torchscript.py create mode 100644 third_party/CMakeLists.txt create mode 100755 third_party/build_codec_helper.sh create mode 100644 third_party/patch/libmad.patch create mode 100644 torchaudio/backend/sox_io_backend.py create mode 100644 torchaudio/csrc/sox_effects.cpp create mode 100644 torchaudio/csrc/sox_effects.h create mode 100644 torchaudio/csrc/sox_io.cpp create mode 100644 torchaudio/csrc/sox_io.h create mode 100644 torchaudio/csrc/sox_utils.cpp create mode 100644 torchaudio/csrc/sox_utils.h delete mode 100644 torchaudio/csrc/typedefs.cpp delete mode 100644 torchaudio/csrc/typedefs.h diff --git a/README.md b/README.md index 94f993ddaa..290bb73543 100644 --- a/README.md +++ b/README.md @@ -113,8 +113,8 @@ python setup.py install MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install ``` -Alternatively, the build process can build SoX (and codecs such as libmad, lame and flac) statically and torchaudio can link them, by setting environment variable `BUILD_SOX=1`. -The build process will fetch and build SoX, liblame, libmad, flac before building extension. +Alternatively, the build process can build libsox and some optional codecs statically and torchaudio can link them, by setting environment variable `BUILD_SOX=1`. +The build process will fetch and build libmad, lame, flac, vorbis, opus, and libsox before building extension. This process requires `cmake` and `pkg-config`. ```bash # Linux diff --git a/build_tools/setup_helpers/build_third_party.sh b/build_tools/setup_helpers/build_third_party.sh deleted file mode 100755 index 9577776cc9..0000000000 --- a/build_tools/setup_helpers/build_third_party.sh +++ /dev/null @@ -1,50 +0,0 @@ -#!/bin/bash -# Build third party libraries (SoX, lame, libmad, and flac) -# Usage: ./build_thid_parth.sh [prefix] [download_only?=false] - -set -e - -this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" -root_dir="${this_dir}/../.." - -prefix="${1:-}" -if [ -z "${prefix}" ]; then - prefix="${root_dir}" -fi -download_only="${2:-false}" - -tp_dir="${prefix}/third_party" -tmp_dir="${tp_dir}/tmp" -build_dir="${tp_dir}/build" - -mkdir -p "${tmp_dir}" "${build_dir}" - -. "${this_dir}/build_third_party_helper.sh" - -if ! found_lame "${build_dir}" ; then - get_lame "${tmp_dir}" - if [ "${download_only}" = "false" ]; then - build_lame "${tmp_dir}" "${build_dir}" - fi -fi - -if ! found_flac "${build_dir}" ; then - get_flac "${tmp_dir}" - if [ "${download_only}" = "false" ]; then - build_flac "${tmp_dir}" "${build_dir}" - fi -fi - -if ! found_mad "${build_dir}" ; then - get_mad "${tmp_dir}" - if [ "${download_only}" = "false" ]; then - build_mad "${tmp_dir}" "${build_dir}" - fi -fi - -if ! found_sox "${build_dir}" ; then - get_sox "${tmp_dir}" - if [ "${download_only}" = "false" ]; then - build_sox "${tmp_dir}" "${build_dir}" - fi -fi diff --git a/build_tools/setup_helpers/build_third_party_helper.sh b/build_tools/setup_helpers/build_third_party_helper.sh deleted file mode 100644 index 7cca812409..0000000000 --- a/build_tools/setup_helpers/build_third_party_helper.sh +++ /dev/null @@ -1,217 +0,0 @@ -#!/usr/bin/env bash - -set -euo pipefail - -# Global options -CURL_OPTS="-L --retry 10 --connect-timeout 5 --max-time 180" -MAKE_OPTS="-j" -CONFIG_OPTS="" - -if [ -z ${DEBUG+x} ]; then - CURL_OPTS="${CURL_OPTS} --silent --show-error" - MAKE_OPTS="${MAKE_OPTS} --quiet" - CONFIG_OPTS="${CONFIG_OPTS} --quiet" -fi - -all_found() { - dir="$1" - shift - while [ "$#" -gt 0 ]; do - if [ ! -f "${dir}/$1" ]; then - return 1 - fi - shift - done -} - - -found_lame() { - all_found "$1" 'include/lame/lame.h' 'lib/libmp3lame.a' -} - -found_flac() { - all_found "$1" \ - 'include/FLAC/format.h' \ - 'include/FLAC/stream_decoder.h' \ - 'include/FLAC/export.h' \ - 'include/FLAC/ordinals.h' \ - 'include/FLAC/all.h' \ - 'include/FLAC/assert.h' \ - 'include/FLAC/callback.h' \ - 'include/FLAC/metadata.h' \ - 'include/FLAC/stream_encoder.h' \ - 'include/FLAC++/export.h' \ - 'include/FLAC++/decoder.h' \ - 'include/FLAC++/all.h' \ - 'include/FLAC++/metadata.h' \ - 'include/FLAC++/encoder.h' \ - 'lib/libFLAC++.a' \ - 'lib/libFLAC.a' -} - -found_mad() { - all_found "$1" 'include/mad.h' 'lib/libmad.a' -} - -found_sox() { - all_found "$1" 'include/sox.h' 'lib/libsox.a' -} - -LAME="lame-3.99.5" -LAME_ARCHIVE="${LAME}.tar.gz" - -get_lame() { - work_dir="$1" - url="https://downloads.sourceforge.net/project/lame/lame/3.99/${LAME_ARCHIVE}" - ( - cd "${work_dir}" - if [ ! -d "${LAME}" ]; then - if [ ! -f "${LAME_ARCHIVE}" ]; then - printf "Fetching liblame from %s\n" "${url}" - curl $CURL_OPTS -O "${url}" - fi - fi - ) -} - -build_lame() { - work_dir="$1" - install_dir="$2" - ( - cd "${work_dir}" - if [ ! -d "${LAME}" ]; then - tar xfp "${LAME_ARCHIVE}" - fi - cd "${LAME}" - # build statically - printf "Building liblame\n" - if [ ! -f Makefile ]; then - ./configure ${CONFIG_OPTS} \ - --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ - --with-pic --disable-debug --disable-dependency-tracking --enable-nasm - fi - make ${MAKE_OPTS} > make.log 2>&1 - make ${MAKE_OPTS} install - ) -} - -FLAC="flac-1.3.2" -FLAC_ARCHIVE="${FLAC}.tar.xz" - -get_flac() { - work_dir="$1" - url="https://downloads.sourceforge.net/project/flac/flac-src/${FLAC_ARCHIVE}" - ( - cd "${work_dir}" - if [ ! -d "${FLAC}" ]; then - if [ ! -f "${FLAC_ARCHIVE}" ]; then - printf "Fetching flac from %s\n" "${url}" - curl $CURL_OPTS -O "${url}" - fi - fi - ) -} - -build_flac() { - work_dir="$1" - install_dir="$2" - ( - cd "${work_dir}" - if [ ! -d "${FLAC}" ]; then - tar xfp "${FLAC_ARCHIVE}" - fi - cd "${FLAC}" - # build statically - printf "Building flac\n" - if [ ! -f Makefile ]; then - ./configure ${CONFIG_OPTS} \ - --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ - --with-pic --disable-debug --disable-dependency-tracking - fi - make ${MAKE_OPTS} > make.log 2>&1 - make ${MAKE_OPTS} install - ) -} - -LIBMAD="libmad-0.15.1b" -LIBMAD_ARCHIVE="${LIBMAD}.tar.gz" - -get_mad() { - work_dir="$1" - url="https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/${LIBMAD_ARCHIVE}" - ( - cd "${work_dir}" - if [ ! -d "${LIBMAD}" ]; then - if [ ! -f "${LIBMAD_ARCHIVE}" ]; then - printf "Fetching mad from %s\n" "${url}" - curl $CURL_OPTS -O "${url}" - fi - fi - ) -} - -build_mad() { - work_dir="$1" - install_dir="$2" - ( - cd "${work_dir}" - if [ ! -d "${LIBMAD}" ]; then - tar xfp "${LIBMAD_ARCHIVE}" - fi - cd "${LIBMAD}" - # build statically - printf "Building mad\n" - if [ ! -f Makefile ]; then - # See https://stackoverflow.com/a/12864879/23845 - sed -i.bak 's/-march=i486//' configure - ./configure ${CONFIG_OPTS} \ - --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ - --with-pic --disable-debug --disable-dependency-tracking - fi - make ${MAKE_OPTS} > make.log 2>&1 - make ${MAKE_OPTS} install - ) -} - -SOX="sox-14.4.2" -SOX_ARCHIVE="${SOX}.tar.bz2" - -get_sox() { - work_dir="$1" - url="https://downloads.sourceforge.net/project/sox/sox/14.4.2/${SOX_ARCHIVE}" - ( - cd "${work_dir}" - if [ ! -d "${SOX}" ]; then - if [ ! -f "${SOX_ARCHIVE}" ]; then - printf "Fetching SoX from %s\n" "${url}" - curl $CURL_OPTS -O "${url}" - fi - fi - ) -} - -build_sox() { - work_dir="$1" - install_dir="$2" - ( - cd "${work_dir}" - if [ ! -d "${SOX}" ]; then - tar xfp "${SOX_ARCHIVE}" - fi - cd "${SOX}" - # build statically - printf "Building SoX\n" - if [ ! -f Makefile ]; then - # --without-png makes OS X build less hazardous; somehow the build - # finds png and enables it. We don't want it; we'd need to package - # it statically if we do. - ./configure ${CONFIG_OPTS} --disable-shared --enable-static --prefix="${install_dir}" \ - LDFLAGS="-L${install_dir}/lib" CPPFLAGS="-I${install_dir}/include" \ - --with-lame --with-flac --with-mad --without-alsa --without-coreaudio \ - --without-png --without-oggvorbis --without-oss --without-sndfile \ - CFLAGS=-fPIC CXXFLAGS=-fPIC --with-pic --disable-debug --disable-dependency-tracking - fi - make ${MAKE_OPTS} > make.log 2>&1 - make ${MAKE_OPTS} install - ) -} diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index dcf61ab259..b9fb30e114 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -17,7 +17,7 @@ _ROOT_DIR = _THIS_DIR.parent.parent.resolve() _CSRC_DIR = _ROOT_DIR / 'torchaudio' / 'csrc' _TP_BASE_DIR = _ROOT_DIR / 'third_party' -_TP_INSTALL_DIR = _TP_BASE_DIR / 'build' +_TP_INSTALL_DIR = _TP_BASE_DIR / 'install' def _get_build_sox(): @@ -76,8 +76,20 @@ def _get_extra_objects(): # NOTE: The order of the library listed bellow matters. # # (the most important thing is that dependencies come after a library - # e.g., sox comes first) - libs = ['libsox.a', 'libmad.a', 'libFLAC.a', 'libmp3lame.a'] + # e.g., sox comes first, flac/vorbis comes before ogg, and + # vorbisenc/vorbisfile comes before vorbis + libs = [ + 'libsox.a', + 'libmad.a', + 'libFLAC.a', + 'libmp3lame.a', + 'libopusfile.a', + 'libopus.a', + 'libvorbisenc.a', + 'libvorbisfile.a', + 'libvorbis.a', + 'libogg.a', + ] for lib in libs: objs.append(str(_TP_INSTALL_DIR / 'lib' / lib)) return objs @@ -87,15 +99,19 @@ def _get_libraries(): return [] if _BUILD_SOX else ['sox'] -def _build_codecs(): +def _build_third_party(): + build_dir = str(_TP_BASE_DIR / 'build') + os.makedirs(build_dir, exist_ok=True) subprocess.run( - args=[str(_THIS_DIR / 'build_third_party.sh')], + args=['cmake', '..'], + cwd=build_dir, + check=True, + ) + subprocess.run( + args=['cmake', '--build', '.'], + cwd=build_dir, check=True, ) - - -def _configure_third_party(): - _build_codecs() _EXT_NAME = 'torchaudio._torchaudio' @@ -120,5 +136,5 @@ def get_ext_modules(debug=False): class BuildExtension(TorchBuildExtension): def build_extension(self, ext): if ext.name == _EXT_NAME and _BUILD_SOX: - _configure_third_party() + _build_third_party() super().build_extension(ext) diff --git a/packaging/build_conda.sh b/packaging/build_conda.sh index 3e011d312f..628eaf0bf8 100755 --- a/packaging/build_conda.sh +++ b/packaging/build_conda.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="conda" export NO_CUDA_PACKAGE=1 -setup_env 0.6.0 +setup_env 0.7.0 export SOURCE_ROOT_DIR="$PWD" setup_conda_pytorch_constraint conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchaudio diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index d08196cb20..3b25c00cd2 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="wheel" export NO_CUDA_PACKAGE=1 -setup_env 0.6.0 +setup_env 0.7.0 setup_wheel_python pip_install numpy future setup_pip_pytorch_version diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index 4030128a98..3ec49a0269 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -171,6 +171,7 @@ setup_pip_pytorch_version() { else pip_install "torch==$PYTORCH_VERSION$PYTORCH_VERSION_SUFFIX" \ -f https://download.pytorch.org/whl/torch_stable.html \ + -f https://download.pytorch.org/whl/test/torch_test.html \ -f https://download.pytorch.org/whl/nightly/torch_nightly.html fi } @@ -184,7 +185,7 @@ setup_conda_pytorch_constraint() { export CONDA_CHANNEL_FLAGS="-c pytorch-nightly" export PYTORCH_VERSION="$(conda search --json 'pytorch[channel=pytorch-nightly]' | python -c "import sys, json, re; print(re.sub(r'\\+.*$', '', json.load(sys.stdin)['pytorch'][-1]['version']))")" else - export CONDA_CHANNEL_FLAGS="-c pytorch -c pytorch-nightly" + export CONDA_CHANNEL_FLAGS="-c pytorch -c pytorch-test -c pytorch-nightly" fi if [[ "$CU_VERSION" == cpu ]]; then export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==$PYTORCH_VERSION${PYTORCH_VERSION_SUFFIX}" diff --git a/setup.py b/setup.py index 5cf1ad680a..aab59b4d8a 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ # Creating the version file -version = '0.6.0a0' +version = '0.7.0a0' sha = 'Unknown' try: diff --git a/test/README.md b/test/README.md index 35a926c120..304bfa9c8e 100644 --- a/test/README.md +++ b/test/README.md @@ -41,6 +41,58 @@ The following test modules are defined for corresponding `torchaudio` module/fun - [assets/kaldi](./assets/kaldi): Contains Kaldi format matrix files used in [./test_compliance_kaldi.py](./test_compliance_kaldi.py). - [compliance](./compliance): Scripts used to generate above Kaldi matrix files. +### Waveforms for Testing Purposes + +When testing transforms we often need waveforms of specific type (ex: pure tone, noise, or voice), with specific bitrate (ex. 8 or 16 kHz) and number of channels (ex. mono, stereo). Below are some tips on how to construct waveforms and guidance around existing audio files. + +#### Load a Waveform from a File + +```python +filepath = common_utils.get_asset_path('filename.wav') +waveform, sample_rate = common_utils.load_wav(filepath) +``` + +*Note: Should you choose to contribute an audio file, please leave a comment in the issue or pull request, mentioning content source and licensing information. WAV files are preferred. Other formats should be used only when there is no alternative. (i.e. dataset implementation comes with hardcoded non-wav extension).* + +#### Pure Tone + +Code: + +```python +waveform = common_utils.get_sinusoid( + frequency=300, + sample_rate=16000, + duration=1, # seconds + n_channels=1, + dtype="float32", + device="cpu", +) +``` + +#### Noise + +Code: + +```python +tensor = common_utils.get_whitenoise() +``` + +Files: + +* `steam-train-whistle-daniel_simon.wav` + +#### Voice + +Files: + +* `CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav` +* `LibriSpeech/dev-clean/1272/128104/1272-128104-0000.flac` +* `LJSpeech-1.1/wavs/LJ001-0001.wav` +* `SpeechCommands/speech_commands_v0.02/go/0a9f9af7_nohash_0.wav` +* `VCTK-Corpus/wav48/p224/p224_002.wav` +* `waves_yesno/0_1_0_1_0_1_1_0.wav` +* `vad-go-stereo-44100.wav` +* `vad-go-mono-32000.wav` ## Adding test diff --git a/test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.mp3 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+1,50 @@ +"""Generate opus file for testing load functions""" + +import argparse +import subprocess + +import scipy.io.wavfile +import torch + + +def _parse_args(): + parser = argparse.ArgumentParser( + description='Generate opus files for test' + ) + parser.add_argument('--num-channels', required=True, type=int) + parser.add_argument('--compression-level', required=True, type=int, choices=list(range(11))) + parser.add_argument('--bitrate', default='96k') + return parser.parse_args() + + +def convert_to_opus( + src_path, dst_path, + *, bitrate, compression_level): + """Convert audio file with `ffmpeg` command.""" + command = ['ffmpeg', '-y', '-i', src_path, '-c:a', 'libopus', '-b:a', bitrate] + if compression_level is not None: + command += ['-compression_level', str(compression_level)] + command += [dst_path] + print(' '.join(command)) + subprocess.run(command, check=True) + + +def _generate(num_channels, compression_level, bitrate): + org_path = 'original.wav' + ops_path = f'{bitrate}_{compression_level}_{num_channels}ch.opus' + + # Note: ffmpeg forces sample rate 48k Hz for opus https://stackoverflow.com/a/39186779 + # 1. generate original wav + data = torch.linspace(-32768, 32767, 32768, dtype=torch.int16).repeat([num_channels, 1]).t() + scipy.io.wavfile.write(org_path, 48000, data.numpy()) + # 2. convert to opus + convert_to_opus(org_path, ops_path, bitrate=bitrate, compression_level=compression_level) + + +def _main(): + args = _parse_args() + _generate(args.num_channels, args.compression_level, args.bitrate) + + +if __name__ == '__main__': + _main() diff --git a/test/common_utils.py b/test/common_utils.py deleted file mode 100644 index fe045d9db8..0000000000 --- a/test/common_utils.py +++ /dev/null @@ -1,171 +0,0 @@ -import os -import tempfile -import unittest -from typing import Union -from shutil import copytree - -import torch -from torch.testing._internal.common_utils import TestCase as PytorchTestCase -import torchaudio - -_TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__)) -BACKENDS = torchaudio.list_audio_backends() - - -def get_asset_path(*paths): - """Return full path of a test asset""" - return os.path.join(_TEST_DIR_PATH, 'assets', *paths) - - -def create_temp_assets_dir(): - """ - Creates a temporary directory and moves all files from test/assets there. - Returns a Tuple[string, TemporaryDirectory] which is the folder path - and object. - """ - tmp_dir = tempfile.TemporaryDirectory() - copytree(os.path.join(_TEST_DIR_PATH, "assets"), - os.path.join(tmp_dir.name, "assets")) - return tmp_dir.name, tmp_dir - - -def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32): - """ Generates random tensors given a seed and size - https://en.wikipedia.org/wiki/Linear_congruential_generator - X_{n + 1} = (a * X_n + c) % m - Using Borland C/C++ values - - The tensor will have values between [0,1) - Inputs: - seed (int): an int - size (Tuple[int]): the size of the output tensor - a (int): the multiplier constant to the generator - c (int): the additive constant to the generator - m (int): the modulus constant to the generator - """ - num_elements = 1 - for s in size: - num_elements *= s - - arr = [(a * seed + c) % m] - for i in range(num_elements - 1): - arr.append((a * arr[i] + c) % m) - - return torch.tensor(arr).float().view(size) / m - - -def filter_backends_with_mp3(backends): - # Filter out backends that do not support mp3 - test_filepath = get_asset_path('steam-train-whistle-daniel_simon.mp3') - - def supports_mp3(backend): - torchaudio.set_audio_backend(backend) - try: - torchaudio.load(test_filepath) - return True - except (RuntimeError, ImportError): - return False - - return [backend for backend in backends if supports_mp3(backend)] - - -BACKENDS_MP3 = filter_backends_with_mp3(BACKENDS) - - -def set_audio_backend(backend): - """Allow additional backend value, 'default'""" - if backend == 'default': - if 'sox' in BACKENDS: - be = 'sox' - elif 'soundfile' in BACKENDS: - be = 'soundfile' - else: - raise unittest.SkipTest('No default backend available') - else: - be = backend - - torchaudio.set_audio_backend(be) - - -class TestBaseMixin: - """Mixin to provide consistent way to define device/dtype/backend aware TestCase""" - dtype = None - device = None - backend = None - - def setUp(self): - super().setUp() - set_audio_backend(self.backend) - - -class TorchaudioTestCase(TestBaseMixin, PytorchTestCase): - pass - - -skipIfNoSoxBackend = unittest.skipIf('sox' not in BACKENDS, 'Sox backend not available') -skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') - - -def get_whitenoise( - *, - sample_rate: int = 16000, - duration: float = 1, # seconds - n_channels: int = 1, - seed: int = 0, - dtype: Union[str, torch.dtype] = "float32", - device: Union[str, torch.device] = "cpu", -): - """Generate pseudo audio data with whitenoise - - Args: - sample_rate: Sampling rate - duration: Length of the resulting Tensor in seconds. - n_channels: Number of channels - seed: Seed value used for random number generation. - Note that this function does not modify global random generator state. - dtype: Torch dtype - device: device - Returns: - Tensor: shape of (n_channels, sample_rate * duration) - """ - if isinstance(dtype, str): - dtype = getattr(torch, dtype) - shape = [n_channels, sample_rate * duration] - # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices, - # so we only folk on CPU, generate values and move the data to the given device - with torch.random.fork_rng([]): - torch.random.manual_seed(seed) - tensor = torch.randn(shape, dtype=dtype, device='cpu') - tensor /= 2.0 - tensor.clamp_(-1.0, 1.0) - return tensor.to(device=device) - - -def get_sinusoid( - *, - frequency: float = 300, - sample_rate: int = 16000, - duration: float = 1, # seconds - n_channels: int = 1, - dtype: Union[str, torch.dtype] = "float32", - device: Union[str, torch.device] = "cpu", -): - """Generate pseudo audio data with sine wave. - - Args: - frequency: Frequency of sine wave - sample_rate: Sampling rate - duration: Length of the resulting Tensor in seconds. - n_channels: Number of channels - dtype: Torch dtype - device: device - - Returns: - Tensor: shape of (n_channels, sample_rate * duration) - """ - if isinstance(dtype, str): - dtype = getattr(torch, dtype) - pie2 = 2 * 3.141592653589793 - end = pie2 * frequency * duration - theta = torch.linspace(0, end, sample_rate * duration, dtype=dtype, device=device) - return torch.sin(theta, out=None).repeat([n_channels, 1]) diff --git a/test/common_utils/__init__.py b/test/common_utils/__init__.py new file mode 100644 index 0000000000..841cd26713 --- /dev/null +++ b/test/common_utils/__init__.py @@ -0,0 +1,31 @@ +from .data_utils import ( + get_asset_path, + get_whitenoise, + get_sinusoid, +) +from .backend_utils import ( + set_audio_backend, + BACKENDS, + BACKENDS_MP3, +) +from .case_utils import ( + TempDirMixin, + TestBaseMixin, + PytorchTestCase, + TorchaudioTestCase, + skipIfNoCuda, + skipIfNoExec, + skipIfNoModule, + skipIfNoExtension, + skipIfNoSoxBackend, +) +from .wav_utils import ( + get_wav_data, + normalize_wav, + load_wav, + save_wav, +) +from .parameterized_utils import ( + load_params, +) +from . import sox_utils diff --git a/test/common_utils/backend_utils.py b/test/common_utils/backend_utils.py new file mode 100644 index 0000000000..158fde87ed --- /dev/null +++ b/test/common_utils/backend_utils.py @@ -0,0 +1,41 @@ +import unittest + +import torchaudio + +from .import data_utils + + +BACKENDS = torchaudio.list_audio_backends() + + +def _filter_backends_with_mp3(backends): + # Filter out backends that do not support mp3 + test_filepath = data_utils.get_asset_path('steam-train-whistle-daniel_simon.mp3') + + def supports_mp3(backend): + torchaudio.set_audio_backend(backend) + try: + torchaudio.load(test_filepath) + return True + except (RuntimeError, ImportError): + return False + + return [backend for backend in backends if supports_mp3(backend)] + + +BACKENDS_MP3 = _filter_backends_with_mp3(BACKENDS) + + +def set_audio_backend(backend): + """Allow additional backend value, 'default'""" + if backend == 'default': + if 'sox' in BACKENDS: + be = 'sox' + elif 'soundfile' in BACKENDS: + be = 'soundfile' + else: + raise unittest.SkipTest('No default backend available') + else: + be = backend + + torchaudio.set_audio_backend(be) diff --git a/test/common_utils/case_utils.py b/test/common_utils/case_utils.py new file mode 100644 index 0000000000..f3b0c343a6 --- /dev/null +++ b/test/common_utils/case_utils.py @@ -0,0 +1,75 @@ +import shutil +import os.path +import tempfile +import unittest + +import torch +from torch.testing._internal.common_utils import TestCase as PytorchTestCase +import torchaudio +from torchaudio._internal.module_utils import is_module_available + +from .backend_utils import set_audio_backend + + +class TempDirMixin: + """Mixin to provide easy access to temp dir""" + temp_dir_ = None + base_temp_dir = None + temp_dir = None + + @classmethod + def setUpClass(cls): + super().setUpClass() + # If TORCHAUDIO_TEST_TEMP_DIR is set, use it instead of temporary directory. + # this is handy for debugging. + key = 'TORCHAUDIO_TEST_TEMP_DIR' + if key in os.environ: + cls.base_temp_dir = os.environ[key] + else: + cls.temp_dir_ = tempfile.TemporaryDirectory() + cls.base_temp_dir = cls.temp_dir_.name + + @classmethod + def tearDownClass(cls): + super().tearDownClass() + if isinstance(cls.temp_dir_, tempfile.TemporaryDirectory): + cls.temp_dir_.cleanup() + + def setUp(self): + super().setUp() + self.temp_dir = os.path.join(self.base_temp_dir, self.id()) + + def get_temp_path(self, *paths): + path = os.path.join(self.temp_dir, *paths) + os.makedirs(os.path.dirname(path), exist_ok=True) + return path + + +class TestBaseMixin: + """Mixin to provide consistent way to define device/dtype/backend aware TestCase""" + dtype = None + device = None + backend = None + + def setUp(self): + super().setUp() + set_audio_backend(self.backend) + + +class TorchaudioTestCase(TestBaseMixin, PytorchTestCase): + pass + + +def skipIfNoExec(cmd): + return unittest.skipIf(shutil.which(cmd) is None, f'`{cmd}` is not available') + + +def skipIfNoModule(module, display_name=None): + display_name = display_name or module + return unittest.skipIf(not is_module_available(module), f'"{display_name}" is not available') + + +skipIfNoSoxBackend = unittest.skipIf( + 'sox' not in torchaudio.list_audio_backends(), 'Sox backend not available') +skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') +skipIfNoExtension = skipIfNoModule('torchaudio._torchaudio', 'torchaudio C++ extension') diff --git a/test/common_utils/data_utils.py b/test/common_utils/data_utils.py new file mode 100644 index 0000000000..b948ce334a --- /dev/null +++ b/test/common_utils/data_utils.py @@ -0,0 +1,94 @@ +import os.path +from typing import Union + +import torch + + +_TEST_DIR_PATH = os.path.realpath( + os.path.join(os.path.dirname(__file__), '..')) + + +def get_asset_path(*paths): + """Return full path of a test asset""" + return os.path.join(_TEST_DIR_PATH, 'assets', *paths) + + +def get_whitenoise( + *, + sample_rate: int = 16000, + duration: float = 1, # seconds + n_channels: int = 1, + seed: int = 0, + dtype: Union[str, torch.dtype] = "float32", + device: Union[str, torch.device] = "cpu", + channels_first=True, + scale_factor: float = 1, +): + """Generate pseudo audio data with whitenoise + Args: + sample_rate: Sampling rate + duration: Length of the resulting Tensor in seconds. + n_channels: Number of channels + seed: Seed value used for random number generation. + Note that this function does not modify global random generator state. + dtype: Torch dtype + device: device + channels_first: whether first dimension is n_channels + scale_factor: scale the Tensor before clamping and quantization + Returns: + Tensor: shape of (n_channels, sample_rate * duration) + """ + if isinstance(dtype, str): + dtype = getattr(torch, dtype) + if dtype not in [torch.float32, torch.int32, torch.int16, torch.uint8]: + raise NotImplementedError(f'dtype {dtype} is not supported.') + # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices, + # so we only folk on CPU, generate values and move the data to the given device + with torch.random.fork_rng([]): + torch.random.manual_seed(seed) + tensor = torch.randn([sample_rate * duration], dtype=torch.float32, device='cpu') + tensor /= 2.0 + tensor *= scale_factor + tensor.clamp_(-1.0, 1.0) + if dtype == torch.int32: + tensor *= (tensor > 0) * 2147483647 + (tensor < 0) * 2147483648 + if dtype == torch.int16: + tensor *= (tensor > 0) * 32767 + (tensor < 0) * 32768 + if dtype == torch.uint8: + tensor *= (tensor > 0) * 127 + (tensor < 0) * 128 + tensor += 128 + tensor = tensor.to(dtype) + tensor = tensor.repeat([n_channels, 1]) + if not channels_first: + tensor = tensor.t() + return tensor.to(device=device) + + +def get_sinusoid( + *, + frequency: float = 300, + sample_rate: int = 16000, + duration: float = 1, # seconds + n_channels: int = 1, + dtype: Union[str, torch.dtype] = "float32", + device: Union[str, torch.device] = "cpu", +): + """Generate pseudo audio data with sine wave. + + Args: + frequency: Frequency of sine wave + sample_rate: Sampling rate + duration: Length of the resulting Tensor in seconds. + n_channels: Number of channels + dtype: Torch dtype + device: device + + Returns: + Tensor: shape of (n_channels, sample_rate * duration) + """ + if isinstance(dtype, str): + dtype = getattr(torch, dtype) + pie2 = 2 * 3.141592653589793 + end = pie2 * frequency * duration + theta = torch.linspace(0, end, sample_rate * duration, dtype=dtype, device=device) + return torch.sin(theta, out=None).repeat([n_channels, 1]) diff --git a/test/common_utils/parameterized_utils.py b/test/common_utils/parameterized_utils.py new file mode 100644 index 0000000000..24404a6edd --- /dev/null +++ b/test/common_utils/parameterized_utils.py @@ -0,0 +1,10 @@ +import json + +from parameterized import param + +from .data_utils import get_asset_path + + +def load_params(*paths): + with open(get_asset_path(*paths), 'r') as file: + return [param(json.loads(line)) for line in file] diff --git a/test/common_utils/sox_utils.py b/test/common_utils/sox_utils.py new file mode 100644 index 0000000000..cd1c247b72 --- /dev/null +++ b/test/common_utils/sox_utils.py @@ -0,0 +1,79 @@ +import subprocess + + +def get_encoding(dtype): + encodings = { + 'float32': 'floating-point', + 'int32': 'signed-integer', + 'int16': 'signed-integer', + 'uint8': 'unsigned-integer', + } + return encodings[dtype] + + +def get_bit_depth(dtype): + bit_depths = { + 'float32': 32, + 'int32': 32, + 'int16': 16, + 'uint8': 8, + } + return bit_depths[dtype] + + +def gen_audio_file( + path, sample_rate, num_channels, + *, encoding=None, bit_depth=None, compression=None, attenuation=None, duration=1, +): + """Generate synthetic audio file with `sox` command.""" + if path.endswith('.wav'): + raise RuntimeError( + 'Use get_wav_data and save_wav to generate wav file for accurate result.') + command = [ + 'sox', + '-V3', # verbose + '-R', + # -R is supposed to be repeatable, though the implementation looks suspicious + # and not setting the seed to a fixed value. + # https://fossies.org/dox/sox-14.4.2/sox_8c_source.html + # search "sox_globals.repeatable" + ] + if bit_depth is not None: + command += ['--bits', str(bit_depth)] + command += [ + '--rate', str(sample_rate), + '--null', # no input + '--channels', str(num_channels), + ] + if compression is not None: + command += ['--compression', str(compression)] + if bit_depth is not None: + command += ['--bits', str(bit_depth)] + if encoding is not None: + command += ['--encoding', str(encoding)] + command += [ + str(path), + 'synth', str(duration), # synthesizes for the given duration [sec] + 'sawtooth', '1', + # saw tooth covers the both ends of value range, which is a good property for test. + # similar to linspace(-1., 1.) + # this introduces bigger boundary effect than sine when converted to mp3 + ] + if attenuation is not None: + command += ['vol', f'-{attenuation}dB'] + print(' '.join(command)) + subprocess.run(command, check=True) + + +def convert_audio_file( + src_path, dst_path, + *, bit_depth=None, compression=None): + """Convert audio file with `sox` command.""" + command = ['sox', '-V3', '-R', str(src_path)] + if bit_depth is not None: + command += ['--bits', str(bit_depth)] + if compression is not None: + command += ['--compression', str(compression)] + command += [dst_path] + print(' '.join(command)) + subprocess.run(command, check=True) diff --git a/test/common_utils/wav_utils.py b/test/common_utils/wav_utils.py new file mode 100644 index 0000000000..bc122ec6cb --- /dev/null +++ b/test/common_utils/wav_utils.py @@ -0,0 +1,86 @@ +from typing import Optional + +import torch +import scipy.io.wavfile + + +def normalize_wav(tensor: torch.Tensor) -> torch.Tensor: + if tensor.dtype == torch.float32: + pass + elif tensor.dtype == torch.int32: + tensor = tensor.to(torch.float32) + tensor[tensor > 0] /= 2147483647. + tensor[tensor < 0] /= 2147483648. + elif tensor.dtype == torch.int16: + tensor = tensor.to(torch.float32) + tensor[tensor > 0] /= 32767. + tensor[tensor < 0] /= 32768. + elif tensor.dtype == torch.uint8: + tensor = tensor.to(torch.float32) - 128 + tensor[tensor > 0] /= 127. + tensor[tensor < 0] /= 128. + return tensor + + +def get_wav_data( + dtype: str, + num_channels: int, + *, + num_frames: Optional[int] = None, + normalize: bool = True, + channels_first: bool = True, +): + """Generate linear signal of the given dtype and num_channels + + Data range is + [-1.0, 1.0] for float32, + [-2147483648, 2147483647] for int32 + [-32768, 32767] for int16 + [0, 255] for uint8 + + num_frames allow to change the linear interpolation parameter. + Default values are 256 for uint8, else 1 << 16. + 1 << 16 as default is so that int16 value range is completely covered. + """ + dtype_ = getattr(torch, dtype) + + if num_frames is None: + if dtype == 'uint8': + num_frames = 256 + else: + num_frames = 1 << 16 + + if dtype == 'uint8': + base = torch.linspace(0, 255, num_frames, dtype=dtype_) + if dtype == 'float32': + base = torch.linspace(-1., 1., num_frames, dtype=dtype_) + if dtype == 'int32': + base = torch.linspace(-2147483648, 2147483647, num_frames, dtype=dtype_) + if dtype == 'int16': + base = torch.linspace(-32768, 32767, num_frames, dtype=dtype_) + data = base.repeat([num_channels, 1]) + if not channels_first: + data = data.transpose(1, 0) + if normalize: + data = normalize_wav(data) + return data + + +def load_wav(path: str, normalize=True, channels_first=True) -> torch.Tensor: + """Load wav file without torchaudio""" + sample_rate, data = scipy.io.wavfile.read(path) + data = torch.from_numpy(data.copy()) + if data.ndim == 1: + data = data.unsqueeze(1) + if normalize: + data = normalize_wav(data) + if channels_first: + data = data.transpose(1, 0) + return data, sample_rate + + +def save_wav(path, data, sample_rate, channels_first=True): + """Save wav file without torchaudio""" + if channels_first: + data = data.transpose(1, 0) + scipy.io.wavfile.write(path, sample_rate, data.numpy()) diff --git a/test/functional_cpu_test.py b/test/functional_cpu_test.py index 470d6ab770..ab5fdaed95 100644 --- a/test/functional_cpu_test.py +++ b/test/functional_cpu_test.py @@ -10,6 +10,31 @@ from .functional_impl import Lfilter +def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32): + """ Generates random tensors given a seed and size + https://en.wikipedia.org/wiki/Linear_congruential_generator + X_{n + 1} = (a * X_n + c) % m + Using Borland C/C++ values + + The tensor will have values between [0,1) + Inputs: + seed (int): an int + size (Tuple[int]): the size of the output tensor + a (int): the multiplier constant to the generator + c (int): the additive constant to the generator + m (int): the modulus constant to the generator + """ + num_elements = 1 + for s in size: + num_elements *= s + + arr = [(a * seed + c) % m] + for i in range(num_elements - 1): + arr.append((a * arr[i] + c) % m) + + return torch.tensor(arr).float().view(size) / m + + class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): dtype = torch.float32 device = torch.device('cpu') @@ -49,7 +74,7 @@ def _test_istft_is_inverse_of_stft(kwargs): for data_size in [(2, 20), (3, 15), (4, 10)]: for i in range(100): - sound = common_utils.random_float_tensor(i, data_size) + sound = random_float_tensor(i, data_size) stft = torch.stft(sound, **kwargs) estimate = torchaudio.functional.istft(stft, length=sound.size(1), **kwargs) @@ -211,8 +236,8 @@ def test_istft_of_sine(self): def _test_linearity_of_istft(self, data_size, kwargs, atol=1e-6, rtol=1e-8): for i in range(self.number_of_trials): - tensor1 = common_utils.random_float_tensor(i, data_size) - tensor2 = common_utils.random_float_tensor(i * 2, data_size) + tensor1 = random_float_tensor(i, data_size) + tensor2 = random_float_tensor(i * 2, data_size) a, b = torch.rand(2) istft1 = torchaudio.functional.istft(tensor1, **kwargs) istft2 = torchaudio.functional.istft(tensor2, **kwargs) @@ -274,8 +299,6 @@ def test_linearity_of_istft4(self): class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): - backend = 'default' - def test_pitch(self): test_filepath_100 = common_utils.get_asset_path("100Hz_44100Hz_16bit_05sec.wav") test_filepath_440 = common_utils.get_asset_path("440Hz_44100Hz_16bit_05sec.wav") @@ -287,7 +310,7 @@ def test_pitch(self): ] for filename, freq_ref in tests: - waveform, sample_rate = torchaudio.load(filename) + waveform, sample_rate = common_utils.load_wav(filename) freq = torchaudio.functional.detect_pitch_frequency(waveform, sample_rate) diff --git a/test/kaldi_compatibility_impl.py b/test/kaldi_compatibility_impl.py index ec58a3fa44..7e51620f63 100644 --- a/test/kaldi_compatibility_impl.py +++ b/test/kaldi_compatibility_impl.py @@ -1,20 +1,19 @@ """Test suites for checking numerical compatibility against Kaldi""" -import json -import shutil -import unittest import subprocess import kaldi_io import torch import torchaudio.functional as F import torchaudio.compliance.kaldi +from parameterized import parameterized -from . import common_utils -from parameterized import parameterized, param - - -def _not_available(cmd): - return shutil.which(cmd) is None +from .common_utils import ( + TestBaseMixin, + load_params, + skipIfNoExec, + get_asset_path, + load_wav +) def _convert_args(**kwargs): @@ -49,19 +48,12 @@ def _run_kaldi(command, input_type, input_value): return torch.from_numpy(result.copy()) # copy supresses some torch warning -def _load_params(path): - with open(path, 'r') as file: - return [param(json.loads(line)) for line in file] - - -class Kaldi(common_utils.TestBaseMixin): - backend = 'sox' - +class Kaldi(TestBaseMixin): def assert_equal(self, output, *, expected, rtol=None, atol=None): expected = expected.to(dtype=self.dtype, device=self.device) self.assertEqual(output, expected, rtol=rtol, atol=atol) - @unittest.skipIf(_not_available('apply-cmvn-sliding'), '`apply-cmvn-sliding` not available') + @skipIfNoExec('apply-cmvn-sliding') def test_sliding_window_cmn(self): """sliding_window_cmn should be numerically compatible with apply-cmvn-sliding""" kwargs = { @@ -77,34 +69,34 @@ def test_sliding_window_cmn(self): kaldi_result = _run_kaldi(command, 'ark', tensor) self.assert_equal(result, expected=kaldi_result) - @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_fbank_args.json'))) - @unittest.skipIf(_not_available('compute-fbank-feats'), '`compute-fbank-feats` not available') + @parameterized.expand(load_params('kaldi_test_fbank_args.json')) + @skipIfNoExec('compute-fbank-feats') def test_fbank(self, kwargs): """fbank should be numerically compatible with compute-fbank-feats""" - wave_file = common_utils.get_asset_path('kaldi_file.wav') - waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + wave_file = get_asset_path('kaldi_file.wav') + waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) result = torchaudio.compliance.kaldi.fbank(waveform, **kwargs) command = ['compute-fbank-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] kaldi_result = _run_kaldi(command, 'scp', wave_file) self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) - @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_spectrogram_args.json'))) - @unittest.skipIf(_not_available('compute-spectrogram-feats'), '`compute-spectrogram-feats` not available') + @parameterized.expand(load_params('kaldi_test_spectrogram_args.json')) + @skipIfNoExec('compute-spectrogram-feats') def test_spectrogram(self, kwargs): """spectrogram should be numerically compatible with compute-spectrogram-feats""" - wave_file = common_utils.get_asset_path('kaldi_file.wav') - waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + wave_file = get_asset_path('kaldi_file.wav') + waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) result = torchaudio.compliance.kaldi.spectrogram(waveform, **kwargs) command = ['compute-spectrogram-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] kaldi_result = _run_kaldi(command, 'scp', wave_file) self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) - @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_mfcc_args.json'))) - @unittest.skipIf(_not_available('compute-mfcc-feats'), '`compute-mfcc-feats` not available') + @parameterized.expand(load_params('kaldi_test_mfcc_args.json')) + @skipIfNoExec('compute-mfcc-feats') def test_mfcc(self, kwargs): """mfcc should be numerically compatible with compute-mfcc-feats""" - wave_file = common_utils.get_asset_path('kaldi_file.wav') - waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + wave_file = get_asset_path('kaldi_file.wav') + waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) result = torchaudio.compliance.kaldi.mfcc(waveform, **kwargs) command = ['compute-mfcc-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] kaldi_result = _run_kaldi(command, 'scp', wave_file) diff --git a/test/sox_io_backend/__init__.py b/test/sox_io_backend/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/sox_io_backend/common.py b/test/sox_io_backend/common.py new file mode 100644 index 0000000000..eb85937236 --- /dev/null +++ b/test/sox_io_backend/common.py @@ -0,0 +1,2 @@ +def name_func(func, _, params): + return f'{func.__name__}_{"_".join(str(arg) for arg in params.args)}' diff --git a/test/sox_io_backend/test_info.py b/test/sox_io_backend/test_info.py new file mode 100644 index 0000000000..da5207a7e5 --- /dev/null +++ b/test/sox_io_backend/test_info.py @@ -0,0 +1,125 @@ +import itertools +from parameterized import parameterized + +from torchaudio.backend import sox_io_backend + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExec, + skipIfNoExtension, + get_asset_path, + get_wav_data, + save_wav, + sox_utils, +) +from .common import ( + name_func, +) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestInfo(TempDirMixin, PytorchTestCase): + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels): + """`sox_io_backend.info` can check wav file correctly""" + duration = 1 + path = self.get_temp_path('data.wav') + data = get_wav_data(dtype, num_channels, normalize=False, num_frames=duration * sample_rate) + save_wav(path, data, sample_rate) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [4, 8, 16, 32], + )), name_func=name_func) + def test_wav_multiple_channels(self, dtype, sample_rate, num_channels): + """`sox_io_backend.info` can check wav file with channels more than 2 correctly""" + duration = 1 + path = self.get_temp_path('data.wav') + data = get_wav_data(dtype, num_channels, normalize=False, num_frames=duration * sample_rate) + save_wav(path, data, sample_rate) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [96, 128, 160, 192, 224, 256, 320], + )), name_func=name_func) + def test_mp3(self, sample_rate, num_channels, bit_rate): + """`sox_io_backend.info` can check mp3 file correctly""" + duration = 1 + path = self.get_temp_path('data.mp3') + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=bit_rate, duration=duration, + ) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + # mp3 does not preserve the number of samples + # assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """`sox_io_backend.info` can check flac file correctly""" + duration = 1 + path = self.get_temp_path('data.flac') + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=compression_level, duration=duration, + ) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-1, 0, 1, 2, 3, 3.6, 5, 10], + )), name_func=name_func) + def test_vorbis(self, sample_rate, num_channels, quality_level): + """`sox_io_backend.info` can check vorbis file correctly""" + duration = 1 + path = self.get_temp_path('data.vorbis') + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=quality_level, duration=duration, + ) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + +@skipIfNoExtension +class TestInfoOpus(PytorchTestCase): + @parameterized.expand(list(itertools.product( + ['96k'], + [1, 2], + [0, 5, 10], + )), name_func=name_func) + def test_opus(self, bitrate, num_channels, compression_level): + """`sox_io_backend.info` can check opus file correcty""" + path = get_asset_path('io', f'{bitrate}_{compression_level}_{num_channels}ch.opus') + info = sox_io_backend.info(path) + assert info.sample_rate == 48000 + assert info.num_frames == 32768 + assert info.num_channels == num_channels diff --git a/test/sox_io_backend/test_load.py b/test/sox_io_backend/test_load.py new file mode 100644 index 0000000000..8366a01f83 --- /dev/null +++ b/test/sox_io_backend/test_load.py @@ -0,0 +1,263 @@ +import itertools + +from torchaudio.backend import sox_io_backend +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExec, + skipIfNoExtension, + get_asset_path, + get_wav_data, + load_wav, + save_wav, + sox_utils, +) +from .common import ( + name_func, +) + + +class LoadTestBase(TempDirMixin, PytorchTestCase): + def assert_wav(self, dtype, sample_rate, num_channels, normalize, duration): + """`sox_io_backend.load` can load wav format correctly. + + Wav data loaded with sox_io backend should match those with scipy + """ + path = self.get_temp_path('reference.wav') + data = get_wav_data(dtype, num_channels, normalize=normalize, num_frames=duration * sample_rate) + save_wav(path, data, sample_rate) + expected = load_wav(path, normalize=normalize)[0] + data, sr = sox_io_backend.load(path, normalize=normalize) + assert sr == sample_rate + self.assertEqual(data, expected) + + def assert_mp3(self, sample_rate, num_channels, bit_rate, duration): + """`sox_io_backend.load` can load mp3 format. + + mp3 encoding introduces delay and boundary effects so + we create reference wav file from mp3 + + x + | + | 1. Generate mp3 with Sox + | + v 2. Convert to wav with Sox + mp3 ------------------------------> wav + | | + | 3. Load with torchaudio | 4. Load with scipy + | | + v v + tensor ----------> x <----------- tensor + 5. Compare + + Underlying assumptions are; + i. Conversion of mp3 to wav with Sox preserves data. + ii. Loading wav file with scipy is correct. + + By combining i & ii, step 2. and 4. allows to load reference mp3 data + without using torchaudio + """ + path = self.get_temp_path('1.original.mp3') + ref_path = self.get_temp_path('2.reference.wav') + + # 1. Generate mp3 with sox + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=bit_rate, duration=duration) + # 2. Convert to wav with sox + sox_utils.convert_audio_file(path, ref_path) + # 3. Load mp3 with torchaudio + data, sr = sox_io_backend.load(path) + # 4. Load wav with scipy + data_ref = load_wav(ref_path)[0] + # 5. Compare + assert sr == sample_rate + self.assertEqual(data, data_ref, atol=3e-03, rtol=1.3e-06) + + def assert_flac(self, sample_rate, num_channels, compression_level, duration): + """`sox_io_backend.load` can load flac format. + + This test takes the same strategy as mp3 to compare the result + """ + path = self.get_temp_path('1.original.flac') + ref_path = self.get_temp_path('2.reference.wav') + + # 1. Generate flac with sox + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=compression_level, bit_depth=16, duration=duration) + # 2. Convert to wav with sox + sox_utils.convert_audio_file(path, ref_path) + # 3. Load flac with torchaudio + data, sr = sox_io_backend.load(path) + # 4. Load wav with scipy + data_ref = load_wav(ref_path)[0] + # 5. Compare + assert sr == sample_rate + self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + + def assert_vorbis(self, sample_rate, num_channels, quality_level, duration): + """`sox_io_backend.load` can load vorbis format. + + This test takes the same strategy as mp3 to compare the result + """ + path = self.get_temp_path('1.original.vorbis') + ref_path = self.get_temp_path('2.reference.wav') + + # 1. Generate vorbis with sox + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=quality_level, bit_depth=16, duration=duration) + # 2. Convert to wav with sox + sox_utils.convert_audio_file(path, ref_path) + # 3. Load vorbis with torchaudio + data, sr = sox_io_backend.load(path) + # 4. Load wav with scipy + data_ref = load_wav(ref_path)[0] + # 5. Compare + assert sr == sample_rate + self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestLoad(LoadTestBase): + """Test the correctness of `sox_io_backend.load` for various formats""" + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + [False, True], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels, normalize): + """`sox_io_backend.load` can load wav format correctly.""" + self.assert_wav(dtype, sample_rate, num_channels, normalize, duration=1) + + @parameterized.expand(list(itertools.product( + ['int16'], + [16000], + [2], + [False], + )), name_func=name_func) + def test_wav_large(self, dtype, sample_rate, num_channels, normalize): + """`sox_io_backend.load` can load large wav file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_wav(dtype, sample_rate, num_channels, normalize, two_hours) + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [4, 8, 16, 32], + )), name_func=name_func) + def test_multiple_channels(self, dtype, num_channels): + """`sox_io_backend.load` can load wav file with more than 2 channels.""" + sample_rate = 8000 + normalize = False + self.assert_wav(dtype, sample_rate, num_channels, normalize, duration=1) + + @parameterized.expand(list(itertools.product( + [8000, 16000, 44100], + [1, 2], + [96, 128, 160, 192, 224, 256, 320], + )), name_func=name_func) + def test_mp3(self, sample_rate, num_channels, bit_rate): + """`sox_io_backend.load` can load mp3 format correctly.""" + self.assert_mp3(sample_rate, num_channels, bit_rate, duration=1) + + @parameterized.expand(list(itertools.product( + [16000], + [2], + [128], + )), name_func=name_func) + def test_mp3_large(self, sample_rate, num_channels, bit_rate): + """`sox_io_backend.load` can load large mp3 file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_mp3(sample_rate, num_channels, bit_rate, two_hours) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """`sox_io_backend.load` can load flac format correctly.""" + self.assert_flac(sample_rate, num_channels, compression_level, duration=1) + + @parameterized.expand(list(itertools.product( + [16000], + [2], + [0], + )), name_func=name_func) + def test_flac_large(self, sample_rate, num_channels, compression_level): + """`sox_io_backend.load` can load large flac file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_flac(sample_rate, num_channels, compression_level, two_hours) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-1, 0, 1, 2, 3, 3.6, 5, 10], + )), name_func=name_func) + def test_vorbis(self, sample_rate, num_channels, quality_level): + """`sox_io_backend.load` can load vorbis format correctly.""" + self.assert_vorbis(sample_rate, num_channels, quality_level, duration=1) + + @parameterized.expand(list(itertools.product( + [16000], + [2], + [10], + )), name_func=name_func) + def test_vorbis_large(self, sample_rate, num_channels, quality_level): + """`sox_io_backend.load` can load large vorbis file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_vorbis(sample_rate, num_channels, quality_level, two_hours) + + @parameterized.expand(list(itertools.product( + ['96k'], + [1, 2], + [0, 5, 10], + )), name_func=name_func) + def test_opus(self, bitrate, num_channels, compression_level): + """`sox_io_backend.load` can load opus file correctly.""" + ops_path = get_asset_path('io', f'{bitrate}_{compression_level}_{num_channels}ch.opus') + wav_path = self.get_temp_path(f'{bitrate}_{compression_level}_{num_channels}ch.opus.wav') + sox_utils.convert_audio_file(ops_path, wav_path) + + expected, sample_rate = load_wav(wav_path) + found, sr = sox_io_backend.load(ops_path) + + assert sample_rate == sr + self.assertEqual(expected, found) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestLoadParams(TempDirMixin, PytorchTestCase): + """Test the correctness of frame parameters of `sox_io_backend.load`""" + original = None + path = None + + def setUp(self): + super().setUp() + sample_rate = 8000 + self.original = get_wav_data('float32', num_channels=2) + self.path = self.get_temp_path('test.wave') + save_wav(self.path, self.original, sample_rate) + + @parameterized.expand(list(itertools.product( + [0, 1, 10, 100, 1000], + [-1, 1, 10, 100, 1000], + )), name_func=name_func) + def test_frame(self, frame_offset, num_frames): + """num_frames and frame_offset correctly specify the region of data""" + found, _ = sox_io_backend.load(self.path, frame_offset, num_frames) + frame_end = None if num_frames == -1 else frame_offset + num_frames + self.assertEqual(found, self.original[:, frame_offset:frame_end]) + + @parameterized.expand([(True, ), (False, )], name_func=name_func) + def test_channels_first(self, channels_first): + """channels_first swaps axes""" + found, _ = sox_io_backend.load(self.path, channels_first=channels_first) + expected = self.original if channels_first else self.original.transpose(1, 0) + self.assertEqual(found, expected) diff --git a/test/sox_io_backend/test_roundtrip.py b/test/sox_io_backend/test_roundtrip.py new file mode 100644 index 0000000000..2a051bebd5 --- /dev/null +++ b/test/sox_io_backend/test_roundtrip.py @@ -0,0 +1,52 @@ +import itertools + +from torchaudio.backend import sox_io_backend +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExec, + skipIfNoExtension, + get_wav_data, +) +from .common import ( + name_func, +) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestRoundTripIO(TempDirMixin, PytorchTestCase): + """save/load round trip should not degrade data for lossless formats""" + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels): + """save/load round trip should not degrade data for wav formats""" + original = get_wav_data(dtype, num_channels, normalize=False) + data = original + for i in range(10): + path = self.get_temp_path(f'{i}.wav') + sox_io_backend.save(path, data, sample_rate) + data, sr = sox_io_backend.load(path, normalize=False) + assert sr == sample_rate + self.assertEqual(original, data) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """save/load round trip should not degrade data for flac formats""" + original = get_wav_data('float32', num_channels) + data = original + for i in range(10): + path = self.get_temp_path(f'{i}.flac') + sox_io_backend.save(path, data, sample_rate, compression=compression_level) + data, sr = sox_io_backend.load(path) + assert sr == sample_rate + self.assertEqual(original, data) diff --git a/test/sox_io_backend/test_save.py b/test/sox_io_backend/test_save.py new file mode 100644 index 0000000000..53588c456b --- /dev/null +++ b/test/sox_io_backend/test_save.py @@ -0,0 +1,304 @@ +import itertools + +from torchaudio.backend import sox_io_backend +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExec, + skipIfNoExtension, + get_wav_data, + load_wav, + save_wav, + sox_utils, +) +from .common import ( + name_func, +) + + +class SaveTestBase(TempDirMixin, PytorchTestCase): + def assert_wav(self, dtype, sample_rate, num_channels, num_frames): + """`sox_io_backend.save` can save wav format.""" + path = self.get_temp_path('data.wav') + expected = get_wav_data(dtype, num_channels, num_frames=num_frames) + sox_io_backend.save(path, expected, sample_rate) + found, sr = load_wav(path) + assert sample_rate == sr + self.assertEqual(found, expected) + + def assert_mp3(self, sample_rate, num_channels, bit_rate, duration): + """`sox_io_backend.save` can save mp3 format. + + mp3 encoding introduces delay and boundary effects so + we convert the resulting mp3 to wav and compare the results there + + | + | 1. Generate original wav file with SciPy + | + v + -------------- wav ---------------- + | | + | 2.1. load with scipy | 3.1. Convert to mp3 with Sox + | then save with torchaudio | + v v + mp3 mp3 + | | + | 2.2. Convert to wav with Sox | 3.2. Convert to wav with Sox + | | + v v + wav wav + | | + | 2.3. load with scipy | 3.3. load with scipy + | | + v v + tensor -------> compare <--------- tensor + + """ + src_path = self.get_temp_path('1.reference.wav') + mp3_path = self.get_temp_path('2.1.torchaudio.mp3') + wav_path = self.get_temp_path('2.2.torchaudio.wav') + mp3_path_sox = self.get_temp_path('3.1.sox.mp3') + wav_path_sox = self.get_temp_path('3.2.sox.wav') + + # 1. Generate original wav + data = get_wav_data('float32', num_channels, normalize=True, num_frames=duration * sample_rate) + save_wav(src_path, data, sample_rate) + # 2.1. Convert the original wav to mp3 with torchaudio + sox_io_backend.save( + mp3_path, load_wav(src_path)[0], sample_rate, compression=bit_rate) + # 2.2. Convert the mp3 to wav with Sox + sox_utils.convert_audio_file(mp3_path, wav_path) + # 2.3. Load + found = load_wav(wav_path)[0] + + # 3.1. Convert the original wav to mp3 with SoX + sox_utils.convert_audio_file(src_path, mp3_path_sox, compression=bit_rate) + # 3.2. Convert the mp3 to wav with Sox + sox_utils.convert_audio_file(mp3_path_sox, wav_path_sox) + # 3.3. Load + expected = load_wav(wav_path_sox)[0] + + self.assertEqual(found, expected) + + def assert_flac(self, sample_rate, num_channels, compression_level, duration): + """`sox_io_backend.save` can save flac format. + + This test takes the same strategy as mp3 to compare the result + """ + src_path = self.get_temp_path('1.reference.wav') + flc_path = self.get_temp_path('2.1.torchaudio.flac') + wav_path = self.get_temp_path('2.2.torchaudio.wav') + flc_path_sox = self.get_temp_path('3.1.sox.flac') + wav_path_sox = self.get_temp_path('3.2.sox.wav') + + # 1. Generate original wav + data = get_wav_data('float32', num_channels, normalize=True, num_frames=duration * sample_rate) + save_wav(src_path, data, sample_rate) + # 2.1. Convert the original wav to flac with torchaudio + sox_io_backend.save( + flc_path, load_wav(src_path)[0], sample_rate, compression=compression_level) + # 2.2. Convert the flac to wav with Sox + # converting to 32 bit because flac file has 24 bit depth which scipy cannot handle. + sox_utils.convert_audio_file(flc_path, wav_path, bit_depth=32) + # 2.3. Load + found = load_wav(wav_path)[0] + + # 3.1. Convert the original wav to flac with SoX + sox_utils.convert_audio_file(src_path, flc_path_sox, compression=compression_level) + # 3.2. Convert the flac to wav with Sox + # converting to 32 bit because flac file has 24 bit depth which scipy cannot handle. + sox_utils.convert_audio_file(flc_path_sox, wav_path_sox, bit_depth=32) + # 3.3. Load + expected = load_wav(wav_path_sox)[0] + + self.assertEqual(found, expected) + + def _assert_vorbis(self, sample_rate, num_channels, quality_level, duration): + """`sox_io_backend.save` can save vorbis format. + + This test takes the same strategy as mp3 to compare the result + """ + src_path = self.get_temp_path('1.reference.wav') + vbs_path = self.get_temp_path('2.1.torchaudio.vorbis') + wav_path = self.get_temp_path('2.2.torchaudio.wav') + vbs_path_sox = self.get_temp_path('3.1.sox.vorbis') + wav_path_sox = self.get_temp_path('3.2.sox.wav') + + # 1. Generate original wav + data = get_wav_data('int16', num_channels, normalize=False, num_frames=duration * sample_rate) + save_wav(src_path, data, sample_rate) + # 2.1. Convert the original wav to vorbis with torchaudio + sox_io_backend.save( + vbs_path, load_wav(src_path)[0], sample_rate, compression=quality_level) + # 2.2. Convert the vorbis to wav with Sox + sox_utils.convert_audio_file(vbs_path, wav_path) + # 2.3. Load + found = load_wav(wav_path)[0] + + # 3.1. Convert the original wav to vorbis with SoX + sox_utils.convert_audio_file(src_path, vbs_path_sox, compression=quality_level) + # 3.2. Convert the vorbis to wav with Sox + sox_utils.convert_audio_file(vbs_path_sox, wav_path_sox) + # 3.3. Load + expected = load_wav(wav_path_sox)[0] + + # sox's vorbis encoding has some random boundary effect, which cause small number of + # samples yields higher descrepency than the others. + # so we allow small portions of data to be outside of absolute torelance. + # make sure to pass somewhat long duration + atol = 1.0e-4 + max_failure_allowed = 0.01 # this percent of samples are allowed to outside of atol. + failure_ratio = ((found - expected).abs() > atol).sum().item() / found.numel() + if failure_ratio > max_failure_allowed: + # it's failed and this will give a better error message. + self.assertEqual(found, expected, atol=atol, rtol=1.3e-6) + + def assert_vorbis(self, *args, **kwargs): + # sox's vorbis encoding has some randomness, so we run tests multiple time + max_retry = 5 + error = None + for _ in range(max_retry): + try: + self._assert_vorbis(*args, **kwargs) + break + except AssertionError as e: + error = e + else: + raise error + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestSave(SaveTestBase): + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels): + """`sox_io_backend.save` can save wav format.""" + self.assert_wav(dtype, sample_rate, num_channels, num_frames=None) + + @parameterized.expand(list(itertools.product( + ['float32'], + [16000], + [2], + )), name_func=name_func) + def test_wav_large(self, dtype, sample_rate, num_channels): + """`sox_io_backend.save` can save large wav file.""" + two_hours = 2 * 60 * 60 * sample_rate + self.assert_wav(dtype, sample_rate, num_channels, num_frames=two_hours) + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [4, 8, 16, 32], + )), name_func=name_func) + def test_multiple_channels(self, dtype, num_channels): + """`sox_io_backend.save` can save wav with more than 2 channels.""" + sample_rate = 8000 + self.assert_wav(dtype, sample_rate, num_channels, num_frames=None) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-4.2, -0.2, 0, 0.2, 96, 128, 160, 192, 224, 256, 320], + )), name_func=name_func) + def test_mp3(self, sample_rate, num_channels, bit_rate): + """`sox_io_backend.save` can save mp3 format.""" + self.assert_mp3(sample_rate, num_channels, bit_rate, duration=1) + + @parameterized.expand(list(itertools.product( + [16000], + [2], + [128], + )), name_func=name_func) + def test_mp3_large(self, sample_rate, num_channels, bit_rate): + """`sox_io_backend.save` can save large mp3 file.""" + two_hours = 2 * 60 * 60 + self.assert_mp3(sample_rate, num_channels, bit_rate, duration=two_hours) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """`sox_io_backend.save` can save flac format.""" + self.assert_flac(sample_rate, num_channels, compression_level, duration=1) + + @parameterized.expand(list(itertools.product( + [16000], + [2], + [0], + )), name_func=name_func) + def test_flac_large(self, sample_rate, num_channels, compression_level): + """`sox_io_backend.save` can save large flac file.""" + two_hours = 2 * 60 * 60 + self.assert_flac(sample_rate, num_channels, compression_level, duration=two_hours) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-1, 0, 1, 2, 3, 3.6, 5, 10], + )), name_func=name_func) + def test_vorbis(self, sample_rate, num_channels, quality_level): + """`sox_io_backend.save` can save vorbis format.""" + self.assert_vorbis(sample_rate, num_channels, quality_level, duration=20) + + # note: torchaudio can load large vorbis file, but cannot save large volbis file + # the following test causes Segmentation fault + # + ''' + @parameterized.expand(list(itertools.product( + [16000], + [2], + [10], + )), name_func=name_func) + def test_vorbis_large(self, sample_rate, num_channels, quality_level): + """`sox_io_backend.save` can save large vorbis file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_vorbis(sample_rate, num_channels, quality_level, two_hours) + ''' + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestSaveParams(TempDirMixin, PytorchTestCase): + """Test the correctness of optional parameters of `sox_io_backend.save`""" + @parameterized.expand([(True, ), (False, )], name_func=name_func) + def test_channels_first(self, channels_first): + """channels_first swaps axes""" + path = self.get_temp_path('data.wav') + data = get_wav_data('int32', 2, channels_first=channels_first) + sox_io_backend.save( + path, data, 8000, channels_first=channels_first) + found = load_wav(path)[0] + expected = data if channels_first else data.transpose(1, 0) + self.assertEqual(found, expected) + + @parameterized.expand([ + 'float32', 'int32', 'int16', 'uint8' + ], name_func=name_func) + def test_noncontiguous(self, dtype): + """Noncontiguous tensors are saved correctly""" + path = self.get_temp_path('data.wav') + expected = get_wav_data(dtype, 4)[::2, ::2] + assert not expected.is_contiguous() + sox_io_backend.save(path, expected, 8000) + found = load_wav(path)[0] + self.assertEqual(found, expected) + + @parameterized.expand([ + 'float32', 'int32', 'int16', 'uint8', + ]) + def test_tensor_preserve(self, dtype): + """save function should not alter Tensor""" + path = self.get_temp_path('data.wav') + expected = get_wav_data(dtype, 4)[::2, ::2] + + data = expected.clone() + sox_io_backend.save(path, data, 8000) + + self.assertEqual(data, expected) diff --git a/test/sox_io_backend/test_torchscript.py b/test/sox_io_backend/test_torchscript.py new file mode 100644 index 0000000000..9a30aab0d2 --- /dev/null +++ b/test/sox_io_backend/test_torchscript.py @@ -0,0 +1,149 @@ +import itertools +from typing import Optional + +import torch +import torchaudio +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + skipIfNoExec, + skipIfNoExtension, + get_wav_data, + save_wav, + load_wav, + sox_utils, +) +from .common import ( + name_func, +) + + +def py_info_func(filepath: str) -> torchaudio.backend.sox_io_backend.AudioMetaData: + return torchaudio.info(filepath) + + +def py_load_func(filepath: str, normalize: bool, channels_first: bool): + return torchaudio.load( + filepath, normalize=normalize, channels_first=channels_first) + + +def py_save_func( + filepath: str, + tensor: torch.Tensor, + sample_rate: int, + channels_first: bool = True, + compression: Optional[float] = None, +): + torchaudio.save(filepath, tensor, sample_rate, channels_first, compression) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class SoxIO(TempDirMixin, TorchaudioTestCase): + """TorchScript-ability Test suite for `sox_io_backend`""" + backend = 'sox_io' + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_info_wav(self, dtype, sample_rate, num_channels): + """`sox_io_backend.info` is torchscript-able and returns the same result""" + audio_path = self.get_temp_path(f'{dtype}_{sample_rate}_{num_channels}.wav') + data = get_wav_data(dtype, num_channels, normalize=False, num_frames=1 * sample_rate) + save_wav(audio_path, data, sample_rate) + + script_path = self.get_temp_path('info_func.zip') + torch.jit.script(py_info_func).save(script_path) + ts_info_func = torch.jit.load(script_path) + + py_info = py_info_func(audio_path) + ts_info = ts_info_func(audio_path) + + assert py_info.sample_rate == ts_info.sample_rate + assert py_info.num_frames == ts_info.num_frames + assert py_info.num_channels == ts_info.num_channels + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + [False, True], + [False, True], + )), name_func=name_func) + def test_load_wav(self, dtype, sample_rate, num_channels, normalize, channels_first): + """`sox_io_backend.load` is torchscript-able and returns the same result""" + audio_path = self.get_temp_path(f'test_load_{dtype}_{sample_rate}_{num_channels}_{normalize}.wav') + data = get_wav_data(dtype, num_channels, normalize=False, num_frames=1 * sample_rate) + save_wav(audio_path, data, sample_rate) + + script_path = self.get_temp_path('load_func.zip') + torch.jit.script(py_load_func).save(script_path) + ts_load_func = torch.jit.load(script_path) + + py_data, py_sr = py_load_func( + audio_path, normalize=normalize, channels_first=channels_first) + ts_data, ts_sr = ts_load_func( + audio_path, normalize=normalize, channels_first=channels_first) + + self.assertEqual(py_sr, ts_sr) + self.assertEqual(py_data, ts_data) + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_save_wav(self, dtype, sample_rate, num_channels): + script_path = self.get_temp_path('save_func.zip') + torch.jit.script(py_save_func).save(script_path) + ts_save_func = torch.jit.load(script_path) + + expected = get_wav_data(dtype, num_channels) + py_path = self.get_temp_path(f'test_save_py_{dtype}_{sample_rate}_{num_channels}.wav') + ts_path = self.get_temp_path(f'test_save_ts_{dtype}_{sample_rate}_{num_channels}.wav') + + py_save_func(py_path, expected, sample_rate, True, None) + ts_save_func(ts_path, expected, sample_rate, True, None) + + py_data, py_sr = load_wav(py_path) + ts_data, ts_sr = load_wav(ts_path) + + self.assertEqual(sample_rate, py_sr) + self.assertEqual(sample_rate, ts_sr) + self.assertEqual(expected, py_data) + self.assertEqual(expected, ts_data) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_save_flac(self, sample_rate, num_channels, compression_level): + script_path = self.get_temp_path('save_func.zip') + torch.jit.script(py_save_func).save(script_path) + ts_save_func = torch.jit.load(script_path) + + expected = get_wav_data('float32', num_channels) + py_path = self.get_temp_path(f'test_save_py_{sample_rate}_{num_channels}_{compression_level}.flac') + ts_path = self.get_temp_path(f'test_save_ts_{sample_rate}_{num_channels}_{compression_level}.flac') + + py_save_func(py_path, expected, sample_rate, True, compression_level) + ts_save_func(ts_path, expected, sample_rate, True, compression_level) + + # converting to 32 bit because flac file has 24 bit depth which scipy cannot handle. + py_path_wav = f'{py_path}.wav' + ts_path_wav = f'{ts_path}.wav' + sox_utils.convert_audio_file(py_path, py_path_wav, bit_depth=32) + sox_utils.convert_audio_file(ts_path, ts_path_wav, bit_depth=32) + + py_data, py_sr = load_wav(py_path_wav, normalize=True) + ts_data, ts_sr = load_wav(ts_path_wav, normalize=True) + + self.assertEqual(sample_rate, py_sr) + self.assertEqual(sample_rate, ts_sr) + self.assertEqual(expected, py_data) + self.assertEqual(expected, ts_data) diff --git a/test/test_backend.py b/test/test_backend.py index 1e8f9e4fd6..6b67cb2898 100644 --- a/test/test_backend.py +++ b/test/test_backend.py @@ -1,7 +1,4 @@ -import unittest - import torchaudio -from torchaudio._internal.module_utils import is_module_available from . import common_utils @@ -28,15 +25,19 @@ class TestBackendSwitch_NoBackend(BackendSwitchMixin, common_utils.TorchaudioTes backend_module = torchaudio.backend.no_backend -@unittest.skipIf( - not is_module_available('torchaudio._torchaudio'), - 'torchaudio C++ extension not available') +@common_utils.skipIfNoExtension class TestBackendSwitch_SoX(BackendSwitchMixin, common_utils.TorchaudioTestCase): backend = 'sox' backend_module = torchaudio.backend.sox_backend -@unittest.skipIf(not is_module_available('soundfile'), '"soundfile" not available') +@common_utils.skipIfNoExtension +class TestBackendSwitch_SoXIO(BackendSwitchMixin, common_utils.TorchaudioTestCase): + backend = 'sox_io' + backend_module = torchaudio.backend.sox_io_backend + + +@common_utils.skipIfNoModule('soundfile') class TestBackendSwitch_soundfile(BackendSwitchMixin, common_utils.TorchaudioTestCase): backend = 'soundfile' backend_module = torchaudio.backend.soundfile_backend diff --git a/test/test_datasets.py b/test/test_datasets.py index 3ac7fae8ac..c3b0c917da 100644 --- a/test/test_datasets.py +++ b/test/test_datasets.py @@ -46,9 +46,8 @@ def test_cmuarctic(self): data[0] -@common_utils.skipIfNoSoxBackend class TestCommonVoice(common_utils.TorchaudioTestCase): - backend = 'sox' + backend = 'default' path = common_utils.get_asset_path() def test_commonvoice(self): diff --git a/test/test_io.py b/test/test_io.py index f58f66ed11..2c8aece85e 100644 --- a/test/test_io.py +++ b/test/test_io.py @@ -1,11 +1,24 @@ import os import math +import shutil +import tempfile import unittest import torch import torchaudio -from .common_utils import BACKENDS, BACKENDS_MP3, create_temp_assets_dir +from .common_utils import BACKENDS, BACKENDS_MP3, get_asset_path + + +def create_temp_assets_dir(): + """ + Creates a temporary directory and moves all files from test/assets there. + Returns a Tuple[string, TemporaryDirectory] which is the folder path + and object. + """ + tmp_dir = tempfile.TemporaryDirectory() + shutil.copytree(get_asset_path(), os.path.join(tmp_dir.name, "assets")) + return tmp_dir.name, tmp_dir class Test_LoadSave(unittest.TestCase): @@ -17,11 +30,15 @@ class Test_LoadSave(unittest.TestCase): def test_1_save(self): for backend in BACKENDS_MP3: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save(self.test_filepath, False) for backend in BACKENDS: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save(self.test_filepath_wav, True) @@ -68,6 +85,8 @@ def _test_1_save(self, test_filepath, normalization): def test_1_save_sine(self): for backend in BACKENDS: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save_sine() @@ -101,11 +120,15 @@ def _test_1_save_sine(self): def test_2_load(self): for backend in BACKENDS_MP3: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_2_load(self.test_filepath, 278756) for backend in BACKENDS: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_2_load(self.test_filepath_wav, 276858) @@ -142,6 +165,8 @@ def _test_2_load(self, test_filepath, length): def test_2_load_nonormalization(self): for backend in BACKENDS_MP3: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_2_load_nonormalization(self.test_filepath, 278756) @@ -159,6 +184,8 @@ def _test_2_load_nonormalization(self, test_filepath, length): def test_3_load_and_save_is_identity(self): for backend in BACKENDS: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_3_load_and_save_is_identity() @@ -197,6 +224,8 @@ def _test_3_load_and_save_is_identity_across_backend(self, backend1, backend2): def test_4_load_partial(self): for backend in BACKENDS_MP3: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_4_load_partial() @@ -239,6 +268,8 @@ def _test_4_load_partial(self): def test_5_get_info(self): for backend in BACKENDS: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_5_get_info() diff --git a/test/test_librosa_compatibility.py b/test/test_librosa_compatibility.py index 62e9d3ca88..aa933535e8 100644 --- a/test/test_librosa_compatibility.py +++ b/test/test_librosa_compatibility.py @@ -160,7 +160,8 @@ class TestTransforms(common_utils.TorchaudioTestCase): """Test suite for functions in `transforms` module.""" def assert_compatibilities(self, n_fft, hop_length, power, n_mels, n_mfcc, sample_rate): common_utils.set_audio_backend('default') - sound, sample_rate = _load_audio_asset('sinewave.wav') + path = common_utils.get_asset_path('sinewave.wav') + sound, sample_rate = common_utils.load_wav(path) sound_librosa = sound.cpu().numpy().squeeze() # (64000) # test core spectrogram @@ -300,9 +301,9 @@ def test_InverseMelScale(self): hop_length = n_fft // 4 # Prepare mel spectrogram input. We use torchaudio to compute one. - common_utils.set_audio_backend('default') - sound, sample_rate = _load_audio_asset( - 'steam-train-whistle-daniel_simon.wav', offset=2**10, num_frames=2**14) + path = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') + sound, sample_rate = common_utils.load_wav(path) + sound = sound[:, 2**10:2**10 + 2**14] sound = sound.mean(dim=0, keepdim=True) spec_orig = F.spectrogram( sound, pad=0, window=torch.hann_window(n_fft), n_fft=n_fft, diff --git a/test/test_models.py b/test/test_models.py index 7bd3f3819d..c54a57cebd 100644 --- a/test/test_models.py +++ b/test/test_models.py @@ -1,8 +1,10 @@ import torch -from torchaudio.models import Wav2Letter, _MelResNet +from torchaudio.models import Wav2Letter, _MelResNet, _UpsampleNetwork, _WaveRNN +from . import common_utils -class TestWav2Letter: + +class TestWav2Letter(common_utils.TorchaudioTestCase): def test_waveform(self): batch_size = 2 @@ -31,21 +33,110 @@ def test_mfcc(self): assert out.size() == (batch_size, num_classes, 2) -class TestMelResNet: +class TestMelResNet(common_utils.TorchaudioTestCase): def test_waveform(self): + """Validate the output dimensions of a _MelResNet block. + """ - batch_size = 2 - num_features = 200 - input_dims = 100 - output_dims = 128 - res_blocks = 10 - hidden_dims = 128 - pad = 2 + n_batch = 2 + n_time = 200 + n_freq = 100 + n_output = 128 + n_res_block = 10 + n_hidden = 128 + kernel_size = 5 - model = _MelResNet(res_blocks, input_dims, hidden_dims, output_dims, pad) + model = _MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) - x = torch.rand(batch_size, input_dims, num_features) + x = torch.rand(n_batch, n_freq, n_time) out = model(x) - assert out.size() == (batch_size, output_dims, num_features - pad * 2) + assert out.size() == (n_batch, n_output, n_time - kernel_size + 1) + + +class TestUpsampleNetwork(common_utils.TorchaudioTestCase): + + def test_waveform(self): + """Validate the output dimensions of a _UpsampleNetwork block. + """ + + upsample_scales = [5, 5, 8] + n_batch = 2 + n_time = 200 + n_freq = 100 + n_output = 256 + n_res_block = 10 + n_hidden = 128 + kernel_size = 5 + + total_scale = 1 + for upsample_scale in upsample_scales: + total_scale *= upsample_scale + + model = _UpsampleNetwork(upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size) + + x = torch.rand(n_batch, n_freq, n_time) + out1, out2 = model(x) + + assert out1.size() == (n_batch, n_freq, total_scale * (n_time - kernel_size + 1)) + assert out2.size() == (n_batch, n_output, total_scale * (n_time - kernel_size + 1)) + + +class TestWaveRNN(common_utils.TorchaudioTestCase): + + def test_waveform(self): + """Validate the output dimensions of a _WaveRNN model in waveform mode. + """ + + upsample_scales = [5, 5, 8] + n_rnn = 512 + n_fc = 512 + n_bits = 9 + sample_rate = 24000 + hop_length = 200 + n_batch = 2 + n_time = 200 + n_freq = 100 + n_output = 256 + n_res_block = 10 + n_hidden = 128 + kernel_size = 5 + mode = 'waveform' + + model = _WaveRNN(upsample_scales, n_bits, sample_rate, hop_length, n_res_block, + n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output, mode) + + x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1)) + mels = torch.rand(n_batch, 1, n_freq, n_time) + out = model(x, mels) + + assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), 2 ** n_bits) + + def test_mol(self): + """Validate the output dimensions of a _WaveRNN model in mol mode. + """ + + upsample_scales = [5, 5, 8] + n_rnn = 512 + n_fc = 512 + n_bits = 9 + sample_rate = 24000 + hop_length = 200 + n_batch = 2 + n_time = 200 + n_freq = 100 + n_output = 256 + n_res_block = 10 + n_hidden = 128 + kernel_size = 5 + mode = 'mol' + + model = _WaveRNN(upsample_scales, n_bits, sample_rate, hop_length, n_res_block, + n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output, mode) + + x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1)) + mels = torch.rand(n_batch, 1, n_freq, n_time) + out = model(x, mels) + + assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), 30) diff --git a/test/test_sox_compatibility.py b/test/test_sox_compatibility.py index 23d58a3fb1..a5d9f8633c 100644 --- a/test/test_sox_compatibility.py +++ b/test/test_sox_compatibility.py @@ -9,12 +9,30 @@ @common_utils.skipIfNoSoxBackend -class TestFunctionalFiltering(common_utils.TorchaudioTestCase): +class TestFunctionalFiltering(common_utils.TempDirMixin, common_utils.TorchaudioTestCase): backend = 'sox' + def setUp(self): + # 1. Create int16 signal to save as PCM wav + # 2. Write to temp file + # 3. Load temp file into tensor to reuse in downstream tests + # Prefer to use common_utils.load_wav() but this implementation does + # not match torchaudio.load and errors on downstream tests + super().setUp() + + self.NOISE_SAMPLE_RATE = 44100 # N.B. 44.1 kHz required by SoX deemph effect + noise_waveform_as_int = common_utils.get_whitenoise( + sample_rate=self.NOISE_SAMPLE_RATE, duration=5, dtype=torch.int16, scale_factor=0.9, + ) + self.noise_filepath = self.get_temp_path("whitenoise.wav") + common_utils.save_wav( + self.noise_filepath, noise_waveform_as_int, self.NOISE_SAMPLE_RATE + ) + self.noise_waveform, _ = torchaudio.load(self.noise_filepath, normalization=True) + def test_gain(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) + waveform, _ = common_utils.load_wav(test_filepath) waveform_gain = F.gain(waveform, 3) self.assertTrue(waveform_gain.abs().max().item(), 1.) @@ -28,7 +46,7 @@ def test_gain(self): def test_dither(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) + waveform, _ = common_utils.load_wav(test_filepath) waveform_dithered = F.dither(waveform) waveform_dithered_noiseshaped = F.dither(waveform, noise_shaping=True) @@ -48,7 +66,7 @@ def test_dither(self): def test_vctk_transform_pipeline(self): test_filepath_vctk = common_utils.get_asset_path('VCTK-Corpus', 'wav48', 'p224', 'p224_002.wav') - wf_vctk, sr_vctk = torchaudio.load(test_filepath_vctk) + wf_vctk, sr_vctk = common_utils.load_wav(test_filepath_vctk) # rate sample = T.Resample(sr_vctk, 16000, resampling_method='sinc_interpolation') @@ -71,17 +89,14 @@ def test_lowpass(self): """ Test biquad lowpass filter, compare to SoX implementation """ - cutoff_freq = 3000 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("lowpass", [cutoff_freq]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.lowpass_biquad(waveform, sample_rate, cutoff_freq) + output_waveform = F.lowpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, cutoff_freq) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -89,37 +104,30 @@ def test_highpass(self): """ Test biquad highpass filter, compare to SoX implementation """ - cutoff_freq = 2000 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("highpass", [cutoff_freq]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.highpass_biquad(waveform, sample_rate, cutoff_freq) + output_waveform = F.highpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, cutoff_freq) - # TBD - this fails at the 1e-4 level, debug why - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-3, rtol=1e-5) def test_allpass(self): """ Test biquad allpass filter, compare to SoX implementation """ - central_freq = 1000 q = 0.707 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("allpass", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.allpass_biquad(waveform, sample_rate, central_freq, q) + output_waveform = F.allpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -127,19 +135,17 @@ def test_bandpass_with_csg(self): """ Test biquad bandpass filter, compare to SoX implementation """ - central_freq = 1000 q = 0.707 const_skirt_gain = True - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("bandpass", ["-c", central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) + output_waveform = F.bandpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, + central_freq, q, const_skirt_gain) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -147,19 +153,17 @@ def test_bandpass_without_csg(self): """ Test biquad bandpass filter, compare to SoX implementation """ - central_freq = 1000 q = 0.707 const_skirt_gain = False - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("bandpass", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) + output_waveform = F.bandpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, + central_freq, q, const_skirt_gain) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -167,18 +171,16 @@ def test_bandreject(self): """ Test biquad bandreject filter, compare to SoX implementation """ - central_freq = 1000 q = 0.707 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("bandreject", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandreject_biquad(waveform, sample_rate, central_freq, q) + output_waveform = F.bandreject_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, + central_freq, q) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -186,19 +188,16 @@ def test_band_with_noise(self): """ Test biquad band filter with noise mode, compare to SoX implementation """ - central_freq = 1000 q = 0.707 noise = True - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("band", ["-n", central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) + output_waveform = F.band_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q, noise) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -211,14 +210,12 @@ def test_band_without_noise(self): q = 0.707 noise = False - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("band", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) + output_waveform = F.band_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q, noise) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -231,14 +228,12 @@ def test_treble(self): q = 0.707 gain = 40 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("treble", [gain, central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.treble_biquad(waveform, sample_rate, gain, central_freq, q) + output_waveform = F.treble_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, gain, central_freq, q) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -251,14 +246,12 @@ def test_bass(self): q = 0.707 gain = 40 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("bass", [gain, central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bass_biquad(waveform, sample_rate, gain, central_freq, q) + output_waveform = F.bass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, gain, central_freq, q) self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-4, rtol=1e-5) @@ -266,15 +259,12 @@ def test_deemph(self): """ Test biquad deemph filter, compare to SoX implementation """ - - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("deemph") sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.deemph_biquad(waveform, sample_rate) + output_waveform = F.deemph_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -282,15 +272,12 @@ def test_riaa(self): """ Test biquad riaa filter, compare to SoX implementation """ - - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("riaa") sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.riaa_biquad(waveform, sample_rate) + output_waveform = F.riaa_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -299,14 +286,13 @@ def test_contrast(self): Test contrast effect, compare to SoX implementation """ enhancement_amount = 80. - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("contrast", [enhancement_amount]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.contrast(waveform, enhancement_amount) + output_waveform = F.contrast(self.noise_waveform, enhancement_amount) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -316,14 +302,13 @@ def test_dcshift_with_limiter(self): """ shift = 0.5 limiter_gain = 0.05 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("dcshift", [shift, limiter_gain]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, _ = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.dcshift(waveform, shift, limiter_gain) + output_waveform = F.dcshift(self.noise_waveform, shift, limiter_gain) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -332,14 +317,13 @@ def test_dcshift_without_limiter(self): Test dcshift effect, compare to SoX implementation """ shift = 0.6 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("dcshift", [shift]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, _ = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.dcshift(waveform, shift) + output_waveform = F.dcshift(self.noise_waveform, shift) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -349,14 +333,13 @@ def test_overdrive(self): """ gain = 30 colour = 40 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("overdrive", [gain, colour]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, _ = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.overdrive(waveform, gain, colour) + output_waveform = F.overdrive(self.noise_waveform, gain, colour) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -369,14 +352,14 @@ def test_phaser_sine(self): delay_ms = 2.0 decay = 0.4 speed = 0.5 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-s"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.phaser(waveform, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) + output_waveform = F.phaser(self.noise_waveform, self.NOISE_SAMPLE_RATE, + gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -389,14 +372,14 @@ def test_phaser_triangle(self): delay_ms = 2.0 decay = 0.4 speed = 0.5 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-t"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.phaser(waveform, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=False) + output_waveform = F.phaser(self.noise_waveform, self.NOISE_SAMPLE_RATE, + gain_in, gain_out, delay_ms, decay, speed, sinusoidal=False) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -410,15 +393,14 @@ def test_flanger_triangle_linear(self): width = 0.9 speed = 0.5 phase = 30 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "linear"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, - modulation='triangular', interpolation='linear') + output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, regen, + width, speed, phase, modulation='triangular', interpolation='linear') torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -432,15 +414,14 @@ def test_flanger_triangle_quad(self): width = 0.4 speed = 0.5 phase = 40 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "quadratic"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, - modulation='triangular', interpolation='quadratic') + output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, + regen, width, speed, phase, modulation='triangular', interpolation='quadratic') torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -454,15 +435,14 @@ def test_flanger_sine_linear(self): width = 0.23 speed = 1.3 phase = 60 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "linear"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, - modulation='sinusoidal', interpolation='linear') + output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, + regen, width, speed, phase, modulation='sinusoidal', interpolation='linear') torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -476,15 +456,14 @@ def test_flanger_sine_quad(self): width = 0.23 speed = 1.3 phase = 25 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "quadratic"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, - modulation='sinusoidal', interpolation='quadratic') + output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, + regen, width, speed, phase, modulation='sinusoidal', interpolation='quadratic') torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -497,21 +476,17 @@ def test_equalizer(self): q = 0.707 gain = 1 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("equalizer", [center_freq, q, gain]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.equalizer_biquad(waveform, sample_rate, center_freq, gain, q) + output_waveform = F.equalizer_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, center_freq, gain, q) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) def test_perf_biquad_filtering(self): - fn_sine = common_utils.get_asset_path('whitenoise.wav') - b0 = 0.4 b1 = 0.2 b2 = 0.9 @@ -521,13 +496,12 @@ def test_perf_biquad_filtering(self): # SoX method E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(fn_sine) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("biquad", [b0, b1, b2, a0, a1, a2]) waveform_sox_out, _ = E.sox_build_flow_effects() - waveform, _ = torchaudio.load(fn_sine, normalization=True) waveform_lfilter_out = F.lfilter( - waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2]) + self.noise_waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2]) ) self.assertEqual(waveform_lfilter_out, waveform_sox_out, atol=1e-4, rtol=1e-5) diff --git a/test/test_sox_effects.py b/test/test_sox_effects.py index 68440db8d2..6b12d7cd06 100644 --- a/test/test_sox_effects.py +++ b/test/test_sox_effects.py @@ -1,3 +1,4 @@ +import sys import math import unittest @@ -11,7 +12,7 @@ class Test_SoxEffectsChain(common_utils.TorchaudioTestCase): backend = 'sox' - test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.mp3") + test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.wav") def test_single_channel(self): fn_sine = common_utils.get_asset_path("sinewave.wav") @@ -34,6 +35,7 @@ def test_rate_channels(self): self.assertEqual(sr, target_rate) self.assertEqual(x.size(0), target_channels) + @unittest.skipIf(sys.platform == 'darwin', 'This test is known to fail on macOS') def test_lowpass_speed(self): speed = .8 si, _ = torchaudio.info(self.test_filepath) diff --git a/test/test_transforms.py b/test/test_transforms.py index af2b834fdb..6df9562bc8 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -45,7 +45,7 @@ def test_mu_law_companding(self): def test_AmplitudeToDB(self): filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, sample_rate = torchaudio.load(filepath) + waveform = common_utils.load_wav(filepath)[0] mag_to_db_transform = transforms.AmplitudeToDB('magnitude', 80.) power_to_db_transform = transforms.AmplitudeToDB('power', 80.) @@ -115,7 +115,7 @@ def test_mel2(self): self.assertTrue(mel_transform2.mel_scale.fb.sum(1).ge(0.).all()) # check on multi-channel audio filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - x_stereo, sr_stereo = torchaudio.load(filepath) # (2, 278756), 44100 + x_stereo = common_utils.load_wav(filepath)[0] # (2, 278756), 44100 spectrogram_stereo = s2db(mel_transform(x_stereo)) # (2, 128, 1394) self.assertTrue(spectrogram_stereo.dim() == 3) self.assertTrue(spectrogram_stereo.size(0) == 2) @@ -166,7 +166,7 @@ def test_mfcc(self): def test_resample_size(self): input_path = common_utils.get_asset_path('sinewave.wav') - waveform, sample_rate = torchaudio.load(input_path) + waveform, sample_rate = common_utils.load_wav(input_path) upsample_rate = sample_rate * 2 downsample_rate = sample_rate // 2 diff --git a/test/torchscript_consistency_impl.py b/test/torchscript_consistency_impl.py index 2527cd5fbf..76444dc18a 100644 --- a/test/torchscript_consistency_impl.py +++ b/test/torchscript_consistency_impl.py @@ -2,7 +2,6 @@ import unittest import torch -import torchaudio import torchaudio.functional as F import torchaudio.transforms as T @@ -616,6 +615,5 @@ def test_SlidingWindowCmn(self): def test_Vad(self): filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") - common_utils.set_audio_backend('default') - waveform, sample_rate = torchaudio.load(filepath) + waveform, sample_rate = common_utils.load_wav(filepath) self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform) diff --git a/third_party/CMakeLists.txt b/third_party/CMakeLists.txt new file mode 100644 index 0000000000..c4be4cb7b0 --- /dev/null +++ b/third_party/CMakeLists.txt @@ -0,0 +1,80 @@ +cmake_minimum_required(VERSION 3.1) + +project(torchaudio_third_parties) +include(ExternalProject) + +set(INSTALL_DIR ${CMAKE_CURRENT_SOURCE_DIR}/install) +set(ARCHIVE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/archives) +set(COMMON_ARGS --quiet --disable-shared --enable-static --prefix=${INSTALL_DIR} --with-pic --disable-dependency-tracking --disable-debug --disable-examples --disable-doc) + +ExternalProject_Add(libmad + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/libmad-0.15.1b.tar.gz + URL_HASH SHA256=bbfac3ed6bfbc2823d3775ebb931087371e142bb0e9bb1bee51a76a6e0078690 + PATCH_COMMAND patch < ${CMAKE_CURRENT_SOURCE_DIR}/patch/libmad.patch + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/libmad/configure ${COMMON_ARGS} +) + +ExternalProject_Add(libmp3lame + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz + URL_HASH SHA256=24346b4158e4af3bd9f2e194bb23eb473c75fb7377011523353196b19b9a23ff + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/libmp3lame/configure ${COMMON_ARGS} --enable-nasm +) + +ExternalProject_Add(libogg + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/ogg/libogg-1.3.3.tar.gz + URL_HASH SHA256=c2e8a485110b97550f453226ec644ebac6cb29d1caef2902c007edab4308d985 + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/libogg/configure ${COMMON_ARGS} +) + +ExternalProject_Add(libflac + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DEPENDS libogg + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/flac/flac-1.3.2.tar.xz + URL_HASH SHA256=91cfc3ed61dc40f47f050a109b08610667d73477af6ef36dcad31c31a4a8d53f + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libflac/configure ${COMMON_ARGS} --with-ogg +) + +ExternalProject_Add(libvorbis + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DEPENDS libogg + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/vorbis/libvorbis-1.3.6.tar.gz + URL_HASH SHA256=6ed40e0241089a42c48604dc00e362beee00036af2d8b3f46338031c9e0351cb + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libvorbis/configure ${COMMON_ARGS} --with-ogg +) + +ExternalProject_Add(libopus + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DEPENDS libogg + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/opus/opus-1.3.1.tar.gz + URL_HASH SHA256=65b58e1e25b2a114157014736a3d9dfeaad8d41be1c8179866f144a2fb44ff9d + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libopus/configure ${COMMON_ARGS} --with-ogg +) + +ExternalProject_Add(opusfile + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DEPENDS libopus + DOWNLOAD_DIR ${ARCHIVE_DIR} + STAMP_DIR ${CMAKE_CURRENT_SOURCE_DIR}/src/opusfile-stamp + SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/src/opusfile + URL https://ftp.osuosl.org/pub/xiph/releases/opus/opusfile-0.12.tar.gz + URL_HASH SHA256=118d8601c12dd6a44f52423e68ca9083cc9f2bfe72da7a8c1acb22a80ae3550b + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/opusfile/configure ${COMMON_ARGS} --disable-http +) + +ExternalProject_Add(libsox + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DEPENDS libogg libflac libvorbis opusfile libmp3lame libmad + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2 + URL_HASH SHA256=81a6956d4330e75b5827316e44ae381e6f1e8928003c6aa45896da9041ea149c + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libsox/configure ${COMMON_ARGS} --with-lame --with-flac --with-mad --with-oggvorbis --without-alsa --without-coreaudio --without-png --without-oss --without-sndfile --with-opus +) diff --git a/third_party/build_codec_helper.sh b/third_party/build_codec_helper.sh new file mode 100755 index 0000000000..e7f2614781 --- /dev/null +++ b/third_party/build_codec_helper.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash +# Helper script for building codecs depending on libogg, such as libopus and opus. +# It is difficult to set environment variable inside of ExternalProject_Add, +# so this script sets necessary environment variables before running the given command + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +install_dir="${this_dir}/install" + +export PKG_CONFIG_PATH="${install_dir}/lib/pkgconfig" +export LDFLAGS="-L${install_dir}/lib ${LDFLAGS}" +export CPPFLAGS="-I${install_dir}/include ${CPPFLAGS}" + +$@ diff --git a/third_party/patch/libmad.patch b/third_party/patch/libmad.patch new file mode 100644 index 0000000000..a805787831 --- /dev/null +++ b/third_party/patch/libmad.patch @@ -0,0 +1,86 @@ +See the followings for the origin of this patch +http://www.linuxfromscratch.org/blfs/view/svn/multimedia/libmad.html +http://www.linuxfromscratch.org/patches/blfs/svn/libmad-0.15.1b-fixes-1.patch +--- src/libmad/configure 2004-02-05 09:34:07.000000000 +0000 ++++ src/libmad/configure.new 2020-06-30 21:10:28.528018931 +0000 +@@ -19083,71 +19083,7 @@ + + if test "$GCC" = yes + then +- if test -z "$arch" +- then +- case "$host" in +- i386-*) ;; +- i?86-*) arch="-march=i486" ;; +- arm*-empeg-*) arch="-march=armv4 -mtune=strongarm1100" ;; +- armv4*-*) arch="-march=armv4 -mtune=strongarm" ;; +- powerpc-*) ;; +- mips*-agenda-*) arch="-mcpu=vr4100" ;; +- mips*-luxsonor-*) arch="-mips1 -mcpu=r3000 -Wa,-m4010" ;; +- esac +- fi +- +- case "$optimize" in +- -O|"-O "*) +- optimize="-O" +- optimize="$optimize -fforce-mem" +- optimize="$optimize -fforce-addr" +- : #x optimize="$optimize -finline-functions" +- : #- optimize="$optimize -fstrength-reduce" +- optimize="$optimize -fthread-jumps" +- optimize="$optimize -fcse-follow-jumps" +- optimize="$optimize -fcse-skip-blocks" +- : #x optimize="$optimize -frerun-cse-after-loop" +- : #x optimize="$optimize -frerun-loop-opt" +- : #x optimize="$optimize -fgcse" +- optimize="$optimize -fexpensive-optimizations" +- optimize="$optimize -fregmove" +- : #* optimize="$optimize -fdelayed-branch" +- : #x optimize="$optimize -fschedule-insns" +- optimize="$optimize -fschedule-insns2" +- : #? optimize="$optimize -ffunction-sections" +- : #? optimize="$optimize -fcaller-saves" +- : #> optimize="$optimize -funroll-loops" +- : #> optimize="$optimize -funroll-all-loops" +- : #x optimize="$optimize -fmove-all-movables" +- : #x optimize="$optimize -freduce-all-givs" +- : #? optimize="$optimize -fstrict-aliasing" +- : #* optimize="$optimize -fstructure-noalias" +- +- case "$host" in +- arm*-*) +- optimize="$optimize -fstrength-reduce" +- ;; +- mips*-*) +- optimize="$optimize -fstrength-reduce" +- optimize="$optimize -finline-functions" +- ;; +- i?86-*) +- optimize="$optimize -fstrength-reduce" +- ;; +- powerpc-apple-*) +- # this triggers an internal compiler error with gcc2 +- : #optimize="$optimize -fstrength-reduce" +- +- # this is really only beneficial with gcc3 +- : #optimize="$optimize -finline-functions" +- ;; +- *) +- # this sometimes provokes bugs in gcc 2.95.2 +- : #optimize="$optimize -fstrength-reduce" +- ;; +- esac +- ;; +- esac ++ optimize="-O2" + fi + + case "$host" in +@@ -21497,6 +21433,7 @@ + then + case "$host" in + i?86-*) FPM="INTEL" ;; ++ x86_64*) FPM="64BIT" ;; + arm*-*) FPM="ARM" ;; + mips*-*) FPM="MIPS" ;; + sparc*-*) FPM="SPARC" ;; diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py new file mode 100644 index 0000000000..4664f733c6 --- /dev/null +++ b/torchaudio/backend/sox_io_backend.py @@ -0,0 +1,134 @@ +from typing import Tuple, Optional + +import torch +from torchaudio._internal import ( + module_utils as _mod_utils, +) + + +class AudioMetaData: + def __init__(self, sample_rate: int, num_frames: int, num_channels: int): + self.sample_rate = sample_rate + self.num_frames = num_frames + self.num_channels = num_channels + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def info(filepath: str) -> AudioMetaData: + """Get signal information of an audio file.""" + sinfo = torch.ops.torchaudio.sox_io_get_info(filepath) + return AudioMetaData(sinfo.get_sample_rate(), sinfo.get_num_frames(), sinfo.get_num_channels()) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def load( + filepath: str, + frame_offset: int = 0, + num_frames: int = -1, + normalize: bool = True, + channels_first: bool = True, +) -> Tuple[torch.Tensor, int]: + """Load audio data from file. + + This function can handle all the codecs that underlying libsox can handle, however note the + followings. + + Note: + This function is tested on the following formats; + - WAV + - 32-bit floating-point + - 32-bit signed integer + - 16-bit signed integer + - 8-bit unsigned integer + - MP3 + - FLAC + - OGG/VORBIS + + By default, this function returns Tensor with ``float32`` dtype and the shape of ``[channel, time]``. + The samples are normalized to fit in the range of ``[-1.0, 1.0]``. + + When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit + signed integer and 8-bit unsigned integer (24-bit signed integer is not supported), + by providing ``normalize=False``, this function can return integer Tensor, where the samples + are expressed within the whole range of the corresponding dtype, that is, ``int32`` tensor + for 32-bit signed PCM, ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. + + ``normalize`` parameter has no effect on 32-bit floating-point WAV and other formats, such as + flac and mp3. For these formats, this function always returns ``float32`` Tensor with values + normalized to ``[-1.0, 1.0]``. + + Args: + filepath: Path to audio file + frame_offset: Number of frames to skip before start reading data. + num_frames: Maximum number of frames to read. -1 reads all the remaining samples, starting + from ``frame_offset``. This function may return the less number of frames if there is + not enough frames in the given file. + normalize: When ``True``, this function always return ``float32``, and sample values are + normalized to ``[-1.0, 1.0]``. If input file is integer WAV, giving ``False`` will change + the resulting Tensor type to integer type. This argument has no effect for formats other + than integer WAV type. + channels_first: When True, the returned Tensor has dimension ``[channel, time]``. + Otherwise, the returned Tensor's dimension is ``[time, channel]``. + + Returns: + torch.Tensor: If the input file has integer wav format and normalization is off, then it has + integer type, else ``float32`` type. If ``channels_first=True``, it has + ``[channel, time]`` else ``[time, channel]``. + """ + signal = torch.ops.torchaudio.sox_io_load_audio_file( + filepath, frame_offset, num_frames, normalize, channels_first) + return signal.get_tensor(), signal.get_sample_rate() + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def save( + filepath: str, + tensor: torch.Tensor, + sample_rate: int, + channels_first: bool = True, + compression: Optional[float] = None, +): + """Save audio data to file. + + Supported formats are; + - WAV + - 32-bit floating-point + - 32-bit signed integer + - 16-bit signed integer + - 8-bit unsigned integer + - MP3 + - FLAC + - OGG/VORBIS + + Args: + filepath: Path to save file. + tensor: Audio data to save. must be 2D tensor. + sample_rate: sampling rate + channels_first: If True, the given tensor is interpreted as ``[channel, time]``. + compression: Used for formats other than WAV. This corresponds to ``-C`` option + of ``sox`` command. + See the detail at http://sox.sourceforge.net/soxformat.html. + - MP3: Either bitrate [kbps] with quality factor, such as ``128.2`` or + VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5`` + - FLAC: compression level. Whole number from ``0`` to ``8``. + ``8`` is default and highest compression. + - OGG/VORBIS: number from -1 to 10; -1 is the highest compression and lowest + quality. Default: ``3``. + """ + if compression is None: + ext = str(filepath)[-3:].lower() + if ext == 'wav': + compression = 0. + elif ext == 'mp3': + compression = -4.5 + elif ext == 'flac': + compression = 8. + elif ext in ['ogg', 'vorbis']: + compression = 3. + else: + raise RuntimeError(f'Unsupported file type: "{ext}"') + signal = torch.classes.torchaudio.TensorSignal(tensor, sample_rate, channels_first) + torch.ops.torchaudio.sox_io_save_audio_file(filepath, signal, compression) + + +load_wav = load diff --git a/torchaudio/backend/utils.py b/torchaudio/backend/utils.py index d537f01daf..cb53b3e02f 100644 --- a/torchaudio/backend/utils.py +++ b/torchaudio/backend/utils.py @@ -7,6 +7,7 @@ from . import ( no_backend, sox_backend, + sox_io_backend, soundfile_backend, ) @@ -24,6 +25,7 @@ def list_audio_backends() -> List[str]: backends.append('soundfile') if is_module_available('torchaudio._torchaudio'): backends.append('sox') + backends.append('sox_io') return backends @@ -43,6 +45,8 @@ def set_audio_backend(backend: Optional[str]) -> None: module = no_backend elif backend == 'sox': module = sox_backend + elif backend == 'sox_io': + module = sox_io_backend elif backend == 'soundfile': module = soundfile_backend else: @@ -69,6 +73,8 @@ def get_audio_backend() -> Optional[str]: return None if torchaudio.load == sox_backend.load: return 'sox' + if torchaudio.load == sox_io_backend.load: + return 'sox_io' if torchaudio.load == soundfile_backend.load: return 'soundfile' raise ValueError('Unknown backend.') diff --git a/torchaudio/csrc/register.cpp b/torchaudio/csrc/register.cpp index 81b1a84c96..3c03232941 100644 --- a/torchaudio/csrc/register.cpp +++ b/torchaudio/csrc/register.cpp @@ -1,17 +1,64 @@ #ifndef TORCHAUDIO_REGISTER_H #define TORCHAUDIO_REGISTER_H -#include +#include +#include +#include namespace torchaudio { namespace { +//////////////////////////////////////////////////////////////////////////////// +// sox_utils.h +//////////////////////////////////////////////////////////////////////////////// +static auto registerTensorSignal = + torch::class_("torchaudio", "TensorSignal") + .def(torch::init()) + .def("get_tensor", &sox_utils::TensorSignal::getTensor) + .def("get_sample_rate", &sox_utils::TensorSignal::getSampleRate) + .def("get_channels_first", &sox_utils::TensorSignal::getChannelsFirst); + +//////////////////////////////////////////////////////////////////////////////// +// sox_io.h +//////////////////////////////////////////////////////////////////////////////// static auto registerSignalInfo = - torch::class_("torchaudio", "SignalInfo") - .def(torch::init()) - .def("get_sample_rate", &SignalInfo::getSampleRate) - .def("get_num_channels", &SignalInfo::getNumChannels) - .def("get_num_samples", &SignalInfo::getNumSamples); + torch::class_("torchaudio", "SignalInfo") + .def("get_sample_rate", &sox_io::SignalInfo::getSampleRate) + .def("get_num_channels", &sox_io::SignalInfo::getNumChannels) + .def("get_num_frames", &sox_io::SignalInfo::getNumFrames); + +static auto registerGetInfo = torch::RegisterOperators().op( + torch::RegisterOperators::options() + .schema( + "torchaudio::sox_io_get_info(str path) -> __torch__.torch.classes.torchaudio.SignalInfo info") + .catchAllKernel()); + +static auto registerLoadAudioFile = torch::RegisterOperators().op( + torch::RegisterOperators::options() + .schema( + "torchaudio::sox_io_load_audio_file(str path, int frame_offset, int num_frames, bool normalize, bool channels_first) -> __torch__.torch.classes.torchaudio.TensorSignal signal") + .catchAllKernel< + decltype(sox_io::load_audio_file), + &sox_io::load_audio_file>()); + +static auto registerSaveAudioFile = torch::RegisterOperators().op( + torch::RegisterOperators::options() + .schema( + "torchaudio::sox_io_save_audio_file(str path, __torch__.torch.classes.torchaudio.TensorSignal signal, float compression) -> ()") + .catchAllKernel< + decltype(sox_io::save_audio_file), + &sox_io::save_audio_file>()); + +//////////////////////////////////////////////////////////////////////////////// +// sox_effects.h +//////////////////////////////////////////////////////////////////////////////// +static auto registerSoxEffects = + torch::RegisterOperators( + "torchaudio::sox_effects_initialize_sox_effects", + &sox_effects::initialize_sox_effects) + .op("torchaudio::sox_effects_shutdown_sox_effects", + &sox_effects::shutdown_sox_effects) + .op("torchaudio::sox_effects_list_effects", &sox_effects::list_effects); } // namespace } // namespace torchaudio diff --git a/torchaudio/csrc/sox.cpp b/torchaudio/csrc/sox.cpp index 3ae81bef19..0f099946fd 100644 --- a/torchaudio/csrc/sox.cpp +++ b/torchaudio/csrc/sox.cpp @@ -82,17 +82,6 @@ std::tuple get_info( return std::make_tuple(fd->signal, fd->encoding); } -std::vector get_effect_names() { - sox_effect_fn_t const * fns = sox_get_effect_fns(); - std::vector sv; - for(int i = 0; fns[i]; ++i) { - const sox_effect_handler_t *eh = fns[i] (); - if(eh && eh->name) - sv.push_back(eh->name); - } - return sv; -} - int read_audio_file( const std::string& file_name, at::Tensor output, @@ -186,16 +175,6 @@ void write_audio_file( } } -int initialize_sox() { - /* Initialization for sox effects. Only initialize once */ - return sox_init(); -} - -int shutdown_sox() { - /* Shutdown for sox effects. Do not shutdown between multiple calls */ - return sox_quit(); -} - int build_flow_effects(const std::string& file_name, at::Tensor otensor, bool ch_first, @@ -489,20 +468,8 @@ PYBIND11_MODULE(_torchaudio, m) { "get_info", &torch::audio::get_info, "Gets information about an audio file"); - m.def( - "get_effect_names", - &torch::audio::get_effect_names, - "Gets the names of all available effects"); m.def( "build_flow_effects", &torch::audio::build_flow_effects, "build effects and flow chain into tensors"); - m.def( - "initialize_sox", - &torch::audio::initialize_sox, - "initialize sox for effects"); - m.def( - "shutdown_sox", - &torch::audio::shutdown_sox, - "shutdown sox for effects"); } diff --git a/torchaudio/csrc/sox.h b/torchaudio/csrc/sox.h index 8d851c9b21..8093f0732e 100644 --- a/torchaudio/csrc/sox.h +++ b/torchaudio/csrc/sox.h @@ -45,13 +45,6 @@ void write_audio_file( std::tuple get_info( const std::string& file_name); -// get names of all sox effects -std::vector get_effect_names(); - -// Initialize and Shutdown SoX effects chain. These functions should only be run once. -int initialize_sox(); -int shutdown_sox(); - // Struct for build_flow_effects function struct SoxEffect { SoxEffect() : ename(""), eopts({""}) { } diff --git a/torchaudio/csrc/sox_effects.cpp b/torchaudio/csrc/sox_effects.cpp new file mode 100644 index 0000000000..9a0c2ddc6f --- /dev/null +++ b/torchaudio/csrc/sox_effects.cpp @@ -0,0 +1,54 @@ +#include +#include + +using namespace torch::indexing; + +namespace torchaudio { +namespace sox_effects { + +namespace { + +enum SoxEffectsResourceState { NotInitialized, Initialized, ShutDown }; +SoxEffectsResourceState SOX_RESOURCE_STATE = NotInitialized; + +} // namespace + +void initialize_sox_effects() { + if (SOX_RESOURCE_STATE == ShutDown) { + throw std::runtime_error( + "SoX Effects has been shut down. Cannot initialize again."); + } + if (SOX_RESOURCE_STATE == NotInitialized) { + if (sox_init() != SOX_SUCCESS) { + throw std::runtime_error("Failed to initialize sox effects."); + }; + SOX_RESOURCE_STATE = Initialized; + } +}; + +void shutdown_sox_effects() { + if (SOX_RESOURCE_STATE == NotInitialized) { + throw std::runtime_error( + "SoX Effects is not initialized. Cannot shutdown."); + } + if (SOX_RESOURCE_STATE == Initialized) { + if (sox_quit() != SOX_SUCCESS) { + throw std::runtime_error("Failed to initialize sox effects."); + }; + SOX_RESOURCE_STATE = ShutDown; + } +} + +std::vector list_effects() { + std::vector names; + const sox_effect_fn_t* fns = sox_get_effect_fns(); + for (int i = 0; fns[i]; ++i) { + const sox_effect_handler_t* handler = fns[i](); + if (handler && handler->name) + names.push_back(handler->name); + } + return names; +} + +} // namespace sox_effects +} // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects.h b/torchaudio/csrc/sox_effects.h new file mode 100644 index 0000000000..14bdbbfabc --- /dev/null +++ b/torchaudio/csrc/sox_effects.h @@ -0,0 +1,18 @@ +#ifndef TORCHAUDIO_SOX_EFFECTS_H +#define TORCHAUDIO_SOX_EFFECTS_H + +#include + +namespace torchaudio { +namespace sox_effects { + +void initialize_sox_effects(); + +void shutdown_sox_effects(); + +std::vector list_effects(); + +} // namespace sox_effects +} // namespace torchaudio + +#endif diff --git a/torchaudio/csrc/sox_io.cpp b/torchaudio/csrc/sox_io.cpp new file mode 100644 index 0000000000..5d308027bb --- /dev/null +++ b/torchaudio/csrc/sox_io.cpp @@ -0,0 +1,170 @@ +#include +#include +#include + +using namespace torch::indexing; +using namespace torchaudio::sox_utils; + +namespace torchaudio { +namespace sox_io { + +SignalInfo::SignalInfo( + const int64_t sample_rate_, + const int64_t num_channels_, + const int64_t num_frames_) + : sample_rate(sample_rate_), + num_channels(num_channels_), + num_frames(num_frames_){}; + +int64_t SignalInfo::getSampleRate() const { + return sample_rate; +} + +int64_t SignalInfo::getNumChannels() const { + return num_channels; +} + +int64_t SignalInfo::getNumFrames() const { + return num_frames; +} + +c10::intrusive_ptr get_info(const std::string& path) { + SoxFormat sf(sox_open_read( + path.c_str(), + /*signal=*/nullptr, + /*encoding=*/nullptr, + /*filetype=*/nullptr)); + + if (static_cast(sf) == nullptr) { + throw std::runtime_error("Error opening audio file"); + } + + return c10::make_intrusive( + static_cast(sf->signal.rate), + static_cast(sf->signal.channels), + static_cast(sf->signal.length / sf->signal.channels)); +} + +c10::intrusive_ptr load_audio_file( + const std::string& path, + const int64_t frame_offset, + const int64_t num_frames, + const bool normalize, + const bool channels_first) { + if (frame_offset < 0) { + throw std::runtime_error( + "Invalid argument: frame_offset must be non-negative."); + } + if (num_frames == 0 || num_frames < -1) { + throw std::runtime_error( + "Invalid argument: num_frames must be -1 or greater than 0."); + } + + SoxFormat sf(sox_open_read( + path.c_str(), + /*signal=*/nullptr, + /*encoding=*/nullptr, + /*filetype=*/nullptr)); + + validate_input_file(sf); + + const int64_t num_channels = sf->signal.channels; + const int64_t num_total_samples = sf->signal.length; + const int64_t sample_start = sf->signal.channels * frame_offset; + + if (sox_seek(sf, sample_start, 0) == SOX_EOF) { + throw std::runtime_error("Error reading audio file: offset past EOF."); + } + + const int64_t sample_end = [&]() { + if (num_frames == -1) + return num_total_samples; + const int64_t sample_end_ = num_channels * num_frames + sample_start; + if (num_total_samples < sample_end_) { + // For lossy encoding, it is difficult to predict exact size of buffer for + // reading the number of samples required. + // So we allocate buffer size of given `num_frames` and ask sox to read as + // much as possible. For lossless format, sox reads exact number of + // samples, but for lossy encoding, sox can end up reading less. (i.e. + // mp3) For the consistent behavior specification between lossy/lossless + // format, we allow users to provide `num_frames` value that exceeds #of + // available samples, and we adjust it here. + return num_total_samples; + } + return sample_end_; + }(); + + const int64_t max_samples = sample_end - sample_start; + + // Read samples into buffer + std::vector buffer; + buffer.reserve(max_samples); + const int64_t num_samples = sox_read(sf, buffer.data(), max_samples); + if (num_samples == 0) { + throw std::runtime_error( + "Error reading audio file: empty file or read operation failed."); + } + // NOTE: num_samples may be smaller than max_samples if the input + // format is compressed (i.e. mp3). + + // Convert to Tensor + auto tensor = convert_to_tensor( + buffer.data(), + num_samples, + num_channels, + get_dtype(sf->encoding.encoding, sf->signal.precision), + normalize, + channels_first); + + return c10::make_intrusive( + tensor, static_cast(sf->signal.rate), channels_first); +} + +void save_audio_file( + const std::string& file_name, + const c10::intrusive_ptr& signal, + const double compression) { + const auto tensor = signal->getTensor(); + const auto sample_rate = signal->getSampleRate(); + const auto channels_first = signal->getChannelsFirst(); + + validate_input_tensor(tensor); + + const auto filetype = get_filetype(file_name); + const auto signal_info = + get_signalinfo(tensor, sample_rate, channels_first, filetype); + const auto encoding_info = + get_encodinginfo(filetype, tensor.dtype(), compression); + + SoxFormat sf(sox_open_write( + file_name.c_str(), + &signal_info, + &encoding_info, + /*filetype=*/filetype.c_str(), + /*oob=*/nullptr, + /*overwrite_permitted=*/nullptr)); + + if (static_cast(sf) == nullptr) { + throw std::runtime_error("Error saving audio file: failed to open file."); + } + + auto tensor_ = tensor; + if (channels_first) { + tensor_ = tensor_.t(); + } + + const int64_t frames_per_chunk = 65536; + for (int64_t i = 0; i < tensor_.size(0); i += frames_per_chunk) { + auto chunk = tensor_.index({Slice(i, i + frames_per_chunk), Slice()}); + chunk = unnormalize_wav(chunk).contiguous(); + + const size_t numel = chunk.numel(); + if (sox_write(sf, chunk.data_ptr(), numel) != numel) { + throw std::runtime_error( + "Error saving audio file: failed to write the entier buffer."); + } + } +} + +} // namespace sox_io +} // namespace torchaudio diff --git a/torchaudio/csrc/sox_io.h b/torchaudio/csrc/sox_io.h new file mode 100644 index 0000000000..5288e911e8 --- /dev/null +++ b/torchaudio/csrc/sox_io.h @@ -0,0 +1,41 @@ +#ifndef TORCHAUDIO_SOX_IO_H +#define TORCHAUDIO_SOX_IO_H + +#include +#include + +namespace torchaudio { +namespace sox_io { + +struct SignalInfo : torch::CustomClassHolder { + int64_t sample_rate; + int64_t num_channels; + int64_t num_frames; + + SignalInfo( + const int64_t sample_rate_, + const int64_t num_channels_, + const int64_t num_frames_); + int64_t getSampleRate() const; + int64_t getNumChannels() const; + int64_t getNumFrames() const; +}; + +c10::intrusive_ptr get_info(const std::string& path); + +c10::intrusive_ptr load_audio_file( + const std::string& path, + const int64_t frame_offset = 0, + const int64_t num_frames = -1, + const bool normalize = true, + const bool channels_first = true); + +void save_audio_file( + const std::string& file_name, + const c10::intrusive_ptr& signal, + const double compression = 0.); + +} // namespace sox_io +} // namespace torchaudio + +#endif diff --git a/torchaudio/csrc/sox_utils.cpp b/torchaudio/csrc/sox_utils.cpp new file mode 100644 index 0000000000..c1fd8383a8 --- /dev/null +++ b/torchaudio/csrc/sox_utils.cpp @@ -0,0 +1,245 @@ +#include +#include +#include + +namespace torchaudio { +namespace sox_utils { + +TensorSignal::TensorSignal( + torch::Tensor tensor_, + int64_t sample_rate_, + bool channels_first_) + : tensor(tensor_), + sample_rate(sample_rate_), + channels_first(channels_first_){}; + +torch::Tensor TensorSignal::getTensor() const { + return tensor; +} +int64_t TensorSignal::getSampleRate() const { + return sample_rate; +} +bool TensorSignal::getChannelsFirst() const { + return channels_first; +} + +SoxFormat::SoxFormat(sox_format_t* fd) noexcept : fd_(fd) {} +SoxFormat::~SoxFormat() { + if (fd_ != nullptr) { + sox_close(fd_); + } +} +sox_format_t* SoxFormat::operator->() const noexcept { + return fd_; +} +SoxFormat::operator sox_format_t*() const noexcept { + return fd_; +} + +void validate_input_file(const SoxFormat& sf) { + if (static_cast(sf) == nullptr) { + throw std::runtime_error("Error loading audio file: failed to open file."); + } + if (sf->encoding.encoding == SOX_ENCODING_UNKNOWN) { + throw std::runtime_error("Error loading audio file: unknown encoding."); + } + if (sf->signal.length == 0) { + throw std::runtime_error("Error reading audio file: unkown length."); + } +} + +void validate_input_tensor(const torch::Tensor tensor) { + if (!tensor.device().is_cpu()) { + throw std::runtime_error("Input tensor has to be on CPU."); + } + + if (tensor.ndimension() != 2) { + throw std::runtime_error("Input tensor has to be 2D."); + } + + const auto dtype = tensor.dtype(); + if (!(dtype == torch::kFloat32 || dtype == torch::kInt32 || + dtype == torch::kInt16 || dtype == torch::kUInt8)) { + throw std::runtime_error( + "Input tensor has to be one of float32, int32, int16 or uint8 type."); + } +} + +caffe2::TypeMeta get_dtype( + const sox_encoding_t encoding, + const unsigned precision) { + const auto dtype = [&]() { + switch (encoding) { + case SOX_ENCODING_UNSIGNED: // 8-bit PCM WAV + return torch::kUInt8; + case SOX_ENCODING_SIGN2: // 16-bit or 32-bit PCM WAV + switch (precision) { + case 16: + return torch::kInt16; + case 32: + return torch::kInt32; + default: + throw std::runtime_error( + "Only 16 and 32 bits are supported for signed PCM."); + } + default: + // default to float32 for the other formats, including + // 32-bit flaoting-point WAV, + // MP3, + // FLAC, + // VORBIS etc... + return torch::kFloat32; + } + }(); + return c10::scalarTypeToTypeMeta(dtype); +} + +torch::Tensor convert_to_tensor( + sox_sample_t* buffer, + const int32_t num_samples, + const int32_t num_channels, + const caffe2::TypeMeta dtype, + const bool normalize, + const bool channels_first) { + auto t = torch::from_blob( + buffer, {num_samples / num_channels, num_channels}, torch::kInt32); + // Note: Tensor created from_blob does not own data but borrwos + // So make sure to create a new copy after processing samples. + if (normalize || dtype == torch::kFloat32) { + t = t.to(torch::kFloat32); + t *= (t > 0) / 2147483647. + (t < 0) / 2147483648.; + } else if (dtype == torch::kInt32) { + t = t.clone(); + } else if (dtype == torch::kInt16) { + t.floor_divide_(1 << 16); + t = t.to(torch::kInt16); + } else if (dtype == torch::kUInt8) { + t.floor_divide_(1 << 24); + t += 128; + t = t.to(torch::kUInt8); + } else { + throw std::runtime_error("Unsupported dtype."); + } + if (channels_first) { + t = t.transpose(1, 0); + } + return t.contiguous(); +} + +torch::Tensor unnormalize_wav(const torch::Tensor input_tensor) { + const auto dtype = input_tensor.dtype(); + auto tensor = input_tensor; + if (dtype == torch::kFloat32) { + double multi_pos = 2147483647.; + double multi_neg = -2147483648.; + auto mult = (tensor > 0) * multi_pos - (tensor < 0) * multi_neg; + tensor = tensor.to(torch::dtype(torch::kFloat64)); + tensor *= mult; + tensor.clamp_(multi_neg, multi_pos); + tensor = tensor.to(torch::dtype(torch::kInt32)); + } else if (dtype == torch::kInt32) { + // already denormalized + } else if (dtype == torch::kInt16) { + tensor = tensor.to(torch::dtype(torch::kInt32)); + tensor *= ((tensor != 0) * 65536); + } else if (dtype == torch::kUInt8) { + tensor = tensor.to(torch::dtype(torch::kInt32)); + tensor -= 128; + tensor *= 16777216; + } else { + throw std::runtime_error("Unexpected dtype."); + } + return tensor; +} + +const std::string get_filetype(const std::string path) { + std::string ext = path.substr(path.find_last_of(".") + 1); + std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower); + return ext; +} + +sox_encoding_t get_encoding( + const std::string filetype, + const caffe2::TypeMeta dtype) { + if (filetype == "mp3") + return SOX_ENCODING_MP3; + if (filetype == "flac") + return SOX_ENCODING_FLAC; + if (filetype == "ogg" || filetype == "vorbis") + return SOX_ENCODING_VORBIS; + if (filetype == "wav") { + if (dtype == torch::kUInt8) + return SOX_ENCODING_UNSIGNED; + if (dtype == torch::kInt16) + return SOX_ENCODING_SIGN2; + if (dtype == torch::kInt32) + return SOX_ENCODING_SIGN2; + if (dtype == torch::kFloat32) + return SOX_ENCODING_FLOAT; + throw std::runtime_error("Unsupported dtype."); + } + throw std::runtime_error("Unsupported file type."); +} + +unsigned get_precision( + const std::string filetype, + const caffe2::TypeMeta dtype) { + if (filetype == "mp3") + return SOX_UNSPEC; + if (filetype == "flac") + return 24; + if (filetype == "ogg" || filetype == "vorbis") + return SOX_UNSPEC; + if (filetype == "wav") { + if (dtype == torch::kUInt8) + return 8; + if (dtype == torch::kInt16) + return 16; + if (dtype == torch::kInt32) + return 32; + if (dtype == torch::kFloat32) + return 32; + throw std::runtime_error("Unsupported dtype."); + } + throw std::runtime_error("Unsupported file type."); +} + +sox_signalinfo_t get_signalinfo( + const torch::Tensor& tensor, + const int64_t sample_rate, + const bool channels_first, + const std::string filetype) { + return sox_signalinfo_t{ + /*rate=*/static_cast(sample_rate), + /*channels=*/static_cast(tensor.size(channels_first ? 0 : 1)), + /*precision=*/get_precision(filetype, tensor.dtype()), + /*length=*/static_cast(tensor.numel())}; +} + +sox_encodinginfo_t get_encodinginfo( + const std::string filetype, + const caffe2::TypeMeta dtype, + const double compression) { + const double compression_ = [&]() { + if (filetype == "mp3") + return compression; + if (filetype == "flac") + return compression; + if (filetype == "ogg" || filetype == "vorbis") + return compression; + if (filetype == "wav") + return 0.; + throw std::runtime_error("Unsupported file type."); + }(); + + return sox_encodinginfo_t{/*encoding=*/get_encoding(filetype, dtype), + /*bits_per_sample=*/get_precision(filetype, dtype), + /*compression=*/compression_, + /*reverse_bytes=*/sox_option_default, + /*reverse_nibbles=*/sox_option_default, + /*reverse_bits=*/sox_option_default, + /*opposite_endian=*/sox_false}; +} + +} // namespace sox_utils +} // namespace torchaudio diff --git a/torchaudio/csrc/sox_utils.h b/torchaudio/csrc/sox_utils.h new file mode 100644 index 0000000000..665187c840 --- /dev/null +++ b/torchaudio/csrc/sox_utils.h @@ -0,0 +1,100 @@ +#ifndef TORCHAUDIO_SOX_UTILS_H +#define TORCHAUDIO_SOX_UTILS_H + +#include +#include + +namespace torchaudio { +namespace sox_utils { + +struct TensorSignal : torch::CustomClassHolder { + torch::Tensor tensor; + int64_t sample_rate; + bool channels_first; + + TensorSignal( + torch::Tensor tensor_, + int64_t sample_rate_, + bool channels_first_); + + torch::Tensor getTensor() const; + int64_t getSampleRate() const; + bool getChannelsFirst() const; +}; + +/// helper class to automatically close sox_format_t* +struct SoxFormat { + explicit SoxFormat(sox_format_t* fd) noexcept; + SoxFormat(const SoxFormat& other) = delete; + SoxFormat(SoxFormat&& other) = delete; + SoxFormat& operator=(const SoxFormat& other) = delete; + SoxFormat& operator=(SoxFormat&& other) = delete; + ~SoxFormat(); + sox_format_t* operator->() const noexcept; + operator sox_format_t*() const noexcept; + + private: + sox_format_t* fd_; +}; + +/// +/// Verify that input file is found, has known encoding, and not empty +void validate_input_file(const SoxFormat& sf); + +/// +/// Verify that input Tensor is 2D, CPU and either uin8, int16, int32 or float32 +void validate_input_tensor(const torch::Tensor); + +/// +/// Get target dtype for the given encoding and precision. +caffe2::TypeMeta get_dtype( + const sox_encoding_t encoding, + const unsigned precision); + +/// +/// Convert sox_sample_t buffer to uint8/int16/int32/float32 Tensor +/// NOTE: This function might modify the values in the input buffer to +/// reduce the number of memory copy. +/// @param buffer Pointer to buffer that contains audio data. +/// @param num_samples The number of samples to read. +/// @param num_channels The number of channels. Used to reshape the resulting +/// Tensor. +/// @param dtype Target dtype. Determines the output dtype and value range in +/// conjunction with normalization. +/// @param noramlize Perform normalization. Only effective when dtype is not +/// kFloat32. When effective, the output tensor is kFloat32 type and value range +/// is [-1.0, 1.0] +/// @param channels_first When True, output Tensor has shape of [num_channels, +/// num_frames]. +torch::Tensor convert_to_tensor( + sox_sample_t* buffer, + const int32_t num_samples, + const int32_t num_channels, + const caffe2::TypeMeta dtype, + const bool normalize, + const bool channels_first); + +/// +/// Convert float32/int32/int16/uint8 Tensor to int32 for Torch -> Sox +/// conversion. +torch::Tensor unnormalize_wav(const torch::Tensor); + +/// Extract extension from file path +const std::string get_filetype(const std::string path); + +/// Get sox_signalinfo_t for passing a torch::Tensor object. +sox_signalinfo_t get_signalinfo( + const torch::Tensor& tensor, + const int64_t sample_rate, + const bool channels_first, + const std::string filetype); + +/// Get sox_encofinginfo_t for saving audoi file +sox_encodinginfo_t get_encodinginfo( + const std::string filetype, + const caffe2::TypeMeta dtype, + const double compression); + +} // namespace sox_utils +} // namespace torchaudio +#endif diff --git a/torchaudio/csrc/typedefs.cpp b/torchaudio/csrc/typedefs.cpp deleted file mode 100644 index 7b81d665dc..0000000000 --- a/torchaudio/csrc/typedefs.cpp +++ /dev/null @@ -1,23 +0,0 @@ -#include - -namespace torchaudio { -SignalInfo::SignalInfo( - const int64_t sample_rate_, - const int64_t num_channels_, - const int64_t num_samples_) - : sample_rate(sample_rate_), - num_channels(num_channels_), - num_samples(num_samples_){}; - -int64_t SignalInfo::getSampleRate() const { - return sample_rate; -} - -int64_t SignalInfo::getNumChannels() const { - return num_channels; -} - -int64_t SignalInfo::getNumSamples() const { - return num_samples; -} -} // namespace torchaudio diff --git a/torchaudio/csrc/typedefs.h b/torchaudio/csrc/typedefs.h deleted file mode 100644 index 646ed09f3d..0000000000 --- a/torchaudio/csrc/typedefs.h +++ /dev/null @@ -1,23 +0,0 @@ -#ifndef TORCHAUDIO_TYPDEFS_H -#define TORCHAUDIO_TYPDEFS_H - -#include - -namespace torchaudio { -struct SignalInfo : torch::CustomClassHolder { - int64_t sample_rate; - int64_t num_channels; - int64_t num_samples; - - SignalInfo( - const int64_t sample_rate_, - const int64_t num_channels_, - const int64_t num_samples_); - int64_t getSampleRate() const; - int64_t getNumChannels() const; - int64_t getNumSamples() const; -}; - -} // namespace torchaudio - -#endif diff --git a/torchaudio/extension/extension.py b/torchaudio/extension/extension.py index 4a2ab82124..b01ba13e39 100644 --- a/torchaudio/extension/extension.py +++ b/torchaudio/extension/extension.py @@ -12,38 +12,9 @@ def _init_extension(): _init_script_module(ext) else: warnings.warn('torchaudio C++ extension is not available.') - _init_dummy_module() def _init_script_module(module): path = importlib.util.find_spec(module).origin torch.classes.load_library(path) torch.ops.load_library(path) - - -def _init_dummy_module(): - class SignalInfo: - """Data class for audio format information - - Used when torchaudio C++ extension is not available for annotating - sox_io backend functions so that torchaudio is still importable - without extension. - This class has to implement the same interface as C++ equivalent. - """ - def __init__(self, sample_rate: int, num_channels: int, num_samples: int): - self.sample_rate = sample_rate - self.num_channels = num_channels - self.num_samples = num_samples - - def get_sample_rate(self): - return self.sample_rate - - def get_num_channels(self): - return self.num_channels - - def get_num_samples(self): - return self.num_samples - - DummyModule = namedtuple('torchaudio', ['SignalInfo']) - module = DummyModule(SignalInfo) - setattr(torch.classes, 'torchaudio', module) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 28cb6a3fa2..78c8c594c9 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -485,9 +485,10 @@ def complex_norm( Returns: Tensor: Power of the normed input tensor. Shape of `(..., )` """ - if power == 1.0: - return torch.norm(complex_tensor, 2, -1) - return torch.norm(complex_tensor, 2, -1).pow(power) + + # Replace by torch.norm once issue is fixed + # https://github.com/pytorch/pytorch/issues/34279 + return complex_tensor.pow(2.).sum(-1).pow(0.5 * power) def angle( diff --git a/torchaudio/models/_wavernn.py b/torchaudio/models/_wavernn.py index 04155fb87c..cd2e89a10c 100644 --- a/torchaudio/models/_wavernn.py +++ b/torchaudio/models/_wavernn.py @@ -1,105 +1,331 @@ +from typing import List + +import torch from torch import Tensor from torch import nn -__all__ = ["_ResBlock", "_MelResNet"] +__all__ = ["_ResBlock", "_MelResNet", "_Stretch2d", "_UpsampleNetwork", "_WaveRNN"] class _ResBlock(nn.Module): - r"""This is a ResNet block layer. This layer is based on the paper "Deep Residual Learning - for Image Recognition". Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. CVPR, 2016. - It is a block used in WaveRNN. WaveRNN is based on the paper "Efficient Neural Audio Synthesis". - Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, - Florian Stimberg, Aaron van den Oord, Sander Dieleman, Koray Kavukcuoglu. arXiv:1802.08435, 2018. + r"""ResNet block based on "Deep Residual Learning for Image Recognition" + + The paper link is https://arxiv.org/pdf/1512.03385.pdf. Args: - num_dims: the number of compute dimensions in the input (default=128). + n_freq: the number of bins in a spectrogram (default=128) - Examples:: - >>> resblock = _ResBlock(num_dims=128) - >>> input = torch.rand(10, 128, 512) - >>> output = resblock(input) + Examples + >>> resblock = _ResBlock() + >>> input = torch.rand(10, 128, 512) # a random spectrogram + >>> output = resblock(input) # shape: (10, 128, 512) """ - def __init__(self, num_dims: int = 128) -> None: + def __init__(self, n_freq: int = 128) -> None: super().__init__() self.resblock_model = nn.Sequential( - nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False), - nn.BatchNorm1d(num_dims), + nn.Conv1d(in_channels=n_freq, out_channels=n_freq, kernel_size=1, bias=False), + nn.BatchNorm1d(n_freq), nn.ReLU(inplace=True), - nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False), - nn.BatchNorm1d(num_dims) + nn.Conv1d(in_channels=n_freq, out_channels=n_freq, kernel_size=1, bias=False), + nn.BatchNorm1d(n_freq) ) - def forward(self, x: Tensor) -> Tensor: + def forward(self, specgram: Tensor) -> Tensor: r"""Pass the input through the _ResBlock layer. - Args: - x: the input sequence to the _ResBlock layer (required). + specgram (Tensor): the input sequence to the _ResBlock layer (n_batch, n_freq, n_time). - Shape: - - x: :math:`(N, S, T)`. - - output: :math:`(N, S, T)`. - where N is the batch size, S is the number of input sequence, - T is the length of input sequence. + Return: + Tensor shape: (n_batch, n_freq, n_time) """ - residual = x - return self.resblock_model(x) + residual + return self.resblock_model(specgram) + specgram class _MelResNet(nn.Module): - r"""This is a MelResNet layer based on a stack of ResBlocks. It is a block used in WaveRNN. - WaveRNN is based on the paper "Efficient Neural Audio Synthesis". Nal Kalchbrenner, Erich Elsen, - Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron van den Oord, - Sander Dieleman, Koray Kavukcuoglu. arXiv:1802.08435, 2018. + r"""MelResNet layer uses a stack of ResBlocks on spectrogram. Args: - res_blocks: the number of ResBlock in stack (default=10). - input_dims: the number of input sequence (default=100). - hidden_dims: the number of compute dimensions (default=128). - output_dims: the number of output sequence (default=128). - pad: the number of kernal size (pad * 2 + 1) in the first Conv1d layer (default=2). - - Examples:: - >>> melresnet = _MelResNet(res_blocks=10, input_dims=100, - hidden_dims=128, output_dims=128, pad=2) - >>> input = torch.rand(10, 100, 512) - >>> output = melresnet(input) + n_res_block: the number of ResBlock in stack (default=10) + n_freq: the number of bins in a spectrogram (default=128) + n_hidden: the number of hidden dimensions (default=128) + n_output: the number of output dimensions (default=128) + kernel_size: the number of kernel size in the first Conv1d layer (default=5) + + Examples + >>> melresnet = _MelResNet() + >>> input = torch.rand(10, 128, 512) # a random spectrogram + >>> output = melresnet(input) # shape: (10, 128, 508) """ - def __init__(self, res_blocks: int = 10, - input_dims: int = 100, - hidden_dims: int = 128, - output_dims: int = 128, - pad: int = 2) -> None: + def __init__(self, + n_res_block: int = 10, + n_freq: int = 128, + n_hidden: int = 128, + n_output: int = 128, + kernel_size: int = 5) -> None: super().__init__() - kernel_size = pad * 2 + 1 - ResBlocks = [] - - for i in range(res_blocks): - ResBlocks.append(_ResBlock(hidden_dims)) + ResBlocks = [_ResBlock(n_hidden) for _ in range(n_res_block)] self.melresnet_model = nn.Sequential( - nn.Conv1d(in_channels=input_dims, out_channels=hidden_dims, kernel_size=kernel_size, bias=False), - nn.BatchNorm1d(hidden_dims), + nn.Conv1d(in_channels=n_freq, out_channels=n_hidden, kernel_size=kernel_size, bias=False), + nn.BatchNorm1d(n_hidden), nn.ReLU(inplace=True), *ResBlocks, - nn.Conv1d(in_channels=hidden_dims, out_channels=output_dims, kernel_size=1) + nn.Conv1d(in_channels=n_hidden, out_channels=n_output, kernel_size=1) ) - def forward(self, x: Tensor) -> Tensor: + def forward(self, specgram: Tensor) -> Tensor: r"""Pass the input through the _MelResNet layer. + Args: + specgram (Tensor): the input sequence to the _MelResNet layer (n_batch, n_freq, n_time). + + Return: + Tensor shape: (n_batch, n_output, n_time - kernel_size + 1) + """ + + return self.melresnet_model(specgram) + + +class _Stretch2d(nn.Module): + r"""Upscale the frequency and time dimensions of a spectrogram. + + Args: + time_scale: the scale factor in time dimension + freq_scale: the scale factor in frequency dimension + + Examples + >>> stretch2d = _Stretch2d(time_scale=10, freq_scale=5) + + >>> input = torch.rand(10, 100, 512) # a random spectrogram + >>> output = stretch2d(input) # shape: (10, 500, 5120) + """ + + def __init__(self, + time_scale: int, + freq_scale: int) -> None: + super().__init__() + + self.freq_scale = freq_scale + self.time_scale = time_scale + + def forward(self, specgram: Tensor) -> Tensor: + r"""Pass the input through the _Stretch2d layer. Args: - x: the input sequence to the _MelResNet layer (required). + specgram (Tensor): the input sequence to the _Stretch2d layer (..., n_freq, n_time). - Shape: - - x: :math:`(N, S, T)`. - - output: :math:`(N, P, T - 2 * pad)`. - where N is the batch size, S is the number of input sequence, - P is the number of output sequence, T is the length of input sequence. + Return: + Tensor shape: (..., n_freq * freq_scale, n_time * time_scale) """ - return self.melresnet_model(x) + return specgram.repeat_interleave(self.freq_scale, -2).repeat_interleave(self.time_scale, -1) + + +class _UpsampleNetwork(nn.Module): + r"""Upscale the dimensions of a spectrogram. + + Args: + upsample_scales: the list of upsample scales + n_res_block: the number of ResBlock in stack (default=10) + n_freq: the number of bins in a spectrogram (default=128) + n_hidden: the number of hidden dimensions (default=128) + n_output: the number of output dimensions (default=128) + kernel_size: the number of kernel size in the first Conv1d layer (default=5) + + Examples + >>> upsamplenetwork = _UpsampleNetwork(upsample_scales=[4, 4, 16]) + >>> input = torch.rand(10, 128, 10) # a random spectrogram + >>> output = upsamplenetwork(input) # shape: (10, 1536, 128), (10, 1536, 128) + """ + + def __init__(self, + upsample_scales: List[int], + n_res_block: int = 10, + n_freq: int = 128, + n_hidden: int = 128, + n_output: int = 128, + kernel_size: int = 5) -> None: + super().__init__() + + total_scale = 1 + for upsample_scale in upsample_scales: + total_scale *= upsample_scale + + self.indent = (kernel_size - 1) // 2 * total_scale + self.resnet = _MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) + self.resnet_stretch = _Stretch2d(total_scale, 1) + + up_layers = [] + for scale in upsample_scales: + stretch = _Stretch2d(scale, 1) + conv = nn.Conv2d(in_channels=1, + out_channels=1, + kernel_size=(1, scale * 2 + 1), + padding=(0, scale), + bias=False) + conv.weight.data.fill_(1. / (scale * 2 + 1)) + up_layers.append(stretch) + up_layers.append(conv) + self.upsample_layers = nn.Sequential(*up_layers) + + def forward(self, specgram: Tensor) -> Tensor: + r"""Pass the input through the _UpsampleNetwork layer. + + Args: + specgram (Tensor): the input sequence to the _UpsampleNetwork layer (n_batch, n_freq, n_time) + + Return: + Tensor shape: (n_batch, n_freq, (n_time - kernel_size + 1) * total_scale), + (n_batch, n_output, (n_time - kernel_size + 1) * total_scale) + where total_scale is the product of all elements in upsample_scales. + """ + + resnet_output = self.resnet(specgram).unsqueeze(1) + resnet_output = self.resnet_stretch(resnet_output) + resnet_output = resnet_output.squeeze(1) + + specgram = specgram.unsqueeze(1) + upsampling_output = self.upsample_layers(specgram) + upsampling_output = upsampling_output.squeeze(1)[:, :, self.indent:-self.indent] + + return upsampling_output, resnet_output + + +class _WaveRNN(nn.Module): + r"""WaveRNN model based on the implementation from `fatchord `_. + + The original implementation was introduced in + `"Efficient Neural Audio Synthesis" `_. + The input channels of waveform and spectrogram have to be 1. The product of + `upsample_scales` must equal `hop_length`. + + Args: + upsample_scales: the list of upsample scales + n_bits: the bits of output waveform + sample_rate: the rate of audio dimensions (samples per second) + hop_length: the number of samples between the starts of consecutive frames + n_res_block: the number of ResBlock in stack (default=10) + n_rnn: the dimension of RNN layer (default=512) + n_fc: the dimension of fully connected layer (default=512) + kernel_size: the number of kernel size in the first Conv1d layer (default=5) + n_freq: the number of bins in a spectrogram (default=128) + n_hidden: the number of hidden dimensions (default=128) + n_output: the number of output dimensions (default=128) + mode: the mode of waveform in ['waveform', 'mol'] (default='waveform') + + Example + >>> wavernn = _waveRNN(upsample_scales=[5,5,8], n_bits=9, sample_rate=24000, hop_length=200) + >>> waveform, sample_rate = torchaudio.load(file) + >>> # waveform shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length) + >>> specgram = MelSpectrogram(sample_rate)(waveform) # shape: (n_batch, n_channel, n_freq, n_time) + >>> output = wavernn(waveform, specgram) + >>> # output shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length, 2 ** n_bits) + """ + + def __init__(self, + upsample_scales: List[int], + n_bits: int, + sample_rate: int, + hop_length: int, + n_res_block: int = 10, + n_rnn: int = 512, + n_fc: int = 512, + kernel_size: int = 5, + n_freq: int = 128, + n_hidden: int = 128, + n_output: int = 128, + mode: str = 'waveform') -> None: + super().__init__() + + self.mode = mode + self.kernel_size = kernel_size + + if self.mode == 'waveform': + self.n_classes = 2 ** n_bits + elif self.mode == 'mol': + self.n_classes = 30 + else: + raise ValueError(f"Expected mode: `waveform` or `mol`, but found {self.mode}") + + self.n_rnn = n_rnn + self.n_aux = n_output // 4 + self.hop_length = hop_length + self.sample_rate = sample_rate + + total_scale = 1 + for upsample_scale in upsample_scales: + total_scale *= upsample_scale + if total_scale != self.hop_length: + raise ValueError(f"Expected: total_scale == hop_length, but found {total_scale} != {hop_length}") + + self.upsample = _UpsampleNetwork(upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size) + self.fc = nn.Linear(n_freq + self.n_aux + 1, n_rnn) + + self.rnn1 = nn.GRU(n_rnn, n_rnn, batch_first=True) + self.rnn2 = nn.GRU(n_rnn + self.n_aux, n_rnn, batch_first=True) + + self.relu1 = nn.ReLU(inplace=True) + self.relu2 = nn.ReLU(inplace=True) + + self.fc1 = nn.Linear(n_rnn + self.n_aux, n_fc) + self.fc2 = nn.Linear(n_fc + self.n_aux, n_fc) + self.fc3 = nn.Linear(n_fc, self.n_classes) + + def forward(self, waveform: Tensor, specgram: Tensor) -> Tensor: + r"""Pass the input through the _WaveRNN model. + + Args: + waveform: the input waveform to the _WaveRNN layer (n_batch, 1, (n_time - kernel_size + 1) * hop_length) + specgram: the input spectrogram to the _WaveRNN layer (n_batch, 1, n_freq, n_time) + + Return: + Tensor shape: (n_batch, 1, (n_time - kernel_size + 1) * hop_length, 2 ** n_bits) + """ + + assert waveform.size(1) == 1, 'Require the input channel of waveform is 1' + assert specgram.size(1) == 1, 'Require the input channel of specgram is 1' + # remove channel dimension until the end + waveform, specgram = waveform.squeeze(1), specgram.squeeze(1) + + batch_size = waveform.size(0) + h1 = torch.zeros(1, batch_size, self.n_rnn, dtype=waveform.dtype, device=waveform.device) + h2 = torch.zeros(1, batch_size, self.n_rnn, dtype=waveform.dtype, device=waveform.device) + # output of upsample: + # specgram: (n_batch, n_freq, (n_time - kernel_size + 1) * total_scale) + # aux: (n_batch, n_output, (n_time - kernel_size + 1) * total_scale) + specgram, aux = self.upsample(specgram) + specgram = specgram.transpose(1, 2) + aux = aux.transpose(1, 2) + + aux_idx = [self.n_aux * i for i in range(5)] + a1 = aux[:, :, aux_idx[0]:aux_idx[1]] + a2 = aux[:, :, aux_idx[1]:aux_idx[2]] + a3 = aux[:, :, aux_idx[2]:aux_idx[3]] + a4 = aux[:, :, aux_idx[3]:aux_idx[4]] + + x = torch.cat([waveform.unsqueeze(-1), specgram, a1], dim=-1) + x = self.fc(x) + res = x + x, _ = self.rnn1(x, h1) + + x = x + res + res = x + x = torch.cat([x, a2], dim=-1) + x, _ = self.rnn2(x, h2) + + x = x + res + x = torch.cat([x, a3], dim=-1) + x = self.fc1(x) + x = self.relu1(x) + + x = torch.cat([x, a4], dim=-1) + x = self.fc2(x) + x = self.relu2(x) + x = self.fc3(x) + + # bring back channel dimension + return x.unsqueeze(1) diff --git a/torchaudio/sox_effects/__init__.py b/torchaudio/sox_effects/__init__.py index 115b70c895..507dc5c3af 100644 --- a/torchaudio/sox_effects/__init__.py +++ b/torchaudio/sox_effects/__init__.py @@ -9,4 +9,6 @@ if _mod_utils.is_module_available('torchaudio._torchaudio'): + import atexit init_sox_effects() + atexit.register(shutdown_sox_effects) diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 6a6b5be0a5..0aee312126 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -1,4 +1,3 @@ -import atexit from typing import Any, Callable, List, Optional, Tuple, Union import torch @@ -13,19 +12,8 @@ from torchaudio import _torchaudio -_SOX_INITIALIZED: Optional[bool] = False -# This variable has a micro lifecycle. (False -> True -> None) -# False: Not initialized -# True: Initialized -# None: Already shut down (should not be initialized again.) - -_SOX_SUCCESS_CODE = 0 -# defined at -# https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93a9ef2b87ec303edfe40751d9a85fadeeb - - @_mod_utils.requires_module('torchaudio._torchaudio') -def init_sox_effects() -> int: +def init_sox_effects() -> None: """Initialize resources required to use ``SoxEffectsChain`` You do not need to call this function manually. It is called automatically. @@ -33,50 +21,26 @@ def init_sox_effects() -> int: Once initialized, you do not need to call this function again across the multiple call of ``SoxEffectsChain.sox_build_flow_effects``, though it is safe to do so as long as ``shutdown_sox_effects`` is not called yet. - Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and calling - this function results in `RuntimeError`. + Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and + initializing again will result in error. Note: This function is not required for simple loading. - - Returns: - int: Code corresponding to sox_error_t enum. See - https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 """ - global _SOX_INITIALIZED - if _SOX_INITIALIZED is None: - raise RuntimeError('SoX effects chain has been already shut down. Can not initialize again.') - if not _SOX_INITIALIZED: - code = _torchaudio.initialize_sox() - if code == _SOX_SUCCESS_CODE: - _SOX_INITIALIZED = True - atexit.register(shutdown_sox_effects) - return code - return _SOX_SUCCESS_CODE + torch.ops.torchaudio.sox_effects_initialize_sox_effects() @_mod_utils.requires_module("torchaudio._torchaudio") -def shutdown_sox_effects() -> int: +def shutdown_sox_effects() -> None: """Clean up resources required to use ``SoxEffectsChain`` You do not need to call this function manually. It is called automatically. It is safe to call this function multiple times. - Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and calling - this function results in `RuntimeError`. - - - Returns: - int: Code corresponding to sox_error_t enum. See - https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and + initializing again will result in error. """ - global _SOX_INITIALIZED - if _SOX_INITIALIZED: - code = _torchaudio.shutdown_sox() - if code == _SOX_INITIALIZED: - _SOX_INITIALIZED = None - return code - return _SOX_SUCCESS_CODE + torch.ops.torchaudio.sox_effects_shutdown_sox_effects() @_mod_utils.requires_module('torchaudio._torchaudio') @@ -88,7 +52,7 @@ def effect_names() -> List[str]: Example >>> EFFECT_NAMES = torchaudio.sox_effects.effect_names() """ - return _torchaudio.get_effect_names() + return torch.ops.torchaudio.sox_effects_list_effects() @_mod_utils.requires_module('torchaudio._torchaudio') From fd7460ecfbfa47ff1612fba939b7dc47aae613dd Mon Sep 17 00:00:00 2001 From: Stanislau Hlebik Date: Fri, 17 Jul 2020 17:07:23 -0700 Subject: [PATCH 11/73] remediation of S205607 fbshipit-source-id: 5113fe0c527595e4227ff827253b7414abbdf7ac --- .python3 | 1 + build_tools/__init__.py | 1 + examples/test/__init__.py | 1 + test/compliance/__init__.py | 1 + test/sox_io_backend/__init__.py | 1 + torchaudio/_internal/__init__.py | 1 + 6 files changed, 6 insertions(+) diff --git a/.python3 b/.python3 index e69de29bb2..56de9c5ee1 100644 --- a/.python3 +++ b/.python3 @@ -0,0 +1 @@ +S205607 diff --git a/build_tools/__init__.py b/build_tools/__init__.py index e69de29bb2..56de9c5ee1 100644 --- a/build_tools/__init__.py +++ b/build_tools/__init__.py @@ -0,0 +1 @@ +S205607 diff --git a/examples/test/__init__.py b/examples/test/__init__.py index e69de29bb2..56de9c5ee1 100644 --- a/examples/test/__init__.py +++ b/examples/test/__init__.py @@ -0,0 +1 @@ +S205607 diff --git a/test/compliance/__init__.py b/test/compliance/__init__.py index e69de29bb2..56de9c5ee1 100644 --- a/test/compliance/__init__.py +++ b/test/compliance/__init__.py @@ -0,0 +1 @@ +S205607 diff --git a/test/sox_io_backend/__init__.py b/test/sox_io_backend/__init__.py index e69de29bb2..56de9c5ee1 100644 --- a/test/sox_io_backend/__init__.py +++ b/test/sox_io_backend/__init__.py @@ -0,0 +1 @@ +S205607 diff --git a/torchaudio/_internal/__init__.py b/torchaudio/_internal/__init__.py index e69de29bb2..56de9c5ee1 100644 --- a/torchaudio/_internal/__init__.py +++ b/torchaudio/_internal/__init__.py @@ -0,0 +1 @@ +S205607 From 4f19eef10cfcef472ab534b5907b3e2e38f2843d Mon Sep 17 00:00:00 2001 From: Stanislau Hlebik Date: Fri, 17 Jul 2020 17:07:23 -0700 Subject: [PATCH 12/73] remediation of S205607 fbshipit-source-id: 798decc90db4f13770e97cdce3c0df7d5421b2a3 --- .python3 | 1 - build_tools/__init__.py | 1 - examples/test/__init__.py | 1 - test/compliance/__init__.py | 1 - test/sox_io_backend/__init__.py | 1 - torchaudio/_internal/__init__.py | 1 - 6 files changed, 6 deletions(-) diff --git a/.python3 b/.python3 index 56de9c5ee1..e69de29bb2 100644 --- a/.python3 +++ b/.python3 @@ -1 +0,0 @@ -S205607 diff --git a/build_tools/__init__.py b/build_tools/__init__.py index 56de9c5ee1..e69de29bb2 100644 --- a/build_tools/__init__.py +++ b/build_tools/__init__.py @@ -1 +0,0 @@ -S205607 diff --git a/examples/test/__init__.py b/examples/test/__init__.py index 56de9c5ee1..e69de29bb2 100644 --- a/examples/test/__init__.py +++ b/examples/test/__init__.py @@ -1 +0,0 @@ -S205607 diff --git a/test/compliance/__init__.py b/test/compliance/__init__.py index 56de9c5ee1..e69de29bb2 100644 --- a/test/compliance/__init__.py +++ b/test/compliance/__init__.py @@ -1 +0,0 @@ -S205607 diff --git a/test/sox_io_backend/__init__.py b/test/sox_io_backend/__init__.py index 56de9c5ee1..e69de29bb2 100644 --- a/test/sox_io_backend/__init__.py +++ b/test/sox_io_backend/__init__.py @@ -1 +0,0 @@ -S205607 diff --git a/torchaudio/_internal/__init__.py b/torchaudio/_internal/__init__.py index 56de9c5ee1..e69de29bb2 100644 --- a/torchaudio/_internal/__init__.py +++ b/torchaudio/_internal/__init__.py @@ -1 +0,0 @@ -S205607 From 57a88cd7d91f0cdeab39b7e1fae55e6bb22bc41c Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Mon, 27 Jul 2020 06:03:09 -0700 Subject: [PATCH 13/73] Import torchaudio 20200723 Summary: Import torchaudio 20200723 #814 Reviewed By: fmassa Differential Revision: D22666393 fbshipit-source-id: 50df07b5c158fe4e95ada7ea54381b2e26f6aecd --- .circleci/config.yml | 1342 +++++++++++++++++ .circleci/config.yml.in | 631 ++++++++ .circleci/regenerate.py | 180 +++ .circleci/smoke_test/docker/Dockerfile | 36 + .circleci/test/test_sort_yaml.py | 14 + .circleci/unittest/linux/README.md | 6 + .circleci/unittest/linux/docker/.dockerignore | 2 + .circleci/unittest/linux/docker/.gitignore | 2 + .circleci/unittest/linux/docker/Dockerfile | 59 + .../unittest/linux/docker/build_and_push.sh | 27 + .../docker/scripts/copy_kaldi_executables.sh | 58 + .../unittest/linux/scripts/environment.yml | 17 + .circleci/unittest/linux/scripts/install.sh | 23 + .../unittest/linux/scripts/post_process.sh | 8 + .../linux/scripts/run_style_checks.sh | 32 + .circleci/unittest/linux/scripts/run_test.sh | 10 + .circleci/unittest/linux/scripts/setup_env.sh | 50 + .circleci/unittest/windows/README.md | 4 + .../unittest/windows/scripts/environment.yml | 18 + .circleci/unittest/windows/scripts/install.sh | 23 + .../windows/scripts/install_conda.bat | 1 + .../unittest/windows/scripts/post_process.sh | 8 + .../unittest/windows/scripts/run_test.sh | 10 + .../unittest/windows/scripts/setup_env.sh | 39 + .clang-format | 88 ++ .clang-tidy | 33 + .flake8 | 4 + .github/ISSUE_TEMPLATE/bug-report.md | 53 + .github/ISSUE_TEMPLATE/documentation.md | 9 + .github/ISSUE_TEMPLATE/feature-request.md | 24 + .../ISSUE_TEMPLATE/questions-help-support.md | 13 + .github/pytorch-probot.yml | 1 + .gitignore | 129 ++ .travis.yml | 32 + docs/source/backend.rst | 138 ++ docs/source/datasets.rst | 8 + docs/source/index.rst | 15 +- docs/source/sox_effects.rst | 47 +- docs/source/torchaudio.rst | 43 + docs/source/utils.rst | 11 + examples/pipeline_wavernn/README.md | 36 + examples/pipeline_wavernn/datasets.py | 115 ++ examples/pipeline_wavernn/losses.py | 119 ++ examples/pipeline_wavernn/main.py | 390 +++++ examples/pipeline_wavernn/processing.py | 58 + examples/pipeline_wavernn/utils.py | 61 + test/README.md | 2 +- test/assets/100Hz_44100Hz_16bit_05sec.wav | Bin 444524 -> 0 bytes test/assets/440Hz_44100Hz_16bit_05sec.wav | Bin 444524 -> 0 bytes test/assets/genres/noise/noise.0000.wav | Bin 223532 -> 0 bytes test/assets/sox_effect_test_args.json | 88 ++ test/assets/sox_effect_test_fir_coeffs.txt | 1 + test/assets/waves_yesno/0_1_0_1_0_1_1_0.wav | Bin 84 -> 0 bytes test/assets/whitenoise.mp3 | Bin 57136 -> 0 bytes test/assets/whitenoise.wav | Bin 441078 -> 0 bytes test/assets/whitenoise_1min.mp3 | Bin 666705 -> 0 bytes test/common_utils/backend_utils.py | 4 +- test/common_utils/case_utils.py | 21 +- test/common_utils/data_utils.py | 44 +- test/common_utils/sox_utils.py | 21 + test/datasets/__init__.py | 0 .../datasets_test.py} | 29 +- test/datasets/gtzan_test.py | 88 ++ test/datasets/libritts_test.py | 71 + test/datasets/utils_test.py | 47 + test/datasets/yesno_test.py | 49 + test/functional_cpu_test.py | 31 +- test/sox_effect/__init__.py | 0 test/sox_effect/common.py | 26 + test/sox_effect/smoke_test.py | 59 + .../sox_effects_chain_test.py} | 58 +- test/sox_effect/test_dataset.py | 114 ++ test/sox_effect/test_sox_effect.py | 221 +++ test/sox_effect/test_torchscript.py | 100 ++ test/sox_io_backend/smoke_test.py | 87 ++ test/test_batch_consistency.py | 32 +- test/test_compliance_kaldi.py | 59 +- test/test_dataloader.py | 4 - test/test_io.py | 3 - test/test_kaldi_io.py | 4 - test/test_librosa_compatibility.py | 11 +- test/test_models.py | 47 +- test/test_sox_compatibility.py | 534 +++---- test/test_transforms.py | 4 - test/utils/__init__.py | 0 test/utils/test_sox_utils.py | 49 + torchaudio/__init__.py | 7 +- torchaudio/_internal/module_utils.py | 2 +- torchaudio/backend/common.py | 26 + torchaudio/backend/sox_io_backend.py | 159 +- torchaudio/backend/utils.py | 19 +- torchaudio/csrc/register.cpp | 33 +- torchaudio/csrc/sox_effects.cpp | 135 +- torchaudio/csrc/sox_effects.h | 11 +- torchaudio/csrc/sox_effects_chain.cpp | 287 ++++ torchaudio/csrc/sox_effects_chain.h | 42 + torchaudio/csrc/sox_io.cpp | 98 +- torchaudio/csrc/sox_utils.cpp | 66 +- torchaudio/csrc/sox_utils.h | 32 +- torchaudio/datasets/__init__.py | 2 + torchaudio/datasets/gtzan.py | 49 +- torchaudio/datasets/libritts.py | 131 ++ torchaudio/datasets/utils.py | 8 +- torchaudio/functional.py | 4 +- torchaudio/models/_wavernn.py | 80 +- torchaudio/models/wav2letter.py | 9 +- torchaudio/sox_effects/__init__.py | 2 + torchaudio/sox_effects/sox_effects.py | 322 +++- torchaudio/transforms.py | 37 +- torchaudio/utils/__init__.py | 9 + torchaudio/utils/sox_utils.py | 94 ++ 111 files changed, 6697 insertions(+), 902 deletions(-) create mode 100644 .circleci/config.yml create mode 100644 .circleci/config.yml.in create mode 100755 .circleci/regenerate.py create mode 100644 .circleci/smoke_test/docker/Dockerfile create mode 100755 .circleci/test/test_sort_yaml.py create mode 100644 .circleci/unittest/linux/README.md create mode 100644 .circleci/unittest/linux/docker/.dockerignore create mode 100644 .circleci/unittest/linux/docker/.gitignore create mode 100644 .circleci/unittest/linux/docker/Dockerfile create mode 100755 .circleci/unittest/linux/docker/build_and_push.sh create mode 100755 .circleci/unittest/linux/docker/scripts/copy_kaldi_executables.sh create mode 100644 .circleci/unittest/linux/scripts/environment.yml create mode 100755 .circleci/unittest/linux/scripts/install.sh create mode 100755 .circleci/unittest/linux/scripts/post_process.sh create mode 100755 .circleci/unittest/linux/scripts/run_style_checks.sh create mode 100755 .circleci/unittest/linux/scripts/run_test.sh create mode 100755 .circleci/unittest/linux/scripts/setup_env.sh create mode 100644 .circleci/unittest/windows/README.md create mode 100644 .circleci/unittest/windows/scripts/environment.yml create mode 100644 .circleci/unittest/windows/scripts/install.sh create mode 100644 .circleci/unittest/windows/scripts/install_conda.bat create mode 100644 .circleci/unittest/windows/scripts/post_process.sh create mode 100644 .circleci/unittest/windows/scripts/run_test.sh create mode 100644 .circleci/unittest/windows/scripts/setup_env.sh create mode 100644 .clang-format create mode 100644 .clang-tidy create mode 100644 .flake8 create mode 100644 .github/ISSUE_TEMPLATE/bug-report.md create mode 100644 .github/ISSUE_TEMPLATE/documentation.md create mode 100644 .github/ISSUE_TEMPLATE/feature-request.md create mode 100644 .github/ISSUE_TEMPLATE/questions-help-support.md create mode 100644 .github/pytorch-probot.yml create mode 100644 .gitignore create mode 100644 .travis.yml create mode 100644 docs/source/backend.rst create mode 100644 docs/source/torchaudio.rst create mode 100644 docs/source/utils.rst create mode 100644 examples/pipeline_wavernn/README.md create mode 100644 examples/pipeline_wavernn/datasets.py create mode 100644 examples/pipeline_wavernn/losses.py create mode 100644 examples/pipeline_wavernn/main.py create mode 100644 examples/pipeline_wavernn/processing.py create mode 100644 examples/pipeline_wavernn/utils.py delete mode 100644 test/assets/100Hz_44100Hz_16bit_05sec.wav delete mode 100644 test/assets/440Hz_44100Hz_16bit_05sec.wav delete mode 100644 test/assets/genres/noise/noise.0000.wav create mode 100644 test/assets/sox_effect_test_args.json create mode 100644 test/assets/sox_effect_test_fir_coeffs.txt delete mode 100644 test/assets/waves_yesno/0_1_0_1_0_1_1_0.wav delete mode 100644 test/assets/whitenoise.mp3 delete mode 100644 test/assets/whitenoise.wav delete mode 100644 test/assets/whitenoise_1min.mp3 create mode 100644 test/datasets/__init__.py rename test/{test_datasets.py => datasets/datasets_test.py} (71%) create mode 100644 test/datasets/gtzan_test.py create mode 100644 test/datasets/libritts_test.py create mode 100644 test/datasets/utils_test.py create mode 100644 test/datasets/yesno_test.py create mode 100644 test/sox_effect/__init__.py create mode 100644 test/sox_effect/common.py create mode 100644 test/sox_effect/smoke_test.py rename test/{test_sox_effects.py => sox_effect/sox_effects_chain_test.py} (82%) create mode 100644 test/sox_effect/test_dataset.py create mode 100644 test/sox_effect/test_sox_effect.py create mode 100644 test/sox_effect/test_torchscript.py create mode 100644 test/sox_io_backend/smoke_test.py create mode 100644 test/utils/__init__.py create mode 100644 test/utils/test_sox_utils.py create mode 100644 torchaudio/csrc/sox_effects_chain.cpp create mode 100644 torchaudio/csrc/sox_effects_chain.h create mode 100644 torchaudio/datasets/libritts.py create mode 100644 torchaudio/utils/__init__.py create mode 100644 torchaudio/utils/sox_utils.py diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 0000000000..771dccc515 --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,1342 @@ +version: 2.1 + +# How to test the Linux jobs: +# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/ +# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.8 +# - Replace binary_linux_wheel_py3.8 with the name of the job you want to test. +# Job names are 'name:' key. + +executors: + windows-cpu: + machine: + resource_class: windows.xlarge + image: windows-server-2019-vs2019:stable + shell: bash.exe + + windows-gpu: + machine: + resource_class: windows.gpu.nvidia.medium + image: windows-server-2019-nvidia:stable + shell: bash.exe + +commands: + generate_cache_key: + description: "Generates a cache key file that changes daily" + steps: + - run: + name: Generate cache key + command: echo "$(date +"%Y-%m-%d")" > .cachekey + designate_upload_channel: + description: "inserts the correct upload channel into ${BASH_ENV}" + steps: + - run: + name: adding UPLOAD_CHANNEL to BASH_ENV + command: | + our_upload_channel=nightly + # On tags upload to test instead + if [[ -n "${CIRCLE_TAG}" ]]; then + our_upload_channel=test + fi + echo "export UPLOAD_CHANNEL=${our_upload_channel}" >> ${BASH_ENV} + install_build_tools_macos: + description: "installs tools required to build torchaudio" + steps: + - run: + name: Install cmake and pkg-config + command: HOMEBREW_NO_AUTO_UPDATE=1 brew install cmake pkg-config wget + # Disable brew auto update which is very slow + +binary_common: &binary_common + parameters: + # Edit these defaults to do a release + build_version: + description: "version number of release binary; by default, build a nightly" + type: string + default: "" + pytorch_version: + description: "PyTorch version to build against; by default, use a nightly" + type: string + default: "" + # Don't edit these + python_version: + description: "Python version to build against (e.g., 3.8)" + type: string + environment: + PYTHON_VERSION: << parameters.python_version >> + BUILD_VERSION: << parameters.build_version >> + PYTORCH_VERSION: << parameters.pytorch_version >> + CU_VERSION: cpu + +smoke_test_common: &smoke_test_common + <<: *binary_common + docker: + - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:56c846a5-acaa-41a7-92f5-46ec66186c61 + aws_auth: + aws_access_key_id: ${ECR_AWS_ACCESS_KEY} + aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} + +jobs: + circleci_consistency: + docker: + - image: circleci/python:3.8 + steps: + - checkout + - run: + command: | + pip install --user --progress-bar off jinja2 pyyaml + python .circleci/regenerate.py + git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1) + + download_third_parties_nix: + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: small + steps: + - checkout + - generate_cache_key + - restore_cache: + + keys: + - tp-nix-v2-{{ checksum ".cachekey" }} + + - run: + command: | + mkdir -p third_party/archives/ + wget --no-clobber --directory-prefix=third_party/archives/ $(awk '/URL /{print $2}' third_party/CMakeLists.txt) + - save_cache: + + key: tp-nix-v2-{{ checksum ".cachekey" }} + + paths: + - third_party/archives + - persist_to_workspace: + root: third_party + paths: + - archives + + binary_linux_wheel: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - run: packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_linux_conda: + <<: *binary_common + docker: + - image: "pytorch/conda-cuda" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - run: packaging/build_conda.sh + - store_artifacts: + path: /opt/conda/conda-bld/linux-64 + - persist_to_workspace: + root: /opt/conda/conda-bld/linux-64 + paths: + - "*" + + binary_macos_wheel: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - run: + # Cannot easily deduplicate this as source'ing activate + # will set environment variables which we need to propagate + # to build_wheel.sh + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_macos_conda: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - run: + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + conda install -yq conda-build + packaging/build_conda.sh + - store_artifacts: + path: /Users/distiller/miniconda3/conda-bld/osx-64 + - persist_to_workspace: + root: /Users/distiller/miniconda3/conda-bld/osx-64 + paths: + - "*" + + binary_windows_wheel: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: build + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate base + bash packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: build + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate base + conda install -yq conda-build "conda-package-handling!=1.5.0" + bash packaging/build_conda.sh + - store_artifacts: + path: C:/tools/miniconda3/conda-bld/win-64 + - persist_to_workspace: + root: C:/tools/miniconda3/conda-bld/win-64 + paths: + - "*" + + # Requires org-member context + binary_conda_upload: + docker: + - image: continuumio/miniconda + steps: + - attach_workspace: + at: ~/workspace + - designate_upload_channel + - run: + command: | + # Prevent credential from leaking + conda install -yq anaconda-client + set -x + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force + + # Requires org-member context + binary_wheel_upload: + docker: + - image: circleci/python:3.8 + steps: + - attach_workspace: + at: ~/workspace + - checkout + - designate_upload_channel + - run: + command: | + pip install --user awscli + export PATH="$HOME/.local/bin:$PATH" + # Prevent credential from leaking + set +x + export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}" + export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}" + set -x + for pkg in ~/workspace/*.whl; do + aws s3 cp "$pkg" "s3://pytorch/whl/${UPLOAD_CHANNEL}/" --acl public-read + done + + smoke_test_linux_conda: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_linux_pip: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_windows_pip: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_docker_image_build: + machine: + image: ubuntu-1604:201903-01 + resource_class: large + environment: + image_name: torchaudio/smoke_test + steps: + - checkout + - run: + name: build_docker image + no_output_timeout: "1h" + command: | + cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} + - run: + name: upload docker image + no_output_timeout: "1h" + command: | + set +x + export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} + export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} + eval $(aws ecr get-login --region us-east-1 --no-include-email) + set -x + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:latest + docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:latest + + unittest_linux_cpu: + <<: *binary_common + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + + keys: + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_linux_gpu: + <<: *binary_common + machine: + image: ubuntu-1604-cuda-10.1:201909-23 + resource_class: gpu.small + environment: + image_name: "pytorch/torchaudio_unittest_base:manylinux-cuda10.1" + steps: + - checkout + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + + keys: + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post Process + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_windows_cpu: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - generate_cache_key + - restore_cache: + + keys: + - env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: .circleci/unittest/windows/scripts/setup_env.sh + - save_cache: + + key: env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - run: + name: Install torchaudio + command: .circleci/unittest/windows/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/windows/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/windows/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_windows_gpu: + <<: *binary_common + executor: + name: windows-gpu + environment: + CUDA_VERSION: "10.1" + steps: + - checkout + - generate_cache_key + - restore_cache: + + keys: + - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: .circleci/unittest/windows/scripts/setup_env.sh + - save_cache: + + key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - run: + name: Install torchaudio + command: .circleci/unittest/windows/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/windows/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/windows/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_macos_cpu: + <<: *binary_common + macos: + xcode: "9.0" + resource_class: large + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + + keys: + - env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + + key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + stylecheck: + <<: *binary_common + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: medium + steps: + - checkout + - generate_cache_key + - restore_cache: + + keys: + - env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + + key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Run style check + command: .circleci/unittest/linux/scripts/run_style_checks.sh + +workflows: + build: + jobs: + - circleci_consistency + - download_third_parties_nix: + name: download_third_parties_nix + - binary_linux_wheel: + name: binary_linux_wheel_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_linux_wheel: + name: binary_linux_wheel_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_linux_wheel: + name: binary_linux_wheel_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_macos_wheel: + name: binary_macos_wheel_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_macos_wheel: + name: binary_macos_wheel_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_macos_wheel: + name: binary_macos_wheel_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_windows_wheel: + name: binary_windows_wheel_py3.6 + python_version: '3.6' + - binary_windows_wheel: + name: binary_windows_wheel_py3.7 + python_version: '3.7' + - binary_windows_wheel: + name: binary_windows_wheel_py3.8 + python_version: '3.8' + - binary_linux_conda: + name: binary_linux_conda_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_linux_conda: + name: binary_linux_conda_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_linux_conda: + name: binary_linux_conda_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_macos_conda: + name: binary_macos_conda_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_macos_conda: + name: binary_macos_conda_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_macos_conda: + name: binary_macos_conda_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_windows_conda: + name: binary_windows_conda_py3.6 + python_version: '3.6' + - binary_windows_conda: + name: binary_windows_conda_py3.7 + python_version: '3.7' + - binary_windows_conda: + name: binary_windows_conda_py3.8 + python_version: '3.8' + unittest: + jobs: + - download_third_parties_nix: + name: download_third_parties_nix + - unittest_linux_cpu: + name: unittest_linux_cpu_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - stylecheck: + name: stylecheck_py3.6 + python_version: '3.6' + - unittest_linux_cpu: + name: unittest_linux_cpu_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - unittest_linux_cpu: + name: unittest_linux_cpu_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - unittest_linux_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_linux_gpu_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - unittest_linux_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_linux_gpu_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - unittest_linux_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_linux_gpu_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - unittest_windows_cpu: + name: unittest_windows_cpu_py3.6 + python_version: '3.6' + - unittest_windows_cpu: + name: unittest_windows_cpu_py3.7 + python_version: '3.7' + - unittest_windows_cpu: + name: unittest_windows_cpu_py3.8 + python_version: '3.8' + - unittest_windows_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_windows_gpu_py3.6 + python_version: '3.6' + - unittest_windows_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_windows_gpu_py3.7 + python_version: '3.7' + - unittest_windows_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_windows_gpu_py3.8 + python_version: '3.8' + - unittest_macos_cpu: + name: unittest_macos_cpu_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - unittest_macos_cpu: + name: unittest_macos_cpu_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - unittest_macos_cpu: + name: unittest_macos_cpu_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + nightly: + jobs: + - circleci_consistency: + filters: + branches: + only: nightly + - download_third_parties_nix: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: download_third_parties_nix + - binary_linux_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.6_upload + requires: + - nightly_binary_linux_wheel_py3.6 + - smoke_test_linux_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.6_smoke_test_pip + python_version: '3.6' + requires: + - nightly_binary_linux_wheel_py3.6_upload + - binary_linux_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.7_upload + requires: + - nightly_binary_linux_wheel_py3.7 + - smoke_test_linux_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.7_smoke_test_pip + python_version: '3.7' + requires: + - nightly_binary_linux_wheel_py3.7_upload + - binary_linux_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.8_upload + requires: + - nightly_binary_linux_wheel_py3.8 + - smoke_test_linux_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.8_smoke_test_pip + python_version: '3.8' + requires: + - nightly_binary_linux_wheel_py3.8_upload + - binary_macos_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.6_upload + requires: + - nightly_binary_macos_wheel_py3.6 + - binary_macos_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.7_upload + requires: + - nightly_binary_macos_wheel_py3.7 + - binary_macos_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.8_upload + requires: + - nightly_binary_macos_wheel_py3.8 + - binary_windows_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.6 + python_version: '3.6' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.6_upload + requires: + - nightly_binary_windows_wheel_py3.6 + - smoke_test_windows_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.6_smoke_test_pip + python_version: '3.6' + requires: + - nightly_binary_windows_wheel_py3.6_upload + - binary_windows_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7 + python_version: '3.7' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_upload + requires: + - nightly_binary_windows_wheel_py3.7 + - smoke_test_windows_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_smoke_test_pip + python_version: '3.7' + requires: + - nightly_binary_windows_wheel_py3.7_upload + - binary_windows_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8 + python_version: '3.8' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_upload + requires: + - nightly_binary_windows_wheel_py3.8 + - smoke_test_windows_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_smoke_test_pip + python_version: '3.8' + requires: + - nightly_binary_windows_wheel_py3.8_upload + - binary_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_upload + requires: + - nightly_binary_linux_conda_py3.6 + - smoke_test_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_linux_conda_py3.6_upload + - binary_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_upload + requires: + - nightly_binary_linux_conda_py3.7 + - smoke_test_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_linux_conda_py3.7_upload + - binary_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_upload + requires: + - nightly_binary_linux_conda_py3.8 + - smoke_test_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_linux_conda_py3.8_upload + - binary_macos_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.6_upload + requires: + - nightly_binary_macos_conda_py3.6 + - binary_macos_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.7_upload + requires: + - nightly_binary_macos_conda_py3.7 + - binary_macos_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.8_upload + requires: + - nightly_binary_macos_conda_py3.8 + - binary_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6 + python_version: '3.6' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_upload + requires: + - nightly_binary_windows_conda_py3.6 + - smoke_test_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_windows_conda_py3.6_upload + - binary_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7 + python_version: '3.7' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7_upload + requires: + - nightly_binary_windows_conda_py3.7 + - smoke_test_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_windows_conda_py3.7_upload + - binary_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8 + python_version: '3.8' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8_upload + requires: + - nightly_binary_windows_conda_py3.8 + - smoke_test_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_windows_conda_py3.8_upload + docker_build: + triggers: + - schedule: + cron: "0 10 * * 0" + filters: + branches: + only: + - master + jobs: + - smoke_test_docker_image_build \ No newline at end of file diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in new file mode 100644 index 0000000000..6d6d77d50f --- /dev/null +++ b/.circleci/config.yml.in @@ -0,0 +1,631 @@ +version: 2.1 + +# How to test the Linux jobs: +# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/ +# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.8 +# - Replace binary_linux_wheel_py3.8 with the name of the job you want to test. +# Job names are 'name:' key. + +executors: + windows-cpu: + machine: + resource_class: windows.xlarge + image: windows-server-2019-vs2019:stable + shell: bash.exe + + windows-gpu: + machine: + resource_class: windows.gpu.nvidia.medium + image: windows-server-2019-nvidia:stable + shell: bash.exe + +commands: + generate_cache_key: + description: "Generates a cache key file that changes daily" + steps: + - run: + name: Generate cache key + command: echo "$(date +"%Y-%m-%d")" > .cachekey + designate_upload_channel: + description: "inserts the correct upload channel into ${BASH_ENV}" + steps: + - run: + name: adding UPLOAD_CHANNEL to BASH_ENV + command: | + our_upload_channel=nightly + # On tags upload to test instead + if [[ -n "${CIRCLE_TAG}" ]]; then + our_upload_channel=test + fi + echo "export UPLOAD_CHANNEL=${our_upload_channel}" >> ${BASH_ENV} + install_build_tools_macos: + description: "installs tools required to build torchaudio" + steps: + - run: + name: Install cmake and pkg-config + command: HOMEBREW_NO_AUTO_UPDATE=1 brew install cmake pkg-config wget + # Disable brew auto update which is very slow + +binary_common: &binary_common + parameters: + # Edit these defaults to do a release + build_version: + description: "version number of release binary; by default, build a nightly" + type: string + default: "" + pytorch_version: + description: "PyTorch version to build against; by default, use a nightly" + type: string + default: "" + # Don't edit these + python_version: + description: "Python version to build against (e.g., 3.8)" + type: string + environment: + PYTHON_VERSION: << parameters.python_version >> + BUILD_VERSION: << parameters.build_version >> + PYTORCH_VERSION: << parameters.pytorch_version >> + CU_VERSION: cpu + +smoke_test_common: &smoke_test_common + <<: *binary_common + docker: + - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:56c846a5-acaa-41a7-92f5-46ec66186c61 + aws_auth: + aws_access_key_id: ${ECR_AWS_ACCESS_KEY} + aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} + +jobs: + circleci_consistency: + docker: + - image: circleci/python:3.8 + steps: + - checkout + - run: + command: | + pip install --user --progress-bar off jinja2 pyyaml + python .circleci/regenerate.py + git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1) + + download_third_parties_nix: + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: small + steps: + - checkout + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - tp-nix-v2-{{ checksum ".cachekey" }} + {% endraw %} + - run: + command: | + mkdir -p third_party/archives/ + wget --no-clobber --directory-prefix=third_party/archives/ $(awk '/URL /{print $2}' third_party/CMakeLists.txt) + - save_cache: + {% raw %} + key: tp-nix-v2-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - third_party/archives + - persist_to_workspace: + root: third_party + paths: + - archives + + binary_linux_wheel: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - run: packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_linux_conda: + <<: *binary_common + docker: + - image: "pytorch/conda-cuda" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - run: packaging/build_conda.sh + - store_artifacts: + path: /opt/conda/conda-bld/linux-64 + - persist_to_workspace: + root: /opt/conda/conda-bld/linux-64 + paths: + - "*" + + binary_macos_wheel: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - run: + # Cannot easily deduplicate this as source'ing activate + # will set environment variables which we need to propagate + # to build_wheel.sh + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_macos_conda: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - run: + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + conda install -yq conda-build + packaging/build_conda.sh + - store_artifacts: + path: /Users/distiller/miniconda3/conda-bld/osx-64 + - persist_to_workspace: + root: /Users/distiller/miniconda3/conda-bld/osx-64 + paths: + - "*" + + binary_windows_wheel: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: build + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate base + bash packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: build + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate base + conda install -yq conda-build "conda-package-handling!=1.5.0" + bash packaging/build_conda.sh + - store_artifacts: + path: C:/tools/miniconda3/conda-bld/win-64 + - persist_to_workspace: + root: C:/tools/miniconda3/conda-bld/win-64 + paths: + - "*" + + # Requires org-member context + binary_conda_upload: + docker: + - image: continuumio/miniconda + steps: + - attach_workspace: + at: ~/workspace + - designate_upload_channel + - run: + command: | + # Prevent credential from leaking + conda install -yq anaconda-client + set -x + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force + + # Requires org-member context + binary_wheel_upload: + docker: + - image: circleci/python:3.8 + steps: + - attach_workspace: + at: ~/workspace + - checkout + - designate_upload_channel + - run: + command: | + pip install --user awscli + export PATH="$HOME/.local/bin:$PATH" + # Prevent credential from leaking + set +x + export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}" + export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}" + set -x + for pkg in ~/workspace/*.whl; do + aws s3 cp "$pkg" "s3://pytorch/whl/${UPLOAD_CHANNEL}/" --acl public-read + done + + smoke_test_linux_conda: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_linux_pip: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_windows_pip: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_docker_image_build: + machine: + image: ubuntu-1604:201903-01 + resource_class: large + environment: + image_name: torchaudio/smoke_test + steps: + - checkout + - run: + name: build_docker image + no_output_timeout: "1h" + command: | + cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} + - run: + name: upload docker image + no_output_timeout: "1h" + command: | + set +x + export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} + export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} + eval $(aws ecr get-login --region us-east-1 --no-include-email) + set -x + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:latest + docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:latest + + unittest_linux_cpu: + <<: *binary_common + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_linux_gpu: + <<: *binary_common + machine: + image: ubuntu-1604-cuda-10.1:201909-23 + resource_class: gpu.small + environment: + image_name: "pytorch/torchaudio_unittest_base:manylinux-cuda10.1" + steps: + - checkout + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post Process + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_windows_cpu: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/windows/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - run: + name: Install torchaudio + command: .circleci/unittest/windows/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/windows/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/windows/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_windows_gpu: + <<: *binary_common + executor: + name: windows-gpu + environment: + CUDA_VERSION: "10.1" + steps: + - checkout + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/windows/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - run: + name: Install torchaudio + command: .circleci/unittest/windows/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/windows/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/windows/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_macos_cpu: + <<: *binary_common + macos: + xcode: "9.0" + resource_class: large + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + stylecheck: + <<: *binary_common + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: medium + steps: + - checkout + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Run style check + command: .circleci/unittest/linux/scripts/run_style_checks.sh + +workflows: + build: + jobs: + - circleci_consistency + {{ build_workflows() }} + unittest: + jobs: + {{ unittest_workflows() }} + nightly: + jobs: + - circleci_consistency: + filters: + branches: + only: nightly + {{ build_workflows(prefix="nightly_", filter_branch="nightly", upload=True) }} + docker_build: + triggers: + - schedule: + cron: "0 10 * * 0" + filters: + branches: + only: + - master + jobs: + - smoke_test_docker_image_build diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py new file mode 100755 index 0000000000..2c9fe7ee4f --- /dev/null +++ b/.circleci/regenerate.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 + +""" +This script should use a very simple, functional programming style. +Avoid Jinja macros in favor of native Python functions. + +Don't go overboard on code generation; use Python only to generate +content that can't be easily declared statically using CircleCI's YAML API. + +Data declarations (e.g. the nested loops for defining the configuration matrix) +should be at the top of the file for easy updating. + +See this comment for design rationale: +https://github.com/pytorch/vision/pull/1321#issuecomment-531033978 +""" + +import jinja2 +import yaml +import os.path + + +PYTHON_VERSIONS = ["3.6", "3.7", "3.8"] + + +def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): + w = [] + w += build_download_job(filter_branch) + for btype in ["wheel", "conda"]: + for os_type in ["linux", "macos", "windows"]: + for python_version in PYTHON_VERSIONS: + w += build_workflow_pair(btype, os_type, python_version, filter_branch, prefix, upload) + + return indent(indentation, w) + + +def build_download_job(filter_branch): + job = { + "name": "download_third_parties_nix", + } + + if filter_branch: + job["filters"] = gen_filter_branch_tree(filter_branch) + return [{"download_third_parties_nix": job}] + + +def build_workflow_pair(btype, os_type, python_version, filter_branch, prefix='', upload=False): + + w = [] + base_workflow_name = "{prefix}binary_{os_type}_{btype}_py{python_version}".format( + prefix=prefix, + os_type=os_type, + btype=btype, + python_version=python_version, + ) + + w.append(generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype)) + + if upload: + + is_py3_linux = os_type in ['linux', "windows"] and not python_version.startswith("2.") + + w.append(generate_upload_workflow(base_workflow_name, filter_branch, btype)) + + if filter_branch == 'nightly' and is_py3_linux: + pydistro = 'pip' if btype == 'wheel' else 'conda' + w.append(generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version, os_type)) + + return w + + +def generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype): + + d = { + "name": base_workflow_name, + "python_version": python_version, + } + + if os_type in ['linux', 'macos']: + d['requires'] = ['download_third_parties_nix'] + + if filter_branch: + d["filters"] = gen_filter_branch_tree(filter_branch) + + return {"binary_{os_type}_{btype}".format(os_type=os_type, btype=btype): d} + + +def gen_filter_branch_tree(*branches): + return { + "branches": { + "only": list(branches), + }, + "tags": { + # Using a raw string here to avoid having to escape + # anything + "only": r"/v[0-9]+(\.[0-9]+)*-rc[0-9]+/" + } + } + + +def generate_upload_workflow(base_workflow_name, filter_branch, btype): + d = { + "name": "{base_workflow_name}_upload".format(base_workflow_name=base_workflow_name), + "context": "org-member", + "requires": [base_workflow_name], + } + + if filter_branch: + d["filters"] = gen_filter_branch_tree(filter_branch) + + return {"binary_{btype}_upload".format(btype=btype): d} + + +def generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version, os_type): + + required_build_suffix = "_upload" + required_build_name = base_workflow_name + required_build_suffix + + smoke_suffix = "smoke_test_{pydistro}".format(pydistro=pydistro) + d = { + "name": "{base_workflow_name}_{smoke_suffix}".format( + base_workflow_name=base_workflow_name, smoke_suffix=smoke_suffix), + "requires": [required_build_name], + "python_version": python_version, + } + + if filter_branch: + d["filters"] = gen_filter_branch_tree(filter_branch) + + return {"smoke_test_{os_type}_{pydistro}".format(os_type=os_type, pydistro=pydistro): d} + + +def indent(indentation, data_list): + return ("\n" + " " * indentation).join(yaml.dump(data_list).splitlines()) + + +def unittest_workflows(indentation=6): + jobs = [] + jobs += build_download_job(None) + for os_type in ["linux", "windows", "macos"]: + for device_type in ["cpu", "gpu"]: + if os_type == "macos" and device_type == "gpu": + continue + + for i, python_version in enumerate(PYTHON_VERSIONS): + job = { + "name": f"unittest_{os_type}_{device_type}_py{python_version}", + "python_version": python_version, + } + + if device_type == 'gpu': + job['filters'] = gen_filter_branch_tree('master', 'nightly') + + if os_type != "windows": + job['requires'] = ['download_third_parties_nix'] + + jobs.append({f"unittest_{os_type}_{device_type}": job}) + + if i == 0 and os_type == "linux" and device_type == "cpu": + jobs.append({ + f"stylecheck": { + "name": f"stylecheck_py{python_version}", + "python_version": python_version, + } + }) + return indent(indentation, jobs) + + +if __name__ == "__main__": + d = os.path.dirname(__file__) + env = jinja2.Environment( + loader=jinja2.FileSystemLoader(d), + lstrip_blocks=True, + autoescape=False, + ) + + with open(os.path.join(d, 'config.yml'), 'w') as f: + f.write(env.get_template('config.yml.in').render( + build_workflows=build_workflows, + unittest_workflows=unittest_workflows, + )) diff --git a/.circleci/smoke_test/docker/Dockerfile b/.circleci/smoke_test/docker/Dockerfile new file mode 100644 index 0000000000..acfac51fcd --- /dev/null +++ b/.circleci/smoke_test/docker/Dockerfile @@ -0,0 +1,36 @@ +# this Dockerfile is for torchaudio smoke test, it will be created periodically via CI system +# if you need to do it locally, follow below steps once you have Docker installed +# assuming you're within the directory where this Dockerfile located +# $ docker build . -t torchaudio/smoketest + +# if you want to push to aws ecr, make sure you have the rights to write to ECR, then run +# $ eval $(aws ecr get-login --region us-east-1 --no-include-email) +# $ export MYTAG=localbuild ## you can choose whatever tag you like +# $ docker tag torchaudio/smoketest 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:${MYTAG} +# $ docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:${MYTAG} + +FROM ubuntu:latest + +RUN apt-get -qq update && apt-get -qq -y install curl bzip2 sox libsox-dev libsox-fmt-all \ + && curl -sSL https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda.sh \ + && bash /tmp/miniconda.sh -bfp /usr/local \ + && rm -rf /tmp/miniconda.sh \ + && conda install -y python=3 \ + && conda update conda \ + && apt-get -qq -y remove curl bzip2 \ + && apt-get -qq -y autoremove \ + && apt-get autoclean \ + && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log \ + && conda clean --all --yes + +ENV PATH /opt/conda/bin:$PATH + +RUN conda create -y --name python3.6 python=3.6 +RUN conda create -y --name python3.7 python=3.7 +RUN conda create -y --name python3.8 python=3.8 +SHELL [ "/bin/bash", "-c" ] +RUN echo "source /usr/local/etc/profile.d/conda.sh" >> ~/.bashrc +RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.6 && conda install -y -c conda-forge sox && conda install -y numpy +RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.7 && conda install -y -c conda-forge sox && conda install -y numpy +RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.8 && conda install -y -c conda-forge sox && conda install -y numpy +CMD [ "/bin/bash"] diff --git a/.circleci/test/test_sort_yaml.py b/.circleci/test/test_sort_yaml.py new file mode 100755 index 0000000000..dc6db481dd --- /dev/null +++ b/.circleci/test/test_sort_yaml.py @@ -0,0 +1,14 @@ +#!/usr/bin/env python3 + +""" +To compare new version with previous: + + ./regenerate.sh + meld <(git show HEAD:./config.yml | ./sort-yaml.py) <(cat config.yml | ./sort-yaml.py) +""" + + +import sys +import yaml + +sys.stdout.write(yaml.dump(yaml.load(sys.stdin, Loader=yaml.FullLoader), sort_keys=True)) diff --git a/.circleci/unittest/linux/README.md b/.circleci/unittest/linux/README.md new file mode 100644 index 0000000000..0a4b0e0e63 --- /dev/null +++ b/.circleci/unittest/linux/README.md @@ -0,0 +1,6 @@ +This directory contains; + + - docker + Docker image definition and scripts to build and update Docker image for unittest. + - scripts + Scripts used by CircleCI to run unit tests. diff --git a/.circleci/unittest/linux/docker/.dockerignore b/.circleci/unittest/linux/docker/.dockerignore new file mode 100644 index 0000000000..1398d409f8 --- /dev/null +++ b/.circleci/unittest/linux/docker/.dockerignore @@ -0,0 +1,2 @@ +* +!scripts diff --git a/.circleci/unittest/linux/docker/.gitignore b/.circleci/unittest/linux/docker/.gitignore new file mode 100644 index 0000000000..7e977058dd --- /dev/null +++ b/.circleci/unittest/linux/docker/.gitignore @@ -0,0 +1,2 @@ +scripts/build_third_parties.sh +Dockerfile.tmp diff --git a/.circleci/unittest/linux/docker/Dockerfile b/.circleci/unittest/linux/docker/Dockerfile new file mode 100644 index 0000000000..d91b5222a4 --- /dev/null +++ b/.circleci/unittest/linux/docker/Dockerfile @@ -0,0 +1,59 @@ +FROM ubuntu:18.04 as builder + +RUN apt update -q + +################################################################################ +# Build Kaldi +################################################################################ +RUN apt install -q -y \ + autoconf \ + automake \ + bzip2 \ + g++ \ + gfortran \ + git \ + libatlas-base-dev \ + libtool \ + make \ + python2.7 \ + python3 \ + sox \ + subversion \ + unzip \ + wget \ + zlib1g-dev + +# KALDI uses MKL as a default math library, but we are going to copy featbin binaries and dependent +# shared libraries to the final image, so we use ATLAS, which is easy to reinstall in the final image. +RUN git clone --depth 1 https://github.com/kaldi-asr/kaldi.git /opt/kaldi && \ + cd /opt/kaldi/tools && \ + make -j $(nproc) && \ + cd /opt/kaldi/src && \ + ./configure --shared --mathlib=ATLAS --use-cuda=no && \ + make featbin -j $(nproc) + +# Copy featbins and dependent libraries +ADD ./scripts /scripts +RUN bash /scripts/copy_kaldi_executables.sh /opt/kaldi /kaldi + +################################################################################ +# Build the final image +################################################################################ +FROM BASE_IMAGE +RUN apt update && apt install -y \ + g++ \ + gfortran \ + git \ + libatlas3-base \ + wget \ + curl \ + make \ + file \ + sox \ + libsox-dev \ + libsox-fmt-all \ + cmake \ + pkg-config \ + && rm -rf /var/lib/apt/lists/* +COPY --from=builder /kaldi /kaldi +ENV PATH="${PATH}:/kaldi/bin" LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/kaldi/lib" diff --git a/.circleci/unittest/linux/docker/build_and_push.sh b/.circleci/unittest/linux/docker/build_and_push.sh new file mode 100755 index 0000000000..8bf33eaaa3 --- /dev/null +++ b/.circleci/unittest/linux/docker/build_and_push.sh @@ -0,0 +1,27 @@ +#!/usr/bin/env bash + +set -euo pipefail + +if [ $# -ne 1 ]; then + printf "Usage %s \n\n" "$0" + exit 1 +fi + +if [ "$1" = "cpu" ]; then + base_image="ubuntu:18.04" + image="pytorch/torchaudio_unittest_base:manylinux" +elif [[ "$1" =~ ^(9.2|10.1)$ ]]; then + base_image="nvidia/cuda:$1-runtime-ubuntu18.04" + image="pytorch/torchaudio_unittest_base:manylinux-cuda$1" +else + printf "Unexpected string: %s" "$1" + exit 1; +fi + +cd "$( dirname "${BASH_SOURCE[0]}" )" + +# docker build also accepts reading from STDIN +# but in that case, no context (other files) can be passed, so we write out Dockerfile +sed "s|BASE_IMAGE|${base_image}|g" Dockerfile > Dockerfile.tmp +docker build -t "${image}" -f Dockerfile.tmp . +docker push "${image}" diff --git a/.circleci/unittest/linux/docker/scripts/copy_kaldi_executables.sh b/.circleci/unittest/linux/docker/scripts/copy_kaldi_executables.sh new file mode 100755 index 0000000000..b0cf207143 --- /dev/null +++ b/.circleci/unittest/linux/docker/scripts/copy_kaldi_executables.sh @@ -0,0 +1,58 @@ +#!/usr/bin/env bash + +list_executables() { + # List up executables in the given directory + find "$1" -type f -executable +} + +list_kaldi_libraries() { + # List up shared libraries used by executables found in the given directory ($1) + # that reside in Kaldi directory ($2) + while read file; do + ldd "${file}" | grep -o "${2}.* "; + done < <(list_executables "$1") | sort -u +} + +set -euo pipefail + +kaldi_root="$(realpath "$1")" +target_dir="$(realpath "$2")" + +bin_dir="${target_dir}/bin" +lib_dir="${target_dir}/lib" + +mkdir -p "${bin_dir}" "${lib_dir}" + +# 1. Copy featbins +printf "Copying executables to %s\n" "${bin_dir}" +while read file; do + printf " %s\n" "${file}" + cp "${file}" "${bin_dir}" +done < <(list_executables "${kaldi_root}/src/featbin") + +# 2. Copy dependent libraries from Kaldi +printf "Copying libraries to %s\n" "${lib_dir}" +while read file; do + printf " %s\n" "$file" + # If it is not symlink, just copy to the target directory + if [ ! -L "${file}" ]; then + cp "${file}" "${lib_dir}" + continue + fi + + # If it is symlink, + # 1. Copy the actual library to the target directory. + library="$(realpath "${file}")" + cp "${library}" "${lib_dir}" + # 2. then if the name of the symlink is different from the actual library name, + # create the symlink in the target directory. + lib_name="$(basename "${library}")" + link_name="$(basename "${file}")" + if [ "${lib_name}" != "${link_name}" ]; then + printf " Linking %s -> %s\n" "${lib_name}" "${link_name}" + ( + cd "${lib_dir}" + ln -sf "${lib_name}" "${link_name}" + ) + fi +done < <(list_kaldi_libraries "${bin_dir}" "${kaldi_root}") diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml new file mode 100644 index 0000000000..108d97d193 --- /dev/null +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -0,0 +1,17 @@ +channels: + - conda-forge + - defaults +dependencies: + - flake8 + - numpy + - pytest + - pytest-cov + - codecov + - librosa + - llvmlite==0.31 # See https://github.com/pytorch/audio/pull/766 + - pip + - pip: + - kaldi-io + - scipy + - parameterized + - numba==0.48 # See https://github.com/librosa/librosa/issues/1160 diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh new file mode 100755 index 0000000000..131c9e43bf --- /dev/null +++ b/.circleci/unittest/linux/scripts/install.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash + +unset PYTORCH_VERSION +# For unittest, nightly PyTorch is used as the following section, +# so no need to set PYTORCH_VERSION. +# In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config. + +set -e + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +if [ -z "${CUDA_VERSION:-}" ] ; then + cudatoolkit="cpuonly" +else + version="$(python -c "print('.'.join(\"${CUDA_VERSION}\".split('.')[:2]))")" + cudatoolkit="cudatoolkit=${version}" +fi +printf "Installing PyTorch with %s\n" "${cudatoolkit}" +conda install -y -c pytorch-nightly pytorch "${cudatoolkit}" + +printf "* Installing torchaudio\n" +BUILD_SOX=1 python setup.py develop diff --git a/.circleci/unittest/linux/scripts/post_process.sh b/.circleci/unittest/linux/scripts/post_process.sh new file mode 100755 index 0000000000..a84a0dea55 --- /dev/null +++ b/.circleci/unittest/linux/scripts/post_process.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +codecov diff --git a/.circleci/unittest/linux/scripts/run_style_checks.sh b/.circleci/unittest/linux/scripts/run_style_checks.sh new file mode 100755 index 0000000000..70b83e19f5 --- /dev/null +++ b/.circleci/unittest/linux/scripts/run_style_checks.sh @@ -0,0 +1,32 @@ +#!/usr/bin/env bash + +set -u + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +# We want to run all the style checks even if one of them fail. + +exit_status=0 + +printf "\x1b[34mRunning flake8: " +flake8 --version +printf "\x1b[0m\n" +flake8 torchaudio test build_tools/setup_helpers +status=$? +exit_status="$((exit_status+status))" +if [ "${status}" -ne 0 ]; then + printf "\x1b[31mflake8 failed. Check the format of Python files.\x1b[0m\n" +fi + +printf "\x1b[34mRunning clang-format: " +clang-format --version +printf "\x1b[0m\n" +git-clang-format origin/master +git diff --exit-code +status=$? +exit_status="$((exit_status+status))" +if [ "${status}" -ne 0 ]; then + printf "\x1b[31mC++ files are not formatted. Please use git-clang-format to format CPP files.\x1b[0m\n" +fi +exit $exit_status diff --git a/.circleci/unittest/linux/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh new file mode 100755 index 0000000000..9ea942851e --- /dev/null +++ b/.circleci/unittest/linux/scripts/run_test.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +python -m torch.utils.collect_env +export PATH="${PWD}/third_party/install/bin/:${PATH}" +pytest --cov=torchaudio --junitxml=test-results/junit.xml -v --durations 20 test diff --git a/.circleci/unittest/linux/scripts/setup_env.sh b/.circleci/unittest/linux/scripts/setup_env.sh new file mode 100755 index 0000000000..290f0f8f97 --- /dev/null +++ b/.circleci/unittest/linux/scripts/setup_env.sh @@ -0,0 +1,50 @@ +#!/usr/bin/env bash + +# This script is for setting up environment in which unit test is ran. +# To speed up the CI time, the resulting environment is cached. +# +# Do not install PyTorch and torchaudio here, otherwise they also get cached. + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +root_dir="$(git rev-parse --show-toplevel)" +conda_dir="${root_dir}/conda" +env_dir="${root_dir}/env" + +cd "${root_dir}" + +case "$(uname -s)" in + Darwin*) os=MacOSX;; + *) os=Linux +esac + +# 1. Install conda at ./conda +if [ ! -d "${conda_dir}" ]; then + printf "* Installing conda\n" + wget -O miniconda.sh "http://repo.continuum.io/miniconda/Miniconda3-latest-${os}-x86_64.sh" + bash ./miniconda.sh -b -f -p "${conda_dir}" +fi +eval "$(${conda_dir}/bin/conda shell.bash hook)" + +# 2. Create test environment at ./env +if [ ! -d "${env_dir}" ]; then + printf "* Creating a test environment\n" + conda create --prefix "${env_dir}" -y python="$PYTHON_VERSION" +fi +conda activate "${env_dir}" + +# 3. Install Conda dependencies +printf "* Installing dependencies (except PyTorch)\n" +conda env update --file "${this_dir}/environment.yml" --prune +if [ "${os}" == Linux ] ; then + pip install clang-format +fi + +# 4. Buld codecs +mkdir -p third_party/build +( + cd third_party/build + cmake .. + cmake --build . +) diff --git a/.circleci/unittest/windows/README.md b/.circleci/unittest/windows/README.md new file mode 100644 index 0000000000..2c06af62bd --- /dev/null +++ b/.circleci/unittest/windows/README.md @@ -0,0 +1,4 @@ +This directory contains; + + - scripts + Scripts used by CircleCI to run unit tests. diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml new file mode 100644 index 0000000000..fb57f0facc --- /dev/null +++ b/.circleci/unittest/windows/scripts/environment.yml @@ -0,0 +1,18 @@ +channels: + - conda-forge + - defaults +dependencies: + - flake8 + - numpy + - pytest + - pytest-cov + - codecov + - pip + - pip: + - scipy == 1.4.1 + - kaldi-io + - PySoundFile + - librosa + - future + - parameterized + - numba==0.48 diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh new file mode 100644 index 0000000000..acbd79ae50 --- /dev/null +++ b/.circleci/unittest/windows/scripts/install.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash + +unset PYTORCH_VERSION +# For unittest, nightly PyTorch is used as the following section, +# so no need to set PYTORCH_VERSION. +# In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config. + +set -e + +eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" +conda activate ./env + +if [ -z "${CUDA_VERSION:-}" ] ; then + cudatoolkit="cpuonly" +else + version="$(python -c "print('.'.join(\"${CUDA_VERSION}\".split('.')[:2]))")" + cudatoolkit="cudatoolkit=${version}" +fi +printf "Installing PyTorch with %s\n" "${cudatoolkit}" +conda install -y -c pytorch-nightly pytorch "${cudatoolkit}" + +printf "* Installing torchaudio\n" +python setup.py develop diff --git a/.circleci/unittest/windows/scripts/install_conda.bat b/.circleci/unittest/windows/scripts/install_conda.bat new file mode 100644 index 0000000000..6052ad08b1 --- /dev/null +++ b/.circleci/unittest/windows/scripts/install_conda.bat @@ -0,0 +1 @@ +start /wait "" "%miniconda_exe%" /S /InstallationType=JustMe /RegisterPython=0 /AddToPath=0 /D=%tmp_conda% diff --git a/.circleci/unittest/windows/scripts/post_process.sh b/.circleci/unittest/windows/scripts/post_process.sh new file mode 100644 index 0000000000..b132113194 --- /dev/null +++ b/.circleci/unittest/windows/scripts/post_process.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" +conda activate ./env + +codecov diff --git a/.circleci/unittest/windows/scripts/run_test.sh b/.circleci/unittest/windows/scripts/run_test.sh new file mode 100644 index 0000000000..252c1e9f46 --- /dev/null +++ b/.circleci/unittest/windows/scripts/run_test.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" +conda activate ./env + +python -m torch.utils.collect_env +pytest --cov=torchaudio --junitxml=test-results/junit.xml -v --durations 20 test +flake8 torchaudio test diff --git a/.circleci/unittest/windows/scripts/setup_env.sh b/.circleci/unittest/windows/scripts/setup_env.sh new file mode 100644 index 0000000000..a45cf856ac --- /dev/null +++ b/.circleci/unittest/windows/scripts/setup_env.sh @@ -0,0 +1,39 @@ +#!/usr/bin/env bash + +# This script is for setting up environment in which unit test is ran. +# To speed up the CI time, the resulting environment is cached. +# +# Do not install PyTorch and torchaudio here, otherwise they also get cached. + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +root_dir="$(git rev-parse --show-toplevel)" +conda_dir="${root_dir}/conda" +env_dir="${root_dir}/env" + +cd "${root_dir}" + +# 1. Install conda at ./conda +if [ ! -d "${conda_dir}" ]; then + printf "* Installing conda\n" + export tmp_conda="$(echo $conda_dir | tr '/' '\\')" + export miniconda_exe="$(echo $root_dir | tr '/' '\\')\\miniconda.exe" + curl --output miniconda.exe https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe -O + "$this_dir/install_conda.bat" + unset tmp_conda + unset miniconda_exe +fi + +eval "$(${conda_dir}/Scripts/conda.exe 'shell.bash' 'hook')" + +# 2. Create test environment at ./env +if [ ! -d "${env_dir}" ]; then + printf "* Creating a test environment\n" + conda create --prefix "${env_dir}" -y python="$PYTHON_VERSION" +fi +conda activate "${env_dir}" + +# 3. Install Conda dependencies +printf "* Installing dependencies (except PyTorch)\n" +conda env update --file "${this_dir}/environment.yml" --prune diff --git a/.clang-format b/.clang-format new file mode 100644 index 0000000000..73304266bd --- /dev/null +++ b/.clang-format @@ -0,0 +1,88 @@ +--- +AccessModifierOffset: -1 +AlignAfterOpenBracket: AlwaysBreak +AlignConsecutiveAssignments: false +AlignConsecutiveDeclarations: false +AlignEscapedNewlinesLeft: true +AlignOperands: false +AlignTrailingComments: false +AllowAllParametersOfDeclarationOnNextLine: false +AllowShortBlocksOnASingleLine: false +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: Empty +AllowShortIfStatementsOnASingleLine: false +AllowShortLoopsOnASingleLine: false +AlwaysBreakAfterReturnType: None +AlwaysBreakBeforeMultilineStrings: true +AlwaysBreakTemplateDeclarations: true +BinPackArguments: false +BinPackParameters: false +BraceWrapping: + AfterClass: false + AfterControlStatement: false + AfterEnum: false + AfterFunction: false + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: false + AfterUnion: false + BeforeCatch: false + BeforeElse: false + IndentBraces: false +BreakBeforeBinaryOperators: None +BreakBeforeBraces: Attach +BreakBeforeTernaryOperators: true +BreakConstructorInitializersBeforeComma: false +BreakAfterJavaFieldAnnotations: false +BreakStringLiterals: false +ColumnLimit: 80 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerAllOnOneLineOrOnePerLine: true +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +Cpp11BracedListStyle: true +DerivePointerAlignment: false +DisableFormat: false +ForEachMacros: [ FOR_EACH_RANGE, FOR_EACH, ] +IncludeCategories: + - Regex: '^<.*\.h(pp)?>' + Priority: 1 + - Regex: '^<.*' + Priority: 2 + - Regex: '.*' + Priority: 3 +IndentCaseLabels: true +IndentWidth: 2 +IndentWrappedFunctionNames: false +KeepEmptyLinesAtTheStartOfBlocks: false +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBlockIndentWidth: 2 +ObjCSpaceAfterProperty: false +ObjCSpaceBeforeProtocolList: false +PenaltyBreakBeforeFirstCallParameter: 1 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 2000000 +PointerAlignment: Left +ReflowComments: true +SortIncludes: true +SpaceAfterCStyleCast: false +SpaceBeforeAssignmentOperators: true +SpaceBeforeParens: ControlStatements +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 1 +SpacesInAngles: false +SpacesInContainerLiterals: true +SpacesInCStyleCastParentheses: false +SpacesInParentheses: false +SpacesInSquareBrackets: false +Standard: Cpp11 +TabWidth: 8 +UseTab: Never +... diff --git a/.clang-tidy b/.clang-tidy new file mode 100644 index 0000000000..a3a40b5907 --- /dev/null +++ b/.clang-tidy @@ -0,0 +1,33 @@ +--- +# NOTE there must be no spaces before the '-', so put the comma first. +Checks: ' + -* + ,bugprone-* + ,-bugprone-forward-declaration-namespace + ,-bugprone-macro-parentheses + ,cppcoreguidelines-* + ,-cppcoreguidelines-interfaces-global-init + ,-cppcoreguidelines-owning-memory + ,-cppcoreguidelines-pro-bounds-array-to-pointer-decay + ,-cppcoreguidelines-pro-bounds-constant-array-index + ,-cppcoreguidelines-pro-bounds-pointer-arithmetic + ,-cppcoreguidelines-pro-type-cstyle-cast + ,-cppcoreguidelines-pro-type-reinterpret-cast + ,-cppcoreguidelines-pro-type-static-cast-downcast + ,-cppcoreguidelines-pro-type-union-access + ,-cppcoreguidelines-pro-type-vararg + ,-cppcoreguidelines-special-member-functions + ,hicpp-exception-baseclass + ,hicpp-avoid-goto + ,modernize-* + ,-modernize-return-braced-init-list + ,-modernize-use-auto + ,-modernize-use-default-member-init + ,-modernize-use-using + ,performance-unnecessary-value-param + ' +WarningsAsErrors: '*' +HeaderFilterRegex: 'torchaudio/.*' +AnalyzeTemporaryDtors: false +CheckOptions: +... diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000000..9d0eaed2bd --- /dev/null +++ b/.flake8 @@ -0,0 +1,4 @@ +[flake8] +max-line-length = 120 +ignore = E305,E402,E721,E741,F401,F403,F405,F999,W503,W504 +exclude = build,docs/source,_ext,third_party diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md new file mode 100644 index 0000000000..fb50bd8504 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug-report.md @@ -0,0 +1,53 @@ +--- +name: "\U0001F41B Bug Report" +about: Submit a bug report to help us improve Torchaudio + +--- + +## ๐Ÿ› Bug + + + +## To Reproduce + +Steps to reproduce the behavior: + +1. +1. +1. + + + +## Expected behavior + + + +## Environment + + - What commands did you used to install torchaudio (conda/pip/build from source)? + - If you are building from source, which commit is it? + - What does `torchaudio.__version__` print? (If applicable) + +Please copy and paste the output from our +[environment collection script](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py) +(or fill out the checklist below manually). + +You can get the script and run it with: +``` +wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py +# For security purposes, please check the contents of collect_env.py before running it. +python collect_env.py +``` + + - PyTorch Version (e.g., 1.0): + - OS (e.g., Linux): + - How you installed PyTorch (`conda`, `pip`, source): + - Build command you used (if compiling from source): + - Python version: + - CUDA/cuDNN version: + - GPU models and configuration: + - Any other relevant information: + +## Additional context + + diff --git a/.github/ISSUE_TEMPLATE/documentation.md b/.github/ISSUE_TEMPLATE/documentation.md new file mode 100644 index 0000000000..ae745121c5 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/documentation.md @@ -0,0 +1,9 @@ +--- +name: "\U0001F4DA Documentation" +about: Report an issue related to https://pytorch.org/audio + +--- + +## ๐Ÿ“š Documentation + + diff --git a/.github/ISSUE_TEMPLATE/feature-request.md b/.github/ISSUE_TEMPLATE/feature-request.md new file mode 100644 index 0000000000..f3896e5368 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature-request.md @@ -0,0 +1,24 @@ +--- +name: "\U0001F680Feature Request" +about: Submit a proposal/request for a new Torchaudio feature + +--- + +## ๐Ÿš€ Feature + + +## Motivation + + + +## Pitch + + + +## Alternatives + + + +## Additional context + + diff --git a/.github/ISSUE_TEMPLATE/questions-help-support.md b/.github/ISSUE_TEMPLATE/questions-help-support.md new file mode 100644 index 0000000000..77bfb55b9a --- /dev/null +++ b/.github/ISSUE_TEMPLATE/questions-help-support.md @@ -0,0 +1,13 @@ +--- +name: "โ“Questions/Help/Support" +about: Do you need support? We have resources. + +--- + +## โ“ Questions and Help + +### Please note that this issue tracker is not a help form and this issue will be closed. + +We have a set of [listed resources available on the website](https://pytorch.org/resources). Our primary means of support is our discussion forum: + +- [Discussion Forum](https://discuss.pytorch.org/) diff --git a/.github/pytorch-probot.yml b/.github/pytorch-probot.yml new file mode 100644 index 0000000000..d9daca0fb2 --- /dev/null +++ b/.github/pytorch-probot.yml @@ -0,0 +1 @@ +tracking_issue: 736 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000..e8a9d4c409 --- /dev/null +++ b/.gitignore @@ -0,0 +1,129 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# temp files +~* +*.swp + +# C extensions / folders +*.so +_ext/ + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +docs/src/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints/ + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# PyCharm project settings +.idea + +# OSX dir files +.DS_Store + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# Generated Files +test/assets/sinewave.wav +torchaudio/version.py +gen.yml + +# Examples +examples/interactive_asr/data/*.txt +examples/interactive_asr/data/*.model +examples/interactive_asr/data/*.pt + +# third parties +third_party/archives/ +third_party/install/ +third_party/src/ +third_party/tmp/ diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000000..81e37b4120 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,32 @@ +# note dist: 'trusty' does not work here +dist: xenial + +language: python + +# cache miniconda installer and similar files +cache: + directories: + - /home/travis/download + +# This matrix tests that the code works on Python 3.5, 3.6, 3.7, passes +# lint and example tests. +matrix: + fast_finish: true + include: + - env: PYTHON_VERSION="3.6" RUN_EXAMPLE_TESTS="true" SKIP_TESTS="true" + allow_failures: + - env: PYTHON_VERSION="3.6" RUN_EXAMPLE_TESTS="true" SKIP_TESTS="true" + +addons: + apt: + packages: + sox + libsox-dev + libsox-fmt-all + portaudio19-dev + +notifications: + email: false + +install: source build_tools/travis/install.sh +script: bash build_tools/travis/test_script.sh diff --git a/docs/source/backend.rst b/docs/source/backend.rst new file mode 100644 index 0000000000..6b0e074d5c --- /dev/null +++ b/docs/source/backend.rst @@ -0,0 +1,138 @@ +.. _backend: + +torchaudio.backend +================== + +:mod:`torchaudio.backend` module provides implementations for audio file I/O, using different backend libraries. +To switch backend, use :py:func:`torchaudio.set_audio_backend`. To check the current backend use :py:func:`torchaudio.get_audio_backend`. + +.. warning:: + Although ``sox`` backend is default for backward compatibility reason, it has a number of issues, therefore it is highly recommended to use ``sox_io`` backend instead. Note, however, that due to the interface refinement, functions defined in ``sox`` backend and those defined in ``sox_io`` backend do not have the same signatures. + +.. note:: + Instead of calling functions in :mod:`torchaudio.backend` directly, please use ``torchaudio.info``, ``torhcaudio.load``, ``torchaudio.load_wav`` and ``torchaudio.save`` with proper backend set with :func:`torchaudio.get_audio_backend`. + +There are currently three implementations available. + + * :ref:`sox` + * :ref:`sox_io` + * :ref:`soundfile` + +``sox`` backend is the original backend which is built on ``libsox``. This module is currently default but is known to have number of issues, such as wrong handling of WAV files other than 16-bit signed integer. Users are encouraged to use ``sox_io`` backend. This backend requires C++ extension module and is not available on Windows system. + +``sox_io`` backend is the new backend which is built on ``libsox`` and bound to Python with ``Torchscript``. This module addresses all the known issues ``sox`` backend has. Function calls to this backend can be Torchscriptable. This backend requires C++ extension module and is not available on Windows system. + +``soundfile`` backend is built on ``PySoundFile``. You need to install ``PySoundFile`` separately. + +Common Data Structure +~~~~~~~~~~~~~~~~~~~~~ + +Structures used to exchange data between Python interface and ``libsox``. They are used by :ref:`sox` and :ref:`soundfile` but not by :ref:`sox_io`. + +.. autoclass:: torchaudio.backend.common.SignalInfo + +.. autoclass:: torchaudio.backend.common.EncodingInfo + +.. _sox_backend: + +Sox Backend +~~~~~~~~~~~ + +``sox`` backend is available on ``torchaudio`` installation with C++ extension. It is currently not available on Windows system. + +It is currently default backend when it's available. You can switch from another backend to ``sox`` backend with the following; + +.. code:: + + torchaudio.set_audio_backend("sox") + +info +---- + +.. autofunction:: torchaudio.backend.sox_backend.info + +load +---- + +.. autofunction:: torchaudio.backend.sox_backend.load + +.. autofunction:: torchaudio.backend.sox_backend.load_wav + + +save +---- + +.. autofunction:: torchaudio.backend.sox_backend.save + +others +------ + +.. automodule:: torchaudio.backend.sox_backend + :members: + :exclude-members: info, load, load_wav, save + +.. _sox_io_backend: + +Sox IO Backend +~~~~~~~~~~~~~~ + +``sox_io`` backend is available on ``torchaudio`` installation with C++ extension. It is currently not available on Windows system. + +This new backend is recommended over ``sox`` backend. You can switch from another backend to ``sox_io`` backend with the following; + +.. code:: + + torchaudio.set_audio_backend("sox_io") + +The function call to this backend can be Torchsript-able. You can apply :func:`torch.jit.script` and dump the object to file, then call it from C++ application. + +info +---- + +.. autoclass:: torchaudio.backend.sox_io_backend.AudioMetaData + +.. autofunction:: torchaudio.backend.sox_io_backend.info + +load +---- + +.. autofunction:: torchaudio.backend.sox_io_backend.load + +.. autofunction:: torchaudio.backend.sox_io_backend.load_wav + + +save +---- + +.. autofunction:: torchaudio.backend.sox_io_backend.save + +.. _soundfile_backend: + +Soundfile Backend +~~~~~~~~~~~~~~~~~ + +``soundfile`` backend is available when ``PySoundFile`` is installed. This backend works on ``torchaudio`` installation without C++ extension. (i.e. Windows) + +You can switch from another backend to ``soundfile`` backend with the following; + +.. code:: + + torchaudio.set_audio_backend("soundfile") + +info +---- + +.. autofunction:: torchaudio.backend.soundfile_backend.info + +load +---- + +.. autofunction:: torchaudio.backend.soundfile_backend.load + +.. autofunction:: torchaudio.backend.soundfile_backend.load_wav + + +save +---- + +.. autofunction:: torchaudio.backend.soundfile_backend.save diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 2f844293f6..96ba045c5e 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -57,6 +57,14 @@ LIBRISPEECH :special-members: +LIBRITTS +~~~~~~~~ + +.. autoclass:: LIBRITTS + :members: __getitem__ + :special-members: + + LJSPEECH ~~~~~~~~ diff --git a/docs/source/index.rst b/docs/source/index.rst index c6d0efde69..8146e545c3 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,5 +1,5 @@ torchaudio -=========== +========== The :mod:`torchaudio` package consists of I/O, popular datasets and common audio transformations. @@ -7,12 +7,13 @@ The :mod:`torchaudio` package consists of I/O, popular datasets and common audio :maxdepth: 2 :caption: Package Reference - sox_effects + torchaudio + backend + functional + transforms datasets + models + sox_effects compliance.kaldi kaldi_io - transforms - functional - -.. automodule:: torchaudio - :members: + utils diff --git a/docs/source/sox_effects.rst b/docs/source/sox_effects.rst index 56cd985d0a..0aa34e7908 100644 --- a/docs/source/sox_effects.rst +++ b/docs/source/sox_effects.rst @@ -1,21 +1,52 @@ -.. role:: hidden - :class: hidden-section +.. _sox_effects: torchaudio.sox_effects ====================== -Create SoX effects chain for preprocessing audio. - .. currentmodule:: torchaudio.sox_effects -:hidden:`SoxEffect` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. warning:: + + The :py:class:`SoxEffect` and :py:class:`SoxEffectsChain` classes are deprecated. Please migrate to :func:`apply_effects_tensor` and :func:`apply_effects_file`. + +Resource initialization / shutdown +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: init_sox_effects + +.. autofunction:: shutdown_sox_effects + +Listing supported effects +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: effect_names + +Applying effects +~~~~~~~~~~~~~~~~ + +Apply SoX effects chain on torch.Tensor or on file and load as torch.Tensor. + +Applying effects on Tensor +-------------------------- + +.. autofunction:: apply_effects_tensor + +Applying effects on file +------------------------ + +.. autofunction:: apply_effects_file + +Legacy +~~~~~~ + +SoxEffect +--------- .. autoclass:: SoxEffect :members: -:hidden:`SoxEffectsChain` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +SoxEffectsChain +--------------- .. autoclass:: SoxEffectsChain :members: append_effect_to_chain, sox_build_flow_effects, clear_chain, set_input_file diff --git a/docs/source/torchaudio.rst b/docs/source/torchaudio.rst new file mode 100644 index 0000000000..f7b826b571 --- /dev/null +++ b/docs/source/torchaudio.rst @@ -0,0 +1,43 @@ +torchaudio +========== + +I/O functionalities +~~~~~~~~~~~~~~~~~~~ + +Audio I/O functions are implemented in :ref:`torchaudio.backend` module, but for the ease of use, the following functions are made available on :mod:`torchaudio` module. There are different backends available and you can switch backends with :func:`set_audio_backend`. + +Refer to :ref:`backend` for the detail. + +.. function:: torchaudio.info(filepath: str, ...) + + Fetch meta data of an audio file. Refer to :ref:`backend` for the detail. + +.. function:: torchaudio.load(filepath: str, ...) + + Load audio file into torch.Tensor object. Refer to :ref:`backend` for the detail. + +.. function:: torchaudio.load_wav(filepath: str, ...) + + Load audio file into torch.Tensor, Refer to :ref:`backend` for the detail. + +.. function:: torchaudio.save(filepath: str, src: torch.Tensor, sample_rate: int, ...) + + Save torch.Tensor object into an audio format. Refer to :ref:`backend` for the detail. + +.. currentmodule:: torchaudio + +Backend Utilities +~~~~~~~~~~~~~~~~~ + +.. autofunction:: list_audio_backends + +.. autofunction:: get_audio_backend + +.. autofunction:: set_audio_backend + +Sox Effects Utilities +~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: initialize_sox + +.. autofunction:: shutdown_sox diff --git a/docs/source/utils.rst b/docs/source/utils.rst new file mode 100644 index 0000000000..dc5ad0fd73 --- /dev/null +++ b/docs/source/utils.rst @@ -0,0 +1,11 @@ +torchaudio.utils +================ + +torchaudio.utils.sox_utils +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Utility module to configure libsox. +This affects functionalities in :ref:`Sox IO backend` and :ref:`Sox Effects`. + +.. automodule:: torchaudio.utils.sox_utils + :members: diff --git a/examples/pipeline_wavernn/README.md b/examples/pipeline_wavernn/README.md new file mode 100644 index 0000000000..865f06c181 --- /dev/null +++ b/examples/pipeline_wavernn/README.md @@ -0,0 +1,36 @@ +This is an example vocoder pipeline using the WaveRNN model trained with LJSpeech. WaveRNN model is based on the implementation from [this repository](https://github.com/fatchord/WaveRNN). The original implementation was +introduced in "Efficient Neural Audio Synthesis". WaveRNN and LJSpeech are available in torchaudio. + +### Usage + +An example can be invoked as follows. +``` +python main.py \ + --batch-size 256 \ + --learning-rate 1e-4 \ + --n-freq 80 \ + --loss 'crossentropy' \ + --n-bits 8 \ +``` + +### Output + +The information reported at each iteration and epoch (e.g. loss) is printed to standard output in the form of one json per line. Here is an example python function to parse the output if redirected to a file. +```python +def read_json(filename): + """ + Convert the standard output saved to filename into a pandas dataframe for analysis. + """ + + import pandas + import json + + with open(filename, "r") as f: + data = f.read() + + # pandas doesn't read single quotes for json + data = data.replace("'", '"') + + data = [json.loads(l) for l in data.splitlines()] + return pandas.DataFrame(data) +``` diff --git a/examples/pipeline_wavernn/datasets.py b/examples/pipeline_wavernn/datasets.py new file mode 100644 index 0000000000..8d3068a229 --- /dev/null +++ b/examples/pipeline_wavernn/datasets.py @@ -0,0 +1,115 @@ +import os +import random + +import torch +import torchaudio +from torch.utils.data.dataset import random_split +from torchaudio.datasets import LJSPEECH +from torchaudio.transforms import MuLawEncoding + +from processing import bits_to_normalized_waveform, normalized_waveform_to_bits + + +class MapMemoryCache(torch.utils.data.Dataset): + r"""Wrap a dataset so that, whenever a new item is returned, it is saved to memory. + """ + + def __init__(self, dataset): + self.dataset = dataset + self._cache = [None] * len(dataset) + + def __getitem__(self, n): + if self._cache[n] is not None: + return self._cache[n] + + item = self.dataset[n] + self._cache[n] = item + + return item + + def __len__(self): + return len(self.dataset) + + +class Processed(torch.utils.data.Dataset): + def __init__(self, dataset, transforms): + self.dataset = dataset + self.transforms = transforms + + def __getitem__(self, key): + item = self.dataset[key] + return self.process_datapoint(item) + + def __len__(self): + return len(self.dataset) + + def process_datapoint(self, item): + specgram = self.transforms(item[0]) + return item[0].squeeze(0), specgram + + +def split_process_ljspeech(args, transforms): + data = LJSPEECH(root=args.file_path, download=False) + + val_length = int(len(data) * args.val_ratio) + lengths = [len(data) - val_length, val_length] + train_dataset, val_dataset = random_split(data, lengths) + + train_dataset = Processed(train_dataset, transforms) + val_dataset = Processed(val_dataset, transforms) + + train_dataset = MapMemoryCache(train_dataset) + val_dataset = MapMemoryCache(val_dataset) + + return train_dataset, val_dataset + + +def collate_factory(args): + def raw_collate(batch): + + pad = (args.kernel_size - 1) // 2 + + # input waveform length + wave_length = args.hop_length * args.seq_len_factor + # input spectrogram length + spec_length = args.seq_len_factor + pad * 2 + + # max start postion in spectrogram + max_offsets = [x[1].shape[-1] - (spec_length + pad * 2) for x in batch] + + # random start postion in spectrogram + spec_offsets = [random.randint(0, offset) for offset in max_offsets] + # random start postion in waveform + wave_offsets = [(offset + pad) * args.hop_length for offset in spec_offsets] + + waveform_combine = [ + x[0][wave_offsets[i]: wave_offsets[i] + wave_length + 1] + for i, x in enumerate(batch) + ] + specgram = [ + x[1][:, spec_offsets[i]: spec_offsets[i] + spec_length] + for i, x in enumerate(batch) + ] + + specgram = torch.stack(specgram) + waveform_combine = torch.stack(waveform_combine) + + waveform = waveform_combine[:, :wave_length] + target = waveform_combine[:, 1:] + + # waveform: [-1, 1], target: [0, 2**bits-1] if loss = 'crossentropy' + if args.loss == "crossentropy": + + if args.mulaw: + mulaw_encode = MuLawEncoding(2 ** args.n_bits) + waveform = mulaw_encode(waveform) + target = mulaw_encode(target) + + waveform = bits_to_normalized_waveform(waveform, args.n_bits) + + else: + target = normalized_waveform_to_bits(target, args.n_bits) + + return waveform.unsqueeze(1), specgram.unsqueeze(1), target.unsqueeze(1) + + return raw_collate diff --git a/examples/pipeline_wavernn/losses.py b/examples/pipeline_wavernn/losses.py new file mode 100644 index 0000000000..a4494b05fb --- /dev/null +++ b/examples/pipeline_wavernn/losses.py @@ -0,0 +1,119 @@ +import math + +import torch +from torch import nn as nn +from torch.nn import functional as F + + +class LongCrossEntropyLoss(nn.Module): + r""" CrossEntropy loss + """ + + def __init__(self): + super(LongCrossEntropyLoss, self).__init__() + + def forward(self, output, target): + output = output.transpose(1, 2) + target = target.long() + + criterion = nn.CrossEntropyLoss() + return criterion(output, target) + + +class MoLLoss(nn.Module): + r""" Discretized mixture of logistic distributions loss + + Adapted from wavenet vocoder + (https://github.com/r9y9/wavenet_vocoder/blob/master/wavenet_vocoder/mixture.py) + Explanation of loss (https://github.com/Rayhane-mamah/Tacotron-2/issues/155) + + Args: + y_hat (Tensor): Predicted output (n_batch x n_time x n_channel) + y (Tensor): Target (n_batch x n_time x 1) + num_classes (int): Number of classes + log_scale_min (float): Log scale minimum value + reduce (bool): If True, the losses are averaged or summed for each minibatch + + Returns + Tensor: loss + """ + + def __init__(self, num_classes=65536, log_scale_min=None, reduce=True): + super(MoLLoss, self).__init__() + self.num_classes = num_classes + self.log_scale_min = log_scale_min + self.reduce = reduce + + def forward(self, y_hat, y): + y = y.unsqueeze(-1) + + if self.log_scale_min is None: + self.log_scale_min = math.log(1e-14) + + assert y_hat.dim() == 3 + assert y_hat.size(-1) % 3 == 0 + + nr_mix = y_hat.size(-1) // 3 + + # unpack parameters (n_batch, n_time, num_mixtures) x 3 + logit_probs = y_hat[:, :, :nr_mix] + means = y_hat[:, :, nr_mix: 2 * nr_mix] + log_scales = torch.clamp( + y_hat[:, :, 2 * nr_mix: 3 * nr_mix], min=self.log_scale_min + ) + + # (n_batch x n_time x 1) to (n_batch x n_time x num_mixtures) + y = y.expand_as(means) + + centered_y = y - means + inv_stdv = torch.exp(-log_scales) + plus_in = inv_stdv * (centered_y + 1.0 / (self.num_classes - 1)) + cdf_plus = torch.sigmoid(plus_in) + min_in = inv_stdv * (centered_y - 1.0 / (self.num_classes - 1)) + cdf_min = torch.sigmoid(min_in) + + # log probability for edge case of 0 (before scaling) + # equivalent: torch.log(F.sigmoid(plus_in)) + log_cdf_plus = plus_in - F.softplus(plus_in) + + # log probability for edge case of 255 (before scaling) + # equivalent: (1 - F.sigmoid(min_in)).log() + log_one_minus_cdf_min = -F.softplus(min_in) + + # probability for all other cases + cdf_delta = cdf_plus - cdf_min + + mid_in = inv_stdv * centered_y + # log probability in the center of the bin, to be used in extreme cases + log_pdf_mid = mid_in - log_scales - 2.0 * F.softplus(mid_in) + + inner_inner_cond = (cdf_delta > 1e-5).float() + + inner_inner_out = inner_inner_cond * torch.log( + torch.clamp(cdf_delta, min=1e-12) + ) + (1.0 - inner_inner_cond) * ( + log_pdf_mid - math.log((self.num_classes - 1) / 2) + ) + inner_cond = (y > 0.999).float() + inner_out = ( + inner_cond * log_one_minus_cdf_min + (1.0 - inner_cond) * inner_inner_out + ) + cond = (y < -0.999).float() + log_probs = cond * log_cdf_plus + (1.0 - cond) * inner_out + + log_probs = log_probs + F.log_softmax(logit_probs, -1) + + if self.reduce: + return -torch.mean(_log_sum_exp(log_probs)) + else: + return -_log_sum_exp(log_probs).unsqueeze(-1) + + +def _log_sum_exp(x): + r""" Numerically stable log_sum_exp implementation that prevents overflow + """ + + axis = len(x.size()) - 1 + m, _ = torch.max(x, dim=axis) + m2, _ = torch.max(x, dim=axis, keepdim=True) + return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis)) diff --git a/examples/pipeline_wavernn/main.py b/examples/pipeline_wavernn/main.py new file mode 100644 index 0000000000..032e9fc70e --- /dev/null +++ b/examples/pipeline_wavernn/main.py @@ -0,0 +1,390 @@ +import argparse +import logging +import os +import signal +from collections import defaultdict +from datetime import datetime +from time import time +from typing import List + +import torch +import torchaudio +from torch import nn as nn +from torch.optim import Adam +from torch.utils.data import DataLoader +from torchaudio.datasets.utils import bg_iterator +from torchaudio.models._wavernn import _WaveRNN + +from datasets import collate_factory, split_process_ljspeech +from losses import LongCrossEntropyLoss, MoLLoss +from processing import LinearToMel, NormalizeDB +from utils import MetricLogger, count_parameters, save_checkpoint + + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--workers", + default=4, + type=int, + metavar="N", + help="number of data loading workers", + ) + parser.add_argument( + "--checkpoint", + default="", + type=str, + metavar="PATH", + help="path to latest checkpoint", + ) + parser.add_argument( + "--epochs", + default=8000, + type=int, + metavar="N", + help="number of total epochs to run", + ) + parser.add_argument( + "--start-epoch", default=0, type=int, metavar="N", help="manual epoch number" + ) + parser.add_argument( + "--print-freq", + default=10, + type=int, + metavar="N", + help="print frequency in epochs", + ) + parser.add_argument( + "--batch-size", default=256, type=int, metavar="N", help="mini-batch size" + ) + parser.add_argument( + "--learning-rate", default=1e-4, type=float, metavar="LR", help="learning rate", + ) + parser.add_argument("--clip-grad", metavar="NORM", type=float, default=4.0) + parser.add_argument( + "--mulaw", + default=True, + action="store_true", + help="if used, waveform is mulaw encoded", + ) + parser.add_argument( + "--jit", default=False, action="store_true", help="if used, model is jitted" + ) + parser.add_argument( + "--upsample-scales", + default=[5, 5, 11], + type=List[int], + help="the list of upsample scales", + ) + parser.add_argument( + "--n-bits", default=8, type=int, help="the bits of output waveform", + ) + parser.add_argument( + "--sample-rate", + default=22050, + type=int, + help="the rate of audio dimensions (samples per second)", + ) + parser.add_argument( + "--hop-length", + default=275, + type=int, + help="the number of samples between the starts of consecutive frames", + ) + parser.add_argument( + "--win-length", default=1100, type=int, help="the length of the STFT window", + ) + parser.add_argument( + "--f-min", default=40.0, type=float, help="the minimum frequency", + ) + parser.add_argument( + "--min-level-db", + default=-100, + type=float, + help="the minimum db value for spectrogam normalization", + ) + parser.add_argument( + "--n-res-block", default=10, type=int, help="the number of ResBlock in stack", + ) + parser.add_argument( + "--n-rnn", default=512, type=int, help="the dimension of RNN layer", + ) + parser.add_argument( + "--n-fc", default=512, type=int, help="the dimension of fully connected layer", + ) + parser.add_argument( + "--kernel-size", + default=5, + type=int, + help="the number of kernel size in the first Conv1d layer", + ) + parser.add_argument( + "--n-freq", default=80, type=int, help="the number of spectrogram bins to use", + ) + parser.add_argument( + "--n-hidden-melresnet", + default=128, + type=int, + help="the number of hidden dimensions of resblock in melresnet", + ) + parser.add_argument( + "--n-output-melresnet", default=128, type=int, help="the output dimension of melresnet", + ) + parser.add_argument( + "--n-fft", default=2048, type=int, help="the number of Fourier bins", + ) + parser.add_argument( + "--loss", + default="crossentropy", + choices=["crossentropy", "mol"], + type=str, + help="the type of loss", + ) + parser.add_argument( + "--seq-len-factor", + default=5, + type=int, + help="the length of each waveform to process per batch = hop_length * seq_len_factor", + ) + parser.add_argument( + "--val-ratio", + default=0.1, + type=float, + help="the ratio of waveforms for validation", + ) + parser.add_argument( + "--file-path", default="", type=str, help="the path of audio files", + ) + + args = parser.parse_args() + return args + + +def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch): + + model.train() + + sums = defaultdict(lambda: 0.0) + start1 = time() + + metric = MetricLogger("train_iteration") + metric["epoch"] = epoch + + for waveform, specgram, target in bg_iterator(data_loader, maxsize=2): + + start2 = time() + + waveform = waveform.to(device) + specgram = specgram.to(device) + target = target.to(device) + + output = model(waveform, specgram) + output, target = output.squeeze(1), target.squeeze(1) + + loss = criterion(output, target) + loss_item = loss.item() + sums["loss"] += loss_item + metric["loss"] = loss_item + + optimizer.zero_grad() + loss.backward() + + if args.clip_grad > 0: + gradient = torch.nn.utils.clip_grad_norm_( + model.parameters(), args.clip_grad + ) + sums["gradient"] += gradient.item() + metric["gradient"] = gradient.item() + + optimizer.step() + + metric["iteration"] = sums["iteration"] + metric["time"] = time() - start2 + metric() + sums["iteration"] += 1 + + avg_loss = sums["loss"] / len(data_loader) + + metric = MetricLogger("train_epoch") + metric["epoch"] = epoch + metric["loss"] = sums["loss"] / len(data_loader) + metric["gradient"] = avg_loss + metric["time"] = time() - start1 + metric() + + +def validate(model, criterion, data_loader, device, epoch): + + with torch.no_grad(): + + model.eval() + sums = defaultdict(lambda: 0.0) + start = time() + + for waveform, specgram, target in bg_iterator(data_loader, maxsize=2): + + waveform = waveform.to(device) + specgram = specgram.to(device) + target = target.to(device) + + output = model(waveform, specgram) + output, target = output.squeeze(1), target.squeeze(1) + + loss = criterion(output, target) + sums["loss"] += loss.item() + + avg_loss = sums["loss"] / len(data_loader) + + metric = MetricLogger("validation") + metric["epoch"] = epoch + metric["loss"] = avg_loss + metric["time"] = time() - start + metric() + + return avg_loss + + +def main(args): + + devices = ["cuda" if torch.cuda.is_available() else "cpu"] + + logging.info("Start time: {}".format(str(datetime.now()))) + + melkwargs = { + "n_fft": args.n_fft, + "power": 1, + "hop_length": args.hop_length, + "win_length": args.win_length, + } + + transforms = torch.nn.Sequential( + torchaudio.transforms.Spectrogram(**melkwargs), + LinearToMel( + sample_rate=args.sample_rate, + n_fft=args.n_fft, + n_mels=args.n_freq, + fmin=args.f_min, + ), + NormalizeDB(min_level_db=args.min_level_db), + ) + + train_dataset, val_dataset = split_process_ljspeech(args, transforms) + + loader_training_params = { + "num_workers": args.workers, + "pin_memory": False, + "shuffle": True, + "drop_last": False, + } + loader_validation_params = loader_training_params.copy() + loader_validation_params["shuffle"] = False + + collate_fn = collate_factory(args) + + train_loader = DataLoader( + train_dataset, + batch_size=args.batch_size, + collate_fn=collate_fn, + **loader_training_params, + ) + val_loader = DataLoader( + val_dataset, + batch_size=args.batch_size, + collate_fn=collate_fn, + **loader_validation_params, + ) + + n_classes = 2 ** args.n_bits if args.loss == "crossentropy" else 30 + + model = _WaveRNN( + upsample_scales=args.upsample_scales, + n_classes=n_classes, + hop_length=args.hop_length, + n_res_block=args.n_res_block, + n_rnn=args.n_rnn, + n_fc=args.n_fc, + kernel_size=args.kernel_size, + n_freq=args.n_freq, + n_hidden=args.n_hidden_melresnet, + n_output=args.n_output_melresnet, + ) + + if args.jit: + model = torch.jit.script(model) + + model = torch.nn.DataParallel(model) + model = model.to(devices[0], non_blocking=True) + + n = count_parameters(model) + logging.info(f"Number of parameters: {n}") + + # Optimizer + optimizer_params = { + "lr": args.learning_rate, + } + + optimizer = Adam(model.parameters(), **optimizer_params) + + criterion = LongCrossEntropyLoss() if args.loss == "crossentropy" else MoLLoss() + + best_loss = 10.0 + + if args.checkpoint and os.path.isfile(args.checkpoint): + logging.info(f"Checkpoint: loading '{args.checkpoint}'") + checkpoint = torch.load(args.checkpoint) + + args.start_epoch = checkpoint["epoch"] + best_loss = checkpoint["best_loss"] + + model.load_state_dict(checkpoint["state_dict"]) + optimizer.load_state_dict(checkpoint["optimizer"]) + + logging.info( + f"Checkpoint: loaded '{args.checkpoint}' at epoch {checkpoint['epoch']}" + ) + else: + logging.info("Checkpoint: not found") + + save_checkpoint( + { + "epoch": args.start_epoch, + "state_dict": model.state_dict(), + "best_loss": best_loss, + "optimizer": optimizer.state_dict(), + }, + False, + args.checkpoint, + ) + + for epoch in range(args.start_epoch, args.epochs): + + train_one_epoch( + model, criterion, optimizer, train_loader, devices[0], epoch, + ) + + if not (epoch + 1) % args.print_freq or epoch == args.epochs - 1: + + sum_loss = validate(model, criterion, val_loader, devices[0], epoch) + + is_best = sum_loss < best_loss + best_loss = min(sum_loss, best_loss) + save_checkpoint( + { + "epoch": epoch + 1, + "state_dict": model.state_dict(), + "best_loss": best_loss, + "optimizer": optimizer.state_dict(), + }, + is_best, + args.checkpoint, + ) + + logging.info(f"End time: {datetime.now()}") + + +if __name__ == "__main__": + + logging.basicConfig(level=logging.INFO) + args = parse_args() + main(args) diff --git a/examples/pipeline_wavernn/processing.py b/examples/pipeline_wavernn/processing.py new file mode 100644 index 0000000000..b22d60dae4 --- /dev/null +++ b/examples/pipeline_wavernn/processing.py @@ -0,0 +1,58 @@ +import librosa +import torch +import torch.nn as nn + + +# TODO Replace by torchaudio, once https://github.com/pytorch/audio/pull/593 is resolved +class LinearToMel(nn.Module): + def __init__(self, sample_rate, n_fft, n_mels, fmin, htk=False, norm="slaney"): + super().__init__() + self.sample_rate = sample_rate + self.n_fft = n_fft + self.n_mels = n_mels + self.fmin = fmin + self.htk = htk + self.norm = norm + + def forward(self, specgram): + specgram = librosa.feature.melspectrogram( + S=specgram.squeeze(0).numpy(), + sr=self.sample_rate, + n_fft=self.n_fft, + n_mels=self.n_mels, + fmin=self.fmin, + htk=self.htk, + norm=self.norm, + ) + return torch.from_numpy(specgram) + + +class NormalizeDB(nn.Module): + r"""Normalize the spectrogram with a minimum db value + """ + + def __init__(self, min_level_db): + super().__init__() + self.min_level_db = min_level_db + + def forward(self, specgram): + specgram = 20 * torch.log10(torch.clamp(specgram, min=1e-5)) + return torch.clamp( + (self.min_level_db - specgram) / self.min_level_db, min=0, max=1 + ) + + +def normalized_waveform_to_bits(waveform, bits): + r"""Transform waveform [-1, 1] to label [0, 2 ** bits - 1] + """ + + assert abs(waveform).max() <= 1.0 + waveform = (waveform + 1.0) * (2 ** bits - 1) / 2 + return torch.clamp(waveform, 0, 2 ** bits - 1).int() + + +def bits_to_normalized_waveform(label, bits): + r"""Transform label [0, 2 ** bits - 1] to waveform [-1, 1] + """ + + return 2 * label / (2 ** bits - 1.0) - 1.0 diff --git a/examples/pipeline_wavernn/utils.py b/examples/pipeline_wavernn/utils.py new file mode 100644 index 0000000000..e924c9f512 --- /dev/null +++ b/examples/pipeline_wavernn/utils.py @@ -0,0 +1,61 @@ +import logging +import os +import shutil +from collections import defaultdict, deque + +import torch + + +class MetricLogger: + r"""Logger for model metrics + """ + + def __init__(self, group, print_freq=1): + self.print_freq = print_freq + self._iter = 0 + self.data = defaultdict(lambda: deque(maxlen=self.print_freq)) + self.data["group"].append(group) + + def __setitem__(self, key, value): + self.data[key].append(value) + + def _get_last(self): + return {k: v[-1] for k, v in self.data.items()} + + def __str__(self): + return str(self._get_last()) + + def __call__(self): + self._iter = (self._iter + 1) % self.print_freq + if not self._iter: + print(self, flush=True) + + +def save_checkpoint(state, is_best, filename): + r"""Save the model to a temporary file first, + then copy it to filename, in case the signal interrupts + the torch.save() process. + """ + + if filename == "": + return + + tempfile = filename + ".temp" + + # Remove tempfile in case interuption during the copying from tempfile to filename + if os.path.isfile(tempfile): + os.remove(tempfile) + + torch.save(state, tempfile) + if os.path.isfile(tempfile): + os.rename(tempfile, filename) + if is_best: + shutil.copyfile(filename, "model_best.pth.tar") + logging.info("Checkpoint: saved") + + +def count_parameters(model): + r"""Count the total number of parameters in the model + """ + + return sum(p.numel() for p in model.parameters() if p.requires_grad) diff --git a/test/README.md b/test/README.md index 304bfa9c8e..c0a4b2a21d 100644 --- a/test/README.md +++ b/test/README.md @@ -29,7 +29,7 @@ The following test modules are defined for corresponding `torchaudio` module/fun - [`torchaudio.transforms`](./test_transforms.py) - [`torchaudio.compliance.kaldi`](./test_compliance_kaldi.py) - 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158fde87ed..beceb6cafb 100644 --- a/test/common_utils/backend_utils.py +++ b/test/common_utils/backend_utils.py @@ -29,8 +29,8 @@ def supports_mp3(backend): def set_audio_backend(backend): """Allow additional backend value, 'default'""" if backend == 'default': - if 'sox' in BACKENDS: - be = 'sox' + if 'sox_io' in BACKENDS: + be = 'sox_io' elif 'soundfile' in BACKENDS: be = 'soundfile' else: diff --git a/test/common_utils/case_utils.py b/test/common_utils/case_utils.py index f3b0c343a6..9c89ef6891 100644 --- a/test/common_utils/case_utils.py +++ b/test/common_utils/case_utils.py @@ -14,33 +14,28 @@ class TempDirMixin: """Mixin to provide easy access to temp dir""" temp_dir_ = None - base_temp_dir = None - temp_dir = None @classmethod - def setUpClass(cls): - super().setUpClass() + def get_base_temp_dir(cls): # If TORCHAUDIO_TEST_TEMP_DIR is set, use it instead of temporary directory. # this is handy for debugging. key = 'TORCHAUDIO_TEST_TEMP_DIR' if key in os.environ: - cls.base_temp_dir = os.environ[key] - else: + return os.environ[key] + if cls.temp_dir_ is None: cls.temp_dir_ = tempfile.TemporaryDirectory() - cls.base_temp_dir = cls.temp_dir_.name + return cls.temp_dir_.name @classmethod def tearDownClass(cls): super().tearDownClass() - if isinstance(cls.temp_dir_, tempfile.TemporaryDirectory): + if cls.temp_dir_ is not None: cls.temp_dir_.cleanup() - - def setUp(self): - super().setUp() - self.temp_dir = os.path.join(self.base_temp_dir, self.id()) + cls.temp_dir_ = None def get_temp_path(self, *paths): - path = os.path.join(self.temp_dir, *paths) + temp_dir = os.path.join(self.get_base_temp_dir(), self.id()) + path = os.path.join(temp_dir, *paths) os.makedirs(os.path.dirname(path), exist_ok=True) return path diff --git a/test/common_utils/data_utils.py b/test/common_utils/data_utils.py index b948ce334a..de19d8174f 100644 --- a/test/common_utils/data_utils.py +++ b/test/common_utils/data_utils.py @@ -13,6 +13,28 @@ def get_asset_path(*paths): return os.path.join(_TEST_DIR_PATH, 'assets', *paths) +def convert_tensor_encoding( + tensor: torch.tensor, + dtype: torch.dtype, +): + """Convert input tensor with values between -1 and 1 to integer encoding + Args: + tensor: input tensor, assumed between -1 and 1 + dtype: desired output tensor dtype + Returns: + Tensor: shape of (n_channels, sample_rate * duration) + """ + if dtype == torch.int32: + tensor *= (tensor > 0) * 2147483647 + (tensor < 0) * 2147483648 + if dtype == torch.int16: + tensor *= (tensor > 0) * 32767 + (tensor < 0) * 32768 + if dtype == torch.uint8: + tensor *= (tensor > 0) * 127 + (tensor < 0) * 128 + tensor += 128 + tensor = tensor.to(dtype) + return tensor + + def get_whitenoise( *, sample_rate: int = 16000, @@ -43,25 +65,17 @@ def get_whitenoise( if dtype not in [torch.float32, torch.int32, torch.int16, torch.uint8]: raise NotImplementedError(f'dtype {dtype} is not supported.') # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices, - # so we only folk on CPU, generate values and move the data to the given device + # so we only fork on CPU, generate values and move the data to the given device with torch.random.fork_rng([]): torch.random.manual_seed(seed) - tensor = torch.randn([sample_rate * duration], dtype=torch.float32, device='cpu') + tensor = torch.randn([int(sample_rate * duration)], dtype=torch.float32, device='cpu') tensor /= 2.0 tensor *= scale_factor tensor.clamp_(-1.0, 1.0) - if dtype == torch.int32: - tensor *= (tensor > 0) * 2147483647 + (tensor < 0) * 2147483648 - if dtype == torch.int16: - tensor *= (tensor > 0) * 32767 + (tensor < 0) * 32768 - if dtype == torch.uint8: - tensor *= (tensor > 0) * 127 + (tensor < 0) * 128 - tensor += 128 - tensor = tensor.to(dtype) tensor = tensor.repeat([n_channels, 1]) if not channels_first: tensor = tensor.t() - return tensor.to(device=device) + return convert_tensor_encoding(tensor, dtype) def get_sinusoid( @@ -72,6 +86,7 @@ def get_sinusoid( n_channels: int = 1, dtype: Union[str, torch.dtype] = "float32", device: Union[str, torch.device] = "cpu", + channels_first: bool = True, ): """Generate pseudo audio data with sine wave. @@ -90,5 +105,8 @@ def get_sinusoid( dtype = getattr(torch, dtype) pie2 = 2 * 3.141592653589793 end = pie2 * frequency * duration - theta = torch.linspace(0, end, sample_rate * duration, dtype=dtype, device=device) - return torch.sin(theta, out=None).repeat([n_channels, 1]) + theta = torch.linspace(0, end, int(sample_rate * duration), dtype=torch.float32, device=device) + tensor = torch.sin(theta, out=None).repeat([n_channels, 1]) + if not channels_first: + tensor = tensor.t() + return convert_tensor_encoding(tensor, dtype) diff --git a/test/common_utils/sox_utils.py b/test/common_utils/sox_utils.py index cd1c247b72..db131cdec5 100644 --- a/test/common_utils/sox_utils.py +++ b/test/common_utils/sox_utils.py @@ -77,3 +77,24 @@ def convert_audio_file( command += [dst_path] print(' '.join(command)) subprocess.run(command, check=True) + + +def _flattern(effects): + if not effects: + return effects + if isinstance(effects[0], str): + return effects + return [item for sublist in effects for item in sublist] + + +def run_sox_effect(input_file, output_file, effect, *, output_sample_rate=None, output_bitdepth=None): + """Run sox effects""" + effect = _flattern(effect) + command = ['sox', '-V', '--no-dither', input_file] + if output_bitdepth: + command += ['--bits', str(output_bitdepth)] + command += [output_file] + effect + if output_sample_rate: + command += ['rate', str(output_sample_rate)] + print(' '.join(command)) + subprocess.run(command, check=True) diff --git a/test/datasets/__init__.py b/test/datasets/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/test_datasets.py b/test/datasets/datasets_test.py similarity index 71% rename from test/test_datasets.py rename to test/datasets/datasets_test.py index c3b0c917da..b8a980f346 100644 --- a/test/test_datasets.py +++ b/test/datasets/datasets_test.py @@ -1,25 +1,20 @@ -import unittest - from torchaudio.datasets.commonvoice import COMMONVOICE from torchaudio.datasets.librispeech import LIBRISPEECH from torchaudio.datasets.speechcommands import SPEECHCOMMANDS from torchaudio.datasets.utils import diskcache_iterator, bg_iterator from torchaudio.datasets.vctk import VCTK -from torchaudio.datasets.yesno import YESNO from torchaudio.datasets.ljspeech import LJSPEECH -from torchaudio.datasets.gtzan import GTZAN from torchaudio.datasets.cmuarctic import CMUARCTIC -from . import common_utils +from ..common_utils import ( + TorchaudioTestCase, + get_asset_path, +) -class TestDatasets(common_utils.TorchaudioTestCase): +class TestDatasets(TorchaudioTestCase): backend = 'default' - path = common_utils.get_asset_path() - - def test_yesno(self): - data = YESNO(self.path) - data[0] + path = get_asset_path() def test_vctk(self): data = VCTK(self.path) @@ -37,18 +32,14 @@ def test_speechcommands(self): data = SPEECHCOMMANDS(self.path) data[0] - def test_gtzan(self): - data = GTZAN(self.path) - data[0] - def test_cmuarctic(self): data = CMUARCTIC(self.path) data[0] -class TestCommonVoice(common_utils.TorchaudioTestCase): +class TestCommonVoice(TorchaudioTestCase): backend = 'default' - path = common_utils.get_asset_path() + path = get_asset_path() def test_commonvoice(self): data = COMMONVOICE(self.path, url="tatar") @@ -67,7 +58,3 @@ def test_commonvoice_bg(self): data = bg_iterator(data, 5) for _ in data: pass - - -if __name__ == "__main__": - unittest.main() diff --git a/test/datasets/gtzan_test.py b/test/datasets/gtzan_test.py new file mode 100644 index 0000000000..14c82a512a --- /dev/null +++ b/test/datasets/gtzan_test.py @@ -0,0 +1,88 @@ +import os + +from torchaudio.datasets import gtzan + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + + +class TestGTZAN(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + samples = [] + training = [] + validation = [] + testing = [] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + sample_rate = 22050 + seed = 0 + for genre in gtzan.gtzan_genres: + base_dir = os.path.join(cls.root_dir, 'genres', genre) + os.makedirs(base_dir, exist_ok=True) + for i in range(100): + filename = f'{genre}.{i:05d}' + path = os.path.join(base_dir, f'{filename}.wav') + data = get_whitenoise(sample_rate=sample_rate, duration=0.01, n_channels=1, dtype='int16', seed=seed) + save_wav(path, data, sample_rate) + sample = (normalize_wav(data), sample_rate, genre) + cls.samples.append(sample) + if filename in gtzan.filtered_test: + cls.testing.append(sample) + if filename in gtzan.filtered_train: + cls.training.append(sample) + if filename in gtzan.filtered_valid: + cls.validation.append(sample) + seed += 1 + + def test_no_subset(self): + dataset = gtzan.GTZAN(self.root_dir) + + n_ite = 0 + for i, (waveform, sample_rate, label) in enumerate(dataset): + self.assertEqual(waveform, self.samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[i][1] + assert label == self.samples[i][2] + n_ite += 1 + assert n_ite == len(self.samples) + + def test_training(self): + dataset = gtzan.GTZAN(self.root_dir, subset='training') + + n_ite = 0 + for i, (waveform, sample_rate, label) in enumerate(dataset): + self.assertEqual(waveform, self.training[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.training[i][1] + assert label == self.training[i][2] + n_ite += 1 + assert n_ite == len(self.training) + + def test_validation(self): + dataset = gtzan.GTZAN(self.root_dir, subset='validation') + + n_ite = 0 + for i, (waveform, sample_rate, label) in enumerate(dataset): + self.assertEqual(waveform, self.validation[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.validation[i][1] + assert label == self.validation[i][2] + n_ite += 1 + assert n_ite == len(self.validation) + + def test_testing(self): + dataset = gtzan.GTZAN(self.root_dir, subset='testing') + + n_ite = 0 + for i, (waveform, sample_rate, label) in enumerate(dataset): + self.assertEqual(waveform, self.testing[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.testing[i][1] + assert label == self.testing[i][2] + n_ite += 1 + assert n_ite == len(self.testing) diff --git a/test/datasets/libritts_test.py b/test/datasets/libritts_test.py new file mode 100644 index 0000000000..f31561d211 --- /dev/null +++ b/test/datasets/libritts_test.py @@ -0,0 +1,71 @@ +import os + +from torchaudio.datasets.libritts import LIBRITTS + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + + +class TestLibriTTS(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + data = [] + utterance_ids = [ + [19, 198, '000000', '000000'], + [26, 495, '000004', '000000'], + ] + original_text = 'this is the original text.' + normalized_text = 'this is the normalized text.' + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + base_dir = os.path.join(cls.root_dir, 'LibriTTS', 'train-clean-100') + for i, utterance_id in enumerate(cls.utterance_ids): + filename = f'{"_".join(str(u) for u in utterance_id)}.wav' + file_dir = os.path.join(base_dir, str(utterance_id[0]), str(utterance_id[1])) + os.makedirs(file_dir, exist_ok=True) + path = os.path.join(file_dir, filename) + + data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16', seed=i) + save_wav(path, data, 8000) + cls.data.append(normalize_wav(data)) + + original_text_filename = f'{"_".join(str(u) for u in utterance_id)}.original.txt' + path_original = os.path.join(file_dir, original_text_filename) + with open(path_original, 'w') as file_: + file_.write(cls.original_text) + + normalized_text_filename = f'{"_".join(str(u) for u in utterance_id)}.normalized.txt' + path_normalized = os.path.join(file_dir, normalized_text_filename) + with open(path_normalized, 'w') as file_: + file_.write(cls.normalized_text) + + def test_libritts(self): + dataset = LIBRITTS(self.root_dir) + n_ites = 0 + for i, (waveform, + sample_rate, + original_text, + normalized_text, + speaker_id, + chapter_id, + utterance_id) in enumerate(dataset): + + expected_ids = self.utterance_ids[i] + expected_data = self.data[i] + self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) + assert sample_rate == 8000 + assert speaker_id == expected_ids[0] + assert chapter_id == expected_ids[1] + assert original_text == self.original_text + assert normalized_text == self.normalized_text + assert utterance_id == f'{"_".join(str(u) for u in expected_ids[-4:])}' + n_ites += 1 + assert n_ites == len(self.utterance_ids) diff --git a/test/datasets/utils_test.py b/test/datasets/utils_test.py new file mode 100644 index 0000000000..e77df09f4c --- /dev/null +++ b/test/datasets/utils_test.py @@ -0,0 +1,47 @@ +import os +from pathlib import Path + +from torchaudio.datasets import utils as dataset_utils + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, +) + + +class TestWalkFiles(TempDirMixin, TorchaudioTestCase): + root = None + expected = None + + def _add_file(self, *parts): + path = self.get_temp_path(*parts) + self.expected.append(path) + Path(path).touch() + + def setUp(self): + self.root = self.get_temp_path() + self.expected = [] + + # level 1 + for filename in ['a.txt', 'b.txt', 'c.txt']: + self._add_file(filename) + + # level 2 + for dir1 in ['d1', 'd2', 'd3']: + for filename in ['d.txt', 'e.txt', 'f.txt']: + self._add_file(dir1, filename) + # level 3 + for dir2 in ['d1', 'd2', 'd3']: + for filename in ['g.txt', 'h.txt', 'i.txt']: + self._add_file(dir1, dir2, filename) + + print('\n'.join(self.expected)) + + def test_walk_files(self): + """walk_files should traverse files in alphabetical order""" + n_ites = 0 + for i, path in enumerate(dataset_utils.walk_files(self.root, '.txt', prefix=True)): + found = os.path.join(self.root, path) + assert found == self.expected[i] + n_ites += 1 + assert n_ites == len(self.expected) diff --git a/test/datasets/yesno_test.py b/test/datasets/yesno_test.py new file mode 100644 index 0000000000..f9c4d0b051 --- /dev/null +++ b/test/datasets/yesno_test.py @@ -0,0 +1,49 @@ +import os + +from torchaudio.datasets import yesno + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + + +class TestYesNo(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + data = [] + labels = [ + [0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 1, 1], + [0, 1, 0, 1, 0, 1, 1, 0], + [1, 1, 1, 1, 0, 0, 0, 0], + [1, 1, 1, 1, 1, 1, 1, 1], + ] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + base_dir = os.path.join(cls.root_dir, 'waves_yesno') + os.makedirs(base_dir, exist_ok=True) + for label in cls.labels: + filename = f'{"_".join(str(l) for l in label)}.wav' + path = os.path.join(base_dir, filename) + data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16') + save_wav(path, data, 8000) + cls.data.append(normalize_wav(data)) + + def test_yesno(self): + dataset = yesno.YESNO(self.root_dir) + n_ite = 0 + for i, (waveform, sample_rate, label) in enumerate(dataset): + expected_label = self.labels[i] + expected_data = self.data[i] + self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) + assert sample_rate == 8000 + assert label == expected_label + n_ite += 1 + assert n_ite == len(self.data) diff --git a/test/functional_cpu_test.py b/test/functional_cpu_test.py index ab5fdaed95..b162df2ebc 100644 --- a/test/functional_cpu_test.py +++ b/test/functional_cpu_test.py @@ -4,6 +4,7 @@ import torch import torchaudio import torchaudio.functional as F +from parameterized import parameterized import pytest from . import common_utils @@ -299,24 +300,18 @@ def test_linearity_of_istft4(self): class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): - def test_pitch(self): - test_filepath_100 = common_utils.get_asset_path("100Hz_44100Hz_16bit_05sec.wav") - test_filepath_440 = common_utils.get_asset_path("440Hz_44100Hz_16bit_05sec.wav") + @parameterized.expand([(100,), (440,)]) + def test_pitch(self, frequency): + sample_rate = 44100 + test_sine_waveform = common_utils.get_sinusoid( + frequency=frequency, sample_rate=sample_rate, duration=5, + ) - # Files from https://www.mediacollege.com/audio/tone/download/ - tests = [ - (test_filepath_100, 100), - (test_filepath_440, 440), - ] + freq = torchaudio.functional.detect_pitch_frequency(test_sine_waveform, sample_rate) - for filename, freq_ref in tests: - waveform, sample_rate = common_utils.load_wav(filename) - - freq = torchaudio.functional.detect_pitch_frequency(waveform, sample_rate) - - threshold = 1 - s = ((freq - freq_ref).abs() > threshold).sum() - self.assertFalse(s) + threshold = 1 + s = ((freq - frequency).abs() > threshold).sum() + self.assertFalse(s) class TestDB_to_amplitude(common_utils.TorchaudioTestCase): @@ -410,7 +405,3 @@ def test_mask_along_axis_iid(mask_param, mask_value, axis): assert mask_specgrams.size() == specgrams.size() assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() - - -if __name__ == '__main__': - unittest.main() diff --git a/test/sox_effect/__init__.py b/test/sox_effect/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/sox_effect/common.py b/test/sox_effect/common.py new file mode 100644 index 0000000000..86edcf6476 --- /dev/null +++ b/test/sox_effect/common.py @@ -0,0 +1,26 @@ +import json + +from parameterized import param + +from ..common_utils import get_asset_path + + +def name_func(func, _, params): + if isinstance(params.args[0], str): + args = "_".join([str(arg) for arg in params.args]) + else: + args = "_".join([str(arg) for arg in params.args[0]]) + return f'{func.__name__}_{args}' + + +def load_params(*paths): + params = [] + with open(get_asset_path(*paths), 'r') as file: + for line in file: + data = json.loads(line) + for effect in data['effects']: + for i, arg in enumerate(effect): + if arg.startswith(""): + effect[i] = arg.replace("", get_asset_path()) + params.append(param(data)) + return params diff --git a/test/sox_effect/smoke_test.py b/test/sox_effect/smoke_test.py new file mode 100644 index 0000000000..4997002170 --- /dev/null +++ b/test/sox_effect/smoke_test.py @@ -0,0 +1,59 @@ +from torchaudio import sox_effects +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + skipIfNoExtension, + get_wav_data, + get_sinusoid, + save_wav, +) +from .common import ( + name_func, + load_params, +) + + +@skipIfNoExtension +class SmokeTest(TempDirMixin, TorchaudioTestCase): + """Run smoke test on various effects + + The purpose of this test suite is to verify that sox_effect functionalities do not exhibit + abnormal behaviors. + + This test suite should be able to run without any additional tools (such as sox command), + however without such tools, the correctness of each function cannot be verified. + """ + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects_tensor(self, args): + """`apply_effects_tensor` should not crash""" + effects = args['effects'] + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + original = get_sinusoid( + frequency=800, sample_rate=input_sr, + n_channels=num_channels, dtype='float32') + _found, _sr = sox_effects.apply_effects_tensor(original, input_sr, effects) + + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects(self, args): + """`apply_effects_file` should return identical data as sox command""" + dtype = 'int32' + channels_first = True + effects = args['effects'] + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + + input_path = self.get_temp_path('input.wav') + data = get_wav_data(dtype, num_channels, channels_first=channels_first) + save_wav(input_path, data, input_sr, channels_first=channels_first) + + _found, _sr = sox_effects.apply_effects_file( + input_path, effects, normalize=False, channels_first=channels_first) diff --git a/test/test_sox_effects.py b/test/sox_effect/sox_effects_chain_test.py similarity index 82% rename from test/test_sox_effects.py rename to test/sox_effect/sox_effects_chain_test.py index 6b12d7cd06..765ab62094 100644 --- a/test/test_sox_effects.py +++ b/test/sox_effect/sox_effects_chain_test.py @@ -5,7 +5,7 @@ import torch import torchaudio -from . import common_utils +from .. import common_utils @common_utils.skipIfNoSoxBackend @@ -45,8 +45,8 @@ def test_lowpass_speed(self): E.append_effect_to_chain("speed", speed) E.append_effect_to_chain("rate", si.rate) x, sr = E.sox_build_flow_effects() - # check if effects worked - self.assertEqual(x.size(1), int((si.length / si.channels) / speed)) + # check if effects worked, add small tolerance for rounding effects + self.assertEqual(x.size(1), int((si.length / si.channels) / speed), atol=1, rtol=1e-8) def test_ulaw_and_siginfo(self): si_out = torchaudio.sox_signalinfo_t() @@ -222,55 +222,3 @@ def test_invalid_effect_options(self): E.append_effect_to_chain("compand", ["0.3", "1", "6:-70,-60,-20", "-5", "-90", "0.2"]) with self.assertRaises(RuntimeError): E.sox_build_flow_effects() - - def test_fade(self): - x_orig, _ = torchaudio.load(self.test_filepath) - fade_in_len = 44100 - fade_out_len = 44100 - - for fade_shape_sox, fade_shape_torchaudio in (("q", "quarter_sine"), ("h", "half_sine"), ("t", "linear")): - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("fade", [fade_shape_sox, 1, "0", 1]) - x, sr = E.sox_build_flow_effects() - - fade = torchaudio.transforms.Fade(fade_in_len, fade_out_len, fade_shape_torchaudio) - - # check if effect worked - self.assertTrue(x.allclose(fade(x_orig), rtol=1e-4, atol=1e-4)) - - def test_vol(self): - x_orig, _ = torchaudio.load(self.test_filepath) - - for gain, gain_type in ((1.1, "amplitude"), (2, "db"), (2, "power")): - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("vol", [gain, gain_type]) - x, sr = E.sox_build_flow_effects() - - vol = torchaudio.transforms.Vol(gain, gain_type) - z = vol(x_orig) - # check if effect worked - self.assertTrue(x.allclose(z, rtol=1e-4, atol=1e-4)) - - def test_vad(self): - sample_files = [ - common_utils.get_asset_path("vad-go-stereo-44100.wav"), - common_utils.get_asset_path("vad-go-mono-32000.wav") - ] - - for sample_file in sample_files: - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(sample_file) - E.append_effect_to_chain("vad") - x, _ = E.sox_build_flow_effects() - - x_orig, sample_rate = torchaudio.load(sample_file) - vad = torchaudio.transforms.Vad(sample_rate) - - y = vad(x_orig) - self.assertTrue(x.allclose(y, rtol=1e-4, atol=1e-4)) - - -if __name__ == '__main__': - unittest.main() diff --git a/test/sox_effect/test_dataset.py b/test/sox_effect/test_dataset.py new file mode 100644 index 0000000000..f1b71eb806 --- /dev/null +++ b/test/sox_effect/test_dataset.py @@ -0,0 +1,114 @@ +from typing import List, Tuple + +import numpy as np +import torch +import torchaudio + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExtension, + get_whitenoise, + save_wav, +) + + +class RandomPerturbationFile(torch.utils.data.Dataset): + """Given flist, apply random speed perturbation""" + def __init__(self, flist: List[str], sample_rate: int): + super().__init__() + self.flist = flist + self.sample_rate = sample_rate + self.rng = None + + def __getitem__(self, index): + speed = self.rng.uniform(0.5, 2.0) + effects = [ + ['gain', '-n', '-10'], + ['speed', f'{speed:.5f}'], # duration of data is 0.5 ~ 2.0 seconds. + ['rate', f'{self.sample_rate}'], + ['pad', '0', '1.5'], # add 1.5 seconds silence at the end + ['trim', '0', '2'], # get the first 2 seconds + ] + data, _ = torchaudio.sox_effects.apply_effects_file(self.flist[index], effects) + return data + + def __len__(self): + return len(self.flist) + + +class RandomPerturbationTensor(torch.utils.data.Dataset): + """Apply speed purturbation to (synthetic) Tensor data""" + def __init__(self, signals: List[Tuple[torch.Tensor, int]], sample_rate: int): + super().__init__() + self.signals = signals + self.sample_rate = sample_rate + self.rng = None + + def __getitem__(self, index): + speed = self.rng.uniform(0.5, 2.0) + effects = [ + ['gain', '-n', '-10'], + ['speed', f'{speed:.5f}'], # duration of data is 0.5 ~ 2.0 seconds. + ['rate', f'{self.sample_rate}'], + ['pad', '0', '1.5'], # add 1.5 seconds silence at the end + ['trim', '0', '2'], # get the first 2 seconds + ] + tensor, sample_rate = self.signals[index] + data, _ = torchaudio.sox_effects.apply_effects_tensor(tensor, sample_rate, effects) + return data + + def __len__(self): + return len(self.signals) + + +def init_random_seed(worker_id): + dataset = torch.utils.data.get_worker_info().dataset + dataset.rng = np.random.RandomState(worker_id) + + +@skipIfNoExtension +class TestSoxEffectsDataset(TempDirMixin, PytorchTestCase): + """Test `apply_effects_file` in multi-process dataloader setting""" + + def _generate_dataset(self, num_samples=128): + flist = [] + for i in range(num_samples): + sample_rate = np.random.choice([8000, 16000, 44100]) + dtype = np.random.choice(['float32', 'int32', 'int16', 'uint8']) + data = get_whitenoise(n_channels=2, sample_rate=sample_rate, duration=1, dtype=dtype) + path = self.get_temp_path(f'{i:03d}_{dtype}_{sample_rate}.wav') + save_wav(path, data, sample_rate) + flist.append(path) + return flist + + def test_apply_effects_file(self): + sample_rate = 12000 + flist = self._generate_dataset() + dataset = RandomPerturbationFile(flist, sample_rate) + loader = torch.utils.data.DataLoader( + dataset, batch_size=32, num_workers=16, + worker_init_fn=init_random_seed, + ) + for batch in loader: + assert batch.shape == (32, 2, 2 * sample_rate) + + def _generate_signals(self, num_samples=128): + signals = [] + for _ in range(num_samples): + sample_rate = np.random.choice([8000, 16000, 44100]) + data = get_whitenoise( + n_channels=2, sample_rate=sample_rate, duration=1, dtype='float32') + signals.append((data, sample_rate)) + return signals + + def test_apply_effects_tensor(self): + sample_rate = 12000 + signals = self._generate_signals() + dataset = RandomPerturbationTensor(signals, sample_rate) + loader = torch.utils.data.DataLoader( + dataset, batch_size=32, num_workers=16, + worker_init_fn=init_random_seed, + ) + for batch in loader: + assert batch.shape == (32, 2, 2 * sample_rate) diff --git a/test/sox_effect/test_sox_effect.py b/test/sox_effect/test_sox_effect.py new file mode 100644 index 0000000000..38ab0fa772 --- /dev/null +++ b/test/sox_effect/test_sox_effect.py @@ -0,0 +1,221 @@ +import itertools + +from torchaudio import sox_effects +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExtension, + get_sinusoid, + get_wav_data, + save_wav, + load_wav, + sox_utils, +) +from .common import ( + load_params, + name_func, +) + + +@skipIfNoExtension +class TestSoxEffects(PytorchTestCase): + def test_init(self): + """Calling init_sox_effects multiple times does not crush""" + for _ in range(3): + sox_effects.init_sox_effects() + + +@skipIfNoExtension +class TestSoxEffectsTensor(TempDirMixin, PytorchTestCase): + """Test suite for `apply_effects_tensor` function""" + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2, 4, 8], + [True, False] + )), name_func=name_func) + def test_apply_no_effect(self, dtype, sample_rate, num_channels, channels_first): + """`apply_effects_tensor` without effects should return identical data as input""" + original = get_wav_data(dtype, num_channels, channels_first=channels_first) + expected = original.clone() + found, output_sample_rate = sox_effects.apply_effects_tensor( + expected, sample_rate, [], channels_first) + + assert output_sample_rate == sample_rate + # SoxEffect should not alter the input Tensor object + self.assertEqual(original, expected) + # SoxEffect should not return the same Tensor object + assert expected is not found + # Returned Tensor should equal to the input Tensor + self.assertEqual(expected, found) + + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects(self, args): + """`apply_effects_tensor` should return identical data as sox command""" + effects = args['effects'] + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + output_sr = args.get("output_sample_rate") + + input_path = self.get_temp_path('input.wav') + reference_path = self.get_temp_path('reference.wav') + + original = get_sinusoid( + frequency=800, sample_rate=input_sr, + n_channels=num_channels, dtype='float32') + save_wav(input_path, original, input_sr) + sox_utils.run_sox_effect( + input_path, reference_path, effects, output_sample_rate=output_sr) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_tensor(original, input_sr, effects) + + assert sr == expected_sr + self.assertEqual(expected, found) + + +@skipIfNoExtension +class TestSoxEffectsFile(TempDirMixin, PytorchTestCase): + """Test suite for `apply_effects_file` function""" + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2, 4, 8], + [False, True], + )), name_func=name_func) + def test_apply_no_effect(self, dtype, sample_rate, num_channels, channels_first): + """`apply_effects_file` without effects should return identical data as input""" + path = self.get_temp_path('input.wav') + expected = get_wav_data(dtype, num_channels, channels_first=channels_first) + save_wav(path, expected, sample_rate, channels_first=channels_first) + + found, output_sample_rate = sox_effects.apply_effects_file( + path, [], normalize=False, channels_first=channels_first) + + assert output_sample_rate == sample_rate + self.assertEqual(expected, found) + + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects(self, args): + """`apply_effects_file` should return identical data as sox command""" + dtype = 'int32' + channels_first = True + effects = args['effects'] + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + output_sr = args.get("output_sample_rate") + + input_path = self.get_temp_path('input.wav') + reference_path = self.get_temp_path('reference.wav') + data = get_wav_data(dtype, num_channels, channels_first=channels_first) + save_wav(input_path, data, input_sr, channels_first=channels_first) + sox_utils.run_sox_effect( + input_path, reference_path, effects, output_sample_rate=output_sr) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + input_path, effects, normalize=False, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected) + + +@skipIfNoExtension +class TestFileFormats(TempDirMixin, PytorchTestCase): + """`apply_effects_file` gives the same result as sox on various file formats""" + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') + def test_wav(self, dtype, sample_rate, num_channels): + """`apply_effects_file` works on various wav format""" + channels_first = True + effects = [['band', '300', '10']] + + input_path = self.get_temp_path('input.wav') + reference_path = self.get_temp_path('reference.wav') + data = get_wav_data(dtype, num_channels, channels_first=channels_first) + save_wav(input_path, data, sample_rate, channels_first=channels_first) + sox_utils.run_sox_effect(input_path, reference_path, effects) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + input_path, effects, normalize=False, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') + def test_mp3(self, sample_rate, num_channels): + """`apply_effects_file` works on various mp3 format""" + channels_first = True + effects = [['band', '300', '10']] + + input_path = self.get_temp_path('input.mp3') + reference_path = self.get_temp_path('reference.wav') + sox_utils.gen_audio_file(input_path, sample_rate, num_channels) + sox_utils.run_sox_effect(input_path, reference_path, effects) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + input_path, effects, channels_first=channels_first) + save_wav(self.get_temp_path('result.wav'), found, sr, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected, atol=1e-4, rtol=1e-8) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') + def test_flac(self, sample_rate, num_channels): + """`apply_effects_file` works on various flac format""" + channels_first = True + effects = [['band', '300', '10']] + + input_path = self.get_temp_path('input.flac') + reference_path = self.get_temp_path('reference.wav') + sox_utils.gen_audio_file(input_path, sample_rate, num_channels) + sox_utils.run_sox_effect(input_path, reference_path, effects, output_bitdepth=32) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + input_path, effects, channels_first=channels_first) + save_wav(self.get_temp_path('result.wav'), found, sr, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') + def test_vorbis(self, sample_rate, num_channels): + """`apply_effects_file` works on various vorbis format""" + channels_first = True + effects = [['band', '300', '10']] + + input_path = self.get_temp_path('input.vorbis') + reference_path = self.get_temp_path('reference.wav') + sox_utils.gen_audio_file(input_path, sample_rate, num_channels) + sox_utils.run_sox_effect(input_path, reference_path, effects, output_bitdepth=32) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + input_path, effects, channels_first=channels_first) + save_wav(self.get_temp_path('result.wav'), found, sr, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected) diff --git a/test/sox_effect/test_torchscript.py b/test/sox_effect/test_torchscript.py new file mode 100644 index 0000000000..ded923ab11 --- /dev/null +++ b/test/sox_effect/test_torchscript.py @@ -0,0 +1,100 @@ +from typing import List + +import torch +from torchaudio import sox_effects +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExtension, + get_sinusoid, + save_wav, +) +from .common import ( + load_params, +) + + +class SoxEffectTensorTransform(torch.nn.Module): + effects: List[List[str]] + + def __init__(self, effects: List[List[str]], sample_rate: int, channels_first: bool): + super().__init__() + self.effects = effects + self.sample_rate = sample_rate + self.channels_first = channels_first + + def forward(self, tensor: torch.Tensor): + return sox_effects.apply_effects_tensor( + tensor, self.sample_rate, self.effects, self.channels_first) + + +class SoxEffectFileTransform(torch.nn.Module): + effects: List[List[str]] + channels_first: bool + + def __init__(self, effects: List[List[str]], channels_first: bool): + super().__init__() + self.effects = effects + self.channels_first = channels_first + + def forward(self, path: str): + return sox_effects.apply_effects_file(path, self.effects, self.channels_first) + + +@skipIfNoExtension +class TestTorchScript(TempDirMixin, PytorchTestCase): + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects_tensor(self, args): + effects = args['effects'] + channels_first = True + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + + trans = SoxEffectTensorTransform(effects, input_sr, channels_first) + + path = self.get_temp_path('sox_effect.zip') + torch.jit.script(trans).save(path) + trans = torch.jit.load(path) + + wav = get_sinusoid( + frequency=800, sample_rate=input_sr, + n_channels=num_channels, dtype='float32', channels_first=channels_first) + found, sr_found = trans(wav) + expected, sr_expected = sox_effects.apply_effects_tensor( + wav, input_sr, effects, channels_first) + + assert sr_found == sr_expected + self.assertEqual(expected, found) + + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects_file(self, args): + effects = args['effects'] + channels_first = True + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + + trans = SoxEffectFileTransform(effects, channels_first) + + path = self.get_temp_path('sox_effect.zip') + torch.jit.script(trans).save(path) + trans = torch.jit.load(path) + + path = self.get_temp_path('input.wav') + wav = get_sinusoid( + frequency=800, sample_rate=input_sr, + n_channels=num_channels, dtype='float32', channels_first=channels_first) + save_wav(path, wav, sample_rate=input_sr, channels_first=channels_first) + + found, sr_found = trans(path) + expected, sr_expected = sox_effects.apply_effects_file(path, effects, channels_first) + + assert sr_found == sr_expected + self.assertEqual(expected, found) diff --git a/test/sox_io_backend/smoke_test.py b/test/sox_io_backend/smoke_test.py new file mode 100644 index 0000000000..1ae9a8d938 --- /dev/null +++ b/test/sox_io_backend/smoke_test.py @@ -0,0 +1,87 @@ +import itertools +import unittest + +from torchaudio.utils import sox_utils +from torchaudio.backend import sox_io_backend +from torchaudio._internal.module_utils import is_module_available +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + skipIfNoExtension, + get_wav_data, +) +from .common import name_func + + +skipIfNoMP3 = unittest.skipIf( + not is_module_available('torchaudio._torchaudio') or + 'mp3' not in sox_utils.list_read_formats() or + 'mp3' not in sox_utils.list_write_formats(), + '"sox_io" backend does not support MP3') + + +@skipIfNoExtension +class SmokeTest(TempDirMixin, TorchaudioTestCase): + """Run smoke test on various audio format + + The purpose of this test suite is to verify that sox_io_backend functionalities do not exhibit + abnormal behaviors. + + This test suite should be able to run without any additional tools (such as sox command), + however without such tools, the correctness of each function cannot be verified. + """ + def run_smoke_test(self, ext, sample_rate, num_channels, *, compression=None, dtype='float32'): + duration = 1 + num_frames = sample_rate * duration + path = self.get_temp_path(f'test.{ext}') + original = get_wav_data(dtype, num_channels, normalize=False, num_frames=num_frames) + + # 1. run save + sox_io_backend.save(path, original, sample_rate, compression=compression) + # 2. run info + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_channels == num_channels + # 3. run load + loaded, sr = sox_io_backend.load(path, normalize=False) + assert sr == sample_rate + assert loaded.shape[0] == num_channels + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels): + """Run smoke test on wav format""" + self.run_smoke_test('wav', sample_rate, num_channels, dtype=dtype) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-4.2, -0.2, 0, 0.2, 96, 128, 160, 192, 224, 256, 320], + ))) + @skipIfNoMP3 + def test_mp3(self, sample_rate, num_channels, bit_rate): + """Run smoke test on mp3 format""" + self.run_smoke_test('mp3', sample_rate, num_channels, compression=bit_rate) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-1, 0, 1, 2, 3, 3.6, 5, 10], + ))) + def test_vorbis(self, sample_rate, num_channels, quality_level): + """Run smoke test on vorbis format""" + self.run_smoke_test('vorbis', sample_rate, num_channels, compression=quality_level) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """Run smoke test on flac format""" + self.run_smoke_test('flac', sample_rate, num_channels, compression=compression_level) diff --git a/test/test_batch_consistency.py b/test/test_batch_consistency.py index 2a88598f88..e7040fdc0b 100644 --- a/test/test_batch_consistency.py +++ b/test/test_batch_consistency.py @@ -1,5 +1,7 @@ """Test numerical consistency among single input and batched input.""" import unittest +import itertools +from parameterized import parameterized import torch import torchaudio @@ -47,17 +49,15 @@ def test_griffinlim(self): F.griffinlim, tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, 0, atol=5e-5 ) - def test_detect_pitch_frequency(self): - filenames = [ - 'steam-train-whistle-daniel_simon.wav', # 2ch 44100Hz - # Files from https://www.mediacollege.com/audio/tone/download/ - '100Hz_44100Hz_16bit_05sec.wav', # 1ch - '440Hz_44100Hz_16bit_05sec.wav', # 1ch - ] - for filename in filenames: - filepath = common_utils.get_asset_path(filename) - waveform, sample_rate = torchaudio.load(filepath) - self.assert_batch_consistencies(F.detect_pitch_frequency, waveform, sample_rate) + @parameterized.expand(list(itertools.product( + [100, 440], + [8000, 16000, 44100], + [1, 2], + )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') + def test_detect_pitch_frequency(self, frequency, sample_rate, n_channels): + waveform = common_utils.get_sinusoid(frequency=frequency, sample_rate=sample_rate, + n_channels=n_channels, duration=5) + self.assert_batch_consistencies(F.detect_pitch_frequency, waveform, sample_rate) def test_istft(self): stft = torch.tensor([ @@ -80,8 +80,10 @@ def test_overdrive(self): self.assert_batch_consistencies(F.overdrive, waveform, gain=45, colour=30) def test_phaser(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath) + sample_rate = 44100 + waveform = common_utils.get_whitenoise( + sample_rate=sample_rate, duration=5, + ) self.assert_batch_consistencies(F.phaser, waveform, sample_rate) def test_flanger(self): @@ -286,7 +288,3 @@ def test_batch_Vol(self): # Batch then transform computed = torchaudio.transforms.Vol(gain=1.1)(waveform.repeat(3, 1, 1)) self.assertEqual(computed, expected) - - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_compliance_kaldi.py b/test/test_compliance_kaldi.py index f9e097e09c..450183d6b3 100644 --- a/test/test_compliance_kaldi.py +++ b/test/test_compliance_kaldi.py @@ -47,14 +47,25 @@ def first_sample_of_frame(frame, window_size, window_shift, snip_edges): @common_utils.skipIfNoSoxBackend -class Test_Kaldi(common_utils.TorchaudioTestCase): +class Test_Kaldi(common_utils.TempDirMixin, common_utils.TorchaudioTestCase): backend = 'sox' - test_filepath = common_utils.get_asset_path('kaldi_file.wav') - test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav') kaldi_output_dir = common_utils.get_asset_path('kaldi') + test_filepath = common_utils.get_asset_path('kaldi_file.wav') test_filepaths = {prefix: [] for prefix in compliance_utils.TEST_PREFIX} + def setUp(self): + super().setUp() + + # 1. test signal for testing resampling + self.test1_signal_sr = 16000 + self.test1_signal = common_utils.get_whitenoise( + sample_rate=self.test1_signal_sr, duration=0.5, + ) + + # 2. test audio file corresponding to saved kaldi ark files + self.test2_filepath = common_utils.get_asset_path('kaldi_file_8000.wav') + # separating test files by their types (e.g 'spec', 'fbank', etc.) for f in os.listdir(kaldi_output_dir): dash_idx = f.find('-') @@ -94,7 +105,6 @@ def test_get_strided(self): def _create_data_set(self): # used to generate the dataset to test on. this is not used in testing (offline procedure) - test_filepath = common_utils.get_asset_path('kaldi_file.wav') sr = 16000 x = torch.arange(0, 20).float() # between [-6,6] @@ -103,8 +113,8 @@ def _create_data_set(self): y = (y / 6 * (1 << 30)).long() # clear the last 16 bits because they aren't used anyways y = ((y >> 16) << 16).float() - torchaudio.save(test_filepath, y, sr) - sound, sample_rate = torchaudio.load(test_filepath, normalization=False) + torchaudio.save(self.test_filepath, y, sr) + sound, sample_rate = torchaudio.load(self.test_filepath, normalization=False) print(y >> 16) self.assertTrue(sample_rate == sr) torch.testing.assert_allclose(y, sound) @@ -123,7 +133,7 @@ def _print_diagnostic(self, output, expect_output): print('relative_mse:', relative_mse.item(), 'relative_max_error:', relative_max_error.item()) def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_files, - expected_num_args, get_output_fn, atol=1e-5, rtol=1e-8): + expected_num_args, get_output_fn, atol=1e-5, rtol=1e-7): """ Inputs: sound_filepath (str): The location of the sound file @@ -135,7 +145,7 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil atol (float): absolute tolerance rtol (float): relative tolerance """ - sound, sample_rate = torchaudio.load_wav(sound_filepath) + sound, sr = torchaudio.load_wav(sound_filepath) files = self.test_filepaths[filepath_key] assert len(files) == expected_num_files, ('number of kaldi %s file changed to %d' % (filepath_key, len(files))) @@ -170,22 +180,19 @@ def get_output_fn(sound, args): output = kaldi.resample_waveform(sound, args[1], args[2]) return output - self._compliance_test_helper(self.test_8000_filepath, 'resample', 32, 3, get_output_fn, atol=1e-2, rtol=1e-5) + self._compliance_test_helper(self.test2_filepath, 'resample', 32, 3, get_output_fn, atol=1e-2, rtol=1e-5) def test_resample_waveform_upsample_size(self): - sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath) - upsample_sound = kaldi.resample_waveform(sound, sample_rate, sample_rate * 2) - self.assertTrue(upsample_sound.size(-1) == sound.size(-1) * 2) + upsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr * 2) + self.assertTrue(upsample_sound.size(-1) == self.test1_signal.size(-1) * 2) def test_resample_waveform_downsample_size(self): - sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath) - downsample_sound = kaldi.resample_waveform(sound, sample_rate, sample_rate // 2) - self.assertTrue(downsample_sound.size(-1) == sound.size(-1) // 2) + downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr // 2) + self.assertTrue(downsample_sound.size(-1) == self.test1_signal.size(-1) // 2) def test_resample_waveform_identity_size(self): - sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath) - downsample_sound = kaldi.resample_waveform(sound, sample_rate, sample_rate) - self.assertTrue(downsample_sound.size(-1) == sound.size(-1)) + downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr) + self.assertTrue(downsample_sound.size(-1) == self.test1_signal.size(-1)) def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_factor=None, atol=1e-1, rtol=1e-4): @@ -226,20 +233,16 @@ def test_resample_waveform_upsample_accuracy(self): def test_resample_waveform_multi_channel(self): num_channels = 3 - sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath) # (1, 8000) - multi_sound = sound.repeat(num_channels, 1) # (num_channels, 8000) + multi_sound = self.test1_signal.repeat(num_channels, 1) # (num_channels, 8000 smp) for i in range(num_channels): multi_sound[i, :] *= (i + 1) * 1.5 - multi_sound_sampled = kaldi.resample_waveform(multi_sound, sample_rate, sample_rate // 2) + multi_sound_sampled = kaldi.resample_waveform(multi_sound, self.test1_signal_sr, self.test1_signal_sr // 2) # check that sampling is same whether using separately or in a tensor of size (c, n) for i in range(num_channels): - single_channel = sound * (i + 1) * 1.5 - single_channel_sampled = kaldi.resample_waveform(single_channel, sample_rate, sample_rate // 2) - torch.testing.assert_allclose(multi_sound_sampled[i, :], single_channel_sampled[0], rtol=1e-4, atol=1e-8) - - -if __name__ == '__main__': - unittest.main() + single_channel = self.test1_signal * (i + 1) * 1.5 + single_channel_sampled = kaldi.resample_waveform(single_channel, self.test1_signal_sr, + self.test1_signal_sr // 2) + torch.testing.assert_allclose(multi_sound_sampled[i, :], single_channel_sampled[0], rtol=1e-4, atol=1e-7) diff --git a/test/test_dataloader.py b/test/test_dataloader.py index 1bf73ebb64..6d1518b70c 100644 --- a/test/test_dataloader.py +++ b/test/test_dataloader.py @@ -37,7 +37,3 @@ def test_1(self): dl = DataLoader(ds, batch_size=2) for x in dl: self.assertTrue(x.size() == expected_size) - - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_io.py b/test/test_io.py index 2c8aece85e..b55885059a 100644 --- a/test/test_io.py +++ b/test/test_io.py @@ -282,6 +282,3 @@ def _test_5_get_info(self): self.assertEqual(si.length, samples) self.assertEqual(si.rate, rate) self.assertEqual(ei.bits_per_sample, precision) - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_kaldi_io.py b/test/test_kaldi_io.py index 4bd0fd56a4..80e6b3dc5a 100644 --- a/test/test_kaldi_io.py +++ b/test/test_kaldi_io.py @@ -33,7 +33,3 @@ def test_read_vec_flt_ark(self): def test_read_mat_ark(self): self._test_helper("mat.ark", [self.data1, self.data2], kio.read_mat_ark, torch.float32) - - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_librosa_compatibility.py b/test/test_librosa_compatibility.py index aa933535e8..01b0f23732 100644 --- a/test/test_librosa_compatibility.py +++ b/test/test_librosa_compatibility.py @@ -270,16 +270,13 @@ def test_basics4(self): } self.assert_compatibilities(**kwargs) - @unittest.skipIf(not common_utils.BACKENDS_MP3, 'no backend to read mp3') def test_MelScale(self): """MelScale transform is comparable to that of librosa""" n_fft = 2048 n_mels = 256 hop_length = n_fft // 4 - - # Prepare spectrogram input. We use torchaudio to compute one. - common_utils.set_audio_backend('default') - sound, sample_rate = _load_audio_asset('whitenoise_1min.mp3') + sample_rate = 44100 + sound = common_utils.get_whitenoise(sample_rate=sample_rate, duration=60) sound = sound.mean(dim=0, keepdim=True) spec_ta = F.spectrogram( sound, pad=0, window=torch.hann_window(n_fft), n_fft=n_fft, @@ -347,7 +344,3 @@ def test_InverseMelScale(self): # torch.dist(spec_lr, spec_ta, p=1) # >>> tensor(943.2759) assert torch.dist(spec_orig, spec_ta, p=1) < threshold - - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_models.py b/test/test_models.py index c54a57cebd..10ca5c827b 100644 --- a/test/test_models.py +++ b/test/test_models.py @@ -74,7 +74,12 @@ def test_waveform(self): for upsample_scale in upsample_scales: total_scale *= upsample_scale - model = _UpsampleNetwork(upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size) + model = _UpsampleNetwork(upsample_scales, + n_res_block, + n_freq, + n_hidden, + n_output, + kernel_size) x = torch.rand(n_batch, n_freq, n_time) out1, out2 = model(x) @@ -86,14 +91,13 @@ def test_waveform(self): class TestWaveRNN(common_utils.TorchaudioTestCase): def test_waveform(self): - """Validate the output dimensions of a _WaveRNN model in waveform mode. + """Validate the output dimensions of a _WaveRNN model. """ upsample_scales = [5, 5, 8] n_rnn = 512 n_fc = 512 - n_bits = 9 - sample_rate = 24000 + n_classes = 512 hop_length = 200 n_batch = 2 n_time = 200 @@ -102,41 +106,12 @@ def test_waveform(self): n_res_block = 10 n_hidden = 128 kernel_size = 5 - mode = 'waveform' - model = _WaveRNN(upsample_scales, n_bits, sample_rate, hop_length, n_res_block, - n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output, mode) + model = _WaveRNN(upsample_scales, n_classes, hop_length, n_res_block, + n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output) x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1)) mels = torch.rand(n_batch, 1, n_freq, n_time) out = model(x, mels) - assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), 2 ** n_bits) - - def test_mol(self): - """Validate the output dimensions of a _WaveRNN model in mol mode. - """ - - upsample_scales = [5, 5, 8] - n_rnn = 512 - n_fc = 512 - n_bits = 9 - sample_rate = 24000 - hop_length = 200 - n_batch = 2 - n_time = 200 - n_freq = 100 - n_output = 256 - n_res_block = 10 - n_hidden = 128 - kernel_size = 5 - mode = 'mol' - - model = _WaveRNN(upsample_scales, n_bits, sample_rate, hop_length, n_res_block, - n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output, mode) - - x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1)) - mels = torch.rand(n_batch, 1, n_freq, n_time) - out = model(x, mels) - - assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), 30) + assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), n_classes) diff --git a/test/test_sox_compatibility.py b/test/test_sox_compatibility.py index a5d9f8633c..260c435b83 100644 --- a/test/test_sox_compatibility.py +++ b/test/test_sox_compatibility.py @@ -1,492 +1,296 @@ import unittest import torch -import torchaudio import torchaudio.functional as F import torchaudio.transforms as T - -from . import common_utils - - -@common_utils.skipIfNoSoxBackend -class TestFunctionalFiltering(common_utils.TempDirMixin, common_utils.TorchaudioTestCase): - backend = 'sox' - - def setUp(self): - # 1. Create int16 signal to save as PCM wav - # 2. Write to temp file - # 3. Load temp file into tensor to reuse in downstream tests - # Prefer to use common_utils.load_wav() but this implementation does - # not match torchaudio.load and errors on downstream tests - super().setUp() - - self.NOISE_SAMPLE_RATE = 44100 # N.B. 44.1 kHz required by SoX deemph effect - noise_waveform_as_int = common_utils.get_whitenoise( - sample_rate=self.NOISE_SAMPLE_RATE, duration=5, dtype=torch.int16, scale_factor=0.9, +from parameterized import parameterized + +from .common_utils import ( + skipIfNoSoxBackend, + skipIfNoExec, + TempDirMixin, + TorchaudioTestCase, + get_asset_path, + sox_utils, + load_wav, + save_wav, + get_whitenoise, +) + + +@skipIfNoSoxBackend +@skipIfNoExec('sox') +class TestFunctionalFiltering(TempDirMixin, TorchaudioTestCase): + def run_sox_effect(self, input_file, effect): + output_file = self.get_temp_path('expected.wav') + sox_utils.run_sox_effect(input_file, output_file, [str(e) for e in effect]) + return load_wav(output_file) + + def assert_sox_effect(self, result, input_path, effects, atol=1e-04, rtol=1e-5): + expected, _ = self.run_sox_effect(input_path, effects) + self.assertEqual(result, expected, atol=atol, rtol=rtol) + + def get_whitenoise(self, sample_rate=8000): + noise = get_whitenoise( + sample_rate=sample_rate, duration=3, scale_factor=0.9, ) - self.noise_filepath = self.get_temp_path("whitenoise.wav") - common_utils.save_wav( - self.noise_filepath, noise_waveform_as_int, self.NOISE_SAMPLE_RATE - ) - self.noise_waveform, _ = torchaudio.load(self.noise_filepath, normalization=True) + path = self.get_temp_path("whitenoise.wav") + save_wav(path, noise, sample_rate) + return noise, path def test_gain(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = common_utils.load_wav(test_filepath) - - waveform_gain = F.gain(waveform, 3) - self.assertTrue(waveform_gain.abs().max().item(), 1.) - - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(test_filepath) - E.append_effect_to_chain("gain", [3]) - sox_gain_waveform = E.sox_build_flow_effects()[0] - - self.assertEqual(waveform_gain, sox_gain_waveform, atol=1e-04, rtol=1e-5) + path = get_asset_path('steam-train-whistle-daniel_simon.wav') + data, _ = load_wav(path) + result = F.gain(data, 3) + self.assert_sox_effect(result, path, ['gain', 3]) def test_dither(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = common_utils.load_wav(test_filepath) - - waveform_dithered = F.dither(waveform) - waveform_dithered_noiseshaped = F.dither(waveform, noise_shaping=True) + path = get_asset_path('steam-train-whistle-daniel_simon.wav') + data, _ = load_wav(path) + result = F.dither(data) + self.assert_sox_effect(result, path, ['dither']) - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(test_filepath) - E.append_effect_to_chain("dither", []) - sox_dither_waveform = E.sox_build_flow_effects()[0] - - self.assertEqual(waveform_dithered, sox_dither_waveform, atol=1e-04, rtol=1e-5) - E.clear_chain() - - E.append_effect_to_chain("dither", ["-s"]) - sox_dither_waveform_ns = E.sox_build_flow_effects()[0] - - self.assertEqual(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02, rtol=1e-5) - - def test_vctk_transform_pipeline(self): - test_filepath_vctk = common_utils.get_asset_path('VCTK-Corpus', 'wav48', 'p224', 'p224_002.wav') - wf_vctk, sr_vctk = common_utils.load_wav(test_filepath_vctk) - - # rate - sample = T.Resample(sr_vctk, 16000, resampling_method='sinc_interpolation') - wf_vctk = sample(wf_vctk) - # dither - wf_vctk = F.dither(wf_vctk, noise_shaping=True) - - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(test_filepath_vctk) - E.append_effect_to_chain("gain", ["-h"]) - E.append_effect_to_chain("channels", [1]) - E.append_effect_to_chain("rate", [16000]) - E.append_effect_to_chain("gain", ["-rh"]) - E.append_effect_to_chain("dither", ["-s"]) - wf_vctk_sox = E.sox_build_flow_effects()[0] - - self.assertEqual(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) + def test_dither_noise(self): + path = get_asset_path('steam-train-whistle-daniel_simon.wav') + data, _ = load_wav(path) + result = F.dither(data, noise_shaping=True) + self.assert_sox_effect(result, path, ['dither', '-s'], atol=1.5e-4) def test_lowpass(self): - """ - Test biquad lowpass filter, compare to SoX implementation - """ cutoff_freq = 3000 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("lowpass", [cutoff_freq]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.lowpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, cutoff_freq) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.lowpass_biquad(data, sample_rate, cutoff_freq) + self.assert_sox_effect(result, path, ['lowpass', cutoff_freq], atol=1.5e-4) def test_highpass(self): - """ - Test biquad highpass filter, compare to SoX implementation - """ cutoff_freq = 2000 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("highpass", [cutoff_freq]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.highpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, cutoff_freq) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-3, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.highpass_biquad(data, sample_rate, cutoff_freq) + self.assert_sox_effect(result, path, ['highpass', cutoff_freq], atol=1.5e-4) def test_allpass(self): - """ - Test biquad allpass filter, compare to SoX implementation - """ central_freq = 1000 q = 0.707 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("allpass", [central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.allpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.allpass_biquad(data, sample_rate, central_freq, q) + self.assert_sox_effect(result, path, ['allpass', central_freq, f'{q}q']) def test_bandpass_with_csg(self): - """ - Test biquad bandpass filter, compare to SoX implementation - """ central_freq = 1000 q = 0.707 const_skirt_gain = True + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("bandpass", ["-c", central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.bandpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, - central_freq, q, const_skirt_gain) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.bandpass_biquad(data, sample_rate, central_freq, q, const_skirt_gain) + self.assert_sox_effect(result, path, ['bandpass', '-c', central_freq, f'{q}q']) def test_bandpass_without_csg(self): - """ - Test biquad bandpass filter, compare to SoX implementation - """ central_freq = 1000 q = 0.707 const_skirt_gain = False + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("bandpass", [central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.bandpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, - central_freq, q, const_skirt_gain) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.bandpass_biquad(data, sample_rate, central_freq, q, const_skirt_gain) + self.assert_sox_effect(result, path, ['bandpass', central_freq, f'{q}q']) def test_bandreject(self): - """ - Test biquad bandreject filter, compare to SoX implementation - """ central_freq = 1000 q = 0.707 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("bandreject", [central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.bandreject_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, - central_freq, q) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.bandreject_biquad(data, sample_rate, central_freq, q) + self.assert_sox_effect(result, path, ['bandreject', central_freq, f'{q}q']) def test_band_with_noise(self): - """ - Test biquad band filter with noise mode, compare to SoX implementation - """ central_freq = 1000 q = 0.707 noise = True + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("band", ["-n", central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.band_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q, noise) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.band_biquad(data, sample_rate, central_freq, q, noise) + self.assert_sox_effect(result, path, ['band', '-n', central_freq, f'{q}q']) def test_band_without_noise(self): - """ - Test biquad band filter without noise mode, compare to SoX implementation - """ - central_freq = 1000 q = 0.707 noise = False + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("band", [central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.band_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q, noise) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.band_biquad(data, sample_rate, central_freq, q, noise) + self.assert_sox_effect(result, path, ['band', central_freq, f'{q}q']) def test_treble(self): - """ - Test biquad treble filter, compare to SoX implementation - """ - central_freq = 1000 q = 0.707 gain = 40 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("treble", [gain, central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.treble_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, gain, central_freq, q) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.treble_biquad(data, sample_rate, gain, central_freq, q) + self.assert_sox_effect(result, path, ['treble', gain, central_freq, f'{q}q']) def test_bass(self): - """ - Test biquad bass filter, compare to SoX implementation - """ - central_freq = 1000 q = 0.707 gain = 40 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("bass", [gain, central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.bass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, gain, central_freq, q) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.bass_biquad(data, sample_rate, gain, central_freq, q) + self.assert_sox_effect(result, path, ['bass', gain, central_freq, f'{q}q'], atol=1.5e-4) def test_deemph(self): - """ - Test biquad deemph filter, compare to SoX implementation - """ - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("deemph") - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.deemph_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + sample_rate = 44100 + data, path = self.get_whitenoise(sample_rate) + result = F.deemph_biquad(data, sample_rate) + self.assert_sox_effect(result, path, ['deemph']) def test_riaa(self): - """ - Test biquad riaa filter, compare to SoX implementation - """ - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("riaa") - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.riaa_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + sample_rate = 44100 + data, path = self.get_whitenoise(sample_rate) + result = F.riaa_biquad(data, sample_rate) + self.assert_sox_effect(result, path, ['riaa']) def test_contrast(self): - """ - Test contrast effect, compare to SoX implementation - """ enhancement_amount = 80. - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("contrast", [enhancement_amount]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.contrast(self.noise_waveform, enhancement_amount) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise() + result = F.contrast(data, enhancement_amount) + self.assert_sox_effect(result, path, ['contrast', enhancement_amount]) def test_dcshift_with_limiter(self): - """ - Test dcshift effect, compare to SoX implementation - """ shift = 0.5 limiter_gain = 0.05 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("dcshift", [shift, limiter_gain]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.dcshift(self.noise_waveform, shift, limiter_gain) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise() + result = F.dcshift(data, shift, limiter_gain) + self.assert_sox_effect(result, path, ['dcshift', shift, limiter_gain]) def test_dcshift_without_limiter(self): - """ - Test dcshift effect, compare to SoX implementation - """ shift = 0.6 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("dcshift", [shift]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.dcshift(self.noise_waveform, shift) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise() + result = F.dcshift(data, shift) + self.assert_sox_effect(result, path, ['dcshift', shift]) def test_overdrive(self): - """ - Test overdrive effect, compare to SoX implementation - """ gain = 30 colour = 40 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("overdrive", [gain, colour]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.overdrive(self.noise_waveform, gain, colour) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise() + result = F.overdrive(data, gain, colour) + self.assert_sox_effect(result, path, ['overdrive', gain, colour]) def test_phaser_sine(self): - """ - Test phaser effect with sine moduldation, compare to SoX implementation - """ gain_in = 0.5 gain_out = 0.8 delay_ms = 2.0 decay = 0.4 speed = 0.5 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-s"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.phaser(self.noise_waveform, self.NOISE_SAMPLE_RATE, - gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.phaser(data, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) + self.assert_sox_effect(result, path, ['phaser', gain_in, gain_out, delay_ms, decay, speed, '-s']) def test_phaser_triangle(self): - """ - Test phaser effect with triangle modulation, compare to SoX implementation - """ gain_in = 0.5 gain_out = 0.8 delay_ms = 2.0 decay = 0.4 speed = 0.5 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-t"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.phaser(self.noise_waveform, self.NOISE_SAMPLE_RATE, - gain_in, gain_out, delay_ms, decay, speed, sinusoidal=False) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.phaser(data, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=False) + self.assert_sox_effect(result, path, ['phaser', gain_in, gain_out, delay_ms, decay, speed, '-t']) def test_flanger_triangle_linear(self): - """ - Test flanger effect with triangle modulation and linear interpolation, compare to SoX implementation - """ delay = 0.6 depth = 0.87 regen = 3.0 width = 0.9 speed = 0.5 phase = 30 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "linear"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, regen, - width, speed, phase, modulation='triangular', interpolation='linear') - - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.flanger( + data, sample_rate, delay, depth, regen, width, speed, phase, + modulation='triangular', interpolation='linear') + self.assert_sox_effect( + result, path, ['flanger', delay, depth, regen, width, speed, 'triangle', phase, 'linear']) def test_flanger_triangle_quad(self): - """ - Test flanger effect with triangle modulation and quadratic interpolation, compare to SoX implementation - """ delay = 0.8 depth = 0.88 regen = 3.0 width = 0.4 speed = 0.5 phase = 40 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "quadratic"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, - regen, width, speed, phase, modulation='triangular', interpolation='quadratic') - - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.flanger( + data, sample_rate, delay, depth, regen, width, speed, phase, + modulation='triangular', interpolation='quadratic') + self.assert_sox_effect( + result, path, ['flanger', delay, depth, regen, width, speed, 'triangle', phase, 'quadratic']) def test_flanger_sine_linear(self): - """ - Test flanger effect with sine modulation and linear interpolation, compare to SoX implementation - """ delay = 0.8 depth = 0.88 regen = 3.0 width = 0.23 speed = 1.3 phase = 60 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "linear"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, - regen, width, speed, phase, modulation='sinusoidal', interpolation='linear') - - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.flanger( + data, sample_rate, delay, depth, regen, width, speed, phase, + modulation='sinusoidal', interpolation='linear') + self.assert_sox_effect( + result, path, ['flanger', delay, depth, regen, width, speed, 'sine', phase, 'linear']) def test_flanger_sine_quad(self): - """ - Test flanger effect with sine modulation and quadratic interpolation, compare to SoX implementation - """ delay = 0.9 depth = 0.9 regen = 4.0 width = 0.23 speed = 1.3 phase = 25 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "quadratic"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, - regen, width, speed, phase, modulation='sinusoidal', interpolation='quadratic') - - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.flanger( + data, sample_rate, delay, depth, regen, width, speed, phase, + modulation='sinusoidal', interpolation='quadratic') + self.assert_sox_effect( + result, path, ['flanger', delay, depth, regen, width, speed, 'sine', phase, 'quadratic']) def test_equalizer(self): - """ - Test biquad peaking equalizer filter, compare to SoX implementation - """ - center_freq = 300 q = 0.707 gain = 1 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("equalizer", [center_freq, q, gain]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.equalizer_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, center_freq, gain, q) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.equalizer_biquad(data, sample_rate, center_freq, gain, q) + self.assert_sox_effect(result, path, ['equalizer', center_freq, q, gain]) def test_perf_biquad_filtering(self): - b0 = 0.4 b1 = 0.2 b2 = 0.9 @@ -494,18 +298,34 @@ def test_perf_biquad_filtering(self): a1 = 0.2 a2 = 0.6 - # SoX method - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("biquad", [b0, b1, b2, a0, a1, a2]) - waveform_sox_out, _ = E.sox_build_flow_effects() - - waveform_lfilter_out = F.lfilter( - self.noise_waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2]) - ) - - self.assertEqual(waveform_lfilter_out, waveform_sox_out, atol=1e-4, rtol=1e-5) - - -if __name__ == "__main__": - unittest.main() + data, path = self.get_whitenoise() + result = F.lfilter(data, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2])) + self.assert_sox_effect(result, path, ['biquad', b0, b1, b2, a0, a1, a2]) + + @parameterized.expand([ + ('q', 'quarter_sine'), + ('h', 'half_sine'), + ('t', 'linear'), + ]) + def test_fade(self, fade_shape_sox, fade_shape): + fade_in_len, fade_out_len = 44100, 44100 + data, path = self.get_whitenoise(sample_rate=44100) + result = T.Fade(fade_in_len, fade_out_len, fade_shape)(data) + self.assert_sox_effect(result, path, ['fade', fade_shape_sox, '1', '0', '1']) + + @parameterized.expand([ + ('amplitude', 1.1), + ('db', 2), + ('power', 2), + ]) + def test_vol(self, gain_type, gain): + data, path = self.get_whitenoise() + result = T.Vol(gain, gain_type)(data) + self.assert_sox_effect(result, path, ['vol', f'{gain}', gain_type]) + + @parameterized.expand(['vad-go-stereo-44100.wav', 'vad-go-mono-32000.wav']) + def test_vad(self, filename): + path = get_asset_path(filename) + data, sample_rate = load_wav(path) + result = T.Vad(sample_rate)(data) + self.assert_sox_effect(result, path, ['vad']) diff --git a/test/test_transforms.py b/test/test_transforms.py index 6df9562bc8..f6942e18a5 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -219,7 +219,3 @@ def test_compute_deltas_twochannel(self): computed = transform(specgram) assert computed.shape == expected.shape, (computed.shape, expected.shape) self.assertEqual(computed, expected, atol=1e-6, rtol=1e-8) - - -if __name__ == '__main__': - unittest.main() diff --git a/test/utils/__init__.py b/test/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/utils/test_sox_utils.py b/test/utils/test_sox_utils.py new file mode 100644 index 0000000000..55a46a235e --- /dev/null +++ b/test/utils/test_sox_utils.py @@ -0,0 +1,49 @@ +from torchaudio.utils import sox_utils + +from ..common_utils import ( + PytorchTestCase, + skipIfNoExtension, +) + + +@skipIfNoExtension +class TestSoxUtils(PytorchTestCase): + """Smoke tests for sox_util module""" + def test_set_seed(self): + """`set_seed` does not crush""" + sox_utils.set_seed(0) + + def test_set_verbosity(self): + """`set_verbosity` does not crush""" + for val in range(6, 0, -1): + sox_utils.set_verbosity(val) + + def test_set_buffer_size(self): + """`set_buffer_size` does not crush""" + sox_utils.set_buffer_size(131072) + # back to default + sox_utils.set_buffer_size(8192) + + def test_set_use_threads(self): + """`set_use_threads` does not crush""" + sox_utils.set_use_threads(True) + # back to default + sox_utils.set_use_threads(False) + + def test_list_effects(self): + """`list_effects` returns the list of available effects""" + effects = sox_utils.list_effects() + # We cannot infer what effects are available, so only check some of them. + assert 'highpass' in effects + assert 'phaser' in effects + assert 'gain' in effects + + def test_list_read_formats(self): + """`list_read_formats` returns the list of supported formats""" + formats = sox_utils.list_read_formats() + assert 'wav' in formats + + def test_list_write_formats(self): + """`list_write_formats` returns the list of supported formats""" + formats = sox_utils.list_write_formats() + assert 'opus' not in formats diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index 16419ff3ff..9a6a5e3c16 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -4,6 +4,7 @@ compliance, datasets, kaldi_io, + utils, sox_effects, transforms ) @@ -34,10 +35,10 @@ @_mod_utils.deprecated( "Please remove the function call to initialize_sox. " "Resource initialization is now automatically handled.") -def initialize_sox() -> int: +def initialize_sox(): """Initialize sox effects. - This function is deprecated. See ``torchaudio.sox_effects.init_sox_effects`` + This function is deprecated. See :py:func:`torchaudio.sox_effects.init_sox_effects` """ _init_sox_effects() @@ -50,6 +51,6 @@ def initialize_sox() -> int: def shutdown_sox(): """Shutdown sox effects. - This function is deprecated. See ``torchaudio.sox_effects.shutdown_sox_effects`` + This function is deprecated. See :py:func:`torchaudio.sox_effects.shutdown_sox_effects` """ _shutdown_sox_effects() diff --git a/torchaudio/_internal/module_utils.py b/torchaudio/_internal/module_utils.py index 575a76c1ed..31bc2f1a31 100644 --- a/torchaudio/_internal/module_utils.py +++ b/torchaudio/_internal/module_utils.py @@ -48,7 +48,7 @@ def decorator(func): def wrapped(*args, **kwargs): message = ( f'{func.__module__}.{func.__name__} has been deprecated ' - f'and will be removed from {"future" if version is None else version} release.' + f'and will be removed from {"future" if version is None else version} release. ' f'{direction}') warnings.warn(message, stacklevel=2) return func(*args, **kwargs) diff --git a/torchaudio/backend/common.py b/torchaudio/backend/common.py index 0593c34bd5..bc61df4746 100644 --- a/torchaudio/backend/common.py +++ b/torchaudio/backend/common.py @@ -2,6 +2,18 @@ class SignalInfo: + """Data class returned ``info`` functions. + + Used by :ref:`sox backend` and :ref:`soundfile backend` + + See https://fossies.org/dox/sox-14.4.2/structsox__signalinfo__t.html + + :ivar Optional[int] channels: The number of channels + :ivar Optional[float] rate: Sampleing rate + :ivar Optional[int] precision: Bit depth + :ivar Optional[int] length: For :ref:`sox backend`, the number of samples. + (frames * channels). For :ref:`soundfile backend`, the number of frames. + """ def __init__(self, channels: Optional[int] = None, rate: Optional[float] = None, @@ -14,6 +26,20 @@ def __init__(self, class EncodingInfo: + """Data class returned ``info`` functions. + + Used by :ref:`sox backend` and :ref:`soundfile backend` + + See https://fossies.org/dox/sox-14.4.2/structsox__encodinginfo__t.html + + :ivar Optional[int] encoding: sox_encoding_t + :ivar Optional[int] bits_per_sample: bit depth + :ivar Optional[float] compression: Compression option + :ivar Any reverse_bytes: + :ivar Any reverse_nibbles: + :ivar Any reverse_bits: + :ivar Optional[bool] opposite_endian: + """ def __init__(self, encoding: Any = None, bits_per_sample: Optional[int] = None, diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 4664f733c6..57d8db7723 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -7,6 +7,12 @@ class AudioMetaData: + """Data class to be returned by :py:func:`~torchaudio.backend.sox_io_backend.info`. + + :ivar int sample_rate: Sample rate + :ivar int num_frames: The number of frames + :ivar int num_channels: The number of channels + """ def __init__(self, sample_rate: int, num_frames: int, num_channels: int): self.sample_rate = sample_rate self.num_frames = num_frames @@ -15,7 +21,14 @@ def __init__(self, sample_rate: int, num_frames: int, num_channels: int): @_mod_utils.requires_module('torchaudio._torchaudio') def info(filepath: str) -> AudioMetaData: - """Get signal information of an audio file.""" + """Get signal information of an audio file. + + Args: + filepath (str): Path to audio file + + Returns: + AudioMetaData: meta data of the given audio. + """ sinfo = torch.ops.torchaudio.sox_io_get_info(filepath) return AudioMetaData(sinfo.get_sample_rate(), sinfo.get_num_frames(), sinfo.get_num_channels()) @@ -30,21 +43,28 @@ def load( ) -> Tuple[torch.Tensor, int]: """Load audio data from file. - This function can handle all the codecs that underlying libsox can handle, however note the - followings. - Note: - This function is tested on the following formats; - - WAV - - 32-bit floating-point - - 32-bit signed integer - - 16-bit signed integer - - 8-bit unsigned integer - - MP3 - - FLAC - - OGG/VORBIS - - By default, this function returns Tensor with ``float32`` dtype and the shape of ``[channel, time]``. + This function can handle all the codecs that underlying libsox can handle, + however it is tested on the following formats; + + * WAV + + * 32-bit floating-point + * 32-bit signed integer + * 16-bit signed integer + * 8-bit unsigned integer + + * MP3 + * FLAC + * OGG/VORBIS + * OPUS + + To load ``MP3``, ``FLAC``, ``OGG/VORBIS``, ``OPUS`` and other codecs ``libsox`` does not + handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` + and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc. + + By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with + ``float32`` dtype and the shape of ``[channel, time]``. The samples are normalized to fit in the range of ``[-1.0, 1.0]``. When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit @@ -54,24 +74,33 @@ def load( for 32-bit signed PCM, ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. ``normalize`` parameter has no effect on 32-bit floating-point WAV and other formats, such as - flac and mp3. For these formats, this function always returns ``float32`` Tensor with values - normalized to ``[-1.0, 1.0]``. + ``flac`` and ``mp3``. + For these formats, this function always returns ``float32`` Tensor with values normalized to + ``[-1.0, 1.0]``. Args: - filepath: Path to audio file - frame_offset: Number of frames to skip before start reading data. - num_frames: Maximum number of frames to read. -1 reads all the remaining samples, starting - from ``frame_offset``. This function may return the less number of frames if there is - not enough frames in the given file. - normalize: When ``True``, this function always return ``float32``, and sample values are - normalized to ``[-1.0, 1.0]``. If input file is integer WAV, giving ``False`` will change - the resulting Tensor type to integer type. This argument has no effect for formats other - than integer WAV type. - channels_first: When True, the returned Tensor has dimension ``[channel, time]``. + filepath (str): + Path to audio file + frame_offset (int): + Number of frames to skip before start reading data. + num_frames (int): + Maximum number of frames to read. ``-1`` reads all the remaining samples, + starting from ``frame_offset``. + This function may return the less number of frames if there is not enough + frames in the given file. + normalize (bool): + When ``True``, this function always return ``float32``, and sample values are + normalized to ``[-1.0, 1.0]``. + If input file is integer WAV, giving ``False`` will change the resulting Tensor type to + integer type. + This argument has no effect for formats other than integer WAV type. + channels_first (bool): + When True, the returned Tensor has dimension ``[channel, time]``. Otherwise, the returned Tensor's dimension is ``[time, channel]``. Returns: - torch.Tensor: If the input file has integer wav format and normalization is off, then it has + torch.Tensor: + If the input file has integer wav format and normalization is off, then it has integer type, else ``float32`` type. If ``channels_first=True``, it has ``[channel, time]`` else ``[time, channel]``. """ @@ -83,37 +112,49 @@ def load( @_mod_utils.requires_module('torchaudio._torchaudio') def save( filepath: str, - tensor: torch.Tensor, + src: torch.Tensor, sample_rate: int, channels_first: bool = True, compression: Optional[float] = None, ): """Save audio data to file. - Supported formats are; - - WAV - - 32-bit floating-point - - 32-bit signed integer - - 16-bit signed integer - - 8-bit unsigned integer - - MP3 - - FLAC - - OGG/VORBIS + Note: + Supported formats are; + + * WAV + + * 32-bit floating-point + * 32-bit signed integer + * 16-bit signed integer + * 8-bit unsigned integer + + * MP3 + * FLAC + * OGG/VORBIS + + To save ``MP3``, ``FLAC``, ``OGG/VORBIS``, and other codecs ``libsox`` does not + handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` + and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc. Args: - filepath: Path to save file. - tensor: Audio data to save. must be 2D tensor. - sample_rate: sampling rate - channels_first: If True, the given tensor is interpreted as ``[channel, time]``. - compression: Used for formats other than WAV. This corresponds to ``-C`` option - of ``sox`` command. + filepath (str): Path to save file. + tensor (torch.Tensor): Audio data to save. must be 2D tensor. + sample_rate (int): sampling rate + channels_first (bool): + If ``True``, the given tensor is interpreted as ``[channel, time]``, + otherwise ``[time, channel]``. + compression (Optional[float]): + Used for formats other than WAV. This corresponds to ``-C`` option of ``sox`` command. + + * | ``MP3``: Either bitrate (in ``kbps``) with quality factor, such as ``128.2``, or + | VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5``. + * | ``FLAC``: compression level. Whole number from ``0`` to ``8``. + | ``8`` is default and highest compression. + * | ``OGG/VORBIS``: number from ``-1`` to ``10``; ``-1`` is the highest compression + | and lowest quality. Default: ``3``. + See the detail at http://sox.sourceforge.net/soxformat.html. - - MP3: Either bitrate [kbps] with quality factor, such as ``128.2`` or - VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5`` - - FLAC: compression level. Whole number from ``0`` to ``8``. - ``8`` is default and highest compression. - - OGG/VORBIS: number from -1 to 10; -1 is the highest compression and lowest - quality. Default: ``3``. """ if compression is None: ext = str(filepath)[-3:].lower() @@ -127,8 +168,22 @@ def save( compression = 3. else: raise RuntimeError(f'Unsupported file type: "{ext}"') - signal = torch.classes.torchaudio.TensorSignal(tensor, sample_rate, channels_first) + signal = torch.classes.torchaudio.TensorSignal(src, sample_rate, channels_first) torch.ops.torchaudio.sox_io_save_audio_file(filepath, signal, compression) -load_wav = load +@_mod_utils.requires_module('torchaudio._torchaudio') +def load_wav( + filepath: str, + frame_offset: int = 0, + num_frames: int = -1, + channels_first: bool = True, +) -> Tuple[torch.Tensor, int]: + """Load wave file. + + + This function is defined only for the purpose of compatibility against other backend + for simple usecases, such as ``torchaudio.load_wav(filepath)``. + The implementation is same as :py:func:`load`. + """ + return load(filepath, frame_offset, num_frames, normalize=False, channels_first=channels_first) diff --git a/torchaudio/backend/utils.py b/torchaudio/backend/utils.py index cb53b3e02f..84ec2fa3f5 100644 --- a/torchaudio/backend/utils.py +++ b/torchaudio/backend/utils.py @@ -19,7 +19,11 @@ def list_audio_backends() -> List[str]: - """List available backends""" + """List available backends + + Returns: + List[str]: The list of available backends. + """ backends = [] if is_module_available('soundfile'): backends.append('soundfile') @@ -29,12 +33,13 @@ def list_audio_backends() -> List[str]: return backends -def set_audio_backend(backend: Optional[str]) -> None: +def set_audio_backend(backend: Optional[str]): """Set the backend for I/O operation Args: - backend (str): Name of the backend. One of "sox" or "soundfile", - based on availability of the system. + backend (Optional[str]): Name of the backend. + One of ``"sox"``, ``"sox_io"`` or ``"soundfile"`` based on availability + of the system. If ``None`` is provided the current backend is unassigned. """ if backend is not None and backend not in list_audio_backends(): raise RuntimeError( @@ -68,7 +73,11 @@ def _init_audio_backend(): def get_audio_backend() -> Optional[str]: - """Get the name of the current backend""" + """Get the name of the current backend + + Returns: + Optional[str]: The name of the current backend or ``None`` if no backend is assigned. + """ if torchaudio.load == no_backend.load: return None if torchaudio.load == sox_backend.load: diff --git a/torchaudio/csrc/register.cpp b/torchaudio/csrc/register.cpp index 3c03232941..476dcc3ebb 100644 --- a/torchaudio/csrc/register.cpp +++ b/torchaudio/csrc/register.cpp @@ -18,6 +18,20 @@ static auto registerTensorSignal = .def("get_sample_rate", &sox_utils::TensorSignal::getSampleRate) .def("get_channels_first", &sox_utils::TensorSignal::getChannelsFirst); +static auto registerSetSoxOptions = + torch::RegisterOperators() + .op("torchaudio::sox_utils_set_seed", &sox_utils::set_seed) + .op("torchaudio::sox_utils_set_verbosity", &sox_utils::set_verbosity) + .op("torchaudio::sox_utils_set_use_threads", + &sox_utils::set_use_threads) + .op("torchaudio::sox_utils_set_buffer_size", + &sox_utils::set_buffer_size) + .op("torchaudio::sox_utils_list_effects", &sox_utils::list_effects) + .op("torchaudio::sox_utils_list_read_formats", + &sox_utils::list_read_formats) + .op("torchaudio::sox_utils_list_write_formats", + &sox_utils::list_write_formats); + //////////////////////////////////////////////////////////////////////////////// // sox_io.h //////////////////////////////////////////////////////////////////////////////// @@ -53,12 +67,23 @@ static auto registerSaveAudioFile = torch::RegisterOperators().op( // sox_effects.h //////////////////////////////////////////////////////////////////////////////// static auto registerSoxEffects = - torch::RegisterOperators( - "torchaudio::sox_effects_initialize_sox_effects", - &sox_effects::initialize_sox_effects) + torch::RegisterOperators() + .op("torchaudio::sox_effects_initialize_sox_effects", + &sox_effects::initialize_sox_effects) .op("torchaudio::sox_effects_shutdown_sox_effects", &sox_effects::shutdown_sox_effects) - .op("torchaudio::sox_effects_list_effects", &sox_effects::list_effects); + .op(torch::RegisterOperators::options() + .schema( + "torchaudio::sox_effects_apply_effects_tensor(__torch__.torch.classes.torchaudio.TensorSignal input_signal, str[][] effects) -> __torch__.torch.classes.torchaudio.TensorSignal output_signal") + .catchAllKernel< + decltype(sox_effects::apply_effects_tensor), + &sox_effects::apply_effects_tensor>()) + .op(torch::RegisterOperators::options() + .schema( + "torchaudio::sox_effects_apply_effects_file(str path, str[][] effects, bool normalize, bool channels_first) -> __torch__.torch.classes.torchaudio.TensorSignal output_signal") + .catchAllKernel< + decltype(sox_effects::apply_effects_file), + &sox_effects::apply_effects_file>()); } // namespace } // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects.cpp b/torchaudio/csrc/sox_effects.cpp index 9a0c2ddc6f..8a7cc7c494 100644 --- a/torchaudio/csrc/sox_effects.cpp +++ b/torchaudio/csrc/sox_effects.cpp @@ -1,7 +1,9 @@ #include #include +#include +#include -using namespace torch::indexing; +using namespace torchaudio::sox_utils; namespace torchaudio { namespace sox_effects { @@ -10,44 +12,125 @@ namespace { enum SoxEffectsResourceState { NotInitialized, Initialized, ShutDown }; SoxEffectsResourceState SOX_RESOURCE_STATE = NotInitialized; +std::mutex SOX_RESOUCE_STATE_MUTEX; } // namespace void initialize_sox_effects() { - if (SOX_RESOURCE_STATE == ShutDown) { - throw std::runtime_error( - "SoX Effects has been shut down. Cannot initialize again."); - } - if (SOX_RESOURCE_STATE == NotInitialized) { - if (sox_init() != SOX_SUCCESS) { - throw std::runtime_error("Failed to initialize sox effects."); - }; - SOX_RESOURCE_STATE = Initialized; + const std::lock_guard lock(SOX_RESOUCE_STATE_MUTEX); + + switch (SOX_RESOURCE_STATE) { + case NotInitialized: + if (sox_init() != SOX_SUCCESS) { + throw std::runtime_error("Failed to initialize sox effects."); + }; + SOX_RESOURCE_STATE = Initialized; + case Initialized: + break; + case ShutDown: + throw std::runtime_error( + "SoX Effects has been shut down. Cannot initialize again."); } }; void shutdown_sox_effects() { - if (SOX_RESOURCE_STATE == NotInitialized) { - throw std::runtime_error( - "SoX Effects is not initialized. Cannot shutdown."); + const std::lock_guard lock(SOX_RESOUCE_STATE_MUTEX); + + switch (SOX_RESOURCE_STATE) { + case NotInitialized: + throw std::runtime_error( + "SoX Effects is not initialized. Cannot shutdown."); + case Initialized: + if (sox_quit() != SOX_SUCCESS) { + throw std::runtime_error("Failed to initialize sox effects."); + }; + SOX_RESOURCE_STATE = ShutDown; + case ShutDown: + break; } - if (SOX_RESOURCE_STATE == Initialized) { - if (sox_quit() != SOX_SUCCESS) { - throw std::runtime_error("Failed to initialize sox effects."); - }; - SOX_RESOURCE_STATE = ShutDown; +} + +c10::intrusive_ptr apply_effects_tensor( + const c10::intrusive_ptr& input_signal, + std::vector> effects) { + auto in_tensor = input_signal->getTensor(); + validate_input_tensor(in_tensor); + + // Create SoxEffectsChain + const auto dtype = in_tensor.dtype(); + torchaudio::sox_effects_chain::SoxEffectsChain chain( + /*input_encoding=*/get_encodinginfo("wav", dtype, 0.), + /*output_encoding=*/get_encodinginfo("wav", dtype, 0.)); + + // Prepare output buffer + std::vector out_buffer; + out_buffer.reserve(in_tensor.numel()); + + // Build and run effects chain + chain.addInputTensor(input_signal.get()); + for (const auto& effect : effects) { + chain.addEffect(effect); } + chain.addOutputBuffer(&out_buffer); + chain.run(); + + // Create tensor from buffer + const auto channels_first = input_signal->getChannelsFirst(); + auto out_tensor = convert_to_tensor( + /*buffer=*/out_buffer.data(), + /*num_samples=*/out_buffer.size(), + /*num_channels=*/chain.getOutputNumChannels(), + dtype, + /*noramlize=*/false, + channels_first); + + return c10::make_intrusive( + out_tensor, chain.getOutputSampleRate(), channels_first); } -std::vector list_effects() { - std::vector names; - const sox_effect_fn_t* fns = sox_get_effect_fns(); - for (int i = 0; fns[i]; ++i) { - const sox_effect_handler_t* handler = fns[i](); - if (handler && handler->name) - names.push_back(handler->name); +c10::intrusive_ptr apply_effects_file( + const std::string path, + std::vector> effects, + const bool normalize, + const bool channels_first) { + // Open input file + SoxFormat sf(sox_open_read( + path.c_str(), + /*signal=*/nullptr, + /*encoding=*/nullptr, + /*filetype=*/nullptr)); + + validate_input_file(sf); + + const auto dtype = get_dtype(sf->encoding.encoding, sf->signal.precision); + + // Prepare output + std::vector out_buffer; + out_buffer.reserve(sf->signal.length); + + // Create and run SoxEffectsChain + torchaudio::sox_effects_chain::SoxEffectsChain chain( + /*input_encoding=*/sf->encoding, + /*output_encoding=*/get_encodinginfo("wav", dtype, 0.)); + + chain.addInputFile(sf); + for (const auto& effect : effects) { + chain.addEffect(effect); } - return names; + chain.addOutputBuffer(&out_buffer); + chain.run(); + + // Create tensor from buffer + auto tensor = convert_to_tensor( + /*buffer=*/out_buffer.data(), + /*num_samples=*/out_buffer.size(), + /*num_channels=*/chain.getOutputNumChannels(), + dtype, + normalize, + channels_first); + + return c10::make_intrusive( + tensor, chain.getOutputSampleRate(), channels_first); } } // namespace sox_effects diff --git a/torchaudio/csrc/sox_effects.h b/torchaudio/csrc/sox_effects.h index 14bdbbfabc..7883a7beac 100644 --- a/torchaudio/csrc/sox_effects.h +++ b/torchaudio/csrc/sox_effects.h @@ -2,6 +2,7 @@ #define TORCHAUDIO_SOX_EFFECTS_H #include +#include namespace torchaudio { namespace sox_effects { @@ -10,7 +11,15 @@ void initialize_sox_effects(); void shutdown_sox_effects(); -std::vector list_effects(); +c10::intrusive_ptr apply_effects_tensor( + const c10::intrusive_ptr& input_signal, + std::vector> effects); + +c10::intrusive_ptr apply_effects_file( + const std::string path, + std::vector> effects, + const bool normalize = true, + const bool channels_first = true); } // namespace sox_effects } // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects_chain.cpp b/torchaudio/csrc/sox_effects_chain.cpp new file mode 100644 index 0000000000..449e33be98 --- /dev/null +++ b/torchaudio/csrc/sox_effects_chain.cpp @@ -0,0 +1,287 @@ +#include +#include + +using namespace torch::indexing; +using namespace torchaudio::sox_utils; + +namespace torchaudio { +namespace sox_effects_chain { + +namespace { + +// Helper struct to safely close sox_effect_t* pointer returned by +// sox_create_effect +struct SoxEffect { + explicit SoxEffect(sox_effect_t* se) noexcept : se_(se){}; + SoxEffect(const SoxEffect& other) = delete; + SoxEffect(const SoxEffect&& other) = delete; + SoxEffect& operator=(const SoxEffect& other) = delete; + SoxEffect& operator=(SoxEffect&& other) = delete; + ~SoxEffect() { + if (se_ != nullptr) { + free(se_); + } + } + operator sox_effect_t*() const { + return se_; + }; + sox_effect_t* operator->() noexcept { + return se_; + } + + private: + sox_effect_t* se_; +}; + +/// helper classes for passing the location of input tensor and output buffer +/// +/// drain/flow callback functions require plaing C style function signature and +/// the way to pass extra data is to attach data to sox_fffect_t::priv pointer. +/// The following structs will be assigned to sox_fffect_t::priv pointer which +/// gives sox_effect_t an access to input Tensor and output buffer object. +struct TensorInputPriv { + size_t index; + TensorSignal* signal; +}; +struct TensorOutputPriv { + std::vector* buffer; +}; +struct FileOutputPriv { + sox_format_t* sf; +}; + +/// Callback function to feed Tensor data to SoxEffectChain. +int tensor_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { + // Retrieve the input Tensor and current index + auto priv = static_cast(effp->priv); + auto index = priv->index; + auto signal = priv->signal; + auto tensor = signal->getTensor(); + auto num_channels = effp->out_signal.channels; + + // Adjust the number of samples to read + const size_t num_samples = tensor.numel(); + if (index + *osamp > num_samples) { + *osamp = num_samples - index; + } + // Ensure that it's a multiple of the number of channels + *osamp -= *osamp % num_channels; + + // Slice the input Tensor and unnormalize the values + const auto tensor_ = [&]() { + auto i_frame = index / num_channels; + auto num_frames = *osamp / num_channels; + auto t = (signal->getChannelsFirst()) + ? tensor.index({Slice(), Slice(i_frame, i_frame + num_frames)}).t() + : tensor.index({Slice(i_frame, i_frame + num_frames), Slice()}); + return unnormalize_wav(t.reshape({-1})).contiguous(); + }(); + priv->index += *osamp; + + // Write data to SoxEffectsChain buffer. + auto ptr = tensor_.data_ptr(); + std::copy(ptr, ptr + *osamp, obuf); + + return (priv->index == num_samples) ? SOX_EOF : SOX_SUCCESS; +} + +/// Callback function to fetch data from SoxEffectChain. +int tensor_output_flow( + sox_effect_t* effp, + sox_sample_t const* ibuf, + sox_sample_t* obuf LSX_UNUSED, + size_t* isamp, + size_t* osamp) { + *osamp = 0; + // Get output buffer + auto out_buffer = static_cast(effp->priv)->buffer; + // Append at the end + out_buffer->insert(out_buffer->end(), ibuf, ibuf + *isamp); + return SOX_SUCCESS; +} + +int file_output_flow( + sox_effect_t* effp, + sox_sample_t const* ibuf, + sox_sample_t* obuf LSX_UNUSED, + size_t* isamp, + size_t* osamp) { + *osamp = 0; + if (*isamp) { + auto sf = static_cast(effp->priv)->sf; + if (sox_write(sf, ibuf, *isamp) != *isamp) { + if (sf->sox_errno) { + std::ostringstream stream; + stream << sf->sox_errstr << " " << sox_strerror(sf->sox_errno) << " " + << sf->filename; + throw std::runtime_error(stream.str()); + } + return SOX_EOF; + } + } + return SOX_SUCCESS; +} + +sox_effect_handler_t* get_tensor_input_handler() { + static sox_effect_handler_t handler{/*name=*/"input_tensor", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/NULL, + /*drain=*/tensor_input_drain, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(TensorInputPriv)}; + return &handler; +} + +sox_effect_handler_t* get_tensor_output_handler() { + static sox_effect_handler_t handler{/*name=*/"output_tensor", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/tensor_output_flow, + /*drain=*/NULL, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(TensorOutputPriv)}; + return &handler; +} + +sox_effect_handler_t* get_file_output_handler() { + static sox_effect_handler_t handler{/*name=*/"output_file", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/file_output_flow, + /*drain=*/NULL, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(FileOutputPriv)}; + return &handler; +} + +} // namespace + +SoxEffectsChain::SoxEffectsChain( + sox_encodinginfo_t input_encoding, + sox_encodinginfo_t output_encoding) + : in_enc_(input_encoding), + out_enc_(output_encoding), + in_sig_(), + interm_sig_(), + out_sig_(), + sec_(sox_create_effects_chain(&in_enc_, &out_enc_)) { + if (!sec_) { + throw std::runtime_error("Failed to create effect chain."); + } +} + +SoxEffectsChain::~SoxEffectsChain() { + if (sec_ != nullptr) { + sox_delete_effects_chain(sec_); + } +} + +void SoxEffectsChain::run() { + sox_flow_effects(sec_, NULL, NULL); +} + +void SoxEffectsChain::addInputTensor(TensorSignal* signal) { + in_sig_ = get_signalinfo(signal, "wav"); + interm_sig_ = in_sig_; + SoxEffect e(sox_create_effect(get_tensor_input_handler())); + auto priv = static_cast(e->priv); + priv->signal = signal; + priv->index = 0; + if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { + throw std::runtime_error("Failed to add effect: input_tensor"); + } +} + +void SoxEffectsChain::addOutputBuffer( + std::vector* output_buffer) { + SoxEffect e(sox_create_effect(get_tensor_output_handler())); + static_cast(e->priv)->buffer = output_buffer; + if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { + throw std::runtime_error("Failed to add effect: output_tensor"); + } +} + +void SoxEffectsChain::addInputFile(sox_format_t* sf) { + in_sig_ = sf->signal; + interm_sig_ = in_sig_; + SoxEffect e(sox_create_effect(sox_find_effect("input"))); + char* opts[] = {(char*)sf}; + sox_effect_options(e, 1, opts); + if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { + std::ostringstream stream; + stream << "Failed to add effect: input " << sf->filename; + throw std::runtime_error(stream.str()); + } +} + +void SoxEffectsChain::addOutputFile(sox_format_t* sf) { + out_sig_ = sf->signal; + SoxEffect e(sox_create_effect(get_file_output_handler())); + static_cast(e->priv)->sf = sf; + if (sox_add_effect(sec_, e, &interm_sig_, &out_sig_) != SOX_SUCCESS) { + std::ostringstream stream; + stream << "Failed to add effect: output " << sf->filename; + throw std::runtime_error(stream.str()); + } +} + +void SoxEffectsChain::addEffect(const std::vector effect) { + const auto num_args = effect.size(); + if (num_args == 0) { + throw std::runtime_error("Invalid argument: empty effect."); + } + const auto name = effect[0]; + if (UNSUPPORTED_EFFECTS.find(name) != UNSUPPORTED_EFFECTS.end()) { + std::ostringstream stream; + stream << "Unsupported effect: " << name; + throw std::runtime_error(stream.str()); + } + + SoxEffect e(sox_create_effect(sox_find_effect(name.c_str()))); + const auto num_options = num_args - 1; + + std::vector opts; + for (size_t i = 1; i < num_args; ++i) { + opts.push_back((char*)effect[i].c_str()); + } + if (sox_effect_options(e, num_options, num_options ? opts.data() : nullptr) != + SOX_SUCCESS) { + std::ostringstream stream; + stream << "Invalid effect option:"; + for (const auto& v : effect) { + stream << " " << v; + } + throw std::runtime_error(stream.str()); + } + + if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { + std::ostringstream stream; + stream << "Failed to add effect: \"" << name; + for (size_t i = 1; i < num_args; ++i) { + stream << " " << effect[i]; + } + stream << "\""; + throw std::runtime_error(stream.str()); + } +} + +int64_t SoxEffectsChain::getOutputNumChannels() { + return interm_sig_.channels; +} + +int64_t SoxEffectsChain::getOutputSampleRate() { + return interm_sig_.rate; +} + +} // namespace sox_effects_chain +} // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects_chain.h b/torchaudio/csrc/sox_effects_chain.h new file mode 100644 index 0000000000..fa55182281 --- /dev/null +++ b/torchaudio/csrc/sox_effects_chain.h @@ -0,0 +1,42 @@ +#ifndef TORCHAUDIO_SOX_EFFECTS_CHAIN_H +#define TORCHAUDIO_SOX_EFFECTS_CHAIN_H + +#include +#include +#include + +namespace torchaudio { +namespace sox_effects_chain { + +// Helper struct to safely close sox_effects_chain_t with handy methods +class SoxEffectsChain { + const sox_encodinginfo_t in_enc_; + const sox_encodinginfo_t out_enc_; + sox_signalinfo_t in_sig_; + sox_signalinfo_t interm_sig_; + sox_signalinfo_t out_sig_; + sox_effects_chain_t* sec_; + + public: + explicit SoxEffectsChain( + sox_encodinginfo_t input_encoding, + sox_encodinginfo_t output_encoding); + SoxEffectsChain(const SoxEffectsChain& other) = delete; + SoxEffectsChain(const SoxEffectsChain&& other) = delete; + SoxEffectsChain& operator=(const SoxEffectsChain& other) = delete; + SoxEffectsChain& operator=(SoxEffectsChain&& other) = delete; + ~SoxEffectsChain(); + void run(); + void addInputTensor(torchaudio::sox_utils::TensorSignal* signal); + void addInputFile(sox_format_t* sf); + void addOutputBuffer(std::vector* output_buffer); + void addOutputFile(sox_format_t* sf); + void addEffect(const std::vector effect); + int64_t getOutputNumChannels(); + int64_t getOutputSampleRate(); +}; + +} // namespace sox_effects_chain +} // namespace torchaudio + +#endif diff --git a/torchaudio/csrc/sox_io.cpp b/torchaudio/csrc/sox_io.cpp index 5d308027bb..4092d215d0 100644 --- a/torchaudio/csrc/sox_io.cpp +++ b/torchaudio/csrc/sox_io.cpp @@ -1,4 +1,6 @@ #include +#include +#include #include #include @@ -60,64 +62,21 @@ c10::intrusive_ptr load_audio_file( "Invalid argument: num_frames must be -1 or greater than 0."); } - SoxFormat sf(sox_open_read( - path.c_str(), - /*signal=*/nullptr, - /*encoding=*/nullptr, - /*filetype=*/nullptr)); - - validate_input_file(sf); - - const int64_t num_channels = sf->signal.channels; - const int64_t num_total_samples = sf->signal.length; - const int64_t sample_start = sf->signal.channels * frame_offset; - - if (sox_seek(sf, sample_start, 0) == SOX_EOF) { - throw std::runtime_error("Error reading audio file: offset past EOF."); + std::vector> effects; + if (num_frames != -1) { + std::ostringstream offset, frames; + offset << frame_offset << "s"; + frames << "+" << num_frames << "s"; + effects.emplace_back( + std::vector{"trim", offset.str(), frames.str()}); + } else if (frame_offset != 0) { + std::ostringstream offset; + offset << frame_offset << "s"; + effects.emplace_back(std::vector{"trim", offset.str()}); } - const int64_t sample_end = [&]() { - if (num_frames == -1) - return num_total_samples; - const int64_t sample_end_ = num_channels * num_frames + sample_start; - if (num_total_samples < sample_end_) { - // For lossy encoding, it is difficult to predict exact size of buffer for - // reading the number of samples required. - // So we allocate buffer size of given `num_frames` and ask sox to read as - // much as possible. For lossless format, sox reads exact number of - // samples, but for lossy encoding, sox can end up reading less. (i.e. - // mp3) For the consistent behavior specification between lossy/lossless - // format, we allow users to provide `num_frames` value that exceeds #of - // available samples, and we adjust it here. - return num_total_samples; - } - return sample_end_; - }(); - - const int64_t max_samples = sample_end - sample_start; - - // Read samples into buffer - std::vector buffer; - buffer.reserve(max_samples); - const int64_t num_samples = sox_read(sf, buffer.data(), max_samples); - if (num_samples == 0) { - throw std::runtime_error( - "Error reading audio file: empty file or read operation failed."); - } - // NOTE: num_samples may be smaller than max_samples if the input - // format is compressed (i.e. mp3). - - // Convert to Tensor - auto tensor = convert_to_tensor( - buffer.data(), - num_samples, - num_channels, - get_dtype(sf->encoding.encoding, sf->signal.precision), - normalize, - channels_first); - - return c10::make_intrusive( - tensor, static_cast(sf->signal.rate), channels_first); + return torchaudio::sox_effects::apply_effects_file( + path, effects, normalize, channels_first); } void save_audio_file( @@ -125,14 +84,11 @@ void save_audio_file( const c10::intrusive_ptr& signal, const double compression) { const auto tensor = signal->getTensor(); - const auto sample_rate = signal->getSampleRate(); - const auto channels_first = signal->getChannelsFirst(); validate_input_tensor(tensor); const auto filetype = get_filetype(file_name); - const auto signal_info = - get_signalinfo(tensor, sample_rate, channels_first, filetype); + const auto signal_info = get_signalinfo(signal.get(), filetype); const auto encoding_info = get_encodinginfo(filetype, tensor.dtype(), compression); @@ -148,22 +104,12 @@ void save_audio_file( throw std::runtime_error("Error saving audio file: failed to open file."); } - auto tensor_ = tensor; - if (channels_first) { - tensor_ = tensor_.t(); - } - - const int64_t frames_per_chunk = 65536; - for (int64_t i = 0; i < tensor_.size(0); i += frames_per_chunk) { - auto chunk = tensor_.index({Slice(i, i + frames_per_chunk), Slice()}); - chunk = unnormalize_wav(chunk).contiguous(); - - const size_t numel = chunk.numel(); - if (sox_write(sf, chunk.data_ptr(), numel) != numel) { - throw std::runtime_error( - "Error saving audio file: failed to write the entier buffer."); - } - } + torchaudio::sox_effects_chain::SoxEffectsChain chain( + /*input_encoding=*/get_encodinginfo("wav", tensor.dtype(), 0.), + /*output_encoding=*/sf->encoding); + chain.addInputTensor(signal.get()); + chain.addOutputFile(sf); + chain.run(); } } // namespace sox_io diff --git a/torchaudio/csrc/sox_utils.cpp b/torchaudio/csrc/sox_utils.cpp index c1fd8383a8..c6b73c5795 100644 --- a/torchaudio/csrc/sox_utils.cpp +++ b/torchaudio/csrc/sox_utils.cpp @@ -5,6 +5,62 @@ namespace torchaudio { namespace sox_utils { +void set_seed(const int64_t seed) { + sox_get_globals()->ranqd1 = static_cast(seed); +} + +void set_verbosity(const int64_t verbosity) { + sox_get_globals()->verbosity = static_cast(verbosity); +} + +void set_use_threads(const bool use_threads) { + sox_get_globals()->use_threads = static_cast(use_threads); +} + +void set_buffer_size(const int64_t buffer_size) { + sox_get_globals()->bufsiz = static_cast(buffer_size); +} + +std::vector> list_effects() { + std::vector> effects; + for (const sox_effect_fn_t* fns = sox_get_effect_fns(); *fns; ++fns) { + const sox_effect_handler_t* handler = (*fns)(); + if (handler && handler->name) { + if (UNSUPPORTED_EFFECTS.find(handler->name) == + UNSUPPORTED_EFFECTS.end()) { + effects.emplace_back(std::vector{ + handler->name, + handler->usage ? std::string(handler->usage) : std::string("")}); + } + } + } + return effects; +} + +std::vector list_write_formats() { + std::vector formats; + for (const sox_format_tab_t* fns = sox_get_format_fns(); fns->fn; ++fns) { + const sox_format_handler_t* handler = fns->fn(); + for (const char* const* names = handler->names; *names; ++names) { + if (!strchr(*names, '/') && handler->write) + formats.emplace_back(*names); + } + } + return formats; +} + +std::vector list_read_formats() { + std::vector formats; + for (const sox_format_tab_t* fns = sox_get_format_fns(); fns->fn; ++fns) { + const sox_format_handler_t* handler = fns->fn(); + for (const char* const* names = handler->names; *names; ++names) { + if (!strchr(*names, '/') && handler->read) + formats.emplace_back(*names); + } + } + return formats; +} + TensorSignal::TensorSignal( torch::Tensor tensor_, int64_t sample_rate_, @@ -205,13 +261,13 @@ unsigned get_precision( } sox_signalinfo_t get_signalinfo( - const torch::Tensor& tensor, - const int64_t sample_rate, - const bool channels_first, + const TensorSignal* signal, const std::string filetype) { + auto tensor = signal->getTensor(); return sox_signalinfo_t{ - /*rate=*/static_cast(sample_rate), - /*channels=*/static_cast(tensor.size(channels_first ? 0 : 1)), + /*rate=*/static_cast(signal->getSampleRate()), + /*channels=*/ + static_cast(tensor.size(signal->getChannelsFirst() ? 0 : 1)), /*precision=*/get_precision(filetype, tensor.dtype()), /*length=*/static_cast(tensor.numel())}; } diff --git a/torchaudio/csrc/sox_utils.h b/torchaudio/csrc/sox_utils.h index 665187c840..c9037e9e2f 100644 --- a/torchaudio/csrc/sox_utils.h +++ b/torchaudio/csrc/sox_utils.h @@ -7,6 +7,27 @@ namespace torchaudio { namespace sox_utils { +//////////////////////////////////////////////////////////////////////////////// +// APIs for Python interaction +//////////////////////////////////////////////////////////////////////////////// + +/// Set sox global options +void set_seed(const int64_t seed); + +void set_verbosity(const int64_t verbosity); + +void set_use_threads(const bool use_threads); + +void set_buffer_size(const int64_t buffer_size); + +std::vector> list_effects(); + +std::vector list_read_formats(); + +std::vector list_write_formats(); + +/// Class for exchanging signal infomation (tensor + meta data) between +/// C++ and Python for read/write operation. struct TensorSignal : torch::CustomClassHolder { torch::Tensor tensor; int64_t sample_rate; @@ -22,6 +43,13 @@ struct TensorSignal : torch::CustomClassHolder { bool getChannelsFirst() const; }; +//////////////////////////////////////////////////////////////////////////////// +// Utilities for sox_io / sox_effects implementations +//////////////////////////////////////////////////////////////////////////////// + +const std::unordered_set UNSUPPORTED_EFFECTS = + {"input", "output", "spectrogram", "noiseprof", "noisered", "splice"}; + /// helper class to automatically close sox_format_t* struct SoxFormat { explicit SoxFormat(sox_format_t* fd) noexcept; @@ -84,9 +112,7 @@ const std::string get_filetype(const std::string path); /// Get sox_signalinfo_t for passing a torch::Tensor object. sox_signalinfo_t get_signalinfo( - const torch::Tensor& tensor, - const int64_t sample_rate, - const bool channels_first, + const TensorSignal* signal, const std::string filetype); /// Get sox_encofinginfo_t for saving audoi file diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index 942f2c83a1..187142db46 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -7,6 +7,7 @@ from .yesno import YESNO from .ljspeech import LJSPEECH from .cmuarctic import CMUARCTIC +from .libritts import LIBRITTS __all__ = ( "COMMONVOICE", @@ -17,6 +18,7 @@ "LJSPEECH", "GTZAN", "CMUARCTIC", + "LIBRITTS" "diskcache_iterator", "bg_iterator", ) diff --git a/torchaudio/datasets/gtzan.py b/torchaudio/datasets/gtzan.py index 13d7e8b68b..e031801bc1 100644 --- a/torchaudio/datasets/gtzan.py +++ b/torchaudio/datasets/gtzan.py @@ -8,7 +8,6 @@ from torchaudio.datasets.utils import ( download_url, extract_archive, - walk_files, ) # The following lists prefixed with `filtered_` provide a filtered split @@ -22,6 +21,19 @@ # Those are used when GTZAN is initialised with the `filtered` keyword. # The split was taken from (github) jordipons/sklearn-audio-transfer-learning. +gtzan_genres = [ + "blues", + "classical", + "country", + "disco", + "hiphop", + "jazz", + "metal", + "pop", + "reggae", + "rock", +] + filtered_test = [ "blues.00012", "blues.00013", @@ -964,7 +976,9 @@ URL = "http://opihi.cs.uvic.ca/sound/genres.tar.gz" FOLDER_IN_ARCHIVE = "genres" -_CHECKSUMS = {"http://opihi.cs.uvic.ca/sound/genres.tar.gz": "5b3d6dddb579ab49814ab86dba69e7c7"} +_CHECKSUMS = { + "http://opihi.cs.uvic.ca/sound/genres.tar.gz": "5b3d6dddb579ab49814ab86dba69e7c7" +} def load_gtzan_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, str]: @@ -1032,10 +1046,33 @@ def __init__( ) if self.subset is None: - walker = walk_files( - self._path, suffix=self._ext_audio, prefix=False, remove_suffix=True - ) - self._walker = list(walker) + # Check every subdirectory under dataset root + # which has the same name as the genres in + # GTZAN (e.g. `root_dir'/blues/, `root_dir'/rock, etc.) + # This lets users remove or move around song files, + # useful when e.g. they want to use only some of the files + # in a genre or want to label other files with a different + # genre. + self._walker = [] + + root = os.path.expanduser(self._path) + + for directory in gtzan_genres: + fulldir = os.path.join(root, directory) + + if not os.path.exists(fulldir): + continue + + songs_in_genre = os.listdir(fulldir) + songs_in_genre.sort() + for fname in songs_in_genre: + name, ext = os.path.splitext(fname) + if ext.lower() == ".wav" and "." in name: + # Check whether the file is of the form + # `gtzan_genre`.`5 digit number`.wav + genre, num = name.split(".") + if genre in gtzan_genres and len(num) == 5 and num.isdigit(): + self._walker.append(name) else: if self.subset == "training": self._walker = filtered_train diff --git a/torchaudio/datasets/libritts.py b/torchaudio/datasets/libritts.py new file mode 100644 index 0000000000..a37d5528fb --- /dev/null +++ b/torchaudio/datasets/libritts.py @@ -0,0 +1,131 @@ +import os +from typing import Tuple + +import torchaudio +from torch import Tensor +from torch.utils.data import Dataset +from torchaudio.datasets.utils import ( + download_url, + extract_archive, + walk_files, +) + +URL = "train-clean-100" +FOLDER_IN_ARCHIVE = "LibriTTS" +_CHECKSUMS = { + "http://www.openslr.org/60/dev-clean.tar.gz": "0c3076c1e5245bb3f0af7d82087ee207", + "http://www.openslr.org/60/dev-other.tar.gz": "815555d8d75995782ac3ccd7f047213d", + "http://www.openslr.org/60/test-clean.tar.gz": "7bed3bdb047c4c197f1ad3bc412db59f", + "http://www.openslr.org/60/test-other.tar.gz": "ae3258249472a13b5abef2a816f733e4", + "http://www.openslr.org/60/train-clean-100.tar.gz": "4a8c202b78fe1bc0c47916a98f3a2ea8", + "http://www.openslr.org/60/train-clean-360.tar.gz": "a84ef10ddade5fd25df69596a2767b2d", + "http://www.openslr.org/60/train-other-500.tar.gz": "7b181dd5ace343a5f38427999684aa6f", +} + + +def load_libritts_item( + fileid: str, + path: str, + ext_audio: str, + ext_original_txt: str, + ext_normalized_txt: str, +) -> Tuple[Tensor, int, str, str, int, int, str]: + speaker_id, chapter_id, segment_id, utterance_id = fileid.split("_") + utterance_id = fileid + + normalized_text = utterance_id + ext_normalized_txt + normalized_text = os.path.join(path, speaker_id, chapter_id, normalized_text) + + original_text = utterance_id + ext_original_txt + original_text = os.path.join(path, speaker_id, chapter_id, original_text) + + file_audio = utterance_id + ext_audio + file_audio = os.path.join(path, speaker_id, chapter_id, file_audio) + + # Load audio + waveform, sample_rate = torchaudio.load(file_audio) + + # Load original text + with open(original_text) as ft: + original_text = ft.readline() + + # Load normalized text + with open(normalized_text, "r") as ft: + normalized_text = ft.readline() + + return ( + waveform, + sample_rate, + original_text, + normalized_text, + int(speaker_id), + int(chapter_id), + utterance_id, + ) + + +class LIBRITTS(Dataset): + """ + Create a Dataset for LibriTTS. Each item is a tuple of the form: + waveform, sample_rate, original_text, normalized_text, speaker_id, chapter_id, utterance_id + """ + + _ext_original_txt = ".original.txt" + _ext_normalized_txt = ".normalized.txt" + _ext_audio = ".wav" + + def __init__( + self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False, + ) -> None: + + if url in [ + "dev-clean", + "dev-other", + "test-clean", + "test-other", + "train-clean-100", + "train-clean-360", + "train-other-500", + ]: + + ext_archive = ".tar.gz" + base_url = "http://www.openslr.org/resources/60/" + + url = os.path.join(base_url, url + ext_archive) + + basename = os.path.basename(url) + archive = os.path.join(root, basename) + + basename = basename.split(".")[0] + folder_in_archive = os.path.join(folder_in_archive, basename) + + self._path = os.path.join(root, folder_in_archive) + + if download: + if not os.path.isdir(self._path): + if not os.path.isfile(archive): + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum) + extract_archive(archive) + + walker = walk_files( + self._path, suffix=self._ext_audio, prefix=False, remove_suffix=True + ) + self._walker = list(walker) + + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int, int, str]: + fileid = self._walker[n] + return load_libritts_item( + fileid, + self._path, + self._ext_audio, + self._ext_original_txt, + self._ext_normalized_txt, + ) + + def __len__(self) -> int: + return len(self._walker) diff --git a/torchaudio/datasets/utils.py b/torchaudio/datasets/utils.py index 2840b43471..d00ffc143b 100644 --- a/torchaudio/datasets/utils.py +++ b/torchaudio/datasets/utils.py @@ -264,7 +264,13 @@ def walk_files(root: str, root = os.path.expanduser(root) - for dirpath, _, files in os.walk(root): + for dirpath, dirs, files in os.walk(root): + dirs.sort() + # `dirs` is the list used in os.walk function and by sorting it in-place here, we change the + # behavior of os.walk to traverse sub directory alphabetically + # see also + # https://stackoverflow.com/questions/6670029/can-i-force-python3s-os-walk-to-visit-directories-in-alphabetical-order-how#comment71993866_6670926 + files.sort() for f in files: if f.endswith(suffix): diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 78c8c594c9..713544cc74 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -1646,11 +1646,11 @@ def compute_deltas( r"""Compute delta coefficients of a tensor, usually a spectrogram: .. math:: - d_t = \frac{\sum_{n=1}^{\text{N}} n (c_{t+n} - c_{t-n})}{2 \sum_{n=1}^{\text{N} n^2} + d_t = \frac{\sum_{n=1}^{\text{N}} n (c_{t+n} - c_{t-n})}{2 \sum_{n=1}^{\text{N}} n^2} where :math:`d_t` is the deltas at time :math:`t`, :math:`c_t` is the spectrogram coeffcients at time :math:`t`, - :math:`N` is (`win_length`-1)//2. + :math:`N` is ``(win_length-1)//2``. Args: specgram (Tensor): Tensor of audio of dimension (..., freq, time) diff --git a/torchaudio/models/_wavernn.py b/torchaudio/models/_wavernn.py index cd2e89a10c..37a06edfc3 100644 --- a/torchaudio/models/_wavernn.py +++ b/torchaudio/models/_wavernn.py @@ -1,4 +1,4 @@ -from typing import List +from typing import List, Tuple import torch from torch import Tensor @@ -13,7 +13,7 @@ class _ResBlock(nn.Module): The paper link is https://arxiv.org/pdf/1512.03385.pdf. Args: - n_freq: the number of bins in a spectrogram (default=128) + n_freq: the number of bins in a spectrogram. (Default: ``128``) Examples >>> resblock = _ResBlock() @@ -48,11 +48,11 @@ class _MelResNet(nn.Module): r"""MelResNet layer uses a stack of ResBlocks on spectrogram. Args: - n_res_block: the number of ResBlock in stack (default=10) - n_freq: the number of bins in a spectrogram (default=128) - n_hidden: the number of hidden dimensions (default=128) - n_output: the number of output dimensions (default=128) - kernel_size: the number of kernel size in the first Conv1d layer (default=5) + n_res_block: the number of ResBlock in stack. (Default: ``10``) + n_freq: the number of bins in a spectrogram. (Default: ``128``) + n_hidden: the number of hidden dimensions of resblock. (Default: ``128``) + n_output: the number of output dimensions of melresnet. (Default: ``128``) + kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``) Examples >>> melresnet = _MelResNet() @@ -129,12 +129,12 @@ class _UpsampleNetwork(nn.Module): r"""Upscale the dimensions of a spectrogram. Args: - upsample_scales: the list of upsample scales - n_res_block: the number of ResBlock in stack (default=10) - n_freq: the number of bins in a spectrogram (default=128) - n_hidden: the number of hidden dimensions (default=128) - n_output: the number of output dimensions (default=128) - kernel_size: the number of kernel size in the first Conv1d layer (default=5) + upsample_scales: the list of upsample scales. + n_res_block: the number of ResBlock in stack. (Default: ``10``) + n_freq: the number of bins in a spectrogram. (Default: ``128``) + n_hidden: the number of hidden dimensions of resblock. (Default: ``128``) + n_output: the number of output dimensions of melresnet. (Default: ``128``) + kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``) Examples >>> upsamplenetwork = _UpsampleNetwork(upsample_scales=[4, 4, 16]) @@ -172,7 +172,7 @@ def __init__(self, up_layers.append(conv) self.upsample_layers = nn.Sequential(*up_layers) - def forward(self, specgram: Tensor) -> Tensor: + def forward(self, specgram: Tensor) -> Tuple[Tensor, Tensor]: r"""Pass the input through the _UpsampleNetwork layer. Args: @@ -204,32 +204,29 @@ class _WaveRNN(nn.Module): `upsample_scales` must equal `hop_length`. Args: - upsample_scales: the list of upsample scales - n_bits: the bits of output waveform - sample_rate: the rate of audio dimensions (samples per second) - hop_length: the number of samples between the starts of consecutive frames - n_res_block: the number of ResBlock in stack (default=10) - n_rnn: the dimension of RNN layer (default=512) - n_fc: the dimension of fully connected layer (default=512) - kernel_size: the number of kernel size in the first Conv1d layer (default=5) - n_freq: the number of bins in a spectrogram (default=128) - n_hidden: the number of hidden dimensions (default=128) - n_output: the number of output dimensions (default=128) - mode: the mode of waveform in ['waveform', 'mol'] (default='waveform') + upsample_scales: the list of upsample scales. + n_classes: the number of output classes. + hop_length: the number of samples between the starts of consecutive frames. + n_res_block: the number of ResBlock in stack. (Default: ``10``) + n_rnn: the dimension of RNN layer. (Default: ``512``) + n_fc: the dimension of fully connected layer. (Default: ``512``) + kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``) + n_freq: the number of bins in a spectrogram. (Default: ``128``) + n_hidden: the number of hidden dimensions of resblock. (Default: ``128``) + n_output: the number of output dimensions of melresnet. (Default: ``128``) Example - >>> wavernn = _waveRNN(upsample_scales=[5,5,8], n_bits=9, sample_rate=24000, hop_length=200) + >>> wavernn = _waveRNN(upsample_scales=[5,5,8], n_classes=512, hop_length=200) >>> waveform, sample_rate = torchaudio.load(file) >>> # waveform shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length) >>> specgram = MelSpectrogram(sample_rate)(waveform) # shape: (n_batch, n_channel, n_freq, n_time) >>> output = wavernn(waveform, specgram) - >>> # output shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length, 2 ** n_bits) + >>> # output shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length, n_classes) """ def __init__(self, upsample_scales: List[int], - n_bits: int, - sample_rate: int, + n_classes: int, hop_length: int, n_res_block: int = 10, n_rnn: int = 512, @@ -237,24 +234,14 @@ def __init__(self, kernel_size: int = 5, n_freq: int = 128, n_hidden: int = 128, - n_output: int = 128, - mode: str = 'waveform') -> None: + n_output: int = 128) -> None: super().__init__() - self.mode = mode self.kernel_size = kernel_size - - if self.mode == 'waveform': - self.n_classes = 2 ** n_bits - elif self.mode == 'mol': - self.n_classes = 30 - else: - raise ValueError(f"Expected mode: `waveform` or `mol`, but found {self.mode}") - self.n_rnn = n_rnn self.n_aux = n_output // 4 self.hop_length = hop_length - self.sample_rate = sample_rate + self.n_classes = n_classes total_scale = 1 for upsample_scale in upsample_scales: @@ -262,7 +249,12 @@ def __init__(self, if total_scale != self.hop_length: raise ValueError(f"Expected: total_scale == hop_length, but found {total_scale} != {hop_length}") - self.upsample = _UpsampleNetwork(upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size) + self.upsample = _UpsampleNetwork(upsample_scales, + n_res_block, + n_freq, + n_hidden, + n_output, + kernel_size) self.fc = nn.Linear(n_freq + self.n_aux + 1, n_rnn) self.rnn1 = nn.GRU(n_rnn, n_rnn, batch_first=True) @@ -283,7 +275,7 @@ def forward(self, waveform: Tensor, specgram: Tensor) -> Tensor: specgram: the input spectrogram to the _WaveRNN layer (n_batch, 1, n_freq, n_time) Return: - Tensor shape: (n_batch, 1, (n_time - kernel_size + 1) * hop_length, 2 ** n_bits) + Tensor shape: (n_batch, 1, (n_time - kernel_size + 1) * hop_length, n_classes) """ assert waveform.size(1) == 1, 'Require the input channel of waveform is 1' diff --git a/torchaudio/models/wav2letter.py b/torchaudio/models/wav2letter.py index 3466e42dd2..acd4dfe4b3 100644 --- a/torchaudio/models/wav2letter.py +++ b/torchaudio/models/wav2letter.py @@ -1,5 +1,3 @@ -from typing import Optional - from torch import Tensor from torch import nn @@ -7,8 +5,9 @@ class Wav2Letter(nn.Module): - r"""Wav2Letter model architecture from the `"Wav2Letter: an End-to-End ConvNet-based Speech Recognition System" - `_ paper. + r"""Wav2Letter model architecture from the `Wav2Letter an End-to-End ConvNet-based Speech Recognition System`_. + + .. _Wav2Letter an End-to-End ConvNet-based Speech Recognition System: https://arxiv.org/abs/1609.03193 :math:`\text{padding} = \frac{\text{ceil}(\text{kernel} - \text{stride})}{2}` @@ -63,7 +62,7 @@ def __init__(self, num_classes: int = 40, def forward(self, x: Tensor) -> Tensor: r""" Args: - x (Tensor): Tensor of dimension (batch_size, num_features, input_length). + x (torch.Tensor): Tensor of dimension (batch_size, num_features, input_length). Returns: Tensor: Predictor tensor of dimension (batch_size, number_of_classes, input_length). diff --git a/torchaudio/sox_effects/__init__.py b/torchaudio/sox_effects/__init__.py index 507dc5c3af..d9650173c5 100644 --- a/torchaudio/sox_effects/__init__.py +++ b/torchaudio/sox_effects/__init__.py @@ -3,6 +3,8 @@ init_sox_effects, shutdown_sox_effects, effect_names, + apply_effects_tensor, + apply_effects_file, SoxEffect, SoxEffectsChain, ) diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 0aee312126..9262786541 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -7,37 +7,37 @@ module_utils as _mod_utils, misc_ops as _misc_ops, ) +from torchaudio.utils.sox_utils import list_effects + if _mod_utils.is_module_available('torchaudio._torchaudio'): from torchaudio import _torchaudio @_mod_utils.requires_module('torchaudio._torchaudio') -def init_sox_effects() -> None: - """Initialize resources required to use ``SoxEffectsChain`` - - You do not need to call this function manually. It is called automatically. - - Once initialized, you do not need to call this function again across the multiple call of - ``SoxEffectsChain.sox_build_flow_effects``, though it is safe to do so as long as - ``shutdown_sox_effects`` is not called yet. - Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and - initializing again will result in error. +def init_sox_effects(): + """Initialize resources required to use sox effects. Note: - This function is not required for simple loading. + You do not need to call this function manually. It is called automatically. + + Once initialized, you do not need to call this function again across the multiple uses of + sox effects though it is safe to do so as long as :func:`shutdown_sox_effects` is not called yet. + Once :func:`shutdown_sox_effects` is called, you can no longer use SoX effects and initializing + again will result in error. """ torch.ops.torchaudio.sox_effects_initialize_sox_effects() @_mod_utils.requires_module("torchaudio._torchaudio") -def shutdown_sox_effects() -> None: - """Clean up resources required to use ``SoxEffectsChain`` +def shutdown_sox_effects(): + """Clean up resources required to use sox effects. - You do not need to call this function manually. It is called automatically. + Note: + You do not need to call this function manually. It is called automatically. It is safe to call this function multiple times. - Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and + Once :py:func:`shutdown_sox_effects` is called, you can no longer use SoX effects and initializing again will result in error. """ torch.ops.torchaudio.sox_effects_shutdown_sox_effects() @@ -47,18 +47,227 @@ def shutdown_sox_effects() -> None: def effect_names() -> List[str]: """Gets list of valid sox effect names - Returns: list[str] + Returns: + List[str]: list of available effect names. Example - >>> EFFECT_NAMES = torchaudio.sox_effects.effect_names() + >>> torchaudio.sox_effects.effect_names() + ['allpass', 'band', 'bandpass', ... ] """ - return torch.ops.torchaudio.sox_effects_list_effects() + return list(list_effects().keys()) @_mod_utils.requires_module('torchaudio._torchaudio') +def apply_effects_tensor( + tensor: torch.Tensor, + sample_rate: int, + effects: List[List[str]], + channels_first: bool = True, +) -> Tuple[torch.Tensor, int]: + """Apply sox effects to given Tensor + + Note: + This function works in the way very similar to ``sox`` command, however there are slight + differences. For example, ``sox`` commnad adds certain effects automatically (such as + ``rate`` effect after ``speed`` and ``pitch`` and other effects), but this function does + only applies the given effects. (Therefore, to actually apply ``speed`` effect, you also + need to give ``rate`` effect with desired sampling rate.) + + Args: + tensor (torch.Tensor): Input 2D Tensor. + sample_rate (int): Sample rate + effects (List[List[str]]): List of effects. + channels_first (bool): Indicates if the input Tensor's dimension is + ``[channels, time]`` or ``[time, channels]`` + + Returns: + Tuple[torch.Tensor, int]: Resulting Tensor and sample rate. + The resulting Tensor has the same ``dtype`` as the input Tensor, and + the same channels order. The shape of the Tensor can be different based on the + effects applied. Sample rate can also be different based on the effects applied. + + Example - Basic usage + >>> + >>> # Defines the effects to apply + >>> effects = [ + ... ['gain', '-n'], # normalises to 0dB + ... ['pitch', '5'], # 5 cent pitch shift + ... ['rate', '8000'], # resample to 8000 Hz + ... ] + >>> + >>> # Generate pseudo wave: + >>> # normalized, channels first, 2ch, sampling rate 16000, 1 second + >>> sample_rate = 16000 + >>> waveform = 2 * torch.rand([2, sample_rate * 1]) - 1 + >>> waveform.shape + torch.Size([2, 16000]) + >>> waveform + tensor([[ 0.3138, 0.7620, -0.9019, ..., -0.7495, -0.4935, 0.5442], + [-0.0832, 0.0061, 0.8233, ..., -0.5176, -0.9140, -0.2434]]) + >>> + >>> # Apply effects + >>> waveform, sample_rate = apply_effects_tensor( + ... wave_form, sample_rate, effects, channels_first=True) + >>> + >>> # Check the result + >>> # The new waveform is sampling rate 8000, 1 second. + >>> # normalization and channel order are preserved + >>> waveform.shape + torch.Size([2, 8000]) + >>> waveform + tensor([[ 0.5054, -0.5518, -0.4800, ..., -0.0076, 0.0096, -0.0110], + [ 0.1331, 0.0436, -0.3783, ..., -0.0035, 0.0012, 0.0008]]) + >>> sample_rate + 8000 + + Example - Torchscript-able transform + >>> + >>> # Use `apply_effects_tensor` in `torch.nn.Module` and dump it to file, + >>> # then run sox effect via Torchscript runtime. + >>> + >>> class SoxEffectTransform(torch.nn.Module): + ... effects: List[List[str]] + ... + ... def __init__(self, effects: List[List[str]]): + ... super().__init__() + ... self.effects = effects + ... + ... def forward(self, tensor: torch.Tensor, sample_rate: int): + ... return sox_effects.apply_effects_tensor( + ... tensor, sample_rate, self.effects) + ... + ... + >>> # Create transform object + >>> effects = [ + ... ["lowpass", "-1", "300"], # apply single-pole lowpass filter + ... ["rate", "8000"], # change sample rate to 8000 + ... ] + >>> transform = SoxEffectTensorTransform(effects, input_sample_rate) + >>> + >>> # Dump it to file and load + >>> path = 'sox_effect.zip' + >>> torch.jit.script(trans).save(path) + >>> transform = torch.jit.load(path) + >>> + >>>> # Run transform + >>> waveform, input_sample_rate = torchaudio.load("input.wav") + >>> waveform, sample_rate = transform(waveform, input_sample_rate) + >>> assert sample_rate == 8000 + """ + in_signal = torch.classes.torchaudio.TensorSignal(tensor, sample_rate, channels_first) + out_signal = torch.ops.torchaudio.sox_effects_apply_effects_tensor(in_signal, effects) + return out_signal.get_tensor(), out_signal.get_sample_rate() + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def apply_effects_file( + path: str, + effects: List[List[str]], + normalize: bool = True, + channels_first: bool = True, +) -> Tuple[torch.Tensor, int]: + """Apply sox effects to the audio file and load the resulting data as Tensor + + Note: + This function works in the way very similar to ``sox`` command, however there are slight + differences. For example, ``sox`` commnad adds certain effects automatically (such as + ``rate`` effect after ``speed``, ``pitch`` etc), but this function only applies the given + effects. Therefore, to actually apply ``speed`` effect, you also need to give ``rate`` + effect with desired sampling rate, because internally, ``speed`` effects only alter sampling + rate and leave samples untouched. + + Args: + path (str): Path to the audio file. + effects (List[List[str]]): List of effects. + normalize (bool): + When ``True``, this function always return ``float32``, and sample values are + normalized to ``[-1.0, 1.0]``. + If input file is integer WAV, giving ``False`` will change the resulting Tensor type to + integer type. This argument has no effect for formats other + than integer WAV type. + channels_first (bool): When True, the returned Tensor has dimension ``[channel, time]``. + Otherwise, the returned Tensor's dimension is ``[time, channel]``. + + Returns: + Tuple[torch.Tensor, int]: Resulting Tensor and sample rate. + If ``normalize=True``, the resulting Tensor is always ``float32`` type. + If ``normalize=False`` and the input audio file is of integer WAV file, then the + resulting Tensor has corresponding integer type. (Note 24 bit integer type is not supported) + If ``channels_first=True``, the resulting Tensor has dimension ``[channel, time]``, + otherwise ``[time, channel]``. + + Example - Basic usage + >>> + >>> # Defines the effects to apply + >>> effects = [ + ... ['gain', '-n'], # normalises to 0dB + ... ['pitch', '5'], # 5 cent pitch shift + ... ['rate', '8000'], # resample to 8000 Hz + ... ] + >>> + >>> # Apply effects and load data with channels_first=True + >>> waveform, sample_rate = apply_effects_file("data.wav", effects, channels_first=True) + >>> + >>> # Check the result + >>> waveform.shape + torch.Size([2, 8000]) + >>> waveform + tensor([[ 5.1151e-03, 1.8073e-02, 2.2188e-02, ..., 1.0431e-07, + -1.4761e-07, 1.8114e-07], + [-2.6924e-03, 2.1860e-03, 1.0650e-02, ..., 6.4122e-07, + -5.6159e-07, 4.8103e-07]]) + >>> sample_rate + 8000 + + Example - Apply random speed perturbation to dataset + >>> + >>> # Load data from file, apply random speed perturbation + >>> class RandomPerturbationFile(torch.utils.data.Dataset): + ... \"\"\"Given flist, apply random speed perturbation + ... + ... Suppose all the input files are at least one second long. + ... \"\"\" + ... def __init__(self, flist: List[str], sample_rate: int): + ... super().__init__() + ... self.flist = flist + ... self.sample_rate = sample_rate + ... self.rng = None + ... + ... def __getitem__(self, index): + ... speed = self.rng.uniform(0.5, 2.0) + ... effects = [ + ... ['gain', '-n', '-10'], # apply 10 db attenuation + ... ['remix', '-'], # merge all the channels + ... ['speed', f'{speed:.5f}'], # duration is now 0.5 ~ 2.0 seconds. + ... ['rate', f'{self.sample_rate}'], + ... ['pad', '0', '1.5'], # add 1.5 seconds silence at the end + ... ['trim', '0', '2'], # get the first 2 seconds + ... ] + ... waveform, _ = torchaudio.sox_effects.apply_effects_file( + ... self.flist[index], effects) + ... return waveform + ... + ... def __len__(self): + ... return len(self.flist) + ... + >>> dataset = RandomPerturbationFile(file_list, sample_rate=8000) + >>> loader = torch.utils.data.DataLoader(dataset, batch_size=32) + >>> for batch in loader: + >>> pass + """ + signal = torch.ops.torchaudio.sox_effects_apply_effects_file(path, effects, normalize, channels_first) + return signal.get_tensor(), signal.get_sample_rate() + + +@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated('Please migrate to `apply_effects_file` or `apply_effects_tensor`.') def SoxEffect(): r"""Create an object for passing sox effect information between python and c++ + Warning: + This function is deprecated. + Please migrate to :func:`apply_effects_file` or :func:`apply_effects_tensor`. + Returns: SoxEffect: An object with the following attributes: ename (str) which is the name of effect, and eopts (List[str]) which is a list of effect options. @@ -66,49 +275,60 @@ def SoxEffect(): return _torchaudio.SoxEffect() +@_mod_utils.deprecated('Please migrate to `apply_effects_file` or `apply_effects_tensor`.') class SoxEffectsChain(object): r"""SoX effects chain class. + Warning: + This class is deprecated. + Please migrate to :func:`apply_effects_file` or :func:`apply_effects_tensor`. + Args: - normalization (bool, number, or callable, optional): If boolean `True`, then output is divided by `1 << 31` - (assumes signed 32-bit audio), and normalizes to `[-1, 1]`. If `number`, then output is divided by that - number. If `callable`, then the output is passed as a parameter to the given function, then the - output is divided by the result. (Default: ``True``) - channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) - out_siginfo (sox_signalinfo_t, optional): a sox_signalinfo_t type, which could be helpful if the - audio type cannot be automatically determined. (Default: ``None``) - out_encinfo (sox_encodinginfo_t, optional): a sox_encodinginfo_t type, which could be set if the - audio type cannot be automatically determined. (Default: ``None``) - filetype (str, optional): a filetype or extension to be set if sox cannot determine it - automatically. . (Default: ``'raw'``) + normalization (bool, number, or callable, optional): + If boolean ``True``, then output is divided by ``1 << 31`` + (assumes signed 32-bit audio), and normalizes to ``[-1, 1]``. + If ``number``, then output is divided by that number. + If ``callable``, then the output is passed as a parameter to the given function, then + the output is divided by the result. (Default: ``True``) + channels_first (bool, optional): + Set channels first or length first in result. (Default: ``True``) + out_siginfo (sox_signalinfo_t, optional): + a sox_signalinfo_t type, which could be helpful if the audio type cannot be + automatically determined. (Default: ``None``) + out_encinfo (sox_encodinginfo_t, optional): + a sox_encodinginfo_t type, which could be set if the audio type cannot be + automatically determined. (Default: ``None``) + filetype (str, optional): + a filetype or extension to be set if sox cannot determine it automatically. + (Default: ``'raw'``) Returns: - Tuple[Tensor, int]: An output Tensor of size `[C x L]` or `[L x C]` where L is the number + Tuple[Tensor, int]: + An output Tensor of size ``[C x L]`` or ``[L x C]`` where L is the number of audio frames and C is the number of channels. An integer which is the sample rate of the audio (as listed in the metadata of the file) Example >>> class MyDataset(Dataset): - >>> def __init__(self, audiodir_path): - >>> self.data = [os.path.join(audiodir_path, fn) for fn in os.listdir(audiodir_path)] - >>> self.E = torchaudio.sox_effects.SoxEffectsChain() - >>> self.E.append_effect_to_chain("rate", [16000]) # resample to 16000hz - >>> self.E.append_effect_to_chain("channels", ["1"]) # mono signal - >>> def __getitem__(self, index): - >>> fn = self.data[index] - >>> self.E.set_input_file(fn) - >>> x, sr = self.E.sox_build_flow_effects() - >>> return x, sr - >>> - >>> def __len__(self): - >>> return len(self.data) - >>> - >>> torchaudio.initialize_sox() + ... def __init__(self, audiodir_path): + ... self.data = [ + ... os.path.join(audiodir_path, fn) + ... for fn in os.listdir(audiodir_path)] + ... self.E = torchaudio.sox_effects.SoxEffectsChain() + ... self.E.append_effect_to_chain("rate", [16000]) # resample to 16000hz + ... self.E.append_effect_to_chain("channels", ["1"]) # mono signal + ... def __getitem__(self, index): + ... fn = self.data[index] + ... self.E.set_input_file(fn) + ... x, sr = self.E.sox_build_flow_effects() + ... return x, sr + ... + ... def __len__(self): + ... return len(self.data) + ... >>> ds = MyDataset(path_to_audio_files) >>> for sig, sr in ds: - >>> [do something here] - >>> torchaudio.shutdown_sox() - + ... pass """ EFFECTS_UNIMPLEMENTED = {"spectrogram", "splice", "noiseprof", "fir"} @@ -165,9 +385,9 @@ def sox_build_flow_effects(self, out (Tensor, optional): Where the output will be written to. (Default: ``None``) Returns: - Tuple[Tensor, int]: An output Tensor of size `[C x L]` or `[L x C]` where L is the number - of audio frames and C is the number of channels. An integer which is the sample rate of the - audio (as listed in the metadata of the file) + Tuple[Tensor, int]: An output Tensor of size `[C x L]` or `[L x C]` where + L is the number of audio frames and C is the number of channels. + An integer which is the sample rate of the audio (as listed in the metadata of the file) """ # initialize output tensor if out is not None: diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index f34f5c468e..72e55c94c8 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -86,20 +86,8 @@ def forward(self, waveform: Tensor) -> Tensor: class GriffinLim(torch.nn.Module): r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation. - Implementation ported from `librosa`. - .. [1] McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. - "librosa: Audio and music signal analysis in python." - In Proceedings of the 14th python in science conference, pp. 18-25. 2015. - - .. [2] Perraudin, N., Balazs, P., & Sรธndergaard, P. L. - "A fast Griffin-Lim algorithm," - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4), - Oct. 2013. - - .. [3] D. W. Griffin and J. S. Lim, - "Signal estimation from modified short-time Fourier transform," - IEEE Trans. ASSP, vol.32, no.2, pp.236โ€“243, Apr. 1984. + Implementation ported from ``librosa`` [1]_, [2]_, [3]_. Args: n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``) @@ -117,6 +105,24 @@ class GriffinLim(torch.nn.Module): Values near 1 can lead to faster convergence, but above 1 may not converge. (Default: ``0.99``) length (int, optional): Array length of the expected output. (Default: ``None``) rand_init (bool, optional): Initializes phase randomly if True and to zero otherwise. (Default: ``True``) + + References: + .. [1] + | McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, + and Oriol Nieto. + | "librosa: Audio and music signal analysis in python." + | In Proceedings of the 14th python in science conference, pp. 18-25. 2015. + + .. [2] + | Perraudin, N., Balazs, P., & Sรธndergaard, P. L. + | "A fast Griffin-Lim algorithm," + | IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4), + | Oct. 2013. + + .. [3] + | D. W. Griffin and J. S. Lim, + | "Signal estimation from modified short-time Fourier transform," + | IEEE Trans. ASSP, vol.32, no.2, pp.236โ€“243, Apr. 1984. """ __constants__ = ['n_fft', 'n_iter', 'win_length', 'hop_length', 'power', 'normalized', 'length', 'momentum', 'rand_init'] @@ -153,8 +159,9 @@ def __init__(self, def forward(self, specgram: Tensor) -> Tensor: r""" Args: - specgram (Tensor): A magnitude-only STFT spectrogram of dimension (..., freq, frames) - where freq is ``n_fft // 2 + 1``. + specgram (Tensor): + A magnitude-only STFT spectrogram of dimension (..., freq, frames) + where freq is ``n_fft // 2 + 1``. Returns: Tensor: waveform of (..., time), where time equals the ``length`` parameter if given. diff --git a/torchaudio/utils/__init__.py b/torchaudio/utils/__init__.py new file mode 100644 index 0000000000..bc11f86893 --- /dev/null +++ b/torchaudio/utils/__init__.py @@ -0,0 +1,9 @@ +from . import ( + sox_utils, +) + +from torchaudio._internal import module_utils as _mod_utils + + +if _mod_utils.is_module_available('torchaudio._torchaudio'): + sox_utils.set_verbosity(1) diff --git a/torchaudio/utils/sox_utils.py b/torchaudio/utils/sox_utils.py new file mode 100644 index 0000000000..59090e4b88 --- /dev/null +++ b/torchaudio/utils/sox_utils.py @@ -0,0 +1,94 @@ +from typing import List, Dict + +import torch + +from torchaudio._internal import ( + module_utils as _mod_utils, +) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def set_seed(seed: int): + """Set libsox's PRNG + + Args: + seed (int): seed value. valid range is int32. + + See Also: + http://sox.sourceforge.net/sox.html + """ + torch.ops.torchaudio.sox_utils_set_seed(seed) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def set_verbosity(verbosity: int): + """Set libsox's verbosity + + Args: + verbosity (int): Set verbosity level of libsox. + 1: failure messages + 2: warnings + 3: details of processing + 4-6: increasing levels of debug messages + + See Also: + http://sox.sourceforge.net/sox.html + """ + torch.ops.torchaudio.sox_utils_set_verbosity(verbosity) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def set_buffer_size(buffer_size: int): + """Set buffer size for sox effect chain + + Args: + buffer_size (int): Set the size in bytes of the buffers used for processing audio. + + See Also: + http://sox.sourceforge.net/sox.html + """ + torch.ops.torchaudio.sox_utils_set_buffer_size(buffer_size) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def set_use_threads(use_threads: bool): + """Set multithread option for sox effect chain + + Args: + use_threads (bool): When ``True``, enables ``libsox``'s parallel effects channels processing. + To use mutlithread, the underlying ``libsox`` has to be compiled with OpenMP support. + + See Also: + http://sox.sourceforge.net/sox.html + """ + torch.ops.torchaudio.sox_utils_set_use_threads(use_threads) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def list_effects() -> Dict[str, str]: + """List the available sox effect names + + Returns: + Dict[str, str]: Mapping from ``effect name`` to ``usage`` + """ + return dict(torch.ops.torchaudio.sox_utils_list_effects()) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def list_read_formats() -> List[str]: + """List the supported audio formats for read + + Returns: + List[str]: List of supported audio formats + """ + return torch.ops.torchaudio.sox_utils_list_read_formats() + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def list_write_formats() -> List[str]: + """List the supported audio formats for write + + Returns: + List[str]: List of supported audio formats + """ + return torch.ops.torchaudio.sox_utils_list_write_formats() From 18be43cd7248272d5096c3695f34ebae26449193 Mon Sep 17 00:00:00 2001 From: Yanan Cao Date: Sat, 1 Aug 2020 13:02:20 -0700 Subject: [PATCH 14/73] Support custom exception message (#41907) Summary: Raise and assert used to have a hard-coded error message "Exception". User provided error message was ignored. This PR adds support to represent user's error message in TorchScript. This breaks backward compatibility because now we actually need to script the user's error message, which can potentially contain unscriptable expressions. Such programs can break when scripting, but saved models can still continue to work. Increased an op count in test_mobile_optimizer.py because now we need aten::format to form the actual exception message. This is built upon an WIP PR: https://github.com/pytorch/pytorch/pull/34112 by driazati Pull Request resolved: https://github.com/pytorch/pytorch/pull/41907 Reviewed By: ngimel Differential Revision: D22778301 Pulled By: gmagogsfm fbshipit-source-id: 2b94f0db4ae9fe70c4cd03f4048e519ea96323ad --- torchaudio/functional.py | 4 ++-- torchaudio/transforms.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 713544cc74..53795fef1a 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -223,8 +223,8 @@ def griffinlim( Returns: torch.Tensor: waveform of (..., time), where time equals the ``length`` parameter if given. """ - assert momentum < 1, 'momentum=%s > 1 can be unstable' % momentum - assert momentum >= 0, 'momentum=%s < 0' % momentum + assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum) + assert momentum >= 0, 'momentum={} < 0'.format(momentum) # pack batch shape = specgram.size() diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 72e55c94c8..ea0d6db492 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -607,7 +607,7 @@ def forward(self, waveform: Tensor) -> Tensor: return waveform - raise ValueError('Invalid resampling method: %s' % (self.resampling_method)) + raise ValueError('Invalid resampling method: ' + str(self.resampling_method)) class ComplexNorm(torch.nn.Module): From 355c1fdbda46a9fffc74ca1bbc061eccc62cc2c1 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Wed, 5 Aug 2020 11:03:05 -0700 Subject: [PATCH 15/73] Import torchaudio 20200804 Summary: Up to #804 Reviewed By: vincentqb Differential Revision: D22947671 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.../kaldi_io_test.py} | 2 +- .../librosa_compatibility_test.py} | 2 +- .../models_test.py} | 28 +- .../sox_compatibility_test.py} | 2 +- .../sox_effect}/__init__.py | 0 .../sox_effect/common.py | 2 +- .../sox_effect/dataset_test.py} | 2 +- .../sox_effect/smoke_test.py | 2 +- .../sox_effect/sox_effect_test.py} | 2 +- .../sox_effect/sox_effects_chain_test.py | 0 .../sox_effect/torchscript_test.py} | 2 +- .../sox_io_backend}/__init__.py | 0 .../sox_io_backend/common.py | 0 .../sox_io_backend/info_test.py} | 2 +- .../sox_io_backend/load_test.py} | 2 +- .../sox_io_backend/roundtrip_test.py} | 2 +- .../sox_io_backend/save_test.py} | 2 +- .../sox_io_backend/smoke_test.py | 2 +- .../sox_io_backend/torchscript_test.py} | 2 +- .../torchscript_consistency_cpu_test.py | 2 +- .../torchscript_consistency_cuda_test.py | 2 +- .../torchscript_consistency_impl.py | 2 +- .../transforms_test.py} | 2 +- test/torchaudio_unittest/utils/__init__.py | 0 .../utils/sox_utils_test.py} | 2 +- torchaudio/compliance/kaldi.py | 12 +- torchaudio/datasets/ljspeech.py | 2 +- torchaudio/functional.py | 98 +---- torchaudio/models/__init__.py | 2 +- torchaudio/models/{_wavernn.py => wavernn.py} | 64 +-- torchaudio/transforms.py | 10 +- 146 files changed, 878 insertions(+), 764 deletions(-) delete mode 100644 test/assets/ARCTIC/cmu_us_aew_arctic/etc/txt.done.data delete mode 100644 test/assets/ARCTIC/cmu_us_aew_arctic/wav/arctic_a0024.wav delete mode 100644 test/assets/LJSpeech-1.1/metadata.csv delete mode 100644 test/assets/LJSpeech-1.1/wavs/LJ001-0001.wav delete mode 100644 test/assets/LibriSpeech/dev-clean/1272/128104/1272-128104-0000.flac delete mode 100644 test/assets/LibriSpeech/dev-clean/1272/128104/1272-128104.trans.txt delete mode 100644 test/assets/SpeechCommands/speech_commands_v0.02/go/0a9f9af7_nohash_0.wav delete mode 100644 test/common_utils/backend_utils.py delete mode 100644 test/datasets/datasets_test.py delete mode 100644 test/functional_cpu_test.py rename test/{ => torchaudio_unittest}/__init__.py (100%) rename test/{ => torchaudio_unittest}/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav (100%) rename test/{ => torchaudio_unittest}/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/train.tsv (100%) rename test/{ => torchaudio_unittest}/assets/VCTK-Corpus/txt/p224/p224_002.txt (100%) rename test/{ => torchaudio_unittest}/assets/VCTK-Corpus/wav48/p224/p224_002.wav (100%) rename test/{ => torchaudio_unittest}/assets/io/96k_0_1ch.opus (100%) rename test/{ => torchaudio_unittest}/assets/io/96k_0_2ch.opus (100%) rename test/{ => torchaudio_unittest}/assets/io/96k_10_1ch.opus (100%) rename test/{ => torchaudio_unittest}/assets/io/96k_10_2ch.opus (100%) rename test/{ => torchaudio_unittest}/assets/io/96k_5_1ch.opus (100%) rename test/{ => torchaudio_unittest}/assets/io/96k_5_2ch.opus (100%) rename test/{ => torchaudio_unittest}/assets/io/generate_opus.py (100%) rename test/{ => 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torchaudio_unittest}/sox_effect/smoke_test.py (97%) rename test/{sox_effect/test_sox_effect.py => torchaudio_unittest/sox_effect/sox_effect_test.py} (99%) rename test/{ => torchaudio_unittest}/sox_effect/sox_effects_chain_test.py (100%) rename test/{sox_effect/test_torchscript.py => torchaudio_unittest/sox_effect/torchscript_test.py} (98%) rename test/{utils => torchaudio_unittest/sox_io_backend}/__init__.py (100%) rename test/{ => torchaudio_unittest}/sox_io_backend/common.py (100%) rename test/{sox_io_backend/test_info.py => torchaudio_unittest/sox_io_backend/info_test.py} (98%) rename test/{sox_io_backend/test_load.py => torchaudio_unittest/sox_io_backend/load_test.py} (99%) rename test/{sox_io_backend/test_roundtrip.py => torchaudio_unittest/sox_io_backend/roundtrip_test.py} (97%) rename test/{sox_io_backend/test_save.py => torchaudio_unittest/sox_io_backend/save_test.py} (99%) rename test/{ => torchaudio_unittest}/sox_io_backend/smoke_test.py (98%) rename test/{sox_io_backend/test_torchscript.py => torchaudio_unittest/sox_io_backend/torchscript_test.py} (99%) rename test/{ => torchaudio_unittest}/torchscript_consistency_cpu_test.py (93%) rename test/{ => torchaudio_unittest}/torchscript_consistency_cuda_test.py (94%) rename test/{ => torchaudio_unittest}/torchscript_consistency_impl.py (99%) rename test/{test_transforms.py => torchaudio_unittest/transforms_test.py} (99%) create mode 100644 test/torchaudio_unittest/utils/__init__.py rename test/{utils/test_sox_utils.py => torchaudio_unittest/utils/sox_utils_test.py} (97%) rename torchaudio/models/{_wavernn.py => wavernn.py} (83%) diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml index 108d97d193..121478165e 100644 --- a/.circleci/unittest/linux/scripts/environment.yml +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -6,6 +6,7 @@ dependencies: - numpy - pytest - pytest-cov + - pytest-xdist - codecov - librosa - llvmlite==0.31 # See https://github.com/pytorch/audio/pull/766 diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh index 131c9e43bf..8346e6c825 100755 --- a/.circleci/unittest/linux/scripts/install.sh +++ b/.circleci/unittest/linux/scripts/install.sh @@ -20,4 +20,4 @@ printf "Installing PyTorch with %s\n" "${cudatoolkit}" conda install -y -c pytorch-nightly pytorch "${cudatoolkit}" printf "* Installing torchaudio\n" -BUILD_SOX=1 python setup.py develop +BUILD_SOX=1 python setup.py install diff --git a/.circleci/unittest/linux/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh index 9ea942851e..50bcf711d6 100755 --- a/.circleci/unittest/linux/scripts/run_test.sh +++ b/.circleci/unittest/linux/scripts/run_test.sh @@ -2,9 +2,28 @@ set -e +case "$(uname -s)" in + Darwin*) os=MacOSX;; + *) os=Linux +esac + + eval "$(./conda/bin/conda shell.bash hook)" conda activate ./env python -m torch.utils.collect_env +export TORCHAUDIO_TEST_FAIL_IF_NO_EXTENSION=1 export PATH="${PWD}/third_party/install/bin/:${PATH}" -pytest --cov=torchaudio --junitxml=test-results/junit.xml -v --durations 20 test + +declare -a common_args=( + '--cov=torchaudio' + "--junitxml=${PWD}/test-results/junit.xml" + '--durations' '20' +) +if [ "${os}" == MacOSX ] ; then + declare -a args=('-q' '-n' 'auto' '--dist=loadscope') +else + declare -a args=('-v') +fi +cd test +pytest "${args[@]}" "${common_args[@]}" torchaudio_unittest diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh index acbd79ae50..2b9c88b4af 100644 --- a/.circleci/unittest/windows/scripts/install.sh +++ b/.circleci/unittest/windows/scripts/install.sh @@ -20,4 +20,4 @@ printf "Installing PyTorch with %s\n" "${cudatoolkit}" conda install -y -c pytorch-nightly pytorch "${cudatoolkit}" printf "* Installing torchaudio\n" -python setup.py develop +python setup.py install diff --git a/.circleci/unittest/windows/scripts/run_test.sh b/.circleci/unittest/windows/scripts/run_test.sh index 252c1e9f46..7fdd1d4c6d 100644 --- a/.circleci/unittest/windows/scripts/run_test.sh +++ b/.circleci/unittest/windows/scripts/run_test.sh @@ -6,5 +6,5 @@ eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" conda activate ./env python -m torch.utils.collect_env -pytest --cov=torchaudio --junitxml=test-results/junit.xml -v --durations 20 test -flake8 torchaudio test +cd test +pytest --cov=torchaudio --junitxml=../test-results/junit.xml -v --durations 20 torchaudio_unittest diff --git a/README.md b/README.md index 290bb73543..70f30be243 100644 --- a/README.md +++ b/README.md @@ -37,6 +37,7 @@ The following is the corresponding ``torchaudio`` versions and supported Python | ``torch`` | ``torchaudio`` | ``python`` | | ------------------------ | ------------------------ | ------------------------------- | | ``master`` / ``nightly`` | ``master`` / ``nightly`` | ``>=3.6`` | +| ``1.6.0`` | ``0.6.0`` | ``>=3.6`` | | ``1.5.0`` | ``0.5.0`` | ``>=3.5`` | | ``1.4.0`` | ``0.4.0`` | ``==2.7``, ``>=3.5``, ``<=3.8`` | diff --git a/docs/source/functional.rst b/docs/source/functional.rst index 12ee165060..89e992bb89 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -8,11 +8,6 @@ torchaudio.functional Functions to perform common audio operations. -:hidden:`istft` -~~~~~~~~~~~~~~~ - -.. autofunction:: istft - :hidden:`spectrogram` ~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/source/models.rst b/docs/source/models.rst index 5b40482976..44eb74444a 100644 --- a/docs/source/models.rst +++ b/docs/source/models.rst @@ -15,3 +15,11 @@ The models subpackage contains definitions of models for addressing common audio .. autoclass:: Wav2Letter .. automethod:: forward + + +:hidden:`WaveRNN` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: WaveRNN + + .. automethod:: forward diff --git a/examples/pipeline_wavernn/datasets.py b/examples/pipeline_wavernn/datasets.py index 8d3068a229..58c2853f3d 100644 --- a/examples/pipeline_wavernn/datasets.py +++ b/examples/pipeline_wavernn/datasets.py @@ -4,7 +4,7 @@ import torch import torchaudio from torch.utils.data.dataset import random_split -from torchaudio.datasets import LJSPEECH +from torchaudio.datasets import LJSPEECH, LIBRITTS from torchaudio.transforms import MuLawEncoding from processing import bits_to_normalized_waveform, normalized_waveform_to_bits @@ -48,12 +48,20 @@ def process_datapoint(self, item): return item[0].squeeze(0), specgram -def split_process_ljspeech(args, transforms): - data = LJSPEECH(root=args.file_path, download=False) +def split_process_dataset(args, transforms): + if args.dataset == 'ljspeech': + data = LJSPEECH(root=args.file_path, download=False) - val_length = int(len(data) * args.val_ratio) - lengths = [len(data) - val_length, val_length] - train_dataset, val_dataset = random_split(data, lengths) + val_length = int(len(data) * args.val_ratio) + lengths = [len(data) - val_length, val_length] + train_dataset, val_dataset = random_split(data, lengths) + + elif args.dataset == 'libritts': + train_dataset = LIBRITTS(root=args.file_path, url='train-clean-100', download=False) + val_dataset = LIBRITTS(root=args.file_path, url='dev-clean', download=False) + + else: + raise ValueError(f"Expected dataset: `ljspeech` or `libritts`, but found {args.dataset}") train_dataset = Processed(train_dataset, transforms) val_dataset = Processed(val_dataset, transforms) diff --git a/examples/pipeline_wavernn/main.py b/examples/pipeline_wavernn/main.py index 032e9fc70e..9620dd5d45 100644 --- a/examples/pipeline_wavernn/main.py +++ b/examples/pipeline_wavernn/main.py @@ -13,9 +13,9 @@ from torch.optim import Adam from torch.utils.data import DataLoader from torchaudio.datasets.utils import bg_iterator -from torchaudio.models._wavernn import _WaveRNN +from torchaudio.models.wavernn import WaveRNN -from datasets import collate_factory, split_process_ljspeech +from datasets import collate_factory, split_process_dataset from losses import LongCrossEntropyLoss, MoLLoss from processing import LinearToMel, NormalizeDB from utils import MetricLogger, count_parameters, save_checkpoint @@ -55,6 +55,13 @@ def parse_args(): metavar="N", help="print frequency in epochs", ) + parser.add_argument( + "--dataset", + default="ljspeech", + choices=["ljspeech", "libritts"], + type=str, + help="select dataset to train with", + ) parser.add_argument( "--batch-size", default=256, type=int, metavar="N", help="mini-batch size" ) @@ -269,7 +276,7 @@ def main(args): NormalizeDB(min_level_db=args.min_level_db), ) - train_dataset, val_dataset = split_process_ljspeech(args, transforms) + train_dataset, val_dataset = split_process_dataset(args, transforms) loader_training_params = { "num_workers": args.workers, @@ -297,7 +304,7 @@ def main(args): n_classes = 2 ** args.n_bits if args.loss == "crossentropy" else 30 - model = _WaveRNN( + model = WaveRNN( upsample_scales=args.upsample_scales, n_classes=n_classes, hop_length=args.hop_length, diff --git a/setup.py b/setup.py index aab59b4d8a..0773b23107 100644 --- a/setup.py +++ b/setup.py @@ -3,8 +3,8 @@ import shutil import subprocess from pathlib import Path -import distutils.command.clean from setuptools import setup, find_packages +import distutils.command.clean from build_tools import setup_helpers @@ -85,5 +85,6 @@ def run(self): cmdclass={ 'build_ext': setup_helpers.BuildExtension.with_options(no_python_abi_suffix=True) }, - install_requires=[pytorch_package_dep] + install_requires=[pytorch_package_dep], + zip_safe=False, ) diff --git a/test/README.md b/test/README.md index c0a4b2a21d..629e5b54be 100644 --- a/test/README.md +++ b/test/README.md @@ -1,5 +1,27 @@ # Torchaudio Test Suite +## How to run test + +You can use `pytest` to run `torchaudio`'s test suites. See https://docs.pytest.org/ for the detail of how to use `pytest` command. + +``` +# List up all the tests +pytest test --collect-only +# Run all the test suites +pytest test +# Run tests on sox_effects module +pytest test/sox_effect +# use -k to apply filter +pytest test/sox_io_backend -k load # only runs tests where their names contain load +# Some other useful options; +# Stop on the first failure -x +# Run failure fast --ff +# Only rerun the failure --lf +``` + +**Note** +We use PyTorch's test utilities instead of `pytest` frameworks when writing tests to avoid reinventing the wheel for Tensor comparison. + ## Structure of tests The following is an overview of the tests and related modules for `torchaudio`. @@ -7,39 +29,39 @@ The following is an overview of the tests and related modules for `torchaudio`. ### Purpose specific test suites #### Numerical compatibility agains existing software -- [Librosa compatibility test](./test_librosa_compatibility.py) +- [Librosa compatibility test](./torchaudio_unittest/librosa_compatibility_test.py) Test suite for numerical compatibility against librosa. -- [SoX compatibility test](./test_sox_compatibility.py) +- [SoX compatibility test](./torchaudio_unittest/sox_compatibility_test.py) Test suite for numerical compatibility against SoX. -- [Kaldi compatibility test](./test_kaldi_compatibility.py) +- [Kaldi compatibility test](./torchaudio_unittest/kaldi_compatibility_test.py) Test suite for numerical compatibility against Kaldi. #### Result consistency with PyTorch framework -- [TorchScript consistency test](./test_torchscript_consistency.py) +- [TorchScript consistency test](./torchaudio_unittest/torchscript_consistency_impl.py) Test suite to check 1. if an API is TorchScript-able, and 2. the results from Python and Torchscript match. -- [Batch consistency test](./test_batch_consistency.py) +- [Batch consistency test](./torchaudio_unittest/batch_consistency_test.py) Test suite to check if functionals/Transforms handle single sample input and batch input and return the same result. ### Module specific test suites The following test modules are defined for corresponding `torchaudio` module/functions. -- [`torchaudio.datasets`](./test_datasets.py) -- [`torchaudio.functional`](./test_functional.py) -- [`torchaudio.transforms`](./test_transforms.py) -- [`torchaudio.compliance.kaldi`](./test_compliance_kaldi.py) -- [`torchaudio.kaldi_io`](./test_kaldi_io.py) +- [`torchaudio.datasets`](./torchaudio_unittest/datasets) +- [`torchaudio.functional`](./torchaudio_unittest/functional) +- [`torchaudio.transforms`](./torchaudio_unittest/transforms_test.py) +- [`torchaudio.compliance.kaldi`](./torchaudio_unittest/compliance_kaldi_test.py) +- [`torchaudio.kaldi_io`](./torchaudio_unittest/kaldi_io_test.py) - [`torchaudio.sox_effects`](test/sox_effects) -- [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](test/test_io.py) +- [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](./torchaudio_unittest/io_test.py) ### Test modules that do not fall into the above categories -- [test_dataloader.py](./test_dataloader.py) +- [test_dataloader.py](./torchaudio_unittest/dataloader_test.py) Simple test for loading data and applying preprocessing. ### Support files -- [assets](./assets): Contain sample audio files. -- [assets/kaldi](./assets/kaldi): Contains Kaldi format matrix files used in [./test_compliance_kaldi.py](./test_compliance_kaldi.py). -- [compliance](./compliance): Scripts used to generate above Kaldi matrix files. +- [assets](./torchaudio_unittest/assets): Contain sample audio files. +- [assets/kaldi](./torchaudio_unittest/assets/kaldi): Contains Kaldi format matrix files used in [./torchaudio_unittest/test_compliance_kaldi.py](./torchaudio_unittest/test_compliance_kaldi.py). +- [compliance](./torchaudio_unittest/compliance): Scripts used to generate above Kaldi matrix files. ### Waveforms for Testing Purposes @@ -86,11 +108,7 @@ Files: Files: * `CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav` -* `LibriSpeech/dev-clean/1272/128104/1272-128104-0000.flac` -* `LJSpeech-1.1/wavs/LJ001-0001.wav` -* `SpeechCommands/speech_commands_v0.02/go/0a9f9af7_nohash_0.wav` * `VCTK-Corpus/wav48/p224/p224_002.wav` -* `waves_yesno/0_1_0_1_0_1_1_0.wav` * `vad-go-stereo-44100.wav` * `vad-go-mono-32000.wav` @@ -98,13 +116,13 @@ Files: The following is the current practice of torchaudio test suite. -1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./common_utils.py) has some data generator functions. +1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./torchaudio_unittest/common_utils) has some data generator functions. 1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless you are writing tests that are common to CPU / CUDA. - Set class memeber `dtype`, `device` and `backend` for the desired behavior. - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail. - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability. -1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchscript_consistency_impl.py) and test definitions for [CPU](./torchscript_consistency_cpu_test.py) and [CUDA](./torchscript_consistency_cuda_test.py) devices. -1. For numerically comparing Tensors, use `assertEqual` method from `common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. +1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchaudio_unittest/torchscript_consistency_impl.py) and test definitions for [CPU](./torchaudio_unittest/torchscript_consistency_cpu_test.py) and [CUDA](./torchaudio_unittest/torchscript_consistency_cuda_test.py) devices. +1. For numerically comparing Tensors, use `assertEqual` method from torchaudio_unittest.common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. 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zidd``W?e7VkES9mcn;^{Ik@^AVxLF=E&mvHVKmI27wQG>{!Oq23gLUd2WMg}yq;gM zI2f79bQT;}4rfHF9jF-I$5o$>WU3xNwRqkPQO7}q1;LSpNrOZ4YDbn=-<$$Q9_)W3_=x%N{|2Cn z@vzn|(^7E!v3fPG`xIjVs`q9i*=)l!rV@$6M*9gq?dA4(t3{7v5#)g!C})Tu{?Ogv zzHqzoNrFnB>?1z#!jYETbn2XAj^BCSHP1N_FnBsv*ZH7ZhhZ;(a#%Yn;HJ${oibd# zR6n7?Fc+$E=Z4e8IJ(iup_}9#csAqV6s&_|Xn_^oPhQ8>0>#fV!1np@tp5ORNF6HT P-k4=vBg5&3AZY#vXap6N diff --git a/test/common_utils/backend_utils.py b/test/common_utils/backend_utils.py deleted file mode 100644 index beceb6cafb..0000000000 --- a/test/common_utils/backend_utils.py +++ /dev/null @@ -1,41 +0,0 @@ -import unittest - -import torchaudio - -from .import data_utils - - -BACKENDS = torchaudio.list_audio_backends() - - -def _filter_backends_with_mp3(backends): - # Filter out backends that do not support mp3 - test_filepath = data_utils.get_asset_path('steam-train-whistle-daniel_simon.mp3') - - def supports_mp3(backend): - torchaudio.set_audio_backend(backend) - try: - torchaudio.load(test_filepath) - return True - except (RuntimeError, ImportError): - return False - - return [backend for backend in backends if supports_mp3(backend)] - - -BACKENDS_MP3 = _filter_backends_with_mp3(BACKENDS) - - -def set_audio_backend(backend): - """Allow additional backend value, 'default'""" - if backend == 'default': - if 'sox_io' in BACKENDS: - be = 'sox_io' - elif 'soundfile' in BACKENDS: - be = 'soundfile' - else: - raise unittest.SkipTest('No default backend available') - else: - be = backend - - torchaudio.set_audio_backend(be) diff --git a/test/datasets/datasets_test.py b/test/datasets/datasets_test.py deleted file mode 100644 index b8a980f346..0000000000 --- a/test/datasets/datasets_test.py +++ /dev/null @@ -1,60 +0,0 @@ -from torchaudio.datasets.commonvoice import COMMONVOICE -from torchaudio.datasets.librispeech import LIBRISPEECH -from torchaudio.datasets.speechcommands import SPEECHCOMMANDS -from torchaudio.datasets.utils import diskcache_iterator, bg_iterator -from torchaudio.datasets.vctk import VCTK -from torchaudio.datasets.ljspeech import LJSPEECH -from torchaudio.datasets.cmuarctic import CMUARCTIC - -from ..common_utils import ( - TorchaudioTestCase, - get_asset_path, -) - - -class TestDatasets(TorchaudioTestCase): - backend = 'default' - path = get_asset_path() - - def test_vctk(self): - data = VCTK(self.path) - data[0] - - def test_librispeech(self): - data = LIBRISPEECH(self.path, "dev-clean") - data[0] - - def test_ljspeech(self): - data = LJSPEECH(self.path) - data[0] - - def test_speechcommands(self): - data = SPEECHCOMMANDS(self.path) - data[0] - - def test_cmuarctic(self): - data = CMUARCTIC(self.path) - data[0] - - -class TestCommonVoice(TorchaudioTestCase): - backend = 'default' - path = get_asset_path() - - def test_commonvoice(self): - data = COMMONVOICE(self.path, url="tatar") - data[0] - - def test_commonvoice_diskcache(self): - data = COMMONVOICE(self.path, url="tatar") - data = diskcache_iterator(data) - # Save - data[0] - # Load - data[0] - - def test_commonvoice_bg(self): - data = COMMONVOICE(self.path, url="tatar") - data = bg_iterator(data, 5) - for _ in data: - pass diff --git a/test/functional_cpu_test.py b/test/functional_cpu_test.py deleted file mode 100644 index b162df2ebc..0000000000 --- a/test/functional_cpu_test.py +++ /dev/null @@ -1,407 +0,0 @@ -import math -import unittest - -import torch -import torchaudio -import torchaudio.functional as F -from parameterized import parameterized -import pytest - -from . import common_utils -from .functional_impl import Lfilter - - -def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32): - """ Generates random tensors given a seed and size - https://en.wikipedia.org/wiki/Linear_congruential_generator - X_{n + 1} = (a * X_n + c) % m - Using Borland C/C++ values - - The tensor will have values between [0,1) - Inputs: - seed (int): an int - size (Tuple[int]): the size of the output tensor - a (int): the multiplier constant to the generator - c (int): the additive constant to the generator - m (int): the modulus constant to the generator - """ - num_elements = 1 - for s in size: - num_elements *= s - - arr = [(a * seed + c) % m] - for i in range(num_elements - 1): - arr.append((a * arr[i] + c) % m) - - return torch.tensor(arr).float().view(size) / m - - -class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): - dtype = torch.float32 - device = torch.device('cpu') - - -class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): - dtype = torch.float64 - device = torch.device('cpu') - - -class TestComputeDeltas(common_utils.TorchaudioTestCase): - """Test suite for correctness of compute_deltas""" - def test_one_channel(self): - specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) - expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) - computed = F.compute_deltas(specgram, win_length=3) - torch.testing.assert_allclose(computed, expected) - - def test_two_channels(self): - specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], - [1.0, 2.0, 3.0, 4.0]]]) - expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], - [0.5, 1.0, 1.0, 0.5]]]) - computed = F.compute_deltas(specgram, win_length=3) - torch.testing.assert_allclose(computed, expected) - - -def _compare_estimate(sound, estimate, atol=1e-6, rtol=1e-8): - # trim sound for case when constructed signal is shorter than original - sound = sound[..., :estimate.size(-1)] - torch.testing.assert_allclose(estimate, sound, atol=atol, rtol=rtol) - - -def _test_istft_is_inverse_of_stft(kwargs): - # generates a random sound signal for each tril and then does the stft/istft - # operation to check whether we can reconstruct signal - for data_size in [(2, 20), (3, 15), (4, 10)]: - for i in range(100): - - sound = random_float_tensor(i, data_size) - - stft = torch.stft(sound, **kwargs) - estimate = torchaudio.functional.istft(stft, length=sound.size(1), **kwargs) - - _compare_estimate(sound, estimate) - - -class TestIstft(common_utils.TorchaudioTestCase): - """Test suite for correctness of istft with various input""" - number_of_trials = 100 - - def test_istft_is_inverse_of_stft1(self): - # hann_window, centered, normalized, onesided - kwargs1 = { - 'n_fft': 12, - 'hop_length': 4, - 'win_length': 12, - 'window': torch.hann_window(12), - 'center': True, - 'pad_mode': 'reflect', - 'normalized': True, - 'onesided': True, - } - _test_istft_is_inverse_of_stft(kwargs1) - - def test_istft_is_inverse_of_stft2(self): - # hann_window, centered, not normalized, not onesided - kwargs2 = { - 'n_fft': 12, - 'hop_length': 2, - 'win_length': 8, - 'window': torch.hann_window(8), - 'center': True, - 'pad_mode': 'reflect', - 'normalized': False, - 'onesided': False, - } - _test_istft_is_inverse_of_stft(kwargs2) - - def test_istft_is_inverse_of_stft3(self): - # hamming_window, centered, normalized, not onesided - kwargs3 = { - 'n_fft': 15, - 'hop_length': 3, - 'win_length': 11, - 'window': torch.hamming_window(11), - 'center': True, - 'pad_mode': 'constant', - 'normalized': True, - 'onesided': False, - } - _test_istft_is_inverse_of_stft(kwargs3) - - def test_istft_is_inverse_of_stft4(self): - # hamming_window, not centered, not normalized, onesided - # window same size as n_fft - kwargs4 = { - 'n_fft': 5, - 'hop_length': 2, - 'win_length': 5, - 'window': torch.hamming_window(5), - 'center': False, - 'pad_mode': 'constant', - 'normalized': False, - 'onesided': True, - } - _test_istft_is_inverse_of_stft(kwargs4) - - def test_istft_is_inverse_of_stft5(self): - # hamming_window, not centered, not normalized, not onesided - # window same size as n_fft - kwargs5 = { - 'n_fft': 3, - 'hop_length': 2, - 'win_length': 3, - 'window': torch.hamming_window(3), - 'center': False, - 'pad_mode': 'reflect', - 'normalized': False, - 'onesided': False, - } - _test_istft_is_inverse_of_stft(kwargs5) - - def test_istft_of_ones(self): - # stft = torch.stft(torch.ones(4), 4) - stft = torch.tensor([ - [[4., 0.], [4., 0.], [4., 0.], [4., 0.], [4., 0.]], - [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]], - [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]] - ]) - - estimate = torchaudio.functional.istft(stft, n_fft=4, length=4) - _compare_estimate(torch.ones(4), estimate) - - def test_istft_of_zeros(self): - # stft = torch.stft(torch.zeros(4), 4) - stft = torch.zeros((3, 5, 2)) - - estimate = torchaudio.functional.istft(stft, n_fft=4, length=4) - _compare_estimate(torch.zeros(4), estimate) - - def test_istft_requires_overlap_windows(self): - # the window is size 1 but it hops 20 so there is a gap which throw an error - stft = torch.zeros((3, 5, 2)) - self.assertRaises(RuntimeError, torchaudio.functional.istft, stft, n_fft=4, - hop_length=20, win_length=1, window=torch.ones(1)) - - def test_istft_requires_nola(self): - stft = torch.zeros((3, 5, 2)) - kwargs_ok = { - 'n_fft': 4, - 'win_length': 4, - 'window': torch.ones(4), - } - - kwargs_not_ok = { - 'n_fft': 4, - 'win_length': 4, - 'window': torch.zeros(4), - } - - # A window of ones meets NOLA but a window of zeros does not. This should - # throw an error. - torchaudio.functional.istft(stft, **kwargs_ok) - self.assertRaises(RuntimeError, torchaudio.functional.istft, stft, **kwargs_not_ok) - - def test_istft_requires_non_empty(self): - self.assertRaises(RuntimeError, torchaudio.functional.istft, torch.zeros((3, 0, 2)), 2) - self.assertRaises(RuntimeError, torchaudio.functional.istft, torch.zeros((0, 3, 2)), 2) - - def _test_istft_of_sine(self, amplitude, L, n): - # stft of amplitude*sin(2*pi/L*n*x) with the hop length and window size equaling L - x = torch.arange(2 * L + 1, dtype=torch.get_default_dtype()) - sound = amplitude * torch.sin(2 * math.pi / L * x * n) - # stft = torch.stft(sound, L, hop_length=L, win_length=L, - # window=torch.ones(L), center=False, normalized=False) - stft = torch.zeros((L // 2 + 1, 2, 2)) - stft_largest_val = (amplitude * L) / 2.0 - if n < stft.size(0): - stft[n, :, 1] = -stft_largest_val - - if 0 <= L - n < stft.size(0): - # symmetric about L // 2 - stft[L - n, :, 1] = stft_largest_val - - estimate = torchaudio.functional.istft(stft, L, hop_length=L, win_length=L, - window=torch.ones(L), center=False, normalized=False) - # There is a larger error due to the scaling of amplitude - _compare_estimate(sound, estimate, atol=1e-3) - - def test_istft_of_sine(self): - self._test_istft_of_sine(amplitude=123, L=5, n=1) - self._test_istft_of_sine(amplitude=150, L=5, n=2) - self._test_istft_of_sine(amplitude=111, L=5, n=3) - self._test_istft_of_sine(amplitude=160, L=7, n=4) - self._test_istft_of_sine(amplitude=145, L=8, n=5) - self._test_istft_of_sine(amplitude=80, L=9, n=6) - self._test_istft_of_sine(amplitude=99, L=10, n=7) - - def _test_linearity_of_istft(self, data_size, kwargs, atol=1e-6, rtol=1e-8): - for i in range(self.number_of_trials): - tensor1 = random_float_tensor(i, data_size) - tensor2 = random_float_tensor(i * 2, data_size) - a, b = torch.rand(2) - istft1 = torchaudio.functional.istft(tensor1, **kwargs) - istft2 = torchaudio.functional.istft(tensor2, **kwargs) - istft = a * istft1 + b * istft2 - estimate = torchaudio.functional.istft(a * tensor1 + b * tensor2, **kwargs) - _compare_estimate(istft, estimate, atol, rtol) - - def test_linearity_of_istft1(self): - # hann_window, centered, normalized, onesided - kwargs1 = { - 'n_fft': 12, - 'window': torch.hann_window(12), - 'center': True, - 'pad_mode': 'reflect', - 'normalized': True, - 'onesided': True, - } - data_size = (2, 7, 7, 2) - self._test_linearity_of_istft(data_size, kwargs1) - - def test_linearity_of_istft2(self): - # hann_window, centered, not normalized, not onesided - kwargs2 = { - 'n_fft': 12, - 'window': torch.hann_window(12), - 'center': True, - 'pad_mode': 'reflect', - 'normalized': False, - 'onesided': False, - } - data_size = (2, 12, 7, 2) - self._test_linearity_of_istft(data_size, kwargs2) - - def test_linearity_of_istft3(self): - # hamming_window, centered, normalized, not onesided - kwargs3 = { - 'n_fft': 12, - 'window': torch.hamming_window(12), - 'center': True, - 'pad_mode': 'constant', - 'normalized': True, - 'onesided': False, - } - data_size = (2, 12, 7, 2) - self._test_linearity_of_istft(data_size, kwargs3) - - def test_linearity_of_istft4(self): - # hamming_window, not centered, not normalized, onesided - kwargs4 = { - 'n_fft': 12, - 'window': torch.hamming_window(12), - 'center': False, - 'pad_mode': 'constant', - 'normalized': False, - 'onesided': True, - } - data_size = (2, 7, 3, 2) - self._test_linearity_of_istft(data_size, kwargs4, atol=1e-5, rtol=1e-8) - - -class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): - @parameterized.expand([(100,), (440,)]) - def test_pitch(self, frequency): - sample_rate = 44100 - test_sine_waveform = common_utils.get_sinusoid( - frequency=frequency, sample_rate=sample_rate, duration=5, - ) - - freq = torchaudio.functional.detect_pitch_frequency(test_sine_waveform, sample_rate) - - threshold = 1 - s = ((freq - frequency).abs() > threshold).sum() - self.assertFalse(s) - - -class TestDB_to_amplitude(common_utils.TorchaudioTestCase): - def test_DB_to_amplitude(self): - # Make some noise - x = torch.rand(1000) - spectrogram = torchaudio.transforms.Spectrogram() - spec = spectrogram(x) - - amin = 1e-10 - ref = 1.0 - db_multiplier = math.log10(max(amin, ref)) - - # Waveform amplitude -> DB -> amplitude - multiplier = 20. - power = 0.5 - - db = F.amplitude_to_DB(torch.abs(x), multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) - - torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) - - # Spectrogram amplitude -> DB -> amplitude - db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) - - torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) - - # Waveform power -> DB -> power - multiplier = 10. - power = 1. - - db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) - - torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) - - # Spectrogram power -> DB -> power - db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) - - torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) - - -@pytest.mark.parametrize('complex_tensor', [ - torch.randn(1, 2, 1025, 400, 2), - torch.randn(1025, 400, 2) -]) -@pytest.mark.parametrize('power', [1, 2, 0.7]) -def test_complex_norm(complex_tensor, power): - expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) - norm_tensor = F.complex_norm(complex_tensor, power) - - torch.testing.assert_allclose(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) - - -@pytest.mark.parametrize('specgram', [ - torch.randn(2, 1025, 400), - torch.randn(1, 201, 100) -]) -@pytest.mark.parametrize('mask_param', [100]) -@pytest.mark.parametrize('mask_value', [0., 30.]) -@pytest.mark.parametrize('axis', [1, 2]) -def test_mask_along_axis(specgram, mask_param, mask_value, axis): - - mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis) - - other_axis = 1 if axis == 2 else 2 - - masked_columns = (mask_specgram == mask_value).sum(other_axis) - num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum() - num_masked_columns //= mask_specgram.size(0) - - assert mask_specgram.size() == specgram.size() - assert num_masked_columns < mask_param - - -@pytest.mark.parametrize('mask_param', [100]) -@pytest.mark.parametrize('mask_value', [0., 30.]) -@pytest.mark.parametrize('axis', [2, 3]) -def test_mask_along_axis_iid(mask_param, mask_value, axis): - torch.random.manual_seed(42) - specgrams = torch.randn(4, 2, 1025, 400) - - mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) - - other_axis = 2 if axis == 3 else 3 - - masked_columns = (mask_specgrams == mask_value).sum(other_axis) - num_masked_columns = (masked_columns == mask_specgrams.size(other_axis)).sum(-1) - - assert mask_specgrams.size() == specgrams.size() - assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() diff --git a/test/__init__.py b/test/torchaudio_unittest/__init__.py similarity index 100% rename from test/__init__.py rename to test/torchaudio_unittest/__init__.py diff --git a/test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav b/test/torchaudio_unittest/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav similarity index 100% rename from test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav rename to test/torchaudio_unittest/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav diff --git a/test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/train.tsv b/test/torchaudio_unittest/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/train.tsv similarity index 100% rename from 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b/test/torchaudio_unittest/assets/vad-go-mono-32000.wav similarity index 100% rename from test/assets/vad-go-mono-32000.wav rename to test/torchaudio_unittest/assets/vad-go-mono-32000.wav diff --git a/test/assets/vad-go-stereo-44100.wav b/test/torchaudio_unittest/assets/vad-go-stereo-44100.wav similarity index 100% rename from test/assets/vad-go-stereo-44100.wav rename to test/torchaudio_unittest/assets/vad-go-stereo-44100.wav diff --git a/test/assets/vec_flt.ark b/test/torchaudio_unittest/assets/vec_flt.ark similarity index 100% rename from test/assets/vec_flt.ark rename to test/torchaudio_unittest/assets/vec_flt.ark diff --git a/test/assets/vec_int.ark b/test/torchaudio_unittest/assets/vec_int.ark similarity index 100% rename from test/assets/vec_int.ark rename to test/torchaudio_unittest/assets/vec_int.ark diff --git a/test/test_backend.py b/test/torchaudio_unittest/backend_test.py similarity index 96% rename from test/test_backend.py rename to test/torchaudio_unittest/backend_test.py index 6b67cb2898..d7a2078a31 100644 --- a/test/test_backend.py +++ b/test/torchaudio_unittest/backend_test.py @@ -1,6 +1,6 @@ import torchaudio -from . import common_utils +from torchaudio_unittest import common_utils class BackendSwitchMixin: diff --git a/test/test_batch_consistency.py b/test/torchaudio_unittest/batch_consistency_test.py similarity index 96% rename from test/test_batch_consistency.py rename to test/torchaudio_unittest/batch_consistency_test.py index e7040fdc0b..696b714df1 100644 --- a/test/test_batch_consistency.py +++ b/test/torchaudio_unittest/batch_consistency_test.py @@ -7,7 +7,7 @@ import torchaudio import torchaudio.functional as F -from . import common_utils +from torchaudio_unittest import common_utils class TestFunctional(common_utils.TorchaudioTestCase): @@ -59,14 +59,6 @@ def test_detect_pitch_frequency(self, frequency, sample_rate, n_channels): n_channels=n_channels, duration=5) self.assert_batch_consistencies(F.detect_pitch_frequency, waveform, sample_rate) - def test_istft(self): - stft = torch.tensor([ - [[4., 0.], [4., 0.], [4., 0.], [4., 0.], [4., 0.]], - [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]], - [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]] - ]) - self.assert_batch_consistencies(F.istft, stft, n_fft=4, length=4) - def test_contrast(self): waveform = torch.rand(2, 100) - 0.5 self.assert_batch_consistencies(F.contrast, waveform, enhancement_amount=80.) diff --git a/test/common_utils/__init__.py b/test/torchaudio_unittest/common_utils/__init__.py similarity index 94% rename from test/common_utils/__init__.py rename to test/torchaudio_unittest/common_utils/__init__.py index 841cd26713..105a054864 100644 --- a/test/common_utils/__init__.py +++ b/test/torchaudio_unittest/common_utils/__init__.py @@ -5,8 +5,6 @@ ) from .backend_utils import ( set_audio_backend, - BACKENDS, - BACKENDS_MP3, ) from .case_utils import ( TempDirMixin, diff --git a/test/torchaudio_unittest/common_utils/backend_utils.py b/test/torchaudio_unittest/common_utils/backend_utils.py new file mode 100644 index 0000000000..7702a3d6f3 --- /dev/null +++ b/test/torchaudio_unittest/common_utils/backend_utils.py @@ -0,0 +1,19 @@ +import unittest + +import torchaudio + + +def set_audio_backend(backend): + """Allow additional backend value, 'default'""" + backends = torchaudio.list_audio_backends() + if backend == 'default': + if 'sox_io' in backends: + be = 'sox_io' + elif 'soundfile' in backends: + be = 'soundfile' + else: + raise unittest.SkipTest('No default backend available') + else: + be = backend + + torchaudio.set_audio_backend(be) diff --git a/test/common_utils/case_utils.py b/test/torchaudio_unittest/common_utils/case_utils.py similarity index 85% rename from test/common_utils/case_utils.py rename to test/torchaudio_unittest/common_utils/case_utils.py index 9c89ef6891..2e0a17b5da 100644 --- a/test/common_utils/case_utils.py +++ b/test/torchaudio_unittest/common_utils/case_utils.py @@ -67,4 +67,11 @@ def skipIfNoModule(module, display_name=None): skipIfNoSoxBackend = unittest.skipIf( 'sox' not in torchaudio.list_audio_backends(), 'Sox backend not available') skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') -skipIfNoExtension = skipIfNoModule('torchaudio._torchaudio', 'torchaudio C++ extension') + + +def skipIfNoExtension(test_item): + if is_module_available('torchaudio._torchaudio'): + return test_item + if 'TORCHAUDIO_TEST_FAIL_IF_NO_EXTENSION' in os.environ: + raise RuntimeError('torchaudio C++ extension is not available.') + return unittest.skip('torchaudio C++ extension is not available')(test_item) diff --git a/test/common_utils/data_utils.py b/test/torchaudio_unittest/common_utils/data_utils.py similarity index 100% rename from test/common_utils/data_utils.py rename to test/torchaudio_unittest/common_utils/data_utils.py diff --git a/test/common_utils/parameterized_utils.py b/test/torchaudio_unittest/common_utils/parameterized_utils.py similarity index 100% rename from test/common_utils/parameterized_utils.py rename to test/torchaudio_unittest/common_utils/parameterized_utils.py diff --git a/test/common_utils/sox_utils.py b/test/torchaudio_unittest/common_utils/sox_utils.py similarity index 100% rename from test/common_utils/sox_utils.py rename to test/torchaudio_unittest/common_utils/sox_utils.py diff --git a/test/common_utils/wav_utils.py b/test/torchaudio_unittest/common_utils/wav_utils.py similarity index 100% rename from test/common_utils/wav_utils.py rename to test/torchaudio_unittest/common_utils/wav_utils.py diff --git a/test/compliance/__init__.py b/test/torchaudio_unittest/compliance/__init__.py similarity index 100% rename from test/compliance/__init__.py rename to test/torchaudio_unittest/compliance/__init__.py diff --git a/test/compliance/generate_fbank_data.py b/test/torchaudio_unittest/compliance/generate_fbank_data.py similarity index 98% rename from test/compliance/generate_fbank_data.py rename to test/torchaudio_unittest/compliance/generate_fbank_data.py index 446ebea284..96a935d116 100644 --- a/test/compliance/generate_fbank_data.py +++ b/test/torchaudio_unittest/compliance/generate_fbank_data.py @@ -92,7 +92,7 @@ def decode(fn, sound_path, exe_path, scp_path, out_dir): 'round_to_power_of_two', 'snip_edges', 'subtract_mean', 'use_energy', 'use_log_fbank', 'use_power', 'vtln_high', 'vtln_low', 'vtln_warp', 'window_type'] fn_split = fn.split('-') - assert len(fn_split) == len(arr), ('Len mismatch: %d and %d' % (len(fn_split), len(arr))) + assert len(fn_split) == len(arr), ('Len mismatch: {} and {}'.format(len(fn_split), len(arr))) inputs = {arr[i]: utils.parse(fn_split[i]) for i in range(len(arr))} # print flags for C++ diff --git a/test/compliance/generate_test_stft_data.py b/test/torchaudio_unittest/compliance/generate_test_stft_data.py similarity index 100% rename from test/compliance/generate_test_stft_data.py rename to test/torchaudio_unittest/compliance/generate_test_stft_data.py diff --git a/test/compliance/utils.py b/test/torchaudio_unittest/compliance/utils.py similarity index 100% rename from test/compliance/utils.py rename to test/torchaudio_unittest/compliance/utils.py diff --git a/test/test_compliance_kaldi.py b/test/torchaudio_unittest/compliance_kaldi_test.py similarity index 98% rename from test/test_compliance_kaldi.py rename to test/torchaudio_unittest/compliance_kaldi_test.py index 450183d6b3..43e9865e2f 100644 --- a/test/test_compliance_kaldi.py +++ b/test/torchaudio_unittest/compliance_kaldi_test.py @@ -6,7 +6,7 @@ import torchaudio import torchaudio.compliance.kaldi as kaldi -from . import common_utils +from torchaudio_unittest import common_utils from .compliance import utils as compliance_utils @@ -148,7 +148,9 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil sound, sr = torchaudio.load_wav(sound_filepath) files = self.test_filepaths[filepath_key] - assert len(files) == expected_num_files, ('number of kaldi %s file changed to %d' % (filepath_key, len(files))) + assert len(files) == expected_num_files, \ + ('number of kaldi {} file changed to {}'.format( + filepath_key, len(files))) for f in files: print(f) diff --git a/test/test_dataloader.py b/test/torchaudio_unittest/dataloader_test.py similarity index 96% rename from test/test_dataloader.py rename to test/torchaudio_unittest/dataloader_test.py index 6d1518b70c..8f1d7e6fb3 100644 --- a/test/test_dataloader.py +++ b/test/torchaudio_unittest/dataloader_test.py @@ -3,7 +3,7 @@ import torchaudio from torch.utils.data import Dataset, DataLoader -from . import common_utils +from torchaudio_unittest import common_utils class TORCHAUDIODS(Dataset): diff --git a/test/datasets/__init__.py b/test/torchaudio_unittest/datasets/__init__.py similarity index 100% rename from test/datasets/__init__.py rename to test/torchaudio_unittest/datasets/__init__.py diff --git a/test/torchaudio_unittest/datasets/cmuarctic_test.py b/test/torchaudio_unittest/datasets/cmuarctic_test.py new file mode 100644 index 0000000000..578b0ec385 --- /dev/null +++ b/test/torchaudio_unittest/datasets/cmuarctic_test.py @@ -0,0 +1,67 @@ +import os + +from torchaudio.datasets import cmuarctic + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + + +class TestCMUARCTIC(TempDirMixin, TorchaudioTestCase): + backend = "default" + + root_dir = None + samples = [] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + sample_rate = 16000 + utterance = "This is a test utterance." + + base_dir = os.path.join(cls.root_dir, "ARCTIC", "cmu_us_aew_arctic") + txt_dir = os.path.join(base_dir, "etc") + os.makedirs(txt_dir, exist_ok=True) + txt_file = os.path.join(txt_dir, "txt.done.data") + audio_dir = os.path.join(base_dir, "wav") + os.makedirs(audio_dir, exist_ok=True) + + seed = 42 + with open(txt_file, "w") as txt: + for c in ["a", "b"]: + for i in range(5): + utterance_id = f"arctic_{c}{i:04d}" + path = os.path.join(audio_dir, f"{utterance_id}.wav") + data = get_whitenoise( + sample_rate=sample_rate, + duration=3, + n_channels=1, + dtype="int16", + seed=seed, + ) + save_wav(path, data, sample_rate) + sample = ( + normalize_wav(data), + sample_rate, + utterance, + utterance_id.split("_")[1], + ) + cls.samples.append(sample) + txt.write(f'( {utterance_id} "{utterance}" )\n') + seed += 1 + + def test_cmuarctic(self): + dataset = cmuarctic.CMUARCTIC(self.root_dir) + n_ite = 0 + for i, (waveform, sample_rate, utterance, utterance_id) in enumerate(dataset): + expected_sample = self.samples[i] + assert sample_rate == expected_sample[1] + assert utterance == expected_sample[2] + assert utterance_id == expected_sample[3] + self.assertEqual(expected_sample[0], waveform, atol=5e-5, rtol=1e-8) + n_ite += 1 + assert n_ite == len(self.samples) diff --git a/test/torchaudio_unittest/datasets/commonvoice_test.py b/test/torchaudio_unittest/datasets/commonvoice_test.py new file mode 100644 index 0000000000..3eedebec70 --- /dev/null +++ b/test/torchaudio_unittest/datasets/commonvoice_test.py @@ -0,0 +1,72 @@ +import os +import csv +import random + +from torchaudio.datasets import commonvoice +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + + +class TestCommonVoice(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + data = [] + _headers = [u"client_ids", u"path", u"sentence", u"up_votes", u"down_votes", u"age", u"gender", u"accent"] + # Note: extension is changed to wav for the sake of test + # Note: the first content is missing values for `age`, `gender` and `accent` as in the original data. + _train_csv_contents = [ + ["9d16c5d980247861130e0480e2719f448be73d86a496c36d01a477cbdecd8cfd1399403d7a77bf458d211a70711b2da0845c", + "common_voice_en_18885784.wav", + "He was accorded a State funeral, and was buried in Drayton and Toowoomba Cemetery.", "2", "0", "", "", ""], + ["c82eb9291328620f06025a1f8112b909099e447e485e99236cb87df008650250e79fea5ca772061fb6a370830847b9c44d20", + "common_voice_en_556542.wav", "Once more into the breach", "2", "0", "thirties", "male", "us"], + ["f74d880c5ad4c5917f314a604d3fc4805159d255796fb9f8defca35333ecc002bdf53dc463503c12674ea840b21b4a507b7c", + "common_voice_en_18607573.wav", + "Caddy, show Miss Clare and Miss Summerson their rooms.", "2", "0", "twenties", "male", "canada"], + ] + _folder_audio = "clips" + sample_rate = 48000 + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + # The path convention commonvoice uses + base_dir = os.path.join(cls.root_dir, commonvoice.FOLDER_IN_ARCHIVE, commonvoice.VERSION, "en") + os.makedirs(base_dir, exist_ok=True) + + # Tsv file name difference does not mean different subset, testing as a whole dataset here + tsv_filename = os.path.join(base_dir, commonvoice.TSV) + with open(tsv_filename, "w", newline='') as tsv: + writer = csv.writer(tsv, delimiter='\t') + writer.writerow(cls._headers) + for i, content in enumerate(cls._train_csv_contents): + audio_filename = audio_filename = content[1] + writer.writerow(content) + + # Generate and store audio + audio_base_path = os.path.join(base_dir, cls._folder_audio) + os.makedirs(audio_base_path, exist_ok=True) + audio_path = os.path.join(audio_base_path, audio_filename) + data = get_whitenoise(sample_rate=cls.sample_rate, duration=1, n_channels=1, seed=i, dtype='float32') + save_wav(audio_path, data, cls.sample_rate) + + # Append data entry + cls.data.append((normalize_wav(data), cls.sample_rate, dict(zip(cls._headers, content)))) + + def test_commonvoice(self): + dataset = commonvoice.COMMONVOICE(self.root_dir) + n_ite = 0 + for i, (waveform, sample_rate, dictionary) in enumerate(dataset): + expected_dictionary = self.data[i][2] + expected_data = self.data[i][0] + self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) + assert sample_rate == TestCommonVoice.sample_rate + assert dictionary == expected_dictionary + n_ite += 1 + assert n_ite == len(self.data) diff --git a/test/torchaudio_unittest/datasets/datasets_test.py b/test/torchaudio_unittest/datasets/datasets_test.py new file mode 100644 index 0000000000..cd04c4d447 --- /dev/null +++ b/test/torchaudio_unittest/datasets/datasets_test.py @@ -0,0 +1,17 @@ +from torchaudio.datasets.librispeech import LIBRISPEECH +from torchaudio.datasets.speechcommands import SPEECHCOMMANDS +from torchaudio.datasets.vctk import VCTK + +from torchaudio_unittest.common_utils import ( + TorchaudioTestCase, + get_asset_path, +) + + +class TestDatasets(TorchaudioTestCase): + backend = 'default' + path = get_asset_path() + + def test_vctk(self): + data = VCTK(self.path) + data[0] diff --git a/test/datasets/gtzan_test.py b/test/torchaudio_unittest/datasets/gtzan_test.py similarity index 98% rename from test/datasets/gtzan_test.py rename to test/torchaudio_unittest/datasets/gtzan_test.py index 14c82a512a..3236535fad 100644 --- a/test/datasets/gtzan_test.py +++ b/test/torchaudio_unittest/datasets/gtzan_test.py @@ -2,7 +2,7 @@ from torchaudio.datasets import gtzan -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, get_whitenoise, diff --git a/test/torchaudio_unittest/datasets/librispeech_test.py b/test/torchaudio_unittest/datasets/librispeech_test.py new file mode 100644 index 0000000000..669b428876 --- /dev/null +++ b/test/torchaudio_unittest/datasets/librispeech_test.py @@ -0,0 +1,112 @@ +import os + +from torchaudio.datasets import librispeech + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + +# Used to generate a unique utterance for each dummy audio file +NUMBERS = [ + 'ZERO', + 'ONE', + 'TWO', + 'THREE', + 'FOUR', + 'FIVE', + 'SIX', + 'SEVEN', + 'EIGHT', + 'NINE' +] + + +class TestLibriSpeech(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + samples = [] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + dataset_dir = os.path.join( + cls.root_dir, librispeech.FOLDER_IN_ARCHIVE, librispeech.URL + ) + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 16000 # 16kHz + seed = 0 + + for speaker_id in range(5): + speaker_path = os.path.join(dataset_dir, str(speaker_id)) + os.makedirs(speaker_path, exist_ok=True) + + for chapter_id in range(3): + chapter_path = os.path.join(speaker_path, str(chapter_id)) + os.makedirs(chapter_path, exist_ok=True) + trans_content = [] + + for utterance_id in range(10): + filename = f'{speaker_id}-{chapter_id}-{utterance_id:04d}.wav' + path = os.path.join(chapter_path, filename) + + utterance = ' '.join( + [NUMBERS[x] for x in [speaker_id, chapter_id, utterance_id]] + ) + trans_content.append( + f'{speaker_id}-{chapter_id}-{utterance_id:04d} {utterance}' + ) + + data = get_whitenoise( + sample_rate=sample_rate, + duration=0.01, + n_channels=1, + dtype='float32', + seed=seed + ) + save_wav(path, data, sample_rate) + sample = ( + normalize_wav(data), + sample_rate, + utterance, + speaker_id, + chapter_id, + utterance_id + ) + cls.samples.append(sample) + + seed += 1 + + trans_filename = f'{speaker_id}-{chapter_id}.trans.txt' + trans_path = os.path.join(chapter_path, trans_filename) + with open(trans_path, 'w') as f: + f.write('\n'.join(trans_content)) + + @classmethod + def tearDownClass(cls): + # In case of test failure + librispeech.LIBRISPEECH._ext_audio = '.flac' + + def test_librispeech(self): + librispeech.LIBRISPEECH._ext_audio = '.wav' + dataset = librispeech.LIBRISPEECH(self.root_dir) + print(dataset._path) + + num_samples = 0 + for i, ( + data, sample_rate, utterance, speaker_id, chapter_id, utterance_id + ) in enumerate(dataset): + self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[i][1] + assert utterance == self.samples[i][2] + assert speaker_id == self.samples[i][3] + assert chapter_id == self.samples[i][4] + assert utterance_id == self.samples[i][5] + num_samples += 1 + + assert num_samples == len(self.samples) + librispeech.LIBRISPEECH._ext_audio = '.flac' diff --git a/test/datasets/libritts_test.py b/test/torchaudio_unittest/datasets/libritts_test.py similarity index 91% rename from test/datasets/libritts_test.py rename to test/torchaudio_unittest/datasets/libritts_test.py index f31561d211..639098446b 100644 --- a/test/datasets/libritts_test.py +++ b/test/torchaudio_unittest/datasets/libritts_test.py @@ -2,7 +2,7 @@ from torchaudio.datasets.libritts import LIBRITTS -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, get_whitenoise, @@ -33,8 +33,8 @@ def setUpClass(cls): os.makedirs(file_dir, exist_ok=True) path = os.path.join(file_dir, filename) - data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16', seed=i) - save_wav(path, data, 8000) + data = get_whitenoise(sample_rate=24000, duration=2, n_channels=1, dtype='int16', seed=i) + save_wav(path, data, 24000) cls.data.append(normalize_wav(data)) original_text_filename = f'{"_".join(str(u) for u in utterance_id)}.original.txt' @@ -61,7 +61,7 @@ def test_libritts(self): expected_ids = self.utterance_ids[i] expected_data = self.data[i] self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) - assert sample_rate == 8000 + assert sample_rate == 24000 assert speaker_id == expected_ids[0] assert chapter_id == expected_ids[1] assert original_text == self.original_text diff --git a/test/torchaudio_unittest/datasets/ljspeech_test.py b/test/torchaudio_unittest/datasets/ljspeech_test.py new file mode 100644 index 0000000000..7c1128c31b --- /dev/null +++ b/test/torchaudio_unittest/datasets/ljspeech_test.py @@ -0,0 +1,74 @@ +import csv +import os + +from torchaudio.datasets import ljspeech + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + normalize_wav, + save_wav, +) + + +class TestLJSpeech(TempDirMixin, TorchaudioTestCase): + backend = "default" + + root_dir = None + data = [] + transcripts = [ + "Test transcript 1", + "Test transcript 2", + "Test transcript 3", + "In 1465 Sweynheim and Pannartz began printing in the monastery of Subiaco near Rome," + ] + + normalized_transcripts = [ + "Test transcript one", + "Test transcript two", + "Test transcript three", + "In fourteen sixty-five Sweynheim and Pannartz began printing in the monastery of Subiaco near Rome," + ] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + base_dir = os.path.join(cls.root_dir, "LJSpeech-1.1") + archive_dir = os.path.join(base_dir, "wavs") + os.makedirs(archive_dir, exist_ok=True) + metadata_path = os.path.join(base_dir, "metadata.csv") + sample_rate = 22050 + + with open(metadata_path, mode="w", newline='') as metadata_file: + metadata_writer = csv.writer( + metadata_file, delimiter="|", quoting=csv.QUOTE_NONE + ) + for i, (transcript, normalized_transcript) in enumerate( + zip(cls.transcripts, cls.normalized_transcripts) + ): + fileid = f'LJ001-{i:04d}' + metadata_writer.writerow([fileid, transcript, normalized_transcript]) + filename = fileid + ".wav" + path = os.path.join(archive_dir, filename) + data = get_whitenoise( + sample_rate=sample_rate, duration=1, n_channels=1, dtype="int16", seed=i + ) + save_wav(path, data, sample_rate) + cls.data.append(normalize_wav(data)) + + def test_ljspeech(self): + dataset = ljspeech.LJSPEECH(self.root_dir) + n_ite = 0 + for i, (waveform, sample_rate, transcript, normalized_transcript) in enumerate( + dataset + ): + expected_transcript = self.transcripts[i] + expected_normalized_transcript = self.normalized_transcripts[i] + expected_data = self.data[i] + self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) + assert sample_rate == sample_rate + assert transcript == expected_transcript + assert normalized_transcript == expected_normalized_transcript + n_ite += 1 + assert n_ite == len(self.data) diff --git a/test/torchaudio_unittest/datasets/speechcommands_test.py b/test/torchaudio_unittest/datasets/speechcommands_test.py new file mode 100644 index 0000000000..7df1ea2007 --- /dev/null +++ b/test/torchaudio_unittest/datasets/speechcommands_test.py @@ -0,0 +1,109 @@ +import os + +from torchaudio.datasets import speechcommands + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + normalize_wav, + save_wav, +) + +LABELS = [ + "bed", + "bird", + "cat", + "dog", + "down", + "eight", + "five", + "follow", + "forward", + "four", + "go", + "happy", + "house", + "learn", + "left", + "marvin", + "nine", + "no", + "off", + "on", + "one", + "right", + "seven", + "sheila", + "six", + "stop", + "three", + "tree", + "two", + "up", + "visual", + "wow", + "yes", + "zero", +] + + +class TestSpeechCommands(TempDirMixin, TorchaudioTestCase): + backend = "default" + + root_dir = None + samples = [] + + @classmethod + def setUp(cls): + cls.root_dir = cls.get_base_temp_dir() + dataset_dir = os.path.join( + cls.root_dir, speechcommands.FOLDER_IN_ARCHIVE, speechcommands.URL + ) + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 16000 # 16kHz sample rate + seed = 0 + for label in LABELS: + path = os.path.join(dataset_dir, label) + os.makedirs(path, exist_ok=True) + for j in range(2): + # generate hash ID for speaker + speaker = "{:08x}".format(j) + + for utterance in range(3): + filename = f"{speaker}{speechcommands.HASH_DIVIDER}{utterance}.wav" + file_path = os.path.join(path, filename) + seed += 1 + data = get_whitenoise( + sample_rate=sample_rate, + duration=0.01, + n_channels=1, + dtype="int16", + seed=seed, + ) + save_wav(file_path, data, sample_rate) + sample = ( + normalize_wav(data), + sample_rate, + label, + speaker, + utterance, + ) + cls.samples.append(sample) + + def testSpeechCommands(self): + dataset = speechcommands.SPEECHCOMMANDS(self.root_dir) + print(dataset._path) + + num_samples = 0 + for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( + dataset + ): + self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[i][1] + assert label == self.samples[i][2] + assert speaker_id == self.samples[i][3] + assert utterance_number == self.samples[i][4] + num_samples += 1 + + assert num_samples == len(self.samples) diff --git a/test/datasets/utils_test.py b/test/torchaudio_unittest/datasets/utils_test.py similarity index 68% rename from test/datasets/utils_test.py rename to test/torchaudio_unittest/datasets/utils_test.py index e77df09f4c..f75ae2319c 100644 --- a/test/datasets/utils_test.py +++ b/test/torchaudio_unittest/datasets/utils_test.py @@ -2,10 +2,12 @@ from pathlib import Path from torchaudio.datasets import utils as dataset_utils +from torchaudio.datasets.commonvoice import COMMONVOICE -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, + get_asset_path, ) @@ -45,3 +47,22 @@ def test_walk_files(self): assert found == self.expected[i] n_ites += 1 assert n_ites == len(self.expected) + + +class TestIterator(TorchaudioTestCase): + backend = 'default' + path = get_asset_path() + + def test_disckcache_iterator(self): + data = COMMONVOICE(self.path, url="tatar") + data = dataset_utils.diskcache_iterator(data) + # Save + data[0] + # Load + data[0] + + def test_bg_iterator(self): + data = COMMONVOICE(self.path, url="tatar") + data = dataset_utils.bg_iterator(data, 5) + for _ in data: + pass diff --git a/test/datasets/yesno_test.py b/test/torchaudio_unittest/datasets/yesno_test.py similarity index 91% rename from test/datasets/yesno_test.py rename to test/torchaudio_unittest/datasets/yesno_test.py index f9c4d0b051..a8a8a04276 100644 --- a/test/datasets/yesno_test.py +++ b/test/torchaudio_unittest/datasets/yesno_test.py @@ -2,7 +2,7 @@ from torchaudio.datasets import yesno -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, get_whitenoise, @@ -29,10 +29,10 @@ def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() base_dir = os.path.join(cls.root_dir, 'waves_yesno') os.makedirs(base_dir, exist_ok=True) - for label in cls.labels: + for i, label in enumerate(cls.labels): filename = f'{"_".join(str(l) for l in label)}.wav' path = os.path.join(base_dir, filename) - data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16') + data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16', seed=i) save_wav(path, data, 8000) cls.data.append(normalize_wav(data)) diff --git a/test/sox_effect/__init__.py b/test/torchaudio_unittest/functional/__init__.py similarity index 100% rename from test/sox_effect/__init__.py rename to test/torchaudio_unittest/functional/__init__.py diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py new file mode 100644 index 0000000000..96aac4e770 --- /dev/null +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -0,0 +1,146 @@ +import math +import unittest + +import torch +import torchaudio +import torchaudio.functional as F +from parameterized import parameterized +import pytest + +from torchaudio_unittest import common_utils +from .functional_impl import Lfilter + + +class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') + + +class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') + + +class TestComputeDeltas(common_utils.TorchaudioTestCase): + """Test suite for correctness of compute_deltas""" + def test_one_channel(self): + specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) + expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) + computed = F.compute_deltas(specgram, win_length=3) + torch.testing.assert_allclose(computed, expected) + + def test_two_channels(self): + specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], + [1.0, 2.0, 3.0, 4.0]]]) + expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], + [0.5, 1.0, 1.0, 0.5]]]) + computed = F.compute_deltas(specgram, win_length=3) + torch.testing.assert_allclose(computed, expected) + + +class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): + @parameterized.expand([(100,), (440,)]) + def test_pitch(self, frequency): + sample_rate = 44100 + test_sine_waveform = common_utils.get_sinusoid( + frequency=frequency, sample_rate=sample_rate, duration=5, + ) + + freq = torchaudio.functional.detect_pitch_frequency(test_sine_waveform, sample_rate) + + threshold = 1 + s = ((freq - frequency).abs() > threshold).sum() + self.assertFalse(s) + + +class TestDB_to_amplitude(common_utils.TorchaudioTestCase): + def test_DB_to_amplitude(self): + # Make some noise + x = torch.rand(1000) + spectrogram = torchaudio.transforms.Spectrogram() + spec = spectrogram(x) + + amin = 1e-10 + ref = 1.0 + db_multiplier = math.log10(max(amin, ref)) + + # Waveform amplitude -> DB -> amplitude + multiplier = 20. + power = 0.5 + + db = F.amplitude_to_DB(torch.abs(x), multiplier, amin, db_multiplier, top_db=None) + x2 = F.DB_to_amplitude(db, ref, power) + + torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) + + # Spectrogram amplitude -> DB -> amplitude + db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) + x2 = F.DB_to_amplitude(db, ref, power) + + torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) + + # Waveform power -> DB -> power + multiplier = 10. + power = 1. + + db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None) + x2 = F.DB_to_amplitude(db, ref, power) + + torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) + + # Spectrogram power -> DB -> power + db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) + x2 = F.DB_to_amplitude(db, ref, power) + + torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) + + +@pytest.mark.parametrize('complex_tensor', [ + torch.randn(1, 2, 1025, 400, 2), + torch.randn(1025, 400, 2) +]) +@pytest.mark.parametrize('power', [1, 2, 0.7]) +def test_complex_norm(complex_tensor, power): + expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) + norm_tensor = F.complex_norm(complex_tensor, power) + + torch.testing.assert_allclose(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) + + +@pytest.mark.parametrize('specgram', [ + torch.randn(2, 1025, 400), + torch.randn(1, 201, 100) +]) +@pytest.mark.parametrize('mask_param', [100]) +@pytest.mark.parametrize('mask_value', [0., 30.]) +@pytest.mark.parametrize('axis', [1, 2]) +def test_mask_along_axis(specgram, mask_param, mask_value, axis): + + mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis) + + other_axis = 1 if axis == 2 else 2 + + masked_columns = (mask_specgram == mask_value).sum(other_axis) + num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum() + num_masked_columns //= mask_specgram.size(0) + + assert mask_specgram.size() == specgram.size() + assert num_masked_columns < mask_param + + +@pytest.mark.parametrize('mask_param', [100]) +@pytest.mark.parametrize('mask_value', [0., 30.]) +@pytest.mark.parametrize('axis', [2, 3]) +def test_mask_along_axis_iid(mask_param, mask_value, axis): + torch.random.manual_seed(42) + specgrams = torch.randn(4, 2, 1025, 400) + + mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) + + other_axis = 2 if axis == 3 else 3 + + masked_columns = (mask_specgrams == mask_value).sum(other_axis) + num_masked_columns = (masked_columns == mask_specgrams.size(other_axis)).sum(-1) + + assert mask_specgrams.size() == specgrams.size() + assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() diff --git a/test/functional_cuda_test.py b/test/torchaudio_unittest/functional/functional_cuda_test.py similarity index 88% rename from test/functional_cuda_test.py rename to test/torchaudio_unittest/functional/functional_cuda_test.py index 9602dcf071..80ac5fa0c2 100644 --- a/test/functional_cuda_test.py +++ b/test/torchaudio_unittest/functional/functional_cuda_test.py @@ -1,6 +1,6 @@ import torch -from . import common_utils +from torchaudio_unittest import common_utils from .functional_impl import Lfilter diff --git a/test/functional_impl.py b/test/torchaudio_unittest/functional/functional_impl.py similarity index 96% rename from test/functional_impl.py rename to test/torchaudio_unittest/functional/functional_impl.py index fb5e7dc7ac..c63001a68e 100644 --- a/test/functional_impl.py +++ b/test/torchaudio_unittest/functional/functional_impl.py @@ -2,7 +2,7 @@ import torch import torchaudio.functional as F -from . import common_utils +from torchaudio_unittest import common_utils class Lfilter(common_utils.TestBaseMixin): diff --git a/test/test_io.py b/test/torchaudio_unittest/io_test.py similarity index 95% rename from test/test_io.py rename to test/torchaudio_unittest/io_test.py index b55885059a..e30f4c3fd3 100644 --- a/test/test_io.py +++ b/test/torchaudio_unittest/io_test.py @@ -6,8 +6,25 @@ import torch import torchaudio +from torchaudio.utils import sox_utils +from torchaudio._internal.module_utils import is_module_available -from .common_utils import BACKENDS, BACKENDS_MP3, get_asset_path +from torchaudio_unittest.common_utils import get_asset_path + +BACKENDS = [] +BACKENDS_MP3 = [] + +if is_module_available('soundfile'): + BACKENDS.append('soundfile') + +if is_module_available('torchaudio._torchaudio'): + BACKENDS.append('sox') + + if ( + 'mp3' in sox_utils.list_read_formats() and + 'mp3' in sox_utils.list_write_formats() + ): + BACKENDS_MP3 = ['sox'] def create_temp_assets_dir(): @@ -30,15 +47,11 @@ class Test_LoadSave(unittest.TestCase): def test_1_save(self): for backend in BACKENDS_MP3: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save(self.test_filepath, False) for backend in BACKENDS: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save(self.test_filepath_wav, True) @@ -85,8 +98,6 @@ def _test_1_save(self, test_filepath, normalization): def test_1_save_sine(self): for backend in BACKENDS: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save_sine() @@ -120,15 +131,11 @@ def _test_1_save_sine(self): def test_2_load(self): for backend in BACKENDS_MP3: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_2_load(self.test_filepath, 278756) for backend in BACKENDS: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_2_load(self.test_filepath_wav, 276858) @@ -224,8 +231,6 @@ def _test_3_load_and_save_is_identity_across_backend(self, backend1, backend2): def test_4_load_partial(self): for backend in BACKENDS_MP3: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_4_load_partial() @@ -268,8 +273,6 @@ def _test_4_load_partial(self): def test_5_get_info(self): for backend in BACKENDS: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_5_get_info() diff --git a/test/kaldi_compatibility_cpu_test.py b/test/torchaudio_unittest/kaldi_compatibility_cpu_test.py similarity index 87% rename from test/kaldi_compatibility_cpu_test.py rename to test/torchaudio_unittest/kaldi_compatibility_cpu_test.py index 3357123296..43be412b47 100644 --- a/test/kaldi_compatibility_cpu_test.py +++ b/test/torchaudio_unittest/kaldi_compatibility_cpu_test.py @@ -1,6 +1,6 @@ import torch -from . import common_utils +from torchaudio_unittest import common_utils from .kaldi_compatibility_impl import Kaldi diff --git a/test/kaldi_compatibility_cuda_test.py b/test/torchaudio_unittest/kaldi_compatibility_cuda_test.py similarity index 88% rename from test/kaldi_compatibility_cuda_test.py rename to test/torchaudio_unittest/kaldi_compatibility_cuda_test.py index e23dbf3f40..28adb7fce5 100644 --- a/test/kaldi_compatibility_cuda_test.py +++ b/test/torchaudio_unittest/kaldi_compatibility_cuda_test.py @@ -1,6 +1,6 @@ import torch -from . import common_utils +from torchaudio_unittest import common_utils from .kaldi_compatibility_impl import Kaldi diff --git a/test/kaldi_compatibility_impl.py b/test/torchaudio_unittest/kaldi_compatibility_impl.py similarity index 98% rename from test/kaldi_compatibility_impl.py rename to test/torchaudio_unittest/kaldi_compatibility_impl.py index 7e51620f63..bae01203f1 100644 --- a/test/kaldi_compatibility_impl.py +++ b/test/torchaudio_unittest/kaldi_compatibility_impl.py @@ -7,7 +7,7 @@ import torchaudio.compliance.kaldi from parameterized import parameterized -from .common_utils import ( +from torchaudio_unittest.common_utils import ( TestBaseMixin, load_params, skipIfNoExec, diff --git a/test/test_kaldi_io.py b/test/torchaudio_unittest/kaldi_io_test.py similarity index 96% rename from test/test_kaldi_io.py rename to test/torchaudio_unittest/kaldi_io_test.py index 80e6b3dc5a..2153b4cc02 100644 --- a/test/test_kaldi_io.py +++ b/test/torchaudio_unittest/kaldi_io_test.py @@ -3,7 +3,7 @@ import torch import torchaudio.kaldi_io as kio -from . import common_utils +from torchaudio_unittest import common_utils class Test_KaldiIO(common_utils.TorchaudioTestCase): diff --git a/test/test_librosa_compatibility.py b/test/torchaudio_unittest/librosa_compatibility_test.py similarity index 99% rename from test/test_librosa_compatibility.py rename to test/torchaudio_unittest/librosa_compatibility_test.py index 01b0f23732..f779f36dae 100644 --- a/test/test_librosa_compatibility.py +++ b/test/torchaudio_unittest/librosa_compatibility_test.py @@ -17,7 +17,7 @@ import pytest -from . import common_utils +from torchaudio_unittest import common_utils @unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") diff --git a/test/test_models.py b/test/torchaudio_unittest/models_test.py similarity index 75% rename from test/test_models.py rename to test/torchaudio_unittest/models_test.py index 10ca5c827b..f9e96edd27 100644 --- a/test/test_models.py +++ b/test/torchaudio_unittest/models_test.py @@ -1,7 +1,7 @@ import torch -from torchaudio.models import Wav2Letter, _MelResNet, _UpsampleNetwork, _WaveRNN +from torchaudio.models import Wav2Letter, MelResNet, UpsampleNetwork, WaveRNN -from . import common_utils +from torchaudio_unittest import common_utils class TestWav2Letter(common_utils.TorchaudioTestCase): @@ -36,7 +36,7 @@ def test_mfcc(self): class TestMelResNet(common_utils.TorchaudioTestCase): def test_waveform(self): - """Validate the output dimensions of a _MelResNet block. + """Validate the output dimensions of a MelResNet block. """ n_batch = 2 @@ -47,7 +47,7 @@ def test_waveform(self): n_hidden = 128 kernel_size = 5 - model = _MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) + model = MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) x = torch.rand(n_batch, n_freq, n_time) out = model(x) @@ -58,7 +58,7 @@ def test_waveform(self): class TestUpsampleNetwork(common_utils.TorchaudioTestCase): def test_waveform(self): - """Validate the output dimensions of a _UpsampleNetwork block. + """Validate the output dimensions of a UpsampleNetwork block. """ upsample_scales = [5, 5, 8] @@ -74,12 +74,12 @@ def test_waveform(self): for upsample_scale in upsample_scales: total_scale *= upsample_scale - model = _UpsampleNetwork(upsample_scales, - n_res_block, - n_freq, - n_hidden, - n_output, - kernel_size) + model = UpsampleNetwork(upsample_scales, + n_res_block, + n_freq, + n_hidden, + n_output, + kernel_size) x = torch.rand(n_batch, n_freq, n_time) out1, out2 = model(x) @@ -91,7 +91,7 @@ def test_waveform(self): class TestWaveRNN(common_utils.TorchaudioTestCase): def test_waveform(self): - """Validate the output dimensions of a _WaveRNN model. + """Validate the output dimensions of a WaveRNN model. """ upsample_scales = [5, 5, 8] @@ -107,8 +107,8 @@ def test_waveform(self): n_hidden = 128 kernel_size = 5 - model = _WaveRNN(upsample_scales, n_classes, hop_length, n_res_block, - n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output) + model = WaveRNN(upsample_scales, n_classes, hop_length, n_res_block, + n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output) x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1)) mels = torch.rand(n_batch, 1, n_freq, n_time) diff --git a/test/test_sox_compatibility.py b/test/torchaudio_unittest/sox_compatibility_test.py similarity index 99% rename from test/test_sox_compatibility.py rename to test/torchaudio_unittest/sox_compatibility_test.py index 260c435b83..ade98b2aeb 100644 --- a/test/test_sox_compatibility.py +++ b/test/torchaudio_unittest/sox_compatibility_test.py @@ -5,7 +5,7 @@ import torchaudio.transforms as T from parameterized import parameterized -from .common_utils import ( +from torchaudio_unittest.common_utils import ( skipIfNoSoxBackend, skipIfNoExec, TempDirMixin, diff --git a/test/sox_io_backend/__init__.py b/test/torchaudio_unittest/sox_effect/__init__.py similarity index 100% rename from test/sox_io_backend/__init__.py rename to test/torchaudio_unittest/sox_effect/__init__.py diff --git a/test/sox_effect/common.py b/test/torchaudio_unittest/sox_effect/common.py similarity index 92% rename from test/sox_effect/common.py rename to test/torchaudio_unittest/sox_effect/common.py index 86edcf6476..00ac4a4565 100644 --- a/test/sox_effect/common.py +++ b/test/torchaudio_unittest/sox_effect/common.py @@ -2,7 +2,7 @@ from parameterized import param -from ..common_utils import get_asset_path +from torchaudio_unittest.common_utils import get_asset_path def name_func(func, _, params): diff --git a/test/sox_effect/test_dataset.py b/test/torchaudio_unittest/sox_effect/dataset_test.py similarity index 98% rename from test/sox_effect/test_dataset.py rename to test/torchaudio_unittest/sox_effect/dataset_test.py index f1b71eb806..e98095d0dd 100644 --- a/test/sox_effect/test_dataset.py +++ b/test/torchaudio_unittest/sox_effect/dataset_test.py @@ -4,7 +4,7 @@ import torch import torchaudio -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExtension, diff --git a/test/sox_effect/smoke_test.py b/test/torchaudio_unittest/sox_effect/smoke_test.py similarity index 97% rename from test/sox_effect/smoke_test.py rename to test/torchaudio_unittest/sox_effect/smoke_test.py index 4997002170..996c62063f 100644 --- a/test/sox_effect/smoke_test.py +++ b/test/torchaudio_unittest/sox_effect/smoke_test.py @@ -1,7 +1,7 @@ from torchaudio import sox_effects from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, skipIfNoExtension, diff --git a/test/sox_effect/test_sox_effect.py b/test/torchaudio_unittest/sox_effect/sox_effect_test.py similarity index 99% rename from test/sox_effect/test_sox_effect.py rename to test/torchaudio_unittest/sox_effect/sox_effect_test.py index 38ab0fa772..27740ca576 100644 --- a/test/sox_effect/test_sox_effect.py +++ b/test/torchaudio_unittest/sox_effect/sox_effect_test.py @@ -3,7 +3,7 @@ from torchaudio import sox_effects from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExtension, diff --git a/test/sox_effect/sox_effects_chain_test.py b/test/torchaudio_unittest/sox_effect/sox_effects_chain_test.py similarity index 100% rename from test/sox_effect/sox_effects_chain_test.py rename to test/torchaudio_unittest/sox_effect/sox_effects_chain_test.py diff --git a/test/sox_effect/test_torchscript.py b/test/torchaudio_unittest/sox_effect/torchscript_test.py similarity index 98% rename from test/sox_effect/test_torchscript.py rename to test/torchaudio_unittest/sox_effect/torchscript_test.py index ded923ab11..8e1a9a3980 100644 --- a/test/sox_effect/test_torchscript.py +++ b/test/torchaudio_unittest/sox_effect/torchscript_test.py @@ -4,7 +4,7 @@ from torchaudio import sox_effects from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExtension, diff --git a/test/utils/__init__.py b/test/torchaudio_unittest/sox_io_backend/__init__.py similarity index 100% rename from test/utils/__init__.py rename to test/torchaudio_unittest/sox_io_backend/__init__.py diff --git a/test/sox_io_backend/common.py b/test/torchaudio_unittest/sox_io_backend/common.py similarity index 100% rename from test/sox_io_backend/common.py rename to test/torchaudio_unittest/sox_io_backend/common.py diff --git a/test/sox_io_backend/test_info.py b/test/torchaudio_unittest/sox_io_backend/info_test.py similarity index 98% rename from test/sox_io_backend/test_info.py rename to test/torchaudio_unittest/sox_io_backend/info_test.py index da5207a7e5..f07b72e112 100644 --- a/test/sox_io_backend/test_info.py +++ b/test/torchaudio_unittest/sox_io_backend/info_test.py @@ -3,7 +3,7 @@ from torchaudio.backend import sox_io_backend -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExec, diff --git a/test/sox_io_backend/test_load.py b/test/torchaudio_unittest/sox_io_backend/load_test.py similarity index 99% rename from test/sox_io_backend/test_load.py rename to test/torchaudio_unittest/sox_io_backend/load_test.py index 8366a01f83..1ff8b76dc5 100644 --- a/test/sox_io_backend/test_load.py +++ b/test/torchaudio_unittest/sox_io_backend/load_test.py @@ -3,7 +3,7 @@ from torchaudio.backend import sox_io_backend from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExec, diff --git a/test/sox_io_backend/test_roundtrip.py b/test/torchaudio_unittest/sox_io_backend/roundtrip_test.py similarity index 97% rename from test/sox_io_backend/test_roundtrip.py rename to test/torchaudio_unittest/sox_io_backend/roundtrip_test.py index 2a051bebd5..85aa3019e0 100644 --- a/test/sox_io_backend/test_roundtrip.py +++ b/test/torchaudio_unittest/sox_io_backend/roundtrip_test.py @@ -3,7 +3,7 @@ from torchaudio.backend import sox_io_backend from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExec, diff --git a/test/sox_io_backend/test_save.py b/test/torchaudio_unittest/sox_io_backend/save_test.py similarity index 99% rename from test/sox_io_backend/test_save.py rename to test/torchaudio_unittest/sox_io_backend/save_test.py index 53588c456b..8e416b7aa9 100644 --- a/test/sox_io_backend/test_save.py +++ b/test/torchaudio_unittest/sox_io_backend/save_test.py @@ -3,7 +3,7 @@ from torchaudio.backend import sox_io_backend from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExec, diff --git a/test/sox_io_backend/smoke_test.py b/test/torchaudio_unittest/sox_io_backend/smoke_test.py similarity index 98% rename from test/sox_io_backend/smoke_test.py rename to test/torchaudio_unittest/sox_io_backend/smoke_test.py index 1ae9a8d938..be734afa77 100644 --- a/test/sox_io_backend/smoke_test.py +++ b/test/torchaudio_unittest/sox_io_backend/smoke_test.py @@ -6,7 +6,7 @@ from torchaudio._internal.module_utils import is_module_available from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, skipIfNoExtension, diff --git a/test/sox_io_backend/test_torchscript.py b/test/torchaudio_unittest/sox_io_backend/torchscript_test.py similarity index 99% rename from test/sox_io_backend/test_torchscript.py rename to test/torchaudio_unittest/sox_io_backend/torchscript_test.py index 9a30aab0d2..dad678cbc0 100644 --- a/test/sox_io_backend/test_torchscript.py +++ b/test/torchaudio_unittest/sox_io_backend/torchscript_test.py @@ -5,7 +5,7 @@ import torchaudio from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, skipIfNoExec, diff --git a/test/torchscript_consistency_cpu_test.py b/test/torchaudio_unittest/torchscript_consistency_cpu_test.py similarity index 93% rename from test/torchscript_consistency_cpu_test.py rename to test/torchaudio_unittest/torchscript_consistency_cpu_test.py index 38fe7961fd..fcc9cb8389 100644 --- a/test/torchscript_consistency_cpu_test.py +++ b/test/torchaudio_unittest/torchscript_consistency_cpu_test.py @@ -1,6 +1,6 @@ import torch -from . import common_utils +from torchaudio_unittest import common_utils from .torchscript_consistency_impl import Functional, Transforms diff --git a/test/torchscript_consistency_cuda_test.py b/test/torchaudio_unittest/torchscript_consistency_cuda_test.py similarity index 94% rename from test/torchscript_consistency_cuda_test.py rename to test/torchaudio_unittest/torchscript_consistency_cuda_test.py index b317334ce4..c205e192ed 100644 --- a/test/torchscript_consistency_cuda_test.py +++ b/test/torchaudio_unittest/torchscript_consistency_cuda_test.py @@ -1,6 +1,6 @@ import torch -from . import common_utils +from torchaudio_unittest import common_utils from .torchscript_consistency_impl import Functional, Transforms diff --git a/test/torchscript_consistency_impl.py b/test/torchaudio_unittest/torchscript_consistency_impl.py similarity index 99% rename from test/torchscript_consistency_impl.py rename to test/torchaudio_unittest/torchscript_consistency_impl.py index 76444dc18a..8d14e538fd 100644 --- a/test/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/torchscript_consistency_impl.py @@ -5,7 +5,7 @@ import torchaudio.functional as F import torchaudio.transforms as T -from . import common_utils +from torchaudio_unittest import common_utils class Functional(common_utils.TestBaseMixin): diff --git a/test/test_transforms.py b/test/torchaudio_unittest/transforms_test.py similarity index 99% rename from test/test_transforms.py rename to test/torchaudio_unittest/transforms_test.py index f6942e18a5..4a91d40bd9 100644 --- a/test/test_transforms.py +++ b/test/torchaudio_unittest/transforms_test.py @@ -6,7 +6,7 @@ import torchaudio.transforms as transforms import torchaudio.functional as F -from . import common_utils +from torchaudio_unittest import common_utils class Tester(common_utils.TorchaudioTestCase): diff --git a/test/torchaudio_unittest/utils/__init__.py b/test/torchaudio_unittest/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/utils/test_sox_utils.py b/test/torchaudio_unittest/utils/sox_utils_test.py similarity index 97% rename from test/utils/test_sox_utils.py rename to test/torchaudio_unittest/utils/sox_utils_test.py index 55a46a235e..7c206fdc0b 100644 --- a/test/utils/test_sox_utils.py +++ b/test/torchaudio_unittest/utils/sox_utils_test.py @@ -1,6 +1,6 @@ from torchaudio.utils import sox_utils -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( PytorchTestCase, skipIfNoExtension, ) diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index 6211b09f2d..792f439ccd 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -135,13 +135,15 @@ def _get_waveform_and_window_properties(waveform: Tensor, r"""Gets the waveform and window properties """ channel = max(channel, 0) - assert channel < waveform.size(0), ('Invalid channel %d for size %d' % (channel, waveform.size(0))) + assert channel < waveform.size(0), ('Invalid channel {} for size {}'.format(channel, waveform.size(0))) waveform = waveform[channel, :] # size (n) window_shift = int(sample_frequency * frame_shift * MILLISECONDS_TO_SECONDS) window_size = int(sample_frequency * frame_length * MILLISECONDS_TO_SECONDS) padded_window_size = _next_power_of_2(window_size) if round_to_power_of_two else window_size - assert 2 <= window_size <= len(waveform), ('choose a window size %d that is [2, %d]' % (window_size, len(waveform))) + assert 2 <= window_size <= len( + waveform), ('choose a window size {} that is [2, {}]' + .format(window_size, len(waveform))) assert 0 < window_shift, '`window_shift` must be greater than 0' assert padded_window_size % 2 == 0, 'the padded `window_size` must be divisible by two.' \ ' use `round_to_power_of_two` or change `frame_length`' @@ -430,7 +432,7 @@ def get_mel_banks(num_bins: int, high_freq += nyquist assert (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq), \ - ('Bad values in options: low-freq %f and high-freq %f vs. nyquist %f' % (low_freq, high_freq, nyquist)) + ('Bad values in options: low-freq {} and high-freq {} vs. nyquist {}'.format(low_freq, high_freq, nyquist)) # fft-bin width [think of it as Nyquist-freq / half-window-length] fft_bin_width = sample_freq / window_length_padded @@ -446,8 +448,8 @@ def get_mel_banks(num_bins: int, assert vtln_warp_factor == 1.0 or ((low_freq < vtln_low < high_freq) and (0.0 < vtln_high < high_freq) and (vtln_low < vtln_high)), \ - ('Bad values in options: vtln-low %f and vtln-high %f, versus low-freq %f and high-freq %f' % - (vtln_low, vtln_high, low_freq, high_freq)) + ('Bad values in options: vtln-low {} and vtln-high {}, versus ' + 'low-freq {} and high-freq {}'.format(vtln_low, vtln_high, low_freq, high_freq)) bin = torch.arange(num_bins).unsqueeze(1) left_mel = mel_low_freq + bin * mel_freq_delta # size(num_bins, 1) diff --git a/torchaudio/datasets/ljspeech.py b/torchaudio/datasets/ljspeech.py index 0b4ee86acc..5e3b6ee7d2 100644 --- a/torchaudio/datasets/ljspeech.py +++ b/torchaudio/datasets/ljspeech.py @@ -63,7 +63,7 @@ def __init__(self, download_url(url, root, hash_value=checksum) extract_archive(archive) - with open(self._metadata_path, "r") as metadata: + with open(self._metadata_path, "r", newline='') as metadata: walker = unicode_csv_reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE) self._walker = list(walker) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 53795fef1a..4b79c01960 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -8,7 +8,6 @@ from torch import Tensor __all__ = [ - "istft", "spectrogram", "griffinlim", "amplitude_to_DB", @@ -45,79 +44,6 @@ ] -def istft( - stft_matrix: Tensor, - n_fft: int, - hop_length: Optional[int] = None, - win_length: Optional[int] = None, - window: Optional[Tensor] = None, - center: bool = True, - pad_mode: Optional[str] = None, - normalized: bool = False, - onesided: bool = True, - length: Optional[int] = None, -) -> Tensor: - r"""Inverse short time Fourier Transform. This is expected to be the inverse of torch.stft. - It has the same parameters (+ additional optional parameter of ``length``) and it should return the - least squares estimation of the original signal. The algorithm will check using the NOLA condition ( - nonzero overlap). - - Important consideration in the parameters ``window`` and ``center`` so that the envelop - created by the summation of all the windows is never zero at certain point in time. Specifically, - :math:`\sum_{t=-\infty}^{\infty} w^2[n-t\times hop\_length] \cancel{=} 0`. - - Since stft discards elements at the end of the signal if they do not fit in a frame, the - istft may return a shorter signal than the original signal (can occur if ``center`` is False - since the signal isn't padded). - - If ``center`` is True, then there will be padding e.g. 'constant', 'reflect', etc. Left padding - can be trimmed off exactly because they can be calculated but right padding cannot be calculated - without additional information. - - Example: Suppose the last window is: - [17, 18, 0, 0, 0] vs [18, 0, 0, 0, 0] - - The n_frame, hop_length, win_length are all the same which prevents the calculation of right padding. - These additional values could be zeros or a reflection of the signal so providing ``length`` - could be useful. If ``length`` is ``None`` then padding will be aggressively removed - (some loss of signal). - - [1] D. W. Griffin and J. S. Lim, "Signal estimation from modified short-time Fourier transform," - IEEE Trans. ASSP, vol.32, no.2, pp.236-243, Apr. 1984. - - Args: - stft_matrix (Tensor): Output of stft where each row of a channel is a frequency and each - column is a window. It has a size of either (..., fft_size, n_frame, 2) - n_fft (int): Size of Fourier transform - hop_length (int or None, optional): The distance between neighboring sliding window frames. - (Default: ``win_length // 4``) - win_length (int or None, optional): The size of window frame and STFT filter. (Default: ``n_fft``) - window (Tensor or None, optional): The optional window function. - (Default: ``torch.ones(win_length)``) - center (bool, optional): Whether ``input`` was padded on both sides so - that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. - (Default: ``True``) - pad_mode: This argument was ignored and to be removed. - normalized (bool, optional): Whether the STFT was normalized. (Default: ``False``) - onesided (bool, optional): Whether the STFT is onesided. (Default: ``True``) - length (int or None, optional): The amount to trim the signal by (i.e. the - original signal length). (Default: whole signal) - - Returns: - Tensor: Least squares estimation of the original signal of size (..., signal_length) - """ - warnings.warn( - 'istft has been moved to PyTorch and will be removed from torchaudio, ' - 'please use torch.istft instead.') - if pad_mode is not None: - warnings.warn( - 'The parameter `pad_mode` was ignored in isftft, and is thus being deprecated. ' - 'Please set `pad_mode` to None to suppress this warning.') - return torch.istft( - input=stft_matrix, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, - center=center, normalized=normalized, onesided=onesided, length=length) - - def spectrogram( waveform: Tensor, pad: int, @@ -250,12 +176,12 @@ def griffinlim( tprev = rebuilt # Invert with our current estimate of the phases - inverse = istft(specgram * angles, - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - window=window, - length=length).float() + inverse = torch.istft(specgram * angles, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + length=length).float() # Rebuild the spectrogram rebuilt = torch.stft(inverse, n_fft, hop_length, win_length, window, @@ -268,12 +194,12 @@ def griffinlim( angles = angles.div(complex_norm(angles).add(1e-16).unsqueeze(-1).expand_as(angles)) # Return the final phase estimates - waveform = istft(specgram * angles, - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - window=window, - length=length) + waveform = torch.istft(specgram * angles, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + length=length) # unpack batch waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:]) diff --git a/torchaudio/models/__init__.py b/torchaudio/models/__init__.py index 20c1bdf265..8e05b8b509 100644 --- a/torchaudio/models/__init__.py +++ b/torchaudio/models/__init__.py @@ -1,2 +1,2 @@ from .wav2letter import * -from ._wavernn import * +from .wavernn import * diff --git a/torchaudio/models/_wavernn.py b/torchaudio/models/wavernn.py similarity index 83% rename from torchaudio/models/_wavernn.py rename to torchaudio/models/wavernn.py index 37a06edfc3..8fbd111a72 100644 --- a/torchaudio/models/_wavernn.py +++ b/torchaudio/models/wavernn.py @@ -4,10 +4,10 @@ from torch import Tensor from torch import nn -__all__ = ["_ResBlock", "_MelResNet", "_Stretch2d", "_UpsampleNetwork", "_WaveRNN"] +__all__ = ["ResBlock", "MelResNet", "Stretch2d", "UpsampleNetwork", "WaveRNN"] -class _ResBlock(nn.Module): +class ResBlock(nn.Module): r"""ResNet block based on "Deep Residual Learning for Image Recognition" The paper link is https://arxiv.org/pdf/1512.03385.pdf. @@ -16,7 +16,7 @@ class _ResBlock(nn.Module): n_freq: the number of bins in a spectrogram. (Default: ``128``) Examples - >>> resblock = _ResBlock() + >>> resblock = ResBlock() >>> input = torch.rand(10, 128, 512) # a random spectrogram >>> output = resblock(input) # shape: (10, 128, 512) """ @@ -33,9 +33,9 @@ def __init__(self, n_freq: int = 128) -> None: ) def forward(self, specgram: Tensor) -> Tensor: - r"""Pass the input through the _ResBlock layer. + r"""Pass the input through the ResBlock layer. Args: - specgram (Tensor): the input sequence to the _ResBlock layer (n_batch, n_freq, n_time). + specgram (Tensor): the input sequence to the ResBlock layer (n_batch, n_freq, n_time). Return: Tensor shape: (n_batch, n_freq, n_time) @@ -44,7 +44,7 @@ def forward(self, specgram: Tensor) -> Tensor: return self.resblock_model(specgram) + specgram -class _MelResNet(nn.Module): +class MelResNet(nn.Module): r"""MelResNet layer uses a stack of ResBlocks on spectrogram. Args: @@ -55,7 +55,7 @@ class _MelResNet(nn.Module): kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``) Examples - >>> melresnet = _MelResNet() + >>> melresnet = MelResNet() >>> input = torch.rand(10, 128, 512) # a random spectrogram >>> output = melresnet(input) # shape: (10, 128, 508) """ @@ -68,7 +68,7 @@ def __init__(self, kernel_size: int = 5) -> None: super().__init__() - ResBlocks = [_ResBlock(n_hidden) for _ in range(n_res_block)] + ResBlocks = [ResBlock(n_hidden) for _ in range(n_res_block)] self.melresnet_model = nn.Sequential( nn.Conv1d(in_channels=n_freq, out_channels=n_hidden, kernel_size=kernel_size, bias=False), @@ -79,9 +79,9 @@ def __init__(self, ) def forward(self, specgram: Tensor) -> Tensor: - r"""Pass the input through the _MelResNet layer. + r"""Pass the input through the MelResNet layer. Args: - specgram (Tensor): the input sequence to the _MelResNet layer (n_batch, n_freq, n_time). + specgram (Tensor): the input sequence to the MelResNet layer (n_batch, n_freq, n_time). Return: Tensor shape: (n_batch, n_output, n_time - kernel_size + 1) @@ -90,7 +90,7 @@ def forward(self, specgram: Tensor) -> Tensor: return self.melresnet_model(specgram) -class _Stretch2d(nn.Module): +class Stretch2d(nn.Module): r"""Upscale the frequency and time dimensions of a spectrogram. Args: @@ -98,7 +98,7 @@ class _Stretch2d(nn.Module): freq_scale: the scale factor in frequency dimension Examples - >>> stretch2d = _Stretch2d(time_scale=10, freq_scale=5) + >>> stretch2d = Stretch2d(time_scale=10, freq_scale=5) >>> input = torch.rand(10, 100, 512) # a random spectrogram >>> output = stretch2d(input) # shape: (10, 500, 5120) @@ -113,10 +113,10 @@ def __init__(self, self.time_scale = time_scale def forward(self, specgram: Tensor) -> Tensor: - r"""Pass the input through the _Stretch2d layer. + r"""Pass the input through the Stretch2d layer. Args: - specgram (Tensor): the input sequence to the _Stretch2d layer (..., n_freq, n_time). + specgram (Tensor): the input sequence to the Stretch2d layer (..., n_freq, n_time). Return: Tensor shape: (..., n_freq * freq_scale, n_time * time_scale) @@ -125,7 +125,7 @@ def forward(self, specgram: Tensor) -> Tensor: return specgram.repeat_interleave(self.freq_scale, -2).repeat_interleave(self.time_scale, -1) -class _UpsampleNetwork(nn.Module): +class UpsampleNetwork(nn.Module): r"""Upscale the dimensions of a spectrogram. Args: @@ -137,7 +137,7 @@ class _UpsampleNetwork(nn.Module): kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``) Examples - >>> upsamplenetwork = _UpsampleNetwork(upsample_scales=[4, 4, 16]) + >>> upsamplenetwork = UpsampleNetwork(upsample_scales=[4, 4, 16]) >>> input = torch.rand(10, 128, 10) # a random spectrogram >>> output = upsamplenetwork(input) # shape: (10, 1536, 128), (10, 1536, 128) """ @@ -156,12 +156,12 @@ def __init__(self, total_scale *= upsample_scale self.indent = (kernel_size - 1) // 2 * total_scale - self.resnet = _MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) - self.resnet_stretch = _Stretch2d(total_scale, 1) + self.resnet = MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) + self.resnet_stretch = Stretch2d(total_scale, 1) up_layers = [] for scale in upsample_scales: - stretch = _Stretch2d(scale, 1) + stretch = Stretch2d(scale, 1) conv = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(1, scale * 2 + 1), @@ -173,10 +173,10 @@ def __init__(self, self.upsample_layers = nn.Sequential(*up_layers) def forward(self, specgram: Tensor) -> Tuple[Tensor, Tensor]: - r"""Pass the input through the _UpsampleNetwork layer. + r"""Pass the input through the UpsampleNetwork layer. Args: - specgram (Tensor): the input sequence to the _UpsampleNetwork layer (n_batch, n_freq, n_time) + specgram (Tensor): the input sequence to the UpsampleNetwork layer (n_batch, n_freq, n_time) Return: Tensor shape: (n_batch, n_freq, (n_time - kernel_size + 1) * total_scale), @@ -195,7 +195,7 @@ def forward(self, specgram: Tensor) -> Tuple[Tensor, Tensor]: return upsampling_output, resnet_output -class _WaveRNN(nn.Module): +class WaveRNN(nn.Module): r"""WaveRNN model based on the implementation from `fatchord `_. The original implementation was introduced in @@ -216,7 +216,7 @@ class _WaveRNN(nn.Module): n_output: the number of output dimensions of melresnet. (Default: ``128``) Example - >>> wavernn = _waveRNN(upsample_scales=[5,5,8], n_classes=512, hop_length=200) + >>> wavernn = WaveRNN(upsample_scales=[5,5,8], n_classes=512, hop_length=200) >>> waveform, sample_rate = torchaudio.load(file) >>> # waveform shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length) >>> specgram = MelSpectrogram(sample_rate)(waveform) # shape: (n_batch, n_channel, n_freq, n_time) @@ -249,12 +249,12 @@ def __init__(self, if total_scale != self.hop_length: raise ValueError(f"Expected: total_scale == hop_length, but found {total_scale} != {hop_length}") - self.upsample = _UpsampleNetwork(upsample_scales, - n_res_block, - n_freq, - n_hidden, - n_output, - kernel_size) + self.upsample = UpsampleNetwork(upsample_scales, + n_res_block, + n_freq, + n_hidden, + n_output, + kernel_size) self.fc = nn.Linear(n_freq + self.n_aux + 1, n_rnn) self.rnn1 = nn.GRU(n_rnn, n_rnn, batch_first=True) @@ -268,11 +268,11 @@ def __init__(self, self.fc3 = nn.Linear(n_fc, self.n_classes) def forward(self, waveform: Tensor, specgram: Tensor) -> Tensor: - r"""Pass the input through the _WaveRNN model. + r"""Pass the input through the WaveRNN model. Args: - waveform: the input waveform to the _WaveRNN layer (n_batch, 1, (n_time - kernel_size + 1) * hop_length) - specgram: the input spectrogram to the _WaveRNN layer (n_batch, 1, n_freq, n_time) + waveform: the input waveform to the WaveRNN layer (n_batch, 1, (n_time - kernel_size + 1) * hop_length) + specgram: the input spectrogram to the WaveRNN layer (n_batch, 1, n_freq, n_time) Return: Tensor shape: (n_batch, 1, (n_time - kernel_size + 1) * hop_length, n_classes) diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index ea0d6db492..ef0a58e637 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -141,8 +141,8 @@ def __init__(self, rand_init: bool = True) -> None: super(GriffinLim, self).__init__() - assert momentum < 1, 'momentum=%s > 1 can be unstable' % momentum - assert momentum > 0, 'momentum=%s < 0' % momentum + assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum) + assert momentum > 0, 'momentum={} < 0'.format(momentum) self.n_fft = n_fft self.n_iter = n_iter @@ -237,7 +237,7 @@ def __init__(self, self.f_max = f_max if f_max is not None else float(sample_rate // 2) self.f_min = f_min - assert f_min <= self.f_max, 'Require f_min: %f < f_max: %f' % (f_min, self.f_max) + assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max) fb = torch.empty(0) if n_stft is None else F.create_fb_matrix( n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate) @@ -313,7 +313,7 @@ def __init__(self, self.tolerance_change = tolerance_change self.sgdargs = sgdargs or {'lr': 0.1, 'momentum': 0.9} - assert f_min <= self.f_max, 'Require f_min: %f < f_max: %f' % (f_min, self.f_max) + assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max) fb = F.create_fb_matrix(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate) self.register_buffer('fb', fb) @@ -607,7 +607,7 @@ def forward(self, waveform: Tensor) -> Tensor: return waveform - raise ValueError('Invalid resampling method: ' + str(self.resampling_method)) + raise ValueError('Invalid resampling method: {}'.format(self.resampling_method)) class ComplexNorm(torch.nn.Module): From 3019ce03a2855abdfc397721f4299ba79fbe7b2e Mon Sep 17 00:00:00 2001 From: Andres Suarez Date: Wed, 5 Aug 2020 18:26:48 -0700 Subject: [PATCH 16/73] Remove .python3 markers Reviewed By: ashwinp-fb Differential Revision: D22955630 fbshipit-source-id: f00ef17a905e4c7cd9196c8924db39f9cdfe8cfa --- .python3 | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .python3 diff --git a/.python3 b/.python3 deleted file mode 100644 index e69de29bb2..0000000000 From aa8c98d3aca64739266a8fb714d299b5f7ff3507 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Tue, 22 Sep 2020 10:59:56 -0700 Subject: [PATCH 17/73] Import torchaudio 20200821 Reviewed By: cpuhrsch Differential Revision: D23273584 fbshipit-source-id: 2fe7effa11b7f7cdf0cee1da6b1cac5556e9f55b --- .circleci/config.yml | 42 +++++- .circleci/config.yml.in | 42 +++++- .circleci/torchscript_bc_test/common.sh | 73 +++++++++ .circleci/torchscript_bc_test/environment.yml | 17 +++ .../torchscript_bc_test/generate_objects.sh | 26 ++++ .../torchscript_bc_test/setup_master_envs.sh | 17 +++ .../torchscript_bc_test/setup_release_envs.sh | 18 +++ .../torchscript_bc_test/validate_objects.sh | 30 ++++ .../unittest/linux/scripts/environment.yml | 5 +- .../unittest/windows/scripts/environment.yml | 3 +- docs/source/datasets.rst | 7 + examples/pipeline_wavernn/main.py | 5 +- examples/pipeline_wavernn/processing.py | 13 +- test/README.md | 131 +--------------- test/torchaudio_unittest/README.md | 131 ++++++++++++++++ .../torchaudio_unittest/datasets/vctk_test.py | 91 ++++++++++++ .../sox_io_backend/info_test.py | 14 ++ .../sox_io_backend/load_test.py | 30 ++++ .../sox_io_backend/save_test.py | 40 +++++ .../torchscript_consistency_impl.py | 7 + test/torchscript_bc_test/README.md | 23 +++ test/torchscript_bc_test/main.py | 71 +++++++++ test/torchscript_bc_test/ver_060.py | 71 +++++++++ torchaudio/backend/sox_io_backend.py | 4 +- torchaudio/csrc/register.cpp | 140 ++++++++---------- torchaudio/csrc/sox_utils.cpp | 6 + torchaudio/datasets/__init__.py | 3 +- torchaudio/datasets/vctk.py | 139 +++++++++++++++-- torchaudio/functional.py | 3 +- 29 files changed, 970 insertions(+), 232 deletions(-) create mode 100644 .circleci/torchscript_bc_test/common.sh create mode 100644 .circleci/torchscript_bc_test/environment.yml create mode 100755 .circleci/torchscript_bc_test/generate_objects.sh create mode 100755 .circleci/torchscript_bc_test/setup_master_envs.sh create mode 100755 .circleci/torchscript_bc_test/setup_release_envs.sh create mode 100755 .circleci/torchscript_bc_test/validate_objects.sh create mode 100644 test/torchaudio_unittest/README.md create mode 100644 test/torchaudio_unittest/datasets/vctk_test.py create mode 100644 test/torchscript_bc_test/README.md create mode 100755 test/torchscript_bc_test/main.py create mode 100644 test/torchscript_bc_test/ver_060.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 771dccc515..70b7c54ef5 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -277,12 +277,13 @@ jobs: steps: - attach_workspace: at: ~/workspace + - designate_upload_channel - run: name: install binaries command: | set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - run: name: smoke test @@ -604,6 +605,42 @@ jobs: name: Run style check command: .circleci/unittest/linux/scripts/run_style_checks.sh + torchscript_bc_test: + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: large + steps: + - checkout + - generate_cache_key + - restore_cache: + + keys: + - torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} + + - run: + name: Generate Objects + command: | + .circleci/torchscript_bc_test/setup_release_envs.sh + .circleci/torchscript_bc_test/generate_objects.sh + - save_cache: + + key: torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} + + paths: + - conda + - envs + - store_artifacts: + path: test/torchscript_bc_test/assets + - persist_to_workspace: + root: . + paths: + - test/torchscript_bc_test/assets + - run: + name: Run BC check test + command: | + .circleci/torchscript_bc_test/setup_master_envs.sh + .circleci/torchscript_bc_test/validate_objects.sh + workflows: build: jobs: @@ -690,6 +727,9 @@ workflows: python_version: '3.8' unittest: jobs: + - torchscript_bc_test: + requires: + - download_third_parties_nix - download_third_parties_nix: name: download_third_parties_nix - unittest_linux_cpu: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 6d6d77d50f..94220d02c0 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -277,12 +277,13 @@ jobs: steps: - attach_workspace: at: ~/workspace + - designate_upload_channel - run: name: install binaries command: | set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - run: name: smoke test @@ -604,6 +605,42 @@ jobs: name: Run style check command: .circleci/unittest/linux/scripts/run_style_checks.sh + torchscript_bc_test: + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: large + steps: + - checkout + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Generate Objects + command: | + .circleci/torchscript_bc_test/setup_release_envs.sh + .circleci/torchscript_bc_test/generate_objects.sh + - save_cache: + {% raw %} + key: torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - envs + - store_artifacts: + path: test/torchscript_bc_test/assets + - persist_to_workspace: + root: . + paths: + - test/torchscript_bc_test/assets + - run: + name: Run BC check test + command: | + .circleci/torchscript_bc_test/setup_master_envs.sh + .circleci/torchscript_bc_test/validate_objects.sh + workflows: build: jobs: @@ -611,6 +648,9 @@ workflows: {{ build_workflows() }} unittest: jobs: + - torchscript_bc_test: + requires: + - download_third_parties_nix {{ unittest_workflows() }} nightly: jobs: diff --git a/.circleci/torchscript_bc_test/common.sh b/.circleci/torchscript_bc_test/common.sh new file mode 100644 index 0000000000..51d258a2d9 --- /dev/null +++ b/.circleci/torchscript_bc_test/common.sh @@ -0,0 +1,73 @@ +#!/usr/bin/env bash + +declare -a TORCHAUDIO_VERSIONS=("0.6.0") +declare -a PYTHON_VERSIONS=("3.6" "3.7" "3.8") + +export TORCHAUDIO_VERSIONS +export PYTHON_VERSIONS + +export KALDI_ROOT="${KALDI_ROOT:-$HOME}" # Just to disable warning emitted from kaldi_io + +_this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +_root_dir="$(git rev-parse --show-toplevel)" +_conda_dir="${_root_dir}/conda" +case "$(uname -s)" in + Darwin*) _os="MacOSX";; + *) _os="Linux" +esac + +install_conda() { + if [ ! -d "${_conda_dir}" ]; then + printf "* Installing conda\n" + wget -nv -O miniconda.sh "http://repo.continuum.io/miniconda/Miniconda3-latest-${_os}-x86_64.sh" + bash ./miniconda.sh -b -f -p "${_conda_dir}" + rm miniconda.sh + fi +} + +init_conda() { + eval "$("${_conda_dir}/bin/conda" shell.bash hook)" +} + +get_name() { + echo "${1}-py${2}" +} + +get_env_dir() { + echo "${_root_dir}/envs/$(get_name "$1" "$2")" +} + +create_env() { + env_dir="$(get_env_dir "$1" "$2")" + if [ ! -d "${env_dir}" ]; then + printf "* Creating environment torchaudio: %s, Python: %s\n" "$1" "$2" + conda create -q --prefix "${env_dir}" -y python="$2" + fi +} + +activate_env() { + printf "* Activating environment torchaudio: %s, Python: %s\n" "$1" "$2" + conda activate "$(get_env_dir "$1" "$2")" +} + +install_release() { + printf "* Installing torchaudio: %s\n" "$1" + conda install -y -q torchaudio="$1" packaging -c pytorch + # packaging is required in test to validate the torchaudio version for dump +} + +install_build_dependencies() { + printf "* Installing torchaudio dependencies except PyTorch - (Python: %s)\n" "$1" + conda env update -q --file "${_this_dir}/environment.yml" --prune + if [ "${_os}" == Linux ]; then + pip install clang-format + fi +} + +build_master() { + printf "* Installing PyTorch (py%s)\n" "$1" + conda install -y -q pytorch "cpuonly" -c pytorch-nightly + printf "* Installing torchaudio\n" + cd "${_root_dir}" || exit 1 + BUILD_SOX=1 python setup.py clean install +} diff --git a/.circleci/torchscript_bc_test/environment.yml b/.circleci/torchscript_bc_test/environment.yml new file mode 100644 index 0000000000..108d97d193 --- /dev/null +++ b/.circleci/torchscript_bc_test/environment.yml @@ -0,0 +1,17 @@ +channels: + - conda-forge + - defaults +dependencies: + - flake8 + - numpy + - pytest + - pytest-cov + - codecov + - librosa + - llvmlite==0.31 # See https://github.com/pytorch/audio/pull/766 + - pip + - pip: + - kaldi-io + - scipy + - parameterized + - numba==0.48 # See https://github.com/librosa/librosa/issues/1160 diff --git a/.circleci/torchscript_bc_test/generate_objects.sh b/.circleci/torchscript_bc_test/generate_objects.sh new file mode 100755 index 0000000000..352f137ad2 --- /dev/null +++ b/.circleci/torchscript_bc_test/generate_objects.sh @@ -0,0 +1,26 @@ +#!/usr/bin/env bash + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +pushd "${this_dir}" +. "common.sh" +popd + +init_conda + +# Move to test directory so that the checked out torchaudio source +# will not shadow the conda-installed version of torchaudio +cd test + +for torchaudio in "${TORCHAUDIO_VERSIONS[@]}" ; do + for python in "${PYTHON_VERSIONS[@]}" ; do + activate_env "${torchaudio}" "${python}" + python -m torch.utils.collect_env + printf "***********************************************************\n" + printf "* Generating\n" + printf " Objects: Python: %s, torchaudio: %s\n" "${python}" "${torchaudio}" + printf "***********************************************************\n" + ./torchscript_bc_test/main.py --mode generate --version "${torchaudio}" + done +done diff --git a/.circleci/torchscript_bc_test/setup_master_envs.sh b/.circleci/torchscript_bc_test/setup_master_envs.sh new file mode 100755 index 0000000000..2321ee0529 --- /dev/null +++ b/.circleci/torchscript_bc_test/setup_master_envs.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash + +set -e + +cd "$( dirname "${BASH_SOURCE[0]}" )" +. "common.sh" + +install_conda +init_conda + +# Install torchaudio environments +for python in "${PYTHON_VERSIONS[@]}" ; do + create_env master "${python}" + activate_env master "${python}" + install_build_dependencies "${python}" + build_master "${python}" +done diff --git a/.circleci/torchscript_bc_test/setup_release_envs.sh b/.circleci/torchscript_bc_test/setup_release_envs.sh new file mode 100755 index 0000000000..3816c091d4 --- /dev/null +++ b/.circleci/torchscript_bc_test/setup_release_envs.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash + +set -e + +cd "$( dirname "${BASH_SOURCE[0]}" )" +. "common.sh" + +install_conda +init_conda + +# Install torchaudio environments +for torchaudio in "${TORCHAUDIO_VERSIONS[@]}" ; do + for python in "${PYTHON_VERSIONS[@]}" ; do + create_env "${torchaudio}" "${python}" + activate_env "${torchaudio}" "${python}" + install_release "${torchaudio}" + done +done diff --git a/.circleci/torchscript_bc_test/validate_objects.sh b/.circleci/torchscript_bc_test/validate_objects.sh new file mode 100755 index 0000000000..852ed0acc8 --- /dev/null +++ b/.circleci/torchscript_bc_test/validate_objects.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +pushd "${this_dir}" +. "common.sh" +popd + +init_conda + +# Move to test directory so that the checked out torchaudio source +# will not shadow the conda-installed version of torchaudio +cd test + +# Validate torchscript objects for each +for runtime_python in "${PYTHON_VERSIONS[@]}" ; do + activate_env master "${runtime_python}" + python -m torch.utils.collect_env + for object_torchaudio in "${TORCHAUDIO_VERSIONS[@]}" ; do + for object_python in "${PYTHON_VERSIONS[@]}" ; do + printf "***********************************************************\n" + printf "* Validating\n" + printf " Runtime: Python: %s, torchaudio: master (%s)\n" "${runtime_python}" "$(python -c 'import torchaudio;print(torchaudio.__version__)')" + printf " Objects: Python: %s, torchaudio: %s\n" "${object_python}" "${object_torchaudio}" + printf "***********************************************************\n" + ./torchscript_bc_test/main.py --mode validate --version "${object_torchaudio}" + done + done +done diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml index 121478165e..f68fb130a2 100644 --- a/.circleci/unittest/linux/scripts/environment.yml +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -3,16 +3,15 @@ channels: - defaults dependencies: - flake8 - - numpy + - numpy >= 1.11 - pytest - pytest-cov - pytest-xdist - codecov - - librosa + - librosa>=0.8.0 - llvmlite==0.31 # See https://github.com/pytorch/audio/pull/766 - pip - pip: - kaldi-io - scipy - parameterized - - numba==0.48 # See https://github.com/librosa/librosa/issues/1160 diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml index fb57f0facc..3a41020645 100644 --- a/.circleci/unittest/windows/scripts/environment.yml +++ b/.circleci/unittest/windows/scripts/environment.yml @@ -12,7 +12,6 @@ dependencies: - scipy == 1.4.1 - kaldi-io - PySoundFile - - librosa + - librosa >= 0.8.0 - future - parameterized - - numba==0.48 diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 96ba045c5e..6cdf44e223 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -88,6 +88,13 @@ VCTK :members: __getitem__ :special-members: + +VCTK_092 +~~~~~~~~ + +.. autoclass:: VCTK_092 + + YESNO ~~~~~ diff --git a/examples/pipeline_wavernn/main.py b/examples/pipeline_wavernn/main.py index 9620dd5d45..28ecb76fb9 100644 --- a/examples/pipeline_wavernn/main.py +++ b/examples/pipeline_wavernn/main.py @@ -163,6 +163,9 @@ def parse_args(): parser.add_argument( "--file-path", default="", type=str, help="the path of audio files", ) + parser.add_argument( + "--normalization", default=True, action="store_true", help="if True, spectrogram is normalized", + ) args = parser.parse_args() return args @@ -273,7 +276,7 @@ def main(args): n_mels=args.n_freq, fmin=args.f_min, ), - NormalizeDB(min_level_db=args.min_level_db), + NormalizeDB(min_level_db=args.min_level_db, normalization=args.normalization), ) train_dataset, val_dataset = split_process_dataset(args, transforms) diff --git a/examples/pipeline_wavernn/processing.py b/examples/pipeline_wavernn/processing.py index b22d60dae4..dca45e97f4 100644 --- a/examples/pipeline_wavernn/processing.py +++ b/examples/pipeline_wavernn/processing.py @@ -31,15 +31,18 @@ class NormalizeDB(nn.Module): r"""Normalize the spectrogram with a minimum db value """ - def __init__(self, min_level_db): + def __init__(self, min_level_db, normalization): super().__init__() self.min_level_db = min_level_db + self.normalization = normalization def forward(self, specgram): - specgram = 20 * torch.log10(torch.clamp(specgram, min=1e-5)) - return torch.clamp( - (self.min_level_db - specgram) / self.min_level_db, min=0, max=1 - ) + specgram = torch.log10(torch.clamp(specgram, min=1e-5)) + if self.normalization: + return torch.clamp( + (self.min_level_db - 20 * specgram) / self.min_level_db, min=0, max=1 + ) + return specgram def normalized_waveform_to_bits(waveform, bits): diff --git a/test/README.md b/test/README.md index 629e5b54be..62d32b428d 100644 --- a/test/README.md +++ b/test/README.md @@ -1,131 +1,4 @@ # Torchaudio Test Suite -## How to run test - -You can use `pytest` to run `torchaudio`'s test suites. See https://docs.pytest.org/ for the detail of how to use `pytest` command. - -``` -# List up all the tests -pytest test --collect-only -# Run all the test suites -pytest test -# Run tests on sox_effects module -pytest test/sox_effect -# use -k to apply filter -pytest test/sox_io_backend -k load # only runs tests where their names contain load -# Some other useful options; -# Stop on the first failure -x -# Run failure fast --ff -# Only rerun the failure --lf -``` - -**Note** -We use PyTorch's test utilities instead of `pytest` frameworks when writing tests to avoid reinventing the wheel for Tensor comparison. - -## Structure of tests - -The following is an overview of the tests and related modules for `torchaudio`. - -### Purpose specific test suites - -#### Numerical compatibility agains existing software -- [Librosa compatibility test](./torchaudio_unittest/librosa_compatibility_test.py) - Test suite for numerical compatibility against librosa. -- [SoX compatibility test](./torchaudio_unittest/sox_compatibility_test.py) - Test suite for numerical compatibility against SoX. -- [Kaldi compatibility test](./torchaudio_unittest/kaldi_compatibility_test.py) - Test suite for numerical compatibility against Kaldi. - -#### Result consistency with PyTorch framework -- [TorchScript consistency test](./torchaudio_unittest/torchscript_consistency_impl.py) - Test suite to check 1. if an API is TorchScript-able, and 2. the results from Python and Torchscript match. -- [Batch consistency test](./torchaudio_unittest/batch_consistency_test.py) - Test suite to check if functionals/Transforms handle single sample input and batch input and return the same result. - -### Module specific test suites - -The following test modules are defined for corresponding `torchaudio` module/functions. - -- [`torchaudio.datasets`](./torchaudio_unittest/datasets) -- [`torchaudio.functional`](./torchaudio_unittest/functional) -- [`torchaudio.transforms`](./torchaudio_unittest/transforms_test.py) -- [`torchaudio.compliance.kaldi`](./torchaudio_unittest/compliance_kaldi_test.py) -- [`torchaudio.kaldi_io`](./torchaudio_unittest/kaldi_io_test.py) -- [`torchaudio.sox_effects`](test/sox_effects) -- [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](./torchaudio_unittest/io_test.py) - -### Test modules that do not fall into the above categories -- [test_dataloader.py](./torchaudio_unittest/dataloader_test.py) - Simple test for loading data and applying preprocessing. - -### Support files -- [assets](./torchaudio_unittest/assets): Contain sample audio files. -- [assets/kaldi](./torchaudio_unittest/assets/kaldi): Contains Kaldi format matrix files used in [./torchaudio_unittest/test_compliance_kaldi.py](./torchaudio_unittest/test_compliance_kaldi.py). -- [compliance](./torchaudio_unittest/compliance): Scripts used to generate above Kaldi matrix files. - -### Waveforms for Testing Purposes - -When testing transforms we often need waveforms of specific type (ex: pure tone, noise, or voice), with specific bitrate (ex. 8 or 16 kHz) and number of channels (ex. mono, stereo). Below are some tips on how to construct waveforms and guidance around existing audio files. - -#### Load a Waveform from a File - -```python -filepath = common_utils.get_asset_path('filename.wav') -waveform, sample_rate = common_utils.load_wav(filepath) -``` - -*Note: Should you choose to contribute an audio file, please leave a comment in the issue or pull request, mentioning content source and licensing information. WAV files are preferred. Other formats should be used only when there is no alternative. (i.e. dataset implementation comes with hardcoded non-wav extension).* - -#### Pure Tone - -Code: - -```python -waveform = common_utils.get_sinusoid( - frequency=300, - sample_rate=16000, - duration=1, # seconds - n_channels=1, - dtype="float32", - device="cpu", -) -``` - -#### Noise - -Code: - -```python -tensor = common_utils.get_whitenoise() -``` - -Files: - -* `steam-train-whistle-daniel_simon.wav` - -#### Voice - -Files: - -* `CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav` -* `VCTK-Corpus/wav48/p224/p224_002.wav` -* `vad-go-stereo-44100.wav` -* `vad-go-mono-32000.wav` - -## Adding test - -The following is the current practice of torchaudio test suite. - -1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./torchaudio_unittest/common_utils) has some data generator functions. -1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless you are writing tests that are common to CPU / CUDA. - - Set class memeber `dtype`, `device` and `backend` for the desired behavior. - - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail. - - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability. -1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchaudio_unittest/torchscript_consistency_impl.py) and test definitions for [CPU](./torchaudio_unittest/torchscript_consistency_cpu_test.py) and [CUDA](./torchaudio_unittest/torchscript_consistency_cuda_test.py) devices. -1. For numerically comparing Tensors, use `assertEqual` method from torchaudio_unittest.common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. - -When you add a new feature(functional/transform), consider the following - -1. When you add a new feature, please make it Torchscript-able and batch-consistent unless it degrades the performance. Please add the tests to see if the new feature meet these requirements. -1. If the feature should be numerical compatible against existing software (SoX, Librosa, Kaldi etc), add a corresponding test. -1. If the new feature is unique to `torchaudio` (not a PyTorch implementation of an existing Software functionality), consider adding correctness tests (wheather the expected output is produced for the set of input) under the corresponding test module (`test_functional.py`, `test_transforms.py`). +- [Unit Test](./torchaudio_unittest/) +- [Torchscript Backward Compatibility Test](./torchscript_bc_test/) \ No newline at end of file diff --git a/test/torchaudio_unittest/README.md b/test/torchaudio_unittest/README.md new file mode 100644 index 0000000000..306ca23098 --- /dev/null +++ b/test/torchaudio_unittest/README.md @@ -0,0 +1,131 @@ +# Torchaudio Unit Test Suite + +## How to run test + +You can use `pytest` to run `torchaudio`'s test suites. See https://docs.pytest.org/ for the detail of how to use `pytest` command. + +``` +# List up all the tests +pytest test --collect-only +# Run all the test suites +pytest test +# Run tests on sox_effects module +pytest test/sox_effect +# use -k to apply filter +pytest test/sox_io_backend -k load # only runs tests where their names contain load +# Some other useful options; +# Stop on the first failure -x +# Run failure fast --ff +# Only rerun the failure --lf +``` + +**Note** +We use PyTorch's test utilities instead of `pytest` frameworks when writing tests to avoid reinventing the wheel for Tensor comparison. + +## Structure of tests + +The following is an overview of the tests and related modules for `torchaudio`. + +### Purpose specific test suites + +#### Numerical compatibility agains existing software +- [Librosa compatibility test](./librosa_compatibility_test.py) + Test suite for numerical compatibility against librosa. +- [SoX compatibility test](./sox_compatibility_test.py) + Test suite for numerical compatibility against SoX. +- [Kaldi compatibility test](./kaldi_compatibility_test.py) + Test suite for numerical compatibility against Kaldi. + +#### Result consistency with PyTorch framework +- [TorchScript consistency test](./torchscript_consistency_impl.py) + Test suite to check 1. if an API is TorchScript-able, and 2. the results from Python and Torchscript match. +- [Batch consistency test](./batch_consistency_test.py) + Test suite to check if functionals/Transforms handle single sample input and batch input and return the same result. + +### Module specific test suites + +The following test modules are defined for corresponding `torchaudio` module/functions. + +- [`torchaudio.datasets`](./datasets) +- [`torchaudio.functional`](./functional) +- [`torchaudio.transforms`](./transforms_test.py) +- [`torchaudio.compliance.kaldi`](./compliance_kaldi_test.py) +- [`torchaudio.kaldi_io`](./kaldi_io_test.py) +- [`torchaudio.sox_effects`](test/sox_effects) +- [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](./io_test.py) + +### Test modules that do not fall into the above categories +- [test_dataloader.py](./dataloader_test.py) + Simple test for loading data and applying preprocessing. + +### Support files +- [assets](./assets): Contain sample audio files. +- [assets/kaldi](./assets/kaldi): Contains Kaldi format matrix files used in [./test_compliance_kaldi.py](./test_compliance_kaldi.py). +- [compliance](./compliance): Scripts used to generate above Kaldi matrix files. + +### Waveforms for Testing Purposes + +When testing transforms we often need waveforms of specific type (ex: pure tone, noise, or voice), with specific bitrate (ex. 8 or 16 kHz) and number of channels (ex. mono, stereo). Below are some tips on how to construct waveforms and guidance around existing audio files. + +#### Load a Waveform from a File + +```python +filepath = common_utils.get_asset_path('filename.wav') +waveform, sample_rate = common_utils.load_wav(filepath) +``` + +*Note: Should you choose to contribute an audio file, please leave a comment in the issue or pull request, mentioning content source and licensing information. WAV files are preferred. Other formats should be used only when there is no alternative. (i.e. dataset implementation comes with hardcoded non-wav extension).* + +#### Pure Tone + +Code: + +```python +waveform = common_utils.get_sinusoid( + frequency=300, + sample_rate=16000, + duration=1, # seconds + n_channels=1, + dtype="float32", + device="cpu", +) +``` + +#### Noise + +Code: + +```python +tensor = common_utils.get_whitenoise() +``` + +Files: + +* `steam-train-whistle-daniel_simon.wav` + +#### Voice + +Files: + +* `CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav` +* `VCTK-Corpus/wav48/p224/p224_002.wav` +* `vad-go-stereo-44100.wav` +* `vad-go-mono-32000.wav` + +## Adding test + +The following is the current practice of torchaudio test suite. + +1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./common_utils) has some data generator functions. +1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless you are writing tests that are common to CPU / CUDA. + - Set class memeber `dtype`, `device` and `backend` for the desired behavior. + - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail. + - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability. +1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchscript_consistency_impl.py) and test definitions for [CPU](./torchscript_consistency_cpu_test.py) and [CUDA](./torchscript_consistency_cuda_test.py) devices. +1. For numerically comparing Tensors, use `assertEqual` method from torchaudio_unittest.common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. + +When you add a new feature(functional/transform), consider the following + +1. When you add a new feature, please make it Torchscript-able and batch-consistent unless it degrades the performance. Please add the tests to see if the new feature meet these requirements. +1. If the feature should be numerical compatible against existing software (SoX, Librosa, Kaldi etc), add a corresponding test. +1. If the new feature is unique to `torchaudio` (not a PyTorch implementation of an existing Software functionality), consider adding correctness tests (wheather the expected output is produced for the set of input) under the corresponding test module (`test_functional.py`, `test_transforms.py`). diff --git a/test/torchaudio_unittest/datasets/vctk_test.py b/test/torchaudio_unittest/datasets/vctk_test.py new file mode 100644 index 0000000000..26b14f5d04 --- /dev/null +++ b/test/torchaudio_unittest/datasets/vctk_test.py @@ -0,0 +1,91 @@ +import os + +from torchaudio.datasets import vctk + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + +# Used to generate a unique utterance for each dummy audio file +UTTERANCE = [ + 'Please call Stella', + 'Ask her to bring these things', + 'with her from the store', + 'Six spoons of fresh snow peas, five thick slabs of blue cheese, and maybe a snack for her brother Bob', + 'We also need a small plastic snake and a big toy frog for the kids', + 'She can scoop these things into three red bags, and we will go meet her Wednesday at the train station', + 'When the sunlight strikes raindrops in the air, they act as a prism and form a rainbow', + 'The rainbow is a division of white light into many beautiful colors', + 'These take the shape of a long round arch, with its path high above, and its two ends \ + apparently beyond the horizon', + 'There is, according to legend, a boiling pot of gold at one end' +] + + +class TestVCTK(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + samples = [] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + dataset_dir = os.path.join(cls.root_dir, 'VCTK-Corpus-0.92') + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 48000 + seed = 0 + + for speaker in range(225, 230): + speaker_id = 'p' + str(speaker) + audio_dir = os.path.join(dataset_dir, 'wav48_silence_trimmed', speaker_id) + os.makedirs(audio_dir, exist_ok=True) + + file_dir = os.path.join(dataset_dir, 'txt', speaker_id) + os.makedirs(file_dir, exist_ok=True) + + for utterance_id in range(1, 11): + filename = f'{speaker_id}_{utterance_id:03d}_mic2' + audio_file_path = os.path.join(audio_dir, filename + '.wav') + + data = get_whitenoise( + sample_rate=sample_rate, + duration=0.01, + n_channels=1, + dtype='float32', + seed=seed + ) + save_wav(audio_file_path, data, sample_rate) + + txt_file_path = os.path.join(file_dir, filename[:-5] + '.txt') + utterance = UTTERANCE[utterance_id - 1] + with open(txt_file_path, 'w') as f: + f.write(utterance) + + sample = ( + normalize_wav(data), + sample_rate, + utterance, + speaker_id, + utterance_id + ) + cls.samples.append(sample) + + seed += 1 + + def test_vctk(self): + dataset = vctk.VCTK_092(self.root_dir, audio_ext=".wav") + num_samples = 0 + for i, (data, sample_rate, utterance, speaker_id, utterance_id) in enumerate(dataset): + self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[i][1] + assert utterance == self.samples[i][2] + assert speaker_id == self.samples[i][3] + assert int(utterance_id) == self.samples[i][4] + num_samples += 1 + + assert num_samples == len(self.samples) diff --git a/test/torchaudio_unittest/sox_io_backend/info_test.py b/test/torchaudio_unittest/sox_io_backend/info_test.py index f07b72e112..b16203cfd3 100644 --- a/test/torchaudio_unittest/sox_io_backend/info_test.py +++ b/test/torchaudio_unittest/sox_io_backend/info_test.py @@ -108,6 +108,20 @@ def test_vorbis(self, sample_rate, num_channels, quality_level): assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_sphere(self, sample_rate, num_channels): + """`sox_io_backend.info` can check sph file correctly""" + duration = 1 + path = self.get_temp_path('data.sph') + sox_utils.gen_audio_file(path, sample_rate, num_channels, duration=duration) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + @skipIfNoExtension class TestInfoOpus(PytorchTestCase): diff --git a/test/torchaudio_unittest/sox_io_backend/load_test.py b/test/torchaudio_unittest/sox_io_backend/load_test.py index 1ff8b76dc5..59bcaea386 100644 --- a/test/torchaudio_unittest/sox_io_backend/load_test.py +++ b/test/torchaudio_unittest/sox_io_backend/load_test.py @@ -120,6 +120,28 @@ def assert_vorbis(self, sample_rate, num_channels, quality_level, duration): assert sr == sample_rate self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + def assert_sphere(self, sample_rate, num_channels, duration): + """`sox_io_backend.load` can load sph format. + + This test takes the same strategy as mp3 to compare the result + """ + path = self.get_temp_path('1.original.sph') + ref_path = self.get_temp_path('2.reference.wav') + + # 1. Generate sph with sox + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + bit_depth=32, duration=duration) + # 2. Convert to wav with sox + sox_utils.convert_audio_file(path, ref_path) + # 3. Load sph with torchaudio + data, sr = sox_io_backend.load(path) + # 4. Load wav with scipy + data_ref = load_wav(ref_path)[0] + # 5. Compare + assert sr == sample_rate + self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + @skipIfNoExec('sox') @skipIfNoExtension @@ -230,6 +252,14 @@ def test_opus(self, bitrate, num_channels, compression_level): assert sample_rate == sr self.assertEqual(expected, found) + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_sphere(self, sample_rate, num_channels): + """`sox_io_backend.load` can load sph format correctly.""" + self.assert_sphere(sample_rate, num_channels, duration=1) + @skipIfNoExec('sox') @skipIfNoExtension diff --git a/test/torchaudio_unittest/sox_io_backend/save_test.py b/test/torchaudio_unittest/sox_io_backend/save_test.py index 8e416b7aa9..f7d4da3931 100644 --- a/test/torchaudio_unittest/sox_io_backend/save_test.py +++ b/test/torchaudio_unittest/sox_io_backend/save_test.py @@ -168,6 +168,38 @@ def assert_vorbis(self, *args, **kwargs): else: raise error + def assert_sphere(self, sample_rate, num_channels, duration): + """`sox_io_backend.save` can save sph format. + + This test takes the same strategy as mp3 to compare the result + """ + src_path = self.get_temp_path('1.reference.wav') + flc_path = self.get_temp_path('2.1.torchaudio.sph') + wav_path = self.get_temp_path('2.2.torchaudio.wav') + flc_path_sox = self.get_temp_path('3.1.sox.sph') + wav_path_sox = self.get_temp_path('3.2.sox.wav') + + # 1. Generate original wav + data = get_wav_data('float32', num_channels, normalize=True, num_frames=duration * sample_rate) + save_wav(src_path, data, sample_rate) + # 2.1. Convert the original wav to sph with torchaudio + sox_io_backend.save(flc_path, load_wav(src_path)[0], sample_rate) + # 2.2. Convert the sph to wav with Sox + # converting to 32 bit because sph file has 24 bit depth which scipy cannot handle. + sox_utils.convert_audio_file(flc_path, wav_path, bit_depth=32) + # 2.3. Load + found = load_wav(wav_path)[0] + + # 3.1. Convert the original wav to sph with SoX + sox_utils.convert_audio_file(src_path, flc_path_sox) + # 3.2. Convert the sph to wav with Sox + # converting to 32 bit because sph file has 24 bit depth which scipy cannot handle. + sox_utils.convert_audio_file(flc_path_sox, wav_path_sox, bit_depth=32) + # 3.3. Load + expected = load_wav(wav_path_sox)[0] + + self.assertEqual(found, expected) + @skipIfNoExec('sox') @skipIfNoExtension @@ -262,6 +294,14 @@ def test_vorbis_large(self, sample_rate, num_channels, quality_level): self.assert_vorbis(sample_rate, num_channels, quality_level, two_hours) ''' + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_sphere(self, sample_rate, num_channels): + """`sox_io_backend.save` can save sph format.""" + self.assert_sphere(sample_rate, num_channels, duration=1) + @skipIfNoExec('sox') @skipIfNoExtension diff --git a/test/torchaudio_unittest/torchscript_consistency_impl.py b/test/torchaudio_unittest/torchscript_consistency_impl.py index 8d14e538fd..82c6a300a6 100644 --- a/test/torchaudio_unittest/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/torchscript_consistency_impl.py @@ -194,6 +194,13 @@ def func(tensor): tensor = common_utils.get_whitenoise(n_channels=2) self._assert_consistency(func, tensor, shape_only=True) + def test_dither_noise_shaping(self): + def func(tensor): + return F.dither(tensor, noise_shaping=True) + + tensor = common_utils.get_whitenoise(n_channels=2) + self._assert_consistency(func, tensor) + def test_lfilter(self): if self.dtype == torch.float64: raise unittest.SkipTest("This test is known to fail for float64") diff --git a/test/torchscript_bc_test/README.md b/test/torchscript_bc_test/README.md new file mode 100644 index 0000000000..4cf0e2b226 --- /dev/null +++ b/test/torchscript_bc_test/README.md @@ -0,0 +1,23 @@ +# Torchscript Backward Compatibility Test Suite + +This directory contains tools to generate Torchscript object of a specific torchaudio version (given that you have the corresponding environments setup correctly) and validate it in the current version. + +## Usage + +### Generate torchscript object + +``` +./main.py --mode generate --version 0.6.0 +``` + +will generate Torchscript dump files in [`assets`](./assets/) directory. This requries your Python runtime to have torchaudio `0.6.0`. + + +### Validate torchscript object + + +``` +./main.py --mode validate --version 0.6.0 +``` + +will validate if the Torchscript files created in the previous step are compatible with the version of torchaudio available in your environment (master). diff --git a/test/torchscript_bc_test/main.py b/test/torchscript_bc_test/main.py new file mode 100755 index 0000000000..3c1c8d974e --- /dev/null +++ b/test/torchscript_bc_test/main.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 +"""Generate torchscript object of specific torhcaudio version. + +This requires that the corresponding torchaudio (and torch) is installed. +""" +import os +import sys +import argparse + + +_BASE_OBJ_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'assets') + + +def _parse_args(): + parser = argparse.ArgumentParser( + description=__doc__ + ) + parser.add_argument( + '--mode', choices=['generate', 'validate'], required=True, + help=( + '"generate" generates Torchscript objects of the specific torchaudio ' + 'in the given directory. ' + '"validate" validates if the objects in the givcen directory are compatible ' + 'with the current torhcaudio.' + ) + ) + parser.add_argument( + '--version', choices=['0.6.0'], required=True, + help='torchaudio version.' + ) + parser.add_argument( + '--base-obj-dir', default=_BASE_OBJ_DIR, + help='Directory where objects are saved/loaded.' + ) + return parser.parse_args() + + +def _generate(version, output_dir): + if version == '0.6.0': + import ver_060 + ver_060.generate(output_dir) + else: + raise ValueError(f'Unexpected torchaudio version: {version}') + + +def _validate(version, input_dir): + if version == '0.6.0': + import ver_060 + ver_060.validate(input_dir) + else: + raise ValueError(f'Unexpected torchaudio version: {version}') + + +def _get_obj_dir(base_dir, version): + py_version = f'{sys.version_info.major}.{sys.version_info.minor}' + return os.path.join(base_dir, f'{version}-py{py_version}') + + +def _main(): + args = _parse_args() + obj_dir = _get_obj_dir(args.base_obj_dir, args.version) + if args.mode == 'generate': + _generate(args.version, obj_dir) + elif args.mode == 'validate': + _validate(args.version, obj_dir) + else: + raise ValueError(f'Unexpected mode: {args.mode}') + + +if __name__ == '__main__': + _main() diff --git a/test/torchscript_bc_test/ver_060.py b/test/torchscript_bc_test/ver_060.py new file mode 100644 index 0000000000..78b8da228c --- /dev/null +++ b/test/torchscript_bc_test/ver_060.py @@ -0,0 +1,71 @@ +import os +import tempfile +from typing import Optional +from packaging import version + +import torch +import torchaudio + +_MIN_VER = version.parse('0.6.0a0') +_MAX_VER = version.parse('0.7.0') +_RUNTIME_VER = version.parse(torchaudio.__version__) + + +def info(filepath: str) -> torchaudio.backend.sox_io_backend.AudioMetaData: + return torchaudio.info(filepath) + + +def load( + filepath: str, + frame_offset: int, + num_frames: int, + normalize: bool, + channels_first: bool): + return torchaudio.load(filepath, frame_offset, num_frames, normalize, channels_first) + + +def save( + filepath: str, + tensor: torch.Tensor, + sample_rate: int, + channels_first: bool = True, + compression: Optional[float] = None, +): + torchaudio.save(filepath, tensor, sample_rate, channels_first, compression) + + +def generate(output_dir): + if not (_MIN_VER <= _RUNTIME_VER < _MAX_VER): + raise RuntimeError(f'Invalid torchaudio runtime version: {_RUNTIME_VER}') + + torchaudio.set_audio_backend('sox_io') + + funcs = [ + info, + load, + save, + ] + + os.makedirs(output_dir, exist_ok=True) + for func in funcs: + torch.jit.script(func).save(os.path.join(output_dir, f'{func.__name__}.zip')) + + +def validate(input_dir): + torchaudio.set_audio_backend('sox_io') + + # See https://github.com/pytorch/pytorch/issues/42258 + # info_ = torch.jit.load(os.path.join(input_dir, 'info.zip')) + load_ = torch.jit.load(os.path.join(input_dir, 'load.zip')) + save_ = torch.jit.load(os.path.join(input_dir, 'save.zip')) + + sample_rate = 44100 + normalize = True + channels_first = True + with tempfile.TemporaryDirectory() as temp_dir: + temp_file = os.path.join(temp_dir, 'test.wav') + temp_data = torch.rand(2, sample_rate, dtype=torch.float32) + + save_(temp_file, temp_data, sample_rate, channels_first, 0.) + # info_(temp_file) + load_(temp_file, 0, -1, normalize, channels_first) diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 57d8db7723..9c0695d723 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -58,6 +58,7 @@ def load( * FLAC * OGG/VORBIS * OPUS + * SPHERE To load ``MP3``, ``FLAC``, ``OGG/VORBIS``, ``OPUS`` and other codecs ``libsox`` does not handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` @@ -132,6 +133,7 @@ def save( * MP3 * FLAC * OGG/VORBIS + * SPHERE To save ``MP3``, ``FLAC``, ``OGG/VORBIS``, and other codecs ``libsox`` does not handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` @@ -158,7 +160,7 @@ def save( """ if compression is None: ext = str(filepath)[-3:].lower() - if ext == 'wav': + if ext in ['wav', 'sph']: compression = 0. elif ext == 'mp3': compression = -4.5 diff --git a/torchaudio/csrc/register.cpp b/torchaudio/csrc/register.cpp index 476dcc3ebb..a1ec8f2254 100644 --- a/torchaudio/csrc/register.cpp +++ b/torchaudio/csrc/register.cpp @@ -5,86 +5,70 @@ #include #include -namespace torchaudio { -namespace { +TORCH_LIBRARY(torchaudio, m) { + ////////////////////////////////////////////////////////////////////////////// + // sox_utils.h + ////////////////////////////////////////////////////////////////////////////// + m.class_("TensorSignal") + .def(torch::init()) + .def("get_tensor", &torchaudio::sox_utils::TensorSignal::getTensor) + .def( + "get_sample_rate", + &torchaudio::sox_utils::TensorSignal::getSampleRate) + .def( + "get_channels_first", + &torchaudio::sox_utils::TensorSignal::getChannelsFirst); -//////////////////////////////////////////////////////////////////////////////// -// sox_utils.h -//////////////////////////////////////////////////////////////////////////////// -static auto registerTensorSignal = - torch::class_("torchaudio", "TensorSignal") - .def(torch::init()) - .def("get_tensor", &sox_utils::TensorSignal::getTensor) - .def("get_sample_rate", &sox_utils::TensorSignal::getSampleRate) - .def("get_channels_first", &sox_utils::TensorSignal::getChannelsFirst); + m.def("torchaudio::sox_utils_set_seed", &torchaudio::sox_utils::set_seed); + m.def( + "torchaudio::sox_utils_set_verbosity", + &torchaudio::sox_utils::set_verbosity); + m.def( + "torchaudio::sox_utils_set_use_threads", + &torchaudio::sox_utils::set_use_threads); + m.def( + "torchaudio::sox_utils_set_buffer_size", + &torchaudio::sox_utils::set_buffer_size); + m.def( + "torchaudio::sox_utils_list_effects", + &torchaudio::sox_utils::list_effects); + m.def( + "torchaudio::sox_utils_list_read_formats", + &torchaudio::sox_utils::list_read_formats); + m.def( + "torchaudio::sox_utils_list_write_formats", + &torchaudio::sox_utils::list_write_formats); -static auto registerSetSoxOptions = - torch::RegisterOperators() - .op("torchaudio::sox_utils_set_seed", &sox_utils::set_seed) - .op("torchaudio::sox_utils_set_verbosity", &sox_utils::set_verbosity) - .op("torchaudio::sox_utils_set_use_threads", - &sox_utils::set_use_threads) - .op("torchaudio::sox_utils_set_buffer_size", - &sox_utils::set_buffer_size) - .op("torchaudio::sox_utils_list_effects", &sox_utils::list_effects) - .op("torchaudio::sox_utils_list_read_formats", - &sox_utils::list_read_formats) - .op("torchaudio::sox_utils_list_write_formats", - &sox_utils::list_write_formats); + ////////////////////////////////////////////////////////////////////////////// + // sox_io.h + ////////////////////////////////////////////////////////////////////////////// + m.class_("SignalInfo") + .def("get_sample_rate", &torchaudio::sox_io::SignalInfo::getSampleRate) + .def("get_num_channels", &torchaudio::sox_io::SignalInfo::getNumChannels) + .def("get_num_frames", &torchaudio::sox_io::SignalInfo::getNumFrames); -//////////////////////////////////////////////////////////////////////////////// -// sox_io.h -//////////////////////////////////////////////////////////////////////////////// -static auto registerSignalInfo = - torch::class_("torchaudio", "SignalInfo") - .def("get_sample_rate", &sox_io::SignalInfo::getSampleRate) - .def("get_num_channels", &sox_io::SignalInfo::getNumChannels) - .def("get_num_frames", &sox_io::SignalInfo::getNumFrames); + m.def("torchaudio::sox_io_get_info", &torchaudio::sox_io::get_info); + m.def( + "torchaudio::sox_io_load_audio_file", + &torchaudio::sox_io::load_audio_file); + m.def( + "torchaudio::sox_io_save_audio_file", + &torchaudio::sox_io::save_audio_file); -static auto registerGetInfo = torch::RegisterOperators().op( - torch::RegisterOperators::options() - .schema( - "torchaudio::sox_io_get_info(str path) -> __torch__.torch.classes.torchaudio.SignalInfo info") - .catchAllKernel()); - -static auto registerLoadAudioFile = torch::RegisterOperators().op( - torch::RegisterOperators::options() - .schema( - "torchaudio::sox_io_load_audio_file(str path, int frame_offset, int num_frames, bool normalize, bool channels_first) -> __torch__.torch.classes.torchaudio.TensorSignal signal") - .catchAllKernel< - decltype(sox_io::load_audio_file), - &sox_io::load_audio_file>()); - -static auto registerSaveAudioFile = torch::RegisterOperators().op( - torch::RegisterOperators::options() - .schema( - "torchaudio::sox_io_save_audio_file(str path, __torch__.torch.classes.torchaudio.TensorSignal signal, float compression) -> ()") - .catchAllKernel< - decltype(sox_io::save_audio_file), - &sox_io::save_audio_file>()); - -//////////////////////////////////////////////////////////////////////////////// -// sox_effects.h -//////////////////////////////////////////////////////////////////////////////// -static auto registerSoxEffects = - torch::RegisterOperators() - .op("torchaudio::sox_effects_initialize_sox_effects", - &sox_effects::initialize_sox_effects) - .op("torchaudio::sox_effects_shutdown_sox_effects", - &sox_effects::shutdown_sox_effects) - .op(torch::RegisterOperators::options() - .schema( - "torchaudio::sox_effects_apply_effects_tensor(__torch__.torch.classes.torchaudio.TensorSignal input_signal, str[][] effects) -> __torch__.torch.classes.torchaudio.TensorSignal output_signal") - .catchAllKernel< - decltype(sox_effects::apply_effects_tensor), - &sox_effects::apply_effects_tensor>()) - .op(torch::RegisterOperators::options() - .schema( - "torchaudio::sox_effects_apply_effects_file(str path, str[][] effects, bool normalize, bool channels_first) -> __torch__.torch.classes.torchaudio.TensorSignal output_signal") - .catchAllKernel< - decltype(sox_effects::apply_effects_file), - &sox_effects::apply_effects_file>()); - -} // namespace -} // namespace torchaudio + ////////////////////////////////////////////////////////////////////////////// + // sox_effects.h + ////////////////////////////////////////////////////////////////////////////// + m.def( + "torchaudio::sox_effects_initialize_sox_effects", + &torchaudio::sox_effects::initialize_sox_effects); + m.def( + "torchaudio::sox_effects_shutdown_sox_effects", + &torchaudio::sox_effects::shutdown_sox_effects); + m.def( + "torchaudio::sox_effects_apply_effects_tensor", + &torchaudio::sox_effects::apply_effects_tensor); + m.def( + "torchaudio::sox_effects_apply_effects_file", + &torchaudio::sox_effects::apply_effects_file); +} #endif diff --git a/torchaudio/csrc/sox_utils.cpp b/torchaudio/csrc/sox_utils.cpp index c6b73c5795..25fe376c8b 100644 --- a/torchaudio/csrc/sox_utils.cpp +++ b/torchaudio/csrc/sox_utils.cpp @@ -234,6 +234,8 @@ sox_encoding_t get_encoding( return SOX_ENCODING_FLOAT; throw std::runtime_error("Unsupported dtype."); } + if (filetype == "sph") + return SOX_ENCODING_SIGN2; throw std::runtime_error("Unsupported file type."); } @@ -257,6 +259,8 @@ unsigned get_precision( return 32; throw std::runtime_error("Unsupported dtype."); } + if (filetype == "sph") + return 32; throw std::runtime_error("Unsupported file type."); } @@ -285,6 +289,8 @@ sox_encodinginfo_t get_encodinginfo( return compression; if (filetype == "wav") return 0.; + if (filetype == "sph") + return 0.; throw std::runtime_error("Unsupported file type."); }(); diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index 187142db46..fb9e0db506 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -2,7 +2,7 @@ from .librispeech import LIBRISPEECH from .speechcommands import SPEECHCOMMANDS from .utils import bg_iterator, diskcache_iterator -from .vctk import VCTK +from .vctk import VCTK, VCTK_092 from .gtzan import GTZAN from .yesno import YESNO from .ljspeech import LJSPEECH @@ -14,6 +14,7 @@ "LIBRISPEECH", "SPEECHCOMMANDS", "VCTK", + "VCTK_092", "YESNO", "LJSPEECH", "GTZAN", diff --git a/torchaudio/datasets/vctk.py b/torchaudio/datasets/vctk.py index c84373713d..8fa874c7cf 100644 --- a/torchaudio/datasets/vctk.py +++ b/torchaudio/datasets/vctk.py @@ -1,6 +1,7 @@ import os import warnings from typing import Any, Tuple +from collections import namedtuple import torchaudio from torch import Tensor @@ -11,13 +12,16 @@ walk_files ) -URL = "http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz" +URL = "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip" FOLDER_IN_ARCHIVE = "VCTK-Corpus" _CHECKSUMS = { - "http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz": - "45e8dede780278ef5541fde0b82ac292" + "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip": "8a6ba2946b36fcbef0212cad601f4bfa" } +Sample = namedtuple( + "Sample", ["waveform", "sample_rate", "utterance", "speaker_id", "utterance_id"] +) + def load_vctk_item(fileid: str, path: str, @@ -96,15 +100,15 @@ def __init__(self, self._path = os.path.join(root, folder_in_archive) if download: - if not os.path.isdir(self._path): - if not os.path.isfile(archive): - checksum = _CHECKSUMS.get(url, None) - download_url(url, root, hash_value=checksum, hash_type="md5") - extract_archive(archive) + raise RuntimeError( + "This Dataset is no longer available. " + "Please use `VCTK_092` class to download the latest version." + ) if not os.path.isdir(self._path): raise RuntimeError( - "Dataset not found. Please use `download=True` to download it." + "Dataset not found. Please use `VCTK_092` class " + "with `download=True` to donwload the latest version." ) walker = walk_files( @@ -136,3 +140,120 @@ def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str]: def __len__(self) -> int: return len(self._walker) + + +class VCTK_092(Dataset): + """Create VCTK 0.92 Dataset + + An item is a ``namedtuple`` of (``waveform``, ``sample_rate``, ``utterance``, + ``speaker_id``, ``utterance_id``) + + Args: + root (str): Root directory where the dataset's top level directory is found. + mic_id (str): Microphone ID. Either ``"mic1"`` or ``"mic2"`` + download (bool, optional): Download the dataset if not found in the given directory. + url (str, optional): URL from which the dataset is downloaded. + audio_ext (str, optional): Custom audio extension if dataset is converted to non-default audio format. + + Note: + * All the speeches from speaker ``p315`` will be skipped due to the lack of the corresponding text files. + * All the speeches from ``p280`` will be skipped for ``mic_id="mic2"`` due to the lack of the audio files. + * Some of the speeches from speaker ``p362`` will be skipped due to the lack of the audio files. + * See Also: https://datashare.is.ed.ac.uk/handle/10283/3443 + """ + + def __init__( + self, + root: str, + mic_id: str = "mic2", + download: bool = False, + url: str = URL, + audio_ext=".flac", + ): + if mic_id not in ["mic1", "mic2"]: + raise RuntimeError( + f'`mic_id` has to be either "mic1" or "mic2". Found: {mic_id}' + ) + + archive = os.path.join(root, "VCTK-Corpus-0.92.zip") + + self._path = os.path.join(root, "VCTK-Corpus-0.92") + self._txt_dir = os.path.join(self._path, "txt") + self._audio_dir = os.path.join(self._path, "wav48_silence_trimmed") + self._mic_id = mic_id + self._audio_ext = audio_ext + + if download: + if not os.path.isdir(self._path): + if not os.path.isfile(archive): + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") + extract_archive(archive, self._path) + + if not os.path.isdir(self._path): + raise RuntimeError( + "Dataset not found. Please use `download=True` to download it." + ) + + # Extracting speaker IDs from the folder structure + self._speaker_ids = sorted(os.listdir(self._txt_dir)) + self._sample_ids = [] + + """ + Due to some insufficient data complexity in the 0.92 version of this dataset, + we start traversing the audio folder structure in accordance with the text folder. + As some of the audio files are missing of either ``mic_1`` or ``mic_2`` but the + text is present for the same, we first check for the existence of the audio file + before adding it to the ``sample_ids`` list. + + Once the ``audio_ids`` are loaded into memory we can quickly access the list for + different parameters required by the user. + """ + for speaker_id in self._speaker_ids: + if speaker_id == "p280" and mic_id == "mic2": + continue + utterance_dir = os.path.join(self._txt_dir, speaker_id) + for utterance_file in sorted( + f for f in os.listdir(utterance_dir) if f.endswith(".txt") + ): + utterance_id = os.path.splitext(utterance_file)[0] + audio_path_mic = os.path.join( + self._audio_dir, + speaker_id, + f"{utterance_id}_{mic_id}{self._audio_ext}", + ) + if speaker_id == "p362" and not os.path.isfile(audio_path_mic): + continue + self._sample_ids.append(utterance_id.split("_")) + + def _load_text(self, file_path) -> str: + with open(file_path) as file_path: + return file_path.readlines()[0] + + def _load_audio(self, file_path) -> Tuple[Tensor, int]: + return torchaudio.load(file_path) + + def _load_sample(self, speaker_id: str, utterance_id: str, mic_id: str) -> Sample: + utterance_path = os.path.join( + self._txt_dir, speaker_id, f"{speaker_id}_{utterance_id}.txt" + ) + audio_path = os.path.join( + self._audio_dir, + speaker_id, + f"{speaker_id}_{utterance_id}_{mic_id}{self._audio_ext}", + ) + + # Reading text + utterance = self._load_text(utterance_path) + + # Reading FLAC + waveform, sample_rate = self._load_audio(audio_path) + + return Sample(waveform, sample_rate, utterance, speaker_id, utterance_id) + + def __getitem__(self, n: int) -> Sample: + speaker_id, utterance_id = self._sample_ids[n] + return self._load_sample(speaker_id, utterance_id, self._mic_id) + + def __len__(self) -> int: + return len(self._sample_ids) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 4b79c01960..4e8e62848e 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -1655,9 +1655,10 @@ def _add_noise_shaping( error = dithered_waveform - waveform # add error[n-1] to dithered_waveform[n], so offset the error by 1 index + zeros = torch.zeros(1, dtype=error.dtype, device=error.device) for index in range(error.size()[0]): err = error[index] - error_offset = torch.cat((torch.zeros(1), err)) + error_offset = torch.cat((zeros, err)) error[index] = error_offset[:waveform.size()[1]] noise_shaped = dithered_waveform + error From 7d5591f5b90c3fe70902fede6af157ea92e3206a Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Wed, 23 Sep 2020 08:59:35 -0700 Subject: [PATCH 18/73] Import torchaudio 20200922 Summary: Up to #914 Reviewed By: vincentqb, cpuhrsch Differential Revision: D23846718 fbshipit-source-id: 9feb4e58563b900965467bd9ff66c979211c50df --- .circleci/config.yml | 22 +- .circleci/config.yml.in | 22 +- .../unittest/linux/scripts/environment.yml | 1 + docs/source/datasets.rst | 15 +- examples/interactive_asr/utils.py | 8 +- packaging/pkg_helpers.bash | 2 +- .../datasets/tedlium_test.py | 153 +++++++++++++ .../functional/functional_cpu_test.py | 17 ++ .../sox_io_backend/save_test.py | 4 +- torchaudio/backend/soundfile_backend.py | 1 + torchaudio/backend/sox_backend.py | 1 + torchaudio/backend/sox_io_backend.py | 22 +- torchaudio/backend/utils.py | 5 + torchaudio/datasets/__init__.py | 4 +- torchaudio/datasets/tedlium.py | 208 ++++++++++++++++++ torchaudio/functional.py | 7 + 16 files changed, 454 insertions(+), 38 deletions(-) create mode 100644 test/torchaudio_unittest/datasets/tedlium_test.py create mode 100644 torchaudio/datasets/tedlium.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 70b7c54ef5..b2b0dd45c3 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -144,14 +144,14 @@ jobs: - store_artifacts: path: /opt/conda/conda-bld/linux-64 - persist_to_workspace: - root: /opt/conda/conda-bld/linux-64 + root: /opt/conda paths: - - "*" + - "conda-bld/*" binary_macos_wheel: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" steps: - checkout - install_build_tools_macos @@ -176,7 +176,7 @@ jobs: binary_macos_conda: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" steps: - checkout - install_build_tools_macos @@ -192,9 +192,9 @@ jobs: - store_artifacts: path: /Users/distiller/miniconda3/conda-bld/osx-64 - persist_to_workspace: - root: /Users/distiller/miniconda3/conda-bld/osx-64 + root: /Users/distiller/miniconda3 paths: - - "*" + - "conda-bld/*" binary_windows_wheel: <<: *binary_common @@ -231,9 +231,9 @@ jobs: - store_artifacts: path: C:/tools/miniconda3/conda-bld/win-64 - persist_to_workspace: - root: C:/tools/miniconda3/conda-bld/win-64 + root: C:/tools/miniconda3 paths: - - "*" + - "conda-bld/*" # Requires org-member context binary_conda_upload: @@ -248,7 +248,7 @@ jobs: # Prevent credential from leaking conda install -yq anaconda-client set -x - anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/conda-bld/*/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force # Requires org-member context binary_wheel_upload: @@ -284,7 +284,7 @@ jobs: set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch - conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + conda install -v -y -c file://$HOME/workspace/conda-bld torchaudio - run: name: smoke test command: | @@ -537,7 +537,7 @@ jobs: unittest_macos_cpu: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" resource_class: large steps: - checkout diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 94220d02c0..b1f40c24c0 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -144,14 +144,14 @@ jobs: - store_artifacts: path: /opt/conda/conda-bld/linux-64 - persist_to_workspace: - root: /opt/conda/conda-bld/linux-64 + root: /opt/conda paths: - - "*" + - "conda-bld/*" binary_macos_wheel: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" steps: - checkout - install_build_tools_macos @@ -176,7 +176,7 @@ jobs: binary_macos_conda: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" steps: - checkout - install_build_tools_macos @@ -192,9 +192,9 @@ jobs: - store_artifacts: path: /Users/distiller/miniconda3/conda-bld/osx-64 - persist_to_workspace: - root: /Users/distiller/miniconda3/conda-bld/osx-64 + root: /Users/distiller/miniconda3 paths: - - "*" + - "conda-bld/*" binary_windows_wheel: <<: *binary_common @@ -231,9 +231,9 @@ jobs: - store_artifacts: path: C:/tools/miniconda3/conda-bld/win-64 - persist_to_workspace: - root: C:/tools/miniconda3/conda-bld/win-64 + root: C:/tools/miniconda3 paths: - - "*" + - "conda-bld/*" # Requires org-member context binary_conda_upload: @@ -248,7 +248,7 @@ jobs: # Prevent credential from leaking conda install -yq anaconda-client set -x - anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/conda-bld/*/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force # Requires org-member context binary_wheel_upload: @@ -284,7 +284,7 @@ jobs: set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch - conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + conda install -v -y -c file://$HOME/workspace/conda-bld torchaudio - run: name: smoke test command: | @@ -537,7 +537,7 @@ jobs: unittest_macos_cpu: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" resource_class: large steps: - checkout diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml index f68fb130a2..42acafb984 100644 --- a/.circleci/unittest/linux/scripts/environment.yml +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -1,3 +1,4 @@ +# a comment for these trying times channels: - conda-forge - defaults diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 6cdf44e223..b3f1b3c725 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -4,11 +4,11 @@ torchaudio.datasets All datasets are subclasses of :class:`torch.utils.data.Dataset` i.e, they have ``__getitem__`` and ``__len__`` methods implemented. Hence, they can all be passed to a :class:`torch.utils.data.DataLoader` -which can load multiple samples parallelly using ``torch.multiprocessing`` workers. +which can load multiple samples parallelly using ``torch.multiprocessing`` workers. For example: :: - + yesno_data = torchaudio.datasets.YESNO('.', download=True) - data_loader = torch.utils.data.DataLoader(yesno_data, + data_loader = torch.utils.data.DataLoader(yesno_data, batch_size=1, shuffle=True, num_workers=args.nThreads) @@ -22,7 +22,7 @@ All the datasets have almost similar API. They all have two common arguments: ``transform`` and ``target_transform`` to transform the input and target respectively. -.. currentmodule:: torchaudio.datasets +.. currentmodule:: torchaudio.datasets CMUARCTIC @@ -81,6 +81,13 @@ SPEECHCOMMANDS :special-members: +TEDLIUM +~~~~~~~~~~~~~~ + +.. autoclass:: TEDLIUM + :members: __getitem__ + :special-members: get_phoneme_dict + VCTK ~~~~ diff --git a/examples/interactive_asr/utils.py b/examples/interactive_asr/utils.py index 713954029a..37f5ae5303 100644 --- a/examples/interactive_asr/utils.py +++ b/examples/interactive_asr/utils.py @@ -55,8 +55,9 @@ def check_args(args): def process_predictions(args, hypos, sp, tgt_dict): res = [] + device = torch.device("cuda:0" if torch.cuda.is_available() and not args.cpu else "cpu") for hypo in hypos[: min(len(hypos), args.nbest)]: - hyp_pieces = tgt_dict.string(hypo["tokens"].int().cpu()) + hyp_pieces = tgt_dict.string(hypo["tokens"].int().to(device)) hyp_words = sp.DecodePieces(hyp_pieces.split()) res.append(hyp_words) return res @@ -96,7 +97,8 @@ def calcMN(features): def transcribe(waveform, args, task, generator, models, sp, tgt_dict): num_features = 80 output = torchaudio.compliance.kaldi.fbank(waveform, num_mel_bins=num_features) - output_cmvn = calcMN(output.cpu().detach()) + device = torch.device("cuda:0" if torch.cuda.is_available() and not args.cpu else "cpu") + output_cmvn = calcMN(output.to(device).detach()) # size (m, n) source = output_cmvn @@ -151,7 +153,7 @@ def setup_asr(args, logger): optimize_models(args, use_cuda, models) # Initialize generator - generator = task.build_generator(args) + generator = task.build_generator(models, args) sp = spm.SentencePieceProcessor() sp.Load(os.path.join(args.data, "spm.model")) diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index 3ec49a0269..57b23b93d3 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -32,7 +32,7 @@ setup_cuda() { # First, compute version suffixes. By default, assume no version suffixes export VERSION_SUFFIX="" export PYTORCH_VERSION_SUFFIX="" - export WHEEL_DIR="" + export WHEEL_DIR="cpu/" # Wheel builds need suffixes (but not if they're on OS X, which never has suffix) if [[ "$BUILD_TYPE" == "wheel" ]] && [[ "$(uname)" != Darwin ]]; then # The default CUDA has no suffix diff --git a/test/torchaudio_unittest/datasets/tedlium_test.py b/test/torchaudio_unittest/datasets/tedlium_test.py new file mode 100644 index 0000000000..c19984cac2 --- /dev/null +++ b/test/torchaudio_unittest/datasets/tedlium_test.py @@ -0,0 +1,153 @@ +import os + +from torchaudio.datasets import tedlium + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + +# Used to generate a unique utterance for each dummy audio file +UTTERANCES = [ + "AaronHuey_2010X 1 AaronHuey_2010X 0.0 2.0 script1\n", + "AaronHuey_2010X 1 AaronHuey_2010X 2.0 4.0 script2\n", + "AaronHuey_2010X 1 AaronHuey_2010X 4.0 6.0 script3\n", + "AaronHuey_2010X 1 AaronHuey_2010X 6.0 8.0 script4\n", + "AaronHuey_2010X 1 AaronHuey_2010X 8.0 10.0 script5\n", +] + +PHONEME = [ + "a AH", + "a(2) EY", + "aachen AA K AH N", + "aad AE D", + "aaden EY D AH N", + "aadmi AE D M IY", + "aae EY EY", +] + + +class TestTedlium(TempDirMixin, TorchaudioTestCase): + backend = "default" + + root_dir = None + samples = {} + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + cls.root_dir = dataset_dir = os.path.join(cls.root_dir, "tedlium") + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 16000 # 16kHz + seed = 0 + + for release in ["release1", "release2", "release3"]: + data = get_whitenoise(sample_rate=sample_rate, duration=10.00, n_channels=1, dtype="float32", seed=seed) + if release in ["release1", "release2"]: + release_dir = os.path.join( + dataset_dir, + tedlium._RELEASE_CONFIGS[release]["folder_in_archive"], + tedlium._RELEASE_CONFIGS[release]["subset"], + ) + else: + release_dir = os.path.join( + dataset_dir, + tedlium._RELEASE_CONFIGS[release]["folder_in_archive"], + tedlium._RELEASE_CONFIGS[release]["data_path"], + ) + os.makedirs(release_dir, exist_ok=True) + os.makedirs(os.path.join(release_dir, "stm"), exist_ok=True) # Subfolder for transcripts + os.makedirs(os.path.join(release_dir, "sph"), exist_ok=True) # Subfolder for audio files + filename = f"{release}.sph" + path = os.path.join(os.path.join(release_dir, "sph"), filename) + save_wav(path, data, sample_rate) + + trans_filename = f"{release}.stm" + trans_path = os.path.join(os.path.join(release_dir, "stm"), trans_filename) + with open(trans_path, "w") as f: + f.write("".join(UTTERANCES)) + + dict_filename = f"{release}.dic" + dict_path = os.path.join(release_dir, dict_filename) + with open(dict_path, "w") as f: + f.write("\n".join(PHONEME)) + + # Create a samples list to compare with + cls.samples[release] = [] + for utterance in UTTERANCES: + talk_id, _, speaker_id, start_time, end_time, identifier, transcript = utterance.split(" ", 6) + start_time = int(float(start_time)) * sample_rate + end_time = int(float(end_time)) * sample_rate + sample = ( + data[:, start_time:end_time], + sample_rate, + transcript, + talk_id, + speaker_id, + identifier, + ) + cls.samples[release].append(sample) + seed += 1 + + def test_tedlium_release1(self): + release = "release1" + dataset = tedlium.TEDLIUM(self.root_dir, release=release) + num_samples = 0 + for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset): + self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[release][i][1] + assert transcript == self.samples[release][i][2] + assert talk_id == self.samples[release][i][3] + assert speaker_id == self.samples[release][i][4] + assert identifier == self.samples[release][i][5] + num_samples += 1 + + assert num_samples == len(self.samples[release]) + + dataset._dict_path = os.path.join(dataset._path, f"{release}.dic") + phoneme_dict = dataset.phoneme_dict + phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] + assert phoenemes == PHONEME + + def test_tedlium_release2(self): + release = "release2" + dataset = tedlium.TEDLIUM(self.root_dir, release=release) + num_samples = 0 + for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset): + self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[release][i][1] + assert transcript == self.samples[release][i][2] + assert talk_id == self.samples[release][i][3] + assert speaker_id == self.samples[release][i][4] + assert identifier == self.samples[release][i][5] + num_samples += 1 + + assert num_samples == len(self.samples[release]) + + dataset._dict_path = os.path.join(dataset._path, f"{release}.dic") + phoneme_dict = dataset.phoneme_dict + phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] + assert phoenemes == PHONEME + + def test_tedlium_release3(self): + release = "release3" + dataset = tedlium.TEDLIUM(self.root_dir, release=release) + num_samples = 0 + for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset): + self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[release][i][1] + assert transcript == self.samples[release][i][2] + assert talk_id == self.samples[release][i][3] + assert speaker_id == self.samples[release][i][4] + assert identifier == self.samples[release][i][5] + num_samples += 1 + + assert num_samples == len(self.samples[release]) + + dataset._dict_path = os.path.join(dataset._path, f"{release}.dic") + phoneme_dict = dataset.phoneme_dict + phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] + assert phoenemes == PHONEME diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py index 96aac4e770..4fbe4d871e 100644 --- a/test/torchaudio_unittest/functional/functional_cpu_test.py +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -21,6 +21,23 @@ class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): device = torch.device('cpu') +class TestCreateFBMatrix(common_utils.TorchaudioTestCase): + def test_no_warning_high_n_freq(self): + with pytest.warns(None) as w: + F.create_fb_matrix(288, 0, 8000, 128, 16000) + assert len(w) == 0 + + def test_no_warning_low_n_mels(self): + with pytest.warns(None) as w: + F.create_fb_matrix(201, 0, 8000, 89, 16000) + assert len(w) == 0 + + def test_warning(self): + with pytest.warns(None) as w: + F.create_fb_matrix(201, 0, 8000, 128, 16000) + assert len(w) == 1 + + class TestComputeDeltas(common_utils.TorchaudioTestCase): """Test suite for correctness of compute_deltas""" def test_one_channel(self): diff --git a/test/torchaudio_unittest/sox_io_backend/save_test.py b/test/torchaudio_unittest/sox_io_backend/save_test.py index f7d4da3931..deed1aa1ad 100644 --- a/test/torchaudio_unittest/sox_io_backend/save_test.py +++ b/test/torchaudio_unittest/sox_io_backend/save_test.py @@ -254,7 +254,7 @@ def test_mp3_large(self, sample_rate, num_channels, bit_rate): @parameterized.expand(list(itertools.product( [8000, 16000], [1, 2], - list(range(9)), + [None] + list(range(9)), )), name_func=name_func) def test_flac(self, sample_rate, num_channels, compression_level): """`sox_io_backend.save` can save flac format.""" @@ -273,7 +273,7 @@ def test_flac_large(self, sample_rate, num_channels, compression_level): @parameterized.expand(list(itertools.product( [8000, 16000], [1, 2], - [-1, 0, 1, 2, 3, 3.6, 5, 10], + [None, -1, 0, 1, 2, 3, 3.6, 5, 10], )), name_func=name_func) def test_vorbis(self, sample_rate, num_channels, quality_level): """`sox_io_backend.save` can save vorbis format.""" diff --git a/torchaudio/backend/soundfile_backend.py b/torchaudio/backend/soundfile_backend.py index 680264bcd6..49cada21b0 100644 --- a/torchaudio/backend/soundfile_backend.py +++ b/torchaudio/backend/soundfile_backend.py @@ -73,6 +73,7 @@ def load(filepath: str, @_mod_utils.requires_module('soundfile') +@_mod_utils.deprecated('Please use "torchaudio.load".', '0.9.0') @common._impl_load_wav def load_wav(filepath, **kwargs): kwargs['normalization'] = 1 << 16 diff --git a/torchaudio/backend/sox_backend.py b/torchaudio/backend/sox_backend.py index 4e2b118f8b..45d75817af 100644 --- a/torchaudio/backend/sox_backend.py +++ b/torchaudio/backend/sox_backend.py @@ -63,6 +63,7 @@ def load(filepath: str, @_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated('Please use "torchaudio.load".', '0.9.0') @common._impl_load_wav def load_wav(filepath, **kwargs): kwargs['normalization'] = 1 << 16 diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 9c0695d723..e2823ada00 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -24,11 +24,15 @@ def info(filepath: str) -> AudioMetaData: """Get signal information of an audio file. Args: - filepath (str): Path to audio file + filepath (str or pathlib.Path): + Path to audio file. This function also handles ``pathlib.Path`` objects, but is annotated as + ``str`` for TorchScript compiler compatibility. Returns: AudioMetaData: meta data of the given audio. """ + # Cast to str in case type is `pathlib.Path` + filepath = str(filepath) sinfo = torch.ops.torchaudio.sox_io_get_info(filepath) return AudioMetaData(sinfo.get_sample_rate(), sinfo.get_num_frames(), sinfo.get_num_channels()) @@ -80,8 +84,9 @@ def load( ``[-1.0, 1.0]``. Args: - filepath (str): - Path to audio file + filepath (str or pathlib.Path): + Path to audio file. This function also handles ``pathlib.Path`` objects, but is + annotated as ``str`` for TorchScript compiler compatibility. frame_offset (int): Number of frames to skip before start reading data. num_frames (int): @@ -105,6 +110,8 @@ def load( integer type, else ``float32`` type. If ``channels_first=True``, it has ``[channel, time]`` else ``[time, channel]``. """ + # Cast to str in case type is `pathlib.Path` + filepath = str(filepath) signal = torch.ops.torchaudio.sox_io_load_audio_file( filepath, frame_offset, num_frames, normalize, channels_first) return signal.get_tensor(), signal.get_sample_rate() @@ -140,7 +147,9 @@ def save( and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc. Args: - filepath (str): Path to save file. + filepath (str or pathlib.Path): + Path to save file. This function also handles ``pathlib.Path`` objects, but is annotated + as ``str`` for TorchScript compiler compatibility. tensor (torch.Tensor): Audio data to save. must be 2D tensor. sample_rate (int): sampling rate channels_first (bool): @@ -158,8 +167,10 @@ def save( See the detail at http://sox.sourceforge.net/soxformat.html. """ + # Cast to str in case type is `pathlib.Path` + filepath = str(filepath) if compression is None: - ext = str(filepath)[-3:].lower() + ext = str(filepath).split('.')[-1].lower() if ext in ['wav', 'sph']: compression = 0. elif ext == 'mp3': @@ -175,6 +186,7 @@ def save( @_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated('Please use "torchaudio.load".', '0.9.0') def load_wav( filepath: str, frame_offset: int = 0, diff --git a/torchaudio/backend/utils.py b/torchaudio/backend/utils.py index 84ec2fa3f5..87ec948fba 100644 --- a/torchaudio/backend/utils.py +++ b/torchaudio/backend/utils.py @@ -49,6 +49,11 @@ def set_audio_backend(backend: Optional[str]): if backend is None: module = no_backend elif backend == 'sox': + warnings.warn( + '"sox" backend is being deprecated. ' + 'The default backend will be changed to "sox_io" backend in 0.8.0 and ' + '"sox" backend will be removed in 0.9.0. Please migrate to "sox_io" backend. ' + 'Please refer to https://github.com/pytorch/audio/issues/903 for the detail.') module = sox_backend elif backend == 'sox_io': module = sox_io_backend diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index fb9e0db506..5e5b9c1118 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -8,6 +8,7 @@ from .ljspeech import LJSPEECH from .cmuarctic import CMUARCTIC from .libritts import LIBRITTS +from .tedlium import TEDLIUM __all__ = ( "COMMONVOICE", @@ -19,7 +20,8 @@ "LJSPEECH", "GTZAN", "CMUARCTIC", - "LIBRITTS" + "LIBRITTS", "diskcache_iterator", "bg_iterator", + "TEDLIUM", ) diff --git a/torchaudio/datasets/tedlium.py b/torchaudio/datasets/tedlium.py new file mode 100644 index 0000000000..4ae1ddeb0f --- /dev/null +++ b/torchaudio/datasets/tedlium.py @@ -0,0 +1,208 @@ +import os +from typing import Tuple + +import torchaudio +from torch import Tensor +from torch.utils.data import Dataset +from torchaudio.datasets.utils import ( + download_url, + extract_archive, +) + + +_RELEASE_CONFIGS = { + "release1": { + "folder_in_archive": "TEDLIUM_release1", + "url": "http://www.openslr.org/resources/7/TEDLIUM_release1.tar.gz", + "checksum": "30301975fd8c5cac4040c261c0852f57cfa8adbbad2ce78e77e4986957445f27", + "data_path": "", + "subset": "train", + "supported_subsets": ["train", "test", "dev"], + "dict": "TEDLIUM.150K.dic", + }, + "release2": { + "folder_in_archive": "TEDLIUM_release2", + "url": "http://www.openslr.org/resources/19/TEDLIUM_release2.tar.gz", + "checksum": "93281b5fcaaae5c88671c9d000b443cb3c7ea3499ad12010b3934ca41a7b9c58", + "data_path": "", + "subset": "train", + "supported_subsets": ["train", "test", "dev"], + "dict": "TEDLIUM.152k.dic", + }, + "release3": { + "folder_in_archive": "TEDLIUM_release-3", + "url": "http://www.openslr.org/resources/51/TEDLIUM_release-3.tgz", + "checksum": "ad1e454d14d1ad550bc2564c462d87c7a7ec83d4dc2b9210f22ab4973b9eccdb", + "data_path": "data/", + "subset": None, + "supported_subsets": [None], + "dict": "TEDLIUM.152k.dic", + }, +} + + +class TEDLIUM(Dataset): + """ + Create a Dataset for Tedlium. It supports releases 1,2 and 3, each item is a list containings: + [waveform, sample_rate, transcript, talk_id, speaker_id, identifier]. + + Constructor arguments: + + Args: + root (str): Path containing dataset or target path where its downloaded if needed + release (str, optional): TEDLIUM identifier (release1,release2,release3). Defaults to RELEASE. + subset (str, optional): train/dev/test for releases 1&2, None for release3. Defaults to Train/None + download (bool, optional): Download dataset in case is not founded in root path. Defaults to False. + audio_ext (str, optional): Overwrite audio extension when loading items. Defaults to ".sph". + + Special functions: + + _load_tedlium_item: Loads a TEDLIUM dataset sample given a file name and corresponding sentence name + + _load_audio: Default load function used in TEDLIUM dataset, you can overwrite this function to customize + functionality and load individual sentences from a full ted audio talk file + + get_phoneme_dict: Returns the phoneme dictionary of a TEDLIUM release + + """ + + def __init__( + self, root: str, release: str = "release1", subset: str = None, download: bool = False, audio_ext=".sph" + ) -> None: + """Constructor for TEDLIUM dataset. + + Args: + root (str): Path containing dataset or target path where its downloaded if needed + release (str, optional): TEDLIUM identifier (release1,release2,release3). Defaults to RELEASE. + subset (str, optional): train/dev/test for releases 1&2, None for release3. Defaults to Train/None + download (bool, optional): Download dataset in case is not founded in root path. Defaults to False. + audio_ext (str, optional): Overwrite audio extension when loading items. Defaults to ".sph". + + Raises: + RuntimeError: If release identifier does not match any supported release, + """ + self._ext_audio = audio_ext + if release in _RELEASE_CONFIGS.keys(): + folder_in_archive = _RELEASE_CONFIGS[release]["folder_in_archive"] + url = _RELEASE_CONFIGS[release]["url"] + subset = subset if subset else _RELEASE_CONFIGS[release]["subset"] + else: + # Raise warning + raise RuntimeError( + "The release {} does not match any of the supported tedlium releases{} ".format( + release, _RELEASE_CONFIGS.keys(), + ) + ) + if subset not in _RELEASE_CONFIGS[release]["supported_subsets"]: + # Raise warning + raise RuntimeError( + "The subset {} does not match any of the supported tedlium subsets{} ".format( + subset, _RELEASE_CONFIGS[release]["supported_subsets"], + ) + ) + + basename = os.path.basename(url) + archive = os.path.join(root, basename) + + basename = basename.split(".")[0] + + self._path = os.path.join(root, folder_in_archive, _RELEASE_CONFIGS[release]["data_path"]) + if subset in ["train", "dev", "test"]: + self._path = os.path.join(self._path, subset) + + if download: + if not os.path.isdir(self._path): + if not os.path.isfile(archive): + checksum = _RELEASE_CONFIGS[release]["checksum"] + download_url(url, root, hash_value=checksum) + extract_archive(archive) + + # Create list for all samples + self._filelist = [] + stm_path = os.path.join(self._path, "stm") + for file in sorted(os.listdir(stm_path)): + if file.endswith(".stm"): + stm_path = os.path.join(self._path, "stm", file) + with open(stm_path) as f: + l = len(f.readlines()) + file = file.replace(".stm", "") + self._filelist.extend((file, line) for line in range(l)) + # Create dict path for later read + self._dict_path = os.path.join(root, folder_in_archive, _RELEASE_CONFIGS[release]["dict"]) + self._phoneme_dict = None + + def _load_tedlium_item(self, fileid: str, line: int, path: str) -> Tuple[Tensor, int, str, int, int, int]: + """Loads a TEDLIUM dataset sample given a file name and corresponding sentence name. + + Args: + fileid (str): File id to identify both text and audio files corresponding to the sample + line (int): Line identifier for the sample inside the text file + path (str): Dataset root path + + Returns: + Tedlium_item: A namedTuple containing [waveform, sample_rate, transcript, talk_id, speaker_id, identifier] + """ + transcript_path = os.path.join(path, "stm", fileid) + with open(transcript_path + ".stm") as f: + transcript = f.readlines()[line] + talk_id, _, speaker_id, start_time, end_time, identifier, transcript = transcript.split(" ", 6) + + wave_path = os.path.join(path, "sph", fileid) + waveform, sample_rate = self._load_audio(wave_path + self._ext_audio, start_time=start_time, end_time=end_time) + + return (waveform, sample_rate, transcript, talk_id, speaker_id, identifier) + + def _load_audio(self, path: str, start_time: float, end_time: float, sample_rate: int = 16000) -> [Tensor, int]: + """Default load function used in TEDLIUM dataset, you can overwrite this function to customize functionality + and load individual sentences from a full ted audio talk file. + + Args: + path (str): Path to audio file + start_time (int, optional): Time in seconds where the sample sentence stars + end_time (int, optional): Time in seconds where the sample sentence finishes + + Returns: + [Tensor, int]: Audio tensor representation and sample rate + """ + start_time = int(float(start_time) * sample_rate) + end_time = int(float(end_time) * sample_rate) + if torchaudio.get_audio_backend() == "sox_io": + return torchaudio.load(path, frame_offset=start_time, num_frames=end_time - start_time) + return torchaudio.load(path)[:, start_time:end_time] + + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]: + """TEDLIUM dataset custom function overwritting default loadbehaviour + Loads a TEDLIUM sample given a index N. + + Args: + n (int): Index of sample to be loaded + + Returns: + Tedlium_item: A namedTuple containing [waveform, sample_rate, transcript, talk_id, speaker_id, identifier] + """ + fileid, line = self._filelist[n] + return self._load_tedlium_item(fileid, line, self._path) + + def __len__(self) -> int: + """TEDLIUM dataset custom function overwritting len default behaviour. + + Returns: + int: TEDLIUM dataset length + """ + return len(self._filelist) + + @property + def phoneme_dict(self): + """Returns the phoneme dictionary of a TEDLIUM release. + + Returns: + dictionary: Phoneme dictionary for the current tedlium release + """ + # Read phoneme dictionary + if not self._phoneme_dict: + self._phoneme_dict = {} + with open(self._dict_path, "r", encoding="utf-8") as f: + for line in f.readlines(): + content = line.strip().split() + self._phoneme_dict[content[0]] = tuple(content[1:]) # content[1:] can be empty list + return self._phoneme_dict.copy() diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 4e8e62848e..a6514c79f2 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -313,6 +313,13 @@ def create_fb_matrix( enorm = 2.0 / (f_pts[2:n_mels + 2] - f_pts[:n_mels]) fb *= enorm.unsqueeze(0) + if (fb.max(dim=0).values == 0.).any(): + warnings.warn( + "At least one mel filterbank has all zero values. " + f"The value for `n_mels` ({n_mels}) may be set too high. " + f"Or, the value for `n_freqs` ({n_freqs}) may be set too low." + ) + return fb From f4611b75da718bbc312b55e5262127bd397059f8 Mon Sep 17 00:00:00 2001 From: Alexei Baevski Date: Tue, 6 Oct 2020 16:59:20 -0700 Subject: [PATCH 19/73] replace max-sentences with batch-size for dependencies Summary: this fixes some regressions introduced by D24121305. fairseq configuration is changing from command line to dataclasses (via hydra eventually) which no longer supports option aliases. one such alias is --max-sentences / --batch-size, and D24121305 removed --max-sentences as --batch-size is more appropriate (fairseq is not just an nlp framework dealing with sentences). unfortunately it seems some existing flows broke and this diff attempts to fix this Differential Revision: D24142488 fbshipit-source-id: 075180ea10a9d706a3f8d64b978d66dfd83c3d2b --- examples/interactive_asr/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/interactive_asr/utils.py b/examples/interactive_asr/utils.py index 37f5ae5303..3aae6c6864 100644 --- a/examples/interactive_asr/utils.py +++ b/examples/interactive_asr/utils.py @@ -124,7 +124,7 @@ def setup_asr(args, logger): check_args(args) import_user_module(args) - if args.max_tokens is None and args.max_sentences is None: + if args.max_tokens is None and args.batch_size is None: args.max_tokens = 30000 logger.info(args) From d0e2281b4e8e3a2089162f43d3efeebe1e79bfec Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Mon, 2 Nov 2020 11:36:53 -0800 Subject: [PATCH 20/73] Import torchaudio #996 758f6c2 Reviewed By: cpuhrsch Differential Revision: D24606263 fbshipit-source-id: 4301b1df84d20c671783ec34c52d5b257374abf1 --- .circleci/config.yml | 47 +- .circleci/config.yml.in | 41 +- .circleci/regenerate.py | 2 +- .circleci/torchscript_bc_test/common.sh | 3 - .../unittest/linux/scripts/environment.yml | 1 - .circleci/unittest/linux/scripts/install.sh | 39 +- .../linux/scripts/run_style_checks.sh | 19 +- .circleci/unittest/linux/scripts/run_test.sh | 17 +- .circleci/unittest/linux/scripts/setup_env.sh | 11 +- .../unittest/windows/scripts/environment.yml | 1 + .circleci/unittest/windows/scripts/install.sh | 20 +- .../unittest/windows/scripts/setup_env.sh | 4 - README.md | 1 + docs/requirements.txt | 2 +- .../source/_static/img/pytorch-logo-flame.png | Bin 0 -> 22916 bytes docs/source/backend.rst | 155 ++-- docs/source/datasets.rst | 43 +- docs/source/index.rst | 35 + docs/source/models.rst | 8 + docs/source/sox_effects.rst | 19 - examples/pipeline_wav2letter/README.md | 45 ++ examples/pipeline_wav2letter/ctc_decoders.py | 15 + examples/pipeline_wav2letter/datasets.py | 113 +++ .../pipeline_wav2letter/languagemodels.py | 38 + examples/pipeline_wav2letter/main.py | 668 ++++++++++++++++++ examples/pipeline_wav2letter/metrics.py | 38 + examples/pipeline_wav2letter/transforms.py | 11 + examples/pipeline_wav2letter/utils.py | 55 ++ examples/source_separation/README.md | 168 +++++ .../source_separation/conv_tasnet/README.md | 52 ++ .../source_separation/conv_tasnet/__init__.py | 4 + .../source_separation/conv_tasnet/train.py | 332 +++++++++ .../source_separation/conv_tasnet/trainer.py | 165 +++++ examples/source_separation/train.py | 176 +++++ examples/source_separation/utils/__init__.py | 5 + .../utils/dataset/__init__.py | 1 + .../source_separation/utils/dataset/utils.py | 83 +++ .../utils/dataset/wsj0mix.py | 70 ++ .../source_separation/utils/dist_utils.py | 86 +++ examples/source_separation/utils/metrics.py | 204 ++++++ packaging/build_conda.sh | 2 +- packaging/build_wheel.sh | 2 +- packaging/pkg_helpers.bash | 5 +- setup.py | 2 +- test/torchaudio_unittest/backend_test.py | 14 +- .../common_utils/backend_utils.py | 6 +- .../common_utils/wav_utils.py | 12 +- test/torchaudio_unittest/dataloader_test.py | 39 - .../datasets/speechcommands_test.py | 143 +++- .../datasets/tedlium_test.py | 23 +- test/torchaudio_unittest/example/__init__.py | 8 + .../example/souce_sepration/__init__.py | 0 .../example/souce_sepration/metrics_test.py | 39 + .../example/souce_sepration/sdr_reference.py | 98 +++ .../example/souce_sepration/wsj0mix_test.py | 111 +++ test/torchaudio_unittest/io_test.py | 3 + test/torchaudio_unittest/models_test.py | 63 +- .../soundfile_backend/__init__.py | 0 .../soundfile_backend/common.py | 34 + .../soundfile_backend/info_test.py | 95 +++ .../soundfile_backend/load_test.py | 265 +++++++ .../soundfile_backend/save_test.py | 211 ++++++ .../sox_effect/sox_effects_chain_test.py | 224 ------ torchaudio/__init__.py | 31 +- torchaudio/backend/_soundfile_backend.py | 223 ++++++ torchaudio/backend/common.py | 30 +- torchaudio/backend/sox_backend.py | 18 + torchaudio/backend/sox_io_backend.py | 20 +- torchaudio/backend/utils.py | 27 +- torchaudio/compliance/kaldi.py | 8 +- torchaudio/csrc/sox.cpp | 201 ------ torchaudio/csrc/sox.h | 22 - torchaudio/datasets/cmuarctic.py | 25 +- torchaudio/datasets/commonvoice.py | 37 +- torchaudio/datasets/gtzan.py | 32 +- torchaudio/datasets/librispeech.py | 24 +- torchaudio/datasets/libritts.py | 25 +- torchaudio/datasets/ljspeech.py | 21 +- torchaudio/datasets/speechcommands.py | 67 +- torchaudio/datasets/tedlium.py | 67 +- torchaudio/datasets/vctk.py | 64 +- torchaudio/datasets/yesno.py | 23 +- torchaudio/functional.py | 18 +- torchaudio/models/__init__.py | 5 +- torchaudio/models/conv_tasnet.py | 327 +++++++++ torchaudio/sox_effects/__init__.py | 2 - torchaudio/sox_effects/sox_effects.py | 198 +----- torchaudio/transforms.py | 3 +- 88 files changed, 4664 insertions(+), 1050 deletions(-) create mode 100644 docs/source/_static/img/pytorch-logo-flame.png create mode 100644 examples/pipeline_wav2letter/README.md create mode 100644 examples/pipeline_wav2letter/ctc_decoders.py create mode 100644 examples/pipeline_wav2letter/datasets.py create mode 100644 examples/pipeline_wav2letter/languagemodels.py create mode 100644 examples/pipeline_wav2letter/main.py create mode 100644 examples/pipeline_wav2letter/metrics.py create mode 100644 examples/pipeline_wav2letter/transforms.py create mode 100644 examples/pipeline_wav2letter/utils.py create mode 100644 examples/source_separation/README.md create mode 100644 examples/source_separation/conv_tasnet/README.md create mode 100644 examples/source_separation/conv_tasnet/__init__.py create mode 100644 examples/source_separation/conv_tasnet/train.py create mode 100644 examples/source_separation/conv_tasnet/trainer.py create mode 100644 examples/source_separation/train.py create mode 100644 examples/source_separation/utils/__init__.py create mode 100644 examples/source_separation/utils/dataset/__init__.py create mode 100644 examples/source_separation/utils/dataset/utils.py create mode 100644 examples/source_separation/utils/dataset/wsj0mix.py create mode 100644 examples/source_separation/utils/dist_utils.py create mode 100644 examples/source_separation/utils/metrics.py delete mode 100644 test/torchaudio_unittest/dataloader_test.py create mode 100644 test/torchaudio_unittest/example/__init__.py create mode 100644 test/torchaudio_unittest/example/souce_sepration/__init__.py create mode 100644 test/torchaudio_unittest/example/souce_sepration/metrics_test.py create mode 100644 test/torchaudio_unittest/example/souce_sepration/sdr_reference.py create mode 100644 test/torchaudio_unittest/example/souce_sepration/wsj0mix_test.py create mode 100644 test/torchaudio_unittest/soundfile_backend/__init__.py create mode 100644 test/torchaudio_unittest/soundfile_backend/common.py create mode 100644 test/torchaudio_unittest/soundfile_backend/info_test.py create mode 100644 test/torchaudio_unittest/soundfile_backend/load_test.py create mode 100644 test/torchaudio_unittest/soundfile_backend/save_test.py delete mode 100644 test/torchaudio_unittest/sox_effect/sox_effects_chain_test.py create mode 100644 torchaudio/backend/_soundfile_backend.py create mode 100644 torchaudio/models/conv_tasnet.py diff --git a/.circleci/config.yml b/.circleci/config.yml index b2b0dd45c3..472c3f0af6 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -34,7 +34,7 @@ commands: command: | our_upload_channel=nightly # On tags upload to test instead - if [[ -n "${CIRCLE_TAG}" ]]; then + if [[ -n "${CIRCLE_TAG}" ]] || [[ ${CIRCLE_BRANCH} =~ release/* ]]; then our_upload_channel=test fi echo "export UPLOAD_CHANNEL=${our_upload_channel}" >> ${BASH_ENV} @@ -296,12 +296,13 @@ jobs: steps: - attach_workspace: at: ~/workspace + - designate_upload_channel - run: name: install binaries command: | set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + pip install $(ls ~/workspace/torchaudio*.whl) -f "https://download.pytorch.org/whl/${UPLOAD_CHANNEL}/torch_${UPLOAD_CHANNEL}.html" - run: name: smoke test command: | @@ -315,6 +316,7 @@ jobs: steps: - attach_workspace: at: ~/workspace + - designate_upload_channel - run: name: install binaries command: | @@ -323,7 +325,7 @@ jobs: conda env remove -n python${PYTHON_VERSION} || true conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - run: name: smoke test @@ -339,6 +341,7 @@ jobs: steps: - attach_workspace: at: ~/workspace + - designate_upload_channel - run: name: install binaries command: | @@ -347,7 +350,7 @@ jobs: conda env remove -n python${PYTHON_VERSION} || true conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} conda activate python${PYTHON_VERSION} - pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + pip install $(ls ~/workspace/torchaudio*.whl) -f "https://download.pytorch.org/whl/${UPLOAD_CHANNEL}/torch_${UPLOAD_CHANNEL}.html" - run: name: smoke test command: | @@ -391,18 +394,19 @@ jobs: - checkout - attach_workspace: at: third_party + - designate_upload_channel - generate_cache_key - restore_cache: keys: - - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - save_cache: - key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} paths: - conda @@ -433,18 +437,19 @@ jobs: - checkout - attach_workspace: at: third_party + - designate_upload_channel - generate_cache_key - restore_cache: keys: - - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - run: name: Setup command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh - save_cache: - key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} paths: - conda @@ -470,18 +475,19 @@ jobs: name: windows-cpu steps: - checkout + - designate_upload_channel - generate_cache_key - restore_cache: keys: - - env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + - env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh - save_cache: - key: env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + key: env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} paths: - conda @@ -506,18 +512,19 @@ jobs: CUDA_VERSION: "10.1" steps: - checkout + - designate_upload_channel - generate_cache_key - restore_cache: keys: - - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh - save_cache: - key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} paths: - conda @@ -544,18 +551,19 @@ jobs: - install_build_tools_macos - attach_workspace: at: third_party + - designate_upload_channel - generate_cache_key - restore_cache: keys: - - env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + - env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - save_cache: - key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} paths: - conda @@ -582,18 +590,19 @@ jobs: resource_class: medium steps: - checkout + - designate_upload_channel - generate_cache_key - restore_cache: keys: - - env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + - env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - save_cache: - key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} paths: - conda @@ -756,6 +765,7 @@ workflows: only: - master - nightly + - release/* tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_linux_gpu_py3.6 @@ -768,6 +778,7 @@ workflows: only: - master - nightly + - release/* tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_linux_gpu_py3.7 @@ -780,6 +791,7 @@ workflows: only: - master - nightly + - release/* tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_linux_gpu_py3.8 @@ -801,6 +813,7 @@ workflows: only: - master - nightly + - release/* tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_windows_gpu_py3.6 @@ -811,6 +824,7 @@ workflows: only: - master - nightly + - release/* tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_windows_gpu_py3.7 @@ -821,6 +835,7 @@ workflows: only: - master - nightly + - release/* tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_windows_gpu_py3.8 diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index b1f40c24c0..6f6838deff 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -34,7 +34,7 @@ commands: command: | our_upload_channel=nightly # On tags upload to test instead - if [[ -n "${CIRCLE_TAG}" ]]; then + if [[ -n "${CIRCLE_TAG}" ]] || [[ ${CIRCLE_BRANCH} =~ release/* ]]; then our_upload_channel=test fi echo "export UPLOAD_CHANNEL=${our_upload_channel}" >> ${BASH_ENV} @@ -296,12 +296,13 @@ jobs: steps: - attach_workspace: at: ~/workspace + - designate_upload_channel - run: name: install binaries command: | set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + pip install $(ls ~/workspace/torchaudio*.whl) -f "https://download.pytorch.org/whl/${UPLOAD_CHANNEL}/torch_${UPLOAD_CHANNEL}.html" - run: name: smoke test command: | @@ -315,6 +316,7 @@ jobs: steps: - attach_workspace: at: ~/workspace + - designate_upload_channel - run: name: install binaries command: | @@ -323,7 +325,7 @@ jobs: conda env remove -n python${PYTHON_VERSION} || true conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - run: name: smoke test @@ -339,6 +341,7 @@ jobs: steps: - attach_workspace: at: ~/workspace + - designate_upload_channel - run: name: install binaries command: | @@ -347,7 +350,7 @@ jobs: conda env remove -n python${PYTHON_VERSION} || true conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} conda activate python${PYTHON_VERSION} - pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + pip install $(ls ~/workspace/torchaudio*.whl) -f "https://download.pytorch.org/whl/${UPLOAD_CHANNEL}/torch_${UPLOAD_CHANNEL}.html" - run: name: smoke test command: | @@ -391,18 +394,19 @@ jobs: - checkout - attach_workspace: at: third_party + - designate_upload_channel - generate_cache_key - restore_cache: {% raw %} keys: - - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} {% endraw %} - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - save_cache: {% raw %} - key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} {% endraw %} paths: - conda @@ -433,18 +437,19 @@ jobs: - checkout - attach_workspace: at: third_party + - designate_upload_channel - generate_cache_key - restore_cache: {% raw %} keys: - - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} {% endraw %} - run: name: Setup command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh - save_cache: {% raw %} - key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} {% endraw %} paths: - conda @@ -470,18 +475,19 @@ jobs: name: windows-cpu steps: - checkout + - designate_upload_channel - generate_cache_key - restore_cache: {% raw %} keys: - - env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + - env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} {% endraw %} - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh - save_cache: {% raw %} - key: env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + key: env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} {% endraw %} paths: - conda @@ -506,18 +512,19 @@ jobs: CUDA_VERSION: "10.1" steps: - checkout + - designate_upload_channel - generate_cache_key - restore_cache: {% raw %} keys: - - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} {% endraw %} - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh - save_cache: {% raw %} - key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} {% endraw %} paths: - conda @@ -544,18 +551,19 @@ jobs: - install_build_tools_macos - attach_workspace: at: third_party + - designate_upload_channel - generate_cache_key - restore_cache: {% raw %} keys: - - env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + - env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} {% endraw %} - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - save_cache: {% raw %} - key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} {% endraw %} paths: - conda @@ -582,18 +590,19 @@ jobs: resource_class: medium steps: - checkout + - designate_upload_channel - generate_cache_key - restore_cache: {% raw %} keys: - - env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + - env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} {% endraw %} - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - save_cache: {% raw %} - key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} {% endraw %} paths: - conda diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index 2c9fe7ee4f..2f4d4088d9 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -148,7 +148,7 @@ def unittest_workflows(indentation=6): } if device_type == 'gpu': - job['filters'] = gen_filter_branch_tree('master', 'nightly') + job['filters'] = gen_filter_branch_tree('master', 'nightly', 'release/*') if os_type != "windows": job['requires'] = ['download_third_parties_nix'] diff --git a/.circleci/torchscript_bc_test/common.sh b/.circleci/torchscript_bc_test/common.sh index 51d258a2d9..24ad5e45fb 100644 --- a/.circleci/torchscript_bc_test/common.sh +++ b/.circleci/torchscript_bc_test/common.sh @@ -59,9 +59,6 @@ install_release() { install_build_dependencies() { printf "* Installing torchaudio dependencies except PyTorch - (Python: %s)\n" "$1" conda env update -q --file "${_this_dir}/environment.yml" --prune - if [ "${_os}" == Linux ]; then - pip install clang-format - fi } build_master() { diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml index 42acafb984..4315a2205b 100644 --- a/.circleci/unittest/linux/scripts/environment.yml +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -7,7 +7,6 @@ dependencies: - numpy >= 1.11 - pytest - pytest-cov - - pytest-xdist - codecov - librosa>=0.8.0 - llvmlite==0.31 # See https://github.com/pytorch/audio/pull/766 diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh index 8346e6c825..27dec79251 100755 --- a/.circleci/unittest/linux/scripts/install.sh +++ b/.circleci/unittest/linux/scripts/install.sh @@ -7,17 +7,48 @@ unset PYTORCH_VERSION set -e -eval "$(./conda/bin/conda shell.bash hook)" -conda activate ./env +root_dir="$(git rev-parse --show-toplevel)" +conda_dir="${root_dir}/conda" +env_dir="${root_dir}/env" +cd "${root_dir}" + +case "$(uname -s)" in + Darwin*) os=MacOSX;; + *) os=Linux +esac + +# 0. Activate conda env +eval "$("${conda_dir}/bin/conda" shell.bash hook)" +conda activate "${env_dir}" + +# 1. Install PyTorch if [ -z "${CUDA_VERSION:-}" ] ; then - cudatoolkit="cpuonly" + if [ "${os}" == MacOSX ] ; then + cudatoolkit='' + else + cudatoolkit="cpuonly" + fi else version="$(python -c "print('.'.join(\"${CUDA_VERSION}\".split('.')[:2]))")" cudatoolkit="cudatoolkit=${version}" fi printf "Installing PyTorch with %s\n" "${cudatoolkit}" -conda install -y -c pytorch-nightly pytorch "${cudatoolkit}" +conda install -y -c "pytorch-${UPLOAD_CHANNEL}" pytorch ${cudatoolkit} +# 2. Install torchaudio printf "* Installing torchaudio\n" BUILD_SOX=1 python setup.py install + +# 3. Install Test tools +printf "* Installing test tools\n" +if [ "${os}" == Linux ] ; then + # TODO: move this to docker + apt install -y -q libsndfile1 + conda install -y -c conda-forge codecov pytest pytest-cov + pip install kaldi-io 'librosa>=0.8.0' parameterized SoundFile scipy +else + # Note: installing librosa via pip fail because it will try to compile numba. + conda install -y -c conda-forge codecov pytest pytest-cov 'librosa>=0.8.0' parameterized scipy + pip install kaldi-io SoundFile +fi diff --git a/.circleci/unittest/linux/scripts/run_style_checks.sh b/.circleci/unittest/linux/scripts/run_style_checks.sh index 70b83e19f5..468fa415b5 100755 --- a/.circleci/unittest/linux/scripts/run_style_checks.sh +++ b/.circleci/unittest/linux/scripts/run_style_checks.sh @@ -2,9 +2,20 @@ set -u -eval "$(./conda/bin/conda shell.bash hook)" -conda activate ./env +root_dir="$(git rev-parse --show-toplevel)" +conda_dir="${root_dir}/conda" +env_dir="${root_dir}/env" +eval "$("${conda_dir}/bin/conda" shell.bash hook)" +conda activate "${env_dir}" + +# 1. Install tools +conda install flake8 +clangformat_path="${root_dir}/clang-format" +curl https://oss-clang-format.s3.us-east-2.amazonaws.com/linux64/clang-format-linux64 -o "${clangformat_path}" +chmod +x "${clangformat_path}" + +# 2. Run style checks # We want to run all the style checks even if one of them fail. exit_status=0 @@ -20,9 +31,9 @@ if [ "${status}" -ne 0 ]; then fi printf "\x1b[34mRunning clang-format: " -clang-format --version +./clang-format --version printf "\x1b[0m\n" -git-clang-format origin/master +git-clang-format --binary ./clang-format origin/master git diff --exit-code status=$? exit_status="$((exit_status+status))" diff --git a/.circleci/unittest/linux/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh index 50bcf711d6..e9016d266c 100755 --- a/.circleci/unittest/linux/scripts/run_test.sh +++ b/.circleci/unittest/linux/scripts/run_test.sh @@ -2,12 +2,6 @@ set -e -case "$(uname -s)" in - Darwin*) os=MacOSX;; - *) os=Linux -esac - - eval "$(./conda/bin/conda shell.bash hook)" conda activate ./env @@ -15,15 +9,12 @@ python -m torch.utils.collect_env export TORCHAUDIO_TEST_FAIL_IF_NO_EXTENSION=1 export PATH="${PWD}/third_party/install/bin/:${PATH}" -declare -a common_args=( +declare -a args=( + '-v' '--cov=torchaudio' "--junitxml=${PWD}/test-results/junit.xml" '--durations' '20' ) -if [ "${os}" == MacOSX ] ; then - declare -a args=('-q' '-n' 'auto' '--dist=loadscope') -else - declare -a args=('-v') -fi + cd test -pytest "${args[@]}" "${common_args[@]}" torchaudio_unittest +pytest "${args[@]}" torchaudio_unittest diff --git a/.circleci/unittest/linux/scripts/setup_env.sh b/.circleci/unittest/linux/scripts/setup_env.sh index 290f0f8f97..6e203dea27 100755 --- a/.circleci/unittest/linux/scripts/setup_env.sh +++ b/.circleci/unittest/linux/scripts/setup_env.sh @@ -7,7 +7,6 @@ set -e -this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" root_dir="$(git rev-parse --show-toplevel)" conda_dir="${root_dir}/conda" env_dir="${root_dir}/env" @@ -34,17 +33,13 @@ if [ ! -d "${env_dir}" ]; then fi conda activate "${env_dir}" -# 3. Install Conda dependencies -printf "* Installing dependencies (except PyTorch)\n" -conda env update --file "${this_dir}/environment.yml" --prune -if [ "${os}" == Linux ] ; then - pip install clang-format -fi +# 3. Install minimal build tools +pip install cmake ninja # 4. Buld codecs mkdir -p third_party/build ( cd third_party/build - cmake .. + cmake -GNinja .. cmake --build . ) diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml index 3a41020645..4a0b71d653 100644 --- a/.circleci/unittest/windows/scripts/environment.yml +++ b/.circleci/unittest/windows/scripts/environment.yml @@ -15,3 +15,4 @@ dependencies: - librosa >= 0.8.0 - future - parameterized + - dataclasses diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh index 2b9c88b4af..901cb40450 100644 --- a/.circleci/unittest/windows/scripts/install.sh +++ b/.circleci/unittest/windows/scripts/install.sh @@ -7,9 +7,17 @@ unset PYTORCH_VERSION set -e -eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" -conda activate ./env +root_dir="$(git rev-parse --show-toplevel)" +conda_dir="${root_dir}/conda" +env_dir="${root_dir}/env" +cd "${root_dir}" + +# 0. Activate conda env +eval "$("${conda_dir}/Scripts/conda.exe" 'shell.bash' 'hook')" +conda activate "${env_dir}" + +# 1. Install PyTorch if [ -z "${CUDA_VERSION:-}" ] ; then cudatoolkit="cpuonly" else @@ -17,7 +25,13 @@ else cudatoolkit="cudatoolkit=${version}" fi printf "Installing PyTorch with %s\n" "${cudatoolkit}" -conda install -y -c pytorch-nightly pytorch "${cudatoolkit}" +conda install -y -c "pytorch-${UPLOAD_CHANNEL}" pytorch "${cudatoolkit}" +# 2. Install torchaudio printf "* Installing torchaudio\n" python setup.py install + +# 3. Install Test tools +printf "* Installing test tools\n" +conda install -y -c conda-forge codecov pytest pytest-cov +pip install kaldi-io 'librosa>=0.8.0' parameterized PySoundFile scipy diff --git a/.circleci/unittest/windows/scripts/setup_env.sh b/.circleci/unittest/windows/scripts/setup_env.sh index a45cf856ac..71b556d2bd 100644 --- a/.circleci/unittest/windows/scripts/setup_env.sh +++ b/.circleci/unittest/windows/scripts/setup_env.sh @@ -33,7 +33,3 @@ if [ ! -d "${env_dir}" ]; then conda create --prefix "${env_dir}" -y python="$PYTHON_VERSION" fi conda activate "${env_dir}" - -# 3. Install Conda dependencies -printf "* Installing dependencies (except PyTorch)\n" -conda env update --file "${this_dir}/environment.yml" --prune diff --git a/README.md b/README.md index 70f30be243..861af2a04b 100644 --- a/README.md +++ b/README.md @@ -37,6 +37,7 @@ The following is the corresponding ``torchaudio`` versions and supported Python | ``torch`` | ``torchaudio`` | ``python`` | | ------------------------ | ------------------------ | ------------------------------- | | ``master`` / ``nightly`` | ``master`` / ``nightly`` | ``>=3.6`` | +| ``1.7.0`` | ``0.7.0`` | ``>=3.6`` | | ``1.6.0`` | ``0.6.0`` | ``>=3.6`` | | ``1.5.0`` | ``0.5.0`` | ``>=3.5`` | | ``1.4.0`` | ``0.4.0`` | ``==2.7``, ``>=3.5``, ``<=3.8`` | diff --git a/docs/requirements.txt b/docs/requirements.txt index c591f08491..99a7811de6 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,4 +1,4 @@ -sphinx +sphinx==2.4.4 -e git+git://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme 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To check the current backend use :py:func:`torchaudio.get_audio_backend`. +Overview +~~~~~~~~ -.. warning:: - Although ``sox`` backend is default for backward compatibility reason, it has a number of issues, therefore it is highly recommended to use ``sox_io`` backend instead. Note, however, that due to the interface refinement, functions defined in ``sox`` backend and those defined in ``sox_io`` backend do not have the same signatures. +:mod:`torchaudio.backend` module provides implementations for audio file I/O functionalities, which are ``torchaudio.info``, ``torchaudio.load``, ``torchaudio.load_wav`` and ``torchaudio.save``. + +There are currently four implementations available. + +* :ref:`"sox_io" ` (default on Linux/macOS) +* :ref:`"sox" ` (deprecated, will be removed in 0.9.0 release) +* :ref:`"soundfile" ` (default on Windows) +* :ref:`"soundfile" (legacy interface) ` (deprecated, will be removed in 0.9.0 release) + +The use of ``"sox"`` backend is strongly discouraged as it cannot correctly handle formats other than 16-bit integer WAV. See `#726 `_ for the detail. .. note:: - Instead of calling functions in :mod:`torchaudio.backend` directly, please use ``torchaudio.info``, ``torhcaudio.load``, ``torchaudio.load_wav`` and ``torchaudio.save`` with proper backend set with :func:`torchaudio.get_audio_backend`. + Instead of calling functions in ``torchaudio.backend`` directly, please use ``torchaudio.info``, ``torchaudio.load``, ``torchaudio.load_wav`` and ``torchaudio.save`` with proper backend set with :func:`torchaudio.set_audio_backend`. + +Availability +------------ -There are currently three implementations available. +``"sox"`` and ``"sox_io"`` backends require C++ extension module, which is included in Linux/macOS binary distributions. These backends are not available on Windows. - * :ref:`sox` - * :ref:`sox_io` - * :ref:`soundfile` +``"soundfile"`` backend requires ``SoundFile``. Please refer to `the SoundFile documentation `_ for the installation. -``sox`` backend is the original backend which is built on ``libsox``. This module is currently default but is known to have number of issues, such as wrong handling of WAV files other than 16-bit signed integer. Users are encouraged to use ``sox_io`` backend. This backend requires C++ extension module and is not available on Windows system. +Changes in default backend and deprecation +------------------------------------------ -``sox_io`` backend is the new backend which is built on ``libsox`` and bound to Python with ``Torchscript``. This module addresses all the known issues ``sox`` backend has. Function calls to this backend can be Torchscriptable. This backend requires C++ extension module and is not available on Windows system. +Backend module is going through a major overhaul. The following table summarizes the timeline for the deprecations and removals. -``soundfile`` backend is built on ``PySoundFile``. You need to install ``PySoundFile`` separately. + +--------------------+-----------------------+------------------------+ + | **Backend** | **0.8.0** | **0.9.0** | + +====================+=======================+========================+ + | ``"sox_io"`` | Default on Linx/macOS | Default on Linux/macOS | + +--------------------+-----------------------+------------------------+ + | ``"sox"`` | Available | Removed | + | (deprecated) | | | + +--------------------+-----------------------+------------------------+ + | ``"soundfile"`` | Default on Windows | Default on Windows | + +--------------------+-----------------------+------------------------+ + | ``"soundfile"`` | Available | Removed | + | (legacy interface, | | | + | deprecated) | | | + +--------------------+-----------------------+------------------------+ + +* The ``"sox"`` and ``"soundfile" (legacy interface)`` backends are deprecated and will be removed in 0.9.0 release. Common Data Structure ~~~~~~~~~~~~~~~~~~~~~ -Structures used to exchange data between Python interface and ``libsox``. They are used by :ref:`sox` and :ref:`soundfile` but not by :ref:`sox_io`. +Structures used to report the metadata of audio files. + +AudioMetaData +------------- + +.. autoclass:: torchaudio.backend.common.AudioMetaData + +SignalInfo (Deprecated) +----------------------- .. autoclass:: torchaudio.backend.common.SignalInfo -.. autoclass:: torchaudio.backend.common.EncodingInfo +EncodingInfo (Deprecated) +------------------------- + +.. autoclass:: torchaudio.backend.common.EncodingInfo + +.. _sox_io_backend: + +Sox IO Backend +~~~~~~~~~~~~~~ + +The ``"sox_io"`` backend is available and default on Linux/macOS and not available on Windows. + +I/O functions of this backend support `TorchScript `_. + +You can switch from another backend to the ``sox_io`` backend with the following; + +.. code:: + + torchaudio.set_audio_backend("sox_io") + +info +---- + +.. autofunction:: torchaudio.backend.sox_io_backend.info + +load +---- + +.. autofunction:: torchaudio.backend.sox_io_backend.load + +.. autofunction:: torchaudio.backend.sox_io_backend.load_wav + + +save +---- + +.. autofunction:: torchaudio.backend.sox_io_backend.save .. _sox_backend: -Sox Backend -~~~~~~~~~~~ +Sox Backend (Deprecated) +~~~~~~~~~~~~~~~~~~~~~~~~ -``sox`` backend is available on ``torchaudio`` installation with C++ extension. It is currently not available on Windows system. +The ``"sox"`` backend is available on Linux/macOS and not available on Windows. This backend is deprecated and will be removed in ``0.9.0`` release. -It is currently default backend when it's available. You can switch from another backend to ``sox`` backend with the following; +You can switch from another backend to ``sox`` backend with the following; .. code:: @@ -71,61 +140,61 @@ others :members: :exclude-members: info, load, load_wav, save -.. _sox_io_backend: +.. _soundfile_backend: -Sox IO Backend -~~~~~~~~~~~~~~ +Soundfile Backend +~~~~~~~~~~~~~~~~~ -``sox_io`` backend is available on ``torchaudio`` installation with C++ extension. It is currently not available on Windows system. +The ``"soundfile"`` backend is available when `SoundFile `_ is installed. This backend is the default on Windows. -This new backend is recommended over ``sox`` backend. You can switch from another backend to ``sox_io`` backend with the following; +You can switch from another backend to the ``"soundfile"`` backend with the following; .. code:: - torchaudio.set_audio_backend("sox_io") + torchaudio.set_audio_backend("soundfile") -The function call to this backend can be Torchsript-able. You can apply :func:`torch.jit.script` and dump the object to file, then call it from C++ application. +.. note:: + If you are switching from `"soundfile" (legacy interface) ` backend, set ``torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE`` flag **before** switching the backend. info ----- - -.. autoclass:: torchaudio.backend.sox_io_backend.AudioMetaData +^^^^ -.. autofunction:: torchaudio.backend.sox_io_backend.info +.. autofunction:: torchaudio.backend._soundfile_backend.info load ----- +^^^^ -.. autofunction:: torchaudio.backend.sox_io_backend.load +.. autofunction:: torchaudio.backend._soundfile_backend.load -.. autofunction:: torchaudio.backend.sox_io_backend.load_wav +.. autofunction:: torchaudio.backend._soundfile_backend.load_wav save ----- +^^^^ -.. autofunction:: torchaudio.backend.sox_io_backend.save +.. autofunction:: torchaudio.backend._soundfile_backend.save -.. _soundfile_backend: +.. _soundfile_legacy_backend: -Soundfile Backend -~~~~~~~~~~~~~~~~~ +Legacy Interface (Deprecated) +----------------------------- -``soundfile`` backend is available when ``PySoundFile`` is installed. This backend works on ``torchaudio`` installation without C++ extension. (i.e. Windows) +``"soundfile"`` backend with legacy interface is made available for backward compatibility reason, however this interface is deprecated and will be removed in the ``0.9.0`` release. -You can switch from another backend to ``soundfile`` backend with the following; +To switch to this backend/interface, set ``torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE`` flag **before** switching the backend. .. code:: - torchaudio.set_audio_backend("soundfile") + torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = True + torchaudio.set_audio_backend("soundfile") # The legacy interface info ----- +^^^^ .. autofunction:: torchaudio.backend.soundfile_backend.info load ----- +^^^^ .. autofunction:: torchaudio.backend.soundfile_backend.load @@ -133,6 +202,6 @@ load save ----- +^^^^ .. autofunction:: torchaudio.backend.soundfile_backend.save diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index b3f1b3c725..2ceb93a440 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -29,82 +29,85 @@ CMUARCTIC ~~~~~~~~~ .. autoclass:: CMUARCTIC - :members: __getitem__ - :special-members: + :members: + :special-members: __getitem__ COMMONVOICE ~~~~~~~~~~~ .. autoclass:: COMMONVOICE - :members: __getitem__ - :special-members: + :members: + :special-members: __getitem__ GTZAN ~~~~~ .. autoclass:: GTZAN - :members: __getitem__ - :special-members: + :members: + :special-members: __getitem__ LIBRISPEECH ~~~~~~~~~~~ .. autoclass:: LIBRISPEECH - :members: __getitem__ - :special-members: + :members: + :special-members: __getitem__ LIBRITTS ~~~~~~~~ .. autoclass:: LIBRITTS - :members: __getitem__ - :special-members: + :members: + :special-members: __getitem__ LJSPEECH ~~~~~~~~ .. autoclass:: LJSPEECH - :members: __getitem__ - :special-members: + :members: + :special-members: __getitem__ SPEECHCOMMANDS ~~~~~~~~~~~~~~ .. autoclass:: SPEECHCOMMANDS - :members: __getitem__ - :special-members: + :members: + :special-members: __getitem__ TEDLIUM ~~~~~~~~~~~~~~ .. autoclass:: TEDLIUM - :members: __getitem__ - :special-members: get_phoneme_dict + :members: + :special-members: __getitem__ + VCTK ~~~~ .. autoclass:: VCTK - :members: __getitem__ - :special-members: + :members: + :special-members: __getitem__ VCTK_092 ~~~~~~~~ .. autoclass:: VCTK_092 + :members: + :special-members: __getitem__ YESNO ~~~~~ .. autoclass:: YESNO - :members: __getitem__ - :special-members: + :members: + :special-members: __getitem__ diff --git a/docs/source/index.rst b/docs/source/index.rst index 8146e545c3..596a636b1b 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,5 +1,27 @@ torchaudio ========== +This library is part of the `PyTorch +`_ project. PyTorch is an open source +machine learning framework. + +Features described in this documentation are classified by release status: + + *Stable:* These features will be maintained long-term and there should generally + be no major performance limitations or gaps in documentation. + We also expect to maintain backwards compatibility (although + breaking changes can happen and notice will be given one release ahead + of time). + + *Beta:* Features are tagged as Beta because the API may change based on + user feedback, because the performance needs to improve, or because + coverage across operators is not yet complete. For Beta features, we are + committing to seeing the feature through to the Stable classification. + We are not, however, committing to backwards compatibility. + + *Prototype:* These features are typically not available as part of + binary distributions like PyPI or Conda, except sometimes behind run-time + flags, and are at an early stage for feedback and testing. + The :mod:`torchaudio` package consists of I/O, popular datasets and common audio transformations. @@ -17,3 +39,16 @@ The :mod:`torchaudio` package consists of I/O, popular datasets and common audio compliance.kaldi kaldi_io utils + + +.. toctree:: + :maxdepth: 1 + :caption: PyTorch Libraries + + PyTorch + torchaudio + torchtext + torchvision + TorchElastic + TorchServe + PyTorch on XLA Devices diff --git a/docs/source/models.rst b/docs/source/models.rst index 44eb74444a..ea86d8b73b 100644 --- a/docs/source/models.rst +++ b/docs/source/models.rst @@ -9,6 +9,14 @@ torchaudio.models The models subpackage contains definitions of models for addressing common audio tasks. +:hidden:`ConvTasNet` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: ConvTasNet + + .. automethod:: forward + + :hidden:`Wav2Letter` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/source/sox_effects.rst b/docs/source/sox_effects.rst index 0aa34e7908..6eee11d8c7 100644 --- a/docs/source/sox_effects.rst +++ b/docs/source/sox_effects.rst @@ -5,10 +5,6 @@ torchaudio.sox_effects .. currentmodule:: torchaudio.sox_effects -.. warning:: - - The :py:class:`SoxEffect` and :py:class:`SoxEffectsChain` classes are deprecated. Please migrate to :func:`apply_effects_tensor` and :func:`apply_effects_file`. - Resource initialization / shutdown ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -35,18 +31,3 @@ Applying effects on file ------------------------ .. autofunction:: apply_effects_file - -Legacy -~~~~~~ - -SoxEffect ---------- - -.. autoclass:: SoxEffect - :members: - -SoxEffectsChain ---------------- - -.. autoclass:: SoxEffectsChain - :members: append_effect_to_chain, sox_build_flow_effects, clear_chain, set_input_file diff --git a/examples/pipeline_wav2letter/README.md b/examples/pipeline_wav2letter/README.md new file mode 100644 index 0000000000..8117d19c91 --- /dev/null +++ b/examples/pipeline_wav2letter/README.md @@ -0,0 +1,45 @@ +This is an example pipeline for speech recognition using a greedy or Viterbi CTC decoder, along with the Wav2Letter model trained on LibriSpeech, see [Wav2Letter: an End-to-End ConvNet-based Speech Recognition System](https://arxiv.org/pdf/1609.03193.pdf). Wav2Letter and LibriSpeech are available in torchaudio. + +### Usage + +More information about each command line parameters is available with the `--help` option. An example can be invoked as follows. +``` +python main.py \ + --reduce-lr-valid \ + --dataset-train train-clean-100 train-clean-360 train-other-500 \ + --dataset-valid dev-clean \ + --batch-size 128 \ + --learning-rate .6 \ + --momentum .8 \ + --weight-decay .00001 \ + --clip-grad 0. \ + --gamma .99 \ + --hop-length 160 \ + --n-hidden-channels 2000 \ + --win-length 400 \ + --n-bins 13 \ + --normalize \ + --optimizer adadelta \ + --scheduler reduceonplateau \ + --epochs 30 +``` +With these default parameters, we get a character error rate of 13.8% on dev-clean after 30 epochs. + +### Output + +The information reported at each iteration and epoch (e.g. loss, character error rate, word error rate) is printed to standard output in the form of one json per line, e.g. +```python +{"name": "train", "epoch": 0, "cer over target length": 1.0, "cumulative cer": 23317.0, "total chars": 23317.0, "cer": 0.0, "cumulative cer over target length": 0.0, "wer over target length": 1.0, "cumulative wer": 4446.0, "total words": 4446.0, "wer": 0.0, "cumulative wer over target length": 0.0, "lr": 0.6, "batch size": 128, "n_channel": 13, "n_time": 2453, "dataset length": 128.0, "iteration": 1.0, "loss": 8.712121963500977, "cumulative loss": 8.712121963500977, "average loss": 8.712121963500977, "iteration time": 41.46276903152466, "epoch time": 41.46276903152466} +{"name": "train", "epoch": 0, "cer over target length": 1.0, "cumulative cer": 46005.0, "total chars": 46005.0, "cer": 0.0, "cumulative cer over target length": 0.0, "wer over target length": 1.0, "cumulative wer": 8762.0, "total words": 8762.0, "wer": 0.0, "cumulative wer over target length": 0.0, "lr": 0.6, "batch size": 128, "n_channel": 13, "n_time": 1703, "dataset length": 256.0, "iteration": 2.0, "loss": 8.918599128723145, "cumulative loss": 17.63072109222412, "average loss": 8.81536054611206, "iteration time": 1.2905676364898682, "epoch time": 42.753336668014526} +{"name": "train", "epoch": 0, "cer over target length": 1.0, "cumulative cer": 70030.0, "total chars": 70030.0, "cer": 0.0, "cumulative cer over target length": 0.0, "wer over target length": 1.0, "cumulative wer": 13348.0, "total words": 13348.0, "wer": 0.0, "cumulative wer over target length": 0.0, "lr": 0.6, "batch size": 128, "n_channel": 13, "n_time": 1713, "dataset length": 384.0, "iteration": 3.0, "loss": 8.550191879272461, "cumulative loss": 26.180912971496582, "average loss": 8.726970990498861, "iteration time": 1.2109291553497314, "epoch time": 43.96426582336426} +``` +One way to import the output in python with pandas is by saving the standard output to a file, and then using `pandas.read_json(filename, lines=True)`. + +## Structure of pipeline + +* `main.py` -- the entry point +* `ctc_decoders.py` -- the greedy CTC decoder +* `datasets.py` -- the function to split and process librispeech, a collate factory function +* `languagemodels.py` -- a class to encode and decode strings +* `metrics.py` -- the levenshtein edit distance +* `utils.py` -- functions to log metrics, save checkpoint, and count parameters diff --git a/examples/pipeline_wav2letter/ctc_decoders.py b/examples/pipeline_wav2letter/ctc_decoders.py new file mode 100644 index 0000000000..b4f155d6fa --- /dev/null +++ b/examples/pipeline_wav2letter/ctc_decoders.py @@ -0,0 +1,15 @@ +from torch import topk + + +class GreedyDecoder: + def __call__(self, outputs): + """Greedy Decoder. Returns highest probability of class labels for each timestep + + Args: + outputs (torch.Tensor): shape (input length, batch size, number of classes (including blank)) + + Returns: + torch.Tensor: class labels per time step. + """ + _, indices = topk(outputs, k=1, dim=-1) + return indices[..., 0] diff --git a/examples/pipeline_wav2letter/datasets.py b/examples/pipeline_wav2letter/datasets.py new file mode 100644 index 0000000000..79b05b2c5b --- /dev/null +++ b/examples/pipeline_wav2letter/datasets.py @@ -0,0 +1,113 @@ +import torch +from torchaudio.datasets import LIBRISPEECH + + +class MapMemoryCache(torch.utils.data.Dataset): + """ + Wrap a dataset so that, whenever a new item is returned, it is saved to memory. + """ + + def __init__(self, dataset): + self.dataset = dataset + self._cache = [None] * len(dataset) + + def __getitem__(self, n): + if self._cache[n] is not None: + return self._cache[n] + + item = self.dataset[n] + self._cache[n] = item + + return item + + def __len__(self): + return len(self.dataset) + + +class Processed(torch.utils.data.Dataset): + def __init__(self, dataset, transforms, encode): + self.dataset = dataset + self.transforms = transforms + self.encode = encode + + def __getitem__(self, key): + item = self.dataset[key] + return self.process_datapoint(item) + + def __len__(self): + return len(self.dataset) + + def process_datapoint(self, item): + transformed = item[0] + target = item[2].lower() + + transformed = self.transforms(transformed) + transformed = transformed[0, ...].transpose(0, -1) + + target = self.encode(target) + target = torch.tensor(target, dtype=torch.long, device=transformed.device) + + return transformed, target + + +def split_process_librispeech( + datasets, transforms, language_model, root, folder_in_archive, +): + def create(tags, cache=True): + + if isinstance(tags, str): + tags = [tags] + if isinstance(transforms, list): + transform_list = transforms + else: + transform_list = [transforms] + + data = torch.utils.data.ConcatDataset( + [ + Processed( + LIBRISPEECH( + root, tag, folder_in_archive=folder_in_archive, download=False, + ), + transform, + language_model.encode, + ) + for tag, transform in zip(tags, transform_list) + ] + ) + + data = MapMemoryCache(data) + return data + + # For performance, we cache all datasets + return tuple(create(dataset) for dataset in datasets) + + +def collate_factory(model_length_function, transforms=None): + + if transforms is None: + transforms = torch.nn.Sequential() + + def collate_fn(batch): + + tensors = [transforms(b[0]) for b in batch if b] + + tensors_lengths = torch.tensor( + [model_length_function(t) for t in tensors], + dtype=torch.long, + device=tensors[0].device, + ) + + tensors = torch.nn.utils.rnn.pad_sequence(tensors, batch_first=True) + tensors = tensors.transpose(1, -1) + + targets = [b[1] for b in batch if b] + target_lengths = torch.tensor( + [target.shape[0] for target in targets], + dtype=torch.long, + device=tensors.device, + ) + targets = torch.nn.utils.rnn.pad_sequence(targets, batch_first=True) + + return tensors, targets, tensors_lengths, target_lengths + + return collate_fn diff --git a/examples/pipeline_wav2letter/languagemodels.py b/examples/pipeline_wav2letter/languagemodels.py new file mode 100644 index 0000000000..eee018ae69 --- /dev/null +++ b/examples/pipeline_wav2letter/languagemodels.py @@ -0,0 +1,38 @@ +import collections +import itertools + + +class LanguageModel: + def __init__(self, labels, char_blank, char_space): + + self.char_space = char_space + self.char_blank = char_blank + + labels = [l for l in labels] + self.length = len(labels) + enumerated = list(enumerate(labels)) + flipped = [(sub[1], sub[0]) for sub in enumerated] + + d1 = collections.OrderedDict(enumerated) + d2 = collections.OrderedDict(flipped) + self.mapping = {**d1, **d2} + + def encode(self, iterable): + if isinstance(iterable, list): + return [self.encode(i) for i in iterable] + else: + return [self.mapping[i] + self.mapping[self.char_blank] for i in iterable] + + def decode(self, tensor): + if len(tensor) > 0 and isinstance(tensor[0], list): + return [self.decode(t) for t in tensor] + else: + # not idempotent, since clean string + x = (self.mapping[i] for i in tensor) + x = "".join(i for i, _ in itertools.groupby(x)) + x = x.replace(self.char_blank, "") + # x = x.strip() + return x + + def __len__(self): + return self.length diff --git a/examples/pipeline_wav2letter/main.py b/examples/pipeline_wav2letter/main.py new file mode 100644 index 0000000000..8f52ec6c3c --- /dev/null +++ b/examples/pipeline_wav2letter/main.py @@ -0,0 +1,668 @@ +import argparse +import logging +import os +import string +from datetime import datetime +from time import time + +import torch +import torchaudio +from torch.optim import SGD, Adadelta, Adam, AdamW +from torch.optim.lr_scheduler import ExponentialLR, ReduceLROnPlateau +from torch.utils.data import DataLoader +from torchaudio.datasets.utils import bg_iterator +from torchaudio.models.wav2letter import Wav2Letter + +from ctc_decoders import GreedyDecoder +from datasets import collate_factory, split_process_librispeech +from languagemodels import LanguageModel +from metrics import levenshtein_distance +from transforms import Normalize, UnsqueezeFirst +from utils import MetricLogger, count_parameters, save_checkpoint + + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--type", + metavar="T", + default="mfcc", + choices=["waveform", "mfcc"], + help="input type for model", + ) + parser.add_argument( + "--freq-mask", + default=0, + type=int, + metavar="N", + help="maximal width of frequency mask", + ) + parser.add_argument( + "--win-length", + default=400, + type=int, + metavar="N", + help="width of spectrogram window", + ) + parser.add_argument( + "--hop-length", + default=160, + type=int, + metavar="N", + help="width of spectrogram window", + ) + parser.add_argument( + "--time-mask", + default=0, + type=int, + metavar="N", + help="maximal width of time mask", + ) + parser.add_argument( + "--workers", + default=0, + type=int, + metavar="N", + help="number of data loading workers", + ) + parser.add_argument( + "--checkpoint", + default="", + type=str, + metavar="PATH", + help="path to latest checkpoint", + ) + parser.add_argument( + "--epochs", + default=200, + type=int, + metavar="N", + help="number of total epochs to run", + ) + parser.add_argument( + "--start-epoch", default=0, type=int, metavar="N", help="manual epoch number" + ) + parser.add_argument( + "--print-freq", + default=10, + type=int, + metavar="N", + help="print frequency in epochs", + ) + parser.add_argument( + "--reduce-lr-valid", + action="store_true", + help="reduce learning rate based on validation loss", + ) + parser.add_argument( + "--normalize", action="store_true", help="normalize model input" + ) + parser.add_argument( + "--progress-bar", action="store_true", help="use progress bar while training" + ) + parser.add_argument( + "--decoder", + metavar="D", + default="greedy", + choices=["greedy"], + help="decoder to use", + ) + parser.add_argument( + "--batch-size", default=128, type=int, metavar="N", help="mini-batch size" + ) + parser.add_argument( + "--n-bins", + default=13, + type=int, + metavar="N", + help="number of bins in transforms", + ) + parser.add_argument( + "--optimizer", + metavar="OPT", + default="adadelta", + choices=["sgd", "adadelta", "adam", "adamw"], + help="optimizer to use", + ) + parser.add_argument( + "--scheduler", + metavar="S", + default="reduceonplateau", + choices=["exponential", "reduceonplateau"], + help="optimizer to use", + ) + parser.add_argument( + "--learning-rate", + default=0.6, + type=float, + metavar="LR", + help="initial learning rate", + ) + parser.add_argument( + "--gamma", + default=0.99, + type=float, + metavar="GAMMA", + help="learning rate exponential decay constant", + ) + parser.add_argument( + "--momentum", default=0.8, type=float, metavar="M", help="momentum" + ) + parser.add_argument( + "--weight-decay", default=1e-5, type=float, metavar="W", help="weight decay" + ) + parser.add_argument("--eps", metavar="EPS", type=float, default=1e-8) + parser.add_argument("--rho", metavar="RHO", type=float, default=0.95) + parser.add_argument("--clip-grad", metavar="NORM", type=float, default=0.0) + parser.add_argument( + "--dataset-root", + type=str, + help="specify dataset root folder", + ) + parser.add_argument( + "--dataset-folder-in-archive", + type=str, + help="specify dataset folder in archive", + ) + parser.add_argument( + "--dataset-train", + default=["train-clean-100"], + nargs="+", + type=str, + help="select which part of librispeech to train with", + ) + parser.add_argument( + "--dataset-valid", + default=["dev-clean"], + nargs="+", + type=str, + help="select which part of librispeech to validate with", + ) + parser.add_argument( + "--distributed", action="store_true", help="enable DistributedDataParallel" + ) + parser.add_argument("--seed", type=int, default=0, help="random seed") + parser.add_argument( + "--world-size", type=int, default=8, help="the world size to initiate DPP" + ) + parser.add_argument("--jit", action="store_true", help="if used, model is jitted") + + args = parser.parse_args() + logging.info(args) + return args + + +def setup_distributed(rank, world_size): + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "12355" + + # initialize the process group + torch.distributed.init_process_group("nccl", rank=rank, world_size=world_size) + + +def model_length_function(tensor): + if tensor.shape[1] == 1: + # waveform mode + return int(tensor.shape[0]) // 160 // 2 + 1 + return int(tensor.shape[0]) // 2 + 1 + + +def compute_error_rates(outputs, targets, decoder, language_model, metric): + output = outputs.transpose(0, 1).to("cpu") + output = decoder(output) + + # Compute CER + + output = language_model.decode(output.tolist()) + target = language_model.decode(targets.tolist()) + + print_length = 20 + for i in range(2): + # Print a few examples + output_print = output[i].ljust(print_length)[:print_length] + target_print = target[i].ljust(print_length)[:print_length] + logging.info("Target: %s Output: %s", target_print, output_print) + + cers = [levenshtein_distance(t, o) for t, o in zip(target, output)] + cers = sum(cers) + n = sum(len(t) for t in target) + metric["cer over target length"] = cers / n + metric["cumulative cer"] += cers + metric["total chars"] += n + metric["cumulative cer over target length"] = metric["cer"] / metric["total chars"] + + # Compute WER + + output = [o.split(language_model.char_space) for o in output] + target = [t.split(language_model.char_space) for t in target] + + wers = [levenshtein_distance(t, o) for t, o in zip(target, output)] + wers = sum(wers) + n = sum(len(t) for t in target) + metric["wer over target length"] = wers / n + metric["cumulative wer"] += wers + metric["total words"] += n + metric["cumulative wer over target length"] = metric["wer"] / metric["total words"] + + +def train_one_epoch( + model, + criterion, + optimizer, + scheduler, + data_loader, + decoder, + language_model, + device, + epoch, + clip_grad, + disable_logger=False, + reduce_lr_on_plateau=False, +): + + model.train() + + metric = MetricLogger("train", disable=disable_logger) + metric["epoch"] = epoch + + for inputs, targets, tensors_lengths, target_lengths in bg_iterator( + data_loader, maxsize=2 + ): + + start = time() + inputs = inputs.to(device, non_blocking=True) + targets = targets.to(device, non_blocking=True) + + # keep batch first for data parallel + outputs = model(inputs).transpose(-1, -2).transpose(0, 1) + + # CTC + # outputs: input length, batch size, number of classes (including blank) + # targets: batch size, max target length + # input_lengths: batch size + # target_lengths: batch size + + loss = criterion(outputs, targets, tensors_lengths, target_lengths) + + optimizer.zero_grad() + loss.backward() + + if clip_grad > 0: + metric["gradient"] = torch.nn.utils.clip_grad_norm_( + model.parameters(), clip_grad + ) + + optimizer.step() + + compute_error_rates(outputs, targets, decoder, language_model, metric) + + try: + metric["lr"] = scheduler.get_last_lr()[0] + except AttributeError: + metric["lr"] = optimizer.param_groups[0]["lr"] + + metric["batch size"] = len(inputs) + metric["n_channel"] = inputs.shape[1] + metric["n_time"] = inputs.shape[-1] + metric["dataset length"] += metric["batch size"] + metric["iteration"] += 1 + metric["loss"] = loss.item() + metric["cumulative loss"] += metric["loss"] + metric["average loss"] = metric["cumulative loss"] / metric["iteration"] + metric["iteration time"] = time() - start + metric["epoch time"] += metric["iteration time"] + metric() + + if reduce_lr_on_plateau and isinstance(scheduler, ReduceLROnPlateau): + scheduler.step(metric["average loss"]) + elif not isinstance(scheduler, ReduceLROnPlateau): + scheduler.step() + + +def evaluate( + model, + criterion, + data_loader, + decoder, + language_model, + device, + epoch, + disable_logger=False, +): + + with torch.no_grad(): + + model.eval() + start = time() + metric = MetricLogger("validation", disable=disable_logger) + metric["epoch"] = epoch + + for inputs, targets, tensors_lengths, target_lengths in bg_iterator( + data_loader, maxsize=2 + ): + + inputs = inputs.to(device, non_blocking=True) + targets = targets.to(device, non_blocking=True) + + # keep batch first for data parallel + outputs = model(inputs).transpose(-1, -2).transpose(0, 1) + + # CTC + # outputs: input length, batch size, number of classes (including blank) + # targets: batch size, max target length + # input_lengths: batch size + # target_lengths: batch size + + metric["cumulative loss"] += criterion( + outputs, targets, tensors_lengths, target_lengths + ).item() + + metric["dataset length"] += len(inputs) + metric["iteration"] += 1 + + compute_error_rates(outputs, targets, decoder, language_model, metric) + + metric["average loss"] = metric["cumulative loss"] / metric["iteration"] + metric["validation time"] = time() - start + metric() + + return metric["average loss"] + + +def main(rank, args): + + # Distributed setup + + if args.distributed: + setup_distributed(rank, args.world_size) + + not_main_rank = args.distributed and rank != 0 + + logging.info("Start time: %s", datetime.now()) + + # Explicitly set seed to make sure models created in separate processes + # start from same random weights and biases + torch.manual_seed(args.seed) + + # Empty CUDA cache + torch.cuda.empty_cache() + + # Change backend for flac files + torchaudio.set_audio_backend("soundfile") + + # Transforms + + melkwargs = { + "n_fft": args.win_length, + "n_mels": args.n_bins, + "hop_length": args.hop_length, + } + + sample_rate_original = 16000 + + if args.type == "mfcc": + transforms = torch.nn.Sequential( + torchaudio.transforms.MFCC( + sample_rate=sample_rate_original, + n_mfcc=args.n_bins, + melkwargs=melkwargs, + ), + ) + num_features = args.n_bins + elif args.type == "waveform": + transforms = torch.nn.Sequential(UnsqueezeFirst()) + num_features = 1 + else: + raise ValueError("Model type not supported") + + if args.normalize: + transforms = torch.nn.Sequential(transforms, Normalize()) + + augmentations = torch.nn.Sequential() + if args.freq_mask: + augmentations = torch.nn.Sequential( + augmentations, + torchaudio.transforms.FrequencyMasking(freq_mask_param=args.freq_mask), + ) + if args.time_mask: + augmentations = torch.nn.Sequential( + augmentations, + torchaudio.transforms.TimeMasking(time_mask_param=args.time_mask), + ) + + # Text preprocessing + + char_blank = "*" + char_space = " " + char_apostrophe = "'" + labels = char_blank + char_space + char_apostrophe + string.ascii_lowercase + language_model = LanguageModel(labels, char_blank, char_space) + + # Dataset + + training, validation = split_process_librispeech( + [args.dataset_train, args.dataset_valid], + [transforms, transforms], + language_model, + root=args.dataset_root, + folder_in_archive=args.dataset_folder_in_archive, + ) + + # Decoder + + if args.decoder == "greedy": + decoder = GreedyDecoder() + else: + raise ValueError("Selected decoder not supported") + + # Model + + model = Wav2Letter( + num_classes=language_model.length, + input_type=args.type, + num_features=num_features, + ) + + if args.jit: + model = torch.jit.script(model) + + if args.distributed: + n = torch.cuda.device_count() // args.world_size + devices = list(range(rank * n, (rank + 1) * n)) + model = model.to(devices[0]) + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=devices) + else: + devices = ["cuda" if torch.cuda.is_available() else "cpu"] + model = model.to(devices[0], non_blocking=True) + model = torch.nn.DataParallel(model) + + n = count_parameters(model) + logging.info("Number of parameters: %s", n) + + # Optimizer + + if args.optimizer == "adadelta": + optimizer = Adadelta( + model.parameters(), + lr=args.learning_rate, + weight_decay=args.weight_decay, + eps=args.eps, + rho=args.rho, + ) + elif args.optimizer == "sgd": + optimizer = SGD( + model.parameters(), + lr=args.learning_rate, + momentum=args.momentum, + weight_decay=args.weight_decay, + ) + elif args.optimizer == "adam": + optimizer = Adam( + model.parameters(), + lr=args.learning_rate, + momentum=args.momentum, + weight_decay=args.weight_decay, + ) + elif args.optimizer == "adamw": + optimizer = AdamW( + model.parameters(), + lr=args.learning_rate, + momentum=args.momentum, + weight_decay=args.weight_decay, + ) + else: + raise ValueError("Selected optimizer not supported") + + if args.scheduler == "exponential": + scheduler = ExponentialLR(optimizer, gamma=args.gamma) + elif args.scheduler == "reduceonplateau": + scheduler = ReduceLROnPlateau(optimizer, patience=10, threshold=1e-3) + else: + raise ValueError("Selected scheduler not supported") + + criterion = torch.nn.CTCLoss( + blank=language_model.mapping[char_blank], zero_infinity=False + ) + + # Data Loader + + collate_fn_train = collate_factory(model_length_function, augmentations) + collate_fn_valid = collate_factory(model_length_function) + + loader_training_params = { + "num_workers": args.workers, + "pin_memory": True, + "shuffle": True, + "drop_last": True, + } + loader_validation_params = loader_training_params.copy() + loader_validation_params["shuffle"] = False + + loader_training = DataLoader( + training, + batch_size=args.batch_size, + collate_fn=collate_fn_train, + **loader_training_params, + ) + loader_validation = DataLoader( + validation, + batch_size=args.batch_size, + collate_fn=collate_fn_valid, + **loader_validation_params, + ) + + # Setup checkpoint + + best_loss = 1.0 + + load_checkpoint = args.checkpoint and os.path.isfile(args.checkpoint) + + if args.distributed: + torch.distributed.barrier() + + if load_checkpoint: + logging.info("Checkpoint: loading %s", args.checkpoint) + checkpoint = torch.load(args.checkpoint) + + args.start_epoch = checkpoint["epoch"] + best_loss = checkpoint["best_loss"] + + model.load_state_dict(checkpoint["state_dict"]) + optimizer.load_state_dict(checkpoint["optimizer"]) + scheduler.load_state_dict(checkpoint["scheduler"]) + + logging.info( + "Checkpoint: loaded '%s' at epoch %s", args.checkpoint, checkpoint["epoch"] + ) + else: + logging.info("Checkpoint: not found") + + save_checkpoint( + { + "epoch": args.start_epoch, + "state_dict": model.state_dict(), + "best_loss": best_loss, + "optimizer": optimizer.state_dict(), + "scheduler": scheduler.state_dict(), + }, + False, + args.checkpoint, + not_main_rank, + ) + + if args.distributed: + torch.distributed.barrier() + + torch.autograd.set_detect_anomaly(False) + + for epoch in range(args.start_epoch, args.epochs): + + logging.info("Epoch: %s", epoch) + + train_one_epoch( + model, + criterion, + optimizer, + scheduler, + loader_training, + decoder, + language_model, + devices[0], + epoch, + args.clip_grad, + not_main_rank, + not args.reduce_lr_valid, + ) + + if not (epoch + 1) % args.print_freq or epoch == args.epochs - 1: + + loss = evaluate( + model, + criterion, + loader_validation, + decoder, + language_model, + devices[0], + epoch, + not_main_rank, + ) + + is_best = loss < best_loss + best_loss = min(loss, best_loss) + save_checkpoint( + { + "epoch": epoch + 1, + "state_dict": model.state_dict(), + "best_loss": best_loss, + "optimizer": optimizer.state_dict(), + "scheduler": scheduler.state_dict(), + }, + is_best, + args.checkpoint, + not_main_rank, + ) + + if args.reduce_lr_valid and isinstance(scheduler, ReduceLROnPlateau): + scheduler.step(loss) + + logging.info("End time: %s", datetime.now()) + + if args.distributed: + torch.distributed.destroy_process_group() + + +def spawn_main(main, args): + if args.distributed: + torch.multiprocessing.spawn( + main, args=(args,), nprocs=args.world_size, join=True + ) + else: + main(0, args) + + +if __name__ == "__main__": + + logging.basicConfig(level=logging.INFO) + args = parse_args() + spawn_main(main, args) diff --git a/examples/pipeline_wav2letter/metrics.py b/examples/pipeline_wav2letter/metrics.py new file mode 100644 index 0000000000..cba6595016 --- /dev/null +++ b/examples/pipeline_wav2letter/metrics.py @@ -0,0 +1,38 @@ +from typing import List, Union + + +def levenshtein_distance(r: Union[str, List[str]], h: Union[str, List[str]]): + """ + Calculate the Levenshtein distance between two lists or strings. + """ + + # Initialisation + dold = list(range(len(h) + 1)) + dnew = list(0 for _ in range(len(h) + 1)) + + # Computation + for i in range(1, len(r) + 1): + dnew[0] = i + for j in range(1, len(h) + 1): + if r[i - 1] == h[j - 1]: + dnew[j] = dold[j - 1] + else: + substitution = dold[j - 1] + 1 + insertion = dnew[j - 1] + 1 + deletion = dold[j] + 1 + dnew[j] = min(substitution, insertion, deletion) + + dnew, dold = dold, dnew + + return dold[-1] + + +if __name__ == "__main__": + assert levenshtein_distance("abc", "abc") == 0 + assert levenshtein_distance("aaa", "aba") == 1 + assert levenshtein_distance("aba", "aaa") == 1 + assert levenshtein_distance("aa", "aaa") == 1 + assert levenshtein_distance("aaa", "aa") == 1 + assert levenshtein_distance("abc", "bcd") == 2 + assert levenshtein_distance(["hello", "world"], ["hello", "world", "!"]) == 1 + assert levenshtein_distance(["hello", "world"], ["world", "hello", "!"]) == 2 diff --git a/examples/pipeline_wav2letter/transforms.py b/examples/pipeline_wav2letter/transforms.py new file mode 100644 index 0000000000..f1d9115c87 --- /dev/null +++ b/examples/pipeline_wav2letter/transforms.py @@ -0,0 +1,11 @@ +import torch + + +class Normalize(torch.nn.Module): + def forward(self, tensor): + return (tensor - tensor.mean(-1, keepdim=True)) / tensor.std(-1, keepdim=True) + + +class UnsqueezeFirst(torch.nn.Module): + def forward(self, tensor): + return tensor.unsqueeze(0) diff --git a/examples/pipeline_wav2letter/utils.py b/examples/pipeline_wav2letter/utils.py new file mode 100644 index 0000000000..7cd07a2a80 --- /dev/null +++ b/examples/pipeline_wav2letter/utils.py @@ -0,0 +1,55 @@ +import json +import logging +import os +import shutil +from collections import defaultdict + +import torch + + +class MetricLogger(defaultdict): + def __init__(self, name, print_freq=1, disable=False): + super().__init__(lambda: 0.0) + self.disable = disable + self.print_freq = print_freq + self._iter = 0 + self["name"] = name + + def __str__(self): + return json.dumps(self) + + def __call__(self): + self._iter = (self._iter + 1) % self.print_freq + if not self.disable and not self._iter: + print(self, flush=True) + + +def save_checkpoint(state, is_best, filename, disable): + """ + Save the model to a temporary file first, + then copy it to filename, in case the signal interrupts + the torch.save() process. + """ + + if disable: + return + + if filename == "": + return + + tempfile = filename + ".temp" + + # Remove tempfile in case interuption during the copying from tempfile to filename + if os.path.isfile(tempfile): + os.remove(tempfile) + + torch.save(state, tempfile) + if os.path.isfile(tempfile): + os.rename(tempfile, filename) + if is_best: + shutil.copyfile(filename, "model_best.pth.tar") + logging.warning("Checkpoint: saved") + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) diff --git a/examples/source_separation/README.md b/examples/source_separation/README.md new file mode 100644 index 0000000000..eeeac93d16 --- /dev/null +++ b/examples/source_separation/README.md @@ -0,0 +1,168 @@ +# Source Separation Example + +This directory contains reference implementations for source separations. For the detail of each model, please checkout the followings. + +- [Conv-TasNet](./conv_tasnet/README.md) + +## Usage + +### Overview + +To training a model, you can use [`train.py`](./train.py). This script takes the form of +`train.py [parameters for distributed training] -- [parameters for model/training]` + + ``` + python train.py \ + [--worker-id WORKER_ID] \ + [--device-id DEVICE_ID] \ + [--num-workers NUM_WORKERS] \ + [--sync-protocol SYNC_PROTOCOL] \ + -- \ + + + # For the detail of the parameter values, use; + python train.py --help + + # For the detail of the model parameters, use; + python train.py -- --help + ``` + +If you would like to just try out the traing script, then try it without any parameters +for distributed training. `train.py -- --sample-rate 8000 --batch-size --dataset-dir --save-dir ` + +This script runs training in Distributed Data Parallel (DDP) framework and has two major +operation modes. This behavior depends on if `--worker-id` argument is given or not. + +1. (`--worker-id` is not given) Launchs training worker subprocesses that performs the actual training. +2. (`--worker-id` is given) Performs the training as a part of distributed training. + +When launching the script without any distributed trainig parameters (operation mode 1), +this script will check the number of GPUs available on the local system and spawns the same +number of training subprocesses (as operaiton mode 2). You can reduce the number of GPUs with +`--num-workers`. If there is no GPU available, only one subprocess is launched and providing +`--num-workers` larger than 1 results in error. + +When launching the script as a worker process of a distributed training, you need to configure +the coordination of the workers. + +- `--num-workers` is the number of training processes being launched. +- `--worker-id` is the process rank (must be unique across all the processes). +- `--device-id` is the GPU device ID (should be unique within node). +- `--sync-protocl` is how each worker process communicate and synchronize. + If the training is carried out on a single node, then the default `"env://"` should do. + If the training processes span across multiple nodes, then you need to provide a protocol that + can communicate over the network. If you know where the master node is located, you can use + `"env://"` in combination with `MASTER_ADDR` and `MASER_PORT` environment variables. If you do + not know where the master node is located beforehand, you can use `"file://..."` protocol to + indicate where the file to which all the worker process have access is located. For other + protocols, please refer to the official documentation. + +### Distributed Training Notes + +
Quick overview on DDP (distributed data parallel) + +DDP is single-program multiple-data training paradigm. +With DDP, the model is replicated on every process, +and every model replica will be fed with a different set of input data samples. + +- **Process**: Worker process (as in Linux process). There are `P` processes per a Node. +- **Node**: A machine. There are `N` machines, each of which holds `P` processes. +- **World**: network of nodes, composed of `N` nodes and `N * P` processes. +- **Rank**: Grobal process ID (unique across nodes) `[0, N * P)` +- **Local Rank**: Local process ID (unique only within a node) `[0, P)` + +``` + Node 0 Node 1 Node N-1 +โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” +โ”‚โ•”โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•— โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚โ”‚โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚ โ”‚โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚ +โ”‚โ•‘ Process โ•Ÿโ”€โ”ค GPU: 0 โ”‚โ”‚โ”‚โ”‚ Process โ”œโ”€โ”ค GPU: 0 โ”‚โ”‚ โ”‚โ”‚ Process โ”œโ”€โ”ค GPU: 0 โ”‚โ”‚ +โ”‚โ•‘ Rank: 0 โ•‘ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ”‚โ”‚โ”‚ Rank:P โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ”‚ โ”‚โ”‚ Rank:NP-P โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ”‚ +โ”‚โ•šโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• โ”‚โ”‚โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ +โ”‚โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚โ”‚โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚ โ”‚โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚ +โ”‚โ”‚ Process โ”œโ”€โ”ค GPU: 1 โ”‚โ”‚โ”‚โ”‚ Process โ”œโ”€โ”ค GPU: 1 โ”‚โ”‚ โ”‚โ”‚ Process โ”œโ”€โ”ค GPU: 1 โ”‚โ”‚ +โ”‚โ”‚ Rank: 1 โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ”‚โ”‚โ”‚ Rank:P+1 โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ”‚ โ”‚โ”‚ Rank:NP-P+1 โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ”‚ +โ”‚โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚โ”‚โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ ... โ”‚โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ +โ”‚ โ”‚โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚ ... โ”‚โ”‚ ... โ”‚ โ”‚ ... โ”‚ +โ”‚ โ”‚โ”‚ โ”‚ โ”‚ โ”‚ +โ”‚โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚โ”‚โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚ โ”‚โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚ +โ”‚โ”‚ Process โ”œโ”€โ”ค GPU:P-1 โ”‚โ”‚โ”‚โ”‚ Process โ”œโ”€โ”ค GPU:P-1 โ”‚โ”‚ โ”‚โ”‚ Process โ”œโ”€โ”ค GPU:P-1 โ”‚โ”‚ +โ”‚โ”‚ Rank:P-1 โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ”‚โ”‚โ”‚ Rank:2P-1 โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ”‚ โ”‚โ”‚ Rank:NP-1 โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ”‚ +โ”‚โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚โ”‚โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +
+ +### SLURM + +When launched as SLURM job, the follwoing environment variables correspond to + +- **SLURM_PROCID*: `--worker-id` (Rank) +- **SLURM_NTASKS** (or legacy **SLURM_NPPROCS**): the number of total processes (`--num-workers` == world size) +- **SLURM_LOCALID**: Local Rank (to be mapped with GPU index*) + +* Even when GPU resource is allocated with `--gpus-per-task=1`, if there are muptiple +tasks allocated on the same node, (thus multiple GPUs of the node are allocated to the job) +each task can see all the GPUs allocated for the tasks. Therefore we need to use +SLURM_LOCALID to tell each task to which GPU it should be using. + +
Example scripts for running the training on SLURM cluster + +- **launch_job.sh** + +```bash +#!/bin/bash + +#SBATCH --job-name=source_separation + +#SBATCH --output=/checkpoint/%u/jobs/%x/%j.out + +#SBATCH --error=/checkpoint/%u/jobs/%x/%j.err + +#SBATCH --nodes=1 + +#SBATCH --ntasks-per-node=8 + +#SBATCH --cpus-per-task=8 + +#SBATCH --mem-per-cpu=16G + +#SBATCH --gpus-per-task=1 + +#srun env +srun wrapper.sh $@ +``` + +- **wrapper.sh** + +```bash +#!/bin/bash +num_speakers=2 +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +save_dir="/checkpoint/${USER}/jobs/${SLURM_JOB_NAME}/${SLURM_JOB_ID}" +dataset_dir="/dataset/wsj0-mix/${num_speakers}speakers/wav8k/min" + +if [ "${SLURM_JOB_NUM_NODES}" -gt 1 ]; then + protocol="file:///checkpoint/${USER}/jobs/source_separation/${SLURM_JOB_ID}/sync" +else + protocol="env://" +fi + +mkdir -p "${save_dir}" + +python -u \ + "${this_dir}/train.py" \ + --worker-id "${SLURM_PROCID}" \ + --num-workers "${SLURM_NTASKS}" \ + --device-id "${SLURM_LOCALID}" \ + --sync-protocol "${protocol}" \ + -- \ + --num-speakers "${num_speakers}" \ + --sample-rate 8000 \ + --dataset-dir "${dataset_dir}" \ + --save-dir "${save_dir}" \ + --batch-size $((16 / SLURM_NTASKS)) +``` + +
diff --git a/examples/source_separation/conv_tasnet/README.md b/examples/source_separation/conv_tasnet/README.md new file mode 100644 index 0000000000..fd401a6b60 --- /dev/null +++ b/examples/source_separation/conv_tasnet/README.md @@ -0,0 +1,52 @@ +# Conv-TasNet + +This is a reference implementation of Conv-TasNet. + +> Luo, Yi, and Nima Mesgarani. "Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27.8 (2019): 1256-1266. Crossref. Web. + +This implementation is based on [arXiv:1809.07454v3](https://arxiv.org/abs/1809.07454v3) and [the reference implementation](https://github.com/naplab/Conv-TasNet) provided by the authors. + +For the usage, please checkout the [source separation README](../README.md). + +## (Default) Training Configurations + +The default training/model configurations follow the best non-causal implementation from the paper. (causal configuration is not implemented.) + + - Sample rate: 8000 Hz + - Batch size: total 16 over distributed training workers + - Epochs: 100 + - Initial learning rate: 1e-3 + - Gradient clipping: maximum L2 norm of 5.0 + - Optimizer: Adam + - Learning rate scheduling: Halved after 3 epochs of no improvement in validation accuracy. + - Objective function: SI-SNRi + - Reported metrics: SI-SNRi, SDRi + - Sample audio length: 4 seconds (randomized position) + - Encoder/Decoder feature dimension (N): 512 + - Encoder/Decoder convolution kernel size (L): 16 + - TCN bottleneck/output feature dimension (B): 128 + - TCN hidden feature dimension (H): 512 + - TCN skip connection feature dimension (Sc): 128 + - TCN convolution kernel size (P): 3 + - The number of TCN convolution block layers (X): 8 + - The number of TCN convolution blocks (R): 3 + +## Evaluation + +The following is the evaluation result of training the model on WSJ0-2mix and WSJ0-3mix datasets. + +### wsj0-mix 2speakers + +| | SI-SNRi (dB) | SDRi (dB) | Epoch | +|:------------------:|-------------:|----------:|------:| +| Reference | 15.3 | 15.6 | | +| Validation dataset | 13.1 | 13.1 | 100 | +| Evaluation dataset | 11.0 | 11.0 | 100 | + +### wsj0-mix 3speakers + +| | SI-SNRi (dB) | SDRi (dB) | Epoch | +|:------------------:|-------------:|----------:|------:| +| Reference | 12.7 | 13.1 | | +| Validation dataset | 11.4 | 11.4 | 100 | +| Evaluation dataset | 8.9 | 8.9 | 100 | diff --git a/examples/source_separation/conv_tasnet/__init__.py b/examples/source_separation/conv_tasnet/__init__.py new file mode 100644 index 0000000000..d5818fc320 --- /dev/null +++ b/examples/source_separation/conv_tasnet/__init__.py @@ -0,0 +1,4 @@ +from . import ( + train, + trainer, +) diff --git a/examples/source_separation/conv_tasnet/train.py b/examples/source_separation/conv_tasnet/train.py new file mode 100644 index 0000000000..028f124245 --- /dev/null +++ b/examples/source_separation/conv_tasnet/train.py @@ -0,0 +1,332 @@ +#!/usr/bin/env python3 +"""Train Conv-TasNet""" +import time +import pathlib +import argparse + +import torch +import torchaudio +import torchaudio.models + +import conv_tasnet +from utils import dist_utils +from utils.dataset import utils as dataset_utils + +_LG = dist_utils.getLogger(__name__) + + +def _parse_args(args): + parser = argparse.ArgumentParser(description=__doc__,) + parser.add_argument( + "--debug", + action="store_true", + help="Enable debug behavior. Each epoch will end with just one batch.") + group = parser.add_argument_group("Model Options") + group.add_argument( + "--num-speakers", required=True, type=int, help="The number of speakers." + ) + group = parser.add_argument_group("Dataset Options") + group.add_argument( + "--sample-rate", + required=True, + type=int, + help="Sample rate of audio files in the given dataset.", + ) + group.add_argument( + "--dataset", + default="wsj0mix", + choices=["wsj0mix"], + help='Dataset type. (default: "wsj0mix")', + ) + group.add_argument( + "--dataset-dir", + required=True, + type=pathlib.Path, + help=( + "Directory where dataset is found. " + 'If the dataset type is "wsj9mix", then this is the directory where ' + '"cv", "tt" and "tr" subdirectories are found.' + ), + ) + group = parser.add_argument_group("Save Options") + group.add_argument( + "--save-dir", + required=True, + type=pathlib.Path, + help=( + "Directory where the checkpoints and logs are saved. " + "Though, only the worker 0 saves checkpoint data, " + "all the worker processes must have access to the directory." + ), + ) + group = parser.add_argument_group("Dataloader Options") + group.add_argument( + "--batch-size", + type=int, + help=f"Batch size. (default: 16 // world_size)", + ) + group = parser.add_argument_group("Training Options") + group.add_argument( + "--epochs", + metavar="NUM_EPOCHS", + default=100, + type=int, + help="The number of epochs to train. (default: 100)", + ) + group.add_argument( + "--learning-rate", + default=1e-3, + type=float, + help="Initial learning rate. (default: 1e-3)", + ) + group.add_argument( + "--grad-clip", + metavar="CLIP_VALUE", + default=5.0, + type=float, + help="Gradient clip value (l2 norm). (default: 5.0)", + ) + group.add_argument( + "--resume", + metavar="CHECKPOINT_PATH", + help="Previous checkpoint file from which the training is resumed.", + ) + + args = parser.parse_args(args) + + # Delaing the default value initialization until parse_args is done because + # if `--help` is given, distributed training is not enabled. + if args.batch_size is None: + args.batch_size = 16 // torch.distributed.get_world_size() + + return args + + +def _get_model( + num_sources, + enc_kernel_size=16, + enc_num_feats=512, + msk_kernel_size=3, + msk_num_feats=128, + msk_num_hidden_feats=512, + msk_num_layers=8, + msk_num_stacks=3, +): + model = torchaudio.models.ConvTasNet( + num_sources=num_sources, + enc_kernel_size=enc_kernel_size, + enc_num_feats=enc_num_feats, + msk_kernel_size=msk_kernel_size, + msk_num_feats=msk_num_feats, + msk_num_hidden_feats=msk_num_hidden_feats, + msk_num_layers=msk_num_layers, + msk_num_stacks=msk_num_stacks, + ) + _LG.info_on_master("Model Configuration:") + _LG.info_on_master(" - N: %d", enc_num_feats) + _LG.info_on_master(" - L: %d", enc_kernel_size) + _LG.info_on_master(" - B: %d", msk_num_feats) + _LG.info_on_master(" - H: %d", msk_num_hidden_feats) + _LG.info_on_master(" - Sc: %d", msk_num_feats) + _LG.info_on_master(" - P: %d", msk_kernel_size) + _LG.info_on_master(" - X: %d", msk_num_layers) + _LG.info_on_master(" - R: %d", msk_num_stacks) + _LG.info_on_master( + " - Receptive Field: %s [samples]", model.mask_generator.receptive_field, + ) + return model + + +def _get_dataloader(dataset_type, dataset_dir, num_speakers, sample_rate, batch_size): + train_dataset, valid_dataset, eval_dataset = dataset_utils.get_dataset( + dataset_type, dataset_dir, num_speakers, sample_rate, + ) + train_collate_fn = dataset_utils.get_collate_fn( + dataset_type, mode='train', sample_rate=sample_rate, duration=4 + ) + + test_collate_fn = dataset_utils.get_collate_fn(dataset_type, mode='test') + + train_loader = torch.utils.data.DataLoader( + train_dataset, + batch_size=batch_size, + sampler=torch.utils.data.distributed.DistributedSampler(train_dataset), + collate_fn=train_collate_fn, + pin_memory=True, + ) + valid_loader = torch.utils.data.DataLoader( + valid_dataset, + batch_size=batch_size, + sampler=torch.utils.data.distributed.DistributedSampler(valid_dataset), + collate_fn=test_collate_fn, + pin_memory=True, + ) + eval_loader = torch.utils.data.DataLoader( + eval_dataset, + batch_size=batch_size, + sampler=torch.utils.data.distributed.DistributedSampler(eval_dataset), + collate_fn=test_collate_fn, + pin_memory=True, + ) + return train_loader, valid_loader, eval_loader + + +def _write_header(log_path, args): + rows = [ + [f"# torch: {torch.__version__}", ], + [f"# torchaudio: {torchaudio.__version__}", ] + ] + rows.append(["# arguments"]) + for key, item in vars(args).items(): + rows.append([f"# {key}: {item}"]) + + dist_utils.write_csv_on_master(log_path, *rows) + + +def train(args): + args = _parse_args(args) + _LG.info("%s", args) + + args.save_dir.mkdir(parents=True, exist_ok=True) + if "sox_io" in torchaudio.list_audio_backends(): + torchaudio.set_audio_backend("sox_io") + + start_epoch = 1 + if args.resume: + checkpoint = torch.load(args.resume) + if args.sample_rate != checkpoint["sample_rate"]: + raise ValueError( + "The provided sample rate ({args.sample_rate}) does not match " + "the sample rate from the check point ({checkpoint['sample_rate']})." + ) + if args.num_speakers != checkpoint["num_speakers"]: + raise ValueError( + "The provided #of speakers ({args.num_speakers}) does not match " + "the #of speakers from the check point ({checkpoint['num_speakers']}.)" + ) + start_epoch = checkpoint["epoch"] + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + _LG.info("Using: %s", device) + + model = _get_model(num_sources=args.num_speakers) + model.to(device) + + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[device] if torch.cuda.is_available() else None + ) + optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate) + + if args.resume: + _LG.info("Loading parameters from the checkpoint...") + model.module.load_state_dict(checkpoint["model"]) + optimizer.load_state_dict(checkpoint["optimizer"]) + else: + dist_utils.synchronize_params( + str(args.save_dir / f"tmp.pt"), device, model, optimizer + ) + + lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, mode="max", factor=0.5, patience=3 + ) + + train_loader, valid_loader, eval_loader = _get_dataloader( + args.dataset, + args.dataset_dir, + args.num_speakers, + args.sample_rate, + args.batch_size, + ) + + num_train_samples = len(train_loader.dataset) + num_valid_samples = len(valid_loader.dataset) + num_eval_samples = len(eval_loader.dataset) + + _LG.info_on_master("Datasets:") + _LG.info_on_master(" - Train: %s", num_train_samples) + _LG.info_on_master(" - Valid: %s", num_valid_samples) + _LG.info_on_master(" - Eval: %s", num_eval_samples) + + trainer = conv_tasnet.trainer.Trainer( + model, + optimizer, + train_loader, + valid_loader, + eval_loader, + args.grad_clip, + device, + debug=args.debug, + ) + + log_path = args.save_dir / f"log.csv" + _write_header(log_path, args) + dist_utils.write_csv_on_master( + log_path, + [ + "epoch", + "learning_rate", + "valid_si_snri", + "valid_sdri", + "eval_si_snri", + "eval_sdri", + ], + ) + + _LG.info_on_master("Running %s epochs", args.epochs) + for epoch in range(start_epoch, start_epoch + args.epochs): + _LG.info_on_master("=" * 70) + _LG.info_on_master("Epoch: %s", epoch) + _LG.info_on_master("Learning rate: %s", optimizer.param_groups[0]["lr"]) + _LG.info_on_master("=" * 70) + + t0 = time.monotonic() + trainer.train_one_epoch() + train_sps = num_train_samples / (time.monotonic() - t0) + + _LG.info_on_master("-" * 70) + + t0 = time.monotonic() + valid_metric = trainer.validate() + valid_sps = num_valid_samples / (time.monotonic() - t0) + _LG.info_on_master("Valid: %s", valid_metric) + + _LG.info_on_master("-" * 70) + + t0 = time.monotonic() + eval_metric = trainer.evaluate() + eval_sps = num_eval_samples / (time.monotonic() - t0) + _LG.info_on_master(" Eval: %s", eval_metric) + + _LG.info_on_master("-" * 70) + + _LG.info_on_master("Train: Speed: %6.2f [samples/sec]", train_sps) + _LG.info_on_master("Valid: Speed: %6.2f [samples/sec]", valid_sps) + _LG.info_on_master(" Eval: Speed: %6.2f [samples/sec]", eval_sps) + + _LG.info_on_master("-" * 70) + + dist_utils.write_csv_on_master( + log_path, + [ + epoch, + optimizer.param_groups[0]["lr"], + valid_metric.si_snri, + valid_metric.sdri, + eval_metric.si_snri, + eval_metric.sdri, + ], + ) + + lr_scheduler.step(valid_metric.si_snri) + + save_path = args.save_dir / f"epoch_{epoch}.pt" + dist_utils.save_on_master( + save_path, + { + "model": model.module.state_dict(), + "optimizer": optimizer.state_dict(), + "num_speakers": args.num_speakers, + "sample_rate": args.sample_rate, + "epoch": epoch, + }, + ) diff --git a/examples/source_separation/conv_tasnet/trainer.py b/examples/source_separation/conv_tasnet/trainer.py new file mode 100644 index 0000000000..af4e715305 --- /dev/null +++ b/examples/source_separation/conv_tasnet/trainer.py @@ -0,0 +1,165 @@ +import time +from typing import Tuple +from collections import namedtuple + +import torch +import torch.distributed as dist + +from utils import dist_utils, metrics + +_LG = dist_utils.getLogger(__name__) + +Metric = namedtuple("SNR", ["si_snri", "sdri"]) +Metric.__str__ = ( + lambda self: f"SI-SNRi: {self.si_snri:10.3e}, SDRi: {self.sdri:10.3e}" +) + + +def si_sdr_improvement( + estimate: torch.Tensor, + reference: torch.Tensor, + mix: torch.Tensor, + mask: torch.Tensor +) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute the improvement of scale-invariant SDR. (SI-SNRi) and bare SDR (SDRi). + + Args: + estimate (torch.Tensor): Estimated source signals. + Shape: [batch, speakers, time frame] + reference (torch.Tensor): Reference (original) source signals. + Shape: [batch, speakers, time frame] + mix (torch.Tensor): Mixed souce signals, from which the setimated signals were generated. + Shape: [batch, speakers == 1, time frame] + mask (torch.Tensor): Mask to indicate padded value (0) or valid value (1). + Shape: [batch, 1, time frame] + + + Returns: + torch.Tensor: Improved SI-SDR. Shape: [batch, ] + torch.Tensor: Absolute SI-SDR. Shape: [batch, ] + + References: + - Conv-TasNet: Surpassing Ideal Time--Frequency Magnitude Masking for Speech Separation + Luo, Yi and Mesgarani, Nima + https://arxiv.org/abs/1809.07454 + """ + with torch.no_grad(): + sdri = metrics.sdri(estimate, reference, mix, mask=mask) + + estimate = estimate - estimate.mean(axis=2, keepdim=True) + reference = reference - reference.mean(axis=2, keepdim=True) + mix = mix - mix.mean(axis=2, keepdim=True) + + si_sdri = metrics.sdri(estimate, reference, mix, mask=mask) + return si_sdri, sdri + + +class OccasionalLogger: + """Simple helper class to log once in a while or when progress is quick enough""" + + def __init__(self, time_interval=180, progress_interval=0.1): + self.time_interval = time_interval + self.progress_interval = progress_interval + + self.last_time = 0.0 + self.last_progress = 0.0 + + def log(self, metric, progress, force=False): + now = time.monotonic() + if ( + force + or now > self.last_time + self.time_interval + or progress > self.last_progress + self.progress_interval + ): + self.last_time = now + self.last_progress = progress + _LG.info_on_master("train: %s [%3d%%]", metric, 100 * progress) + + +class Trainer: + def __init__( + self, + model, + optimizer, + train_loader, + valid_loader, + eval_loader, + grad_clip, + device, + *, + debug, + ): + self.model = model + self.optimizer = optimizer + self.train_loader = train_loader + self.valid_loader = valid_loader + self.eval_loader = eval_loader + self.grad_clip = grad_clip + self.device = device + self.debug = debug + + def train_one_epoch(self): + self.model.train() + logger = OccasionalLogger() + + num_batches = len(self.train_loader) + for i, batch in enumerate(self.train_loader, start=1): + mix = batch.mix.to(self.device) + src = batch.src.to(self.device) + mask = batch.mask.to(self.device) + + estimate = self.model(mix) + + si_snri, sdri = si_sdr_improvement(estimate, src, mix, mask) + si_snri = si_snri.mean() + sdri = sdri.mean() + + loss = -si_snri + self.optimizer.zero_grad() + loss.backward() + torch.nn.utils.clip_grad_norm_( + self.model.parameters(), self.grad_clip, norm_type=2.0 + ) + self.optimizer.step() + + metric = Metric(si_snri.item(), sdri.item()) + logger.log(metric, progress=i / num_batches, force=i == num_batches) + + if self.debug: + break + + def evaluate(self): + with torch.no_grad(): + return self._test(self.eval_loader) + + def validate(self): + with torch.no_grad(): + return self._test(self.valid_loader) + + def _test(self, loader): + self.model.eval() + + total_si_snri = torch.zeros(1, dtype=torch.float32, device=self.device) + total_sdri = torch.zeros(1, dtype=torch.float32, device=self.device) + + for batch in loader: + mix = batch.mix.to(self.device) + src = batch.src.to(self.device) + mask = batch.mask.to(self.device) + + estimate = self.model(mix) + + si_snri, sdri = si_sdr_improvement(estimate, src, mix, mask) + + total_si_snri += si_snri.sum() + total_sdri += sdri.sum() + + if self.debug: + break + + dist.all_reduce(total_si_snri, dist.ReduceOp.SUM) + dist.all_reduce(total_sdri, dist.ReduceOp.SUM) + + num_samples = len(loader.dataset) + metric = Metric(total_si_snri.item() / num_samples, total_sdri.item() / num_samples) + return metric diff --git a/examples/source_separation/train.py b/examples/source_separation/train.py new file mode 100644 index 0000000000..a969d33fb8 --- /dev/null +++ b/examples/source_separation/train.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +"""Launch souce separation training. + +This script runs training in Distributed Data Parallel (DDP) framework and has two major +operation modes. This behavior depends on if `--worker-id` argument is given or not. + +1. (`--worker-id` is not given) Launchs worker subprocesses that performs the actual training. +2. (`--worker-id` is given) Performs the training as a part of distributed training. + +When launching the script without any distributed trainig parameters (operation mode 1), +this script will check the number of GPUs available on the local system and spawns the same +number of training subprocesses (as operaiton mode 2). You can reduce the number of GPUs with +`--num-workers`. If there is no GPU available, only one subprocess is launched. + +When launching the script as a worker process of a distributed training, you need to configure +the coordination of the workers. +""" +import sys +import logging +import argparse +import subprocess + +import torch + +from utils import dist_utils + +_LG = dist_utils.getLogger(__name__) + + +def _parse_args(args=None): + max_world_size = torch.cuda.device_count() or 1 + + parser = argparse.ArgumentParser( + description=__doc__, + ) + parser.add_argument("--debug", action="store_true", help="Enable debug log") + group = parser.add_argument_group("Distributed Training") + group.add_argument( + "--worker-id", + type=int, + help=( + "If not provided, the launched process serves as a master process of " + "single-node, multi-worker training and spawns the worker subprocesses. " + "If provided, the launched process serves as a worker process, which " + "performs the actual training. The valid value is [0, --num-workers)." + ), + ) + group.add_argument( + "--device-id", + type=int, + help="The CUDA device ID. Allowed only when --worker-id is provided.", + ) + group.add_argument( + "--num-workers", + type=int, + default=max_world_size, + help=( + "The size of distributed trainig workers. " + "If launching a training as single-node, multi-worker training, " + "(i.e. --worker-id is not provided) then this value should not exceed " + "the number of available GPUs. " + "If launching the training process as a multi-node, multi-gpu training, " + "(i.e. --worker-id is provided) then the value has to match " + f"the number of workers across nodes. (default: {max_world_size})" + ), + ) + group.add_argument( + "--sync-protocol", + type=str, + default="env://", + help=( + "Synchronization protocol for distributed training. " + "This value is passed as `init_method` argument of " + "`torch.distributed.init_process_group` function." + 'If you are using `"env://"`, you can additionally configure ' + 'environment variables "MASTER_ADDR" and "MASTER_PORT". ' + 'If you are using `"file://..."`, then the process has to have ' + "the access to the designated file. " + "See the documentation for `torch.distributed` for the detail. " + 'If you are running the training in a single node, `"env://"` ' + "should do. If you are running the training in multiple nodes, " + "you need to provide the file location where all the nodes have " + 'access, using `"file://..."` protocol. (default: "env://")' + ), + ) + group.add_argument( + "--random-seed", + type=int, + help="Set random seed value. (default: None)", + ) + parser.add_argument( + "rest", nargs=argparse.REMAINDER, help="Model-specific arguments." + ) + namespace = parser.parse_args(args) + if namespace.worker_id is None: + if namespace.device_id is not None: + raise ValueError( + "`--device-id` cannot be provided when runing as master process." + ) + if namespace.num_workers > max_world_size: + raise ValueError( + "--num-workers ({num_workers}) cannot exceed {device_count}." + ) + if namespace.rest[:1] == ["--"]: + namespace.rest = namespace.rest[1:] + return namespace + + +def _main(cli_args): + args = _parse_args(cli_args) + + if any(arg in ["--help", "-h"] for arg in args.rest): + _run_training(args.rest) + + _init_logger(args.worker_id, args.debug) + if args.worker_id is None: + _run_training_subprocesses(args.num_workers, cli_args) + else: + dist_utils.setup_distributed( + world_size=args.num_workers, + rank=args.worker_id, + local_rank=args.device_id, + backend='nccl' if torch.cuda.is_available() else 'gloo', + init_method=args.sync_protocol, + ) + if args.random_seed is not None: + torch.manual_seed(args.random_seed) + if torch.cuda.is_available(): + torch.cuda.set_device(args.device_id) + _LG.info("CUDA device set to %s", args.device_id) + _run_training(args.rest) + + +def _run_training_subprocesses(num_workers, original_args): + workers = [] + _LG.info("Spawning %s workers", num_workers) + for i in range(num_workers): + worker_arg = ["--worker-id", f"{i}", "--num-workers", f"{num_workers}"] + device_arg = ["--device-id", f"{i}"] if torch.cuda.is_available() else [] + command = ( + [sys.executable, "-u", sys.argv[0]] + + worker_arg + + device_arg + + original_args + ) + _LG.info("Launching worker %s: `%s`", i, " ".join(command)) + worker = subprocess.Popen(command) + workers.append(worker) + + num_failed = 0 + for worker in workers: + worker.wait() + if worker.returncode != 0: + num_failed += 1 + sys.exit(num_failed) + + +def _run_training(args): + import conv_tasnet.train + + conv_tasnet.train.train(args) + + +def _init_logger(rank=None, debug=False): + worker_fmt = "[master]" if rank is None else f"[worker {rank:2d}]" + message_fmt = ( + "%(levelname)5s: %(funcName)10s: %(message)s" if debug else "%(message)s" + ) + logging.basicConfig( + level=logging.DEBUG if debug else logging.INFO, + format=f"%(asctime)s: {worker_fmt} {message_fmt}", + ) + + +if __name__ == "__main__": + _main(sys.argv[1:]) diff --git a/examples/source_separation/utils/__init__.py b/examples/source_separation/utils/__init__.py new file mode 100644 index 0000000000..28d451cde3 --- /dev/null +++ b/examples/source_separation/utils/__init__.py @@ -0,0 +1,5 @@ +from . import ( + dataset, + dist_utils, + metrics, +) diff --git a/examples/source_separation/utils/dataset/__init__.py b/examples/source_separation/utils/dataset/__init__.py new file mode 100644 index 0000000000..1a5e0b10c9 --- /dev/null +++ b/examples/source_separation/utils/dataset/__init__.py @@ -0,0 +1 @@ +from . import utils, wsj0mix diff --git a/examples/source_separation/utils/dataset/utils.py b/examples/source_separation/utils/dataset/utils.py new file mode 100644 index 0000000000..5b4b9cf2cc --- /dev/null +++ b/examples/source_separation/utils/dataset/utils.py @@ -0,0 +1,83 @@ +from typing import List +from functools import partial +from collections import namedtuple + +import torch + +from . import wsj0mix + +Batch = namedtuple("Batch", ["mix", "src", "mask"]) + + +def get_dataset(dataset_type, root_dir, num_speakers, sample_rate): + if dataset_type == "wsj0mix": + train = wsj0mix.WSJ0Mix(root_dir / "tr", num_speakers, sample_rate) + validation = wsj0mix.WSJ0Mix(root_dir / "cv", num_speakers, sample_rate) + evaluation = wsj0mix.WSJ0Mix(root_dir / "tt", num_speakers, sample_rate) + else: + raise ValueError(f"Unexpected dataset: {dataset_type}") + return train, validation, evaluation + + +def _fix_num_frames(sample: wsj0mix.SampleType, target_num_frames: int, random_start=False): + """Ensure waveform has exact number of frames by slicing or padding""" + mix = sample[1] # [1, num_frames] + src = torch.cat(sample[2], 0) # [num_sources, num_frames] + + num_channels, num_frames = src.shape + if num_frames >= target_num_frames: + if random_start and num_frames > target_num_frames: + start_frame = torch.randint(num_frames - target_num_frames, [1]) + mix = mix[:, start_frame:] + src = src[:, start_frame:] + mix = mix[:, :target_num_frames] + src = src[:, :target_num_frames] + mask = torch.ones_like(mix) + else: + num_padding = target_num_frames - num_frames + pad = torch.zeros([1, num_padding], dtype=mix.dtype, device=mix.device) + mix = torch.cat([mix, pad], 1) + src = torch.cat([src, pad.expand(num_channels, -1)], 1) + mask = torch.ones_like(mix) + mask[..., num_frames:] = 0 + return mix, src, mask + + + +def collate_fn_wsj0mix_train(samples: List[wsj0mix.SampleType], sample_rate, duration): + target_num_frames = int(duration * sample_rate) + + mixes, srcs, masks = [], [], [] + for sample in samples: + mix, src, mask = _fix_num_frames(sample, target_num_frames, random_start=True) + + mixes.append(mix) + srcs.append(src) + masks.append(mask) + + return Batch(torch.stack(mixes, 0), torch.stack(srcs, 0), torch.stack(masks, 0)) + + +def collate_fn_wsj0mix_test(samples: List[wsj0mix.SampleType]): + max_num_frames = max(s[1].shape[-1] for s in samples) + + mixes, srcs, masks = [], [], [] + for sample in samples: + mix, src, mask = _fix_num_frames(sample, max_num_frames, random_start=False) + + mixes.append(mix) + srcs.append(src) + masks.append(mask) + + return Batch(torch.stack(mixes, 0), torch.stack(srcs, 0), torch.stack(masks, 0)) + + +def get_collate_fn(dataset_type, mode, sample_rate=None, duration=4): + assert mode in ["train", "test"] + if dataset_type == "wsj0mix": + if mode == 'train': + if sample_rate is None: + raise ValueError("sample_rate is not given.") + return partial(collate_fn_wsj0mix_train, sample_rate=sample_rate, duration=duration) + return collate_fn_wsj0mix_test + raise ValueError(f"Unexpected dataset: {dataset_type}") diff --git a/examples/source_separation/utils/dataset/wsj0mix.py b/examples/source_separation/utils/dataset/wsj0mix.py new file mode 100644 index 0000000000..efd9268971 --- /dev/null +++ b/examples/source_separation/utils/dataset/wsj0mix.py @@ -0,0 +1,70 @@ +from pathlib import Path +from typing import Union, Tuple, List + +import torch +from torch.utils.data import Dataset + +import torchaudio + +SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]] + + +class WSJ0Mix(Dataset): + """Create a Dataset for wsj0-mix. + + Args: + root (str or Path): Path to the directory where the dataset is found. + num_speakers (int): The number of speakers, which determines the directories + to traverse. The Dataset will traverse ``s1`` to ``sN`` directories to collect + N source audios. + sample_rate (int): Expected sample rate of audio files. If any of the audio has a + different sample rate, raises ``ValueError``. + audio_ext (str): The extension of audio files to find. (default: ".wav") + """ + def __init__( + self, + root: Union[str, Path], + num_speakers: int, + sample_rate: int, + audio_ext: str = ".wav", + ): + self.root = Path(root) + self.sample_rate = sample_rate + self.mix_dir = (self.root / "mix").resolve() + self.src_dirs = [(self.root / f"s{i+1}").resolve() for i in range(num_speakers)] + + self.files = [p.name for p in self.mix_dir.glob(f"*{audio_ext}")] + self.files.sort() + + def _load_audio(self, path) -> torch.Tensor: + waveform, sample_rate = torchaudio.load(path) + if sample_rate != self.sample_rate: + raise ValueError( + f"The dataset contains audio file of sample rate {sample_rate}. " + "Where the requested sample rate is {self.sample_rate}." + ) + return waveform + + def _load_sample(self, filename) -> SampleType: + mixed = self._load_audio(str(self.mix_dir / filename)) + srcs = [] + for i, dir_ in enumerate(self.src_dirs): + src = self._load_audio(str(dir_ / filename)) + if mixed.shape != src.shape: + raise ValueError( + f"Different waveform shapes. mixed: {mixed.shape}, src[{i}]: {src.shape}" + ) + srcs.append(src) + return self.sample_rate, mixed, srcs + + def __len__(self) -> int: + return len(self.files) + + def __getitem__(self, key: int) -> SampleType: + """Load the n-th sample from the dataset. + Args: + n (int): The index of the sample to be loaded + Returns: + tuple: ``(sample_rate, mix_waveform, list_of_source_waveforms)`` + """ + return self._load_sample(self.files[key]) diff --git a/examples/source_separation/utils/dist_utils.py b/examples/source_separation/utils/dist_utils.py new file mode 100644 index 0000000000..380358b87b --- /dev/null +++ b/examples/source_separation/utils/dist_utils.py @@ -0,0 +1,86 @@ +import os +import csv +import types +import logging + +import torch +import torch.distributed as dist + + +def _info_on_master(self, *args, **kwargs): + if dist.get_rank() == 0: + self.info(*args, **kwargs) + + +def getLogger(name): + """Get logging.Logger module with additional ``info_on_master`` method.""" + logger = logging.getLogger(name) + logger.info_on_master = types.MethodType(_info_on_master, logger) + return logger + + +_LG = getLogger(__name__) + + +def setup_distributed( + world_size, rank, local_rank, backend="nccl", init_method="env://" +): + """Perform env setup and initialization for distributed training""" + if init_method == "env://": + _set_env_vars(world_size, rank, local_rank) + if world_size > 1 and "OMP_NUM_THREADS" not in os.environ: + _LG.info("Setting OMP_NUM_THREADS == 1") + os.environ["OMP_NUM_THREADS"] = "1" + params = { + "backend": backend, + "init_method": init_method, + "world_size": world_size, + "rank": rank, + } + _LG.info("Initializing distributed process group with %s", params) + dist.init_process_group(**params) + _LG.info("Initialized distributed process group.") + + +def _set_env_vars(world_size, rank, local_rank): + for key, default in [("MASTER_ADDR", "127.0.0.1"), ("MASTER_PORT", "29500")]: + if key not in os.environ: + os.environ[key] = default + + os.environ["WORLD_SIZE"] = str(world_size) + os.environ["RANK"] = str(rank) + os.environ["LOCAL_RANK"] = str(local_rank) + + +def save_on_master(path, obj): + if dist.get_rank() == 0: + _LG.info("Saving %s", path) + torch.save(obj, path) + + +def write_csv_on_master(path, *rows): + if dist.get_rank() == 0: + with open(path, "a", newline="") as fileobj: + writer = csv.writer(fileobj) + for row in rows: + writer.writerow(row) + + +def synchronize_params(path, device, *modules): + if dist.get_world_size() < 2: + return + rank = dist.get_rank() + if rank == 0: + _LG.info("[Parameter Sync]: Saving parameters to a temp file...") + torch.save({f"{i}": m.state_dict() for i, m in enumerate(modules)}, path) + dist.barrier() + if rank != 0: + _LG.info("[Parameter Sync]: Loading parameters...") + data = torch.load(path, map_location=device) + for i, m in enumerate(modules): + m.load_state_dict(data[f"{i}"]) + dist.barrier() + if rank == 0: + _LG.info("[Parameter Sync]: Removing the temp file...") + os.remove(path) + _LG.info_on_master("[Parameter Sync]: Complete.") diff --git a/examples/source_separation/utils/metrics.py b/examples/source_separation/utils/metrics.py new file mode 100644 index 0000000000..238851b4e9 --- /dev/null +++ b/examples/source_separation/utils/metrics.py @@ -0,0 +1,204 @@ +import math +from typing import Optional +from itertools import permutations + +import torch + + +def sdr( + estimate: torch.Tensor, + reference: torch.Tensor, + mask: Optional[torch.Tensor] = None, + epsilon: float = 1e-8 +) -> torch.Tensor: + """Computes source-to-distortion ratio. + + 1. scale the reference signal with power(s_est * s_ref) / powr(s_ref * s_ref) + 2. compute SNR between adjusted estimate and reference. + + Args: + estimate (torch.Tensor): Estimtaed signal. + Shape: [batch, speakers (can be 1), time frame] + reference (torch.Tensor): Reference signal. + Shape: [batch, speakers, time frame] + mask (Optional[torch.Tensor]): Binary mask to indicate padded value (0) or valid value (1). + Shape: [batch, 1, time frame] + epsilon (float): constant value used to stabilize division. + + Returns: + torch.Tensor: scale-invariant source-to-distortion ratio. + Shape: [batch, speaker] + + References: + - Single-channel multi-speaker separation using deep clustering + Y. Isik, J. Le Roux, Z. Chen, S. Watanabe, and J. R. Hershey, + - Conv-TasNet: Surpassing Ideal Time--Frequency Magnitude Masking for Speech Separation + Luo, Yi and Mesgarani, Nima + https://arxiv.org/abs/1809.07454 + + Notes: + This function is tested to produce the exact same result as + https://github.com/naplab/Conv-TasNet/blob/e66d82a8f956a69749ec8a4ae382217faa097c5c/utility/sdr.py#L34-L56 + """ + reference_pow = reference.pow(2).mean(axis=2, keepdim=True) + mix_pow = (estimate * reference).mean(axis=2, keepdim=True) + scale = mix_pow / (reference_pow + epsilon) + + reference = scale * reference + error = estimate - reference + + reference_pow = reference.pow(2) + error_pow = error.pow(2) + + if mask is None: + reference_pow = reference_pow.mean(axis=2) + error_pow = error_pow.mean(axis=2) + else: + denom = mask.sum(axis=2) + reference_pow = (mask * reference_pow).sum(axis=2) / denom + error_pow = (mask * error_pow).sum(axis=2) / denom + + return 10 * torch.log10(reference_pow) - 10 * torch.log10(error_pow) + + +class PIT(torch.nn.Module): + """Applies utterance-level speaker permutation + + Computes the maxium possible value of the given utility function + over the permutations of the speakers. + + Args: + utility_func (function): + Function that computes the utility (opposite of loss) with signature of + (extimate: torch.Tensor, reference: torch.Tensor) -> torch.Tensor + where input Tensors are shape of [batch, speakers, frame] and + the output Tensor is shape of [batch, speakers]. + + References: + - Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of + Deep Recurrent Neural Networks + Morten Kolbรฆk, Dong Yu, Zheng-Hua Tan and Jesper Jensen + https://arxiv.org/abs/1703.06284 + """ + + def __init__(self, utility_func): + super().__init__() + self.utility_func = utility_func + + def forward( + self, + estimate: torch.Tensor, + reference: torch.Tensor, + mask: Optional[torch.Tensor] = None, + epsilon: float = 1e-8 + ) -> torch.Tensor: + """Compute utterance-level PIT Loss + + Args: + estimate (torch.Tensor): Estimated source signals. + Shape: [bacth, speakers, time frame] + reference (torch.Tensor): Reference (original) source signals. + Shape: [batch, speakers, time frame] + mask (Optional[torch.Tensor]): Binary mask to indicate padded value (0) or valid value (1). + Shape: [batch, 1, time frame] + epsilon (float): constant value used to stabilize division. + + Returns: + torch.Tensor: Maximum criterion over the speaker permutation. + Shape: [batch, ] + """ + assert estimate.shape == reference.shape + + batch_size, num_speakers = reference.shape[:2] + num_permute = math.factorial(num_speakers) + + util_mat = torch.zeros( + batch_size, num_permute, dtype=estimate.dtype, device=estimate.device + ) + for i, idx in enumerate(permutations(range(num_speakers))): + util = self.utility_func(estimate, reference[:, idx, :], mask=mask, epsilon=epsilon) + util_mat[:, i] = util.mean(dim=1) # take the average over speaker dimension + return util_mat.max(dim=1).values + + +_sdr_pit = PIT(utility_func=sdr) + + +def sdr_pit( + estimate: torch.Tensor, + reference: torch.Tensor, + mask: Optional[torch.Tensor] = None, + epsilon: float = 1e-8): + """Computes scale-invariant source-to-distortion ratio. + + 1. adjust both estimate and reference to have 0-mean + 2. scale the reference signal with power(s_est * s_ref) / powr(s_ref * s_ref) + 3. compute SNR between adjusted estimate and reference. + + Args: + estimate (torch.Tensor): Estimtaed signal. + Shape: [batch, speakers (can be 1), time frame] + reference (torch.Tensor): Reference signal. + Shape: [batch, speakers, time frame] + mask (Optional[torch.Tensor]): Binary mask to indicate padded value (0) or valid value (1). + Shape: [batch, 1, time frame] + epsilon (float): constant value used to stabilize division. + + Returns: + torch.Tensor: scale-invariant source-to-distortion ratio. + Shape: [batch, speaker] + + References: + - Single-channel multi-speaker separation using deep clustering + Y. Isik, J. Le Roux, Z. Chen, S. Watanabe, and J. R. Hershey, + - Conv-TasNet: Surpassing Ideal Time--Frequency Magnitude Masking for Speech Separation + Luo, Yi and Mesgarani, Nima + https://arxiv.org/abs/1809.07454 + + Notes: + This function is tested to produce the exact same result as the reference implementation, + *when the inputs have 0-mean* + https://github.com/naplab/Conv-TasNet/blob/e66d82a8f956a69749ec8a4ae382217faa097c5c/utility/sdr.py#L107-L153 + """ + return _sdr_pit(estimate, reference, mask, epsilon) + + +def sdri( + estimate: torch.Tensor, + reference: torch.Tensor, + mix: torch.Tensor, + mask: Optional[torch.Tensor] = None, + epsilon: float = 1e-8, +) -> torch.Tensor: + """Compute the improvement of SDR (SDRi). + + This function compute how much SDR is improved if the estimation is changed from + the original mixture signal to the actual estimated source signals. That is, + ``SDR(estimate, reference) - SDR(mix, reference)``. + + For computing ``SDR(estimate, reference)``, PIT (permutation invariant training) is applied, + so that best combination of sources between the reference signals and the esimate signals + are picked. + + Args: + estimate (torch.Tensor): Estimated source signals. + Shape: [batch, speakers, time frame] + reference (torch.Tensor): Reference (original) source signals. + Shape: [batch, speakers, time frame] + mix (torch.Tensor): Mixed souce signals, from which the setimated signals were generated. + Shape: [batch, speakers == 1, time frame] + mask (Optional[torch.Tensor]): Binary mask to indicate padded value (0) or valid value (1). + Shape: [batch, 1, time frame] + epsilon (float): constant value used to stabilize division. + + Returns: + torch.Tensor: Improved SDR. Shape: [batch, ] + + References: + - Conv-TasNet: Surpassing Ideal Time--Frequency Magnitude Masking for Speech Separation + Luo, Yi and Mesgarani, Nima + https://arxiv.org/abs/1809.07454 + """ + sdr_ = sdr_pit(estimate, reference, mask=mask, epsilon=epsilon) # [batch, ] + base_sdr = sdr(mix, reference, mask=mask, epsilon=epsilon) # [batch, speaker] + return (sdr_.unsqueeze(1) - base_sdr).mean(dim=1) diff --git a/packaging/build_conda.sh b/packaging/build_conda.sh index 628eaf0bf8..488d0fe871 100755 --- a/packaging/build_conda.sh +++ b/packaging/build_conda.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="conda" export NO_CUDA_PACKAGE=1 -setup_env 0.7.0 +setup_env 0.8.0 export SOURCE_ROOT_DIR="$PWD" setup_conda_pytorch_constraint conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchaudio diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index 3b25c00cd2..ad45a08d59 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="wheel" export NO_CUDA_PACKAGE=1 -setup_env 0.7.0 +setup_env 0.8.0 setup_wheel_python pip_install numpy future setup_pip_pytorch_version diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index 57b23b93d3..3316f789e2 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -171,8 +171,7 @@ setup_pip_pytorch_version() { else pip_install "torch==$PYTORCH_VERSION$PYTORCH_VERSION_SUFFIX" \ -f https://download.pytorch.org/whl/torch_stable.html \ - -f https://download.pytorch.org/whl/test/torch_test.html \ - -f https://download.pytorch.org/whl/nightly/torch_nightly.html + -f "https://download.pytorch.org/whl/${UPLOAD_CHANNEL}/torch_${UPLOAD_CHANNEL}.html" fi } @@ -185,7 +184,7 @@ setup_conda_pytorch_constraint() { export CONDA_CHANNEL_FLAGS="-c pytorch-nightly" export PYTORCH_VERSION="$(conda search --json 'pytorch[channel=pytorch-nightly]' | python -c "import sys, json, re; print(re.sub(r'\\+.*$', '', json.load(sys.stdin)['pytorch'][-1]['version']))")" else - export CONDA_CHANNEL_FLAGS="-c pytorch -c pytorch-test -c pytorch-nightly" + export CONDA_CHANNEL_FLAGS="-c pytorch -c pytorch-${UPLOAD_CHANNEL}" fi if [[ "$CU_VERSION" == cpu ]]; then export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==$PYTORCH_VERSION${PYTORCH_VERSION_SUFFIX}" diff --git a/setup.py b/setup.py index 0773b23107..4ced5fc8fd 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ # Creating the version file -version = '0.7.0a0' +version = '0.8.0a0' sha = 'Unknown' try: diff --git a/test/torchaudio_unittest/backend_test.py b/test/torchaudio_unittest/backend_test.py index d7a2078a31..3f355be0cb 100644 --- a/test/torchaudio_unittest/backend_test.py +++ b/test/torchaudio_unittest/backend_test.py @@ -38,6 +38,18 @@ class TestBackendSwitch_SoXIO(BackendSwitchMixin, common_utils.TorchaudioTestCas @common_utils.skipIfNoModule('soundfile') -class TestBackendSwitch_soundfile(BackendSwitchMixin, common_utils.TorchaudioTestCase): +class TestBackendSwitch_soundfile_legacy(BackendSwitchMixin, common_utils.TorchaudioTestCase): backend = 'soundfile' backend_module = torchaudio.backend.soundfile_backend + + def setUp(self): + torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = True + + def tearDown(self): + torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = None + + +@common_utils.skipIfNoModule('soundfile') +class TestBackendSwitch_soundfile(BackendSwitchMixin, common_utils.TorchaudioTestCase): + backend = 'soundfile' + backend_module = torchaudio.backend._soundfile_backend diff --git a/test/torchaudio_unittest/common_utils/backend_utils.py b/test/torchaudio_unittest/common_utils/backend_utils.py index 7702a3d6f3..7e519c80eb 100644 --- a/test/torchaudio_unittest/common_utils/backend_utils.py +++ b/test/torchaudio_unittest/common_utils/backend_utils.py @@ -6,11 +6,15 @@ def set_audio_backend(backend): """Allow additional backend value, 'default'""" backends = torchaudio.list_audio_backends() - if backend == 'default': + if backend == 'soundfile-new': + be = 'soundfile' + torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = False + elif backend == 'default': if 'sox_io' in backends: be = 'sox_io' elif 'soundfile' in backends: be = 'soundfile' + torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = True else: raise unittest.SkipTest('No default backend available') else: diff --git a/test/torchaudio_unittest/common_utils/wav_utils.py b/test/torchaudio_unittest/common_utils/wav_utils.py index bc122ec6cb..b4b7944805 100644 --- a/test/torchaudio_unittest/common_utils/wav_utils.py +++ b/test/torchaudio_unittest/common_utils/wav_utils.py @@ -52,12 +52,18 @@ def get_wav_data( if dtype == 'uint8': base = torch.linspace(0, 255, num_frames, dtype=dtype_) - if dtype == 'float32': + elif dtype == 'int8': + base = torch.linspace(-128, 127, num_frames, dtype=dtype_) + elif dtype == 'float32': base = torch.linspace(-1., 1., num_frames, dtype=dtype_) - if dtype == 'int32': + elif dtype == 'float64': + base = torch.linspace(-1., 1., num_frames, dtype=dtype_) + elif dtype == 'int32': base = torch.linspace(-2147483648, 2147483647, num_frames, dtype=dtype_) - if dtype == 'int16': + elif dtype == 'int16': base = torch.linspace(-32768, 32767, num_frames, dtype=dtype_) + else: + raise NotImplementedError(f'Unsupported dtype {dtype}') data = base.repeat([num_channels, 1]) if not channels_first: data = data.transpose(1, 0) diff --git a/test/torchaudio_unittest/dataloader_test.py b/test/torchaudio_unittest/dataloader_test.py deleted file mode 100644 index 8f1d7e6fb3..0000000000 --- a/test/torchaudio_unittest/dataloader_test.py +++ /dev/null @@ -1,39 +0,0 @@ -import unittest - -import torchaudio -from torch.utils.data import Dataset, DataLoader - -from torchaudio_unittest import common_utils - - -class TORCHAUDIODS(Dataset): - def __init__(self): - sound_files = ["sinewave.wav", "steam-train-whistle-daniel_simon.mp3"] - self.data = [common_utils.get_asset_path(fn) for fn in sound_files] - self.si, self.ei = torchaudio.info(common_utils.get_asset_path("sinewave.wav")) - self.si.precision = 16 - self.E = torchaudio.sox_effects.SoxEffectsChain() - self.E.append_effect_to_chain("rate", [self.si.rate]) # resample to 16000hz - self.E.append_effect_to_chain("channels", [self.si.channels]) # mono signal - self.E.append_effect_to_chain("trim", [0, "16000s"]) # first 16000 samples of audio - - def __getitem__(self, index): - fn = self.data[index] - self.E.set_input_file(fn) - x, sr = self.E.sox_build_flow_effects() - return x - - def __len__(self): - return len(self.data) - - -class Test_DataLoader(common_utils.TorchaudioTestCase): - backend = 'sox' - - @common_utils.skipIfNoSoxBackend - def test_1(self): - expected_size = (2, 1, 16000) - ds = TORCHAUDIODS() - dl = DataLoader(ds, batch_size=2) - for x in dl: - self.assertTrue(x.size() == expected_size) diff --git a/test/torchaudio_unittest/datasets/speechcommands_test.py b/test/torchaudio_unittest/datasets/speechcommands_test.py index 7df1ea2007..5a331bdeca 100644 --- a/test/torchaudio_unittest/datasets/speechcommands_test.py +++ b/test/torchaudio_unittest/datasets/speechcommands_test.py @@ -53,9 +53,12 @@ class TestSpeechCommands(TempDirMixin, TorchaudioTestCase): root_dir = None samples = [] + train_samples = [] + valid_samples = [] + test_samples = [] @classmethod - def setUp(cls): + def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() dataset_dir = os.path.join( cls.root_dir, speechcommands.FOLDER_IN_ARCHIVE, speechcommands.URL @@ -63,37 +66,47 @@ def setUp(cls): os.makedirs(dataset_dir, exist_ok=True) sample_rate = 16000 # 16kHz sample rate seed = 0 - for label in LABELS: - path = os.path.join(dataset_dir, label) - os.makedirs(path, exist_ok=True) - for j in range(2): - # generate hash ID for speaker - speaker = "{:08x}".format(j) - - for utterance in range(3): - filename = f"{speaker}{speechcommands.HASH_DIVIDER}{utterance}.wav" - file_path = os.path.join(path, filename) - seed += 1 - data = get_whitenoise( - sample_rate=sample_rate, - duration=0.01, - n_channels=1, - dtype="int16", - seed=seed, - ) - save_wav(file_path, data, sample_rate) - sample = ( - normalize_wav(data), - sample_rate, - label, - speaker, - utterance, - ) - cls.samples.append(sample) + valid_file = os.path.join(dataset_dir, "validation_list.txt") + test_file = os.path.join(dataset_dir, "testing_list.txt") + with open(valid_file, "w") as valid, open(test_file, "w") as test: + for label in LABELS: + path = os.path.join(dataset_dir, label) + os.makedirs(path, exist_ok=True) + for j in range(6): + # generate hash ID for speaker + speaker = "{:08x}".format(j) + + for utterance in range(3): + filename = f"{speaker}{speechcommands.HASH_DIVIDER}{utterance}.wav" + file_path = os.path.join(path, filename) + seed += 1 + data = get_whitenoise( + sample_rate=sample_rate, + duration=0.01, + n_channels=1, + dtype="int16", + seed=seed, + ) + save_wav(file_path, data, sample_rate) + sample = ( + normalize_wav(data), + sample_rate, + label, + speaker, + utterance, + ) + cls.samples.append(sample) + if j < 2: + cls.train_samples.append(sample) + elif j < 4: + valid.write(f'{label}/{filename}\n') + cls.valid_samples.append(sample) + elif j < 6: + test.write(f'{label}/{filename}\n') + cls.test_samples.append(sample) def testSpeechCommands(self): dataset = speechcommands.SPEECHCOMMANDS(self.root_dir) - print(dataset._path) num_samples = 0 for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( @@ -107,3 +120,75 @@ def testSpeechCommands(self): num_samples += 1 assert num_samples == len(self.samples) + + def testSpeechCommandsNone(self): + dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset=None) + + num_samples = 0 + for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( + dataset + ): + self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[i][1] + assert label == self.samples[i][2] + assert speaker_id == self.samples[i][3] + assert utterance_number == self.samples[i][4] + num_samples += 1 + + assert num_samples == len(self.samples) + + def testSpeechCommandsSubsetTrain(self): + dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="training") + + num_samples = 0 + for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( + dataset + ): + self.assertEqual(data, self.train_samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.train_samples[i][1] + assert label == self.train_samples[i][2] + assert speaker_id == self.train_samples[i][3] + assert utterance_number == self.train_samples[i][4] + num_samples += 1 + + assert num_samples == len(self.train_samples) + + def testSpeechCommandsSubsetValid(self): + dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="validation") + + num_samples = 0 + for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( + dataset + ): + self.assertEqual(data, self.valid_samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.valid_samples[i][1] + assert label == self.valid_samples[i][2] + assert speaker_id == self.valid_samples[i][3] + assert utterance_number == self.valid_samples[i][4] + num_samples += 1 + + assert num_samples == len(self.valid_samples) + + def testSpeechCommandsSubsetTest(self): + dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="testing") + + num_samples = 0 + for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( + dataset + ): + self.assertEqual(data, self.test_samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.test_samples[i][1] + assert label == self.test_samples[i][2] + assert speaker_id == self.test_samples[i][3] + assert utterance_number == self.test_samples[i][4] + num_samples += 1 + + assert num_samples == len(self.test_samples) + + def testSpeechCommandsSum(self): + dataset_all = speechcommands.SPEECHCOMMANDS(self.root_dir) + dataset_train = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="training") + dataset_valid = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="validation") + dataset_test = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="testing") + + assert len(dataset_train) + len(dataset_valid) + len(dataset_test) == len(dataset_all) diff --git a/test/torchaudio_unittest/datasets/tedlium_test.py b/test/torchaudio_unittest/datasets/tedlium_test.py index c19984cac2..f5becb53ab 100644 --- a/test/torchaudio_unittest/datasets/tedlium_test.py +++ b/test/torchaudio_unittest/datasets/tedlium_test.py @@ -1,8 +1,11 @@ import os +import platform +import unittest from torchaudio.datasets import tedlium from torchaudio_unittest.common_utils import ( + TestBaseMixin, TempDirMixin, TorchaudioTestCase, get_whitenoise, @@ -30,9 +33,7 @@ ] -class TestTedlium(TempDirMixin, TorchaudioTestCase): - backend = "default" - +class Tedlium(TempDirMixin): root_dir = None samples = {} @@ -151,3 +152,19 @@ def test_tedlium_release3(self): phoneme_dict = dataset.phoneme_dict phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] assert phoenemes == PHONEME + + +class TestTedliumSoundfile(Tedlium, TorchaudioTestCase): + backend = "soundfile" + + +class TestTedliumSoundfileNew(Tedlium, TorchaudioTestCase): + backend = "soundfile-new" + + +if platform.system() != "Windows": + class TestTedliumSox(Tedlium, TorchaudioTestCase): + backend = "sox" + + class TestTedliumSoxIO(Tedlium, TorchaudioTestCase): + backend = "sox_io" diff --git a/test/torchaudio_unittest/example/__init__.py b/test/torchaudio_unittest/example/__init__.py new file mode 100644 index 0000000000..9e4fa81989 --- /dev/null +++ b/test/torchaudio_unittest/example/__init__.py @@ -0,0 +1,8 @@ +import os +import sys + + +sys.path.append( + os.path.join( + os.path.dirname(__file__), + '..', '..', '..', 'examples')) diff --git a/test/torchaudio_unittest/example/souce_sepration/__init__.py b/test/torchaudio_unittest/example/souce_sepration/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/torchaudio_unittest/example/souce_sepration/metrics_test.py b/test/torchaudio_unittest/example/souce_sepration/metrics_test.py new file mode 100644 index 0000000000..b7793b07ba --- /dev/null +++ b/test/torchaudio_unittest/example/souce_sepration/metrics_test.py @@ -0,0 +1,39 @@ +from itertools import product + +import torch +from torch.testing._internal.common_utils import TestCase +from parameterized import parameterized + +from . import sdr_reference +from source_separation.utils import metrics + + +class TestSDR(TestCase): + @parameterized.expand([(1, ), (2, ), (32, )]) + def test_sdr(self, batch_size): + """sdr produces the same result as the reference implementation""" + num_frames = 256 + + estimation = torch.rand(batch_size, num_frames) + origin = torch.rand(batch_size, num_frames) + + sdr_ref = sdr_reference.calc_sdr_torch(estimation, origin) + sdr = metrics.sdr(estimation.unsqueeze(1), origin.unsqueeze(1)).squeeze(1) + + self.assertEqual(sdr, sdr_ref) + + @parameterized.expand(list(product([1, 2, 32], [2, 3, 4, 5]))) + def test_sdr_pit(self, batch_size, num_sources): + """sdr_pit produces the same result as the reference implementation""" + num_frames = 256 + + estimation = torch.randn(batch_size, num_sources, num_frames) + origin = torch.randn(batch_size, num_sources, num_frames) + + estimation -= estimation.mean(axis=2, keepdim=True) + origin -= origin.mean(axis=2, keepdim=True) + + batch_sdr_ref = sdr_reference.batch_SDR_torch(estimation, origin) + batch_sdr = metrics.sdr_pit(estimation, origin) + + self.assertEqual(batch_sdr, batch_sdr_ref) diff --git a/test/torchaudio_unittest/example/souce_sepration/sdr_reference.py b/test/torchaudio_unittest/example/souce_sepration/sdr_reference.py new file mode 100644 index 0000000000..7652fab0e6 --- /dev/null +++ b/test/torchaudio_unittest/example/souce_sepration/sdr_reference.py @@ -0,0 +1,98 @@ +"""Reference Implementation of SDR and PIT SDR. + +This module was taken from the following implementation + +https://github.com/naplab/Conv-TasNet/blob/e66d82a8f956a69749ec8a4ae382217faa097c5c/utility/sdr.py + +which was made available by Yi Luo under the following liscence, + +Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License. + +The module was modified in the following manner; + - Remove the functions other than `calc_sdr_torch` and `batch_SDR_torch`, + - Remove the import statements required only for the removed functions. + - Add `# flake8: noqa` so as not to report any format issue on this module. + +The implementation of the retained functions and their formats are kept as-is. +""" + +# flake8: noqa + +import numpy as np +from itertools import permutations + +import torch + + +def calc_sdr_torch(estimation, origin, mask=None): + """ + batch-wise SDR caculation for one audio file on pytorch Variables. + estimation: (batch, nsample) + origin: (batch, nsample) + mask: optional, (batch, nsample), binary + """ + + if mask is not None: + origin = origin * mask + estimation = estimation * mask + + origin_power = torch.pow(origin, 2).sum(1, keepdim=True) + 1e-8 # (batch, 1) + + scale = torch.sum(origin*estimation, 1, keepdim=True) / origin_power # (batch, 1) + + est_true = scale * origin # (batch, nsample) + est_res = estimation - est_true # (batch, nsample) + + true_power = torch.pow(est_true, 2).sum(1) + res_power = torch.pow(est_res, 2).sum(1) + + return 10*torch.log10(true_power) - 10*torch.log10(res_power) # (batch, 1) + + +def batch_SDR_torch(estimation, origin, mask=None): + """ + batch-wise SDR caculation for multiple audio files. + estimation: (batch, nsource, nsample) + origin: (batch, nsource, nsample) + mask: optional, (batch, nsample), binary + """ + + batch_size_est, nsource_est, nsample_est = estimation.size() + batch_size_ori, nsource_ori, nsample_ori = origin.size() + + assert batch_size_est == batch_size_ori, "Estimation and original sources should have same shape." + assert nsource_est == nsource_ori, "Estimation and original sources should have same shape." + assert nsample_est == nsample_ori, "Estimation and original sources should have same shape." + + assert nsource_est < nsample_est, "Axis 1 should be the number of sources, and axis 2 should be the signal." + + batch_size = batch_size_est + nsource = nsource_est + nsample = nsample_est + + # zero mean signals + estimation = estimation - torch.mean(estimation, 2, keepdim=True).expand_as(estimation) + origin = origin - torch.mean(origin, 2, keepdim=True).expand_as(estimation) + + # possible permutations + perm = list(set(permutations(np.arange(nsource)))) + + # pair-wise SDR + SDR = torch.zeros((batch_size, nsource, nsource)).type(estimation.type()) + for i in range(nsource): + for j in range(nsource): + SDR[:,i,j] = calc_sdr_torch(estimation[:,i], origin[:,j], mask) + + # choose the best permutation + SDR_max = [] + SDR_perm = [] + for permute in perm: + sdr = [] + for idx in range(len(permute)): + sdr.append(SDR[:,idx,permute[idx]].view(batch_size,-1)) + sdr = torch.sum(torch.cat(sdr, 1), 1) + SDR_perm.append(sdr.view(batch_size, 1)) + SDR_perm = torch.cat(SDR_perm, 1) + SDR_max, _ = torch.max(SDR_perm, dim=1) + + return SDR_max / nsource diff --git a/test/torchaudio_unittest/example/souce_sepration/wsj0mix_test.py b/test/torchaudio_unittest/example/souce_sepration/wsj0mix_test.py new file mode 100644 index 0000000000..46927b182f --- /dev/null +++ b/test/torchaudio_unittest/example/souce_sepration/wsj0mix_test.py @@ -0,0 +1,111 @@ +import os + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + +from source_separation.utils.dataset import wsj0mix + + +_FILENAMES = [ + "012c0207_1.9952_01cc0202_-1.9952.wav", + "01co0302_1.63_014c020q_-1.63.wav", + "01do0316_0.24011_205a0104_-0.24011.wav", + "01lc020x_1.1301_027o030r_-1.1301.wav", + "01mc0202_0.34056_205o0106_-0.34056.wav", + "01nc020t_0.53821_018o030w_-0.53821.wav", + "01po030f_2.2136_40ko031a_-2.2136.wav", + "01ra010o_2.4098_403a010f_-2.4098.wav", + "01xo030b_0.22377_016o031a_-0.22377.wav", + "02ac020x_0.68566_01ec020b_-0.68566.wav", + "20co010m_0.82801_019c0212_-0.82801.wav", + "20da010u_1.2483_017c0211_-1.2483.wav", + "20oo010d_1.0631_01ic020s_-1.0631.wav", + "20sc0107_2.0222_20fo010h_-2.0222.wav", + "20tc010f_0.051456_404a0110_-0.051456.wav", + "407c0214_1.1712_02ca0113_-1.1712.wav", + "40ao030w_2.4697_20vc010a_-2.4697.wav", + "40pa0101_1.1087_40ea0107_-1.1087.wav", +] + + +def _mock_dataset(root_dir, num_speaker): + dirnames = ["mix"] + [f"s{i+1}" for i in range(num_speaker)] + for dirname in dirnames: + os.makedirs(os.path.join(root_dir, dirname), exist_ok=True) + + seed = 0 + sample_rate = 8000 + expected = [] + for filename in _FILENAMES: + mix = None + src = [] + for dirname in dirnames: + waveform = get_whitenoise( + sample_rate=8000, duration=1, n_channels=1, dtype="int16", seed=seed + ) + seed += 1 + + path = os.path.join(root_dir, dirname, filename) + save_wav(path, waveform, sample_rate) + waveform = normalize_wav(waveform) + + if dirname == "mix": + mix = waveform + else: + src.append(waveform) + expected.append((sample_rate, mix, src)) + return expected + + +class TestWSJ0Mix2(TempDirMixin, TorchaudioTestCase): + backend = "default" + root_dir = None + expected = None + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + cls.expected = _mock_dataset(cls.root_dir, 2) + + def test_wsj0mix(self): + dataset = wsj0mix.WSJ0Mix(self.root_dir, num_speakers=2, sample_rate=8000) + + n_ite = 0 + for i, sample in enumerate(dataset): + (_, sample_mix, sample_src) = sample + (_, expected_mix, expected_src) = self.expected[i] + self.assertEqual(sample_mix, expected_mix, atol=5e-5, rtol=1e-8) + self.assertEqual(sample_src[0], expected_src[0], atol=5e-5, rtol=1e-8) + self.assertEqual(sample_src[1], expected_src[1], atol=5e-5, rtol=1e-8) + n_ite += 1 + assert n_ite == len(self.expected) + + +class TestWSJ0Mix3(TempDirMixin, TorchaudioTestCase): + backend = "default" + root_dir = None + expected = None + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + cls.expected = _mock_dataset(cls.root_dir, 3) + + def test_wsj0mix(self): + dataset = wsj0mix.WSJ0Mix(self.root_dir, num_speakers=3, sample_rate=8000) + + n_ite = 0 + for i, sample in enumerate(dataset): + (_, sample_mix, sample_src) = sample + (_, expected_mix, expected_src) = self.expected[i] + self.assertEqual(sample_mix, expected_mix, atol=5e-5, rtol=1e-8) + self.assertEqual(sample_src[0], expected_src[0], atol=5e-5, rtol=1e-8) + self.assertEqual(sample_src[1], expected_src[1], atol=5e-5, rtol=1e-8) + self.assertEqual(sample_src[2], expected_src[2], atol=5e-5, rtol=1e-8) + n_ite += 1 + assert n_ite == len(self.expected) diff --git a/test/torchaudio_unittest/io_test.py b/test/torchaudio_unittest/io_test.py index e30f4c3fd3..3dbc419bde 100644 --- a/test/torchaudio_unittest/io_test.py +++ b/test/torchaudio_unittest/io_test.py @@ -45,6 +45,9 @@ class Test_LoadSave(unittest.TestCase): test_filepath_wav = os.path.join(test_dirpath, "assets", "steam-train-whistle-daniel_simon.wav") + def setUp(self): + torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = True + def test_1_save(self): for backend in BACKENDS_MP3: with self.subTest(): diff --git a/test/torchaudio_unittest/models_test.py b/test/torchaudio_unittest/models_test.py index f9e96edd27..484ec6c10c 100644 --- a/test/torchaudio_unittest/models_test.py +++ b/test/torchaudio_unittest/models_test.py @@ -1,6 +1,10 @@ -import torch -from torchaudio.models import Wav2Letter, MelResNet, UpsampleNetwork, WaveRNN +import itertools +from collections import namedtuple +import torch +from parameterized import parameterized +from torchaudio.models import ConvTasNet, Wav2Letter, WaveRNN +from torchaudio.models.wavernn import MelResNet, UpsampleNetwork from torchaudio_unittest import common_utils @@ -115,3 +119,58 @@ def test_waveform(self): out = model(x, mels) assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), n_classes) + + +_ConvTasNetParams = namedtuple( + '_ConvTasNetParams', + [ + 'enc_num_feats', + 'enc_kernel_size', + 'msk_num_feats', + 'msk_num_hidden_feats', + 'msk_kernel_size', + 'msk_num_layers', + 'msk_num_stacks', + ] +) + + +class TestConvTasNet(common_utils.TorchaudioTestCase): + @parameterized.expand(list(itertools.product( + [2, 3], + [ + _ConvTasNetParams(128, 40, 128, 256, 3, 7, 2), + _ConvTasNetParams(256, 40, 128, 256, 3, 7, 2), + _ConvTasNetParams(512, 40, 128, 256, 3, 7, 2), + _ConvTasNetParams(512, 40, 128, 256, 3, 7, 2), + _ConvTasNetParams(512, 40, 128, 512, 3, 7, 2), + _ConvTasNetParams(512, 40, 128, 512, 3, 7, 2), + _ConvTasNetParams(512, 40, 256, 256, 3, 7, 2), + _ConvTasNetParams(512, 40, 256, 512, 3, 7, 2), + _ConvTasNetParams(512, 40, 256, 512, 3, 7, 2), + _ConvTasNetParams(512, 40, 128, 512, 3, 6, 4), + _ConvTasNetParams(512, 40, 128, 512, 3, 4, 6), + _ConvTasNetParams(512, 40, 128, 512, 3, 8, 3), + _ConvTasNetParams(512, 32, 128, 512, 3, 8, 3), + _ConvTasNetParams(512, 16, 128, 512, 3, 8, 3), + ], + ))) + def test_paper_configuration(self, num_sources, model_params): + """ConvTasNet model works on the valid configurations in the paper""" + batch_size = 32 + num_frames = 8000 + + model = ConvTasNet( + num_sources=num_sources, + enc_kernel_size=model_params.enc_kernel_size, + enc_num_feats=model_params.enc_num_feats, + msk_kernel_size=model_params.msk_kernel_size, + msk_num_feats=model_params.msk_num_feats, + msk_num_hidden_feats=model_params.msk_num_hidden_feats, + msk_num_layers=model_params.msk_num_layers, + msk_num_stacks=model_params.msk_num_stacks, + ) + tensor = torch.rand(batch_size, 1, num_frames) + output = model(tensor) + + assert output.shape == (batch_size, num_sources, num_frames) diff --git a/test/torchaudio_unittest/soundfile_backend/__init__.py b/test/torchaudio_unittest/soundfile_backend/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/torchaudio_unittest/soundfile_backend/common.py b/test/torchaudio_unittest/soundfile_backend/common.py new file mode 100644 index 0000000000..4da0873fa1 --- /dev/null +++ b/test/torchaudio_unittest/soundfile_backend/common.py @@ -0,0 +1,34 @@ +import itertools +from unittest import skipIf + +from parameterized import parameterized +from torchaudio._internal.module_utils import is_module_available + + +def name_func(func, _, params): + return f'{func.__name__}_{"_".join(str(arg) for arg in params.args)}' + + +def dtype2subtype(dtype): + return { + "float64": "DOUBLE", + "float32": "FLOAT", + "int32": "PCM_32", + "int16": "PCM_16", + "uint8": "PCM_U8", + "int8": "PCM_S8", + }[dtype] + + +def skipIfFormatNotSupported(fmt): + fmts = [] + if is_module_available("soundfile"): + import soundfile + + fmts = soundfile.available_formats() + return skipIf(fmt not in fmts, f'"{fmt}" is not supported by sondfile') + return skipIf(True, '"soundfile" not available.') + + +def parameterize(*params): + return parameterized.expand(list(itertools.product(*params)), name_func=name_func) diff --git a/test/torchaudio_unittest/soundfile_backend/info_test.py b/test/torchaudio_unittest/soundfile_backend/info_test.py new file mode 100644 index 0000000000..71acb20689 --- /dev/null +++ b/test/torchaudio_unittest/soundfile_backend/info_test.py @@ -0,0 +1,95 @@ +import torch +from torchaudio.backend import _soundfile_backend as soundfile_backend +from torchaudio._internal import module_utils as _mod_utils + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoModule, + get_wav_data, + save_wav, +) +from .common import skipIfFormatNotSupported, parameterize + +if _mod_utils.is_module_available("soundfile"): + import soundfile + + +@skipIfNoModule("soundfile") +class TestInfo(TempDirMixin, PytorchTestCase): + @parameterize( + ["float32", "int32", "int16", "uint8"], [8000, 16000], [1, 2], + ) + def test_wav(self, dtype, sample_rate, num_channels): + """`soundfile_backend.info` can check wav file correctly""" + duration = 1 + path = self.get_temp_path("data.wav") + data = get_wav_data( + dtype, num_channels, normalize=False, num_frames=duration * sample_rate + ) + save_wav(path, data, sample_rate) + info = soundfile_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterize( + ["float32", "int32", "int16", "uint8"], [8000, 16000], [4, 8, 16, 32], + ) + def test_wav_multiple_channels(self, dtype, sample_rate, num_channels): + """`soundfile_backend.info` can check wav file with channels more than 2 correctly""" + duration = 1 + path = self.get_temp_path("data.wav") + data = get_wav_data( + dtype, num_channels, normalize=False, num_frames=duration * sample_rate + ) + save_wav(path, data, sample_rate) + info = soundfile_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterize([8000, 16000], [1, 2]) + @skipIfFormatNotSupported("FLAC") + def test_flac(self, sample_rate, num_channels): + """`soundfile_backend.info` can check flac file correctly""" + duration = 1 + num_frames = sample_rate * duration + data = torch.randn(num_frames, num_channels).numpy() + path = self.get_temp_path("data.flac") + soundfile.write(path, data, sample_rate) + + info = soundfile_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == num_frames + assert info.num_channels == num_channels + + @parameterize([8000, 16000], [1, 2]) + @skipIfFormatNotSupported("OGG") + def test_ogg(self, sample_rate, num_channels): + """`soundfile_backend.info` can check ogg file correctly""" + duration = 1 + num_frames = sample_rate * duration + data = torch.randn(num_frames, num_channels).numpy() + path = self.get_temp_path("data.ogg") + soundfile.write(path, data, sample_rate) + + info = soundfile_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterize([8000, 16000], [1, 2]) + @skipIfFormatNotSupported("NIST") + def test_sphere(self, sample_rate, num_channels): + """`soundfile_backend.info` can check sph file correctly""" + duration = 1 + num_frames = sample_rate * duration + data = torch.randn(num_frames, num_channels).numpy() + path = self.get_temp_path("data.nist") + soundfile.write(path, data, sample_rate) + + info = soundfile_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels diff --git a/test/torchaudio_unittest/soundfile_backend/load_test.py b/test/torchaudio_unittest/soundfile_backend/load_test.py new file mode 100644 index 0000000000..8860263eed --- /dev/null +++ b/test/torchaudio_unittest/soundfile_backend/load_test.py @@ -0,0 +1,265 @@ +import itertools +from unittest.mock import patch + +import torch +from torchaudio._internal import module_utils as _mod_utils +from torchaudio.backend import _soundfile_backend as soundfile_backend +from parameterized import parameterized + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoModule, + get_wav_data, + normalize_wav, + load_wav, + save_wav, +) +from .common import ( + parameterize, + dtype2subtype, + skipIfFormatNotSupported, +) + +if _mod_utils.is_module_available("soundfile"): + import soundfile + + +def _get_mock_path( + ext: str, dtype: str, sample_rate: int, num_channels: int, num_frames: int, +): + return f"{dtype}_{sample_rate}_{num_channels}_{num_frames}.{ext}" + + +def _get_mock_params(path: str): + filename, ext = path.split(".") + parts = filename.split("_") + return { + "ext": ext, + "dtype": parts[0], + "sample_rate": int(parts[1]), + "num_channels": int(parts[2]), + "num_frames": int(parts[3]), + } + + +class SoundFileMock: + def __init__(self, path, mode): + assert mode == "r" + self.path = path + self._params = _get_mock_params(path) + self._start = None + + @property + def samplerate(self): + return self._params["sample_rate"] + + @property + def format(self): + if self._params["ext"] == "wav": + return "WAV" + if self._params["ext"] == "flac": + return "FLAC" + if self._params["ext"] == "ogg": + return "OGG" + if self._params["ext"] in ["sph", "nis", "nist"]: + return "NIST" + + @property + def subtype(self): + if self._params["ext"] == "ogg": + return "VORBIS" + return dtype2subtype(self._params["dtype"]) + + def _prepare_read(self, start, stop, frames): + assert stop is None + self._start = start + return frames + + def read(self, frames, dtype, always_2d): + assert always_2d + data = get_wav_data( + dtype, + self._params["num_channels"], + normalize=False, + num_frames=self._params["num_frames"], + channels_first=False, + ).numpy() + return data[self._start:self._start + frames] + + def __enter__(self): + return self + + def __exit__(self, *args, **kwargs): + pass + + +class MockedLoadTest(PytorchTestCase): + def assert_dtype( + self, ext, dtype, sample_rate, num_channels, normalize, channels_first + ): + """When format is WAV or NIST, normalize=False will return the native dtype Tensor, otherwise float32""" + num_frames = 3 * sample_rate + path = _get_mock_path(ext, dtype, sample_rate, num_channels, num_frames) + expected_dtype = ( + torch.float32 + if normalize or ext not in ["wav", "nist"] + else getattr(torch, dtype) + ) + with patch("soundfile.SoundFile", SoundFileMock): + found, sr = soundfile_backend.load( + path, normalize=normalize, channels_first=channels_first + ) + assert found.dtype == expected_dtype + assert sample_rate == sr + + @parameterize( + ["uint8", "int16", "int32", "float32", "float64"], + [8000, 16000], + [1, 2], + [True, False], + [True, False], + ) + def test_wav(self, dtype, sample_rate, num_channels, normalize, channels_first): + """Returns native dtype when normalize=False else float32""" + self.assert_dtype( + "wav", dtype, sample_rate, num_channels, normalize, channels_first + ) + + @parameterize( + ["int8", "int16", "int32"], [8000, 16000], [1, 2], [True, False], [True, False], + ) + def test_sphere(self, dtype, sample_rate, num_channels, normalize, channels_first): + """Returns float32 always""" + self.assert_dtype( + "sph", dtype, sample_rate, num_channels, normalize, channels_first + ) + + @parameterize([8000, 16000], [1, 2], [True, False], [True, False]) + def test_ogg(self, sample_rate, num_channels, normalize, channels_first): + """Returns float32 always""" + self.assert_dtype( + "ogg", "int16", sample_rate, num_channels, normalize, channels_first + ) + + @parameterize([8000, 16000], [1, 2], [True, False], [True, False]) + def test_flac(self, sample_rate, num_channels, normalize, channels_first): + """`soundfile_backend.load` can load ogg format.""" + self.assert_dtype( + "flac", "int16", sample_rate, num_channels, normalize, channels_first + ) + + +class LoadTestBase(TempDirMixin, PytorchTestCase): + def assert_wav( + self, + dtype, + sample_rate, + num_channels, + normalize, + channels_first=True, + duration=1, + ): + """`soundfile_backend.load` can load wav format correctly. + + Wav data loaded with soundfile backend should match those with scipy + """ + path = self.get_temp_path("reference.wav") + num_frames = duration * sample_rate + data = get_wav_data( + dtype, + num_channels, + normalize=normalize, + num_frames=num_frames, + channels_first=channels_first, + ) + save_wav(path, data, sample_rate, channels_first=channels_first) + expected = load_wav(path, normalize=normalize, channels_first=channels_first)[0] + data, sr = soundfile_backend.load( + path, normalize=normalize, channels_first=channels_first + ) + assert sr == sample_rate + self.assertEqual(data, expected) + + def assert_sphere( + self, dtype, sample_rate, num_channels, channels_first=True, duration=1, + ): + """`soundfile_backend.load` can load SPHERE format correctly.""" + path = self.get_temp_path("reference.sph") + num_frames = duration * sample_rate + raw = get_wav_data( + dtype, + num_channels, + num_frames=num_frames, + normalize=False, + channels_first=False, + ) + soundfile.write( + path, raw, sample_rate, subtype=dtype2subtype(dtype), format="NIST" + ) + expected = normalize_wav(raw.t() if channels_first else raw) + data, sr = soundfile_backend.load(path, channels_first=channels_first) + assert sr == sample_rate + self.assertEqual(data, expected, atol=1e-4, rtol=1e-8) + + def assert_flac( + self, dtype, sample_rate, num_channels, channels_first=True, duration=1, + ): + """`soundfile_backend.load` can load FLAC format correctly.""" + path = self.get_temp_path("reference.flac") + num_frames = duration * sample_rate + raw = get_wav_data( + dtype, + num_channels, + num_frames=num_frames, + normalize=False, + channels_first=False, + ) + soundfile.write(path, raw, sample_rate) + expected = normalize_wav(raw.t() if channels_first else raw) + data, sr = soundfile_backend.load(path, channels_first=channels_first) + assert sr == sample_rate + self.assertEqual(data, expected, atol=1e-4, rtol=1e-8) + + +@skipIfNoModule("soundfile") +class TestLoad(LoadTestBase): + """Test the correctness of `soundfile_backend.load` for various formats""" + + @parameterize( + ["float32", "int32", "int16"], + [8000, 16000], + [1, 2], + [False, True], + [False, True], + ) + def test_wav(self, dtype, sample_rate, num_channels, normalize, channels_first): + """`soundfile_backend.load` can load wav format correctly.""" + self.assert_wav(dtype, sample_rate, num_channels, normalize, channels_first) + + @parameterize( + ["int16"], [16000], [2], [False], + ) + def test_wav_large(self, dtype, sample_rate, num_channels, normalize): + """`soundfile_backend.load` can load large wav file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_wav(dtype, sample_rate, num_channels, normalize, duration=two_hours) + + @parameterize(["float32", "int32", "int16"], [4, 8, 16, 32], [False, True]) + def test_multiple_channels(self, dtype, num_channels, channels_first): + """`soundfile_backend.load` can load wav file with more than 2 channels.""" + sample_rate = 8000 + normalize = False + self.assert_wav(dtype, sample_rate, num_channels, normalize, channels_first) + + @parameterize(["int32", "int16"], [8000, 16000], [1, 2], [False, True]) + @skipIfFormatNotSupported("NIST") + def test_sphere(self, dtype, sample_rate, num_channels, channels_first): + """`soundfile_backend.load` can load sphere format correctly.""" + self.assert_sphere(dtype, sample_rate, num_channels, channels_first) + + @parameterize(["int32", "int16"], [8000, 16000], [1, 2], [False, True]) + @skipIfFormatNotSupported("FLAC") + def test_flac(self, dtype, sample_rate, num_channels, channels_first): + """`soundfile_backend.load` can load flac format correctly.""" + self.assert_flac(dtype, sample_rate, num_channels, channels_first) diff --git a/test/torchaudio_unittest/soundfile_backend/save_test.py b/test/torchaudio_unittest/soundfile_backend/save_test.py new file mode 100644 index 0000000000..a99c69b0c8 --- /dev/null +++ b/test/torchaudio_unittest/soundfile_backend/save_test.py @@ -0,0 +1,211 @@ +import itertools +from unittest.mock import patch + +from torchaudio._internal import module_utils as _mod_utils +from torchaudio.backend import _soundfile_backend as soundfile_backend +from parameterized import parameterized + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoModule, + get_wav_data, + load_wav, +) +from .common import parameterize, dtype2subtype, skipIfFormatNotSupported + +if _mod_utils.is_module_available("soundfile"): + import soundfile + + +class MockedSaveTest(PytorchTestCase): + @parameterize( + ["float32", "int32", "int16", "uint8"], [8000, 16000], [1, 2], [False, True], + ) + @patch("soundfile.write") + def test_wav(self, dtype, sample_rate, num_channels, channels_first, mocked_write): + """soundfile_backend.save passes correct subtype to soundfile.write when WAV""" + filepath = "foo.wav" + input_tensor = get_wav_data( + dtype, + num_channels, + num_frames=3 * sample_rate, + normalize=dtype == "flaot32", + channels_first=channels_first, + ).t() + + soundfile_backend.save( + filepath, input_tensor, sample_rate, channels_first=channels_first + ) + + # on +Py3.8 call_args.kwargs is more descreptive + args = mocked_write.call_args[1] + assert args["file"] == filepath + assert args["samplerate"] == sample_rate + assert args["subtype"] == dtype2subtype(dtype) + assert args["format"] is None + self.assertEqual( + args["data"], input_tensor.t() if channels_first else input_tensor + ) + + @patch("soundfile.write") + def assert_non_wav( + self, fmt, dtype, sample_rate, num_channels, channels_first, mocked_write + ): + """soundfile_backend.save passes correct subtype and format to soundfile.write when SPHERE""" + filepath = f"foo.{fmt}" + input_tensor = get_wav_data( + dtype, + num_channels, + num_frames=3 * sample_rate, + normalize=False, + channels_first=channels_first, + ).t() + expected_data = input_tensor.t() if channels_first else input_tensor + + soundfile_backend.save( + filepath, input_tensor, sample_rate, channels_first=channels_first + ) + + # on +Py3.8 call_args.kwargs is more descreptive + args = mocked_write.call_args[1] + assert args["file"] == filepath + assert args["samplerate"] == sample_rate + assert args["subtype"] is None + if fmt in ["sph", "nist", "nis"]: + assert args["format"] == "NIST" + else: + assert args["format"] is None + self.assertEqual(args["data"], expected_data) + + @parameterize( + ["sph", "nist", "nis"], + ["int32", "int16"], + [8000, 16000], + [1, 2], + [False, True], + ) + def test_sph(self, fmt, dtype, sample_rate, num_channels, channels_first): + """soundfile_backend.save passes default format and subtype (None-s) to + soundfile.write when not WAV""" + self.assert_non_wav(fmt, dtype, sample_rate, num_channels, channels_first) + + @parameterize( + ["int32", "int16"], [8000, 16000], [1, 2], [False, True], + ) + def test_flac(self, dtype, sample_rate, num_channels, channels_first): + """soundfile_backend.save passes default format and subtype (None-s) to + soundfile.write when not WAV""" + self.assert_non_wav("flac", dtype, sample_rate, num_channels, channels_first) + + @parameterize( + ["int32", "int16"], [8000, 16000], [1, 2], [False, True], + ) + def test_ogg(self, dtype, sample_rate, num_channels, channels_first): + """soundfile_backend.save passes default format and subtype (None-s) to + soundfile.write when not WAV""" + self.assert_non_wav("ogg", dtype, sample_rate, num_channels, channels_first) + + +@skipIfNoModule("soundfile") +class SaveTestBase(TempDirMixin, PytorchTestCase): + def assert_wav(self, dtype, sample_rate, num_channels, num_frames): + """`soundfile_backend.save` can save wav format.""" + path = self.get_temp_path("data.wav") + expected = get_wav_data( + dtype, num_channels, num_frames=num_frames, normalize=False + ) + soundfile_backend.save(path, expected, sample_rate) + found, sr = load_wav(path, normalize=False) + assert sample_rate == sr + self.assertEqual(found, expected) + + def _assert_non_wav(self, fmt, dtype, sample_rate, num_channels): + """`soundfile_backend.save` can save non-wav format. + + Due to precision missmatch, and the lack of alternative way to decode the + resulting files without using soundfile, only meta data are validated. + """ + num_frames = sample_rate * 3 + path = self.get_temp_path(f"data.{fmt}") + expected = get_wav_data( + dtype, num_channels, num_frames=num_frames, normalize=False + ) + soundfile_backend.save(path, expected, sample_rate) + sinfo = soundfile.info(path) + assert sinfo.format == fmt.upper() + assert sinfo.frames == num_frames + assert sinfo.channels == num_channels + assert sinfo.samplerate == sample_rate + + def assert_flac(self, dtype, sample_rate, num_channels): + """`soundfile_backend.save` can save flac format.""" + self._assert_non_wav("flac", dtype, sample_rate, num_channels) + + def assert_sphere(self, dtype, sample_rate, num_channels): + """`soundfile_backend.save` can save sph format.""" + self._assert_non_wav("nist", dtype, sample_rate, num_channels) + + def assert_ogg(self, dtype, sample_rate, num_channels): + """`soundfile_backend.save` can save ogg format. + + As we cannot inspect the OGG format (it's lossy), we only check the metadata. + """ + self._assert_non_wav("ogg", dtype, sample_rate, num_channels) + + +@skipIfNoModule("soundfile") +class TestSave(SaveTestBase): + @parameterize( + ["float32", "int32", "int16"], [8000, 16000], [1, 2], + ) + def test_wav(self, dtype, sample_rate, num_channels): + """`soundfile_backend.save` can save wav format.""" + self.assert_wav(dtype, sample_rate, num_channels, num_frames=None) + + @parameterize( + ["float32", "int32", "int16"], [4, 8, 16, 32], + ) + def test_multiple_channels(self, dtype, num_channels): + """`soundfile_backend.save` can save wav with more than 2 channels.""" + sample_rate = 8000 + self.assert_wav(dtype, sample_rate, num_channels, num_frames=None) + + @parameterize( + ["int32", "int16"], [8000, 16000], [1, 2], + ) + @skipIfFormatNotSupported("NIST") + def test_sphere(self, dtype, sample_rate, num_channels): + """`soundfile_backend.save` can save sph format.""" + self.assert_sphere(dtype, sample_rate, num_channels) + + @parameterize( + [8000, 16000], [1, 2], + ) + @skipIfFormatNotSupported("FLAC") + def test_flac(self, sample_rate, num_channels): + """`soundfile_backend.save` can save flac format.""" + self.assert_flac("float32", sample_rate, num_channels) + + @parameterize( + [8000, 16000], [1, 2], + ) + @skipIfFormatNotSupported("OGG") + def test_ogg(self, sample_rate, num_channels): + """`soundfile_backend.save` can save ogg/vorbis format.""" + self.assert_ogg("float32", sample_rate, num_channels) + + +@skipIfNoModule("soundfile") +class TestSaveParams(TempDirMixin, PytorchTestCase): + """Test the correctness of optional parameters of `soundfile_backend.save`""" + + @parameterize([(True,), (False,)]) + def test_channels_first(self, channels_first): + """channels_first swaps axes""" + path = self.get_temp_path("data.wav") + data = get_wav_data("int32", 2, channels_first=channels_first) + soundfile_backend.save(path, data, 8000, channels_first=channels_first) + found = load_wav(path)[0] + expected = data if channels_first else data.transpose(1, 0) + self.assertEqual(found, expected, atol=1e-4, rtol=1e-8) diff --git a/test/torchaudio_unittest/sox_effect/sox_effects_chain_test.py b/test/torchaudio_unittest/sox_effect/sox_effects_chain_test.py deleted file mode 100644 index 765ab62094..0000000000 --- a/test/torchaudio_unittest/sox_effect/sox_effects_chain_test.py +++ /dev/null @@ -1,224 +0,0 @@ -import sys -import math -import unittest - -import torch -import torchaudio - -from .. import common_utils - - -@common_utils.skipIfNoSoxBackend -class Test_SoxEffectsChain(common_utils.TorchaudioTestCase): - backend = 'sox' - - test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.wav") - - def test_single_channel(self): - fn_sine = common_utils.get_asset_path("sinewave.wav") - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(fn_sine) - E.append_effect_to_chain("echos", [0.8, 0.7, 40, 0.25, 63, 0.3]) - x, sr = E.sox_build_flow_effects() - # check if effects worked - # print(x.size()) - - def test_rate_channels(self): - target_rate = 16000 - target_channels = 1 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("rate", [target_rate]) - E.append_effect_to_chain("channels", [target_channels]) - x, sr = E.sox_build_flow_effects() - # check if effects worked - self.assertEqual(sr, target_rate) - self.assertEqual(x.size(0), target_channels) - - @unittest.skipIf(sys.platform == 'darwin', 'This test is known to fail on macOS') - def test_lowpass_speed(self): - speed = .8 - si, _ = torchaudio.info(self.test_filepath) - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("lowpass", 100) - E.append_effect_to_chain("speed", speed) - E.append_effect_to_chain("rate", si.rate) - x, sr = E.sox_build_flow_effects() - # check if effects worked, add small tolerance for rounding effects - self.assertEqual(x.size(1), int((si.length / si.channels) / speed), atol=1, rtol=1e-8) - - def test_ulaw_and_siginfo(self): - si_out = torchaudio.sox_signalinfo_t() - ei_out = torchaudio.sox_encodinginfo_t() - si_out.precision = 8 - ei_out.encoding = torchaudio.get_sox_encoding_t(9) - ei_out.bits_per_sample = 8 - si_in, ei_in = torchaudio.info(self.test_filepath) - si_out.rate = 44100 - si_out.channels = 2 - E = torchaudio.sox_effects.SoxEffectsChain(out_siginfo=si_out, out_encinfo=ei_out) - E.set_input_file(self.test_filepath) - x, sr = E.sox_build_flow_effects() - # Note: the output was encoded into ulaw because the - # number of unique values in the output is less than 256. - self.assertLess(x.unique().size(0), 2**8 + 1) - self.assertEqual(x.numel(), si_in.length) - - def test_band_chorus(self): - si_in, ei_in = torchaudio.info(self.test_filepath) - ei_in.encoding = torchaudio.get_sox_encoding_t(1) - E = torchaudio.sox_effects.SoxEffectsChain(out_encinfo=ei_in, out_siginfo=si_in) - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("band", ["-n", "10k", "3.5k"]) - E.append_effect_to_chain("chorus", [.5, .7, 55, 0.4, .25, 2, '-s']) - E.append_effect_to_chain("rate", [si_in.rate]) - E.append_effect_to_chain("channels", [si_in.channels]) - x, sr = E.sox_build_flow_effects() - # The chorus effect will make the output file longer than the input - self.assertEqual(x.size(0), si_in.channels) - self.assertGreaterEqual(x.size(1) * x.size(0), si_in.length) - - def test_synth(self): - si_in, ei_in = torchaudio.info(self.test_filepath) - len_in_seconds = si_in.length / si_in.channels / si_in.rate - ei_in.encoding = torchaudio.get_sox_encoding_t(1) - E = torchaudio.sox_effects.SoxEffectsChain(out_encinfo=ei_in, out_siginfo=si_in) - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("synth", [str(len_in_seconds), "pinknoise", "mix"]) - E.append_effect_to_chain("rate", [44100]) - E.append_effect_to_chain("channels", [2]) - x, sr = E.sox_build_flow_effects() - self.assertEqual(x.size(0), si_in.channels) - self.assertEqual(si_in.length, x.size(0) * x.size(1)) - - def test_gain(self): - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("gain", ["5"]) - x, sr = E.sox_build_flow_effects() - E.clear_chain() - self.assertTrue(x.abs().max().item(), 1.) - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("gain", ["-e", "-5"]) - x, sr = E.sox_build_flow_effects() - E.clear_chain() - self.assertLess(x.abs().max().item(), 1.) - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("gain", ["-b", "8"]) - x, sr = E.sox_build_flow_effects() - E.clear_chain() - self.assertTrue(x.abs().max().item(), 1.) - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("gain", ["-n", "-10"]) - x, sr = E.sox_build_flow_effects() - E.clear_chain() - self.assertLess(x.abs().max().item(), 1.) - - def test_tempo_or_speed(self): - tempo = .8 - si, _ = torchaudio.info(self.test_filepath) - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("tempo", ["-s", tempo]) - x, sr = E.sox_build_flow_effects() - # check if effect worked - self.assertAlmostEqual(x.size(1), math.ceil((si.length / si.channels) / tempo), delta=1) - # tempo > 1 - E.clear_chain() - tempo = 1.2 - E.append_effect_to_chain("tempo", ["-s", tempo]) - x, sr = E.sox_build_flow_effects() - # check if effect worked - self.assertAlmostEqual(x.size(1), math.ceil((si.length / si.channels) / tempo), delta=1) - # tempo > 1 - E.clear_chain() - speed = 1.2 - E.append_effect_to_chain("speed", [speed]) - E.append_effect_to_chain("rate", [si.rate]) - x, sr = E.sox_build_flow_effects() - # check if effect worked - self.assertAlmostEqual(x.size(1), math.ceil((si.length / si.channels) / speed), delta=1) - # speed < 1 - E.clear_chain() - speed = 0.8 - E.append_effect_to_chain("speed", [speed]) - E.append_effect_to_chain("rate", [si.rate]) - x, sr = E.sox_build_flow_effects() - # check if effect worked - self.assertAlmostEqual(x.size(1), math.ceil((si.length / si.channels) / speed), delta=1) - - def test_trim(self): - x_orig, _ = torchaudio.load(self.test_filepath) - offset = "10000s" - offset_int = int(offset[:-1]) - num_frames = "20000s" - num_frames_int = int(num_frames[:-1]) - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("trim", [offset, num_frames]) - x, sr = E.sox_build_flow_effects() - # check if effect worked - self.assertTrue(x.allclose(x_orig[:, offset_int:(offset_int + num_frames_int)], rtol=1e-4, atol=1e-4)) - - def test_silence_contrast(self): - si, _ = torchaudio.info(self.test_filepath) - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("silence", [1, 100, 1]) - E.append_effect_to_chain("contrast", []) - x, sr = E.sox_build_flow_effects() - # check if effect worked - self.assertLess(x.numel(), si.length) - - def test_reverse(self): - x_orig, _ = torchaudio.load(self.test_filepath) - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("reverse", "") - x_rev, _ = E.sox_build_flow_effects() - # check if effect worked - rev_idx = torch.LongTensor(range(x_orig.size(1))[::-1]) - self.assertTrue(x_orig.allclose(x_rev[:, rev_idx], rtol=1e-5, atol=2e-5)) - - def test_compand_fade(self): - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("compand", ["0.3,1", "6:-70,-60,-20", "-5", "-90", "0.2"]) - E.append_effect_to_chain("fade", ["q", "0.25", "0", "0.33"]) - x, _ = E.sox_build_flow_effects() - # check if effect worked - # print(x.size()) - - def test_biquad_delay(self): - si, _ = torchaudio.info(self.test_filepath) - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("biquad", ["0.25136437", "0.50272873", "0.25136437", - "1.0", "-0.17123075", "0.17668821"]) - E.append_effect_to_chain("delay", ["15000s"]) - x, _ = E.sox_build_flow_effects() - # check if effect worked - self.assertTrue(x.size(1) == (si.length / si.channels) + 15000) - - def test_invalid_effect_name(self): - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - # there is no effect named "special" - with self.assertRaises(LookupError): - E.append_effect_to_chain("special", [""]) - - def test_unimplemented_effect(self): - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - # the sox spectrogram function is not implemented in torchaudio - with self.assertRaises(NotImplementedError): - E.append_effect_to_chain("spectrogram", [""]) - - def test_invalid_effect_options(self): - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - # first two options should be combined to "0.3,1" - E.append_effect_to_chain("compand", ["0.3", "1", "6:-70,-60,-20", "-5", "-90", "0.2"]) - with self.assertRaises(RuntimeError): - E.sox_build_flow_effects() diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index 9a6a5e3c16..be46d8a0f6 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -8,6 +8,9 @@ sox_effects, transforms ) + +USE_SOUNDFILE_LEGACY_INTERFACE = None + from torchaudio.backend import ( list_audio_backends, get_audio_backend, @@ -21,36 +24,8 @@ SignalInfo, EncodingInfo, ) -from torchaudio.sox_effects import ( - init_sox_effects as _init_sox_effects, - shutdown_sox_effects as _shutdown_sox_effects, -) try: from .version import __version__, git_version # noqa: F401 except ImportError: pass - - -@_mod_utils.deprecated( - "Please remove the function call to initialize_sox. " - "Resource initialization is now automatically handled.") -def initialize_sox(): - """Initialize sox effects. - - This function is deprecated. See :py:func:`torchaudio.sox_effects.init_sox_effects` - """ - _init_sox_effects() - - -@_mod_utils.deprecated( - "Please remove the function call to torchaudio.shutdown_sox. " - "Resource clean up is now automatically handled. " - "In the unlikely event that you need to manually shutdown sox, " - "please use torchaudio.sox_effects.shutdown_sox_effects.") -def shutdown_sox(): - """Shutdown sox effects. - - This function is deprecated. See :py:func:`torchaudio.sox_effects.shutdown_sox_effects` - """ - _shutdown_sox_effects() diff --git a/torchaudio/backend/_soundfile_backend.py b/torchaudio/backend/_soundfile_backend.py new file mode 100644 index 0000000000..83d3cbc3cf --- /dev/null +++ b/torchaudio/backend/_soundfile_backend.py @@ -0,0 +1,223 @@ +"""The new soundfile backend which will become default in 0.8.0 onward""" +from typing import Tuple, Optional +import warnings + +import torch +from torchaudio._internal import module_utils as _mod_utils +from .common import AudioMetaData + + +if _mod_utils.is_module_available("soundfile"): + import soundfile + + +@_mod_utils.requires_module("soundfile") +def info(filepath: str) -> AudioMetaData: + """Get signal information of an audio file. + + Args: + filepath (str or pathlib.Path): Path to audio file. + This functionalso handles ``pathlib.Path`` objects, but is annotated as ``str`` + for the consistency with "sox_io" backend, which has a restriction on type annotation + for TorchScript compiler compatiblity. + + Returns: + AudioMetaData: meta data of the given audio. + """ + sinfo = soundfile.info(filepath) + return AudioMetaData(sinfo.samplerate, sinfo.frames, sinfo.channels) + + +_SUBTYPE2DTYPE = { + "PCM_S8": "int8", + "PCM_U8": "uint8", + "PCM_16": "int16", + "PCM_32": "int32", + "FLOAT": "float32", + "DOUBLE": "float64", +} + + +@_mod_utils.requires_module("soundfile") +def load( + filepath: str, + frame_offset: int = 0, + num_frames: int = -1, + normalize: bool = True, + channels_first: bool = True, +) -> Tuple[torch.Tensor, int]: + """Load audio data from file. + + Note: + The formats this function can handle depend on the soundfile installation. + This function is tested on the following formats; + + * WAV + + * 32-bit floating-point + * 32-bit signed integer + * 16-bit signed integer + * 8-bit unsigned integer + + * FLAC + * OGG/VORBIS + * SPHERE + + By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with + ``float32`` dtype and the shape of ``[channel, time]``. + The samples are normalized to fit in the range of ``[-1.0, 1.0]``. + + When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit + signed integer and 8-bit unsigned integer (24-bit signed integer is not supported), + by providing ``normalize=False``, this function can return integer Tensor, where the samples + are expressed within the whole range of the corresponding dtype, that is, ``int32`` tensor + for 32-bit signed PCM, ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. + + ``normalize`` parameter has no effect on 32-bit floating-point WAV and other formats, such as + ``flac`` and ``mp3``. + For these formats, this function always returns ``float32`` Tensor with values normalized to + ``[-1.0, 1.0]``. + + Args: + filepath (str or pathlib.Path): Path to audio file. + This functionalso handles ``pathlib.Path`` objects, but is annotated as ``str`` + for the consistency with "sox_io" backend, which has a restriction on type annotation + for TorchScript compiler compatiblity. + frame_offset (int): + Number of frames to skip before start reading data. + num_frames (int): + Maximum number of frames to read. ``-1`` reads all the remaining samples, + starting from ``frame_offset``. + This function may return the less number of frames if there is not enough + frames in the given file. + normalize (bool): + When ``True``, this function always return ``float32``, and sample values are + normalized to ``[-1.0, 1.0]``. + If input file is integer WAV, giving ``False`` will change the resulting Tensor type to + integer type. + This argument has no effect for formats other than integer WAV type. + channels_first (bool): + When True, the returned Tensor has dimension ``[channel, time]``. + Otherwise, the returned Tensor's dimension is ``[time, channel]``. + + Returns: + torch.Tensor: + If the input file has integer wav format and normalization is off, then it has + integer type, else ``float32`` type. If ``channels_first=True``, it has + ``[channel, time]`` else ``[time, channel]``. + """ + with soundfile.SoundFile(filepath, "r") as file_: + if file_.format != "WAV" or normalize: + dtype = "float32" + elif file_.subtype not in _SUBTYPE2DTYPE: + raise ValueError(f"Unsupported subtype: {file_.subtype}") + else: + dtype = _SUBTYPE2DTYPE[file_.subtype] + + frames = file_._prepare_read(frame_offset, None, num_frames) + waveform = file_.read(frames, dtype, always_2d=True) + sample_rate = file_.samplerate + + waveform = torch.from_numpy(waveform) + if channels_first: + waveform = waveform.t() + return waveform, sample_rate + + +@_mod_utils.requires_module("soundfile") +def save( + filepath: str, + src: torch.Tensor, + sample_rate: int, + channels_first: bool = True, + compression: Optional[float] = None, +): + """Save audio data to file. + + Note: + The formats this function can handle depend on the soundfile installation. + This function is tested on the following formats; + + * WAV + + * 32-bit floating-point + * 32-bit signed integer + * 16-bit signed integer + * 8-bit unsigned integer + + * FLAC + * OGG/VORBIS + * SPHERE + + Args: + filepath (str or pathlib.Path): Path to audio file. + This functionalso handles ``pathlib.Path`` objects, but is annotated as ``str`` + for the consistency with "sox_io" backend, which has a restriction on type annotation + for TorchScript compiler compatiblity. + tensor (torch.Tensor): Audio data to save. must be 2D tensor. + sample_rate (int): sampling rate + channels_first (bool): + If ``True``, the given tensor is interpreted as ``[channel, time]``, + otherwise ``[time, channel]``. + compression (Optional[float]): + Not used. It is here only for interface compatibility reson with "sox_io" backend. + """ + if src.ndim != 2: + raise ValueError(f"Expected 2D Tensor, got {src.ndim}D.") + if compression is not None: + warnings.warn( + '`save` function of "soundfile" backend does not support "compression" parameter. ' + "The argument is silently ignored." + ) + + ext = str(filepath).split(".")[-1].lower() + if ext != "wav": + subtype = None + elif src.dtype == torch.uint8: + subtype = "PCM_U8" + elif src.dtype == torch.int16: + subtype = "PCM_16" + elif src.dtype == torch.int32: + subtype = "PCM_32" + elif src.dtype == torch.float32: + subtype = "FLOAT" + elif src.dtype == torch.float64: + subtype = "DOUBLE" + else: + raise ValueError(f"Unsupported dtype for WAV: {src.dtype}") + + format_ = None + # sph is a extension used in TED-LIUM but soundfile does not recognize it as NIST format, + # so we extend the extensions manually here + if ext in ["nis", "nist", "sph"]: + format_ = "NIST" + + if channels_first: + src = src.t() + + soundfile.write( + file=filepath, data=src, samplerate=sample_rate, subtype=subtype, format=format_ + ) + + +@_mod_utils.requires_module("soundfile") +@_mod_utils.deprecated('Please use "torchaudio.load".', "0.9.0") +def load_wav( + filepath: str, + frame_offset: int = 0, + num_frames: int = -1, + channels_first: bool = True, +) -> Tuple[torch.Tensor, int]: + """Load wave file. + + This function is defined only for the purpose of compatibility against other backend + for simple usecases, such as ``torchaudio.load_wav(filepath)``. + The implementation is same as :py:func:`load`. + """ + return load( + filepath, + frame_offset, + num_frames, + normalize=False, + channels_first=channels_first, + ) diff --git a/torchaudio/backend/common.py b/torchaudio/backend/common.py index bc61df4746..135a18caee 100644 --- a/torchaudio/backend/common.py +++ b/torchaudio/backend/common.py @@ -1,10 +1,30 @@ from typing import Any, Optional +from torchaudio._internal import module_utils as _mod_utils + +class AudioMetaData: + """Return type of ``torchaudio.info`` function. + + This class is used by :ref:`"sox_io" backend` and + :ref:`"soundfile" backend with the new interface`. + + :ivar int sample_rate: Sample rate + :ivar int num_frames: The number of frames + :ivar int num_channels: The number of channels + """ + def __init__(self, sample_rate: int, num_frames: int, num_channels: int): + self.sample_rate = sample_rate + self.num_frames = num_frames + self.num_channels = num_channels + + +@_mod_utils.deprecated('Please migrate to `AudioMetaData`.', '0.9.0') class SignalInfo: - """Data class returned ``info`` functions. + """One of return types of ``torchaudio.info`` functions. - Used by :ref:`sox backend` and :ref:`soundfile backend` + This class is used by :ref:`"sox" backend (deprecated)` and + :ref:`"soundfile" backend with the legacy interface (deprecated)`. See https://fossies.org/dox/sox-14.4.2/structsox__signalinfo__t.html @@ -25,10 +45,12 @@ def __init__(self, self.length = length +@_mod_utils.deprecated('Please migrate to `AudioMetaData`.', '0.9.0') class EncodingInfo: - """Data class returned ``info`` functions. + """One of return types of ``torchaudio.info`` functions. - Used by :ref:`sox backend` and :ref:`soundfile backend` + This class is used by :ref:`"sox" backend (deprecated)` and + :ref:`"soundfile" backend with the legacy interface (deprecated)`. See https://fossies.org/dox/sox-14.4.2/structsox__encodinginfo__t.html diff --git a/torchaudio/backend/sox_backend.py b/torchaudio/backend/sox_backend.py index 45d75817af..bf167dd195 100644 --- a/torchaudio/backend/sox_backend.py +++ b/torchaudio/backend/sox_backend.py @@ -92,6 +92,9 @@ def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: @_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated( + 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', + '0.9.0') def save_encinfo(filepath: str, src: Tensor, channels_first: bool = True, @@ -163,6 +166,9 @@ def save_encinfo(filepath: str, @_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated( + 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', + '0.9.0') def sox_signalinfo_t() -> SignalInfo: r"""Create a sox_signalinfo_t object. This object can be used to set the sample rate, number of channels, length, bit precision and headroom multiplier @@ -186,6 +192,9 @@ def sox_signalinfo_t() -> SignalInfo: @_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated( + 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', + '0.9.0') def sox_encodinginfo_t() -> EncodingInfo: r"""Create a sox_encodinginfo_t object. This object can be used to set the encoding type, bit precision, compression factor, reverse bytes, reverse nibbles, @@ -224,6 +233,9 @@ def sox_encodinginfo_t() -> EncodingInfo: @_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated( + 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', + '0.9.0') def get_sox_encoding_t(i: int = None) -> EncodingInfo: r"""Get enum of sox_encoding_t for sox encodings. @@ -242,6 +254,9 @@ def get_sox_encoding_t(i: int = None) -> EncodingInfo: @_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated( + 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', + '0.9.0') def get_sox_option_t(i: int = 2) -> Any: r"""Get enum of sox_option_t for sox encodinginfo options. @@ -259,6 +274,9 @@ def get_sox_option_t(i: int = 2) -> Any: @_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated( + 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', + '0.9.0') def get_sox_bool(i: int = 0) -> Any: r"""Get enum of sox_bool for sox encodinginfo options. diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index e2823ada00..1e57e92834 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -5,18 +5,7 @@ module_utils as _mod_utils, ) - -class AudioMetaData: - """Data class to be returned by :py:func:`~torchaudio.backend.sox_io_backend.info`. - - :ivar int sample_rate: Sample rate - :ivar int num_frames: The number of frames - :ivar int num_channels: The number of channels - """ - def __init__(self, sample_rate: int, num_frames: int, num_channels: int): - self.sample_rate = sample_rate - self.num_frames = num_frames - self.num_channels = num_channels +from .common import AudioMetaData @_mod_utils.requires_module('torchaudio._torchaudio') @@ -25,11 +14,11 @@ def info(filepath: str) -> AudioMetaData: Args: filepath (str or pathlib.Path): - Path to audio file. This function also handles ``pathlib.Path`` objects, but is annotated as - ``str`` for TorchScript compiler compatibility. + Path to audio file. This function also handles ``pathlib.Path`` objects, + but is annotated as ``str`` for TorchScript compatibility. Returns: - AudioMetaData: meta data of the given audio. + AudioMetaData: Metadata of the given audio. """ # Cast to str in case type is `pathlib.Path` filepath = str(filepath) @@ -195,7 +184,6 @@ def load_wav( ) -> Tuple[torch.Tensor, int]: """Load wave file. - This function is defined only for the purpose of compatibility against other backend for simple usecases, such as ``torchaudio.load_wav(filepath)``. The implementation is same as :py:func:`load`. diff --git a/torchaudio/backend/utils.py b/torchaudio/backend/utils.py index 87ec948fba..b888c04c48 100644 --- a/torchaudio/backend/utils.py +++ b/torchaudio/backend/utils.py @@ -9,6 +9,7 @@ sox_backend, sox_io_backend, soundfile_backend, + _soundfile_backend, ) __all__ = [ @@ -41,6 +42,12 @@ def set_audio_backend(backend: Optional[str]): One of ``"sox"``, ``"sox_io"`` or ``"soundfile"`` based on availability of the system. If ``None`` is provided the current backend is unassigned. """ + if torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE is not None: + warnings.warn( + '"torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE" flag is depredated and will be removed in 0.9.0. ' + 'Please remove the use of flag.' + ) + if backend is not None and backend not in list_audio_backends(): raise RuntimeError( f'Backend "{backend}" is not one of ' @@ -50,15 +57,21 @@ def set_audio_backend(backend: Optional[str]): module = no_backend elif backend == 'sox': warnings.warn( - '"sox" backend is being deprecated. ' - 'The default backend will be changed to "sox_io" backend in 0.8.0 and ' - '"sox" backend will be removed in 0.9.0. Please migrate to "sox_io" backend. ' - 'Please refer to https://github.com/pytorch/audio/issues/903 for the detail.') + '"sox" backend is depredated and will be removed in 0.9.0. ' + 'Please use "sox_io" backend.' + ) module = sox_backend elif backend == 'sox_io': module = sox_io_backend elif backend == 'soundfile': - module = soundfile_backend + if torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE: + warnings.warn( + 'The legacy interface of "soundfile" backend is depredated and will be removed in 0.9.0. ' + 'Please migrate to the new interface.' + ) + module = soundfile_backend + else: + module = _soundfile_backend else: raise NotImplementedError(f'Unexpected backend "{backend}"') @@ -69,7 +82,7 @@ def set_audio_backend(backend: Optional[str]): def _init_audio_backend(): backends = list_audio_backends() if 'sox' in backends: - set_audio_backend('sox') + set_audio_backend('sox_io') elif 'soundfile' in backends: set_audio_backend('soundfile') else: @@ -89,6 +102,6 @@ def get_audio_backend() -> Optional[str]: return 'sox' if torchaudio.load == sox_io_backend.load: return 'sox_io' - if torchaudio.load == soundfile_backend.load: + if torchaudio.load in [soundfile_backend.load, _soundfile_backend.load]: return 'soundfile' raise ValueError('Unknown backend.') diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index 792f439ccd..1160595992 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -573,7 +573,7 @@ def fbank(waveform: Tensor, # size (m, padded_window_size // 2 + 1, 2) fft = torch.rfft(strided_input, 1, normalized=False, onesided=True) - power_spectrum = fft.pow(2).sum(2).unsqueeze(1) # size (m, 1, padded_window_size // 2 + 1) + power_spectrum = fft.pow(2).sum(2) # size (m, padded_window_size // 2 + 1) if not use_power: power_spectrum = power_spectrum.pow(0.5) @@ -582,11 +582,11 @@ def fbank(waveform: Tensor, low_freq, high_freq, vtln_low, vtln_high, vtln_warp) mel_energies = mel_energies.to(device=device, dtype=dtype) - # pad right column with zeros and add dimension, size (1, num_mel_bins, padded_window_size // 2 + 1) - mel_energies = torch.nn.functional.pad(mel_energies, (0, 1), mode='constant', value=0).unsqueeze(0) + # pad right column with zeros and add dimension, size (num_mel_bins, padded_window_size // 2 + 1) + mel_energies = torch.nn.functional.pad(mel_energies, (0, 1), mode='constant', value=0) # sum with mel fiterbanks over the power spectrum, size (m, num_mel_bins) - mel_energies = (power_spectrum * mel_energies).sum(dim=2) + mel_energies = torch.mm(power_spectrum, mel_energies.T) if use_log_fbank: # avoid log of zero (which should be prevented anyway by dithering) mel_energies = torch.max(mel_energies, _get_epsilon(device, dtype)).log() diff --git a/torchaudio/csrc/sox.cpp b/torchaudio/csrc/sox.cpp index 0f099946fd..3be7d01314 100644 --- a/torchaudio/csrc/sox.cpp +++ b/torchaudio/csrc/sox.cpp @@ -175,207 +175,10 @@ void write_audio_file( } } -int build_flow_effects(const std::string& file_name, - at::Tensor otensor, - bool ch_first, - sox_signalinfo_t* target_signal, - sox_encodinginfo_t* target_encoding, - const char* file_type, - std::vector pyeffs, - int max_num_eopts) { - - /* This function builds an effects flow and puts the results into a tensor. - It can also be used to re-encode audio using any of the available encoding - options in SoX including sample rate and channel re-encoding. */ - - // open input - sox_format_t* input = sox_open_read(file_name.c_str(), nullptr, nullptr, nullptr); - if (input == nullptr) { - throw std::runtime_error("Error opening audio file"); - } - - // only used if target signal or encoding are null - sox_signalinfo_t empty_signal; - sox_encodinginfo_t empty_encoding; - - // set signalinfo and encodinginfo if blank - if(target_signal == nullptr) { - target_signal = &empty_signal; - target_signal->rate = input->signal.rate; - target_signal->channels = input->signal.channels; - target_signal->length = SOX_UNSPEC; - target_signal->precision = input->signal.precision; -#if SOX_LIB_VERSION_CODE >= 918272 // >= 14.3.0 - target_signal->mult = nullptr; -#endif - } - if(target_encoding == nullptr) { - target_encoding = &empty_encoding; - target_encoding->encoding = SOX_ENCODING_SIGN2; // Sample format - target_encoding->bits_per_sample = input->signal.precision; // Bits per sample - target_encoding->compression = 0.0; // Compression factor - target_encoding->reverse_bytes = sox_option_default; // Should bytes be reversed - target_encoding->reverse_nibbles = sox_option_default; // Should nibbles be reversed - target_encoding->reverse_bits = sox_option_default; // Should bits be reversed (pairs of bits?) - target_encoding->opposite_endian = sox_false; // Reverse endianness - } - - // check for rate or channels effect and change the output signalinfo accordingly - for (SoxEffect se : pyeffs) { - if (se.ename == "rate") { - target_signal->rate = std::stod(se.eopts[0]); - } else if (se.ename == "channels") { - target_signal->channels = std::stoi(se.eopts[0]); - } - } - - // create interm_signal for effects, intermediate steps change this in-place - sox_signalinfo_t interm_signal = input->signal; - -#ifdef __APPLE__ - // According to Mozilla Deepspeech sox_open_memstream_write doesn't work - // with OSX - char tmp_name[] = "/tmp/fileXXXXXX"; - int tmp_fd = mkstemp(tmp_name); - close(tmp_fd); - sox_format_t* output = sox_open_write(tmp_name, target_signal, - target_encoding, "wav", nullptr, nullptr); -#else - // create buffer and buffer_size for output in memwrite - char* buffer; - size_t buffer_size; - // in-memory descriptor (this may not work for OSX) - sox_format_t* output = sox_open_memstream_write(&buffer, - &buffer_size, - target_signal, - target_encoding, - file_type, nullptr); -#endif - if (output == nullptr) { - throw std::runtime_error("Error opening output memstream/temporary file"); - } - // Setup the effects chain to decode/resample - sox_effects_chain_t* chain = - sox_create_effects_chain(&input->encoding, &output->encoding); - - sox_effect_t* e = sox_create_effect(sox_find_effect("input")); - char* io_args[1]; - io_args[0] = (char*)input; - sox_effect_options(e, 1, io_args); - sox_add_effect(chain, e, &interm_signal, &input->signal); - free(e); - - for(SoxEffect tae : pyeffs) { - if(tae.ename == "no_effects") break; - e = sox_create_effect(sox_find_effect(tae.ename.c_str())); - e->global_info->global_info->verbosity = 1; - if(tae.eopts[0] == "") { - sox_effect_options(e, 0, nullptr); - } else { - int num_opts = tae.eopts.size(); - char* sox_args[max_num_eopts]; - for(std::vector::size_type i = 0; i != tae.eopts.size(); i++) { - sox_args[i] = (char*) tae.eopts[i].c_str(); - } - if(sox_effect_options(e, num_opts, sox_args) != SOX_SUCCESS) { -#ifdef __APPLE__ - unlink(tmp_name); -#endif - throw std::runtime_error("invalid effect options, see SoX docs for details"); - } - } - sox_add_effect(chain, e, &interm_signal, &output->signal); - free(e); - } - - e = sox_create_effect(sox_find_effect("output")); - io_args[0] = (char*)output; - sox_effect_options(e, 1, io_args); - sox_add_effect(chain, e, &interm_signal, &output->signal); - free(e); - - // Finally run the effects chain - sox_flow_effects(chain, nullptr, nullptr); - sox_delete_effects_chain(chain); - - // Close sox handles, buffer does not get properly sized until these are closed - sox_close(output); - sox_close(input); - - int sr; - // Read the in-memory audio buffer or temp file that we just wrote. -#ifdef __APPLE__ - /* - Temporary filetype must have a valid header. Wav seems to work here while - raw does not. Certain effects like chorus caused strange behavior on the mac. - */ - // read_audio_file reads the temporary file and returns the sr and otensor - sr = read_audio_file(tmp_name, otensor, ch_first, 0, 0, - target_signal, target_encoding, "wav"); - // delete temporary audio file - unlink(tmp_name); -#else - // Resize output tensor to desired dimensions, different effects result in output->signal.length, - // interm_signal.length and buffer size being inconsistent with the result of the file output. - // We prioritize in the order: output->signal.length > interm_signal.length > buffer_size - // Could be related to: https://sourceforge.net/p/sox/bugs/314/ - int nc, ns; - if (output->signal.length == 0) { - // sometimes interm_signal length is extremely large, but the buffer_size - // is double the length of the output signal - if (interm_signal.length > (buffer_size * 10)) { - ns = buffer_size / 2; - } else { - ns = interm_signal.length; - } - nc = interm_signal.channels; - } else { - nc = output->signal.channels; - ns = output->signal.length; - } - otensor.resize_({ns/nc, nc}); - otensor = otensor.contiguous(); - - input = sox_open_mem_read(buffer, buffer_size, target_signal, target_encoding, file_type); - std::vector samples(buffer_size); - const int64_t samples_read = sox_read(input, samples.data(), buffer_size); - assert(samples_read != nc * ns && samples_read != 0); - AT_DISPATCH_ALL_TYPES(otensor.scalar_type(), "effects_buffer", [&] { - auto* data = otensor.data_ptr(); - std::copy(samples.begin(), samples.begin() + samples_read, data); - }); - // free buffer and close mem_read - sox_close(input); - free(buffer); - - if (ch_first) { - otensor.transpose_(1, 0); - } - sr = target_signal->rate; - -#endif - // return sample rate, output tensor modified in-place - return sr; -} } // namespace audio } // namespace torch PYBIND11_MODULE(_torchaudio, m) { - py::class_(m, "SoxEffect") - .def(py::init<>()) - .def("__repr__", [](const torch::audio::SoxEffect &self) { - std::stringstream ss; - std::string sep; - ss << "SoxEffect (" << self.ename << " ,["; - for(std::string s : self.eopts) { - ss << sep << "\"" << s << "\""; - sep = ", "; - } - ss << "])\n"; - return ss.str(); - }) - .def_readwrite("ename", &torch::audio::SoxEffect::ename) - .def_readwrite("eopts", &torch::audio::SoxEffect::eopts); py::class_(m, "sox_signalinfo_t") .def(py::init<>()) .def("__repr__", [](const sox_signalinfo_t &self) { @@ -468,8 +271,4 @@ PYBIND11_MODULE(_torchaudio, m) { "get_info", &torch::audio::get_info, "Gets information about an audio file"); - m.def( - "build_flow_effects", - &torch::audio::build_flow_effects, - "build effects and flow chain into tensors"); } diff --git a/torchaudio/csrc/sox.h b/torchaudio/csrc/sox.h index 8093f0732e..1344031330 100644 --- a/torchaudio/csrc/sox.h +++ b/torchaudio/csrc/sox.h @@ -44,26 +44,4 @@ void write_audio_file( /// error occurred during reading of the audio data. std::tuple get_info( const std::string& file_name); - -// Struct for build_flow_effects function -struct SoxEffect { - SoxEffect() : ename(""), eopts({""}) { } - std::string ename; - std::vector eopts; -}; - -/// Build a SoX chain, flow the effects, and capture the results in a tensor. -/// An audio file from the given `path` flows through an effects chain given -/// by a list of effects and effect options to an output buffer which is encoded -/// into memory to a target signal type and target signal encoding. The resulting -/// buffer is then placed into a tensor. This function returns the output tensor -/// and the sample rate of the output tensor. -int build_flow_effects(const std::string& file_name, - at::Tensor otensor, - bool ch_first, - sox_signalinfo_t* target_signal, - sox_encodinginfo_t* target_encoding, - const char* file_type, - std::vector pyeffs, - int max_num_eopts); }} // namespace torch::audio diff --git a/torchaudio/datasets/cmuarctic.py b/torchaudio/datasets/cmuarctic.py index 7aa6d541e5..7ea4ca4dae 100644 --- a/torchaudio/datasets/cmuarctic.py +++ b/torchaudio/datasets/cmuarctic.py @@ -76,9 +76,20 @@ def load_cmuarctic_item(line: str, class CMUARCTIC(Dataset): - """ - Create a Dataset for CMU_arctic. Each item is a tuple of the form: - waveform, sample_rate, utterance, utterance_id + """Create a Dataset for CMU_ARCTIC. + + Args: + root (str): Path to the directory where the dataset is found or downloaded. + url (str, optional): + The URL to download the dataset from or the type of the dataset to dowload. + (default: ``"aew"``) + Allowed type values are ``"aew"``, ``"ahw"``, ``"aup"``, ``"awb"``, ``"axb"``, ``"bdl"``, + ``"clb"``, ``"eey"``, ``"fem"``, ``"gka"``, ``"jmk"``, ``"ksp"``, ``"ljm"``, ``"lnh"``, + ``"rms"``, ``"rxr"``, ``"slp"`` or ``"slt"``. + folder_in_archive (str, optional): + The top-level directory of the dataset. (default: ``"ARCTIC"``) + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). """ _file_text = "txt.done.data" @@ -143,6 +154,14 @@ def __init__(self, self._walker = list(walker) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, utterance, utterance_id)`` + """ line = self._walker[n] return load_cmuarctic_item(line, self._path, self._folder_audio, self._ext_audio) diff --git a/torchaudio/datasets/commonvoice.py b/torchaudio/datasets/commonvoice.py index a11b9dc1c8..825cf3e9ab 100644 --- a/torchaudio/datasets/commonvoice.py +++ b/torchaudio/datasets/commonvoice.py @@ -100,11 +100,28 @@ def load_commonvoice_item(line: List[str], class COMMONVOICE(Dataset): - """ - Create a Dataset for CommonVoice. Each item is a tuple of the form: - (waveform, sample_rate, dictionary) - where dictionary is a dictionary built from the tsv file with the following keys: - client_id, path, sentence, up_votes, down_votes, age, gender, accent. + """Create a Dataset for CommonVoice. + + Args: + root (str): Path to the directory where the dataset is found or downloaded. + tsv (str, optional): The name of the tsv file used to construct the metadata. + (default: ``"train.tsv"``) + url (str, optional): The URL to download the dataset from, or the language of + the dataset to download. (default: ``"english"``). + Allowed language values are ``"tatar"``, ``"english"``, ``"german"``, + ``"french"``, ``"welsh"``, ``"breton"``, ``"chuvash"``, ``"turkish"``, ``"kyrgyz"``, + ``"irish"``, ``"kabyle"``, ``"catalan"``, ``"taiwanese"``, ``"slovenian"``, + ``"italian"``, ``"dutch"``, ``"hakha chin"``, ``"esperanto"``, ``"estonian"``, + ``"persian"``, ``"portuguese"``, ``"basque"``, ``"spanish"``, ``"chinese"``, + ``"mongolian"``, ``"sakha"``, ``"dhivehi"``, ``"kinyarwanda"``, ``"swedish"``, + ``"russian"``, ``"indonesian"``, ``"arabic"``, ``"tamil"``, ``"interlingua"``, + ``"latvian"``, ``"japanese"``, ``"votic"``, ``"abkhaz"``, ``"cantonese"`` and + ``"romansh sursilvan"``. + folder_in_archive (str, optional): The top-level directory of the dataset. + version (str): Version string. (default: ``"cv-corpus-4-2019-12-10"``) + For the other allowed values, Please checkout https://commonvoice.mozilla.org/en/datasets. + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). """ _ext_txt = ".txt" @@ -192,6 +209,16 @@ def __init__(self, self._walker = list(walker) def __getitem__(self, n: int) -> Tuple[Tensor, int, Dict[str, str]]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, dictionary)``, where dictionary is built + from the TSV file with the following keys: ``client_id``, ``path``, ``sentence``, + ``up_votes``, ``down_votes``, ``age``, ``gender`` and ``accent``. + """ line = self._walker[n] return load_commonvoice_item(line, self._header, self._path, self._folder_audio) diff --git a/torchaudio/datasets/gtzan.py b/torchaudio/datasets/gtzan.py index e031801bc1..160898c73d 100644 --- a/torchaudio/datasets/gtzan.py +++ b/torchaudio/datasets/gtzan.py @@ -1,6 +1,6 @@ import os import warnings -from typing import Any, Tuple +from typing import Any, Tuple, Optional import torchaudio from torch import Tensor @@ -998,12 +998,22 @@ def load_gtzan_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, str class GTZAN(Dataset): - """ - Create a Dataset for GTZAN. Each item is a tuple of the form: - waveform, sample_rate, label. + """Create a Dataset for GTZAN. + + Note: + Please see http://marsyas.info/downloads/datasets.html if you are planning to use + this dataset to publish results. - Please see http://marsyas.info/downloads/datasets.html - if you are planning to use this dataset to publish results. + Args: + root (str): Path to the directory where the dataset is found or downloaded. + url (str, optional): The URL to download the dataset from. + (default: ``"http://opihi.cs.uvic.ca/sound/genres.tar.gz"``) + folder_in_archive (str, optional): The top-level directory of the dataset. + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). + subset (str, optional): Which subset of the dataset to use. + One of ``"training"``, ``"validation"``, ``"testing"`` or ``None``. + If ``None``, the entire dataset is used. (default: ``None``). """ _ext_audio = ".wav" @@ -1014,7 +1024,7 @@ def __init__( url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False, - subset: Any = None, + subset: Optional[str] = None, ) -> None: # super(GTZAN, self).__init__() @@ -1082,6 +1092,14 @@ def __init__( self._walker = filtered_test def __getitem__(self, n: int) -> Tuple[Tensor, int, str]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, label)`` + """ fileid = self._walker[n] item = load_gtzan_item(fileid, self._path, self._ext_audio) waveform, sample_rate, label = item diff --git a/torchaudio/datasets/librispeech.py b/torchaudio/datasets/librispeech.py index 449ab744d3..24da90aa66 100644 --- a/torchaudio/datasets/librispeech.py +++ b/torchaudio/datasets/librispeech.py @@ -67,9 +67,19 @@ def load_librispeech_item(fileid: str, class LIBRISPEECH(Dataset): - """ - Create a Dataset for LibriSpeech. Each item is a tuple of the form: - waveform, sample_rate, utterance, speaker_id, chapter_id, utterance_id + """Create a Dataset for LibriSpeech. + + Args: + root (str): Path to the directory where the dataset is found or downloaded. + url (str, optional): The URL to download the dataset from, + or the type of the dataset to dowload. + Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``, + ``"test-other"``, ``"train-clean-100"``, ``"train-clean-360"`` and + ``"train-other-500"``. (default: ``"train-clean-100"``) + folder_in_archive (str, optional): + The top-level directory of the dataset. (default: ``"LibriSpeech"``) + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). """ _ext_txt = ".trans.txt" @@ -117,6 +127,14 @@ def __init__(self, self._walker = list(walker) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, utterance, speaker_id, chapter_id, utterance_id)`` + """ fileid = self._walker[n] return load_librispeech_item(fileid, self._path, self._ext_audio, self._ext_txt) diff --git a/torchaudio/datasets/libritts.py b/torchaudio/datasets/libritts.py index a37d5528fb..5d4e94cca5 100644 --- a/torchaudio/datasets/libritts.py +++ b/torchaudio/datasets/libritts.py @@ -65,9 +65,19 @@ def load_libritts_item( class LIBRITTS(Dataset): - """ - Create a Dataset for LibriTTS. Each item is a tuple of the form: - waveform, sample_rate, original_text, normalized_text, speaker_id, chapter_id, utterance_id + """Create a Dataset for LibriTTS. + + Args: + root (str): Path to the directory where the dataset is found or downloaded. + url (str, optional): The URL to download the dataset from, + or the type of the dataset to dowload. + Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``, + ``"test-other"``, ``"train-clean-100"``, ``"train-clean-360"`` and + ``"train-other-500"``. (default: ``"train-clean-100"``) + folder_in_archive (str, optional): + The top-level directory of the dataset. (default: ``"LibriTTS"``) + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). """ _ext_original_txt = ".original.txt" @@ -118,6 +128,15 @@ def __init__( self._walker = list(walker) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int, int, str]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, original_text, normalized_text, speaker_id, + chapter_id, utterance_id)`` + """ fileid = self._walker[n] return load_libritts_item( fileid, diff --git a/torchaudio/datasets/ljspeech.py b/torchaudio/datasets/ljspeech.py index 5e3b6ee7d2..28c3a5dfc8 100644 --- a/torchaudio/datasets/ljspeech.py +++ b/torchaudio/datasets/ljspeech.py @@ -33,9 +33,16 @@ def load_ljspeech_item(line: List[str], path: str, ext_audio: str) -> Tuple[Tens class LJSPEECH(Dataset): - """ - Create a Dataset for LJSpeech-1.1. Each item is a tuple of the form: - waveform, sample_rate, transcript, normalized_transcript + """Create a Dataset for LJSpeech-1.1. + + Args: + root (str): Path to the directory where the dataset is found or downloaded. + url (str, optional): The URL to download the dataset from. + (default: ``"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"``) + folder_in_archive (str, optional): + The top-level directory of the dataset. (default: ``"wavs"``) + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). """ _ext_audio = ".wav" @@ -68,6 +75,14 @@ def __init__(self, self._walker = list(walker) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, transcript, normalized_transcript)`` + """ line = self._walker[n] return load_ljspeech_item(line, self._path, self._ext_audio) diff --git a/torchaudio/datasets/speechcommands.py b/torchaudio/datasets/speechcommands.py index 9943d5965b..2bcd2d199f 100644 --- a/torchaudio/datasets/speechcommands.py +++ b/torchaudio/datasets/speechcommands.py @@ -1,5 +1,5 @@ import os -from typing import Tuple +from typing import Tuple, Optional import torchaudio from torch.utils.data import Dataset @@ -22,6 +22,15 @@ } +def _load_list(root, *filenames): + output = [] + for filename in filenames: + filepath = os.path.join(root, filename) + with open(filepath) as fileobj: + output += [os.path.normpath(os.path.join(root, line.strip())) for line in fileobj] + return output + + def load_speechcommands_item(filepath: str, path: str) -> Tuple[Tensor, int, str, str, int]: relpath = os.path.relpath(filepath, path) label, filename = os.path.split(relpath) @@ -36,16 +45,37 @@ def load_speechcommands_item(filepath: str, path: str) -> Tuple[Tensor, int, str class SPEECHCOMMANDS(Dataset): - """ - Create a Dataset for Speech Commands. Each item is a tuple of the form: - waveform, sample_rate, label, speaker_id, utterance_number + """Create a Dataset for Speech Commands. + + Args: + root (str): Path to the directory where the dataset is found or downloaded. + url (str, optional): The URL to download the dataset from, + or the type of the dataset to dowload. + Allowed type values are ``"speech_commands_v0.01"`` and ``"speech_commands_v0.02"`` + (default: ``"speech_commands_v0.02"``) + folder_in_archive (str, optional): + The top-level directory of the dataset. (default: ``"SpeechCommands"``) + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). + subset (Optional[str]): + Select a subset of the dataset [None, "training", "validation", "testing"]. None means + the whole dataset. "validation" and "testing" are defined in "validation_list.txt" and + "testing_list.txt", respectively, and "training" is the rest. (default: ``None``) """ def __init__(self, root: str, url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, - download: bool = False) -> None: + download: bool = False, + subset: Optional[str] = None, + ) -> None: + + assert subset is None or subset in ["training", "validation", "testing"], ( + "When `subset` not None, it must take a value from " + + "{'training', 'validation', 'testing'}." + ) + if url in [ "speech_commands_v0.01", "speech_commands_v0.02", @@ -70,11 +100,32 @@ def __init__(self, download_url(url, root, hash_value=checksum, hash_type="md5") extract_archive(archive, self._path) - walker = walk_files(self._path, suffix=".wav", prefix=True) - walker = filter(lambda w: HASH_DIVIDER in w and EXCEPT_FOLDER not in w, walker) - self._walker = list(walker) + if subset == "validation": + self._walker = _load_list(self._path, "validation_list.txt") + elif subset == "testing": + self._walker = _load_list(self._path, "testing_list.txt") + elif subset == "training": + excludes = set(_load_list(self._path, "validation_list.txt", "testing_list.txt")) + walker = walk_files(self._path, suffix=".wav", prefix=True) + self._walker = [ + w for w in walker + if HASH_DIVIDER in w + and EXCEPT_FOLDER not in w + and os.path.normpath(w) not in excludes + ] + else: + walker = walk_files(self._path, suffix=".wav", prefix=True) + self._walker = [w for w in walker if HASH_DIVIDER in w and EXCEPT_FOLDER not in w] def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, label, speaker_id, utterance_number)`` + """ fileid = self._walker[n] return load_speechcommands_item(fileid, self._path) diff --git a/torchaudio/datasets/tedlium.py b/torchaudio/datasets/tedlium.py index 4ae1ddeb0f..7ac347cd3c 100644 --- a/torchaudio/datasets/tedlium.py +++ b/torchaudio/datasets/tedlium.py @@ -43,44 +43,21 @@ class TEDLIUM(Dataset): """ - Create a Dataset for Tedlium. It supports releases 1,2 and 3, each item is a list containings: - [waveform, sample_rate, transcript, talk_id, speaker_id, identifier]. - - Constructor arguments: + Create a Dataset for Tedlium. It supports releases 1,2 and 3. Args: - root (str): Path containing dataset or target path where its downloaded if needed - release (str, optional): TEDLIUM identifier (release1,release2,release3). Defaults to RELEASE. - subset (str, optional): train/dev/test for releases 1&2, None for release3. Defaults to Train/None - download (bool, optional): Download dataset in case is not founded in root path. Defaults to False. - audio_ext (str, optional): Overwrite audio extension when loading items. Defaults to ".sph". - - Special functions: - - _load_tedlium_item: Loads a TEDLIUM dataset sample given a file name and corresponding sentence name - - _load_audio: Default load function used in TEDLIUM dataset, you can overwrite this function to customize - functionality and load individual sentences from a full ted audio talk file - - get_phoneme_dict: Returns the phoneme dictionary of a TEDLIUM release - + root (str): Path to the directory where the dataset is found or downloaded. + release (str, optional): Release version. + Allowed values are ``"release1"``, ``"release2"`` or ``"release3"``. + (default: ``"release1"``). + subset (str, optional): The subset of dataset to use. Valid options are ``"train"``, ``"dev"``, + and ``"test"`` for releases 1&2, ``None`` for release3. Defaults to ``"train"`` or ``None``. + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). """ - def __init__( self, root: str, release: str = "release1", subset: str = None, download: bool = False, audio_ext=".sph" ) -> None: - """Constructor for TEDLIUM dataset. - - Args: - root (str): Path containing dataset or target path where its downloaded if needed - release (str, optional): TEDLIUM identifier (release1,release2,release3). Defaults to RELEASE. - subset (str, optional): train/dev/test for releases 1&2, None for release3. Defaults to Train/None - download (bool, optional): Download dataset in case is not founded in root path. Defaults to False. - audio_ext (str, optional): Overwrite audio extension when loading items. Defaults to ".sph". - - Raises: - RuntimeError: If release identifier does not match any supported release, - """ self._ext_audio = audio_ext if release in _RELEASE_CONFIGS.keys(): folder_in_archive = _RELEASE_CONFIGS[release]["folder_in_archive"] @@ -140,7 +117,7 @@ def _load_tedlium_item(self, fileid: str, line: int, path: str) -> Tuple[Tensor, path (str): Dataset root path Returns: - Tedlium_item: A namedTuple containing [waveform, sample_rate, transcript, talk_id, speaker_id, identifier] + tuple: ``(waveform, sample_rate, transcript, talk_id, speaker_id, identifier)`` """ transcript_path = os.path.join(path, "stm", fileid) with open(transcript_path + ".stm") as f: @@ -166,19 +143,23 @@ def _load_audio(self, path: str, start_time: float, end_time: float, sample_rate """ start_time = int(float(start_time) * sample_rate) end_time = int(float(end_time) * sample_rate) - if torchaudio.get_audio_backend() == "sox_io": - return torchaudio.load(path, frame_offset=start_time, num_frames=end_time - start_time) - return torchaudio.load(path)[:, start_time:end_time] + + backend = torchaudio.get_audio_backend() + if backend == "sox" or (backend == "soundfile" and torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE): + kwargs = {"offset": start_time, "num_frames": end_time - start_time} + else: + kwargs = {"frame_offset": start_time, "num_frames": end_time - start_time} + + return torchaudio.load(path, **kwargs) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]: - """TEDLIUM dataset custom function overwritting default loadbehaviour - Loads a TEDLIUM sample given a index N. + """Load the n-th sample from the dataset. Args: - n (int): Index of sample to be loaded + n (int): The index of the sample to be loaded Returns: - Tedlium_item: A namedTuple containing [waveform, sample_rate, transcript, talk_id, speaker_id, identifier] + tuple: ``(waveform, sample_rate, transcript, talk_id, speaker_id, identifier)`` """ fileid, line = self._filelist[n] return self._load_tedlium_item(fileid, line, self._path) @@ -193,10 +174,8 @@ def __len__(self) -> int: @property def phoneme_dict(self): - """Returns the phoneme dictionary of a TEDLIUM release. - - Returns: - dictionary: Phoneme dictionary for the current tedlium release + """dict[str, tuple[str]]: Phonemes. Mapping from word to tuple of phonemes. + Note that some words have empty phonemes. """ # Read phoneme dictionary if not self._phoneme_dict: diff --git a/torchaudio/datasets/vctk.py b/torchaudio/datasets/vctk.py index 8fa874c7cf..a8fca59df2 100644 --- a/torchaudio/datasets/vctk.py +++ b/torchaudio/datasets/vctk.py @@ -1,7 +1,6 @@ import os import warnings from typing import Any, Tuple -from collections import namedtuple import torchaudio from torch import Tensor @@ -18,10 +17,6 @@ "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip": "8a6ba2946b36fcbef0212cad601f4bfa" } -Sample = namedtuple( - "Sample", ["waveform", "sample_rate", "utterance", "speaker_id", "utterance_id"] -) - def load_vctk_item(fileid: str, path: str, @@ -54,12 +49,25 @@ def load_vctk_item(fileid: str, class VCTK(Dataset): - """ - Create a Dataset for VCTK. Each item is a tuple of the form: - (waveform, sample_rate, utterance, speaker_id, utterance_id) + """Create a Dataset for VCTK. + + Note: + * **This dataset is no longer publicly available.** Please use :py:class:`VCTK_092` + * Directory ``p315`` is ignored because there is no corresponding text files. + For more information about the dataset visit: https://datashare.is.ed.ac.uk/handle/10283/3443 - Folder `p315` will be ignored due to the non-existent corresponding text files. - For more information about the dataset visit: https://datashare.is.ed.ac.uk/handle/10283/3443 + Args: + root (str): Path to the directory where the dataset is found or downloaded. + url (str, optional): Not used as the dataset is no longer publicly available. + folder_in_archive (str, optional): + The top-level directory of the dataset. (default: ``"VCTK-Corpus"``) + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). + Giving ``download=True`` will result in error as the dataset is no longer + publicly available. + downsample (bool, optional): Not used. + transform (callable, optional): Optional transform applied on waveform. (default: ``None``) + target_transform (callable, optional): Optional transform applied on utterance. (default: ``None``) """ _folder_txt = "txt" @@ -118,6 +126,14 @@ def __init__(self, self._walker = list(walker) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, utterance, speaker_id, utterance_id)`` + """ fileid = self._walker[n] item = load_vctk_item( fileid, @@ -142,17 +158,19 @@ def __len__(self) -> int: return len(self._walker) +SampleType = Tuple[Tensor, int, str, str, str] + + class VCTK_092(Dataset): """Create VCTK 0.92 Dataset - An item is a ``namedtuple`` of (``waveform``, ``sample_rate``, ``utterance``, - ``speaker_id``, ``utterance_id``) - Args: root (str): Root directory where the dataset's top level directory is found. - mic_id (str): Microphone ID. Either ``"mic1"`` or ``"mic2"`` - download (bool, optional): Download the dataset if not found in the given directory. - url (str, optional): URL from which the dataset is downloaded. + mic_id (str): Microphone ID. Either ``"mic1"`` or ``"mic2"``. (default: ``"mic2"``) + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). + url (str, optional): The URL to download the dataset from. + (default: ``"https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip"``) audio_ext (str, optional): Custom audio extension if dataset is converted to non-default audio format. Note: @@ -233,7 +251,7 @@ def _load_text(self, file_path) -> str: def _load_audio(self, file_path) -> Tuple[Tensor, int]: return torchaudio.load(file_path) - def _load_sample(self, speaker_id: str, utterance_id: str, mic_id: str) -> Sample: + def _load_sample(self, speaker_id: str, utterance_id: str, mic_id: str) -> SampleType: utterance_path = os.path.join( self._txt_dir, speaker_id, f"{speaker_id}_{utterance_id}.txt" ) @@ -249,9 +267,17 @@ def _load_sample(self, speaker_id: str, utterance_id: str, mic_id: str) -> Sampl # Reading FLAC waveform, sample_rate = self._load_audio(audio_path) - return Sample(waveform, sample_rate, utterance, speaker_id, utterance_id) + return (waveform, sample_rate, utterance, speaker_id, utterance_id) - def __getitem__(self, n: int) -> Sample: + def __getitem__(self, n: int) -> SampleType: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, utterance, speaker_id, utterance_id)`` + """ speaker_id, utterance_id = self._sample_ids[n] return self._load_sample(speaker_id, utterance_id, self._mic_id) diff --git a/torchaudio/datasets/yesno.py b/torchaudio/datasets/yesno.py index e45b157c35..5a20539e28 100644 --- a/torchaudio/datasets/yesno.py +++ b/torchaudio/datasets/yesno.py @@ -31,9 +31,18 @@ def load_yesno_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, int class YESNO(Dataset): - """ - Create a Dataset for YesNo. Each item is a tuple of the form: - (waveform, sample_rate, labels) + """Create a Dataset for YesNo. + + Args: + root (str): Path to the directory where the dataset is found or downloaded. + url (str, optional): The URL to download the dataset from. + (default: ``"http://www.openslr.org/resources/1/waves_yesno.tar.gz"``) + folder_in_archive (str, optional): + The top-level directory of the dataset. (default: ``"waves_yesno"``) + download (bool, optional): + Whether to download the dataset if it is not found at root path. (default: ``False``). + transform (callable, optional): Optional transform applied on waveform. (default: ``None``) + target_transform (callable, optional): Optional transform applied on utterance. (default: ``None``) """ _ext_audio = ".wav" @@ -78,6 +87,14 @@ def __init__(self, self._walker = list(walker) def __getitem__(self, n: int) -> Tuple[Tensor, int, List[int]]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, labels)`` + """ fileid = self._walker[n] item = load_yesno_item(fileid, self._path, self._ext_audio) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index a6514c79f2..d8adfe86e6 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -1268,15 +1268,23 @@ def phaser( delay_pos = 0 mod_pos = 0 - output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) + output_waveform_pre_gain_list = [] + waveform = waveform * gain_in + delay_buf = delay_buf * decay + waveform_list = [waveform[:, i] for i in range(waveform.size(1))] + delay_buf_list = [delay_buf[:, i] for i in range(delay_buf.size(1))] + mod_buf_list = [mod_buf[i] for i in range(mod_buf.size(0))] for i in range(waveform.shape[-1]): - idx = int((delay_pos + mod_buf[mod_pos]) % delay_buf_len) - temp = (waveform[:, i] * gain_in) + (delay_buf[:, idx] * decay) + idx = int((delay_pos + mod_buf_list[mod_pos]) % delay_buf_len) mod_pos = (mod_pos + 1) % mod_buf_len delay_pos = (delay_pos + 1) % delay_buf_len - delay_buf[:, delay_pos] = temp - output_waveform[:, i] = temp * gain_out + temp = (waveform_list[i]) + (delay_buf_list[idx]) + delay_buf_list[delay_pos] = temp * decay + output_waveform_pre_gain_list.append(temp) + + output_waveform = torch.stack(output_waveform_pre_gain_list, dim=1).to(dtype=dtype, device=device) + output_waveform.mul_(gain_out) return output_waveform.clamp(min=-1, max=1).view(actual_shape) diff --git a/torchaudio/models/__init__.py b/torchaudio/models/__init__.py index 8e05b8b509..55498fd7f6 100644 --- a/torchaudio/models/__init__.py +++ b/torchaudio/models/__init__.py @@ -1,2 +1,3 @@ -from .wav2letter import * -from .wavernn import * +from .wav2letter import Wav2Letter +from .wavernn import WaveRNN +from .conv_tasnet import ConvTasNet diff --git a/torchaudio/models/conv_tasnet.py b/torchaudio/models/conv_tasnet.py new file mode 100644 index 0000000000..eb5f121fae --- /dev/null +++ b/torchaudio/models/conv_tasnet.py @@ -0,0 +1,327 @@ +"""Implements Conv-TasNet with building blocks of it. + +Based on https://github.com/naplab/Conv-TasNet/tree/e66d82a8f956a69749ec8a4ae382217faa097c5c +""" + +from typing import Tuple, Optional + +import torch + + +class ConvBlock(torch.nn.Module): + """1D Convolutional block. + + Args: + io_channels (int): The number of input/output channels, + hidden_channels (int): The number of channels in the internal layers, . + kernel_size (int): The convolution kernel size of the middle layer,

. + padding (int): Padding value of the convolution in the middle layer. + dilation (int): Dilation value of the convolution in the middle layer. + no_redisual (bool): Disable residual block/output. + + Note: + This implementation corresponds to the "non-causal" setting in the paper. + + Reference: + - Conv-TasNet: Surpassing Ideal Time--Frequency Magnitude Masking for Speech Separation + + Luo, Yi and Mesgarani, Nima + + https://arxiv.org/abs/1809.07454 + """ + + def __init__( + self, + io_channels: int, + hidden_channels: int, + kernel_size: int, + padding: int, + dilation: int = 1, + no_residual: bool = False, + ): + super().__init__() + + self.conv_layers = torch.nn.Sequential( + torch.nn.Conv1d( + in_channels=io_channels, out_channels=hidden_channels, kernel_size=1 + ), + torch.nn.PReLU(), + torch.nn.GroupNorm(num_groups=1, num_channels=hidden_channels, eps=1e-08), + torch.nn.Conv1d( + in_channels=hidden_channels, + out_channels=hidden_channels, + kernel_size=kernel_size, + padding=padding, + dilation=dilation, + groups=hidden_channels, + ), + torch.nn.PReLU(), + torch.nn.GroupNorm(num_groups=1, num_channels=hidden_channels, eps=1e-08), + ) + + self.res_out = ( + None + if no_residual + else torch.nn.Conv1d( + in_channels=hidden_channels, out_channels=io_channels, kernel_size=1 + ) + ) + self.skip_out = torch.nn.Conv1d( + in_channels=hidden_channels, out_channels=io_channels, kernel_size=1 + ) + + def forward( + self, input: torch.Tensor + ) -> Tuple[Optional[torch.Tensor], torch.Tensor]: + feature = self.conv_layers(input) + if self.res_out is None: + residual = None + else: + residual = self.res_out(feature) + skip_out = self.skip_out(feature) + return residual, skip_out + + +class MaskGenerator(torch.nn.Module): + """TCN (Temporal Convolution Network) Separation Module + + Generates masks for separation. + + Args: + input_dim (int): Input feature dimension, . + num_sources (int): The number of sources to separate. + kernel_size (int): The convolution kernel size of conv blocks,

. + num_featrs (int): Input/output feature dimenstion of conv blocks, . + num_hidden (int): Intermediate feature dimention of conv blocks, + num_layers (int): The number of conv blocks in one stack, . + num_stacks (int): The number of conv block stacks, . + + Note: + This implementation corresponds to the "non-causal" setting in the paper. + + References: + - Conv-TasNet: Surpassing Ideal Time--Frequency Magnitude Masking for Speech Separation + Luo, Yi and Mesgarani, Nima + https://arxiv.org/abs/1809.07454 + """ + + def __init__( + self, + input_dim: int, + num_sources: int, + kernel_size: int, + num_feats: int, + num_hidden: int, + num_layers: int, + num_stacks: int, + ): + super().__init__() + + self.input_dim = input_dim + self.num_sources = num_sources + + self.input_norm = torch.nn.GroupNorm( + num_groups=1, num_channels=input_dim, eps=1e-8 + ) + self.input_conv = torch.nn.Conv1d( + in_channels=input_dim, out_channels=num_feats, kernel_size=1 + ) + + self.receptive_field = 0 + self.conv_layers = torch.nn.ModuleList([]) + for s in range(num_stacks): + for l in range(num_layers): + multi = 2 ** l + self.conv_layers.append( + ConvBlock( + io_channels=num_feats, + hidden_channels=num_hidden, + kernel_size=kernel_size, + dilation=multi, + padding=multi, + # The last ConvBlock does not need residual + no_residual=(l == (num_layers - 1) and s == (num_stacks - 1)), + ) + ) + self.receptive_field += ( + kernel_size if s == 0 and l == 0 else (kernel_size - 1) * multi + ) + self.output_prelu = torch.nn.PReLU() + self.output_conv = torch.nn.Conv1d( + in_channels=num_feats, out_channels=input_dim * num_sources, kernel_size=1, + ) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + """Generate separation mask. + + Args: + input (torch.Tensor): 3D Tensor with shape [batch, features, frames] + + Returns: + torch.Tensor: shape [batch, num_sources, features, frames] + """ + batch_size = input.shape[0] + feats = self.input_norm(input) + feats = self.input_conv(feats) + output = 0.0 + for layer in self.conv_layers: + residual, skip = layer(feats) + if residual is not None: # the last conv layer does not produce residual + feats = feats + residual + output = output + skip + output = self.output_prelu(output) + output = self.output_conv(output) + output = torch.sigmoid(output) + return output.view(batch_size, self.num_sources, self.input_dim, -1) + + +class ConvTasNet(torch.nn.Module): + """Conv-TasNet: a fully-convolutional time-domain audio separation network + + Args: + num_sources (int): The number of sources to split. + enc_kernel_size (int): The convolution kernel size of the encoder/decoder, . + enc_num_feats (int): The feature dimensions passed to mask generator, . + msk_kernel_size (int): The convolution kernel size of the mask generator,

Click here for the examples on how to install SoX - -OSX (Homebrew): -```bash -brew install sox -``` - -Linux (Ubuntu): -```bash -sudo apt-get install sox libsox-dev libsox-fmt-all -``` - -Anaconda -```bash -conda install -c conda-forge sox -``` - -
- -```bash -# Linux -python setup.py install - -# OSX -MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install -``` - -Alternatively, the build process can build libsox and some optional codecs statically and torchaudio can link them, by setting environment variable `BUILD_SOX=1`. +The build process builds libsox and some codecs that torchaudio need to link to. This is achieve by setting the environment variable `BUILD_SOX=1`. The build process will fetch and build libmad, lame, flac, vorbis, opus, and libsox before building extension. This process requires `cmake` and `pkg-config`. ```bash @@ -195,16 +164,16 @@ Conventions With torchaudio being a machine learning library and built on top of PyTorch, torchaudio is standardized around the following naming conventions. Tensors are -assumed to have channels as the first dimension and time as the last +assumed to have "channel" as the first dimension and time as the last dimension (when applicable). This makes it consistent with PyTorch's dimensions. For size names, the prefix `n_` is used (e.g. "a tensor of size (`n_freq`, `n_mel`)") whereas dimension names do not have this prefix (e.g. "a tensor of -dimension (channels, time)") +dimension (channel, time)") -* `waveform`: a tensor of audio samples with dimensions (channels, time) +* `waveform`: a tensor of audio samples with dimensions (channel, time) * `sample_rate`: the rate of audio dimensions (samples per second) -* `specgram`: a tensor of spectrogram with dimensions (channels, freq, time) -* `mel_specgram`: a mel spectrogram with dimensions (channels, mel, time) +* `specgram`: a tensor of spectrogram with dimensions (channel, freq, time) +* `mel_specgram`: a mel spectrogram with dimensions (channel, mel, time) * `hop_length`: the number of samples between the starts of consecutive frames * `n_fft`: the number of Fourier bins * `n_mel`, `n_mfcc`: the number of mel and MFCC bins @@ -216,16 +185,16 @@ dimension (channels, time)") Transforms expect and return the following dimensions. -* `Spectrogram`: (channels, time) -> (channels, freq, time) -* `AmplitudeToDB`: (channels, freq, time) -> (channels, freq, time) -* `MelScale`: (channels, freq, time) -> (channels, mel, time) -* `MelSpectrogram`: (channels, time) -> (channels, mel, time) -* `MFCC`: (channels, time) -> (channel, mfcc, time) -* `MuLawEncode`: (channels, time) -> (channels, time) -* `MuLawDecode`: (channels, time) -> (channels, time) -* `Resample`: (channels, time) -> (channels, time) -* `Fade`: (channels, time) -> (channels, time) -* `Vol`: (channels, time) -> (channels, time) +* `Spectrogram`: (channel, time) -> (channel, freq, time) +* `AmplitudeToDB`: (channel, freq, time) -> (channel, freq, time) +* `MelScale`: (channel, freq, time) -> (channel, mel, time) +* `MelSpectrogram`: (channel, time) -> (channel, mel, time) +* `MFCC`: (channel, time) -> (channel, mfcc, time) +* `MuLawEncode`: (channel, time) -> (channel, time) +* `MuLawDecode`: (channel, time) -> (channel, time) +* `Resample`: (channel, time) -> (channel, time) +* `Fade`: (channel, time) -> (channel, time) +* `Vol`: (channel, time) -> (channel, time) Complex numbers are supported via tensors of dimension (..., 2), and torchaudio provides `complex_norm` and `angle` to convert such a tensor into its magnitude and phase. Here, and in the documentation, we use an ellipsis "..." as a placeholder for the rest of the dimensions of a tensor, e.g. optional batching and channel dimensions. diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index d34464ed37..4e04097716 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -18,7 +18,10 @@ _TORCHAUDIO_DIR = _ROOT_DIR / 'torchaudio' -def _get_build(var): +def _get_build(var, default=False): + if var not in os.environ: + return default + val = os.environ.get(var, '0') trues = ['1', 'true', 'TRUE', 'on', 'ON', 'yes', 'YES'] falses = ['0', 'false', 'FALSE', 'off', 'OFF', 'no', 'NO'] @@ -31,13 +34,12 @@ def _get_build(var): return False -_BUILD_SOX = _get_build("BUILD_SOX") +_BUILD_SOX = False if platform.system() == 'Windows' else _get_build("BUILD_SOX") +_BUILD_KALDI = False if platform.system() == 'Windows' else _get_build("BUILD_KALDI", True) _BUILD_TRANSDUCER = _get_build("BUILD_TRANSDUCER") def get_ext_modules(): - if platform.system() == 'Windows': - return None return [Extension(name='torchaudio._torchaudio', sources=[])] @@ -68,6 +70,7 @@ def build_extension(self, ext): '-DCMAKE_VERBOSE_MAKEFILE=ON', f"-DPython_INCLUDE_DIR={distutils.sysconfig.get_python_inc()}", f"-DBUILD_SOX:BOOL={'ON' if _BUILD_SOX else 'OFF'}", + f"-DBUILD_KALDI:BOOL={'ON' if _BUILD_KALDI else 'OFF'}", f"-DBUILD_TRANSDUCER:BOOL={'ON' if _BUILD_TRANSDUCER else 'OFF'}", "-DBUILD_TORCHAUDIO_PYTHON_EXTENSION:BOOL=ON", "-DBUILD_LIBTORCHAUDIO:BOOL=OFF", @@ -77,8 +80,16 @@ def build_extension(self, ext): ] # Default to Ninja - if 'CMAKE_GENERATOR' not in os.environ: + if 'CMAKE_GENERATOR' not in os.environ or platform.system() == 'Windows': cmake_args += ["-GNinja"] + if platform.system() == 'Windows': + import sys + python_version = sys.version_info + cmake_args += [ + "-DCMAKE_C_COMPILER=cl", + "-DCMAKE_CXX_COMPILER=cl", + f"-DPYTHON_VERSION={python_version.major}.{python_version.minor}", + ] # Set CMAKE_BUILD_PARALLEL_LEVEL to control the parallel build level # across all generators. diff --git a/docs/Makefile b/docs/Makefile index 630a95dc19..76b604393a 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -2,7 +2,7 @@ # # You can set these variables from the command line. -SPHINXOPTS = +SPHINXOPTS = -W # converts warnings into error SPHINXBUILD = sphinx-build SPHINXPROJ = torchaudio SOURCEDIR = source diff --git a/docs/source/_templates/layout.html b/docs/source/_templates/layout.html new file mode 100644 index 0000000000..8727b6ce5d --- /dev/null +++ b/docs/source/_templates/layout.html @@ -0,0 +1,8 @@ +{% extends "!layout.html" %} + +{% block sidebartitle %} +
+ {{ version }} ▼ +
+ {% include "searchbox.html" %} +{% endblock %} diff --git a/docs/source/backend.rst b/docs/source/backend.rst index 1cdb1c923e..6eda225307 100644 --- a/docs/source/backend.rst +++ b/docs/source/backend.rst @@ -6,49 +6,23 @@ torchaudio.backend Overview ~~~~~~~~ -:mod:`torchaudio.backend` module provides implementations for audio file I/O functionalities, which are ``torchaudio.info``, ``torchaudio.load``, ``torchaudio.load_wav`` and ``torchaudio.save``. +:mod:`torchaudio.backend` module provides implementations for audio file I/O functionalities, which are ``torchaudio.info``, ``torchaudio.load``, and ``torchaudio.save``. There are currently four implementations available. * :ref:`"sox_io" ` (default on Linux/macOS) -* :ref:`"sox" ` (deprecated, will be removed in 0.9.0 release) * :ref:`"soundfile" ` (default on Windows) -* :ref:`"soundfile" (legacy interface) ` (deprecated, will be removed in 0.9.0 release) - -The use of ``"sox"`` backend is strongly discouraged as it cannot correctly handle formats other than 16-bit integer WAV. See `#726 `_ for the detail. .. note:: - Instead of calling functions in ``torchaudio.backend`` directly, please use ``torchaudio.info``, ``torchaudio.load``, ``torchaudio.load_wav`` and ``torchaudio.save`` with proper backend set with :func:`torchaudio.set_audio_backend`. + Instead of calling functions in ``torchaudio.backend`` directly, please use ``torchaudio.info``, ``torchaudio.load``, and ``torchaudio.save`` with proper backend set with :func:`torchaudio.set_audio_backend`. Availability ------------ -``"sox"`` and ``"sox_io"`` backends require C++ extension module, which is included in Linux/macOS binary distributions. These backends are not available on Windows. +``"sox_io"`` backend requires C++ extension module, which is included in Linux/macOS binary distributions. This backend is not available on Windows. ``"soundfile"`` backend requires ``SoundFile``. Please refer to `the SoundFile documentation `_ for the installation. -Changes in default backend and deprecation ------------------------------------------- - -Backend module is going through a major overhaul. The following table summarizes the timeline for the deprecations and removals. - - +--------------------+-----------------------+------------------------+ - | **Backend** | **0.8.0** | **0.9.0** | - +====================+=======================+========================+ - | ``"sox_io"`` | Default on Linx/macOS | Default on Linux/macOS | - +--------------------+-----------------------+------------------------+ - | ``"sox"`` | Available | Removed | - | (deprecated) | | | - +--------------------+-----------------------+------------------------+ - | ``"soundfile"`` | Default on Windows | Default on Windows | - +--------------------+-----------------------+------------------------+ - | ``"soundfile"`` | Available | Removed | - | (legacy interface, | | | - | deprecated) | | | - +--------------------+-----------------------+------------------------+ - -* The ``"sox"`` and ``"soundfile" (legacy interface)`` backends are deprecated and will be removed in 0.9.0 release. - Common Data Structure ~~~~~~~~~~~~~~~~~~~~~ @@ -59,16 +33,6 @@ AudioMetaData .. autoclass:: torchaudio.backend.common.AudioMetaData -SignalInfo (Deprecated) ------------------------ - -.. autoclass:: torchaudio.backend.common.SignalInfo - -EncodingInfo (Deprecated) -------------------------- - -.. autoclass:: torchaudio.backend.common.EncodingInfo - .. _sox_io_backend: Sox IO Backend @@ -94,52 +58,11 @@ load .. autofunction:: torchaudio.backend.sox_io_backend.load -.. autofunction:: torchaudio.backend.sox_io_backend.load_wav - - save ---- .. autofunction:: torchaudio.backend.sox_io_backend.save -.. _sox_backend: - -Sox Backend (Deprecated) -~~~~~~~~~~~~~~~~~~~~~~~~ - -The ``"sox"`` backend is available on Linux/macOS and not available on Windows. This backend is deprecated and will be removed in ``0.9.0`` release. - -You can switch from another backend to ``sox`` backend with the following; - -.. code:: - - torchaudio.set_audio_backend("sox") - -info ----- - -.. autofunction:: torchaudio.backend.sox_backend.info - -load ----- - -.. autofunction:: torchaudio.backend.sox_backend.load - -.. autofunction:: torchaudio.backend.sox_backend.load_wav - - -save ----- - -.. autofunction:: torchaudio.backend.sox_backend.save - -others ------- - -.. automodule:: torchaudio.backend.sox_backend - :members: - :exclude-members: info, load, load_wav, save - .. _soundfile_backend: Soundfile Backend @@ -153,55 +76,17 @@ You can switch from another backend to the ``"soundfile"`` backend with the foll torchaudio.set_audio_backend("soundfile") -.. note:: - If you are switching from `"soundfile" (legacy interface) ` backend, set ``torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE`` flag **before** switching the backend. - info -^^^^ - -.. autofunction:: torchaudio.backend._soundfile_backend.info - -load -^^^^ - -.. autofunction:: torchaudio.backend._soundfile_backend.load - -.. autofunction:: torchaudio.backend._soundfile_backend.load_wav - - -save -^^^^ - -.. autofunction:: torchaudio.backend._soundfile_backend.save - -.. _soundfile_legacy_backend: - -Legacy Interface (Deprecated) ------------------------------ - -``"soundfile"`` backend with legacy interface is made available for backward compatibility reason, however this interface is deprecated and will be removed in the ``0.9.0`` release. - -To switch to this backend/interface, set ``torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE`` flag **before** switching the backend. - -.. code:: - - torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = True - torchaudio.set_audio_backend("soundfile") # The legacy interface - -info -^^^^ +---- .. autofunction:: torchaudio.backend.soundfile_backend.info load -^^^^ +---- .. autofunction:: torchaudio.backend.soundfile_backend.load -.. autofunction:: torchaudio.backend.soundfile_backend.load_wav - - save -^^^^ +---- .. autofunction:: torchaudio.backend.soundfile_backend.save diff --git a/docs/source/conf.py b/docs/source/conf.py index 9d8faaa61b..3bc89e999d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -20,8 +20,6 @@ # import os # import sys # sys.path.insert(0, os.path.abspath('.')) -import torch -import torchaudio import pytorch_sphinx_theme # -- General configuration ------------------------------------------------ @@ -58,6 +56,8 @@ ''' napoleon_use_ivar = True +napoleon_numpy_docstring = False +napoleon_google_docstring = True # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] @@ -123,6 +123,7 @@ 'collapse_navigation': False, 'display_version': True, 'logo_only': True, + 'navigation_with_keys': True } html_logo = '_static/img/pytorch-logo-dark.svg' @@ -132,6 +133,7 @@ # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] + def setup(app): # NOTE: in Sphinx 1.8+ `html_css_files` is an official configuration value # and can be moved outside of this function (and the setup(app) function @@ -142,7 +144,7 @@ def setup(app): # In Sphinx 1.8 it was renamed to `add_css_file`, 1.7 and prior it is # `add_stylesheet` (deprecated in 1.8). - add_css = getattr(app, 'add_css_file', getattr(app, 'add_stylesheet')) + add_css = getattr(app, 'add_css_file', app.add_stylesheet) for css_file in html_css_files: add_css(css_file) @@ -223,7 +225,7 @@ def patched_make_field(self, types, domain, items, **kw): # `kw` catches `env=None` needed for newer sphinx while maintaining # backwards compatibility when passed along further down! - # type: (List, unicode, Tuple) -> nodes.field + # type: (list, str, tuple) -> nodes.field def handle_item(fieldarg, content): par = nodes.paragraph() par += addnodes.literal_strong('', fieldarg) # Patch: this line added diff --git a/docs/source/functional.rst b/docs/source/functional.rst index ebed60de6b..e52e83ca42 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -8,6 +8,75 @@ torchaudio.functional Functions to perform common audio operations. +:hidden:`Utility` +~~~~~~~~~~~~~~~~~ + +amplitude_to_DB +--------------- + +.. autofunction:: amplitude_to_DB + +DB_to_amplitude +--------------- + +.. autofunction:: DB_to_amplitude + +create_fb_matrix +---------------- + +.. autofunction:: create_fb_matrix + +create_dct +---------- + +.. autofunction:: create_dct + +mask_along_axis +--------------- + +.. autofunction:: mask_along_axis + +mask_along_axis_iid +------------------- + +.. autofunction:: mask_along_axis_iid + +mu_law_encoding +--------------- + +.. autofunction:: mu_law_encoding + +mu_law_decoding +--------------- + +.. autofunction:: mu_law_decoding + +apply_codec +----------- + +.. autofunction:: apply_codec + +:hidden:`Complex Utility` +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Utilities for pseudo complex tensor. This is not for the native complex dtype, such as `cfloat64`, but for tensors with real-value type and have extra dimension at the end for real and imaginary parts. + +angle +----- + +.. autofunction:: angle + +complex_norm +------------ + +.. autofunction:: complex_norm + + +magphase +-------- + +.. autofunction:: magphase + :hidden:`Filtering` ~~~~~~~~~~~~~~~~~~~ @@ -117,89 +186,47 @@ treble_biquad vad --- +:hidden:`Feature Extractions` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + .. autofunction:: vad :hidden:`spectrogram` -~~~~~~~~~~~~~~~~~~~~~ +--------------------- .. autofunction:: spectrogram :hidden:`griffinlim` -~~~~~~~~~~~~~~~~~~~~~~~~~ +-------------------- .. autofunction:: griffinlim -:hidden:`amplitude_to_DB` -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: amplitude_to_DB - -:hidden:`DB_to_amplitude` -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: DB_to_amplitude - -:hidden:`create_fb_matrix` -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: create_fb_matrix - -:hidden:`create_dct` -~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: create_dct - -:hidden:`mu_law_encoding` -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: mu_law_encoding - -:hidden:`mu_law_decoding` -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: mu_law_decoding - -:hidden:`complex_norm` -~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: complex_norm - -:hidden:`angle` -~~~~~~~~~~~~~~~ - -.. autofunction:: angle - -:hidden:`magphase` -~~~~~~~~~~~~~~~~~~ - -.. autofunction:: magphase - :hidden:`phase_vocoder` -~~~~~~~~~~~~~~~~~~~~~~~ +----------------------- .. autofunction:: phase_vocoder -:hidden:`mask_along_axis` -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: mask_along_axis - -:hidden:`mask_along_axis_iid` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: mask_along_axis_iid - :hidden:`compute_deltas` -~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------ .. autofunction:: compute_deltas :hidden:`detect_pitch_frequency` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +-------------------------------- .. autofunction:: detect_pitch_frequency :hidden:`sliding_window_cmn` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +---------------------------- .. autofunction:: sliding_window_cmn + +:hidden:`compute_kaldi_pitch` +----------------------------- + +.. autofunction:: compute_kaldi_pitch + +:hidden:`spectral_centroid` +--------------------------- + +.. autofunction:: spectral_centroid diff --git a/docs/source/torchaudio.rst b/docs/source/torchaudio.rst index 480be4393e..cb616d01aa 100644 --- a/docs/source/torchaudio.rst +++ b/docs/source/torchaudio.rst @@ -16,10 +16,6 @@ Refer to :ref:`backend` for the detail. Load audio file into torch.Tensor object. Refer to :ref:`backend` for the detail. -.. function:: torchaudio.load_wav(filepath: str, ...) - - Load audio file into torch.Tensor, Refer to :ref:`backend` for the detail. - .. function:: torchaudio.save(filepath: str, src: torch.Tensor, sample_rate: int, ...) Save torch.Tensor object into an audio format. Refer to :ref:`backend` for the detail. diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst index fc6a4d4a9f..8269193e22 100644 --- a/docs/source/transforms.rst +++ b/docs/source/transforms.rst @@ -136,6 +136,13 @@ Transforms are common audio transforms. They can be chained together using :clas .. automethod:: forward +:hidden:`SpectralCentroid` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: SpectralCentroid + + .. automethod:: forward + :hidden:`Vad` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/examples/interactive_asr/vad.py b/examples/interactive_asr/vad.py index 6a363941b0..bc942032b0 100644 --- a/examples/interactive_asr/vad.py +++ b/examples/interactive_asr/vad.py @@ -35,7 +35,7 @@ def compute_spectral_flatness(frame, epsilon=0.01): return -10 * torch.log10(epsilon + geometric_mean / arithmetic_mean) -class VoiceActivityDetection(object): +class VoiceActivityDetection: def __init__( self, num_init_frames=30, @@ -148,7 +148,7 @@ def iter(self, frame): return self.silence_mark -class MicrophoneStream(object): +class MicrophoneStream: """Opens a recording stream as a generator yielding the audio chunks.""" def __init__(self, device=None, rate=22050, chunk=2205): diff --git a/examples/libtorchaudio/.gitignore b/examples/libtorchaudio/.gitignore new file mode 100644 index 0000000000..f03338dc7d --- /dev/null +++ b/examples/libtorchaudio/.gitignore @@ -0,0 +1,3 @@ +build +data/output.wav +data/pipeline.zip diff --git a/examples/libtorchaudio/CMakeLists.txt b/examples/libtorchaudio/CMakeLists.txt new file mode 100644 index 0000000000..264949bc12 --- /dev/null +++ b/examples/libtorchaudio/CMakeLists.txt @@ -0,0 +1,20 @@ +cmake_minimum_required(VERSION 3.5) + +project(libtorchaudio-cpp-example) + +SET(BUILD_LIBTORCHAUDIO ON CACHE BOOL "Build libtorchaudio") +SET(BUILD_SOX ON CACHE BOOL "Build libsox into libtorchaudio") + +SET(BUILD_KALDI OFF CACHE BOOL "Build Kaldi into libtorchaudio") +SET(BUILD_TRANSDUCER OFF CACHE BOOL "Build Python binding") +SET(BUILD_TORCHAUDIO_PYTHON_EXTENSION OFF CACHE BOOL "Build Python binding") + +find_package(Torch REQUIRED) +message("libtorchaudio CMakeLists: ${TORCH_CXX_FLAGS}") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}") + +add_subdirectory(../.. libtorchaudio) + +add_executable(main main.cpp) +target_link_libraries(main "${TORCH_LIBRARIES}" "${TORCHAUDIO_LIBRARY}") +set_property(TARGET main PROPERTY CXX_STANDARD 14) diff --git a/examples/libtorchaudio/README.md b/examples/libtorchaudio/README.md new file mode 100644 index 0000000000..10f1228894 --- /dev/null +++ b/examples/libtorchaudio/README.md @@ -0,0 +1,11 @@ +# Example usage: libtorchaudio + +This example demonstrates how you can use torchaudio's I/O features in C++ application, in addition to PyTorch's operations. + +To try this example, simply run `./run.sh`. This script will + +1. Create an audio preprocessing pipeline with TorchScript and dump it to a file. +2. Build the application using `libtorch` and `libtorchaudio`. +3. Execute the preprocessing pipeline on an example audio. + +The detail of the preprocessing pipeline can be found in [`create_jittable_pipeline.py`](./create_jittable_pipeline.py). diff --git a/examples/libtorchaudio/create_jittable_pipeline.py b/examples/libtorchaudio/create_jittable_pipeline.py new file mode 100755 index 0000000000..14a7eefd50 --- /dev/null +++ b/examples/libtorchaudio/create_jittable_pipeline.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 +""" +Create a data preprocess pipeline that can be run with libtorchaudio +""" +import os +import argparse + +import torch +import torchaudio + + +class Pipeline(torch.nn.Module): + """Example audio process pipeline. + + This example load waveform from a file then apply effects and save it to a file. + """ + def __init__(self): + super().__init__() + rir, sample_rate = _load_rir() + self.register_buffer('rir', rir) + self.rir_sample_rate: int = sample_rate + + def forward(self, input_path: str, output_path: str): + torchaudio.sox_effects.init_sox_effects() + + # 1. load audio + waveform, sample_rate = torchaudio.load(input_path) + + # 2. Add background noise + alpha = 0.01 + waveform = alpha * torch.randn_like(waveform) + (1 - alpha) * waveform + + # 3. Reample the RIR filter to much the audio sample rate + rir, _ = torchaudio.sox_effects.apply_effects_tensor( + self.rir, self.rir_sample_rate, effects=[["rate", str(sample_rate)]]) + rir = rir / torch.norm(rir, p=2) + rir = torch.flip(rir, [1]) + + # 4. Apply RIR filter + waveform = torch.nn.functional.pad(waveform, (rir.shape[1] - 1, 0)) + waveform = torch.nn.functional.conv1d(waveform[None, ...], rir[None, ...])[0] + + # Save + torchaudio.save(output_path, waveform, sample_rate) + + +def _create_jit_pipeline(output_path): + module = torch.jit.script(Pipeline()) + print("*" * 40) + print("* Pipeline code") + print("*" * 40) + print() + print(module.code) + print("*" * 40) + module.save(output_path) + + +def _get_path(*paths): + return os.path.join(os.path.dirname(__file__), *paths) + + +def _load_rir(): + path = _get_path("data", "rir.wav") + return torchaudio.load(path) + + +def _parse_args(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--output-path", + default=_get_path("data", "pipeline.zip"), + help="Output JIT file." + ) + return parser.parse_args() + + +def _main(): + args = _parse_args() + _create_jit_pipeline(args.output_path) + + +if __name__ == '__main__': + _main() diff --git a/examples/libtorchaudio/data/README.md b/examples/libtorchaudio/data/README.md new file mode 100644 index 0000000000..6e2ff19097 --- /dev/null +++ b/examples/libtorchaudio/data/README.md @@ -0,0 +1,5 @@ +The files in this directory are originated from [VOiCES](https://iqtlabs.github.io/voices/) dataset, which is licensed under Creative Commos BY 4.0. They are modified to fit into the tutorial. + +* `input.wav`: `VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav` + +* `rir.wav`: `VOiCES_devkit/distant-16k/room-response/rm1/impulse/Lab41-SRI-VOiCES-rm1-impulse-mc01-stu-clo.wav` diff --git a/examples/libtorchaudio/data/input.wav b/examples/libtorchaudio/data/input.wav new file mode 100644 index 0000000000000000000000000000000000000000..004a33532ea2547c10c0074b967733ba91edd9f8 GIT binary patch literal 108844 zcmXV&1y~zP*T;7QAqj-wu5F?2a_jEye(UaX>+bIE?(XgeHR>(YfkJV2$nN)>yx%j= zZg$7#%$alk=ge%nv~JNN=%g(5Y}}*y@NrWD9VJPU2@4wVFjtZ^lA|L@P6Ey=gLwN3` zX-?>>>7c2isj2D2Q;?=WcGQ&7IB5(TdyQGnmh~DJO`aSnhs&YzCHaheN4_i{miNf} zyVx+gyLn%c1A$^ffOBNEAV`c!?SURBSi*VOy!QFW8LSG}R$QtzlQ)!*d&t$rb2hFZuyN{v^oq&W4Dny)%Z zh3YHvsH#cIQd3n~(n^2TNHtG&lY%50$w?|JRhELKl2UQ0hSWr=BQ>Q*U8L4hEADNi z?ow}QhO|_gCXFR5lvYWrr7f2GG-(d+>!fX@kCG;GpH12}M!THc^Q1kDc!xAg+8~{h zb}{bF(j{oIR+<7e4ik4yI&TT5$hDCcrbxqSYc$kZAuVId7f@z~G?V)}YMVpJIno$v 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. + msk_num_feats (int): The input/output feature dimension of conv block in the mask generator, . + msk_num_hidden_feats (int): The internal feature dimension of conv block of the mask generator, . + msk_num_layers (int): The number of layers in one conv block of the mask generator, . + msk_num_stacks (int): The numbr of conv blocks of the mask generator, . + + Note: + This implementation corresponds to the "non-causal" setting in the paper. + + Reference: + - Conv-TasNet: Surpassing Ideal Time--Frequency Magnitude Masking for Speech Separation + + Luo, Yi and Mesgarani, Nima + + https://arxiv.org/abs/1809.07454 + """ + + def __init__( + self, + num_sources: int = 2, + # encoder/decoder parameters + enc_kernel_size: int = 16, + enc_num_feats: int = 512, + # mask generator parameters + msk_kernel_size: int = 3, + msk_num_feats: int = 128, + msk_num_hidden_feats: int = 512, + msk_num_layers: int = 8, + msk_num_stacks: int = 3, + ): + super().__init__() + + self.num_sources = num_sources + self.enc_num_feats = enc_num_feats + self.enc_kernel_size = enc_kernel_size + self.enc_stride = enc_kernel_size // 2 + + self.encoder = torch.nn.Conv1d( + in_channels=1, + out_channels=enc_num_feats, + kernel_size=enc_kernel_size, + stride=self.enc_stride, + padding=self.enc_stride, + bias=False, + ) + self.mask_generator = MaskGenerator( + input_dim=enc_num_feats, + num_sources=num_sources, + kernel_size=msk_kernel_size, + num_feats=msk_num_feats, + num_hidden=msk_num_hidden_feats, + num_layers=msk_num_layers, + num_stacks=msk_num_stacks, + ) + self.decoder = torch.nn.ConvTranspose1d( + in_channels=enc_num_feats, + out_channels=1, + kernel_size=enc_kernel_size, + stride=self.enc_stride, + padding=self.enc_stride, + bias=False, + ) + + def _align_num_frames_with_strides( + self, input: torch.Tensor + ) -> Tuple[torch.Tensor, int]: + """Pad input Tensor so that the end of the input tensor corresponds with + + 1. (if kernel size is odd) the center of the last convolution kernel + or 2. (if kernel size is even) the end of the first half of the last convolution kernel + + Assumption: + The resulting Tensor will be padded with the size of stride (== kernel_width // 2) + on the both ends in Conv1D + + |<--- k_1 --->| + | | |<-- k_n-1 -->| + | | | |<--- k_n --->| + | | | | | + | | | | | + | v v v | + |<---->|<--- input signal --->|<--->|<---->| + stride PAD stride + + Args: + input (torch.Tensor): 3D Tensor with shape (batch_size, channels==1, frames) + + Returns: + torch.Tensor: Padded Tensor + int: Number of paddings performed + """ + batch_size, num_channels, num_frames = input.shape + is_odd = self.enc_kernel_size % 2 + num_strides = (num_frames - is_odd) // self.enc_stride + num_remainings = num_frames - (is_odd + num_strides * self.enc_stride) + if num_remainings == 0: + return input, 0 + + num_paddings = self.enc_stride - num_remainings + pad = torch.zeros( + batch_size, + num_channels, + num_paddings, + dtype=input.dtype, + device=input.device, + ) + return torch.cat([input, pad], 2), num_paddings + + def forward(self, input: torch.Tensor) -> torch.Tensor: + """Perform source separation. Generate audio source waveforms. + + Args: + input (torch.Tensor): 3D Tensor with shape [batch, channel==1, frames] + + Returns: + torch.Tensor: 3D Tensor with shape [batch, channel==num_sources, frames] + """ + if input.ndim != 3 or input.shape[1] != 1: + raise ValueError( + f"Expected 3D tensor (batch, channel==1, frames). Found: {input.shape}" + ) + + # B: batch size + # L: input frame length + # L': padded input frame length + # F: feature dimension + # M: feature frame length + # S: number of sources + + padded, num_pads = self._align_num_frames_with_strides(input) # B, 1, L' + batch_size, num_padded_frames = padded.shape[0], padded.shape[2] + feats = self.encoder(padded) # B, F, M + masked = self.mask_generator(feats) * feats.unsqueeze(1) # B, S, F, M + masked = masked.view( + batch_size * self.num_sources, self.enc_num_feats, -1 + ) # B*S, F, M + decoded = self.decoder(masked) # B*S, 1, L' + output = decoded.view( + batch_size, self.num_sources, num_padded_frames + ) # B, S, L' + if num_pads > 0: + output = output[..., :-num_pads] # B, S, L + return output diff --git a/torchaudio/sox_effects/__init__.py b/torchaudio/sox_effects/__init__.py index d9650173c5..5baf406b10 100644 --- a/torchaudio/sox_effects/__init__.py +++ b/torchaudio/sox_effects/__init__.py @@ -5,8 +5,6 @@ effect_names, apply_effects_tensor, apply_effects_file, - SoxEffect, - SoxEffectsChain, ) diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 9262786541..8b50d19aa7 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -1,19 +1,11 @@ -from typing import Any, Callable, List, Optional, Tuple, Union +from typing import List, Tuple import torch -from torch import Tensor -from torchaudio._internal import ( - module_utils as _mod_utils, - misc_ops as _misc_ops, -) +from torchaudio._internal import module_utils as _mod_utils from torchaudio.utils.sox_utils import list_effects -if _mod_utils.is_module_available('torchaudio._torchaudio'): - from torchaudio import _torchaudio - - @_mod_utils.requires_module('torchaudio._torchaudio') def init_sox_effects(): """Initialize resources required to use sox effects. @@ -257,189 +249,3 @@ def apply_effects_file( """ signal = torch.ops.torchaudio.sox_effects_apply_effects_file(path, effects, normalize, channels_first) return signal.get_tensor(), signal.get_sample_rate() - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@_mod_utils.deprecated('Please migrate to `apply_effects_file` or `apply_effects_tensor`.') -def SoxEffect(): - r"""Create an object for passing sox effect information between python and c++ - - Warning: - This function is deprecated. - Please migrate to :func:`apply_effects_file` or :func:`apply_effects_tensor`. - - Returns: - SoxEffect: An object with the following attributes: ename (str) which is the - name of effect, and eopts (List[str]) which is a list of effect options. - """ - return _torchaudio.SoxEffect() - - -@_mod_utils.deprecated('Please migrate to `apply_effects_file` or `apply_effects_tensor`.') -class SoxEffectsChain(object): - r"""SoX effects chain class. - - Warning: - This class is deprecated. - Please migrate to :func:`apply_effects_file` or :func:`apply_effects_tensor`. - - Args: - normalization (bool, number, or callable, optional): - If boolean ``True``, then output is divided by ``1 << 31`` - (assumes signed 32-bit audio), and normalizes to ``[-1, 1]``. - If ``number``, then output is divided by that number. - If ``callable``, then the output is passed as a parameter to the given function, then - the output is divided by the result. (Default: ``True``) - channels_first (bool, optional): - Set channels first or length first in result. (Default: ``True``) - out_siginfo (sox_signalinfo_t, optional): - a sox_signalinfo_t type, which could be helpful if the audio type cannot be - automatically determined. (Default: ``None``) - out_encinfo (sox_encodinginfo_t, optional): - a sox_encodinginfo_t type, which could be set if the audio type cannot be - automatically determined. (Default: ``None``) - filetype (str, optional): - a filetype or extension to be set if sox cannot determine it automatically. - (Default: ``'raw'``) - - Returns: - Tuple[Tensor, int]: - An output Tensor of size ``[C x L]`` or ``[L x C]`` where L is the number - of audio frames and C is the number of channels. An integer which is the sample rate of the - audio (as listed in the metadata of the file) - - Example - >>> class MyDataset(Dataset): - ... def __init__(self, audiodir_path): - ... self.data = [ - ... os.path.join(audiodir_path, fn) - ... for fn in os.listdir(audiodir_path)] - ... self.E = torchaudio.sox_effects.SoxEffectsChain() - ... self.E.append_effect_to_chain("rate", [16000]) # resample to 16000hz - ... self.E.append_effect_to_chain("channels", ["1"]) # mono signal - ... def __getitem__(self, index): - ... fn = self.data[index] - ... self.E.set_input_file(fn) - ... x, sr = self.E.sox_build_flow_effects() - ... return x, sr - ... - ... def __len__(self): - ... return len(self.data) - ... - >>> ds = MyDataset(path_to_audio_files) - >>> for sig, sr in ds: - ... pass - """ - - EFFECTS_UNIMPLEMENTED = {"spectrogram", "splice", "noiseprof", "fir"} - - def __init__(self, - normalization: Union[bool, float, Callable] = True, - channels_first: bool = True, - out_siginfo: Any = None, - out_encinfo: Any = None, - filetype: str = "raw") -> None: - self.input_file: Optional[str] = None - self.chain: List[str] = [] - self.MAX_EFFECT_OPTS = 20 - self.out_siginfo = out_siginfo - self.out_encinfo = out_encinfo - self.filetype = filetype - self.normalization = normalization - self.channels_first = channels_first - - # Define in __init__ to avoid calling at import time - self.EFFECTS_AVAILABLE = set(effect_names()) - - def append_effect_to_chain(self, - ename: str, - eargs: Optional[Union[List[str], str]] = None) -> None: - r"""Append effect to a sox effects chain. - - Args: - ename (str): which is the name of effect - eargs (List[str] or str, optional): which is a list of effect options. (Default: ``None``) - """ - e = SoxEffect() - # check if we have a valid effect - ename = self._check_effect(ename) - if eargs is None or eargs == []: - eargs = [""] - elif not isinstance(eargs, list): - eargs = [eargs] - eargs = self._flatten(eargs) - if len(eargs) > self.MAX_EFFECT_OPTS: - raise RuntimeError("Number of effect options ({}) is greater than max " - "suggested number of options {}. Increase MAX_EFFECT_OPTS " - "or lower the number of effect options".format(len(eargs), self.MAX_EFFECT_OPTS)) - e.ename = ename - e.eopts = eargs - self.chain.append(e) - - @_mod_utils.requires_module('torchaudio._torchaudio') - def sox_build_flow_effects(self, - out: Optional[Tensor] = None) -> Tuple[Tensor, int]: - r"""Build effects chain and flow effects from input file to output tensor - - Args: - out (Tensor, optional): Where the output will be written to. (Default: ``None``) - - Returns: - Tuple[Tensor, int]: An output Tensor of size `[C x L]` or `[L x C]` where - L is the number of audio frames and C is the number of channels. - An integer which is the sample rate of the audio (as listed in the metadata of the file) - """ - # initialize output tensor - if out is not None: - _misc_ops.check_input(out) - else: - out = torch.FloatTensor() - if not len(self.chain): - e = SoxEffect() - e.ename = "no_effects" - e.eopts = [""] - self.chain.append(e) - - # print("effect options:", [x.eopts for x in self.chain]) - - sr = _torchaudio.build_flow_effects(self.input_file, - out, - self.channels_first, - self.out_siginfo, - self.out_encinfo, - self.filetype, - self.chain, - self.MAX_EFFECT_OPTS) - - _misc_ops.normalize_audio(out, self.normalization) - - return out, sr - - def clear_chain(self) -> None: - r"""Clear effects chain in python - """ - self.chain = [] - - def set_input_file(self, input_file: str) -> None: - r"""Set input file for input of chain - - Args: - input_file (str): The path to the input file. - """ - self.input_file = input_file - - def _check_effect(self, e: str) -> str: - if e.lower() in self.EFFECTS_UNIMPLEMENTED: - raise NotImplementedError("This effect ({}) is not implement in torchaudio".format(e)) - elif e.lower() not in self.EFFECTS_AVAILABLE: - raise LookupError("Effect name, {}, not valid".format(e.lower())) - return e.lower() - - # https://stackoverflow.com/questions/12472338/flattening-a-list-recursively - # convenience function to flatten list recursively - def _flatten(self, x: list) -> list: - if x == []: - return [] - if isinstance(x[0], list): - return self._flatten(x[:1]) + self._flatten(x[:1]) - return [str(a) for a in x[:1]] + self._flatten(x[1:]) diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index ef0a58e637..2e7e4f37ee 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -198,7 +198,8 @@ def __init__(self, stype: str = 'power', top_db: Optional[float] = None) -> None def forward(self, x: Tensor) -> Tensor: r"""Numerically stable implementation from Librosa. - https://librosa.github.io/librosa/_modules/librosa/core/spectrum.html + + https://librosa.org/doc/latest/generated/librosa.amplitude_to_db.html Args: x (Tensor): Input tensor before being converted to decibel scale. From 719ebb855a7deaa4913da9215b85521a02ae9371 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Wed, 11 Nov 2020 13:00:49 -0800 Subject: [PATCH 21/73] Import torchaudio #1004 5e54c77 Summary: Import torchaudio up to #1004 5e54c77 Reviewed By: vincentqb, cpuhrsch Differential Revision: D24841498 fbshipit-source-id: 3829130636f36779d84f01ff0d0120b80b2396d7 --- .circleci/config.yml | 4 +- .circleci/config.yml.in | 2 +- .circleci/regenerate.py | 1 + docs/source/functional.rst | 190 +- docs/source/torchaudio.rst | 7 - examples/pipeline_wav2letter/main.py | 59 +- .../sox_effect/dataset_test.py | 10 + torchaudio/_internal/fft.py | 27 + torchaudio/compliance/kaldi.py | 20 +- torchaudio/functional.py | 2332 ----------------- torchaudio/functional/__init__.py | 42 + torchaudio/functional/filtering.py | 1469 +++++++++++ torchaudio/functional/functional.py | 912 +++++++ 13 files changed, 2602 insertions(+), 2473 deletions(-) create mode 100644 torchaudio/_internal/fft.py delete mode 100644 torchaudio/functional.py create mode 100644 torchaudio/functional/__init__.py create mode 100644 torchaudio/functional/filtering.py create mode 100644 torchaudio/functional/functional.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 472c3f0af6..1f6d664d7e 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -459,7 +459,7 @@ jobs: - third_party/src - run: name: Install torchaudio - command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh + command: docker run -t --gpus all -e UPLOAD_CHANNEL -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh - run: name: Run tests command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh @@ -1394,4 +1394,4 @@ workflows: only: - master jobs: - - smoke_test_docker_image_build \ No newline at end of file + - smoke_test_docker_image_build diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 6f6838deff..b9fccbaf30 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -459,7 +459,7 @@ jobs: - third_party/src - run: name: Install torchaudio - command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh + command: docker run -t --gpus all -e UPLOAD_CHANNEL -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh - run: name: Run tests command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index 2f4d4088d9..419db311c1 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -178,3 +178,4 @@ def unittest_workflows(indentation=6): build_workflows=build_workflows, unittest_workflows=unittest_workflows, )) + f.write("\n") diff --git a/docs/source/functional.rst b/docs/source/functional.rst index 89e992bb89..a0f3a272cf 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -8,145 +8,166 @@ torchaudio.functional Functions to perform common audio operations. -:hidden:`spectrogram` -~~~~~~~~~~~~~~~~~~~~~ +:hidden:`Filtering` +~~~~~~~~~~~~~~~~~~~ -.. autofunction:: spectrogram -:hidden:`amplitude_to_DB` -~~~~~~~~~~~~~~~~~~~~~~~~~ +allpass_biquad +-------------- -.. autofunction:: amplitude_to_DB +.. autofunction:: allpass_biquad -:hidden:`create_fb_matrix` -~~~~~~~~~~~~~~~~~~~~~~~~~~ +band_biquad +----------- -.. autofunction:: create_fb_matrix +.. autofunction:: band_biquad -:hidden:`create_dct` -~~~~~~~~~~~~~~~~~~~~ +bandpass_biquad +--------------- -.. autofunction:: create_dct +.. autofunction:: bandpass_biquad -:hidden:`mu_law_encoding` -~~~~~~~~~~~~~~~~~~~~~~~~~ +bandreject_biquad +----------------- -.. autofunction:: mu_law_encoding +.. autofunction:: bandreject_biquad -:hidden:`mu_law_decoding` -~~~~~~~~~~~~~~~~~~~~~~~~~ +bass_biquad +----------- -.. autofunction:: mu_law_decoding +.. autofunction:: bass_biquad -:hidden:`complex_norm` -~~~~~~~~~~~~~~~~~~~~~~ +biquad +------ -.. autofunction:: complex_norm +.. autofunction:: biquad -:hidden:`angle` -~~~~~~~~~~~~~~~ +contrast +-------- -.. autofunction:: angle +.. autofunction:: contrast -:hidden:`magphase` -~~~~~~~~~~~~~~~~~~ +dcshift +------- -.. autofunction:: magphase +.. autofunction:: dcshift -:hidden:`phase_vocoder` -~~~~~~~~~~~~~~~~~~~~~~~ +deemph_biquad +------------- -.. autofunction:: phase_vocoder +.. autofunction:: deemph_biquad -:hidden:`lfilter` -~~~~~~~~~~~~~~~~~ -.. autofunction:: lfilter +dither +------ -:hidden:`biquad` -~~~~~~~~~~~~~~~~ +.. autofunction:: dither -.. autofunction:: biquad +equalizer_biquad +---------------- -:hidden:`lowpass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: equalizer_biquad -.. autofunction:: lowpass_biquad +flanger +------- -:hidden:`highpass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: flanger + +gain +---- + +.. autofunction:: gain + +highpass_biquad +--------------- .. autofunction:: highpass_biquad -:hidden:`allpass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~ +lfilter +------- -.. autofunction:: allpass_biquad +.. autofunction:: lfilter -:hidden:`equalizer_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~ +lowpass_biquad +-------------- -.. autofunction:: equalizer_biquad +.. autofunction:: lowpass_biquad -:hidden:`bandpass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +overdrive +--------- -.. autofunction:: bandpass_biquad +.. autofunction:: overdrive -:hidden:`bandreject_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +phaser +------ -.. autofunction:: bandreject_biquad +.. autofunction:: phaser -:hidden:`band_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +riaa_biquad +----------- -.. autofunction:: band_biquad +.. autofunction:: riaa_biquad -:hidden:`treble_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +treble_biquad +------------- .. autofunction:: treble_biquad -:hidden:`bass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. autofunction:: bass_biquad +vad +--- -:hidden:`deemph_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: vad -.. autofunction:: deemph_biquad +:hidden:`spectrogram` +~~~~~~~~~~~~~~~~~~~~~ -:hidden:`riaa_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: spectrogram -.. autofunction:: riaa_biquad +:hidden:`amplitude_to_DB` +~~~~~~~~~~~~~~~~~~~~~~~~~ -:hidden:`contrast` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: amplitude_to_DB -.. autofunction:: contrast +:hidden:`create_fb_matrix` +~~~~~~~~~~~~~~~~~~~~~~~~~~ -:hidden:`dcshift` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: create_fb_matrix -.. autofunction:: dcshift +:hidden:`create_dct` +~~~~~~~~~~~~~~~~~~~~ -:hidden:`overdrive` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: create_dct -.. autofunction:: overdrive +:hidden:`mu_law_encoding` +~~~~~~~~~~~~~~~~~~~~~~~~~ -:hidden:`phaser` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: mu_law_encoding -.. autofunction:: phaser +:hidden:`mu_law_decoding` +~~~~~~~~~~~~~~~~~~~~~~~~~ -:hidden:`flanger` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: mu_law_decoding -.. autofunction:: flanger +:hidden:`complex_norm` +~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: complex_norm + +:hidden:`angle` +~~~~~~~~~~~~~~~ + +.. autofunction:: angle + +:hidden:`magphase` +~~~~~~~~~~~~~~~~~~ + +.. autofunction:: magphase + +:hidden:`phase_vocoder` +~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: phase_vocoder :hidden:`mask_along_axis` ~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -172,8 +193,3 @@ Functions to perform common audio operations. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: sliding_window_cmn - -:hidden:`vad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: vad diff --git a/docs/source/torchaudio.rst b/docs/source/torchaudio.rst index f7b826b571..480be4393e 100644 --- a/docs/source/torchaudio.rst +++ b/docs/source/torchaudio.rst @@ -34,10 +34,3 @@ Backend Utilities .. autofunction:: get_audio_backend .. autofunction:: set_audio_backend - -Sox Effects Utilities -~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: initialize_sox - -.. autofunction:: shutdown_sox diff --git a/examples/pipeline_wav2letter/main.py b/examples/pipeline_wav2letter/main.py index 8f52ec6c3c..549f3dcdeb 100644 --- a/examples/pipeline_wav2letter/main.py +++ b/examples/pipeline_wav2letter/main.py @@ -83,13 +83,6 @@ def parse_args(): parser.add_argument( "--start-epoch", default=0, type=int, metavar="N", help="manual epoch number" ) - parser.add_argument( - "--print-freq", - default=10, - type=int, - metavar="N", - help="print frequency in epochs", - ) parser.add_argument( "--reduce-lr-valid", action="store_true", @@ -615,37 +608,35 @@ def main(rank, args): not args.reduce_lr_valid, ) - if not (epoch + 1) % args.print_freq or epoch == args.epochs - 1: - - loss = evaluate( - model, - criterion, - loader_validation, - decoder, - language_model, - devices[0], - epoch, - not_main_rank, - ) - - is_best = loss < best_loss - best_loss = min(loss, best_loss) - save_checkpoint( - { - "epoch": epoch + 1, - "state_dict": model.state_dict(), - "best_loss": best_loss, - "optimizer": optimizer.state_dict(), - "scheduler": scheduler.state_dict(), - }, - is_best, - args.checkpoint, - not_main_rank, - ) + loss = evaluate( + model, + criterion, + loader_validation, + decoder, + language_model, + devices[0], + epoch, + not_main_rank, + ) if args.reduce_lr_valid and isinstance(scheduler, ReduceLROnPlateau): scheduler.step(loss) + is_best = loss < best_loss + best_loss = min(loss, best_loss) + save_checkpoint( + { + "epoch": epoch + 1, + "state_dict": model.state_dict(), + "best_loss": best_loss, + "optimizer": optimizer.state_dict(), + "scheduler": scheduler.state_dict(), + }, + is_best, + args.checkpoint, + not_main_rank, + ) + logging.info("End time: %s", datetime.now()) if args.distributed: diff --git a/test/torchaudio_unittest/sox_effect/dataset_test.py b/test/torchaudio_unittest/sox_effect/dataset_test.py index e98095d0dd..647e4d2a81 100644 --- a/test/torchaudio_unittest/sox_effect/dataset_test.py +++ b/test/torchaudio_unittest/sox_effect/dataset_test.py @@ -1,3 +1,6 @@ +import sys +import platform +from unittest import skipIf from typing import List, Tuple import numpy as np @@ -68,6 +71,13 @@ def init_random_seed(worker_id): @skipIfNoExtension +@skipIf( + platform.system() == 'Darwin' and + sys.version_info.major == 3 and + sys.version_info.minor in [6, 7], + 'This test is known to get stuck for macOS with Python < 3.8. ' + 'See https://github.com/pytorch/pytorch/issues/46409' +) class TestSoxEffectsDataset(TempDirMixin, PytorchTestCase): """Test `apply_effects_file` in multi-process dataloader setting""" diff --git a/torchaudio/_internal/fft.py b/torchaudio/_internal/fft.py new file mode 100644 index 0000000000..45350a4972 --- /dev/null +++ b/torchaudio/_internal/fft.py @@ -0,0 +1,27 @@ +"""Compatibility module for fft-related functions + +In PyTorch 1.7, the new `torch.fft` module was introduced. + +To use this new module, one has to explicitly import `torch.fft`. however this will change +the reference `torch.fft` is pointing from function to module. +And this change takes effect not only in the client code but also in already-imported libraries too. +Similarly, if a library does the explicit import, the rest of the application code must use the +`torch.fft.fft` function. + +For this reason, to migrate the deprecated functions of fft-family, we need to use the new +implementation under `torch.fft` without explicitly importing `torch.fft` module. + +This module provides a simple interface for the migration, abstracting away +the access to the underlying C functions. + +Once the deprecated functions are removed from PyTorch and `torch.fft` starts to always represent +the new module, we can get rid of this module and call functions under `torch.fft` directly. +""" +from typing import Optional + +import torch + + +def rfft(input: torch.Tensor, n: Optional[int] = None, dim: int = -1, norm: Optional[str] = None) -> torch.Tensor: + # see: https://pytorch.org/docs/master/fft.html#torch.fft.rfft + return torch._C._fft.fft_rfft(input, n, dim, norm) diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index 1160595992..afabebeb6a 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -2,9 +2,11 @@ import math import torch -import torchaudio from torch import Tensor +import torchaudio +import torchaudio._internal.fft + __all__ = [ 'get_mel_banks', 'inverse_mel_scale', @@ -289,10 +291,10 @@ def spectrogram(waveform: Tensor, snip_edges, raw_energy, energy_floor, dither, remove_dc_offset, preemphasis_coefficient) # size (m, padded_window_size // 2 + 1, 2) - fft = torch.rfft(strided_input, 1, normalized=False, onesided=True) + fft = torchaudio._internal.fft.rfft(strided_input) # Convert the FFT into a power spectrum - power_spectrum = torch.max(fft.pow(2).sum(2), epsilon).log() # size (m, padded_window_size // 2 + 1) + power_spectrum = torch.max(fft.abs().pow(2.), epsilon).log() # size (m, padded_window_size // 2 + 1) power_spectrum[:, 0] = signal_log_energy power_spectrum = _subtract_column_mean(power_spectrum, subtract_mean) @@ -570,12 +572,10 @@ def fbank(waveform: Tensor, waveform, padded_window_size, window_size, window_shift, window_type, blackman_coeff, snip_edges, raw_energy, energy_floor, dither, remove_dc_offset, preemphasis_coefficient) - # size (m, padded_window_size // 2 + 1, 2) - fft = torch.rfft(strided_input, 1, normalized=False, onesided=True) - - power_spectrum = fft.pow(2).sum(2) # size (m, padded_window_size // 2 + 1) - if not use_power: - power_spectrum = power_spectrum.pow(0.5) + # size (m, padded_window_size // 2 + 1) + spectrum = torchaudio._internal.fft.rfft(strided_input).abs() + if use_power: + spectrum = spectrum.pow(2.) # size (num_mel_bins, padded_window_size // 2) mel_energies, _ = get_mel_banks(num_mel_bins, padded_window_size, sample_frequency, @@ -586,7 +586,7 @@ def fbank(waveform: Tensor, mel_energies = torch.nn.functional.pad(mel_energies, (0, 1), mode='constant', value=0) # sum with mel fiterbanks over the power spectrum, size (m, num_mel_bins) - mel_energies = torch.mm(power_spectrum, mel_energies.T) + mel_energies = torch.mm(spectrum, mel_energies.T) if use_log_fbank: # avoid log of zero (which should be prevented anyway by dithering) mel_energies = torch.max(mel_energies, _get_epsilon(device, dtype)).log() diff --git a/torchaudio/functional.py b/torchaudio/functional.py deleted file mode 100644 index d8adfe86e6..0000000000 --- a/torchaudio/functional.py +++ /dev/null @@ -1,2332 +0,0 @@ -# -*- coding: utf-8 -*- - -import math -from typing import Optional, Tuple -import warnings - -import torch -from torch import Tensor - -__all__ = [ - "spectrogram", - "griffinlim", - "amplitude_to_DB", - "create_fb_matrix", - "create_dct", - "mu_law_encoding", - "mu_law_decoding", - "complex_norm", - "angle", - "magphase", - "phase_vocoder", - "lfilter", - "lowpass_biquad", - "highpass_biquad", - "allpass_biquad", - "bandpass_biquad", - "bandreject_biquad", - "equalizer_biquad", - "band_biquad", - "treble_biquad", - "bass_biquad", - "deemph_biquad", - "riaa_biquad", - "biquad", - "contrast", - "dcshift", - "overdrive", - "phaser", - "flanger", - 'mask_along_axis', - 'mask_along_axis_iid', - 'sliding_window_cmn', - 'vad', -] - - -def spectrogram( - waveform: Tensor, - pad: int, - window: Tensor, - n_fft: int, - hop_length: int, - win_length: int, - power: Optional[float], - normalized: bool -) -> Tensor: - r"""Create a spectrogram or a batch of spectrograms from a raw audio signal. - The spectrogram can be either magnitude-only or complex. - - Args: - waveform (Tensor): Tensor of audio of dimension (..., time) - pad (int): Two sided padding of signal - window (Tensor): Window tensor that is applied/multiplied to each frame/window - n_fft (int): Size of FFT - hop_length (int): Length of hop between STFT windows - win_length (int): Window size - power (float or None): Exponent for the magnitude spectrogram, - (must be > 0) e.g., 1 for energy, 2 for power, etc. - If None, then the complex spectrum is returned instead. - normalized (bool): Whether to normalize by magnitude after stft - - Returns: - Tensor: Dimension (..., freq, time), freq is - ``n_fft // 2 + 1`` and ``n_fft`` is the number of - Fourier bins, and time is the number of window hops (n_frame). - """ - - if pad > 0: - # TODO add "with torch.no_grad():" back when JIT supports it - waveform = torch.nn.functional.pad(waveform, (pad, pad), "constant") - - # pack batch - shape = waveform.size() - waveform = waveform.reshape(-1, shape[-1]) - - # default values are consistent with librosa.core.spectrum._spectrogram - spec_f = torch.stft( - waveform, n_fft, hop_length, win_length, window, True, "reflect", False, True - ) - - # unpack batch - spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-3:]) - - if normalized: - spec_f /= window.pow(2.).sum().sqrt() - if power is not None: - spec_f = complex_norm(spec_f, power=power) - - return spec_f - - -def griffinlim( - specgram: Tensor, - window: Tensor, - n_fft: int, - hop_length: int, - win_length: int, - power: float, - normalized: bool, - n_iter: int, - momentum: float, - length: Optional[int], - rand_init: bool -) -> Tensor: - r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation. - Implementation ported from `librosa`. - - .. [1] McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. - "librosa: Audio and music signal analysis in python." - In Proceedings of the 14th python in science conference, pp. 18-25. 2015. - - .. [2] Perraudin, N., Balazs, P., & Sรธndergaard, P. L. - "A fast Griffin-Lim algorithm," - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4), - Oct. 2013. - - .. [3] D. W. Griffin and J. S. Lim, - "Signal estimation from modified short-time Fourier transform," - IEEE Trans. ASSP, vol.32, no.2, pp.236โ€“243, Apr. 1984. - - Args: - specgram (Tensor): A magnitude-only STFT spectrogram of dimension (..., freq, frames) - where freq is ``n_fft // 2 + 1``. - window (Tensor): Window tensor that is applied/multiplied to each frame/window - n_fft (int): Size of FFT, creates ``n_fft // 2 + 1`` bins - hop_length (int): Length of hop between STFT windows. ( - Default: ``win_length // 2``) - win_length (int): Window size. (Default: ``n_fft``) - power (float): Exponent for the magnitude spectrogram, - (must be > 0) e.g., 1 for energy, 2 for power, etc. - normalized (bool): Whether to normalize by magnitude after stft. - n_iter (int): Number of iteration for phase recovery process. - momentum (float): The momentum parameter for fast Griffin-Lim. - Setting this to 0 recovers the original Griffin-Lim method. - Values near 1 can lead to faster convergence, but above 1 may not converge. - length (int or None): Array length of the expected output. - rand_init (bool): Initializes phase randomly if True, to zero otherwise. - - Returns: - torch.Tensor: waveform of (..., time), where time equals the ``length`` parameter if given. - """ - assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum) - assert momentum >= 0, 'momentum={} < 0'.format(momentum) - - # pack batch - shape = specgram.size() - specgram = specgram.reshape([-1] + list(shape[-2:])) - - specgram = specgram.pow(1 / power) - - # randomly initialize the phase - batch, freq, frames = specgram.size() - if rand_init: - angles = 2 * math.pi * torch.rand(batch, freq, frames) - else: - angles = torch.zeros(batch, freq, frames) - angles = torch.stack([angles.cos(), angles.sin()], dim=-1) \ - .to(dtype=specgram.dtype, device=specgram.device) - specgram = specgram.unsqueeze(-1).expand_as(angles) - - # And initialize the previous iterate to 0 - rebuilt = torch.tensor(0.) - - for _ in range(n_iter): - # Store the previous iterate - tprev = rebuilt - - # Invert with our current estimate of the phases - inverse = torch.istft(specgram * angles, - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - window=window, - length=length).float() - - # Rebuild the spectrogram - rebuilt = torch.stft(inverse, n_fft, hop_length, win_length, window, - True, 'reflect', False, True) - - # Update our phase estimates - angles = rebuilt - if momentum: - angles = angles - tprev.mul_(momentum / (1 + momentum)) - angles = angles.div(complex_norm(angles).add(1e-16).unsqueeze(-1).expand_as(angles)) - - # Return the final phase estimates - waveform = torch.istft(specgram * angles, - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - window=window, - length=length) - - # unpack batch - waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:]) - - return waveform - - -def amplitude_to_DB( - x: Tensor, - multiplier: float, - amin: float, - db_multiplier: float, - top_db: Optional[float] = None -) -> Tensor: - r"""Turn a tensor from the power/amplitude scale to the decibel scale. - - This output depends on the maximum value in the input tensor, and so - may return different values for an audio clip split into snippets vs. a - a full clip. - - Args: - x (Tensor): Input tensor before being converted to decibel scale - multiplier (float): Use 10. for power and 20. for amplitude - amin (float): Number to clamp ``x`` - db_multiplier (float): Log10(max(reference value and amin)) - top_db (float or None, optional): Minimum negative cut-off in decibels. A reasonable number - is 80. (Default: ``None``) - - Returns: - Tensor: Output tensor in decibel scale - """ - x_db = multiplier * torch.log10(torch.clamp(x, min=amin)) - x_db -= multiplier * db_multiplier - - if top_db is not None: - x_db = x_db.clamp(min=x_db.max().item() - top_db) - - return x_db - - -def DB_to_amplitude( - x: Tensor, - ref: float, - power: float -) -> Tensor: - r"""Turn a tensor from the decibel scale to the power/amplitude scale. - - Args: - x (Tensor): Input tensor before being converted to power/amplitude scale. - ref (float): Reference which the output will be scaled by. - power (float): If power equals 1, will compute DB to power. If 0.5, will compute DB to amplitude. - - Returns: - Tensor: Output tensor in power/amplitude scale. - """ - return ref * torch.pow(torch.pow(10.0, 0.1 * x), power) - - -def create_fb_matrix( - n_freqs: int, - f_min: float, - f_max: float, - n_mels: int, - sample_rate: int, - norm: Optional[str] = None -) -> Tensor: - r"""Create a frequency bin conversion matrix. - - Args: - n_freqs (int): Number of frequencies to highlight/apply - f_min (float): Minimum frequency (Hz) - f_max (float): Maximum frequency (Hz) - n_mels (int): Number of mel filterbanks - sample_rate (int): Sample rate of the audio waveform - norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band - (area normalization). (Default: ``None``) - - Returns: - Tensor: Triangular filter banks (fb matrix) of size (``n_freqs``, ``n_mels``) - meaning number of frequencies to highlight/apply to x the number of filterbanks. - Each column is a filterbank so that assuming there is a matrix A of - size (..., ``n_freqs``), the applied result would be - ``A * create_fb_matrix(A.size(-1), ...)``. - """ - - if norm is not None and norm != "slaney": - raise ValueError("norm must be one of None or 'slaney'") - - # freq bins - # Equivalent filterbank construction by Librosa - all_freqs = torch.linspace(0, sample_rate // 2, n_freqs) - - # calculate mel freq bins - # hertz to mel(f) is 2595. * math.log10(1. + (f / 700.)) - m_min = 2595.0 * math.log10(1.0 + (f_min / 700.0)) - m_max = 2595.0 * math.log10(1.0 + (f_max / 700.0)) - m_pts = torch.linspace(m_min, m_max, n_mels + 2) - # mel to hertz(mel) is 700. * (10**(mel / 2595.) - 1.) - f_pts = 700.0 * (10 ** (m_pts / 2595.0) - 1.0) - # calculate the difference between each mel point and each stft freq point in hertz - f_diff = f_pts[1:] - f_pts[:-1] # (n_mels + 1) - slopes = f_pts.unsqueeze(0) - all_freqs.unsqueeze(1) # (n_freqs, n_mels + 2) - # create overlapping triangles - zero = torch.zeros(1) - down_slopes = (-1.0 * slopes[:, :-2]) / f_diff[:-1] # (n_freqs, n_mels) - up_slopes = slopes[:, 2:] / f_diff[1:] # (n_freqs, n_mels) - fb = torch.max(zero, torch.min(down_slopes, up_slopes)) - - if norm is not None and norm == "slaney": - # Slaney-style mel is scaled to be approx constant energy per channel - enorm = 2.0 / (f_pts[2:n_mels + 2] - f_pts[:n_mels]) - fb *= enorm.unsqueeze(0) - - if (fb.max(dim=0).values == 0.).any(): - warnings.warn( - "At least one mel filterbank has all zero values. " - f"The value for `n_mels` ({n_mels}) may be set too high. " - f"Or, the value for `n_freqs` ({n_freqs}) may be set too low." - ) - - return fb - - -def create_dct( - n_mfcc: int, - n_mels: int, - norm: Optional[str] -) -> Tensor: - r"""Create a DCT transformation matrix with shape (``n_mels``, ``n_mfcc``), - normalized depending on norm. - - Args: - n_mfcc (int): Number of mfc coefficients to retain - n_mels (int): Number of mel filterbanks - norm (str or None): Norm to use (either 'ortho' or None) - - Returns: - Tensor: The transformation matrix, to be right-multiplied to - row-wise data of size (``n_mels``, ``n_mfcc``). - """ - # http://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II - n = torch.arange(float(n_mels)) - k = torch.arange(float(n_mfcc)).unsqueeze(1) - dct = torch.cos(math.pi / float(n_mels) * (n + 0.5) * k) # size (n_mfcc, n_mels) - if norm is None: - dct *= 2.0 - else: - assert norm == "ortho" - dct[0] *= 1.0 / math.sqrt(2.0) - dct *= math.sqrt(2.0 / float(n_mels)) - return dct.t() - - -def mu_law_encoding( - x: Tensor, - quantization_channels: int -) -> Tensor: - r"""Encode signal based on mu-law companding. For more info see the - `Wikipedia Entry `_ - - This algorithm assumes the signal has been scaled to between -1 and 1 and - returns a signal encoded with values from 0 to quantization_channels - 1. - - Args: - x (Tensor): Input tensor - quantization_channels (int): Number of channels - - Returns: - Tensor: Input after mu-law encoding - """ - mu = quantization_channels - 1.0 - if not x.is_floating_point(): - x = x.to(torch.float) - mu = torch.tensor(mu, dtype=x.dtype) - x_mu = torch.sign(x) * torch.log1p(mu * torch.abs(x)) / torch.log1p(mu) - x_mu = ((x_mu + 1) / 2 * mu + 0.5).to(torch.int64) - return x_mu - - -def mu_law_decoding( - x_mu: Tensor, - quantization_channels: int -) -> Tensor: - r"""Decode mu-law encoded signal. For more info see the - `Wikipedia Entry `_ - - This expects an input with values between 0 and quantization_channels - 1 - and returns a signal scaled between -1 and 1. - - Args: - x_mu (Tensor): Input tensor - quantization_channels (int): Number of channels - - Returns: - Tensor: Input after mu-law decoding - """ - mu = quantization_channels - 1.0 - if not x_mu.is_floating_point(): - x_mu = x_mu.to(torch.float) - mu = torch.tensor(mu, dtype=x_mu.dtype) - x = ((x_mu) / mu) * 2 - 1.0 - x = torch.sign(x) * (torch.exp(torch.abs(x) * torch.log1p(mu)) - 1.0) / mu - return x - - -def complex_norm( - complex_tensor: Tensor, - power: float = 1.0 -) -> Tensor: - r"""Compute the norm of complex tensor input. - - Args: - complex_tensor (Tensor): Tensor shape of `(..., complex=2)` - power (float): Power of the norm. (Default: `1.0`). - - Returns: - Tensor: Power of the normed input tensor. Shape of `(..., )` - """ - - # Replace by torch.norm once issue is fixed - # https://github.com/pytorch/pytorch/issues/34279 - return complex_tensor.pow(2.).sum(-1).pow(0.5 * power) - - -def angle( - complex_tensor: Tensor -) -> Tensor: - r"""Compute the angle of complex tensor input. - - Args: - complex_tensor (Tensor): Tensor shape of `(..., complex=2)` - - Return: - Tensor: Angle of a complex tensor. Shape of `(..., )` - """ - return torch.atan2(complex_tensor[..., 1], complex_tensor[..., 0]) - - -def magphase( - complex_tensor: Tensor, - power: float = 1.0 -) -> Tuple[Tensor, Tensor]: - r"""Separate a complex-valued spectrogram with shape `(..., 2)` into its magnitude and phase. - - Args: - complex_tensor (Tensor): Tensor shape of `(..., complex=2)` - power (float): Power of the norm. (Default: `1.0`) - - Returns: - (Tensor, Tensor): The magnitude and phase of the complex tensor - """ - mag = complex_norm(complex_tensor, power) - phase = angle(complex_tensor) - return mag, phase - - -def phase_vocoder( - complex_specgrams: Tensor, - rate: float, - phase_advance: Tensor -) -> Tensor: - r"""Given a STFT tensor, speed up in time without modifying pitch by a - factor of ``rate``. - - Args: - complex_specgrams (Tensor): Dimension of `(..., freq, time, complex=2)` - rate (float): Speed-up factor - phase_advance (Tensor): Expected phase advance in each bin. Dimension of (freq, 1) - - Returns: - Tensor: Complex Specgrams Stretch with dimension of `(..., freq, ceil(time/rate), complex=2)` - - Example - >>> freq, hop_length = 1025, 512 - >>> # (channel, freq, time, complex=2) - >>> complex_specgrams = torch.randn(2, freq, 300, 2) - >>> rate = 1.3 # Speed up by 30% - >>> phase_advance = torch.linspace( - >>> 0, math.pi * hop_length, freq)[..., None] - >>> x = phase_vocoder(complex_specgrams, rate, phase_advance) - >>> x.shape # with 231 == ceil(300 / 1.3) - torch.Size([2, 1025, 231, 2]) - """ - - # pack batch - shape = complex_specgrams.size() - complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-3:])) - - time_steps = torch.arange(0, - complex_specgrams.size(-2), - rate, - device=complex_specgrams.device, - dtype=complex_specgrams.dtype) - - alphas = time_steps % 1.0 - phase_0 = angle(complex_specgrams[..., :1, :]) - - # Time Padding - complex_specgrams = torch.nn.functional.pad(complex_specgrams, [0, 0, 0, 2]) - - # (new_bins, freq, 2) - complex_specgrams_0 = complex_specgrams.index_select(-2, time_steps.long()) - complex_specgrams_1 = complex_specgrams.index_select(-2, (time_steps + 1).long()) - - angle_0 = angle(complex_specgrams_0) - angle_1 = angle(complex_specgrams_1) - - norm_0 = torch.norm(complex_specgrams_0, p=2, dim=-1) - norm_1 = torch.norm(complex_specgrams_1, p=2, dim=-1) - - phase = angle_1 - angle_0 - phase_advance - phase = phase - 2 * math.pi * torch.round(phase / (2 * math.pi)) - - # Compute Phase Accum - phase = phase + phase_advance - phase = torch.cat([phase_0, phase[..., :-1]], dim=-1) - phase_acc = torch.cumsum(phase, -1) - - mag = alphas * norm_1 + (1 - alphas) * norm_0 - - real_stretch = mag * torch.cos(phase_acc) - imag_stretch = mag * torch.sin(phase_acc) - - complex_specgrams_stretch = torch.stack([real_stretch, imag_stretch], dim=-1) - - # unpack batch - complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-3] + complex_specgrams_stretch.shape[1:]) - - return complex_specgrams_stretch - - -def lfilter( - waveform: Tensor, - a_coeffs: Tensor, - b_coeffs: Tensor, - clamp: bool = True, -) -> Tensor: - r"""Perform an IIR filter by evaluating difference equation. - - Args: - waveform (Tensor): audio waveform of dimension of ``(..., time)``. Must be normalized to -1 to 1. - a_coeffs (Tensor): denominator coefficients of difference equation of dimension of ``(n_order + 1)``. - Lower delays coefficients are first, e.g. ``[a0, a1, a2, ...]``. - Must be same size as b_coeffs (pad with 0's as necessary). - b_coeffs (Tensor): numerator coefficients of difference equation of dimension of ``(n_order + 1)``. - Lower delays coefficients are first, e.g. ``[b0, b1, b2, ...]``. - Must be same size as a_coeffs (pad with 0's as necessary). - clamp (bool, optional): If ``True``, clamp the output signal to be in the range [-1, 1] (Default: ``True``) - - Returns: - Tensor: Waveform with dimension of ``(..., time)``. - """ - # pack batch - shape = waveform.size() - waveform = waveform.reshape(-1, shape[-1]) - - assert (a_coeffs.size(0) == b_coeffs.size(0)) - assert (len(waveform.size()) == 2) - assert (waveform.device == a_coeffs.device) - assert (b_coeffs.device == a_coeffs.device) - - device = waveform.device - dtype = waveform.dtype - n_channel, n_sample = waveform.size() - n_order = a_coeffs.size(0) - n_sample_padded = n_sample + n_order - 1 - assert (n_order > 0) - - # Pad the input and create output - padded_waveform = torch.zeros(n_channel, n_sample_padded, dtype=dtype, device=device) - padded_waveform[:, (n_order - 1):] = waveform - padded_output_waveform = torch.zeros(n_channel, n_sample_padded, dtype=dtype, device=device) - - # Set up the coefficients matrix - # Flip coefficients' order - a_coeffs_flipped = a_coeffs.flip(0) - b_coeffs_flipped = b_coeffs.flip(0) - - # calculate windowed_input_signal in parallel - # create indices of original with shape (n_channel, n_order, n_sample) - window_idxs = torch.arange(n_sample, device=device).unsqueeze(0) + torch.arange(n_order, device=device).unsqueeze(1) - window_idxs = window_idxs.repeat(n_channel, 1, 1) - window_idxs += (torch.arange(n_channel, device=device).unsqueeze(-1).unsqueeze(-1) * n_sample_padded) - window_idxs = window_idxs.long() - # (n_order, ) matmul (n_channel, n_order, n_sample) -> (n_channel, n_sample) - input_signal_windows = torch.matmul(b_coeffs_flipped, torch.take(padded_waveform, window_idxs)) - - input_signal_windows.div_(a_coeffs[0]) - a_coeffs_flipped.div_(a_coeffs[0]) - for i_sample, o0 in enumerate(input_signal_windows.t()): - windowed_output_signal = padded_output_waveform[:, i_sample:(i_sample + n_order)] - o0.addmv_(windowed_output_signal, a_coeffs_flipped, alpha=-1) - padded_output_waveform[:, i_sample + n_order - 1] = o0 - - output = padded_output_waveform[:, (n_order - 1):] - - if clamp: - output = torch.clamp(output, min=-1., max=1.) - - # unpack batch - output = output.reshape(shape[:-1] + output.shape[-1:]) - - return output - - -def biquad( - waveform: Tensor, - b0: float, - b1: float, - b2: float, - a0: float, - a1: float, - a2: float -) -> Tensor: - r"""Perform a biquad filter of input tensor. Initial conditions set to 0. - https://en.wikipedia.org/wiki/Digital_biquad_filter - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - b0 (float): numerator coefficient of current input, x[n] - b1 (float): numerator coefficient of input one time step ago x[n-1] - b2 (float): numerator coefficient of input two time steps ago x[n-2] - a0 (float): denominator coefficient of current output y[n], typically 1 - a1 (float): denominator coefficient of current output y[n-1] - a2 (float): denominator coefficient of current output y[n-2] - - Returns: - Tensor: Waveform with dimension of `(..., time)` - """ - - device = waveform.device - dtype = waveform.dtype - - output_waveform = lfilter( - waveform, - torch.tensor([a0, a1, a2], dtype=dtype, device=device), - torch.tensor([b0, b1, b2], dtype=dtype, device=device) - ) - return output_waveform - - -def _dB2Linear(x: float) -> float: - return math.exp(x * math.log(10) / 20.0) - - -def highpass_biquad( - waveform: Tensor, - sample_rate: int, - cutoff_freq: float, - Q: float = 0.707 -) -> Tensor: - r"""Design biquad highpass filter and perform filtering. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - cutoff_freq (float): filter cutoff frequency - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) - - Returns: - Tensor: Waveform dimension of `(..., time)` - """ - w0 = 2 * math.pi * cutoff_freq / sample_rate - alpha = math.sin(w0) / 2. / Q - - b0 = (1 + math.cos(w0)) / 2 - b1 = -1 - math.cos(w0) - b2 = b0 - a0 = 1 + alpha - a1 = -2 * math.cos(w0) - a2 = 1 - alpha - return biquad(waveform, b0, b1, b2, a0, a1, a2) - - -def lowpass_biquad( - waveform: Tensor, - sample_rate: int, - cutoff_freq: float, - Q: float = 0.707 -) -> Tensor: - r"""Design biquad lowpass filter and perform filtering. Similar to SoX implementation. - - Args: - waveform (torch.Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - cutoff_freq (float): filter cutoff frequency - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) - - Returns: - Tensor: Waveform of dimension of `(..., time)` - """ - w0 = 2 * math.pi * cutoff_freq / sample_rate - alpha = math.sin(w0) / 2 / Q - - b0 = (1 - math.cos(w0)) / 2 - b1 = 1 - math.cos(w0) - b2 = b0 - a0 = 1 + alpha - a1 = -2 * math.cos(w0) - a2 = 1 - alpha - return biquad(waveform, b0, b1, b2, a0, a1, a2) - - -def allpass_biquad( - waveform: Tensor, - sample_rate: int, - central_freq: float, - Q: float = 0.707 -) -> Tensor: - r"""Design two-pole all-pass filter. Similar to SoX implementation. - - Args: - waveform(torch.Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - central_freq (float): central frequency (in Hz) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF - """ - w0 = 2 * math.pi * central_freq / sample_rate - alpha = math.sin(w0) / 2 / Q - - b0 = 1 - alpha - b1 = -2 * math.cos(w0) - b2 = 1 + alpha - a0 = 1 + alpha - a1 = -2 * math.cos(w0) - a2 = 1 - alpha - return biquad(waveform, b0, b1, b2, a0, a1, a2) - - -def bandpass_biquad( - waveform: Tensor, - sample_rate: int, - central_freq: float, - Q: float = 0.707, - const_skirt_gain: bool = False -) -> Tensor: - r"""Design two-pole band-pass filter. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - central_freq (float): central frequency (in Hz) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) - const_skirt_gain (bool, optional) : If ``True``, uses a constant skirt gain (peak gain = Q). - If ``False``, uses a constant 0dB peak gain. (Default: ``False``) - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF - """ - w0 = 2 * math.pi * central_freq / sample_rate - alpha = math.sin(w0) / 2 / Q - - temp = math.sin(w0) / 2 if const_skirt_gain else alpha - b0 = temp - b1 = 0. - b2 = -temp - a0 = 1 + alpha - a1 = -2 * math.cos(w0) - a2 = 1 - alpha - return biquad(waveform, b0, b1, b2, a0, a1, a2) - - -def bandreject_biquad( - waveform: Tensor, - sample_rate: int, - central_freq: float, - Q: float = 0.707 -) -> Tensor: - r"""Design two-pole band-reject filter. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - central_freq (float): central frequency (in Hz) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF - """ - w0 = 2 * math.pi * central_freq / sample_rate - alpha = math.sin(w0) / 2 / Q - - b0 = 1. - b1 = -2 * math.cos(w0) - b2 = 1. - a0 = 1 + alpha - a1 = -2 * math.cos(w0) - a2 = 1 - alpha - return biquad(waveform, b0, b1, b2, a0, a1, a2) - - -def equalizer_biquad( - waveform: Tensor, - sample_rate: int, - center_freq: float, - gain: float, - Q: float = 0.707 -) -> Tensor: - r"""Design biquad peaking equalizer filter and perform filtering. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - center_freq (float): filter's central frequency - gain (float): desired gain at the boost (or attenuation) in dB - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) - - Returns: - Tensor: Waveform of dimension of `(..., time)` - """ - w0 = 2 * math.pi * center_freq / sample_rate - A = math.exp(gain / 40.0 * math.log(10)) - alpha = math.sin(w0) / 2 / Q - - b0 = 1 + alpha * A - b1 = -2 * math.cos(w0) - b2 = 1 - alpha * A - a0 = 1 + alpha / A - a1 = -2 * math.cos(w0) - a2 = 1 - alpha / A - return biquad(waveform, b0, b1, b2, a0, a1, a2) - - -def band_biquad( - waveform: Tensor, - sample_rate: int, - central_freq: float, - Q: float = 0.707, - noise: bool = False -) -> Tensor: - r"""Design two-pole band filter. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - central_freq (float): central frequency (in Hz) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). - noise (bool, optional) : If ``True``, uses the alternate mode for un-pitched audio (e.g. percussion). - If ``False``, uses mode oriented to pitched audio, i.e. voice, singing, - or instrumental music (Default: ``False``). - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF - """ - w0 = 2 * math.pi * central_freq / sample_rate - bw_Hz = central_freq / Q - - a0 = 1. - a2 = math.exp(-2 * math.pi * bw_Hz / sample_rate) - a1 = -4 * a2 / (1 + a2) * math.cos(w0) - - b0 = math.sqrt(1 - a1 * a1 / (4 * a2)) * (1 - a2) - - if noise: - mult = math.sqrt(((1 + a2) * (1 + a2) - a1 * a1) * (1 - a2) / (1 + a2)) / b0 - b0 *= mult - - b1 = 0. - b2 = 0. - - return biquad(waveform, b0, b1, b2, a0, a1, a2) - - -def treble_biquad( - waveform: Tensor, - sample_rate: int, - gain: float, - central_freq: float = 3000, - Q: float = 0.707 -) -> Tensor: - r"""Design a treble tone-control effect. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - gain (float): desired gain at the boost (or attenuation) in dB. - central_freq (float, optional): central frequency (in Hz). (Default: ``3000``) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF - """ - w0 = 2 * math.pi * central_freq / sample_rate - alpha = math.sin(w0) / 2 / Q - A = math.exp(gain / 40 * math.log(10)) - - temp1 = 2 * math.sqrt(A) * alpha - temp2 = (A - 1) * math.cos(w0) - temp3 = (A + 1) * math.cos(w0) - - b0 = A * ((A + 1) + temp2 + temp1) - b1 = -2 * A * ((A - 1) + temp3) - b2 = A * ((A + 1) + temp2 - temp1) - a0 = (A + 1) - temp2 + temp1 - a1 = 2 * ((A - 1) - temp3) - a2 = (A + 1) - temp2 - temp1 - - return biquad(waveform, b0, b1, b2, a0, a1, a2) - - -def bass_biquad( - waveform: Tensor, - sample_rate: int, - gain: float, - central_freq: float = 100, - Q: float = 0.707 -) -> Tensor: - r"""Design a bass tone-control effect. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - gain (float): desired gain at the boost (or attenuation) in dB. - central_freq (float, optional): central frequency (in Hz). (Default: ``100``) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF - """ - w0 = 2 * math.pi * central_freq / sample_rate - alpha = math.sin(w0) / 2 / Q - A = math.exp(gain / 40 * math.log(10)) - - temp1 = 2 * math.sqrt(A) * alpha - temp2 = (A - 1) * math.cos(w0) - temp3 = (A + 1) * math.cos(w0) - - b0 = A * ((A + 1) - temp2 + temp1) - b1 = 2 * A * ((A - 1) - temp3) - b2 = A * ((A + 1) - temp2 - temp1) - a0 = (A + 1) + temp2 + temp1 - a1 = -2 * ((A - 1) + temp3) - a2 = (A + 1) + temp2 - temp1 - - return biquad(waveform, b0 / a0, b1 / a0, b2 / a0, a0 / a0, a1 / a0, a2 / a0) - - -def deemph_biquad( - waveform: Tensor, - sample_rate: int -) -> Tensor: - r"""Apply ISO 908 CD de-emphasis (shelving) IIR filter. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, Allowed sample rate ``44100`` or ``48000`` - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF - """ - - if sample_rate == 44100: - central_freq = 5283 - width_slope = 0.4845 - gain = -9.477 - elif sample_rate == 48000: - central_freq = 5356 - width_slope = 0.479 - gain = -9.62 - else: - raise ValueError("Sample rate must be 44100 (audio-CD) or 48000 (DAT)") - - w0 = 2 * math.pi * central_freq / sample_rate - A = math.exp(gain / 40.0 * math.log(10)) - alpha = math.sin(w0) / 2 * math.sqrt((A + 1 / A) * (1 / width_slope - 1) + 2) - - temp1 = 2 * math.sqrt(A) * alpha - temp2 = (A - 1) * math.cos(w0) - temp3 = (A + 1) * math.cos(w0) - - b0 = A * ((A + 1) + temp2 + temp1) - b1 = -2 * A * ((A - 1) + temp3) - b2 = A * ((A + 1) + temp2 - temp1) - a0 = (A + 1) - temp2 + temp1 - a1 = 2 * ((A - 1) - temp3) - a2 = (A + 1) - temp2 - temp1 - - return biquad(waveform, b0, b1, b2, a0, a1, a2) - - -def riaa_biquad( - waveform: Tensor, - sample_rate: int -) -> Tensor: - r"""Apply RIAA vinyl playback equalisation. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz). - Allowed sample rates in Hz : ``44100``,``48000``,``88200``,``96000`` - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF - """ - - if (sample_rate == 44100): - zeros = [-0.2014898, 0.9233820] - poles = [0.7083149, 0.9924091] - - elif (sample_rate == 48000): - zeros = [-0.1766069, 0.9321590] - poles = [0.7396325, 0.9931330] - - elif (sample_rate == 88200): - zeros = [-0.1168735, 0.9648312] - poles = [0.8590646, 0.9964002] - - elif (sample_rate == 96000): - zeros = [-0.1141486, 0.9676817] - poles = [0.8699137, 0.9966946] - - else: - raise ValueError("Sample rate must be 44.1k, 48k, 88.2k, or 96k") - - # polynomial coefficients with roots zeros[0] and zeros[1] - b0 = 1. - b1 = -(zeros[0] + zeros[1]) - b2 = (zeros[0] * zeros[1]) - - # polynomial coefficients with roots poles[0] and poles[1] - a0 = 1. - a1 = -(poles[0] + poles[1]) - a2 = (poles[0] * poles[1]) - - # Normalise to 0dB at 1kHz - y = 2 * math.pi * 1000 / sample_rate - b_re = b0 + b1 * math.cos(-y) + b2 * math.cos(-2 * y) - a_re = a0 + a1 * math.cos(-y) + a2 * math.cos(-2 * y) - b_im = b1 * math.sin(-y) + b2 * math.sin(-2 * y) - a_im = a1 * math.sin(-y) + a2 * math.sin(-2 * y) - g = 1 / math.sqrt((b_re ** 2 + b_im ** 2) / (a_re ** 2 + a_im ** 2)) - - b0 *= g - b1 *= g - b2 *= g - - return biquad(waveform, b0, b1, b2, a0, a1, a2) - - -def contrast( - waveform: Tensor, - enhancement_amount: float = 75. -) -> Tensor: - r"""Apply contrast effect. Similar to SoX implementation. - Comparable with compression, this effect modifies an audio signal to make it sound louder - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - enhancement_amount (float): controls the amount of the enhancement - Allowed range of values for enhancement_amount : 0-100 - Note that enhancement_amount = 0 still gives a significant contrast enhancement - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - """ - - if not 0 <= enhancement_amount <= 100: - raise ValueError("Allowed range of values for enhancement_amount : 0-100") - - contrast = enhancement_amount / 750. - - temp1 = waveform * (math.pi / 2) - temp2 = contrast * torch.sin(temp1 * 4) - output_waveform = torch.sin(temp1 + temp2) - - return output_waveform - - -def dcshift( - waveform: Tensor, - shift: float, - limiter_gain: Optional[float] = None -) -> Tensor: - r"""Apply a DC shift to the audio. Similar to SoX implementation. - This can be useful to remove a DC offset - (caused perhaps by a hardware problem in the recording chain) from the audio - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - shift (float): indicates the amount to shift the audio - Allowed range of values for shift : -2.0 to +2.0 - limiter_gain (float): It is used only on peaks to prevent clipping - It should have a value much less than 1 (e.g. 0.05 or 0.02) - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - """ - output_waveform = waveform - limiter_threshold = 0. - - if limiter_gain is not None: - limiter_threshold = 1.0 - (abs(shift) - limiter_gain) - - if limiter_gain is not None and shift > 0: - mask = waveform > limiter_threshold - temp = (waveform[mask] - limiter_threshold) * limiter_gain / (1 - limiter_threshold) - output_waveform[mask] = (temp + limiter_threshold + shift).clamp(max=limiter_threshold) - output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1) - elif limiter_gain is not None and shift < 0: - mask = waveform < -limiter_threshold - temp = (waveform[mask] + limiter_threshold) * limiter_gain / (1 - limiter_threshold) - output_waveform[mask] = (temp - limiter_threshold + shift).clamp(min=-limiter_threshold) - output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1) - else: - output_waveform = (waveform + shift).clamp(min=-1, max=1) - - return output_waveform - - -def overdrive( - waveform: Tensor, - gain: float = 20, - colour: float = 20 -) -> Tensor: - r"""Apply a overdrive effect to the audio. Similar to SoX implementation. - This effect applies a non linear distortion to the audio signal. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - gain (float): desired gain at the boost (or attenuation) in dB - Allowed range of values are 0 to 100 - colour (float): controls the amount of even harmonic content in the over-driven output - Allowed range of values are 0 to 100 - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - """ - actual_shape = waveform.shape - device, dtype = waveform.device, waveform.dtype - - # convert to 2D (..,time) - waveform = waveform.view(-1, actual_shape[-1]) - - gain = _dB2Linear(gain) - colour = colour / 200 - last_in = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device) - last_out = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device) - - temp = waveform * gain + colour - - mask1 = temp < -1 - temp[mask1] = torch.tensor(-2.0 / 3.0, dtype=dtype, device=device) - # Wrapping the constant with Tensor is required for Torchscript - - mask2 = temp > 1 - temp[mask2] = torch.tensor(2.0 / 3.0, dtype=dtype, device=device) - - mask3 = (~mask1 & ~mask2) - temp[mask3] = temp[mask3] - (temp[mask3]**3) * (1. / 3) - - output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) - - # TODO: Implement a torch CPP extension - for i in range(waveform.shape[-1]): - last_out = temp[:, i] - last_in + 0.995 * last_out - last_in = temp[:, i] - output_waveform[:, i] = waveform[:, i] * 0.5 + last_out * 0.75 - - return output_waveform.clamp(min=-1, max=1).view(actual_shape) - - -def phaser( - waveform: Tensor, - sample_rate: int, - gain_in: float = 0.4, - gain_out: float = 0.74, - delay_ms: float = 3.0, - decay: float = 0.4, - mod_speed: float = 0.5, - sinusoidal: bool = True -) -> Tensor: - r"""Apply a phasing effect to the audio. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., time)` - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - gain_in (float): desired input gain at the boost (or attenuation) in dB - Allowed range of values are 0 to 1 - gain_out (float): desired output gain at the boost (or attenuation) in dB - Allowed range of values are 0 to 1e9 - delay_ms (float): desired delay in milli seconds - Allowed range of values are 0 to 5.0 - decay (float): desired decay relative to gain-in - Allowed range of values are 0 to 0.99 - mod_speed (float): modulation speed in Hz - Allowed range of values are 0.1 to 2 - sinusoidal (bool): If ``True``, uses sinusoidal modulation (preferable for multiple instruments) - If ``False``, uses triangular modulation (gives single instruments a sharper phasing effect) - (Default: ``True``) - - Returns: - Tensor: Waveform of dimension of `(..., time)` - - References: - http://sox.sourceforge.net/sox.html - Scott Lehman, Effects Explained, http://harmony-central.com/Effects/effects-explained.html - """ - actual_shape = waveform.shape - device, dtype = waveform.device, waveform.dtype - - # convert to 2D (channels,time) - waveform = waveform.view(-1, actual_shape[-1]) - - delay_buf_len = int((delay_ms * .001 * sample_rate) + .5) - delay_buf = torch.zeros(waveform.shape[0], delay_buf_len, dtype=dtype, device=device) - - mod_buf_len = int(sample_rate / mod_speed + .5) - - if sinusoidal: - wave_type = 'SINE' - else: - wave_type = 'TRIANGLE' - - mod_buf = _generate_wave_table(wave_type=wave_type, - data_type='INT', - table_size=mod_buf_len, - min=1., - max=float(delay_buf_len), - phase=math.pi / 2, - device=device) - - delay_pos = 0 - mod_pos = 0 - - output_waveform_pre_gain_list = [] - waveform = waveform * gain_in - delay_buf = delay_buf * decay - waveform_list = [waveform[:, i] for i in range(waveform.size(1))] - delay_buf_list = [delay_buf[:, i] for i in range(delay_buf.size(1))] - mod_buf_list = [mod_buf[i] for i in range(mod_buf.size(0))] - - for i in range(waveform.shape[-1]): - idx = int((delay_pos + mod_buf_list[mod_pos]) % delay_buf_len) - mod_pos = (mod_pos + 1) % mod_buf_len - delay_pos = (delay_pos + 1) % delay_buf_len - temp = (waveform_list[i]) + (delay_buf_list[idx]) - delay_buf_list[delay_pos] = temp * decay - output_waveform_pre_gain_list.append(temp) - - output_waveform = torch.stack(output_waveform_pre_gain_list, dim=1).to(dtype=dtype, device=device) - output_waveform.mul_(gain_out) - - return output_waveform.clamp(min=-1, max=1).view(actual_shape) - - -def _generate_wave_table( - wave_type: str, - data_type: str, - table_size: int, - min: float, - max: float, - phase: float, - device: torch.device -) -> Tensor: - r"""A helper fucntion for phaser. Generates a table with given parameters - - Args: - wave_type (str): SINE or TRIANGULAR - data_type (str): desired data_type ( `INT` or `FLOAT` ) - table_size (int): desired table size - min (float): desired min value - max (float): desired max value - phase (float): desired phase - device (torch.device): Torch device on which table must be generated - Returns: - Tensor: A 1D tensor with wave table values - """ - - phase_offset = int(phase / math.pi / 2 * table_size + 0.5) - - t = torch.arange(table_size, device=device, dtype=torch.int32) - - point = (t + phase_offset) % table_size - - d = torch.zeros_like(point, device=device, dtype=torch.float64) - - if wave_type == 'SINE': - d = (torch.sin(point.to(torch.float64) / table_size * 2 * math.pi) + 1) / 2 - elif wave_type == 'TRIANGLE': - d = point.to(torch.float64) * 2 / table_size - value = 4 * point // table_size - d[value == 0] = d[value == 0] + 0.5 - d[value == 1] = 1.5 - d[value == 1] - d[value == 2] = 1.5 - d[value == 2] - d[value == 3] = d[value == 3] - 1.5 - - d = d * (max - min) + min - - if data_type == 'INT': - mask = d < 0 - d[mask] = d[mask] - 0.5 - d[~mask] = d[~mask] + 0.5 - d = d.to(torch.int32) - elif data_type == 'FLOAT': - d = d.to(torch.float32) - - return d - - -def flanger( - waveform: Tensor, - sample_rate: int, - delay: float = 0., - depth: float = 2., - regen: float = 0., - width: float = 71., - speed: float = 0.5, - phase: float = 25., - modulation: str = 'sinusoidal', - interpolation: str = 'linear' -) -> Tensor: - r"""Apply a flanger effect to the audio. Similar to SoX implementation. - - Args: - waveform (Tensor): audio waveform of dimension of `(..., channel, time)` . - Max 4 channels allowed - sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - delay (float): desired delay in milliseconds(ms) - Allowed range of values are 0 to 30 - depth (float): desired delay depth in milliseconds(ms) - Allowed range of values are 0 to 10 - regen (float): desired regen(feeback gain) in dB - Allowed range of values are -95 to 95 - width (float): desired width(delay gain) in dB - Allowed range of values are 0 to 100 - speed (float): modulation speed in Hz - Allowed range of values are 0.1 to 10 - phase (float): percentage phase-shift for multi-channel - Allowed range of values are 0 to 100 - modulation (str): Use either "sinusoidal" or "triangular" modulation. (Default: ``sinusoidal``) - interpolation (str): Use either "linear" or "quadratic" for delay-line interpolation. (Default: ``linear``) - - Returns: - Tensor: Waveform of dimension of `(..., channel, time)` - - References: - http://sox.sourceforge.net/sox.html - - Scott Lehman, Effects Explained, - https://web.archive.org/web/20051125072557/http://www.harmony-central.com/Effects/effects-explained.html - """ - - if modulation not in ('sinusoidal', 'triangular'): - raise ValueError("Only 'sinusoidal' or 'triangular' modulation allowed") - - if interpolation not in ('linear', 'quadratic'): - raise ValueError("Only 'linear' or 'quadratic' interpolation allowed") - - actual_shape = waveform.shape - device, dtype = waveform.device, waveform.dtype - - if actual_shape[-2] > 4: - raise ValueError("Max 4 channels allowed") - - # convert to 3D (batch, channels, time) - waveform = waveform.view(-1, actual_shape[-2], actual_shape[-1]) - - # Scaling - feedback_gain = regen / 100 - delay_gain = width / 100 - channel_phase = phase / 100 - delay_min = delay / 1000 - delay_depth = depth / 1000 - - n_channels = waveform.shape[-2] - - if modulation == 'sinusoidal': - wave_type = 'SINE' - else: - wave_type = 'TRIANGLE' - - # Balance output: - in_gain = 1. / (1 + delay_gain) - delay_gain = delay_gain / (1 + delay_gain) - - # Balance feedback loop: - delay_gain = delay_gain * (1 - abs(feedback_gain)) - - delay_buf_length = int((delay_min + delay_depth) * sample_rate + 0.5) - delay_buf_length = delay_buf_length + 2 - - delay_bufs = torch.zeros(waveform.shape[0], n_channels, delay_buf_length, dtype=dtype, device=device) - delay_last = torch.zeros(waveform.shape[0], n_channels, dtype=dtype, device=device) - - lfo_length = int(sample_rate / speed) - - table_min = math.floor(delay_min * sample_rate + 0.5) - table_max = delay_buf_length - 2. - - lfo = _generate_wave_table(wave_type=wave_type, - data_type='FLOAT', - table_size=lfo_length, - min=float(table_min), - max=float(table_max), - phase=3 * math.pi / 2, - device=device) - - output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) - - delay_buf_pos = 0 - lfo_pos = 0 - channel_idxs = torch.arange(0, n_channels, device=device) - - for i in range(waveform.shape[-1]): - - delay_buf_pos = (delay_buf_pos + delay_buf_length - 1) % delay_buf_length - - cur_channel_phase = (channel_idxs * lfo_length * channel_phase + .5).to(torch.int64) - delay_tensor = lfo[(lfo_pos + cur_channel_phase) % lfo_length] - frac_delay = torch.frac(delay_tensor) - delay_tensor = torch.floor(delay_tensor) - - int_delay = delay_tensor.to(torch.int64) - - temp = waveform[:, :, i] - - delay_bufs[:, :, delay_buf_pos] = temp + delay_last * feedback_gain - - delayed_0 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length] - - int_delay = int_delay + 1 - - delayed_1 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length] - - int_delay = int_delay + 1 - - if interpolation == 'linear': - delayed = delayed_0 + (delayed_1 - delayed_0) * frac_delay - else: - delayed_2 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length] - - int_delay = int_delay + 1 - - delayed_2 = delayed_2 - delayed_0 - delayed_1 = delayed_1 - delayed_0 - a = delayed_2 * .5 - delayed_1 - b = delayed_1 * 2 - delayed_2 * .5 - - delayed = delayed_0 + (a * frac_delay + b) * frac_delay - - delay_last = delayed - output_waveform[:, :, i] = waveform[:, :, i] * in_gain + delayed * delay_gain - - lfo_pos = (lfo_pos + 1) % lfo_length - - return output_waveform.clamp(min=-1, max=1).view(actual_shape) - - -def mask_along_axis_iid( - specgrams: Tensor, - mask_param: int, - mask_value: float, - axis: int -) -> Tensor: - r""" - Apply a mask along ``axis``. Mask will be applied from indices ``[v_0, v_0 + v)``, where - ``v`` is sampled from ``uniform(0, mask_param)``, and ``v_0`` from ``uniform(0, max_v - v)``. - - Args: - specgrams (Tensor): Real spectrograms (batch, channel, freq, time) - mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param] - mask_value (float): Value to assign to the masked columns - axis (int): Axis to apply masking on (2 -> frequency, 3 -> time) - - Returns: - Tensor: Masked spectrograms of dimensions (batch, channel, freq, time) - """ - - if axis != 2 and axis != 3: - raise ValueError('Only Frequency and Time masking are supported') - - device = specgrams.device - dtype = specgrams.dtype - - value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * mask_param - min_value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * (specgrams.size(axis) - value) - - # Create broadcastable mask - mask_start = min_value[..., None, None] - mask_end = (min_value + value)[..., None, None] - mask = torch.arange(0, specgrams.size(axis), device=device, dtype=dtype) - - # Per batch example masking - specgrams = specgrams.transpose(axis, -1) - specgrams.masked_fill_((mask >= mask_start) & (mask < mask_end), mask_value) - specgrams = specgrams.transpose(axis, -1) - - return specgrams - - -def mask_along_axis( - specgram: Tensor, - mask_param: int, - mask_value: float, - axis: int -) -> Tensor: - r""" - Apply a mask along ``axis``. Mask will be applied from indices ``[v_0, v_0 + v)``, where - ``v`` is sampled from ``uniform(0, mask_param)``, and ``v_0`` from ``uniform(0, max_v - v)``. - All examples will have the same mask interval. - - Args: - specgram (Tensor): Real spectrogram (channel, freq, time) - mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param] - mask_value (float): Value to assign to the masked columns - axis (int): Axis to apply masking on (1 -> frequency, 2 -> time) - - Returns: - Tensor: Masked spectrogram of dimensions (channel, freq, time) - """ - - # pack batch - shape = specgram.size() - specgram = specgram.reshape([-1] + list(shape[-2:])) - - value = torch.rand(1) * mask_param - min_value = torch.rand(1) * (specgram.size(axis) - value) - - mask_start = (min_value.long()).squeeze() - mask_end = (min_value.long() + value.long()).squeeze() - - assert mask_end - mask_start < mask_param - if axis == 1: - specgram[:, mask_start:mask_end] = mask_value - elif axis == 2: - specgram[:, :, mask_start:mask_end] = mask_value - else: - raise ValueError('Only Frequency and Time masking are supported') - - # unpack batch - specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:]) - - return specgram - - -def compute_deltas( - specgram: Tensor, - win_length: int = 5, - mode: str = "replicate" -) -> Tensor: - r"""Compute delta coefficients of a tensor, usually a spectrogram: - - .. math:: - d_t = \frac{\sum_{n=1}^{\text{N}} n (c_{t+n} - c_{t-n})}{2 \sum_{n=1}^{\text{N}} n^2} - - where :math:`d_t` is the deltas at time :math:`t`, - :math:`c_t` is the spectrogram coeffcients at time :math:`t`, - :math:`N` is ``(win_length-1)//2``. - - Args: - specgram (Tensor): Tensor of audio of dimension (..., freq, time) - win_length (int, optional): The window length used for computing delta (Default: ``5``) - mode (str, optional): Mode parameter passed to padding (Default: ``"replicate"``) - - Returns: - Tensor: Tensor of deltas of dimension (..., freq, time) - - Example - >>> specgram = torch.randn(1, 40, 1000) - >>> delta = compute_deltas(specgram) - >>> delta2 = compute_deltas(delta) - """ - device = specgram.device - dtype = specgram.dtype - - # pack batch - shape = specgram.size() - specgram = specgram.reshape(1, -1, shape[-1]) - - assert win_length >= 3 - - n = (win_length - 1) // 2 - - # twice sum of integer squared - denom = n * (n + 1) * (2 * n + 1) / 3 - - specgram = torch.nn.functional.pad(specgram, (n, n), mode=mode) - - kernel = torch.arange(-n, n + 1, 1, device=device, dtype=dtype).repeat(specgram.shape[1], 1, 1) - - output = torch.nn.functional.conv1d(specgram, kernel, groups=specgram.shape[1]) / denom - - # unpack batch - output = output.reshape(shape) - - return output - - -def gain( - waveform: Tensor, - gain_db: float = 1.0 -) -> Tensor: - r"""Apply amplification or attenuation to the whole waveform. - - Args: - waveform (Tensor): Tensor of audio of dimension (..., time). - gain_db (float, optional) Gain adjustment in decibels (dB) (Default: ``1.0``). - - Returns: - Tensor: the whole waveform amplified by gain_db. - """ - if (gain_db == 0): - return waveform - - ratio = 10 ** (gain_db / 20) - - return waveform * ratio - - -def _add_noise_shaping( - dithered_waveform: Tensor, - waveform: Tensor -) -> Tensor: - r"""Noise shaping is calculated by error: - error[n] = dithered[n] - original[n] - noise_shaped_waveform[n] = dithered[n] + error[n-1] - """ - wf_shape = waveform.size() - waveform = waveform.reshape(-1, wf_shape[-1]) - - dithered_shape = dithered_waveform.size() - dithered_waveform = dithered_waveform.reshape(-1, dithered_shape[-1]) - - error = dithered_waveform - waveform - - # add error[n-1] to dithered_waveform[n], so offset the error by 1 index - zeros = torch.zeros(1, dtype=error.dtype, device=error.device) - for index in range(error.size()[0]): - err = error[index] - error_offset = torch.cat((zeros, err)) - error[index] = error_offset[:waveform.size()[1]] - - noise_shaped = dithered_waveform + error - return noise_shaped.reshape(dithered_shape[:-1] + noise_shaped.shape[-1:]) - - -def _apply_probability_distribution( - waveform: Tensor, - density_function: str = "TPDF" -) -> Tensor: - r"""Apply a probability distribution function on a waveform. - - Triangular probability density function (TPDF) dither noise has a - triangular distribution; values in the center of the range have a higher - probability of occurring. - - Rectangular probability density function (RPDF) dither noise has a - uniform distribution; any value in the specified range has the same - probability of occurring. - - Gaussian probability density function (GPDF) has a normal distribution. - The relationship of probabilities of results follows a bell-shaped, - or Gaussian curve, typical of dither generated by analog sources. - Args: - waveform (Tensor): Tensor of audio of dimension (..., time) - probability_density_function (str, optional): The density function of a - continuous random variable (Default: ``"TPDF"``) - Options: Triangular Probability Density Function - `TPDF` - Rectangular Probability Density Function - `RPDF` - Gaussian Probability Density Function - `GPDF` - Returns: - Tensor: waveform dithered with TPDF - """ - - # pack batch - shape = waveform.size() - waveform = waveform.reshape(-1, shape[-1]) - - channel_size = waveform.size()[0] - 1 - time_size = waveform.size()[-1] - 1 - - random_channel = int(torch.randint(channel_size, [1, ]).item()) if channel_size > 0 else 0 - random_time = int(torch.randint(time_size, [1, ]).item()) if time_size > 0 else 0 - - number_of_bits = 16 - up_scaling = 2 ** (number_of_bits - 1) - 2 - signal_scaled = waveform * up_scaling - down_scaling = 2 ** (number_of_bits - 1) - - signal_scaled_dis = waveform - if (density_function == "RPDF"): - RPDF = waveform[random_channel][random_time] - 0.5 - - signal_scaled_dis = signal_scaled + RPDF - elif (density_function == "GPDF"): - # TODO Replace by distribution code once - # https://github.com/pytorch/pytorch/issues/29843 is resolved - # gaussian = torch.distributions.normal.Normal(torch.mean(waveform, -1), 1).sample() - - num_rand_variables = 6 - - gaussian = waveform[random_channel][random_time] - for ws in num_rand_variables * [time_size]: - rand_chan = int(torch.randint(channel_size, [1, ]).item()) - gaussian += waveform[rand_chan][int(torch.randint(ws, [1, ]).item())] - - signal_scaled_dis = signal_scaled + gaussian - else: - # dtype needed for https://github.com/pytorch/pytorch/issues/32358 - TPDF = torch.bartlett_window(time_size + 1, dtype=signal_scaled.dtype, device=signal_scaled.device) - TPDF = TPDF.repeat((channel_size + 1), 1) - signal_scaled_dis = signal_scaled + TPDF - - quantised_signal_scaled = torch.round(signal_scaled_dis) - quantised_signal = quantised_signal_scaled / down_scaling - - # unpack batch - return quantised_signal.reshape(shape[:-1] + quantised_signal.shape[-1:]) - - -def dither( - waveform: Tensor, - density_function: str = "TPDF", - noise_shaping: bool = False -) -> Tensor: - r"""Dither increases the perceived dynamic range of audio stored at a - particular bit-depth by eliminating nonlinear truncation distortion - (i.e. adding minimally perceived noise to mask distortion caused by quantization). - Args: - waveform (Tensor): Tensor of audio of dimension (..., time) - density_function (str, optional): The density function of a continuous random variable (Default: ``"TPDF"``) - Options: Triangular Probability Density Function - `TPDF` - Rectangular Probability Density Function - `RPDF` - Gaussian Probability Density Function - `GPDF` - noise_shaping (bool, optional): a filtering process that shapes the spectral - energy of quantisation error (Default: ``False``) - - Returns: - Tensor: waveform dithered - """ - dithered = _apply_probability_distribution(waveform, density_function=density_function) - - if noise_shaping: - return _add_noise_shaping(dithered, waveform) - else: - return dithered - - -def _compute_nccf( - waveform: Tensor, - sample_rate: int, - frame_time: float, - freq_low: int -) -> Tensor: - r""" - Compute Normalized Cross-Correlation Function (NCCF). - - .. math:: - \phi_i(m) = \frac{\sum_{n=b_i}^{b_i + N-1} w(n) w(m+n)}{\sqrt{E(b_i) E(m+b_i)}}, - - where - :math:`\phi_i(m)` is the NCCF at frame :math:`i` with lag :math:`m`, - :math:`w` is the waveform, - :math:`N` is the length of a frame, - :math:`b_i` is the beginning of frame :math:`i`, - :math:`E(j)` is the energy :math:`\sum_{n=j}^{j+N-1} w^2(n)`. - """ - - EPSILON = 10 ** (-9) - - # Number of lags to check - lags = int(math.ceil(sample_rate / freq_low)) - - frame_size = int(math.ceil(sample_rate * frame_time)) - - waveform_length = waveform.size()[-1] - num_of_frames = int(math.ceil(waveform_length / frame_size)) - - p = lags + num_of_frames * frame_size - waveform_length - waveform = torch.nn.functional.pad(waveform, (0, p)) - - # Compute lags - output_lag = [] - for lag in range(1, lags + 1): - s1 = waveform[..., :-lag].unfold(-1, frame_size, frame_size)[..., :num_of_frames, :] - s2 = waveform[..., lag:].unfold(-1, frame_size, frame_size)[..., :num_of_frames, :] - - output_frames = ( - (s1 * s2).sum(-1) - / (EPSILON + torch.norm(s1, p=2, dim=-1)).pow(2) - / (EPSILON + torch.norm(s2, p=2, dim=-1)).pow(2) - ) - - output_lag.append(output_frames.unsqueeze(-1)) - - nccf = torch.cat(output_lag, -1) - - return nccf - - -def _combine_max( - a: Tuple[Tensor, Tensor], - b: Tuple[Tensor, Tensor], - thresh: float = 0.99 -) -> Tuple[Tensor, Tensor]: - """ - Take value from first if bigger than a multiplicative factor of the second, elementwise. - """ - mask = (a[0] > thresh * b[0]) - values = mask * a[0] + ~mask * b[0] - indices = mask * a[1] + ~mask * b[1] - return values, indices - - -def _find_max_per_frame( - nccf: Tensor, - sample_rate: int, - freq_high: int -) -> Tensor: - r""" - For each frame, take the highest value of NCCF, - apply centered median smoothing, and convert to frequency. - - Note: If the max among all the lags is very close - to the first half of lags, then the latter is taken. - """ - - lag_min = int(math.ceil(sample_rate / freq_high)) - - # Find near enough max that is smallest - - best = torch.max(nccf[..., lag_min:], -1) - - half_size = nccf.shape[-1] // 2 - half = torch.max(nccf[..., lag_min:half_size], -1) - - best = _combine_max(half, best) - indices = best[1] - - # Add back minimal lag - indices += lag_min - # Add 1 empirical calibration offset - indices += 1 - - return indices - - -def _median_smoothing( - indices: Tensor, - win_length: int -) -> Tensor: - r""" - Apply median smoothing to the 1D tensor over the given window. - """ - - # Centered windowed - pad_length = (win_length - 1) // 2 - - # "replicate" padding in any dimension - indices = torch.nn.functional.pad( - indices, (pad_length, 0), mode="constant", value=0. - ) - - indices[..., :pad_length] = torch.cat(pad_length * [indices[..., pad_length].unsqueeze(-1)], dim=-1) - roll = indices.unfold(-1, win_length, 1) - - values, _ = torch.median(roll, -1) - return values - - -def detect_pitch_frequency( - waveform: Tensor, - sample_rate: int, - frame_time: float = 10 ** (-2), - win_length: int = 30, - freq_low: int = 85, - freq_high: int = 3400, -) -> Tensor: - r"""Detect pitch frequency. - - It is implemented using normalized cross-correlation function and median smoothing. - - Args: - waveform (Tensor): Tensor of audio of dimension (..., freq, time) - sample_rate (int): The sample rate of the waveform (Hz) - frame_time (float, optional): Duration of a frame (Default: ``10 ** (-2)``). - win_length (int, optional): The window length for median smoothing (in number of frames) (Default: ``30``). - freq_low (int, optional): Lowest frequency that can be detected (Hz) (Default: ``85``). - freq_high (int, optional): Highest frequency that can be detected (Hz) (Default: ``3400``). - - Returns: - Tensor: Tensor of freq of dimension (..., frame) - """ - # pack batch - shape = list(waveform.size()) - waveform = waveform.reshape([-1] + shape[-1:]) - - nccf = _compute_nccf(waveform, sample_rate, frame_time, freq_low) - indices = _find_max_per_frame(nccf, sample_rate, freq_high) - indices = _median_smoothing(indices, win_length) - - # Convert indices to frequency - EPSILON = 10 ** (-9) - freq = sample_rate / (EPSILON + indices.to(torch.float)) - - # unpack batch - freq = freq.reshape(shape[:-1] + list(freq.shape[-1:])) - - return freq - - -def sliding_window_cmn( - waveform: Tensor, - cmn_window: int = 600, - min_cmn_window: int = 100, - center: bool = False, - norm_vars: bool = False, -) -> Tensor: - r""" - Apply sliding-window cepstral mean (and optionally variance) normalization per utterance. - - Args: - waveform (Tensor): Tensor of audio of dimension (..., freq, time) - cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600) - min_cmn_window (int, optional): Minimum CMN window used at start of decoding (adds latency only at start). - Only applicable if center == false, ignored if center==true (int, default = 100) - center (bool, optional): If true, use a window centered on the current frame - (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false) - norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false) - - Returns: - Tensor: Tensor of freq of dimension (..., frame) - """ - input_shape = waveform.shape - num_frames, num_feats = input_shape[-2:] - waveform = waveform.view(-1, num_frames, num_feats) - num_channels = waveform.shape[0] - - dtype = waveform.dtype - device = waveform.device - last_window_start = last_window_end = -1 - cur_sum = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) - cur_sumsq = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) - cmn_waveform = torch.zeros( - num_channels, num_frames, num_feats, dtype=dtype, device=device) - for t in range(num_frames): - window_start = 0 - window_end = 0 - if center: - window_start = t - cmn_window // 2 - window_end = window_start + cmn_window - else: - window_start = t - cmn_window - window_end = t + 1 - if window_start < 0: - window_end -= window_start - window_start = 0 - if not center: - if window_end > t: - window_end = max(t + 1, min_cmn_window) - if window_end > num_frames: - window_start -= (window_end - num_frames) - window_end = num_frames - if window_start < 0: - window_start = 0 - if last_window_start == -1: - input_part = waveform[:, window_start: window_end - window_start, :] - cur_sum += torch.sum(input_part, 1) - if norm_vars: - cur_sumsq += torch.cumsum(input_part ** 2, 1)[:, -1, :] - else: - if window_start > last_window_start: - frame_to_remove = waveform[:, last_window_start, :] - cur_sum -= frame_to_remove - if norm_vars: - cur_sumsq -= (frame_to_remove ** 2) - if window_end > last_window_end: - frame_to_add = waveform[:, last_window_end, :] - cur_sum += frame_to_add - if norm_vars: - cur_sumsq += (frame_to_add ** 2) - window_frames = window_end - window_start - last_window_start = window_start - last_window_end = window_end - cmn_waveform[:, t, :] = waveform[:, t, :] - cur_sum / window_frames - if norm_vars: - if window_frames == 1: - cmn_waveform[:, t, :] = torch.zeros( - num_channels, num_feats, dtype=dtype, device=device) - else: - variance = cur_sumsq - variance = variance / window_frames - variance -= ((cur_sum ** 2) / (window_frames ** 2)) - variance = torch.pow(variance, -0.5) - cmn_waveform[:, t, :] *= variance - - cmn_waveform = cmn_waveform.view(input_shape[:-2] + (num_frames, num_feats)) - if len(input_shape) == 2: - cmn_waveform = cmn_waveform.squeeze(0) - return cmn_waveform - - -def _measure( - measure_len_ws: int, - samples: Tensor, - spectrum: Tensor, - noise_spectrum: Tensor, - spectrum_window: Tensor, - spectrum_start: int, - spectrum_end: int, - cepstrum_window: Tensor, - cepstrum_start: int, - cepstrum_end: int, - noise_reduction_amount: float, - measure_smooth_time_mult: float, - noise_up_time_mult: float, - noise_down_time_mult: float, - index_ns: int, - boot_count: int -) -> float: - - assert spectrum.size()[-1] == noise_spectrum.size()[-1] - - samplesLen_ns = samples.size()[-1] - dft_len_ws = spectrum.size()[-1] - - dftBuf = torch.zeros(dft_len_ws) - - _index_ns = torch.tensor([index_ns] + [ - (index_ns + i) % samplesLen_ns - for i in range(1, measure_len_ws) - ]) - dftBuf[:measure_len_ws] = \ - samples[_index_ns] * spectrum_window[:measure_len_ws] - - # memset(c->dftBuf + i, 0, (p->dft_len_ws - i) * sizeof(*c->dftBuf)); - dftBuf[measure_len_ws:dft_len_ws].zero_() - - # lsx_safe_rdft((int)p->dft_len_ws, 1, c->dftBuf); - _dftBuf = torch.rfft(dftBuf, 1) - - # memset(c->dftBuf, 0, p->spectrum_start * sizeof(*c->dftBuf)); - _dftBuf[:spectrum_start].zero_() - - mult: float = boot_count / (1. + boot_count) \ - if boot_count >= 0 \ - else measure_smooth_time_mult - - _d = complex_norm(_dftBuf[spectrum_start:spectrum_end]) - spectrum[spectrum_start:spectrum_end].mul_(mult).add_(_d * (1 - mult)) - _d = spectrum[spectrum_start:spectrum_end] ** 2 - - _zeros = torch.zeros(spectrum_end - spectrum_start) - _mult = _zeros \ - if boot_count >= 0 \ - else torch.where( - _d > noise_spectrum[spectrum_start:spectrum_end], - torch.tensor(noise_up_time_mult), # if - torch.tensor(noise_down_time_mult) # else - ) - - noise_spectrum[spectrum_start:spectrum_end].mul_(_mult).add_(_d * (1 - _mult)) - _d = torch.sqrt( - torch.max( - _zeros, - _d - noise_reduction_amount * noise_spectrum[spectrum_start:spectrum_end])) - - _cepstrum_Buf: Tensor = torch.zeros(dft_len_ws >> 1) - _cepstrum_Buf[spectrum_start:spectrum_end] = _d * cepstrum_window - _cepstrum_Buf[spectrum_end:dft_len_ws >> 1].zero_() - - # lsx_safe_rdft((int)p->dft_len_ws >> 1, 1, c->dftBuf); - _cepstrum_Buf = torch.rfft(_cepstrum_Buf, 1) - - result: float = float(torch.sum( - complex_norm( - _cepstrum_Buf[cepstrum_start:cepstrum_end], - power=2.0))) - result = \ - math.log(result / (cepstrum_end - cepstrum_start)) \ - if result > 0 \ - else -math.inf - return max(0, 21 + result) - - -def vad( - waveform: Tensor, - sample_rate: int, - trigger_level: float = 7.0, - trigger_time: float = 0.25, - search_time: float = 1.0, - allowed_gap: float = 0.25, - pre_trigger_time: float = 0.0, - # Fine-tuning parameters - boot_time: float = .35, - noise_up_time: float = .1, - noise_down_time: float = .01, - noise_reduction_amount: float = 1.35, - measure_freq: float = 20.0, - measure_duration: Optional[float] = None, - measure_smooth_time: float = .4, - hp_filter_freq: float = 50., - lp_filter_freq: float = 6000., - hp_lifter_freq: float = 150., - lp_lifter_freq: float = 2000., -) -> Tensor: - r"""Voice Activity Detector. Similar to SoX implementation. - Attempts to trim silence and quiet background sounds from the ends of recordings of speech. - The algorithm currently uses a simple cepstral power measurement to detect voice, - so may be fooled by other things, especially music. - - The effect can trim only from the front of the audio, - so in order to trim from the back, the reverse effect must also be used. - - Args: - waveform (Tensor): Tensor of audio of dimension `(..., time)` - sample_rate (int): Sample rate of audio signal. - trigger_level (float, optional): The measurement level used to trigger activity detection. - This may need to be cahnged depending on the noise level, signal level, - and other characteristics of the input audio. (Default: 7.0) - trigger_time (float, optional): The time constant (in seconds) - used to help ignore short bursts of sound. (Default: 0.25) - search_time (float, optional): The amount of audio (in seconds) - to search for quieter/shorter bursts of audio to include prior - to the detected trigger point. (Default: 1.0) - allowed_gap (float, optional): The allowed gap (in seconds) between - quiteter/shorter bursts of audio to include prior - to the detected trigger point. (Default: 0.25) - pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve - before the trigger point and any found quieter/shorter bursts. (Default: 0.0) - boot_time (float, optional) The algorithm (internally) uses adaptive noise - estimation/reduction in order to detect the start of the wanted audio. - This option sets the time for the initial noise estimate. (Default: 0.35) - noise_up_time (float, optional) Time constant used by the adaptive noise estimator - for when the noise level is increasing. (Default: 0.1) - noise_down_time (float, optional) Time constant used by the adaptive noise estimator - for when the noise level is decreasing. (Default: 0.01) - noise_reduction_amount (float, optional) Amount of noise reduction to use in - the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35) - measure_freq (float, optional) Frequency of the algorithmโ€™s - processing/measurements. (Default: 20.0) - measure_duration: (float, optional) Measurement duration. - (Default: Twice the measurement period; i.e. with overlap.) - measure_smooth_time (float, optional) Time constant used to smooth - spectral measurements. (Default: 0.4) - hp_filter_freq (float, optional) "Brick-wall" frequency of high-pass filter applied - at the input to the detector algorithm. (Default: 50.0) - lp_filter_freq (float, optional) "Brick-wall" frequency of low-pass filter applied - at the input to the detector algorithm. (Default: 6000.0) - hp_lifter_freq (float, optional) "Brick-wall" frequency of high-pass lifter used - in the detector algorithm. (Default: 150.0) - lp_lifter_freq (float, optional) "Brick-wall" frequency of low-pass lifter used - in the detector algorithm. (Default: 2000.0) - - Returns: - Tensor: Tensor of audio of dimension (..., time). - - References: - http://sox.sourceforge.net/sox.html - """ - - measure_duration: float = 2.0 / measure_freq \ - if measure_duration is None \ - else measure_duration - - measure_len_ws = int(sample_rate * measure_duration + .5) - measure_len_ns = measure_len_ws - # for (dft_len_ws = 16; dft_len_ws < measure_len_ws; dft_len_ws <<= 1); - dft_len_ws = 16 - while (dft_len_ws < measure_len_ws): - dft_len_ws *= 2 - - measure_period_ns = int(sample_rate / measure_freq + .5) - measures_len = math.ceil(search_time * measure_freq) - search_pre_trigger_len_ns = measures_len * measure_period_ns - gap_len = int(allowed_gap * measure_freq + .5) - - fixed_pre_trigger_len_ns = int(pre_trigger_time * sample_rate + .5) - samplesLen_ns = fixed_pre_trigger_len_ns + search_pre_trigger_len_ns + measure_len_ns - - spectrum_window = torch.zeros(measure_len_ws) - for i in range(measure_len_ws): - # sox.h:741 define SOX_SAMPLE_MIN (sox_sample_t)SOX_INT_MIN(32) - spectrum_window[i] = 2. / math.sqrt(float(measure_len_ws)) - # lsx_apply_hann(spectrum_window, (int)measure_len_ws); - spectrum_window *= torch.hann_window(measure_len_ws, dtype=torch.float) - - spectrum_start: int = int(hp_filter_freq / sample_rate * dft_len_ws + .5) - spectrum_start: int = max(spectrum_start, 1) - spectrum_end: int = int(lp_filter_freq / sample_rate * dft_len_ws + .5) - spectrum_end: int = min(spectrum_end, dft_len_ws // 2) - - cepstrum_window = torch.zeros(spectrum_end - spectrum_start) - for i in range(spectrum_end - spectrum_start): - cepstrum_window[i] = 2. / math.sqrt(float(spectrum_end) - spectrum_start) - # lsx_apply_hann(cepstrum_window,(int)(spectrum_end - spectrum_start)); - cepstrum_window *= torch.hann_window(spectrum_end - spectrum_start, dtype=torch.float) - - cepstrum_start = math.ceil(sample_rate * .5 / lp_lifter_freq) - cepstrum_end = math.floor(sample_rate * .5 / hp_lifter_freq) - cepstrum_end = min(cepstrum_end, dft_len_ws // 4) - - assert cepstrum_end > cepstrum_start - - noise_up_time_mult = math.exp(-1. / (noise_up_time * measure_freq)) - noise_down_time_mult = math.exp(-1. / (noise_down_time * measure_freq)) - measure_smooth_time_mult = math.exp(-1. / (measure_smooth_time * measure_freq)) - trigger_meas_time_mult = math.exp(-1. / (trigger_time * measure_freq)) - - boot_count_max = int(boot_time * measure_freq - .5) - measure_timer_ns = measure_len_ns - boot_count = measures_index = flushedLen_ns = samplesIndex_ns = 0 - - # pack batch - shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) - - n_channels, ilen = waveform.size() - - mean_meas = torch.zeros(n_channels) - samples = torch.zeros(n_channels, samplesLen_ns) - spectrum = torch.zeros(n_channels, dft_len_ws) - noise_spectrum = torch.zeros(n_channels, dft_len_ws) - measures = torch.zeros(n_channels, measures_len) - - has_triggered: bool = False - num_measures_to_flush: int = 0 - pos: int = 0 - - while (pos < ilen and not has_triggered): - measure_timer_ns -= 1 - for i in range(n_channels): - samples[i, samplesIndex_ns] = waveform[i, pos] - # if (!p->measure_timer_ns) { - if (measure_timer_ns == 0): - index_ns: int = \ - (samplesIndex_ns + samplesLen_ns - measure_len_ns) % samplesLen_ns - meas: float = _measure( - measure_len_ws=measure_len_ws, - samples=samples[i], - spectrum=spectrum[i], - noise_spectrum=noise_spectrum[i], - spectrum_window=spectrum_window, - spectrum_start=spectrum_start, - spectrum_end=spectrum_end, - cepstrum_window=cepstrum_window, - cepstrum_start=cepstrum_start, - cepstrum_end=cepstrum_end, - noise_reduction_amount=noise_reduction_amount, - measure_smooth_time_mult=measure_smooth_time_mult, - noise_up_time_mult=noise_up_time_mult, - noise_down_time_mult=noise_down_time_mult, - index_ns=index_ns, - boot_count=boot_count) - measures[i, measures_index] = meas - mean_meas[i] = mean_meas[i] * trigger_meas_time_mult + meas * (1. - trigger_meas_time_mult) - - has_triggered = has_triggered or (mean_meas[i] >= trigger_level) - if has_triggered: - n: int = measures_len - k: int = measures_index - jTrigger: int = n - jZero: int = n - j: int = 0 - - for j in range(n): - if (measures[i, k] >= trigger_level) and (j <= jTrigger + gap_len): - jZero = jTrigger = j - elif (measures[i, k] == 0) and (jTrigger >= jZero): - jZero = j - k = (k + n - 1) % n - j = min(j, jZero) - # num_measures_to_flush = range_limit(j, num_measures_to_flush, n); - num_measures_to_flush = (min(max(num_measures_to_flush, j), n)) - # end if has_triggered - # end if (measure_timer_ns == 0): - # end for - samplesIndex_ns += 1 - pos += 1 - # end while - if samplesIndex_ns == samplesLen_ns: - samplesIndex_ns = 0 - if measure_timer_ns == 0: - measure_timer_ns = measure_period_ns - measures_index += 1 - measures_index = measures_index % measures_len - if boot_count >= 0: - boot_count = -1 if boot_count == boot_count_max else boot_count + 1 - - if has_triggered: - flushedLen_ns = (measures_len - num_measures_to_flush) * measure_period_ns - samplesIndex_ns = (samplesIndex_ns + flushedLen_ns) % samplesLen_ns - - res = waveform[:, pos - samplesLen_ns + flushedLen_ns:] - # unpack batch - return res.view(shape[:-1] + res.shape[-1:]) diff --git a/torchaudio/functional/__init__.py b/torchaudio/functional/__init__.py new file mode 100644 index 0000000000..3d1aa00f86 --- /dev/null +++ b/torchaudio/functional/__init__.py @@ -0,0 +1,42 @@ +from .functional import ( + amplitude_to_DB, + angle, + complex_norm, + compute_deltas, + create_dct, + create_fb_matrix, + DB_to_amplitude, + detect_pitch_frequency, + griffinlim, + magphase, + mask_along_axis, + mask_along_axis_iid, + mu_law_encoding, + mu_law_decoding, + phase_vocoder, + sliding_window_cmn, + spectrogram, +) +from .filtering import ( + allpass_biquad, + band_biquad, + bandpass_biquad, + bandreject_biquad, + bass_biquad, + biquad, + contrast, + dither, + dcshift, + deemph_biquad, + equalizer_biquad, + flanger, + gain, + highpass_biquad, + lfilter, + lowpass_biquad, + overdrive, + phaser, + riaa_biquad, + treble_biquad, + vad, +) diff --git a/torchaudio/functional/filtering.py b/torchaudio/functional/filtering.py new file mode 100644 index 0000000000..aa9f95f2e0 --- /dev/null +++ b/torchaudio/functional/filtering.py @@ -0,0 +1,1469 @@ +import math +from typing import Optional + +import torch +from torch import Tensor + +import torchaudio._internal.fft + + +def _dB2Linear(x: float) -> float: + return math.exp(x * math.log(10) / 20.0) + + +def _generate_wave_table( + wave_type: str, + data_type: str, + table_size: int, + min: float, + max: float, + phase: float, + device: torch.device, +) -> Tensor: + r"""A helper fucntion for phaser. Generates a table with given parameters + + Args: + wave_type (str): SINE or TRIANGULAR + data_type (str): desired data_type ( `INT` or `FLOAT` ) + table_size (int): desired table size + min (float): desired min value + max (float): desired max value + phase (float): desired phase + device (torch.device): Torch device on which table must be generated + Returns: + Tensor: A 1D tensor with wave table values + """ + + phase_offset = int(phase / math.pi / 2 * table_size + 0.5) + + t = torch.arange(table_size, device=device, dtype=torch.int32) + + point = (t + phase_offset) % table_size + + d = torch.zeros_like(point, device=device, dtype=torch.float64) + + if wave_type == "SINE": + d = (torch.sin(point.to(torch.float64) / table_size * 2 * math.pi) + 1) / 2 + elif wave_type == "TRIANGLE": + d = point.to(torch.float64) * 2 / table_size + value = 4 * point // table_size + d[value == 0] = d[value == 0] + 0.5 + d[value == 1] = 1.5 - d[value == 1] + d[value == 2] = 1.5 - d[value == 2] + d[value == 3] = d[value == 3] - 1.5 + + d = d * (max - min) + min + + if data_type == "INT": + mask = d < 0 + d[mask] = d[mask] - 0.5 + d[~mask] = d[~mask] + 0.5 + d = d.to(torch.int32) + elif data_type == "FLOAT": + d = d.to(torch.float32) + + return d + + +def allpass_biquad( + waveform: Tensor, sample_rate: int, central_freq: float, Q: float = 0.707 +) -> Tensor: + r"""Design two-pole all-pass filter. Similar to SoX implementation. + + Args: + waveform(torch.Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + central_freq (float): central frequency (in Hz) + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF + """ + w0 = 2 * math.pi * central_freq / sample_rate + alpha = math.sin(w0) / 2 / Q + + b0 = 1 - alpha + b1 = -2 * math.cos(w0) + b2 = 1 + alpha + a0 = 1 + alpha + a1 = -2 * math.cos(w0) + a2 = 1 - alpha + return biquad(waveform, b0, b1, b2, a0, a1, a2) + + +def band_biquad( + waveform: Tensor, + sample_rate: int, + central_freq: float, + Q: float = 0.707, + noise: bool = False, +) -> Tensor: + r"""Design two-pole band filter. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + central_freq (float): central frequency (in Hz) + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). + noise (bool, optional) : If ``True``, uses the alternate mode for un-pitched audio (e.g. percussion). + If ``False``, uses mode oriented to pitched audio, i.e. voice, singing, + or instrumental music (Default: ``False``). + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF + """ + w0 = 2 * math.pi * central_freq / sample_rate + bw_Hz = central_freq / Q + + a0 = 1.0 + a2 = math.exp(-2 * math.pi * bw_Hz / sample_rate) + a1 = -4 * a2 / (1 + a2) * math.cos(w0) + + b0 = math.sqrt(1 - a1 * a1 / (4 * a2)) * (1 - a2) + + if noise: + mult = math.sqrt(((1 + a2) * (1 + a2) - a1 * a1) * (1 - a2) / (1 + a2)) / b0 + b0 *= mult + + b1 = 0.0 + b2 = 0.0 + + return biquad(waveform, b0, b1, b2, a0, a1, a2) + + +def bandpass_biquad( + waveform: Tensor, + sample_rate: int, + central_freq: float, + Q: float = 0.707, + const_skirt_gain: bool = False, +) -> Tensor: + r"""Design two-pole band-pass filter. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + central_freq (float): central frequency (in Hz) + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + const_skirt_gain (bool, optional) : If ``True``, uses a constant skirt gain (peak gain = Q). + If ``False``, uses a constant 0dB peak gain. (Default: ``False``) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF + """ + w0 = 2 * math.pi * central_freq / sample_rate + alpha = math.sin(w0) / 2 / Q + + temp = math.sin(w0) / 2 if const_skirt_gain else alpha + b0 = temp + b1 = 0.0 + b2 = -temp + a0 = 1 + alpha + a1 = -2 * math.cos(w0) + a2 = 1 - alpha + return biquad(waveform, b0, b1, b2, a0, a1, a2) + + +def bandreject_biquad( + waveform: Tensor, sample_rate: int, central_freq: float, Q: float = 0.707 +) -> Tensor: + r"""Design two-pole band-reject filter. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + central_freq (float): central frequency (in Hz) + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF + """ + w0 = 2 * math.pi * central_freq / sample_rate + alpha = math.sin(w0) / 2 / Q + + b0 = 1.0 + b1 = -2 * math.cos(w0) + b2 = 1.0 + a0 = 1 + alpha + a1 = -2 * math.cos(w0) + a2 = 1 - alpha + return biquad(waveform, b0, b1, b2, a0, a1, a2) + + +def bass_biquad( + waveform: Tensor, + sample_rate: int, + gain: float, + central_freq: float = 100, + Q: float = 0.707, +) -> Tensor: + r"""Design a bass tone-control effect. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + gain (float): desired gain at the boost (or attenuation) in dB. + central_freq (float, optional): central frequency (in Hz). (Default: ``100``) + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF + """ + w0 = 2 * math.pi * central_freq / sample_rate + alpha = math.sin(w0) / 2 / Q + A = math.exp(gain / 40 * math.log(10)) + + temp1 = 2 * math.sqrt(A) * alpha + temp2 = (A - 1) * math.cos(w0) + temp3 = (A + 1) * math.cos(w0) + + b0 = A * ((A + 1) - temp2 + temp1) + b1 = 2 * A * ((A - 1) - temp3) + b2 = A * ((A + 1) - temp2 - temp1) + a0 = (A + 1) + temp2 + temp1 + a1 = -2 * ((A - 1) + temp3) + a2 = (A + 1) + temp2 - temp1 + + return biquad(waveform, b0 / a0, b1 / a0, b2 / a0, a0 / a0, a1 / a0, a2 / a0) + + +def biquad( + waveform: Tensor, b0: float, b1: float, b2: float, a0: float, a1: float, a2: float +) -> Tensor: + r"""Perform a biquad filter of input tensor. Initial conditions set to 0. + https://en.wikipedia.org/wiki/Digital_biquad_filter + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + b0 (float): numerator coefficient of current input, x[n] + b1 (float): numerator coefficient of input one time step ago x[n-1] + b2 (float): numerator coefficient of input two time steps ago x[n-2] + a0 (float): denominator coefficient of current output y[n], typically 1 + a1 (float): denominator coefficient of current output y[n-1] + a2 (float): denominator coefficient of current output y[n-2] + + Returns: + Tensor: Waveform with dimension of `(..., time)` + """ + + device = waveform.device + dtype = waveform.dtype + + output_waveform = lfilter( + waveform, + torch.tensor([a0, a1, a2], dtype=dtype, device=device), + torch.tensor([b0, b1, b2], dtype=dtype, device=device), + ) + return output_waveform + + +def contrast(waveform: Tensor, enhancement_amount: float = 75.0) -> Tensor: + r"""Apply contrast effect. Similar to SoX implementation. + Comparable with compression, this effect modifies an audio signal to make it sound louder + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + enhancement_amount (float): controls the amount of the enhancement + Allowed range of values for enhancement_amount : 0-100 + Note that enhancement_amount = 0 still gives a significant contrast enhancement + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + """ + + if not 0 <= enhancement_amount <= 100: + raise ValueError("Allowed range of values for enhancement_amount : 0-100") + + contrast = enhancement_amount / 750.0 + + temp1 = waveform * (math.pi / 2) + temp2 = contrast * torch.sin(temp1 * 4) + output_waveform = torch.sin(temp1 + temp2) + + return output_waveform + + +def dcshift( + waveform: Tensor, shift: float, limiter_gain: Optional[float] = None +) -> Tensor: + r"""Apply a DC shift to the audio. Similar to SoX implementation. + This can be useful to remove a DC offset + (caused perhaps by a hardware problem in the recording chain) from the audio + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + shift (float): indicates the amount to shift the audio + Allowed range of values for shift : -2.0 to +2.0 + limiter_gain (float): It is used only on peaks to prevent clipping + It should have a value much less than 1 (e.g. 0.05 or 0.02) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + """ + output_waveform = waveform + limiter_threshold = 0.0 + + if limiter_gain is not None: + limiter_threshold = 1.0 - (abs(shift) - limiter_gain) + + if limiter_gain is not None and shift > 0: + mask = waveform > limiter_threshold + temp = ( + (waveform[mask] - limiter_threshold) + * limiter_gain + / (1 - limiter_threshold) + ) + output_waveform[mask] = (temp + limiter_threshold + shift).clamp( + max=limiter_threshold + ) + output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1) + elif limiter_gain is not None and shift < 0: + mask = waveform < -limiter_threshold + temp = ( + (waveform[mask] + limiter_threshold) + * limiter_gain + / (1 - limiter_threshold) + ) + output_waveform[mask] = (temp - limiter_threshold + shift).clamp( + min=-limiter_threshold + ) + output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1) + else: + output_waveform = (waveform + shift).clamp(min=-1, max=1) + + return output_waveform + + +def deemph_biquad(waveform: Tensor, sample_rate: int) -> Tensor: + r"""Apply ISO 908 CD de-emphasis (shelving) IIR filter. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, Allowed sample rate ``44100`` or ``48000`` + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF + """ + + if sample_rate == 44100: + central_freq = 5283 + width_slope = 0.4845 + gain = -9.477 + elif sample_rate == 48000: + central_freq = 5356 + width_slope = 0.479 + gain = -9.62 + else: + raise ValueError("Sample rate must be 44100 (audio-CD) or 48000 (DAT)") + + w0 = 2 * math.pi * central_freq / sample_rate + A = math.exp(gain / 40.0 * math.log(10)) + alpha = math.sin(w0) / 2 * math.sqrt((A + 1 / A) * (1 / width_slope - 1) + 2) + + temp1 = 2 * math.sqrt(A) * alpha + temp2 = (A - 1) * math.cos(w0) + temp3 = (A + 1) * math.cos(w0) + + b0 = A * ((A + 1) + temp2 + temp1) + b1 = -2 * A * ((A - 1) + temp3) + b2 = A * ((A + 1) + temp2 - temp1) + a0 = (A + 1) - temp2 + temp1 + a1 = 2 * ((A - 1) - temp3) + a2 = (A + 1) - temp2 - temp1 + + return biquad(waveform, b0, b1, b2, a0, a1, a2) + + +def _add_noise_shaping(dithered_waveform: Tensor, waveform: Tensor) -> Tensor: + r"""Noise shaping is calculated by error: + error[n] = dithered[n] - original[n] + noise_shaped_waveform[n] = dithered[n] + error[n-1] + """ + wf_shape = waveform.size() + waveform = waveform.reshape(-1, wf_shape[-1]) + + dithered_shape = dithered_waveform.size() + dithered_waveform = dithered_waveform.reshape(-1, dithered_shape[-1]) + + error = dithered_waveform - waveform + + # add error[n-1] to dithered_waveform[n], so offset the error by 1 index + zeros = torch.zeros(1, dtype=error.dtype, device=error.device) + for index in range(error.size()[0]): + err = error[index] + error_offset = torch.cat((zeros, err)) + error[index] = error_offset[: waveform.size()[1]] + + noise_shaped = dithered_waveform + error + return noise_shaped.reshape(dithered_shape[:-1] + noise_shaped.shape[-1:]) + + +def _apply_probability_distribution( + waveform: Tensor, density_function: str = "TPDF" +) -> Tensor: + r"""Apply a probability distribution function on a waveform. + + Triangular probability density function (TPDF) dither noise has a + triangular distribution; values in the center of the range have a higher + probability of occurring. + + Rectangular probability density function (RPDF) dither noise has a + uniform distribution; any value in the specified range has the same + probability of occurring. + + Gaussian probability density function (GPDF) has a normal distribution. + The relationship of probabilities of results follows a bell-shaped, + or Gaussian curve, typical of dither generated by analog sources. + Args: + waveform (Tensor): Tensor of audio of dimension (..., time) + probability_density_function (str, optional): The density function of a + continuous random variable (Default: ``"TPDF"``) + Options: Triangular Probability Density Function - `TPDF` + Rectangular Probability Density Function - `RPDF` + Gaussian Probability Density Function - `GPDF` + Returns: + Tensor: waveform dithered with TPDF + """ + + # pack batch + shape = waveform.size() + waveform = waveform.reshape(-1, shape[-1]) + + channel_size = waveform.size()[0] - 1 + time_size = waveform.size()[-1] - 1 + + random_channel = ( + int( + torch.randint( + channel_size, + [ + 1, + ], + ).item() + ) + if channel_size > 0 + else 0 + ) + random_time = ( + int( + torch.randint( + time_size, + [ + 1, + ], + ).item() + ) + if time_size > 0 + else 0 + ) + + number_of_bits = 16 + up_scaling = 2 ** (number_of_bits - 1) - 2 + signal_scaled = waveform * up_scaling + down_scaling = 2 ** (number_of_bits - 1) + + signal_scaled_dis = waveform + if density_function == "RPDF": + RPDF = waveform[random_channel][random_time] - 0.5 + + signal_scaled_dis = signal_scaled + RPDF + elif density_function == "GPDF": + # TODO Replace by distribution code once + # https://github.com/pytorch/pytorch/issues/29843 is resolved + # gaussian = torch.distributions.normal.Normal(torch.mean(waveform, -1), 1).sample() + + num_rand_variables = 6 + + gaussian = waveform[random_channel][random_time] + for ws in num_rand_variables * [time_size]: + rand_chan = int( + torch.randint( + channel_size, + [ + 1, + ], + ).item() + ) + gaussian += waveform[rand_chan][ + int( + torch.randint( + ws, + [ + 1, + ], + ).item() + ) + ] + + signal_scaled_dis = signal_scaled + gaussian + else: + # dtype needed for https://github.com/pytorch/pytorch/issues/32358 + TPDF = torch.bartlett_window( + time_size + 1, dtype=signal_scaled.dtype, device=signal_scaled.device + ) + TPDF = TPDF.repeat((channel_size + 1), 1) + signal_scaled_dis = signal_scaled + TPDF + + quantised_signal_scaled = torch.round(signal_scaled_dis) + quantised_signal = quantised_signal_scaled / down_scaling + + # unpack batch + return quantised_signal.reshape(shape[:-1] + quantised_signal.shape[-1:]) + + +def dither( + waveform: Tensor, density_function: str = "TPDF", noise_shaping: bool = False +) -> Tensor: + r"""Dither increases the perceived dynamic range of audio stored at a + particular bit-depth by eliminating nonlinear truncation distortion + (i.e. adding minimally perceived noise to mask distortion caused by quantization). + + Args: + waveform (Tensor): Tensor of audio of dimension (..., time) + density_function (str, optional): + The density function of a continuous random variable. One of + ``"TPDF"`` (Triangular Probability Density Function), + ``"RPDF"`` (Rectangular Probability Density Function) or + ``"GPDF"`` (Gaussian Probability Density Function) (Default: ``"TPDF"``). + noise_shaping (bool, optional): a filtering process that shapes the spectral + energy of quantisation error (Default: ``False``) + + Returns: + Tensor: waveform dithered + """ + dithered = _apply_probability_distribution( + waveform, density_function=density_function + ) + + if noise_shaping: + return _add_noise_shaping(dithered, waveform) + else: + return dithered + + +def equalizer_biquad( + waveform: Tensor, + sample_rate: int, + center_freq: float, + gain: float, + Q: float = 0.707, +) -> Tensor: + r"""Design biquad peaking equalizer filter and perform filtering. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + center_freq (float): filter's central frequency + gain (float): desired gain at the boost (or attenuation) in dB + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + """ + w0 = 2 * math.pi * center_freq / sample_rate + A = math.exp(gain / 40.0 * math.log(10)) + alpha = math.sin(w0) / 2 / Q + + b0 = 1 + alpha * A + b1 = -2 * math.cos(w0) + b2 = 1 - alpha * A + a0 = 1 + alpha / A + a1 = -2 * math.cos(w0) + a2 = 1 - alpha / A + return biquad(waveform, b0, b1, b2, a0, a1, a2) + + +def flanger( + waveform: Tensor, + sample_rate: int, + delay: float = 0.0, + depth: float = 2.0, + regen: float = 0.0, + width: float = 71.0, + speed: float = 0.5, + phase: float = 25.0, + modulation: str = "sinusoidal", + interpolation: str = "linear", +) -> Tensor: + r"""Apply a flanger effect to the audio. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., channel, time)` . + Max 4 channels allowed + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + delay (float): desired delay in milliseconds(ms) + Allowed range of values are 0 to 30 + depth (float): desired delay depth in milliseconds(ms) + Allowed range of values are 0 to 10 + regen (float): desired regen(feeback gain) in dB + Allowed range of values are -95 to 95 + width (float): desired width(delay gain) in dB + Allowed range of values are 0 to 100 + speed (float): modulation speed in Hz + Allowed range of values are 0.1 to 10 + phase (float): percentage phase-shift for multi-channel + Allowed range of values are 0 to 100 + modulation (str): Use either "sinusoidal" or "triangular" modulation. (Default: ``sinusoidal``) + interpolation (str): Use either "linear" or "quadratic" for delay-line interpolation. (Default: ``linear``) + + Returns: + Tensor: Waveform of dimension of `(..., channel, time)` + + References: + http://sox.sourceforge.net/sox.html + + Scott Lehman, Effects Explained, + https://web.archive.org/web/20051125072557/http://www.harmony-central.com/Effects/effects-explained.html + """ + + if modulation not in ("sinusoidal", "triangular"): + raise ValueError("Only 'sinusoidal' or 'triangular' modulation allowed") + + if interpolation not in ("linear", "quadratic"): + raise ValueError("Only 'linear' or 'quadratic' interpolation allowed") + + actual_shape = waveform.shape + device, dtype = waveform.device, waveform.dtype + + if actual_shape[-2] > 4: + raise ValueError("Max 4 channels allowed") + + # convert to 3D (batch, channels, time) + waveform = waveform.view(-1, actual_shape[-2], actual_shape[-1]) + + # Scaling + feedback_gain = regen / 100 + delay_gain = width / 100 + channel_phase = phase / 100 + delay_min = delay / 1000 + delay_depth = depth / 1000 + + n_channels = waveform.shape[-2] + + if modulation == "sinusoidal": + wave_type = "SINE" + else: + wave_type = "TRIANGLE" + + # Balance output: + in_gain = 1.0 / (1 + delay_gain) + delay_gain = delay_gain / (1 + delay_gain) + + # Balance feedback loop: + delay_gain = delay_gain * (1 - abs(feedback_gain)) + + delay_buf_length = int((delay_min + delay_depth) * sample_rate + 0.5) + delay_buf_length = delay_buf_length + 2 + + delay_bufs = torch.zeros( + waveform.shape[0], n_channels, delay_buf_length, dtype=dtype, device=device + ) + delay_last = torch.zeros(waveform.shape[0], n_channels, dtype=dtype, device=device) + + lfo_length = int(sample_rate / speed) + + table_min = math.floor(delay_min * sample_rate + 0.5) + table_max = delay_buf_length - 2.0 + + lfo = _generate_wave_table( + wave_type=wave_type, + data_type="FLOAT", + table_size=lfo_length, + min=float(table_min), + max=float(table_max), + phase=3 * math.pi / 2, + device=device, + ) + + output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) + + delay_buf_pos = 0 + lfo_pos = 0 + channel_idxs = torch.arange(0, n_channels, device=device) + + for i in range(waveform.shape[-1]): + + delay_buf_pos = (delay_buf_pos + delay_buf_length - 1) % delay_buf_length + + cur_channel_phase = (channel_idxs * lfo_length * channel_phase + 0.5).to( + torch.int64 + ) + delay_tensor = lfo[(lfo_pos + cur_channel_phase) % lfo_length] + frac_delay = torch.frac(delay_tensor) + delay_tensor = torch.floor(delay_tensor) + + int_delay = delay_tensor.to(torch.int64) + + temp = waveform[:, :, i] + + delay_bufs[:, :, delay_buf_pos] = temp + delay_last * feedback_gain + + delayed_0 = delay_bufs[ + :, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length + ] + + int_delay = int_delay + 1 + + delayed_1 = delay_bufs[ + :, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length + ] + + int_delay = int_delay + 1 + + if interpolation == "linear": + delayed = delayed_0 + (delayed_1 - delayed_0) * frac_delay + else: + delayed_2 = delay_bufs[ + :, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length + ] + + int_delay = int_delay + 1 + + delayed_2 = delayed_2 - delayed_0 + delayed_1 = delayed_1 - delayed_0 + a = delayed_2 * 0.5 - delayed_1 + b = delayed_1 * 2 - delayed_2 * 0.5 + + delayed = delayed_0 + (a * frac_delay + b) * frac_delay + + delay_last = delayed + output_waveform[:, :, i] = waveform[:, :, i] * in_gain + delayed * delay_gain + + lfo_pos = (lfo_pos + 1) % lfo_length + + return output_waveform.clamp(min=-1, max=1).view(actual_shape) + + +def gain(waveform: Tensor, gain_db: float = 1.0) -> Tensor: + r"""Apply amplification or attenuation to the whole waveform. + + Args: + waveform (Tensor): Tensor of audio of dimension (..., time). + gain_db (float, optional) Gain adjustment in decibels (dB) (Default: ``1.0``). + + Returns: + Tensor: the whole waveform amplified by gain_db. + """ + if gain_db == 0: + return waveform + + ratio = 10 ** (gain_db / 20) + + return waveform * ratio + + +def highpass_biquad( + waveform: Tensor, sample_rate: int, cutoff_freq: float, Q: float = 0.707 +) -> Tensor: + r"""Design biquad highpass filter and perform filtering. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + cutoff_freq (float): filter cutoff frequency + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + + Returns: + Tensor: Waveform dimension of `(..., time)` + """ + w0 = 2 * math.pi * cutoff_freq / sample_rate + alpha = math.sin(w0) / 2.0 / Q + + b0 = (1 + math.cos(w0)) / 2 + b1 = -1 - math.cos(w0) + b2 = b0 + a0 = 1 + alpha + a1 = -2 * math.cos(w0) + a2 = 1 - alpha + return biquad(waveform, b0, b1, b2, a0, a1, a2) + + +def lfilter( + waveform: Tensor, + a_coeffs: Tensor, + b_coeffs: Tensor, + clamp: bool = True, +) -> Tensor: + r"""Perform an IIR filter by evaluating difference equation. + + Args: + waveform (Tensor): audio waveform of dimension of ``(..., time)``. Must be normalized to -1 to 1. + a_coeffs (Tensor): denominator coefficients of difference equation of dimension of ``(n_order + 1)``. + Lower delays coefficients are first, e.g. ``[a0, a1, a2, ...]``. + Must be same size as b_coeffs (pad with 0's as necessary). + b_coeffs (Tensor): numerator coefficients of difference equation of dimension of ``(n_order + 1)``. + Lower delays coefficients are first, e.g. ``[b0, b1, b2, ...]``. + Must be same size as a_coeffs (pad with 0's as necessary). + clamp (bool, optional): If ``True``, clamp the output signal to be in the range [-1, 1] (Default: ``True``) + + Returns: + Tensor: Waveform with dimension of ``(..., time)``. + """ + # pack batch + shape = waveform.size() + waveform = waveform.reshape(-1, shape[-1]) + + assert a_coeffs.size(0) == b_coeffs.size(0) + assert len(waveform.size()) == 2 + assert waveform.device == a_coeffs.device + assert b_coeffs.device == a_coeffs.device + + device = waveform.device + dtype = waveform.dtype + n_channel, n_sample = waveform.size() + n_order = a_coeffs.size(0) + n_sample_padded = n_sample + n_order - 1 + assert n_order > 0 + + # Pad the input and create output + padded_waveform = torch.zeros( + n_channel, n_sample_padded, dtype=dtype, device=device + ) + padded_waveform[:, n_order - 1:] = waveform + padded_output_waveform = torch.zeros( + n_channel, n_sample_padded, dtype=dtype, device=device + ) + + # Set up the coefficients matrix + # Flip coefficients' order + a_coeffs_flipped = a_coeffs.flip(0) + b_coeffs_flipped = b_coeffs.flip(0) + + # calculate windowed_input_signal in parallel + # create indices of original with shape (n_channel, n_order, n_sample) + window_idxs = torch.arange(n_sample, device=device).unsqueeze(0) + torch.arange( + n_order, device=device + ).unsqueeze(1) + window_idxs = window_idxs.repeat(n_channel, 1, 1) + window_idxs += ( + torch.arange(n_channel, device=device).unsqueeze(-1).unsqueeze(-1) + * n_sample_padded + ) + window_idxs = window_idxs.long() + # (n_order, ) matmul (n_channel, n_order, n_sample) -> (n_channel, n_sample) + input_signal_windows = torch.matmul( + b_coeffs_flipped, torch.take(padded_waveform, window_idxs) + ) + + input_signal_windows.div_(a_coeffs[0]) + a_coeffs_flipped.div_(a_coeffs[0]) + for i_sample, o0 in enumerate(input_signal_windows.t()): + windowed_output_signal = padded_output_waveform[ + :, i_sample:i_sample + n_order + ] + o0.addmv_(windowed_output_signal, a_coeffs_flipped, alpha=-1) + padded_output_waveform[:, i_sample + n_order - 1] = o0 + + output = padded_output_waveform[:, n_order - 1:] + + if clamp: + output = torch.clamp(output, min=-1.0, max=1.0) + + # unpack batch + output = output.reshape(shape[:-1] + output.shape[-1:]) + + return output + + +def lowpass_biquad( + waveform: Tensor, sample_rate: int, cutoff_freq: float, Q: float = 0.707 +) -> Tensor: + r"""Design biquad lowpass filter and perform filtering. Similar to SoX implementation. + + Args: + waveform (torch.Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + cutoff_freq (float): filter cutoff frequency + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + """ + w0 = 2 * math.pi * cutoff_freq / sample_rate + alpha = math.sin(w0) / 2 / Q + + b0 = (1 - math.cos(w0)) / 2 + b1 = 1 - math.cos(w0) + b2 = b0 + a0 = 1 + alpha + a1 = -2 * math.cos(w0) + a2 = 1 - alpha + return biquad(waveform, b0, b1, b2, a0, a1, a2) + + +def overdrive(waveform: Tensor, gain: float = 20, colour: float = 20) -> Tensor: + r"""Apply a overdrive effect to the audio. Similar to SoX implementation. + This effect applies a non linear distortion to the audio signal. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + gain (float): desired gain at the boost (or attenuation) in dB + Allowed range of values are 0 to 100 + colour (float): controls the amount of even harmonic content in the over-driven output + Allowed range of values are 0 to 100 + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + """ + actual_shape = waveform.shape + device, dtype = waveform.device, waveform.dtype + + # convert to 2D (..,time) + waveform = waveform.view(-1, actual_shape[-1]) + + gain = _dB2Linear(gain) + colour = colour / 200 + last_in = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device) + last_out = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device) + + temp = waveform * gain + colour + + mask1 = temp < -1 + temp[mask1] = torch.tensor(-2.0 / 3.0, dtype=dtype, device=device) + # Wrapping the constant with Tensor is required for Torchscript + + mask2 = temp > 1 + temp[mask2] = torch.tensor(2.0 / 3.0, dtype=dtype, device=device) + + mask3 = ~mask1 & ~mask2 + temp[mask3] = temp[mask3] - (temp[mask3] ** 3) * (1.0 / 3) + + output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) + + # TODO: Implement a torch CPP extension + for i in range(waveform.shape[-1]): + last_out = temp[:, i] - last_in + 0.995 * last_out + last_in = temp[:, i] + output_waveform[:, i] = waveform[:, i] * 0.5 + last_out * 0.75 + + return output_waveform.clamp(min=-1, max=1).view(actual_shape) + + +def phaser( + waveform: Tensor, + sample_rate: int, + gain_in: float = 0.4, + gain_out: float = 0.74, + delay_ms: float = 3.0, + decay: float = 0.4, + mod_speed: float = 0.5, + sinusoidal: bool = True, +) -> Tensor: + r"""Apply a phasing effect to the audio. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + gain_in (float): desired input gain at the boost (or attenuation) in dB + Allowed range of values are 0 to 1 + gain_out (float): desired output gain at the boost (or attenuation) in dB + Allowed range of values are 0 to 1e9 + delay_ms (float): desired delay in milli seconds + Allowed range of values are 0 to 5.0 + decay (float): desired decay relative to gain-in + Allowed range of values are 0 to 0.99 + mod_speed (float): modulation speed in Hz + Allowed range of values are 0.1 to 2 + sinusoidal (bool): If ``True``, uses sinusoidal modulation (preferable for multiple instruments) + If ``False``, uses triangular modulation (gives single instruments a sharper phasing effect) + (Default: ``True``) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + Scott Lehman, Effects Explained, http://harmony-central.com/Effects/effects-explained.html + """ + actual_shape = waveform.shape + device, dtype = waveform.device, waveform.dtype + + # convert to 2D (channels,time) + waveform = waveform.view(-1, actual_shape[-1]) + + delay_buf_len = int((delay_ms * 0.001 * sample_rate) + 0.5) + delay_buf = torch.zeros( + waveform.shape[0], delay_buf_len, dtype=dtype, device=device + ) + + mod_buf_len = int(sample_rate / mod_speed + 0.5) + + if sinusoidal: + wave_type = "SINE" + else: + wave_type = "TRIANGLE" + + mod_buf = _generate_wave_table( + wave_type=wave_type, + data_type="INT", + table_size=mod_buf_len, + min=1.0, + max=float(delay_buf_len), + phase=math.pi / 2, + device=device, + ) + + delay_pos = 0 + mod_pos = 0 + + output_waveform_pre_gain_list = [] + waveform = waveform * gain_in + delay_buf = delay_buf * decay + waveform_list = [waveform[:, i] for i in range(waveform.size(1))] + delay_buf_list = [delay_buf[:, i] for i in range(delay_buf.size(1))] + mod_buf_list = [mod_buf[i] for i in range(mod_buf.size(0))] + + for i in range(waveform.shape[-1]): + idx = int((delay_pos + mod_buf_list[mod_pos]) % delay_buf_len) + mod_pos = (mod_pos + 1) % mod_buf_len + delay_pos = (delay_pos + 1) % delay_buf_len + temp = (waveform_list[i]) + (delay_buf_list[idx]) + delay_buf_list[delay_pos] = temp * decay + output_waveform_pre_gain_list.append(temp) + + output_waveform = torch.stack(output_waveform_pre_gain_list, dim=1).to( + dtype=dtype, device=device + ) + output_waveform.mul_(gain_out) + + return output_waveform.clamp(min=-1, max=1).view(actual_shape) + + +def riaa_biquad(waveform: Tensor, sample_rate: int) -> Tensor: + r"""Apply RIAA vinyl playback equalisation. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz). + Allowed sample rates in Hz : ``44100``,``48000``,``88200``,``96000`` + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF + """ + + if sample_rate == 44100: + zeros = [-0.2014898, 0.9233820] + poles = [0.7083149, 0.9924091] + + elif sample_rate == 48000: + zeros = [-0.1766069, 0.9321590] + poles = [0.7396325, 0.9931330] + + elif sample_rate == 88200: + zeros = [-0.1168735, 0.9648312] + poles = [0.8590646, 0.9964002] + + elif sample_rate == 96000: + zeros = [-0.1141486, 0.9676817] + poles = [0.8699137, 0.9966946] + + else: + raise ValueError("Sample rate must be 44.1k, 48k, 88.2k, or 96k") + + # polynomial coefficients with roots zeros[0] and zeros[1] + b0 = 1.0 + b1 = -(zeros[0] + zeros[1]) + b2 = zeros[0] * zeros[1] + + # polynomial coefficients with roots poles[0] and poles[1] + a0 = 1.0 + a1 = -(poles[0] + poles[1]) + a2 = poles[0] * poles[1] + + # Normalise to 0dB at 1kHz + y = 2 * math.pi * 1000 / sample_rate + b_re = b0 + b1 * math.cos(-y) + b2 * math.cos(-2 * y) + a_re = a0 + a1 * math.cos(-y) + a2 * math.cos(-2 * y) + b_im = b1 * math.sin(-y) + b2 * math.sin(-2 * y) + a_im = a1 * math.sin(-y) + a2 * math.sin(-2 * y) + g = 1 / math.sqrt((b_re ** 2 + b_im ** 2) / (a_re ** 2 + a_im ** 2)) + + b0 *= g + b1 *= g + b2 *= g + + return biquad(waveform, b0, b1, b2, a0, a1, a2) + + +def treble_biquad( + waveform: Tensor, + sample_rate: int, + gain: float, + central_freq: float = 3000, + Q: float = 0.707, +) -> Tensor: + r"""Design a treble tone-control effect. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + gain (float): desired gain at the boost (or attenuation) in dB. + central_freq (float, optional): central frequency (in Hz). (Default: ``3000``) + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF + """ + w0 = 2 * math.pi * central_freq / sample_rate + alpha = math.sin(w0) / 2 / Q + A = math.exp(gain / 40 * math.log(10)) + + temp1 = 2 * math.sqrt(A) * alpha + temp2 = (A - 1) * math.cos(w0) + temp3 = (A + 1) * math.cos(w0) + + b0 = A * ((A + 1) + temp2 + temp1) + b1 = -2 * A * ((A - 1) + temp3) + b2 = A * ((A + 1) + temp2 - temp1) + a0 = (A + 1) - temp2 + temp1 + a1 = 2 * ((A - 1) - temp3) + a2 = (A + 1) - temp2 - temp1 + + return biquad(waveform, b0, b1, b2, a0, a1, a2) + + +def _measure( + measure_len_ws: int, + samples: Tensor, + spectrum: Tensor, + noise_spectrum: Tensor, + spectrum_window: Tensor, + spectrum_start: int, + spectrum_end: int, + cepstrum_window: Tensor, + cepstrum_start: int, + cepstrum_end: int, + noise_reduction_amount: float, + measure_smooth_time_mult: float, + noise_up_time_mult: float, + noise_down_time_mult: float, + index_ns: int, + boot_count: int, +) -> float: + + assert spectrum.size()[-1] == noise_spectrum.size()[-1] + + samplesLen_ns = samples.size()[-1] + dft_len_ws = spectrum.size()[-1] + + dftBuf = torch.zeros(dft_len_ws) + + _index_ns = torch.tensor( + [index_ns] + [(index_ns + i) % samplesLen_ns for i in range(1, measure_len_ws)] + ) + dftBuf[:measure_len_ws] = samples[_index_ns] * spectrum_window[:measure_len_ws] + + # memset(c->dftBuf + i, 0, (p->dft_len_ws - i) * sizeof(*c->dftBuf)); + dftBuf[measure_len_ws:dft_len_ws].zero_() + + # lsx_safe_rdft((int)p->dft_len_ws, 1, c->dftBuf); + _dftBuf = torchaudio._internal.fft.rfft(dftBuf) + + # memset(c->dftBuf, 0, p->spectrum_start * sizeof(*c->dftBuf)); + _dftBuf[:spectrum_start].zero_() + + mult: float = ( + boot_count / (1.0 + boot_count) if boot_count >= 0 else measure_smooth_time_mult + ) + + _d = _dftBuf[spectrum_start:spectrum_end].abs() + spectrum[spectrum_start:spectrum_end].mul_(mult).add_(_d * (1 - mult)) + _d = spectrum[spectrum_start:spectrum_end] ** 2 + + _zeros = torch.zeros(spectrum_end - spectrum_start) + _mult = ( + _zeros + if boot_count >= 0 + else torch.where( + _d > noise_spectrum[spectrum_start:spectrum_end], + torch.tensor(noise_up_time_mult), # if + torch.tensor(noise_down_time_mult), # else + ) + ) + + noise_spectrum[spectrum_start:spectrum_end].mul_(_mult).add_(_d * (1 - _mult)) + _d = torch.sqrt( + torch.max( + _zeros, + _d - noise_reduction_amount * noise_spectrum[spectrum_start:spectrum_end], + ) + ) + + _cepstrum_Buf: Tensor = torch.zeros(dft_len_ws >> 1) + _cepstrum_Buf[spectrum_start:spectrum_end] = _d * cepstrum_window + _cepstrum_Buf[spectrum_end:dft_len_ws >> 1].zero_() + + # lsx_safe_rdft((int)p->dft_len_ws >> 1, 1, c->dftBuf); + _cepstrum_Buf = torchaudio._internal.fft.rfft(_cepstrum_Buf) + + result: float = float( + torch.sum(_cepstrum_Buf[cepstrum_start:cepstrum_end].abs().pow(2)) + ) + result = ( + math.log(result / (cepstrum_end - cepstrum_start)) if result > 0 else -math.inf + ) + return max(0, 21 + result) + + +def vad( + waveform: Tensor, + sample_rate: int, + trigger_level: float = 7.0, + trigger_time: float = 0.25, + search_time: float = 1.0, + allowed_gap: float = 0.25, + pre_trigger_time: float = 0.0, + # Fine-tuning parameters + boot_time: float = 0.35, + noise_up_time: float = 0.1, + noise_down_time: float = 0.01, + noise_reduction_amount: float = 1.35, + measure_freq: float = 20.0, + measure_duration: Optional[float] = None, + measure_smooth_time: float = 0.4, + hp_filter_freq: float = 50.0, + lp_filter_freq: float = 6000.0, + hp_lifter_freq: float = 150.0, + lp_lifter_freq: float = 2000.0, +) -> Tensor: + r"""Voice Activity Detector. Similar to SoX implementation. + Attempts to trim silence and quiet background sounds from the ends of recordings of speech. + The algorithm currently uses a simple cepstral power measurement to detect voice, + so may be fooled by other things, especially music. + + The effect can trim only from the front of the audio, + so in order to trim from the back, the reverse effect must also be used. + + Args: + waveform (Tensor): Tensor of audio of dimension `(..., time)` + sample_rate (int): Sample rate of audio signal. + trigger_level (float, optional): The measurement level used to trigger activity detection. + This may need to be cahnged depending on the noise level, signal level, + and other characteristics of the input audio. (Default: 7.0) + trigger_time (float, optional): The time constant (in seconds) + used to help ignore short bursts of sound. (Default: 0.25) + search_time (float, optional): The amount of audio (in seconds) + to search for quieter/shorter bursts of audio to include prior + to the detected trigger point. (Default: 1.0) + allowed_gap (float, optional): The allowed gap (in seconds) between + quiteter/shorter bursts of audio to include prior + to the detected trigger point. (Default: 0.25) + pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve + before the trigger point and any found quieter/shorter bursts. (Default: 0.0) + boot_time (float, optional) The algorithm (internally) uses adaptive noise + estimation/reduction in order to detect the start of the wanted audio. + This option sets the time for the initial noise estimate. (Default: 0.35) + noise_up_time (float, optional) Time constant used by the adaptive noise estimator + for when the noise level is increasing. (Default: 0.1) + noise_down_time (float, optional) Time constant used by the adaptive noise estimator + for when the noise level is decreasing. (Default: 0.01) + noise_reduction_amount (float, optional) Amount of noise reduction to use in + the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35) + measure_freq (float, optional) Frequency of the algorithmโ€™s + processing/measurements. (Default: 20.0) + measure_duration: (float, optional) Measurement duration. + (Default: Twice the measurement period; i.e. with overlap.) + measure_smooth_time (float, optional) Time constant used to smooth + spectral measurements. (Default: 0.4) + hp_filter_freq (float, optional) "Brick-wall" frequency of high-pass filter applied + at the input to the detector algorithm. (Default: 50.0) + lp_filter_freq (float, optional) "Brick-wall" frequency of low-pass filter applied + at the input to the detector algorithm. (Default: 6000.0) + hp_lifter_freq (float, optional) "Brick-wall" frequency of high-pass lifter used + in the detector algorithm. (Default: 150.0) + lp_lifter_freq (float, optional) "Brick-wall" frequency of low-pass lifter used + in the detector algorithm. (Default: 2000.0) + + Returns: + Tensor: Tensor of audio of dimension (..., time). + + References: + http://sox.sourceforge.net/sox.html + """ + + measure_duration: float = ( + 2.0 / measure_freq if measure_duration is None else measure_duration + ) + + measure_len_ws = int(sample_rate * measure_duration + 0.5) + measure_len_ns = measure_len_ws + # for (dft_len_ws = 16; dft_len_ws < measure_len_ws; dft_len_ws <<= 1); + dft_len_ws = 16 + while dft_len_ws < measure_len_ws: + dft_len_ws *= 2 + + measure_period_ns = int(sample_rate / measure_freq + 0.5) + measures_len = math.ceil(search_time * measure_freq) + search_pre_trigger_len_ns = measures_len * measure_period_ns + gap_len = int(allowed_gap * measure_freq + 0.5) + + fixed_pre_trigger_len_ns = int(pre_trigger_time * sample_rate + 0.5) + samplesLen_ns = ( + fixed_pre_trigger_len_ns + search_pre_trigger_len_ns + measure_len_ns + ) + + spectrum_window = torch.zeros(measure_len_ws) + for i in range(measure_len_ws): + # sox.h:741 define SOX_SAMPLE_MIN (sox_sample_t)SOX_INT_MIN(32) + spectrum_window[i] = 2.0 / math.sqrt(float(measure_len_ws)) + # lsx_apply_hann(spectrum_window, (int)measure_len_ws); + spectrum_window *= torch.hann_window(measure_len_ws, dtype=torch.float) + + spectrum_start: int = int(hp_filter_freq / sample_rate * dft_len_ws + 0.5) + spectrum_start: int = max(spectrum_start, 1) + spectrum_end: int = int(lp_filter_freq / sample_rate * dft_len_ws + 0.5) + spectrum_end: int = min(spectrum_end, dft_len_ws // 2) + + cepstrum_window = torch.zeros(spectrum_end - spectrum_start) + for i in range(spectrum_end - spectrum_start): + cepstrum_window[i] = 2.0 / math.sqrt(float(spectrum_end) - spectrum_start) + # lsx_apply_hann(cepstrum_window,(int)(spectrum_end - spectrum_start)); + cepstrum_window *= torch.hann_window( + spectrum_end - spectrum_start, dtype=torch.float + ) + + cepstrum_start = math.ceil(sample_rate * 0.5 / lp_lifter_freq) + cepstrum_end = math.floor(sample_rate * 0.5 / hp_lifter_freq) + cepstrum_end = min(cepstrum_end, dft_len_ws // 4) + + assert cepstrum_end > cepstrum_start + + noise_up_time_mult = math.exp(-1.0 / (noise_up_time * measure_freq)) + noise_down_time_mult = math.exp(-1.0 / (noise_down_time * measure_freq)) + measure_smooth_time_mult = math.exp(-1.0 / (measure_smooth_time * measure_freq)) + trigger_meas_time_mult = math.exp(-1.0 / (trigger_time * measure_freq)) + + boot_count_max = int(boot_time * measure_freq - 0.5) + measure_timer_ns = measure_len_ns + boot_count = measures_index = flushedLen_ns = samplesIndex_ns = 0 + + # pack batch + shape = waveform.size() + waveform = waveform.view(-1, shape[-1]) + + n_channels, ilen = waveform.size() + + mean_meas = torch.zeros(n_channels) + samples = torch.zeros(n_channels, samplesLen_ns) + spectrum = torch.zeros(n_channels, dft_len_ws) + noise_spectrum = torch.zeros(n_channels, dft_len_ws) + measures = torch.zeros(n_channels, measures_len) + + has_triggered: bool = False + num_measures_to_flush: int = 0 + pos: int = 0 + + while pos < ilen and not has_triggered: + measure_timer_ns -= 1 + for i in range(n_channels): + samples[i, samplesIndex_ns] = waveform[i, pos] + # if (!p->measure_timer_ns) { + if measure_timer_ns == 0: + index_ns: int = ( + samplesIndex_ns + samplesLen_ns - measure_len_ns + ) % samplesLen_ns + meas: float = _measure( + measure_len_ws=measure_len_ws, + samples=samples[i], + spectrum=spectrum[i], + noise_spectrum=noise_spectrum[i], + spectrum_window=spectrum_window, + spectrum_start=spectrum_start, + spectrum_end=spectrum_end, + cepstrum_window=cepstrum_window, + cepstrum_start=cepstrum_start, + cepstrum_end=cepstrum_end, + noise_reduction_amount=noise_reduction_amount, + measure_smooth_time_mult=measure_smooth_time_mult, + noise_up_time_mult=noise_up_time_mult, + noise_down_time_mult=noise_down_time_mult, + index_ns=index_ns, + boot_count=boot_count, + ) + measures[i, measures_index] = meas + mean_meas[i] = mean_meas[i] * trigger_meas_time_mult + meas * ( + 1.0 - trigger_meas_time_mult + ) + + has_triggered = has_triggered or (mean_meas[i] >= trigger_level) + if has_triggered: + n: int = measures_len + k: int = measures_index + jTrigger: int = n + jZero: int = n + j: int = 0 + + for j in range(n): + if (measures[i, k] >= trigger_level) and ( + j <= jTrigger + gap_len + ): + jZero = jTrigger = j + elif (measures[i, k] == 0) and (jTrigger >= jZero): + jZero = j + k = (k + n - 1) % n + j = min(j, jZero) + # num_measures_to_flush = range_limit(j, num_measures_to_flush, n); + num_measures_to_flush = min(max(num_measures_to_flush, j), n) + # end if has_triggered + # end if (measure_timer_ns == 0): + # end for + samplesIndex_ns += 1 + pos += 1 + # end while + if samplesIndex_ns == samplesLen_ns: + samplesIndex_ns = 0 + if measure_timer_ns == 0: + measure_timer_ns = measure_period_ns + measures_index += 1 + measures_index = measures_index % measures_len + if boot_count >= 0: + boot_count = -1 if boot_count == boot_count_max else boot_count + 1 + + if has_triggered: + flushedLen_ns = (measures_len - num_measures_to_flush) * measure_period_ns + samplesIndex_ns = (samplesIndex_ns + flushedLen_ns) % samplesLen_ns + + res = waveform[:, pos - samplesLen_ns + flushedLen_ns:] + # unpack batch + return res.view(shape[:-1] + res.shape[-1:]) diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py new file mode 100644 index 0000000000..b967904f2a --- /dev/null +++ b/torchaudio/functional/functional.py @@ -0,0 +1,912 @@ +# -*- coding: utf-8 -*- + +import math +from typing import Optional, Tuple +import warnings + +import torch +from torch import Tensor + +__all__ = [ + "spectrogram", + "griffinlim", + "amplitude_to_DB", + "DB_to_amplitude", + "compute_deltas", + "create_fb_matrix", + "create_dct", + "compute_deltas", + "detect_pitch_frequency", + "DB_to_amplitude", + "mu_law_encoding", + "mu_law_decoding", + "complex_norm", + "angle", + "magphase", + "phase_vocoder", + "detect_pitch_frequency", + 'mask_along_axis', + 'mask_along_axis_iid', + 'sliding_window_cmn', +] + + +def spectrogram( + waveform: Tensor, + pad: int, + window: Tensor, + n_fft: int, + hop_length: int, + win_length: int, + power: Optional[float], + normalized: bool +) -> Tensor: + r"""Create a spectrogram or a batch of spectrograms from a raw audio signal. + The spectrogram can be either magnitude-only or complex. + + Args: + waveform (Tensor): Tensor of audio of dimension (..., time) + pad (int): Two sided padding of signal + window (Tensor): Window tensor that is applied/multiplied to each frame/window + n_fft (int): Size of FFT + hop_length (int): Length of hop between STFT windows + win_length (int): Window size + power (float or None): Exponent for the magnitude spectrogram, + (must be > 0) e.g., 1 for energy, 2 for power, etc. + If None, then the complex spectrum is returned instead. + normalized (bool): Whether to normalize by magnitude after stft + + Returns: + Tensor: Dimension (..., freq, time), freq is + ``n_fft // 2 + 1`` and ``n_fft`` is the number of + Fourier bins, and time is the number of window hops (n_frame). + """ + + if pad > 0: + # TODO add "with torch.no_grad():" back when JIT supports it + waveform = torch.nn.functional.pad(waveform, (pad, pad), "constant") + + # pack batch + shape = waveform.size() + waveform = waveform.reshape(-1, shape[-1]) + + # default values are consistent with librosa.core.spectrum._spectrogram + spec_f = torch.stft( + waveform, n_fft, hop_length, win_length, window, True, "reflect", False, True + ) + + # unpack batch + spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-3:]) + + if normalized: + spec_f /= window.pow(2.).sum().sqrt() + if power is not None: + spec_f = complex_norm(spec_f, power=power) + + return spec_f + + +def griffinlim( + specgram: Tensor, + window: Tensor, + n_fft: int, + hop_length: int, + win_length: int, + power: float, + normalized: bool, + n_iter: int, + momentum: float, + length: Optional[int], + rand_init: bool +) -> Tensor: + r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation. + Implementation ported from `librosa`. + + .. [1] McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. + "librosa: Audio and music signal analysis in python." + In Proceedings of the 14th python in science conference, pp. 18-25. 2015. + + .. [2] Perraudin, N., Balazs, P., & Sรธndergaard, P. L. + "A fast Griffin-Lim algorithm," + IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4), + Oct. 2013. + + .. [3] D. W. Griffin and J. S. Lim, + "Signal estimation from modified short-time Fourier transform," + IEEE Trans. ASSP, vol.32, no.2, pp.236โ€“243, Apr. 1984. + + Args: + specgram (Tensor): A magnitude-only STFT spectrogram of dimension (..., freq, frames) + where freq is ``n_fft // 2 + 1``. + window (Tensor): Window tensor that is applied/multiplied to each frame/window + n_fft (int): Size of FFT, creates ``n_fft // 2 + 1`` bins + hop_length (int): Length of hop between STFT windows. ( + Default: ``win_length // 2``) + win_length (int): Window size. (Default: ``n_fft``) + power (float): Exponent for the magnitude spectrogram, + (must be > 0) e.g., 1 for energy, 2 for power, etc. + normalized (bool): Whether to normalize by magnitude after stft. + n_iter (int): Number of iteration for phase recovery process. + momentum (float): The momentum parameter for fast Griffin-Lim. + Setting this to 0 recovers the original Griffin-Lim method. + Values near 1 can lead to faster convergence, but above 1 may not converge. + length (int or None): Array length of the expected output. + rand_init (bool): Initializes phase randomly if True, to zero otherwise. + + Returns: + torch.Tensor: waveform of (..., time), where time equals the ``length`` parameter if given. + """ + assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum) + assert momentum >= 0, 'momentum={} < 0'.format(momentum) + + # pack batch + shape = specgram.size() + specgram = specgram.reshape([-1] + list(shape[-2:])) + + specgram = specgram.pow(1 / power) + + # randomly initialize the phase + batch, freq, frames = specgram.size() + if rand_init: + angles = 2 * math.pi * torch.rand(batch, freq, frames) + else: + angles = torch.zeros(batch, freq, frames) + angles = torch.stack([angles.cos(), angles.sin()], dim=-1) \ + .to(dtype=specgram.dtype, device=specgram.device) + specgram = specgram.unsqueeze(-1).expand_as(angles) + + # And initialize the previous iterate to 0 + rebuilt = torch.tensor(0.) + + for _ in range(n_iter): + # Store the previous iterate + tprev = rebuilt + + # Invert with our current estimate of the phases + inverse = torch.istft(specgram * angles, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + length=length).float() + + # Rebuild the spectrogram + rebuilt = torch.stft(inverse, n_fft, hop_length, win_length, window, + True, 'reflect', False, True) + + # Update our phase estimates + angles = rebuilt + if momentum: + angles = angles - tprev.mul_(momentum / (1 + momentum)) + angles = angles.div(complex_norm(angles).add(1e-16).unsqueeze(-1).expand_as(angles)) + + # Return the final phase estimates + waveform = torch.istft(specgram * angles, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + length=length) + + # unpack batch + waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:]) + + return waveform + + +def amplitude_to_DB( + x: Tensor, + multiplier: float, + amin: float, + db_multiplier: float, + top_db: Optional[float] = None +) -> Tensor: + r"""Turn a tensor from the power/amplitude scale to the decibel scale. + + This output depends on the maximum value in the input tensor, and so + may return different values for an audio clip split into snippets vs. a + a full clip. + + Args: + x (Tensor): Input tensor before being converted to decibel scale + multiplier (float): Use 10. for power and 20. for amplitude + amin (float): Number to clamp ``x`` + db_multiplier (float): Log10(max(reference value and amin)) + top_db (float or None, optional): Minimum negative cut-off in decibels. A reasonable number + is 80. (Default: ``None``) + + Returns: + Tensor: Output tensor in decibel scale + """ + x_db = multiplier * torch.log10(torch.clamp(x, min=amin)) + x_db -= multiplier * db_multiplier + + if top_db is not None: + x_db = x_db.clamp(min=x_db.max().item() - top_db) + + return x_db + + +def DB_to_amplitude( + x: Tensor, + ref: float, + power: float +) -> Tensor: + r"""Turn a tensor from the decibel scale to the power/amplitude scale. + + Args: + x (Tensor): Input tensor before being converted to power/amplitude scale. + ref (float): Reference which the output will be scaled by. + power (float): If power equals 1, will compute DB to power. If 0.5, will compute DB to amplitude. + + Returns: + Tensor: Output tensor in power/amplitude scale. + """ + return ref * torch.pow(torch.pow(10.0, 0.1 * x), power) + + +def create_fb_matrix( + n_freqs: int, + f_min: float, + f_max: float, + n_mels: int, + sample_rate: int, + norm: Optional[str] = None +) -> Tensor: + r"""Create a frequency bin conversion matrix. + + Args: + n_freqs (int): Number of frequencies to highlight/apply + f_min (float): Minimum frequency (Hz) + f_max (float): Maximum frequency (Hz) + n_mels (int): Number of mel filterbanks + sample_rate (int): Sample rate of the audio waveform + norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band + (area normalization). (Default: ``None``) + + Returns: + Tensor: Triangular filter banks (fb matrix) of size (``n_freqs``, ``n_mels``) + meaning number of frequencies to highlight/apply to x the number of filterbanks. + Each column is a filterbank so that assuming there is a matrix A of + size (..., ``n_freqs``), the applied result would be + ``A * create_fb_matrix(A.size(-1), ...)``. + """ + + if norm is not None and norm != "slaney": + raise ValueError("norm must be one of None or 'slaney'") + + # freq bins + # Equivalent filterbank construction by Librosa + all_freqs = torch.linspace(0, sample_rate // 2, n_freqs) + + # calculate mel freq bins + # hertz to mel(f) is 2595. * math.log10(1. + (f / 700.)) + m_min = 2595.0 * math.log10(1.0 + (f_min / 700.0)) + m_max = 2595.0 * math.log10(1.0 + (f_max / 700.0)) + m_pts = torch.linspace(m_min, m_max, n_mels + 2) + # mel to hertz(mel) is 700. * (10**(mel / 2595.) - 1.) + f_pts = 700.0 * (10 ** (m_pts / 2595.0) - 1.0) + # calculate the difference between each mel point and each stft freq point in hertz + f_diff = f_pts[1:] - f_pts[:-1] # (n_mels + 1) + slopes = f_pts.unsqueeze(0) - all_freqs.unsqueeze(1) # (n_freqs, n_mels + 2) + # create overlapping triangles + zero = torch.zeros(1) + down_slopes = (-1.0 * slopes[:, :-2]) / f_diff[:-1] # (n_freqs, n_mels) + up_slopes = slopes[:, 2:] / f_diff[1:] # (n_freqs, n_mels) + fb = torch.max(zero, torch.min(down_slopes, up_slopes)) + + if norm is not None and norm == "slaney": + # Slaney-style mel is scaled to be approx constant energy per channel + enorm = 2.0 / (f_pts[2:n_mels + 2] - f_pts[:n_mels]) + fb *= enorm.unsqueeze(0) + + if (fb.max(dim=0).values == 0.).any(): + warnings.warn( + "At least one mel filterbank has all zero values. " + f"The value for `n_mels` ({n_mels}) may be set too high. " + f"Or, the value for `n_freqs` ({n_freqs}) may be set too low." + ) + + return fb + + +def create_dct( + n_mfcc: int, + n_mels: int, + norm: Optional[str] +) -> Tensor: + r"""Create a DCT transformation matrix with shape (``n_mels``, ``n_mfcc``), + normalized depending on norm. + + Args: + n_mfcc (int): Number of mfc coefficients to retain + n_mels (int): Number of mel filterbanks + norm (str or None): Norm to use (either 'ortho' or None) + + Returns: + Tensor: The transformation matrix, to be right-multiplied to + row-wise data of size (``n_mels``, ``n_mfcc``). + """ + # http://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II + n = torch.arange(float(n_mels)) + k = torch.arange(float(n_mfcc)).unsqueeze(1) + dct = torch.cos(math.pi / float(n_mels) * (n + 0.5) * k) # size (n_mfcc, n_mels) + if norm is None: + dct *= 2.0 + else: + assert norm == "ortho" + dct[0] *= 1.0 / math.sqrt(2.0) + dct *= math.sqrt(2.0 / float(n_mels)) + return dct.t() + + +def mu_law_encoding( + x: Tensor, + quantization_channels: int +) -> Tensor: + r"""Encode signal based on mu-law companding. For more info see the + `Wikipedia Entry `_ + + This algorithm assumes the signal has been scaled to between -1 and 1 and + returns a signal encoded with values from 0 to quantization_channels - 1. + + Args: + x (Tensor): Input tensor + quantization_channels (int): Number of channels + + Returns: + Tensor: Input after mu-law encoding + """ + mu = quantization_channels - 1.0 + if not x.is_floating_point(): + x = x.to(torch.float) + mu = torch.tensor(mu, dtype=x.dtype) + x_mu = torch.sign(x) * torch.log1p(mu * torch.abs(x)) / torch.log1p(mu) + x_mu = ((x_mu + 1) / 2 * mu + 0.5).to(torch.int64) + return x_mu + + +def mu_law_decoding( + x_mu: Tensor, + quantization_channels: int +) -> Tensor: + r"""Decode mu-law encoded signal. For more info see the + `Wikipedia Entry `_ + + This expects an input with values between 0 and quantization_channels - 1 + and returns a signal scaled between -1 and 1. + + Args: + x_mu (Tensor): Input tensor + quantization_channels (int): Number of channels + + Returns: + Tensor: Input after mu-law decoding + """ + mu = quantization_channels - 1.0 + if not x_mu.is_floating_point(): + x_mu = x_mu.to(torch.float) + mu = torch.tensor(mu, dtype=x_mu.dtype) + x = ((x_mu) / mu) * 2 - 1.0 + x = torch.sign(x) * (torch.exp(torch.abs(x) * torch.log1p(mu)) - 1.0) / mu + return x + + +def complex_norm( + complex_tensor: Tensor, + power: float = 1.0 +) -> Tensor: + r"""Compute the norm of complex tensor input. + + Args: + complex_tensor (Tensor): Tensor shape of `(..., complex=2)` + power (float): Power of the norm. (Default: `1.0`). + + Returns: + Tensor: Power of the normed input tensor. Shape of `(..., )` + """ + + # Replace by torch.norm once issue is fixed + # https://github.com/pytorch/pytorch/issues/34279 + return complex_tensor.pow(2.).sum(-1).pow(0.5 * power) + + +def angle( + complex_tensor: Tensor +) -> Tensor: + r"""Compute the angle of complex tensor input. + + Args: + complex_tensor (Tensor): Tensor shape of `(..., complex=2)` + + Return: + Tensor: Angle of a complex tensor. Shape of `(..., )` + """ + return torch.atan2(complex_tensor[..., 1], complex_tensor[..., 0]) + + +def magphase( + complex_tensor: Tensor, + power: float = 1.0 +) -> Tuple[Tensor, Tensor]: + r"""Separate a complex-valued spectrogram with shape `(..., 2)` into its magnitude and phase. + + Args: + complex_tensor (Tensor): Tensor shape of `(..., complex=2)` + power (float): Power of the norm. (Default: `1.0`) + + Returns: + (Tensor, Tensor): The magnitude and phase of the complex tensor + """ + mag = complex_norm(complex_tensor, power) + phase = angle(complex_tensor) + return mag, phase + + +def phase_vocoder( + complex_specgrams: Tensor, + rate: float, + phase_advance: Tensor +) -> Tensor: + r"""Given a STFT tensor, speed up in time without modifying pitch by a + factor of ``rate``. + + Args: + complex_specgrams (Tensor): Dimension of `(..., freq, time, complex=2)` + rate (float): Speed-up factor + phase_advance (Tensor): Expected phase advance in each bin. Dimension of (freq, 1) + + Returns: + Tensor: Complex Specgrams Stretch with dimension of `(..., freq, ceil(time/rate), complex=2)` + + Example + >>> freq, hop_length = 1025, 512 + >>> # (channel, freq, time, complex=2) + >>> complex_specgrams = torch.randn(2, freq, 300, 2) + >>> rate = 1.3 # Speed up by 30% + >>> phase_advance = torch.linspace( + >>> 0, math.pi * hop_length, freq)[..., None] + >>> x = phase_vocoder(complex_specgrams, rate, phase_advance) + >>> x.shape # with 231 == ceil(300 / 1.3) + torch.Size([2, 1025, 231, 2]) + """ + + # pack batch + shape = complex_specgrams.size() + complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-3:])) + + time_steps = torch.arange(0, + complex_specgrams.size(-2), + rate, + device=complex_specgrams.device, + dtype=complex_specgrams.dtype) + + alphas = time_steps % 1.0 + phase_0 = angle(complex_specgrams[..., :1, :]) + + # Time Padding + complex_specgrams = torch.nn.functional.pad(complex_specgrams, [0, 0, 0, 2]) + + # (new_bins, freq, 2) + complex_specgrams_0 = complex_specgrams.index_select(-2, time_steps.long()) + complex_specgrams_1 = complex_specgrams.index_select(-2, (time_steps + 1).long()) + + angle_0 = angle(complex_specgrams_0) + angle_1 = angle(complex_specgrams_1) + + norm_0 = torch.norm(complex_specgrams_0, p=2, dim=-1) + norm_1 = torch.norm(complex_specgrams_1, p=2, dim=-1) + + phase = angle_1 - angle_0 - phase_advance + phase = phase - 2 * math.pi * torch.round(phase / (2 * math.pi)) + + # Compute Phase Accum + phase = phase + phase_advance + phase = torch.cat([phase_0, phase[..., :-1]], dim=-1) + phase_acc = torch.cumsum(phase, -1) + + mag = alphas * norm_1 + (1 - alphas) * norm_0 + + real_stretch = mag * torch.cos(phase_acc) + imag_stretch = mag * torch.sin(phase_acc) + + complex_specgrams_stretch = torch.stack([real_stretch, imag_stretch], dim=-1) + + # unpack batch + complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-3] + complex_specgrams_stretch.shape[1:]) + + return complex_specgrams_stretch + + +def mask_along_axis_iid( + specgrams: Tensor, + mask_param: int, + mask_value: float, + axis: int +) -> Tensor: + r""" + Apply a mask along ``axis``. Mask will be applied from indices ``[v_0, v_0 + v)``, where + ``v`` is sampled from ``uniform(0, mask_param)``, and ``v_0`` from ``uniform(0, max_v - v)``. + + Args: + specgrams (Tensor): Real spectrograms (batch, channel, freq, time) + mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param] + mask_value (float): Value to assign to the masked columns + axis (int): Axis to apply masking on (2 -> frequency, 3 -> time) + + Returns: + Tensor: Masked spectrograms of dimensions (batch, channel, freq, time) + """ + + if axis != 2 and axis != 3: + raise ValueError('Only Frequency and Time masking are supported') + + device = specgrams.device + dtype = specgrams.dtype + + value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * mask_param + min_value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * (specgrams.size(axis) - value) + + # Create broadcastable mask + mask_start = min_value[..., None, None] + mask_end = (min_value + value)[..., None, None] + mask = torch.arange(0, specgrams.size(axis), device=device, dtype=dtype) + + # Per batch example masking + specgrams = specgrams.transpose(axis, -1) + specgrams.masked_fill_((mask >= mask_start) & (mask < mask_end), mask_value) + specgrams = specgrams.transpose(axis, -1) + + return specgrams + + +def mask_along_axis( + specgram: Tensor, + mask_param: int, + mask_value: float, + axis: int +) -> Tensor: + r""" + Apply a mask along ``axis``. Mask will be applied from indices ``[v_0, v_0 + v)``, where + ``v`` is sampled from ``uniform(0, mask_param)``, and ``v_0`` from ``uniform(0, max_v - v)``. + All examples will have the same mask interval. + + Args: + specgram (Tensor): Real spectrogram (channel, freq, time) + mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param] + mask_value (float): Value to assign to the masked columns + axis (int): Axis to apply masking on (1 -> frequency, 2 -> time) + + Returns: + Tensor: Masked spectrogram of dimensions (channel, freq, time) + """ + + # pack batch + shape = specgram.size() + specgram = specgram.reshape([-1] + list(shape[-2:])) + + value = torch.rand(1) * mask_param + min_value = torch.rand(1) * (specgram.size(axis) - value) + + mask_start = (min_value.long()).squeeze() + mask_end = (min_value.long() + value.long()).squeeze() + + assert mask_end - mask_start < mask_param + if axis == 1: + specgram[:, mask_start:mask_end] = mask_value + elif axis == 2: + specgram[:, :, mask_start:mask_end] = mask_value + else: + raise ValueError('Only Frequency and Time masking are supported') + + # unpack batch + specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:]) + + return specgram + + +def compute_deltas( + specgram: Tensor, + win_length: int = 5, + mode: str = "replicate" +) -> Tensor: + r"""Compute delta coefficients of a tensor, usually a spectrogram: + + .. math:: + d_t = \frac{\sum_{n=1}^{\text{N}} n (c_{t+n} - c_{t-n})}{2 \sum_{n=1}^{\text{N}} n^2} + + where :math:`d_t` is the deltas at time :math:`t`, + :math:`c_t` is the spectrogram coeffcients at time :math:`t`, + :math:`N` is ``(win_length-1)//2``. + + Args: + specgram (Tensor): Tensor of audio of dimension (..., freq, time) + win_length (int, optional): The window length used for computing delta (Default: ``5``) + mode (str, optional): Mode parameter passed to padding (Default: ``"replicate"``) + + Returns: + Tensor: Tensor of deltas of dimension (..., freq, time) + + Example + >>> specgram = torch.randn(1, 40, 1000) + >>> delta = compute_deltas(specgram) + >>> delta2 = compute_deltas(delta) + """ + device = specgram.device + dtype = specgram.dtype + + # pack batch + shape = specgram.size() + specgram = specgram.reshape(1, -1, shape[-1]) + + assert win_length >= 3 + + n = (win_length - 1) // 2 + + # twice sum of integer squared + denom = n * (n + 1) * (2 * n + 1) / 3 + + specgram = torch.nn.functional.pad(specgram, (n, n), mode=mode) + + kernel = torch.arange(-n, n + 1, 1, device=device, dtype=dtype).repeat(specgram.shape[1], 1, 1) + + output = torch.nn.functional.conv1d(specgram, kernel, groups=specgram.shape[1]) / denom + + # unpack batch + output = output.reshape(shape) + + return output + + +def _compute_nccf( + waveform: Tensor, + sample_rate: int, + frame_time: float, + freq_low: int +) -> Tensor: + r""" + Compute Normalized Cross-Correlation Function (NCCF). + + .. math:: + \phi_i(m) = \frac{\sum_{n=b_i}^{b_i + N-1} w(n) w(m+n)}{\sqrt{E(b_i) E(m+b_i)}}, + + where + :math:`\phi_i(m)` is the NCCF at frame :math:`i` with lag :math:`m`, + :math:`w` is the waveform, + :math:`N` is the length of a frame, + :math:`b_i` is the beginning of frame :math:`i`, + :math:`E(j)` is the energy :math:`\sum_{n=j}^{j+N-1} w^2(n)`. + """ + + EPSILON = 10 ** (-9) + + # Number of lags to check + lags = int(math.ceil(sample_rate / freq_low)) + + frame_size = int(math.ceil(sample_rate * frame_time)) + + waveform_length = waveform.size()[-1] + num_of_frames = int(math.ceil(waveform_length / frame_size)) + + p = lags + num_of_frames * frame_size - waveform_length + waveform = torch.nn.functional.pad(waveform, (0, p)) + + # Compute lags + output_lag = [] + for lag in range(1, lags + 1): + s1 = waveform[..., :-lag].unfold(-1, frame_size, frame_size)[..., :num_of_frames, :] + s2 = waveform[..., lag:].unfold(-1, frame_size, frame_size)[..., :num_of_frames, :] + + output_frames = ( + (s1 * s2).sum(-1) + / (EPSILON + torch.norm(s1, p=2, dim=-1)).pow(2) + / (EPSILON + torch.norm(s2, p=2, dim=-1)).pow(2) + ) + + output_lag.append(output_frames.unsqueeze(-1)) + + nccf = torch.cat(output_lag, -1) + + return nccf + + +def _combine_max( + a: Tuple[Tensor, Tensor], + b: Tuple[Tensor, Tensor], + thresh: float = 0.99 +) -> Tuple[Tensor, Tensor]: + """ + Take value from first if bigger than a multiplicative factor of the second, elementwise. + """ + mask = (a[0] > thresh * b[0]) + values = mask * a[0] + ~mask * b[0] + indices = mask * a[1] + ~mask * b[1] + return values, indices + + +def _find_max_per_frame( + nccf: Tensor, + sample_rate: int, + freq_high: int +) -> Tensor: + r""" + For each frame, take the highest value of NCCF, + apply centered median smoothing, and convert to frequency. + + Note: If the max among all the lags is very close + to the first half of lags, then the latter is taken. + """ + + lag_min = int(math.ceil(sample_rate / freq_high)) + + # Find near enough max that is smallest + + best = torch.max(nccf[..., lag_min:], -1) + + half_size = nccf.shape[-1] // 2 + half = torch.max(nccf[..., lag_min:half_size], -1) + + best = _combine_max(half, best) + indices = best[1] + + # Add back minimal lag + indices += lag_min + # Add 1 empirical calibration offset + indices += 1 + + return indices + + +def _median_smoothing( + indices: Tensor, + win_length: int +) -> Tensor: + r""" + Apply median smoothing to the 1D tensor over the given window. + """ + + # Centered windowed + pad_length = (win_length - 1) // 2 + + # "replicate" padding in any dimension + indices = torch.nn.functional.pad( + indices, (pad_length, 0), mode="constant", value=0. + ) + + indices[..., :pad_length] = torch.cat(pad_length * [indices[..., pad_length].unsqueeze(-1)], dim=-1) + roll = indices.unfold(-1, win_length, 1) + + values, _ = torch.median(roll, -1) + return values + + +def detect_pitch_frequency( + waveform: Tensor, + sample_rate: int, + frame_time: float = 10 ** (-2), + win_length: int = 30, + freq_low: int = 85, + freq_high: int = 3400, +) -> Tensor: + r"""Detect pitch frequency. + + It is implemented using normalized cross-correlation function and median smoothing. + + Args: + waveform (Tensor): Tensor of audio of dimension (..., freq, time) + sample_rate (int): The sample rate of the waveform (Hz) + frame_time (float, optional): Duration of a frame (Default: ``10 ** (-2)``). + win_length (int, optional): The window length for median smoothing (in number of frames) (Default: ``30``). + freq_low (int, optional): Lowest frequency that can be detected (Hz) (Default: ``85``). + freq_high (int, optional): Highest frequency that can be detected (Hz) (Default: ``3400``). + + Returns: + Tensor: Tensor of freq of dimension (..., frame) + """ + # pack batch + shape = list(waveform.size()) + waveform = waveform.reshape([-1] + shape[-1:]) + + nccf = _compute_nccf(waveform, sample_rate, frame_time, freq_low) + indices = _find_max_per_frame(nccf, sample_rate, freq_high) + indices = _median_smoothing(indices, win_length) + + # Convert indices to frequency + EPSILON = 10 ** (-9) + freq = sample_rate / (EPSILON + indices.to(torch.float)) + + # unpack batch + freq = freq.reshape(shape[:-1] + list(freq.shape[-1:])) + + return freq + + +def sliding_window_cmn( + waveform: Tensor, + cmn_window: int = 600, + min_cmn_window: int = 100, + center: bool = False, + norm_vars: bool = False, +) -> Tensor: + r""" + Apply sliding-window cepstral mean (and optionally variance) normalization per utterance. + + Args: + waveform (Tensor): Tensor of audio of dimension (..., freq, time) + cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600) + min_cmn_window (int, optional): Minimum CMN window used at start of decoding (adds latency only at start). + Only applicable if center == false, ignored if center==true (int, default = 100) + center (bool, optional): If true, use a window centered on the current frame + (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false) + norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false) + + Returns: + Tensor: Tensor of freq of dimension (..., frame) + """ + input_shape = waveform.shape + num_frames, num_feats = input_shape[-2:] + waveform = waveform.view(-1, num_frames, num_feats) + num_channels = waveform.shape[0] + + dtype = waveform.dtype + device = waveform.device + last_window_start = last_window_end = -1 + cur_sum = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) + cur_sumsq = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) + cmn_waveform = torch.zeros( + num_channels, num_frames, num_feats, dtype=dtype, device=device) + for t in range(num_frames): + window_start = 0 + window_end = 0 + if center: + window_start = t - cmn_window // 2 + window_end = window_start + cmn_window + else: + window_start = t - cmn_window + window_end = t + 1 + if window_start < 0: + window_end -= window_start + window_start = 0 + if not center: + if window_end > t: + window_end = max(t + 1, min_cmn_window) + if window_end > num_frames: + window_start -= (window_end - num_frames) + window_end = num_frames + if window_start < 0: + window_start = 0 + if last_window_start == -1: + input_part = waveform[:, window_start: window_end - window_start, :] + cur_sum += torch.sum(input_part, 1) + if norm_vars: + cur_sumsq += torch.cumsum(input_part ** 2, 1)[:, -1, :] + else: + if window_start > last_window_start: + frame_to_remove = waveform[:, last_window_start, :] + cur_sum -= frame_to_remove + if norm_vars: + cur_sumsq -= (frame_to_remove ** 2) + if window_end > last_window_end: + frame_to_add = waveform[:, last_window_end, :] + cur_sum += frame_to_add + if norm_vars: + cur_sumsq += (frame_to_add ** 2) + window_frames = window_end - window_start + last_window_start = window_start + last_window_end = window_end + cmn_waveform[:, t, :] = waveform[:, t, :] - cur_sum / window_frames + if norm_vars: + if window_frames == 1: + cmn_waveform[:, t, :] = torch.zeros( + num_channels, num_feats, dtype=dtype, device=device) + else: + variance = cur_sumsq + variance = variance / window_frames + variance -= ((cur_sum ** 2) / (window_frames ** 2)) + variance = torch.pow(variance, -0.5) + cmn_waveform[:, t, :] *= variance + + cmn_waveform = cmn_waveform.view(input_shape[:-2] + (num_frames, num_feats)) + if len(input_shape) == 2: + cmn_waveform = cmn_waveform.squeeze(0) + return cmn_waveform From d14991a3745b687e02560a26e2a679755816b1df Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Mon, 16 Nov 2020 13:23:59 -0800 Subject: [PATCH 22/73] Import torchaudio #1034 70f429a Summary: Import torchaudio #1027 0cf4b8a Reviewed By: vincentqb, cpuhrsch Differential Revision: D24958707 fbshipit-source-id: d06dd6b59197cc2c16bec5a9012cbf33a172b6b3 --- .circleci/build_docs/build_docs.sh | 13 ++ .circleci/build_docs/commit_docs.sh | 31 +++++ .circleci/build_docs/install_wheels.sh | 12 ++ .circleci/config.yml | 113 ++++++++++-------- .circleci/config.yml.in | 49 +++++++- .circleci/regenerate.py | 38 +++++- .circleci/unittest/linux/scripts/setup_env.sh | 16 ++- .../unittest/windows/scripts/setup_env.sh | 14 ++- README.md | 4 +- .../datasets/cmuarctic_test.py | 12 +- .../datasets/commonvoice_test.py | 12 +- .../datasets/gtzan_test.py | 40 +++++-- .../datasets/ljspeech_test.py | 12 +- .../torchaudio_unittest/datasets/vctk_test.py | 12 +- .../datasets/yesno_test.py | 12 +- torchaudio/datasets/cmuarctic.py | 10 +- torchaudio/datasets/commonvoice.py | 10 +- torchaudio/datasets/gtzan.py | 11 +- torchaudio/datasets/ljspeech.py | 10 +- torchaudio/datasets/vctk.py | 10 +- torchaudio/datasets/yesno.py | 10 +- torchaudio/transforms.py | 4 +- 22 files changed, 347 insertions(+), 108 deletions(-) create mode 100755 .circleci/build_docs/build_docs.sh create mode 100755 .circleci/build_docs/commit_docs.sh create mode 100755 .circleci/build_docs/install_wheels.sh diff --git a/.circleci/build_docs/build_docs.sh b/.circleci/build_docs/build_docs.sh new file mode 100755 index 0000000000..2864a72974 --- /dev/null +++ b/.circleci/build_docs/build_docs.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash + +set -ex +# shellcheck disable=SC1091 +source ./packaging/pkg_helpers.bash +export NO_CUDA_PACKAGE=1 +setup_env 0.8.0 +setup_wheel_python + +pushd docs +pip install -r requirements.txt +make html +popd diff --git a/.circleci/build_docs/commit_docs.sh b/.circleci/build_docs/commit_docs.sh new file mode 100755 index 0000000000..7ac380029f --- /dev/null +++ b/.circleci/build_docs/commit_docs.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash + +set -ex + + +if [ "$2" == "" ]; then + echo call as "$0" "" "" + echo where src is the built documentation and + echo branch should be "master" or "1.7" or so + exit 1 +fi + +src=$1 +target=$2 + +set -ex +echo "committing docs from ${src} to ${target}" + +git checkout gh-pages +rm -rf docs/"${target}"/* +cp -r "${src}"/build/html/* docs/"${target}" +if [ "${target}" == "master" ]; then + rm -rf docs/_static/* + cp -r "${src}"/build/html/_static/* docs/_static +fi +git add docs || true +git config user.email "soumith+bot@pytorch.org" +git config user.name "pytorchbot" +# If there aren't changes, don't make a commit; push is no-op +git commit -m "auto-generating sphinx docs" || true +git push -u origin gh-pages diff --git a/.circleci/build_docs/install_wheels.sh b/.circleci/build_docs/install_wheels.sh new file mode 100755 index 0000000000..3e15154092 --- /dev/null +++ b/.circleci/build_docs/install_wheels.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash + +set -ex + +# shellcheck disable=SC1091 +source ./packaging/pkg_helpers.bash +export NO_CUDA_PACKAGE=1 +setup_env 0.8.0 +setup_wheel_python +setup_pip_pytorch_version +# pytorch is already installed +pip install --no-deps ~/workspace/torchaudio* diff --git a/.circleci/config.yml b/.circleci/config.yml index 1f6d664d7e..7fb19c0276 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -61,7 +61,7 @@ binary_common: &binary_common python_version: description: "Python version to build against (e.g., 3.8)" type: string - environment: + environment: &environment PYTHON_VERSION: << parameters.python_version >> BUILD_VERSION: << parameters.build_version >> PYTORCH_VERSION: << parameters.pytorch_version >> @@ -432,6 +432,7 @@ jobs: image: ubuntu-1604-cuda-10.1:201909-23 resource_class: gpu.small environment: + <<: *environment image_name: "pytorch/torchaudio_unittest_base:manylinux-cuda10.1" steps: - checkout @@ -444,9 +445,12 @@ jobs: keys: - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + - run: + name: Pull Docker image + command: docker pull --quiet "${image_name}" - run: name: Setup - command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh + command: docker run -t --gpus all -e PYTHON_VERSION -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh - save_cache: key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} @@ -509,6 +513,7 @@ jobs: executor: name: windows-gpu environment: + <<: *environment CUDA_VERSION: "10.1" steps: - checkout @@ -649,6 +654,46 @@ jobs: command: | .circleci/torchscript_bc_test/setup_master_envs.sh .circleci/torchscript_bc_test/validate_objects.sh + build_docs: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - attach_workspace: + at: ~/workspace + - designate_upload_channel + - checkout + - run: + name: Install pytorch-audio + command: .circleci/build_docs/install_wheels.sh + - run: + name: Build docs + command: .circleci/build_docs/build_docs.sh + - persist_to_workspace: + root: docs + paths: + - "*" + + upload_docs: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - attach_workspace: + at: ~/workspace + - designate_upload_channel + - checkout + - run: + name: Upload docs + command: | + set -ex + echo $PWD + ls ~/workspace + tag=${CIRCLE_TAG:1:5} + target=${tag:-master} + .circleci/build_docs/commit_docs.sh ~/workspace $target workflows: build: @@ -734,6 +779,22 @@ workflows: - binary_windows_conda: name: binary_windows_conda_py3.8 python_version: '3.8' + - build_docs: + name: build_docs + python_version: '3.8' + requires: + - binary_linux_wheel_py3.8 + - upload_docs: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: upload_docs + python_version: '3.8' + requires: + - build_docs unittest: jobs: - torchscript_bc_test: @@ -760,40 +821,16 @@ workflows: requires: - download_third_parties_nix - unittest_linux_gpu: - filters: - branches: - only: - - master - - nightly - - release/* - tags: - only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_linux_gpu_py3.6 python_version: '3.6' requires: - download_third_parties_nix - unittest_linux_gpu: - filters: - branches: - only: - - master - - nightly - - release/* - tags: - only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_linux_gpu_py3.7 python_version: '3.7' requires: - download_third_parties_nix - unittest_linux_gpu: - filters: - branches: - only: - - master - - nightly - - release/* - tags: - only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_linux_gpu_py3.8 python_version: '3.8' requires: @@ -808,36 +845,12 @@ workflows: name: unittest_windows_cpu_py3.8 python_version: '3.8' - unittest_windows_gpu: - filters: - branches: - only: - - master - - nightly - - release/* - tags: - only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_windows_gpu_py3.6 python_version: '3.6' - unittest_windows_gpu: - filters: - branches: - only: - - master - - nightly - - release/* - tags: - only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_windows_gpu_py3.7 python_version: '3.7' - unittest_windows_gpu: - filters: - branches: - only: - - master - - nightly - - release/* - tags: - only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: unittest_windows_gpu_py3.8 python_version: '3.8' - unittest_macos_cpu: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index b9fccbaf30..60006ff9c7 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -61,7 +61,7 @@ binary_common: &binary_common python_version: description: "Python version to build against (e.g., 3.8)" type: string - environment: + environment: &environment PYTHON_VERSION: << parameters.python_version >> BUILD_VERSION: << parameters.build_version >> PYTORCH_VERSION: << parameters.pytorch_version >> @@ -432,6 +432,7 @@ jobs: image: ubuntu-1604-cuda-10.1:201909-23 resource_class: gpu.small environment: + <<: *environment image_name: "pytorch/torchaudio_unittest_base:manylinux-cuda10.1" steps: - checkout @@ -444,9 +445,12 @@ jobs: keys: - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} {% endraw %} + - run: + name: Pull Docker image + command: docker pull --quiet "${image_name}" - run: name: Setup - command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh + command: docker run -t --gpus all -e PYTHON_VERSION -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh - save_cache: {% raw %} key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} @@ -509,6 +513,7 @@ jobs: executor: name: windows-gpu environment: + <<: *environment CUDA_VERSION: "10.1" steps: - checkout @@ -649,6 +654,46 @@ jobs: command: | .circleci/torchscript_bc_test/setup_master_envs.sh .circleci/torchscript_bc_test/validate_objects.sh + build_docs: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - attach_workspace: + at: ~/workspace + - designate_upload_channel + - checkout + - run: + name: Install pytorch-audio + command: .circleci/build_docs/install_wheels.sh + - run: + name: Build docs + command: .circleci/build_docs/build_docs.sh + - persist_to_workspace: + root: docs + paths: + - "*" + + upload_docs: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - attach_workspace: + at: ~/workspace + - designate_upload_channel + - checkout + - run: + name: Upload docs + command: | + set -ex + echo $PWD + ls ~/workspace + tag=${CIRCLE_TAG:1:5} + target=${tag:-master} + .circleci/build_docs/commit_docs.sh ~/workspace $target workflows: build: diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index 419db311c1..e89ea4e914 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -21,6 +21,8 @@ PYTHON_VERSIONS = ["3.6", "3.7", "3.8"] +DOC_VERSION = ('linux', '3.8') + def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): w = [] @@ -30,6 +32,11 @@ def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): for python_version in PYTHON_VERSIONS: w += build_workflow_pair(btype, os_type, python_version, filter_branch, prefix, upload) + if not filter_branch: + # Build on every pull request, but upload only on nightly and tags + w += build_doc_job(None) + w += upload_doc_job('nightly') + return indent(indentation, w) @@ -68,6 +75,30 @@ def build_workflow_pair(btype, os_type, python_version, filter_branch, prefix='' return w +def build_doc_job(filter_branch): + job = { + "name": "build_docs", + "python_version": "3.8", + "requires": ["binary_linux_wheel_py3.8"], + } + + if filter_branch: + job["filters"] = gen_filter_branch_tree(filter_branch) + return [{"build_docs": job}] + + +def upload_doc_job(filter_branch): + job = { + "name": "upload_docs", + "python_version": "3.8", + "requires": ["build_docs"], + } + + if filter_branch: + job["filters"] = gen_filter_branch_tree(filter_branch) + return [{"upload_docs": job}] + + def generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype): d = { @@ -81,7 +112,7 @@ def generate_base_workflow(base_workflow_name, python_version, filter_branch, os if filter_branch: d["filters"] = gen_filter_branch_tree(filter_branch) - return {"binary_{os_type}_{btype}".format(os_type=os_type, btype=btype): d} + return {f"binary_{os_type}_{btype}": d} def gen_filter_branch_tree(*branches): @@ -147,9 +178,6 @@ def unittest_workflows(indentation=6): "python_version": python_version, } - if device_type == 'gpu': - job['filters'] = gen_filter_branch_tree('master', 'nightly', 'release/*') - if os_type != "windows": job['requires'] = ['download_third_parties_nix'] @@ -157,7 +185,7 @@ def unittest_workflows(indentation=6): if i == 0 and os_type == "linux" and device_type == "cpu": jobs.append({ - f"stylecheck": { + "stylecheck": { "name": f"stylecheck_py{python_version}", "python_version": python_version, } diff --git a/.circleci/unittest/linux/scripts/setup_env.sh b/.circleci/unittest/linux/scripts/setup_env.sh index 6e203dea27..26292a00d5 100755 --- a/.circleci/unittest/linux/scripts/setup_env.sh +++ b/.circleci/unittest/linux/scripts/setup_env.sh @@ -21,20 +21,26 @@ esac # 1. Install conda at ./conda if [ ! -d "${conda_dir}" ]; then printf "* Installing conda\n" - wget -O miniconda.sh "http://repo.continuum.io/miniconda/Miniconda3-latest-${os}-x86_64.sh" + wget --quiet -O miniconda.sh "http://repo.continuum.io/miniconda/Miniconda3-latest-${os}-x86_64.sh" bash ./miniconda.sh -b -f -p "${conda_dir}" + eval "$("${conda_dir}/bin/conda" shell.bash hook)" + conda update --quiet -y conda + printf "* Updating the base Python version to %s\n" "${PYTHON_VERSION}" + conda install --quiet -y python="${PYTHON_VERSION}" +else + eval "$("${conda_dir}/bin/conda" shell.bash hook)" fi -eval "$(${conda_dir}/bin/conda shell.bash hook)" + # 2. Create test environment at ./env if [ ! -d "${env_dir}" ]; then - printf "* Creating a test environment\n" - conda create --prefix "${env_dir}" -y python="$PYTHON_VERSION" + printf "* Creating a test environment with PYTHON_VERSION=%s\n" "${PYTHON_VERSION}\n" + conda create --prefix "${env_dir}" -y python="${PYTHON_VERSION}" fi conda activate "${env_dir}" # 3. Install minimal build tools -pip install cmake ninja +pip --quiet install cmake ninja # 4. Buld codecs mkdir -p third_party/build diff --git a/.circleci/unittest/windows/scripts/setup_env.sh b/.circleci/unittest/windows/scripts/setup_env.sh index 71b556d2bd..929a98cb8f 100644 --- a/.circleci/unittest/windows/scripts/setup_env.sh +++ b/.circleci/unittest/windows/scripts/setup_env.sh @@ -19,17 +19,21 @@ if [ ! -d "${conda_dir}" ]; then printf "* Installing conda\n" export tmp_conda="$(echo $conda_dir | tr '/' '\\')" export miniconda_exe="$(echo $root_dir | tr '/' '\\')\\miniconda.exe" - curl --output miniconda.exe https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe -O + curl --silent --output miniconda.exe https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe -O "$this_dir/install_conda.bat" unset tmp_conda unset miniconda_exe + eval "$("${conda_dir}/Scripts/conda.exe" 'shell.bash' 'hook')" + conda update --quiet -y conda + printf "* Updating the base Python version to %s\n" "${PYTHON_VERSION}" + conda install --quiet -y python="$PYTHON_VERSION" +else + eval "$("${conda_dir}/Scripts/conda.exe" 'shell.bash' 'hook')" fi -eval "$(${conda_dir}/Scripts/conda.exe 'shell.bash' 'hook')" - # 2. Create test environment at ./env if [ ! -d "${env_dir}" ]; then - printf "* Creating a test environment\n" - conda create --prefix "${env_dir}" -y python="$PYTHON_VERSION" + printf "* Creating a test environment with PYTHON_VERSION=%s\n" "${PYTHON_VERSION}" + conda create --prefix "${env_dir}" -y python="${PYTHON_VERSION}" fi conda activate "${env_dir}" diff --git a/README.md b/README.md index 861af2a04b..9bef876136 100644 --- a/README.md +++ b/README.md @@ -10,8 +10,8 @@ the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal -processing library. The benefits of Pytorch is be seen in torchaudio through -having all the computations be through Pytorch operations which makes it easy +processing library. The benefits of PyTorch is be seen in torchaudio through +having all the computations be through PyTorch operations which makes it easy to use and feel like a natural extension. - [Support audio I/O (Load files, Save files)](http://pytorch.org/audio/) diff --git a/test/torchaudio_unittest/datasets/cmuarctic_test.py b/test/torchaudio_unittest/datasets/cmuarctic_test.py index 578b0ec385..f8f7969c25 100644 --- a/test/torchaudio_unittest/datasets/cmuarctic_test.py +++ b/test/torchaudio_unittest/datasets/cmuarctic_test.py @@ -1,4 +1,5 @@ import os +from pathlib import Path from torchaudio.datasets import cmuarctic @@ -54,8 +55,7 @@ def setUpClass(cls): txt.write(f'( {utterance_id} "{utterance}" )\n') seed += 1 - def test_cmuarctic(self): - dataset = cmuarctic.CMUARCTIC(self.root_dir) + def _test_cmuarctic(self, dataset): n_ite = 0 for i, (waveform, sample_rate, utterance, utterance_id) in enumerate(dataset): expected_sample = self.samples[i] @@ -65,3 +65,11 @@ def test_cmuarctic(self): self.assertEqual(expected_sample[0], waveform, atol=5e-5, rtol=1e-8) n_ite += 1 assert n_ite == len(self.samples) + + def test_cmuarctic_str(self): + dataset = cmuarctic.CMUARCTIC(self.root_dir) + self._test_cmuarctic(dataset) + + def test_cmuarctic_path(self): + dataset = cmuarctic.CMUARCTIC(Path(self.root_dir)) + self._test_cmuarctic(dataset) diff --git a/test/torchaudio_unittest/datasets/commonvoice_test.py b/test/torchaudio_unittest/datasets/commonvoice_test.py index 3eedebec70..731c4e07cc 100644 --- a/test/torchaudio_unittest/datasets/commonvoice_test.py +++ b/test/torchaudio_unittest/datasets/commonvoice_test.py @@ -1,6 +1,7 @@ import os import csv import random +from pathlib import Path from torchaudio.datasets import commonvoice from torchaudio_unittest.common_utils import ( @@ -59,8 +60,7 @@ def setUpClass(cls): # Append data entry cls.data.append((normalize_wav(data), cls.sample_rate, dict(zip(cls._headers, content)))) - def test_commonvoice(self): - dataset = commonvoice.COMMONVOICE(self.root_dir) + def _test_commonvoice(self, dataset): n_ite = 0 for i, (waveform, sample_rate, dictionary) in enumerate(dataset): expected_dictionary = self.data[i][2] @@ -70,3 +70,11 @@ def test_commonvoice(self): assert dictionary == expected_dictionary n_ite += 1 assert n_ite == len(self.data) + + def test_commonvoice_str(self): + dataset = commonvoice.COMMONVOICE(self.root_dir) + self._test_commonvoice(dataset) + + def test_commonvoice_path(self): + dataset = commonvoice.COMMONVOICE(Path(self.root_dir)) + self._test_commonvoice(dataset) diff --git a/test/torchaudio_unittest/datasets/gtzan_test.py b/test/torchaudio_unittest/datasets/gtzan_test.py index 3236535fad..763e0a866f 100644 --- a/test/torchaudio_unittest/datasets/gtzan_test.py +++ b/test/torchaudio_unittest/datasets/gtzan_test.py @@ -1,4 +1,5 @@ import os +from pathlib import Path from torchaudio.datasets import gtzan @@ -54,9 +55,7 @@ def test_no_subset(self): n_ite += 1 assert n_ite == len(self.samples) - def test_training(self): - dataset = gtzan.GTZAN(self.root_dir, subset='training') - + def _test_training(self, dataset): n_ite = 0 for i, (waveform, sample_rate, label) in enumerate(dataset): self.assertEqual(waveform, self.training[i][0], atol=5e-5, rtol=1e-8) @@ -65,9 +64,7 @@ def test_training(self): n_ite += 1 assert n_ite == len(self.training) - def test_validation(self): - dataset = gtzan.GTZAN(self.root_dir, subset='validation') - + def _test_validation(self, dataset): n_ite = 0 for i, (waveform, sample_rate, label) in enumerate(dataset): self.assertEqual(waveform, self.validation[i][0], atol=5e-5, rtol=1e-8) @@ -76,9 +73,7 @@ def test_validation(self): n_ite += 1 assert n_ite == len(self.validation) - def test_testing(self): - dataset = gtzan.GTZAN(self.root_dir, subset='testing') - + def _test_testing(self, dataset): n_ite = 0 for i, (waveform, sample_rate, label) in enumerate(dataset): self.assertEqual(waveform, self.testing[i][0], atol=5e-5, rtol=1e-8) @@ -86,3 +81,30 @@ def test_testing(self): assert label == self.testing[i][2] n_ite += 1 assert n_ite == len(self.testing) + + def test_training_str(self): + train_dataset = gtzan.GTZAN(self.root_dir, subset='training') + self._test_training(train_dataset) + + def test_validation_str(self): + val_dataset = gtzan.GTZAN(self.root_dir, subset='validation') + self._test_validation(val_dataset) + + def test_testing_str(self): + test_dataset = gtzan.GTZAN(self.root_dir, subset='testing') + self._test_testing(test_dataset) + + def test_training_path(self): + root_dir = Path(self.root_dir) + train_dataset = gtzan.GTZAN(root_dir, subset='training') + self._test_training(train_dataset) + + def test_validation_path(self): + root_dir = Path(self.root_dir) + val_dataset = gtzan.GTZAN(root_dir, subset='validation') + self._test_validation(val_dataset) + + def test_testing_path(self): + root_dir = Path(self.root_dir) + test_dataset = gtzan.GTZAN(root_dir, subset='testing') + self._test_testing(test_dataset) diff --git a/test/torchaudio_unittest/datasets/ljspeech_test.py b/test/torchaudio_unittest/datasets/ljspeech_test.py index 7c1128c31b..33458fd6e1 100644 --- a/test/torchaudio_unittest/datasets/ljspeech_test.py +++ b/test/torchaudio_unittest/datasets/ljspeech_test.py @@ -1,5 +1,6 @@ import csv import os +from pathlib import Path from torchaudio.datasets import ljspeech @@ -57,8 +58,7 @@ def setUpClass(cls): save_wav(path, data, sample_rate) cls.data.append(normalize_wav(data)) - def test_ljspeech(self): - dataset = ljspeech.LJSPEECH(self.root_dir) + def _test_ljspeech(self, dataset): n_ite = 0 for i, (waveform, sample_rate, transcript, normalized_transcript) in enumerate( dataset @@ -72,3 +72,11 @@ def test_ljspeech(self): assert normalized_transcript == expected_normalized_transcript n_ite += 1 assert n_ite == len(self.data) + + def test_ljspeech_str(self): + dataset = ljspeech.LJSPEECH(self.root_dir) + self._test_ljspeech(dataset) + + def test_ljspeech_path(self): + dataset = ljspeech.LJSPEECH(Path(self.root_dir)) + self._test_ljspeech(dataset) diff --git a/test/torchaudio_unittest/datasets/vctk_test.py b/test/torchaudio_unittest/datasets/vctk_test.py index 26b14f5d04..76bbf29936 100644 --- a/test/torchaudio_unittest/datasets/vctk_test.py +++ b/test/torchaudio_unittest/datasets/vctk_test.py @@ -1,4 +1,5 @@ import os +from pathlib import Path from torchaudio.datasets import vctk @@ -77,8 +78,7 @@ def setUpClass(cls): seed += 1 - def test_vctk(self): - dataset = vctk.VCTK_092(self.root_dir, audio_ext=".wav") + def _test_vctk(self, dataset): num_samples = 0 for i, (data, sample_rate, utterance, speaker_id, utterance_id) in enumerate(dataset): self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) @@ -89,3 +89,11 @@ def test_vctk(self): num_samples += 1 assert num_samples == len(self.samples) + + def test_vctk_str(self): + dataset = vctk.VCTK_092(self.root_dir, audio_ext=".wav") + self._test_vctk(dataset) + + def test_vctk_path(self): + dataset = vctk.VCTK_092(Path(self.root_dir), audio_ext=".wav") + self._test_vctk(dataset) diff --git a/test/torchaudio_unittest/datasets/yesno_test.py b/test/torchaudio_unittest/datasets/yesno_test.py index a8a8a04276..6b094aaece 100644 --- a/test/torchaudio_unittest/datasets/yesno_test.py +++ b/test/torchaudio_unittest/datasets/yesno_test.py @@ -1,4 +1,5 @@ import os +from pathlib import Path from torchaudio.datasets import yesno @@ -36,8 +37,7 @@ def setUpClass(cls): save_wav(path, data, 8000) cls.data.append(normalize_wav(data)) - def test_yesno(self): - dataset = yesno.YESNO(self.root_dir) + def _test_yesno(self, dataset): n_ite = 0 for i, (waveform, sample_rate, label) in enumerate(dataset): expected_label = self.labels[i] @@ -47,3 +47,11 @@ def test_yesno(self): assert label == expected_label n_ite += 1 assert n_ite == len(self.data) + + def test_yesno_str(self): + dataset = yesno.YESNO(self.root_dir) + self._test_yesno(dataset) + + def test_yesno_path(self): + dataset = yesno.YESNO(Path(self.root_dir)) + self._test_yesno(dataset) diff --git a/torchaudio/datasets/cmuarctic.py b/torchaudio/datasets/cmuarctic.py index 7ea4ca4dae..f8674127fd 100644 --- a/torchaudio/datasets/cmuarctic.py +++ b/torchaudio/datasets/cmuarctic.py @@ -1,5 +1,6 @@ import os -from typing import Tuple +from pathlib import Path +from typing import Tuple, Union import torchaudio from torch import Tensor @@ -79,7 +80,7 @@ class CMUARCTIC(Dataset): """Create a Dataset for CMU_ARCTIC. Args: - root (str): Path to the directory where the dataset is found or downloaded. + root (str or Path): Path to the directory where the dataset is found or downloaded. url (str, optional): The URL to download the dataset from or the type of the dataset to dowload. (default: ``"aew"``) @@ -98,7 +99,7 @@ class CMUARCTIC(Dataset): _folder_audio = "wav" def __init__(self, - root: str, + root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False) -> None: @@ -130,6 +131,9 @@ def __init__(self, url = os.path.join(base_url, url + ext_archive) + # Get string representation of 'root' in case Path object is passed + root = os.fspath(root) + basename = os.path.basename(url) root = os.path.join(root, folder_in_archive) if not os.path.isdir(root): diff --git a/torchaudio/datasets/commonvoice.py b/torchaudio/datasets/commonvoice.py index 825cf3e9ab..963777cf38 100644 --- a/torchaudio/datasets/commonvoice.py +++ b/torchaudio/datasets/commonvoice.py @@ -1,5 +1,6 @@ import os -from typing import List, Dict, Tuple +from pathlib import Path +from typing import List, Dict, Tuple, Union import torchaudio from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader @@ -103,7 +104,7 @@ class COMMONVOICE(Dataset): """Create a Dataset for CommonVoice. Args: - root (str): Path to the directory where the dataset is found or downloaded. + root (str or Path): Path to the directory where the dataset is found or downloaded. tsv (str, optional): The name of the tsv file used to construct the metadata. (default: ``"train.tsv"``) url (str, optional): The URL to download the dataset from, or the language of @@ -129,7 +130,7 @@ class COMMONVOICE(Dataset): _folder_audio = "clips" def __init__(self, - root: str, + root: Union[str, Path], tsv: str = TSV, url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, @@ -186,6 +187,9 @@ def __init__(self, base_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com" url = os.path.join(base_url, version, language + ext_archive) + # Get string representation of 'root' in case Path object is passed + root = os.fspath(root) + basename = os.path.basename(url) archive = os.path.join(root, basename) diff --git a/torchaudio/datasets/gtzan.py b/torchaudio/datasets/gtzan.py index 160898c73d..4c91894be1 100644 --- a/torchaudio/datasets/gtzan.py +++ b/torchaudio/datasets/gtzan.py @@ -1,6 +1,7 @@ import os import warnings -from typing import Any, Tuple, Optional +from pathlib import Path +from typing import Any, Tuple, Optional, Union import torchaudio from torch import Tensor @@ -1005,7 +1006,7 @@ class GTZAN(Dataset): this dataset to publish results. Args: - root (str): Path to the directory where the dataset is found or downloaded. + root (str or Path): Path to the directory where the dataset is found or downloaded. url (str, optional): The URL to download the dataset from. (default: ``"http://opihi.cs.uvic.ca/sound/genres.tar.gz"``) folder_in_archive (str, optional): The top-level directory of the dataset. @@ -1020,7 +1021,7 @@ class GTZAN(Dataset): def __init__( self, - root: str, + root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False, @@ -1028,6 +1029,10 @@ def __init__( ) -> None: # super(GTZAN, self).__init__() + + # Get string representation of 'root' in case Path object is passed + root = os.fspath(root) + self.root = root self.url = url self.folder_in_archive = folder_in_archive diff --git a/torchaudio/datasets/ljspeech.py b/torchaudio/datasets/ljspeech.py index 28c3a5dfc8..2d653fbf46 100644 --- a/torchaudio/datasets/ljspeech.py +++ b/torchaudio/datasets/ljspeech.py @@ -1,6 +1,7 @@ import os import csv -from typing import List, Tuple +from typing import List, Tuple, Union +from pathlib import Path import torchaudio from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader @@ -36,7 +37,7 @@ class LJSPEECH(Dataset): """Create a Dataset for LJSpeech-1.1. Args: - root (str): Path to the directory where the dataset is found or downloaded. + root (str or Path): Path to the directory where the dataset is found or downloaded. url (str, optional): The URL to download the dataset from. (default: ``"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"``) folder_in_archive (str, optional): @@ -49,11 +50,14 @@ class LJSPEECH(Dataset): _ext_archive = '.tar.bz2' def __init__(self, - root: str, + root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False) -> None: + # Get string representation of 'root' in case Path object is passed + root = os.fspath(root) + basename = os.path.basename(url) archive = os.path.join(root, basename) diff --git a/torchaudio/datasets/vctk.py b/torchaudio/datasets/vctk.py index a8fca59df2..9bf843f685 100644 --- a/torchaudio/datasets/vctk.py +++ b/torchaudio/datasets/vctk.py @@ -1,6 +1,7 @@ import os import warnings -from typing import Any, Tuple +from typing import Any, Tuple, Union +from pathlib import Path import torchaudio from torch import Tensor @@ -57,7 +58,7 @@ class VCTK(Dataset): For more information about the dataset visit: https://datashare.is.ed.ac.uk/handle/10283/3443 Args: - root (str): Path to the directory where the dataset is found or downloaded. + root (str or Path): Path to the directory where the dataset is found or downloaded. url (str, optional): Not used as the dataset is no longer publicly available. folder_in_archive (str, optional): The top-level directory of the dataset. (default: ``"VCTK-Corpus"``) @@ -77,7 +78,7 @@ class VCTK(Dataset): _except_folder = "p315" def __init__(self, - root: str, + root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False, @@ -103,6 +104,9 @@ def __init__(self, self.transform = transform self.target_transform = target_transform + # Get string representation of 'root' in case Path object is passed + root = os.fspath(root) + archive = os.path.basename(url) archive = os.path.join(root, archive) self._path = os.path.join(root, folder_in_archive) diff --git a/torchaudio/datasets/yesno.py b/torchaudio/datasets/yesno.py index 5a20539e28..182d224ba4 100644 --- a/torchaudio/datasets/yesno.py +++ b/torchaudio/datasets/yesno.py @@ -1,6 +1,7 @@ import os import warnings -from typing import Any, List, Tuple +from typing import Any, List, Tuple, Union +from pathlib import Path import torchaudio from torch import Tensor @@ -34,7 +35,7 @@ class YESNO(Dataset): """Create a Dataset for YesNo. Args: - root (str): Path to the directory where the dataset is found or downloaded. + root (str or Path): Path to the directory where the dataset is found or downloaded. url (str, optional): The URL to download the dataset from. (default: ``"http://www.openslr.org/resources/1/waves_yesno.tar.gz"``) folder_in_archive (str, optional): @@ -48,7 +49,7 @@ class YESNO(Dataset): _ext_audio = ".wav" def __init__(self, - root: str, + root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False, @@ -65,6 +66,9 @@ def __init__(self, self.transform = transform self.target_transform = target_transform + # Get string representation of 'root' in case Path object is passed + root = os.fspath(root) + archive = os.path.basename(url) archive = os.path.join(root, archive) self._path = os.path.join(root, folder_in_archive) diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 2e7e4f37ee..3306491b21 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -1002,7 +1002,7 @@ def __init__(self, self.pre_trigger_time = pre_trigger_time self.boot_time = boot_time self.noise_up_time = noise_up_time - self.noise_down_time = noise_up_time + self.noise_down_time = noise_down_time self.noise_reduction_amount = noise_reduction_amount self.measure_freq = measure_freq self.measure_duration = measure_duration @@ -1027,7 +1027,7 @@ def forward(self, waveform: Tensor) -> Tensor: pre_trigger_time=self.pre_trigger_time, boot_time=self.boot_time, noise_up_time=self.noise_up_time, - noise_down_time=self.noise_up_time, + noise_down_time=self.noise_down_time, noise_reduction_amount=self.noise_reduction_amount, measure_freq=self.measure_freq, measure_duration=self.measure_duration, From 7eead14b2c99c777fa45b9999813599c98fee1b7 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Tue, 8 Dec 2020 09:54:35 -0800 Subject: [PATCH 23/73] Import torchaudio #1066 4406a6b Summary: Import up to #1066 Reviewed By: cpuhrsch Differential Revision: D25373068 fbshipit-source-id: 890d36a25259b93428b3037c3123ff5a2cacfa04 --- .circleci/build_docs/commit_docs.sh | 14 +- .circleci/config.yml | 11 ++ .circleci/config.yml.in | 10 + .circleci/regenerate.py | 5 +- README.md | 48 ++--- build_tools/setup_helpers/extension.py | 2 + docs/source/datasets.rst | 2 +- docs/source/functional.rst | 10 + .../datasets/librispeech_test.py | 17 +- .../datasets/libritts_test.py | 12 +- .../datasets/speechcommands_test.py | 79 ++------ .../datasets/tedlium_test.py | 49 ++--- .../datasets/utils_test.py | 4 +- .../sox_effect/dataset_test.py | 34 ++++ .../sox_effect/sox_effect_test.py | 26 ++- .../sox_io_backend/info_test.py | 30 +++ .../sox_io_backend/load_test.py | 61 ++++++ .../sox_io_backend/save_test.py | 75 ++++++++ third_party/CMakeLists.txt | 14 +- torchaudio/backend/sox_io_backend.py | 10 +- torchaudio/csrc/sox_effects.cpp | 11 +- torchaudio/csrc/sox_effects.h | 4 +- torchaudio/csrc/sox_io.cpp | 40 ++-- torchaudio/csrc/sox_io.h | 8 +- torchaudio/csrc/sox_utils.cpp | 22 ++- torchaudio/datasets/commonvoice.py | 177 ++++++++++-------- torchaudio/datasets/librispeech.py | 10 +- torchaudio/datasets/libritts.py | 10 +- torchaudio/datasets/speechcommands.py | 10 +- torchaudio/datasets/tedlium.py | 15 +- torchaudio/functional/filtering.py | 3 +- torchaudio/functional/functional.py | 16 +- torchaudio/sox_effects/sox_effects.py | 8 +- torchaudio/utils/sox_utils.py | 9 +- 34 files changed, 569 insertions(+), 287 deletions(-) diff --git a/.circleci/build_docs/commit_docs.sh b/.circleci/build_docs/commit_docs.sh index 7ac380029f..9e9548936a 100755 --- a/.circleci/build_docs/commit_docs.sh +++ b/.circleci/build_docs/commit_docs.sh @@ -13,17 +13,19 @@ fi src=$1 target=$2 -set -ex echo "committing docs from ${src} to ${target}" git checkout gh-pages -rm -rf docs/"${target}"/* -cp -r "${src}"/build/html/* docs/"${target}" +mkdir -p ./"${target}" +rm -rf ./"${target}"/* +cp -r "${src}/build/html/"* ./"$target" if [ "${target}" == "master" ]; then - rm -rf docs/_static/* - cp -r "${src}"/build/html/_static/* docs/_static + mkdir -p ./_static + rm -rf ./_static/* + cp -r "${src}/build/html/_static/"* ./_static + git add --all ./_static || true fi -git add docs || true +git add --all ./"${target}" || true git config user.email "soumith+bot@pytorch.org" git config user.name "pytorchbot" # If there aren't changes, don't make a commit; push is no-op diff --git a/.circleci/config.yml b/.circleci/config.yml index 7fb19c0276..f0ee6764b1 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -684,6 +684,16 @@ jobs: - attach_workspace: at: ~/workspace - designate_upload_channel + - run: + name: Generate netrc + command: | + # set credentials for https pushing + # requires the org-member context + cat > ~/.netrc \< ~/.netrc \< (channel, freq, time) -* `AmplitudeToDB`: (channel, freq, time) -> (channel, freq, time) -* `MelScale`: (channel, freq, time) -> (channel, mel, time) -* `MelSpectrogram`: (channel, time) -> (channel, mel, time) -* `MFCC`: (channel, time) -> (channel, mfcc, time) -* `MuLawEncode`: (channel, time) -> (channel, time) -* `MuLawDecode`: (channel, time) -> (channel, time) -* `Resample`: (channel, time) -> (channel, time) -* `Fade`: (channel, time) -> (channel, time) -* `Vol`: (channel, time) -> (channel, time) +* `Spectrogram`: (channels, time) -> (channels, freq, time) +* `AmplitudeToDB`: (channels, freq, time) -> (channels, freq, time) +* `MelScale`: (channels, freq, time) -> (channels, mel, time) +* `MelSpectrogram`: (channels, time) -> (channels, mel, time) +* `MFCC`: (channels, time) -> (channel, mfcc, time) +* `MuLawEncode`: (channels, time) -> (channels, time) +* `MuLawDecode`: (channels, time) -> (channels, time) +* `Resample`: (channels, time) -> (channels, time) +* `Fade`: (channels, time) -> (channels, time) +* `Vol`: (channels, time) -> (channels, time) Complex numbers are supported via tensors of dimension (..., 2), and torchaudio provides `complex_norm` and `angle` to convert such a tensor into its magnitude and phase. Here, and in the documentation, we use an ellipsis "..." as a placeholder for the rest of the dimensions of a tensor, e.g. optional batching and channel dimensions. diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index b9fb30e114..8e6cd337cb 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -89,6 +89,8 @@ def _get_extra_objects(): 'libvorbisfile.a', 'libvorbis.a', 'libogg.a', + 'libopencore-amrnb.a', + 'libopencore-amrwb.a', ] for lib in libs: objs.append(str(_TP_INSTALL_DIR / 'lib' / lib)) diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 2ceb93a440..2fbb2c7929 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -2,7 +2,7 @@ torchaudio.datasets ==================== All datasets are subclasses of :class:`torch.utils.data.Dataset` -i.e, they have ``__getitem__`` and ``__len__`` methods implemented. +and have ``__getitem__`` and ``__len__`` methods implemented. Hence, they can all be passed to a :class:`torch.utils.data.DataLoader` which can load multiple samples parallelly using ``torch.multiprocessing`` workers. For example: :: diff --git a/docs/source/functional.rst b/docs/source/functional.rst index a0f3a272cf..ebed60de6b 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -124,11 +124,21 @@ vad .. autofunction:: spectrogram +:hidden:`griffinlim` +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: griffinlim + :hidden:`amplitude_to_DB` ~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: amplitude_to_DB +:hidden:`DB_to_amplitude` +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: DB_to_amplitude + :hidden:`create_fb_matrix` ~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/test/torchaudio_unittest/datasets/librispeech_test.py b/test/torchaudio_unittest/datasets/librispeech_test.py index 669b428876..b0eabb767c 100644 --- a/test/torchaudio_unittest/datasets/librispeech_test.py +++ b/test/torchaudio_unittest/datasets/librispeech_test.py @@ -1,4 +1,5 @@ import os +from pathlib import Path from torchaudio.datasets import librispeech @@ -91,11 +92,7 @@ def tearDownClass(cls): # In case of test failure librispeech.LIBRISPEECH._ext_audio = '.flac' - def test_librispeech(self): - librispeech.LIBRISPEECH._ext_audio = '.wav' - dataset = librispeech.LIBRISPEECH(self.root_dir) - print(dataset._path) - + def _test_librispeech(self, dataset): num_samples = 0 for i, ( data, sample_rate, utterance, speaker_id, chapter_id, utterance_id @@ -110,3 +107,13 @@ def test_librispeech(self): assert num_samples == len(self.samples) librispeech.LIBRISPEECH._ext_audio = '.flac' + + def test_librispeech_str(self): + librispeech.LIBRISPEECH._ext_audio = '.wav' + dataset = librispeech.LIBRISPEECH(self.root_dir) + self._test_librispeech(dataset) + + def test_librispeech_path(self): + librispeech.LIBRISPEECH._ext_audio = '.wav' + dataset = librispeech.LIBRISPEECH(Path(self.root_dir)) + self._test_librispeech(dataset) diff --git a/test/torchaudio_unittest/datasets/libritts_test.py b/test/torchaudio_unittest/datasets/libritts_test.py index 639098446b..04d47e95f4 100644 --- a/test/torchaudio_unittest/datasets/libritts_test.py +++ b/test/torchaudio_unittest/datasets/libritts_test.py @@ -1,4 +1,5 @@ import os +from pathlib import Path from torchaudio.datasets.libritts import LIBRITTS @@ -47,8 +48,7 @@ def setUpClass(cls): with open(path_normalized, 'w') as file_: file_.write(cls.normalized_text) - def test_libritts(self): - dataset = LIBRITTS(self.root_dir) + def _test_libritts(self, dataset): n_ites = 0 for i, (waveform, sample_rate, @@ -69,3 +69,11 @@ def test_libritts(self): assert utterance_id == f'{"_".join(str(u) for u in expected_ids[-4:])}' n_ites += 1 assert n_ites == len(self.utterance_ids) + + def test_libritts_str(self): + dataset = LIBRITTS(self.root_dir) + self._test_libritts(dataset) + + def test_libritts_path(self): + dataset = LIBRITTS(Path(self.root_dir)) + self._test_libritts(dataset) diff --git a/test/torchaudio_unittest/datasets/speechcommands_test.py b/test/torchaudio_unittest/datasets/speechcommands_test.py index 5a331bdeca..6351d41b27 100644 --- a/test/torchaudio_unittest/datasets/speechcommands_test.py +++ b/test/torchaudio_unittest/datasets/speechcommands_test.py @@ -1,4 +1,5 @@ import os +from pathlib import Path from torchaudio.datasets import speechcommands @@ -105,85 +106,39 @@ def setUpClass(cls): test.write(f'{label}/{filename}\n') cls.test_samples.append(sample) - def testSpeechCommands(self): - dataset = speechcommands.SPEECHCOMMANDS(self.root_dir) - + def _testSpeechCommands(self, dataset, data_samples): num_samples = 0 for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( dataset ): - self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) - assert sample_rate == self.samples[i][1] - assert label == self.samples[i][2] - assert speaker_id == self.samples[i][3] - assert utterance_number == self.samples[i][4] + self.assertEqual(data, data_samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == data_samples[i][1] + assert label == data_samples[i][2] + assert speaker_id == data_samples[i][3] + assert utterance_number == data_samples[i][4] num_samples += 1 - assert num_samples == len(self.samples) + assert num_samples == len(data_samples) - def testSpeechCommandsNone(self): - dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset=None) - - num_samples = 0 - for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( - dataset - ): - self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) - assert sample_rate == self.samples[i][1] - assert label == self.samples[i][2] - assert speaker_id == self.samples[i][3] - assert utterance_number == self.samples[i][4] - num_samples += 1 + def testSpeechCommands_str(self): + dataset = speechcommands.SPEECHCOMMANDS(self.root_dir) + self._testSpeechCommands(dataset, self.samples) - assert num_samples == len(self.samples) + def testSpeechCommands_path(self): + dataset = speechcommands.SPEECHCOMMANDS(Path(self.root_dir)) + self._testSpeechCommands(dataset, self.samples) def testSpeechCommandsSubsetTrain(self): dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="training") - - num_samples = 0 - for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( - dataset - ): - self.assertEqual(data, self.train_samples[i][0], atol=5e-5, rtol=1e-8) - assert sample_rate == self.train_samples[i][1] - assert label == self.train_samples[i][2] - assert speaker_id == self.train_samples[i][3] - assert utterance_number == self.train_samples[i][4] - num_samples += 1 - - assert num_samples == len(self.train_samples) + self._testSpeechCommands(dataset, self.train_samples) def testSpeechCommandsSubsetValid(self): dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="validation") - - num_samples = 0 - for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( - dataset - ): - self.assertEqual(data, self.valid_samples[i][0], atol=5e-5, rtol=1e-8) - assert sample_rate == self.valid_samples[i][1] - assert label == self.valid_samples[i][2] - assert speaker_id == self.valid_samples[i][3] - assert utterance_number == self.valid_samples[i][4] - num_samples += 1 - - assert num_samples == len(self.valid_samples) + self._testSpeechCommands(dataset, self.valid_samples) def testSpeechCommandsSubsetTest(self): dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="testing") - - num_samples = 0 - for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( - dataset - ): - self.assertEqual(data, self.test_samples[i][0], atol=5e-5, rtol=1e-8) - assert sample_rate == self.test_samples[i][1] - assert label == self.test_samples[i][2] - assert speaker_id == self.test_samples[i][3] - assert utterance_number == self.test_samples[i][4] - num_samples += 1 - - assert num_samples == len(self.test_samples) + self._testSpeechCommands(dataset, self.test_samples) def testSpeechCommandsSum(self): dataset_all = speechcommands.SPEECHCOMMANDS(self.root_dir) diff --git a/test/torchaudio_unittest/datasets/tedlium_test.py b/test/torchaudio_unittest/datasets/tedlium_test.py index f5becb53ab..3ea4d25eab 100644 --- a/test/torchaudio_unittest/datasets/tedlium_test.py +++ b/test/torchaudio_unittest/datasets/tedlium_test.py @@ -1,6 +1,7 @@ import os import platform import unittest +from pathlib import Path from torchaudio.datasets import tedlium @@ -93,9 +94,7 @@ def setUpClass(cls): cls.samples[release].append(sample) seed += 1 - def test_tedlium_release1(self): - release = "release1" - dataset = tedlium.TEDLIUM(self.root_dir, release=release) + def _test_tedlium(self, dataset, release): num_samples = 0 for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset): self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8) @@ -113,45 +112,25 @@ def test_tedlium_release1(self): phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] assert phoenemes == PHONEME - def test_tedlium_release2(self): - release = "release2" + def test_tedlium_release1_str(self): + release = "release1" dataset = tedlium.TEDLIUM(self.root_dir, release=release) - num_samples = 0 - for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset): - self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8) - assert sample_rate == self.samples[release][i][1] - assert transcript == self.samples[release][i][2] - assert talk_id == self.samples[release][i][3] - assert speaker_id == self.samples[release][i][4] - assert identifier == self.samples[release][i][5] - num_samples += 1 + self._test_tedlium(dataset, release) - assert num_samples == len(self.samples[release]) + def test_tedlium_release1_path(self): + release = "release1" + dataset = tedlium.TEDLIUM(Path(self.root_dir), release=release) + self._test_tedlium(dataset, release) - dataset._dict_path = os.path.join(dataset._path, f"{release}.dic") - phoneme_dict = dataset.phoneme_dict - phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] - assert phoenemes == PHONEME + def test_tedlium_release2(self): + release = "release2" + dataset = tedlium.TEDLIUM(self.root_dir, release=release) + self._test_tedlium(dataset, release) def test_tedlium_release3(self): release = "release3" dataset = tedlium.TEDLIUM(self.root_dir, release=release) - num_samples = 0 - for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset): - self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8) - assert sample_rate == self.samples[release][i][1] - assert transcript == self.samples[release][i][2] - assert talk_id == self.samples[release][i][3] - assert speaker_id == self.samples[release][i][4] - assert identifier == self.samples[release][i][5] - num_samples += 1 - - assert num_samples == len(self.samples[release]) - - dataset._dict_path = os.path.join(dataset._path, f"{release}.dic") - phoneme_dict = dataset.phoneme_dict - phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] - assert phoenemes == PHONEME + self._test_tedlium(dataset, release) class TestTedliumSoundfile(Tedlium, TorchaudioTestCase): diff --git a/test/torchaudio_unittest/datasets/utils_test.py b/test/torchaudio_unittest/datasets/utils_test.py index f75ae2319c..a9e13a1e38 100644 --- a/test/torchaudio_unittest/datasets/utils_test.py +++ b/test/torchaudio_unittest/datasets/utils_test.py @@ -54,7 +54,7 @@ class TestIterator(TorchaudioTestCase): path = get_asset_path() def test_disckcache_iterator(self): - data = COMMONVOICE(self.path, url="tatar") + data = COMMONVOICE(self.path, version="cv-corpus-4-2019-12-10", language="tatar") data = dataset_utils.diskcache_iterator(data) # Save data[0] @@ -62,7 +62,7 @@ def test_disckcache_iterator(self): data[0] def test_bg_iterator(self): - data = COMMONVOICE(self.path, url="tatar") + data = COMMONVOICE(self.path, version="cv-corpus-4-2019-12-10", language="tatar") data = dataset_utils.bg_iterator(data, 5) for _ in data: pass diff --git a/test/torchaudio_unittest/sox_effect/dataset_test.py b/test/torchaudio_unittest/sox_effect/dataset_test.py index 647e4d2a81..7e89960186 100644 --- a/test/torchaudio_unittest/sox_effect/dataset_test.py +++ b/test/torchaudio_unittest/sox_effect/dataset_test.py @@ -2,6 +2,7 @@ import platform from unittest import skipIf from typing import List, Tuple +from concurrent.futures import ProcessPoolExecutor import numpy as np import torch @@ -122,3 +123,36 @@ def test_apply_effects_tensor(self): ) for batch in loader: assert batch.shape == (32, 2, 2 * sample_rate) + + +def speed(path): + wav, sample_rate = torchaudio.backend.sox_io_backend.load(path) + effects = [ + ['speed', '1.03756523535464655'], + ['rate', f'{sample_rate}'], + ] + return torchaudio.sox_effects.apply_effects_tensor(wav, sample_rate, effects)[0] + + +@skipIfNoExtension +class TestProcessPoolExecutor(TempDirMixin, PytorchTestCase): + backend = "sox_io" + + def setUp(self): + sample_rate = 16000 + self.flist = [] + for i in range(10): + path = self.get_temp_path(f'{i}.wav') + data = get_whitenoise(n_channels=1, sample_rate=sample_rate, duration=1, dtype='float') + save_wav(path, data, sample_rate) + self.flist.append(path) + + def test_executor(self): + """Test that apply_effects_tensor with speed + rate does not crush + + https://github.com/pytorch/audio/issues/1021 + """ + executor = ProcessPoolExecutor(1) + futures = [executor.submit(speed, path) for path in self.flist] + for future in futures: + future.result() diff --git a/test/torchaudio_unittest/sox_effect/sox_effect_test.py b/test/torchaudio_unittest/sox_effect/sox_effect_test.py index 27740ca576..c5b57eaf5f 100644 --- a/test/torchaudio_unittest/sox_effect/sox_effect_test.py +++ b/test/torchaudio_unittest/sox_effect/sox_effect_test.py @@ -1,4 +1,5 @@ import itertools +from pathlib import Path from torchaudio import sox_effects from parameterized import parameterized @@ -104,7 +105,7 @@ def test_apply_no_effect(self, dtype, sample_rate, num_channels, channels_first) load_params("sox_effect_test_args.json"), name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', ) - def test_apply_effects(self, args): + def test_apply_effects_str(self, args): """`apply_effects_file` should return identical data as sox command""" dtype = 'int32' channels_first = True @@ -127,6 +128,29 @@ def test_apply_effects(self, args): assert sr == expected_sr self.assertEqual(found, expected) + def test_apply_effects_path(self): + """`apply_effects_file` should return identical data as sox command when file path is given as a Path Object""" + dtype = 'int32' + channels_first = True + effects = [["hilbert"]] + num_channels = 2 + input_sr = 8000 + output_sr = 8000 + + input_path = self.get_temp_path('input.wav') + reference_path = self.get_temp_path('reference.wav') + data = get_wav_data(dtype, num_channels, channels_first=channels_first) + save_wav(input_path, data, input_sr, channels_first=channels_first) + sox_utils.run_sox_effect( + input_path, reference_path, effects, output_sample_rate=output_sr) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + Path(input_path), effects, normalize=False, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected) + @skipIfNoExtension class TestFileFormats(TempDirMixin, PytorchTestCase): diff --git a/test/torchaudio_unittest/sox_io_backend/info_test.py b/test/torchaudio_unittest/sox_io_backend/info_test.py index b16203cfd3..9b928f3ae0 100644 --- a/test/torchaudio_unittest/sox_io_backend/info_test.py +++ b/test/torchaudio_unittest/sox_io_backend/info_test.py @@ -122,6 +122,36 @@ def test_sphere(self, sample_rate, num_channels): assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_amb(self, dtype, sample_rate, num_channels): + """`sox_io_backend.info` can check amb file correctly""" + duration = 1 + path = self.get_temp_path('data.amb') + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + bit_depth=sox_utils.get_bit_depth(dtype), duration=duration) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + def test_amr_nb(self): + """`sox_io_backend.info` can check amr-nb file correctly""" + duration = 1 + num_channels = 1 + sample_rate = 8000 + path = self.get_temp_path('data.amr-nb') + sox_utils.gen_audio_file( + path, sample_rate=sample_rate, num_channels=num_channels, bit_depth=16, duration=duration) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + @skipIfNoExtension class TestInfoOpus(PytorchTestCase): diff --git a/test/torchaudio_unittest/sox_io_backend/load_test.py b/test/torchaudio_unittest/sox_io_backend/load_test.py index 59bcaea386..156ad9ad3e 100644 --- a/test/torchaudio_unittest/sox_io_backend/load_test.py +++ b/test/torchaudio_unittest/sox_io_backend/load_test.py @@ -142,6 +142,53 @@ def assert_sphere(self, sample_rate, num_channels, duration): assert sr == sample_rate self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + def assert_amb(self, dtype, sample_rate, num_channels, normalize, duration): + """`sox_io_backend.load` can load amb format. + + This test takes the same strategy as mp3 to compare the result + """ + path = self.get_temp_path('1.original.amb') + ref_path = self.get_temp_path('2.reference.wav') + + # 1. Generate amb with sox + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + encoding=sox_utils.get_encoding(dtype), + bit_depth=sox_utils.get_bit_depth(dtype), duration=duration) + # 2. Convert to wav with sox + sox_utils.convert_audio_file(path, ref_path) + # 3. Load amb with torchaudio + data, sr = sox_io_backend.load(path, normalize=normalize) + # 4. Load wav with scipy + data_ref = load_wav(ref_path, normalize=normalize)[0] + # 5. Compare + assert sr == sample_rate + self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + + def assert_amr_nb(self, duration): + """`sox_io_backend.load` can load amr-nb format. + + This test takes the same strategy as mp3 to compare the result + """ + sample_rate = 8000 + num_channels = 1 + path = self.get_temp_path('1.original.amr-nb') + ref_path = self.get_temp_path('2.reference.wav') + + # 1. Generate amr-nb with sox + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + bit_depth=32, duration=duration) + # 2. Convert to wav with sox + sox_utils.convert_audio_file(path, ref_path) + # 3. Load amr-nb with torchaudio + data, sr = sox_io_backend.load(path) + # 4. Load wav with scipy + data_ref = load_wav(ref_path)[0] + # 5. Compare + assert sr == sample_rate + self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + @skipIfNoExec('sox') @skipIfNoExtension @@ -260,6 +307,20 @@ def test_sphere(self, sample_rate, num_channels): """`sox_io_backend.load` can load sph format correctly.""" self.assert_sphere(sample_rate, num_channels, duration=1) + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16'], + [8000, 16000], + [1, 2], + [False, True], + )), name_func=name_func) + def test_amb(self, dtype, sample_rate, num_channels, normalize): + """`sox_io_backend.load` can load sph format correctly.""" + self.assert_amb(dtype, sample_rate, num_channels, normalize, duration=1) + + def test_amr_nb(self): + """`sox_io_backend.load` can load amr_nb format correctly.""" + self.assert_amr_nb(duration=1) + @skipIfNoExec('sox') @skipIfNoExtension diff --git a/test/torchaudio_unittest/sox_io_backend/save_test.py b/test/torchaudio_unittest/sox_io_backend/save_test.py index deed1aa1ad..b0ee25e01c 100644 --- a/test/torchaudio_unittest/sox_io_backend/save_test.py +++ b/test/torchaudio_unittest/sox_io_backend/save_test.py @@ -200,6 +200,68 @@ def assert_sphere(self, sample_rate, num_channels, duration): self.assertEqual(found, expected) + def assert_amb(self, dtype, sample_rate, num_channels, duration): + """`sox_io_backend.save` can save amb format. + + This test takes the same strategy as mp3 to compare the result + """ + src_path = self.get_temp_path('1.reference.wav') + amb_path = self.get_temp_path('2.1.torchaudio.amb') + wav_path = self.get_temp_path('2.2.torchaudio.wav') + amb_path_sox = self.get_temp_path('3.1.sox.amb') + wav_path_sox = self.get_temp_path('3.2.sox.wav') + + # 1. Generate original wav + data = get_wav_data(dtype, num_channels, normalize=False, num_frames=duration * sample_rate) + save_wav(src_path, data, sample_rate) + # 2.1. Convert the original wav to amb with torchaudio + sox_io_backend.save(amb_path, load_wav(src_path, normalize=False)[0], sample_rate) + # 2.2. Convert the amb to wav with Sox + sox_utils.convert_audio_file(amb_path, wav_path) + # 2.3. Load + found = load_wav(wav_path)[0] + + # 3.1. Convert the original wav to amb with SoX + sox_utils.convert_audio_file(src_path, amb_path_sox) + # 3.2. Convert the amb to wav with Sox + sox_utils.convert_audio_file(amb_path_sox, wav_path_sox) + # 3.3. Load + expected = load_wav(wav_path_sox)[0] + + self.assertEqual(found, expected) + + def assert_amr_nb(self, duration): + """`sox_io_backend.save` can save amr_nb format. + + This test takes the same strategy as mp3 to compare the result + """ + sample_rate = 8000 + num_channels = 1 + src_path = self.get_temp_path('1.reference.wav') + amr_path = self.get_temp_path('2.1.torchaudio.amr-nb') + wav_path = self.get_temp_path('2.2.torchaudio.wav') + amr_path_sox = self.get_temp_path('3.1.sox.amr-nb') + wav_path_sox = self.get_temp_path('3.2.sox.wav') + + # 1. Generate original wav + data = get_wav_data('int16', num_channels, normalize=False, num_frames=duration * sample_rate) + save_wav(src_path, data, sample_rate) + # 2.1. Convert the original wav to amr_nb with torchaudio + sox_io_backend.save(amr_path, load_wav(src_path, normalize=False)[0], sample_rate) + # 2.2. Convert the amr_nb to wav with Sox + sox_utils.convert_audio_file(amr_path, wav_path) + # 2.3. Load + found = load_wav(wav_path)[0] + + # 3.1. Convert the original wav to amr_nb with SoX + sox_utils.convert_audio_file(src_path, amr_path_sox) + # 3.2. Convert the amr_nb to wav with Sox + sox_utils.convert_audio_file(amr_path_sox, wav_path_sox) + # 3.3. Load + expected = load_wav(wav_path_sox)[0] + + self.assertEqual(found, expected) + @skipIfNoExec('sox') @skipIfNoExtension @@ -302,6 +364,19 @@ def test_sphere(self, sample_rate, num_channels): """`sox_io_backend.save` can save sph format.""" self.assert_sphere(sample_rate, num_channels, duration=1) + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_amb(self, dtype, sample_rate, num_channels): + """`sox_io_backend.save` can save amb format.""" + self.assert_amb(dtype, sample_rate, num_channels, duration=1) + + def test_amr_nb(self): + """`sox_io_backend.save` can save amr-nb format.""" + self.assert_amr_nb(duration=1) + @skipIfNoExec('sox') @skipIfNoExtension diff --git a/third_party/CMakeLists.txt b/third_party/CMakeLists.txt index c4be4cb7b0..ad0eac582b 100644 --- a/third_party/CMakeLists.txt +++ b/third_party/CMakeLists.txt @@ -16,6 +16,14 @@ ExternalProject_Add(libmad CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/libmad/configure ${COMMON_ARGS} ) +ExternalProject_Add(amr + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://sourceforge.net/projects/opencore-amr/files/opencore-amr/opencore-amr-0.1.5.tar.gz + URL_HASH SHA256=2c006cb9d5f651bfb5e60156dbff6af3c9d35c7bbcc9015308c0aff1e14cd341 + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/amr/configure ${COMMON_ARGS} +) + ExternalProject_Add(libmp3lame PREFIX ${CMAKE_CURRENT_SOURCE_DIR} DOWNLOAD_DIR ${ARCHIVE_DIR} @@ -72,9 +80,11 @@ ExternalProject_Add(opusfile ExternalProject_Add(libsox PREFIX ${CMAKE_CURRENT_SOURCE_DIR} - DEPENDS libogg libflac libvorbis opusfile libmp3lame libmad + DEPENDS libogg libflac libvorbis opusfile libmp3lame libmad amr DOWNLOAD_DIR ${ARCHIVE_DIR} URL https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2 URL_HASH SHA256=81a6956d4330e75b5827316e44ae381e6f1e8928003c6aa45896da9041ea149c - CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libsox/configure ${COMMON_ARGS} --with-lame --with-flac --with-mad --with-oggvorbis --without-alsa --without-coreaudio --without-png --without-oss --without-sndfile --with-opus + # OpenMP is by default compiled against GNU OpenMP, which conflicts with the version of OpenMP that PyTorch uses. + # See https://github.com/pytorch/audio/pull/1026 + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libsox/configure ${COMMON_ARGS} --with-lame --with-flac --with-mad --with-oggvorbis --without-alsa --without-coreaudio --without-png --without-oss --without-sndfile --with-opus --with-amrwb --with-amrnb --disable-openmp ) diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 1e57e92834..5293223977 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -40,18 +40,19 @@ def load( This function can handle all the codecs that underlying libsox can handle, however it is tested on the following formats; - * WAV + * WAV, AMB * 32-bit floating-point * 32-bit signed integer * 16-bit signed integer - * 8-bit unsigned integer + * 8-bit unsigned integer (WAV only) * MP3 * FLAC * OGG/VORBIS * OPUS * SPHERE + * AMR-NB To load ``MP3``, ``FLAC``, ``OGG/VORBIS``, ``OPUS`` and other codecs ``libsox`` does not handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` @@ -119,7 +120,7 @@ def save( Note: Supported formats are; - * WAV + * WAV, AMB * 32-bit floating-point * 32-bit signed integer @@ -130,6 +131,7 @@ def save( * FLAC * OGG/VORBIS * SPHERE + * AMR-NB To save ``MP3``, ``FLAC``, ``OGG/VORBIS``, and other codecs ``libsox`` does not handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` @@ -160,7 +162,7 @@ def save( filepath = str(filepath) if compression is None: ext = str(filepath).split('.')[-1].lower() - if ext in ['wav', 'sph']: + if ext in ['wav', 'sph', 'amb', 'amr-nb']: compression = 0. elif ext == 'mp3': compression = -4.5 diff --git a/torchaudio/csrc/sox_effects.cpp b/torchaudio/csrc/sox_effects.cpp index 8a7cc7c494..7bd24b6bc2 100644 --- a/torchaudio/csrc/sox_effects.cpp +++ b/torchaudio/csrc/sox_effects.cpp @@ -91,8 +91,8 @@ c10::intrusive_ptr apply_effects_tensor( c10::intrusive_ptr apply_effects_file( const std::string path, std::vector> effects, - const bool normalize, - const bool channels_first) { + c10::optional& normalize, + c10::optional& channels_first) { // Open input file SoxFormat sf(sox_open_read( path.c_str(), @@ -121,16 +121,17 @@ c10::intrusive_ptr apply_effects_file( chain.run(); // Create tensor from buffer + bool channels_first_ = channels_first.value_or(true); auto tensor = convert_to_tensor( /*buffer=*/out_buffer.data(), /*num_samples=*/out_buffer.size(), /*num_channels=*/chain.getOutputNumChannels(), dtype, - normalize, - channels_first); + normalize.value_or(true), + channels_first_); return c10::make_intrusive( - tensor, chain.getOutputSampleRate(), channels_first); + tensor, chain.getOutputSampleRate(), channels_first_); } } // namespace sox_effects diff --git a/torchaudio/csrc/sox_effects.h b/torchaudio/csrc/sox_effects.h index 7883a7beac..669c86ac39 100644 --- a/torchaudio/csrc/sox_effects.h +++ b/torchaudio/csrc/sox_effects.h @@ -18,8 +18,8 @@ c10::intrusive_ptr apply_effects_tensor( c10::intrusive_ptr apply_effects_file( const std::string path, std::vector> effects, - const bool normalize = true, - const bool channels_first = true); + c10::optional& normalize, + c10::optional& channels_first); } // namespace sox_effects } // namespace torchaudio diff --git a/torchaudio/csrc/sox_io.cpp b/torchaudio/csrc/sox_io.cpp index 4092d215d0..9acab1c030 100644 --- a/torchaudio/csrc/sox_io.cpp +++ b/torchaudio/csrc/sox_io.cpp @@ -49,30 +49,32 @@ c10::intrusive_ptr get_info(const std::string& path) { c10::intrusive_ptr load_audio_file( const std::string& path, - const int64_t frame_offset, - const int64_t num_frames, - const bool normalize, - const bool channels_first) { - if (frame_offset < 0) { + c10::optional& frame_offset, + c10::optional& num_frames, + c10::optional& normalize, + c10::optional& channels_first) { + const auto offset = frame_offset.value_or(0); + if (offset < 0) { throw std::runtime_error( "Invalid argument: frame_offset must be non-negative."); } - if (num_frames == 0 || num_frames < -1) { + const auto frames = num_frames.value_or(-1); + if (frames == 0 || frames < -1) { throw std::runtime_error( "Invalid argument: num_frames must be -1 or greater than 0."); } std::vector> effects; - if (num_frames != -1) { - std::ostringstream offset, frames; - offset << frame_offset << "s"; - frames << "+" << num_frames << "s"; + if (frames != -1) { + std::ostringstream os_offset, os_frames; + os_offset << offset << "s"; + os_frames << "+" << frames << "s"; effects.emplace_back( - std::vector{"trim", offset.str(), frames.str()}); - } else if (frame_offset != 0) { - std::ostringstream offset; - offset << frame_offset << "s"; - effects.emplace_back(std::vector{"trim", offset.str()}); + std::vector{"trim", os_offset.str(), os_frames.str()}); + } else if (offset != 0) { + std::ostringstream os_offset; + os_offset << offset << "s"; + effects.emplace_back(std::vector{"trim", os_offset.str()}); } return torchaudio::sox_effects::apply_effects_file( @@ -83,11 +85,17 @@ void save_audio_file( const std::string& file_name, const c10::intrusive_ptr& signal, const double compression) { - const auto tensor = signal->getTensor(); + auto tensor = signal->tensor; validate_input_tensor(tensor); const auto filetype = get_filetype(file_name); + if (filetype == "amr-nb") { + const auto num_channels = tensor.size(signal->channels_first ? 0 : 1); + TORCH_CHECK( + num_channels == 1, "amr-nb format only supports single channel audio."); + tensor = (unnormalize_wav(tensor) / 65536).to(torch::kInt16); + } const auto signal_info = get_signalinfo(signal.get(), filetype); const auto encoding_info = get_encodinginfo(filetype, tensor.dtype(), compression); diff --git a/torchaudio/csrc/sox_io.h b/torchaudio/csrc/sox_io.h index 5288e911e8..d62997243e 100644 --- a/torchaudio/csrc/sox_io.h +++ b/torchaudio/csrc/sox_io.h @@ -25,10 +25,10 @@ c10::intrusive_ptr get_info(const std::string& path); c10::intrusive_ptr load_audio_file( const std::string& path, - const int64_t frame_offset = 0, - const int64_t num_frames = -1, - const bool normalize = true, - const bool channels_first = true); + c10::optional& frame_offset, + c10::optional& num_frames, + c10::optional& normalize, + c10::optional& channels_first); void save_audio_file( const std::string& file_name, diff --git a/torchaudio/csrc/sox_utils.cpp b/torchaudio/csrc/sox_utils.cpp index 25fe376c8b..656cc63348 100644 --- a/torchaudio/csrc/sox_utils.cpp +++ b/torchaudio/csrc/sox_utils.cpp @@ -223,7 +223,7 @@ sox_encoding_t get_encoding( return SOX_ENCODING_FLAC; if (filetype == "ogg" || filetype == "vorbis") return SOX_ENCODING_VORBIS; - if (filetype == "wav") { + if (filetype == "wav" || filetype == "amb") { if (dtype == torch::kUInt8) return SOX_ENCODING_UNSIGNED; if (dtype == torch::kInt16) @@ -236,7 +236,9 @@ sox_encoding_t get_encoding( } if (filetype == "sph") return SOX_ENCODING_SIGN2; - throw std::runtime_error("Unsupported file type."); + if (filetype == "amr-nb") + return SOX_ENCODING_AMR_NB; + throw std::runtime_error("Unsupported file type: " + filetype); } unsigned get_precision( @@ -248,7 +250,7 @@ unsigned get_precision( return 24; if (filetype == "ogg" || filetype == "vorbis") return SOX_UNSPEC; - if (filetype == "wav") { + if (filetype == "wav" || filetype == "amb") { if (dtype == torch::kUInt8) return 8; if (dtype == torch::kInt16) @@ -261,7 +263,13 @@ unsigned get_precision( } if (filetype == "sph") return 32; - throw std::runtime_error("Unsupported file type."); + if (filetype == "amr-nb") { + TORCH_INTERNAL_ASSERT( + dtype == torch::kInt16, + "When saving to AMR-NB format, the input tensor must be int16 type."); + return 16; + } + throw std::runtime_error("Unsupported file type: " + filetype); } sox_signalinfo_t get_signalinfo( @@ -287,11 +295,13 @@ sox_encodinginfo_t get_encodinginfo( return compression; if (filetype == "ogg" || filetype == "vorbis") return compression; - if (filetype == "wav") + if (filetype == "wav" || filetype == "amb") return 0.; if (filetype == "sph") return 0.; - throw std::runtime_error("Unsupported file type."); + if (filetype == "amr-nb") + return 0.; + throw std::runtime_error("Unsupported file type: " + filetype); }(); return sox_encodinginfo_t{/*encoding=*/get_encoding(filetype, dtype), diff --git a/torchaudio/datasets/commonvoice.py b/torchaudio/datasets/commonvoice.py index 963777cf38..1a1f4273a4 100644 --- a/torchaudio/datasets/commonvoice.py +++ b/torchaudio/datasets/commonvoice.py @@ -1,9 +1,10 @@ import os +import warnings from pathlib import Path -from typing import List, Dict, Tuple, Union +from typing import List, Dict, Tuple, Optional, Union import torchaudio -from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader +from torchaudio.datasets.utils import extract_archive, unicode_csv_reader, validate_file from torch import Tensor from torch.utils.data import Dataset @@ -16,68 +17,39 @@ # validated.tsv FOLDER_IN_ARCHIVE = "CommonVoice" -URL = "english" -VERSION = "cv-corpus-4-2019-12-10" +LANGUAGE = "english" +VERSION = "cv-corpus-5.1-2020-06-22" TSV = "train.tsv" _CHECKSUMS = { - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/tt.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/en.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/de.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/fr.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/cy.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/br.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/cv.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/tr.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ky.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ga-IE.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/kab.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ca.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/zh-TW.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/sl.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/it.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/nl.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/cnh.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/eo.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/et.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/fa.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/eu.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/es.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/zh-CN.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/mn.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/sah.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/dv.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/rw.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/sv-SE.tar.gz": - None, - "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ru.tar.gz": - None + "cv-corpus-5.1-2020-06-22/tt.tar.gz": None, + "cv-corpus-5.1-2020-06-22/en.tar.gz": None, + "cv-corpus-5.1-2020-06-22/de.tar.gz": None, + "cv-corpus-5.1-2020-06-22/fr.tar.gz": None, + "cv-corpus-5.1-2020-06-22/cy.tar.gz": None, + "cv-corpus-5.1-2020-06-22/br.tar.gz": None, + "cv-corpus-5.1-2020-06-22/cv.tar.gz": None, + "cv-corpus-5.1-2020-06-22/tr.tar.gz": None, + "cv-corpus-5.1-2020-06-22/ky.tar.gz": None, + "cv-corpus-5.1-2020-06-22/ga-IE.tar.gz": None, + "cv-corpus-5.1-2020-06-22/kab.tar.gz": None, + "cv-corpus-5.1-2020-06-22/ca.tar.gz": None, + "cv-corpus-5.1-2020-06-22/zh-TW.tar.gz": None, + "cv-corpus-5.1-2020-06-22/sl.tar.gz": None, + "cv-corpus-5.1-2020-06-22/it.tar.gz": None, + "cv-corpus-5.1-2020-06-22/nl.tar.gz": None, + "cv-corpus-5.1-2020-06-22/cnh.tar.gz": None, + "cv-corpus-5.1-2020-06-22/eo.tar.gz": None, + "cv-corpus-5.1-2020-06-22/et.tar.gz": None, + "cv-corpus-5.1-2020-06-22/fa.tar.gz": None, + "cv-corpus-5.1-2020-06-22/eu.tar.gz": None, + "cv-corpus-5.1-2020-06-22/es.tar.gz": None, + "cv-corpus-5.1-2020-06-22/zh-CN.tar.gz": None, + "cv-corpus-5.1-2020-06-22/mn.tar.gz": None, + "cv-corpus-5.1-2020-06-22/sah.tar.gz": None, + "cv-corpus-5.1-2020-06-22/dv.tar.gz": None, + "cv-corpus-5.1-2020-06-22/rw.tar.gz": None, + "cv-corpus-5.1-2020-06-22/sv-SE.tar.gz": None, + "cv-corpus-5.1-2020-06-22/ru.tar.gz": None, } @@ -101,15 +73,18 @@ def load_commonvoice_item(line: List[str], class COMMONVOICE(Dataset): - """Create a Dataset for CommonVoice. + """Create a Dataset for `CommonVoice `_. Args: root (str or Path): Path to the directory where the dataset is found or downloaded. tsv (str, optional): The name of the tsv file used to construct the metadata. (default: ``"train.tsv"``) - url (str, optional): The URL to download the dataset from, or the language of - the dataset to download. (default: ``"english"``). - Allowed language values are ``"tatar"``, ``"english"``, ``"german"``, + url (str, optional): Deprecated. + folder_in_archive (str, optional): The top-level directory of the dataset. + version (str): Version string. (default: ``"cv-corpus-5.1-2020-06-22"``) + language (str, optional): Language of the dataset. (default: None) + The following values are mapped to their corresponding shortened version: + ``"tatar"``, ``"english"``, ``"german"``, ``"french"``, ``"welsh"``, ``"breton"``, ``"chuvash"``, ``"turkish"``, ``"kyrgyz"``, ``"irish"``, ``"kabyle"``, ``"catalan"``, ``"taiwanese"``, ``"slovenian"``, ``"italian"``, ``"dutch"``, ``"hakha chin"``, ``"esperanto"``, ``"estonian"``, @@ -118,11 +93,8 @@ class COMMONVOICE(Dataset): ``"russian"``, ``"indonesian"``, ``"arabic"``, ``"tamil"``, ``"interlingua"``, ``"latvian"``, ``"japanese"``, ``"votic"``, ``"abkhaz"``, ``"cantonese"`` and ``"romansh sursilvan"``. - folder_in_archive (str, optional): The top-level directory of the dataset. - version (str): Version string. (default: ``"cv-corpus-4-2019-12-10"``) For the other allowed values, Please checkout https://commonvoice.mozilla.org/en/datasets. - download (bool, optional): - Whether to download the dataset if it is not found at root path. (default: ``False``). + download (bool, optional): Deprecated. """ _ext_txt = ".txt" @@ -132,10 +104,30 @@ class COMMONVOICE(Dataset): def __init__(self, root: Union[str, Path], tsv: str = TSV, - url: str = URL, + url: Optional[str] = None, folder_in_archive: str = FOLDER_IN_ARCHIVE, version: str = VERSION, - download: bool = False) -> None: + language: str = LANGUAGE, + download: Optional[bool] = False) -> None: + + if download is True: + raise RuntimeError( + "The dataset is no longer publicly accessible. You need to " + "download the archives externally and place them in the root " + "directory." + ) + elif download is False: + warnings.warn( + "The use of the download flag is deprecated, since the dataset " + "is no longer directly accessible.", RuntimeWarning + ) + + if url is not None: + warnings.warn( + "The use of the url flag is deprecated, since the dataset " + "is no longer publicly accessible. To specify the language of the dataset, " + "please use the language parameter instead.", RuntimeWarning + ) languages = { "tatar": "tt", @@ -180,12 +172,22 @@ def __init__(self, "romansh sursilvan": "rm-sursilv" } - if url in languages: + if language in languages: ext_archive = ".tar.gz" - language = languages[url] - - base_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com" - url = os.path.join(base_url, version, language + ext_archive) + language = languages[language] + url = os.path.join(version, language + ext_archive) + else: + raise ValueError( + 'Allowed language values are "tatar", "english", "german",' + '"french", "welsh", "breton", "chuvash", "turkish", "kyrgyz",' + '"irish", "kabyle", "catalan", "taiwanese", "slovenian",' + '"italian", "dutch", "hakha chin", "esperanto", "estonian",' + '"persian", "portuguese", "basque", "spanish", "chinese",' + '"mongolian", "sakha", "dhivehi", "kinyarwanda", "swedish",' + '"russian", "indonesian", "arabic", "tamil", "interlingua",' + '"latvian", "japanese", "votic", "abkhaz", "cantonese" and' + '"romansh sursilvan".' + ) # Get string representation of 'root' in case Path object is passed root = os.fspath(root) @@ -198,12 +200,23 @@ def __init__(self, self._path = os.path.join(root, folder_in_archive) - if download: - if not os.path.isdir(self._path): - if not os.path.isfile(archive): - checksum = _CHECKSUMS.get(url, None) - download_url(url, root, hash_value=checksum) + if not os.path.isdir(self._path): + if os.path.isfile(archive): + checksum = _CHECKSUMS.get(url, None) + if checksum: + filepath = os.path.basename(url) + with open(filepath, "rb") as file_obj: + if not validate_file(file_obj, checksum, "sha256"): + raise RuntimeError( + f"The hash of {filepath} does not match. Delete the file manually and retry." + ) extract_archive(archive) + else: + raise RuntimeError( + "The dataset is no longer publicly accessible. You need to " + "download the archives externally and place them in the root " + "directory." + ) self._tsv = os.path.join(root, folder_in_archive, tsv) diff --git a/torchaudio/datasets/librispeech.py b/torchaudio/datasets/librispeech.py index 24da90aa66..c13fb312b8 100644 --- a/torchaudio/datasets/librispeech.py +++ b/torchaudio/datasets/librispeech.py @@ -1,5 +1,6 @@ import os -from typing import Tuple +from typing import Tuple, Union +from pathlib import Path import torchaudio from torch import Tensor @@ -70,7 +71,7 @@ class LIBRISPEECH(Dataset): """Create a Dataset for LibriSpeech. Args: - root (str): Path to the directory where the dataset is found or downloaded. + root (str or Path): Path to the directory where the dataset is found or downloaded. url (str, optional): The URL to download the dataset from, or the type of the dataset to dowload. Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``, @@ -86,7 +87,7 @@ class LIBRISPEECH(Dataset): _ext_audio = ".flac" def __init__(self, - root: str, + root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False) -> None: @@ -106,6 +107,9 @@ def __init__(self, url = os.path.join(base_url, url + ext_archive) + # Get string representation of 'root' in case Path object is passed + root = os.fspath(root) + basename = os.path.basename(url) archive = os.path.join(root, basename) diff --git a/torchaudio/datasets/libritts.py b/torchaudio/datasets/libritts.py index 5d4e94cca5..8ed57d9b52 100644 --- a/torchaudio/datasets/libritts.py +++ b/torchaudio/datasets/libritts.py @@ -1,5 +1,6 @@ import os -from typing import Tuple +from typing import Tuple, Union +from pathlib import Path import torchaudio from torch import Tensor @@ -68,7 +69,7 @@ class LIBRITTS(Dataset): """Create a Dataset for LibriTTS. Args: - root (str): Path to the directory where the dataset is found or downloaded. + root (str or Path): Path to the directory where the dataset is found or downloaded. url (str, optional): The URL to download the dataset from, or the type of the dataset to dowload. Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``, @@ -86,7 +87,7 @@ class LIBRITTS(Dataset): def __init__( self, - root: str, + root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False, @@ -107,6 +108,9 @@ def __init__( url = os.path.join(base_url, url + ext_archive) + # Get string representation of 'root' in case Path object is passed + root = os.fspath(root) + basename = os.path.basename(url) archive = os.path.join(root, basename) diff --git a/torchaudio/datasets/speechcommands.py b/torchaudio/datasets/speechcommands.py index 2bcd2d199f..7d774da943 100644 --- a/torchaudio/datasets/speechcommands.py +++ b/torchaudio/datasets/speechcommands.py @@ -1,5 +1,6 @@ import os -from typing import Tuple, Optional +from typing import Tuple, Optional, Union +from pathlib import Path import torchaudio from torch.utils.data import Dataset @@ -48,7 +49,7 @@ class SPEECHCOMMANDS(Dataset): """Create a Dataset for Speech Commands. Args: - root (str): Path to the directory where the dataset is found or downloaded. + root (str or Path): Path to the directory where the dataset is found or downloaded. url (str, optional): The URL to download the dataset from, or the type of the dataset to dowload. Allowed type values are ``"speech_commands_v0.01"`` and ``"speech_commands_v0.02"`` @@ -64,7 +65,7 @@ class SPEECHCOMMANDS(Dataset): """ def __init__(self, - root: str, + root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False, @@ -85,6 +86,9 @@ def __init__(self, url = os.path.join(base_url, url + ext_archive) + # Get string representation of 'root' in case Path object is passed + root = os.fspath(root) + basename = os.path.basename(url) archive = os.path.join(root, basename) diff --git a/torchaudio/datasets/tedlium.py b/torchaudio/datasets/tedlium.py index 7ac347cd3c..e8d1d1cefc 100644 --- a/torchaudio/datasets/tedlium.py +++ b/torchaudio/datasets/tedlium.py @@ -1,5 +1,6 @@ import os -from typing import Tuple +from typing import Tuple, Union +from pathlib import Path import torchaudio from torch import Tensor @@ -46,7 +47,7 @@ class TEDLIUM(Dataset): Create a Dataset for Tedlium. It supports releases 1,2 and 3. Args: - root (str): Path to the directory where the dataset is found or downloaded. + root (str or Path): Path to the directory where the dataset is found or downloaded. release (str, optional): Release version. Allowed values are ``"release1"``, ``"release2"`` or ``"release3"``. (default: ``"release1"``). @@ -56,7 +57,12 @@ class TEDLIUM(Dataset): Whether to download the dataset if it is not found at root path. (default: ``False``). """ def __init__( - self, root: str, release: str = "release1", subset: str = None, download: bool = False, audio_ext=".sph" + self, + root: Union[str, Path], + release: str = "release1", + subset: str = None, + download: bool = False, + audio_ext=".sph" ) -> None: self._ext_audio = audio_ext if release in _RELEASE_CONFIGS.keys(): @@ -78,6 +84,9 @@ def __init__( ) ) + # Get string representation of 'root' in case Path object is passed + root = os.fspath(root) + basename = os.path.basename(url) archive = os.path.join(root, basename) diff --git a/torchaudio/functional/filtering.py b/torchaudio/functional/filtering.py index aa9f95f2e0..55cf05a51f 100644 --- a/torchaudio/functional/filtering.py +++ b/torchaudio/functional/filtering.py @@ -1006,7 +1006,8 @@ def phaser( References: http://sox.sourceforge.net/sox.html - Scott Lehman, Effects Explained, http://harmony-central.com/Effects/effects-explained.html + Scott Lehman, Effects Explained, + https://web.archive.org/web/20051125072557/http://www.harmony-central.com/Effects/effects-explained.html """ actual_shape = waveform.shape device, dtype = waveform.device, waveform.dtype diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index b967904f2a..20b71e4e33 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -102,16 +102,14 @@ def griffinlim( r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation. Implementation ported from `librosa`. - .. [1] McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. + * [1] McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. "librosa: Audio and music signal analysis in python." In Proceedings of the 14th python in science conference, pp. 18-25. 2015. - - .. [2] Perraudin, N., Balazs, P., & Sรธndergaard, P. L. + * [2] Perraudin, N., Balazs, P., & Sรธndergaard, P. L. "A fast Griffin-Lim algorithm," IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4), Oct. 2013. - - .. [3] D. W. Griffin and J. S. Lim, + * [3] D. W. Griffin and J. S. Lim, "Signal estimation from modified short-time Fourier transform," IEEE Trans. ASSP, vol.32, no.2, pp.236โ€“243, Apr. 1984. @@ -139,6 +137,12 @@ def griffinlim( assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum) assert momentum >= 0, 'momentum={} < 0'.format(momentum) + if normalized: + warnings.warn( + "The argument normalized is not used in Griffin-Lim, " + "and will be removed in v0.9.0 release. To suppress this warning, " + "please use `normalized=False`.") + # pack batch shape = specgram.size() specgram = specgram.reshape([-1] + list(shape[-2:])) @@ -205,7 +209,7 @@ def amplitude_to_DB( This output depends on the maximum value in the input tensor, and so may return different values for an audio clip split into snippets vs. a - a full clip. + full clip. Args: x (Tensor): Input tensor before being converted to decibel scale diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 8b50d19aa7..857df5d899 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -169,7 +169,8 @@ def apply_effects_file( rate and leave samples untouched. Args: - path (str): Path to the audio file. + path (str or pathlib.Path): Path to the audio file. This function also handles ``pathlib.Path`` objects, but is + annotated as ``str`` for TorchScript compiler compatibility. effects (List[List[str]]): List of effects. normalize (bool): When ``True``, this function always return ``float32``, and sample values are @@ -223,10 +224,9 @@ def apply_effects_file( ... super().__init__() ... self.flist = flist ... self.sample_rate = sample_rate - ... self.rng = None ... ... def __getitem__(self, index): - ... speed = self.rng.uniform(0.5, 2.0) + ... speed = 0.5 + 1.5 * torch.rand() ... effects = [ ... ['gain', '-n', '-10'], # apply 10 db attenuation ... ['remix', '-'], # merge all the channels @@ -247,5 +247,7 @@ def apply_effects_file( >>> for batch in loader: >>> pass """ + # Get string representation of 'path' in case Path object is passed + path = str(path) signal = torch.ops.torchaudio.sox_effects_apply_effects_file(path, effects, normalize, channels_first) return signal.get_tensor(), signal.get_sample_rate() diff --git a/torchaudio/utils/sox_utils.py b/torchaudio/utils/sox_utils.py index 59090e4b88..3f9ad0b5ec 100644 --- a/torchaudio/utils/sox_utils.py +++ b/torchaudio/utils/sox_utils.py @@ -26,10 +26,11 @@ def set_verbosity(verbosity: int): Args: verbosity (int): Set verbosity level of libsox. - 1: failure messages - 2: warnings - 3: details of processing - 4-6: increasing levels of debug messages + + * ``1`` failure messages + * ``2`` warnings + * ``3`` details of processing + * ``4``-``6`` increasing levels of debug messages See Also: http://sox.sourceforge.net/sox.html From 5f7a11cc588e5bd8196683df826dab5e29261572 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Tue, 22 Dec 2020 09:19:16 -0800 Subject: [PATCH 24/73] Import torchaudio #1105 37692d8 Summary: Import torchaudio up to #1105 37692d8 Reviewed By: datumbox Differential Revision: D25671497 fbshipit-source-id: 5af11c801321f2bb964245ac6ed74979310f4b5f --- .circleci/build_docs/commit_docs.sh | 10 +- .circleci/config.yml | 15 +- .circleci/config.yml.in | 15 +- test/torchaudio_unittest/README.md | 18 +- .../batch_consistency_test.py | 25 +- .../common_utils/sox_utils.py | 4 +- .../datasets/commonvoice_test.py | 21 +- .../datasets/utils_test.py | 6 +- .../soundfile_backend/load_test.py | 38 ++- .../sox_io_backend/load_test.py | 28 ++- third_party/CMakeLists.txt | 1 + third_party/patch/libsox.patch | 24 ++ torchaudio/datasets/cmuarctic.py | 4 +- torchaudio/datasets/commonvoice.py | 220 ++++-------------- torchaudio/datasets/librispeech.py | 6 +- torchaudio/datasets/libritts.py | 6 +- torchaudio/datasets/ljspeech.py | 4 +- torchaudio/datasets/speechcommands.py | 5 +- torchaudio/datasets/utils.py | 46 +--- torchaudio/datasets/vctk.py | 5 +- torchaudio/datasets/yesno.py | 6 +- torchaudio/functional/functional.py | 32 ++- torchaudio/sox_effects/sox_effects.py | 2 +- 23 files changed, 220 insertions(+), 321 deletions(-) create mode 100644 third_party/patch/libsox.patch diff --git a/.circleci/build_docs/commit_docs.sh b/.circleci/build_docs/commit_docs.sh index 9e9548936a..7d18214eef 100755 --- a/.circleci/build_docs/commit_docs.sh +++ b/.circleci/build_docs/commit_docs.sh @@ -5,7 +5,7 @@ set -ex if [ "$2" == "" ]; then echo call as "$0" "" "" - echo where src is the built documentation and + echo where src is the root of the built documentation git checkout and echo branch should be "master" or "1.7" or so exit 1 fi @@ -15,14 +15,15 @@ target=$2 echo "committing docs from ${src} to ${target}" +pushd "${src}" git checkout gh-pages mkdir -p ./"${target}" rm -rf ./"${target}"/* -cp -r "${src}/build/html/"* ./"$target" +cp -r "${src}/docs/build/html/"* ./"$target" if [ "${target}" == "master" ]; then mkdir -p ./_static rm -rf ./_static/* - cp -r "${src}/build/html/_static/"* ./_static + cp -r "${src}/docs/build/html/_static/"* ./_static git add --all ./_static || true fi git add --all ./"${target}" || true @@ -30,4 +31,5 @@ git config user.email "soumith+bot@pytorch.org" git config user.name "pytorchbot" # If there aren't changes, don't make a commit; push is no-op git commit -m "auto-generating sphinx docs" || true -git push -u origin gh-pages +git remote add https https://github.com/pytorch/audio.git +git push -u https gh-pages diff --git a/.circleci/config.yml b/.circleci/config.yml index f0ee6764b1..ee442189e4 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -283,7 +283,7 @@ jobs: command: | set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch + conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch cpuonly conda install -v -y -c file://$HOME/workspace/conda-bld torchaudio - run: name: smoke test @@ -325,7 +325,7 @@ jobs: conda env remove -n python${PYTHON_VERSION} || true conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch + conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch cpuonly conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - run: name: smoke test @@ -662,7 +662,6 @@ jobs: steps: - attach_workspace: at: ~/workspace - - designate_upload_channel - checkout - run: name: Install pytorch-audio @@ -671,7 +670,7 @@ jobs: name: Build docs command: .circleci/build_docs/build_docs.sh - persist_to_workspace: - root: docs + root: ./ paths: - "*" @@ -683,7 +682,6 @@ jobs: steps: - attach_workspace: at: ~/workspace - - designate_upload_channel - run: name: Generate netrc command: | @@ -694,16 +692,15 @@ jobs: login pytorchbot password ${GITHUB_PYTORCHBOT_TOKEN} DONE - - checkout - run: name: Upload docs command: | + # Don't use "checkout" step since it uses ssh, which cannot git push + # https://circleci.com/docs/2.0/configuration-reference/#checkout set -ex - echo $PWD - ls ~/workspace tag=${CIRCLE_TAG:1:5} target=${tag:-master} - .circleci/build_docs/commit_docs.sh ~/workspace $target + ~/workspace/.circleci/build_docs/commit_docs.sh ~/workspace $target workflows: build: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index bdbc3c774d..5c9c4d7693 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -283,7 +283,7 @@ jobs: command: | set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch + conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch cpuonly conda install -v -y -c file://$HOME/workspace/conda-bld torchaudio - run: name: smoke test @@ -325,7 +325,7 @@ jobs: conda env remove -n python${PYTHON_VERSION} || true conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch + conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch cpuonly conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - run: name: smoke test @@ -662,7 +662,6 @@ jobs: steps: - attach_workspace: at: ~/workspace - - designate_upload_channel - checkout - run: name: Install pytorch-audio @@ -671,7 +670,7 @@ jobs: name: Build docs command: .circleci/build_docs/build_docs.sh - persist_to_workspace: - root: docs + root: ./ paths: - "*" @@ -683,7 +682,6 @@ jobs: steps: - attach_workspace: at: ~/workspace - - designate_upload_channel - run: name: Generate netrc command: | @@ -694,16 +692,15 @@ jobs: login pytorchbot password ${GITHUB_PYTORCHBOT_TOKEN} DONE - - checkout - run: name: Upload docs command: | + # Don't use "checkout" step since it uses ssh, which cannot git push + # https://circleci.com/docs/2.0/configuration-reference/#checkout set -ex - echo $PWD - ls ~/workspace tag=${CIRCLE_TAG:1:5} target=${tag:-master} - .circleci/build_docs/commit_docs.sh ~/workspace $target + ~/workspace/.circleci/build_docs/commit_docs.sh ~/workspace $target workflows: build: diff --git a/test/torchaudio_unittest/README.md b/test/torchaudio_unittest/README.md index 306ca23098..119c644687 100644 --- a/test/torchaudio_unittest/README.md +++ b/test/torchaudio_unittest/README.md @@ -10,9 +10,9 @@ pytest test --collect-only # Run all the test suites pytest test # Run tests on sox_effects module -pytest test/sox_effect +pytest test/torchaudio_unittest/sox_effect # use -k to apply filter -pytest test/sox_io_backend -k load # only runs tests where their names contain load +pytest test/torchaudio_unittest/sox_io_backend -k load # only runs tests where their names contain load # Some other useful options; # Stop on the first failure -x # Run failure fast --ff @@ -51,7 +51,7 @@ The following test modules are defined for corresponding `torchaudio` module/fun - [`torchaudio.transforms`](./transforms_test.py) - [`torchaudio.compliance.kaldi`](./compliance_kaldi_test.py) - [`torchaudio.kaldi_io`](./kaldi_io_test.py) -- [`torchaudio.sox_effects`](test/sox_effects) +- [`torchaudio.sox_effects`](./sox_effect) - [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](./io_test.py) ### Test modules that do not fall into the above categories @@ -82,12 +82,12 @@ Code: ```python waveform = common_utils.get_sinusoid( - frequency=300, - sample_rate=16000, - duration=1, # seconds - n_channels=1, - dtype="float32", - device="cpu", + frequency=300, + sample_rate=16000, + duration=1, # seconds + n_channels=1, + dtype="float32", + device="cpu", ) ``` diff --git a/test/torchaudio_unittest/batch_consistency_test.py b/test/torchaudio_unittest/batch_consistency_test.py index 696b714df1..d4edf65e98 100644 --- a/test/torchaudio_unittest/batch_consistency_test.py +++ b/test/torchaudio_unittest/batch_consistency_test.py @@ -227,19 +227,22 @@ def test_batch_TimeStretch(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 - kwargs = { - 'n_fft': 2048, - 'hop_length': 512, - 'win_length': 2048, - 'window': torch.hann_window(2048), - 'center': True, - 'pad_mode': 'reflect', - 'normalized': True, - 'onesided': True, - } rate = 2 - complex_specgrams = torch.stft(waveform, **kwargs) + complex_specgrams = torch.view_as_real( + torch.stft( + input=waveform, + n_fft=2048, + hop_length=512, + win_length=2048, + window=torch.hann_window(2048), + center=True, + pad_mode='reflect', + normalized=True, + onesided=True, + return_complex=True, + ) + ) # Single then transform then batch expected = torchaudio.transforms.TimeStretch( diff --git a/test/torchaudio_unittest/common_utils/sox_utils.py b/test/torchaudio_unittest/common_utils/sox_utils.py index db131cdec5..0267017393 100644 --- a/test/torchaudio_unittest/common_utils/sox_utils.py +++ b/test/torchaudio_unittest/common_utils/sox_utils.py @@ -1,4 +1,5 @@ import subprocess +import warnings def get_encoding(dtype): @@ -27,8 +28,7 @@ def gen_audio_file( ): """Generate synthetic audio file with `sox` command.""" if path.endswith('.wav'): - raise RuntimeError( - 'Use get_wav_data and save_wav to generate wav file for accurate result.') + warnings.warn('Use get_wav_data and save_wav to generate wav file for accurate result.') command = [ 'sox', '-V3', # verbose diff --git a/test/torchaudio_unittest/datasets/commonvoice_test.py b/test/torchaudio_unittest/datasets/commonvoice_test.py index 731c4e07cc..22c811a67b 100644 --- a/test/torchaudio_unittest/datasets/commonvoice_test.py +++ b/test/torchaudio_unittest/datasets/commonvoice_test.py @@ -1,9 +1,8 @@ import os import csv -import random from pathlib import Path -from torchaudio.datasets import commonvoice +from torchaudio.datasets import COMMONVOICE from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, @@ -31,29 +30,23 @@ class TestCommonVoice(TempDirMixin, TorchaudioTestCase): "common_voice_en_18607573.wav", "Caddy, show Miss Clare and Miss Summerson their rooms.", "2", "0", "twenties", "male", "canada"], ] - _folder_audio = "clips" sample_rate = 48000 @classmethod def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() - # The path convention commonvoice uses - base_dir = os.path.join(cls.root_dir, commonvoice.FOLDER_IN_ARCHIVE, commonvoice.VERSION, "en") - os.makedirs(base_dir, exist_ok=True) - # Tsv file name difference does not mean different subset, testing as a whole dataset here - tsv_filename = os.path.join(base_dir, commonvoice.TSV) + tsv_filename = os.path.join(cls.root_dir, "train.tsv") + audio_base_path = os.path.join(cls.root_dir, "clips") + os.makedirs(audio_base_path, exist_ok=True) with open(tsv_filename, "w", newline='') as tsv: writer = csv.writer(tsv, delimiter='\t') writer.writerow(cls._headers) for i, content in enumerate(cls._train_csv_contents): - audio_filename = audio_filename = content[1] writer.writerow(content) # Generate and store audio - audio_base_path = os.path.join(base_dir, cls._folder_audio) - os.makedirs(audio_base_path, exist_ok=True) - audio_path = os.path.join(audio_base_path, audio_filename) + audio_path = os.path.join(audio_base_path, content[1]) data = get_whitenoise(sample_rate=cls.sample_rate, duration=1, n_channels=1, seed=i, dtype='float32') save_wav(audio_path, data, cls.sample_rate) @@ -72,9 +65,9 @@ def _test_commonvoice(self, dataset): assert n_ite == len(self.data) def test_commonvoice_str(self): - dataset = commonvoice.COMMONVOICE(self.root_dir) + dataset = COMMONVOICE(self.root_dir) self._test_commonvoice(dataset) def test_commonvoice_path(self): - dataset = commonvoice.COMMONVOICE(Path(self.root_dir)) + dataset = COMMONVOICE(Path(self.root_dir)) self._test_commonvoice(dataset) diff --git a/test/torchaudio_unittest/datasets/utils_test.py b/test/torchaudio_unittest/datasets/utils_test.py index a9e13a1e38..5263319e51 100644 --- a/test/torchaudio_unittest/datasets/utils_test.py +++ b/test/torchaudio_unittest/datasets/utils_test.py @@ -51,10 +51,10 @@ def test_walk_files(self): class TestIterator(TorchaudioTestCase): backend = 'default' - path = get_asset_path() + path = get_asset_path('CommonVoice', 'cv-corpus-4-2019-12-10', 'tt') def test_disckcache_iterator(self): - data = COMMONVOICE(self.path, version="cv-corpus-4-2019-12-10", language="tatar") + data = COMMONVOICE(self.path, url="tatar") data = dataset_utils.diskcache_iterator(data) # Save data[0] @@ -62,7 +62,7 @@ def test_disckcache_iterator(self): data[0] def test_bg_iterator(self): - data = COMMONVOICE(self.path, version="cv-corpus-4-2019-12-10", language="tatar") + data = COMMONVOICE(self.path, url="tatar") data = dataset_utils.bg_iterator(data, 5) for _ in data: pass diff --git a/test/torchaudio_unittest/soundfile_backend/load_test.py b/test/torchaudio_unittest/soundfile_backend/load_test.py index 8860263eed..4277ac03e9 100644 --- a/test/torchaudio_unittest/soundfile_backend/load_test.py +++ b/test/torchaudio_unittest/soundfile_backend/load_test.py @@ -1,4 +1,4 @@ -import itertools +import os from unittest.mock import patch import torch @@ -263,3 +263,39 @@ def test_sphere(self, dtype, sample_rate, num_channels, channels_first): def test_flac(self, dtype, sample_rate, num_channels, channels_first): """`soundfile_backend.load` can load flac format correctly.""" self.assert_flac(dtype, sample_rate, num_channels, channels_first) + + +@skipIfNoModule("soundfile") +class TestLoadFormat(TempDirMixin, PytorchTestCase): + """Given `format` parameter, `so.load` can load files without extension""" + original = None + path = None + + def _make_file(self, format_): + sample_rate = 8000 + path_with_ext = self.get_temp_path(f'test.{format_}') + data = get_wav_data('float32', num_channels=2).numpy().T + soundfile.write(path_with_ext, data, sample_rate) + expected = soundfile.read(path_with_ext, dtype='float32')[0].T + path = os.path.splitext(path_with_ext)[0] + os.rename(path_with_ext, path) + return path, expected + + def _test_format(self, format_): + """Providing format allows to read file without extension""" + path, expected = self._make_file(format_) + found, _ = soundfile_backend.load(path) + self.assertEqual(found, expected) + + @parameterized.expand([ + ('WAV', ), ('wav', ), + ]) + def test_wav(self, format_): + self._test_format(format_) + + @parameterized.expand([ + ('FLAC', ), ('flac',), + ]) + @skipIfFormatNotSupported("FLAC") + def test_flac(self, format_): + self._test_format(format_) diff --git a/test/torchaudio_unittest/sox_io_backend/load_test.py b/test/torchaudio_unittest/sox_io_backend/load_test.py index 156ad9ad3e..2b4e916861 100644 --- a/test/torchaudio_unittest/sox_io_backend/load_test.py +++ b/test/torchaudio_unittest/sox_io_backend/load_test.py @@ -1,3 +1,4 @@ +import os import itertools from torchaudio.backend import sox_io_backend @@ -333,7 +334,7 @@ def setUp(self): super().setUp() sample_rate = 8000 self.original = get_wav_data('float32', num_channels=2) - self.path = self.get_temp_path('test.wave') + self.path = self.get_temp_path('test.wav') save_wav(self.path, self.original, sample_rate) @parameterized.expand(list(itertools.product( @@ -352,3 +353,28 @@ def test_channels_first(self, channels_first): found, _ = sox_io_backend.load(self.path, channels_first=channels_first) expected = self.original if channels_first else self.original.transpose(1, 0) self.assertEqual(found, expected) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestLoadExtensionLess(TempDirMixin, PytorchTestCase): + """Given `format` parameter, `sox_io_backend.load` can load files without extension""" + original = None + path = None + + def _make_file(self, format_): + sample_rate = 8000 + path = self.get_temp_path(f'test.{format_}') + sox_utils.gen_audio_file(f'{path}', sample_rate, num_channels=2) + self.original = sox_io_backend.load(path)[0] + self.path = os.path.splitext(path)[0] + os.rename(path, self.path) + + @parameterized.expand([ + ('WAV', ), ('wav', ), ('MP3', ), ('mp3', ), ('FLAC', ), ('flac',), + ], name_func=name_func) + def test_format(self, format_): + """Providing format allows to read file without extension""" + self._make_file(format_) + found, _ = sox_io_backend.load(self.path) + self.assertEqual(found, self.original) diff --git a/third_party/CMakeLists.txt b/third_party/CMakeLists.txt index ad0eac582b..50c7696eb5 100644 --- a/third_party/CMakeLists.txt +++ b/third_party/CMakeLists.txt @@ -84,6 +84,7 @@ ExternalProject_Add(libsox DOWNLOAD_DIR ${ARCHIVE_DIR} URL https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2 URL_HASH SHA256=81a6956d4330e75b5827316e44ae381e6f1e8928003c6aa45896da9041ea149c + PATCH_COMMAND patch -p0 < ${CMAKE_CURRENT_SOURCE_DIR}/patch/libsox.patch # OpenMP is by default compiled against GNU OpenMP, which conflicts with the version of OpenMP that PyTorch uses. # See https://github.com/pytorch/audio/pull/1026 CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libsox/configure ${COMMON_ARGS} --with-lame --with-flac --with-mad --with-oggvorbis --without-alsa --without-coreaudio --without-png --without-oss --without-sndfile --with-opus --with-amrwb --with-amrnb --disable-openmp diff --git a/third_party/patch/libsox.patch b/third_party/patch/libsox.patch new file mode 100644 index 0000000000..996bafdd57 --- /dev/null +++ b/third_party/patch/libsox.patch @@ -0,0 +1,24 @@ +--- src/formats.c 2014-10-27 02:55:50.000000000 +0000 ++++ src/formats_new.c 2020-11-18 19:14:17.398689371 +0000 +@@ -91,6 +91,21 @@ + + if (ext && !strcasecmp(ext, "snd")) + CHECK(sndr , 7, 1, "" , 0, 2, "\0") ++ ++#if defined HAVE_MP3 && (defined STATIC_MP3 || !defined HAVE_LIBLTDL) ++ // http://www.mp3-tech.org/programmer/frame_header.html ++ // Check the first two bytes are ++ // expected 1111_1111 111X_X01X ++ // mask \xEEE6 (1111_1111 1110_0110) ++ // masked value \xEEE2 (1111_1111 1110_0010) ++ if (len >= 2 && !memcmp(data, "\xFF", 1)) { ++ unsigned char second_byte = data[1]; ++ unsigned char mask = 0xE6; ++ unsigned char expected = 0xE2; ++ if ((second_byte & mask) == expected) ++ return "mp3"; ++ } ++#endif + #undef CHECK + + #if HAVE_MAGIC diff --git a/torchaudio/datasets/cmuarctic.py b/torchaudio/datasets/cmuarctic.py index f8674127fd..9bc5577616 100644 --- a/torchaudio/datasets/cmuarctic.py +++ b/torchaudio/datasets/cmuarctic.py @@ -1,4 +1,5 @@ import os +import csv from pathlib import Path from typing import Tuple, Union @@ -8,7 +9,6 @@ from torchaudio.datasets.utils import ( download_url, extract_archive, - unicode_csv_reader, ) URL = "aew" @@ -154,7 +154,7 @@ def __init__(self, self._text = os.path.join(self._path, self._folder_text, self._file_text) with open(self._text, "r") as text: - walker = unicode_csv_reader(text, delimiter="\n") + walker = csv.reader(text, delimiter="\n") self._walker = list(walker) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: diff --git a/torchaudio/datasets/commonvoice.py b/torchaudio/datasets/commonvoice.py index 1a1f4273a4..d387a8ca2a 100644 --- a/torchaudio/datasets/commonvoice.py +++ b/torchaudio/datasets/commonvoice.py @@ -1,57 +1,13 @@ import os +import csv import warnings from pathlib import Path -from typing import List, Dict, Tuple, Optional, Union +from typing import List, Dict, Tuple, Union, Optional import torchaudio -from torchaudio.datasets.utils import extract_archive, unicode_csv_reader, validate_file from torch import Tensor from torch.utils.data import Dataset -# Default TSV should be one of -# dev.tsv -# invalidated.tsv -# other.tsv -# test.tsv -# train.tsv -# validated.tsv - -FOLDER_IN_ARCHIVE = "CommonVoice" -LANGUAGE = "english" -VERSION = "cv-corpus-5.1-2020-06-22" -TSV = "train.tsv" -_CHECKSUMS = { - "cv-corpus-5.1-2020-06-22/tt.tar.gz": None, - "cv-corpus-5.1-2020-06-22/en.tar.gz": None, - "cv-corpus-5.1-2020-06-22/de.tar.gz": None, - "cv-corpus-5.1-2020-06-22/fr.tar.gz": None, - "cv-corpus-5.1-2020-06-22/cy.tar.gz": None, - "cv-corpus-5.1-2020-06-22/br.tar.gz": None, - "cv-corpus-5.1-2020-06-22/cv.tar.gz": None, - "cv-corpus-5.1-2020-06-22/tr.tar.gz": None, - "cv-corpus-5.1-2020-06-22/ky.tar.gz": None, - "cv-corpus-5.1-2020-06-22/ga-IE.tar.gz": None, - "cv-corpus-5.1-2020-06-22/kab.tar.gz": None, - "cv-corpus-5.1-2020-06-22/ca.tar.gz": None, - "cv-corpus-5.1-2020-06-22/zh-TW.tar.gz": None, - "cv-corpus-5.1-2020-06-22/sl.tar.gz": None, - "cv-corpus-5.1-2020-06-22/it.tar.gz": None, - "cv-corpus-5.1-2020-06-22/nl.tar.gz": None, - "cv-corpus-5.1-2020-06-22/cnh.tar.gz": None, - "cv-corpus-5.1-2020-06-22/eo.tar.gz": None, - "cv-corpus-5.1-2020-06-22/et.tar.gz": None, - "cv-corpus-5.1-2020-06-22/fa.tar.gz": None, - "cv-corpus-5.1-2020-06-22/eu.tar.gz": None, - "cv-corpus-5.1-2020-06-22/es.tar.gz": None, - "cv-corpus-5.1-2020-06-22/zh-CN.tar.gz": None, - "cv-corpus-5.1-2020-06-22/mn.tar.gz": None, - "cv-corpus-5.1-2020-06-22/sah.tar.gz": None, - "cv-corpus-5.1-2020-06-22/dv.tar.gz": None, - "cv-corpus-5.1-2020-06-22/rw.tar.gz": None, - "cv-corpus-5.1-2020-06-22/sv-SE.tar.gz": None, - "cv-corpus-5.1-2020-06-22/ru.tar.gz": None, -} - def load_commonvoice_item(line: List[str], header: List[str], @@ -73,28 +29,19 @@ def load_commonvoice_item(line: List[str], class COMMONVOICE(Dataset): - """Create a Dataset for `CommonVoice `_. + """Create a Dataset for CommonVoice. Args: - root (str or Path): Path to the directory where the dataset is found or downloaded. - tsv (str, optional): The name of the tsv file used to construct the metadata. - (default: ``"train.tsv"``) - url (str, optional): Deprecated. - folder_in_archive (str, optional): The top-level directory of the dataset. - version (str): Version string. (default: ``"cv-corpus-5.1-2020-06-22"``) - language (str, optional): Language of the dataset. (default: None) - The following values are mapped to their corresponding shortened version: - ``"tatar"``, ``"english"``, ``"german"``, - ``"french"``, ``"welsh"``, ``"breton"``, ``"chuvash"``, ``"turkish"``, ``"kyrgyz"``, - ``"irish"``, ``"kabyle"``, ``"catalan"``, ``"taiwanese"``, ``"slovenian"``, - ``"italian"``, ``"dutch"``, ``"hakha chin"``, ``"esperanto"``, ``"estonian"``, - ``"persian"``, ``"portuguese"``, ``"basque"``, ``"spanish"``, ``"chinese"``, - ``"mongolian"``, ``"sakha"``, ``"dhivehi"``, ``"kinyarwanda"``, ``"swedish"``, - ``"russian"``, ``"indonesian"``, ``"arabic"``, ``"tamil"``, ``"interlingua"``, - ``"latvian"``, ``"japanese"``, ``"votic"``, ``"abkhaz"``, ``"cantonese"`` and - ``"romansh sursilvan"``. - For the other allowed values, Please checkout https://commonvoice.mozilla.org/en/datasets. - download (bool, optional): Deprecated. + root (str or Path): Path to the directory where the dataset is located. + (Where the ``tsv`` file is present.) + tsv (str, optional): + The name of the tsv file used to construct the metadata, such as + ``"train.tsv"``, ``"test.tsv"``, ``"dev.tsv"``, ``"invalidated.tsv"``, + ``"validated.tsv"`` and ``"other.tsv"``. (default: ``"train.tsv"``) + url (str, optional): Deprecated, not used. + folder_in_archive (str, optional): Deprecated, not used. + version (str): Deprecated, not used. + download (bool, optional): Deprecated, not used. """ _ext_txt = ".txt" @@ -103,125 +50,36 @@ class COMMONVOICE(Dataset): def __init__(self, root: Union[str, Path], - tsv: str = TSV, + tsv: str = "train.tsv", url: Optional[str] = None, - folder_in_archive: str = FOLDER_IN_ARCHIVE, - version: str = VERSION, - language: str = LANGUAGE, - download: Optional[bool] = False) -> None: - - if download is True: + folder_in_archive: Optional[str] = None, + version: Optional[str] = None, + download: Optional[bool] = None) -> None: + if download: raise RuntimeError( - "The dataset is no longer publicly accessible. You need to " - "download the archives externally and place them in the root " - "directory." - ) - elif download is False: - warnings.warn( - "The use of the download flag is deprecated, since the dataset " - "is no longer directly accessible.", RuntimeWarning - ) - - if url is not None: - warnings.warn( - "The use of the url flag is deprecated, since the dataset " - "is no longer publicly accessible. To specify the language of the dataset, " - "please use the language parameter instead.", RuntimeWarning - ) - - languages = { - "tatar": "tt", - "english": "en", - "german": "de", - "french": "fr", - "welsh": "cy", - "breton": "br", - "chuvash": "cv", - "turkish": "tr", - "kyrgyz": "ky", - "irish": "ga-IE", - "kabyle": "kab", - "catalan": "ca", - "taiwanese": "zh-TW", - "slovenian": "sl", - "italian": "it", - "dutch": "nl", - "hakha chin": "cnh", - "esperanto": "eo", - "estonian": "et", - "persian": "fa", - "portuguese": "pt", - "basque": "eu", - "spanish": "es", - "chinese": "zh-CN", - "mongolian": "mn", - "sakha": "sah", - "dhivehi": "dv", - "kinyarwanda": "rw", - "swedish": "sv-SE", - "russian": "ru", - "indonesian": "id", - "arabic": "ar", - "tamil": "ta", - "interlingua": "ia", - "latvian": "lv", - "japanese": "ja", - "votic": "vot", - "abkhaz": "ab", - "cantonese": "zh-HK", - "romansh sursilvan": "rm-sursilv" - } - - if language in languages: - ext_archive = ".tar.gz" - language = languages[language] - url = os.path.join(version, language + ext_archive) - else: - raise ValueError( - 'Allowed language values are "tatar", "english", "german",' - '"french", "welsh", "breton", "chuvash", "turkish", "kyrgyz",' - '"irish", "kabyle", "catalan", "taiwanese", "slovenian",' - '"italian", "dutch", "hakha chin", "esperanto", "estonian",' - '"persian", "portuguese", "basque", "spanish", "chinese",' - '"mongolian", "sakha", "dhivehi", "kinyarwanda", "swedish",' - '"russian", "indonesian", "arabic", "tamil", "interlingua",' - '"latvian", "japanese", "votic", "abkhaz", "cantonese" and' - '"romansh sursilvan".' - ) + "Common Voice dataset requires user agreement on the usage term, " + "and torchaudio no longer provides the download feature. " + "Please download the dataset and extract it manually.") + + deprecated = [ + ('url', url), + ('folder_in_archive', folder_in_archive), + ('version', version), + ('download', download) + ] + for name, val in deprecated: + if val is not None: + warnings.warn( + f"`{name}` argument is no longer used and deprecated. " + "It will be removed in 0.9.0 releaase. " + "Please remove it from the function call") # Get string representation of 'root' in case Path object is passed - root = os.fspath(root) - - basename = os.path.basename(url) - archive = os.path.join(root, basename) - - basename = basename.rsplit(".", 2)[0] - folder_in_archive = os.path.join(folder_in_archive, version, basename) - - self._path = os.path.join(root, folder_in_archive) - - if not os.path.isdir(self._path): - if os.path.isfile(archive): - checksum = _CHECKSUMS.get(url, None) - if checksum: - filepath = os.path.basename(url) - with open(filepath, "rb") as file_obj: - if not validate_file(file_obj, checksum, "sha256"): - raise RuntimeError( - f"The hash of {filepath} does not match. Delete the file manually and retry." - ) - extract_archive(archive) - else: - raise RuntimeError( - "The dataset is no longer publicly accessible. You need to " - "download the archives externally and place them in the root " - "directory." - ) - - self._tsv = os.path.join(root, folder_in_archive, tsv) - - with open(self._tsv, "r") as tsv: - walker = unicode_csv_reader(tsv, delimiter="\t") + self._path = os.fspath(root) + self._tsv = os.path.join(self._path, tsv) + + with open(self._tsv, "r") as tsv_: + walker = csv.reader(tsv_, delimiter="\t") self._header = next(walker) self._walker = list(walker) diff --git a/torchaudio/datasets/librispeech.py b/torchaudio/datasets/librispeech.py index c13fb312b8..8447c4c8a2 100644 --- a/torchaudio/datasets/librispeech.py +++ b/torchaudio/datasets/librispeech.py @@ -8,7 +8,6 @@ from torchaudio.datasets.utils import ( download_url, extract_archive, - walk_files, ) URL = "train-clean-100" @@ -125,10 +124,7 @@ def __init__(self, download_url(url, root, hash_value=checksum) extract_archive(archive) - walker = walk_files( - self._path, suffix=self._ext_audio, prefix=False, remove_suffix=True - ) - self._walker = list(walker) + self._walker = sorted(str(p.stem) for p in Path(self._path).glob('*/*/*' + self._ext_audio)) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]: """Load the n-th sample from the dataset. diff --git a/torchaudio/datasets/libritts.py b/torchaudio/datasets/libritts.py index 8ed57d9b52..9f0c38a751 100644 --- a/torchaudio/datasets/libritts.py +++ b/torchaudio/datasets/libritts.py @@ -8,7 +8,6 @@ from torchaudio.datasets.utils import ( download_url, extract_archive, - walk_files, ) URL = "train-clean-100" @@ -126,10 +125,7 @@ def __init__( download_url(url, root, hash_value=checksum) extract_archive(archive) - walker = walk_files( - self._path, suffix=self._ext_audio, prefix=False, remove_suffix=True - ) - self._walker = list(walker) + self._walker = sorted(str(p.stem) for p in Path(self._path).glob('*/*/*' + self._ext_audio)) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int, int, str]: """Load the n-th sample from the dataset. diff --git a/torchaudio/datasets/ljspeech.py b/torchaudio/datasets/ljspeech.py index 2d653fbf46..01d3fe7d95 100644 --- a/torchaudio/datasets/ljspeech.py +++ b/torchaudio/datasets/ljspeech.py @@ -4,7 +4,7 @@ from pathlib import Path import torchaudio -from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader +from torchaudio.datasets.utils import download_url, extract_archive from torch import Tensor from torch.utils.data import Dataset @@ -75,7 +75,7 @@ def __init__(self, extract_archive(archive) with open(self._metadata_path, "r", newline='') as metadata: - walker = unicode_csv_reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE) + walker = csv.reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE) self._walker = list(walker) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: diff --git a/torchaudio/datasets/speechcommands.py b/torchaudio/datasets/speechcommands.py index 7d774da943..5264ea24de 100644 --- a/torchaudio/datasets/speechcommands.py +++ b/torchaudio/datasets/speechcommands.py @@ -8,7 +8,6 @@ from torchaudio.datasets.utils import ( download_url, extract_archive, - walk_files ) FOLDER_IN_ARCHIVE = "SpeechCommands" @@ -110,7 +109,7 @@ def __init__(self, self._walker = _load_list(self._path, "testing_list.txt") elif subset == "training": excludes = set(_load_list(self._path, "validation_list.txt", "testing_list.txt")) - walker = walk_files(self._path, suffix=".wav", prefix=True) + walker = sorted(str(p) for p in Path(self._path).glob('*/*.wav')) self._walker = [ w for w in walker if HASH_DIVIDER in w @@ -118,7 +117,7 @@ def __init__(self, and os.path.normpath(w) not in excludes ] else: - walker = walk_files(self._path, suffix=".wav", prefix=True) + walker = sorted(str(p) for p in Path(self._path).glob('*/*.wav')) self._walker = [w for w in walker if HASH_DIVIDER in w and EXCEPT_FOLDER not in w] def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int]: diff --git a/torchaudio/datasets/utils.py b/torchaudio/datasets/utils.py index d00ffc143b..c187d0d814 100644 --- a/torchaudio/datasets/utils.py +++ b/torchaudio/datasets/utils.py @@ -1,4 +1,3 @@ -import csv import errno import hashlib import logging @@ -18,49 +17,6 @@ from torch.utils.model_zoo import tqdm -def unicode_csv_reader(unicode_csv_data: TextIOWrapper, **kwargs: Any) -> Any: - r"""Since the standard csv library does not handle unicode in Python 2, we need a wrapper. - Borrowed and slightly modified from the Python docs: - https://docs.python.org/2/library/csv.html#csv-examples - Args: - unicode_csv_data (TextIOWrapper): unicode csv data (see example below) - - Examples: - >>> from torchaudio.datasets.utils import unicode_csv_reader - >>> import io - >>> with io.open(data_path, encoding="utf8") as f: - >>> reader = unicode_csv_reader(f) - """ - - # Fix field larger than field limit error - maxInt = sys.maxsize - while True: - # decrease the maxInt value by factor 10 - # as long as the OverflowError occurs. - try: - csv.field_size_limit(maxInt) - break - except OverflowError: - maxInt = int(maxInt / 10) - csv.field_size_limit(maxInt) - - for line in csv.reader(unicode_csv_data, **kwargs): - yield line - - -def makedir_exist_ok(dirpath: str) -> None: - """ - Python2 support for os.makedirs(.., exist_ok=True) - """ - try: - os.makedirs(dirpath) - except OSError as e: - if e.errno == errno.EEXIST: - pass - else: - raise - - def stream_url(url: str, start_byte: Optional[int] = None, block_size: int = 32 * 1024, @@ -305,7 +261,7 @@ def __getitem__(self, n: int) -> Any: item = self.dataset[n] self._cache[n] = f - makedir_exist_ok(self.location) + os.makedirs(self.location, exist_ok=True) torch.save(item, f) return item diff --git a/torchaudio/datasets/vctk.py b/torchaudio/datasets/vctk.py index 9bf843f685..c71962bad8 100644 --- a/torchaudio/datasets/vctk.py +++ b/torchaudio/datasets/vctk.py @@ -9,7 +9,6 @@ from torchaudio.datasets.utils import ( download_url, extract_archive, - walk_files ) URL = "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip" @@ -123,9 +122,7 @@ def __init__(self, "with `download=True` to donwload the latest version." ) - walker = walk_files( - self._path, suffix=self._ext_audio, prefix=False, remove_suffix=True - ) + walker = sorted(str(p.stem) for p in Path(self._path).glob('**/*' + self._ext_audio)) walker = filter(lambda w: self._except_folder not in w, walker) self._walker = list(walker) diff --git a/torchaudio/datasets/yesno.py b/torchaudio/datasets/yesno.py index 182d224ba4..21d67f8ecc 100644 --- a/torchaudio/datasets/yesno.py +++ b/torchaudio/datasets/yesno.py @@ -9,7 +9,6 @@ from torchaudio.datasets.utils import ( download_url, extract_archive, - walk_files ) URL = "http://www.openslr.org/resources/1/waves_yesno.tar.gz" @@ -85,10 +84,7 @@ def __init__(self, "Dataset not found. Please use `download=True` to download it." ) - walker = walk_files( - self._path, suffix=self._ext_audio, prefix=False, remove_suffix=True - ) - self._walker = list(walker) + self._walker = sorted(str(p.stem) for p in Path(self._path).glob('*' + self._ext_audio)) def __getitem__(self, n: int) -> Tuple[Tensor, int, List[int]]: """Load the n-th sample from the dataset. diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index 20b71e4e33..3fedf93d09 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -24,7 +24,6 @@ "angle", "magphase", "phase_vocoder", - "detect_pitch_frequency", 'mask_along_axis', 'mask_along_axis_iid', 'sliding_window_cmn', @@ -71,8 +70,19 @@ def spectrogram( waveform = waveform.reshape(-1, shape[-1]) # default values are consistent with librosa.core.spectrum._spectrogram - spec_f = torch.stft( - waveform, n_fft, hop_length, win_length, window, True, "reflect", False, True + spec_f = torch.view_as_real( + torch.stft( + input=waveform, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + center=True, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) ) # unpack batch @@ -175,8 +185,20 @@ def griffinlim( length=length).float() # Rebuild the spectrogram - rebuilt = torch.stft(inverse, n_fft, hop_length, win_length, window, - True, 'reflect', False, True) + rebuilt = torch.view_as_real( + torch.stft( + input=inverse, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + center=True, + pad_mode='reflect', + normalized=False, + onesided=True, + return_complex=True, + ) + ) # Update our phase estimates angles = rebuilt diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 857df5d899..82ee613ba0 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -226,7 +226,7 @@ def apply_effects_file( ... self.sample_rate = sample_rate ... ... def __getitem__(self, index): - ... speed = 0.5 + 1.5 * torch.rand() + ... speed = 0.5 + 1.5 * random.randn() ... effects = [ ... ['gain', '-n', '-10'], # apply 10 db attenuation ... ['remix', '-'], # merge all the channels From 17c0f55ea4459a8a3181b140b526771ebb2404b2 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Mon, 11 Jan 2021 13:01:15 -0800 Subject: [PATCH 25/73] Import torchaudio #1161 7a36c55 Summary: Import torchaudio #1161 7a36c55 Reviewed By: cpuhrsch Differential Revision: D25827050 fbshipit-source-id: 31e07ace85f7e1417884cd721bc80c5c6c33960f --- .circleci/config.yml | 14 +- .circleci/config.yml.in | 14 +- .circleci/torchscript_bc_test/common.sh | 3 +- .circleci/unittest/linux/scripts/install.sh | 6 +- .circleci/unittest/linux/scripts/run_test.sh | 2 +- .circleci/unittest/linux/scripts/setup_env.sh | 1 + .gitignore | 4 +- .gitmodules | 4 + build_tools/setup_helpers/extension.py | 48 ++- packaging/build_wheel.sh | 2 +- packaging/pkg_helpers.bash | 1 + packaging/torchaudio/build.sh | 2 +- setup.py | 4 +- test/torchaudio_unittest/README.md | 2 +- .../tt/clips/common_voice_tt_00000000.wav | Bin 80700 -> 0 bytes .../cv-corpus-4-2019-12-10/tt/train.tsv | 3 - .../assets/mp3_without_ext | Bin 0 -> 15453 bytes .../common_utils/__init__.py | 1 + .../common_utils/case_utils.py | 28 ++ .../datasets/cmuarctic_test.py | 75 ++--- .../datasets/commonvoice_test.py | 145 ++++++--- .../datasets/gtzan_test.py | 55 ++-- .../datasets/librispeech_test.py | 119 ++++---- .../datasets/libritts_test.py | 76 ++--- .../datasets/ljspeech_test.py | 92 +++--- .../datasets/speechcommands_test.py | 102 ++++--- .../datasets/tedlium_test.py | 124 ++++---- .../datasets/utils_test.py | 63 +--- .../torchaudio_unittest/datasets/vctk_test.py | 92 +++--- .../datasets/yesno_test.py | 26 +- .../functional/functional_cpu_test.py | 107 ++++--- .../functional/functional_cuda_test.py | 14 +- .../functional/functional_impl.py | 23 ++ .../kaldi_compatibility_impl.py | 2 +- .../librosa_compatibility_test.py | 79 ++--- .../soundfile_backend/load_test.py | 56 ++++ .../sox_effect/sox_effect_test.py | 19 ++ .../sox_io_backend/info_test.py | 17 ++ .../sox_io_backend/load_test.py | 193 ++++++++++-- test/torchaudio_unittest/transducer_test.py | 276 ++++++++++++++++++ third_party/CMakeLists.txt | 95 +----- third_party/build_codec_helper.sh | 13 - third_party/patch/libsox.patch | 24 -- third_party/sox/CMakeLists.txt | 95 ++++++ third_party/{ => sox}/patch/libmad.patch | 0 third_party/transducer/CMakeLists.txt | 27 ++ torchaudio/backend/_soundfile_backend.py | 19 +- torchaudio/backend/sox_io_backend.py | 45 ++- torchaudio/csrc/{sox.cpp => pybind.cpp} | 185 +----------- .../csrc/{sox_effects.cpp => sox/effects.cpp} | 94 +++++- .../csrc/{sox_effects.h => sox/effects.h} | 20 +- .../effects_chain.cpp} | 141 ++++++++- .../effects_chain.h} | 17 +- torchaudio/csrc/{sox_io.cpp => sox/io.cpp} | 53 +++- torchaudio/csrc/{sox_io.h => sox/io.h} | 25 +- torchaudio/csrc/sox/legacy.cpp | 174 +++++++++++ torchaudio/csrc/{sox.h => sox/legacy.h} | 11 +- torchaudio/csrc/{ => sox}/register.cpp | 21 +- .../csrc/{sox_utils.cpp => sox/utils.cpp} | 8 +- torchaudio/csrc/{sox_utils.h => sox/utils.h} | 2 +- torchaudio/csrc/transducer.cpp | 93 ++++++ torchaudio/datasets/commonvoice.py | 14 +- torchaudio/datasets/utils.py | 55 +--- torchaudio/datasets/vctk.py | 39 +-- torchaudio/datasets/yesno.py | 28 +- torchaudio/functional/functional.py | 33 +-- torchaudio/prototype/__init__.py | 0 torchaudio/prototype/transducer.py | 159 ++++++++++ torchaudio/sox_effects/sox_effects.py | 10 +- 69 files changed, 2327 insertions(+), 1067 deletions(-) create mode 100644 .gitmodules delete mode 100644 test/torchaudio_unittest/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav delete mode 100644 test/torchaudio_unittest/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/train.tsv create mode 100644 test/torchaudio_unittest/assets/mp3_without_ext create mode 100644 test/torchaudio_unittest/transducer_test.py delete mode 100755 third_party/build_codec_helper.sh delete mode 100644 third_party/patch/libsox.patch create mode 100644 third_party/sox/CMakeLists.txt rename third_party/{ => sox}/patch/libmad.patch (100%) create mode 100755 third_party/transducer/CMakeLists.txt rename torchaudio/csrc/{sox.cpp => pybind.cpp} (52%) rename torchaudio/csrc/{sox_effects.cpp => sox/effects.cpp} (51%) rename torchaudio/csrc/{sox_effects.h => sox/effects.h} (54%) rename torchaudio/csrc/{sox_effects_chain.cpp => sox/effects_chain.cpp} (64%) rename torchaudio/csrc/{sox_effects_chain.h => sox/effects_chain.h} (78%) rename torchaudio/csrc/{sox_io.cpp => sox/io.cpp} (72%) rename torchaudio/csrc/{sox_io.h => sox/io.h} (56%) create mode 100644 torchaudio/csrc/sox/legacy.cpp rename torchaudio/csrc/{sox.h => sox/legacy.h} (91%) rename torchaudio/csrc/{ => sox}/register.cpp (85%) rename torchaudio/csrc/{sox_utils.cpp => sox/utils.cpp} (97%) rename torchaudio/csrc/{sox_utils.h => sox/utils.h} (98%) create mode 100644 torchaudio/csrc/transducer.cpp create mode 100644 torchaudio/prototype/__init__.py create mode 100644 torchaudio/prototype/transducer.py diff --git a/.circleci/config.yml b/.circleci/config.yml index ee442189e4..d51bb3ca96 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -101,14 +101,14 @@ jobs: - run: command: | - mkdir -p third_party/archives/ - wget --no-clobber --directory-prefix=third_party/archives/ $(awk '/URL /{print $2}' third_party/CMakeLists.txt) + mkdir -p third_party/sox/archives/ + wget --no-clobber --directory-prefix=third_party/sox/archives/ $(awk '/URL /{print $2}' third_party/sox/CMakeLists.txt) - save_cache: key: tp-nix-v2-{{ checksum ".cachekey" }} paths: - - third_party/archives + - third_party/sox/archives - persist_to_workspace: root: third_party paths: @@ -411,9 +411,7 @@ jobs: paths: - conda - env - - third_party/build - third_party/install - - third_party/src - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -458,9 +456,7 @@ jobs: paths: - conda - env - - third_party/build - third_party/install - - third_party/src - run: name: Install torchaudio command: docker run -t --gpus all -e UPLOAD_CHANNEL -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh @@ -573,9 +569,7 @@ jobs: paths: - conda - env - - third_party/build - third_party/install - - third_party/src - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -612,9 +606,7 @@ jobs: paths: - conda - env - - third_party/build - third_party/install - - third_party/src - run: name: Run style check command: .circleci/unittest/linux/scripts/run_style_checks.sh diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 5c9c4d7693..9f6f41a379 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -101,14 +101,14 @@ jobs: {% endraw %} - run: command: | - mkdir -p third_party/archives/ - wget --no-clobber --directory-prefix=third_party/archives/ $(awk '/URL /{print $2}' third_party/CMakeLists.txt) + mkdir -p third_party/sox/archives/ + wget --no-clobber --directory-prefix=third_party/sox/archives/ $(awk '/URL /{print $2}' third_party/sox/CMakeLists.txt) - save_cache: {% raw %} key: tp-nix-v2-{{ checksum ".cachekey" }} {% endraw %} paths: - - third_party/archives + - third_party/sox/archives - persist_to_workspace: root: third_party paths: @@ -411,9 +411,7 @@ jobs: paths: - conda - env - - third_party/build - third_party/install - - third_party/src - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -458,9 +456,7 @@ jobs: paths: - conda - env - - third_party/build - third_party/install - - third_party/src - run: name: Install torchaudio command: docker run -t --gpus all -e UPLOAD_CHANNEL -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh @@ -573,9 +569,7 @@ jobs: paths: - conda - env - - third_party/build - third_party/install - - third_party/src - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -612,9 +606,7 @@ jobs: paths: - conda - env - - third_party/build - third_party/install - - third_party/src - run: name: Run style check command: .circleci/unittest/linux/scripts/run_style_checks.sh diff --git a/.circleci/torchscript_bc_test/common.sh b/.circleci/torchscript_bc_test/common.sh index 24ad5e45fb..6f64e7204c 100644 --- a/.circleci/torchscript_bc_test/common.sh +++ b/.circleci/torchscript_bc_test/common.sh @@ -66,5 +66,6 @@ build_master() { conda install -y -q pytorch "cpuonly" -c pytorch-nightly printf "* Installing torchaudio\n" cd "${_root_dir}" || exit 1 - BUILD_SOX=1 python setup.py clean install + git submodule update --init --recursive + BUILD_TRANSDUCER=1 BUILD_SOX=1 python setup.py clean install } diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh index 27dec79251..72b7c9c88f 100755 --- a/.circleci/unittest/linux/scripts/install.sh +++ b/.circleci/unittest/linux/scripts/install.sh @@ -38,7 +38,7 @@ conda install -y -c "pytorch-${UPLOAD_CHANNEL}" pytorch ${cudatoolkit} # 2. Install torchaudio printf "* Installing torchaudio\n" -BUILD_SOX=1 python setup.py install +BUILD_TRANSDUCER=1 BUILD_SOX=1 python setup.py install # 3. Install Test tools printf "* Installing test tools\n" @@ -46,9 +46,9 @@ if [ "${os}" == Linux ] ; then # TODO: move this to docker apt install -y -q libsndfile1 conda install -y -c conda-forge codecov pytest pytest-cov - pip install kaldi-io 'librosa>=0.8.0' parameterized SoundFile scipy + pip install kaldi-io 'librosa>=0.8.0' parameterized SoundFile scipy 'requests>=2.20' else # Note: installing librosa via pip fail because it will try to compile numba. conda install -y -c conda-forge codecov pytest pytest-cov 'librosa>=0.8.0' parameterized scipy - pip install kaldi-io SoundFile + pip install kaldi-io SoundFile 'requests>=2.20' fi diff --git a/.circleci/unittest/linux/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh index e9016d266c..2f9c10476d 100755 --- a/.circleci/unittest/linux/scripts/run_test.sh +++ b/.circleci/unittest/linux/scripts/run_test.sh @@ -17,4 +17,4 @@ declare -a args=( ) cd test -pytest "${args[@]}" torchaudio_unittest +pytest "${args[@]}" torchaudio_unittest -k "not fileobj" diff --git a/.circleci/unittest/linux/scripts/setup_env.sh b/.circleci/unittest/linux/scripts/setup_env.sh index 26292a00d5..f56b211ac9 100755 --- a/.circleci/unittest/linux/scripts/setup_env.sh +++ b/.circleci/unittest/linux/scripts/setup_env.sh @@ -43,6 +43,7 @@ conda activate "${env_dir}" pip --quiet install cmake ninja # 4. Buld codecs +git submodule update --init --recursive mkdir -p third_party/build ( cd third_party/build diff --git a/.gitignore b/.gitignore index e8a9d4c409..ae628691f5 100644 --- a/.gitignore +++ b/.gitignore @@ -123,7 +123,5 @@ examples/interactive_asr/data/*.model examples/interactive_asr/data/*.pt # third parties -third_party/archives/ third_party/install/ -third_party/src/ -third_party/tmp/ +third_party/sox/archives/ diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000000..c01f8c91ad --- /dev/null +++ b/.gitmodules @@ -0,0 +1,4 @@ +[submodule "third_party/warp_transducer/submodule"] + path = third_party/transducer/submodule + url = https://github.com/HawkAaron/warp-transducer + ignore = dirty diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 8e6cd337cb..0dc306f07e 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -20,20 +20,21 @@ _TP_INSTALL_DIR = _TP_BASE_DIR / 'install' -def _get_build_sox(): - val = os.environ.get('BUILD_SOX', '0') +def _get_build(var): + val = os.environ.get(var, '0') trues = ['1', 'true', 'TRUE', 'on', 'ON', 'yes', 'YES'] falses = ['0', 'false', 'FALSE', 'off', 'OFF', 'no', 'NO'] if val in trues: return True if val not in falses: print( - f'WARNING: Unexpected environment variable value `BUILD_SOX={val}`. ' + f'WARNING: Unexpected environment variable value `{var}={val}`. ' f'Expected one of {trues + falses}') return False -_BUILD_SOX = _get_build_sox() +_BUILD_SOX = _get_build("BUILD_SOX") +_BUILD_TRANSDUCER = _get_build("BUILD_TRANSDUCER") def _get_eca(debug): @@ -42,6 +43,8 @@ def _get_eca(debug): eca += ["-O0", "-g"] else: eca += ["-O3"] + if _BUILD_TRANSDUCER: + eca += ['-DBUILD_TRANSDUCER'] return eca @@ -58,27 +61,31 @@ def _get_ela(debug): def _get_srcs(): - return [str(p) for p in _CSRC_DIR.glob('**/*.cpp')] + srcs = [_CSRC_DIR / 'pybind.cpp'] + srcs += list(_CSRC_DIR.glob('sox/**/*.cpp')) + if _BUILD_TRANSDUCER: + srcs += [_CSRC_DIR / 'transducer.cpp'] + return [str(path) for path in srcs] def _get_include_dirs(): dirs = [ str(_ROOT_DIR), ] - if _BUILD_SOX: + if _BUILD_SOX or _BUILD_TRANSDUCER: dirs.append(str(_TP_INSTALL_DIR / 'include')) return dirs def _get_extra_objects(): - objs = [] + libs = [] if _BUILD_SOX: # NOTE: The order of the library listed bellow matters. # # (the most important thing is that dependencies come after a library # e.g., sox comes first, flac/vorbis comes before ogg, and # vorbisenc/vorbisfile comes before vorbis - libs = [ + libs += [ 'libsox.a', 'libmad.a', 'libFLAC.a', @@ -92,25 +99,34 @@ def _get_extra_objects(): 'libopencore-amrnb.a', 'libopencore-amrwb.a', ] - for lib in libs: - objs.append(str(_TP_INSTALL_DIR / 'lib' / lib)) - return objs + if _BUILD_TRANSDUCER: + libs += ['libwarprnnt.a'] + + return [str(_TP_INSTALL_DIR / 'lib' / lib) for lib in libs] def _get_libraries(): return [] if _BUILD_SOX else ['sox'] -def _build_third_party(): - build_dir = str(_TP_BASE_DIR / 'build') +def _build_third_party(base_build_dir): + build_dir = os.path.join(base_build_dir, 'third_party') os.makedirs(build_dir, exist_ok=True) subprocess.run( - args=['cmake', '..'], + args=[ + 'cmake', + f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', + f'-DBUILD_SOX={"ON" if _BUILD_SOX else "OFF"}', + f'-DBUILD_TRANSDUCER={"ON" if _BUILD_TRANSDUCER else "OFF"}', + f'{_TP_BASE_DIR}'], cwd=build_dir, check=True, ) + command = ['cmake', '--build', '.'] + if _BUILD_TRANSDUCER: + command += ['--target', 'install'] subprocess.run( - args=['cmake', '--build', '.'], + args=command, cwd=build_dir, check=True, ) @@ -138,5 +154,5 @@ def get_ext_modules(debug=False): class BuildExtension(TorchBuildExtension): def build_extension(self, ext): if ext.name == _EXT_NAME and _BUILD_SOX: - _build_third_party() + _build_third_party(self.build_temp) super().build_extension(ext) diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index ad45a08d59..957b41e048 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -15,5 +15,5 @@ if [[ "$OSTYPE" == "msys" ]]; then python_tag="$(echo "cp$PYTHON_VERSION" | tr -d '.')" python setup.py bdist_wheel --plat-name win_amd64 --python-tag $python_tag else - BUILD_SOX=1 python setup.py bdist_wheel + BUILD_TRANSDUCER=1 BUILD_SOX=1 python setup.py bdist_wheel fi diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index 3316f789e2..635bfa3a14 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -103,6 +103,7 @@ setup_macos() { # # Usage: setup_env 0.2.0 setup_env() { + git submodule update --init --recursive setup_cuda setup_build_version "$1" setup_macos diff --git a/packaging/torchaudio/build.sh b/packaging/torchaudio/build.sh index 99c17b6913..88bbfce375 100644 --- a/packaging/torchaudio/build.sh +++ b/packaging/torchaudio/build.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash set -ex -BUILD_SOX=1 python setup.py install --single-version-externally-managed --record=record.txt +BUILD_TRANSDUCER=1 BUILD_SOX=1 python setup.py install --single-version-externally-managed --record=record.txt diff --git a/setup.py b/setup.py index 4ced5fc8fd..8168472ecf 100644 --- a/setup.py +++ b/setup.py @@ -50,7 +50,6 @@ def run(self): # Remove build directory build_dirs = [ ROOT_DIR / 'build', - ROOT_DIR / 'third_party' / 'build', ] for path in build_dirs: if path.exists(): @@ -83,7 +82,8 @@ def run(self): packages=find_packages(exclude=["build*", "test*", "torchaudio.csrc*", "third_party*", "build_tools*"]), ext_modules=setup_helpers.get_ext_modules(), cmdclass={ - 'build_ext': setup_helpers.BuildExtension.with_options(no_python_abi_suffix=True) + 'build_ext': setup_helpers.BuildExtension.with_options(no_python_abi_suffix=True), + 'clean': clean, }, install_requires=[pytorch_package_dep], zip_safe=False, diff --git a/test/torchaudio_unittest/README.md b/test/torchaudio_unittest/README.md index 119c644687..50d17d9948 100644 --- a/test/torchaudio_unittest/README.md +++ b/test/torchaudio_unittest/README.md @@ -2,7 +2,7 @@ ## How to run test -You can use `pytest` to run `torchaudio`'s test suites. See https://docs.pytest.org/ for the detail of how to use `pytest` command. +You can use `pytest` to run `torchaudio`'s test suites. See https://docs.pytest.org/ for the detail of how to use `pytest` command. The `parametrized` package is also needed; it can be installed via `pip` or with ` conda install -c conda-forge parameterized`. ``` # List up all the tests diff --git a/test/torchaudio_unittest/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav b/test/torchaudio_unittest/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav deleted file mode 100644 index 8f9d80d7e152b418703d0421f8ea5e0b710d6a89..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 80700 zcmZU31#}ci*LL@~yJj+zOxzPf5-d0bcL)x_-CY)UcLFRdy0`||#UX?Q0wL~`ad-Eb z{@HKOd*1Imf6b}ss^{LP>dx)cXR527u|o<9a(aNk#J+_C=Pz3uO#uJ^{PpP*1pqsG z0RRl30CQ)pokjWO^WOpj(691e1cHG7LH^RP{ogVCH=2J~|JU!|`TxQGFaCdf|MLFD{{8;{$NVqy-`2lT{=@huo`3Lv>VL-m zH&_4uu75fI&BA|t{*#~oUjH5QzjOR=&i`ip+x}=z3EpBjWvKoG(J{$HK}2=}X!f3@L22(th3!UGye0r3F@zyMgkdJWJK00+>3Zop|s z2dxB5;gt{(Kmt9%93UDR@XKxskAXDd;lK{S2u%dIU>A@F>YxJPAXEx90+RqHa2OJY zSA#17cZdSGf3eGeOo#$b1(6{OgaT7R6&13^Mb2^pTWPvTx1A1 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TorchaudioTestCase, diff --git a/test/torchaudio_unittest/common_utils/case_utils.py b/test/torchaudio_unittest/common_utils/case_utils.py index 2e0a17b5da..9a9d491541 100644 --- a/test/torchaudio_unittest/common_utils/case_utils.py +++ b/test/torchaudio_unittest/common_utils/case_utils.py @@ -1,6 +1,8 @@ import shutil import os.path +import subprocess import tempfile +import time import unittest import torch @@ -40,6 +42,32 @@ def get_temp_path(self, *paths): return path +class HttpServerMixin(TempDirMixin): + """Mixin that serves temporary directory as web server + + This class creates temporary directory and serve the directory as HTTP service. + The server is up through the execution of all the test suite defined under the subclass. + """ + _proc = None + _port = 8000 + + @classmethod + def setUpClass(cls): + super().setUpClass() + cls._proc = subprocess.Popen( + ['python', '-m', 'http.server', f'{cls._port}'], + cwd=cls.get_base_temp_dir()) + time.sleep(1.0) + + @classmethod + def tearDownClass(cls): + super().tearDownClass() + cls._proc.kill() + + def get_url(self, *route): + return f'http://localhost:{self._port}/{self.id()}/{"/".join(route)}' + + class TestBaseMixin: """Mixin to provide consistent way to define device/dtype/backend aware TestCase""" dtype = None diff --git a/test/torchaudio_unittest/datasets/cmuarctic_test.py b/test/torchaudio_unittest/datasets/cmuarctic_test.py index f8f7969c25..dd91edafca 100644 --- a/test/torchaudio_unittest/datasets/cmuarctic_test.py +++ b/test/torchaudio_unittest/datasets/cmuarctic_test.py @@ -12,6 +12,47 @@ ) +def get_mock_dataset(root_dir): + """ + root_dir: directory to the mocked dataset + """ + mocked_data = [] + sample_rate = 16000 + utterance = "This is a test utterance." + + base_dir = os.path.join(root_dir, "ARCTIC", "cmu_us_aew_arctic") + txt_dir = os.path.join(base_dir, "etc") + os.makedirs(txt_dir, exist_ok=True) + txt_file = os.path.join(txt_dir, "txt.done.data") + audio_dir = os.path.join(base_dir, "wav") + os.makedirs(audio_dir, exist_ok=True) + + seed = 42 + with open(txt_file, "w") as txt: + for c in ["a", "b"]: + for i in range(5): + utterance_id = f"arctic_{c}{i:04d}" + path = os.path.join(audio_dir, f"{utterance_id}.wav") + data = get_whitenoise( + sample_rate=sample_rate, + duration=3, + n_channels=1, + dtype="int16", + seed=seed, + ) + save_wav(path, data, sample_rate) + sample = ( + normalize_wav(data), + sample_rate, + utterance, + utterance_id.split("_")[1], + ) + mocked_data.append(sample) + txt.write(f'( {utterance_id} "{utterance}" )\n') + seed += 1 + return mocked_data + + class TestCMUARCTIC(TempDirMixin, TorchaudioTestCase): backend = "default" @@ -21,39 +62,7 @@ class TestCMUARCTIC(TempDirMixin, TorchaudioTestCase): @classmethod def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() - sample_rate = 16000 - utterance = "This is a test utterance." - - base_dir = os.path.join(cls.root_dir, "ARCTIC", "cmu_us_aew_arctic") - txt_dir = os.path.join(base_dir, "etc") - os.makedirs(txt_dir, exist_ok=True) - txt_file = os.path.join(txt_dir, "txt.done.data") - audio_dir = os.path.join(base_dir, "wav") - os.makedirs(audio_dir, exist_ok=True) - - seed = 42 - with open(txt_file, "w") as txt: - for c in ["a", "b"]: - for i in range(5): - utterance_id = f"arctic_{c}{i:04d}" - path = os.path.join(audio_dir, f"{utterance_id}.wav") - data = get_whitenoise( - sample_rate=sample_rate, - duration=3, - n_channels=1, - dtype="int16", - seed=seed, - ) - save_wav(path, data, sample_rate) - sample = ( - normalize_wav(data), - sample_rate, - utterance, - utterance_id.split("_")[1], - ) - cls.samples.append(sample) - txt.write(f'( {utterance_id} "{utterance}" )\n') - seed += 1 + cls.samples = get_mock_dataset(cls.root_dir) def _test_cmuarctic(self, dataset): n_ite = 0 diff --git a/test/torchaudio_unittest/datasets/commonvoice_test.py b/test/torchaudio_unittest/datasets/commonvoice_test.py index 22c811a67b..4d7c269f2a 100644 --- a/test/torchaudio_unittest/datasets/commonvoice_test.py +++ b/test/torchaudio_unittest/datasets/commonvoice_test.py @@ -1,8 +1,9 @@ -import os import csv +import os from pathlib import Path +from typing import Tuple, Dict -from torchaudio.datasets import COMMONVOICE +from torch import Tensor from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, @@ -11,47 +12,97 @@ normalize_wav, ) +from torchaudio.datasets import COMMONVOICE -class TestCommonVoice(TempDirMixin, TorchaudioTestCase): - backend = 'default' +_ORIGINAL_EXT_AUDIO = COMMONVOICE._ext_audio +_SAMPLE_RATE = 48000 +_HEADERS = [u"client_ids", u"path", u"sentence", u"up_votes", u"down_votes", u"age", u"gender", u"accent"] +_EN_TRAIN_CSV_CONTENTS = [ + ["9d16c5d980247861130e0480e2719f448be73d86a496c36d01a477cbdecd8cfd1399403d7a77bf458d211a70711b2da0845c", + "common_voice_en_18885784.wav", + "He was accorded a State funeral, and was buried in Drayton and Toowoomba Cemetery.", "2", "0", "", "", + ""], + ["c82eb9291328620f06025a1f8112b909099e447e485e99236cb87df008650250e79fea5ca772061fb6a370830847b9c44d20", + "common_voice_en_556542.wav", "Once more into the breach", "2", "0", "thirties", "male", "us"], + ["f74d880c5ad4c5917f314a604d3fc4805159d255796fb9f8defca35333ecc002bdf53dc463503c12674ea840b21b4a507b7c", + "common_voice_en_18607573.wav", + "Caddy, show Miss Clare and Miss Summerson their rooms.", "2", "0", "twenties", "male", "canada"], +] - root_dir = None - data = [] - _headers = [u"client_ids", u"path", u"sentence", u"up_votes", u"down_votes", u"age", u"gender", u"accent"] +_FR_TRAIN_CSV_CONTENTS = [ + [ + "a2e8e1e1cc74d08c92a53d7b9ff84e077eb90410edd85b8882f16fd037cecfcb6a19413c6c63ce6458cfea9579878fa91cef" + "18343441c601cae0597a4b0d3144", + "89e67e7682b36786a0b4b4022c4d42090c86edd96c78c12d30088e62522b8fe466ea4912e6a1055dfb91b296a0743e0a2bbe" + "16cebac98ee5349e3e8262cb9329", + "Or sur ce point nous nโ€™avons aucune rรฉponse de votre part.", "2", "0", "twenties", "male", "france"], + [ + "a2e8e1e1cc74d08c92a53d7b9ff84e077eb90410edd85b8882f16fd037cecfcb6a19413c6c63ce6458cfea9579878fa91cef18" + "343441c601cae0597a4b0d3144", + "87d71819a26179e93acfee149d0b21b7bf5e926e367d80b2b3792d45f46e04853a514945783ff764c1fc237b4eb0ee2b0a7a7" + "cbd395acbdfcfa9d76a6e199bbd", + "Monsieur de La Verpilliรจre, laissez parler le ministre", "2", "0", "twenties", "male", "france"], + +] + + +def get_mock_dataset(root_dir, train_csv_contents, ext_audio) -> Tuple[Tensor, int, Dict[str, str]]: + """ + prepares mocked dataset + """ + mocked_data = [] # Note: extension is changed to wav for the sake of test # Note: the first content is missing values for `age`, `gender` and `accent` as in the original data. - _train_csv_contents = [ - ["9d16c5d980247861130e0480e2719f448be73d86a496c36d01a477cbdecd8cfd1399403d7a77bf458d211a70711b2da0845c", - "common_voice_en_18885784.wav", - "He was accorded a State funeral, and was buried in Drayton and Toowoomba Cemetery.", "2", "0", "", "", ""], - ["c82eb9291328620f06025a1f8112b909099e447e485e99236cb87df008650250e79fea5ca772061fb6a370830847b9c44d20", - "common_voice_en_556542.wav", "Once more into the breach", "2", "0", "thirties", "male", "us"], - ["f74d880c5ad4c5917f314a604d3fc4805159d255796fb9f8defca35333ecc002bdf53dc463503c12674ea840b21b4a507b7c", - "common_voice_en_18607573.wav", - "Caddy, show Miss Clare and Miss Summerson their rooms.", "2", "0", "twenties", "male", "canada"], - ] - sample_rate = 48000 + # Tsv file name difference does not mean different subset, testing as a whole dataset here + tsv_filename = os.path.join(root_dir, "train.tsv") + audio_base_path = os.path.join(root_dir, "clips") + os.makedirs(audio_base_path, exist_ok=True) + with open(tsv_filename, "w", newline='') as tsv: + writer = csv.writer(tsv, delimiter='\t') + writer.writerow(_HEADERS) + for i, content in enumerate(train_csv_contents): + content[2] = str(content[2].encode("utf-8")) + writer.writerow(content) + if not content[1].endswith(ext_audio): + audio_path = os.path.join(audio_base_path, content[1] + ext_audio) + else: + audio_path = os.path.join(audio_base_path, content[1]) + + data = get_whitenoise(sample_rate=_SAMPLE_RATE, duration=1, n_channels=1, seed=i, dtype='float32') + save_wav(audio_path, data, _SAMPLE_RATE) + # Append data entry + mocked_data.append((normalize_wav(data), _SAMPLE_RATE, dict(zip(_HEADERS, content)))) + return mocked_data + + +def get_mock_dataset_en(root_dir, ext_audio) -> Tuple[Tensor, int, Dict[str, str]]: + """ + prepares english mocked dataset + """ + return get_mock_dataset(root_dir, _EN_TRAIN_CSV_CONTENTS, ext_audio) + + +def get_mock_dataset_fr(root_dir, ext_audio) -> Tuple[Tensor, int, Dict[str, str]]: + """ + prepares french mocked dataset + """ + return get_mock_dataset(root_dir, _FR_TRAIN_CSV_CONTENTS, ext_audio) + + +class BaseTestCommonVoice(TempDirMixin): + root_dir = None + data = None @classmethod def setUpClass(cls): + super().setUpClass() cls.root_dir = cls.get_base_temp_dir() - # Tsv file name difference does not mean different subset, testing as a whole dataset here - tsv_filename = os.path.join(cls.root_dir, "train.tsv") - audio_base_path = os.path.join(cls.root_dir, "clips") - os.makedirs(audio_base_path, exist_ok=True) - with open(tsv_filename, "w", newline='') as tsv: - writer = csv.writer(tsv, delimiter='\t') - writer.writerow(cls._headers) - for i, content in enumerate(cls._train_csv_contents): - writer.writerow(content) - - # Generate and store audio - audio_path = os.path.join(audio_base_path, content[1]) - data = get_whitenoise(sample_rate=cls.sample_rate, duration=1, n_channels=1, seed=i, dtype='float32') - save_wav(audio_path, data, cls.sample_rate) + COMMONVOICE._ext_audio = ".wav" - # Append data entry - cls.data.append((normalize_wav(data), cls.sample_rate, dict(zip(cls._headers, content)))) + @classmethod + def tearDownClass(cls): + super().tearDownClass() + COMMONVOICE._ext_audio = _ORIGINAL_EXT_AUDIO def _test_commonvoice(self, dataset): n_ite = 0 @@ -59,11 +110,21 @@ def _test_commonvoice(self, dataset): expected_dictionary = self.data[i][2] expected_data = self.data[i][0] self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) - assert sample_rate == TestCommonVoice.sample_rate + assert sample_rate == _SAMPLE_RATE assert dictionary == expected_dictionary n_ite += 1 assert n_ite == len(self.data) + +class TestCommonVoiceEN(BaseTestCommonVoice, TorchaudioTestCase): + backend = 'default' + root_dir = None + + @classmethod + def setUpClass(cls): + super().setUpClass() + cls.data = get_mock_dataset_en(cls.root_dir, COMMONVOICE._ext_audio) + def test_commonvoice_str(self): dataset = COMMONVOICE(self.root_dir) self._test_commonvoice(dataset) @@ -71,3 +132,17 @@ def test_commonvoice_str(self): def test_commonvoice_path(self): dataset = COMMONVOICE(Path(self.root_dir)) self._test_commonvoice(dataset) + + +class TestCommonVoiceFR(BaseTestCommonVoice, TorchaudioTestCase): + backend = 'default' + root_dir = None + + @classmethod + def setUpClass(cls): + super().setUpClass() + cls.data = get_mock_dataset_fr(cls.root_dir, COMMONVOICE._ext_audio) + + def test_commonvoice_str(self): + dataset = COMMONVOICE(self.root_dir) + self._test_commonvoice(dataset) diff --git a/test/torchaudio_unittest/datasets/gtzan_test.py b/test/torchaudio_unittest/datasets/gtzan_test.py index 763e0a866f..838292f55d 100644 --- a/test/torchaudio_unittest/datasets/gtzan_test.py +++ b/test/torchaudio_unittest/datasets/gtzan_test.py @@ -12,6 +12,37 @@ ) +def get_mock_dataset(root_dir): + """ + root_dir: directory to the mocked dataset + """ + mocked_samples = [] + mocked_training = [] + mocked_validation = [] + mocked_testing = [] + sample_rate = 22050 + + seed = 0 + for genre in gtzan.gtzan_genres: + base_dir = os.path.join(root_dir, 'genres', genre) + os.makedirs(base_dir, exist_ok=True) + for i in range(100): + filename = f'{genre}.{i:05d}' + path = os.path.join(base_dir, f'{filename}.wav') + data = get_whitenoise(sample_rate=sample_rate, duration=0.01, n_channels=1, dtype='int16', seed=seed) + save_wav(path, data, sample_rate) + sample = (normalize_wav(data), sample_rate, genre) + mocked_samples.append(sample) + if filename in gtzan.filtered_test: + mocked_testing.append(sample) + if filename in gtzan.filtered_train: + mocked_training.append(sample) + if filename in gtzan.filtered_valid: + mocked_validation.append(sample) + seed += 1 + return (mocked_samples, mocked_training, mocked_validation, mocked_testing) + + class TestGTZAN(TempDirMixin, TorchaudioTestCase): backend = 'default' @@ -24,25 +55,11 @@ class TestGTZAN(TempDirMixin, TorchaudioTestCase): @classmethod def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() - sample_rate = 22050 - seed = 0 - for genre in gtzan.gtzan_genres: - base_dir = os.path.join(cls.root_dir, 'genres', genre) - os.makedirs(base_dir, exist_ok=True) - for i in range(100): - filename = f'{genre}.{i:05d}' - path = os.path.join(base_dir, f'{filename}.wav') - data = get_whitenoise(sample_rate=sample_rate, duration=0.01, n_channels=1, dtype='int16', seed=seed) - save_wav(path, data, sample_rate) - sample = (normalize_wav(data), sample_rate, genre) - cls.samples.append(sample) - if filename in gtzan.filtered_test: - cls.testing.append(sample) - if filename in gtzan.filtered_train: - cls.training.append(sample) - if filename in gtzan.filtered_valid: - cls.validation.append(sample) - seed += 1 + mocked_data = get_mock_dataset(cls.root_dir) + cls.samples = mocked_data[0] + cls.training = mocked_data[1] + cls.validation = mocked_data[2] + cls.testing = mocked_data[3] def test_no_subset(self): dataset = gtzan.GTZAN(self.root_dir) diff --git a/test/torchaudio_unittest/datasets/librispeech_test.py b/test/torchaudio_unittest/datasets/librispeech_test.py index b0eabb767c..0fab5356f6 100644 --- a/test/torchaudio_unittest/datasets/librispeech_test.py +++ b/test/torchaudio_unittest/datasets/librispeech_test.py @@ -1,8 +1,6 @@ import os from pathlib import Path -from torchaudio.datasets import librispeech - from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, @@ -11,8 +9,10 @@ normalize_wav, ) +from torchaudio.datasets import librispeech + # Used to generate a unique utterance for each dummy audio file -NUMBERS = [ +_NUMBERS = [ 'ZERO', 'ONE', 'TWO', @@ -26,6 +26,65 @@ ] +def get_mock_dataset(root_dir): + """ + root_dir: directory to the mocked dataset + """ + mocked_data = [] + dataset_dir = os.path.join( + root_dir, librispeech.FOLDER_IN_ARCHIVE, librispeech.URL + ) + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 16000 # 16kHz + seed = 0 + + for speaker_id in range(5): + speaker_path = os.path.join(dataset_dir, str(speaker_id)) + os.makedirs(speaker_path, exist_ok=True) + + for chapter_id in range(3): + chapter_path = os.path.join(speaker_path, str(chapter_id)) + os.makedirs(chapter_path, exist_ok=True) + trans_content = [] + + for utterance_id in range(10): + filename = f'{speaker_id}-{chapter_id}-{utterance_id:04d}.wav' + path = os.path.join(chapter_path, filename) + + utterance = ' '.join( + [_NUMBERS[x] for x in [speaker_id, chapter_id, utterance_id]] + ) + trans_content.append( + f'{speaker_id}-{chapter_id}-{utterance_id:04d} {utterance}' + ) + + data = get_whitenoise( + sample_rate=sample_rate, + duration=0.01, + n_channels=1, + dtype='float32', + seed=seed + ) + save_wav(path, data, sample_rate) + sample = ( + normalize_wav(data), + sample_rate, + utterance, + speaker_id, + chapter_id, + utterance_id + ) + mocked_data.append(sample) + + seed += 1 + + trans_filename = f'{speaker_id}-{chapter_id}.trans.txt' + trans_path = os.path.join(chapter_path, trans_filename) + with open(trans_path, 'w') as f: + f.write('\n'.join(trans_content)) + return mocked_data + + class TestLibriSpeech(TempDirMixin, TorchaudioTestCase): backend = 'default' @@ -35,57 +94,7 @@ class TestLibriSpeech(TempDirMixin, TorchaudioTestCase): @classmethod def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() - dataset_dir = os.path.join( - cls.root_dir, librispeech.FOLDER_IN_ARCHIVE, librispeech.URL - ) - os.makedirs(dataset_dir, exist_ok=True) - sample_rate = 16000 # 16kHz - seed = 0 - - for speaker_id in range(5): - speaker_path = os.path.join(dataset_dir, str(speaker_id)) - os.makedirs(speaker_path, exist_ok=True) - - for chapter_id in range(3): - chapter_path = os.path.join(speaker_path, str(chapter_id)) - os.makedirs(chapter_path, exist_ok=True) - trans_content = [] - - for utterance_id in range(10): - filename = f'{speaker_id}-{chapter_id}-{utterance_id:04d}.wav' - path = os.path.join(chapter_path, filename) - - utterance = ' '.join( - [NUMBERS[x] for x in [speaker_id, chapter_id, utterance_id]] - ) - trans_content.append( - f'{speaker_id}-{chapter_id}-{utterance_id:04d} {utterance}' - ) - - data = get_whitenoise( - sample_rate=sample_rate, - duration=0.01, - n_channels=1, - dtype='float32', - seed=seed - ) - save_wav(path, data, sample_rate) - sample = ( - normalize_wav(data), - sample_rate, - utterance, - speaker_id, - chapter_id, - utterance_id - ) - cls.samples.append(sample) - - seed += 1 - - trans_filename = f'{speaker_id}-{chapter_id}.trans.txt' - trans_path = os.path.join(chapter_path, trans_filename) - with open(trans_path, 'w') as f: - f.write('\n'.join(trans_content)) + cls.samples = get_mock_dataset(cls.root_dir) @classmethod def tearDownClass(cls): @@ -95,7 +104,7 @@ def tearDownClass(cls): def _test_librispeech(self, dataset): num_samples = 0 for i, ( - data, sample_rate, utterance, speaker_id, chapter_id, utterance_id + data, sample_rate, utterance, speaker_id, chapter_id, utterance_id ) in enumerate(dataset): self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) assert sample_rate == self.samples[i][1] diff --git a/test/torchaudio_unittest/datasets/libritts_test.py b/test/torchaudio_unittest/datasets/libritts_test.py index 04d47e95f4..32e6dbcf73 100644 --- a/test/torchaudio_unittest/datasets/libritts_test.py +++ b/test/torchaudio_unittest/datasets/libritts_test.py @@ -1,8 +1,6 @@ import os from pathlib import Path -from torchaudio.datasets.libritts import LIBRITTS - from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, @@ -11,42 +9,55 @@ normalize_wav, ) +from torchaudio.datasets.libritts import LIBRITTS + +_UTTERANCE_IDS = [ + [19, 198, '000000', '000000'], + [26, 495, '000004', '000000'], +] +_ORIGINAL_TEXT = 'this is the original text.' +_NORMALIZED_TEXT = 'this is the normalized text.' + + +def get_mock_dataset(root_dir): + """ + root_dir: directory to the mocked dataset + """ + mocked_data = [] + base_dir = os.path.join(root_dir, 'LibriTTS', 'train-clean-100') + for i, utterance_id in enumerate(_UTTERANCE_IDS): + filename = f'{"_".join(str(u) for u in utterance_id)}.wav' + file_dir = os.path.join(base_dir, str(utterance_id[0]), str(utterance_id[1])) + os.makedirs(file_dir, exist_ok=True) + path = os.path.join(file_dir, filename) + + data = get_whitenoise(sample_rate=24000, duration=2, n_channels=1, dtype='int16', seed=i) + save_wav(path, data, 24000) + mocked_data.append(normalize_wav(data)) + + original_text_filename = f'{"_".join(str(u) for u in utterance_id)}.original.txt' + path_original = os.path.join(file_dir, original_text_filename) + with open(path_original, 'w') as file_: + file_.write(_ORIGINAL_TEXT) + + normalized_text_filename = f'{"_".join(str(u) for u in utterance_id)}.normalized.txt' + path_normalized = os.path.join(file_dir, normalized_text_filename) + with open(path_normalized, 'w') as file_: + file_.write(_NORMALIZED_TEXT) + return mocked_data, _UTTERANCE_IDS, _ORIGINAL_TEXT, _NORMALIZED_TEXT + class TestLibriTTS(TempDirMixin, TorchaudioTestCase): backend = 'default' root_dir = None data = [] - utterance_ids = [ - [19, 198, '000000', '000000'], - [26, 495, '000004', '000000'], - ] - original_text = 'this is the original text.' - normalized_text = 'this is the normalized text.' + _utterance_ids, _original_text, _normalized_text = [], [], [] @classmethod def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() - base_dir = os.path.join(cls.root_dir, 'LibriTTS', 'train-clean-100') - for i, utterance_id in enumerate(cls.utterance_ids): - filename = f'{"_".join(str(u) for u in utterance_id)}.wav' - file_dir = os.path.join(base_dir, str(utterance_id[0]), str(utterance_id[1])) - os.makedirs(file_dir, exist_ok=True) - path = os.path.join(file_dir, filename) - - data = get_whitenoise(sample_rate=24000, duration=2, n_channels=1, dtype='int16', seed=i) - save_wav(path, data, 24000) - cls.data.append(normalize_wav(data)) - - original_text_filename = f'{"_".join(str(u) for u in utterance_id)}.original.txt' - path_original = os.path.join(file_dir, original_text_filename) - with open(path_original, 'w') as file_: - file_.write(cls.original_text) - - normalized_text_filename = f'{"_".join(str(u) for u in utterance_id)}.normalized.txt' - path_normalized = os.path.join(file_dir, normalized_text_filename) - with open(path_normalized, 'w') as file_: - file_.write(cls.normalized_text) + cls.data, cls._utterance_ids, cls._original_text, cls._normalized_text = get_mock_dataset(cls.root_dir) def _test_libritts(self, dataset): n_ites = 0 @@ -57,18 +68,17 @@ def _test_libritts(self, dataset): speaker_id, chapter_id, utterance_id) in enumerate(dataset): - - expected_ids = self.utterance_ids[i] + expected_ids = self._utterance_ids[i] expected_data = self.data[i] self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) assert sample_rate == 24000 assert speaker_id == expected_ids[0] assert chapter_id == expected_ids[1] - assert original_text == self.original_text - assert normalized_text == self.normalized_text + assert original_text == self._original_text + assert normalized_text == self._normalized_text assert utterance_id == f'{"_".join(str(u) for u in expected_ids[-4:])}' n_ites += 1 - assert n_ites == len(self.utterance_ids) + assert n_ites == len(self._utterance_ids) def test_libritts_str(self): dataset = LIBRITTS(self.root_dir) diff --git a/test/torchaudio_unittest/datasets/ljspeech_test.py b/test/torchaudio_unittest/datasets/ljspeech_test.py index 33458fd6e1..4cf834b226 100644 --- a/test/torchaudio_unittest/datasets/ljspeech_test.py +++ b/test/torchaudio_unittest/datasets/ljspeech_test.py @@ -2,8 +2,6 @@ import os from pathlib import Path -from torchaudio.datasets import ljspeech - from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, @@ -12,59 +10,71 @@ save_wav, ) +from torchaudio.datasets import ljspeech + +_TRANSCRIPTS = [ + "Test transcript 1", + "Test transcript 2", + "Test transcript 3", + "In 1465 Sweynheim and Pannartz began printing in the monastery of Subiaco near Rome," +] + +_NORMALIZED_TRANSCRIPT = [ + "Test transcript one", + "Test transcript two", + "Test transcript three", + "In fourteen sixty-five Sweynheim and Pannartz began printing in the monastery of Subiaco near Rome," +] + + +def get_mock_dataset(root_dir): + """ + root_dir: path to the mocked dataset + """ + mocked_data = [] + base_dir = os.path.join(root_dir, "LJSpeech-1.1") + archive_dir = os.path.join(base_dir, "wavs") + os.makedirs(archive_dir, exist_ok=True) + metadata_path = os.path.join(base_dir, "metadata.csv") + sample_rate = 22050 + + with open(metadata_path, mode="w", newline='') as metadata_file: + metadata_writer = csv.writer( + metadata_file, delimiter="|", quoting=csv.QUOTE_NONE + ) + for i, (transcript, normalized_transcript) in enumerate( + zip(_TRANSCRIPTS, _NORMALIZED_TRANSCRIPT) + ): + fileid = f'LJ001-{i:04d}' + metadata_writer.writerow([fileid, transcript, normalized_transcript]) + filename = fileid + ".wav" + path = os.path.join(archive_dir, filename) + data = get_whitenoise( + sample_rate=sample_rate, duration=1, n_channels=1, dtype="int16", seed=i + ) + save_wav(path, data, sample_rate) + mocked_data.append(normalize_wav(data)) + return mocked_data, _TRANSCRIPTS, _NORMALIZED_TRANSCRIPT + class TestLJSpeech(TempDirMixin, TorchaudioTestCase): backend = "default" root_dir = None - data = [] - transcripts = [ - "Test transcript 1", - "Test transcript 2", - "Test transcript 3", - "In 1465 Sweynheim and Pannartz began printing in the monastery of Subiaco near Rome," - ] - - normalized_transcripts = [ - "Test transcript one", - "Test transcript two", - "Test transcript three", - "In fourteen sixty-five Sweynheim and Pannartz began printing in the monastery of Subiaco near Rome," - ] + data, _transcripts, _normalized_transcript = [], [], [] @classmethod def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() - base_dir = os.path.join(cls.root_dir, "LJSpeech-1.1") - archive_dir = os.path.join(base_dir, "wavs") - os.makedirs(archive_dir, exist_ok=True) - metadata_path = os.path.join(base_dir, "metadata.csv") - sample_rate = 22050 - - with open(metadata_path, mode="w", newline='') as metadata_file: - metadata_writer = csv.writer( - metadata_file, delimiter="|", quoting=csv.QUOTE_NONE - ) - for i, (transcript, normalized_transcript) in enumerate( - zip(cls.transcripts, cls.normalized_transcripts) - ): - fileid = f'LJ001-{i:04d}' - metadata_writer.writerow([fileid, transcript, normalized_transcript]) - filename = fileid + ".wav" - path = os.path.join(archive_dir, filename) - data = get_whitenoise( - sample_rate=sample_rate, duration=1, n_channels=1, dtype="int16", seed=i - ) - save_wav(path, data, sample_rate) - cls.data.append(normalize_wav(data)) + cls.data, cls._transcripts, cls._normalized_transcript = get_mock_dataset(cls.root_dir) def _test_ljspeech(self, dataset): n_ite = 0 for i, (waveform, sample_rate, transcript, normalized_transcript) in enumerate( - dataset + dataset ): - expected_transcript = self.transcripts[i] - expected_normalized_transcript = self.normalized_transcripts[i] + expected_transcript = self._transcripts[i] + expected_normalized_transcript = self._normalized_transcript[i] expected_data = self.data[i] self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) assert sample_rate == sample_rate diff --git a/test/torchaudio_unittest/datasets/speechcommands_test.py b/test/torchaudio_unittest/datasets/speechcommands_test.py index 6351d41b27..19a352ee16 100644 --- a/test/torchaudio_unittest/datasets/speechcommands_test.py +++ b/test/torchaudio_unittest/datasets/speechcommands_test.py @@ -1,8 +1,6 @@ import os from pathlib import Path -from torchaudio.datasets import speechcommands - from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, @@ -11,7 +9,9 @@ save_wav, ) -LABELS = [ +from torchaudio.datasets import speechcommands + +_LABELS = [ "bed", "bird", "cat", @@ -49,6 +49,58 @@ ] +def get_mock_dataset(dataset_dir): + """ + dataset_dir: directory to the mocked dataset + """ + mocked_samples = [] + mocked_train_samples = [] + mocked_valid_samples = [] + mocked_test_samples = [] + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 16000 # 16kHz sample rate + seed = 0 + valid_file = os.path.join(dataset_dir, "validation_list.txt") + test_file = os.path.join(dataset_dir, "testing_list.txt") + with open(valid_file, "w") as valid, open(test_file, "w") as test: + for label in _LABELS: + path = os.path.join(dataset_dir, label) + os.makedirs(path, exist_ok=True) + for j in range(6): + # generate hash ID for speaker + speaker = "{:08x}".format(j) + + for utterance in range(3): + filename = f"{speaker}{speechcommands.HASH_DIVIDER}{utterance}.wav" + file_path = os.path.join(path, filename) + seed += 1 + data = get_whitenoise( + sample_rate=sample_rate, + duration=0.01, + n_channels=1, + dtype="int16", + seed=seed, + ) + save_wav(file_path, data, sample_rate) + sample = ( + normalize_wav(data), + sample_rate, + label, + speaker, + utterance, + ) + mocked_samples.append(sample) + if j < 2: + mocked_train_samples.append(sample) + elif j < 4: + valid.write(f'{label}/{filename}\n') + mocked_valid_samples.append(sample) + elif j < 6: + test.write(f'{label}/{filename}\n') + mocked_test_samples.append(sample) + return mocked_samples, mocked_train_samples, mocked_valid_samples, mocked_test_samples + + class TestSpeechCommands(TempDirMixin, TorchaudioTestCase): backend = "default" @@ -64,52 +116,12 @@ def setUpClass(cls): dataset_dir = os.path.join( cls.root_dir, speechcommands.FOLDER_IN_ARCHIVE, speechcommands.URL ) - os.makedirs(dataset_dir, exist_ok=True) - sample_rate = 16000 # 16kHz sample rate - seed = 0 - valid_file = os.path.join(dataset_dir, "validation_list.txt") - test_file = os.path.join(dataset_dir, "testing_list.txt") - with open(valid_file, "w") as valid, open(test_file, "w") as test: - for label in LABELS: - path = os.path.join(dataset_dir, label) - os.makedirs(path, exist_ok=True) - for j in range(6): - # generate hash ID for speaker - speaker = "{:08x}".format(j) - - for utterance in range(3): - filename = f"{speaker}{speechcommands.HASH_DIVIDER}{utterance}.wav" - file_path = os.path.join(path, filename) - seed += 1 - data = get_whitenoise( - sample_rate=sample_rate, - duration=0.01, - n_channels=1, - dtype="int16", - seed=seed, - ) - save_wav(file_path, data, sample_rate) - sample = ( - normalize_wav(data), - sample_rate, - label, - speaker, - utterance, - ) - cls.samples.append(sample) - if j < 2: - cls.train_samples.append(sample) - elif j < 4: - valid.write(f'{label}/{filename}\n') - cls.valid_samples.append(sample) - elif j < 6: - test.write(f'{label}/{filename}\n') - cls.test_samples.append(sample) + cls.samples, cls.train_samples, cls.valid_samples, cls.test_samples = get_mock_dataset(dataset_dir) def _testSpeechCommands(self, dataset, data_samples): num_samples = 0 for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( - dataset + dataset ): self.assertEqual(data, data_samples[i][0], atol=5e-5, rtol=1e-8) assert sample_rate == data_samples[i][1] diff --git a/test/torchaudio_unittest/datasets/tedlium_test.py b/test/torchaudio_unittest/datasets/tedlium_test.py index 3ea4d25eab..abb6ea5b5c 100644 --- a/test/torchaudio_unittest/datasets/tedlium_test.py +++ b/test/torchaudio_unittest/datasets/tedlium_test.py @@ -1,21 +1,18 @@ import os import platform -import unittest from pathlib import Path -from torchaudio.datasets import tedlium - from torchaudio_unittest.common_utils import ( - TestBaseMixin, TempDirMixin, TorchaudioTestCase, get_whitenoise, save_wav, - normalize_wav, ) +from torchaudio.datasets import tedlium + # Used to generate a unique utterance for each dummy audio file -UTTERANCES = [ +_UTTERANCES = [ "AaronHuey_2010X 1 AaronHuey_2010X 0.0 2.0 script1\n", "AaronHuey_2010X 1 AaronHuey_2010X 2.0 4.0 script2\n", "AaronHuey_2010X 1 AaronHuey_2010X 4.0 6.0 script3\n", @@ -23,7 +20,7 @@ "AaronHuey_2010X 1 AaronHuey_2010X 8.0 10.0 script5\n", ] -PHONEME = [ +_PHONEME = [ "a AH", "a(2) EY", "aachen AA K AH N", @@ -34,6 +31,65 @@ ] +def get_mock_dataset(dataset_dir): + """ + dataset_dir: directory of the mocked dataset + """ + mocked_samples = {} + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 16000 # 16kHz + seed = 0 + + for release in ["release1", "release2", "release3"]: + data = get_whitenoise(sample_rate=sample_rate, duration=10.00, n_channels=1, dtype="float32", seed=seed) + if release in ["release1", "release2"]: + release_dir = os.path.join( + dataset_dir, + tedlium._RELEASE_CONFIGS[release]["folder_in_archive"], + tedlium._RELEASE_CONFIGS[release]["subset"], + ) + else: + release_dir = os.path.join( + dataset_dir, + tedlium._RELEASE_CONFIGS[release]["folder_in_archive"], + tedlium._RELEASE_CONFIGS[release]["data_path"], + ) + os.makedirs(release_dir, exist_ok=True) + os.makedirs(os.path.join(release_dir, "stm"), exist_ok=True) # Subfolder for transcripts + os.makedirs(os.path.join(release_dir, "sph"), exist_ok=True) # Subfolder for audio files + filename = f"{release}.sph" + path = os.path.join(os.path.join(release_dir, "sph"), filename) + save_wav(path, data, sample_rate) + + trans_filename = f"{release}.stm" + trans_path = os.path.join(os.path.join(release_dir, "stm"), trans_filename) + with open(trans_path, "w") as f: + f.write("".join(_UTTERANCES)) + + dict_filename = f"{release}.dic" + dict_path = os.path.join(release_dir, dict_filename) + with open(dict_path, "w") as f: + f.write("\n".join(_PHONEME)) + + # Create a samples list to compare with + mocked_samples[release] = [] + for utterance in _UTTERANCES: + talk_id, _, speaker_id, start_time, end_time, identifier, transcript = utterance.split(" ", 6) + start_time = int(float(start_time)) * sample_rate + end_time = int(float(end_time)) * sample_rate + sample = ( + data[:, start_time:end_time], + sample_rate, + transcript, + talk_id, + speaker_id, + identifier, + ) + mocked_samples[release].append(sample) + seed += 1 + return mocked_samples + + class Tedlium(TempDirMixin): root_dir = None samples = {} @@ -42,57 +98,7 @@ class Tedlium(TempDirMixin): def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() cls.root_dir = dataset_dir = os.path.join(cls.root_dir, "tedlium") - os.makedirs(dataset_dir, exist_ok=True) - sample_rate = 16000 # 16kHz - seed = 0 - - for release in ["release1", "release2", "release3"]: - data = get_whitenoise(sample_rate=sample_rate, duration=10.00, n_channels=1, dtype="float32", seed=seed) - if release in ["release1", "release2"]: - release_dir = os.path.join( - dataset_dir, - tedlium._RELEASE_CONFIGS[release]["folder_in_archive"], - tedlium._RELEASE_CONFIGS[release]["subset"], - ) - else: - release_dir = os.path.join( - dataset_dir, - tedlium._RELEASE_CONFIGS[release]["folder_in_archive"], - tedlium._RELEASE_CONFIGS[release]["data_path"], - ) - os.makedirs(release_dir, exist_ok=True) - os.makedirs(os.path.join(release_dir, "stm"), exist_ok=True) # Subfolder for transcripts - os.makedirs(os.path.join(release_dir, "sph"), exist_ok=True) # Subfolder for audio files - filename = f"{release}.sph" - path = os.path.join(os.path.join(release_dir, "sph"), filename) - save_wav(path, data, sample_rate) - - trans_filename = f"{release}.stm" - trans_path = os.path.join(os.path.join(release_dir, "stm"), trans_filename) - with open(trans_path, "w") as f: - f.write("".join(UTTERANCES)) - - dict_filename = f"{release}.dic" - dict_path = os.path.join(release_dir, dict_filename) - with open(dict_path, "w") as f: - f.write("\n".join(PHONEME)) - - # Create a samples list to compare with - cls.samples[release] = [] - for utterance in UTTERANCES: - talk_id, _, speaker_id, start_time, end_time, identifier, transcript = utterance.split(" ", 6) - start_time = int(float(start_time)) * sample_rate - end_time = int(float(end_time)) * sample_rate - sample = ( - data[:, start_time:end_time], - sample_rate, - transcript, - talk_id, - speaker_id, - identifier, - ) - cls.samples[release].append(sample) - seed += 1 + cls.samples = get_mock_dataset(dataset_dir) def _test_tedlium(self, dataset, release): num_samples = 0 @@ -110,7 +116,7 @@ def _test_tedlium(self, dataset, release): dataset._dict_path = os.path.join(dataset._path, f"{release}.dic") phoneme_dict = dataset.phoneme_dict phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] - assert phoenemes == PHONEME + assert phoenemes == _PHONEME def test_tedlium_release1_str(self): release = "release1" diff --git a/test/torchaudio_unittest/datasets/utils_test.py b/test/torchaudio_unittest/datasets/utils_test.py index 5263319e51..8906991434 100644 --- a/test/torchaudio_unittest/datasets/utils_test.py +++ b/test/torchaudio_unittest/datasets/utils_test.py @@ -1,68 +1,37 @@ -import os -from pathlib import Path - -from torchaudio.datasets import utils as dataset_utils -from torchaudio.datasets.commonvoice import COMMONVOICE - +import torch from torchaudio_unittest.common_utils import ( - TempDirMixin, TorchaudioTestCase, - get_asset_path, + TempDirMixin ) +from torchaudio.datasets import utils as dataset_utils -class TestWalkFiles(TempDirMixin, TorchaudioTestCase): - root = None - expected = None - - def _add_file(self, *parts): - path = self.get_temp_path(*parts) - self.expected.append(path) - Path(path).touch() - - def setUp(self): - self.root = self.get_temp_path() - self.expected = [] - - # level 1 - for filename in ['a.txt', 'b.txt', 'c.txt']: - self._add_file(filename) - # level 2 - for dir1 in ['d1', 'd2', 'd3']: - for filename in ['d.txt', 'e.txt', 'f.txt']: - self._add_file(dir1, filename) - # level 3 - for dir2 in ['d1', 'd2', 'd3']: - for filename in ['g.txt', 'h.txt', 'i.txt']: - self._add_file(dir1, dir2, filename) +class Dataset(torch.utils.data.Dataset): + def __getitem__(self, n): + sample_rate = 8000 + waveform = n * torch.ones(2, 256) + return waveform, sample_rate - print('\n'.join(self.expected)) + def __len__(self) -> int: + return 2 - def test_walk_files(self): - """walk_files should traverse files in alphabetical order""" - n_ites = 0 - for i, path in enumerate(dataset_utils.walk_files(self.root, '.txt', prefix=True)): - found = os.path.join(self.root, path) - assert found == self.expected[i] - n_ites += 1 - assert n_ites == len(self.expected) + def __iter__(self): + for i in range(len(self)): + yield self[i] -class TestIterator(TorchaudioTestCase): +class TestIterator(TorchaudioTestCase, TempDirMixin): backend = 'default' - path = get_asset_path('CommonVoice', 'cv-corpus-4-2019-12-10', 'tt') def test_disckcache_iterator(self): - data = COMMONVOICE(self.path, url="tatar") - data = dataset_utils.diskcache_iterator(data) + data = dataset_utils.diskcache_iterator(Dataset(), self.get_base_temp_dir()) # Save data[0] # Load data[0] def test_bg_iterator(self): - data = COMMONVOICE(self.path, url="tatar") - data = dataset_utils.bg_iterator(data, 5) + data = dataset_utils.bg_iterator(Dataset(), 5) for _ in data: pass diff --git a/test/torchaudio_unittest/datasets/vctk_test.py b/test/torchaudio_unittest/datasets/vctk_test.py index 76bbf29936..03a797daeb 100644 --- a/test/torchaudio_unittest/datasets/vctk_test.py +++ b/test/torchaudio_unittest/datasets/vctk_test.py @@ -12,7 +12,7 @@ ) # Used to generate a unique utterance for each dummy audio file -UTTERANCE = [ +_UTTERANCE = [ 'Please call Stella', 'Ask her to bring these things', 'with her from the store', @@ -27,6 +27,54 @@ ] +def get_mock_dataset(root_dir): + """ + root_dir: root directory of the mocked data + """ + mocked_samples = [] + dataset_dir = os.path.join(root_dir, 'VCTK-Corpus-0.92') + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 48000 + seed = 0 + + for speaker in range(225, 230): + speaker_id = 'p' + str(speaker) + audio_dir = os.path.join(dataset_dir, 'wav48_silence_trimmed', speaker_id) + os.makedirs(audio_dir, exist_ok=True) + + file_dir = os.path.join(dataset_dir, 'txt', speaker_id) + os.makedirs(file_dir, exist_ok=True) + + for utterance_id in range(1, 11): + filename = f'{speaker_id}_{utterance_id:03d}_mic2' + audio_file_path = os.path.join(audio_dir, filename + '.wav') + + data = get_whitenoise( + sample_rate=sample_rate, + duration=0.01, + n_channels=1, + dtype='float32', + seed=seed + ) + save_wav(audio_file_path, data, sample_rate) + + txt_file_path = os.path.join(file_dir, filename[:-5] + '.txt') + utterance = _UTTERANCE[utterance_id - 1] + with open(txt_file_path, 'w') as f: + f.write(utterance) + + sample = ( + normalize_wav(data), + sample_rate, + utterance, + speaker_id, + utterance_id + ) + mocked_samples.append(sample) + seed += 1 + return mocked_samples + + class TestVCTK(TempDirMixin, TorchaudioTestCase): backend = 'default' @@ -36,47 +84,7 @@ class TestVCTK(TempDirMixin, TorchaudioTestCase): @classmethod def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() - dataset_dir = os.path.join(cls.root_dir, 'VCTK-Corpus-0.92') - os.makedirs(dataset_dir, exist_ok=True) - sample_rate = 48000 - seed = 0 - - for speaker in range(225, 230): - speaker_id = 'p' + str(speaker) - audio_dir = os.path.join(dataset_dir, 'wav48_silence_trimmed', speaker_id) - os.makedirs(audio_dir, exist_ok=True) - - file_dir = os.path.join(dataset_dir, 'txt', speaker_id) - os.makedirs(file_dir, exist_ok=True) - - for utterance_id in range(1, 11): - filename = f'{speaker_id}_{utterance_id:03d}_mic2' - audio_file_path = os.path.join(audio_dir, filename + '.wav') - - data = get_whitenoise( - sample_rate=sample_rate, - duration=0.01, - n_channels=1, - dtype='float32', - seed=seed - ) - save_wav(audio_file_path, data, sample_rate) - - txt_file_path = os.path.join(file_dir, filename[:-5] + '.txt') - utterance = UTTERANCE[utterance_id - 1] - with open(txt_file_path, 'w') as f: - f.write(utterance) - - sample = ( - normalize_wav(data), - sample_rate, - utterance, - speaker_id, - utterance_id - ) - cls.samples.append(sample) - - seed += 1 + cls.samples = get_mock_dataset(cls.root_dir) def _test_vctk(self, dataset): num_samples = 0 diff --git a/test/torchaudio_unittest/datasets/yesno_test.py b/test/torchaudio_unittest/datasets/yesno_test.py index 6b094aaece..2f4144985d 100644 --- a/test/torchaudio_unittest/datasets/yesno_test.py +++ b/test/torchaudio_unittest/datasets/yesno_test.py @@ -12,6 +12,23 @@ ) +def get_mock_data(root_dir, labels): + """ + root_dir: path + labels: list of labels + """ + mocked_data = [] + base_dir = os.path.join(root_dir, 'waves_yesno') + os.makedirs(base_dir, exist_ok=True) + for i, label in enumerate(labels): + filename = f'{"_".join(str(l) for l in label)}.wav' + path = os.path.join(base_dir, filename) + data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16', seed=i) + save_wav(path, data, 8000) + mocked_data.append(normalize_wav(data)) + return mocked_data + + class TestYesNo(TempDirMixin, TorchaudioTestCase): backend = 'default' @@ -28,14 +45,7 @@ class TestYesNo(TempDirMixin, TorchaudioTestCase): @classmethod def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() - base_dir = os.path.join(cls.root_dir, 'waves_yesno') - os.makedirs(base_dir, exist_ok=True) - for i, label in enumerate(cls.labels): - filename = f'{"_".join(str(l) for l in label)}.wav' - path = os.path.join(base_dir, filename) - data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16', seed=i) - save_wav(path, data, 8000) - cls.data.append(normalize_wav(data)) + cls.data = get_mock_data(cls.root_dir, cls.labels) def _test_yesno(self, dataset): n_ite = 0 diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py index 4fbe4d871e..4073654011 100644 --- a/test/torchaudio_unittest/functional/functional_cpu_test.py +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -1,14 +1,14 @@ import math -import unittest import torch import torchaudio import torchaudio.functional as F from parameterized import parameterized import pytest +import itertools from torchaudio_unittest import common_utils -from .functional_impl import Lfilter +from .functional_impl import Lfilter, Spectrogram class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): @@ -21,6 +21,16 @@ class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): device = torch.device('cpu') +class TestSpectrogramFloat32(Spectrogram, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') + + +class TestSpectrogramFloat64(Spectrogram, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') + + class TestCreateFBMatrix(common_utils.TorchaudioTestCase): def test_no_warning_high_n_freq(self): with pytest.warns(None) as w: @@ -44,7 +54,7 @@ def test_one_channel(self): specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) computed = F.compute_deltas(specgram, win_length=3) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_two_channels(self): specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], @@ -52,7 +62,7 @@ def test_two_channels(self): expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], [0.5, 1.0, 1.0, 0.5]]]) computed = F.compute_deltas(specgram, win_length=3) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): @@ -88,13 +98,13 @@ def test_DB_to_amplitude(self): db = F.amplitude_to_DB(torch.abs(x), multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) + self.assertEqual(x2, torch.abs(x), atol=5e-5, rtol=1e-5) # Spectrogram amplitude -> DB -> amplitude db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) + self.assertEqual(x2, spec, atol=5e-5, rtol=1e-5) # Waveform power -> DB -> power multiplier = 10. @@ -103,61 +113,66 @@ def test_DB_to_amplitude(self): db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) + self.assertEqual(x2, torch.abs(x), atol=5e-5, rtol=1e-5) # Spectrogram power -> DB -> power db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) - - -@pytest.mark.parametrize('complex_tensor', [ - torch.randn(1, 2, 1025, 400, 2), - torch.randn(1025, 400, 2) -]) -@pytest.mark.parametrize('power', [1, 2, 0.7]) -def test_complex_norm(complex_tensor, power): - expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) - norm_tensor = F.complex_norm(complex_tensor, power) + self.assertEqual(x2, spec, atol=5e-5, rtol=1e-5) - torch.testing.assert_allclose(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) +class TestComplexNorm(common_utils.TorchaudioTestCase): + @parameterized.expand(list(itertools.product( + [(1, 2, 1025, 400, 2), (1025, 400, 2)], + [1, 2, 0.7] + ))) + def test_complex_norm(self, shape, power): + torch.random.manual_seed(42) + complex_tensor = torch.randn(*shape) + expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) + norm_tensor = F.complex_norm(complex_tensor, power) + self.assertEqual(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) -@pytest.mark.parametrize('specgram', [ - torch.randn(2, 1025, 400), - torch.randn(1, 201, 100) -]) -@pytest.mark.parametrize('mask_param', [100]) -@pytest.mark.parametrize('mask_value', [0., 30.]) -@pytest.mark.parametrize('axis', [1, 2]) -def test_mask_along_axis(specgram, mask_param, mask_value, axis): - mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis) +class TestMaskAlongAxis(common_utils.TorchaudioTestCase): + @parameterized.expand(list(itertools.product( + [(2, 1025, 400), (1, 201, 100)], + [100], + [0., 30.], + [1, 2] + ))) + def test_mask_along_axis(self, shape, mask_param, mask_value, axis): + torch.random.manual_seed(42) + specgram = torch.randn(*shape) + mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis) - other_axis = 1 if axis == 2 else 2 + other_axis = 1 if axis == 2 else 2 - masked_columns = (mask_specgram == mask_value).sum(other_axis) - num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum() - num_masked_columns //= mask_specgram.size(0) + masked_columns = (mask_specgram == mask_value).sum(other_axis) + num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum() + num_masked_columns //= mask_specgram.size(0) - assert mask_specgram.size() == specgram.size() - assert num_masked_columns < mask_param + assert mask_specgram.size() == specgram.size() + assert num_masked_columns < mask_param -@pytest.mark.parametrize('mask_param', [100]) -@pytest.mark.parametrize('mask_value', [0., 30.]) -@pytest.mark.parametrize('axis', [2, 3]) -def test_mask_along_axis_iid(mask_param, mask_value, axis): - torch.random.manual_seed(42) - specgrams = torch.randn(4, 2, 1025, 400) +class TestMaskAlongAxisIID(common_utils.TorchaudioTestCase): + @parameterized.expand(list(itertools.product( + [100], + [0., 30.], + [2, 3] + ))) + def test_mask_along_axis_iid(self, mask_param, mask_value, axis): + torch.random.manual_seed(42) + specgrams = torch.randn(4, 2, 1025, 400) - mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) + mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) - other_axis = 2 if axis == 3 else 3 + other_axis = 2 if axis == 3 else 3 - masked_columns = (mask_specgrams == mask_value).sum(other_axis) - num_masked_columns = (masked_columns == mask_specgrams.size(other_axis)).sum(-1) + masked_columns = (mask_specgrams == mask_value).sum(other_axis) + num_masked_columns = (masked_columns == mask_specgrams.size(other_axis)).sum(-1) - assert mask_specgrams.size() == specgrams.size() - assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() + assert mask_specgrams.size() == specgrams.size() + assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() diff --git a/test/torchaudio_unittest/functional/functional_cuda_test.py b/test/torchaudio_unittest/functional/functional_cuda_test.py index 80ac5fa0c2..c89795be01 100644 --- a/test/torchaudio_unittest/functional/functional_cuda_test.py +++ b/test/torchaudio_unittest/functional/functional_cuda_test.py @@ -1,7 +1,7 @@ import torch from torchaudio_unittest import common_utils -from .functional_impl import Lfilter +from .functional_impl import Lfilter, Spectrogram @common_utils.skipIfNoCuda @@ -14,3 +14,15 @@ class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): dtype = torch.float64 device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestSpectrogramFloat32(Spectrogram, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestSpectrogramFloat64(Spectrogram, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/torchaudio_unittest/functional/functional_impl.py b/test/torchaudio_unittest/functional/functional_impl.py index c63001a68e..1ba5e21fbb 100644 --- a/test/torchaudio_unittest/functional/functional_impl.py +++ b/test/torchaudio_unittest/functional/functional_impl.py @@ -1,6 +1,7 @@ """Test defintion common to CPU and CUDA""" import torch import torchaudio.functional as F +from parameterized import parameterized from torchaudio_unittest import common_utils @@ -29,3 +30,25 @@ def test_clamp(self): assert output_signal.max() <= 1 output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=False) assert output_signal.max() > 1 + + +class Spectrogram(common_utils.TestBaseMixin): + @parameterized.expand([(0., ), (1., ), (2., ), (3., )]) + def test_grad_at_zero(self, power): + """The gradient of power spectrogram should not be nan but zero near x=0 + + https://github.com/pytorch/audio/issues/993 + """ + x = torch.zeros(1, 22050, requires_grad=True) + spec = F.spectrogram( + x, + pad=0, + window=None, + n_fft=2048, + hop_length=None, + win_length=None, + power=power, + normalized=False, + ) + spec.sum().backward() + assert not x.grad.isnan().sum() diff --git a/test/torchaudio_unittest/kaldi_compatibility_impl.py b/test/torchaudio_unittest/kaldi_compatibility_impl.py index bae01203f1..983805fff5 100644 --- a/test/torchaudio_unittest/kaldi_compatibility_impl.py +++ b/test/torchaudio_unittest/kaldi_compatibility_impl.py @@ -28,7 +28,7 @@ def _convert_args(**kwargs): def _run_kaldi(command, input_type, input_value): """Run provided Kaldi command, pass a tensor and get the resulting tensor - Arguments: + Args: input_type: str 'ark' or 'scp' input_value: diff --git a/test/torchaudio_unittest/librosa_compatibility_test.py b/test/torchaudio_unittest/librosa_compatibility_test.py index f779f36dae..e1ef80fa79 100644 --- a/test/torchaudio_unittest/librosa_compatibility_test.py +++ b/test/torchaudio_unittest/librosa_compatibility_test.py @@ -7,6 +7,8 @@ import torchaudio import torchaudio.functional as F from torchaudio._internal.module_utils import is_module_available +from parameterized import parameterized +import itertools LIBROSA_AVAILABLE = is_module_available('librosa') @@ -111,42 +113,49 @@ def test_amplitude_to_DB(self): self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) -@pytest.mark.parametrize('complex_specgrams', [ - torch.randn(2, 1025, 400, 2) -]) -@pytest.mark.parametrize('rate', [0.5, 1.01, 1.3]) -@pytest.mark.parametrize('hop_length', [256]) @unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") -def test_phase_vocoder(complex_specgrams, rate, hop_length): - # Due to cummulative sum, numerical error in using torch.float32 will - # result in bottom right values of the stretched sectrogram to not - # match with librosa. - - complex_specgrams = complex_specgrams.type(torch.float64) - phase_advance = torch.linspace(0, np.pi * hop_length, complex_specgrams.shape[-3], dtype=torch.float64)[..., None] - - complex_specgrams_stretch = F.phase_vocoder(complex_specgrams, rate=rate, phase_advance=phase_advance) - - # == Test shape - expected_size = list(complex_specgrams.size()) - expected_size[-2] = int(np.ceil(expected_size[-2] / rate)) - - assert complex_specgrams.dim() == complex_specgrams_stretch.dim() - assert complex_specgrams_stretch.size() == torch.Size(expected_size) - - # == Test values - index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3 - mono_complex_specgram = complex_specgrams[index].numpy() - mono_complex_specgram = mono_complex_specgram[..., 0] + \ - mono_complex_specgram[..., 1] * 1j - expected_complex_stretch = librosa.phase_vocoder(mono_complex_specgram, - rate=rate, - hop_length=hop_length) - - complex_stretch = complex_specgrams_stretch[index].numpy() - complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1] - - assert np.allclose(complex_stretch, expected_complex_stretch, atol=1e-5) +class TestPhaseVocoder(common_utils.TorchaudioTestCase): + @parameterized.expand(list(itertools.product( + [(2, 1025, 400, 2)], + [0.5, 1.01, 1.3], + [256] + ))) + def test_phase_vocoder(self, shape, rate, hop_length): + # Due to cummulative sum, numerical error in using torch.float32 will + # result in bottom right values of the stretched sectrogram to not + # match with librosa. + torch.random.manual_seed(42) + complex_specgrams = torch.randn(*shape) + complex_specgrams = complex_specgrams.type(torch.float64) + phase_advance = torch.linspace( + 0, + np.pi * hop_length, + complex_specgrams.shape[-3], + dtype=torch.float64)[..., None] + + complex_specgrams_stretch = F.phase_vocoder(complex_specgrams, rate=rate, phase_advance=phase_advance) + + # == Test shape + expected_size = list(complex_specgrams.size()) + expected_size[-2] = int(np.ceil(expected_size[-2] / rate)) + + assert complex_specgrams.dim() == complex_specgrams_stretch.dim() + assert complex_specgrams_stretch.size() == torch.Size(expected_size) + + # == Test values + index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3 + mono_complex_specgram = complex_specgrams[index].numpy() + mono_complex_specgram = mono_complex_specgram[..., 0] + \ + mono_complex_specgram[..., 1] * 1j + expected_complex_stretch = librosa.phase_vocoder( + mono_complex_specgram, + rate=rate, + hop_length=hop_length) + + complex_stretch = complex_specgrams_stretch[index].numpy() + complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1] + + self.assertEqual(complex_stretch, torch.from_numpy(expected_complex_stretch), atol=1e-5, rtol=1e-5) def _load_audio_asset(*asset_paths, **kwargs): diff --git a/test/torchaudio_unittest/soundfile_backend/load_test.py b/test/torchaudio_unittest/soundfile_backend/load_test.py index 4277ac03e9..399266de8f 100644 --- a/test/torchaudio_unittest/soundfile_backend/load_test.py +++ b/test/torchaudio_unittest/soundfile_backend/load_test.py @@ -1,4 +1,5 @@ import os +import tarfile from unittest.mock import patch import torch @@ -299,3 +300,58 @@ def test_wav(self, format_): @skipIfFormatNotSupported("FLAC") def test_flac(self, format_): self._test_format(format_) + + +@skipIfNoModule("soundfile") +class TestFileObject(TempDirMixin, PytorchTestCase): + def _test_fileobj(self, ext): + """Loading audio via file-like object works""" + sample_rate = 16000 + path = self.get_temp_path(f'test.{ext}') + + data = get_wav_data('float32', num_channels=2).numpy().T + soundfile.write(path, data, sample_rate) + expected = soundfile.read(path, dtype='float32')[0].T + + with open(path, 'rb') as fileobj: + found, sr = soundfile_backend.load(fileobj) + assert sr == sample_rate + self.assertEqual(expected, found) + + def test_fileobj_wav(self): + """Loading audio via file-like object works""" + self._test_fileobj('wav') + + @skipIfFormatNotSupported("FLAC") + def test_fileobj_flac(self): + """Loading audio via file-like object works""" + self._test_fileobj('flac') + + def _test_tarfile(self, ext): + """Loading audio via file-like object works""" + sample_rate = 16000 + audio_file = f'test.{ext}' + audio_path = self.get_temp_path(audio_file) + archive_path = self.get_temp_path('archive.tar.gz') + + data = get_wav_data('float32', num_channels=2).numpy().T + soundfile.write(audio_path, data, sample_rate) + expected = soundfile.read(audio_path, dtype='float32')[0].T + + with tarfile.TarFile(archive_path, 'w') as tarobj: + tarobj.add(audio_path, arcname=audio_file) + with tarfile.TarFile(archive_path, 'r') as tarobj: + fileobj = tarobj.extractfile(audio_file) + found, sr = soundfile_backend.load(fileobj) + + assert sr == sample_rate + self.assertEqual(expected, found) + + def test_tarfile_wav(self): + """Loading audio via file-like object works""" + self._test_tarfile('wav') + + @skipIfFormatNotSupported("FLAC") + def test_tarfile_flac(self): + """Loading audio via file-like object works""" + self._test_tarfile('flac') diff --git a/test/torchaudio_unittest/sox_effect/sox_effect_test.py b/test/torchaudio_unittest/sox_effect/sox_effect_test.py index c5b57eaf5f..c25e376359 100644 --- a/test/torchaudio_unittest/sox_effect/sox_effect_test.py +++ b/test/torchaudio_unittest/sox_effect/sox_effect_test.py @@ -8,6 +8,7 @@ TempDirMixin, PytorchTestCase, skipIfNoExtension, + get_asset_path, get_sinusoid, get_wav_data, save_wav, @@ -243,3 +244,21 @@ def test_vorbis(self, sample_rate, num_channels): assert sr == expected_sr self.assertEqual(found, expected) + + +@skipIfNoExtension +class TestApplyEffectFileWithoutExtension(PytorchTestCase): + def test_mp3(self): + """Providing format allows to read mp3 without extension + + libsox does not check header for mp3 + + https://github.com/pytorch/audio/issues/1040 + + The file was generated with the following command + ffmpeg -f lavfi -i "sine=frequency=1000:duration=5" -ar 16000 -f mp3 test_noext + """ + effects = [['band', '300', '10']] + path = get_asset_path("mp3_without_ext") + _, sr = sox_effects.apply_effects_file(path, effects, format="mp3") + assert sr == 16000 diff --git a/test/torchaudio_unittest/sox_io_backend/info_test.py b/test/torchaudio_unittest/sox_io_backend/info_test.py index 9b928f3ae0..49fc797354 100644 --- a/test/torchaudio_unittest/sox_io_backend/info_test.py +++ b/test/torchaudio_unittest/sox_io_backend/info_test.py @@ -167,3 +167,20 @@ def test_opus(self, bitrate, num_channels, compression_level): assert info.sample_rate == 48000 assert info.num_frames == 32768 assert info.num_channels == num_channels + + +@skipIfNoExtension +class TestLoadWithoutExtension(PytorchTestCase): + def test_mp3(self): + """Providing `format` allows to read mp3 without extension + + libsox does not check header for mp3 + + https://github.com/pytorch/audio/issues/1040 + + The file was generated with the following command + ffmpeg -f lavfi -i "sine=frequency=1000:duration=5" -ar 16000 -f mp3 test_noext + """ + path = get_asset_path("mp3_without_ext") + sinfo = sox_io_backend.info(path, format="mp3") + assert sinfo.sample_rate == 16000 diff --git a/test/torchaudio_unittest/sox_io_backend/load_test.py b/test/torchaudio_unittest/sox_io_backend/load_test.py index 2b4e916861..3a4b0ba8fe 100644 --- a/test/torchaudio_unittest/sox_io_backend/load_test.py +++ b/test/torchaudio_unittest/sox_io_backend/load_test.py @@ -1,14 +1,18 @@ -import os +import io import itertools +import tarfile -from torchaudio.backend import sox_io_backend from parameterized import parameterized +from torchaudio.backend import sox_io_backend +from torchaudio._internal import module_utils as _mod_utils from torchaudio_unittest.common_utils import ( TempDirMixin, + HttpServerMixin, PytorchTestCase, skipIfNoExec, skipIfNoExtension, + skipIfNoModule, get_asset_path, get_wav_data, load_wav, @@ -20,6 +24,10 @@ ) +if _mod_utils.is_module_available("requests"): + import requests + + class LoadTestBase(TempDirMixin, PytorchTestCase): def assert_wav(self, dtype, sample_rate, num_channels, normalize, duration): """`sox_io_backend.load` can load wav format correctly. @@ -355,26 +363,171 @@ def test_channels_first(self, channels_first): self.assertEqual(found, expected) +@skipIfNoExtension +class TestLoadWithoutExtension(PytorchTestCase): + def test_mp3(self): + """Providing format allows to read mp3 without extension + + libsox does not check header for mp3 + + https://github.com/pytorch/audio/issues/1040 + + The file was generated with the following command + ffmpeg -f lavfi -i "sine=frequency=1000:duration=5" -ar 16000 -f mp3 test_noext + """ + path = get_asset_path("mp3_without_ext") + _, sr = sox_io_backend.load(path, format="mp3") + assert sr == 16000 + + +@skipIfNoExtension @skipIfNoExec('sox') +class TestFileObject(TempDirMixin, PytorchTestCase): + """ + In this test suite, the result of file-like object input is compared against file path input, + because `load` function is rigrously tested for file path inputs to match libsox's result, + """ + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_fileobj(self, ext, compression): + """Loading audio via file object returns the same result as via file path.""" + sample_rate = 16000 + format_ = ext if ext in ['mp3'] else None + path = self.get_temp_path(f'test.{ext}') + + sox_utils.gen_audio_file( + path, sample_rate, num_channels=2, + compression=compression) + expected, _ = sox_io_backend.load(path) + + with open(path, 'rb') as fileobj: + found, sr = sox_io_backend.load(fileobj, format=format_) + + assert sr == sample_rate + self.assertEqual(expected, found) + + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_bytesio(self, ext, compression): + """Loading audio via BytesIO object returns the same result as via file path.""" + sample_rate = 16000 + format_ = ext if ext in ['mp3'] else None + path = self.get_temp_path(f'test.{ext}') + + sox_utils.gen_audio_file( + path, sample_rate, num_channels=2, + compression=compression) + expected, _ = sox_io_backend.load(path) + + with open(path, 'rb') as file_: + fileobj = io.BytesIO(file_.read()) + found, sr = sox_io_backend.load(fileobj, format=format_) + + assert sr == sample_rate + self.assertEqual(expected, found) + + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_tarfile(self, ext, compression): + """Loading compressed audio via file-like object returns the same result as via file path.""" + sample_rate = 16000 + format_ = ext if ext in ['mp3'] else None + audio_file = f'test.{ext}' + audio_path = self.get_temp_path(audio_file) + archive_path = self.get_temp_path('archive.tar.gz') + + sox_utils.gen_audio_file( + audio_path, sample_rate, num_channels=2, + compression=compression) + expected, _ = sox_io_backend.load(audio_path) + + with tarfile.TarFile(archive_path, 'w') as tarobj: + tarobj.add(audio_path, arcname=audio_file) + with tarfile.TarFile(archive_path, 'r') as tarobj: + fileobj = tarobj.extractfile(audio_file) + found, sr = sox_io_backend.load(fileobj, format=format_) + + assert sr == sample_rate + self.assertEqual(expected, found) + + @skipIfNoExtension -class TestLoadExtensionLess(TempDirMixin, PytorchTestCase): - """Given `format` parameter, `sox_io_backend.load` can load files without extension""" - original = None - path = None +@skipIfNoExec('sox') +@skipIfNoModule("requests") +class TestFileObjectHttp(HttpServerMixin, PytorchTestCase): + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_requests(self, ext, compression): + sample_rate = 16000 + format_ = ext if ext in ['mp3'] else None + audio_file = f'test.{ext}' + audio_path = self.get_temp_path(audio_file) - def _make_file(self, format_): + sox_utils.gen_audio_file( + audio_path, sample_rate, num_channels=2, compression=compression) + expected, _ = sox_io_backend.load(audio_path) + + url = self.get_url(audio_file) + with requests.get(url, stream=True) as resp: + found, sr = sox_io_backend.load(resp.raw, format=format_) + + assert sr == sample_rate + self.assertEqual(expected, found) + + @parameterized.expand(list(itertools.product( + [0, 1, 10, 100, 1000], + [-1, 1, 10, 100, 1000], + )), name_func=name_func) + def test_frame(self, frame_offset, num_frames): + """num_frames and frame_offset correctly specify the region of data""" sample_rate = 8000 - path = self.get_temp_path(f'test.{format_}') - sox_utils.gen_audio_file(f'{path}', sample_rate, num_channels=2) - self.original = sox_io_backend.load(path)[0] - self.path = os.path.splitext(path)[0] - os.rename(path, self.path) + audio_file = 'test.wav' + audio_path = self.get_temp_path(audio_file) - @parameterized.expand([ - ('WAV', ), ('wav', ), ('MP3', ), ('mp3', ), ('FLAC', ), ('flac',), - ], name_func=name_func) - def test_format(self, format_): - """Providing format allows to read file without extension""" - self._make_file(format_) - found, _ = sox_io_backend.load(self.path) - self.assertEqual(found, self.original) + original = get_wav_data('float32', num_channels=2) + save_wav(audio_path, original, sample_rate) + frame_end = None if num_frames == -1 else frame_offset + num_frames + expected = original[:, frame_offset:frame_end] + + url = self.get_url(audio_file) + with requests.get(url, stream=True) as resp: + found, sr = sox_io_backend.load(resp.raw, frame_offset, num_frames) + + assert sr == sample_rate + self.assertEqual(expected, found) diff --git a/test/torchaudio_unittest/transducer_test.py b/test/torchaudio_unittest/transducer_test.py new file mode 100644 index 0000000000..3d8dc0683d --- /dev/null +++ b/test/torchaudio_unittest/transducer_test.py @@ -0,0 +1,276 @@ +import torch +from torchaudio.prototype.transducer import RNNTLoss + +from torchaudio_unittest import common_utils + + +def get_data_basic(device): + # Example provided + # in 6f73a2513dc784c59eec153a45f40bc528355b18 + # of https://github.com/HawkAaron/warp-transducer + + acts = torch.tensor( + [ + [ + [ + [0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.1, 0.6, 0.1, 0.1], + [0.1, 0.1, 0.2, 0.8, 0.1], + ], + [ + [0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.1, 0.2, 0.1, 0.1], + [0.7, 0.1, 0.2, 0.1, 0.1], + ], + ] + ], + dtype=torch.float, + ) + labels = torch.tensor([[1, 2]], dtype=torch.int) + act_length = torch.tensor([2], dtype=torch.int) + label_length = torch.tensor([2], dtype=torch.int) + + acts = acts.to(device) + labels = labels.to(device) + act_length = act_length.to(device) + label_length = label_length.to(device) + + acts.requires_grad_(True) + + return acts, labels, act_length, label_length + + +def get_data_B2_T4_U3_D3(dtype=torch.float32, device="cpu"): + # Test from D21322854 + + logits = torch.tensor( + [ + 0.065357, + 0.787530, + 0.081592, + 0.529716, + 0.750675, + 0.754135, + 0.609764, + 0.868140, + 0.622532, + 0.668522, + 0.858039, + 0.164539, + 0.989780, + 0.944298, + 0.603168, + 0.946783, + 0.666203, + 0.286882, + 0.094184, + 0.366674, + 0.736168, + 0.166680, + 0.714154, + 0.399400, + 0.535982, + 0.291821, + 0.612642, + 0.324241, + 0.800764, + 0.524106, + 0.779195, + 0.183314, + 0.113745, + 0.240222, + 0.339470, + 0.134160, + 0.505562, + 0.051597, + 0.640290, + 0.430733, + 0.829473, + 0.177467, + 0.320700, + 0.042883, + 0.302803, + 0.675178, + 0.569537, + 0.558474, + 0.083132, + 0.060165, + 0.107958, + 0.748615, + 0.943918, + 0.486356, + 0.418199, + 0.652408, + 0.024243, + 0.134582, + 0.366342, + 0.295830, + 0.923670, + 0.689929, + 0.741898, + 0.250005, + 0.603430, + 0.987289, + 0.592606, + 0.884672, + 0.543450, + 0.660770, + 0.377128, + 0.358021, + ], + dtype=dtype, + ).reshape(2, 4, 3, 3) + + targets = torch.tensor([[1, 2], [1, 1]], dtype=torch.int32) + src_lengths = torch.tensor([4, 4], dtype=torch.int32) + tgt_lengths = torch.tensor([2, 2], dtype=torch.int32) + + blank = 0 + + ref_costs = torch.tensor([4.2806528590890736, 3.9384369822503591], dtype=dtype) + + ref_gradients = torch.tensor( + [ + -0.186844, + -0.062555, + 0.249399, + -0.203377, + 0.202399, + 0.000977, + -0.141016, + 0.079123, + 0.061893, + -0.011552, + -0.081280, + 0.092832, + -0.154257, + 0.229433, + -0.075176, + -0.246593, + 0.146405, + 0.100188, + -0.012918, + -0.061593, + 0.074512, + -0.055986, + 0.219831, + -0.163845, + -0.497627, + 0.209240, + 0.288387, + 0.013605, + -0.030220, + 0.016615, + 0.113925, + 0.062781, + -0.176706, + -0.667078, + 0.367659, + 0.299419, + -0.356344, + -0.055347, + 0.411691, + -0.096922, + 0.029459, + 0.067463, + -0.063518, + 0.027654, + 0.035863, + -0.154499, + -0.073942, + 0.228441, + -0.166790, + -0.000088, + 0.166878, + -0.172370, + 0.105565, + 0.066804, + 0.023875, + -0.118256, + 0.094381, + -0.104707, + -0.108934, + 0.213642, + -0.369844, + 0.180118, + 0.189726, + 0.025714, + -0.079462, + 0.053748, + 0.122328, + -0.238789, + 0.116460, + -0.598687, + 0.302203, + 0.296484, + ], + dtype=dtype, + ).reshape(2, 4, 3, 3) + + logits.requires_grad_(True) + logits = logits.to(device) + + def grad_hook(grad): + logits.saved_grad = grad.clone() + + logits.register_hook(grad_hook) + + data = { + "logits": logits, + "targets": targets, + "src_lengths": src_lengths, + "tgt_lengths": tgt_lengths, + "blank": blank, + } + + return data, ref_costs, ref_gradients + + +def compute_with_pytorch_transducer(data): + costs = RNNTLoss(blank=data["blank"], reduction="none")( + acts=data["logits"], + labels=data["targets"], + act_lens=data["src_lengths"], + label_lens=data["tgt_lengths"], + ) + + loss = torch.sum(costs) + loss.backward() + costs = costs.cpu() + gradients = data["logits"].saved_grad.cpu() + return costs, gradients + + +class TransducerTester: + def test_basic_fp16_error(self): + rnnt_loss = RNNTLoss() + acts, labels, act_length, label_length = get_data_basic(self.device) + acts = acts.to(torch.float16) + # RuntimeError raised by log_softmax before reaching transducer's bindings + self.assertRaises( + RuntimeError, rnnt_loss, acts, labels, act_length, label_length + ) + + def test_basic_backward(self): + rnnt_loss = RNNTLoss() + acts, labels, act_length, label_length = get_data_basic(self.device) + loss = rnnt_loss(acts, labels, act_length, label_length) + loss.backward() + + def test_costs_and_gradients_B2_T4_U3_D3_fp32(self): + + data, ref_costs, ref_gradients = get_data_B2_T4_U3_D3( + dtype=torch.float32, device=self.device + ) + logits_shape = data["logits"].shape + costs, gradients = compute_with_pytorch_transducer(data=data) + + atol, rtol = 1e-6, 1e-2 + self.assertEqual(costs, ref_costs, atol=atol, rtol=rtol) + self.assertEqual(logits_shape, gradients.shape) + self.assertEqual(gradients, ref_gradients, atol=atol, rtol=rtol) + + +@common_utils.skipIfNoExtension +class CPUTransducerTester(TransducerTester, common_utils.PytorchTestCase): + device = "cpu" diff --git a/third_party/CMakeLists.txt b/third_party/CMakeLists.txt index 50c7696eb5..1284affa88 100644 --- a/third_party/CMakeLists.txt +++ b/third_party/CMakeLists.txt @@ -1,91 +1,16 @@ -cmake_minimum_required(VERSION 3.1) +cmake_minimum_required(VERSION 3.5) project(torchaudio_third_parties) -include(ExternalProject) -set(INSTALL_DIR ${CMAKE_CURRENT_SOURCE_DIR}/install) -set(ARCHIVE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/archives) -set(COMMON_ARGS --quiet --disable-shared --enable-static --prefix=${INSTALL_DIR} --with-pic --disable-dependency-tracking --disable-debug --disable-examples --disable-doc) +option(BUILD_SOX "Build libsox statically") +option(BUILD_TRANSDUCER "Build transducer statically") -ExternalProject_Add(libmad - PREFIX ${CMAKE_CURRENT_SOURCE_DIR} - DOWNLOAD_DIR ${ARCHIVE_DIR} - URL https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/libmad-0.15.1b.tar.gz - URL_HASH SHA256=bbfac3ed6bfbc2823d3775ebb931087371e142bb0e9bb1bee51a76a6e0078690 - PATCH_COMMAND patch < ${CMAKE_CURRENT_SOURCE_DIR}/patch/libmad.patch - CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/libmad/configure ${COMMON_ARGS} -) +set(CMAKE_POSITION_INDEPENDENT_CODE ON) -ExternalProject_Add(amr - PREFIX ${CMAKE_CURRENT_SOURCE_DIR} - DOWNLOAD_DIR ${ARCHIVE_DIR} - URL https://sourceforge.net/projects/opencore-amr/files/opencore-amr/opencore-amr-0.1.5.tar.gz - URL_HASH SHA256=2c006cb9d5f651bfb5e60156dbff6af3c9d35c7bbcc9015308c0aff1e14cd341 - CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/amr/configure ${COMMON_ARGS} -) +if (BUILD_SOX) + add_subdirectory(sox) +endif() -ExternalProject_Add(libmp3lame - PREFIX ${CMAKE_CURRENT_SOURCE_DIR} - DOWNLOAD_DIR ${ARCHIVE_DIR} - URL https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz - URL_HASH SHA256=24346b4158e4af3bd9f2e194bb23eb473c75fb7377011523353196b19b9a23ff - CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/libmp3lame/configure ${COMMON_ARGS} --enable-nasm -) - -ExternalProject_Add(libogg - PREFIX ${CMAKE_CURRENT_SOURCE_DIR} - DOWNLOAD_DIR ${ARCHIVE_DIR} - URL https://ftp.osuosl.org/pub/xiph/releases/ogg/libogg-1.3.3.tar.gz - URL_HASH SHA256=c2e8a485110b97550f453226ec644ebac6cb29d1caef2902c007edab4308d985 - CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/libogg/configure ${COMMON_ARGS} -) - -ExternalProject_Add(libflac - PREFIX ${CMAKE_CURRENT_SOURCE_DIR} - DEPENDS libogg - DOWNLOAD_DIR ${ARCHIVE_DIR} - URL https://ftp.osuosl.org/pub/xiph/releases/flac/flac-1.3.2.tar.xz - URL_HASH SHA256=91cfc3ed61dc40f47f050a109b08610667d73477af6ef36dcad31c31a4a8d53f - CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libflac/configure ${COMMON_ARGS} --with-ogg -) - -ExternalProject_Add(libvorbis - PREFIX ${CMAKE_CURRENT_SOURCE_DIR} - DEPENDS libogg - DOWNLOAD_DIR ${ARCHIVE_DIR} - URL https://ftp.osuosl.org/pub/xiph/releases/vorbis/libvorbis-1.3.6.tar.gz - URL_HASH SHA256=6ed40e0241089a42c48604dc00e362beee00036af2d8b3f46338031c9e0351cb - CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libvorbis/configure ${COMMON_ARGS} --with-ogg -) - -ExternalProject_Add(libopus - PREFIX ${CMAKE_CURRENT_SOURCE_DIR} - DEPENDS libogg - DOWNLOAD_DIR ${ARCHIVE_DIR} - URL https://ftp.osuosl.org/pub/xiph/releases/opus/opus-1.3.1.tar.gz - URL_HASH SHA256=65b58e1e25b2a114157014736a3d9dfeaad8d41be1c8179866f144a2fb44ff9d - CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libopus/configure ${COMMON_ARGS} --with-ogg -) - -ExternalProject_Add(opusfile - PREFIX ${CMAKE_CURRENT_SOURCE_DIR} - DEPENDS libopus - DOWNLOAD_DIR ${ARCHIVE_DIR} - STAMP_DIR ${CMAKE_CURRENT_SOURCE_DIR}/src/opusfile-stamp - SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/src/opusfile - URL https://ftp.osuosl.org/pub/xiph/releases/opus/opusfile-0.12.tar.gz - URL_HASH SHA256=118d8601c12dd6a44f52423e68ca9083cc9f2bfe72da7a8c1acb22a80ae3550b - CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/opusfile/configure ${COMMON_ARGS} --disable-http -) - -ExternalProject_Add(libsox - PREFIX ${CMAKE_CURRENT_SOURCE_DIR} - DEPENDS libogg libflac libvorbis opusfile libmp3lame libmad amr - DOWNLOAD_DIR ${ARCHIVE_DIR} - URL https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2 - URL_HASH SHA256=81a6956d4330e75b5827316e44ae381e6f1e8928003c6aa45896da9041ea149c - PATCH_COMMAND patch -p0 < ${CMAKE_CURRENT_SOURCE_DIR}/patch/libsox.patch - # OpenMP is by default compiled against GNU OpenMP, which conflicts with the version of OpenMP that PyTorch uses. - # See https://github.com/pytorch/audio/pull/1026 - CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libsox/configure ${COMMON_ARGS} --with-lame --with-flac --with-mad --with-oggvorbis --without-alsa --without-coreaudio --without-png --without-oss --without-sndfile --with-opus --with-amrwb --with-amrnb --disable-openmp -) +if(BUILD_TRANSDUCER) + add_subdirectory(transducer) +endif() diff --git a/third_party/build_codec_helper.sh b/third_party/build_codec_helper.sh deleted file mode 100755 index e7f2614781..0000000000 --- a/third_party/build_codec_helper.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/usr/bin/env bash -# Helper script for building codecs depending on libogg, such as libopus and opus. -# It is difficult to set environment variable inside of ExternalProject_Add, -# so this script sets necessary environment variables before running the given command - -this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" -install_dir="${this_dir}/install" - -export PKG_CONFIG_PATH="${install_dir}/lib/pkgconfig" -export LDFLAGS="-L${install_dir}/lib ${LDFLAGS}" -export CPPFLAGS="-I${install_dir}/include ${CPPFLAGS}" - -$@ diff --git a/third_party/patch/libsox.patch b/third_party/patch/libsox.patch deleted file mode 100644 index 996bafdd57..0000000000 --- a/third_party/patch/libsox.patch +++ /dev/null @@ -1,24 +0,0 @@ ---- src/formats.c 2014-10-27 02:55:50.000000000 +0000 -+++ src/formats_new.c 2020-11-18 19:14:17.398689371 +0000 -@@ -91,6 +91,21 @@ - - if (ext && !strcasecmp(ext, "snd")) - CHECK(sndr , 7, 1, "" , 0, 2, "\0") -+ -+#if defined HAVE_MP3 && (defined STATIC_MP3 || !defined HAVE_LIBLTDL) -+ // http://www.mp3-tech.org/programmer/frame_header.html -+ // Check the first two bytes are -+ // expected 1111_1111 111X_X01X -+ // mask \xEEE6 (1111_1111 1110_0110) -+ // masked value \xEEE2 (1111_1111 1110_0010) -+ if (len >= 2 && !memcmp(data, "\xFF", 1)) { -+ unsigned char second_byte = data[1]; -+ unsigned char mask = 0xE6; -+ unsigned char expected = 0xE2; -+ if ((second_byte & mask) == expected) -+ return "mp3"; -+ } -+#endif - #undef CHECK - - #if HAVE_MAGIC diff --git a/third_party/sox/CMakeLists.txt b/third_party/sox/CMakeLists.txt new file mode 100644 index 0000000000..dcad72bbfe --- /dev/null +++ b/third_party/sox/CMakeLists.txt @@ -0,0 +1,95 @@ +include(ExternalProject) + +set(INSTALL_DIR ${PROJECT_SOURCE_DIR}/install) +set(ARCHIVE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/archives) +set(COMMON_ARGS --quiet --disable-shared --enable-static --prefix=${INSTALL_DIR} --with-pic --disable-dependency-tracking --disable-debug --disable-examples --disable-doc) + +# To pass custom environment variables to ExternalProject_Add command, +# we need to do `${CMAKE_COMMAND} -E env ${envs} `. +# https://stackoverflow.com/a/62437353 +# We constrcut the custom environment variables here +set(envs + "PKG_CONFIG_PATH=${INSTALL_DIR}/lib/pkgconfig" + "LDFLAGS=-L${INSTALL_DIR}/lib $ENV{LDFLAGS}" + "CPPFLAGS=-I${INSTALL_DIR}/include $ENV{CPPFLAGS}" +) + +ExternalProject_Add(mad + PREFIX ${CMAKE_CURRENT_BINARY_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/libmad-0.15.1b.tar.gz + URL_HASH SHA256=bbfac3ed6bfbc2823d3775ebb931087371e142bb0e9bb1bee51a76a6e0078690 + PATCH_COMMAND patch < ${CMAKE_CURRENT_SOURCE_DIR}/patch/libmad.patch + CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/mad/configure ${COMMON_ARGS} +) + +ExternalProject_Add(amr + PREFIX ${CMAKE_CURRENT_BINARY_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://sourceforge.net/projects/opencore-amr/files/opencore-amr/opencore-amr-0.1.5.tar.gz + URL_HASH SHA256=2c006cb9d5f651bfb5e60156dbff6af3c9d35c7bbcc9015308c0aff1e14cd341 + CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/amr/configure ${COMMON_ARGS} +) + +ExternalProject_Add(lame + PREFIX ${CMAKE_CURRENT_BINARY_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz + URL_HASH SHA256=24346b4158e4af3bd9f2e194bb23eb473c75fb7377011523353196b19b9a23ff + CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/lame/configure ${COMMON_ARGS} --enable-nasm +) + +ExternalProject_Add(ogg + PREFIX ${CMAKE_CURRENT_BINARY_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/ogg/libogg-1.3.3.tar.gz + URL_HASH SHA256=c2e8a485110b97550f453226ec644ebac6cb29d1caef2902c007edab4308d985 + CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/ogg/configure ${COMMON_ARGS} +) + +ExternalProject_Add(flac + PREFIX ${CMAKE_CURRENT_BINARY_DIR} + DEPENDS ogg + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/flac/flac-1.3.2.tar.xz + URL_HASH SHA256=91cfc3ed61dc40f47f050a109b08610667d73477af6ef36dcad31c31a4a8d53f + CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/flac/configure ${COMMON_ARGS} --with-ogg --disable-cpplibs +) + +ExternalProject_Add(vorbis + PREFIX ${CMAKE_CURRENT_BINARY_DIR} + DEPENDS ogg + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/vorbis/libvorbis-1.3.6.tar.gz + URL_HASH SHA256=6ed40e0241089a42c48604dc00e362beee00036af2d8b3f46338031c9e0351cb + CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/vorbis/configure ${COMMON_ARGS} --with-ogg +) + +ExternalProject_Add(opus + PREFIX ${CMAKE_CURRENT_BINARY_DIR} + DEPENDS ogg + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/opus/opus-1.3.1.tar.gz + URL_HASH SHA256=65b58e1e25b2a114157014736a3d9dfeaad8d41be1c8179866f144a2fb44ff9d + CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/opus/configure ${COMMON_ARGS} --with-ogg +) + +ExternalProject_Add(opusfile + PREFIX ${CMAKE_CURRENT_BINARY_DIR} + DEPENDS opus + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/opus/opusfile-0.12.tar.gz + URL_HASH SHA256=118d8601c12dd6a44f52423e68ca9083cc9f2bfe72da7a8c1acb22a80ae3550b + CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/opusfile/configure ${COMMON_ARGS} --disable-http +) + +ExternalProject_Add(sox + PREFIX ${CMAKE_CURRENT_BINARY_DIR} + DEPENDS ogg flac vorbis opusfile lame mad amr + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2 + URL_HASH SHA256=81a6956d4330e75b5827316e44ae381e6f1e8928003c6aa45896da9041ea149c + # OpenMP is by default compiled against GNU OpenMP, which conflicts with the version of OpenMP that PyTorch uses. + # See https://github.com/pytorch/audio/pull/1026 + CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/sox/configure ${COMMON_ARGS} --with-lame --with-flac --with-mad --with-oggvorbis --without-alsa --without-coreaudio --without-png --without-oss --without-sndfile --with-opus --with-amrwb --with-amrnb --disable-openmp --without-sndio --without-pulseaudio +) diff --git a/third_party/patch/libmad.patch b/third_party/sox/patch/libmad.patch similarity index 100% rename from third_party/patch/libmad.patch rename to third_party/sox/patch/libmad.patch diff --git a/third_party/transducer/CMakeLists.txt b/third_party/transducer/CMakeLists.txt new file mode 100755 index 0000000000..c35f0c8552 --- /dev/null +++ b/third_party/transducer/CMakeLists.txt @@ -0,0 +1,27 @@ +ADD_DEFINITIONS(-DRNNT_DISABLE_OMP) + +IF(APPLE) + ADD_DEFINITIONS(-DAPPLE) + EXEC_PROGRAM(uname ARGS -v OUTPUT_VARIABLE DARWIN_VERSION) + STRING(REGEX MATCH "[0-9]+" DARWIN_VERSION ${DARWIN_VERSION}) + MESSAGE(STATUS "DARWIN_VERSION=${DARWIN_VERSION}") + + # for el capitain have to use rpath + IF(DARWIN_VERSION LESS 15) + SET(CMAKE_SKIP_RPATH TRUE) + ENDIF() + +ELSE() + # always skip for linux + SET(CMAKE_SKIP_RPATH TRUE) +ENDIF() + +ADD_LIBRARY(warprnnt STATIC submodule/src/rnnt_entrypoint.cpp) +target_include_directories(warprnnt PUBLIC submodule/include) +set_target_properties(warprnnt PROPERTIES PUBLIC_HEADER submodule/include/rnnt.h) + + +INSTALL( + TARGETS warprnnt + ARCHIVE DESTINATION "lib" + PUBLIC_HEADER DESTINATION "include") diff --git a/torchaudio/backend/_soundfile_backend.py b/torchaudio/backend/_soundfile_backend.py index 83d3cbc3cf..719224b827 100644 --- a/torchaudio/backend/_soundfile_backend.py +++ b/torchaudio/backend/_soundfile_backend.py @@ -12,7 +12,7 @@ @_mod_utils.requires_module("soundfile") -def info(filepath: str) -> AudioMetaData: +def info(filepath: str, format: Optional[str] = None) -> AudioMetaData: """Get signal information of an audio file. Args: @@ -20,6 +20,8 @@ def info(filepath: str) -> AudioMetaData: This functionalso handles ``pathlib.Path`` objects, but is annotated as ``str`` for the consistency with "sox_io" backend, which has a restriction on type annotation for TorchScript compiler compatiblity. + format (str, optional): + Not used. PySoundFile does not accept format hint. Returns: AudioMetaData: meta data of the given audio. @@ -45,6 +47,7 @@ def load( num_frames: int = -1, normalize: bool = True, channels_first: bool = True, + format: Optional[str] = None, ) -> Tuple[torch.Tensor, int]: """Load audio data from file. @@ -79,10 +82,12 @@ def load( ``[-1.0, 1.0]``. Args: - filepath (str or pathlib.Path): Path to audio file. - This functionalso handles ``pathlib.Path`` objects, but is annotated as ``str`` - for the consistency with "sox_io" backend, which has a restriction on type annotation - for TorchScript compiler compatiblity. + filepath (path-like object or file-like object): + Source of audio data. + Note: + * This argument is intentionally annotated as ``str`` only, + for the consistency with "sox_io" backend, which has a restriction + on type annotation due to TorchScript compiler compatiblity. frame_offset (int): Number of frames to skip before start reading data. num_frames (int): @@ -99,9 +104,11 @@ def load( channels_first (bool): When True, the returned Tensor has dimension ``[channel, time]``. Otherwise, the returned Tensor's dimension is ``[time, channel]``. + format (str, optional): + Not used. PySoundFile does not accept format hint. Returns: - torch.Tensor: + Tuple[torch.Tensor, int]: Resulting Tensor and sample rate. If the input file has integer wav format and normalization is off, then it has integer type, else ``float32`` type. If ``channels_first=True``, it has ``[channel, time]`` else ``[time, channel]``. diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 5293223977..1e6d417cb8 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -1,3 +1,4 @@ +import os from typing import Tuple, Optional import torch @@ -5,24 +6,32 @@ module_utils as _mod_utils, ) +import torchaudio from .common import AudioMetaData @_mod_utils.requires_module('torchaudio._torchaudio') -def info(filepath: str) -> AudioMetaData: +def info( + filepath: str, + format: Optional[str] = None, +) -> AudioMetaData: """Get signal information of an audio file. Args: filepath (str or pathlib.Path): Path to audio file. This function also handles ``pathlib.Path`` objects, but is annotated as ``str`` for TorchScript compatibility. + format (str, optional): + Override the format detection with the given format. + Providing the argument might help when libsox can not infer the format + from header or extension, Returns: AudioMetaData: Metadata of the given audio. """ # Cast to str in case type is `pathlib.Path` filepath = str(filepath) - sinfo = torch.ops.torchaudio.sox_io_get_info(filepath) + sinfo = torch.ops.torchaudio.sox_io_get_info(filepath, format) return AudioMetaData(sinfo.get_sample_rate(), sinfo.get_num_frames(), sinfo.get_num_channels()) @@ -33,6 +42,7 @@ def load( num_frames: int = -1, normalize: bool = True, channels_first: bool = True, + format: Optional[str] = None, ) -> Tuple[torch.Tensor, int]: """Load audio data from file. @@ -74,9 +84,17 @@ def load( ``[-1.0, 1.0]``. Args: - filepath (str or pathlib.Path): - Path to audio file. This function also handles ``pathlib.Path`` objects, but is - annotated as ``str`` for TorchScript compiler compatibility. + filepath (path-like object or file-like object): + Source of audio data. When the function is not compiled by TorchScript, + (e.g. ``torch.jit.script``), the following types are accepted; + * ``path-like``: file path + * ``file-like``: Object with ``read(size: int) -> bytes`` method, + which returns byte string of at most ``size`` length. + When the function is compiled by TorchScript, only ``str`` type is allowed. + + Note: + * This argument is intentionally annotated as ``str`` only due to + TorchScript compiler compatibility. frame_offset (int): Number of frames to skip before start reading data. num_frames (int): @@ -93,17 +111,26 @@ def load( channels_first (bool): When True, the returned Tensor has dimension ``[channel, time]``. Otherwise, the returned Tensor's dimension is ``[time, channel]``. + format (str, optional): + Override the format detection with the given format. + Providing the argument might help when libsox can not infer the format + from header or extension, Returns: - torch.Tensor: + Tuple[torch.Tensor, int]: Resulting Tensor and sample rate. If the input file has integer wav format and normalization is off, then it has integer type, else ``float32`` type. If ``channels_first=True``, it has ``[channel, time]`` else ``[time, channel]``. """ - # Cast to str in case type is `pathlib.Path` - filepath = str(filepath) + if not torch.jit.is_scripting(): + if hasattr(filepath, 'read'): + return torchaudio._torchaudio.load_audio_fileobj( + filepath, frame_offset, num_frames, normalize, channels_first, format) + signal = torch.ops.torchaudio.sox_io_load_audio_file( + os.fspath(filepath), frame_offset, num_frames, normalize, channels_first, format) + return signal.get_tensor(), signal.get_sample_rate() signal = torch.ops.torchaudio.sox_io_load_audio_file( - filepath, frame_offset, num_frames, normalize, channels_first) + filepath, frame_offset, num_frames, normalize, channels_first, format) return signal.get_tensor(), signal.get_sample_rate() diff --git a/torchaudio/csrc/sox.cpp b/torchaudio/csrc/pybind.cpp similarity index 52% rename from torchaudio/csrc/sox.cpp rename to torchaudio/csrc/pybind.cpp index 3be7d01314..caf9ad9b19 100644 --- a/torchaudio/csrc/sox.cpp +++ b/torchaudio/csrc/pybind.cpp @@ -1,182 +1,7 @@ -#include +#include +#include +#include -#include -#include -#include -#include - -namespace torch { -namespace audio { -namespace { -/// Helper struct to safely close the sox_format_t descriptor. -struct SoxDescriptor { - explicit SoxDescriptor(sox_format_t* fd) noexcept : fd_(fd) {} - SoxDescriptor(const SoxDescriptor& other) = delete; - SoxDescriptor(SoxDescriptor&& other) = delete; - SoxDescriptor& operator=(const SoxDescriptor& other) = delete; - SoxDescriptor& operator=(SoxDescriptor&& other) = delete; - ~SoxDescriptor() { - if (fd_ != nullptr) { - sox_close(fd_); - } - } - sox_format_t* operator->() noexcept { - return fd_; - } - sox_format_t* get() noexcept { - return fd_; - } - - private: - sox_format_t* fd_; -}; - -int64_t write_audio(SoxDescriptor& fd, at::Tensor tensor) { - std::vector buffer(tensor.numel()); - - AT_DISPATCH_ALL_TYPES(tensor.scalar_type(), "write_audio_buffer", [&] { - auto* data = tensor.data_ptr(); - std::copy(data, data + tensor.numel(), buffer.begin()); - }); - - const auto samples_written = - sox_write(fd.get(), buffer.data(), buffer.size()); - - return samples_written; -} - -void read_audio( - SoxDescriptor& fd, - at::Tensor output, - int64_t buffer_length) { - std::vector buffer(buffer_length); - - int number_of_channels = fd->signal.channels; - const int64_t samples_read = sox_read(fd.get(), buffer.data(), buffer_length); - if (samples_read == 0) { - throw std::runtime_error( - "Error reading audio file: empty file or read failed in sox_read"); - } - - output.resize_({samples_read / number_of_channels, number_of_channels}); - output = output.contiguous(); - - AT_DISPATCH_ALL_TYPES(output.scalar_type(), "read_audio_buffer", [&] { - auto* data = output.data_ptr(); - std::copy(buffer.begin(), buffer.begin() + samples_read, data); - }); -} -} // namespace - -std::tuple get_info( - const std::string& file_name - ) { - SoxDescriptor fd(sox_open_read( - file_name.c_str(), - /*signal=*/nullptr, - /*encoding=*/nullptr, - /*filetype=*/nullptr)); - if (fd.get() == nullptr) { - throw std::runtime_error("Error opening audio file"); - } - return std::make_tuple(fd->signal, fd->encoding); -} - -int read_audio_file( - const std::string& file_name, - at::Tensor output, - bool ch_first, - int64_t nframes, - int64_t offset, - sox_signalinfo_t* si, - sox_encodinginfo_t* ei, - const char* ft) { - - SoxDescriptor fd(sox_open_read(file_name.c_str(), si, ei, ft)); - if (fd.get() == nullptr) { - throw std::runtime_error("Error opening audio file"); - } - - // signal info - - const int number_of_channels = fd->signal.channels; - const int sample_rate = fd->signal.rate; - const int64_t total_length = fd->signal.length; - - // multiply offset and number of frames by number of channels - offset *= number_of_channels; - nframes *= number_of_channels; - - if (total_length == 0) { - throw std::runtime_error("Error reading audio file: unknown length"); - } - if (offset > total_length) { - throw std::runtime_error("Offset past EOF"); - } - - // calculate buffer length - int64_t buffer_length = total_length; - if (offset > 0) { - buffer_length -= offset; - } - if (nframes > 0 && buffer_length > nframes) { - buffer_length = nframes; - } - - // seek to offset point before reading data - if (sox_seek(fd.get(), offset, 0) == SOX_EOF) { - throw std::runtime_error("sox_seek reached EOF, try reducing offset or num_samples"); - } - - // read data and fill output tensor - read_audio(fd, output, buffer_length); - - // L x C -> C x L, if desired - if (ch_first) { - output.transpose_(1, 0); - } - - return sample_rate; -} - -void write_audio_file( - const std::string& file_name, - const at::Tensor& tensor, - sox_signalinfo_t* si, - sox_encodinginfo_t* ei, - const char* file_type) { - if (!tensor.is_contiguous()) { - throw std::runtime_error( - "Error writing audio file: input tensor must be contiguous"); - } - -#if SOX_LIB_VERSION_CODE >= 918272 // >= 14.3.0 - si->mult = nullptr; -#endif - - SoxDescriptor fd(sox_open_write( - file_name.c_str(), - si, - ei, - file_type, - /*oob=*/nullptr, - /*overwrite=*/nullptr)); - - if (fd.get() == nullptr) { - throw std::runtime_error( - "Error writing audio file: could not open file for writing"); - } - - const auto samples_written = write_audio(fd, tensor); - - if (samples_written != tensor.numel()) { - throw std::runtime_error( - "Error writing audio file: could not write entire buffer"); - } -} - -} // namespace audio -} // namespace torch PYBIND11_MODULE(_torchaudio, m) { py::class_(m, "sox_signalinfo_t") @@ -271,4 +96,8 @@ PYBIND11_MODULE(_torchaudio, m) { "get_info", &torch::audio::get_info, "Gets information about an audio file"); + m.def( + "load_audio_fileobj", + &torchaudio::sox_io::load_audio_fileobj, + "Load audio from file object."); } diff --git a/torchaudio/csrc/sox_effects.cpp b/torchaudio/csrc/sox/effects.cpp similarity index 51% rename from torchaudio/csrc/sox_effects.cpp rename to torchaudio/csrc/sox/effects.cpp index 7bd24b6bc2..ffc85b92ed 100644 --- a/torchaudio/csrc/sox_effects.cpp +++ b/torchaudio/csrc/sox/effects.cpp @@ -1,7 +1,7 @@ #include -#include -#include -#include +#include +#include +#include using namespace torchaudio::sox_utils; @@ -92,13 +92,14 @@ c10::intrusive_ptr apply_effects_file( const std::string path, std::vector> effects, c10::optional& normalize, - c10::optional& channels_first) { + c10::optional& channels_first, + c10::optional& format) { // Open input file SoxFormat sf(sox_open_read( path.c_str(), /*signal=*/nullptr, /*encoding=*/nullptr, - /*filetype=*/nullptr)); + /*filetype=*/format.has_value() ? format.value().c_str() : nullptr)); validate_input_file(sf); @@ -134,5 +135,88 @@ c10::intrusive_ptr apply_effects_file( tensor, chain.getOutputSampleRate(), channels_first_); } +#ifdef TORCH_API_INCLUDE_EXTENSION_H + +std::tuple apply_effects_fileobj( + py::object fileobj, + std::vector> effects, + c10::optional& normalize, + c10::optional& channels_first, + c10::optional& format) { + + // Streaming decoding over file-like object is tricky because libsox operates on FILE pointer. + // The folloing is what `sox` and `play` commands do + // - file input -> FILE pointer + // - URL input -> call wget in suprocess and pipe the data -> FILE pointer + // - stdin -> FILE pointer + // + // We want to, instead, fetch byte strings chunk by chunk, consume them, and discard. + // + // Here is the approach + // 1. Initialize sox_format_t using sox_open_mem_read, providing the initial chunk of byte string + // This will perform header-based format detection, if necessary, then fill the metadata of + // sox_format_t. Internally, sox_open_mem_read uses fmemopen, which returns FILE* which points the + // buffer of the provided byte string. + // 2. Each time sox reads a chunk from the FILE*, we update the underlying buffer in a way that it + // starts with unseen data, and append the new data read from the given fileobj. + // This will trick libsox as if it keeps reading from the FILE* continuously. + + // Prepare the buffer used throughout the lifecycle of SoxEffectChain. + // Using std::string and let it manage memory. + // 4096 is minimum size required by auto_detect_format + // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/formats.c#L40-L48 + const size_t in_buffer_size = 4096; + std::string in_buffer(in_buffer_size, 'x'); + auto* in_buf = const_cast(in_buffer.data()); + + // Fetch the header, and copy it to the buffer. + auto header = static_cast(static_cast(fileobj.attr("read")(4096))); + memcpy(static_cast(in_buf), + static_cast(const_cast(header.data())), header.length()); + + // Open file (this starts reading the header) + SoxFormat sf(sox_open_mem_read( + in_buf, + in_buffer_size, + /*signal=*/nullptr, + /*encoding=*/nullptr, + /*filetype=*/format.has_value() ? format.value().c_str() : nullptr)); + + // In case of streamed data, length can be 0 + validate_input_file(sf, /*check_length=*/false); + + // Prepare output buffer + std::vector out_buffer; + out_buffer.reserve(sf->signal.length); + + // Create and run SoxEffectsChain + const auto dtype = get_dtype(sf->encoding.encoding, sf->signal.precision); + torchaudio::sox_effects_chain::SoxEffectsChain chain( + /*input_encoding=*/sf->encoding, + /*output_encoding=*/get_encodinginfo("wav", dtype, 0.)); + chain.addInputFileObj(sf, in_buf, in_buffer_size, &fileobj); + for (const auto& effect : effects) { + chain.addEffect(effect); + } + chain.addOutputBuffer(&out_buffer); + chain.run(); + + // Create tensor from buffer + bool channels_first_ = channels_first.value_or(true); + auto tensor = convert_to_tensor( + /*buffer=*/out_buffer.data(), + /*num_samples=*/out_buffer.size(), + /*num_channels=*/chain.getOutputNumChannels(), + dtype, + normalize.value_or(true), + channels_first_); + + return std::make_tuple( + tensor, + static_cast(chain.getOutputSampleRate())); +} + +#endif // TORCH_API_INCLUDE_EXTENSION_H + } // namespace sox_effects } // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects.h b/torchaudio/csrc/sox/effects.h similarity index 54% rename from torchaudio/csrc/sox_effects.h rename to torchaudio/csrc/sox/effects.h index 669c86ac39..a2a598b408 100644 --- a/torchaudio/csrc/sox_effects.h +++ b/torchaudio/csrc/sox/effects.h @@ -1,8 +1,12 @@ #ifndef TORCHAUDIO_SOX_EFFECTS_H #define TORCHAUDIO_SOX_EFFECTS_H +#ifdef TORCH_API_INCLUDE_EXTENSION_H +#include +#endif // TORCH_API_INCLUDE_EXTENSION_H + #include -#include +#include namespace torchaudio { namespace sox_effects { @@ -19,7 +23,19 @@ c10::intrusive_ptr apply_effects_file( const std::string path, std::vector> effects, c10::optional& normalize, - c10::optional& channels_first); + c10::optional& channels_first, + c10::optional& format); + +#ifdef TORCH_API_INCLUDE_EXTENSION_H + +std::tuple apply_effects_fileobj( + py::object fileobj, + std::vector> effects, + c10::optional& normalize, + c10::optional& channels_first, + c10::optional& format); + +#endif // TORCH_API_INCLUDE_EXTENSION_H } // namespace sox_effects } // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects_chain.cpp b/torchaudio/csrc/sox/effects_chain.cpp similarity index 64% rename from torchaudio/csrc/sox_effects_chain.cpp rename to torchaudio/csrc/sox/effects_chain.cpp index 449e33be98..37a6f32df8 100644 --- a/torchaudio/csrc/sox_effects_chain.cpp +++ b/torchaudio/csrc/sox/effects_chain.cpp @@ -1,5 +1,5 @@ -#include -#include +#include +#include using namespace torch::indexing; using namespace torchaudio::sox_utils; @@ -198,7 +198,7 @@ void SoxEffectsChain::addInputTensor(TensorSignal* signal) { priv->signal = signal; priv->index = 0; if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { - throw std::runtime_error("Failed to add effect: input_tensor"); + throw std::runtime_error("Internal Error: Failed to add effect: input_tensor"); } } @@ -207,7 +207,7 @@ void SoxEffectsChain::addOutputBuffer( SoxEffect e(sox_create_effect(get_tensor_output_handler())); static_cast(e->priv)->buffer = output_buffer; if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { - throw std::runtime_error("Failed to add effect: output_tensor"); + throw std::runtime_error("Internal Error: Failed to add effect: output_tensor"); } } @@ -219,7 +219,7 @@ void SoxEffectsChain::addInputFile(sox_format_t* sf) { sox_effect_options(e, 1, opts); if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { std::ostringstream stream; - stream << "Failed to add effect: input " << sf->filename; + stream << "Internal Error: Failed to add effect: input " << sf->filename; throw std::runtime_error(stream.str()); } } @@ -230,7 +230,7 @@ void SoxEffectsChain::addOutputFile(sox_format_t* sf) { static_cast(e->priv)->sf = sf; if (sox_add_effect(sec_, e, &interm_sig_, &out_sig_) != SOX_SUCCESS) { std::ostringstream stream; - stream << "Failed to add effect: output " << sf->filename; + stream << "Internal Error: Failed to add effect: output " << sf->filename; throw std::runtime_error(stream.str()); } } @@ -266,7 +266,7 @@ void SoxEffectsChain::addEffect(const std::vector effect) { if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { std::ostringstream stream; - stream << "Failed to add effect: \"" << name; + stream << "Internal Error: Failed to add effect: \"" << name; for (size_t i = 1; i < num_args; ++i) { stream << " " << effect[i]; } @@ -283,5 +283,132 @@ int64_t SoxEffectsChain::getOutputSampleRate() { return interm_sig_.rate; } +#ifdef TORCH_API_INCLUDE_EXTENSION_H + +namespace { + +/// helper classes for passing file-like object to SoxEffectChain +struct FileObjInputPriv { + sox_format_t* sf; + py::object* fileobj; + char* buffer; + uint64_t buffer_size; +}; + +/// Callback function to feed byte string +/// https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/sox.h#L1268-L1278 +int fileobj_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { + auto priv = static_cast(effp->priv); + auto sf = priv->sf; + auto fileobj = priv->fileobj; + auto buffer = priv->buffer; + auto buffer_size = priv->buffer_size; + + // 1. Refresh the buffer + // + // NOTE: + // Since the underlying FILE* was opened with `fmemopen`, the only way + // libsox detect EOF is reaching the end of the buffer. (null byte won't help) + // Therefore we need to align the content at the end of buffer, otherwise, + // libsox will keep reading the content beyond intended length. + // + // Before: + // + // |<--------consumed------->|<-remaining->| + // |*************************|-------------| + // ^ ftell + // + // After: + // + // |<-offset->|<-remaining->|<--new data-->| + // |**********|-------------|++++++++++++++| + // ^ ftell + + const auto num_consumed = sf->tell_off; + const auto num_remain = buffer_size - num_consumed; + + // 1.1. First, we fetch the data to see if there is data to fill the buffer + py::bytes chunk_ = fileobj->attr("read")(num_consumed); + const auto num_refill = py::len(chunk_); + const auto offset = buffer_size - (num_remain + num_refill); + + if(num_refill > num_consumed) { + std::ostringstream message; + message << "Tried to read up to " << num_consumed << " bytes but, " + << "received " << num_refill << " bytes. " + << "The given object does not confirm to read protocol of file object."; + throw std::runtime_error(message.str()); + } + + // 1.2. Move the unconsumed data towards the beginning of buffer. + if (num_remain) { + auto src = static_cast(buffer + num_consumed); + auto dst = static_cast(buffer + offset); + memmove(dst, src, num_remain); + } + + // 1.3. Refill the remaining buffer. + if (num_refill) { + auto chunk = static_cast(chunk_); + auto src = static_cast(const_cast(chunk.c_str())); + auto dst = buffer + offset + num_remain; + memcpy(dst, src, num_refill); + } + + // 1.4. Set the file pointer to the new offset + sf->tell_off = offset; + fseek ((FILE*)sf->fp, offset, SEEK_SET); + + // 2. Perform decoding operation + // The following part is practically same as "input" effect + // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/input.c#L30-L48 + + // Ensure that it's a multiple of the number of channels + *osamp -= *osamp % effp->out_signal.channels; + + // Read up to *osamp samples into obuf; + // store the actual number read back to *osamp + *osamp = sox_read(sf, obuf, *osamp); + + return *osamp? SOX_SUCCESS : SOX_EOF; +} + +sox_effect_handler_t* get_fileobj_input_handler() { + static sox_effect_handler_t handler{/*name=*/"input_fileobj_object", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/NULL, + /*drain=*/fileobj_input_drain, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(FileObjInputPriv)}; + return &handler; +} + +} // namespace + +void SoxEffectsChain::addInputFileObj( + sox_format_t* sf, + char* buffer, + uint64_t buffer_size, + py::object* fileobj) { + in_sig_ = sf->signal; + interm_sig_ = in_sig_; + + SoxEffect e(sox_create_effect(get_fileobj_input_handler())); + auto priv = static_cast(e->priv); + priv->sf = sf; + priv->fileobj = fileobj; + priv->buffer = buffer; + priv->buffer_size = buffer_size; + if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { + throw std::runtime_error("Internal Error: Failed to add effect: input fileobj"); + } +} + +#endif // TORCH_API_INCLUDE_EXTENSION_H + } // namespace sox_effects_chain } // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects_chain.h b/torchaudio/csrc/sox/effects_chain.h similarity index 78% rename from torchaudio/csrc/sox_effects_chain.h rename to torchaudio/csrc/sox/effects_chain.h index fa55182281..b096b3eb3d 100644 --- a/torchaudio/csrc/sox_effects_chain.h +++ b/torchaudio/csrc/sox/effects_chain.h @@ -2,8 +2,11 @@ #define TORCHAUDIO_SOX_EFFECTS_CHAIN_H #include -#include -#include +#include + +#ifdef TORCH_API_INCLUDE_EXTENSION_H +#include +#endif // TORCH_API_INCLUDE_EXTENSION_H namespace torchaudio { namespace sox_effects_chain { @@ -34,6 +37,16 @@ class SoxEffectsChain { void addEffect(const std::vector effect); int64_t getOutputNumChannels(); int64_t getOutputSampleRate(); + +#ifdef TORCH_API_INCLUDE_EXTENSION_H + + void addInputFileObj( + sox_format_t* sf, + char* buffer, + uint64_t buffer_size, + py::object* fileobj); + +#endif // TORCH_API_INCLUDE_EXTENSION_H }; } // namespace sox_effects_chain diff --git a/torchaudio/csrc/sox_io.cpp b/torchaudio/csrc/sox/io.cpp similarity index 72% rename from torchaudio/csrc/sox_io.cpp rename to torchaudio/csrc/sox/io.cpp index 9acab1c030..e381be14f8 100644 --- a/torchaudio/csrc/sox_io.cpp +++ b/torchaudio/csrc/sox/io.cpp @@ -1,8 +1,8 @@ #include -#include -#include -#include -#include +#include +#include +#include +#include using namespace torch::indexing; using namespace torchaudio::sox_utils; @@ -30,12 +30,14 @@ int64_t SignalInfo::getNumFrames() const { return num_frames; } -c10::intrusive_ptr get_info(const std::string& path) { +c10::intrusive_ptr get_info( + const std::string& path, + c10::optional& format) { SoxFormat sf(sox_open_read( path.c_str(), /*signal=*/nullptr, /*encoding=*/nullptr, - /*filetype=*/nullptr)); + /*filetype=*/format.has_value() ? format.value().c_str() : nullptr)); if (static_cast(sf) == nullptr) { throw std::runtime_error("Error opening audio file"); @@ -47,12 +49,11 @@ c10::intrusive_ptr get_info(const std::string& path) { static_cast(sf->signal.length / sf->signal.channels)); } -c10::intrusive_ptr load_audio_file( - const std::string& path, +namespace { + +std::vector> get_effects( c10::optional& frame_offset, - c10::optional& num_frames, - c10::optional& normalize, - c10::optional& channels_first) { + c10::optional& num_frames) { const auto offset = frame_offset.value_or(0); if (offset < 0) { throw std::runtime_error( @@ -76,9 +77,21 @@ c10::intrusive_ptr load_audio_file( os_offset << offset << "s"; effects.emplace_back(std::vector{"trim", os_offset.str()}); } + return effects; +} + +} // namespace +c10::intrusive_ptr load_audio_file( + const std::string& path, + c10::optional& frame_offset, + c10::optional& num_frames, + c10::optional& normalize, + c10::optional& channels_first, + c10::optional& format) { + auto effects = get_effects(frame_offset, num_frames); return torchaudio::sox_effects::apply_effects_file( - path, effects, normalize, channels_first); + path, effects, normalize, channels_first, format); } void save_audio_file( @@ -120,5 +133,21 @@ void save_audio_file( chain.run(); } +#ifdef TORCH_API_INCLUDE_EXTENSION_H + +std::tuple load_audio_fileobj( + py::object fileobj, + c10::optional& frame_offset, + c10::optional& num_frames, + c10::optional& normalize, + c10::optional& channels_first, + c10::optional& format) { + auto effects = get_effects(frame_offset, num_frames); + return torchaudio::sox_effects::apply_effects_fileobj( + fileobj, effects, normalize, channels_first, format); +} + +#endif // TORCH_API_INCLUDE_EXTENSION_H + } // namespace sox_io } // namespace torchaudio diff --git a/torchaudio/csrc/sox_io.h b/torchaudio/csrc/sox/io.h similarity index 56% rename from torchaudio/csrc/sox_io.h rename to torchaudio/csrc/sox/io.h index d62997243e..d6e5310077 100644 --- a/torchaudio/csrc/sox_io.h +++ b/torchaudio/csrc/sox/io.h @@ -1,8 +1,12 @@ #ifndef TORCHAUDIO_SOX_IO_H #define TORCHAUDIO_SOX_IO_H +#ifdef TORCH_API_INCLUDE_EXTENSION_H +#include +#endif // TORCH_API_INCLUDE_EXTENSION_H + #include -#include +#include namespace torchaudio { namespace sox_io { @@ -21,20 +25,35 @@ struct SignalInfo : torch::CustomClassHolder { int64_t getNumFrames() const; }; -c10::intrusive_ptr get_info(const std::string& path); +c10::intrusive_ptr get_info( + const std::string& path, + c10::optional& format); c10::intrusive_ptr load_audio_file( const std::string& path, c10::optional& frame_offset, c10::optional& num_frames, c10::optional& normalize, - c10::optional& channels_first); + c10::optional& channels_first, + c10::optional& format); void save_audio_file( const std::string& file_name, const c10::intrusive_ptr& signal, const double compression = 0.); +#ifdef TORCH_API_INCLUDE_EXTENSION_H + +std::tuple load_audio_fileobj( + py::object fileobj, + c10::optional& frame_offset, + c10::optional& num_frames, + c10::optional& normalize, + c10::optional& channels_first, + c10::optional& format); + +#endif // TORCH_API_INCLUDE_EXTENSION_H + } // namespace sox_io } // namespace torchaudio diff --git a/torchaudio/csrc/sox/legacy.cpp b/torchaudio/csrc/sox/legacy.cpp new file mode 100644 index 0000000000..97e1d1d32b --- /dev/null +++ b/torchaudio/csrc/sox/legacy.cpp @@ -0,0 +1,174 @@ +#include + +namespace torch { +namespace audio { +namespace { +/// Helper struct to safely close the sox_format_t descriptor. +struct SoxDescriptor { + explicit SoxDescriptor(sox_format_t* fd) noexcept : fd_(fd) {} + SoxDescriptor(const SoxDescriptor& other) = delete; + SoxDescriptor(SoxDescriptor&& other) = delete; + SoxDescriptor& operator=(const SoxDescriptor& other) = delete; + SoxDescriptor& operator=(SoxDescriptor&& other) = delete; + ~SoxDescriptor() { + if (fd_ != nullptr) { + sox_close(fd_); + } + } + sox_format_t* operator->() noexcept { + return fd_; + } + sox_format_t* get() noexcept { + return fd_; + } + + private: + sox_format_t* fd_; +}; + +int64_t write_audio(SoxDescriptor& fd, at::Tensor tensor) { + std::vector buffer(tensor.numel()); + + AT_DISPATCH_ALL_TYPES(tensor.scalar_type(), "write_audio_buffer", [&] { + auto* data = tensor.data_ptr(); + std::copy(data, data + tensor.numel(), buffer.begin()); + }); + + const auto samples_written = + sox_write(fd.get(), buffer.data(), buffer.size()); + + return samples_written; +} + +void read_audio( + SoxDescriptor& fd, + at::Tensor output, + int64_t buffer_length) { + std::vector buffer(buffer_length); + + int number_of_channels = fd->signal.channels; + const int64_t samples_read = sox_read(fd.get(), buffer.data(), buffer_length); + if (samples_read == 0) { + throw std::runtime_error( + "Error reading audio file: empty file or read failed in sox_read"); + } + + output.resize_({samples_read / number_of_channels, number_of_channels}); + output = output.contiguous(); + + AT_DISPATCH_ALL_TYPES(output.scalar_type(), "read_audio_buffer", [&] { + auto* data = output.data_ptr(); + std::copy(buffer.begin(), buffer.begin() + samples_read, data); + }); +} +} // namespace + +std::tuple get_info( + const std::string& file_name + ) { + SoxDescriptor fd(sox_open_read( + file_name.c_str(), + /*signal=*/nullptr, + /*encoding=*/nullptr, + /*filetype=*/nullptr)); + if (fd.get() == nullptr) { + throw std::runtime_error("Error opening audio file"); + } + return std::make_tuple(fd->signal, fd->encoding); +} + +int read_audio_file( + const std::string& file_name, + at::Tensor output, + bool ch_first, + int64_t nframes, + int64_t offset, + sox_signalinfo_t* si, + sox_encodinginfo_t* ei, + const char* ft) { + + SoxDescriptor fd(sox_open_read(file_name.c_str(), si, ei, ft)); + if (fd.get() == nullptr) { + throw std::runtime_error("Error opening audio file"); + } + + // signal info + + const int number_of_channels = fd->signal.channels; + const int sample_rate = fd->signal.rate; + const int64_t total_length = fd->signal.length; + + // multiply offset and number of frames by number of channels + offset *= number_of_channels; + nframes *= number_of_channels; + + if (total_length == 0) { + throw std::runtime_error("Error reading audio file: unknown length"); + } + if (offset > total_length) { + throw std::runtime_error("Offset past EOF"); + } + + // calculate buffer length + int64_t buffer_length = total_length; + if (offset > 0) { + buffer_length -= offset; + } + if (nframes > 0 && buffer_length > nframes) { + buffer_length = nframes; + } + + // seek to offset point before reading data + if (sox_seek(fd.get(), offset, 0) == SOX_EOF) { + throw std::runtime_error("sox_seek reached EOF, try reducing offset or num_samples"); + } + + // read data and fill output tensor + read_audio(fd, output, buffer_length); + + // L x C -> C x L, if desired + if (ch_first) { + output.transpose_(1, 0); + } + + return sample_rate; +} + +void write_audio_file( + const std::string& file_name, + const at::Tensor& tensor, + sox_signalinfo_t* si, + sox_encodinginfo_t* ei, + const char* file_type) { + if (!tensor.is_contiguous()) { + throw std::runtime_error( + "Error writing audio file: input tensor must be contiguous"); + } + +#if SOX_LIB_VERSION_CODE >= 918272 // >= 14.3.0 + si->mult = nullptr; +#endif + + SoxDescriptor fd(sox_open_write( + file_name.c_str(), + si, + ei, + file_type, + /*oob=*/nullptr, + /*overwrite=*/nullptr)); + + if (fd.get() == nullptr) { + throw std::runtime_error( + "Error writing audio file: could not open file for writing"); + } + + const auto samples_written = write_audio(fd, tensor); + + if (samples_written != tensor.numel()) { + throw std::runtime_error( + "Error writing audio file: could not write entire buffer"); + } +} + +} // namespace audio +} // namespace torch diff --git a/torchaudio/csrc/sox.h b/torchaudio/csrc/sox/legacy.h similarity index 91% rename from torchaudio/csrc/sox.h rename to torchaudio/csrc/sox/legacy.h index 1344031330..6b3f8383b2 100644 --- a/torchaudio/csrc/sox.h +++ b/torchaudio/csrc/sox/legacy.h @@ -1,14 +1,5 @@ #include - -#include -#include -#include - -#include - -namespace at { -struct Tensor; -} // namespace at +#include namespace torch { namespace audio { diff --git a/torchaudio/csrc/register.cpp b/torchaudio/csrc/sox/register.cpp similarity index 85% rename from torchaudio/csrc/register.cpp rename to torchaudio/csrc/sox/register.cpp index a1ec8f2254..7c65bebe2d 100644 --- a/torchaudio/csrc/register.cpp +++ b/torchaudio/csrc/sox/register.cpp @@ -1,11 +1,8 @@ -#ifndef TORCHAUDIO_REGISTER_H -#define TORCHAUDIO_REGISTER_H +#include +#include +#include -#include -#include -#include - -TORCH_LIBRARY(torchaudio, m) { +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { ////////////////////////////////////////////////////////////////////////////// // sox_utils.h ////////////////////////////////////////////////////////////////////////////// @@ -49,7 +46,14 @@ TORCH_LIBRARY(torchaudio, m) { m.def("torchaudio::sox_io_get_info", &torchaudio::sox_io::get_info); m.def( - "torchaudio::sox_io_load_audio_file", + "torchaudio::sox_io_load_audio_file(" + "str path," + "int? frame_offset=None," + "int? num_frames=None," + "bool? normalize=True," + "bool? channels_first=False," + "str? format=None" + ") -> __torch__.torch.classes.torchaudio.TensorSignal", &torchaudio::sox_io::load_audio_file); m.def( "torchaudio::sox_io_save_audio_file", @@ -71,4 +75,3 @@ TORCH_LIBRARY(torchaudio, m) { "torchaudio::sox_effects_apply_effects_file", &torchaudio::sox_effects::apply_effects_file); } -#endif diff --git a/torchaudio/csrc/sox_utils.cpp b/torchaudio/csrc/sox/utils.cpp similarity index 97% rename from torchaudio/csrc/sox_utils.cpp rename to torchaudio/csrc/sox/utils.cpp index 656cc63348..44f00084e8 100644 --- a/torchaudio/csrc/sox_utils.cpp +++ b/torchaudio/csrc/sox/utils.cpp @@ -1,6 +1,6 @@ #include #include -#include +#include namespace torchaudio { namespace sox_utils { @@ -92,15 +92,15 @@ SoxFormat::operator sox_format_t*() const noexcept { return fd_; } -void validate_input_file(const SoxFormat& sf) { +void validate_input_file(const SoxFormat& sf, bool check_length) { if (static_cast(sf) == nullptr) { throw std::runtime_error("Error loading audio file: failed to open file."); } if (sf->encoding.encoding == SOX_ENCODING_UNKNOWN) { throw std::runtime_error("Error loading audio file: unknown encoding."); } - if (sf->signal.length == 0) { - throw std::runtime_error("Error reading audio file: unkown length."); + if (check_length && sf->signal.length == 0) { + throw std::runtime_error("Error reading audio file: unknown length."); } } diff --git a/torchaudio/csrc/sox_utils.h b/torchaudio/csrc/sox/utils.h similarity index 98% rename from torchaudio/csrc/sox_utils.h rename to torchaudio/csrc/sox/utils.h index c9037e9e2f..ee8d1baa66 100644 --- a/torchaudio/csrc/sox_utils.h +++ b/torchaudio/csrc/sox/utils.h @@ -67,7 +67,7 @@ struct SoxFormat { /// /// Verify that input file is found, has known encoding, and not empty -void validate_input_file(const SoxFormat& sf); +void validate_input_file(const SoxFormat& sf, bool check_length=true); /// /// Verify that input Tensor is 2D, CPU and either uin8, int16, int32 or float32 diff --git a/torchaudio/csrc/transducer.cpp b/torchaudio/csrc/transducer.cpp new file mode 100644 index 0000000000..3ca7a5ebde --- /dev/null +++ b/torchaudio/csrc/transducer.cpp @@ -0,0 +1,93 @@ +#include +#include +#include +#include + +#include +#include "rnnt.h" + +namespace { + +int64_t cpu_rnnt_loss(torch::Tensor acts, + torch::Tensor labels, + torch::Tensor input_lengths, + torch::Tensor label_lengths, + torch::Tensor costs, + torch::Tensor grads, + int64_t blank_label, + int64_t num_threads) { + + int maxT = acts.size(1); + int maxU = acts.size(2); + int minibatch_size = acts.size(0); + int alphabet_size = acts.size(3); + + rnntOptions options; + memset(&options, 0, sizeof(options)); + options.maxT = maxT; + options.maxU = maxU; + options.blank_label = blank_label; + options.batch_first = true; + options.loc = RNNT_CPU; + options.num_threads = num_threads; + + // have to use at least one + options.num_threads = std::max(options.num_threads, (unsigned int) 1); + + size_t cpu_size_bytes = 0; + switch (acts.scalar_type()) { + case torch::ScalarType::Float: + { + get_workspace_size(maxT, maxU, minibatch_size, + false, &cpu_size_bytes); + + std::vector cpu_workspace(cpu_size_bytes / sizeof(float), 0); + + compute_rnnt_loss(acts.data_ptr(), grads.data_ptr(), + labels.data_ptr(), label_lengths.data_ptr(), + input_lengths.data_ptr(), alphabet_size, + minibatch_size, costs.data_ptr(), + cpu_workspace.data(), options); + + return 0; + } + case torch::ScalarType::Double: + { + get_workspace_size(maxT, maxU, minibatch_size, + false, &cpu_size_bytes, + sizeof(double)); + + std::vector cpu_workspace(cpu_size_bytes / sizeof(double), 0); + + compute_rnnt_loss_fp64(acts.data_ptr(), grads.data_ptr(), + labels.data_ptr(), label_lengths.data_ptr(), + input_lengths.data_ptr(), alphabet_size, + minibatch_size, costs.data_ptr(), + cpu_workspace.data(), options); + + return 0; + } + default: + TORCH_CHECK(false, + std::string(__func__) + " not implemented for '" + toString(acts.scalar_type()) + "'" + ); + } + return -1; +} + +} // namespace + +TORCH_LIBRARY_IMPL(torchaudio, CPU, m) { + m.impl("rnnt_loss", &cpu_rnnt_loss); +} + +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("rnnt_loss(Tensor acts," + "Tensor labels," + "Tensor input_lengths," + "Tensor label_lengths," + "Tensor costs," + "Tensor grads," + "int blank_label," + "int num_threads) -> int"); +} diff --git a/torchaudio/datasets/commonvoice.py b/torchaudio/datasets/commonvoice.py index d387a8ca2a..75fd84843f 100644 --- a/torchaudio/datasets/commonvoice.py +++ b/torchaudio/datasets/commonvoice.py @@ -1,26 +1,28 @@ -import os import csv +import os import warnings from pathlib import Path from typing import List, Dict, Tuple, Union, Optional -import torchaudio from torch import Tensor from torch.utils.data import Dataset +import torchaudio + def load_commonvoice_item(line: List[str], header: List[str], path: str, - folder_audio: str) -> Tuple[Tensor, int, Dict[str, str]]: + folder_audio: str, + ext_audio: str) -> Tuple[Tensor, int, Dict[str, str]]: # Each line as the following data: # client_id, path, sentence, up_votes, down_votes, age, gender, accent assert header[1] == "path" fileid = line[1] - filename = os.path.join(path, folder_audio, fileid) - + if not filename.endswith(ext_audio): + filename += ext_audio waveform, sample_rate = torchaudio.load(filename) dic = dict(zip(header, line)) @@ -95,7 +97,7 @@ def __getitem__(self, n: int) -> Tuple[Tensor, int, Dict[str, str]]: ``up_votes``, ``down_votes``, ``age``, ``gender`` and ``accent``. """ line = self._walker[n] - return load_commonvoice_item(line, self._header, self._path, self._folder_audio) + return load_commonvoice_item(line, self._header, self._path, self._folder_audio, self._ext_audio) def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/utils.py b/torchaudio/datasets/utils.py index c187d0d814..189866374f 100644 --- a/torchaudio/datasets/utils.py +++ b/torchaudio/datasets/utils.py @@ -1,18 +1,15 @@ -import errno import hashlib import logging import os -import sys import tarfile import threading +import urllib +import urllib.request import zipfile -from _io import TextIOWrapper from queue import Queue -from typing import Any, Iterable, List, Optional, Tuple, Union +from typing import Any, Iterable, List, Optional import torch -import urllib -import urllib.request from torch.utils.data import Dataset from torch.utils.model_zoo import tqdm @@ -41,11 +38,11 @@ def stream_url(url: str, req.headers["Range"] = "bytes={}-".format(start_byte) with urllib.request.urlopen(req) as upointer, tqdm( - unit="B", - unit_scale=True, - unit_divisor=1024, - total=url_size, - disable=not progress_bar, + unit="B", + unit_scale=True, + unit_divisor=1024, + total=url_size, + disable=not progress_bar, ) as pbar: num_bytes = 0 @@ -203,42 +200,6 @@ def extract_archive(from_path: str, to_path: Optional[str] = None, overwrite: bo raise NotImplementedError("We currently only support tar.gz, tgz, and zip achives.") -def walk_files(root: str, - suffix: Union[str, Tuple[str]], - prefix: bool = False, - remove_suffix: bool = False) -> Iterable[str]: - """List recursively all files ending with a suffix at a given root - Args: - root (str): Path to directory whose folders need to be listed - suffix (str or tuple): Suffix of the files to match, e.g. '.png' or ('.jpg', '.png'). - It uses the Python "str.endswith" method and is passed directly - prefix (bool, optional): If true, prepends the full path to each result, otherwise - only returns the name of the files found (Default: ``False``) - remove_suffix (bool, optional): If true, removes the suffix to each result defined in suffix, - otherwise will return the result as found (Default: ``False``). - """ - - root = os.path.expanduser(root) - - for dirpath, dirs, files in os.walk(root): - dirs.sort() - # `dirs` is the list used in os.walk function and by sorting it in-place here, we change the - # behavior of os.walk to traverse sub directory alphabetically - # see also - # https://stackoverflow.com/questions/6670029/can-i-force-python3s-os-walk-to-visit-directories-in-alphabetical-order-how#comment71993866_6670926 - files.sort() - for f in files: - if f.endswith(suffix): - - if remove_suffix: - f = f[: -len(suffix)] - - if prefix: - f = os.path.join(dirpath, f) - - yield f - - class _DiskCache(Dataset): """ Wrap a dataset so that, whenever a new item is returned, it is saved to disk. diff --git a/torchaudio/datasets/vctk.py b/torchaudio/datasets/vctk.py index c71962bad8..4e845502d6 100644 --- a/torchaudio/datasets/vctk.py +++ b/torchaudio/datasets/vctk.py @@ -1,11 +1,12 @@ import os import warnings -from typing import Any, Tuple, Union from pathlib import Path +from typing import Tuple, Union -import torchaudio from torch import Tensor from torch.utils.data import Dataset + +import torchaudio from torchaudio.datasets.utils import ( download_url, extract_archive, @@ -66,8 +67,6 @@ class VCTK(Dataset): Giving ``download=True`` will result in error as the dataset is no longer publicly available. downsample (bool, optional): Not used. - transform (callable, optional): Optional transform applied on waveform. (default: ``None``) - target_transform (callable, optional): Optional transform applied on utterance. (default: ``None``) """ _folder_txt = "txt" @@ -81,9 +80,7 @@ def __init__(self, url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False, - downsample: bool = False, - transform: Any = None, - target_transform: Any = None) -> None: + downsample: bool = False) -> None: if downsample: warnings.warn( @@ -92,17 +89,7 @@ def __init__(self, "and suppress this warning." ) - if transform is not None or target_transform is not None: - warnings.warn( - "In the next version, transforms will not be part of the dataset. " - "Please remove the option `transform=True` and " - "`target_transform=True` to suppress this warning." - ) - self.downsample = downsample - self.transform = transform - self.target_transform = target_transform - # Get string representation of 'root' in case Path object is passed root = os.fspath(root) @@ -149,10 +136,6 @@ def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str]: # return item waveform, sample_rate, utterance, speaker_id, utterance_id = item - if self.transform is not None: - waveform = self.transform(waveform) - if self.target_transform is not None: - utterance = self.target_transform(utterance) return waveform, sample_rate, utterance, speaker_id, utterance_id def __len__(self) -> int: @@ -182,12 +165,12 @@ class VCTK_092(Dataset): """ def __init__( - self, - root: str, - mic_id: str = "mic2", - download: bool = False, - url: str = URL, - audio_ext=".flac", + self, + root: str, + mic_id: str = "mic2", + download: bool = False, + url: str = URL, + audio_ext=".flac", ): if mic_id not in ["mic1", "mic2"]: raise RuntimeError( @@ -233,7 +216,7 @@ def __init__( continue utterance_dir = os.path.join(self._txt_dir, speaker_id) for utterance_file in sorted( - f for f in os.listdir(utterance_dir) if f.endswith(".txt") + f for f in os.listdir(utterance_dir) if f.endswith(".txt") ): utterance_id = os.path.splitext(utterance_file)[0] audio_path_mic = os.path.join( diff --git a/torchaudio/datasets/yesno.py b/torchaudio/datasets/yesno.py index 21d67f8ecc..7178b8332c 100644 --- a/torchaudio/datasets/yesno.py +++ b/torchaudio/datasets/yesno.py @@ -1,11 +1,11 @@ import os -import warnings -from typing import Any, List, Tuple, Union from pathlib import Path +from typing import List, Tuple, Union -import torchaudio from torch import Tensor from torch.utils.data import Dataset + +import torchaudio from torchaudio.datasets.utils import ( download_url, extract_archive, @@ -15,7 +15,7 @@ FOLDER_IN_ARCHIVE = "waves_yesno" _CHECKSUMS = { "http://www.openslr.org/resources/1/waves_yesno.tar.gz": - "962ff6e904d2df1126132ecec6978786" + "962ff6e904d2df1126132ecec6978786" } @@ -41,8 +41,6 @@ class YESNO(Dataset): The top-level directory of the dataset. (default: ``"waves_yesno"``) download (bool, optional): Whether to download the dataset if it is not found at root path. (default: ``False``). - transform (callable, optional): Optional transform applied on waveform. (default: ``None``) - target_transform (callable, optional): Optional transform applied on utterance. (default: ``None``) """ _ext_audio = ".wav" @@ -51,19 +49,7 @@ def __init__(self, root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, - download: bool = False, - transform: Any = None, - target_transform: Any = None) -> None: - - if transform is not None or target_transform is not None: - warnings.warn( - "In the next version, transforms will not be part of the dataset. " - "Please remove the option `transform=True` and " - "`target_transform=True` to suppress this warning." - ) - - self.transform = transform - self.target_transform = target_transform + download: bool = False) -> None: # Get string representation of 'root' in case Path object is passed root = os.fspath(root) @@ -102,10 +88,6 @@ def __getitem__(self, n: int) -> Tuple[Tensor, int, List[int]]: # return item waveform, sample_rate, labels = item - if self.transform is not None: - waveform = self.transform(waveform) - if self.target_transform is not None: - labels = self.target_transform(labels) return waveform, sample_rate, labels def __len__(self) -> int: diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index 3fedf93d09..0c07ec0847 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -70,30 +70,29 @@ def spectrogram( waveform = waveform.reshape(-1, shape[-1]) # default values are consistent with librosa.core.spectrum._spectrogram - spec_f = torch.view_as_real( - torch.stft( - input=waveform, - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - window=window, - center=True, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=True, - ) + spec_f = torch.stft( + input=waveform, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + center=True, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, ) # unpack batch - spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-3:]) + spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-2:]) if normalized: spec_f /= window.pow(2.).sum().sqrt() if power is not None: - spec_f = complex_norm(spec_f, power=power) - - return spec_f + if power == 1.0: + return spec_f.abs() + return spec_f.abs().pow(power) + return torch.view_as_real(spec_f) def griffinlim( diff --git a/torchaudio/prototype/__init__.py b/torchaudio/prototype/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/torchaudio/prototype/transducer.py b/torchaudio/prototype/transducer.py new file mode 100644 index 0000000000..d08ab6ea17 --- /dev/null +++ b/torchaudio/prototype/transducer.py @@ -0,0 +1,159 @@ +import torch +from torch.autograd import Function +from torch.nn import Module +from torchaudio._internal import ( + module_utils as _mod_utils, +) + +__all__ = ["rnnt_loss", "RNNTLoss"] + + +class _RNNT(Function): + @staticmethod + def forward(ctx, acts, labels, act_lens, label_lens, blank, reduction): + """ + See documentation for RNNTLoss. + """ + + device = acts.device + check_inputs(acts, labels, act_lens, label_lens) + + acts = acts.to("cpu") + labels = labels.to("cpu") + act_lens = act_lens.to("cpu") + label_lens = label_lens.to("cpu") + + loss_func = torch.ops.torchaudio.rnnt_loss + + grads = torch.zeros_like(acts) + minibatch_size = acts.size(0) + costs = torch.zeros(minibatch_size, dtype=acts.dtype) + + loss_func(acts, labels, act_lens, label_lens, costs, grads, blank, 0) + + if reduction in ["sum", "mean"]: + costs = costs.sum().unsqueeze_(-1) + if reduction == "mean": + costs /= minibatch_size + grads /= minibatch_size + + costs = costs.to(device) + ctx.grads = grads.to(device) + + return costs + + @staticmethod + def backward(ctx, grad_output): + grad_output = grad_output.view(-1, 1, 1, 1).to(ctx.grads) + return ctx.grads.mul_(grad_output), None, None, None, None, None + + +@_mod_utils.requires_module("torchaudio._torchaudio") +def rnnt_loss(acts, labels, act_lens, label_lens, blank=0, reduction="mean"): + """Compute the RNN Transducer Loss. + + The RNN Transducer loss (`Graves 2012 `__) extends the CTC loss by defining + a distribution over output sequences of all lengths, and by jointly modelling both input-output and output-output + dependencies. + + The implementation uses `warp-transducer `__. + + Args: + acts (Tensor): Tensor of dimension (batch, time, label, class) containing output from network + before applying ``torch.nn.functional.log_softmax``. + labels (Tensor): Tensor of dimension (batch, max label length) containing the labels padded by zero + act_lens (Tensor): Tensor of dimension (batch) containing the length of each output sequence + label_lens (Tensor): Tensor of dimension (batch) containing the length of each output sequence + blank (int): blank label. (Default: ``0``) + reduction (string): If ``'sum'``, the output losses will be summed. + If ``'mean'``, the output losses will be divided by the target lengths and + then the mean over the batch is taken. If ``'none'``, no reduction will be applied. + (Default: ``'mean'``) + """ + + # NOTE manually done log_softmax for CPU version, + # log_softmax is computed within GPU version. + acts = torch.nn.functional.log_softmax(acts, -1) + return _RNNT.apply(acts, labels, act_lens, label_lens, blank, reduction) + + +@_mod_utils.requires_module("torchaudio._torchaudio") +class RNNTLoss(Module): + """Compute the RNN Transducer Loss. + + The RNN Transducer loss (`Graves 2012 `__) extends the CTC loss by defining + a distribution over output sequences of all lengths, and by jointly modelling both input-output and output-output + dependencies. + + The implementation uses `warp-transducer `__. + + Args: + blank (int): blank label. (Default: ``0``) + reduction (string): If ``'sum'``, the output losses will be summed. + If ``'mean'``, the output losses will be divided by the target lengths and + then the mean over the batch is taken. If ``'none'``, no reduction will be applied. + (Default: ``'mean'``) + """ + + def __init__(self, blank=0, reduction="mean"): + super(RNNTLoss, self).__init__() + self.blank = blank + self.reduction = reduction + self.loss = _RNNT.apply + + def forward(self, acts, labels, act_lens, label_lens): + """ + Args: + acts (Tensor): Tensor of dimension (batch, time, label, class) containing output from network + before applying ``torch.nn.functional.log_softmax``. + labels (Tensor): Tensor of dimension (batch, max label length) containing the labels padded by zero + act_lens (Tensor): Tensor of dimension (batch) containing the length of each output sequence + label_lens (Tensor): Tensor of dimension (batch) containing the length of each output sequence + """ + + # NOTE manually done log_softmax for CPU version, + # log_softmax is computed within GPU version. + acts = torch.nn.functional.log_softmax(acts, -1) + return self.loss(acts, labels, act_lens, label_lens, self.blank, self.reduction) + + +def check_type(var, t, name): + if var.dtype is not t: + raise TypeError("{} must be {}".format(name, t)) + + +def check_contiguous(var, name): + if not var.is_contiguous(): + raise ValueError("{} must be contiguous".format(name)) + + +def check_dim(var, dim, name): + if len(var.shape) != dim: + raise ValueError("{} must be {}D".format(name, dim)) + + +def check_inputs(log_probs, labels, lengths, label_lengths): + check_type(labels, torch.int32, "labels") + check_type(label_lengths, torch.int32, "label_lengths") + check_type(lengths, torch.int32, "lengths") + check_contiguous(log_probs, "log_probs") + check_contiguous(labels, "labels") + check_contiguous(label_lengths, "label_lengths") + check_contiguous(lengths, "lengths") + + if lengths.shape[0] != log_probs.shape[0]: + raise ValueError("must have a length per example.") + if label_lengths.shape[0] != log_probs.shape[0]: + raise ValueError("must have a label length per example.") + + check_dim(log_probs, 4, "log_probs") + check_dim(labels, 2, "labels") + check_dim(lengths, 1, "lengths") + check_dim(label_lengths, 1, "label_lengths") + max_T = torch.max(lengths) + max_U = torch.max(label_lengths) + T, U = log_probs.shape[1:3] + if T != max_T: + raise ValueError("Input length mismatch") + if U != max_U + 1: + raise ValueError("Output length mismatch") diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 82ee613ba0..7534714965 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -1,4 +1,4 @@ -from typing import List, Tuple +from typing import List, Tuple, Optional import torch @@ -157,6 +157,7 @@ def apply_effects_file( effects: List[List[str]], normalize: bool = True, channels_first: bool = True, + format: Optional[str] = None, ) -> Tuple[torch.Tensor, int]: """Apply sox effects to the audio file and load the resulting data as Tensor @@ -180,6 +181,10 @@ def apply_effects_file( than integer WAV type. channels_first (bool): When True, the returned Tensor has dimension ``[channel, time]``. Otherwise, the returned Tensor's dimension is ``[time, channel]``. + format (str, optional): + Override the format detection with the given format. + Providing the argument might help when libsox can not infer the format + from header or extension, Returns: Tuple[torch.Tensor, int]: Resulting Tensor and sample rate. @@ -249,5 +254,6 @@ def apply_effects_file( """ # Get string representation of 'path' in case Path object is passed path = str(path) - signal = torch.ops.torchaudio.sox_effects_apply_effects_file(path, effects, normalize, channels_first) + signal = torch.ops.torchaudio.sox_effects_apply_effects_file( + path, effects, normalize, channels_first, format) return signal.get_tensor(), signal.get_sample_rate() From 227068af153ec718d62aec755394823f59a30da8 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Wed, 20 Jan 2021 14:06:25 -0800 Subject: [PATCH 26/73] Import torchaudio #1182 d53e404 Summary: Import torchaudio #1182 d53e404 Reviewed By: datumbox Differential Revision: D25975367 fbshipit-source-id: feac3187a82b0e3de23562fde11fcfc5bb13461d --- .circleci/config.yml | 6 +- .circleci/config.yml.in | 6 +- .circleci/unittest/linux/scripts/run_test.sh | 2 +- .flake8 | 2 +- build_tools/setup_helpers/extension.py | 11 + docs/source/index.rst | 1 + docs/source/transducer.rst | 23 ++ packaging/pkg_helpers.bash | 9 +- setup.py | 5 +- .../functional/functional_cpu_test.py | 11 +- .../librosa_compatibility_test.py | 2 - .../soundfile_backend/save_test.py | 42 ++- .../sox_io_backend/save_test.py | 86 ++++++ torchaudio/backend/_soundfile_backend.py | 18 +- torchaudio/backend/sox_io_backend.py | 44 +-- torchaudio/compliance/kaldi.py | 265 +++++------------- torchaudio/csrc/pybind.cpp | 4 + torchaudio/csrc/sox/effects.cpp | 10 +- torchaudio/csrc/sox/effects_chain.cpp | 79 +++++- torchaudio/csrc/sox/effects_chain.h | 6 + torchaudio/csrc/sox/io.cpp | 100 ++++++- torchaudio/csrc/sox/io.h | 17 +- torchaudio/csrc/sox/utils.cpp | 46 +-- torchaudio/csrc/sox/utils.h | 8 +- torchaudio/datasets/yesno.py | 65 ++--- tox.ini | 4 - 26 files changed, 536 insertions(+), 336 deletions(-) create mode 100644 docs/source/transducer.rst delete mode 100644 tox.ini diff --git a/.circleci/config.yml b/.circleci/config.yml index d51bb3ca96..c40054d0e3 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -112,7 +112,7 @@ jobs: - persist_to_workspace: root: third_party paths: - - archives + - sox/archives binary_linux_wheel: <<: *binary_common @@ -411,7 +411,6 @@ jobs: paths: - conda - env - - third_party/install - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -456,7 +455,6 @@ jobs: paths: - conda - env - - third_party/install - run: name: Install torchaudio command: docker run -t --gpus all -e UPLOAD_CHANNEL -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh @@ -569,7 +567,6 @@ jobs: paths: - conda - env - - third_party/install - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -606,7 +603,6 @@ jobs: paths: - conda - env - - third_party/install - run: name: Run style check command: .circleci/unittest/linux/scripts/run_style_checks.sh diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 9f6f41a379..535729d72e 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -112,7 +112,7 @@ jobs: - persist_to_workspace: root: third_party paths: - - archives + - sox/archives binary_linux_wheel: <<: *binary_common @@ -411,7 +411,6 @@ jobs: paths: - conda - env - - third_party/install - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -456,7 +455,6 @@ jobs: paths: - conda - env - - third_party/install - run: name: Install torchaudio command: docker run -t --gpus all -e UPLOAD_CHANNEL -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh @@ -569,7 +567,6 @@ jobs: paths: - conda - env - - third_party/install - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -606,7 +603,6 @@ jobs: paths: - conda - env - - third_party/install - run: name: Run style check command: .circleci/unittest/linux/scripts/run_style_checks.sh diff --git a/.circleci/unittest/linux/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh index 2f9c10476d..e9016d266c 100755 --- a/.circleci/unittest/linux/scripts/run_test.sh +++ b/.circleci/unittest/linux/scripts/run_test.sh @@ -17,4 +17,4 @@ declare -a args=( ) cd test -pytest "${args[@]}" torchaudio_unittest -k "not fileobj" +pytest "${args[@]}" torchaudio_unittest diff --git a/.flake8 b/.flake8 index 9d0eaed2bd..b4a5069f16 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,4 @@ [flake8] max-line-length = 120 -ignore = E305,E402,E721,E741,F401,F403,F405,F999,W503,W504 +ignore = E305,E402,E721,E741,F401,F403,F405,W503,W504,F999 exclude = build,docs/source,_ext,third_party diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 0dc306f07e..7f0ba7a14f 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -3,6 +3,7 @@ import subprocess from pathlib import Path +import torch from torch.utils.cpp_extension import ( CppExtension, BuildExtension as TorchBuildExtension @@ -109,12 +110,22 @@ def _get_libraries(): return [] if _BUILD_SOX else ['sox'] +def _get_cxx11_abi(): + try: + value = int(torch._C._GLIBCXX_USE_CXX11_ABI) + except ImportError: + value = 0 + return f'-D_GLIBCXX_USE_CXX11_ABI={value}' + + def _build_third_party(base_build_dir): build_dir = os.path.join(base_build_dir, 'third_party') os.makedirs(build_dir, exist_ok=True) subprocess.run( args=[ 'cmake', + f"-DCMAKE_CXX_FLAGS='{_get_cxx11_abi()}'", + '-DCMAKE_VERBOSE_MAKEFILE:BOOL=ON', f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', f'-DBUILD_SOX={"ON" if _BUILD_SOX else "OFF"}', f'-DBUILD_TRANSDUCER={"ON" if _BUILD_TRANSDUCER else "OFF"}', diff --git a/docs/source/index.rst b/docs/source/index.rst index 596a636b1b..49ffb9af22 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -39,6 +39,7 @@ The :mod:`torchaudio` package consists of I/O, popular datasets and common audio compliance.kaldi kaldi_io utils + transducer .. toctree:: diff --git a/docs/source/transducer.rst b/docs/source/transducer.rst new file mode 100644 index 0000000000..505ab0187c --- /dev/null +++ b/docs/source/transducer.rst @@ -0,0 +1,23 @@ +.. role:: hidden + :class: hidden-section + +torchaudio.prototype.transducer +=============================== + +.. currentmodule:: torchaudio.prototype.transducer + +.. note:: + + The RNN transducer loss is a prototype feature, see `here `_ to learn more about the nomenclature. It is only available within the nightlies, and also needs to be imported explicitly using: :code:`from torchaudio.prototype.transducer import rnnt_loss, RNNTLoss`. + +rnnt_loss +--------- + +.. autofunction:: rnnt_loss + +:hidden:`RNNTLoss` +~~~~~~~~~~~~~~~~~~ + +.. autoclass:: RNNTLoss + + .. automethod:: forward diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index 635bfa3a14..36760b9484 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -162,13 +162,8 @@ setup_pip_pytorch_version() { # Install latest prerelease version of torch, per our nightlies, consistent # with the requested cuda version pip_install --pre torch -f "https://download.pytorch.org/whl/nightly/${WHEEL_DIR}torch_nightly.html" - if [[ "$CUDA_VERSION" == "cpu" ]]; then - # CUDA and CPU are ABI compatible on the CPU-only parts, so strip - # in this case - export PYTORCH_VERSION="$(pip show torch | grep ^Version: | sed 's/Version: *//' | sed 's/+.\+//')" - else - export PYTORCH_VERSION="$(pip show torch | grep ^Version: | sed 's/Version: *//')" - fi + # CUDA and CPU are ABI compatible on the CPU-only parts, so strip in this case + export PYTORCH_VERSION="$(pip show torch | grep ^Version: | sed 's/Version: *//' | sed 's/+.\+//')" else pip_install "torch==$PYTORCH_VERSION$PYTORCH_VERSION_SUFFIX" \ -f https://download.pytorch.org/whl/torch_stable.html \ diff --git a/setup.py b/setup.py index 8168472ecf..8ecc41c1cc 100644 --- a/setup.py +++ b/setup.py @@ -73,8 +73,9 @@ def run(self): "Operating System :: Microsoft :: Windows", "Operating System :: POSIX", "Programming Language :: C++", - "Programming Language :: Python :: 2.7", - "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.6", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", "Programming Language :: Python :: Implementation :: CPython", "Topic :: Multimedia :: Sound/Audio", "Topic :: Scientific/Engineering :: Artificial Intelligence" diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py index 4073654011..c4ba42391d 100644 --- a/test/torchaudio_unittest/functional/functional_cpu_test.py +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -1,10 +1,10 @@ import math +import warnings import torch import torchaudio import torchaudio.functional as F from parameterized import parameterized -import pytest import itertools from torchaudio_unittest import common_utils @@ -33,17 +33,20 @@ class TestSpectrogramFloat64(Spectrogram, common_utils.PytorchTestCase): class TestCreateFBMatrix(common_utils.TorchaudioTestCase): def test_no_warning_high_n_freq(self): - with pytest.warns(None) as w: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") F.create_fb_matrix(288, 0, 8000, 128, 16000) assert len(w) == 0 def test_no_warning_low_n_mels(self): - with pytest.warns(None) as w: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") F.create_fb_matrix(201, 0, 8000, 89, 16000) assert len(w) == 0 def test_warning(self): - with pytest.warns(None) as w: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") F.create_fb_matrix(201, 0, 8000, 128, 16000) assert len(w) == 1 diff --git a/test/torchaudio_unittest/librosa_compatibility_test.py b/test/torchaudio_unittest/librosa_compatibility_test.py index e1ef80fa79..92d5baff06 100644 --- a/test/torchaudio_unittest/librosa_compatibility_test.py +++ b/test/torchaudio_unittest/librosa_compatibility_test.py @@ -17,8 +17,6 @@ import librosa import scipy -import pytest - from torchaudio_unittest import common_utils diff --git a/test/torchaudio_unittest/soundfile_backend/save_test.py b/test/torchaudio_unittest/soundfile_backend/save_test.py index a99c69b0c8..5c36e0d126 100644 --- a/test/torchaudio_unittest/soundfile_backend/save_test.py +++ b/test/torchaudio_unittest/soundfile_backend/save_test.py @@ -1,9 +1,9 @@ +import io import itertools from unittest.mock import patch from torchaudio._internal import module_utils as _mod_utils from torchaudio.backend import _soundfile_backend as soundfile_backend -from parameterized import parameterized from torchaudio_unittest.common_utils import ( TempDirMixin, @@ -209,3 +209,43 @@ def test_channels_first(self, channels_first): found = load_wav(path)[0] expected = data if channels_first else data.transpose(1, 0) self.assertEqual(found, expected, atol=1e-4, rtol=1e-8) + + +@skipIfNoModule("soundfile") +class TestFileObject(TempDirMixin, PytorchTestCase): + def _test_fileobj(self, ext): + """Saving audio to file-like object works""" + sample_rate = 16000 + path = self.get_temp_path(f'test.{ext}') + + subtype = 'FLOAT' if ext == 'wav' else None + data = get_wav_data('float32', num_channels=2) + soundfile.write(path, data.numpy().T, sample_rate, subtype=subtype) + expected = soundfile.read(path, dtype='float32')[0] + + fileobj = io.BytesIO() + soundfile_backend.save(fileobj, data, sample_rate, format=ext) + fileobj.seek(0) + found, sr = soundfile.read(fileobj, dtype='float32') + + assert sr == sample_rate + self.assertEqual(expected, found) + + def test_fileobj_wav(self): + """Saving audio via file-like object works""" + self._test_fileobj('wav') + + @skipIfFormatNotSupported("FLAC") + def test_fileobj_flac(self): + """Saving audio via file-like object works""" + self._test_fileobj('flac') + + @skipIfFormatNotSupported("NIST") + def test_fileobj_nist(self): + """Saving audio via file-like object works""" + self._test_fileobj('NIST') + + @skipIfFormatNotSupported("OGG") + def test_fileobj_ogg(self): + """Saving audio via file-like object works""" + self._test_fileobj('OGG') diff --git a/test/torchaudio_unittest/sox_io_backend/save_test.py b/test/torchaudio_unittest/sox_io_backend/save_test.py index b0ee25e01c..3f2faff6d4 100644 --- a/test/torchaudio_unittest/sox_io_backend/save_test.py +++ b/test/torchaudio_unittest/sox_io_backend/save_test.py @@ -1,3 +1,4 @@ +import io import itertools from torchaudio.backend import sox_io_backend @@ -417,3 +418,88 @@ def test_tensor_preserve(self, dtype): sox_io_backend.save(path, data, 8000) self.assertEqual(data, expected) + + +@skipIfNoExtension +@skipIfNoExec('sox') +class TestFileObject(SaveTestBase): + """ + We campare the result of file-like object input against file path input because + `save` function is rigrously tested for file path inputs to match libsox's result, + """ + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_fileobj(self, ext, compression): + """Saving audio to file object returns the same result as via file path.""" + sample_rate = 16000 + dtype = 'float32' + num_channels = 2 + num_frames = 16000 + channels_first = True + + data = get_wav_data(dtype, num_channels, num_frames=num_frames) + + ref_path = self.get_temp_path(f'reference.{ext}') + res_path = self.get_temp_path(f'test.{ext}') + sox_io_backend.save( + ref_path, data, channels_first=channels_first, + sample_rate=sample_rate, compression=compression) + with open(res_path, 'wb') as fileobj: + sox_io_backend.save( + fileobj, data, channels_first=channels_first, + sample_rate=sample_rate, compression=compression, format=ext) + + expected_data, _ = sox_io_backend.load(ref_path) + data, sr = sox_io_backend.load(res_path) + + assert sample_rate == sr + self.assertEqual(expected_data, data) + + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_bytesio(self, ext, compression): + """Saving audio to BytesIO object returns the same result as via file path.""" + sample_rate = 16000 + dtype = 'float32' + num_channels = 2 + num_frames = 16000 + channels_first = True + + data = get_wav_data(dtype, num_channels, num_frames=num_frames) + + ref_path = self.get_temp_path(f'reference.{ext}') + res_path = self.get_temp_path(f'test.{ext}') + sox_io_backend.save( + ref_path, data, channels_first=channels_first, + sample_rate=sample_rate, compression=compression) + fileobj = io.BytesIO() + sox_io_backend.save( + fileobj, data, channels_first=channels_first, + sample_rate=sample_rate, compression=compression, format=ext) + fileobj.seek(0) + with open(res_path, 'wb') as file_: + file_.write(fileobj.read()) + + expected_data, _ = sox_io_backend.load(ref_path) + data, sr = sox_io_backend.load(res_path) + + assert sample_rate == sr + self.assertEqual(expected_data, data) diff --git a/torchaudio/backend/_soundfile_backend.py b/torchaudio/backend/_soundfile_backend.py index 719224b827..3366780bdb 100644 --- a/torchaudio/backend/_soundfile_backend.py +++ b/torchaudio/backend/_soundfile_backend.py @@ -138,6 +138,7 @@ def save( sample_rate: int, channels_first: bool = True, compression: Optional[float] = None, + format: Optional[str] = None, ): """Save audio data to file. @@ -168,6 +169,9 @@ def save( otherwise ``[time, channel]``. compression (Optional[float]): Not used. It is here only for interface compatibility reson with "sox_io" backend. + format (str, optional): + Output audio format. This is required when the output audio format cannot be infered from + ``filepath``, (such as file extension or ``name`` attribute of the given file object). """ if src.ndim != 2: raise ValueError(f"Expected 2D Tensor, got {src.ndim}D.") @@ -176,8 +180,13 @@ def save( '`save` function of "soundfile" backend does not support "compression" parameter. ' "The argument is silently ignored." ) + if hasattr(filepath, 'write'): + if format is None: + raise RuntimeError('`format` is required when saving to file object.') + ext = format + else: + ext = str(filepath).split(".")[-1].lower() - ext = str(filepath).split(".")[-1].lower() if ext != "wav": subtype = None elif src.dtype == torch.uint8: @@ -193,17 +202,16 @@ def save( else: raise ValueError(f"Unsupported dtype for WAV: {src.dtype}") - format_ = None # sph is a extension used in TED-LIUM but soundfile does not recognize it as NIST format, # so we extend the extensions manually here - if ext in ["nis", "nist", "sph"]: - format_ = "NIST" + if ext in ["nis", "nist", "sph"] and format is None: + format = "NIST" if channels_first: src = src.t() soundfile.write( - file=filepath, data=src, samplerate=sample_rate, subtype=subtype, format=format_ + file=filepath, data=src, samplerate=sample_rate, subtype=subtype, format=format ) diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 1e6d417cb8..18296ef7e2 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -134,6 +134,25 @@ def load( return signal.get_tensor(), signal.get_sample_rate() +@torch.jit.unused +def _save( + filepath: str, + src: torch.Tensor, + sample_rate: int, + channels_first: bool = True, + compression: Optional[float] = None, + format: Optional[str] = None, +): + if hasattr(filepath, 'write'): + if format is None: + raise RuntimeError('`format` is required when saving to file object.') + torchaudio._torchaudio.save_audio_fileobj( + filepath, src, sample_rate, channels_first, compression, format) + else: + torch.ops.torchaudio.sox_io_save_audio_file( + os.fspath(filepath), src, sample_rate, channels_first, compression, format) + + @_mod_utils.requires_module('torchaudio._torchaudio') def save( filepath: str, @@ -141,6 +160,7 @@ def save( sample_rate: int, channels_first: bool = True, compression: Optional[float] = None, + format: Optional[str] = None, ): """Save audio data to file. @@ -184,23 +204,15 @@ def save( | and lowest quality. Default: ``3``. See the detail at http://sox.sourceforge.net/soxformat.html. + format (str, optional): + Output audio format. This is required when the output audio format cannot be infered from + ``filepath``, (such as file extension or ``name`` attribute of the given file object). """ - # Cast to str in case type is `pathlib.Path` - filepath = str(filepath) - if compression is None: - ext = str(filepath).split('.')[-1].lower() - if ext in ['wav', 'sph', 'amb', 'amr-nb']: - compression = 0. - elif ext == 'mp3': - compression = -4.5 - elif ext == 'flac': - compression = 8. - elif ext in ['ogg', 'vorbis']: - compression = 3. - else: - raise RuntimeError(f'Unsupported file type: "{ext}"') - signal = torch.classes.torchaudio.TensorSignal(src, sample_rate, channels_first) - torch.ops.torchaudio.sox_io_save_audio_file(filepath, signal, compression) + if not torch.jit.is_scripting(): + _save(filepath, src, sample_rate, channels_first, compression, format) + return + torch.ops.torchaudio.sox_io_save_audio_file( + filepath, src, sample_rate, channels_first, compression, format) @_mod_utils.requires_module('torchaudio._torchaudio') diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index afabebeb6a..5b47ef3350 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -3,6 +3,7 @@ import math import torch from torch import Tensor +from torch.nn import functional as F import torchaudio import torchaudio._internal.fft @@ -752,141 +753,54 @@ def mfcc( return feature -def _get_LR_indices_and_weights(orig_freq: float, - new_freq: float, - output_samples_in_unit: int, - window_width: float, - lowpass_cutoff: float, - lowpass_filter_width: int, - device: torch.device, - dtype: int) -> Tuple[Tensor, Tensor]: - r"""Based on LinearResample::SetIndexesAndWeights where it retrieves the weights for - resampling as well as the indices in which they are valid. LinearResample (LR) means - that the output signal is at linearly spaced intervals (i.e the output signal has a - frequency of ``new_freq``). It uses sinc/bandlimited interpolation to upsample/downsample - the signal. - - The reason why the same filter is not used for multiple convolutions is because the - sinc function could sampled at different points in time. For example, suppose - a signal is sampled at the timestamps (seconds) - 0 16 32 - and we want it to be sampled at the timestamps (seconds) - 0 5 10 15 20 25 30 35 - at the timestamp of 16, the delta timestamps are - 16 11 6 1 4 9 14 19 - at the timestamp of 32, the delta timestamps are - 32 27 22 17 12 8 2 3 - - As we can see from deltas, the sinc function is sampled at different points of time - assuming the center of the sinc function is at 0, 16, and 32 (the deltas [..., 6, 1, 4, ....] - for 16 vs [...., 2, 3, ....] for 32) - - Example, one case is when the ``orig_freq`` and ``new_freq`` are multiples of each other then - there needs to be one filter. - - A windowed filter function (i.e. Hanning * sinc) because the ideal case of sinc function - has infinite support (non-zero for all values) so instead it is truncated and multiplied by - a window function which gives it less-than-perfect rolloff [1]. - - [1] Chapter 16: Windowed-Sinc Filters, https://www.dspguide.com/ch16/1.htm - - Args: - orig_freq (float): The original frequency of the signal - new_freq (float): The desired frequency - output_samples_in_unit (int): The number of output samples in the smallest repeating unit: - num_samp_out = new_freq / Gcd(orig_freq, new_freq) - window_width (float): The width of the window which is nonzero - lowpass_cutoff (float): The filter cutoff in Hz. The filter cutoff needs to be less - than samp_rate_in_hz/2 and less than samp_rate_out_hz/2. - lowpass_filter_width (int): Controls the sharpness of the filter, more == sharper but less - efficient. We suggest around 4 to 10 for normal use - - Returns: - (Tensor, Tensor): A tuple of ``min_input_index`` (which is the minimum indices - where the window is valid, size (``output_samples_in_unit``)) and ``weights`` (which is the weights - which correspond with min_input_index, size (``output_samples_in_unit``, ``max_weight_width``)). - """ - assert lowpass_cutoff < min(orig_freq, new_freq) / 2 - output_t = torch.arange(0., output_samples_in_unit, device=device, dtype=dtype) / new_freq - min_t = output_t - window_width - max_t = output_t + window_width - - min_input_index = torch.ceil(min_t * orig_freq) # size (output_samples_in_unit) - max_input_index = torch.floor(max_t * orig_freq) # size (output_samples_in_unit) - num_indices = max_input_index - min_input_index + 1 # size (output_samples_in_unit) - - max_weight_width = num_indices.max() - # create a group of weights of size (output_samples_in_unit, max_weight_width) - j = torch.arange(max_weight_width, device=device, dtype=dtype).unsqueeze(0) - input_index = min_input_index.unsqueeze(1) + j - delta_t = (input_index / orig_freq) - output_t.unsqueeze(1) - - weights = torch.zeros_like(delta_t) - inside_window_indices = delta_t.abs().lt(window_width) - # raised-cosine (Hanning) window with width `window_width` - weights[inside_window_indices] = 0.5 * (1 + torch.cos(2 * math.pi * lowpass_cutoff / - lowpass_filter_width * delta_t[inside_window_indices])) - - t_eq_zero_indices = delta_t.eq(0.0) - t_not_eq_zero_indices = ~t_eq_zero_indices - # sinc filter function - weights[t_not_eq_zero_indices] *= torch.sin( - 2 * math.pi * lowpass_cutoff * delta_t[t_not_eq_zero_indices]) / (math.pi * delta_t[t_not_eq_zero_indices]) - # limit of the function at t = 0 - weights[t_eq_zero_indices] *= 2 * lowpass_cutoff - - weights /= orig_freq # size (output_samples_in_unit, max_weight_width) - return min_input_index, weights - - -def _lcm(a: int, b: int) -> int: - return abs(a * b) // math.gcd(a, b) - - -def _get_num_LR_output_samples(input_num_samp: int, - samp_rate_in: float, - samp_rate_out: float) -> int: - r"""Based on LinearResample::GetNumOutputSamples. LinearResample (LR) means that - the output signal is at linearly spaced intervals (i.e the output signal has a - frequency of ``new_freq``). It uses sinc/bandlimited interpolation to upsample/downsample - the signal. - - Args: - input_num_samp (int): The number of samples in the input - samp_rate_in (float): The original frequency of the signal - samp_rate_out (float): The desired frequency - - Returns: - int: The number of output samples - """ - # For exact computation, we measure time in "ticks" of 1.0 / tick_freq, - # where tick_freq is the least common multiple of samp_rate_in and - # samp_rate_out. - samp_rate_in = int(samp_rate_in) - samp_rate_out = int(samp_rate_out) - - tick_freq = _lcm(samp_rate_in, samp_rate_out) - ticks_per_input_period = tick_freq // samp_rate_in - - # work out the number of ticks in the time interval - # [ 0, input_num_samp/samp_rate_in ). - interval_length_in_ticks = input_num_samp * ticks_per_input_period - if interval_length_in_ticks <= 0: - return 0 - ticks_per_output_period = tick_freq // samp_rate_out - # Get the last output-sample in the closed interval, i.e. replacing [ ) with - # [ ]. Note: integer division rounds down. See - # http://en.wikipedia.org/wiki/Interval_(mathematics) for an explanation of - # the notation. - last_output_samp = interval_length_in_ticks // ticks_per_output_period - # We need the last output-sample in the open interval, so if it takes us to - # the end of the interval exactly, subtract one. - if last_output_samp * ticks_per_output_period == interval_length_in_ticks: - last_output_samp -= 1 - # First output-sample index is zero, so the number of output samples - # is the last output-sample plus one. - num_output_samp = last_output_samp + 1 - return num_output_samp +def _get_sinc_resample_kernel(orig_freq: int, new_freq: int, lowpass_filter_width: int, + device: torch.device, dtype: torch.dtype): + assert lowpass_filter_width > 0 + kernels = [] + base_freq = min(orig_freq, new_freq) + # This will perform antialiasing filtering by removing the highest frequencies. + # At first I thought I only needed this when downsampling, but when upsampling + # you will get edge artifacts without this, as the edge is equivalent to zero padding, + # which will add high freq artifacts. + base_freq *= 0.99 + + # The key idea of the algorithm is that x(t) can be exactly reconstructed from x[i] (tensor) + # using the sinc interpolation formula: + # x(t) = sum_i x[i] sinc(pi * orig_freq * (i / orig_freq - t)) + # We can then sample the function x(t) with a different sample rate: + # y[j] = x(j / new_freq) + # or, + # y[j] = sum_i x[i] sinc(pi * orig_freq * (i / orig_freq - j / new_freq)) + + # We see here that y[j] is the convolution of x[i] with a specific filter, for which + # we take an FIR approximation, stopping when we see at least `lowpass_filter_width` zeros crossing. + # But y[j+1] is going to have a different set of weights and so on, until y[j + new_freq]. + # Indeed: + # y[j + new_freq] = sum_i x[i] sinc(pi * orig_freq * ((i / orig_freq - (j + new_freq) / new_freq)) + # = sum_i x[i] sinc(pi * orig_freq * ((i - orig_freq) / orig_freq - j / new_freq)) + # = sum_i x[i + orig_freq] sinc(pi * orig_freq * (i / orig_freq - j / new_freq)) + # so y[j+new_freq] uses the same filter as y[j], but on a shifted version of x by `orig_freq`. + # This will explain the F.conv1d after, with a stride of orig_freq. + width = math.ceil(lowpass_filter_width * orig_freq / base_freq) + # If orig_freq is still big after GCD reduction, most filters will be very unbalanced, i.e., + # they will have a lot of almost zero values to the left or to the right... + # There is probably a way to evaluate those filters more efficiently, but this is kept for + # future work. + idx = torch.arange(-width, width + orig_freq, device=device, dtype=dtype) + + for i in range(new_freq): + t = (-i / new_freq + idx / orig_freq) * base_freq + t = t.clamp_(-lowpass_filter_width, lowpass_filter_width) + t *= math.pi + # we do not use torch.hann_window here as we need to evaluate the window + # at spectifics positions, not over a regular grid. + window = torch.cos(t / lowpass_filter_width / 2)**2 + kernel = torch.where(t == 0, torch.tensor(1.).to(t), torch.sin(t) / t) + kernel.mul_(window) + kernels.append(kernel) + + scale = base_freq / orig_freq + return torch.stack(kernels).view(new_freq, 1, -1).mul_(scale), width def resample_waveform(waveform: Tensor, @@ -912,72 +826,21 @@ def resample_waveform(waveform: Tensor, Returns: Tensor: The waveform at the new frequency """ - device, dtype = waveform.device, waveform.dtype - assert waveform.dim() == 2 assert orig_freq > 0.0 and new_freq > 0.0 - min_freq = min(orig_freq, new_freq) - lowpass_cutoff = 0.99 * 0.5 * min_freq - - assert lowpass_cutoff * 2 <= min_freq - - base_freq = math.gcd(int(orig_freq), int(new_freq)) - input_samples_in_unit = int(orig_freq) // base_freq - output_samples_in_unit = int(new_freq) // base_freq - - window_width = lowpass_filter_width / (2.0 * lowpass_cutoff) - first_indices, weights = _get_LR_indices_and_weights( - orig_freq, new_freq, output_samples_in_unit, - window_width, lowpass_cutoff, lowpass_filter_width, device, dtype) - - assert first_indices.dim() == 1 - # TODO figure a better way to do this. conv1d reaches every element i*stride + padding - # all the weights have the same stride but have different padding. - # Current implementation takes the input and applies the various padding before - # doing a conv1d for that specific weight. - conv_stride = input_samples_in_unit - conv_transpose_stride = output_samples_in_unit - num_channels, wave_len = waveform.size() - window_size = weights.size(1) - tot_output_samp = _get_num_LR_output_samples(wave_len, orig_freq, new_freq) - output = torch.zeros((num_channels, tot_output_samp), - device=device, dtype=dtype) - # eye size: (num_channels, num_channels, 1) - eye = torch.eye(num_channels, device=device, dtype=dtype).unsqueeze(2) - for i in range(first_indices.size(0)): - wave_to_conv = waveform - first_index = int(first_indices[i].item()) - if first_index >= 0: - # trim the signal as the filter will not be applied before the first_index - wave_to_conv = wave_to_conv[..., first_index:] - - # pad the right of the signal to allow partial convolutions meaning compute - # values for partial windows (e.g. end of the window is outside the signal length) - max_unit_index = (tot_output_samp - 1) // output_samples_in_unit - end_index_of_last_window = max_unit_index * conv_stride + window_size - current_wave_len = wave_len - first_index - right_padding = max(0, end_index_of_last_window + 1 - current_wave_len) - - left_padding = max(0, -first_index) - if left_padding != 0 or right_padding != 0: - wave_to_conv = torch.nn.functional.pad(wave_to_conv, (left_padding, right_padding)) - - conv_wave = torch.nn.functional.conv1d( - wave_to_conv.unsqueeze(0), weights[i].repeat(num_channels, 1, 1), - stride=conv_stride, groups=num_channels) - - # we want conv_wave[:, i] to be at output[:, i + n*conv_transpose_stride] - dilated_conv_wave = torch.nn.functional.conv_transpose1d( - conv_wave, eye, stride=conv_transpose_stride).squeeze(0) - - # pad dilated_conv_wave so it reaches the output length if needed. - dialated_conv_wave_len = dilated_conv_wave.size(-1) - left_padding = i - right_padding = max(0, tot_output_samp - (left_padding + dialated_conv_wave_len)) - dilated_conv_wave = torch.nn.functional.pad( - dilated_conv_wave, (left_padding, right_padding))[..., :tot_output_samp] - - output += dilated_conv_wave - - return output + orig_freq = int(orig_freq) + new_freq = int(new_freq) + gcd = math.gcd(orig_freq, new_freq) + orig_freq = orig_freq // gcd + new_freq = new_freq // gcd + + kernel, width = _get_sinc_resample_kernel(orig_freq, new_freq, lowpass_filter_width, + waveform.device, waveform.dtype) + + num_wavs, length = waveform.shape + waveform = F.pad(waveform, (width, width + orig_freq)) + resampled = F.conv1d(waveform[:, None], kernel, stride=orig_freq) + resampled = resampled.transpose(1, 2).reshape(num_wavs, -1) + target_length = int(math.ceil(new_freq * length / orig_freq)) + return resampled[..., :target_length] diff --git a/torchaudio/csrc/pybind.cpp b/torchaudio/csrc/pybind.cpp index caf9ad9b19..eb8c30b96a 100644 --- a/torchaudio/csrc/pybind.cpp +++ b/torchaudio/csrc/pybind.cpp @@ -100,4 +100,8 @@ PYBIND11_MODULE(_torchaudio, m) { "load_audio_fileobj", &torchaudio::sox_io::load_audio_fileobj, "Load audio from file object."); + m.def( + "save_audio_fileobj", + &torchaudio::sox_io::save_audio_fileobj, + "Save audio to file obj."); } diff --git a/torchaudio/csrc/sox/effects.cpp b/torchaudio/csrc/sox/effects.cpp index ffc85b92ed..03f06c2602 100644 --- a/torchaudio/csrc/sox/effects.cpp +++ b/torchaudio/csrc/sox/effects.cpp @@ -59,8 +59,8 @@ c10::intrusive_ptr apply_effects_tensor( // Create SoxEffectsChain const auto dtype = in_tensor.dtype(); torchaudio::sox_effects_chain::SoxEffectsChain chain( - /*input_encoding=*/get_encodinginfo("wav", dtype, 0.), - /*output_encoding=*/get_encodinginfo("wav", dtype, 0.)); + /*input_encoding=*/get_encodinginfo("wav", dtype), + /*output_encoding=*/get_encodinginfo("wav", dtype)); // Prepare output buffer std::vector out_buffer; @@ -112,7 +112,7 @@ c10::intrusive_ptr apply_effects_file( // Create and run SoxEffectsChain torchaudio::sox_effects_chain::SoxEffectsChain chain( /*input_encoding=*/sf->encoding, - /*output_encoding=*/get_encodinginfo("wav", dtype, 0.)); + /*output_encoding=*/get_encodinginfo("wav", dtype)); chain.addInputFile(sf); for (const auto& effect : effects) { @@ -163,7 +163,7 @@ std::tuple apply_effects_fileobj( // Prepare the buffer used throughout the lifecycle of SoxEffectChain. // Using std::string and let it manage memory. - // 4096 is minimum size required by auto_detect_format + // 4096 is minimum size requried by auto_detect_format // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/formats.c#L40-L48 const size_t in_buffer_size = 4096; std::string in_buffer(in_buffer_size, 'x'); @@ -193,7 +193,7 @@ std::tuple apply_effects_fileobj( const auto dtype = get_dtype(sf->encoding.encoding, sf->signal.precision); torchaudio::sox_effects_chain::SoxEffectsChain chain( /*input_encoding=*/sf->encoding, - /*output_encoding=*/get_encodinginfo("wav", dtype, 0.)); + /*output_encoding=*/get_encodinginfo("wav", dtype)); chain.addInputFileObj(sf, in_buf, in_buffer_size, &fileobj); for (const auto& effect : effects) { chain.addEffect(effect); diff --git a/torchaudio/csrc/sox/effects_chain.cpp b/torchaudio/csrc/sox/effects_chain.cpp index 37a6f32df8..9c365b72aa 100644 --- a/torchaudio/csrc/sox/effects_chain.cpp +++ b/torchaudio/csrc/sox/effects_chain.cpp @@ -295,6 +295,13 @@ struct FileObjInputPriv { uint64_t buffer_size; }; +struct FileObjOutputPriv { + sox_format_t* sf; + py::object* fileobj; + char** buffer; + size_t* buffer_size; +}; + /// Callback function to feed byte string /// https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/sox.h#L1268-L1278 int fileobj_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { @@ -335,7 +342,7 @@ int fileobj_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { if(num_refill > num_consumed) { std::ostringstream message; message << "Tried to read up to " << num_consumed << " bytes but, " - << "received " << num_refill << " bytes. " + << "recieved " << num_refill << " bytes. " << "The given object does not confirm to read protocol of file object."; throw std::runtime_error(message.str()); } @@ -373,6 +380,45 @@ int fileobj_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { return *osamp? SOX_SUCCESS : SOX_EOF; } +int fileobj_output_flow( + sox_effect_t* effp, + sox_sample_t const* ibuf, + sox_sample_t* obuf LSX_UNUSED, + size_t* isamp, + size_t* osamp) { + *osamp = 0; + if (*isamp) { + auto priv = static_cast(effp->priv); + auto sf = priv->sf; + auto fp = static_cast(sf->fp); + auto fileobj = priv->fileobj; + auto buffer = priv->buffer; + auto buffer_size = priv->buffer_size; + + // Encode chunk + auto num_samples_written = sox_write(sf, ibuf, *isamp); + fflush(fp); + + // Copy the encoded chunk to python object. + fileobj->attr("write")(py::bytes(*buffer, *buffer_size)); + + // Reset FILE* + sf->tell_off = 0; + fseek(fp, 0, SEEK_SET); + + if (num_samples_written != *isamp) { + if (sf->sox_errno) { + std::ostringstream stream; + stream << sf->sox_errstr << " " << sox_strerror(sf->sox_errno) << " " + << sf->filename; + throw std::runtime_error(stream.str()); + } + return SOX_EOF; + } + } + return SOX_SUCCESS; +} + sox_effect_handler_t* get_fileobj_input_handler() { static sox_effect_handler_t handler{/*name=*/"input_fileobj_object", /*usage=*/NULL, @@ -387,6 +433,20 @@ sox_effect_handler_t* get_fileobj_input_handler() { return &handler; } +sox_effect_handler_t* get_fileobj_output_handler() { + static sox_effect_handler_t handler{/*name=*/"output_fileobj_object", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/fileobj_output_flow, + /*drain=*/NULL, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(FileObjOutputPriv)}; + return &handler; +} + } // namespace void SoxEffectsChain::addInputFileObj( @@ -408,6 +468,23 @@ void SoxEffectsChain::addInputFileObj( } } +void SoxEffectsChain::addOutputFileObj( + sox_format_t* sf, + char** buffer, + size_t* buffer_size, + py::object* fileobj) { + out_sig_ = sf->signal; + SoxEffect e(sox_create_effect(get_fileobj_output_handler())); + auto priv = static_cast(e->priv); + priv->sf = sf; + priv->fileobj = fileobj; + priv->buffer = buffer; + priv->buffer_size = buffer_size; + if (sox_add_effect(sec_, e, &interm_sig_, &out_sig_) != SOX_SUCCESS) { + throw std::runtime_error("Internal Error: Failed to add effect: output fileobj"); + } +} + #endif // TORCH_API_INCLUDE_EXTENSION_H } // namespace sox_effects_chain diff --git a/torchaudio/csrc/sox/effects_chain.h b/torchaudio/csrc/sox/effects_chain.h index b096b3eb3d..a4797b3486 100644 --- a/torchaudio/csrc/sox/effects_chain.h +++ b/torchaudio/csrc/sox/effects_chain.h @@ -46,6 +46,12 @@ class SoxEffectsChain { uint64_t buffer_size, py::object* fileobj); + void addOutputFileObj( + sox_format_t* sf, + char** buffer, + size_t* buffer_size, + py::object* fileobj); + #endif // TORCH_API_INCLUDE_EXTENSION_H }; diff --git a/torchaudio/csrc/sox/io.cpp b/torchaudio/csrc/sox/io.cpp index e381be14f8..c6531eb77e 100644 --- a/torchaudio/csrc/sox/io.cpp +++ b/torchaudio/csrc/sox/io.cpp @@ -95,26 +95,31 @@ c10::intrusive_ptr load_audio_file( } void save_audio_file( - const std::string& file_name, - const c10::intrusive_ptr& signal, - const double compression) { - auto tensor = signal->tensor; - + const std::string& path, + torch::Tensor tensor, + int64_t sample_rate, + bool channels_first, + c10::optional compression, + c10::optional format) { validate_input_tensor(tensor); - const auto filetype = get_filetype(file_name); + auto signal = TensorSignal(tensor, sample_rate, channels_first); + + const auto filetype = [&](){ + if (format.has_value()) return format.value(); + return get_filetype(path); + }(); if (filetype == "amr-nb") { - const auto num_channels = tensor.size(signal->channels_first ? 0 : 1); + const auto num_channels = tensor.size(channels_first ? 0 : 1); TORCH_CHECK( num_channels == 1, "amr-nb format only supports single channel audio."); tensor = (unnormalize_wav(tensor) / 65536).to(torch::kInt16); } - const auto signal_info = get_signalinfo(signal.get(), filetype); - const auto encoding_info = - get_encodinginfo(filetype, tensor.dtype(), compression); + const auto signal_info = get_signalinfo(&signal, filetype); + const auto encoding_info = get_encodinginfo(filetype, tensor.dtype(), compression); SoxFormat sf(sox_open_write( - file_name.c_str(), + path.c_str(), &signal_info, &encoding_info, /*filetype=*/filetype.c_str(), @@ -126,9 +131,9 @@ void save_audio_file( } torchaudio::sox_effects_chain::SoxEffectsChain chain( - /*input_encoding=*/get_encodinginfo("wav", tensor.dtype(), 0.), + /*input_encoding=*/get_encodinginfo("wav", tensor.dtype()), /*output_encoding=*/sf->encoding); - chain.addInputTensor(signal.get()); + chain.addInputTensor(&signal); chain.addOutputFile(sf); chain.run(); } @@ -147,6 +152,75 @@ std::tuple load_audio_fileobj( fileobj, effects, normalize, channels_first, format); } +namespace { + +// helper class to automatically release buffer, to be used by save_audio_fileobj +struct AutoReleaseBuffer { + char* ptr; + size_t size; + + AutoReleaseBuffer() : ptr(nullptr), size(0) {} + AutoReleaseBuffer(const AutoReleaseBuffer& other) = delete; + AutoReleaseBuffer(AutoReleaseBuffer&& other) = delete; + AutoReleaseBuffer& operator=(const AutoReleaseBuffer& other) = delete; + AutoReleaseBuffer& operator=(AutoReleaseBuffer&& other) = delete; + ~AutoReleaseBuffer() { + if (ptr) { + free(ptr); + } + } +}; + +} // namespace + +void save_audio_fileobj( + py::object fileobj, + torch::Tensor tensor, + int64_t sample_rate, + bool channels_first, + c10::optional compression, + std::string filetype) { + validate_input_tensor(tensor); + + auto signal = TensorSignal(tensor, sample_rate, channels_first); + + if (filetype == "amr-nb") { + const auto num_channels = tensor.size(channels_first ? 0 : 1); + if (num_channels != 1) { + throw std::runtime_error("amr-nb format only supports single channel audio."); + } + tensor = (unnormalize_wav(tensor) / 65536).to(torch::kInt16); + } + const auto signal_info = get_signalinfo(&signal, filetype); + const auto encoding_info = get_encodinginfo(filetype, tensor.dtype(), compression); + + AutoReleaseBuffer buffer; + + SoxFormat sf(sox_open_memstream_write( + &buffer.ptr, + &buffer.size, + &signal_info, + &encoding_info, + filetype.c_str(), + /*oob=*/nullptr)); + + if (static_cast(sf) == nullptr) { + throw std::runtime_error("Error saving audio file: failed to open memory stream."); + } + + torchaudio::sox_effects_chain::SoxEffectsChain chain( + /*input_encoding=*/get_encodinginfo("wav", tensor.dtype()), + /*output_encoding=*/sf->encoding); + chain.addInputTensor(&signal); + chain.addOutputFileObj(sf, &buffer.ptr, &buffer.size, &fileobj); + chain.run(); + + // Closing the sox_format_t is necessary for flushing the last chunk to the buffer + sf.close(); + + fileobj.attr("write")(py::bytes(buffer.ptr, buffer.size)); +} + #endif // TORCH_API_INCLUDE_EXTENSION_H } // namespace sox_io diff --git a/torchaudio/csrc/sox/io.h b/torchaudio/csrc/sox/io.h index d6e5310077..ac7191527f 100644 --- a/torchaudio/csrc/sox/io.h +++ b/torchaudio/csrc/sox/io.h @@ -38,9 +38,12 @@ c10::intrusive_ptr load_audio_file( c10::optional& format); void save_audio_file( - const std::string& file_name, - const c10::intrusive_ptr& signal, - const double compression = 0.); + const std::string& path, + torch::Tensor tensor, + int64_t sample_rate, + bool channels_first, + c10::optional compression, + c10::optional format); #ifdef TORCH_API_INCLUDE_EXTENSION_H @@ -52,6 +55,14 @@ std::tuple load_audio_fileobj( c10::optional& channels_first, c10::optional& format); +void save_audio_fileobj( + py::object fileobj, + torch::Tensor tensor, + int64_t sample_rate, + bool channels_first, + c10::optional compression, + std::string filetype); + #endif // TORCH_API_INCLUDE_EXTENSION_H } // namespace sox_io diff --git a/torchaudio/csrc/sox/utils.cpp b/torchaudio/csrc/sox/utils.cpp index 44f00084e8..0ea95cbbb3 100644 --- a/torchaudio/csrc/sox/utils.cpp +++ b/torchaudio/csrc/sox/utils.cpp @@ -80,11 +80,8 @@ bool TensorSignal::getChannelsFirst() const { } SoxFormat::SoxFormat(sox_format_t* fd) noexcept : fd_(fd) {} -SoxFormat::~SoxFormat() { - if (fd_ != nullptr) { - sox_close(fd_); - } -} +SoxFormat::~SoxFormat() { close(); } + sox_format_t* SoxFormat::operator->() const noexcept { return fd_; } @@ -92,6 +89,13 @@ SoxFormat::operator sox_format_t*() const noexcept { return fd_; } +void SoxFormat::close() { + if (fd_ != nullptr) { + sox_close(fd_); + fd_ = nullptr; + } +} + void validate_input_file(const SoxFormat& sf, bool check_length) { if (static_cast(sf) == nullptr) { throw std::runtime_error("Error loading audio file: failed to open file."); @@ -286,27 +290,23 @@ sox_signalinfo_t get_signalinfo( sox_encodinginfo_t get_encodinginfo( const std::string filetype, - const caffe2::TypeMeta dtype, - const double compression) { - const double compression_ = [&]() { - if (filetype == "mp3") - return compression; - if (filetype == "flac") - return compression; - if (filetype == "ogg" || filetype == "vorbis") - return compression; - if (filetype == "wav" || filetype == "amb") - return 0.; - if (filetype == "sph") - return 0.; - if (filetype == "amr-nb") - return 0.; - throw std::runtime_error("Unsupported file type: " + filetype); - }(); + const caffe2::TypeMeta dtype) { + return sox_encodinginfo_t{/*encoding=*/get_encoding(filetype, dtype), + /*bits_per_sample=*/get_precision(filetype, dtype), + /*compression=*/HUGE_VAL, + /*reverse_bytes=*/sox_option_default, + /*reverse_nibbles=*/sox_option_default, + /*reverse_bits=*/sox_option_default, + /*opposite_endian=*/sox_false}; +} +sox_encodinginfo_t get_encodinginfo( + const std::string filetype, + const caffe2::TypeMeta dtype, + c10::optional& compression) { return sox_encodinginfo_t{/*encoding=*/get_encoding(filetype, dtype), /*bits_per_sample=*/get_precision(filetype, dtype), - /*compression=*/compression_, + /*compression=*/compression.value_or(HUGE_VAL), /*reverse_bytes=*/sox_option_default, /*reverse_nibbles=*/sox_option_default, /*reverse_bits=*/sox_option_default, diff --git a/torchaudio/csrc/sox/utils.h b/torchaudio/csrc/sox/utils.h index ee8d1baa66..2d434d6f72 100644 --- a/torchaudio/csrc/sox/utils.h +++ b/torchaudio/csrc/sox/utils.h @@ -61,6 +61,8 @@ struct SoxFormat { sox_format_t* operator->() const noexcept; operator sox_format_t*() const noexcept; + void close(); + private: sox_format_t* fd_; }; @@ -116,10 +118,14 @@ sox_signalinfo_t get_signalinfo( const std::string filetype); /// Get sox_encofinginfo_t for saving audoi file +sox_encodinginfo_t get_encodinginfo( + const std::string filetype, + const caffe2::TypeMeta dtype); + sox_encodinginfo_t get_encodinginfo( const std::string filetype, const caffe2::TypeMeta dtype, - const double compression); + c10::optional& compression); } // namespace sox_utils } // namespace torchaudio diff --git a/torchaudio/datasets/yesno.py b/torchaudio/datasets/yesno.py index 7178b8332c..6d0d2c0a5f 100644 --- a/torchaudio/datasets/yesno.py +++ b/torchaudio/datasets/yesno.py @@ -11,23 +11,14 @@ extract_archive, ) -URL = "http://www.openslr.org/resources/1/waves_yesno.tar.gz" -FOLDER_IN_ARCHIVE = "waves_yesno" -_CHECKSUMS = { - "http://www.openslr.org/resources/1/waves_yesno.tar.gz": - "962ff6e904d2df1126132ecec6978786" -} - - -def load_yesno_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, int, List[int]]: - # Read label - labels = [int(c) for c in fileid.split("_")] - # Read wav - file_audio = os.path.join(path, fileid + ext_audio) - waveform, sample_rate = torchaudio.load(file_audio) - - return waveform, sample_rate, labels +_RELEASE_CONFIGS = { + "release1": { + "folder_in_archive": "waves_yesno", + "url": "http://www.openslr.org/resources/1/waves_yesno.tar.gz", + "checksum": "30301975fd8c5cac4040c261c0852f57cfa8adbbad2ce78e77e4986957445f27", + } +} class YESNO(Dataset): @@ -43,25 +34,26 @@ class YESNO(Dataset): Whether to download the dataset if it is not found at root path. (default: ``False``). """ - _ext_audio = ".wav" - - def __init__(self, - root: Union[str, Path], - url: str = URL, - folder_in_archive: str = FOLDER_IN_ARCHIVE, - download: bool = False) -> None: + def __init__( + self, + root: Union[str, Path], + url: str = _RELEASE_CONFIGS["release1"]["url"], + folder_in_archive: str = _RELEASE_CONFIGS["release1"]["folder_in_archive"], + download: bool = False + ) -> None: - # Get string representation of 'root' in case Path object is passed - root = os.fspath(root) + self._parse_filesystem(root, url, folder_in_archive, download) + def _parse_filesystem(self, root: str, url: str, folder_in_archive: str, download: bool) -> None: + root = Path(root) archive = os.path.basename(url) - archive = os.path.join(root, archive) - self._path = os.path.join(root, folder_in_archive) + archive = root / archive + self._path = root / folder_in_archive if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - checksum = _CHECKSUMS.get(url, None) + checksum = _RELEASE_CONFIGS["release1"]["checksum"] download_url(url, root, hash_value=checksum, hash_type="md5") extract_archive(archive) @@ -70,7 +62,13 @@ def __init__(self, "Dataset not found. Please use `download=True` to download it." ) - self._walker = sorted(str(p.stem) for p in Path(self._path).glob('*' + self._ext_audio)) + self._walker = sorted(str(p.stem) for p in Path(self._path).glob("*.wav")) + + def _load_item(self, fileid: str, path: str): + labels = [int(c) for c in fileid.split("_")] + file_audio = os.path.join(path, fileid + ".wav") + waveform, sample_rate = torchaudio.load(file_audio) + return waveform, sample_rate, labels def __getitem__(self, n: int) -> Tuple[Tensor, int, List[int]]: """Load the n-th sample from the dataset. @@ -82,13 +80,8 @@ def __getitem__(self, n: int) -> Tuple[Tensor, int, List[int]]: tuple: ``(waveform, sample_rate, labels)`` """ fileid = self._walker[n] - item = load_yesno_item(fileid, self._path, self._ext_audio) - - # TODO Upon deprecation, uncomment line below and remove following code - # return item - - waveform, sample_rate, labels = item - return waveform, sample_rate, labels + item = self._load_item(fileid, self._path) + return item def __len__(self) -> int: return len(self._walker) diff --git a/tox.ini b/tox.ini deleted file mode 100644 index 793ccc2c3b..0000000000 --- a/tox.ini +++ /dev/null @@ -1,4 +0,0 @@ -[flake8] -max-line-length = 120 -ignore = E305,E402,E721,E741,F401,F403,F405,F821,F841,F999 -exclude = docs/src,venv,torch/lib/gloo,torch/lib/pybind11,torch/lib/nanopb From d580296c74df83da1569cf2bc45ef95574ccdaeb Mon Sep 17 00:00:00 2001 From: Vincent Quenneville-Belair Date: Mon, 1 Feb 2021 16:03:20 -0800 Subject: [PATCH 27/73] Import #1217 828df46a95310453dda13599a29fa22a1cc049da Summary: Import [#1217](https://github.com/pytorch/audio/pull/1217) 828df46a95310453dda13599a29fa22a1cc049da Reviewed By: cpuhrsch Differential Revision: D26180248 fbshipit-source-id: 34b1e18e86436472f47070c4d3c748a10a4153a3 --- .circleci/config.yml | 120 +------ .circleci/config.yml.in | 120 +------ .circleci/unittest/linux/docker/Dockerfile | 5 +- .../unittest/linux/docker/build_and_push.sh | 11 +- .circleci/unittest/linux/scripts/install.sh | 2 - .../linux/scripts/run-clang-format.py | 340 ++++++++++++++++++ .../linux/scripts/run_style_checks.sh | 26 +- .../batch_consistency_test.py | 11 + .../librosa_compatibility_test.py | 13 + .../soundfile_backend/common.py | 2 +- .../soundfile_backend/info_test.py | 123 ++++++- .../sox_effect/smoke_test.py | 22 +- .../sox_effect/sox_effect_test.py | 161 ++++++++- .../sox_io_backend/info_test.py | 291 ++++++++++++++- .../sox_io_backend/load_test.py | 75 ++++ .../sox_io_backend/save_test.py | 43 ++- .../sox_io_backend/smoke_test.py | 68 ++++ .../torchscript_consistency_impl.py | 19 + test/torchaudio_unittest/transforms_test.py | 16 + third_party/sox/CMakeLists.txt | 122 ++++++- torchaudio/backend/_soundfile_backend.py | 88 ++++- torchaudio/backend/common.py | 14 +- torchaudio/backend/sox_io_backend.py | 68 +++- torchaudio/csrc/pybind.cpp | 165 +++++---- torchaudio/csrc/sox/effects.cpp | 80 +++-- torchaudio/csrc/sox/effects_chain.cpp | 179 ++++----- torchaudio/csrc/sox/io.cpp | 153 +++++++- torchaudio/csrc/sox/io.h | 20 +- torchaudio/csrc/sox/legacy.cpp | 16 +- torchaudio/csrc/sox/legacy.h | 12 +- torchaudio/csrc/sox/register.cpp | 20 +- torchaudio/csrc/sox/utils.cpp | 81 ++++- torchaudio/csrc/sox/utils.h | 14 +- torchaudio/csrc/transducer.cpp | 154 ++++---- torchaudio/datasets/ljspeech.py | 74 ++-- torchaudio/functional/__init__.py | 1 + torchaudio/functional/functional.py | 54 ++- torchaudio/sox_effects/sox_effects.py | 22 +- torchaudio/transforms.py | 98 ++++- 39 files changed, 2214 insertions(+), 689 deletions(-) create mode 100755 .circleci/unittest/linux/scripts/run-clang-format.py diff --git a/.circleci/config.yml b/.circleci/config.yml index c40054d0e3..ef2a4ff76c 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -388,29 +388,16 @@ jobs: unittest_linux_cpu: <<: *binary_common docker: - - image: "pytorch/torchaudio_unittest_base:manylinux" + - image: pytorch/torchaudio_unittest_base:manylinux-20210121 resource_class: 2xlarge+ steps: - checkout - attach_workspace: at: third_party - designate_upload_channel - - generate_cache_key - - restore_cache: - - keys: - - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - - save_cache: - - key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - - paths: - - conda - - env - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -430,31 +417,18 @@ jobs: resource_class: gpu.small environment: <<: *environment - image_name: "pytorch/torchaudio_unittest_base:manylinux-cuda10.1" + image_name: pytorch/torchaudio_unittest_base:manylinux-cuda10.1-cudnn7-20210121 steps: - checkout - attach_workspace: at: third_party - designate_upload_channel - - generate_cache_key - - restore_cache: - - keys: - - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - - run: name: Pull Docker image command: docker pull --quiet "${image_name}" - run: name: Setup command: docker run -t --gpus all -e PYTHON_VERSION -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh - - save_cache: - - key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - - paths: - - conda - - env - run: name: Install torchaudio command: docker run -t --gpus all -e UPLOAD_CHANNEL -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh @@ -474,22 +448,9 @@ jobs: steps: - checkout - designate_upload_channel - - generate_cache_key - - restore_cache: - - keys: - - env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh - - save_cache: - - key: env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - - paths: - - conda - - env - run: name: Install torchaudio command: .circleci/unittest/windows/scripts/install.sh @@ -512,22 +473,9 @@ jobs: steps: - checkout - designate_upload_channel - - generate_cache_key - - restore_cache: - - keys: - - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh - - save_cache: - - key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - - paths: - - conda - - env - run: name: Install torchaudio command: .circleci/unittest/windows/scripts/install.sh @@ -551,22 +499,9 @@ jobs: - attach_workspace: at: third_party - designate_upload_channel - - generate_cache_key - - restore_cache: - - keys: - - env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - - save_cache: - - key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - - paths: - - conda - - env - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -587,61 +522,13 @@ jobs: steps: - checkout - designate_upload_channel - - generate_cache_key - - restore_cache: - - keys: - - env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - - save_cache: - - key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - - paths: - - conda - - env - run: name: Run style check command: .circleci/unittest/linux/scripts/run_style_checks.sh - torchscript_bc_test: - docker: - - image: "pytorch/torchaudio_unittest_base:manylinux" - resource_class: large - steps: - - checkout - - generate_cache_key - - restore_cache: - - keys: - - torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} - - - run: - name: Generate Objects - command: | - .circleci/torchscript_bc_test/setup_release_envs.sh - .circleci/torchscript_bc_test/generate_objects.sh - - save_cache: - - key: torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} - - paths: - - conda - - envs - - store_artifacts: - path: test/torchscript_bc_test/assets - - persist_to_workspace: - root: . - paths: - - test/torchscript_bc_test/assets - - run: - name: Run BC check test - command: | - .circleci/torchscript_bc_test/setup_master_envs.sh - .circleci/torchscript_bc_test/validate_objects.sh build_docs: <<: *binary_common docker: @@ -793,9 +680,6 @@ workflows: - build_docs unittest: jobs: - - torchscript_bc_test: - requires: - - download_third_parties_nix - download_third_parties_nix: name: download_third_parties_nix - unittest_linux_cpu: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 535729d72e..84e25911a3 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -388,29 +388,16 @@ jobs: unittest_linux_cpu: <<: *binary_common docker: - - image: "pytorch/torchaudio_unittest_base:manylinux" + - image: pytorch/torchaudio_unittest_base:manylinux-20210121 resource_class: 2xlarge+ steps: - checkout - attach_workspace: at: third_party - designate_upload_channel - - generate_cache_key - - restore_cache: - {% raw %} - keys: - - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - {% endraw %} - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - - save_cache: - {% raw %} - key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - {% endraw %} - paths: - - conda - - env - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -430,31 +417,18 @@ jobs: resource_class: gpu.small environment: <<: *environment - image_name: "pytorch/torchaudio_unittest_base:manylinux-cuda10.1" + image_name: pytorch/torchaudio_unittest_base:manylinux-cuda10.1-cudnn7-20210121 steps: - checkout - attach_workspace: at: third_party - designate_upload_channel - - generate_cache_key - - restore_cache: - {% raw %} - keys: - - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - {% endraw %} - run: name: Pull Docker image command: docker pull --quiet "${image_name}" - run: name: Setup command: docker run -t --gpus all -e PYTHON_VERSION -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh - - save_cache: - {% raw %} - key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - {% endraw %} - paths: - - conda - - env - run: name: Install torchaudio command: docker run -t --gpus all -e UPLOAD_CHANNEL -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh @@ -474,22 +448,9 @@ jobs: steps: - checkout - designate_upload_channel - - generate_cache_key - - restore_cache: - {% raw %} - keys: - - env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - {% endraw %} - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh - - save_cache: - {% raw %} - key: env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - {% endraw %} - paths: - - conda - - env - run: name: Install torchaudio command: .circleci/unittest/windows/scripts/install.sh @@ -512,22 +473,9 @@ jobs: steps: - checkout - designate_upload_channel - - generate_cache_key - - restore_cache: - {% raw %} - keys: - - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - {% endraw %} - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh - - save_cache: - {% raw %} - key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - {% endraw %} - paths: - - conda - - env - run: name: Install torchaudio command: .circleci/unittest/windows/scripts/install.sh @@ -551,22 +499,9 @@ jobs: - attach_workspace: at: third_party - designate_upload_channel - - generate_cache_key - - restore_cache: - {% raw %} - keys: - - env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - {% endraw %} - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - - save_cache: - {% raw %} - key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} - {% endraw %} - paths: - - conda - - env - run: name: Install torchaudio command: .circleci/unittest/linux/scripts/install.sh @@ -587,61 +522,13 @@ jobs: steps: - checkout - designate_upload_channel - - generate_cache_key - - restore_cache: - {% raw %} - keys: - - env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - {% endraw %} - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh - - save_cache: - {% raw %} - key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/setup_env.sh" }}-{{ checksum ".cachekey" }} - {% endraw %} - paths: - - conda - - env - run: name: Run style check command: .circleci/unittest/linux/scripts/run_style_checks.sh - torchscript_bc_test: - docker: - - image: "pytorch/torchaudio_unittest_base:manylinux" - resource_class: large - steps: - - checkout - - generate_cache_key - - restore_cache: - {% raw %} - keys: - - torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} - {% endraw %} - - run: - name: Generate Objects - command: | - .circleci/torchscript_bc_test/setup_release_envs.sh - .circleci/torchscript_bc_test/generate_objects.sh - - save_cache: - {% raw %} - key: torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} - {% endraw %} - paths: - - conda - - envs - - store_artifacts: - path: test/torchscript_bc_test/assets - - persist_to_workspace: - root: . - paths: - - test/torchscript_bc_test/assets - - run: - name: Run BC check test - command: | - .circleci/torchscript_bc_test/setup_master_envs.sh - .circleci/torchscript_bc_test/validate_objects.sh build_docs: <<: *binary_common docker: @@ -697,9 +584,6 @@ workflows: {{ build_workflows() }} unittest: jobs: - - torchscript_bc_test: - requires: - - download_third_parties_nix {{ unittest_workflows() }} nightly: jobs: diff --git a/.circleci/unittest/linux/docker/Dockerfile b/.circleci/unittest/linux/docker/Dockerfile index d91b5222a4..c47a896348 100644 --- a/.circleci/unittest/linux/docker/Dockerfile +++ b/.circleci/unittest/linux/docker/Dockerfile @@ -45,14 +45,11 @@ RUN apt update && apt install -y \ gfortran \ git \ libatlas3-base \ + libsndfile1 \ wget \ curl \ make \ file \ - sox \ - libsox-dev \ - libsox-fmt-all \ - cmake \ pkg-config \ && rm -rf /var/lib/apt/lists/* COPY --from=builder /kaldi /kaldi diff --git a/.circleci/unittest/linux/docker/build_and_push.sh b/.circleci/unittest/linux/docker/build_and_push.sh index 8bf33eaaa3..e7ced13ad3 100755 --- a/.circleci/unittest/linux/docker/build_and_push.sh +++ b/.circleci/unittest/linux/docker/build_and_push.sh @@ -7,15 +7,14 @@ if [ $# -ne 1 ]; then exit 1 fi +datestr="$(date "+%Y%m%d")" if [ "$1" = "cpu" ]; then base_image="ubuntu:18.04" - image="pytorch/torchaudio_unittest_base:manylinux" -elif [[ "$1" =~ ^(9.2|10.1)$ ]]; then - base_image="nvidia/cuda:$1-runtime-ubuntu18.04" - image="pytorch/torchaudio_unittest_base:manylinux-cuda$1" + image="pytorch/torchaudio_unittest_base:manylinux-${datestr}" else - printf "Unexpected string: %s" "$1" - exit 1; + base_image="nvidia/cuda:$1-devel-ubuntu18.04" + docker pull "${base_image}" + image="pytorch/torchaudio_unittest_base:manylinux-cuda$1-${datestr}" fi cd "$( dirname "${BASH_SOURCE[0]}" )" diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh index 72b7c9c88f..fd43260194 100755 --- a/.circleci/unittest/linux/scripts/install.sh +++ b/.circleci/unittest/linux/scripts/install.sh @@ -43,8 +43,6 @@ BUILD_TRANSDUCER=1 BUILD_SOX=1 python setup.py install # 3. Install Test tools printf "* Installing test tools\n" if [ "${os}" == Linux ] ; then - # TODO: move this to docker - apt install -y -q libsndfile1 conda install -y -c conda-forge codecov pytest pytest-cov pip install kaldi-io 'librosa>=0.8.0' parameterized SoundFile scipy 'requests>=2.20' else diff --git a/.circleci/unittest/linux/scripts/run-clang-format.py b/.circleci/unittest/linux/scripts/run-clang-format.py new file mode 100755 index 0000000000..fd2913bd70 --- /dev/null +++ b/.circleci/unittest/linux/scripts/run-clang-format.py @@ -0,0 +1,340 @@ +#!/usr/bin/env python +"""A wrapper script around clang-format, suitable for linting multiple files +and to use for continuous integration. + +This is an alternative API for the clang-format command line. +It runs over multiple files and directories in parallel. +A diff output is produced and a sensible exit code is returned. + +""" + +import argparse +import codecs +import difflib +import fnmatch +import io +import multiprocessing +import os +import signal +import subprocess +import sys +import traceback + +from functools import partial + +try: + from subprocess import DEVNULL # py3k +except ImportError: + DEVNULL = open(os.devnull, "wb") + + +DEFAULT_EXTENSIONS = 'c,h,C,H,cpp,hpp,cc,hh,c++,h++,cxx,hxx,cu' + + +class ExitStatus: + SUCCESS = 0 + DIFF = 1 + TROUBLE = 2 + + +def list_files(files, recursive=False, extensions=None, exclude=None): + if extensions is None: + extensions = [] + if exclude is None: + exclude = [] + + out = [] + for file in files: + if recursive and os.path.isdir(file): + for dirpath, dnames, fnames in os.walk(file): + fpaths = [os.path.join(dirpath, fname) for fname in fnames] + for pattern in exclude: + # os.walk() supports trimming down the dnames list + # by modifying it in-place, + # to avoid unnecessary directory listings. + dnames[:] = [ + x for x in dnames + if + not fnmatch.fnmatch(os.path.join(dirpath, x), pattern) + ] + fpaths = [ + x for x in fpaths if not fnmatch.fnmatch(x, pattern) + ] + for f in fpaths: + ext = os.path.splitext(f)[1][1:] + if ext in extensions: + out.append(f) + else: + out.append(file) + return out + + +def make_diff(file, original, reformatted): + return list( + difflib.unified_diff( + original, + reformatted, + fromfile='{}\t(original)'.format(file), + tofile='{}\t(reformatted)'.format(file), + n=3)) + + +class DiffError(Exception): + def __init__(self, message, errs=None): + super(DiffError, self).__init__(message) + self.errs = errs or [] + + +class UnexpectedError(Exception): + def __init__(self, message, exc=None): + super(UnexpectedError, self).__init__(message) + self.formatted_traceback = traceback.format_exc() + self.exc = exc + + +def run_clang_format_diff_wrapper(args, file): + try: + ret = run_clang_format_diff(args, file) + return ret + except DiffError: + raise + except Exception as e: + raise UnexpectedError('{}: {}: {}'.format(file, e.__class__.__name__, + e), e) + + +def run_clang_format_diff(args, file): + try: + with io.open(file, 'r', encoding='utf-8') as f: + original = f.readlines() + except IOError as exc: + raise DiffError(str(exc)) + invocation = [args.clang_format_executable, file] + + # Use of utf-8 to decode the process output. + # + # Hopefully, this is the correct thing to do. + # + # It's done due to the following assumptions (which may be incorrect): + # - clang-format will returns the bytes read from the files as-is, + # without conversion, and it is already assumed that the files use utf-8. + # - if the diagnostics were internationalized, they would use utf-8: + # > Adding Translations to Clang + # > + # > Not possible yet! + # > Diagnostic strings should be written in UTF-8, + # > the client can translate to the relevant code page if needed. + # > Each translation completely replaces the format string + # > for the diagnostic. + # > -- http://clang.llvm.org/docs/InternalsManual.html#internals-diag-translation + + try: + proc = subprocess.Popen( + invocation, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + encoding='utf-8') + except OSError as exc: + raise DiffError( + "Command '{}' failed to start: {}".format( + subprocess.list2cmdline(invocation), exc + ) + ) + proc_stdout = proc.stdout + proc_stderr = proc.stderr + + # hopefully the stderr pipe won't get full and block the process + outs = list(proc_stdout.readlines()) + errs = list(proc_stderr.readlines()) + proc.wait() + if proc.returncode: + raise DiffError( + "Command '{}' returned non-zero exit status {}".format( + subprocess.list2cmdline(invocation), proc.returncode + ), + errs, + ) + return make_diff(file, original, outs), errs + + +def bold_red(s): + return '\x1b[1m\x1b[31m' + s + '\x1b[0m' + + +def colorize(diff_lines): + def bold(s): + return '\x1b[1m' + s + '\x1b[0m' + + def cyan(s): + return '\x1b[36m' + s + '\x1b[0m' + + def green(s): + return '\x1b[32m' + s + '\x1b[0m' + + def red(s): + return '\x1b[31m' + s + '\x1b[0m' + + for line in diff_lines: + if line[:4] in ['--- ', '+++ ']: + yield bold(line) + elif line.startswith('@@ '): + yield cyan(line) + elif line.startswith('+'): + yield green(line) + elif line.startswith('-'): + yield red(line) + else: + yield line + + +def print_diff(diff_lines, use_color): + if use_color: + diff_lines = colorize(diff_lines) + sys.stdout.writelines(diff_lines) + + +def print_trouble(prog, message, use_colors): + error_text = 'error:' + if use_colors: + error_text = bold_red(error_text) + print("{}: {} {}".format(prog, error_text, message), file=sys.stderr) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + '--clang-format-executable', + metavar='EXECUTABLE', + help='path to the clang-format executable', + default='clang-format') + parser.add_argument( + '--extensions', + help='comma separated list of file extensions (default: {})'.format( + DEFAULT_EXTENSIONS), + default=DEFAULT_EXTENSIONS) + parser.add_argument( + '-r', + '--recursive', + action='store_true', + help='run recursively over directories') + parser.add_argument('files', metavar='file', nargs='+') + parser.add_argument( + '-q', + '--quiet', + action='store_true') + parser.add_argument( + '-j', + metavar='N', + type=int, + default=0, + help='run N clang-format jobs in parallel' + ' (default number of cpus + 1)') + parser.add_argument( + '--color', + default='auto', + choices=['auto', 'always', 'never'], + help='show colored diff (default: auto)') + parser.add_argument( + '-e', + '--exclude', + metavar='PATTERN', + action='append', + default=[], + help='exclude paths matching the given glob-like pattern(s)' + ' from recursive search') + + args = parser.parse_args() + + # use default signal handling, like diff return SIGINT value on ^C + # https://bugs.python.org/issue14229#msg156446 + signal.signal(signal.SIGINT, signal.SIG_DFL) + try: + signal.SIGPIPE + except AttributeError: + # compatibility, SIGPIPE does not exist on Windows + pass + else: + signal.signal(signal.SIGPIPE, signal.SIG_DFL) + + colored_stdout = False + colored_stderr = False + if args.color == 'always': + colored_stdout = True + colored_stderr = True + elif args.color == 'auto': + colored_stdout = sys.stdout.isatty() + colored_stderr = sys.stderr.isatty() + + version_invocation = [args.clang_format_executable, str("--version")] + try: + subprocess.check_call(version_invocation, stdout=DEVNULL) + except subprocess.CalledProcessError as e: + print_trouble(parser.prog, str(e), use_colors=colored_stderr) + return ExitStatus.TROUBLE + except OSError as e: + print_trouble( + parser.prog, + "Command '{}' failed to start: {}".format( + subprocess.list2cmdline(version_invocation), e + ), + use_colors=colored_stderr, + ) + return ExitStatus.TROUBLE + + retcode = ExitStatus.SUCCESS + files = list_files( + args.files, + recursive=args.recursive, + exclude=args.exclude, + extensions=args.extensions.split(',')) + + if not files: + return + + njobs = args.j + if njobs == 0: + njobs = multiprocessing.cpu_count() + 1 + njobs = min(len(files), njobs) + + if njobs == 1: + # execute directly instead of in a pool, + # less overhead, simpler stacktraces + it = (run_clang_format_diff_wrapper(args, file) for file in files) + pool = None + else: + pool = multiprocessing.Pool(njobs) + it = pool.imap_unordered( + partial(run_clang_format_diff_wrapper, args), files) + while True: + try: + outs, errs = next(it) + except StopIteration: + break + except DiffError as e: + print_trouble(parser.prog, str(e), use_colors=colored_stderr) + retcode = ExitStatus.TROUBLE + sys.stderr.writelines(e.errs) + except UnexpectedError as e: + print_trouble(parser.prog, str(e), use_colors=colored_stderr) + sys.stderr.write(e.formatted_traceback) + retcode = ExitStatus.TROUBLE + # stop at the first unexpected error, + # something could be very wrong, + # don't process all files unnecessarily + if pool: + pool.terminate() + break + else: + sys.stderr.writelines(errs) + if outs == []: + continue + if not args.quiet: + print_diff(outs, use_color=colored_stdout) + if retcode == ExitStatus.SUCCESS: + retcode = ExitStatus.DIFF + return retcode + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/.circleci/unittest/linux/scripts/run_style_checks.sh b/.circleci/unittest/linux/scripts/run_style_checks.sh index 468fa415b5..d47bae71a7 100755 --- a/.circleci/unittest/linux/scripts/run_style_checks.sh +++ b/.circleci/unittest/linux/scripts/run_style_checks.sh @@ -1,28 +1,34 @@ #!/usr/bin/env bash -set -u +set -eux root_dir="$(git rev-parse --show-toplevel)" conda_dir="${root_dir}/conda" env_dir="${root_dir}/env" +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" eval "$("${conda_dir}/bin/conda" shell.bash hook)" conda activate "${env_dir}" # 1. Install tools conda install flake8 +printf "Installed flake8: " +flake8 --version + clangformat_path="${root_dir}/clang-format" curl https://oss-clang-format.s3.us-east-2.amazonaws.com/linux64/clang-format-linux64 -o "${clangformat_path}" chmod +x "${clangformat_path}" +printf "Installed clang-fortmat" +"${clangformat_path}" --version # 2. Run style checks # We want to run all the style checks even if one of them fail. +set +e + exit_status=0 -printf "\x1b[34mRunning flake8: " -flake8 --version -printf "\x1b[0m\n" +printf "\x1b[34mRunning flake8:\x1b[0m\n" flake8 torchaudio test build_tools/setup_helpers status=$? exit_status="$((exit_status+status))" @@ -30,14 +36,14 @@ if [ "${status}" -ne 0 ]; then printf "\x1b[31mflake8 failed. Check the format of Python files.\x1b[0m\n" fi -printf "\x1b[34mRunning clang-format: " -./clang-format --version -printf "\x1b[0m\n" -git-clang-format --binary ./clang-format origin/master -git diff --exit-code +printf "\x1b[34mRunning clang-format:\x1b[0m\n" +"${this_dir}"/run-clang-format.py \ + -r torchaudio/csrc \ + --clang-format-executable "${clangformat_path}" \ + && git diff --exit-code status=$? exit_status="$((exit_status+status))" if [ "${status}" -ne 0 ]; then - printf "\x1b[31mC++ files are not formatted. Please use git-clang-format to format CPP files.\x1b[0m\n" + printf "\x1b[31mC++ files are not formatted. Please use clang-format to format CPP files.\x1b[0m\n" fi exit $exit_status diff --git a/test/torchaudio_unittest/batch_consistency_test.py b/test/torchaudio_unittest/batch_consistency_test.py index d4edf65e98..78e3cd62f9 100644 --- a/test/torchaudio_unittest/batch_consistency_test.py +++ b/test/torchaudio_unittest/batch_consistency_test.py @@ -283,3 +283,14 @@ def test_batch_Vol(self): # Batch then transform computed = torchaudio.transforms.Vol(gain=1.1)(waveform.repeat(3, 1, 1)) self.assertEqual(computed, expected) + + def test_batch_spectral_centroid(self): + sample_rate = 44100 + waveform = common_utils.get_whitenoise(sample_rate=sample_rate) + + # Single then transform then batch + expected = torchaudio.transforms.SpectralCentroid(sample_rate)(waveform).repeat(3, 1, 1) + + # Batch then transform + computed = torchaudio.transforms.SpectralCentroid(sample_rate)(waveform.repeat(3, 1, 1)) + self.assertEqual(computed, expected) diff --git a/test/torchaudio_unittest/librosa_compatibility_test.py b/test/torchaudio_unittest/librosa_compatibility_test.py index 92d5baff06..9e070b141a 100644 --- a/test/torchaudio_unittest/librosa_compatibility_test.py +++ b/test/torchaudio_unittest/librosa_compatibility_test.py @@ -231,6 +231,19 @@ def assert_compatibilities(self, n_fft, hop_length, power, n_mels, n_mfcc, sampl self.assertEqual( torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3, rtol=1e-5) + self.assert_compatibilities_spectral_centroid(sample_rate, n_fft, hop_length, sound, sound_librosa) + + def assert_compatibilities_spectral_centroid(self, sample_rate, n_fft, hop_length, sound, sound_librosa): + spect_centroid = torchaudio.transforms.SpectralCentroid( + sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length) + out_torch = spect_centroid(sound).squeeze().cpu() + + out_librosa = librosa.feature.spectral_centroid( + y=sound_librosa, sr=sample_rate, n_fft=n_fft, hop_length=hop_length) + out_librosa = torch.from_numpy(out_librosa)[0] + + self.assertEqual(out_torch.type(out_librosa.dtype), out_librosa, atol=1e-5, rtol=1e-5) + def test_basics1(self): kwargs = { 'n_fft': 400, diff --git a/test/torchaudio_unittest/soundfile_backend/common.py b/test/torchaudio_unittest/soundfile_backend/common.py index 4da0873fa1..8f991fb0f8 100644 --- a/test/torchaudio_unittest/soundfile_backend/common.py +++ b/test/torchaudio_unittest/soundfile_backend/common.py @@ -26,7 +26,7 @@ def skipIfFormatNotSupported(fmt): import soundfile fmts = soundfile.available_formats() - return skipIf(fmt not in fmts, f'"{fmt}" is not supported by sondfile') + return skipIf(fmt not in fmts, f'"{fmt}" is not supported by soundfile') return skipIf(True, '"soundfile" not available.') diff --git a/test/torchaudio_unittest/soundfile_backend/info_test.py b/test/torchaudio_unittest/soundfile_backend/info_test.py index 71acb20689..d4aa2b2b2e 100644 --- a/test/torchaudio_unittest/soundfile_backend/info_test.py +++ b/test/torchaudio_unittest/soundfile_backend/info_test.py @@ -1,3 +1,7 @@ +from unittest.mock import patch +import warnings +import tarfile + import torch from torchaudio.backend import _soundfile_backend as soundfile_backend from torchaudio._internal import module_utils as _mod_utils @@ -9,6 +13,8 @@ get_wav_data, save_wav, ) +# TODO refactor and move these to common location +from torchaudio_unittest.sox_io_backend.info_test import get_encoding, get_bits_per_sample from .common import skipIfFormatNotSupported, parameterize if _mod_utils.is_module_available("soundfile"): @@ -32,22 +38,8 @@ def test_wav(self, dtype, sample_rate, num_channels): assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels - - @parameterize( - ["float32", "int32", "int16", "uint8"], [8000, 16000], [4, 8, 16, 32], - ) - def test_wav_multiple_channels(self, dtype, sample_rate, num_channels): - """`soundfile_backend.info` can check wav file with channels more than 2 correctly""" - duration = 1 - path = self.get_temp_path("data.wav") - data = get_wav_data( - dtype, num_channels, normalize=False, num_frames=duration * sample_rate - ) - save_wav(path, data, sample_rate) - info = soundfile_backend.info(path) - assert info.sample_rate == sample_rate - assert info.num_frames == sample_rate * duration - assert info.num_channels == num_channels + assert info.bits_per_sample == get_bits_per_sample("wav", dtype) + assert info.encoding == get_encoding("wav", dtype) @parameterize([8000, 16000], [1, 2]) @skipIfFormatNotSupported("FLAC") @@ -63,6 +55,8 @@ def test_flac(self, sample_rate, num_channels): assert info.sample_rate == sample_rate assert info.num_frames == num_frames assert info.num_channels == num_channels + assert info.bits_per_sample == 16 + assert info.encoding == "FLAC" @parameterize([8000, 16000], [1, 2]) @skipIfFormatNotSupported("OGG") @@ -78,18 +72,109 @@ def test_ogg(self, sample_rate, num_channels): assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + assert info.bits_per_sample == 0 + assert info.encoding == "VORBIS" - @parameterize([8000, 16000], [1, 2]) + @parameterize([8000, 16000], [1, 2], [('PCM_24', 24), ('PCM_32', 32)]) @skipIfFormatNotSupported("NIST") - def test_sphere(self, sample_rate, num_channels): + def test_sphere(self, sample_rate, num_channels, subtype_and_bit_depth): """`soundfile_backend.info` can check sph file correctly""" duration = 1 num_frames = sample_rate * duration data = torch.randn(num_frames, num_channels).numpy() path = self.get_temp_path("data.nist") - soundfile.write(path, data, sample_rate) + subtype, bits_per_sample = subtype_and_bit_depth + soundfile.write(path, data, sample_rate, subtype=subtype) info = soundfile_backend.info(path) assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + assert info.bits_per_sample == bits_per_sample + assert info.encoding == "PCM_S" + + def test_unknown_subtype_warning(self): + """soundfile_backend.info issues a warning when the subtype is unknown + + This will happen if a new subtype is supported in SoundFile: the _SUBTYPE_TO_BITS_PER_SAMPLE + dict should be updated. + """ + def _mock_info_func(_): + class MockSoundFileInfo: + samplerate = 8000 + frames = 356 + channels = 2 + subtype = 'UNSEEN_SUBTYPE' + format = 'UNKNOWN' + return MockSoundFileInfo() + + with patch("soundfile.info", _mock_info_func): + with warnings.catch_warnings(record=True) as w: + info = soundfile_backend.info("foo") + assert len(w) == 1 + assert "UNSEEN_SUBTYPE subtype is unknown to TorchAudio" in str(w[-1].message) + assert info.bits_per_sample == 0 + + +@skipIfNoModule("soundfile") +class TestFileObject(TempDirMixin, PytorchTestCase): + def _test_fileobj(self, ext, subtype, bits_per_sample): + """Query audio via file-like object works""" + duration = 2 + sample_rate = 16000 + num_channels = 2 + num_frames = sample_rate * duration + path = self.get_temp_path(f'test.{ext}') + + data = torch.randn(num_frames, num_channels).numpy() + soundfile.write(path, data, sample_rate, subtype=subtype) + + with open(path, 'rb') as fileobj: + info = soundfile_backend.info(fileobj) + assert info.sample_rate == sample_rate + assert info.num_frames == num_frames + assert info.num_channels == num_channels + assert info.bits_per_sample == bits_per_sample + assert info.encoding == "FLAC" if ext == 'flac' else "PCM_S" + + def test_fileobj_wav(self): + """Loading audio via file-like object works""" + self._test_fileobj('wav', 'PCM_16', 16) + + @skipIfFormatNotSupported("FLAC") + def test_fileobj_flac(self): + """Loading audio via file-like object works""" + self._test_fileobj('flac', 'PCM_16', 16) + + def _test_tarobj(self, ext, subtype, bits_per_sample): + """Query compressed audio via file-like object works""" + duration = 2 + sample_rate = 16000 + num_channels = 2 + num_frames = sample_rate * duration + audio_file = f'test.{ext}' + audio_path = self.get_temp_path(audio_file) + archive_path = self.get_temp_path('archive.tar.gz') + + data = torch.randn(num_frames, num_channels).numpy() + soundfile.write(audio_path, data, sample_rate, subtype=subtype) + + with tarfile.TarFile(archive_path, 'w') as tarobj: + tarobj.add(audio_path, arcname=audio_file) + with tarfile.TarFile(archive_path, 'r') as tarobj: + fileobj = tarobj.extractfile(audio_file) + info = soundfile_backend.info(fileobj) + assert info.sample_rate == sample_rate + assert info.num_frames == num_frames + assert info.num_channels == num_channels + assert info.bits_per_sample == bits_per_sample + assert info.encoding == "FLAC" if ext == 'flac' else "PCM_S" + + def test_tarobj_wav(self): + """Query compressed audio via file-like object works""" + self._test_tarobj('wav', 'PCM_16', 16) + + @skipIfFormatNotSupported("FLAC") + def test_tarobj_flac(self): + """Query compressed audio via file-like object works""" + self._test_tarobj('flac', 'PCM_16', 16) diff --git a/test/torchaudio_unittest/sox_effect/smoke_test.py b/test/torchaudio_unittest/sox_effect/smoke_test.py index 996c62063f..2a8d9e7ff9 100644 --- a/test/torchaudio_unittest/sox_effect/smoke_test.py +++ b/test/torchaudio_unittest/sox_effect/smoke_test.py @@ -43,7 +43,7 @@ def test_apply_effects_tensor(self, args): load_params("sox_effect_test_args.json"), name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', ) - def test_apply_effects(self, args): + def test_apply_effects_file(self, args): """`apply_effects_file` should return identical data as sox command""" dtype = 'int32' channels_first = True @@ -57,3 +57,23 @@ def test_apply_effects(self, args): _found, _sr = sox_effects.apply_effects_file( input_path, effects, normalize=False, channels_first=channels_first) + + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects_fileobj(self, args): + """`apply_effects_file` should return identical data as sox command""" + dtype = 'int32' + channels_first = True + effects = args['effects'] + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + + input_path = self.get_temp_path('input.wav') + data = get_wav_data(dtype, num_channels, channels_first=channels_first) + save_wav(input_path, data, input_sr, channels_first=channels_first) + + with open(input_path, 'rb') as fileobj: + _found, _sr = sox_effects.apply_effects_file( + fileobj, effects, normalize=False, channels_first=channels_first) diff --git a/test/torchaudio_unittest/sox_effect/sox_effect_test.py b/test/torchaudio_unittest/sox_effect/sox_effect_test.py index c25e376359..ab334613e3 100644 --- a/test/torchaudio_unittest/sox_effect/sox_effect_test.py +++ b/test/torchaudio_unittest/sox_effect/sox_effect_test.py @@ -1,13 +1,19 @@ +import io import itertools from pathlib import Path +import tarfile -from torchaudio import sox_effects from parameterized import parameterized +from torchaudio import sox_effects +from torchaudio._internal import module_utils as _mod_utils from torchaudio_unittest.common_utils import ( TempDirMixin, + HttpServerMixin, PytorchTestCase, skipIfNoExtension, + skipIfNoModule, + skipIfNoExec, get_asset_path, get_sinusoid, get_wav_data, @@ -21,6 +27,10 @@ ) +if _mod_utils.is_module_available("requests"): + import requests + + @skipIfNoExtension class TestSoxEffects(PytorchTestCase): def test_init(self): @@ -262,3 +272,152 @@ def test_mp3(self): path = get_asset_path("mp3_without_ext") _, sr = sox_effects.apply_effects_file(path, effects, format="mp3") assert sr == 16000 + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestFileObject(TempDirMixin, PytorchTestCase): + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_fileobj(self, ext, compression): + """Applying effects via file object works""" + sample_rate = 16000 + channels_first = True + effects = [['band', '300', '10']] + format_ = ext if ext in ['mp3'] else None + input_path = self.get_temp_path(f'input.{ext}') + reference_path = self.get_temp_path('reference.wav') + + sox_utils.gen_audio_file( + input_path, sample_rate, num_channels=2, compression=compression) + sox_utils.run_sox_effect( + input_path, reference_path, effects, output_bitdepth=32) + expected, expected_sr = load_wav(reference_path) + + with open(input_path, 'rb') as fileobj: + found, sr = sox_effects.apply_effects_file( + fileobj, effects, channels_first=channels_first, format=format_) + save_wav(self.get_temp_path('result.wav'), found, sr, channels_first=channels_first) + assert sr == expected_sr + self.assertEqual(found, expected) + + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_bytesio(self, ext, compression): + """Applying effects via BytesIO object works""" + sample_rate = 16000 + channels_first = True + effects = [['band', '300', '10']] + format_ = ext if ext in ['mp3'] else None + input_path = self.get_temp_path(f'input.{ext}') + reference_path = self.get_temp_path('reference.wav') + + sox_utils.gen_audio_file( + input_path, sample_rate, num_channels=2, compression=compression) + sox_utils.run_sox_effect( + input_path, reference_path, effects, output_bitdepth=32) + expected, expected_sr = load_wav(reference_path) + + with open(input_path, 'rb') as file_: + fileobj = io.BytesIO(file_.read()) + found, sr = sox_effects.apply_effects_file( + fileobj, effects, channels_first=channels_first, format=format_) + save_wav(self.get_temp_path('result.wav'), found, sr, channels_first=channels_first) + assert sr == expected_sr + self.assertEqual(found, expected) + + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_tarfile(self, ext, compression): + """Applying effects to compressed audio via file-like file works""" + sample_rate = 16000 + channels_first = True + effects = [['band', '300', '10']] + format_ = ext if ext in ['mp3'] else None + audio_file = f'input.{ext}' + + input_path = self.get_temp_path(audio_file) + reference_path = self.get_temp_path('reference.wav') + archive_path = self.get_temp_path('archive.tar.gz') + + sox_utils.gen_audio_file( + input_path, sample_rate, num_channels=2, compression=compression) + sox_utils.run_sox_effect( + input_path, reference_path, effects, output_bitdepth=32) + expected, expected_sr = load_wav(reference_path) + + with tarfile.TarFile(archive_path, 'w') as tarobj: + tarobj.add(input_path, arcname=audio_file) + with tarfile.TarFile(archive_path, 'r') as tarobj: + fileobj = tarobj.extractfile(audio_file) + found, sr = sox_effects.apply_effects_file( + fileobj, effects, channels_first=channels_first, format=format_) + save_wav(self.get_temp_path('result.wav'), found, sr, channels_first=channels_first) + assert sr == expected_sr + self.assertEqual(found, expected) + + +@skipIfNoExtension +@skipIfNoExec('sox') +@skipIfNoModule("requests") +class TestFileObjectHttp(HttpServerMixin, PytorchTestCase): + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_requests(self, ext, compression): + sample_rate = 16000 + channels_first = True + effects = [['band', '300', '10']] + format_ = ext if ext in ['mp3'] else None + audio_file = f'input.{ext}' + input_path = self.get_temp_path(audio_file) + reference_path = self.get_temp_path('reference.wav') + + sox_utils.gen_audio_file( + input_path, sample_rate, num_channels=2, compression=compression) + sox_utils.run_sox_effect( + input_path, reference_path, effects, output_bitdepth=32) + expected, expected_sr = load_wav(reference_path) + + url = self.get_url(audio_file) + with requests.get(url, stream=True) as resp: + found, sr = sox_effects.apply_effects_file( + resp.raw, effects, channels_first=channels_first, format=format_) + save_wav(self.get_temp_path('result.wav'), found, sr, channels_first=channels_first) + assert sr == expected_sr + self.assertEqual(found, expected) diff --git a/test/torchaudio_unittest/sox_io_backend/info_test.py b/test/torchaudio_unittest/sox_io_backend/info_test.py index 49fc797354..9a26b0d2ea 100644 --- a/test/torchaudio_unittest/sox_io_backend/info_test.py +++ b/test/torchaudio_unittest/sox_io_backend/info_test.py @@ -1,13 +1,19 @@ +import io +import os import itertools -from parameterized import parameterized +import tarfile +from parameterized import parameterized from torchaudio.backend import sox_io_backend +from torchaudio._internal import module_utils as _mod_utils from torchaudio_unittest.common_utils import ( TempDirMixin, + HttpServerMixin, PytorchTestCase, skipIfNoExec, skipIfNoExtension, + skipIfNoModule, get_asset_path, get_wav_data, save_wav, @@ -18,6 +24,34 @@ ) +if _mod_utils.is_module_available("requests"): + import requests + + +def get_encoding(ext, dtype): + exts = { + 'mp3', + 'flac', + 'vorbis', + } + encodings = { + 'float32': 'PCM_F', + 'int32': 'PCM_S', + 'int16': 'PCM_S', + 'uint8': 'PCM_U', + } + return ext.upper() if ext in exts else encodings[dtype] + + +def get_bits_per_sample(ext, dtype): + bits_per_samples = { + 'flac': 24, + 'mp3': 0, + 'vorbis': 0, + } + return bits_per_samples.get(ext, sox_utils.get_bit_depth(dtype)) + + @skipIfNoExec('sox') @skipIfNoExtension class TestInfo(TempDirMixin, PytorchTestCase): @@ -36,6 +70,8 @@ def test_wav(self, dtype, sample_rate, num_channels): assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + assert info.bits_per_sample == sox_utils.get_bit_depth(dtype) + assert info.encoding == get_encoding('wav', dtype) @parameterized.expand(list(itertools.product( ['float32', 'int32', 'int16', 'uint8'], @@ -52,6 +88,8 @@ def test_wav_multiple_channels(self, dtype, sample_rate, num_channels): assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + assert info.bits_per_sample == sox_utils.get_bit_depth(dtype) + assert info.encoding == get_encoding('wav', dtype) @parameterized.expand(list(itertools.product( [8000, 16000], @@ -71,6 +109,8 @@ def test_mp3(self, sample_rate, num_channels, bit_rate): # mp3 does not preserve the number of samples # assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + assert info.bits_per_sample == 0 # bit_per_sample is irrelevant for compressed formats + assert info.encoding == "MP3" @parameterized.expand(list(itertools.product( [8000, 16000], @@ -89,6 +129,8 @@ def test_flac(self, sample_rate, num_channels, compression_level): assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + assert info.bits_per_sample == 24 # FLAC standard + assert info.encoding == "FLAC" @parameterized.expand(list(itertools.product( [8000, 16000], @@ -107,23 +149,28 @@ def test_vorbis(self, sample_rate, num_channels, quality_level): assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + assert info.bits_per_sample == 0 # bit_per_sample is irrelevant for compressed formats + assert info.encoding == "VORBIS" @parameterized.expand(list(itertools.product( [8000, 16000], [1, 2], + [16, 32], )), name_func=name_func) - def test_sphere(self, sample_rate, num_channels): + def test_sphere(self, sample_rate, num_channels, bits_per_sample): """`sox_io_backend.info` can check sph file correctly""" duration = 1 path = self.get_temp_path('data.sph') - sox_utils.gen_audio_file(path, sample_rate, num_channels, duration=duration) + sox_utils.gen_audio_file(path, sample_rate, num_channels, duration=duration, bit_depth=bits_per_sample) info = sox_io_backend.info(path) assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + assert info.bits_per_sample == bits_per_sample + assert info.encoding == "PCM_S" @parameterized.expand(list(itertools.product( - ['float32', 'int32', 'int16', 'uint8'], + ['int32', 'int16', 'uint8'], [8000, 16000], [1, 2], )), name_func=name_func) @@ -131,13 +178,16 @@ def test_amb(self, dtype, sample_rate, num_channels): """`sox_io_backend.info` can check amb file correctly""" duration = 1 path = self.get_temp_path('data.amb') + bits_per_sample = sox_utils.get_bit_depth(dtype) sox_utils.gen_audio_file( path, sample_rate, num_channels, - bit_depth=sox_utils.get_bit_depth(dtype), duration=duration) + bit_depth=bits_per_sample, duration=duration) info = sox_io_backend.info(path) assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + assert info.bits_per_sample == bits_per_sample + assert info.encoding == get_encoding("amb", dtype) def test_amr_nb(self): """`sox_io_backend.info` can check amr-nb file correctly""" @@ -146,11 +196,48 @@ def test_amr_nb(self): sample_rate = 8000 path = self.get_temp_path('data.amr-nb') sox_utils.gen_audio_file( - path, sample_rate=sample_rate, num_channels=num_channels, bit_depth=16, duration=duration) + path, sample_rate=sample_rate, num_channels=num_channels, bit_depth=16, + duration=duration) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + assert info.bits_per_sample == 0 + assert info.encoding == "AMR_NB" + + def test_ulaw(self): + """`sox_io_backend.info` can check ulaw file correctly""" + duration = 1 + num_channels = 1 + sample_rate = 8000 + path = self.get_temp_path('data.wav') + sox_utils.gen_audio_file( + path, sample_rate=sample_rate, num_channels=num_channels, + bit_depth=8, encoding='u-law', + duration=duration) info = sox_io_backend.info(path) assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + assert info.bits_per_sample == 8 + assert info.encoding == "ULAW" + + def test_alaw(self): + """`sox_io_backend.info` can check ulaw file correctly""" + duration = 1 + num_channels = 1 + sample_rate = 8000 + path = self.get_temp_path('data.wav') + sox_utils.gen_audio_file( + path, sample_rate=sample_rate, num_channels=num_channels, + bit_depth=8, encoding='a-law', + duration=duration) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + assert info.bits_per_sample == 8 + assert info.encoding == "ALAW" @skipIfNoExtension @@ -167,6 +254,8 @@ def test_opus(self, bitrate, num_channels, compression_level): assert info.sample_rate == 48000 assert info.num_frames == 32768 assert info.num_channels == num_channels + assert info.bits_per_sample == 0 # bit_per_sample is irrelevant for compressed formats + assert info.encoding == "OPUS" @skipIfNoExtension @@ -184,3 +273,193 @@ def test_mp3(self): path = get_asset_path("mp3_without_ext") sinfo = sox_io_backend.info(path, format="mp3") assert sinfo.sample_rate == 16000 + assert sinfo.num_frames == 81216 + assert sinfo.num_channels == 1 + assert sinfo.bits_per_sample == 0 # bit_per_sample is irrelevant for compressed formats + assert sinfo.encoding == "MP3" + + +class FileObjTestBase(TempDirMixin): + def _gen_file(self, ext, dtype, sample_rate, num_channels, num_frames): + path = self.get_temp_path(f'test.{ext}') + bit_depth = sox_utils.get_bit_depth(dtype) + duration = num_frames / sample_rate + + sox_utils.gen_audio_file( + path, sample_rate, num_channels=num_channels, + encoding=sox_utils.get_encoding(dtype), + bit_depth=bit_depth, + duration=duration) + return path + + +@skipIfNoExtension +@skipIfNoExec('sox') +class TestFileObject(FileObjTestBase, PytorchTestCase): + def _query_fileobj(self, ext, dtype, sample_rate, num_channels, num_frames): + path = self._gen_file(ext, dtype, sample_rate, num_channels, num_frames) + format_ = ext if ext in ['mp3'] else None + with open(path, 'rb') as fileobj: + return sox_io_backend.info(fileobj, format_) + + def _query_bytesio(self, ext, dtype, sample_rate, num_channels, num_frames): + path = self._gen_file(ext, dtype, sample_rate, num_channels, num_frames) + format_ = ext if ext in ['mp3'] else None + with open(path, 'rb') as file_: + fileobj = io.BytesIO(file_.read()) + return sox_io_backend.info(fileobj, format_) + + def _query_tarfile(self, ext, dtype, sample_rate, num_channels, num_frames): + audio_path = self._gen_file(ext, dtype, sample_rate, num_channels, num_frames) + audio_file = os.path.basename(audio_path) + archive_path = self.get_temp_path('archive.tar.gz') + with tarfile.TarFile(archive_path, 'w') as tarobj: + tarobj.add(audio_path, arcname=audio_file) + format_ = ext if ext in ['mp3'] else None + with tarfile.TarFile(archive_path, 'r') as tarobj: + fileobj = tarobj.extractfile(audio_file) + return sox_io_backend.info(fileobj, format_) + + @parameterized.expand([ + ('wav', "float32"), + ('wav', "int32"), + ('wav', "int16"), + ('wav', "uint8"), + ('mp3', "float32"), + ('flac', "float32"), + ('vorbis', "float32"), + ('amb', "int16"), + ]) + def test_fileobj(self, ext, dtype): + """Querying audio via file object works""" + sample_rate = 16000 + num_frames = 3 * sample_rate + num_channels = 2 + sinfo = self._query_fileobj(ext, dtype, sample_rate, num_channels, num_frames) + + bits_per_sample = get_bits_per_sample(ext, dtype) + num_frames = 0 if ext in ['mp3', 'vorbis'] else num_frames + + assert sinfo.sample_rate == sample_rate + assert sinfo.num_channels == num_channels + assert sinfo.num_frames == num_frames + assert sinfo.bits_per_sample == bits_per_sample + assert sinfo.encoding == get_encoding(ext, dtype) + + @parameterized.expand([ + ('wav', "float32"), + ('wav', "int32"), + ('wav', "int16"), + ('wav', "uint8"), + ('mp3', "float32"), + ('flac', "float32"), + ('vorbis', "float32"), + ('amb', "int16"), + ]) + def test_bytesio(self, ext, dtype): + """Querying audio via ByteIO object works for small data""" + sample_rate = 16000 + num_frames = 3 * sample_rate + num_channels = 2 + sinfo = self._query_bytesio(ext, dtype, sample_rate, num_channels, num_frames) + + bits_per_sample = get_bits_per_sample(ext, dtype) + num_frames = 0 if ext in ['mp3', 'vorbis'] else num_frames + + assert sinfo.sample_rate == sample_rate + assert sinfo.num_channels == num_channels + assert sinfo.num_frames == num_frames + assert sinfo.bits_per_sample == bits_per_sample + assert sinfo.encoding == get_encoding(ext, dtype) + + @parameterized.expand([ + ('wav', "float32"), + ('wav', "int32"), + ('wav', "int16"), + ('wav', "uint8"), + ('mp3', "float32"), + ('flac', "float32"), + ('vorbis', "float32"), + ('amb', "int16"), + ]) + def test_bytesio_tiny(self, ext, dtype): + """Querying audio via ByteIO object works for small data""" + sample_rate = 8000 + num_frames = 4 + num_channels = 2 + sinfo = self._query_bytesio(ext, dtype, sample_rate, num_channels, num_frames) + + bits_per_sample = get_bits_per_sample(ext, dtype) + num_frames = 0 if ext in ['mp3', 'vorbis'] else num_frames + + assert sinfo.sample_rate == sample_rate + assert sinfo.num_channels == num_channels + assert sinfo.num_frames == num_frames + assert sinfo.bits_per_sample == bits_per_sample + assert sinfo.encoding == get_encoding(ext, dtype) + + @parameterized.expand([ + ('wav', "float32"), + ('wav', "int32"), + ('wav', "int16"), + ('wav', "uint8"), + ('mp3', "float32"), + ('flac', "float32"), + ('vorbis', "float32"), + ('amb', "int16"), + ]) + def test_tarfile(self, ext, dtype): + """Querying compressed audio via file-like object works""" + sample_rate = 16000 + num_frames = 3.0 * sample_rate + num_channels = 2 + sinfo = self._query_tarfile(ext, dtype, sample_rate, num_channels, num_frames) + + bits_per_sample = get_bits_per_sample(ext, dtype) + num_frames = 0 if ext in ['mp3', 'vorbis'] else num_frames + + assert sinfo.sample_rate == sample_rate + assert sinfo.num_channels == num_channels + assert sinfo.num_frames == num_frames + assert sinfo.bits_per_sample == bits_per_sample + assert sinfo.encoding == get_encoding(ext, dtype) + + +@skipIfNoExtension +@skipIfNoExec('sox') +@skipIfNoModule("requests") +class TestFileObjectHttp(HttpServerMixin, FileObjTestBase, PytorchTestCase): + def _query_http(self, ext, dtype, sample_rate, num_channels, num_frames): + audio_path = self._gen_file(ext, dtype, sample_rate, num_channels, num_frames) + audio_file = os.path.basename(audio_path) + + url = self.get_url(audio_file) + format_ = ext if ext in ['mp3'] else None + with requests.get(url, stream=True) as resp: + return sox_io_backend.info(resp.raw, format=format_) + + @parameterized.expand([ + ('wav', "float32"), + ('wav', "int32"), + ('wav', "int16"), + ('wav', "uint8"), + ('mp3', "float32"), + ('flac', "float32"), + ('vorbis', "float32"), + ('amb', "int16"), + ]) + def test_requests(self, ext, dtype): + """Querying compressed audio via requests works""" + sample_rate = 16000 + num_frames = 3.0 * sample_rate + num_channels = 2 + sinfo = self._query_http(ext, dtype, sample_rate, num_channels, num_frames) + + bits_per_sample = get_bits_per_sample(ext, dtype) + num_frames = 0 if ext in ['mp3', 'vorbis'] else num_frames + + assert sinfo.sample_rate == sample_rate + assert sinfo.num_channels == num_channels + assert sinfo.num_frames == num_frames + assert sinfo.bits_per_sample == bits_per_sample + assert sinfo.encoding == get_encoding(ext, dtype) diff --git a/test/torchaudio_unittest/sox_io_backend/load_test.py b/test/torchaudio_unittest/sox_io_backend/load_test.py index 3a4b0ba8fe..f2a514ea61 100644 --- a/test/torchaudio_unittest/sox_io_backend/load_test.py +++ b/test/torchaudio_unittest/sox_io_backend/load_test.py @@ -380,6 +380,19 @@ def test_mp3(self): assert sr == 16000 +class CloggedFileObj: + def __init__(self, fileobj): + self.fileobj = fileobj + self.buffer = b'' + + def read(self, n): + if not self.buffer: + self.buffer += self.fileobj.read(n) + ret = self.buffer[:2] + self.buffer = self.buffer[2:] + return ret + + @skipIfNoExtension @skipIfNoExec('sox') class TestFileObject(TempDirMixin, PytorchTestCase): @@ -444,6 +457,68 @@ def test_bytesio(self, ext, compression): assert sr == sample_rate self.assertEqual(expected, found) + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_bytesio_clogged(self, ext, compression): + """Loading audio via clogged file object returns the same result as via file path. + + This test case validates the case where fileobject returns shorter bytes than requeted. + """ + sample_rate = 16000 + format_ = ext if ext in ['mp3'] else None + path = self.get_temp_path(f'test.{ext}') + + sox_utils.gen_audio_file( + path, sample_rate, num_channels=2, + compression=compression) + expected, _ = sox_io_backend.load(path) + + with open(path, 'rb') as file_: + fileobj = CloggedFileObj(io.BytesIO(file_.read())) + found, sr = sox_io_backend.load(fileobj, format=format_) + + assert sr == sample_rate + self.assertEqual(expected, found) + + @parameterized.expand([ + ('wav', None), + ('mp3', 128), + ('mp3', 320), + ('flac', 0), + ('flac', 5), + ('flac', 8), + ('vorbis', -1), + ('vorbis', 10), + ('amb', None), + ]) + def test_bytesio_tiny(self, ext, compression): + """Loading very small audio via file object returns the same result as via file path. + """ + sample_rate = 16000 + format_ = ext if ext in ['mp3'] else None + path = self.get_temp_path(f'test.{ext}') + + sox_utils.gen_audio_file( + path, sample_rate, num_channels=2, + compression=compression, duration=1 / 1600) + expected, _ = sox_io_backend.load(path) + + with open(path, 'rb') as file_: + fileobj = io.BytesIO(file_.read()) + found, sr = sox_io_backend.load(fileobj, format=format_) + + assert sr == sample_rate + self.assertEqual(expected, found) + @parameterized.expand([ ('wav', None), ('mp3', 128), diff --git a/test/torchaudio_unittest/sox_io_backend/save_test.py b/test/torchaudio_unittest/sox_io_backend/save_test.py index 3f2faff6d4..fc378f9c70 100644 --- a/test/torchaudio_unittest/sox_io_backend/save_test.py +++ b/test/torchaudio_unittest/sox_io_backend/save_test.py @@ -1,6 +1,7 @@ import io import itertools +import torch from torchaudio.backend import sox_io_backend from parameterized import parameterized @@ -24,7 +25,7 @@ def assert_wav(self, dtype, sample_rate, num_channels, num_frames): """`sox_io_backend.save` can save wav format.""" path = self.get_temp_path('data.wav') expected = get_wav_data(dtype, num_channels, num_frames=num_frames) - sox_io_backend.save(path, expected, sample_rate) + sox_io_backend.save(path, expected, sample_rate, dtype=None) found, sr = load_wav(path) assert sample_rate == sr self.assertEqual(found, expected) @@ -68,7 +69,7 @@ def assert_mp3(self, sample_rate, num_channels, bit_rate, duration): save_wav(src_path, data, sample_rate) # 2.1. Convert the original wav to mp3 with torchaudio sox_io_backend.save( - mp3_path, load_wav(src_path)[0], sample_rate, compression=bit_rate) + mp3_path, load_wav(src_path)[0], sample_rate, compression=bit_rate, dtype=None) # 2.2. Convert the mp3 to wav with Sox sox_utils.convert_audio_file(mp3_path, wav_path) # 2.3. Load @@ -99,7 +100,7 @@ def assert_flac(self, sample_rate, num_channels, compression_level, duration): save_wav(src_path, data, sample_rate) # 2.1. Convert the original wav to flac with torchaudio sox_io_backend.save( - flc_path, load_wav(src_path)[0], sample_rate, compression=compression_level) + flc_path, load_wav(src_path)[0], sample_rate, compression=compression_level, dtype=None) # 2.2. Convert the flac to wav with Sox # converting to 32 bit because flac file has 24 bit depth which scipy cannot handle. sox_utils.convert_audio_file(flc_path, wav_path, bit_depth=32) @@ -132,7 +133,7 @@ def _assert_vorbis(self, sample_rate, num_channels, quality_level, duration): save_wav(src_path, data, sample_rate) # 2.1. Convert the original wav to vorbis with torchaudio sox_io_backend.save( - vbs_path, load_wav(src_path)[0], sample_rate, compression=quality_level) + vbs_path, load_wav(src_path)[0], sample_rate, compression=quality_level, dtype=None) # 2.2. Convert the vorbis to wav with Sox sox_utils.convert_audio_file(vbs_path, wav_path) # 2.3. Load @@ -184,7 +185,7 @@ def assert_sphere(self, sample_rate, num_channels, duration): data = get_wav_data('float32', num_channels, normalize=True, num_frames=duration * sample_rate) save_wav(src_path, data, sample_rate) # 2.1. Convert the original wav to sph with torchaudio - sox_io_backend.save(flc_path, load_wav(src_path)[0], sample_rate) + sox_io_backend.save(flc_path, load_wav(src_path)[0], sample_rate, dtype=None) # 2.2. Convert the sph to wav with Sox # converting to 32 bit because sph file has 24 bit depth which scipy cannot handle. sox_utils.convert_audio_file(flc_path, wav_path, bit_depth=32) @@ -216,7 +217,7 @@ def assert_amb(self, dtype, sample_rate, num_channels, duration): data = get_wav_data(dtype, num_channels, normalize=False, num_frames=duration * sample_rate) save_wav(src_path, data, sample_rate) # 2.1. Convert the original wav to amb with torchaudio - sox_io_backend.save(amb_path, load_wav(src_path, normalize=False)[0], sample_rate) + sox_io_backend.save(amb_path, load_wav(src_path, normalize=False)[0], sample_rate, dtype=None) # 2.2. Convert the amb to wav with Sox sox_utils.convert_audio_file(amb_path, wav_path) # 2.3. Load @@ -248,7 +249,7 @@ def assert_amr_nb(self, duration): data = get_wav_data('int16', num_channels, normalize=False, num_frames=duration * sample_rate) save_wav(src_path, data, sample_rate) # 2.1. Convert the original wav to amr_nb with torchaudio - sox_io_backend.save(amr_path, load_wav(src_path, normalize=False)[0], sample_rate) + sox_io_backend.save(amr_path, load_wav(src_path, normalize=False)[0], sample_rate, dtype=None) # 2.2. Convert the amr_nb to wav with Sox sox_utils.convert_audio_file(amr_path, wav_path) # 2.3. Load @@ -389,7 +390,7 @@ def test_channels_first(self, channels_first): path = self.get_temp_path('data.wav') data = get_wav_data('int32', 2, channels_first=channels_first) sox_io_backend.save( - path, data, 8000, channels_first=channels_first) + path, data, 8000, channels_first=channels_first, dtype=None) found = load_wav(path)[0] expected = data if channels_first else data.transpose(1, 0) self.assertEqual(found, expected) @@ -402,7 +403,7 @@ def test_noncontiguous(self, dtype): path = self.get_temp_path('data.wav') expected = get_wav_data(dtype, 4)[::2, ::2] assert not expected.is_contiguous() - sox_io_backend.save(path, expected, 8000) + sox_io_backend.save(path, expected, 8000, dtype=None) found = load_wav(path)[0] self.assertEqual(found, expected) @@ -415,10 +416,24 @@ def test_tensor_preserve(self, dtype): expected = get_wav_data(dtype, 4)[::2, ::2] data = expected.clone() - sox_io_backend.save(path, data, 8000) + sox_io_backend.save(path, data, 8000, dtype=None) self.assertEqual(data, expected) + @parameterized.expand([ + ('float32', torch.tensor([-1.0, -0.5, 0, 0.5, 1.0]).to(torch.float32)), + ('int32', torch.tensor([-2147483648, -1073741824, 0, 1073741824, 2147483647]).to(torch.int32)), + ('int16', torch.tensor([-32768, -16384, 0, 16384, 32767]).to(torch.int16)), + ('uint8', torch.tensor([0, 64, 128, 192, 255]).to(torch.uint8)), + ]) + def test_dtype_conversion(self, dtype, expected): + """`save` performs dtype conversion on float32 src tensors only.""" + path = self.get_temp_path("data.wav") + data = torch.tensor([-1.0, -0.5, 0, 0.5, 1.0]).to(torch.float32).view(-1, 1) + sox_io_backend.save(path, data, 8000, dtype=dtype) + found = load_wav(path, normalize=False)[0] + self.assertEqual(found, expected.view(-1, 1)) + @skipIfNoExtension @skipIfNoExec('sox') @@ -452,11 +467,11 @@ def test_fileobj(self, ext, compression): res_path = self.get_temp_path(f'test.{ext}') sox_io_backend.save( ref_path, data, channels_first=channels_first, - sample_rate=sample_rate, compression=compression) + sample_rate=sample_rate, compression=compression, dtype=None) with open(res_path, 'wb') as fileobj: sox_io_backend.save( fileobj, data, channels_first=channels_first, - sample_rate=sample_rate, compression=compression, format=ext) + sample_rate=sample_rate, compression=compression, format=ext, dtype=None) expected_data, _ = sox_io_backend.load(ref_path) data, sr = sox_io_backend.load(res_path) @@ -489,11 +504,11 @@ def test_bytesio(self, ext, compression): res_path = self.get_temp_path(f'test.{ext}') sox_io_backend.save( ref_path, data, channels_first=channels_first, - sample_rate=sample_rate, compression=compression) + sample_rate=sample_rate, compression=compression, dtype=None) fileobj = io.BytesIO() sox_io_backend.save( fileobj, data, channels_first=channels_first, - sample_rate=sample_rate, compression=compression, format=ext) + sample_rate=sample_rate, compression=compression, format=ext, dtype=None) fileobj.seek(0) with open(res_path, 'wb') as file_: file_.write(fileobj.read()) diff --git a/test/torchaudio_unittest/sox_io_backend/smoke_test.py b/test/torchaudio_unittest/sox_io_backend/smoke_test.py index be734afa77..598a98bd3d 100644 --- a/test/torchaudio_unittest/sox_io_backend/smoke_test.py +++ b/test/torchaudio_unittest/sox_io_backend/smoke_test.py @@ -1,3 +1,4 @@ +import io import itertools import unittest @@ -85,3 +86,70 @@ def test_vorbis(self, sample_rate, num_channels, quality_level): def test_flac(self, sample_rate, num_channels, compression_level): """Run smoke test on flac format""" self.run_smoke_test('flac', sample_rate, num_channels, compression=compression_level) + + +@skipIfNoExtension +class SmokeTestFileObj(TorchaudioTestCase): + """Run smoke test on various audio format + + The purpose of this test suite is to verify that sox_io_backend functionalities do not exhibit + abnormal behaviors. + + This test suite should be able to run without any additional tools (such as sox command), + however without such tools, the correctness of each function cannot be verified. + """ + def run_smoke_test(self, ext, sample_rate, num_channels, *, compression=None, dtype='float32'): + duration = 1 + num_frames = sample_rate * duration + original = get_wav_data(dtype, num_channels, normalize=False, num_frames=num_frames) + + fileobj = io.BytesIO() + # 1. run save + sox_io_backend.save(fileobj, original, sample_rate, compression=compression, format=ext) + # 2. run info + fileobj.seek(0) + info = sox_io_backend.info(fileobj, format=ext) + assert info.sample_rate == sample_rate + assert info.num_channels == num_channels + # 3. run load + fileobj.seek(0) + loaded, sr = sox_io_backend.load(fileobj, normalize=False, format=ext) + assert sr == sample_rate + assert loaded.shape[0] == num_channels + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels): + """Run smoke test on wav format""" + self.run_smoke_test('wav', sample_rate, num_channels, dtype=dtype) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-4.2, -0.2, 0, 0.2, 96, 128, 160, 192, 224, 256, 320], + ))) + @skipIfNoMP3 + def test_mp3(self, sample_rate, num_channels, bit_rate): + """Run smoke test on mp3 format""" + self.run_smoke_test('mp3', sample_rate, num_channels, compression=bit_rate) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-1, 0, 1, 2, 3, 3.6, 5, 10], + ))) + def test_vorbis(self, sample_rate, num_channels, quality_level): + """Run smoke test on vorbis format""" + self.run_smoke_test('vorbis', sample_rate, num_channels, compression=quality_level) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """Run smoke test on flac format""" + self.run_smoke_test('flac', sample_rate, num_channels, compression=compression_level) diff --git a/test/torchaudio_unittest/torchscript_consistency_impl.py b/test/torchaudio_unittest/torchscript_consistency_impl.py index 82c6a300a6..ab681b999b 100644 --- a/test/torchaudio_unittest/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/torchscript_consistency_impl.py @@ -535,6 +535,20 @@ def func(tensor): self._assert_consistency(func, waveform) + def test_spectral_centroid(self): + + def func(tensor): + sample_rate = 44100 + n_fft = 400 + ws = 400 + hop = 200 + pad = 0 + window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) + return F.spectral_centroid(tensor, sample_rate, pad, window, n_fft, hop, ws) + + tensor = common_utils.get_whitenoise(sample_rate=44100) + self._assert_consistency(func, tensor) + class Transforms(common_utils.TestBaseMixin): """Implements test for Transforms that are performed for different devices""" @@ -624,3 +638,8 @@ def test_Vad(self): filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") waveform, sample_rate = common_utils.load_wav(filepath) self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform) + + def test_SpectralCentroid(self): + sample_rate = 44100 + waveform = common_utils.get_whitenoise(sample_rate=sample_rate) + self._assert_consistency(T.SpectralCentroid(sample_rate=sample_rate), waveform) diff --git a/test/torchaudio_unittest/transforms_test.py b/test/torchaudio_unittest/transforms_test.py index 4a91d40bd9..76628b1315 100644 --- a/test/torchaudio_unittest/transforms_test.py +++ b/test/torchaudio_unittest/transforms_test.py @@ -219,3 +219,19 @@ def test_compute_deltas_twochannel(self): computed = transform(specgram) assert computed.shape == expected.shape, (computed.shape, expected.shape) self.assertEqual(computed, expected, atol=1e-6, rtol=1e-8) + + +class SmokeTest(common_utils.TorchaudioTestCase): + + def test_spectrogram(self): + specgram = transforms.Spectrogram(center=False, pad_mode="reflect", onesided=False) + self.assertEqual(specgram.center, False) + self.assertEqual(specgram.pad_mode, "reflect") + self.assertEqual(specgram.onesided, False) + + def test_melspectrogram(self): + melspecgram = transforms.MelSpectrogram(center=True, pad_mode="reflect", onesided=False) + specgram = melspecgram.spectrogram + self.assertEqual(specgram.center, True) + self.assertEqual(specgram.pad_mode, "reflect") + self.assertEqual(specgram.onesided, False) diff --git a/third_party/sox/CMakeLists.txt b/third_party/sox/CMakeLists.txt index dcad72bbfe..930b84496a 100644 --- a/third_party/sox/CMakeLists.txt +++ b/third_party/sox/CMakeLists.txt @@ -1,6 +1,6 @@ include(ExternalProject) -set(INSTALL_DIR ${PROJECT_SOURCE_DIR}/install) +set(INSTALL_DIR ${CMAKE_CURRENT_SOURCE_DIR}/../install) set(ARCHIVE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/archives) set(COMMON_ARGS --quiet --disable-shared --enable-static --prefix=${INSTALL_DIR} --with-pic --disable-dependency-tracking --disable-debug --disable-examples --disable-doc) @@ -21,6 +21,14 @@ ExternalProject_Add(mad URL_HASH SHA256=bbfac3ed6bfbc2823d3775ebb931087371e142bb0e9bb1bee51a76a6e0078690 PATCH_COMMAND patch < ${CMAKE_CURRENT_SOURCE_DIR}/patch/libmad.patch CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/mad/configure ${COMMON_ARGS} + DOWNLOAD_NO_PROGRESS ON + LOG_DOWNLOAD ON + LOG_UPDATE ON + LOG_CONFIGURE ON + LOG_BUILD ON + LOG_INSTALL ON + LOG_MERGED_STDOUTERR ON + LOG_OUTPUT_ON_FAILURE ON ) ExternalProject_Add(amr @@ -29,6 +37,14 @@ ExternalProject_Add(amr URL https://sourceforge.net/projects/opencore-amr/files/opencore-amr/opencore-amr-0.1.5.tar.gz URL_HASH SHA256=2c006cb9d5f651bfb5e60156dbff6af3c9d35c7bbcc9015308c0aff1e14cd341 CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/amr/configure ${COMMON_ARGS} + DOWNLOAD_NO_PROGRESS ON + LOG_DOWNLOAD ON + LOG_UPDATE ON + LOG_CONFIGURE ON + LOG_BUILD ON + LOG_INSTALL ON + LOG_MERGED_STDOUTERR ON + LOG_OUTPUT_ON_FAILURE ON ) ExternalProject_Add(lame @@ -37,6 +53,14 @@ ExternalProject_Add(lame URL https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz URL_HASH SHA256=24346b4158e4af3bd9f2e194bb23eb473c75fb7377011523353196b19b9a23ff CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/lame/configure ${COMMON_ARGS} --enable-nasm + DOWNLOAD_NO_PROGRESS ON + LOG_DOWNLOAD ON + LOG_UPDATE ON + LOG_CONFIGURE ON + LOG_BUILD ON + LOG_INSTALL ON + LOG_MERGED_STDOUTERR ON + LOG_OUTPUT_ON_FAILURE ON ) ExternalProject_Add(ogg @@ -45,6 +69,14 @@ ExternalProject_Add(ogg URL https://ftp.osuosl.org/pub/xiph/releases/ogg/libogg-1.3.3.tar.gz URL_HASH SHA256=c2e8a485110b97550f453226ec644ebac6cb29d1caef2902c007edab4308d985 CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/ogg/configure ${COMMON_ARGS} + DOWNLOAD_NO_PROGRESS ON + LOG_DOWNLOAD ON + LOG_UPDATE ON + LOG_CONFIGURE ON + LOG_BUILD ON + LOG_INSTALL ON + LOG_MERGED_STDOUTERR ON + LOG_OUTPUT_ON_FAILURE ON ) ExternalProject_Add(flac @@ -54,6 +86,14 @@ ExternalProject_Add(flac URL https://ftp.osuosl.org/pub/xiph/releases/flac/flac-1.3.2.tar.xz URL_HASH SHA256=91cfc3ed61dc40f47f050a109b08610667d73477af6ef36dcad31c31a4a8d53f CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/flac/configure ${COMMON_ARGS} --with-ogg --disable-cpplibs + DOWNLOAD_NO_PROGRESS ON + LOG_DOWNLOAD ON + LOG_UPDATE ON + LOG_CONFIGURE ON + LOG_BUILD ON + LOG_INSTALL ON + LOG_MERGED_STDOUTERR ON + LOG_OUTPUT_ON_FAILURE ON ) ExternalProject_Add(vorbis @@ -63,6 +103,14 @@ ExternalProject_Add(vorbis URL https://ftp.osuosl.org/pub/xiph/releases/vorbis/libvorbis-1.3.6.tar.gz URL_HASH SHA256=6ed40e0241089a42c48604dc00e362beee00036af2d8b3f46338031c9e0351cb CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/vorbis/configure ${COMMON_ARGS} --with-ogg + DOWNLOAD_NO_PROGRESS ON + LOG_DOWNLOAD ON + LOG_UPDATE ON + LOG_CONFIGURE ON + LOG_BUILD ON + LOG_INSTALL ON + LOG_MERGED_STDOUTERR ON + LOG_OUTPUT_ON_FAILURE ON ) ExternalProject_Add(opus @@ -72,6 +120,14 @@ ExternalProject_Add(opus URL https://ftp.osuosl.org/pub/xiph/releases/opus/opus-1.3.1.tar.gz URL_HASH SHA256=65b58e1e25b2a114157014736a3d9dfeaad8d41be1c8179866f144a2fb44ff9d CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/opus/configure ${COMMON_ARGS} --with-ogg + DOWNLOAD_NO_PROGRESS ON + LOG_DOWNLOAD ON + LOG_UPDATE ON + LOG_CONFIGURE ON + LOG_BUILD ON + LOG_INSTALL ON + LOG_MERGED_STDOUTERR ON + LOG_OUTPUT_ON_FAILURE ON ) ExternalProject_Add(opusfile @@ -81,15 +137,73 @@ ExternalProject_Add(opusfile URL https://ftp.osuosl.org/pub/xiph/releases/opus/opusfile-0.12.tar.gz URL_HASH SHA256=118d8601c12dd6a44f52423e68ca9083cc9f2bfe72da7a8c1acb22a80ae3550b CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/opusfile/configure ${COMMON_ARGS} --disable-http + DOWNLOAD_NO_PROGRESS ON + LOG_DOWNLOAD ON + LOG_UPDATE ON + LOG_CONFIGURE ON + LOG_BUILD ON + LOG_INSTALL ON + LOG_MERGED_STDOUTERR ON + LOG_OUTPUT_ON_FAILURE ON ) +# OpenMP is by default compiled against GNU OpenMP, which conflicts with the version of OpenMP that PyTorch uses. +# See https://github.com/pytorch/audio/pull/1026 +# TODO: Add flags like https://github.com/suphoff/pytorch_parallel_extension_cpp/blob/master/setup.py +set(SOX_OPTIONS + --disable-openmp + --with-amrnb + --with-amrwb + --with-flac + --with-lame + --with-mad + --with-oggvorbis + --with-opus + --without-alsa + --without-ao + --without-coreaudio + --without-oss + --without-id3tag + --without-ladspa + --without-magic + --without-png + --without-pulseaudio + --without-sndfile + --without-sndio + --without-sunaudio + --without-waveaudio + --without-twolame + ) + +set(SOX_LIBRARIES + ${INSTALL_DIR}/lib/libsox.a + ${INSTALL_DIR}/lib/libopencore-amrnb.a + ${INSTALL_DIR}/lib/libopencore-amrwb.a + ${INSTALL_DIR}/lib/libmad.a + ${INSTALL_DIR}/lib/libmp3lame.a + ${INSTALL_DIR}/lib/libFLAC.a + ${INSTALL_DIR}/lib/libopusfile.a + ${INSTALL_DIR}/lib/libopus.a + ${INSTALL_DIR}/lib/libvorbisenc.a + ${INSTALL_DIR}/lib/libvorbisfile.a + ${INSTALL_DIR}/lib/libvorbis.a + ${INSTALL_DIR}/lib/libogg.a + ) + ExternalProject_Add(sox PREFIX ${CMAKE_CURRENT_BINARY_DIR} DEPENDS ogg flac vorbis opusfile lame mad amr DOWNLOAD_DIR ${ARCHIVE_DIR} URL https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2 URL_HASH SHA256=81a6956d4330e75b5827316e44ae381e6f1e8928003c6aa45896da9041ea149c - # OpenMP is by default compiled against GNU OpenMP, which conflicts with the version of OpenMP that PyTorch uses. - # See https://github.com/pytorch/audio/pull/1026 - CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/sox/configure ${COMMON_ARGS} --with-lame --with-flac --with-mad --with-oggvorbis --without-alsa --without-coreaudio --without-png --without-oss --without-sndfile --with-opus --with-amrwb --with-amrnb --disable-openmp --without-sndio --without-pulseaudio + CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/sox/configure ${COMMON_ARGS} ${SOX_OPTIONS} + BUILD_BYPRODUCTS ${SOX_LIBRARIES} + DOWNLOAD_NO_PROGRESS ON + LOG_DOWNLOAD ON + LOG_UPDATE ON + LOG_CONFIGURE ON + LOG_BUILD ON + LOG_INSTALL ON + LOG_MERGED_STDOUTERR ON + LOG_OUTPUT_ON_FAILURE ON ) diff --git a/torchaudio/backend/_soundfile_backend.py b/torchaudio/backend/_soundfile_backend.py index 3366780bdb..e3c992b6fd 100644 --- a/torchaudio/backend/_soundfile_backend.py +++ b/torchaudio/backend/_soundfile_backend.py @@ -11,15 +11,87 @@ import soundfile +# Mapping from soundfile subtype to number of bits per sample. +# This is mostly heuristical and the value is set to 0 when it is irrelevant +# (lossy formats) or when it can't be inferred. +# For ADPCM (and G72X) subtypes, it's hard to infer the bit depth because it's not part of the standard: +# According to https://en.wikipedia.org/wiki/Adaptive_differential_pulse-code_modulation#In_telephony, +# the default seems to be 8 bits but it can be compressed further to 4 bits. +# The dict is inspired from +# https://github.com/bastibe/python-soundfile/blob/744efb4b01abc72498a96b09115b42a4cabd85e4/soundfile.py#L66-L94 +_SUBTYPE_TO_BITS_PER_SAMPLE = { + 'PCM_S8': 8, # Signed 8 bit data + 'PCM_16': 16, # Signed 16 bit data + 'PCM_24': 24, # Signed 24 bit data + 'PCM_32': 32, # Signed 32 bit data + 'PCM_U8': 8, # Unsigned 8 bit data (WAV and RAW only) + 'FLOAT': 32, # 32 bit float data + 'DOUBLE': 64, # 64 bit float data + 'ULAW': 8, # U-Law encoded. See https://en.wikipedia.org/wiki/G.711#Types + 'ALAW': 8, # A-Law encoded. See https://en.wikipedia.org/wiki/G.711#Types + 'IMA_ADPCM': 0, # IMA ADPCM. + 'MS_ADPCM': 0, # Microsoft ADPCM. + 'GSM610': 0, # GSM 6.10 encoding. (Wikipedia says 1.625 bit depth?? https://en.wikipedia.org/wiki/Full_Rate) + 'VOX_ADPCM': 0, # OKI / Dialogix ADPCM + 'G721_32': 0, # 32kbs G721 ADPCM encoding. + 'G723_24': 0, # 24kbs G723 ADPCM encoding. + 'G723_40': 0, # 40kbs G723 ADPCM encoding. + 'DWVW_12': 12, # 12 bit Delta Width Variable Word encoding. + 'DWVW_16': 16, # 16 bit Delta Width Variable Word encoding. + 'DWVW_24': 24, # 24 bit Delta Width Variable Word encoding. + 'DWVW_N': 0, # N bit Delta Width Variable Word encoding. + 'DPCM_8': 8, # 8 bit differential PCM (XI only) + 'DPCM_16': 16, # 16 bit differential PCM (XI only) + 'VORBIS': 0, # Xiph Vorbis encoding. (lossy) + 'ALAC_16': 16, # Apple Lossless Audio Codec (16 bit). + 'ALAC_20': 20, # Apple Lossless Audio Codec (20 bit). + 'ALAC_24': 24, # Apple Lossless Audio Codec (24 bit). + 'ALAC_32': 32, # Apple Lossless Audio Codec (32 bit). +} + + +def _get_bit_depth(subtype): + if subtype not in _SUBTYPE_TO_BITS_PER_SAMPLE: + warnings.warn( + f"The {subtype} subtype is unknown to TorchAudio. As a result, the bits_per_sample " + "attribute will be set to 0. If you are seeing this warning, please " + "report by opening an issue on github (after checking for existing/closed ones). " + "You may otherwise ignore this warning." + ) + return _SUBTYPE_TO_BITS_PER_SAMPLE.get(subtype, 0) + + +_SUBTYPE_TO_ENCODING = { + 'PCM_S8': 'PCM_S', + 'PCM_16': 'PCM_S', + 'PCM_24': 'PCM_S', + 'PCM_32': 'PCM_S', + 'PCM_U8': 'PCM_U', + 'FLOAT': 'PCM_F', + 'DOUBLE': 'PCM_F', + 'ULAW': 'ULAW', + 'ALAW': 'ALAW', + 'VORBIS': 'VORBIS', +} + + +def _get_encoding(format: str, subtype: str): + if format == 'FLAC': + return 'FLAC' + return _SUBTYPE_TO_ENCODING.get(subtype, 'UNKNOWN') + + @_mod_utils.requires_module("soundfile") def info(filepath: str, format: Optional[str] = None) -> AudioMetaData: """Get signal information of an audio file. Args: - filepath (str or pathlib.Path): Path to audio file. - This functionalso handles ``pathlib.Path`` objects, but is annotated as ``str`` - for the consistency with "sox_io" backend, which has a restriction on type annotation - for TorchScript compiler compatiblity. + filepath (path-like object or file-like object): + Source of audio data. + Note: + * This argument is intentionally annotated as ``str`` only, + for the consistency with "sox_io" backend, which has a restriction + on type annotation due to TorchScript compiler compatiblity. format (str, optional): Not used. PySoundFile does not accept format hint. @@ -27,7 +99,13 @@ def info(filepath: str, format: Optional[str] = None) -> AudioMetaData: AudioMetaData: meta data of the given audio. """ sinfo = soundfile.info(filepath) - return AudioMetaData(sinfo.samplerate, sinfo.frames, sinfo.channels) + return AudioMetaData( + sinfo.samplerate, + sinfo.frames, + sinfo.channels, + bits_per_sample=_get_bit_depth(sinfo.subtype), + encoding=_get_encoding(sinfo.format, sinfo.subtype), + ) _SUBTYPE2DTYPE = { diff --git a/torchaudio/backend/common.py b/torchaudio/backend/common.py index 135a18caee..c0eb90c994 100644 --- a/torchaudio/backend/common.py +++ b/torchaudio/backend/common.py @@ -12,11 +12,23 @@ class AudioMetaData: :ivar int sample_rate: Sample rate :ivar int num_frames: The number of frames :ivar int num_channels: The number of channels + :ivar int bits_per_sample: The number of bits per sample. This is 0 for lossy formats, + or when it cannot be accurately inferred. + :ivar str encoding: Audio encoding. """ - def __init__(self, sample_rate: int, num_frames: int, num_channels: int): + def __init__( + self, + sample_rate: int, + num_frames: int, + num_channels: int, + bits_per_sample: int, + encoding: str, + ): self.sample_rate = sample_rate self.num_frames = num_frames self.num_channels = num_channels + self.bits_per_sample = bits_per_sample + self.encoding = encoding @_mod_utils.deprecated('Please migrate to `AudioMetaData`.', '0.9.0') diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 18296ef7e2..513bff4393 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -1,4 +1,5 @@ import os +import warnings from typing import Tuple, Optional import torch @@ -10,6 +11,24 @@ from .common import AudioMetaData +@torch.jit.unused +def _info( + filepath: str, + format: Optional[str] = None, +) -> AudioMetaData: + if hasattr(filepath, 'read'): + sinfo = torchaudio._torchaudio.get_info_fileobj(filepath, format) + return AudioMetaData(*sinfo) + sinfo = torch.ops.torchaudio.sox_io_get_info(os.fspath(filepath), format) + return AudioMetaData( + sinfo.get_sample_rate(), + sinfo.get_num_frames(), + sinfo.get_num_channels(), + sinfo.get_bits_per_sample(), + sinfo.get_encoding(), + ) + + @_mod_utils.requires_module('torchaudio._torchaudio') def info( filepath: str, @@ -18,9 +37,21 @@ def info( """Get signal information of an audio file. Args: - filepath (str or pathlib.Path): - Path to audio file. This function also handles ``pathlib.Path`` objects, - but is annotated as ``str`` for TorchScript compatibility. + filepath (path-like object or file-like object): + Source of audio data. When the function is not compiled by TorchScript, + (e.g. ``torch.jit.script``), the following types are accepted; + * ``path-like``: file path + * ``file-like``: Object with ``read(size: int) -> bytes`` method, + which returns byte string of at most ``size`` length. + When the function is compiled by TorchScript, only ``str`` type is allowed. + + Note: + * When the input type is file-like object, this function cannot + get the correct length (``num_samples``) for certain formats, + such as ``mp3`` and ``vorbis``. + In this case, the value of ``num_samples`` is ``0``. + * This argument is intentionally annotated as ``str`` only due to + TorchScript compiler compatibility. format (str, optional): Override the format detection with the given format. Providing the argument might help when libsox can not infer the format @@ -29,10 +60,15 @@ def info( Returns: AudioMetaData: Metadata of the given audio. """ - # Cast to str in case type is `pathlib.Path` - filepath = str(filepath) + if not torch.jit.is_scripting(): + return _info(filepath, format) sinfo = torch.ops.torchaudio.sox_io_get_info(filepath, format) - return AudioMetaData(sinfo.get_sample_rate(), sinfo.get_num_frames(), sinfo.get_num_channels()) + return AudioMetaData( + sinfo.get_sample_rate(), + sinfo.get_num_frames(), + sinfo.get_num_channels(), + sinfo.get_bits_per_sample(), + sinfo.get_encoding()) @_mod_utils.requires_module('torchaudio._torchaudio') @@ -142,15 +178,16 @@ def _save( channels_first: bool = True, compression: Optional[float] = None, format: Optional[str] = None, + dtype: Optional[str] = None, ): if hasattr(filepath, 'write'): if format is None: raise RuntimeError('`format` is required when saving to file object.') torchaudio._torchaudio.save_audio_fileobj( - filepath, src, sample_rate, channels_first, compression, format) + filepath, src, sample_rate, channels_first, compression, format, dtype) else: torch.ops.torchaudio.sox_io_save_audio_file( - os.fspath(filepath), src, sample_rate, channels_first, compression, format) + os.fspath(filepath), src, sample_rate, channels_first, compression, format, dtype) @_mod_utils.requires_module('torchaudio._torchaudio') @@ -161,6 +198,7 @@ def save( channels_first: bool = True, compression: Optional[float] = None, format: Optional[str] = None, + dtype: Optional[str] = None, ): """Save audio data to file. @@ -207,12 +245,22 @@ def save( format (str, optional): Output audio format. This is required when the output audio format cannot be infered from ``filepath``, (such as file extension or ``name`` attribute of the given file object). + dtype (str, optional) + Output tensor dtype. + Valid values: ``"uint8", "int16", "int32", "float32", "float64", None`` + ``dtype=None`` means no conversion is performed. + ``dtype`` parameter is only effective for ``float32`` Tensor. """ + if src.dtype == torch.float32 and dtype is None: + warnings.warn( + '`dtype` default value will be changed to `int16` in 0.9 release.' + 'Specify `dtype` to suppress this warning.' + ) if not torch.jit.is_scripting(): - _save(filepath, src, sample_rate, channels_first, compression, format) + _save(filepath, src, sample_rate, channels_first, compression, format, dtype) return torch.ops.torchaudio.sox_io_save_audio_file( - filepath, src, sample_rate, channels_first, compression, format) + filepath, src, sample_rate, channels_first, compression, format, dtype) @_mod_utils.requires_module('torchaudio._torchaudio') diff --git a/torchaudio/csrc/pybind.cpp b/torchaudio/csrc/pybind.cpp index eb8c30b96a..373b9d0d96 100644 --- a/torchaudio/csrc/pybind.cpp +++ b/torchaudio/csrc/pybind.cpp @@ -1,89 +1,94 @@ #include +#include #include #include - PYBIND11_MODULE(_torchaudio, m) { py::class_(m, "sox_signalinfo_t") - .def(py::init<>()) - .def("__repr__", [](const sox_signalinfo_t &self) { - std::stringstream ss; - ss << "sox_signalinfo_t {\n" - << " rate-> " << self.rate << "\n" - << " channels-> " << self.channels << "\n" - << " precision-> " << self.precision << "\n" - << " length-> " << self.length << "\n" - << " mult-> " << self.mult << "\n" - << "}\n"; - return ss.str(); - }) - .def_readwrite("rate", &sox_signalinfo_t::rate) - .def_readwrite("channels", &sox_signalinfo_t::channels) - .def_readwrite("precision", &sox_signalinfo_t::precision) - .def_readwrite("length", &sox_signalinfo_t::length) - .def_readwrite("mult", &sox_signalinfo_t::mult); + .def(py::init<>()) + .def( + "__repr__", + [](const sox_signalinfo_t& self) { + std::stringstream ss; + ss << "sox_signalinfo_t {\n" + << " rate-> " << self.rate << "\n" + << " channels-> " << self.channels << "\n" + << " precision-> " << self.precision << "\n" + << " length-> " << self.length << "\n" + << " mult-> " << self.mult << "\n" + << "}\n"; + return ss.str(); + }) + .def_readwrite("rate", &sox_signalinfo_t::rate) + .def_readwrite("channels", &sox_signalinfo_t::channels) + .def_readwrite("precision", &sox_signalinfo_t::precision) + .def_readwrite("length", &sox_signalinfo_t::length) + .def_readwrite("mult", &sox_signalinfo_t::mult); py::class_(m, "sox_encodinginfo_t") - .def(py::init<>()) - .def("__repr__", [](const sox_encodinginfo_t &self) { - std::stringstream ss; - ss << "sox_encodinginfo_t {\n" - << " encoding-> " << self.encoding << "\n" - << " bits_per_sample-> " << self.bits_per_sample << "\n" - << " compression-> " << self.compression << "\n" - << " reverse_bytes-> " << self.reverse_bytes << "\n" - << " reverse_nibbles-> " << self.reverse_nibbles << "\n" - << " reverse_bits-> " << self.reverse_bits << "\n" - << " opposite_endian-> " << self.opposite_endian << "\n" - << "}\n"; - return ss.str(); - }) - .def_readwrite("encoding", &sox_encodinginfo_t::encoding) - .def_readwrite("bits_per_sample", &sox_encodinginfo_t::bits_per_sample) - .def_readwrite("compression", &sox_encodinginfo_t::compression) - .def_readwrite("reverse_bytes", &sox_encodinginfo_t::reverse_bytes) - .def_readwrite("reverse_nibbles", &sox_encodinginfo_t::reverse_nibbles) - .def_readwrite("reverse_bits", &sox_encodinginfo_t::reverse_bits) - .def_readwrite("opposite_endian", &sox_encodinginfo_t::opposite_endian); + .def(py::init<>()) + .def( + "__repr__", + [](const sox_encodinginfo_t& self) { + std::stringstream ss; + ss << "sox_encodinginfo_t {\n" + << " encoding-> " << self.encoding << "\n" + << " bits_per_sample-> " << self.bits_per_sample << "\n" + << " compression-> " << self.compression << "\n" + << " reverse_bytes-> " << self.reverse_bytes << "\n" + << " reverse_nibbles-> " << self.reverse_nibbles << "\n" + << " reverse_bits-> " << self.reverse_bits << "\n" + << " opposite_endian-> " << self.opposite_endian << "\n" + << "}\n"; + return ss.str(); + }) + .def_readwrite("encoding", &sox_encodinginfo_t::encoding) + .def_readwrite("bits_per_sample", &sox_encodinginfo_t::bits_per_sample) + .def_readwrite("compression", &sox_encodinginfo_t::compression) + .def_readwrite("reverse_bytes", &sox_encodinginfo_t::reverse_bytes) + .def_readwrite("reverse_nibbles", &sox_encodinginfo_t::reverse_nibbles) + .def_readwrite("reverse_bits", &sox_encodinginfo_t::reverse_bits) + .def_readwrite("opposite_endian", &sox_encodinginfo_t::opposite_endian); py::enum_(m, "sox_encoding_t") - .value("SOX_ENCODING_UNKNOWN", sox_encoding_t::SOX_ENCODING_UNKNOWN) - .value("SOX_ENCODING_SIGN2", sox_encoding_t::SOX_ENCODING_SIGN2) - .value("SOX_ENCODING_UNSIGNED", sox_encoding_t::SOX_ENCODING_UNSIGNED) - .value("SOX_ENCODING_FLOAT", sox_encoding_t::SOX_ENCODING_FLOAT) - .value("SOX_ENCODING_FLOAT_TEXT", sox_encoding_t::SOX_ENCODING_FLOAT_TEXT) - .value("SOX_ENCODING_FLAC", sox_encoding_t::SOX_ENCODING_FLAC) - .value("SOX_ENCODING_HCOM", sox_encoding_t::SOX_ENCODING_HCOM) - .value("SOX_ENCODING_WAVPACK", sox_encoding_t::SOX_ENCODING_WAVPACK) - .value("SOX_ENCODING_WAVPACKF", sox_encoding_t::SOX_ENCODING_WAVPACKF) - .value("SOX_ENCODING_ULAW", sox_encoding_t::SOX_ENCODING_ULAW) - .value("SOX_ENCODING_ALAW", sox_encoding_t::SOX_ENCODING_ALAW) - .value("SOX_ENCODING_G721", sox_encoding_t::SOX_ENCODING_G721) - .value("SOX_ENCODING_G723", sox_encoding_t::SOX_ENCODING_G723) - .value("SOX_ENCODING_CL_ADPCM", sox_encoding_t::SOX_ENCODING_CL_ADPCM) - .value("SOX_ENCODING_CL_ADPCM16", sox_encoding_t::SOX_ENCODING_CL_ADPCM16) - .value("SOX_ENCODING_MS_ADPCM", sox_encoding_t::SOX_ENCODING_MS_ADPCM) - .value("SOX_ENCODING_IMA_ADPCM", sox_encoding_t::SOX_ENCODING_IMA_ADPCM) - .value("SOX_ENCODING_OKI_ADPCM", sox_encoding_t::SOX_ENCODING_OKI_ADPCM) - .value("SOX_ENCODING_DPCM", sox_encoding_t::SOX_ENCODING_DPCM) - .value("SOX_ENCODING_DWVW", sox_encoding_t::SOX_ENCODING_DWVW) - .value("SOX_ENCODING_DWVWN", sox_encoding_t::SOX_ENCODING_DWVWN) - .value("SOX_ENCODING_GSM", sox_encoding_t::SOX_ENCODING_GSM) - .value("SOX_ENCODING_MP3", sox_encoding_t::SOX_ENCODING_MP3) - .value("SOX_ENCODING_VORBIS", sox_encoding_t::SOX_ENCODING_VORBIS) - .value("SOX_ENCODING_AMR_WB", sox_encoding_t::SOX_ENCODING_AMR_WB) - .value("SOX_ENCODING_AMR_NB", sox_encoding_t::SOX_ENCODING_AMR_NB) - .value("SOX_ENCODING_LPC10", sox_encoding_t::SOX_ENCODING_LPC10) - //.value("SOX_ENCODING_OPUS", sox_encoding_t::SOX_ENCODING_OPUS) // creates a compile error - .value("SOX_ENCODINGS", sox_encoding_t::SOX_ENCODINGS) - .export_values(); + .value("SOX_ENCODING_UNKNOWN", sox_encoding_t::SOX_ENCODING_UNKNOWN) + .value("SOX_ENCODING_SIGN2", sox_encoding_t::SOX_ENCODING_SIGN2) + .value("SOX_ENCODING_UNSIGNED", sox_encoding_t::SOX_ENCODING_UNSIGNED) + .value("SOX_ENCODING_FLOAT", sox_encoding_t::SOX_ENCODING_FLOAT) + .value("SOX_ENCODING_FLOAT_TEXT", sox_encoding_t::SOX_ENCODING_FLOAT_TEXT) + .value("SOX_ENCODING_FLAC", sox_encoding_t::SOX_ENCODING_FLAC) + .value("SOX_ENCODING_HCOM", sox_encoding_t::SOX_ENCODING_HCOM) + .value("SOX_ENCODING_WAVPACK", sox_encoding_t::SOX_ENCODING_WAVPACK) + .value("SOX_ENCODING_WAVPACKF", sox_encoding_t::SOX_ENCODING_WAVPACKF) + .value("SOX_ENCODING_ULAW", sox_encoding_t::SOX_ENCODING_ULAW) + .value("SOX_ENCODING_ALAW", sox_encoding_t::SOX_ENCODING_ALAW) + .value("SOX_ENCODING_G721", sox_encoding_t::SOX_ENCODING_G721) + .value("SOX_ENCODING_G723", sox_encoding_t::SOX_ENCODING_G723) + .value("SOX_ENCODING_CL_ADPCM", sox_encoding_t::SOX_ENCODING_CL_ADPCM) + .value("SOX_ENCODING_CL_ADPCM16", sox_encoding_t::SOX_ENCODING_CL_ADPCM16) + .value("SOX_ENCODING_MS_ADPCM", sox_encoding_t::SOX_ENCODING_MS_ADPCM) + .value("SOX_ENCODING_IMA_ADPCM", sox_encoding_t::SOX_ENCODING_IMA_ADPCM) + .value("SOX_ENCODING_OKI_ADPCM", sox_encoding_t::SOX_ENCODING_OKI_ADPCM) + .value("SOX_ENCODING_DPCM", sox_encoding_t::SOX_ENCODING_DPCM) + .value("SOX_ENCODING_DWVW", sox_encoding_t::SOX_ENCODING_DWVW) + .value("SOX_ENCODING_DWVWN", sox_encoding_t::SOX_ENCODING_DWVWN) + .value("SOX_ENCODING_GSM", sox_encoding_t::SOX_ENCODING_GSM) + .value("SOX_ENCODING_MP3", sox_encoding_t::SOX_ENCODING_MP3) + .value("SOX_ENCODING_VORBIS", sox_encoding_t::SOX_ENCODING_VORBIS) + .value("SOX_ENCODING_AMR_WB", sox_encoding_t::SOX_ENCODING_AMR_WB) + .value("SOX_ENCODING_AMR_NB", sox_encoding_t::SOX_ENCODING_AMR_NB) + .value("SOX_ENCODING_LPC10", sox_encoding_t::SOX_ENCODING_LPC10) + //.value("SOX_ENCODING_OPUS", sox_encoding_t::SOX_ENCODING_OPUS) // + // creates a compile error + .value("SOX_ENCODINGS", sox_encoding_t::SOX_ENCODINGS) + .export_values(); py::enum_(m, "sox_option_t") - .value("sox_option_no", sox_option_t::sox_option_no) - .value("sox_option_yes", sox_option_t::sox_option_yes) - .value("sox_option_default", sox_option_t::sox_option_default) - .export_values(); + .value("sox_option_no", sox_option_t::sox_option_no) + .value("sox_option_yes", sox_option_t::sox_option_yes) + .value("sox_option_default", sox_option_t::sox_option_default) + .export_values(); py::enum_(m, "sox_bool") - .value("sox_false", sox_bool::sox_false) - .value("sox_true", sox_bool::sox_true) - .export_values(); + .value("sox_false", sox_bool::sox_false) + .value("sox_true", sox_bool::sox_true) + .export_values(); m.def( "read_audio_file", &torch::audio::read_audio_file, @@ -96,6 +101,10 @@ PYBIND11_MODULE(_torchaudio, m) { "get_info", &torch::audio::get_info, "Gets information about an audio file"); + m.def( + "get_info_fileobj", + &torchaudio::sox_io::get_info_fileobj, + "Get metadata of audio in file object."); m.def( "load_audio_fileobj", &torchaudio::sox_io::load_audio_fileobj, @@ -104,4 +113,8 @@ PYBIND11_MODULE(_torchaudio, m) { "save_audio_fileobj", &torchaudio::sox_io::save_audio_fileobj, "Save audio to file obj."); + m.def( + "apply_effects_fileobj", + &torchaudio::sox_effects::apply_effects_fileobj, + "Decode audio data from file-like obj and apply effects."); } diff --git a/torchaudio/csrc/sox/effects.cpp b/torchaudio/csrc/sox/effects.cpp index 03f06c2602..e195e25731 100644 --- a/torchaudio/csrc/sox/effects.cpp +++ b/torchaudio/csrc/sox/effects.cpp @@ -137,44 +137,65 @@ c10::intrusive_ptr apply_effects_file( #ifdef TORCH_API_INCLUDE_EXTENSION_H +// Streaming decoding over file-like object is tricky because libsox operates on +// FILE pointer. The folloing is what `sox` and `play` commands do +// - file input -> FILE pointer +// - URL input -> call wget in suprocess and pipe the data -> FILE pointer +// - stdin -> FILE pointer +// +// We want to, instead, fetch byte strings chunk by chunk, consume them, and +// discard. +// +// Here is the approach +// 1. Initialize sox_format_t using sox_open_mem_read, providing the initial +// chunk of byte string +// This will perform header-based format detection, if necessary, then fill +// the metadata of sox_format_t. Internally, sox_open_mem_read uses fmemopen, +// which returns FILE* which points the buffer of the provided byte string. +// 2. Each time sox reads a chunk from the FILE*, we update the underlying +// buffer in a way that it +// starts with unseen data, and append the new data read from the given +// fileobj. This will trick libsox as if it keeps reading from the FILE* +// continuously. +// For Step 2. see `fileobj_input_drain` function in effects_chain.cpp std::tuple apply_effects_fileobj( py::object fileobj, std::vector> effects, c10::optional& normalize, c10::optional& channels_first, c10::optional& format) { - - // Streaming decoding over file-like object is tricky because libsox operates on FILE pointer. - // The folloing is what `sox` and `play` commands do - // - file input -> FILE pointer - // - URL input -> call wget in suprocess and pipe the data -> FILE pointer - // - stdin -> FILE pointer + // Prepare the buffer used throughout the lifecycle of SoxEffectChain. // - // We want to, instead, fetch byte strings chunk by chunk, consume them, and discard. + // For certain format (such as FLAC), libsox keeps reading the content at + // the initialization unless it reaches EOF even when the header is properly + // parsed. (Making buffer size 8192, which is way bigger than the header, + // resulted in libsox consuming all the buffer content at the time it opens + // the file.) Therefore buffer has to always contain valid data, except after + // EOF. We default to `sox_get_globals()->bufsiz`* for buffer size and we + // first check if there is enough data to fill the buffer. `read_fileobj` + // repeatedly calls `read` method until it receives the requested length of + // bytes or it reaches EOF. If we get bytes shorter than requested, that means + // the whole audio data are fetched. // - // Here is the approach - // 1. Initialize sox_format_t using sox_open_mem_read, providing the initial chunk of byte string - // This will perform header-based format detection, if necessary, then fill the metadata of - // sox_format_t. Internally, sox_open_mem_read uses fmemopen, which returns FILE* which points the - // buffer of the provided byte string. - // 2. Each time sox reads a chunk from the FILE*, we update the underlying buffer in a way that it - // starts with unseen data, and append the new data read from the given fileobj. - // This will trick libsox as if it keeps reading from the FILE* continuously. - - // Prepare the buffer used throughout the lifecycle of SoxEffectChain. - // Using std::string and let it manage memory. - // 4096 is minimum size requried by auto_detect_format - // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/formats.c#L40-L48 - const size_t in_buffer_size = 4096; - std::string in_buffer(in_buffer_size, 'x'); - auto* in_buf = const_cast(in_buffer.data()); - - // Fetch the header, and copy it to the buffer. - auto header = static_cast(static_cast(fileobj.attr("read")(4096))); - memcpy(static_cast(in_buf), - static_cast(const_cast(header.data())), header.length()); + // * This can be changed with `torchaudio.utils.sox_utils.set_buffer_size`. + auto capacity = + (sox_get_globals()->bufsiz > 256) ? sox_get_globals()->bufsiz : 256; + std::string buffer(capacity, '\0'); + auto* in_buf = const_cast(buffer.data()); + auto num_read = read_fileobj(&fileobj, capacity, in_buf); + // If the file is shorter than 256, then libsox cannot read the header. + auto in_buffer_size = (num_read > 256) ? num_read : 256; // Open file (this starts reading the header) + // When opening a file there are two functions that can touches FILE*. + // * `auto_detect_format` + // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/formats.c#L43 + // * `startread` handler of detected format. + // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/formats.c#L574 + // To see the handler of a particular format, go to + // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/.c + // For example, voribs can be found + // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/vorbis.c#L97-L158 SoxFormat sf(sox_open_mem_read( in_buf, in_buffer_size, @@ -212,8 +233,7 @@ std::tuple apply_effects_fileobj( channels_first_); return std::make_tuple( - tensor, - static_cast(chain.getOutputSampleRate())); + tensor, static_cast(chain.getOutputSampleRate())); } #endif // TORCH_API_INCLUDE_EXTENSION_H diff --git a/torchaudio/csrc/sox/effects_chain.cpp b/torchaudio/csrc/sox/effects_chain.cpp index 9c365b72aa..80885b296c 100644 --- a/torchaudio/csrc/sox/effects_chain.cpp +++ b/torchaudio/csrc/sox/effects_chain.cpp @@ -123,44 +123,47 @@ int file_output_flow( } sox_effect_handler_t* get_tensor_input_handler() { - static sox_effect_handler_t handler{/*name=*/"input_tensor", - /*usage=*/NULL, - /*flags=*/SOX_EFF_MCHAN, - /*getopts=*/NULL, - /*start=*/NULL, - /*flow=*/NULL, - /*drain=*/tensor_input_drain, - /*stop=*/NULL, - /*kill=*/NULL, - /*priv_size=*/sizeof(TensorInputPriv)}; + static sox_effect_handler_t handler{ + /*name=*/"input_tensor", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/NULL, + /*drain=*/tensor_input_drain, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(TensorInputPriv)}; return &handler; } sox_effect_handler_t* get_tensor_output_handler() { - static sox_effect_handler_t handler{/*name=*/"output_tensor", - /*usage=*/NULL, - /*flags=*/SOX_EFF_MCHAN, - /*getopts=*/NULL, - /*start=*/NULL, - /*flow=*/tensor_output_flow, - /*drain=*/NULL, - /*stop=*/NULL, - /*kill=*/NULL, - /*priv_size=*/sizeof(TensorOutputPriv)}; + static sox_effect_handler_t handler{ + /*name=*/"output_tensor", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/tensor_output_flow, + /*drain=*/NULL, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(TensorOutputPriv)}; return &handler; } sox_effect_handler_t* get_file_output_handler() { - static sox_effect_handler_t handler{/*name=*/"output_file", - /*usage=*/NULL, - /*flags=*/SOX_EFF_MCHAN, - /*getopts=*/NULL, - /*start=*/NULL, - /*flow=*/file_output_flow, - /*drain=*/NULL, - /*stop=*/NULL, - /*kill=*/NULL, - /*priv_size=*/sizeof(FileOutputPriv)}; + static sox_effect_handler_t handler{ + /*name=*/"output_file", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/file_output_flow, + /*drain=*/NULL, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(FileOutputPriv)}; return &handler; } @@ -198,7 +201,8 @@ void SoxEffectsChain::addInputTensor(TensorSignal* signal) { priv->signal = signal; priv->index = 0; if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { - throw std::runtime_error("Internal Error: Failed to add effect: input_tensor"); + throw std::runtime_error( + "Internal Error: Failed to add effect: input_tensor"); } } @@ -207,7 +211,8 @@ void SoxEffectsChain::addOutputBuffer( SoxEffect e(sox_create_effect(get_tensor_output_handler())); static_cast(e->priv)->buffer = output_buffer; if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { - throw std::runtime_error("Internal Error: Failed to add effect: output_tensor"); + throw std::runtime_error( + "Internal Error: Failed to add effect: output_tensor"); } } @@ -291,6 +296,7 @@ namespace { struct FileObjInputPriv { sox_format_t* sf; py::object* fileobj; + bool eof_reached; char* buffer; uint64_t buffer_size; }; @@ -305,47 +311,54 @@ struct FileObjOutputPriv { /// Callback function to feed byte string /// https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/sox.h#L1268-L1278 int fileobj_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { - auto priv = static_cast(effp->priv); + auto priv = static_cast(effp->priv); auto sf = priv->sf; - auto fileobj = priv->fileobj; auto buffer = priv->buffer; - auto buffer_size = priv->buffer_size; // 1. Refresh the buffer // // NOTE: // Since the underlying FILE* was opened with `fmemopen`, the only way - // libsox detect EOF is reaching the end of the buffer. (null byte won't help) - // Therefore we need to align the content at the end of buffer, otherwise, - // libsox will keep reading the content beyond intended length. + // libsox detect EOF is reaching the end of the buffer. (null byte won't + // help) Therefore we need to align the content at the end of buffer, + // otherwise, libsox will keep reading the content beyond intended length. // // Before: // - // |<--------consumed------->|<-remaining->| - // |*************************|-------------| - // ^ ftell + // |<-------consumed------>|<---remaining--->| + // |***********************|-----------------| + // ^ ftell // // After: // - // |<-offset->|<-remaining->|<--new data-->| - // |**********|-------------|++++++++++++++| + // |<-offset->|<---remaining--->|<-new data->| + // |**********|-----------------|++++++++++++| // ^ ftell - const auto num_consumed = sf->tell_off; - const auto num_remain = buffer_size - num_consumed; + // NOTE: + // Do not use `sf->tell_off` here. Presumably, `tell_off` and `fseek` are + // supposed to be in sync, but there are cases (Vorbis) they are not + // in sync and `tell_off` has seemingly uninitialized value, which + // leads num_remain to be negative and cause segmentation fault + // in `memmove`. + const auto num_consumed = ftell((FILE*)sf->fp); + if (num_consumed > priv->buffer_size) { + throw std::runtime_error("Internal Error: buffer overrun."); + } - // 1.1. First, we fetch the data to see if there is data to fill the buffer - py::bytes chunk_ = fileobj->attr("read")(num_consumed); - const auto num_refill = py::len(chunk_); - const auto offset = buffer_size - (num_remain + num_refill); + const auto num_remain = priv->buffer_size - num_consumed; - if(num_refill > num_consumed) { - std::ostringstream message; - message << "Tried to read up to " << num_consumed << " bytes but, " - << "recieved " << num_refill << " bytes. " - << "The given object does not confirm to read protocol of file object."; - throw std::runtime_error(message.str()); + // 1.1. Fetch the data to see if there is data to fill the buffer + uint64_t num_refill = 0; + std::string chunk(num_consumed, '\0'); + if (num_consumed && !priv->eof_reached) { + num_refill = read_fileobj( + priv->fileobj, num_consumed, const_cast(chunk.data())); + if (num_refill < num_consumed) { + priv->eof_reached = true; + } } + const auto offset = num_consumed - num_refill; // 1.2. Move the unconsumed data towards the beginning of buffer. if (num_remain) { @@ -356,7 +369,6 @@ int fileobj_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { // 1.3. Refill the remaining buffer. if (num_refill) { - auto chunk = static_cast(chunk_); auto src = static_cast(const_cast(chunk.c_str())); auto dst = buffer + offset + num_remain; memcpy(dst, src, num_refill); @@ -364,7 +376,7 @@ int fileobj_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { // 1.4. Set the file pointer to the new offset sf->tell_off = offset; - fseek ((FILE*)sf->fp, offset, SEEK_SET); + fseek((FILE*)sf->fp, offset, SEEK_SET); // 2. Perform decoding operation // The following part is practically same as "input" effect @@ -377,7 +389,9 @@ int fileobj_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { // store the actual number read back to *osamp *osamp = sox_read(sf, obuf, *osamp); - return *osamp? SOX_SUCCESS : SOX_EOF; + // Decoding is finished when fileobject is exhausted and sox can no longer + // decode a sample. + return (priv->eof_reached && !*osamp) ? SOX_EOF : SOX_SUCCESS; } int fileobj_output_flow( @@ -420,30 +434,32 @@ int fileobj_output_flow( } sox_effect_handler_t* get_fileobj_input_handler() { - static sox_effect_handler_t handler{/*name=*/"input_fileobj_object", - /*usage=*/NULL, - /*flags=*/SOX_EFF_MCHAN, - /*getopts=*/NULL, - /*start=*/NULL, - /*flow=*/NULL, - /*drain=*/fileobj_input_drain, - /*stop=*/NULL, - /*kill=*/NULL, - /*priv_size=*/sizeof(FileObjInputPriv)}; + static sox_effect_handler_t handler{ + /*name=*/"input_fileobj_object", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/NULL, + /*drain=*/fileobj_input_drain, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(FileObjInputPriv)}; return &handler; } sox_effect_handler_t* get_fileobj_output_handler() { - static sox_effect_handler_t handler{/*name=*/"output_fileobj_object", - /*usage=*/NULL, - /*flags=*/SOX_EFF_MCHAN, - /*getopts=*/NULL, - /*start=*/NULL, - /*flow=*/fileobj_output_flow, - /*drain=*/NULL, - /*stop=*/NULL, - /*kill=*/NULL, - /*priv_size=*/sizeof(FileObjOutputPriv)}; + static sox_effect_handler_t handler{ + /*name=*/"output_fileobj_object", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/fileobj_output_flow, + /*drain=*/NULL, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(FileObjOutputPriv)}; return &handler; } @@ -461,10 +477,12 @@ void SoxEffectsChain::addInputFileObj( auto priv = static_cast(e->priv); priv->sf = sf; priv->fileobj = fileobj; + priv->eof_reached = false; priv->buffer = buffer; priv->buffer_size = buffer_size; if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { - throw std::runtime_error("Internal Error: Failed to add effect: input fileobj"); + throw std::runtime_error( + "Internal Error: Failed to add effect: input fileobj"); } } @@ -481,7 +499,8 @@ void SoxEffectsChain::addOutputFileObj( priv->buffer = buffer; priv->buffer_size = buffer_size; if (sox_add_effect(sec_, e, &interm_sig_, &out_sig_) != SOX_SUCCESS) { - throw std::runtime_error("Internal Error: Failed to add effect: output fileobj"); + throw std::runtime_error( + "Internal Error: Failed to add effect: output fileobj"); } } diff --git a/torchaudio/csrc/sox/io.cpp b/torchaudio/csrc/sox/io.cpp index c6531eb77e..4594fa4875 100644 --- a/torchaudio/csrc/sox/io.cpp +++ b/torchaudio/csrc/sox/io.cpp @@ -13,10 +13,14 @@ namespace sox_io { SignalInfo::SignalInfo( const int64_t sample_rate_, const int64_t num_channels_, - const int64_t num_frames_) + const int64_t num_frames_, + const int64_t bits_per_sample_, + const std::string encoding_) : sample_rate(sample_rate_), num_channels(num_channels_), - num_frames(num_frames_){}; + num_frames(num_frames_), + bits_per_sample(bits_per_sample_), + encoding(encoding_){}; int64_t SignalInfo::getSampleRate() const { return sample_rate; @@ -30,7 +34,50 @@ int64_t SignalInfo::getNumFrames() const { return num_frames; } -c10::intrusive_ptr get_info( +int64_t SignalInfo::getBitsPerSample() const { + return bits_per_sample; +} + +std::string SignalInfo::getEncoding() const { + return encoding; +} + +namespace { + +std::string get_encoding(sox_encoding_t encoding) { + switch (encoding) { + case SOX_ENCODING_UNKNOWN: + return "UNKNOWN"; + case SOX_ENCODING_SIGN2: + return "PCM_S"; + case SOX_ENCODING_UNSIGNED: + return "PCM_U"; + case SOX_ENCODING_FLOAT: + return "PCM_F"; + case SOX_ENCODING_FLAC: + return "FLAC"; + case SOX_ENCODING_ULAW: + return "ULAW"; + case SOX_ENCODING_ALAW: + return "ALAW"; + case SOX_ENCODING_MP3: + return "MP3"; + case SOX_ENCODING_VORBIS: + return "VORBIS"; + case SOX_ENCODING_AMR_WB: + return "AMR_WB"; + case SOX_ENCODING_AMR_NB: + return "AMR_NB"; + case SOX_ENCODING_OPUS: + return "OPUS"; + default: + return "UNKNOWN"; + } +} + +} // namespace + +c10::intrusive_ptr get_info_file( const std::string& path, c10::optional& format) { SoxFormat sf(sox_open_read( @@ -46,7 +93,9 @@ c10::intrusive_ptr get_info( return c10::make_intrusive( static_cast(sf->signal.rate), static_cast(sf->signal.channels), - static_cast(sf->signal.length / sf->signal.channels)); + static_cast(sf->signal.length / sf->signal.channels), + static_cast(sf->encoding.bits_per_sample), + get_encoding(sf->encoding.encoding)); } namespace { @@ -100,13 +149,23 @@ void save_audio_file( int64_t sample_rate, bool channels_first, c10::optional compression, - c10::optional format) { + c10::optional format, + c10::optional dtype) { validate_input_tensor(tensor); auto signal = TensorSignal(tensor, sample_rate, channels_first); - - const auto filetype = [&](){ - if (format.has_value()) return format.value(); + if (tensor.dtype() != torch::kFloat32 && dtype.has_value()) { + throw std::runtime_error( + "dtype conversion only supported for float32 tensors"); + } + const auto tgt_dtype = + (tensor.dtype() == torch::kFloat32 && dtype.has_value()) + ? get_dtype_from_str(dtype.value()) + : tensor.dtype(); + + const auto filetype = [&]() { + if (format.has_value()) + return format.value(); return get_filetype(path); }(); if (filetype == "amr-nb") { @@ -116,7 +175,7 @@ void save_audio_file( tensor = (unnormalize_wav(tensor) / 65536).to(torch::kInt16); } const auto signal_info = get_signalinfo(&signal, filetype); - const auto encoding_info = get_encodinginfo(filetype, tensor.dtype(), compression); + const auto encoding_info = get_encodinginfo(filetype, tgt_dtype, compression); SoxFormat sf(sox_open_write( path.c_str(), @@ -140,6 +199,57 @@ void save_audio_file( #ifdef TORCH_API_INCLUDE_EXTENSION_H +std::tuple get_info_fileobj( + py::object fileobj, + c10::optional& format) { + // Prepare in-memory file object + // When libsox opens a file, it also reads the header. + // When opening a file there are two functions that might touch FILE* (and the + // underlying buffer). + // * `auto_detect_format` + // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/formats.c#L43 + // * `startread` handler of detected format. + // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/formats.c#L574 + // To see the handler of a particular format, go to + // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/.c + // For example, voribs can be found + // https://github.com/dmkrepo/libsox/blob/b9dd1a86e71bbd62221904e3e59dfaa9e5e72046/src/vorbis.c#L97-L158 + // + // `auto_detect_format` function only requires 256 bytes, but format-dependent + // `startread` handler might require more data. In case of vorbis, the size of + // header is unbounded, but typically 4kB maximum. + // + // "The header size is unbounded, although for streaming a rule-of-thumb of + // 4kB or less is recommended (and Xiph.Org's Vorbis encoder follows this + // suggestion)." + // + // See: + // https://xiph.org/vorbis/doc/Vorbis_I_spec.html + auto capacity = 4096; + std::string buffer(capacity, '\0'); + auto* buf = const_cast(buffer.data()); + auto num_read = read_fileobj(&fileobj, capacity, buf); + // If the file is shorter than 256, then libsox cannot read the header. + auto buf_size = (num_read > 256) ? num_read : 256; + + SoxFormat sf(sox_open_mem_read( + buf, + buf_size, + /*signal=*/nullptr, + /*encoding=*/nullptr, + /*filetype=*/format.has_value() ? format.value().c_str() : nullptr)); + + // In case of streamed data, length can be 0 + validate_input_file(sf, /*check_length=*/false); + + return std::make_tuple( + static_cast(sf->signal.rate), + static_cast(sf->signal.length / sf->signal.channels), + static_cast(sf->signal.channels), + static_cast(sf->encoding.bits_per_sample), + get_encoding(sf->encoding.encoding)); +} + std::tuple load_audio_fileobj( py::object fileobj, c10::optional& frame_offset, @@ -154,7 +264,8 @@ std::tuple load_audio_fileobj( namespace { -// helper class to automatically release buffer, to be used by save_audio_fileobj +// helper class to automatically release buffer, to be used by +// save_audio_fileobj struct AutoReleaseBuffer { char* ptr; size_t size; @@ -179,20 +290,30 @@ void save_audio_fileobj( int64_t sample_rate, bool channels_first, c10::optional compression, - std::string filetype) { + std::string filetype, + c10::optional dtype) { validate_input_tensor(tensor); auto signal = TensorSignal(tensor, sample_rate, channels_first); + if (tensor.dtype() != torch::kFloat32 && dtype.has_value()) { + throw std::runtime_error( + "dtype conversion only supported for float32 tensors"); + } + const auto tgt_dtype = + (tensor.dtype() == torch::kFloat32 && dtype.has_value()) + ? get_dtype_from_str(dtype.value()) + : tensor.dtype(); if (filetype == "amr-nb") { const auto num_channels = tensor.size(channels_first ? 0 : 1); if (num_channels != 1) { - throw std::runtime_error("amr-nb format only supports single channel audio."); + throw std::runtime_error( + "amr-nb format only supports single channel audio."); } tensor = (unnormalize_wav(tensor) / 65536).to(torch::kInt16); } const auto signal_info = get_signalinfo(&signal, filetype); - const auto encoding_info = get_encodinginfo(filetype, tensor.dtype(), compression); + const auto encoding_info = get_encodinginfo(filetype, tgt_dtype, compression); AutoReleaseBuffer buffer; @@ -205,7 +326,8 @@ void save_audio_fileobj( /*oob=*/nullptr)); if (static_cast(sf) == nullptr) { - throw std::runtime_error("Error saving audio file: failed to open memory stream."); + throw std::runtime_error( + "Error saving audio file: failed to open memory stream."); } torchaudio::sox_effects_chain::SoxEffectsChain chain( @@ -215,7 +337,8 @@ void save_audio_fileobj( chain.addOutputFileObj(sf, &buffer.ptr, &buffer.size, &fileobj); chain.run(); - // Closing the sox_format_t is necessary for flushing the last chunk to the buffer + // Closing the sox_format_t is necessary for flushing the last chunk to the + // buffer sf.close(); fileobj.attr("write")(py::bytes(buffer.ptr, buffer.size)); diff --git a/torchaudio/csrc/sox/io.h b/torchaudio/csrc/sox/io.h index ac7191527f..fa1f3d61f9 100644 --- a/torchaudio/csrc/sox/io.h +++ b/torchaudio/csrc/sox/io.h @@ -15,17 +15,23 @@ struct SignalInfo : torch::CustomClassHolder { int64_t sample_rate; int64_t num_channels; int64_t num_frames; + int64_t bits_per_sample; + std::string encoding; SignalInfo( const int64_t sample_rate_, const int64_t num_channels_, - const int64_t num_frames_); + const int64_t num_frames_, + const int64_t bits_per_sample_, + const std::string encoding_); int64_t getSampleRate() const; int64_t getNumChannels() const; int64_t getNumFrames() const; + int64_t getBitsPerSample() const; + std::string getEncoding() const; }; -c10::intrusive_ptr get_info( +c10::intrusive_ptr get_info_file( const std::string& path, c10::optional& format); @@ -43,10 +49,15 @@ void save_audio_file( int64_t sample_rate, bool channels_first, c10::optional compression, - c10::optional format); + c10::optional format, + c10::optional dtype); #ifdef TORCH_API_INCLUDE_EXTENSION_H +std::tuple get_info_fileobj( + py::object fileobj, + c10::optional& format); + std::tuple load_audio_fileobj( py::object fileobj, c10::optional& frame_offset, @@ -61,7 +72,8 @@ void save_audio_fileobj( int64_t sample_rate, bool channels_first, c10::optional compression, - std::string filetype); + std::string filetype, + c10::optional dtype); #endif // TORCH_API_INCLUDE_EXTENSION_H diff --git a/torchaudio/csrc/sox/legacy.cpp b/torchaudio/csrc/sox/legacy.cpp index 97e1d1d32b..858ad15149 100644 --- a/torchaudio/csrc/sox/legacy.cpp +++ b/torchaudio/csrc/sox/legacy.cpp @@ -40,10 +40,7 @@ int64_t write_audio(SoxDescriptor& fd, at::Tensor tensor) { return samples_written; } -void read_audio( - SoxDescriptor& fd, - at::Tensor output, - int64_t buffer_length) { +void read_audio(SoxDescriptor& fd, at::Tensor output, int64_t buffer_length) { std::vector buffer(buffer_length); int number_of_channels = fd->signal.channels; @@ -64,8 +61,7 @@ void read_audio( } // namespace std::tuple get_info( - const std::string& file_name - ) { + const std::string& file_name) { SoxDescriptor fd(sox_open_read( file_name.c_str(), /*signal=*/nullptr, @@ -86,7 +82,6 @@ int read_audio_file( sox_signalinfo_t* si, sox_encodinginfo_t* ei, const char* ft) { - SoxDescriptor fd(sox_open_read(file_name.c_str(), si, ei, ft)); if (fd.get() == nullptr) { throw std::runtime_error("Error opening audio file"); @@ -112,15 +107,16 @@ int read_audio_file( // calculate buffer length int64_t buffer_length = total_length; if (offset > 0) { - buffer_length -= offset; + buffer_length -= offset; } if (nframes > 0 && buffer_length > nframes) { - buffer_length = nframes; + buffer_length = nframes; } // seek to offset point before reading data if (sox_seek(fd.get(), offset, 0) == SOX_EOF) { - throw std::runtime_error("sox_seek reached EOF, try reducing offset or num_samples"); + throw std::runtime_error( + "sox_seek reached EOF, try reducing offset or num_samples"); } // read data and fill output tensor diff --git a/torchaudio/csrc/sox/legacy.h b/torchaudio/csrc/sox/legacy.h index 6b3f8383b2..5869695bfe 100644 --- a/torchaudio/csrc/sox/legacy.h +++ b/torchaudio/csrc/sox/legacy.h @@ -1,7 +1,8 @@ #include #include -namespace torch { namespace audio { +namespace torch { +namespace audio { /// Reads an audio file from the given `path` into the `output` `Tensor` and /// returns the sample rate of the audio file. @@ -30,9 +31,10 @@ void write_audio_file( /// Reads an audio file from the given `path` and returns a tuple of /// sox_signalinfo_t and sox_encodinginfo_t, which contain information about -/// the audio file such as sample rate, length, bit precision, encoding and more. -/// Throws `std::runtime_error` if the audio file could not be opened, or an -/// error occurred during reading of the audio data. +/// the audio file such as sample rate, length, bit precision, encoding and +/// more. Throws `std::runtime_error` if the audio file could not be opened, or +/// an error occurred during reading of the audio data. std::tuple get_info( const std::string& file_name); -}} // namespace torch::audio +} // namespace audio +} // namespace torch diff --git a/torchaudio/csrc/sox/register.cpp b/torchaudio/csrc/sox/register.cpp index 7c65bebe2d..d90a3b6df1 100644 --- a/torchaudio/csrc/sox/register.cpp +++ b/torchaudio/csrc/sox/register.cpp @@ -42,17 +42,21 @@ TORCH_LIBRARY_FRAGMENT(torchaudio, m) { m.class_("SignalInfo") .def("get_sample_rate", &torchaudio::sox_io::SignalInfo::getSampleRate) .def("get_num_channels", &torchaudio::sox_io::SignalInfo::getNumChannels) - .def("get_num_frames", &torchaudio::sox_io::SignalInfo::getNumFrames); + .def("get_num_frames", &torchaudio::sox_io::SignalInfo::getNumFrames) + .def( + "get_bits_per_sample", + &torchaudio::sox_io::SignalInfo::getBitsPerSample) + .def("get_encoding", &torchaudio::sox_io::SignalInfo::getEncoding); - m.def("torchaudio::sox_io_get_info", &torchaudio::sox_io::get_info); + m.def("torchaudio::sox_io_get_info", &torchaudio::sox_io::get_info_file); m.def( "torchaudio::sox_io_load_audio_file(" - "str path," - "int? frame_offset=None," - "int? num_frames=None," - "bool? normalize=True," - "bool? channels_first=False," - "str? format=None" + "str path," + "int? frame_offset=None," + "int? num_frames=None," + "bool? normalize=True," + "bool? channels_first=False," + "str? format=None" ") -> __torch__.torch.classes.torchaudio.TensorSignal", &torchaudio::sox_io::load_audio_file); m.def( diff --git a/torchaudio/csrc/sox/utils.cpp b/torchaudio/csrc/sox/utils.cpp index 0ea95cbbb3..f6bd3c8137 100644 --- a/torchaudio/csrc/sox/utils.cpp +++ b/torchaudio/csrc/sox/utils.cpp @@ -80,7 +80,9 @@ bool TensorSignal::getChannelsFirst() const { } SoxFormat::SoxFormat(sox_format_t* fd) noexcept : fd_(fd) {} -SoxFormat::~SoxFormat() { close(); } +SoxFormat::~SoxFormat() { + close(); +} sox_format_t* SoxFormat::operator->() const noexcept { return fd_; @@ -154,6 +156,24 @@ caffe2::TypeMeta get_dtype( return c10::scalarTypeToTypeMeta(dtype); } +caffe2::TypeMeta get_dtype_from_str(const std::string dtype) { + const auto tgt_dtype = [&]() { + if (dtype == "uint8") + return torch::kUInt8; + else if (dtype == "int16") + return torch::kInt16; + else if (dtype == "int32") + return torch::kInt32; + else if (dtype == "float32") + return torch::kFloat32; + else if (dtype == "float64") + return torch::kFloat64; + else + throw std::runtime_error("Unsupported dtype"); + }(); + return c10::scalarTypeToTypeMeta(tgt_dtype); +} + torch::Tensor convert_to_tensor( sox_sample_t* buffer, const int32_t num_samples, @@ -291,27 +311,58 @@ sox_signalinfo_t get_signalinfo( sox_encodinginfo_t get_encodinginfo( const std::string filetype, const caffe2::TypeMeta dtype) { - return sox_encodinginfo_t{/*encoding=*/get_encoding(filetype, dtype), - /*bits_per_sample=*/get_precision(filetype, dtype), - /*compression=*/HUGE_VAL, - /*reverse_bytes=*/sox_option_default, - /*reverse_nibbles=*/sox_option_default, - /*reverse_bits=*/sox_option_default, - /*opposite_endian=*/sox_false}; + return sox_encodinginfo_t{ + /*encoding=*/get_encoding(filetype, dtype), + /*bits_per_sample=*/get_precision(filetype, dtype), + /*compression=*/HUGE_VAL, + /*reverse_bytes=*/sox_option_default, + /*reverse_nibbles=*/sox_option_default, + /*reverse_bits=*/sox_option_default, + /*opposite_endian=*/sox_false}; } sox_encodinginfo_t get_encodinginfo( const std::string filetype, const caffe2::TypeMeta dtype, c10::optional& compression) { - return sox_encodinginfo_t{/*encoding=*/get_encoding(filetype, dtype), - /*bits_per_sample=*/get_precision(filetype, dtype), - /*compression=*/compression.value_or(HUGE_VAL), - /*reverse_bytes=*/sox_option_default, - /*reverse_nibbles=*/sox_option_default, - /*reverse_bits=*/sox_option_default, - /*opposite_endian=*/sox_false}; + return sox_encodinginfo_t{ + /*encoding=*/get_encoding(filetype, dtype), + /*bits_per_sample=*/get_precision(filetype, dtype), + /*compression=*/compression.value_or(HUGE_VAL), + /*reverse_bytes=*/sox_option_default, + /*reverse_nibbles=*/sox_option_default, + /*reverse_bits=*/sox_option_default, + /*opposite_endian=*/sox_false}; } +#ifdef TORCH_API_INCLUDE_EXTENSION_H + +uint64_t read_fileobj(py::object* fileobj, const uint64_t size, char* buffer) { + uint64_t num_read = 0; + while (num_read < size) { + auto request = size - num_read; + auto chunk = static_cast( + static_cast(fileobj->attr("read")(request))); + auto chunk_len = chunk.length(); + if (chunk_len == 0) { + break; + } + if (chunk_len > request) { + std::ostringstream message; + message + << "Requested up to " << request << " bytes but, " + << "received " << chunk_len << " bytes. " + << "The given object does not confirm to read protocol of file object."; + throw std::runtime_error(message.str()); + } + memcpy(buffer, chunk.data(), chunk_len); + buffer += chunk_len; + num_read += chunk_len; + } + return num_read; +} + +#endif // TORCH_API_INCLUDE_EXTENSION_H + } // namespace sox_utils } // namespace torchaudio diff --git a/torchaudio/csrc/sox/utils.h b/torchaudio/csrc/sox/utils.h index 2d434d6f72..1d99158378 100644 --- a/torchaudio/csrc/sox/utils.h +++ b/torchaudio/csrc/sox/utils.h @@ -4,6 +4,10 @@ #include #include +#ifdef TORCH_API_INCLUDE_EXTENSION_H +#include +#endif // TORCH_API_INCLUDE_EXTENSION_H + namespace torchaudio { namespace sox_utils { @@ -69,7 +73,7 @@ struct SoxFormat { /// /// Verify that input file is found, has known encoding, and not empty -void validate_input_file(const SoxFormat& sf, bool check_length=true); +void validate_input_file(const SoxFormat& sf, bool check_length = true); /// /// Verify that input Tensor is 2D, CPU and either uin8, int16, int32 or float32 @@ -81,6 +85,8 @@ caffe2::TypeMeta get_dtype( const sox_encoding_t encoding, const unsigned precision); +caffe2::TypeMeta get_dtype_from_str(const std::string dtype); + /// /// Convert sox_sample_t buffer to uint8/int16/int32/float32 Tensor /// NOTE: This function might modify the values in the input buffer to @@ -127,6 +133,12 @@ sox_encodinginfo_t get_encodinginfo( const caffe2::TypeMeta dtype, c10::optional& compression); +#ifdef TORCH_API_INCLUDE_EXTENSION_H + +uint64_t read_fileobj(py::object* fileobj, uint64_t size, char* buffer); + +#endif // TORCH_API_INCLUDE_EXTENSION_H + } // namespace sox_utils } // namespace torchaudio #endif diff --git a/torchaudio/csrc/transducer.cpp b/torchaudio/csrc/transducer.cpp index 3ca7a5ebde..f5d06c1ba9 100644 --- a/torchaudio/csrc/transducer.cpp +++ b/torchaudio/csrc/transducer.cpp @@ -8,71 +8,80 @@ namespace { -int64_t cpu_rnnt_loss(torch::Tensor acts, - torch::Tensor labels, - torch::Tensor input_lengths, - torch::Tensor label_lengths, - torch::Tensor costs, - torch::Tensor grads, - int64_t blank_label, - int64_t num_threads) { - - int maxT = acts.size(1); - int maxU = acts.size(2); - int minibatch_size = acts.size(0); - int alphabet_size = acts.size(3); - - rnntOptions options; - memset(&options, 0, sizeof(options)); - options.maxT = maxT; - options.maxU = maxU; - options.blank_label = blank_label; - options.batch_first = true; - options.loc = RNNT_CPU; - options.num_threads = num_threads; - - // have to use at least one - options.num_threads = std::max(options.num_threads, (unsigned int) 1); - - size_t cpu_size_bytes = 0; - switch (acts.scalar_type()) { - case torch::ScalarType::Float: - { - get_workspace_size(maxT, maxU, minibatch_size, - false, &cpu_size_bytes); - - std::vector cpu_workspace(cpu_size_bytes / sizeof(float), 0); - - compute_rnnt_loss(acts.data_ptr(), grads.data_ptr(), - labels.data_ptr(), label_lengths.data_ptr(), - input_lengths.data_ptr(), alphabet_size, - minibatch_size, costs.data_ptr(), - cpu_workspace.data(), options); - - return 0; - } - case torch::ScalarType::Double: - { - get_workspace_size(maxT, maxU, minibatch_size, - false, &cpu_size_bytes, - sizeof(double)); - - std::vector cpu_workspace(cpu_size_bytes / sizeof(double), 0); - - compute_rnnt_loss_fp64(acts.data_ptr(), grads.data_ptr(), - labels.data_ptr(), label_lengths.data_ptr(), - input_lengths.data_ptr(), alphabet_size, - minibatch_size, costs.data_ptr(), - cpu_workspace.data(), options); - - return 0; - } - default: - TORCH_CHECK(false, - std::string(__func__) + " not implemented for '" + toString(acts.scalar_type()) + "'" - ); +int64_t cpu_rnnt_loss( + torch::Tensor acts, + torch::Tensor labels, + torch::Tensor input_lengths, + torch::Tensor label_lengths, + torch::Tensor costs, + torch::Tensor grads, + int64_t blank_label, + int64_t num_threads) { + int maxT = acts.size(1); + int maxU = acts.size(2); + int minibatch_size = acts.size(0); + int alphabet_size = acts.size(3); + + rnntOptions options; + memset(&options, 0, sizeof(options)); + options.maxT = maxT; + options.maxU = maxU; + options.blank_label = blank_label; + options.batch_first = true; + options.loc = RNNT_CPU; + options.num_threads = num_threads; + + // have to use at least one + options.num_threads = std::max(options.num_threads, (unsigned int)1); + + size_t cpu_size_bytes = 0; + switch (acts.scalar_type()) { + case torch::ScalarType::Float: { + get_workspace_size(maxT, maxU, minibatch_size, false, &cpu_size_bytes); + + std::vector cpu_workspace(cpu_size_bytes / sizeof(float), 0); + + compute_rnnt_loss( + acts.data_ptr(), + grads.data_ptr(), + labels.data_ptr(), + label_lengths.data_ptr(), + input_lengths.data_ptr(), + alphabet_size, + minibatch_size, + costs.data_ptr(), + cpu_workspace.data(), + options); + + return 0; + } + case torch::ScalarType::Double: { + get_workspace_size( + maxT, maxU, minibatch_size, false, &cpu_size_bytes, sizeof(double)); + + std::vector cpu_workspace(cpu_size_bytes / sizeof(double), 0); + + compute_rnnt_loss_fp64( + acts.data_ptr(), + grads.data_ptr(), + labels.data_ptr(), + label_lengths.data_ptr(), + input_lengths.data_ptr(), + alphabet_size, + minibatch_size, + costs.data_ptr(), + cpu_workspace.data(), + options); + + return 0; } - return -1; + default: + TORCH_CHECK( + false, + std::string(__func__) + " not implemented for '" + + toString(acts.scalar_type()) + "'"); + } + return -1; } } // namespace @@ -82,12 +91,13 @@ TORCH_LIBRARY_IMPL(torchaudio, CPU, m) { } TORCH_LIBRARY_FRAGMENT(torchaudio, m) { - m.def("rnnt_loss(Tensor acts," - "Tensor labels," - "Tensor input_lengths," - "Tensor label_lengths," - "Tensor costs," - "Tensor grads," - "int blank_label," - "int num_threads) -> int"); + m.def( + "rnnt_loss(Tensor acts," + "Tensor labels," + "Tensor input_lengths," + "Tensor label_lengths," + "Tensor costs," + "Tensor grads," + "int blank_label," + "int num_threads) -> int"); } diff --git a/torchaudio/datasets/ljspeech.py b/torchaudio/datasets/ljspeech.py index 01d3fe7d95..99058dd83e 100644 --- a/torchaudio/datasets/ljspeech.py +++ b/torchaudio/datasets/ljspeech.py @@ -8,31 +8,15 @@ from torch import Tensor from torch.utils.data import Dataset -URL = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2" -FOLDER_IN_ARCHIVE = "wavs" -_CHECKSUMS = { - "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2": - "be1a30453f28eb8dd26af4101ae40cbf2c50413b1bb21936cbcdc6fae3de8aa5" +_RELEASE_CONFIGS = { + "release1": { + "folder_in_archive": "wavs", + "url": "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2", + "checksum": "be1a30453f28eb8dd26af4101ae40cbf2c50413b1bb21936cbcdc6fae3de8aa5", + } } -def load_ljspeech_item(line: List[str], path: str, ext_audio: str) -> Tuple[Tensor, int, str, str]: - assert len(line) == 3 - fileid, transcript, normalized_transcript = line - fileid_audio = fileid + ext_audio - fileid_audio = os.path.join(path, fileid_audio) - - # Load audio - waveform, sample_rate = torchaudio.load(fileid_audio) - - return ( - waveform, - sample_rate, - transcript, - normalized_transcript, - ) - - class LJSPEECH(Dataset): """Create a Dataset for LJSpeech-1.1. @@ -46,37 +30,36 @@ class LJSPEECH(Dataset): Whether to download the dataset if it is not found at root path. (default: ``False``). """ - _ext_audio = ".wav" - _ext_archive = '.tar.bz2' - def __init__(self, root: Union[str, Path], - url: str = URL, - folder_in_archive: str = FOLDER_IN_ARCHIVE, + url: str = _RELEASE_CONFIGS["release1"]["url"], + folder_in_archive: str = _RELEASE_CONFIGS["release1"]["folder_in_archive"], download: bool = False) -> None: - # Get string representation of 'root' in case Path object is passed - root = os.fspath(root) + self._parse_filesystem(root, url, folder_in_archive, download) + + def _parse_filesystem(self, root: str, url: str, folder_in_archive: str, download: bool) -> None: + root = Path(root) basename = os.path.basename(url) - archive = os.path.join(root, basename) + archive = root / basename - basename = basename.split(self._ext_archive)[0] - folder_in_archive = os.path.join(basename, folder_in_archive) + basename = Path(basename.split(".tar.bz2")[0]) + folder_in_archive = basename / folder_in_archive - self._path = os.path.join(root, folder_in_archive) - self._metadata_path = os.path.join(root, basename, 'metadata.csv') + self._path = root / folder_in_archive + self._metadata_path = root / basename / 'metadata.csv' if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - checksum = _CHECKSUMS.get(url, None) + checksum = _RELEASE_CONFIGS["release1"]["checksum"] download_url(url, root, hash_value=checksum) extract_archive(archive) with open(self._metadata_path, "r", newline='') as metadata: - walker = csv.reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE) - self._walker = list(walker) + flist = csv.reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE) + self._flist = list(flist) def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: """Load the n-th sample from the dataset. @@ -87,8 +70,19 @@ def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: Returns: tuple: ``(waveform, sample_rate, transcript, normalized_transcript)`` """ - line = self._walker[n] - return load_ljspeech_item(line, self._path, self._ext_audio) + line = self._flist[n] + fileid, transcript, normalized_transcript = line + fileid_audio = self._path / (fileid + ".wav") + + # Load audio + waveform, sample_rate = torchaudio.load(fileid_audio) + + return ( + waveform, + sample_rate, + transcript, + normalized_transcript, + ) def __len__(self) -> int: - return len(self._walker) + return len(self._flist) diff --git a/torchaudio/functional/__init__.py b/torchaudio/functional/__init__.py index 3d1aa00f86..688708296b 100644 --- a/torchaudio/functional/__init__.py +++ b/torchaudio/functional/__init__.py @@ -16,6 +16,7 @@ phase_vocoder, sliding_window_cmn, spectrogram, + spectral_centroid, ) from .filtering import ( allpass_biquad, diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index 0c07ec0847..73941946ec 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -27,6 +27,7 @@ 'mask_along_axis', 'mask_along_axis_iid', 'sliding_window_cmn', + "spectral_centroid", ] @@ -38,7 +39,10 @@ def spectrogram( hop_length: int, win_length: int, power: Optional[float], - normalized: bool + normalized: bool, + center: bool = True, + pad_mode: str = "reflect", + onesided: bool = True ) -> Tensor: r"""Create a spectrogram or a batch of spectrograms from a raw audio signal. The spectrogram can be either magnitude-only or complex. @@ -54,6 +58,13 @@ def spectrogram( (must be > 0) e.g., 1 for energy, 2 for power, etc. If None, then the complex spectrum is returned instead. normalized (bool): Whether to normalize by magnitude after stft + center (bool, optional): whether to pad :attr:`waveform` on both sides so + that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. + Default: ``True`` + pad_mode (string, optional): controls the padding method used when + :attr:`center` is ``True``. Default: ``"reflect"`` + onesided (bool, optional): controls whether to return half of results to + avoid redundancy. Default: ``True`` Returns: Tensor: Dimension (..., freq, time), freq is @@ -76,10 +87,10 @@ def spectrogram( hop_length=hop_length, win_length=win_length, window=window, - center=True, - pad_mode="reflect", + center=center, + pad_mode=pad_mode, normalized=False, - onesided=True, + onesided=onesided, return_complex=True, ) @@ -935,3 +946,38 @@ def sliding_window_cmn( if len(input_shape) == 2: cmn_waveform = cmn_waveform.squeeze(0) return cmn_waveform + + +def spectral_centroid( + waveform: Tensor, + sample_rate: int, + pad: int, + window: Tensor, + n_fft: int, + hop_length: int, + win_length: int, +) -> Tensor: + r""" + Compute the spectral centroid for each channel along the time axis. + + The spectral centroid is defined as the weighted average of the + frequency values, weighted by their magnitude. + + Args: + waveform (Tensor): Tensor of audio of dimension (..., time) + sample_rate (int): Sample rate of the audio waveform + pad (int): Two sided padding of signal + window (Tensor): Window tensor that is applied/multiplied to each frame/window + n_fft (int): Size of FFT + hop_length (int): Length of hop between STFT windows + win_length (int): Window size + + Returns: + Tensor: Dimension (..., time) + """ + specgram = spectrogram(waveform, pad=pad, window=window, n_fft=n_fft, hop_length=hop_length, + win_length=win_length, power=1., normalized=False) + freqs = torch.linspace(0, sample_rate // 2, steps=1 + n_fft // 2, + device=specgram.device).reshape((-1, 1)) + freq_dim = -2 + return (freqs * specgram).sum(dim=freq_dim) / specgram.sum(dim=freq_dim) diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 7534714965..d94a776cd6 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -1,7 +1,10 @@ +import os +from pathlib import Path from typing import List, Tuple, Optional import torch +import torchaudio from torchaudio._internal import module_utils as _mod_utils from torchaudio.utils.sox_utils import list_effects @@ -170,8 +173,16 @@ def apply_effects_file( rate and leave samples untouched. Args: - path (str or pathlib.Path): Path to the audio file. This function also handles ``pathlib.Path`` objects, but is - annotated as ``str`` for TorchScript compiler compatibility. + path (path-like object or file-like object): + Source of audio data. When the function is not compiled by TorchScript, + (e.g. ``torch.jit.script``), the following types are accepted; + * ``path-like``: file path + * ``file-like``: Object with ``read(size: int) -> bytes`` method, + which returns byte string of at most ``size`` length. + When the function is compiled by TorchScript, only ``str`` type is allowed. + Note: + * This argument is intentionally annotated as ``str`` only for + TorchScript compiler compatibility. effects (List[List[str]]): List of effects. normalize (bool): When ``True``, this function always return ``float32``, and sample values are @@ -252,8 +263,11 @@ def apply_effects_file( >>> for batch in loader: >>> pass """ - # Get string representation of 'path' in case Path object is passed - path = str(path) + if not torch.jit.is_scripting(): + if hasattr(path, 'read'): + return torchaudio._torchaudio.apply_effects_fileobj( + path, effects, normalize, channels_first, format) + path = os.fspath(path) signal = torch.ops.torchaudio.sox_effects_apply_effects_file( path, effects, normalize, channels_first, format) return signal.get_tensor(), signal.get_sample_rate() diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 3306491b21..dc7e2ddfe1 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -28,6 +28,7 @@ 'TimeMasking', 'SlidingWindowCmn', 'Vad', + 'SpectralCentroid', ] @@ -46,6 +47,13 @@ class Spectrogram(torch.nn.Module): If None, then the complex spectrum is returned instead. (Default: ``2``) normalized (bool, optional): Whether to normalize by magnitude after stft. (Default: ``False``) wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``) + center (bool, optional): whether to pad :attr:`waveform` on both sides so + that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. + Default: ``True`` + pad_mode (string, optional): controls the padding method used when + :attr:`center` is ``True``. Default: ``"reflect"`` + onesided (bool, optional): controls whether to return half of results to + avoid redundancy Default: ``True`` """ __constants__ = ['n_fft', 'win_length', 'hop_length', 'pad', 'power', 'normalized'] @@ -57,7 +65,10 @@ def __init__(self, window_fn: Callable[..., Tensor] = torch.hann_window, power: Optional[float] = 2., normalized: bool = False, - wkwargs: Optional[dict] = None) -> None: + wkwargs: Optional[dict] = None, + center: bool = True, + pad_mode: str = "reflect", + onesided: bool = True) -> None: super(Spectrogram, self).__init__() self.n_fft = n_fft # number of FFT bins. the returned STFT result will have n_fft // 2 + 1 @@ -69,6 +80,9 @@ def __init__(self, self.pad = pad self.power = power self.normalized = normalized + self.center = center + self.pad_mode = pad_mode + self.onesided = onesided def forward(self, waveform: Tensor) -> Tensor: r""" @@ -80,8 +94,19 @@ def forward(self, waveform: Tensor) -> Tensor: ``n_fft // 2 + 1`` where ``n_fft`` is the number of Fourier bins, and time is the number of window hops (n_frame). """ - return F.spectrogram(waveform, self.pad, self.window, self.n_fft, self.hop_length, - self.win_length, self.power, self.normalized) + return F.spectrogram( + waveform, + self.pad, + self.window, + self.n_fft, + self.hop_length, + self.win_length, + self.power, + self.normalized, + self.center, + self.pad_mode, + self.onesided + ) class GriffinLim(torch.nn.Module): @@ -386,6 +411,13 @@ class MelSpectrogram(torch.nn.Module): window_fn (Callable[..., Tensor], optional): A function to create a window tensor that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``) wkwargs (Dict[..., ...] or None, optional): Arguments for window function. (Default: ``None``) + center (bool, optional): whether to pad :attr:`waveform` on both sides so + that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. + Default: ``True`` + pad_mode (string, optional): controls the padding method used when + :attr:`center` is ``True``. Default: ``"reflect"`` + onesided (bool, optional): controls whether to return half of results to + avoid redundancy. Default: ``True`` Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalization=True) @@ -405,7 +437,10 @@ def __init__(self, window_fn: Callable[..., Tensor] = torch.hann_window, power: Optional[float] = 2., normalized: bool = False, - wkwargs: Optional[dict] = None) -> None: + wkwargs: Optional[dict] = None, + center: bool = True, + pad_mode: str = "reflect", + onesided: bool = True) -> None: super(MelSpectrogram, self).__init__() self.sample_rate = sample_rate self.n_fft = n_fft @@ -420,7 +455,8 @@ def __init__(self, self.spectrogram = Spectrogram(n_fft=self.n_fft, win_length=self.win_length, hop_length=self.hop_length, pad=self.pad, window_fn=window_fn, power=self.power, - normalized=self.normalized, wkwargs=wkwargs) + normalized=self.normalized, wkwargs=wkwargs, + center=center, pad_mode=pad_mode, onesided=onesided) self.mel_scale = MelScale(self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1) def forward(self, waveform: Tensor) -> Tensor: @@ -1037,3 +1073,55 @@ def forward(self, waveform: Tensor) -> Tensor: hp_lifter_freq=self.hp_lifter_freq, lp_lifter_freq=self.lp_lifter_freq, ) + + +class SpectralCentroid(torch.nn.Module): + r"""Compute the spectral centroid for each channel along the time axis. + + The spectral centroid is defined as the weighted average of the + frequency values, weighted by their magnitude. + + Args: + sample_rate (int): Sample rate of audio signal. + n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``) + win_length (int or None, optional): Window size. (Default: ``n_fft``) + hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``) + pad (int, optional): Two sided padding of signal. (Default: ``0``) + window_fn (Callable[..., Tensor], optional): A function to create a window tensor + that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``) + wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``) + + Example + >>> waveform, sample_rate = torchaudio.load('test.wav', normalization=True) + >>> spectral_centroid = transforms.SpectralCentroid(sample_rate)(waveform) # (channel, time) + """ + __constants__ = ['sample_rate', 'n_fft', 'win_length', 'hop_length', 'pad'] + + def __init__(self, + sample_rate: int, + n_fft: int = 400, + win_length: Optional[int] = None, + hop_length: Optional[int] = None, + pad: int = 0, + window_fn: Callable[..., Tensor] = torch.hann_window, + wkwargs: Optional[dict] = None) -> None: + super(SpectralCentroid, self).__init__() + self.sample_rate = sample_rate + self.n_fft = n_fft + self.win_length = win_length if win_length is not None else n_fft + self.hop_length = hop_length if hop_length is not None else self.win_length // 2 + window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs) + self.register_buffer('window', window) + self.pad = pad + + def forward(self, waveform: Tensor) -> Tensor: + r""" + Args: + waveform (Tensor): Tensor of audio of dimension (..., time). + + Returns: + Tensor: Spectral Centroid of size (..., time). + """ + + return F.spectral_centroid(waveform, self.sample_rate, self.pad, self.window, self.n_fft, self.hop_length, + self.win_length) From 587743e19295352f99afb3bc1cbc59d6b3eda9f5 Mon Sep 17 00:00:00 2001 From: Nicolas Hug Date: Thu, 4 Feb 2021 07:54:55 -0800 Subject: [PATCH 28/73] Import torchaudio #1233 135e966d Reviewed By: mthrok Differential Revision: D26228762 fbshipit-source-id: 9acc587adb5e7ca7867d8a5df44ba73166099fd9 --- .../linux/scripts/run_clang_format.py | 340 ++++++++++++++++++ .../linux/scripts/run_style_checks.sh | 2 +- .circleci/unittest/linux/scripts/run_test.sh | 8 + torchaudio/__init__.py | 3 +- torchaudio/backend/sox_io_backend.py | 37 +- torchaudio/csrc/sox/effects.cpp | 51 ++- torchaudio/csrc/sox/effects.h | 10 +- torchaudio/csrc/sox/effects_chain.cpp | 32 +- torchaudio/csrc/sox/effects_chain.h | 5 +- torchaudio/csrc/sox/io.cpp | 72 ++-- torchaudio/csrc/sox/io.h | 24 +- torchaudio/csrc/sox/register.cpp | 81 ----- torchaudio/csrc/sox/utils.cpp | 87 +++-- torchaudio/csrc/sox/utils.h | 32 +- torchaudio/sox_effects/sox_effects.py | 10 +- torchaudio/transforms.py | 25 +- 16 files changed, 537 insertions(+), 282 deletions(-) create mode 100755 .circleci/unittest/linux/scripts/run_clang_format.py delete mode 100644 torchaudio/csrc/sox/register.cpp diff --git a/.circleci/unittest/linux/scripts/run_clang_format.py b/.circleci/unittest/linux/scripts/run_clang_format.py new file mode 100755 index 0000000000..fd2913bd70 --- /dev/null +++ b/.circleci/unittest/linux/scripts/run_clang_format.py @@ -0,0 +1,340 @@ +#!/usr/bin/env python +"""A wrapper script around clang-format, suitable for linting multiple files +and to use for continuous integration. + +This is an alternative API for the clang-format command line. +It runs over multiple files and directories in parallel. +A diff output is produced and a sensible exit code is returned. + +""" + +import argparse +import codecs +import difflib +import fnmatch +import io +import multiprocessing +import os +import signal +import subprocess +import sys +import traceback + +from functools import partial + +try: + from subprocess import DEVNULL # py3k +except ImportError: + DEVNULL = open(os.devnull, "wb") + + +DEFAULT_EXTENSIONS = 'c,h,C,H,cpp,hpp,cc,hh,c++,h++,cxx,hxx,cu' + + +class ExitStatus: + SUCCESS = 0 + DIFF = 1 + TROUBLE = 2 + + +def list_files(files, recursive=False, extensions=None, exclude=None): + if extensions is None: + extensions = [] + if exclude is None: + exclude = [] + + out = [] + for file in files: + if recursive and os.path.isdir(file): + for dirpath, dnames, fnames in os.walk(file): + fpaths = [os.path.join(dirpath, fname) for fname in fnames] + for pattern in exclude: + # os.walk() supports trimming down the dnames list + # by modifying it in-place, + # to avoid unnecessary directory listings. + dnames[:] = [ + x for x in dnames + if + not fnmatch.fnmatch(os.path.join(dirpath, x), pattern) + ] + fpaths = [ + x for x in fpaths if not fnmatch.fnmatch(x, pattern) + ] + for f in fpaths: + ext = os.path.splitext(f)[1][1:] + if ext in extensions: + out.append(f) + else: + out.append(file) + return out + + +def make_diff(file, original, reformatted): + return list( + difflib.unified_diff( + original, + reformatted, + fromfile='{}\t(original)'.format(file), + tofile='{}\t(reformatted)'.format(file), + n=3)) + + +class DiffError(Exception): + def __init__(self, message, errs=None): + super(DiffError, self).__init__(message) + self.errs = errs or [] + + +class UnexpectedError(Exception): + def __init__(self, message, exc=None): + super(UnexpectedError, self).__init__(message) + self.formatted_traceback = traceback.format_exc() + self.exc = exc + + +def run_clang_format_diff_wrapper(args, file): + try: + ret = run_clang_format_diff(args, file) + return ret + except DiffError: + raise + except Exception as e: + raise UnexpectedError('{}: {}: {}'.format(file, e.__class__.__name__, + e), e) + + +def run_clang_format_diff(args, file): + try: + with io.open(file, 'r', encoding='utf-8') as f: + original = f.readlines() + except IOError as exc: + raise DiffError(str(exc)) + invocation = [args.clang_format_executable, file] + + # Use of utf-8 to decode the process output. + # + # Hopefully, this is the correct thing to do. + # + # It's done due to the following assumptions (which may be incorrect): + # - clang-format will returns the bytes read from the files as-is, + # without conversion, and it is already assumed that the files use utf-8. + # - if the diagnostics were internationalized, they would use utf-8: + # > Adding Translations to Clang + # > + # > Not possible yet! + # > Diagnostic strings should be written in UTF-8, + # > the client can translate to the relevant code page if needed. + # > Each translation completely replaces the format string + # > for the diagnostic. + # > -- http://clang.llvm.org/docs/InternalsManual.html#internals-diag-translation + + try: + proc = subprocess.Popen( + invocation, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + encoding='utf-8') + except OSError as exc: + raise DiffError( + "Command '{}' failed to start: {}".format( + subprocess.list2cmdline(invocation), exc + ) + ) + proc_stdout = proc.stdout + proc_stderr = proc.stderr + + # hopefully the stderr pipe won't get full and block the process + outs = list(proc_stdout.readlines()) + errs = list(proc_stderr.readlines()) + proc.wait() + if proc.returncode: + raise DiffError( + "Command '{}' returned non-zero exit status {}".format( + subprocess.list2cmdline(invocation), proc.returncode + ), + errs, + ) + return make_diff(file, original, outs), errs + + +def bold_red(s): + return '\x1b[1m\x1b[31m' + s + '\x1b[0m' + + +def colorize(diff_lines): + def bold(s): + return '\x1b[1m' + s + '\x1b[0m' + + def cyan(s): + return '\x1b[36m' + s + '\x1b[0m' + + def green(s): + return '\x1b[32m' + s + '\x1b[0m' + + def red(s): + return '\x1b[31m' + s + '\x1b[0m' + + for line in diff_lines: + if line[:4] in ['--- ', '+++ ']: + yield bold(line) + elif line.startswith('@@ '): + yield cyan(line) + elif line.startswith('+'): + yield green(line) + elif line.startswith('-'): + yield red(line) + else: + yield line + + +def print_diff(diff_lines, use_color): + if use_color: + diff_lines = colorize(diff_lines) + sys.stdout.writelines(diff_lines) + + +def print_trouble(prog, message, use_colors): + error_text = 'error:' + if use_colors: + error_text = bold_red(error_text) + print("{}: {} {}".format(prog, error_text, message), file=sys.stderr) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + '--clang-format-executable', + metavar='EXECUTABLE', + help='path to the clang-format executable', + default='clang-format') + parser.add_argument( + '--extensions', + help='comma separated list of file extensions (default: {})'.format( + DEFAULT_EXTENSIONS), + default=DEFAULT_EXTENSIONS) + parser.add_argument( + '-r', + '--recursive', + action='store_true', + help='run recursively over directories') + parser.add_argument('files', metavar='file', nargs='+') + parser.add_argument( + '-q', + '--quiet', + action='store_true') + parser.add_argument( + '-j', + metavar='N', + type=int, + default=0, + help='run N clang-format jobs in parallel' + ' (default number of cpus + 1)') + parser.add_argument( + '--color', + default='auto', + choices=['auto', 'always', 'never'], + help='show colored diff (default: auto)') + parser.add_argument( + '-e', + '--exclude', + metavar='PATTERN', + action='append', + default=[], + help='exclude paths matching the given glob-like pattern(s)' + ' from recursive search') + + args = parser.parse_args() + + # use default signal handling, like diff return SIGINT value on ^C + # https://bugs.python.org/issue14229#msg156446 + signal.signal(signal.SIGINT, signal.SIG_DFL) + try: + signal.SIGPIPE + except AttributeError: + # compatibility, SIGPIPE does not exist on Windows + pass + else: + signal.signal(signal.SIGPIPE, signal.SIG_DFL) + + colored_stdout = False + colored_stderr = False + if args.color == 'always': + colored_stdout = True + colored_stderr = True + elif args.color == 'auto': + colored_stdout = sys.stdout.isatty() + colored_stderr = sys.stderr.isatty() + + version_invocation = [args.clang_format_executable, str("--version")] + try: + subprocess.check_call(version_invocation, stdout=DEVNULL) + except subprocess.CalledProcessError as e: + print_trouble(parser.prog, str(e), use_colors=colored_stderr) + return ExitStatus.TROUBLE + except OSError as e: + print_trouble( + parser.prog, + "Command '{}' failed to start: {}".format( + subprocess.list2cmdline(version_invocation), e + ), + use_colors=colored_stderr, + ) + return ExitStatus.TROUBLE + + retcode = ExitStatus.SUCCESS + files = list_files( + args.files, + recursive=args.recursive, + exclude=args.exclude, + extensions=args.extensions.split(',')) + + if not files: + return + + njobs = args.j + if njobs == 0: + njobs = multiprocessing.cpu_count() + 1 + njobs = min(len(files), njobs) + + if njobs == 1: + # execute directly instead of in a pool, + # less overhead, simpler stacktraces + it = (run_clang_format_diff_wrapper(args, file) for file in files) + pool = None + else: + pool = multiprocessing.Pool(njobs) + it = pool.imap_unordered( + partial(run_clang_format_diff_wrapper, args), files) + while True: + try: + outs, errs = next(it) + except StopIteration: + break + except DiffError as e: + print_trouble(parser.prog, str(e), use_colors=colored_stderr) + retcode = ExitStatus.TROUBLE + sys.stderr.writelines(e.errs) + except UnexpectedError as e: + print_trouble(parser.prog, str(e), use_colors=colored_stderr) + sys.stderr.write(e.formatted_traceback) + retcode = ExitStatus.TROUBLE + # stop at the first unexpected error, + # something could be very wrong, + # don't process all files unnecessarily + if pool: + pool.terminate() + break + else: + sys.stderr.writelines(errs) + if outs == []: + continue + if not args.quiet: + print_diff(outs, use_color=colored_stdout) + if retcode == ExitStatus.SUCCESS: + retcode = ExitStatus.DIFF + return retcode + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/.circleci/unittest/linux/scripts/run_style_checks.sh b/.circleci/unittest/linux/scripts/run_style_checks.sh index d47bae71a7..d8af8824f5 100755 --- a/.circleci/unittest/linux/scripts/run_style_checks.sh +++ b/.circleci/unittest/linux/scripts/run_style_checks.sh @@ -37,7 +37,7 @@ if [ "${status}" -ne 0 ]; then fi printf "\x1b[34mRunning clang-format:\x1b[0m\n" -"${this_dir}"/run-clang-format.py \ +"${this_dir}"/run_clang_format.py \ -r torchaudio/csrc \ --clang-format-executable "${clangformat_path}" \ && git diff --exit-code diff --git a/.circleci/unittest/linux/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh index e9016d266c..cbd96f79a8 100755 --- a/.circleci/unittest/linux/scripts/run_test.sh +++ b/.circleci/unittest/linux/scripts/run_test.sh @@ -5,7 +5,15 @@ set -e eval "$(./conda/bin/conda shell.bash hook)" conda activate ./env +case "$(uname -s)" in + Darwin*) os=MacOSX;; + *) os=Linux +esac + python -m torch.utils.collect_env +if [ "${os}" == Linux ]; then + cat /proc/cpuinfo +fi export TORCHAUDIO_TEST_FAIL_IF_NO_EXTENSION=1 export PATH="${PWD}/third_party/install/bin/:${PATH}" diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index be46d8a0f6..cbb2ba3370 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -3,10 +3,11 @@ from torchaudio import ( compliance, datasets, + functional, kaldi_io, utils, sox_effects, - transforms + transforms, ) USE_SOUNDFILE_LEGACY_INTERFACE = None diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 513bff4393..0eaf02118a 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -11,24 +11,6 @@ from .common import AudioMetaData -@torch.jit.unused -def _info( - filepath: str, - format: Optional[str] = None, -) -> AudioMetaData: - if hasattr(filepath, 'read'): - sinfo = torchaudio._torchaudio.get_info_fileobj(filepath, format) - return AudioMetaData(*sinfo) - sinfo = torch.ops.torchaudio.sox_io_get_info(os.fspath(filepath), format) - return AudioMetaData( - sinfo.get_sample_rate(), - sinfo.get_num_frames(), - sinfo.get_num_channels(), - sinfo.get_bits_per_sample(), - sinfo.get_encoding(), - ) - - @_mod_utils.requires_module('torchaudio._torchaudio') def info( filepath: str, @@ -61,14 +43,12 @@ def info( AudioMetaData: Metadata of the given audio. """ if not torch.jit.is_scripting(): - return _info(filepath, format) + if hasattr(filepath, 'read'): + sinfo = torchaudio._torchaudio.get_info_fileobj(filepath, format) + return AudioMetaData(*sinfo) + filepath = os.fspath(filepath) sinfo = torch.ops.torchaudio.sox_io_get_info(filepath, format) - return AudioMetaData( - sinfo.get_sample_rate(), - sinfo.get_num_frames(), - sinfo.get_num_channels(), - sinfo.get_bits_per_sample(), - sinfo.get_encoding()) + return AudioMetaData(*sinfo) @_mod_utils.requires_module('torchaudio._torchaudio') @@ -162,12 +142,9 @@ def load( if hasattr(filepath, 'read'): return torchaudio._torchaudio.load_audio_fileobj( filepath, frame_offset, num_frames, normalize, channels_first, format) - signal = torch.ops.torchaudio.sox_io_load_audio_file( - os.fspath(filepath), frame_offset, num_frames, normalize, channels_first, format) - return signal.get_tensor(), signal.get_sample_rate() - signal = torch.ops.torchaudio.sox_io_load_audio_file( + filepath = os.fspath(filepath) + return torch.ops.torchaudio.sox_io_load_audio_file( filepath, frame_offset, num_frames, normalize, channels_first, format) - return signal.get_tensor(), signal.get_sample_rate() @torch.jit.unused diff --git a/torchaudio/csrc/sox/effects.cpp b/torchaudio/csrc/sox/effects.cpp index e195e25731..eb4bf24549 100644 --- a/torchaudio/csrc/sox/effects.cpp +++ b/torchaudio/csrc/sox/effects.cpp @@ -50,24 +50,25 @@ void shutdown_sox_effects() { } } -c10::intrusive_ptr apply_effects_tensor( - const c10::intrusive_ptr& input_signal, - std::vector> effects) { - auto in_tensor = input_signal->getTensor(); - validate_input_tensor(in_tensor); +std::tuple apply_effects_tensor( + torch::Tensor waveform, + int64_t sample_rate, + std::vector> effects, + bool channels_first) { + validate_input_tensor(waveform); // Create SoxEffectsChain - const auto dtype = in_tensor.dtype(); + const auto dtype = waveform.dtype(); torchaudio::sox_effects_chain::SoxEffectsChain chain( - /*input_encoding=*/get_encodinginfo("wav", dtype), - /*output_encoding=*/get_encodinginfo("wav", dtype)); + /*input_encoding=*/get_tensor_encodinginfo(dtype), + /*output_encoding=*/get_tensor_encodinginfo(dtype)); // Prepare output buffer std::vector out_buffer; - out_buffer.reserve(in_tensor.numel()); + out_buffer.reserve(waveform.numel()); // Build and run effects chain - chain.addInputTensor(input_signal.get()); + chain.addInputTensor(&waveform, sample_rate, channels_first); for (const auto& effect : effects) { chain.addEffect(effect); } @@ -75,7 +76,6 @@ c10::intrusive_ptr apply_effects_tensor( chain.run(); // Create tensor from buffer - const auto channels_first = input_signal->getChannelsFirst(); auto out_tensor = convert_to_tensor( /*buffer=*/out_buffer.data(), /*num_samples=*/out_buffer.size(), @@ -84,11 +84,11 @@ c10::intrusive_ptr apply_effects_tensor( /*noramlize=*/false, channels_first); - return c10::make_intrusive( - out_tensor, chain.getOutputSampleRate(), channels_first); + return std::tuple( + out_tensor, chain.getOutputSampleRate()); } -c10::intrusive_ptr apply_effects_file( +std::tuple apply_effects_file( const std::string path, std::vector> effects, c10::optional& normalize, @@ -112,7 +112,7 @@ c10::intrusive_ptr apply_effects_file( // Create and run SoxEffectsChain torchaudio::sox_effects_chain::SoxEffectsChain chain( /*input_encoding=*/sf->encoding, - /*output_encoding=*/get_encodinginfo("wav", dtype)); + /*output_encoding=*/get_tensor_encodinginfo(dtype)); chain.addInputFile(sf); for (const auto& effect : effects) { @@ -131,8 +131,8 @@ c10::intrusive_ptr apply_effects_file( normalize.value_or(true), channels_first_); - return c10::make_intrusive( - tensor, chain.getOutputSampleRate(), channels_first_); + return std::tuple( + tensor, chain.getOutputSampleRate()); } #ifdef TORCH_API_INCLUDE_EXTENSION_H @@ -214,7 +214,7 @@ std::tuple apply_effects_fileobj( const auto dtype = get_dtype(sf->encoding.encoding, sf->signal.precision); torchaudio::sox_effects_chain::SoxEffectsChain chain( /*input_encoding=*/sf->encoding, - /*output_encoding=*/get_encodinginfo("wav", dtype)); + /*output_encoding=*/get_tensor_encodinginfo(dtype)); chain.addInputFileObj(sf, in_buf, in_buffer_size, &fileobj); for (const auto& effect : effects) { chain.addEffect(effect); @@ -238,5 +238,20 @@ std::tuple apply_effects_fileobj( #endif // TORCH_API_INCLUDE_EXTENSION_H +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def( + "torchaudio::sox_effects_initialize_sox_effects", + &torchaudio::sox_effects::initialize_sox_effects); + m.def( + "torchaudio::sox_effects_shutdown_sox_effects", + &torchaudio::sox_effects::shutdown_sox_effects); + m.def( + "torchaudio::sox_effects_apply_effects_tensor", + &torchaudio::sox_effects::apply_effects_tensor); + m.def( + "torchaudio::sox_effects_apply_effects_file", + &torchaudio::sox_effects::apply_effects_file); +} + } // namespace sox_effects } // namespace torchaudio diff --git a/torchaudio/csrc/sox/effects.h b/torchaudio/csrc/sox/effects.h index a2a598b408..8ce9757824 100644 --- a/torchaudio/csrc/sox/effects.h +++ b/torchaudio/csrc/sox/effects.h @@ -15,11 +15,13 @@ void initialize_sox_effects(); void shutdown_sox_effects(); -c10::intrusive_ptr apply_effects_tensor( - const c10::intrusive_ptr& input_signal, - std::vector> effects); +std::tuple apply_effects_tensor( + torch::Tensor waveform, + int64_t sample_rate, + std::vector> effects, + bool channels_first); -c10::intrusive_ptr apply_effects_file( +std::tuple apply_effects_file( const std::string path, std::vector> effects, c10::optional& normalize, diff --git a/torchaudio/csrc/sox/effects_chain.cpp b/torchaudio/csrc/sox/effects_chain.cpp index 80885b296c..cbe78684f7 100644 --- a/torchaudio/csrc/sox/effects_chain.cpp +++ b/torchaudio/csrc/sox/effects_chain.cpp @@ -36,12 +36,14 @@ struct SoxEffect { /// helper classes for passing the location of input tensor and output buffer /// /// drain/flow callback functions require plaing C style function signature and -/// the way to pass extra data is to attach data to sox_fffect_t::priv pointer. -/// The following structs will be assigned to sox_fffect_t::priv pointer which +/// the way to pass extra data is to attach data to sox_effect_t::priv pointer. +/// The following structs will be assigned to sox_effect_t::priv pointer which /// gives sox_effect_t an access to input Tensor and output buffer object. struct TensorInputPriv { size_t index; - TensorSignal* signal; + torch::Tensor* waveform; + int64_t sample_rate; + bool channels_first; }; struct TensorOutputPriv { std::vector* buffer; @@ -55,8 +57,7 @@ int tensor_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { // Retrieve the input Tensor and current index auto priv = static_cast(effp->priv); auto index = priv->index; - auto signal = priv->signal; - auto tensor = signal->getTensor(); + auto tensor = *(priv->waveform); auto num_channels = effp->out_signal.channels; // Adjust the number of samples to read @@ -71,7 +72,7 @@ int tensor_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { const auto tensor_ = [&]() { auto i_frame = index / num_channels; auto num_frames = *osamp / num_channels; - auto t = (signal->getChannelsFirst()) + auto t = (priv->channels_first) ? tensor.index({Slice(), Slice(i_frame, i_frame + num_frames)}).t() : tensor.index({Slice(i_frame, i_frame + num_frames), Slice()}); return unnormalize_wav(t.reshape({-1})).contiguous(); @@ -193,13 +194,18 @@ void SoxEffectsChain::run() { sox_flow_effects(sec_, NULL, NULL); } -void SoxEffectsChain::addInputTensor(TensorSignal* signal) { - in_sig_ = get_signalinfo(signal, "wav"); +void SoxEffectsChain::addInputTensor( + torch::Tensor* waveform, + int64_t sample_rate, + bool channels_first) { + in_sig_ = get_signalinfo(waveform, sample_rate, "wav", channels_first); interm_sig_ = in_sig_; SoxEffect e(sox_create_effect(get_tensor_input_handler())); auto priv = static_cast(e->priv); - priv->signal = signal; priv->index = 0; + priv->waveform = waveform; + priv->sample_rate = sample_rate; + priv->channels_first = channels_first; if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { throw std::runtime_error( "Internal Error: Failed to add effect: input_tensor"); @@ -252,7 +258,13 @@ void SoxEffectsChain::addEffect(const std::vector effect) { throw std::runtime_error(stream.str()); } - SoxEffect e(sox_create_effect(sox_find_effect(name.c_str()))); + auto returned_effect = sox_find_effect(name.c_str()); + if (!returned_effect) { + std::ostringstream stream; + stream << "Unsupported effect: " << name; + throw std::runtime_error(stream.str()); + } + SoxEffect e(sox_create_effect(returned_effect)); const auto num_options = num_args - 1; std::vector opts; diff --git a/torchaudio/csrc/sox/effects_chain.h b/torchaudio/csrc/sox/effects_chain.h index a4797b3486..62e9b8685c 100644 --- a/torchaudio/csrc/sox/effects_chain.h +++ b/torchaudio/csrc/sox/effects_chain.h @@ -30,7 +30,10 @@ class SoxEffectsChain { SoxEffectsChain& operator=(SoxEffectsChain&& other) = delete; ~SoxEffectsChain(); void run(); - void addInputTensor(torchaudio::sox_utils::TensorSignal* signal); + void addInputTensor( + torch::Tensor* waveform, + int64_t sample_rate, + bool channels_first); void addInputFile(sox_format_t* sf); void addOutputBuffer(std::vector* output_buffer); void addOutputFile(sox_format_t* sf); diff --git a/torchaudio/csrc/sox/io.cpp b/torchaudio/csrc/sox/io.cpp index 4594fa4875..ac10911507 100644 --- a/torchaudio/csrc/sox/io.cpp +++ b/torchaudio/csrc/sox/io.cpp @@ -10,38 +10,6 @@ using namespace torchaudio::sox_utils; namespace torchaudio { namespace sox_io { -SignalInfo::SignalInfo( - const int64_t sample_rate_, - const int64_t num_channels_, - const int64_t num_frames_, - const int64_t bits_per_sample_, - const std::string encoding_) - : sample_rate(sample_rate_), - num_channels(num_channels_), - num_frames(num_frames_), - bits_per_sample(bits_per_sample_), - encoding(encoding_){}; - -int64_t SignalInfo::getSampleRate() const { - return sample_rate; -} - -int64_t SignalInfo::getNumChannels() const { - return num_channels; -} - -int64_t SignalInfo::getNumFrames() const { - return num_frames; -} - -int64_t SignalInfo::getBitsPerSample() const { - return bits_per_sample; -} - -std::string SignalInfo::getEncoding() const { - return encoding; -} - namespace { std::string get_encoding(sox_encoding_t encoding) { @@ -77,7 +45,7 @@ std::string get_encoding(sox_encoding_t encoding) { } // namespace -c10::intrusive_ptr get_info_file( +std::tuple get_info_file( const std::string& path, c10::optional& format) { SoxFormat sf(sox_open_read( @@ -90,10 +58,10 @@ c10::intrusive_ptr get_info_file( throw std::runtime_error("Error opening audio file"); } - return c10::make_intrusive( + return std::make_tuple( static_cast(sf->signal.rate), - static_cast(sf->signal.channels), static_cast(sf->signal.length / sf->signal.channels), + static_cast(sf->signal.channels), static_cast(sf->encoding.bits_per_sample), get_encoding(sf->encoding.encoding)); } @@ -131,7 +99,7 @@ std::vector> get_effects( } // namespace -c10::intrusive_ptr load_audio_file( +std::tuple load_audio_file( const std::string& path, c10::optional& frame_offset, c10::optional& num_frames, @@ -153,7 +121,6 @@ void save_audio_file( c10::optional dtype) { validate_input_tensor(tensor); - auto signal = TensorSignal(tensor, sample_rate, channels_first); if (tensor.dtype() != torch::kFloat32 && dtype.has_value()) { throw std::runtime_error( "dtype conversion only supported for float32 tensors"); @@ -174,8 +141,10 @@ void save_audio_file( num_channels == 1, "amr-nb format only supports single channel audio."); tensor = (unnormalize_wav(tensor) / 65536).to(torch::kInt16); } - const auto signal_info = get_signalinfo(&signal, filetype); - const auto encoding_info = get_encodinginfo(filetype, tgt_dtype, compression); + const auto signal_info = + get_signalinfo(&tensor, sample_rate, filetype, channels_first); + const auto encoding_info = + get_encodinginfo_for_save(filetype, tgt_dtype, compression); SoxFormat sf(sox_open_write( path.c_str(), @@ -190,9 +159,9 @@ void save_audio_file( } torchaudio::sox_effects_chain::SoxEffectsChain chain( - /*input_encoding=*/get_encodinginfo("wav", tensor.dtype()), + /*input_encoding=*/get_tensor_encodinginfo(tensor.dtype()), /*output_encoding=*/sf->encoding); - chain.addInputTensor(&signal); + chain.addInputTensor(&tensor, sample_rate, channels_first); chain.addOutputFile(sf); chain.run(); } @@ -294,7 +263,6 @@ void save_audio_fileobj( c10::optional dtype) { validate_input_tensor(tensor); - auto signal = TensorSignal(tensor, sample_rate, channels_first); if (tensor.dtype() != torch::kFloat32 && dtype.has_value()) { throw std::runtime_error( "dtype conversion only supported for float32 tensors"); @@ -312,8 +280,10 @@ void save_audio_fileobj( } tensor = (unnormalize_wav(tensor) / 65536).to(torch::kInt16); } - const auto signal_info = get_signalinfo(&signal, filetype); - const auto encoding_info = get_encodinginfo(filetype, tgt_dtype, compression); + const auto signal_info = + get_signalinfo(&tensor, sample_rate, filetype, channels_first); + const auto encoding_info = + get_encodinginfo_for_save(filetype, tgt_dtype, compression); AutoReleaseBuffer buffer; @@ -331,9 +301,9 @@ void save_audio_fileobj( } torchaudio::sox_effects_chain::SoxEffectsChain chain( - /*input_encoding=*/get_encodinginfo("wav", tensor.dtype()), + /*input_encoding=*/get_tensor_encodinginfo(tensor.dtype()), /*output_encoding=*/sf->encoding); - chain.addInputTensor(&signal); + chain.addInputTensor(&tensor, sample_rate, channels_first); chain.addOutputFileObj(sf, &buffer.ptr, &buffer.size, &fileobj); chain.run(); @@ -346,5 +316,15 @@ void save_audio_fileobj( #endif // TORCH_API_INCLUDE_EXTENSION_H +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("torchaudio::sox_io_get_info", &torchaudio::sox_io::get_info_file); + m.def( + "torchaudio::sox_io_load_audio_file", + &torchaudio::sox_io::load_audio_file); + m.def( + "torchaudio::sox_io_save_audio_file", + &torchaudio::sox_io::save_audio_file); +} + } // namespace sox_io } // namespace torchaudio diff --git a/torchaudio/csrc/sox/io.h b/torchaudio/csrc/sox/io.h index fa1f3d61f9..6129fac1b7 100644 --- a/torchaudio/csrc/sox/io.h +++ b/torchaudio/csrc/sox/io.h @@ -11,31 +11,11 @@ namespace torchaudio { namespace sox_io { -struct SignalInfo : torch::CustomClassHolder { - int64_t sample_rate; - int64_t num_channels; - int64_t num_frames; - int64_t bits_per_sample; - std::string encoding; - - SignalInfo( - const int64_t sample_rate_, - const int64_t num_channels_, - const int64_t num_frames_, - const int64_t bits_per_sample_, - const std::string encoding_); - int64_t getSampleRate() const; - int64_t getNumChannels() const; - int64_t getNumFrames() const; - int64_t getBitsPerSample() const; - std::string getEncoding() const; -}; - -c10::intrusive_ptr get_info_file( +std::tuple get_info_file( const std::string& path, c10::optional& format); -c10::intrusive_ptr load_audio_file( +std::tuple load_audio_file( const std::string& path, c10::optional& frame_offset, c10::optional& num_frames, diff --git a/torchaudio/csrc/sox/register.cpp b/torchaudio/csrc/sox/register.cpp deleted file mode 100644 index d90a3b6df1..0000000000 --- a/torchaudio/csrc/sox/register.cpp +++ /dev/null @@ -1,81 +0,0 @@ -#include -#include -#include - -TORCH_LIBRARY_FRAGMENT(torchaudio, m) { - ////////////////////////////////////////////////////////////////////////////// - // sox_utils.h - ////////////////////////////////////////////////////////////////////////////// - m.class_("TensorSignal") - .def(torch::init()) - .def("get_tensor", &torchaudio::sox_utils::TensorSignal::getTensor) - .def( - "get_sample_rate", - &torchaudio::sox_utils::TensorSignal::getSampleRate) - .def( - "get_channels_first", - &torchaudio::sox_utils::TensorSignal::getChannelsFirst); - - m.def("torchaudio::sox_utils_set_seed", &torchaudio::sox_utils::set_seed); - m.def( - "torchaudio::sox_utils_set_verbosity", - &torchaudio::sox_utils::set_verbosity); - m.def( - "torchaudio::sox_utils_set_use_threads", - &torchaudio::sox_utils::set_use_threads); - m.def( - "torchaudio::sox_utils_set_buffer_size", - &torchaudio::sox_utils::set_buffer_size); - m.def( - "torchaudio::sox_utils_list_effects", - &torchaudio::sox_utils::list_effects); - m.def( - "torchaudio::sox_utils_list_read_formats", - &torchaudio::sox_utils::list_read_formats); - m.def( - "torchaudio::sox_utils_list_write_formats", - &torchaudio::sox_utils::list_write_formats); - - ////////////////////////////////////////////////////////////////////////////// - // sox_io.h - ////////////////////////////////////////////////////////////////////////////// - m.class_("SignalInfo") - .def("get_sample_rate", &torchaudio::sox_io::SignalInfo::getSampleRate) - .def("get_num_channels", &torchaudio::sox_io::SignalInfo::getNumChannels) - .def("get_num_frames", &torchaudio::sox_io::SignalInfo::getNumFrames) - .def( - "get_bits_per_sample", - &torchaudio::sox_io::SignalInfo::getBitsPerSample) - .def("get_encoding", &torchaudio::sox_io::SignalInfo::getEncoding); - - m.def("torchaudio::sox_io_get_info", &torchaudio::sox_io::get_info_file); - m.def( - "torchaudio::sox_io_load_audio_file(" - "str path," - "int? frame_offset=None," - "int? num_frames=None," - "bool? normalize=True," - "bool? channels_first=False," - "str? format=None" - ") -> __torch__.torch.classes.torchaudio.TensorSignal", - &torchaudio::sox_io::load_audio_file); - m.def( - "torchaudio::sox_io_save_audio_file", - &torchaudio::sox_io::save_audio_file); - - ////////////////////////////////////////////////////////////////////////////// - // sox_effects.h - ////////////////////////////////////////////////////////////////////////////// - m.def( - "torchaudio::sox_effects_initialize_sox_effects", - &torchaudio::sox_effects::initialize_sox_effects); - m.def( - "torchaudio::sox_effects_shutdown_sox_effects", - &torchaudio::sox_effects::shutdown_sox_effects); - m.def( - "torchaudio::sox_effects_apply_effects_tensor", - &torchaudio::sox_effects::apply_effects_tensor); - m.def( - "torchaudio::sox_effects_apply_effects_file", - &torchaudio::sox_effects::apply_effects_file); -} diff --git a/torchaudio/csrc/sox/utils.cpp b/torchaudio/csrc/sox/utils.cpp index f6bd3c8137..983b7829fe 100644 --- a/torchaudio/csrc/sox/utils.cpp +++ b/torchaudio/csrc/sox/utils.cpp @@ -61,24 +61,6 @@ std::vector list_read_formats() { return formats; } -TensorSignal::TensorSignal( - torch::Tensor tensor_, - int64_t sample_rate_, - bool channels_first_) - : tensor(tensor_), - sample_rate(sample_rate_), - channels_first(channels_first_){}; - -torch::Tensor TensorSignal::getTensor() const { - return tensor; -} -int64_t TensorSignal::getSampleRate() const { - return sample_rate; -} -bool TensorSignal::getChannelsFirst() const { - return channels_first; -} - SoxFormat::SoxFormat(sox_format_t* fd) noexcept : fd_(fd) {} SoxFormat::~SoxFormat() { close(); @@ -297,23 +279,44 @@ unsigned get_precision( } sox_signalinfo_t get_signalinfo( - const TensorSignal* signal, - const std::string filetype) { - auto tensor = signal->getTensor(); + const torch::Tensor* waveform, + const int64_t sample_rate, + const std::string filetype, + const bool channels_first) { return sox_signalinfo_t{ - /*rate=*/static_cast(signal->getSampleRate()), + /*rate=*/static_cast(sample_rate), /*channels=*/ - static_cast(tensor.size(signal->getChannelsFirst() ? 0 : 1)), - /*precision=*/get_precision(filetype, tensor.dtype()), - /*length=*/static_cast(tensor.numel())}; + static_cast(waveform->size(channels_first ? 0 : 1)), + /*precision=*/get_precision(filetype, waveform->dtype()), + /*length=*/static_cast(waveform->numel())}; } -sox_encodinginfo_t get_encodinginfo( - const std::string filetype, - const caffe2::TypeMeta dtype) { +sox_encodinginfo_t get_tensor_encodinginfo(const caffe2::TypeMeta dtype) { + sox_encoding_t encoding = [&]() { + if (dtype == torch::kUInt8) + return SOX_ENCODING_UNSIGNED; + if (dtype == torch::kInt16) + return SOX_ENCODING_SIGN2; + if (dtype == torch::kInt32) + return SOX_ENCODING_SIGN2; + if (dtype == torch::kFloat32) + return SOX_ENCODING_FLOAT; + throw std::runtime_error("Unsupported dtype."); + }(); + unsigned bits_per_sample = [&]() { + if (dtype == torch::kUInt8) + return 8; + if (dtype == torch::kInt16) + return 16; + if (dtype == torch::kInt32) + return 32; + if (dtype == torch::kFloat32) + return 32; + throw std::runtime_error("Unsupported dtype."); + }(); return sox_encodinginfo_t{ - /*encoding=*/get_encoding(filetype, dtype), - /*bits_per_sample=*/get_precision(filetype, dtype), + /*encoding=*/encoding, + /*bits_per_sample=*/bits_per_sample, /*compression=*/HUGE_VAL, /*reverse_bytes=*/sox_option_default, /*reverse_nibbles=*/sox_option_default, @@ -321,7 +324,7 @@ sox_encodinginfo_t get_encodinginfo( /*opposite_endian=*/sox_false}; } -sox_encodinginfo_t get_encodinginfo( +sox_encodinginfo_t get_encodinginfo_for_save( const std::string filetype, const caffe2::TypeMeta dtype, c10::optional& compression) { @@ -364,5 +367,27 @@ uint64_t read_fileobj(py::object* fileobj, const uint64_t size, char* buffer) { #endif // TORCH_API_INCLUDE_EXTENSION_H +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("torchaudio::sox_utils_set_seed", &torchaudio::sox_utils::set_seed); + m.def( + "torchaudio::sox_utils_set_verbosity", + &torchaudio::sox_utils::set_verbosity); + m.def( + "torchaudio::sox_utils_set_use_threads", + &torchaudio::sox_utils::set_use_threads); + m.def( + "torchaudio::sox_utils_set_buffer_size", + &torchaudio::sox_utils::set_buffer_size); + m.def( + "torchaudio::sox_utils_list_effects", + &torchaudio::sox_utils::list_effects); + m.def( + "torchaudio::sox_utils_list_read_formats", + &torchaudio::sox_utils::list_read_formats); + m.def( + "torchaudio::sox_utils_list_write_formats", + &torchaudio::sox_utils::list_write_formats); +} + } // namespace sox_utils } // namespace torchaudio diff --git a/torchaudio/csrc/sox/utils.h b/torchaudio/csrc/sox/utils.h index 1d99158378..ea2a6a2953 100644 --- a/torchaudio/csrc/sox/utils.h +++ b/torchaudio/csrc/sox/utils.h @@ -30,23 +30,6 @@ std::vector list_read_formats(); std::vector list_write_formats(); -/// Class for exchanging signal infomation (tensor + meta data) between -/// C++ and Python for read/write operation. -struct TensorSignal : torch::CustomClassHolder { - torch::Tensor tensor; - int64_t sample_rate; - bool channels_first; - - TensorSignal( - torch::Tensor tensor_, - int64_t sample_rate_, - bool channels_first_); - - torch::Tensor getTensor() const; - int64_t getSampleRate() const; - bool getChannelsFirst() const; -}; - //////////////////////////////////////////////////////////////////////////////// // Utilities for sox_io / sox_effects implementations //////////////////////////////////////////////////////////////////////////////// @@ -120,15 +103,16 @@ const std::string get_filetype(const std::string path); /// Get sox_signalinfo_t for passing a torch::Tensor object. sox_signalinfo_t get_signalinfo( - const TensorSignal* signal, - const std::string filetype); - -/// Get sox_encofinginfo_t for saving audoi file -sox_encodinginfo_t get_encodinginfo( + const torch::Tensor* waveform, + const int64_t sample_rate, const std::string filetype, - const caffe2::TypeMeta dtype); + const bool channels_first); + +/// Get sox_encodinginfo_t for Tensor I/O +sox_encodinginfo_t get_tensor_encodinginfo(const caffe2::TypeMeta dtype); -sox_encodinginfo_t get_encodinginfo( +/// Get sox_encodinginfo_t for saving to file/file object +sox_encodinginfo_t get_encodinginfo_for_save( const std::string filetype, const caffe2::TypeMeta dtype, c10::optional& compression); diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index d94a776cd6..4d1d0234d9 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -63,7 +63,7 @@ def apply_effects_tensor( Note: This function works in the way very similar to ``sox`` command, however there are slight - differences. For example, ``sox`` commnad adds certain effects automatically (such as + differences. For example, ``sox`` command adds certain effects automatically (such as ``rate`` effect after ``speed`` and ``pitch`` and other effects), but this function does only applies the given effects. (Therefore, to actually apply ``speed`` effect, you also need to give ``rate`` effect with desired sampling rate.) @@ -149,9 +149,8 @@ def apply_effects_tensor( >>> waveform, sample_rate = transform(waveform, input_sample_rate) >>> assert sample_rate == 8000 """ - in_signal = torch.classes.torchaudio.TensorSignal(tensor, sample_rate, channels_first) - out_signal = torch.ops.torchaudio.sox_effects_apply_effects_tensor(in_signal, effects) - return out_signal.get_tensor(), out_signal.get_sample_rate() + return torch.ops.torchaudio.sox_effects_apply_effects_tensor( + tensor, sample_rate, effects, channels_first) @_mod_utils.requires_module('torchaudio._torchaudio') @@ -268,6 +267,5 @@ def apply_effects_file( return torchaudio._torchaudio.apply_effects_fileobj( path, effects, normalize, channels_first, format) path = os.fspath(path) - signal = torch.ops.torchaudio.sox_effects_apply_effects_file( + return torch.ops.torchaudio.sox_effects_apply_effects_file( path, effects, normalize, channels_first, format) - return signal.get_tensor(), signal.get_sample_rate() diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index dc7e2ddfe1..7d7903b752 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -248,6 +248,8 @@ class MelScale(torch.nn.Module): f_max (float or None, optional): Maximum frequency. (Default: ``sample_rate // 2``) n_stft (int, optional): Number of bins in STFT. Calculated from first input if None is given. See ``n_fft`` in :class:`Spectrogram`. (Default: ``None``) + norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band + (area normalization). (Default: ``None``) """ __constants__ = ['n_mels', 'sample_rate', 'f_min', 'f_max'] @@ -256,17 +258,19 @@ def __init__(self, sample_rate: int = 16000, f_min: float = 0., f_max: Optional[float] = None, - n_stft: Optional[int] = None) -> None: + n_stft: Optional[int] = None, + norm: Optional[str] = None) -> None: super(MelScale, self).__init__() self.n_mels = n_mels self.sample_rate = sample_rate self.f_max = f_max if f_max is not None else float(sample_rate // 2) self.f_min = f_min + self.norm = norm assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max) fb = torch.empty(0) if n_stft is None else F.create_fb_matrix( - n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate) + n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, self.norm) self.register_buffer('fb', fb) def forward(self, specgram: Tensor) -> Tensor: @@ -283,7 +287,8 @@ def forward(self, specgram: Tensor) -> Tensor: specgram = specgram.reshape(-1, shape[-2], shape[-1]) if self.fb.numel() == 0: - tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max, self.n_mels, self.sample_rate) + tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max, + self.n_mels, self.sample_rate, self.norm) # Attributes cannot be reassigned outside __init__ so workaround self.fb.resize_(tmp_fb.size()) self.fb.copy_(tmp_fb) @@ -315,6 +320,8 @@ class InverseMelScale(torch.nn.Module): tolerance_loss (float, optional): Value of loss to stop optimization at. (Default: ``1e-5``) tolerance_change (float, optional): Difference in losses to stop optimization at. (Default: ``1e-8``) sgdargs (dict or None, optional): Arguments for the SGD optimizer. (Default: ``None``) + norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band + (area normalization). (Default: ``None``) """ __constants__ = ['n_stft', 'n_mels', 'sample_rate', 'f_min', 'f_max', 'max_iter', 'tolerance_loss', 'tolerance_change', 'sgdargs'] @@ -328,7 +335,8 @@ def __init__(self, max_iter: int = 100000, tolerance_loss: float = 1e-5, tolerance_change: float = 1e-8, - sgdargs: Optional[dict] = None) -> None: + sgdargs: Optional[dict] = None, + norm: Optional[str] = None) -> None: super(InverseMelScale, self).__init__() self.n_mels = n_mels self.sample_rate = sample_rate @@ -341,7 +349,7 @@ def __init__(self, assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max) - fb = F.create_fb_matrix(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate) + fb = F.create_fb_matrix(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, norm) self.register_buffer('fb', fb) def forward(self, melspec: Tensor) -> Tensor: @@ -418,6 +426,8 @@ class MelSpectrogram(torch.nn.Module): :attr:`center` is ``True``. Default: ``"reflect"`` onesided (bool, optional): controls whether to return half of results to avoid redundancy. Default: ``True`` + norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band + (area normalization). (Default: ``None``) Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalization=True) @@ -440,7 +450,8 @@ def __init__(self, wkwargs: Optional[dict] = None, center: bool = True, pad_mode: str = "reflect", - onesided: bool = True) -> None: + onesided: bool = True, + norm: Optional[str] = None) -> None: super(MelSpectrogram, self).__init__() self.sample_rate = sample_rate self.n_fft = n_fft @@ -457,7 +468,7 @@ def __init__(self, pad=self.pad, window_fn=window_fn, power=self.power, normalized=self.normalized, wkwargs=wkwargs, center=center, pad_mode=pad_mode, onesided=onesided) - self.mel_scale = MelScale(self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1) + self.mel_scale = MelScale(self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1, norm) def forward(self, waveform: Tensor) -> Tensor: r""" From ac3369cd7b7020ed56c7a32fd756eb261b4f61ea Mon Sep 17 00:00:00 2001 From: Vasilis Vryniotis Date: Wed, 10 Feb 2021 01:15:54 -0800 Subject: [PATCH 29/73] Import torchaudio #1250 5a69911 Summary: Imported from Github Reviewed By: mthrok Differential Revision: D26344055 fbshipit-source-id: 163f308e43f514c0b885f4ed0ed87efc0ad26982 --- .circleci/config.yml | 4 +- .circleci/config.yml.in | 4 +- .circleci/torchscript_bc_test/environment.yml | 4 +- .circleci/unittest/linux/scripts/install.sh | 1 + .circleci/unittest/linux/scripts/setup_env.sh | 11 +- .flake8 | 2 +- CMakeLists.txt | 60 ++ CONTRIBUTING.md | 117 ++++ README.md | 10 +- build_tools/setup_helpers/extension.py | 202 +++---- packaging/build_wheel.sh | 2 +- packaging/torchaudio/meta.yaml | 3 +- setup.py | 2 +- test/torchaudio_unittest/README.md | 25 +- .../__init__.py | 0 test/torchaudio_unittest/backend/common.py | 25 + .../{io_test.py => backend/legacy_test.py} | 0 .../soundfile}/__init__.py | 0 .../soundfile}/common.py | 0 .../soundfile}/info_test.py | 6 +- .../soundfile}/load_test.py | 0 .../soundfile}/save_test.py | 1 - .../backend/sox_io/__init__.py | 0 .../sox_io}/common.py | 0 .../sox_io}/info_test.py | 32 +- .../sox_io}/load_test.py | 0 .../sox_io}/roundtrip_test.py | 0 .../sox_io}/save_test.py | 0 .../sox_io}/smoke_test.py | 0 .../sox_io}/torchscript_test.py | 0 .../utils_test.py} | 0 .../batch_consistency_test.py | 77 ++- .../common_utils/__init__.py | 6 +- .../compliance_kaldi_test.py | 1 - .../datasets/datasets_test.py | 2 - .../functional/functional_cpu_test.py | 94 ++- .../torchscript_consistency_cpu_test.py | 14 + .../torchscript_consistency_cuda_test.py | 16 + .../torchscript_consistency_impl.py | 549 ++++++++++++++++++ test/torchaudio_unittest/kaldi_io_test.py | 2 - .../sox_compatibility_test.py | 2 - .../sox_effect/smoke_test.py | 1 - .../torchscript_consistency_cpu_test.py | 18 +- .../torchscript_consistency_cuda_test.py | 24 +- .../torchscript_consistency_impl.py | 544 ----------------- test/torchaudio_unittest/transforms_test.py | 1 - third_party/CMakeLists.txt | 28 +- third_party/sox/CMakeLists.txt | 6 +- third_party/transducer/CMakeLists.txt | 28 +- torchaudio/__init__.py | 25 +- torchaudio/backend/__init__.py | 1 + torchaudio/compliance/__init__.py | 4 + torchaudio/csrc/CMakeLists.txt | 91 +++ torchaudio/csrc/sox/effects_chain.cpp | 8 +- torchaudio/datasets/__init__.py | 4 +- torchaudio/datasets/gtzan.py | 3 +- torchaudio/datasets/ljspeech.py | 2 +- torchaudio/extension/extension.py | 1 - torchaudio/functional/__init__.py | 42 ++ torchaudio/functional/functional.py | 22 +- torchaudio/models/__init__.py | 6 + torchaudio/models/wav2letter.py | 4 +- torchaudio/models/wavernn.py | 8 +- torchaudio/prototype/transducer.py | 5 +- torchaudio/sox_effects/__init__.py | 8 + torchaudio/sox_effects/sox_effects.py | 1 - torchaudio/transforms.py | 15 +- 67 files changed, 1297 insertions(+), 877 deletions(-) create mode 100644 CMakeLists.txt create mode 100644 CONTRIBUTING.md rename test/torchaudio_unittest/{soundfile_backend => backend}/__init__.py (100%) create mode 100644 test/torchaudio_unittest/backend/common.py rename test/torchaudio_unittest/{io_test.py => backend/legacy_test.py} (100%) rename test/torchaudio_unittest/{sox_io_backend => backend/soundfile}/__init__.py (100%) rename test/torchaudio_unittest/{soundfile_backend => backend/soundfile}/common.py (100%) rename test/torchaudio_unittest/{soundfile_backend => backend/soundfile}/info_test.py (98%) rename test/torchaudio_unittest/{soundfile_backend => backend/soundfile}/load_test.py (100%) rename test/torchaudio_unittest/{soundfile_backend => backend/soundfile}/save_test.py (99%) create mode 100644 test/torchaudio_unittest/backend/sox_io/__init__.py rename test/torchaudio_unittest/{sox_io_backend => backend/sox_io}/common.py (100%) rename test/torchaudio_unittest/{sox_io_backend => backend/sox_io}/info_test.py (96%) rename test/torchaudio_unittest/{sox_io_backend => backend/sox_io}/load_test.py (100%) rename test/torchaudio_unittest/{sox_io_backend => backend/sox_io}/roundtrip_test.py (100%) rename test/torchaudio_unittest/{sox_io_backend => backend/sox_io}/save_test.py (100%) rename test/torchaudio_unittest/{sox_io_backend => backend/sox_io}/smoke_test.py (100%) rename test/torchaudio_unittest/{sox_io_backend => backend/sox_io}/torchscript_test.py (100%) rename test/torchaudio_unittest/{backend_test.py => backend/utils_test.py} (100%) create mode 100644 test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py create mode 100644 test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py create mode 100644 test/torchaudio_unittest/functional/torchscript_consistency_impl.py create mode 100644 torchaudio/csrc/CMakeLists.txt diff --git a/.circleci/config.yml b/.circleci/config.yml index ef2a4ff76c..1cae2f6c78 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -42,8 +42,8 @@ commands: description: "installs tools required to build torchaudio" steps: - run: - name: Install cmake and pkg-config - command: HOMEBREW_NO_AUTO_UPDATE=1 brew install cmake pkg-config wget + name: Install pkg-config + command: HOMEBREW_NO_AUTO_UPDATE=1 brew install pkg-config wget # Disable brew auto update which is very slow binary_common: &binary_common diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 84e25911a3..fa1e202cb9 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -42,8 +42,8 @@ commands: description: "installs tools required to build torchaudio" steps: - run: - name: Install cmake and pkg-config - command: HOMEBREW_NO_AUTO_UPDATE=1 brew install cmake pkg-config wget + name: Install pkg-config + command: HOMEBREW_NO_AUTO_UPDATE=1 brew install pkg-config wget # Disable brew auto update which is very slow binary_common: &binary_common diff --git a/.circleci/torchscript_bc_test/environment.yml b/.circleci/torchscript_bc_test/environment.yml index 108d97d193..f528ce99f4 100644 --- a/.circleci/torchscript_bc_test/environment.yml +++ b/.circleci/torchscript_bc_test/environment.yml @@ -7,10 +7,12 @@ dependencies: - pytest - pytest-cov - codecov - - librosa + - librosa>=0.8.0 - llvmlite==0.31 # See https://github.com/pytorch/audio/pull/766 - pip - pip: + - cmake + - ninja - kaldi-io - scipy - parameterized diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh index fd43260194..209c86be71 100755 --- a/.circleci/unittest/linux/scripts/install.sh +++ b/.circleci/unittest/linux/scripts/install.sh @@ -38,6 +38,7 @@ conda install -y -c "pytorch-${UPLOAD_CHANNEL}" pytorch ${cudatoolkit} # 2. Install torchaudio printf "* Installing torchaudio\n" +git submodule update --init --recursive BUILD_TRANSDUCER=1 BUILD_SOX=1 python setup.py install # 3. Install Test tools diff --git a/.circleci/unittest/linux/scripts/setup_env.sh b/.circleci/unittest/linux/scripts/setup_env.sh index f56b211ac9..085dc61a5b 100755 --- a/.circleci/unittest/linux/scripts/setup_env.sh +++ b/.circleci/unittest/linux/scripts/setup_env.sh @@ -5,7 +5,7 @@ # # Do not install PyTorch and torchaudio here, otherwise they also get cached. -set -e +set -ex root_dir="$(git rev-parse --show-toplevel)" conda_dir="${root_dir}/conda" @@ -41,12 +41,3 @@ conda activate "${env_dir}" # 3. Install minimal build tools pip --quiet install cmake ninja - -# 4. Buld codecs -git submodule update --init --recursive -mkdir -p third_party/build -( - cd third_party/build - cmake -GNinja .. - cmake --build . -) diff --git a/.flake8 b/.flake8 index b4a5069f16..f344aec879 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,4 @@ [flake8] max-line-length = 120 -ignore = E305,E402,E721,E741,F401,F403,F405,W503,W504,F999 +ignore = E305,E402,E721,E741,F405,W503,W504,F999 exclude = build,docs/source,_ext,third_party diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 0000000000..d72639a71e --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,60 @@ +cmake_minimum_required(VERSION 3.5 FATAL_ERROR) + +# Most of the configurations are taken from PyTorch +# https://github.com/pytorch/pytorch/blob/0c9fb4aff0d60eaadb04e4d5d099fb1e1d5701a9/CMakeLists.txt + +# Use compiler ID "AppleClang" instead of "Clang" for XCode. +# Not setting this sometimes makes XCode C compiler gets detected as "Clang", +# even when the C++ one is detected as "AppleClang". +cmake_policy(SET CMP0010 NEW) +cmake_policy(SET CMP0025 NEW) + +# Suppress warning flags in default MSVC configuration. It's not +# mandatory that we do this (and we don't if cmake is old), but it's +# nice when it's possible, and it's possible on our Windows configs. +if(NOT CMAKE_VERSION VERSION_LESS 3.15.0) + cmake_policy(SET CMP0092 NEW) +endif() + +project(torchaudio) + +# check and set CMAKE_CXX_STANDARD +string(FIND "${CMAKE_CXX_FLAGS}" "-std=c++" env_cxx_standard) +if(env_cxx_standard GREATER -1) + message( + WARNING "C++ standard version definition detected in environment variable." + "PyTorch requires -std=c++14. Please remove -std=c++ settings in your environment.") +endif() +set(CMAKE_CXX_STANDARD 14) +set(CMAKE_C_STANDARD 11) + +set(CMAKE_EXPORT_COMPILE_COMMANDS ON) +set(CMAKE_POSITION_INDEPENDENT_CODE ON) + +# Apple specific +if(APPLE) + # Get clang version on macOS + execute_process( COMMAND ${CMAKE_CXX_COMPILER} --version OUTPUT_VARIABLE clang_full_version_string ) + string(REGEX REPLACE "Apple LLVM version ([0-9]+\\.[0-9]+).*" "\\1" CLANG_VERSION_STRING ${clang_full_version_string}) + message( STATUS "CLANG_VERSION_STRING: " ${CLANG_VERSION_STRING} ) + + # RPATH stuff + set(CMAKE_MACOSX_RPATH ON) + + set(CMAKE_SHARED_LIBRARY_SUFFIX ".so") +endif() + + +# Options +option(BUILD_SOX "Build libsox statically" OFF) +option(BUILD_TRANSDUCER "Enable transducer" OFF) +option(BUILD_LIBTORCHAUDIO "Build C++ Library" ON) +option(BUILD_TORCHAUDIO_PYTHON_EXTENSION "Build Python extension" OFF) + +find_package(Torch REQUIRED) + +# TORCH_CXX_FLAGS contains the same -D_GLIBCXX_USE_CXX11_ABI value as PyTorch +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall ${TORCH_CXX_FLAGS}") + +add_subdirectory(third_party) +add_subdirectory(torchaudio/csrc) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000..73674b42c2 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,117 @@ +# Contributing to Torchaudio +We want to make contributing to this project as easy and transparent as possible. + +## TL;DR + +Please let us know if you encounter a bug by filing an [issue](https://github.com/pytorch/audio/issues). + +We appreciate all contributions. If you are planning to contribute back +bug-fixes, please do so without any further discussion. + +If you plan to contribute new features, utility functions or extensions to the +core, please first open an issue and discuss the feature with us. Sending a PR +without discussion might end up resulting in a rejected PR, because we might be +taking the core in a different direction than you might be aware of. + +Fixing bugs and implementing new features are not the only way you can +contribute. It also helps the project when you report problems you're facing, +and when you give a :+1: on issues that others reported and that are relevant +to you. + +You can also help by improving the documentation. This is no less important +than improving the library itself! If you find a typo in the documentation, +do not hesitate to submit a pull request. + +If you're not sure what you want to work on, you can pick an issue from the +[list of open issues labelled as "help +wanted"](https://github.com/pytorch/audio/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22). +Comment on the issue that you want to work on it and send a PR with your fix +(see below). + +## Development installation + +We recommend using a `conda` environment to contribute efficiently to +torchaudio. + +### Install PyTorch Nightly + +```bash +conda install pytorch -c pytorch-nightly +``` + +### Install Torchaudio + +```bash +pip install cmake ninja +``` + +```bash +git clone https://github.com/pytorch/audio.git +cd audio +git submodule update --init --recursive +BUILD_SOX=1 python setup.py develop +# or, for OSX +# BUILD_SOX=1 MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py develop +# for C++ debugging, please use DEBUG=1 +# DEBUG=1 python setup.py install +``` + +Note: you don't need to use `BUILD_SOX=1` if you have `libsox-dev` installed +already. If you built sox however, set the `PATH` variable so that the tests +properly use the newly built `sox` binary: + +```bash +export PATH="/third_party/install/bin:${PATH}" +``` + +The following dependencies are also needed for testing: + +```bash +pip install typing pytest scipy numpy parametrized +``` + +## Development Process + +If you plan to modify the code or documentation, please follow the steps below: + +1. Fork the repository and create your branch from `master`: `$ git checkout master && git checkout -b my_cool_feature` +2. If you have modified the code (new feature or bug-fix), [please add tests](.test/torchaudio_unittest/). +3. If you have changed APIs, [update the documentation](#Documentation). + +For more details about pull requests, +please read [GitHub's guides](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request). + +If you would like to contribute a new model, please see [here](#New-model). + +If you would like to contribute a new dataset, please see [here](#New-dataset). + +## Testing + +Please refer to our [testing guidelines](.test/torchaudio_unittest/) for more +details. + +## Documentation + +Torchaudio uses [Google style](http://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) +for formatting docstrings. Length of line inside docstrings block must be limited to 120 characters. + +To build the docs, first install the requirements: + +```bash +cd docs +pip install -r requirements.txt +``` + +Then: + +```bash +cd docs +make html +``` + +The built docs should now be available in `docs/build/html` + +## License + +By contributing to Torchaudio, you agree that your contributions will be licensed +under the LICENSE file in the root directory of this source tree. diff --git a/README.md b/README.md index b3664de5f6..527b1a22da 100644 --- a/README.md +++ b/README.md @@ -232,15 +232,7 @@ Complex numbers are supported via tensors of dimension (..., 2), and torchaudio Contributing Guidelines ----------------------- -Please let us know if you encounter a bug by filing an [issue](https://github.com/pytorch/audio/issues). - -We appreciate all contributions. If you are planning to contribute back -bug-fixes, please do so without any further discussion. - -If you plan to contribute new features, utility functions or extensions to the -core, please first open an issue and discuss the feature with us. Sending a PR -without discussion might end up resulting in a rejected PR, because we might be -taking the core in a different direction than you might be aware of. +Please refer to [CONTRIBUTING.md](./CONTRIBUTING.md) Disclaimer on Datasets ---------------------- diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 7f0ba7a14f..d34464ed37 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -2,23 +2,20 @@ import platform import subprocess from pathlib import Path +import distutils.sysconfig +from setuptools import Extension +from setuptools.command.build_ext import build_ext import torch -from torch.utils.cpp_extension import ( - CppExtension, - BuildExtension as TorchBuildExtension -) __all__ = [ 'get_ext_modules', - 'BuildExtension', + 'CMakeBuild', ] _THIS_DIR = Path(__file__).parent.resolve() _ROOT_DIR = _THIS_DIR.parent.parent.resolve() -_CSRC_DIR = _ROOT_DIR / 'torchaudio' / 'csrc' -_TP_BASE_DIR = _ROOT_DIR / 'third_party' -_TP_INSTALL_DIR = _TP_BASE_DIR / 'install' +_TORCHAUDIO_DIR = _ROOT_DIR / 'torchaudio' def _get_build(var): @@ -38,132 +35,71 @@ def _get_build(var): _BUILD_TRANSDUCER = _get_build("BUILD_TRANSDUCER") -def _get_eca(debug): - eca = [] - if debug: - eca += ["-O0", "-g"] - else: - eca += ["-O3"] - if _BUILD_TRANSDUCER: - eca += ['-DBUILD_TRANSDUCER'] - return eca - - -def _get_ela(debug): - ela = [] - if debug: - if platform.system() == "Windows": - ela += ["/DEBUG:FULL"] - else: - ela += ["-O0", "-g"] - else: - ela += ["-O3"] - return ela - - -def _get_srcs(): - srcs = [_CSRC_DIR / 'pybind.cpp'] - srcs += list(_CSRC_DIR.glob('sox/**/*.cpp')) - if _BUILD_TRANSDUCER: - srcs += [_CSRC_DIR / 'transducer.cpp'] - return [str(path) for path in srcs] - - -def _get_include_dirs(): - dirs = [ - str(_ROOT_DIR), - ] - if _BUILD_SOX or _BUILD_TRANSDUCER: - dirs.append(str(_TP_INSTALL_DIR / 'include')) - return dirs - - -def _get_extra_objects(): - libs = [] - if _BUILD_SOX: - # NOTE: The order of the library listed bellow matters. - # - # (the most important thing is that dependencies come after a library - # e.g., sox comes first, flac/vorbis comes before ogg, and - # vorbisenc/vorbisfile comes before vorbis - libs += [ - 'libsox.a', - 'libmad.a', - 'libFLAC.a', - 'libmp3lame.a', - 'libopusfile.a', - 'libopus.a', - 'libvorbisenc.a', - 'libvorbisfile.a', - 'libvorbis.a', - 'libogg.a', - 'libopencore-amrnb.a', - 'libopencore-amrwb.a', - ] - if _BUILD_TRANSDUCER: - libs += ['libwarprnnt.a'] - - return [str(_TP_INSTALL_DIR / 'lib' / lib) for lib in libs] - - -def _get_libraries(): - return [] if _BUILD_SOX else ['sox'] - - -def _get_cxx11_abi(): - try: - value = int(torch._C._GLIBCXX_USE_CXX11_ABI) - except ImportError: - value = 0 - return f'-D_GLIBCXX_USE_CXX11_ABI={value}' - - -def _build_third_party(base_build_dir): - build_dir = os.path.join(base_build_dir, 'third_party') - os.makedirs(build_dir, exist_ok=True) - subprocess.run( - args=[ - 'cmake', - f"-DCMAKE_CXX_FLAGS='{_get_cxx11_abi()}'", - '-DCMAKE_VERBOSE_MAKEFILE:BOOL=ON', - f'-DCMAKE_INSTALL_PREFIX={_TP_INSTALL_DIR}', - f'-DBUILD_SOX={"ON" if _BUILD_SOX else "OFF"}', - f'-DBUILD_TRANSDUCER={"ON" if _BUILD_TRANSDUCER else "OFF"}', - f'{_TP_BASE_DIR}'], - cwd=build_dir, - check=True, - ) - command = ['cmake', '--build', '.'] - if _BUILD_TRANSDUCER: - command += ['--target', 'install'] - subprocess.run( - args=command, - cwd=build_dir, - check=True, - ) - +def get_ext_modules(): + if platform.system() == 'Windows': + return None + return [Extension(name='torchaudio._torchaudio', sources=[])] -_EXT_NAME = 'torchaudio._torchaudio' +# Based off of +# https://github.com/pybind/cmake_example/blob/580c5fd29d4651db99d8874714b07c0c49a53f8a/setup.py +class CMakeBuild(build_ext): + def run(self): + try: + subprocess.check_output(['cmake', '--version']) + except OSError: + raise RuntimeError("CMake is not available.") + super().run() -def get_ext_modules(debug=False): - if platform.system() == 'Windows': - return None - return [ - CppExtension( - _EXT_NAME, - _get_srcs(), - libraries=_get_libraries(), - include_dirs=_get_include_dirs(), - extra_compile_args=_get_eca(debug), - extra_objects=_get_extra_objects(), - extra_link_args=_get_ela(debug), - ), - ] - - -class BuildExtension(TorchBuildExtension): def build_extension(self, ext): - if ext.name == _EXT_NAME and _BUILD_SOX: - _build_third_party(self.build_temp) - super().build_extension(ext) + extdir = os.path.abspath( + os.path.dirname(self.get_ext_fullpath(ext.name))) + + # required for auto-detection of auxiliary "native" libs + if not extdir.endswith(os.path.sep): + extdir += os.path.sep + + cfg = "Debug" if self.debug else "Release" + + cmake_args = [ + f"-DCMAKE_BUILD_TYPE={cfg}", + f"-DCMAKE_PREFIX_PATH={torch.utils.cmake_prefix_path}", + f"-DCMAKE_INSTALL_PREFIX={extdir}", + '-DCMAKE_VERBOSE_MAKEFILE=ON', + f"-DPython_INCLUDE_DIR={distutils.sysconfig.get_python_inc()}", + f"-DBUILD_SOX:BOOL={'ON' if _BUILD_SOX else 'OFF'}", + f"-DBUILD_TRANSDUCER:BOOL={'ON' if _BUILD_TRANSDUCER else 'OFF'}", + "-DBUILD_TORCHAUDIO_PYTHON_EXTENSION:BOOL=ON", + "-DBUILD_LIBTORCHAUDIO:BOOL=OFF", + ] + build_args = [ + '--target', 'install' + ] + + # Default to Ninja + if 'CMAKE_GENERATOR' not in os.environ: + cmake_args += ["-GNinja"] + + # Set CMAKE_BUILD_PARALLEL_LEVEL to control the parallel build level + # across all generators. + if "CMAKE_BUILD_PARALLEL_LEVEL" not in os.environ: + # self.parallel is a Python 3 only way to set parallel jobs by hand + # using -j in the build_ext call, not supported by pip or PyPA-build. + if hasattr(self, "parallel") and self.parallel: + # CMake 3.12+ only. + build_args += ["-j{}".format(self.parallel)] + + if not os.path.exists(self.build_temp): + os.makedirs(self.build_temp) + + subprocess.check_call( + ["cmake", str(_ROOT_DIR)] + cmake_args, cwd=self.build_temp) + subprocess.check_call( + ["cmake", "--build", "."] + build_args, cwd=self.build_temp) + + def get_ext_filename(self, fullname): + ext_filename = super().get_ext_filename(fullname) + ext_filename_parts = ext_filename.split('.') + without_abi = ext_filename_parts[:-2] + ext_filename_parts[-1:] + ext_filename = '.'.join(without_abi) + return ext_filename diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index 957b41e048..11e8f07bfd 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -8,7 +8,7 @@ export BUILD_TYPE="wheel" export NO_CUDA_PACKAGE=1 setup_env 0.8.0 setup_wheel_python -pip_install numpy future +pip_install numpy future cmake ninja setup_pip_pytorch_version python setup.py clean if [[ "$OSTYPE" == "msys" ]]; then diff --git a/packaging/torchaudio/meta.yaml b/packaging/torchaudio/meta.yaml index bd4f004a1b..ad12821d0e 100644 --- a/packaging/torchaudio/meta.yaml +++ b/packaging/torchaudio/meta.yaml @@ -14,11 +14,12 @@ requirements: - python - setuptools - cpuonly + - cmake + - ninja {{ environ.get('CONDA_PYTORCH_BUILD_CONSTRAINT') }} run: - python - - typing # [py2k] {{ environ.get('CONDA_PYTORCH_CONSTRAINT') }} build: diff --git a/setup.py b/setup.py index 8ecc41c1cc..1ee56dfcb7 100644 --- a/setup.py +++ b/setup.py @@ -83,7 +83,7 @@ def run(self): packages=find_packages(exclude=["build*", "test*", "torchaudio.csrc*", "third_party*", "build_tools*"]), ext_modules=setup_helpers.get_ext_modules(), cmdclass={ - 'build_ext': setup_helpers.BuildExtension.with_options(no_python_abi_suffix=True), + 'build_ext': setup_helpers.CMakeBuild, 'clean': clean, }, install_requires=[pytorch_package_dep], diff --git a/test/torchaudio_unittest/README.md b/test/torchaudio_unittest/README.md index 50d17d9948..d61c64b015 100644 --- a/test/torchaudio_unittest/README.md +++ b/test/torchaudio_unittest/README.md @@ -2,9 +2,25 @@ ## How to run test -You can use `pytest` to run `torchaudio`'s test suites. See https://docs.pytest.org/ for the detail of how to use `pytest` command. The `parametrized` package is also needed; it can be installed via `pip` or with ` conda install -c conda-forge parameterized`. +You can use `pytest` to run `torchaudio`'s test suites. See +https://docs.pytest.org/ for the detail of how to use `pytest` command. +For testing, the following packages are needed: + +```bash +pip install typing pytest scipy numpy parametrized +``` + +Make sure to follow the installation instruction in the [contributing +guide](../../CONTRIBUTING.md) first. For running `kaldi`-related tests: + +```bash +export PATH="${PATH}:/src/featbin/" ``` + +Some useful pytest commands: + +```bash # List up all the tests pytest test --collect-only # Run all the test suites @@ -21,6 +37,11 @@ pytest test/torchaudio_unittest/sox_io_backend -k load # only runs tests where **Note** We use PyTorch's test utilities instead of `pytest` frameworks when writing tests to avoid reinventing the wheel for Tensor comparison. +Also, while we recommend using `pytest` for *running* the tests, we cannot +make `pytest` a testing dependency of `torchaudio`. As a result, you should +not import `pytest` or its submodules in the test files; Use the Python +`unittest` builtin module instead, or the `parametrized` package to +parametrize tests. ## Structure of tests @@ -28,7 +49,7 @@ The following is an overview of the tests and related modules for `torchaudio`. ### Purpose specific test suites -#### Numerical compatibility agains existing software +#### Numerical compatibility against existing software - [Librosa compatibility test](./librosa_compatibility_test.py) Test suite for numerical compatibility against librosa. - [SoX compatibility test](./sox_compatibility_test.py) diff --git a/test/torchaudio_unittest/soundfile_backend/__init__.py b/test/torchaudio_unittest/backend/__init__.py similarity index 100% rename from test/torchaudio_unittest/soundfile_backend/__init__.py rename to test/torchaudio_unittest/backend/__init__.py diff --git a/test/torchaudio_unittest/backend/common.py b/test/torchaudio_unittest/backend/common.py new file mode 100644 index 0000000000..dc8e90bf77 --- /dev/null +++ b/test/torchaudio_unittest/backend/common.py @@ -0,0 +1,25 @@ +from torchaudio_unittest.common_utils import sox_utils + + +def get_encoding(ext, dtype): + exts = { + 'mp3', + 'flac', + 'vorbis', + } + encodings = { + 'float32': 'PCM_F', + 'int32': 'PCM_S', + 'int16': 'PCM_S', + 'uint8': 'PCM_U', + } + return ext.upper() if ext in exts else encodings[dtype] + + +def get_bits_per_sample(ext, dtype): + bits_per_samples = { + 'flac': 24, + 'mp3': 0, + 'vorbis': 0, + } + return bits_per_samples.get(ext, sox_utils.get_bit_depth(dtype)) diff --git a/test/torchaudio_unittest/io_test.py b/test/torchaudio_unittest/backend/legacy_test.py similarity index 100% rename from test/torchaudio_unittest/io_test.py rename to test/torchaudio_unittest/backend/legacy_test.py diff --git a/test/torchaudio_unittest/sox_io_backend/__init__.py b/test/torchaudio_unittest/backend/soundfile/__init__.py similarity index 100% rename from test/torchaudio_unittest/sox_io_backend/__init__.py rename to test/torchaudio_unittest/backend/soundfile/__init__.py diff --git a/test/torchaudio_unittest/soundfile_backend/common.py b/test/torchaudio_unittest/backend/soundfile/common.py similarity index 100% rename from test/torchaudio_unittest/soundfile_backend/common.py rename to test/torchaudio_unittest/backend/soundfile/common.py diff --git a/test/torchaudio_unittest/soundfile_backend/info_test.py b/test/torchaudio_unittest/backend/soundfile/info_test.py similarity index 98% rename from test/torchaudio_unittest/soundfile_backend/info_test.py rename to test/torchaudio_unittest/backend/soundfile/info_test.py index d4aa2b2b2e..3b3f792281 100644 --- a/test/torchaudio_unittest/soundfile_backend/info_test.py +++ b/test/torchaudio_unittest/backend/soundfile/info_test.py @@ -13,8 +13,10 @@ get_wav_data, save_wav, ) -# TODO refactor and move these to common location -from torchaudio_unittest.sox_io_backend.info_test import get_encoding, get_bits_per_sample +from torchaudio_unittest.backend.common import ( + get_bits_per_sample, + get_encoding, +) from .common import skipIfFormatNotSupported, parameterize if _mod_utils.is_module_available("soundfile"): diff --git a/test/torchaudio_unittest/soundfile_backend/load_test.py b/test/torchaudio_unittest/backend/soundfile/load_test.py similarity index 100% rename from test/torchaudio_unittest/soundfile_backend/load_test.py rename to test/torchaudio_unittest/backend/soundfile/load_test.py diff --git a/test/torchaudio_unittest/soundfile_backend/save_test.py b/test/torchaudio_unittest/backend/soundfile/save_test.py similarity index 99% rename from test/torchaudio_unittest/soundfile_backend/save_test.py rename to test/torchaudio_unittest/backend/soundfile/save_test.py index 5c36e0d126..2f2741c303 100644 --- a/test/torchaudio_unittest/soundfile_backend/save_test.py +++ b/test/torchaudio_unittest/backend/soundfile/save_test.py @@ -1,5 +1,4 @@ import io -import itertools from unittest.mock import patch from torchaudio._internal import module_utils as _mod_utils diff --git a/test/torchaudio_unittest/backend/sox_io/__init__.py b/test/torchaudio_unittest/backend/sox_io/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/torchaudio_unittest/sox_io_backend/common.py b/test/torchaudio_unittest/backend/sox_io/common.py similarity index 100% rename from test/torchaudio_unittest/sox_io_backend/common.py rename to test/torchaudio_unittest/backend/sox_io/common.py diff --git a/test/torchaudio_unittest/sox_io_backend/info_test.py b/test/torchaudio_unittest/backend/sox_io/info_test.py similarity index 96% rename from test/torchaudio_unittest/sox_io_backend/info_test.py rename to test/torchaudio_unittest/backend/sox_io/info_test.py index 9a26b0d2ea..a2a93648a1 100644 --- a/test/torchaudio_unittest/sox_io_backend/info_test.py +++ b/test/torchaudio_unittest/backend/sox_io/info_test.py @@ -7,6 +7,10 @@ from torchaudio.backend import sox_io_backend from torchaudio._internal import module_utils as _mod_utils +from torchaudio_unittest.backend.common import ( + get_bits_per_sample, + get_encoding, +) from torchaudio_unittest.common_utils import ( TempDirMixin, HttpServerMixin, @@ -28,30 +32,6 @@ import requests -def get_encoding(ext, dtype): - exts = { - 'mp3', - 'flac', - 'vorbis', - } - encodings = { - 'float32': 'PCM_F', - 'int32': 'PCM_S', - 'int16': 'PCM_S', - 'uint8': 'PCM_U', - } - return ext.upper() if ext in exts else encodings[dtype] - - -def get_bits_per_sample(ext, dtype): - bits_per_samples = { - 'flac': 24, - 'mp3': 0, - 'vorbis': 0, - } - return bits_per_samples.get(ext, sox_utils.get_bit_depth(dtype)) - - @skipIfNoExec('sox') @skipIfNoExtension class TestInfo(TempDirMixin, PytorchTestCase): @@ -161,7 +141,9 @@ def test_sphere(self, sample_rate, num_channels, bits_per_sample): """`sox_io_backend.info` can check sph file correctly""" duration = 1 path = self.get_temp_path('data.sph') - sox_utils.gen_audio_file(path, sample_rate, num_channels, duration=duration, bit_depth=bits_per_sample) + sox_utils.gen_audio_file( + path, sample_rate, num_channels, duration=duration, + bit_depth=bits_per_sample) info = sox_io_backend.info(path) assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration diff --git a/test/torchaudio_unittest/sox_io_backend/load_test.py b/test/torchaudio_unittest/backend/sox_io/load_test.py similarity index 100% rename from test/torchaudio_unittest/sox_io_backend/load_test.py rename to test/torchaudio_unittest/backend/sox_io/load_test.py diff --git a/test/torchaudio_unittest/sox_io_backend/roundtrip_test.py b/test/torchaudio_unittest/backend/sox_io/roundtrip_test.py similarity index 100% rename from test/torchaudio_unittest/sox_io_backend/roundtrip_test.py rename to test/torchaudio_unittest/backend/sox_io/roundtrip_test.py diff --git a/test/torchaudio_unittest/sox_io_backend/save_test.py b/test/torchaudio_unittest/backend/sox_io/save_test.py similarity index 100% rename from test/torchaudio_unittest/sox_io_backend/save_test.py rename to test/torchaudio_unittest/backend/sox_io/save_test.py diff --git a/test/torchaudio_unittest/sox_io_backend/smoke_test.py b/test/torchaudio_unittest/backend/sox_io/smoke_test.py similarity index 100% rename from test/torchaudio_unittest/sox_io_backend/smoke_test.py rename to test/torchaudio_unittest/backend/sox_io/smoke_test.py diff --git a/test/torchaudio_unittest/sox_io_backend/torchscript_test.py b/test/torchaudio_unittest/backend/sox_io/torchscript_test.py similarity index 100% rename from test/torchaudio_unittest/sox_io_backend/torchscript_test.py rename to test/torchaudio_unittest/backend/sox_io/torchscript_test.py diff --git a/test/torchaudio_unittest/backend_test.py b/test/torchaudio_unittest/backend/utils_test.py similarity index 100% rename from test/torchaudio_unittest/backend_test.py rename to test/torchaudio_unittest/backend/utils_test.py diff --git a/test/torchaudio_unittest/batch_consistency_test.py b/test/torchaudio_unittest/batch_consistency_test.py index 78e3cd62f9..4644fe7dd3 100644 --- a/test/torchaudio_unittest/batch_consistency_test.py +++ b/test/torchaudio_unittest/batch_consistency_test.py @@ -1,8 +1,9 @@ """Test numerical consistency among single input and batched input.""" -import unittest import itertools from parameterized import parameterized +import math + import torch import torchaudio import torchaudio.functional as F @@ -59,6 +60,78 @@ def test_detect_pitch_frequency(self, frequency, sample_rate, n_channels): n_channels=n_channels, duration=5) self.assert_batch_consistencies(F.detect_pitch_frequency, waveform, sample_rate) + def test_amplitude_to_DB(self): + torch.manual_seed(0) + spec = torch.rand(2, 100, 100) * 200 + + amplitude_mult = 20. + amin = 1e-10 + ref = 1.0 + db_mult = math.log10(max(amin, ref)) + + # Test with & without a `top_db` clamp + self.assert_batch_consistencies(F.amplitude_to_DB, spec, amplitude_mult, + amin, db_mult, top_db=None) + self.assert_batch_consistencies(F.amplitude_to_DB, spec, amplitude_mult, + amin, db_mult, top_db=40.) + + def test_amplitude_to_DB_itemwise_clamps(self): + """Ensure that the clamps are separate for each spectrogram in a batch. + + The clamp was determined per-batch in a prior implementation, which + meant it was determined by the loudest item, thus items weren't + independent. See: + + https://github.com/pytorch/audio/issues/994 + + """ + amplitude_mult = 20. + amin = 1e-10 + ref = 1.0 + db_mult = math.log10(max(amin, ref)) + top_db = 20. + + # Make a batch of noise + torch.manual_seed(0) + spec = torch.rand([2, 2, 100, 100]) * 200 + # Make one item blow out the other + spec[0] += 50 + + batchwise_dbs = F.amplitude_to_DB(spec, amplitude_mult, amin, + db_mult, top_db=top_db) + itemwise_dbs = torch.stack([ + F.amplitude_to_DB(item, amplitude_mult, amin, + db_mult, top_db=top_db) + for item in spec + ]) + + self.assertEqual(batchwise_dbs, itemwise_dbs) + + def test_amplitude_to_DB_not_channelwise_clamps(self): + """Check that clamps are applied per-item, not per channel.""" + amplitude_mult = 20. + amin = 1e-10 + ref = 1.0 + db_mult = math.log10(max(amin, ref)) + top_db = 40. + + torch.manual_seed(0) + spec = torch.rand([1, 2, 100, 100]) * 200 + # Make one channel blow out the other + spec[:, 0] += 50 + + specwise_dbs = F.amplitude_to_DB(spec, amplitude_mult, amin, + db_mult, top_db=top_db) + channelwise_dbs = torch.stack([ + F.amplitude_to_DB(spec[:, i], amplitude_mult, amin, + db_mult, top_db=top_db) + for i in range(spec.size(-3)) + ]) + + # Just check channelwise gives a different answer. + difference = (specwise_dbs - channelwise_dbs).abs() + assert (difference >= 1e-5).any() + def test_contrast(self): waveform = torch.rand(2, 100) - 0.5 self.assert_batch_consistencies(F.contrast, waveform, enhancement_amount=80.) @@ -103,7 +176,7 @@ class TestTransforms(common_utils.TorchaudioTestCase): """Test suite for classes defined in `transforms` module""" def test_batch_AmplitudeToDB(self): - spec = torch.rand((6, 201)) + spec = torch.rand((2, 6, 201)) # Single then transform then batch expected = torchaudio.transforms.AmplitudeToDB()(spec).repeat(3, 1, 1) diff --git a/test/torchaudio_unittest/common_utils/__init__.py b/test/torchaudio_unittest/common_utils/__init__.py index cf3e717116..8cc44c3529 100644 --- a/test/torchaudio_unittest/common_utils/__init__.py +++ b/test/torchaudio_unittest/common_utils/__init__.py @@ -27,4 +27,8 @@ from .parameterized_utils import ( load_params, ) -from . import sox_utils + +__all__ = ['get_asset_path', 'get_whitenoise', 'get_sinusoid', 'set_audio_backend', + 'TempDirMixin', 'HttpServerMixin', 'TestBaseMixin', 'PytorchTestCase', 'TorchaudioTestCase', + 'skipIfNoCuda', 'skipIfNoExec', 'skipIfNoModule', 'skipIfNoExtension', 'skipIfNoSoxBackend', + 'get_wav_data', 'normalize_wav', 'load_wav', 'save_wav', 'load_params'] diff --git a/test/torchaudio_unittest/compliance_kaldi_test.py b/test/torchaudio_unittest/compliance_kaldi_test.py index 43e9865e2f..c7b4149819 100644 --- a/test/torchaudio_unittest/compliance_kaldi_test.py +++ b/test/torchaudio_unittest/compliance_kaldi_test.py @@ -1,6 +1,5 @@ import os import math -import unittest import torch import torchaudio diff --git a/test/torchaudio_unittest/datasets/datasets_test.py b/test/torchaudio_unittest/datasets/datasets_test.py index cd04c4d447..fda0804ea6 100644 --- a/test/torchaudio_unittest/datasets/datasets_test.py +++ b/test/torchaudio_unittest/datasets/datasets_test.py @@ -1,5 +1,3 @@ -from torchaudio.datasets.librispeech import LIBRISPEECH -from torchaudio.datasets.speechcommands import SPEECHCOMMANDS from torchaudio.datasets.vctk import VCTK from torchaudio_unittest.common_utils import ( diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py index c4ba42391d..21e5a3e1b7 100644 --- a/test/torchaudio_unittest/functional/functional_cpu_test.py +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -83,46 +83,78 @@ def test_pitch(self, frequency): self.assertFalse(s) -class TestDB_to_amplitude(common_utils.TorchaudioTestCase): - def test_DB_to_amplitude(self): - # Make some noise - x = torch.rand(1000) - spectrogram = torchaudio.transforms.Spectrogram() - spec = spectrogram(x) - +class Testamplitude_to_DB(common_utils.TorchaudioTestCase): + @parameterized.expand([ + ([100, 100],), + ([2, 100, 100],), + ([2, 2, 100, 100],), + ]) + def test_reversible(self, shape): + """Round trip between amplitude and db should return the original for various shape + + This implicitly also tests `DB_to_amplitude`. + + """ + amplitude_mult = 20. + power_mult = 10. amin = 1e-10 ref = 1.0 - db_multiplier = math.log10(max(amin, ref)) - - # Waveform amplitude -> DB -> amplitude - multiplier = 20. - power = 0.5 - - db = F.amplitude_to_DB(torch.abs(x), multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) + db_mult = math.log10(max(amin, ref)) - self.assertEqual(x2, torch.abs(x), atol=5e-5, rtol=1e-5) + torch.manual_seed(0) + spec = torch.rand(*shape) * 200 # Spectrogram amplitude -> DB -> amplitude - db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) + db = F.amplitude_to_DB(spec, amplitude_mult, amin, db_mult, top_db=None) + x2 = F.DB_to_amplitude(db, ref, 0.5) self.assertEqual(x2, spec, atol=5e-5, rtol=1e-5) - # Waveform power -> DB -> power - multiplier = 10. - power = 1. - - db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) - - self.assertEqual(x2, torch.abs(x), atol=5e-5, rtol=1e-5) - # Spectrogram power -> DB -> power - db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) - - self.assertEqual(x2, spec, atol=5e-5, rtol=1e-5) + db = F.amplitude_to_DB(spec, power_mult, amin, db_mult, top_db=None) + x2 = F.DB_to_amplitude(db, ref, 1.) + + self.assertEqual(x2, spec) + + @parameterized.expand([ + ([100, 100],), + ([2, 100, 100],), + ([2, 2, 100, 100],), + ]) + def test_top_db_clamp(self, shape): + """Ensure values are properly clamped when `top_db` is supplied.""" + amplitude_mult = 20. + amin = 1e-10 + ref = 1.0 + db_mult = math.log10(max(amin, ref)) + top_db = 40. + + torch.manual_seed(0) + # A random tensor is used for increased entropy, but the max and min for + # each spectrogram still need to be predictable. The max determines the + # decibel cutoff, and the distance from the min must be large enough + # that it triggers a clamp. + spec = torch.rand(*shape) + # Ensure each spectrogram has a min of 0 and a max of 1. + spec -= spec.amin([-2, -1])[..., None, None] + spec /= spec.amax([-2, -1])[..., None, None] + # Expand the range to (0, 200) - wide enough to properly test clamping. + spec *= 200 + + decibels = F.amplitude_to_DB(spec, amplitude_mult, amin, + db_mult, top_db=top_db) + # Ensure the clamp was applied + below_limit = decibels < 6.0205 + assert not below_limit.any(), ( + "{} decibel values were below the expected cutoff:\n{}".format( + below_limit.sum().item(), decibels + ) + ) + # Ensure it didn't over-clamp + close_to_limit = decibels < 6.0207 + assert close_to_limit.any(), ( + f"No values were close to the limit. Did it over-clamp?\n{decibels}" + ) class TestComplexNorm(common_utils.TorchaudioTestCase): diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py b/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py new file mode 100644 index 0000000000..06871f9bde --- /dev/null +++ b/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py @@ -0,0 +1,14 @@ +import torch + +from torchaudio_unittest.common_utils import PytorchTestCase +from .torchscript_consistency_impl import Functional + + +class TestFunctionalFloat32(Functional, PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') + + +class TestFunctionalFloat64(Functional, PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py b/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py new file mode 100644 index 0000000000..53ccbad3c9 --- /dev/null +++ b/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py @@ -0,0 +1,16 @@ +import torch + +from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase +from .torchscript_consistency_impl import Functional + + +@skipIfNoCuda +class TestFunctionalFloat32(Functional, PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@skipIfNoCuda +class TestFunctionalFloat64(Functional, PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py new file mode 100644 index 0000000000..83f7e8e95d --- /dev/null +++ b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py @@ -0,0 +1,549 @@ +"""Test suites for jit-ability and its numerical compatibility""" +import unittest + +import torch +import torchaudio.functional as F + +from torchaudio_unittest import common_utils + + +class Functional(common_utils.TestBaseMixin): + """Implements test for `functinoal` modul that are performed for different devices""" + def _assert_consistency(self, func, tensor, shape_only=False): + tensor = tensor.to(device=self.device, dtype=self.dtype) + + ts_func = torch.jit.script(func) + output = func(tensor) + ts_output = ts_func(tensor) + if shape_only: + ts_output = ts_output.shape + output = output.shape + self.assertEqual(ts_output, output) + + def test_spectrogram(self): + def func(tensor): + n_fft = 400 + ws = 400 + hop = 200 + pad = 0 + window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) + power = 2. + normalize = False + return F.spectrogram(tensor, pad, window, n_fft, hop, ws, power, normalize) + + tensor = common_utils.get_whitenoise() + self._assert_consistency(func, tensor) + + def test_griffinlim(self): + def func(tensor): + n_fft = 400 + ws = 400 + hop = 200 + window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) + power = 2. + normalize = False + momentum = 0.99 + n_iter = 32 + length = 1000 + rand_int = False + return F.griffinlim(tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, rand_int) + + tensor = torch.rand((1, 201, 6)) + self._assert_consistency(func, tensor) + + def test_compute_deltas(self): + def func(tensor): + win_length = 2 * 7 + 1 + return F.compute_deltas(tensor, win_length=win_length) + + channel = 13 + n_mfcc = channel * 3 + time = 1021 + tensor = torch.randn(channel, n_mfcc, time) + self._assert_consistency(func, tensor) + + def test_detect_pitch_frequency(self): + waveform = common_utils.get_sinusoid(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + return F.detect_pitch_frequency(tensor, sample_rate) + + self._assert_consistency(func, waveform) + + def test_create_fb_matrix(self): + if self.device != torch.device('cpu'): + raise unittest.SkipTest('No need to perform test on device other than CPU') + + def func(_): + n_stft = 100 + f_min = 0.0 + f_max = 20.0 + n_mels = 10 + sample_rate = 16000 + norm = "slaney" + return F.create_fb_matrix(n_stft, f_min, f_max, n_mels, sample_rate, norm) + + dummy = torch.zeros(1, 1) + self._assert_consistency(func, dummy) + + def test_amplitude_to_DB(self): + def func(tensor): + multiplier = 10.0 + amin = 1e-10 + db_multiplier = 0.0 + top_db = 80.0 + return F.amplitude_to_DB(tensor, multiplier, amin, db_multiplier, top_db) + + tensor = torch.rand((6, 201)) + self._assert_consistency(func, tensor) + + def test_DB_to_amplitude(self): + def func(tensor): + ref = 1. + power = 1. + return F.DB_to_amplitude(tensor, ref, power) + + tensor = torch.rand((1, 100)) + self._assert_consistency(func, tensor) + + def test_create_dct(self): + if self.device != torch.device('cpu'): + raise unittest.SkipTest('No need to perform test on device other than CPU') + + def func(_): + n_mfcc = 40 + n_mels = 128 + norm = "ortho" + return F.create_dct(n_mfcc, n_mels, norm) + + dummy = torch.zeros(1, 1) + self._assert_consistency(func, dummy) + + def test_mu_law_encoding(self): + def func(tensor): + qc = 256 + return F.mu_law_encoding(tensor, qc) + + waveform = common_utils.get_whitenoise() + self._assert_consistency(func, waveform) + + def test_mu_law_decoding(self): + def func(tensor): + qc = 256 + return F.mu_law_decoding(tensor, qc) + + tensor = torch.rand((1, 10)) + self._assert_consistency(func, tensor) + + def test_complex_norm(self): + def func(tensor): + power = 2. + return F.complex_norm(tensor, power) + + tensor = torch.randn(1, 2, 1025, 400, 2) + self._assert_consistency(func, tensor) + + def test_mask_along_axis(self): + def func(tensor): + mask_param = 100 + mask_value = 30. + axis = 2 + return F.mask_along_axis(tensor, mask_param, mask_value, axis) + + tensor = torch.randn(2, 1025, 400) + self._assert_consistency(func, tensor) + + def test_mask_along_axis_iid(self): + def func(tensor): + mask_param = 100 + mask_value = 30. + axis = 2 + return F.mask_along_axis_iid(tensor, mask_param, mask_value, axis) + + tensor = torch.randn(4, 2, 1025, 400) + self._assert_consistency(func, tensor) + + def test_gain(self): + def func(tensor): + gainDB = 2.0 + return F.gain(tensor, gainDB) + + tensor = torch.rand((1, 1000)) + self._assert_consistency(func, tensor) + + def test_dither_TPDF(self): + def func(tensor): + return F.dither(tensor, 'TPDF') + + tensor = common_utils.get_whitenoise(n_channels=2) + self._assert_consistency(func, tensor, shape_only=True) + + def test_dither_RPDF(self): + def func(tensor): + return F.dither(tensor, 'RPDF') + + tensor = common_utils.get_whitenoise(n_channels=2) + self._assert_consistency(func, tensor, shape_only=True) + + def test_dither_GPDF(self): + def func(tensor): + return F.dither(tensor, 'GPDF') + + tensor = common_utils.get_whitenoise(n_channels=2) + self._assert_consistency(func, tensor, shape_only=True) + + def test_dither_noise_shaping(self): + def func(tensor): + return F.dither(tensor, noise_shaping=True) + + tensor = common_utils.get_whitenoise(n_channels=2) + self._assert_consistency(func, tensor) + + def test_lfilter(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise() + + def func(tensor): + # Design an IIR lowpass filter using scipy.signal filter design + # https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirdesign.html#scipy.signal.iirdesign + # + # Example + # >>> from scipy.signal import iirdesign + # >>> b, a = iirdesign(0.2, 0.3, 1, 60) + b_coeffs = torch.tensor( + [ + 0.00299893, + -0.0051152, + 0.00841964, + -0.00747802, + 0.00841964, + -0.0051152, + 0.00299893, + ], + device=tensor.device, + dtype=tensor.dtype, + ) + a_coeffs = torch.tensor( + [ + 1.0, + -4.8155751, + 10.2217618, + -12.14481273, + 8.49018171, + -3.3066882, + 0.56088705, + ], + device=tensor.device, + dtype=tensor.dtype, + ) + return F.lfilter(tensor, a_coeffs, b_coeffs) + + self._assert_consistency(func, waveform) + + def test_lowpass(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + cutoff_freq = 3000. + return F.lowpass_biquad(tensor, sample_rate, cutoff_freq) + + self._assert_consistency(func, waveform) + + def test_highpass(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + cutoff_freq = 2000. + return F.highpass_biquad(tensor, sample_rate, cutoff_freq) + + self._assert_consistency(func, waveform) + + def test_allpass(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + return F.allpass_biquad(tensor, sample_rate, central_freq, q) + + self._assert_consistency(func, waveform) + + def test_bandpass_with_csg(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + const_skirt_gain = True + return F.bandpass_biquad(tensor, sample_rate, central_freq, q, const_skirt_gain) + + self._assert_consistency(func, waveform) + + def test_bandpass_without_csg(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + const_skirt_gain = True + return F.bandpass_biquad(tensor, sample_rate, central_freq, q, const_skirt_gain) + + self._assert_consistency(func, waveform) + + def test_bandreject(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + return F.bandreject_biquad(tensor, sample_rate, central_freq, q) + + self._assert_consistency(func, waveform) + + def test_band_with_noise(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + noise = True + return F.band_biquad(tensor, sample_rate, central_freq, q, noise) + + self._assert_consistency(func, waveform) + + def test_band_without_noise(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + noise = False + return F.band_biquad(tensor, sample_rate, central_freq, q, noise) + + self._assert_consistency(func, waveform) + + def test_treble(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + gain = 40. + central_freq = 1000. + q = 0.707 + return F.treble_biquad(tensor, sample_rate, gain, central_freq, q) + + self._assert_consistency(func, waveform) + + def test_bass(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + gain = 40. + central_freq = 1000. + q = 0.707 + return F.bass_biquad(tensor, sample_rate, gain, central_freq, q) + + self._assert_consistency(func, waveform) + + def test_deemph(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + return F.deemph_biquad(tensor, sample_rate) + + self._assert_consistency(func, waveform) + + def test_riaa(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + return F.riaa_biquad(tensor, sample_rate) + + self._assert_consistency(func, waveform) + + def test_equalizer(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + center_freq = 300. + gain = 1. + q = 0.707 + return F.equalizer_biquad(tensor, sample_rate, center_freq, gain, q) + + self._assert_consistency(func, waveform) + + def test_perf_biquad_filtering(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise() + + def func(tensor): + a = torch.tensor([0.7, 0.2, 0.6], device=tensor.device, dtype=tensor.dtype) + b = torch.tensor([0.4, 0.2, 0.9], device=tensor.device, dtype=tensor.dtype) + return F.lfilter(tensor, a, b) + + self._assert_consistency(func, waveform) + + def test_sliding_window_cmn(self): + def func(tensor): + cmn_window = 600 + min_cmn_window = 100 + center = False + norm_vars = False + a = torch.tensor( + [ + [ + -1.915875792503357, + 1.147700309753418 + ], + [ + 1.8242558240890503, + 1.3869990110397339 + ] + ], + device=tensor.device, + dtype=tensor.dtype + ) + return F.sliding_window_cmn(a, cmn_window, min_cmn_window, center, norm_vars) + b = torch.tensor( + [ + [ + -1.8701, + -0.1196 + ], + [ + 1.8701, + 0.1196 + ] + ] + ) + self._assert_consistency(func, b) + + def test_contrast(self): + waveform = common_utils.get_whitenoise() + + def func(tensor): + enhancement_amount = 80. + return F.contrast(tensor, enhancement_amount) + + self._assert_consistency(func, waveform) + + def test_dcshift(self): + waveform = common_utils.get_whitenoise() + + def func(tensor): + shift = 0.5 + limiter_gain = 0.05 + return F.dcshift(tensor, shift, limiter_gain) + + self._assert_consistency(func, waveform) + + def test_overdrive(self): + waveform = common_utils.get_whitenoise() + + def func(tensor): + gain = 30. + colour = 50. + return F.overdrive(tensor, gain, colour) + + self._assert_consistency(func, waveform) + + def test_phaser(self): + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + gain_in = 0.5 + gain_out = 0.8 + delay_ms = 2.0 + decay = 0.4 + speed = 0.5 + sample_rate = 44100 + return F.phaser(tensor, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) + + self._assert_consistency(func, waveform) + + def test_flanger(self): + torch.random.manual_seed(40) + waveform = torch.rand(2, 100) - 0.5 + + def func(tensor): + delay = 0.8 + depth = 0.88 + regen = 3.0 + width = 0.23 + speed = 1.3 + phase = 60. + sample_rate = 44100 + return F.flanger(tensor, sample_rate, delay, depth, regen, width, speed, + phase, modulation='sinusoidal', interpolation='linear') + + self._assert_consistency(func, waveform) + + def test_spectral_centroid(self): + + def func(tensor): + sample_rate = 44100 + n_fft = 400 + ws = 400 + hop = 200 + pad = 0 + window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) + return F.spectral_centroid(tensor, sample_rate, pad, window, n_fft, hop, ws) + + tensor = common_utils.get_whitenoise(sample_rate=44100) + self._assert_consistency(func, tensor) diff --git a/test/torchaudio_unittest/kaldi_io_test.py b/test/torchaudio_unittest/kaldi_io_test.py index 2153b4cc02..4ceeeebbc5 100644 --- a/test/torchaudio_unittest/kaldi_io_test.py +++ b/test/torchaudio_unittest/kaldi_io_test.py @@ -1,5 +1,3 @@ -import unittest - import torch import torchaudio.kaldi_io as kio diff --git a/test/torchaudio_unittest/sox_compatibility_test.py b/test/torchaudio_unittest/sox_compatibility_test.py index ade98b2aeb..c68e529427 100644 --- a/test/torchaudio_unittest/sox_compatibility_test.py +++ b/test/torchaudio_unittest/sox_compatibility_test.py @@ -1,5 +1,3 @@ -import unittest - import torch import torchaudio.functional as F import torchaudio.transforms as T diff --git a/test/torchaudio_unittest/sox_effect/smoke_test.py b/test/torchaudio_unittest/sox_effect/smoke_test.py index 2a8d9e7ff9..3b2b34d3ec 100644 --- a/test/torchaudio_unittest/sox_effect/smoke_test.py +++ b/test/torchaudio_unittest/sox_effect/smoke_test.py @@ -10,7 +10,6 @@ save_wav, ) from .common import ( - name_func, load_params, ) diff --git a/test/torchaudio_unittest/torchscript_consistency_cpu_test.py b/test/torchaudio_unittest/torchscript_consistency_cpu_test.py index fcc9cb8389..9092f3a64b 100644 --- a/test/torchaudio_unittest/torchscript_consistency_cpu_test.py +++ b/test/torchaudio_unittest/torchscript_consistency_cpu_test.py @@ -1,24 +1,14 @@ import torch -from torchaudio_unittest import common_utils -from .torchscript_consistency_impl import Functional, Transforms +from torchaudio_unittest.common_utils import PytorchTestCase +from .torchscript_consistency_impl import Transforms -class TestFunctionalFloat32(Functional, common_utils.PytorchTestCase): +class TestTransformsFloat32(Transforms, PytorchTestCase): dtype = torch.float32 device = torch.device('cpu') -class TestFunctionalFloat64(Functional, common_utils.PytorchTestCase): - dtype = torch.float64 - device = torch.device('cpu') - - -class TestTransformsFloat32(Transforms, common_utils.PytorchTestCase): - dtype = torch.float32 - device = torch.device('cpu') - - -class TestTransformsFloat64(Transforms, common_utils.PytorchTestCase): +class TestTransformsFloat64(Transforms, PytorchTestCase): dtype = torch.float64 device = torch.device('cpu') diff --git a/test/torchaudio_unittest/torchscript_consistency_cuda_test.py b/test/torchaudio_unittest/torchscript_consistency_cuda_test.py index c205e192ed..7425647bab 100644 --- a/test/torchaudio_unittest/torchscript_consistency_cuda_test.py +++ b/test/torchaudio_unittest/torchscript_consistency_cuda_test.py @@ -1,28 +1,16 @@ import torch -from torchaudio_unittest import common_utils -from .torchscript_consistency_impl import Functional, Transforms +from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase +from .torchscript_consistency_impl import Transforms -@common_utils.skipIfNoCuda -class TestFunctionalFloat32(Functional, common_utils.PytorchTestCase): +@skipIfNoCuda +class TestTransformsFloat32(Transforms, PytorchTestCase): dtype = torch.float32 device = torch.device('cuda') -@common_utils.skipIfNoCuda -class TestFunctionalFloat64(Functional, common_utils.PytorchTestCase): - dtype = torch.float64 - device = torch.device('cuda') - - -@common_utils.skipIfNoCuda -class TestTransformsFloat32(Transforms, common_utils.PytorchTestCase): - dtype = torch.float32 - device = torch.device('cuda') - - -@common_utils.skipIfNoCuda -class TestTransformsFloat64(Transforms, common_utils.PytorchTestCase): +@skipIfNoCuda +class TestTransformsFloat64(Transforms, PytorchTestCase): dtype = torch.float64 device = torch.device('cuda') diff --git a/test/torchaudio_unittest/torchscript_consistency_impl.py b/test/torchaudio_unittest/torchscript_consistency_impl.py index ab681b999b..492e1e4a92 100644 --- a/test/torchaudio_unittest/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/torchscript_consistency_impl.py @@ -1,555 +1,11 @@ """Test suites for jit-ability and its numerical compatibility""" -import unittest import torch -import torchaudio.functional as F import torchaudio.transforms as T from torchaudio_unittest import common_utils -class Functional(common_utils.TestBaseMixin): - """Implements test for `functinoal` modul that are performed for different devices""" - def _assert_consistency(self, func, tensor, shape_only=False): - tensor = tensor.to(device=self.device, dtype=self.dtype) - - ts_func = torch.jit.script(func) - output = func(tensor) - ts_output = ts_func(tensor) - if shape_only: - ts_output = ts_output.shape - output = output.shape - self.assertEqual(ts_output, output) - - def test_spectrogram(self): - def func(tensor): - n_fft = 400 - ws = 400 - hop = 200 - pad = 0 - window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) - power = 2. - normalize = False - return F.spectrogram(tensor, pad, window, n_fft, hop, ws, power, normalize) - - tensor = common_utils.get_whitenoise() - self._assert_consistency(func, tensor) - - def test_griffinlim(self): - def func(tensor): - n_fft = 400 - ws = 400 - hop = 200 - window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) - power = 2. - normalize = False - momentum = 0.99 - n_iter = 32 - length = 1000 - rand_int = False - return F.griffinlim(tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, rand_int) - - tensor = torch.rand((1, 201, 6)) - self._assert_consistency(func, tensor) - - def test_compute_deltas(self): - def func(tensor): - win_length = 2 * 7 + 1 - return F.compute_deltas(tensor, win_length=win_length) - - channel = 13 - n_mfcc = channel * 3 - time = 1021 - tensor = torch.randn(channel, n_mfcc, time) - self._assert_consistency(func, tensor) - - def test_detect_pitch_frequency(self): - waveform = common_utils.get_sinusoid(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - return F.detect_pitch_frequency(tensor, sample_rate) - - self._assert_consistency(func, waveform) - - def test_create_fb_matrix(self): - if self.device != torch.device('cpu'): - raise unittest.SkipTest('No need to perform test on device other than CPU') - - def func(_): - n_stft = 100 - f_min = 0.0 - f_max = 20.0 - n_mels = 10 - sample_rate = 16000 - norm = "slaney" - return F.create_fb_matrix(n_stft, f_min, f_max, n_mels, sample_rate, norm) - - dummy = torch.zeros(1, 1) - self._assert_consistency(func, dummy) - - def test_amplitude_to_DB(self): - def func(tensor): - multiplier = 10.0 - amin = 1e-10 - db_multiplier = 0.0 - top_db = 80.0 - return F.amplitude_to_DB(tensor, multiplier, amin, db_multiplier, top_db) - - tensor = torch.rand((6, 201)) - self._assert_consistency(func, tensor) - - def test_DB_to_amplitude(self): - def func(tensor): - ref = 1. - power = 1. - return F.DB_to_amplitude(tensor, ref, power) - - tensor = torch.rand((1, 100)) - self._assert_consistency(func, tensor) - - def test_create_dct(self): - if self.device != torch.device('cpu'): - raise unittest.SkipTest('No need to perform test on device other than CPU') - - def func(_): - n_mfcc = 40 - n_mels = 128 - norm = "ortho" - return F.create_dct(n_mfcc, n_mels, norm) - - dummy = torch.zeros(1, 1) - self._assert_consistency(func, dummy) - - def test_mu_law_encoding(self): - def func(tensor): - qc = 256 - return F.mu_law_encoding(tensor, qc) - - waveform = common_utils.get_whitenoise() - self._assert_consistency(func, waveform) - - def test_mu_law_decoding(self): - def func(tensor): - qc = 256 - return F.mu_law_decoding(tensor, qc) - - tensor = torch.rand((1, 10)) - self._assert_consistency(func, tensor) - - def test_complex_norm(self): - def func(tensor): - power = 2. - return F.complex_norm(tensor, power) - - tensor = torch.randn(1, 2, 1025, 400, 2) - self._assert_consistency(func, tensor) - - def test_mask_along_axis(self): - def func(tensor): - mask_param = 100 - mask_value = 30. - axis = 2 - return F.mask_along_axis(tensor, mask_param, mask_value, axis) - - tensor = torch.randn(2, 1025, 400) - self._assert_consistency(func, tensor) - - def test_mask_along_axis_iid(self): - def func(tensor): - mask_param = 100 - mask_value = 30. - axis = 2 - return F.mask_along_axis_iid(tensor, mask_param, mask_value, axis) - - tensor = torch.randn(4, 2, 1025, 400) - self._assert_consistency(func, tensor) - - def test_gain(self): - def func(tensor): - gainDB = 2.0 - return F.gain(tensor, gainDB) - - tensor = torch.rand((1, 1000)) - self._assert_consistency(func, tensor) - - def test_dither_TPDF(self): - def func(tensor): - return F.dither(tensor, 'TPDF') - - tensor = common_utils.get_whitenoise(n_channels=2) - self._assert_consistency(func, tensor, shape_only=True) - - def test_dither_RPDF(self): - def func(tensor): - return F.dither(tensor, 'RPDF') - - tensor = common_utils.get_whitenoise(n_channels=2) - self._assert_consistency(func, tensor, shape_only=True) - - def test_dither_GPDF(self): - def func(tensor): - return F.dither(tensor, 'GPDF') - - tensor = common_utils.get_whitenoise(n_channels=2) - self._assert_consistency(func, tensor, shape_only=True) - - def test_dither_noise_shaping(self): - def func(tensor): - return F.dither(tensor, noise_shaping=True) - - tensor = common_utils.get_whitenoise(n_channels=2) - self._assert_consistency(func, tensor) - - def test_lfilter(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise() - - def func(tensor): - # Design an IIR lowpass filter using scipy.signal filter design - # https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirdesign.html#scipy.signal.iirdesign - # - # Example - # >>> from scipy.signal import iirdesign - # >>> b, a = iirdesign(0.2, 0.3, 1, 60) - b_coeffs = torch.tensor( - [ - 0.00299893, - -0.0051152, - 0.00841964, - -0.00747802, - 0.00841964, - -0.0051152, - 0.00299893, - ], - device=tensor.device, - dtype=tensor.dtype, - ) - a_coeffs = torch.tensor( - [ - 1.0, - -4.8155751, - 10.2217618, - -12.14481273, - 8.49018171, - -3.3066882, - 0.56088705, - ], - device=tensor.device, - dtype=tensor.dtype, - ) - return F.lfilter(tensor, a_coeffs, b_coeffs) - - self._assert_consistency(func, waveform) - - def test_lowpass(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - cutoff_freq = 3000. - return F.lowpass_biquad(tensor, sample_rate, cutoff_freq) - - self._assert_consistency(func, waveform) - - def test_highpass(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - cutoff_freq = 2000. - return F.highpass_biquad(tensor, sample_rate, cutoff_freq) - - self._assert_consistency(func, waveform) - - def test_allpass(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - central_freq = 1000. - q = 0.707 - return F.allpass_biquad(tensor, sample_rate, central_freq, q) - - self._assert_consistency(func, waveform) - - def test_bandpass_with_csg(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - central_freq = 1000. - q = 0.707 - const_skirt_gain = True - return F.bandpass_biquad(tensor, sample_rate, central_freq, q, const_skirt_gain) - - self._assert_consistency(func, waveform) - - def test_bandpass_without_csg(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - central_freq = 1000. - q = 0.707 - const_skirt_gain = True - return F.bandpass_biquad(tensor, sample_rate, central_freq, q, const_skirt_gain) - - self._assert_consistency(func, waveform) - - def test_bandreject(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - central_freq = 1000. - q = 0.707 - return F.bandreject_biquad(tensor, sample_rate, central_freq, q) - - self._assert_consistency(func, waveform) - - def test_band_with_noise(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - central_freq = 1000. - q = 0.707 - noise = True - return F.band_biquad(tensor, sample_rate, central_freq, q, noise) - - self._assert_consistency(func, waveform) - - def test_band_without_noise(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - central_freq = 1000. - q = 0.707 - noise = False - return F.band_biquad(tensor, sample_rate, central_freq, q, noise) - - self._assert_consistency(func, waveform) - - def test_treble(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - gain = 40. - central_freq = 1000. - q = 0.707 - return F.treble_biquad(tensor, sample_rate, gain, central_freq, q) - - self._assert_consistency(func, waveform) - - def test_bass(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - gain = 40. - central_freq = 1000. - q = 0.707 - return F.bass_biquad(tensor, sample_rate, gain, central_freq, q) - - self._assert_consistency(func, waveform) - - def test_deemph(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - return F.deemph_biquad(tensor, sample_rate) - - self._assert_consistency(func, waveform) - - def test_riaa(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - return F.riaa_biquad(tensor, sample_rate) - - self._assert_consistency(func, waveform) - - def test_equalizer(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - sample_rate = 44100 - center_freq = 300. - gain = 1. - q = 0.707 - return F.equalizer_biquad(tensor, sample_rate, center_freq, gain, q) - - self._assert_consistency(func, waveform) - - def test_perf_biquad_filtering(self): - if self.dtype == torch.float64: - raise unittest.SkipTest("This test is known to fail for float64") - - waveform = common_utils.get_whitenoise() - - def func(tensor): - a = torch.tensor([0.7, 0.2, 0.6], device=tensor.device, dtype=tensor.dtype) - b = torch.tensor([0.4, 0.2, 0.9], device=tensor.device, dtype=tensor.dtype) - return F.lfilter(tensor, a, b) - - self._assert_consistency(func, waveform) - - def test_sliding_window_cmn(self): - def func(tensor): - cmn_window = 600 - min_cmn_window = 100 - center = False - norm_vars = False - a = torch.tensor( - [ - [ - -1.915875792503357, - 1.147700309753418 - ], - [ - 1.8242558240890503, - 1.3869990110397339 - ] - ], - device=tensor.device, - dtype=tensor.dtype - ) - return F.sliding_window_cmn(a, cmn_window, min_cmn_window, center, norm_vars) - b = torch.tensor( - [ - [ - -1.8701, - -0.1196 - ], - [ - 1.8701, - 0.1196 - ] - ] - ) - self._assert_consistency(func, b) - - def test_contrast(self): - waveform = common_utils.get_whitenoise() - - def func(tensor): - enhancement_amount = 80. - return F.contrast(tensor, enhancement_amount) - - self._assert_consistency(func, waveform) - - def test_dcshift(self): - waveform = common_utils.get_whitenoise() - - def func(tensor): - shift = 0.5 - limiter_gain = 0.05 - return F.dcshift(tensor, shift, limiter_gain) - - self._assert_consistency(func, waveform) - - def test_overdrive(self): - waveform = common_utils.get_whitenoise() - - def func(tensor): - gain = 30. - colour = 50. - return F.overdrive(tensor, gain, colour) - - self._assert_consistency(func, waveform) - - def test_phaser(self): - waveform = common_utils.get_whitenoise(sample_rate=44100) - - def func(tensor): - gain_in = 0.5 - gain_out = 0.8 - delay_ms = 2.0 - decay = 0.4 - speed = 0.5 - sample_rate = 44100 - return F.phaser(tensor, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) - - self._assert_consistency(func, waveform) - - def test_flanger(self): - torch.random.manual_seed(40) - waveform = torch.rand(2, 100) - 0.5 - - def func(tensor): - delay = 0.8 - depth = 0.88 - regen = 3.0 - width = 0.23 - speed = 1.3 - phase = 60. - sample_rate = 44100 - return F.flanger(tensor, sample_rate, delay, depth, regen, width, speed, - phase, modulation='sinusoidal', interpolation='linear') - - self._assert_consistency(func, waveform) - - def test_spectral_centroid(self): - - def func(tensor): - sample_rate = 44100 - n_fft = 400 - ws = 400 - hop = 200 - pad = 0 - window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) - return F.spectral_centroid(tensor, sample_rate, pad, window, n_fft, hop, ws) - - tensor = common_utils.get_whitenoise(sample_rate=44100) - self._assert_consistency(func, tensor) - - class Transforms(common_utils.TestBaseMixin): """Implements test for Transforms that are performed for different devices""" def _assert_consistency(self, transform, tensor): diff --git a/test/torchaudio_unittest/transforms_test.py b/test/torchaudio_unittest/transforms_test.py index 76628b1315..ab7b2ed981 100644 --- a/test/torchaudio_unittest/transforms_test.py +++ b/test/torchaudio_unittest/transforms_test.py @@ -1,5 +1,4 @@ import math -import unittest import torch import torchaudio diff --git a/third_party/CMakeLists.txt b/third_party/CMakeLists.txt index 1284affa88..3e1ab962fe 100644 --- a/third_party/CMakeLists.txt +++ b/third_party/CMakeLists.txt @@ -1,16 +1,28 @@ -cmake_minimum_required(VERSION 3.5) +set(TORCHAUDIO_THIRD_PARTIES "") -project(torchaudio_third_parties) - -option(BUILD_SOX "Build libsox statically") -option(BUILD_TRANSDUCER "Build transducer statically") - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden") +################################################################################ +# sox +################################################################################ +add_library(libsox INTERFACE) if (BUILD_SOX) add_subdirectory(sox) + target_include_directories(libsox INTERFACE ${SOX_INCLUDE_DIR}) + target_link_libraries(libsox INTERFACE ${SOX_LIBRARIES}) +else() + # If not building and linking libsox statically, then we expect that + # sox library and header are found in search path + target_link_libraries(libsox INTERFACE -lsox) endif() +list(APPEND TORCHAUDIO_THIRD_PARTIES libsox) -if(BUILD_TRANSDUCER) +################################################################################ +# transducer +################################################################################ +if (BUILD_TRANSDUCER) add_subdirectory(transducer) + list(APPEND TORCHAUDIO_THIRD_PARTIES warprnnt) endif() + +set_property(GLOBAL PROPERTY TORCHAUDIO_THIRD_PARTIES "${TORCHAUDIO_THIRD_PARTIES}") diff --git a/third_party/sox/CMakeLists.txt b/third_party/sox/CMakeLists.txt index 930b84496a..840130f055 100644 --- a/third_party/sox/CMakeLists.txt +++ b/third_party/sox/CMakeLists.txt @@ -11,7 +11,7 @@ set(COMMON_ARGS --quiet --disable-shared --enable-static --prefix=${INSTALL_DIR} set(envs "PKG_CONFIG_PATH=${INSTALL_DIR}/lib/pkgconfig" "LDFLAGS=-L${INSTALL_DIR}/lib $ENV{LDFLAGS}" - "CPPFLAGS=-I${INSTALL_DIR}/include $ENV{CPPFLAGS}" + "CFLAGS=-I${INSTALL_DIR}/include -fvisibility=hidden $ENV{CFLAGS}" ) ExternalProject_Add(mad @@ -207,3 +207,7 @@ ExternalProject_Add(sox LOG_MERGED_STDOUTERR ON LOG_OUTPUT_ON_FAILURE ON ) + +add_dependencies(libsox sox) +set(SOX_INCLUDE_DIR ${INSTALL_DIR}/include PARENT_SCOPE) +set(SOX_LIBRARIES ${SOX_LIBRARIES} PARENT_SCOPE) diff --git a/third_party/transducer/CMakeLists.txt b/third_party/transducer/CMakeLists.txt index c35f0c8552..359af3e3e3 100755 --- a/third_party/transducer/CMakeLists.txt +++ b/third_party/transducer/CMakeLists.txt @@ -1,27 +1,3 @@ -ADD_DEFINITIONS(-DRNNT_DISABLE_OMP) - -IF(APPLE) - ADD_DEFINITIONS(-DAPPLE) - EXEC_PROGRAM(uname ARGS -v OUTPUT_VARIABLE DARWIN_VERSION) - STRING(REGEX MATCH "[0-9]+" DARWIN_VERSION ${DARWIN_VERSION}) - MESSAGE(STATUS "DARWIN_VERSION=${DARWIN_VERSION}") - - # for el capitain have to use rpath - IF(DARWIN_VERSION LESS 15) - SET(CMAKE_SKIP_RPATH TRUE) - ENDIF() - -ELSE() - # always skip for linux - SET(CMAKE_SKIP_RPATH TRUE) -ENDIF() - -ADD_LIBRARY(warprnnt STATIC submodule/src/rnnt_entrypoint.cpp) +add_library(warprnnt STATIC submodule/src/rnnt_entrypoint.cpp) +target_compile_definitions(warprnnt PRIVATE RNNT_DISABLE_OMP) target_include_directories(warprnnt PUBLIC submodule/include) -set_target_properties(warprnnt PROPERTIES PUBLIC_HEADER submodule/include/rnnt.h) - - -INSTALL( - TARGETS warprnnt - ARCHIVE DESTINATION "lib" - PUBLIC_HEADER DESTINATION "include") diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index cbb2ba3370..35f5fd41d5 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -1,5 +1,5 @@ -from . import extension -from torchaudio._internal import module_utils as _mod_utils +from . import extension # noqa: F401 +from torchaudio._internal import module_utils as _mod_utils # noqa: F401 from torchaudio import ( compliance, datasets, @@ -30,3 +30,24 @@ from .version import __version__, git_version # noqa: F401 except ImportError: pass + +__all__ = [ + 'compliance', + 'datasets', + 'functional', + 'kaldi_io', + 'utils', + 'sox_effects', + 'transforms', + 'list_audio_backends', + 'get_audio_backend', + 'set_audio_backend', + 'save_encinfo', + 'sox_signalinfo_t', + 'sox_encodinginfo_t', + 'get_sox_option_t', + 'get_sox_encoding_t', + 'get_sox_bool', + 'SignalInfo', + 'EncodingInfo', +] diff --git a/torchaudio/backend/__init__.py b/torchaudio/backend/__init__.py index 0812c09cf4..361935229f 100644 --- a/torchaudio/backend/__init__.py +++ b/torchaudio/backend/__init__.py @@ -1,3 +1,4 @@ +# flake8: noqa from . import utils from .utils import ( list_audio_backends, diff --git a/torchaudio/compliance/__init__.py b/torchaudio/compliance/__init__.py index ba354c371e..795065dc8d 100644 --- a/torchaudio/compliance/__init__.py +++ b/torchaudio/compliance/__init__.py @@ -1 +1,5 @@ from . import kaldi + +__all__ = [ + 'kaldi', +] diff --git a/torchaudio/csrc/CMakeLists.txt b/torchaudio/csrc/CMakeLists.txt new file mode 100644 index 0000000000..f9c3a4d08f --- /dev/null +++ b/torchaudio/csrc/CMakeLists.txt @@ -0,0 +1,91 @@ +get_property(TORCHAUDIO_THIRD_PARTIES GLOBAL PROPERTY TORCHAUDIO_THIRD_PARTIES) + +################################################################################ +# Stuff common to libtorchaudio and _torchaudio.so +################################################################################ +set( + LIBTORCHAUDIO_SOURCES + sox/io.cpp + sox/utils.cpp + sox/effects.cpp + sox/effects_chain.cpp + ) + +if(BUILD_TRANSDUCER) + list(APPEND LIBTORCHAUDIO_SOURCES transducer.cpp) +endif() + +################################################################################ +# libtorchaudio.so +################################################################################ +if(BUILD_LIBTORCHAUDIO) + add_library( + libtorchaudio + SHARED + ${LIBTORCHAUDIO_SOURCES} + ) + set_target_properties(libtorchaudio PROPERTIES PREFIX "") + + target_include_directories( + libtorchaudio + PUBLIC + ${PROJECT_SOURCE_DIR} + ) + + target_link_libraries( + libtorchaudio + ${TORCHAUDIO_THIRD_PARTIES} + ) + + install( + TARGETS + libtorchaudio + LIBRARY DESTINATION lib + ) + + set(TORCHAUDIO_LIBRARY -Wl,--no-as-needed libtorchaudio -Wl,--as-needed CACHE INTERNAL "") +endif() + +################################################################################ +# _torchaudio.so +################################################################################ +if (BUILD_TORCHAUDIO_PYTHON_EXTENSION) + add_library( + _torchaudio + SHARED + pybind.cpp + sox/legacy.cpp + ${LIBTORCHAUDIO_SOURCES} + ) + + set_target_properties(_torchaudio PROPERTIES PREFIX "") + + if (APPLE) + # https://github.com/facebookarchive/caffe2/issues/854#issuecomment-364538485 + # https://github.com/pytorch/pytorch/commit/73f6715f4725a0723d8171d3131e09ac7abf0666 + set_target_properties(_torchaudio PROPERTIES LINK_FLAGS "-undefined dynamic_lookup") + endif() + + target_compile_definitions( + _torchaudio PRIVATE TORCH_API_INCLUDE_EXTENSION_H + ) + + target_include_directories( + _torchaudio + PRIVATE + ${PROJECT_SOURCE_DIR} + ${Python_INCLUDE_DIR} + ) + + # See https://github.com/pytorch/pytorch/issues/38122 + find_library(TORCH_PYTHON_LIBRARY torch_python PATHS "${TORCH_INSTALL_PREFIX}/lib") + + target_link_libraries( + _torchaudio + ${TORCH_LIBRARIES} + ${TORCH_PYTHON_LIBRARY} + ${TORCHAUDIO_THIRD_PARTIES} + ) + + install(TARGETS _torchaudio LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}) +endif() diff --git a/torchaudio/csrc/sox/effects_chain.cpp b/torchaudio/csrc/sox/effects_chain.cpp index cbe78684f7..b98010f67b 100644 --- a/torchaudio/csrc/sox/effects_chain.cpp +++ b/torchaudio/csrc/sox/effects_chain.cpp @@ -353,7 +353,11 @@ int fileobj_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { // in sync and `tell_off` has seemingly uninitialized value, which // leads num_remain to be negative and cause segmentation fault // in `memmove`. - const auto num_consumed = ftell((FILE*)sf->fp); + const auto tell = ftell((FILE*)sf->fp); + if (tell < 0) { + throw std::runtime_error("Internal Error: ftell failed."); + } + const auto num_consumed = static_cast(tell); if (num_consumed > priv->buffer_size) { throw std::runtime_error("Internal Error: buffer overrun."); } @@ -361,7 +365,7 @@ int fileobj_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { const auto num_remain = priv->buffer_size - num_consumed; // 1.1. Fetch the data to see if there is data to fill the buffer - uint64_t num_refill = 0; + size_t num_refill = 0; std::string chunk(num_consumed, '\0'); if (num_consumed && !priv->eof_reached) { num_refill = read_fileobj( diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index 5e5b9c1118..a83bf33c82 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -10,7 +10,7 @@ from .libritts import LIBRITTS from .tedlium import TEDLIUM -__all__ = ( +__all__ = [ "COMMONVOICE", "LIBRISPEECH", "SPEECHCOMMANDS", @@ -24,4 +24,4 @@ "diskcache_iterator", "bg_iterator", "TEDLIUM", -) +] diff --git a/torchaudio/datasets/gtzan.py b/torchaudio/datasets/gtzan.py index 4c91894be1..6c0b6246f3 100644 --- a/torchaudio/datasets/gtzan.py +++ b/torchaudio/datasets/gtzan.py @@ -1,7 +1,6 @@ import os -import warnings from pathlib import Path -from typing import Any, Tuple, Optional, Union +from typing import Tuple, Optional, Union import torchaudio from torch import Tensor diff --git a/torchaudio/datasets/ljspeech.py b/torchaudio/datasets/ljspeech.py index 99058dd83e..1a519242c3 100644 --- a/torchaudio/datasets/ljspeech.py +++ b/torchaudio/datasets/ljspeech.py @@ -1,6 +1,6 @@ import os import csv -from typing import List, Tuple, Union +from typing import Tuple, Union from pathlib import Path import torchaudio diff --git a/torchaudio/extension/extension.py b/torchaudio/extension/extension.py index b01ba13e39..5875f41023 100644 --- a/torchaudio/extension/extension.py +++ b/torchaudio/extension/extension.py @@ -1,6 +1,5 @@ import warnings import importlib -from collections import namedtuple import torch from torchaudio._internal import module_utils as _mod_utils diff --git a/torchaudio/functional/__init__.py b/torchaudio/functional/__init__.py index 688708296b..d59400e82e 100644 --- a/torchaudio/functional/__init__.py +++ b/torchaudio/functional/__init__.py @@ -41,3 +41,45 @@ treble_biquad, vad, ) + +__all__ = [ + 'amplitude_to_DB', + 'angle', + 'complex_norm', + 'compute_deltas', + 'create_dct', + 'create_fb_matrix', + 'DB_to_amplitude', + 'detect_pitch_frequency', + 'griffinlim', + 'magphase', + 'mask_along_axis', + 'mask_along_axis_iid', + 'mu_law_encoding', + 'mu_law_decoding', + 'phase_vocoder', + 'sliding_window_cmn', + 'spectrogram', + 'spectral_centroid', + 'allpass_biquad', + 'band_biquad', + 'bandpass_biquad', + 'bandreject_biquad', + 'bass_biquad', + 'biquad', + 'contrast', + 'dither', + 'dcshift', + 'deemph_biquad', + 'equalizer_biquad', + 'flanger', + 'gain', + 'highpass_biquad', + 'lfilter', + 'lowpass_biquad', + 'overdrive', + 'phaser', + 'riaa_biquad', + 'treble_biquad', + 'vad', +] diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index 73941946ec..4dde324a31 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -237,14 +237,16 @@ def amplitude_to_DB( db_multiplier: float, top_db: Optional[float] = None ) -> Tensor: - r"""Turn a tensor from the power/amplitude scale to the decibel scale. + r"""Turn a spectrogram from the power/amplitude scale to the decibel scale. - This output depends on the maximum value in the input tensor, and so - may return different values for an audio clip split into snippets vs. a - full clip. + The output of each tensor in a batch depends on the maximum value of that tensor, + and so may return different values for an audio clip split into snippets vs. a full clip. Args: - x (Tensor): Input tensor before being converted to decibel scale + + x (Tensor): Input spectrogram(s) before being converted to decibel scale. Input should take + the form `(..., freq, time)`. Batched inputs should include a channel dimension and + have the form `(batch, channel, freq, time)`. multiplier (float): Use 10. for power and 20. for amplitude amin (float): Number to clamp ``x`` db_multiplier (float): Log10(max(reference value and amin)) @@ -258,7 +260,15 @@ def amplitude_to_DB( x_db -= multiplier * db_multiplier if top_db is not None: - x_db = x_db.clamp(min=x_db.max().item() - top_db) + # Expand batch + shape = x_db.size() + packed_channels = shape[-3] if x_db.dim() > 2 else 1 + x_db = x_db.reshape(-1, packed_channels, shape[-2], shape[-1]) + + x_db = torch.max(x_db, (x_db.amax(dim=(-3, -2, -1)) - top_db).view(-1, 1, 1, 1)) + + # Repack batch + x_db = x_db.reshape(shape) return x_db diff --git a/torchaudio/models/__init__.py b/torchaudio/models/__init__.py index 55498fd7f6..5b134345af 100644 --- a/torchaudio/models/__init__.py +++ b/torchaudio/models/__init__.py @@ -1,3 +1,9 @@ from .wav2letter import Wav2Letter from .wavernn import WaveRNN from .conv_tasnet import ConvTasNet + +__all__ = [ + 'Wav2Letter', + 'WaveRNN', + 'ConvTasNet', +] diff --git a/torchaudio/models/wav2letter.py b/torchaudio/models/wav2letter.py index acd4dfe4b3..20a665a784 100644 --- a/torchaudio/models/wav2letter.py +++ b/torchaudio/models/wav2letter.py @@ -1,7 +1,9 @@ from torch import Tensor from torch import nn -__all__ = ["Wav2Letter"] +__all__ = [ + "Wav2Letter", +] class Wav2Letter(nn.Module): diff --git a/torchaudio/models/wavernn.py b/torchaudio/models/wavernn.py index 8fbd111a72..eedbd3c589 100644 --- a/torchaudio/models/wavernn.py +++ b/torchaudio/models/wavernn.py @@ -4,7 +4,13 @@ from torch import Tensor from torch import nn -__all__ = ["ResBlock", "MelResNet", "Stretch2d", "UpsampleNetwork", "WaveRNN"] +__all__ = [ + "ResBlock", + "MelResNet", + "Stretch2d", + "UpsampleNetwork", + "WaveRNN", +] class ResBlock(nn.Module): diff --git a/torchaudio/prototype/transducer.py b/torchaudio/prototype/transducer.py index d08ab6ea17..843cf5f9a4 100644 --- a/torchaudio/prototype/transducer.py +++ b/torchaudio/prototype/transducer.py @@ -5,7 +5,10 @@ module_utils as _mod_utils, ) -__all__ = ["rnnt_loss", "RNNTLoss"] +__all__ = [ + "rnnt_loss", + "RNNTLoss", +] class _RNNT(Function): diff --git a/torchaudio/sox_effects/__init__.py b/torchaudio/sox_effects/__init__.py index 5baf406b10..b3e2708ad1 100644 --- a/torchaudio/sox_effects/__init__.py +++ b/torchaudio/sox_effects/__init__.py @@ -12,3 +12,11 @@ import atexit init_sox_effects() atexit.register(shutdown_sox_effects) + +__all__ = [ + 'init_sox_effects', + 'shutdown_sox_effects', + 'effect_names', + 'apply_effects_tensor', + 'apply_effects_file', +] diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 4d1d0234d9..e183932788 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -1,5 +1,4 @@ import os -from pathlib import Path from typing import List, Tuple, Optional import torch diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 7d7903b752..af89d5b250 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -2,7 +2,6 @@ import math from typing import Callable, Optional -from warnings import warn import torch from torch import Tensor @@ -541,24 +540,16 @@ def forward(self, waveform: Tensor) -> Tensor: Returns: Tensor: specgram_mel_db of size (..., ``n_mfcc``, time). """ - - # pack batch - shape = waveform.size() - waveform = waveform.reshape(-1, shape[-1]) - mel_specgram = self.MelSpectrogram(waveform) if self.log_mels: log_offset = 1e-6 mel_specgram = torch.log(mel_specgram + log_offset) else: mel_specgram = self.amplitude_to_DB(mel_specgram) - # (channel, n_mels, time).tranpose(...) dot (n_mels, n_mfcc) - # -> (channel, time, n_mfcc).tranpose(...) - mfcc = torch.matmul(mel_specgram.transpose(1, 2), self.dct_mat).transpose(1, 2) - - # unpack batch - mfcc = mfcc.reshape(shape[:-1] + mfcc.shape[-2:]) + # (..., channel, n_mels, time).tranpose(...) dot (n_mels, n_mfcc) + # -> (..., channel, time, n_mfcc).tranpose(...) + mfcc = torch.matmul(mel_specgram.transpose(-2, -1), self.dct_mat).transpose(-2, -1) return mfcc From a0446251ef7c2806fe36c601631fcae54667318d Mon Sep 17 00:00:00 2001 From: Scott Wolchok Date: Wed, 17 Feb 2021 10:33:13 -0800 Subject: [PATCH 30/73] Remove reference_cast in make_boxed_from_unboxed_functor (#51319) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/51319 We were going out of our way to accommodate `IValue::to` returning a copy of the inner Tensor. `IValue::toTensor` is capable of returning a reference without copying, so if we use it directly, we can allow kernels that want to take `Tensor &` to do so! As a bonus, we get reduced build times. ghstack-source-id: 121378961 Reviewed By: bhosmer Differential Revision: D26138549 fbshipit-source-id: b0f830527da360c542c815bef2f7e1692615b32a --- torchaudio/csrc/sox/effects.cpp | 6 +++--- torchaudio/csrc/sox/effects.h | 6 +++--- torchaudio/csrc/sox/io.cpp | 16 ++++++++-------- torchaudio/csrc/sox/io.h | 12 ++++++------ 4 files changed, 20 insertions(+), 20 deletions(-) diff --git a/torchaudio/csrc/sox/effects.cpp b/torchaudio/csrc/sox/effects.cpp index eb4bf24549..d2d2a0f5ad 100644 --- a/torchaudio/csrc/sox/effects.cpp +++ b/torchaudio/csrc/sox/effects.cpp @@ -91,9 +91,9 @@ std::tuple apply_effects_tensor( std::tuple apply_effects_file( const std::string path, std::vector> effects, - c10::optional& normalize, - c10::optional& channels_first, - c10::optional& format) { + c10::optional normalize, + c10::optional channels_first, + const c10::optional& format) { // Open input file SoxFormat sf(sox_open_read( path.c_str(), diff --git a/torchaudio/csrc/sox/effects.h b/torchaudio/csrc/sox/effects.h index 8ce9757824..efec06971c 100644 --- a/torchaudio/csrc/sox/effects.h +++ b/torchaudio/csrc/sox/effects.h @@ -24,9 +24,9 @@ std::tuple apply_effects_tensor( std::tuple apply_effects_file( const std::string path, std::vector> effects, - c10::optional& normalize, - c10::optional& channels_first, - c10::optional& format); + c10::optional normalize, + c10::optional channels_first, + const c10::optional& format); #ifdef TORCH_API_INCLUDE_EXTENSION_H diff --git a/torchaudio/csrc/sox/io.cpp b/torchaudio/csrc/sox/io.cpp index ac10911507..90532dd3ad 100644 --- a/torchaudio/csrc/sox/io.cpp +++ b/torchaudio/csrc/sox/io.cpp @@ -47,7 +47,7 @@ std::string get_encoding(sox_encoding_t encoding) { std::tuple get_info_file( const std::string& path, - c10::optional& format) { + const c10::optional& format) { SoxFormat sf(sox_open_read( path.c_str(), /*signal=*/nullptr, @@ -69,8 +69,8 @@ std::tuple get_info_file( namespace { std::vector> get_effects( - c10::optional& frame_offset, - c10::optional& num_frames) { + const c10::optional& frame_offset, + const c10::optional& num_frames) { const auto offset = frame_offset.value_or(0); if (offset < 0) { throw std::runtime_error( @@ -101,11 +101,11 @@ std::vector> get_effects( std::tuple load_audio_file( const std::string& path, - c10::optional& frame_offset, - c10::optional& num_frames, - c10::optional& normalize, - c10::optional& channels_first, - c10::optional& format) { + const c10::optional& frame_offset, + const c10::optional& num_frames, + c10::optional normalize, + c10::optional channels_first, + const c10::optional& format) { auto effects = get_effects(frame_offset, num_frames); return torchaudio::sox_effects::apply_effects_file( path, effects, normalize, channels_first, format); diff --git a/torchaudio/csrc/sox/io.h b/torchaudio/csrc/sox/io.h index 6129fac1b7..cd203e9c5c 100644 --- a/torchaudio/csrc/sox/io.h +++ b/torchaudio/csrc/sox/io.h @@ -13,15 +13,15 @@ namespace sox_io { std::tuple get_info_file( const std::string& path, - c10::optional& format); + const c10::optional& format); std::tuple load_audio_file( const std::string& path, - c10::optional& frame_offset, - c10::optional& num_frames, - c10::optional& normalize, - c10::optional& channels_first, - c10::optional& format); + const c10::optional& frame_offset, + const c10::optional& num_frames, + c10::optional normalize, + c10::optional channels_first, + const c10::optional& format); void save_audio_file( const std::string& path, From d6321ae070da4155712a156e232c7e9443c1e570 Mon Sep 17 00:00:00 2001 From: cpuhrsch Date: Fri, 2 Apr 2021 13:34:42 -0400 Subject: [PATCH 31/73] Add missing file to faciliate fixup patch (#1417) --- .circleci/unittest/windows/scripts/environment.yml | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 .circleci/unittest/windows/scripts/environment.yml diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml new file mode 100644 index 0000000000..e69de29bb2 From ece130122fb7e2a34d79bb59addb301aef53e324 Mon Sep 17 00:00:00 2001 From: cpuhrsch Date: Fri, 2 Apr 2021 13:44:12 -0400 Subject: [PATCH 32/73] Sync environment.yml such that patch applies (#1418) --- .../unittest/windows/scripts/environment.yml | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml index e69de29bb2..4a0b71d653 100644 --- a/.circleci/unittest/windows/scripts/environment.yml +++ b/.circleci/unittest/windows/scripts/environment.yml @@ -0,0 +1,18 @@ +channels: + - conda-forge + - defaults +dependencies: + - flake8 + - numpy + - pytest + - pytest-cov + - codecov + - pip + - pip: + - scipy == 1.4.1 + - kaldi-io + - PySoundFile + - librosa >= 0.8.0 + - future + - parameterized + - dataclasses From 76167470d0eee470020908cc42549c5faf7cd1ee Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Thu, 18 Mar 2021 11:38:04 -0700 Subject: [PATCH 33/73] Import #1396 dd76e9d Summary: Import #1396 dd76e9d Reviewed By: vincentqb Differential Revision: D26772272 fbshipit-source-id: 5fb10b8e4bfe955372eaf588d33ab96e1a83ef8d --- .circleci/config.yml | 269 ++++++- .circleci/config.yml.in | 45 +- .circleci/regenerate.py | 16 +- .circleci/unittest/linux/scripts/install.sh | 23 +- .../linux/scripts/run_style_checks.sh | 4 +- .../unittest/windows/scripts/environment.yml | 4 +- .circleci/unittest/windows/scripts/install.sh | 19 +- .../unittest/windows/scripts/setup_env.sh | 3 + .gitmodules | 4 + CMakeLists.txt | 1 + CONTRIBUTING.md | 30 +- README.md | 75 +- build_tools/setup_helpers/extension.py | 21 +- docs/Makefile | 2 +- docs/source/_templates/layout.html | 8 + docs/source/backend.rst | 127 +-- docs/source/conf.py | 10 +- docs/source/functional.rst | 149 ++-- docs/source/torchaudio.rst | 4 - docs/source/transforms.rst | 7 + examples/interactive_asr/vad.py | 4 +- examples/libtorchaudio/.gitignore | 3 + examples/libtorchaudio/CMakeLists.txt | 20 + examples/libtorchaudio/README.md | 11 + .../libtorchaudio/create_jittable_pipeline.py | 83 ++ examples/libtorchaudio/data/README.md | 5 + examples/libtorchaudio/data/input.wav | Bin 0 -> 108844 bytes examples/libtorchaudio/data/rir.wav | Bin 0 -> 9338 bytes examples/libtorchaudio/main.cpp | 21 + examples/libtorchaudio/run.sh | 24 + .../pipeline_wav2letter/languagemodels.py | 2 +- .../utils/dataset/wsj0mix.py | 2 +- packaging/build_conda.sh | 3 +- packaging/build_wheel.sh | 4 +- packaging/pkg_helpers.bash | 20 +- packaging/torchaudio/meta.yaml | 2 + packaging/vc_env_helper.bat | 39 + packaging/vs2019/activate.bat | 44 + packaging/vs2019/conda_build_config.yaml | 24 + packaging/vs2019/install_activate.bat | 30 + packaging/vs2019/install_runtime.bat | 49 ++ packaging/vs2019/meta.yaml | 24 + setup.cfg | 2 + setup.py | 3 +- test/torchaudio_unittest/README.md | 18 +- ..._args.json => kaldi_test_fbank_args.jsonl} | 0 ...c_args.json => kaldi_test_mfcc_args.jsonl} | 0 .../assets/kaldi_test_pitch_args.jsonl | 5 + ...json => kaldi_test_spectrogram_args.jsonl} | 0 ...t_args.json => sox_effect_test_args.jsonl} | 0 .../backend/legacy_test.py | 290 ------- .../backend/soundfile/common.py | 23 + .../backend/soundfile/info_test.py | 12 +- .../backend/soundfile/load_test.py | 2 +- .../backend/soundfile/save_test.py | 81 +- .../backend/sox_io/common.py | 12 + .../backend/sox_io/info_test.py | 45 +- .../backend/sox_io/load_test.py | 226 ++---- .../backend/sox_io/roundtrip_test.py | 8 +- .../backend/sox_io/save_test.py | 755 ++++++++---------- .../backend/sox_io/smoke_test.py | 10 +- .../backend/sox_io/torchscript_test.py | 26 +- .../torchaudio_unittest/backend/utils_test.py | 23 +- .../common_utils/__init__.py | 9 +- .../common_utils/backend_utils.py | 4 +- .../common_utils/case_utils.py | 16 +- .../common_utils/data_utils.py | 4 +- .../common_utils/kaldi_utils.py | 38 + .../common_utils/parameterized_utils.py | 19 +- .../common_utils/sox_utils.py | 12 +- .../compliance/generate_fbank_data.py | 3 +- .../compliance_kaldi_test.py | 20 +- .../datasets/tedlium_test.py | 9 +- .../functional/autograd_cpu_test.py | 8 + .../functional/autograd_cuda_test.py | 9 + .../functional/autograd_impl.py | 40 + .../functional/batch_consistency_test.py | 224 ++++++ .../functional/functional_cpu_test.py | 53 ++ .../functional/functional_cuda_test.py | 5 + .../functional/functional_impl.py | 37 + .../kaldi_compatibility_cpu_test.py | 19 + .../kaldi_compatibility_cuda_test.py | 16 + .../kaldi_compatibility_test_impl.py | 60 ++ .../functional/librosa_compatibility_test.py | 155 ++++ .../sox_compatibility_test.py | 30 - .../torchscript_consistency_impl.py | 30 +- .../kaldi_compatibility_impl.py | 103 --- .../librosa_compatibility_test.py | 366 --------- .../sox_effect/dataset_test.py | 6 +- .../sox_effect/smoke_test.py | 10 +- .../sox_effect/sox_effect_test.py | 20 +- .../sox_effect/torchscript_test.py | 8 +- test/torchaudio_unittest/transducer_test.py | 15 +- .../transforms/__init__.py | 0 .../batch_consistency_test.py | 166 ---- .../kaldi_compatibility_cpu_test.py | 0 .../kaldi_compatibility_cuda_test.py | 0 .../transforms/kaldi_compatibility_impl.py | 55 ++ .../transforms/librosa_compatibility_test.py | 245 ++++++ .../transforms/sox_compatibility_test.py | 53 ++ .../torchscript_consistency_cpu_test.py | 0 .../torchscript_consistency_cuda_test.py | 0 .../torchscript_consistency_impl.py | 0 .../{ => transforms}/transforms_test.py | 0 .../utils/sox_utils_test.py | 4 +- third_party/CMakeLists.txt | 14 +- third_party/kaldi/CMakeLists.txt | 30 + third_party/kaldi/README.md | 6 + third_party/kaldi/kaldi.patch | 76 ++ third_party/kaldi/src/matrix/kaldi-matrix.cc | 39 + third_party/kaldi/src/matrix/kaldi-matrix.h | 178 +++++ third_party/kaldi/src/matrix/kaldi-vector.cc | 42 + third_party/kaldi/src/matrix/kaldi-vector.h | 313 ++++++++ third_party/sox/CMakeLists.txt | 1 + third_party/sox/patch/sox.patch | 16 + torchaudio/__init__.py | 10 - torchaudio/_internal/module_utils.py | 40 +- torchaudio/backend/__init__.py | 12 - torchaudio/backend/_soundfile_backend.py | 316 -------- torchaudio/backend/common.py | 202 +---- torchaudio/backend/no_backend.py | 17 +- torchaudio/backend/soundfile_backend.py | 495 +++++++++--- torchaudio/backend/sox_backend.py | 294 ------- torchaudio/backend/sox_io_backend.py | 256 +++--- torchaudio/backend/utils.py | 40 +- torchaudio/compliance/kaldi.py | 2 +- torchaudio/csrc/CMakeLists.txt | 48 +- torchaudio/csrc/kaldi.cpp | 93 +++ torchaudio/csrc/lfilter.cpp | 268 +++++++ torchaudio/csrc/overdrive.cpp | 52 ++ torchaudio/csrc/pybind.cpp | 103 +-- torchaudio/csrc/sox/effects.cpp | 8 +- torchaudio/csrc/sox/effects.h | 6 +- torchaudio/csrc/sox/effects_chain.cpp | 45 +- torchaudio/csrc/sox/io.cpp | 115 ++- torchaudio/csrc/sox/io.h | 20 +- torchaudio/csrc/sox/legacy.cpp | 170 ---- torchaudio/csrc/sox/legacy.h | 40 - torchaudio/csrc/sox/types.cpp | 139 ++++ torchaudio/csrc/sox/types.h | 60 ++ torchaudio/csrc/sox/utils.cpp | 404 +++++++--- torchaudio/csrc/sox/utils.h | 15 +- torchaudio/csrc/transducer.cpp | 29 + torchaudio/csrc/utils.cpp | 30 + torchaudio/datasets/speechcommands.py | 13 +- torchaudio/datasets/tedlium.py | 6 +- torchaudio/extension/extension.py | 1 + torchaudio/functional/__init__.py | 4 + torchaudio/functional/filtering.py | 167 ++-- torchaudio/functional/functional.py | 284 ++++++- torchaudio/kaldi_io.py | 2 +- torchaudio/prototype/transducer.py | 43 - torchaudio/sox_effects/__init__.py | 2 +- torchaudio/sox_effects/sox_effects.py | 20 +- torchaudio/transforms.py | 43 +- torchaudio/utils/__init__.py | 3 +- torchaudio/utils/sox_utils.py | 19 +- 157 files changed, 5521 insertions(+), 3777 deletions(-) create mode 100644 docs/source/_templates/layout.html create mode 100644 examples/libtorchaudio/.gitignore create mode 100644 examples/libtorchaudio/CMakeLists.txt create mode 100644 examples/libtorchaudio/README.md create mode 100755 examples/libtorchaudio/create_jittable_pipeline.py create mode 100644 examples/libtorchaudio/data/README.md create mode 100644 examples/libtorchaudio/data/input.wav create mode 100644 examples/libtorchaudio/data/rir.wav create mode 100644 examples/libtorchaudio/main.cpp create mode 100755 examples/libtorchaudio/run.sh create mode 100644 packaging/vc_env_helper.bat create mode 100644 packaging/vs2019/activate.bat create mode 100644 packaging/vs2019/conda_build_config.yaml create mode 100644 packaging/vs2019/install_activate.bat create mode 100644 packaging/vs2019/install_runtime.bat create mode 100644 packaging/vs2019/meta.yaml create mode 100644 setup.cfg rename test/torchaudio_unittest/assets/{kaldi_test_fbank_args.json => kaldi_test_fbank_args.jsonl} (100%) rename test/torchaudio_unittest/assets/{kaldi_test_mfcc_args.json => kaldi_test_mfcc_args.jsonl} (100%) create mode 100644 test/torchaudio_unittest/assets/kaldi_test_pitch_args.jsonl rename test/torchaudio_unittest/assets/{kaldi_test_spectrogram_args.json => kaldi_test_spectrogram_args.jsonl} (100%) rename test/torchaudio_unittest/assets/{sox_effect_test_args.json => sox_effect_test_args.jsonl} (100%) delete mode 100644 test/torchaudio_unittest/backend/legacy_test.py create mode 100644 test/torchaudio_unittest/common_utils/kaldi_utils.py create mode 100644 test/torchaudio_unittest/functional/autograd_cpu_test.py create mode 100644 test/torchaudio_unittest/functional/autograd_cuda_test.py create mode 100644 test/torchaudio_unittest/functional/autograd_impl.py create mode 100644 test/torchaudio_unittest/functional/batch_consistency_test.py create mode 100644 test/torchaudio_unittest/functional/kaldi_compatibility_cpu_test.py create mode 100644 test/torchaudio_unittest/functional/kaldi_compatibility_cuda_test.py create mode 100644 test/torchaudio_unittest/functional/kaldi_compatibility_test_impl.py create mode 100644 test/torchaudio_unittest/functional/librosa_compatibility_test.py rename test/torchaudio_unittest/{ => functional}/sox_compatibility_test.py (90%) delete mode 100644 test/torchaudio_unittest/kaldi_compatibility_impl.py delete mode 100644 test/torchaudio_unittest/librosa_compatibility_test.py create mode 100644 test/torchaudio_unittest/transforms/__init__.py rename test/torchaudio_unittest/{ => transforms}/batch_consistency_test.py (54%) rename test/torchaudio_unittest/{ => transforms}/kaldi_compatibility_cpu_test.py (100%) rename test/torchaudio_unittest/{ => transforms}/kaldi_compatibility_cuda_test.py (100%) create mode 100644 test/torchaudio_unittest/transforms/kaldi_compatibility_impl.py create mode 100644 test/torchaudio_unittest/transforms/librosa_compatibility_test.py create mode 100644 test/torchaudio_unittest/transforms/sox_compatibility_test.py rename test/torchaudio_unittest/{ => transforms}/torchscript_consistency_cpu_test.py (100%) rename test/torchaudio_unittest/{ => transforms}/torchscript_consistency_cuda_test.py (100%) rename test/torchaudio_unittest/{ => transforms}/torchscript_consistency_impl.py (100%) rename test/torchaudio_unittest/{ => transforms}/transforms_test.py (100%) create mode 100644 third_party/kaldi/CMakeLists.txt create mode 100644 third_party/kaldi/README.md create mode 100644 third_party/kaldi/kaldi.patch create mode 100644 third_party/kaldi/src/matrix/kaldi-matrix.cc create mode 100644 third_party/kaldi/src/matrix/kaldi-matrix.h create mode 100644 third_party/kaldi/src/matrix/kaldi-vector.cc create mode 100644 third_party/kaldi/src/matrix/kaldi-vector.h create mode 100644 third_party/sox/patch/sox.patch delete mode 100644 torchaudio/backend/_soundfile_backend.py delete mode 100644 torchaudio/backend/sox_backend.py create mode 100644 torchaudio/csrc/kaldi.cpp create mode 100644 torchaudio/csrc/lfilter.cpp create mode 100644 torchaudio/csrc/overdrive.cpp delete mode 100644 torchaudio/csrc/sox/legacy.cpp delete mode 100644 torchaudio/csrc/sox/legacy.h create mode 100644 torchaudio/csrc/sox/types.cpp create mode 100644 torchaudio/csrc/sox/types.h create mode 100644 torchaudio/csrc/utils.cpp diff --git a/.circleci/config.yml b/.circleci/config.yml index 1cae2f6c78..3bf72c3a6d 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -45,6 +45,16 @@ commands: name: Install pkg-config command: HOMEBREW_NO_AUTO_UPDATE=1 brew install pkg-config wget # Disable brew auto update which is very slow + load_conda_channel_flags: + description: "Determines whether we need extra conda channels" + steps: + - run: + name: Adding CONDA_CHANNEL_FLAGS to BASH_ENV + command: | + CONDA_CHANNEL_FLAGS="" + if [[ "${PYTHON_VERSION}" = *3.9* ]]; then + echo "export CONDA_CHANNEL_FLAGS=-c=conda-forge" >> ${BASH_ENV} + fi binary_common: &binary_common parameters: @@ -117,7 +127,7 @@ jobs: binary_linux_wheel: <<: *binary_common docker: - - image: "pytorch/manylinux-cuda100" + - image: "pytorch/manylinux-cuda102" resource_class: 2xlarge+ steps: - checkout @@ -138,6 +148,7 @@ jobs: resource_class: 2xlarge+ steps: - checkout + - load_conda_channel_flags - attach_workspace: at: third_party - run: packaging/build_conda.sh @@ -155,6 +166,7 @@ jobs: steps: - checkout - install_build_tools_macos + - load_conda_channel_flags - attach_workspace: at: third_party - run: @@ -180,6 +192,7 @@ jobs: steps: - checkout - install_build_tools_macos + - load_conda_channel_flags - attach_workspace: at: third_party - run: @@ -202,6 +215,7 @@ jobs: name: windows-cpu steps: - checkout + - load_conda_channel_flags - run: name: build command: | @@ -221,6 +235,7 @@ jobs: name: windows-cpu steps: - checkout + - load_conda_channel_flags - run: name: build command: | @@ -278,6 +293,7 @@ jobs: - attach_workspace: at: ~/workspace - designate_upload_channel + - load_conda_channel_flags - run: name: install binaries command: | @@ -297,6 +313,7 @@ jobs: - attach_workspace: at: ~/workspace - designate_upload_channel + - load_conda_channel_flags - run: name: install binaries command: | @@ -317,6 +334,7 @@ jobs: - attach_workspace: at: ~/workspace - designate_upload_channel + - load_conda_channel_flags - run: name: install binaries command: | @@ -342,6 +360,7 @@ jobs: - attach_workspace: at: ~/workspace - designate_upload_channel + - load_conda_channel_flags - run: name: install binaries command: | @@ -395,6 +414,7 @@ jobs: - attach_workspace: at: third_party - designate_upload_channel + - load_conda_channel_flags - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh @@ -423,6 +443,7 @@ jobs: - attach_workspace: at: third_party - designate_upload_channel + - load_conda_channel_flags - run: name: Pull Docker image command: docker pull --quiet "${image_name}" @@ -431,7 +452,7 @@ jobs: command: docker run -t --gpus all -e PYTHON_VERSION -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh - run: name: Install torchaudio - command: docker run -t --gpus all -e UPLOAD_CHANNEL -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh + command: docker run -t --gpus all -e UPLOAD_CHANNEL -e CONDA_CHANNEL_FLAGS -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh - run: name: Run tests command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh @@ -448,6 +469,7 @@ jobs: steps: - checkout - designate_upload_channel + - load_conda_channel_flags - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh @@ -473,6 +495,7 @@ jobs: steps: - checkout - designate_upload_channel + - load_conda_channel_flags - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh @@ -496,6 +519,7 @@ jobs: steps: - checkout - install_build_tools_macos + - load_conda_channel_flags - attach_workspace: at: third_party - designate_upload_channel @@ -522,6 +546,7 @@ jobs: steps: - checkout - designate_upload_channel + - load_conda_channel_flags - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh @@ -538,6 +563,7 @@ jobs: - attach_workspace: at: ~/workspace - checkout + - load_conda_channel_flags - run: name: Install pytorch-audio command: .circleci/build_docs/install_wheels.sh @@ -548,6 +574,9 @@ jobs: root: ./ paths: - "*" + - store_artifacts: + path: ./docs/build/html + destination: docs upload_docs: <<: *binary_common @@ -577,6 +606,18 @@ jobs: target=${tag:-master} ~/workspace/.circleci/build_docs/commit_docs.sh ~/workspace $target + docstring_parameters_sync: + <<: *binary_common + docker: + - image: circleci/python:3.8 + steps: + - checkout + - run: + name: Check parameters docstring sync + command: | + pip install --user pydocstyle + pydocstyle torchaudio + workflows: build: jobs: @@ -598,6 +639,11 @@ workflows: python_version: '3.8' requires: - download_third_parties_nix + - binary_linux_wheel: + name: binary_linux_wheel_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix - binary_macos_wheel: name: binary_macos_wheel_py3.6 python_version: '3.6' @@ -613,6 +659,11 @@ workflows: python_version: '3.8' requires: - download_third_parties_nix + - binary_macos_wheel: + name: binary_macos_wheel_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix - binary_windows_wheel: name: binary_windows_wheel_py3.6 python_version: '3.6' @@ -622,6 +673,9 @@ workflows: - binary_windows_wheel: name: binary_windows_wheel_py3.8 python_version: '3.8' + - binary_windows_wheel: + name: binary_windows_wheel_py3.9 + python_version: '3.9' - binary_linux_conda: name: binary_linux_conda_py3.6 python_version: '3.6' @@ -637,6 +691,11 @@ workflows: python_version: '3.8' requires: - download_third_parties_nix + - binary_linux_conda: + name: binary_linux_conda_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix - binary_macos_conda: name: binary_macos_conda_py3.6 python_version: '3.6' @@ -652,6 +711,11 @@ workflows: python_version: '3.8' requires: - download_third_parties_nix + - binary_macos_conda: + name: binary_macos_conda_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix - binary_windows_conda: name: binary_windows_conda_py3.6 python_version: '3.6' @@ -661,6 +725,9 @@ workflows: - binary_windows_conda: name: binary_windows_conda_py3.8 python_version: '3.8' + - binary_windows_conda: + name: binary_windows_conda_py3.9 + python_version: '3.9' - build_docs: name: build_docs python_version: '3.8' @@ -678,6 +745,11 @@ workflows: python_version: '3.8' requires: - build_docs + - docstring_parameters_sync: + name: docstring_parameters_sync + python_version: '3.8' + requires: + - binary_linux_wheel_py3.8 unittest: jobs: - download_third_parties_nix: @@ -700,6 +772,11 @@ workflows: python_version: '3.8' requires: - download_third_parties_nix + - unittest_linux_cpu: + name: unittest_linux_cpu_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix - unittest_linux_gpu: name: unittest_linux_gpu_py3.6 python_version: '3.6' @@ -715,6 +792,11 @@ workflows: python_version: '3.8' requires: - download_third_parties_nix + - unittest_linux_gpu: + name: unittest_linux_gpu_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix - unittest_windows_cpu: name: unittest_windows_cpu_py3.6 python_version: '3.6' @@ -724,6 +806,9 @@ workflows: - unittest_windows_cpu: name: unittest_windows_cpu_py3.8 python_version: '3.8' + - unittest_windows_cpu: + name: unittest_windows_cpu_py3.9 + python_version: '3.9' - unittest_windows_gpu: name: unittest_windows_gpu_py3.6 python_version: '3.6' @@ -733,6 +818,9 @@ workflows: - unittest_windows_gpu: name: unittest_windows_gpu_py3.8 python_version: '3.8' + - unittest_windows_gpu: + name: unittest_windows_gpu_py3.9 + python_version: '3.9' - unittest_macos_cpu: name: unittest_macos_cpu_py3.6 python_version: '3.6' @@ -748,6 +836,11 @@ workflows: python_version: '3.8' requires: - download_third_parties_nix + - unittest_macos_cpu: + name: unittest_macos_cpu_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix nightly: jobs: - circleci_consistency: @@ -861,6 +954,39 @@ workflows: python_version: '3.8' requires: - nightly_binary_linux_wheel_py3.8_upload + - binary_linux_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.9_upload + requires: + - nightly_binary_linux_wheel_py3.9 + - smoke_test_linux_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.9_smoke_test_pip + python_version: '3.9' + requires: + - nightly_binary_linux_wheel_py3.9_upload - binary_macos_wheel: filters: branches: @@ -927,6 +1053,28 @@ workflows: name: nightly_binary_macos_wheel_py3.8_upload requires: - nightly_binary_macos_wheel_py3.8 + - binary_macos_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.9_upload + requires: + - nightly_binary_macos_wheel_py3.9 - binary_windows_wheel: filters: branches: @@ -1020,6 +1168,37 @@ workflows: python_version: '3.8' requires: - nightly_binary_windows_wheel_py3.8_upload + - binary_windows_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9 + python_version: '3.9' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_upload + requires: + - nightly_binary_windows_wheel_py3.9 + - smoke_test_windows_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_smoke_test_pip + python_version: '3.9' + requires: + - nightly_binary_windows_wheel_py3.9_upload - binary_linux_conda: filters: branches: @@ -1119,6 +1298,39 @@ workflows: python_version: '3.8' requires: - nightly_binary_linux_conda_py3.8_upload + - binary_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_upload + requires: + - nightly_binary_linux_conda_py3.9 + - smoke_test_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_smoke_test_conda + python_version: '3.9' + requires: + - nightly_binary_linux_conda_py3.9_upload - binary_macos_conda: filters: branches: @@ -1185,6 +1397,28 @@ workflows: name: nightly_binary_macos_conda_py3.8_upload requires: - nightly_binary_macos_conda_py3.8 + - binary_macos_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.9 + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.9_upload + requires: + - nightly_binary_macos_conda_py3.9 - binary_windows_conda: filters: branches: @@ -1278,6 +1512,37 @@ workflows: python_version: '3.8' requires: - nightly_binary_windows_conda_py3.8_upload + - binary_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.9 + python_version: '3.9' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.9_upload + requires: + - nightly_binary_windows_conda_py3.9 + - smoke_test_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.9_smoke_test_conda + python_version: '3.9' + requires: + - nightly_binary_windows_conda_py3.9_upload docker_build: triggers: - schedule: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index fa1e202cb9..8e01b1da56 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -45,6 +45,16 @@ commands: name: Install pkg-config command: HOMEBREW_NO_AUTO_UPDATE=1 brew install pkg-config wget # Disable brew auto update which is very slow + load_conda_channel_flags: + description: "Determines whether we need extra conda channels" + steps: + - run: + name: Adding CONDA_CHANNEL_FLAGS to BASH_ENV + command: | + CONDA_CHANNEL_FLAGS="" + if [[ "${PYTHON_VERSION}" = *3.9* ]]; then + echo "export CONDA_CHANNEL_FLAGS=-c=conda-forge" >> ${BASH_ENV} + fi binary_common: &binary_common parameters: @@ -117,7 +127,7 @@ jobs: binary_linux_wheel: <<: *binary_common docker: - - image: "pytorch/manylinux-cuda100" + - image: "pytorch/manylinux-cuda102" resource_class: 2xlarge+ steps: - checkout @@ -138,6 +148,7 @@ jobs: resource_class: 2xlarge+ steps: - checkout + - load_conda_channel_flags - attach_workspace: at: third_party - run: packaging/build_conda.sh @@ -155,6 +166,7 @@ jobs: steps: - checkout - install_build_tools_macos + - load_conda_channel_flags - attach_workspace: at: third_party - run: @@ -180,6 +192,7 @@ jobs: steps: - checkout - install_build_tools_macos + - load_conda_channel_flags - attach_workspace: at: third_party - run: @@ -202,6 +215,7 @@ jobs: name: windows-cpu steps: - checkout + - load_conda_channel_flags - run: name: build command: | @@ -221,6 +235,7 @@ jobs: name: windows-cpu steps: - checkout + - load_conda_channel_flags - run: name: build command: | @@ -278,6 +293,7 @@ jobs: - attach_workspace: at: ~/workspace - designate_upload_channel + - load_conda_channel_flags - run: name: install binaries command: | @@ -297,6 +313,7 @@ jobs: - attach_workspace: at: ~/workspace - designate_upload_channel + - load_conda_channel_flags - run: name: install binaries command: | @@ -317,6 +334,7 @@ jobs: - attach_workspace: at: ~/workspace - designate_upload_channel + - load_conda_channel_flags - run: name: install binaries command: | @@ -342,6 +360,7 @@ jobs: - attach_workspace: at: ~/workspace - designate_upload_channel + - load_conda_channel_flags - run: name: install binaries command: | @@ -395,6 +414,7 @@ jobs: - attach_workspace: at: third_party - designate_upload_channel + - load_conda_channel_flags - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh @@ -423,6 +443,7 @@ jobs: - attach_workspace: at: third_party - designate_upload_channel + - load_conda_channel_flags - run: name: Pull Docker image command: docker pull --quiet "${image_name}" @@ -431,7 +452,7 @@ jobs: command: docker run -t --gpus all -e PYTHON_VERSION -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh - run: name: Install torchaudio - command: docker run -t --gpus all -e UPLOAD_CHANNEL -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh + command: docker run -t --gpus all -e UPLOAD_CHANNEL -e CONDA_CHANNEL_FLAGS -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh - run: name: Run tests command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh @@ -448,6 +469,7 @@ jobs: steps: - checkout - designate_upload_channel + - load_conda_channel_flags - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh @@ -473,6 +495,7 @@ jobs: steps: - checkout - designate_upload_channel + - load_conda_channel_flags - run: name: Setup command: .circleci/unittest/windows/scripts/setup_env.sh @@ -496,6 +519,7 @@ jobs: steps: - checkout - install_build_tools_macos + - load_conda_channel_flags - attach_workspace: at: third_party - designate_upload_channel @@ -522,6 +546,7 @@ jobs: steps: - checkout - designate_upload_channel + - load_conda_channel_flags - run: name: Setup command: .circleci/unittest/linux/scripts/setup_env.sh @@ -538,6 +563,7 @@ jobs: - attach_workspace: at: ~/workspace - checkout + - load_conda_channel_flags - run: name: Install pytorch-audio command: .circleci/build_docs/install_wheels.sh @@ -548,6 +574,9 @@ jobs: root: ./ paths: - "*" + - store_artifacts: + path: ./docs/build/html + destination: docs upload_docs: <<: *binary_common @@ -577,6 +606,18 @@ jobs: target=${tag:-master} ~/workspace/.circleci/build_docs/commit_docs.sh ~/workspace $target + docstring_parameters_sync: + <<: *binary_common + docker: + - image: circleci/python:3.8 + steps: + - checkout + - run: + name: Check parameters docstring sync + command: | + pip install --user pydocstyle + pydocstyle torchaudio + workflows: build: jobs: diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index ea4639ad86..0985c1f9f6 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -19,7 +19,7 @@ import os.path -PYTHON_VERSIONS = ["3.6", "3.7", "3.8"] +PYTHON_VERSIONS = ["3.6", "3.7", "3.8", "3.9"] DOC_VERSION = ('linux', '3.8') @@ -36,6 +36,8 @@ def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): # Build on every pull request, but upload only on nightly and tags w += build_doc_job(None) w += upload_doc_job('nightly') + w += docstring_parameters_sync_job(None) + return indent(indentation, w) @@ -100,6 +102,18 @@ def upload_doc_job(filter_branch): return [{"upload_docs": job}] +def docstring_parameters_sync_job(filter_branch): + job = { + "name": "docstring_parameters_sync", + "python_version": "3.8", + "requires": ["binary_linux_wheel_py3.8", ], + } + + if filter_branch: + job["filters"] = gen_filter_branch_tree(filter_branch) + return [{"docstring_parameters_sync": job}] + + def generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype): d = { diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh index 209c86be71..5638a28850 100755 --- a/.circleci/unittest/linux/scripts/install.sh +++ b/.circleci/unittest/linux/scripts/install.sh @@ -34,7 +34,10 @@ else cudatoolkit="cudatoolkit=${version}" fi printf "Installing PyTorch with %s\n" "${cudatoolkit}" -conda install -y -c "pytorch-${UPLOAD_CHANNEL}" pytorch ${cudatoolkit} +( + set -x + conda install ${CONDA_CHANNEL_FLAGS:-} -y -c "pytorch-${UPLOAD_CHANNEL}" "pytorch-${UPLOAD_CHANNEL}::pytorch" ${cudatoolkit} +) # 2. Install torchaudio printf "* Installing torchaudio\n" @@ -43,11 +46,15 @@ BUILD_TRANSDUCER=1 BUILD_SOX=1 python setup.py install # 3. Install Test tools printf "* Installing test tools\n" -if [ "${os}" == Linux ] ; then - conda install -y -c conda-forge codecov pytest pytest-cov - pip install kaldi-io 'librosa>=0.8.0' parameterized SoundFile scipy 'requests>=2.20' -else - # Note: installing librosa via pip fail because it will try to compile numba. - conda install -y -c conda-forge codecov pytest pytest-cov 'librosa>=0.8.0' parameterized scipy - pip install kaldi-io SoundFile 'requests>=2.20' +NUMBA_DEV_CHANNEL="" +if [[ "$(python --version)" = *3.9* ]]; then + # Numba isn't available for Python 3.9 except on the numba dev channel and building from source fails + # See https://github.com/librosa/librosa/issues/1270#issuecomment-759065048 + NUMBA_DEV_CHANNEL="-c numba/label/dev" fi +# Note: installing librosa via pip fail because it will try to compile numba. +( + set -x + conda install -y -c conda-forge ${NUMBA_DEV_CHANNEL} 'librosa>=0.8.0' parameterized 'requests>=2.20' + pip install kaldi-io SoundFile codecov pytest pytest-cov scipy +) diff --git a/.circleci/unittest/linux/scripts/run_style_checks.sh b/.circleci/unittest/linux/scripts/run_style_checks.sh index d8af8824f5..8b2d8e2548 100755 --- a/.circleci/unittest/linux/scripts/run_style_checks.sh +++ b/.circleci/unittest/linux/scripts/run_style_checks.sh @@ -29,7 +29,7 @@ set +e exit_status=0 printf "\x1b[34mRunning flake8:\x1b[0m\n" -flake8 torchaudio test build_tools/setup_helpers +flake8 torchaudio test build_tools/setup_helpers docs/source/conf.py status=$? exit_status="$((exit_status+status))" if [ "${status}" -ne 0 ]; then @@ -38,7 +38,7 @@ fi printf "\x1b[34mRunning clang-format:\x1b[0m\n" "${this_dir}"/run_clang_format.py \ - -r torchaudio/csrc \ + -r torchaudio/csrc third_party/kaldi/src \ --clang-format-executable "${clangformat_path}" \ && git diff --exit-code status=$? diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml index 4a0b71d653..27322ccc01 100644 --- a/.circleci/unittest/windows/scripts/environment.yml +++ b/.circleci/unittest/windows/scripts/environment.yml @@ -3,16 +3,14 @@ channels: - defaults dependencies: - flake8 - - numpy - pytest - pytest-cov - codecov + - scipy >= 1.4.1 - pip - pip: - - scipy == 1.4.1 - kaldi-io - PySoundFile - - librosa >= 0.8.0 - future - parameterized - dataclasses diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh index 901cb40450..be0c890614 100644 --- a/.circleci/unittest/windows/scripts/install.sh +++ b/.circleci/unittest/windows/scripts/install.sh @@ -25,13 +25,24 @@ else cudatoolkit="cudatoolkit=${version}" fi printf "Installing PyTorch with %s\n" "${cudatoolkit}" -conda install -y -c "pytorch-${UPLOAD_CHANNEL}" pytorch "${cudatoolkit}" +conda install ${CONDA_CHANNEL_FLAGS:-} -y -c "pytorch-${UPLOAD_CHANNEL}" "pytorch-${UPLOAD_CHANNEL}::pytorch" ${cudatoolkit} # 2. Install torchaudio printf "* Installing torchaudio\n" -python setup.py install +git submodule update --init --recursive +"$root_dir/packaging/vc_env_helper.bat" python setup.py install # 3. Install Test tools printf "* Installing test tools\n" -conda install -y -c conda-forge codecov pytest pytest-cov -pip install kaldi-io 'librosa>=0.8.0' parameterized PySoundFile scipy +NUMBA_DEV_CHANNEL="" +if [[ "$(python --version)" = *3.9* ]]; then + # Numba isn't available for Python 3.9 except on the numba dev channel and building from source fails + # See https://github.com/librosa/librosa/issues/1270#issuecomment-759065048 + NUMBA_DEV_CHANNEL="-c numba/label/dev" +fi +# Note: installing librosa via pip fail because it will try to compile numba. +( + set -x + conda install -y -c conda-forge ${NUMBA_DEV_CHANNEL} 'librosa>=0.8.0' parameterized 'requests>=2.20' + pip install kaldi-io SoundFile codecov pytest pytest-cov scipy +) diff --git a/.circleci/unittest/windows/scripts/setup_env.sh b/.circleci/unittest/windows/scripts/setup_env.sh index 929a98cb8f..5f092bfceb 100644 --- a/.circleci/unittest/windows/scripts/setup_env.sh +++ b/.circleci/unittest/windows/scripts/setup_env.sh @@ -37,3 +37,6 @@ if [ ! -d "${env_dir}" ]; then conda create --prefix "${env_dir}" -y python="${PYTHON_VERSION}" fi conda activate "${env_dir}" + +# 3. Install minimal build tools +pip --quiet install cmake ninja diff --git a/.gitmodules b/.gitmodules index c01f8c91ad..dd47fc1e91 100644 --- a/.gitmodules +++ b/.gitmodules @@ -2,3 +2,7 @@ path = third_party/transducer/submodule url = https://github.com/HawkAaron/warp-transducer ignore = dirty +[submodule "kaldi"] + path = third_party/kaldi/submodule + url = https://github.com/kaldi-asr/kaldi + ignore = dirty diff --git a/CMakeLists.txt b/CMakeLists.txt index d72639a71e..87abef1262 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -47,6 +47,7 @@ endif() # Options option(BUILD_SOX "Build libsox statically" OFF) +option(BUILD_KALDI "Build kaldi statically" ON) option(BUILD_TRANSDUCER "Enable transducer" OFF) option(BUILD_LIBTORCHAUDIO "Build C++ Library" ON) option(BUILD_TORCHAUDIO_PYTHON_EXTENSION "Build Python extension" OFF) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 73674b42c2..94526661e0 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -13,6 +13,10 @@ core, please first open an issue and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR, because we might be taking the core in a different direction than you might be aware of. +Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the +safe disclosure of security bugs. In those cases, please go through the +process outlined on that page and do not file a public issue. + Fixing bugs and implementing new features are not the only way you can contribute. It also helps the project when you report problems you're facing, and when you give a :+1: on issues that others reported and that are relevant @@ -28,12 +32,18 @@ wanted"](https://github.com/pytorch/audio/issues?q=is%3Aopen+is%3Aissue+label%3A Comment on the issue that you want to work on it and send a PR with your fix (see below). +## Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Facebook's open source projects. + +Complete your CLA here: + ## Development installation We recommend using a `conda` environment to contribute efficiently to torchaudio. -### Install PyTorch Nightly +### Install PyTorch Nightly ```bash conda install pytorch -c pytorch-nightly @@ -53,12 +63,10 @@ BUILD_SOX=1 python setup.py develop # or, for OSX # BUILD_SOX=1 MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py develop # for C++ debugging, please use DEBUG=1 -# DEBUG=1 python setup.py install +# DEBUG=1 python setup.py develop ``` -Note: you don't need to use `BUILD_SOX=1` if you have `libsox-dev` installed -already. If you built sox however, set the `PATH` variable so that the tests -properly use the newly built `sox` binary: +If you built sox, set the `PATH` variable so that the tests properly use the newly built `sox` binary: ```bash export PATH="/third_party/install/bin:${PATH}" @@ -67,7 +75,7 @@ export PATH="/third_party/install/bin:${PATH}" The following dependencies are also needed for testing: ```bash -pip install typing pytest scipy numpy parametrized +pip install typing pytest scipy numpy parameterized ``` ## Development Process @@ -75,19 +83,19 @@ pip install typing pytest scipy numpy parametrized If you plan to modify the code or documentation, please follow the steps below: 1. Fork the repository and create your branch from `master`: `$ git checkout master && git checkout -b my_cool_feature` -2. If you have modified the code (new feature or bug-fix), [please add tests](.test/torchaudio_unittest/). +2. If you have modified the code (new feature or bug-fix), [please add tests](test/torchaudio_unittest/). 3. If you have changed APIs, [update the documentation](#Documentation). -For more details about pull requests, -please read [GitHub's guides](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request). +For more details about pull requests, +please read [GitHub's guides](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request). If you would like to contribute a new model, please see [here](#New-model). -If you would like to contribute a new dataset, please see [here](#New-dataset). +If you would like to contribute a new dataset, please see [here](#New-dataset). ## Testing -Please refer to our [testing guidelines](.test/torchaudio_unittest/) for more +Please refer to our [testing guidelines](test/torchaudio_unittest/) for more details. ## Documentation diff --git a/README.md b/README.md index 527b1a22da..2cff4bf076 100644 --- a/README.md +++ b/README.md @@ -29,17 +29,18 @@ to use and feel like a natural extension. Dependencies ------------ * PyTorch (See below for the compatible versions) -* libsox v14.3.2 or above (only required when building from source) * [optional] vesis84/kaldi-io-for-python commit cb46cb1f44318a5d04d4941cf39084c5b021241e or above The following are the corresponding ``torchaudio`` versions and supported Python versions. | ``torch`` | ``torchaudio`` | ``python`` | | ------------------------ | ------------------------ | ------------------------------- | -| ``master`` / ``nightly`` | ``master`` / ``nightly`` | ``>=3.6`` | -| ``1.7.0`` | ``0.7.0`` | ``>=3.6`` | -| ``1.6.0`` | ``0.6.0`` | ``>=3.6`` | -| ``1.5.0`` | ``0.5.0`` | ``>=3.5`` | +| ``master`` / ``nightly`` | ``master`` / ``nightly`` | ``>=3.6``, ``<=3.9`` | +| ``1.8.0`` | ``0.8.0`` | ``>=3.6``, ``<=3.9`` | +| ``1.7.1`` | ``0.7.2`` | ``>=3.6``, ``<=3.9`` | +| ``1.7.0`` | ``0.7.0`` | ``>=3.6``, ``<=3.8`` | +| ``1.6.0`` | ``0.6.0`` | ``>=3.6``, ``<=3.8`` | +| ``1.5.0`` | ``0.5.0`` | ``>=3.5``, ``<=3.8`` | | ``1.4.0`` | ``0.4.0`` | ``==2.7``, ``>=3.5``, ``<=3.8`` | @@ -83,39 +84,7 @@ conda install -y -c pytorch-nightly torchaudio ### From Source -If your system configuration is not among the supported configurations -above, you can build torchaudio from source. - -This will require libsox v14.3.2 or above. - -

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zZwlJYtlh-A_01FB>qYKMD@yughrpFzg?dm@s=e&%?(p6hG7D$lV3yt&ws6t^xIBBV zpsc)B(uV^5m+`&r;%8BYHH71J`>KN{eBGzMgQ;ElmE)Gox$yRK&V!~NmU2FrG=U$x zF_$==bB77ao+0oTU!%w>frw3l0(*s>8X@t+Db9zR!ft2gzo!M>+$@L(_~@Hopi&2} zeSXc;Odvj%zxUk1cK(Sg&ipVuGdht5buA4xISrZ5SpI1YT#eM(+)ps$Ai*H~MIz2QRkp$#=A59aGLLz&HZZy+F# z=w0?YvzgcLa1Ez;zzdG&T@RzmI`Fb + +int main(int argc, char* argv[]) { + if (argc !=4) { + std::cerr << "Usage: " << argv[0] << " " << std::endl; + return -1; + } + + torch::jit::script::Module module; + std::cout << "Loading module from: " << argv[0] << std::endl; + try { + module = torch::jit::load(argv[1]); + } catch (const c10::Error &error) { + std::cerr << "Failed to load the module:" << error.what() << std::endl; + return -1; + } + + std::cout << "Performing the process ..." << std::endl; + module.forward({c10::IValue(argv[2]), c10::IValue(argv[3])}); + std::cout << "Done." << std::endl; +} diff --git a/examples/libtorchaudio/run.sh b/examples/libtorchaudio/run.sh new file mode 100755 index 0000000000..a55614e909 --- /dev/null +++ b/examples/libtorchaudio/run.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash + +set -eux + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" + +build_dir="${this_dir}/build" +data_dir="${this_dir}/data" +jit_file="${data_dir}/pipeline.zip" +input_file="${data_dir}/input.wav" +output_file="${data_dir}/output.wav" + +cd "${this_dir}" +python create_jittable_pipeline.py + +mkdir -p "${build_dir}" +cd "${build_dir}" +cmake -GNinja \ + -DCMAKE_PREFIX_PATH="$(python -c 'import torch;print(torch.utils.cmake_prefix_path)')" \ + -DBUILD_SOX=ON \ + -DBUILD_KALDI=OFF \ + .. +cmake --build . --target main +./main "${jit_file}" "${input_file}" "${output_file}" diff --git a/examples/pipeline_wav2letter/languagemodels.py b/examples/pipeline_wav2letter/languagemodels.py index eee018ae69..d66858ea95 100644 --- a/examples/pipeline_wav2letter/languagemodels.py +++ b/examples/pipeline_wav2letter/languagemodels.py @@ -8,7 +8,7 @@ def __init__(self, labels, char_blank, char_space): self.char_space = char_space self.char_blank = char_blank - labels = [l for l in labels] + labels = list(labels) self.length = len(labels) enumerated = list(enumerate(labels)) flipped = [(sub[1], sub[0]) for sub in enumerated] diff --git a/examples/source_separation/utils/dataset/wsj0mix.py b/examples/source_separation/utils/dataset/wsj0mix.py index efd9268971..c67bbd8205 100644 --- a/examples/source_separation/utils/dataset/wsj0mix.py +++ b/examples/source_separation/utils/dataset/wsj0mix.py @@ -63,7 +63,7 @@ def __len__(self) -> int: def __getitem__(self, key: int) -> SampleType: """Load the n-th sample from the dataset. Args: - n (int): The index of the sample to be loaded + key (int): The index of the sample to be loaded Returns: tuple: ``(sample_rate, mix_waveform, list_of_source_waveforms)`` """ diff --git a/packaging/build_conda.sh b/packaging/build_conda.sh index 488d0fe871..161c48ad43 100755 --- a/packaging/build_conda.sh +++ b/packaging/build_conda.sh @@ -6,7 +6,8 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="conda" export NO_CUDA_PACKAGE=1 -setup_env 0.8.0 +setup_env 0.9.0 export SOURCE_ROOT_DIR="$PWD" setup_conda_pytorch_constraint +setup_visual_studio_constraint conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchaudio diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index 11e8f07bfd..9b4194afda 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -6,14 +6,14 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="wheel" export NO_CUDA_PACKAGE=1 -setup_env 0.8.0 +setup_env 0.9.0 setup_wheel_python pip_install numpy future cmake ninja setup_pip_pytorch_version python setup.py clean if [[ "$OSTYPE" == "msys" ]]; then python_tag="$(echo "cp$PYTHON_VERSION" | tr -d '.')" - python setup.py bdist_wheel --plat-name win_amd64 --python-tag $python_tag + "$script_dir/vc_env_helper.bat" python setup.py bdist_wheel --plat-name win_amd64 --python-tag $python_tag else BUILD_TRANSDUCER=1 BUILD_SOX=1 python setup.py bdist_wheel fi diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index 36760b9484..5dc5397042 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -141,6 +141,7 @@ setup_wheel_python() { 3.6) python_abi=cp36-cp36m ;; 3.7) python_abi=cp37-cp37m ;; 3.8) python_abi=cp38-cp38 ;; + 3.9) python_abi=cp39-cp39 ;; *) echo "Unrecognized PYTHON_VERSION=$PYTHON_VERSION" exit 1 @@ -176,11 +177,16 @@ setup_pip_pytorch_version() { # # You MUST have populated PYTORCH_VERSION_SUFFIX before hand. setup_conda_pytorch_constraint() { + CONDA_CHANNEL_FLAGS="${CONDA_CHANNEL_FLAGS}" if [[ -z "$PYTORCH_VERSION" ]]; then - export CONDA_CHANNEL_FLAGS="-c pytorch-nightly" + export CONDA_CHANNEL_FLAGS="${CONDA_CHANNEL_FLAGS} -c pytorch-nightly" export PYTORCH_VERSION="$(conda search --json 'pytorch[channel=pytorch-nightly]' | python -c "import sys, json, re; print(re.sub(r'\\+.*$', '', json.load(sys.stdin)['pytorch'][-1]['version']))")" else - export CONDA_CHANNEL_FLAGS="-c pytorch -c pytorch-${UPLOAD_CHANNEL}" + export CONDA_CHANNEL_FLAGS="${CONDA_CHANNEL_FLAGS} -c pytorch -c pytorch-test -c pytorch-nightly" + fi + # Some dependencies for Python 3.9 are only on conda-forge + if [[ "${PYTHON_VERSION}" = "3.9" ]]; then + export CONDA_CHANNEL_FLAGS="${CONDA_CHANNEL_FLAGS} -c conda-forge" fi if [[ "$CU_VERSION" == cpu ]]; then export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==$PYTORCH_VERSION${PYTORCH_VERSION_SUFFIX}" @@ -215,3 +221,13 @@ setup_conda_cudatoolkit_constraint() { esac fi } + +# Build the proper compiler package before building the final package +setup_visual_studio_constraint() { + if [[ "$OSTYPE" == "msys" ]]; then + export VSTOOLCHAIN_PACKAGE=vs2019 + export VSDEVCMD_ARGS='' + conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload packaging/$VSTOOLCHAIN_PACKAGE + cp packaging/$VSTOOLCHAIN_PACKAGE/conda_build_config.yaml packaging/torchaudio/conda_build_config.yaml + fi +} diff --git a/packaging/torchaudio/meta.yaml b/packaging/torchaudio/meta.yaml index ad12821d0e..5f9e485eba 100644 --- a/packaging/torchaudio/meta.yaml +++ b/packaging/torchaudio/meta.yaml @@ -16,10 +16,12 @@ requirements: - cpuonly - cmake - ninja + - defaults::numpy >=1.11 {{ environ.get('CONDA_PYTORCH_BUILD_CONSTRAINT') }} run: - python + - defaults::numpy >=1.11 {{ environ.get('CONDA_PYTORCH_CONSTRAINT') }} build: diff --git a/packaging/vc_env_helper.bat b/packaging/vc_env_helper.bat new file mode 100644 index 0000000000..9410135677 --- /dev/null +++ b/packaging/vc_env_helper.bat @@ -0,0 +1,39 @@ +@echo on + +set VC_VERSION_LOWER=16 +set VC_VERSION_UPPER=17 + +for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [%VC_VERSION_LOWER%^,%VC_VERSION_UPPER%^) -property installationPath`) do ( + if exist "%%i" if exist "%%i\VC\Auxiliary\Build\vcvarsall.bat" ( + set "VS15INSTALLDIR=%%i" + set "VS15VCVARSALL=%%i\VC\Auxiliary\Build\vcvarsall.bat" + goto vswhere + ) +) + +:vswhere +if "%VSDEVCMD_ARGS%" == "" ( + call "%VS15VCVARSALL%" x64 || exit /b 1 +) else ( + call "%VS15VCVARSALL%" x64 %VSDEVCMD_ARGS% || exit /b 1 +) + +@echo on + +set DISTUTILS_USE_SDK=1 + +set args=%1 +shift +:start +if [%1] == [] goto done +set args=%args% %1 +shift +goto start + +:done +if "%args%" == "" ( + echo Usage: vc_env_helper.bat [command] [args] + echo e.g. vc_env_helper.bat cl /c test.cpp +) + +%args% || exit /b 1 diff --git a/packaging/vs2019/activate.bat b/packaging/vs2019/activate.bat new file mode 100644 index 0000000000..6f607ba751 --- /dev/null +++ b/packaging/vs2019/activate.bat @@ -0,0 +1,44 @@ +:: Set env vars that tell distutils to use the compiler that we put on path +SET DISTUTILS_USE_SDK=1 +SET MSSdk=1 + +SET "VS_VERSION=16.0" +SET "VS_MAJOR=16" +SET "VS_YEAR=2019" + +set "MSYS2_ARG_CONV_EXCL=/AI;/AL;/OUT;/out" +set "MSYS2_ENV_CONV_EXCL=CL" + +:: For Python 3.5+, ensure that we link with the dynamic runtime. See +:: http://stevedower.id.au/blog/building-for-python-3-5-part-two/ for more info +set "PY_VCRUNTIME_REDIST=%PREFIX%\\bin\\vcruntime140.dll" + +for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [16^,17^) -property installationPath`) do ( + if exist "%%i" if exist "%%i\VC\Auxiliary\Build\vcvarsall.bat" ( + set "VSINSTALLDIR=%%i\" + goto :vswhere + ) +) + +:vswhere + +:: Shorten PATH to avoid the `input line too long` error. +SET MyPath=%PATH% + +setlocal EnableDelayedExpansion + +SET TempPath="%MyPath:;=";"%" +SET var= +FOR %%a IN (%TempPath%) DO ( + IF EXIST %%~sa ( + SET "var=!var!;%%~sa" + ) +) + +set "TempPath=!var:~1!" +endlocal & set "PATH=%TempPath%" + +:: Shorten current directory too +FOR %%A IN (.) DO CD "%%~sA" + +:: other things added by install_activate.bat at package build time diff --git a/packaging/vs2019/conda_build_config.yaml b/packaging/vs2019/conda_build_config.yaml new file mode 100644 index 0000000000..358052ec01 --- /dev/null +++ b/packaging/vs2019/conda_build_config.yaml @@ -0,0 +1,24 @@ +blas_impl: + - mkl # [x86_64] +c_compiler: + - vs2019 # [win] +cxx_compiler: + - vs2019 # [win] +python: + - 3.5 + - 3.6 +# This differs from target_platform in that it determines what subdir the compiler +# will target, not what subdir the compiler package will be itself. +# For example, we need a win-64 vs2008_win-32 package, so that we compile win-32 +# code on win-64 miniconda. +cross_compiler_target_platform: + - win-64 # [win] +target_platform: + - win-64 # [win] +vc: + - 14 +zip_keys: + - # [win] + - vc # [win] + - c_compiler # [win] + - cxx_compiler # [win] diff --git a/packaging/vs2019/install_activate.bat b/packaging/vs2019/install_activate.bat new file mode 100644 index 0000000000..3c38253aa5 --- /dev/null +++ b/packaging/vs2019/install_activate.bat @@ -0,0 +1,30 @@ +set YEAR=2019 +set VER=16 + +mkdir "%PREFIX%\etc\conda\activate.d" +COPY "%RECIPE_DIR%\activate.bat" "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + +IF "%cross_compiler_target_platform%" == "win-64" ( + set "target_platform=amd64" + echo SET "CMAKE_GENERATOR=Visual Studio %VER% %YEAR% Win64" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo pushd "%%VSINSTALLDIR%%" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + IF "%VSDEVCMD_ARGS%" == "" ( + echo CALL "VC\Auxiliary\Build\vcvarsall.bat" x64 >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo popd >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo pushd "%%VSINSTALLDIR%%" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo CALL "VC\Auxiliary\Build\vcvarsall.bat" x86_amd64 >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + ) ELSE ( + echo CALL "VC\Auxiliary\Build\vcvarsall.bat" x64 %VSDEVCMD_ARGS% >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo popd >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo pushd "%%VSINSTALLDIR%%" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo CALL "VC\Auxiliary\Build\vcvarsall.bat" x86_amd64 %VSDEVCMD_ARGS% >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + ) + echo popd >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + ) else ( + set "target_platform=x86" + echo SET "CMAKE_GENERATOR=Visual Studio %VER% %YEAR%" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo pushd "%%VSINSTALLDIR%%" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo CALL "VC\Auxiliary\Build\vcvars32.bat" >> "%PREFIX%\etc\conda\activate.d\vs%YEAR%_compiler_vars.bat" + echo popd + ) + diff --git a/packaging/vs2019/install_runtime.bat b/packaging/vs2019/install_runtime.bat new file mode 100644 index 0000000000..e09a5ccfb0 --- /dev/null +++ b/packaging/vs2019/install_runtime.bat @@ -0,0 +1,49 @@ +set VC_PATH=x86 +if "%ARCH%"=="64" ( + set VC_PATH=x64 +) + +set MSC_VER=2019 + +rem :: This should always be present for VC installed with VS. Not sure about VC installed with Visual C++ Build Tools 2015 +rem FOR /F "usebackq tokens=3*" %%A IN (`REG QUERY "HKEY_LOCAL_MACHINE\Software\Microsoft\DevDiv\VC\Servicing\14.0\IDE.x64" /v UpdateVersion`) DO ( +rem set SP=%%A +rem ) + +rem if not "%SP%" == "%PKG_VERSION%" ( +rem echo "Version detected from registry: %SP%" +rem echo "does not match version of package being built (%PKG_VERSION%)" +rem echo "Do you have current updates for VS 2015 installed?" +rem exit 1 +rem ) + + +REM ========== REQUIRES Win 10 SDK be installed, or files otherwise copied to location below! +robocopy "C:\Program Files (x86)\Windows Kits\10\Redist\ucrt\DLLs\%VC_PATH%" "%LIBRARY_BIN%" *.dll /E +robocopy "C:\Program Files (x86)\Windows Kits\10\Redist\ucrt\DLLs\%VC_PATH%" "%PREFIX%" *.dll /E +if %ERRORLEVEL% GEQ 8 exit 1 + +REM ========== This one comes from visual studio 2019 +set "VC_VER=142" + +for /f "usebackq tokens=*" %%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -legacy -products * -version [16^,17^) -property installationPath`) do ( + if exist "%%i" if exist "%%i\VC\Auxiliary\Build\vcvarsall.bat" ( + set "VS15VCVARSALL=%%i\VC\Auxiliary\Build\vcvarsall.bat" + goto :eof + ) +) + +@setlocal +call "%VS15VARSALL%" x64 + +set "REDIST_ROOT=%VCToolsRedistDir%%VC_PATH%" + +robocopy "%REDIST_ROOT%\Microsoft.VC%VC_VER%.CRT" "%LIBRARY_BIN%" *.dll /E +if %ERRORLEVEL% LSS 8 exit 0 +robocopy "%REDIST_ROOT%\Microsoft.VC%VC_VER%.CRT" "%PREFIX%" *.dll /E +if %ERRORLEVEL% LSS 8 exit 0 +robocopy "%REDIST_ROOT%\Microsoft.VC%VC_VER%.OpenMP" "%LIBRARY_BIN%" *.dll /E +if %ERRORLEVEL% LSS 8 exit 0 +robocopy "%REDIST_ROOT%\Microsoft.VC%VC_VER%.OpenMP" "%PREFIX%" *.dll /E +if %ERRORLEVEL% LSS 8 exit 0 +@endlocal diff --git a/packaging/vs2019/meta.yaml b/packaging/vs2019/meta.yaml new file mode 100644 index 0000000000..94a0ed4db3 --- /dev/null +++ b/packaging/vs2019/meta.yaml @@ -0,0 +1,24 @@ +{% set vcver="14.2" %} +{% set vcfeature="14" %} +{% set vsyear="2019" %} +{% set fullver="15.4.27004.2010" %} + +package: + name: vs{{ vsyear }} + version: {{ fullver }} + +build: + skip: True [not win] + script_env: + - VSDEVCMD_ARGS # [win] + +outputs: + - name: vs{{ vsyear }}_{{ cross_compiler_target_platform }} + script: install_activate.bat + track_features: + # VS 2019 is binary-compatible with VS 2017/vc 14.1 and 2015/vc14. Tools are "v142". + strong: + - vc{{ vcfeature }} + about: + summary: Activation and version verification of MSVC {{ vcver }} (VS {{ vsyear }}) compiler + license: BSD 3-clause diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000000..182c9f7e3f --- /dev/null +++ b/setup.cfg @@ -0,0 +1,2 @@ +[pydocstyle] +select = D417 # Missing argument descriptions in the docstring diff --git a/setup.py b/setup.py index 1ee56dfcb7..255ac9894d 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ # Creating the version file -version = '0.8.0a0' +version = '0.9.0a0' sha = 'Unknown' try: @@ -76,6 +76,7 @@ def run(self): "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", "Programming Language :: Python :: Implementation :: CPython", "Topic :: Multimedia :: Sound/Audio", "Topic :: Scientific/Engineering :: Artificial Intelligence" diff --git a/test/torchaudio_unittest/README.md b/test/torchaudio_unittest/README.md index d61c64b015..69f8498f05 100644 --- a/test/torchaudio_unittest/README.md +++ b/test/torchaudio_unittest/README.md @@ -8,7 +8,7 @@ https://docs.pytest.org/ for the detail of how to use `pytest` command. For testing, the following packages are needed: ```bash -pip install typing pytest scipy numpy parametrized +pip install typing pytest scipy numpy parameterized ``` Make sure to follow the installation instruction in the [contributing @@ -40,7 +40,7 @@ We use PyTorch's test utilities instead of `pytest` frameworks when writing test Also, while we recommend using `pytest` for *running* the tests, we cannot make `pytest` a testing dependency of `torchaudio`. As a result, you should not import `pytest` or its submodules in the test files; Use the Python -`unittest` builtin module instead, or the `parametrized` package to +`unittest` builtin module instead, or the `parameterized` package to parametrize tests. ## Structure of tests @@ -50,17 +50,17 @@ The following is an overview of the tests and related modules for `torchaudio`. ### Purpose specific test suites #### Numerical compatibility against existing software -- [Librosa compatibility test](./librosa_compatibility_test.py) +- [Librosa compatibility test](./transforms/librosa_compatibility_test.py) Test suite for numerical compatibility against librosa. -- [SoX compatibility test](./sox_compatibility_test.py) +- [SoX compatibility test](./transforms/sox_compatibility_test.py) Test suite for numerical compatibility against SoX. -- [Kaldi compatibility test](./kaldi_compatibility_test.py) +- [Kaldi compatibility test](./transforms/kaldi_compatibility_impl.py) Test suite for numerical compatibility against Kaldi. #### Result consistency with PyTorch framework -- [TorchScript consistency test](./torchscript_consistency_impl.py) +- [TorchScript consistency test](./transforms/torchscript_consistency_impl.py) Test suite to check 1. if an API is TorchScript-able, and 2. the results from Python and Torchscript match. -- [Batch consistency test](./batch_consistency_test.py) +- [Batch consistency test](./transforms/batch_consistency_test.py) Test suite to check if functionals/Transforms handle single sample input and batch input and return the same result. ### Module specific test suites @@ -69,7 +69,7 @@ The following test modules are defined for corresponding `torchaudio` module/fun - [`torchaudio.datasets`](./datasets) - [`torchaudio.functional`](./functional) -- [`torchaudio.transforms`](./transforms_test.py) +- [`torchaudio.transforms`](./transforms/transforms_test.py) - [`torchaudio.compliance.kaldi`](./compliance_kaldi_test.py) - [`torchaudio.kaldi_io`](./kaldi_io_test.py) - [`torchaudio.sox_effects`](./sox_effect) @@ -142,7 +142,7 @@ The following is the current practice of torchaudio test suite. - Set class memeber `dtype`, `device` and `backend` for the desired behavior. - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail. - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability. -1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchscript_consistency_impl.py) and test definitions for [CPU](./torchscript_consistency_cpu_test.py) and [CUDA](./torchscript_consistency_cuda_test.py) devices. +1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./transforms/torchscript_consistency_impl.py) and test definitions for [CPU](./transforms/torchscript_consistency_cpu_test.py) and [CUDA](./transforms/torchscript_consistency_cuda_test.py) devices. 1. For numerically comparing Tensors, use `assertEqual` method from torchaudio_unittest.common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. When you add a new feature(functional/transform), consider the following diff --git a/test/torchaudio_unittest/assets/kaldi_test_fbank_args.json b/test/torchaudio_unittest/assets/kaldi_test_fbank_args.jsonl similarity index 100% rename from test/torchaudio_unittest/assets/kaldi_test_fbank_args.json rename to test/torchaudio_unittest/assets/kaldi_test_fbank_args.jsonl diff --git a/test/torchaudio_unittest/assets/kaldi_test_mfcc_args.json b/test/torchaudio_unittest/assets/kaldi_test_mfcc_args.jsonl similarity index 100% rename from test/torchaudio_unittest/assets/kaldi_test_mfcc_args.json rename to test/torchaudio_unittest/assets/kaldi_test_mfcc_args.jsonl diff --git a/test/torchaudio_unittest/assets/kaldi_test_pitch_args.jsonl b/test/torchaudio_unittest/assets/kaldi_test_pitch_args.jsonl new file mode 100644 index 0000000000..9844bd6c72 --- /dev/null +++ b/test/torchaudio_unittest/assets/kaldi_test_pitch_args.jsonl @@ -0,0 +1,5 @@ +{"sample_rate": 8000} +{"sample_rate": 8000, "frames_per_chunk": 200} +{"sample_rate": 8000, "frames_per_chunk": 200, "simulate_first_pass_online": true} +{"sample_rate": 16000} +{"sample_rate": 44100} diff --git a/test/torchaudio_unittest/assets/kaldi_test_spectrogram_args.json b/test/torchaudio_unittest/assets/kaldi_test_spectrogram_args.jsonl similarity index 100% rename from test/torchaudio_unittest/assets/kaldi_test_spectrogram_args.json rename to test/torchaudio_unittest/assets/kaldi_test_spectrogram_args.jsonl diff --git a/test/torchaudio_unittest/assets/sox_effect_test_args.json b/test/torchaudio_unittest/assets/sox_effect_test_args.jsonl similarity index 100% rename from test/torchaudio_unittest/assets/sox_effect_test_args.json rename to test/torchaudio_unittest/assets/sox_effect_test_args.jsonl diff --git a/test/torchaudio_unittest/backend/legacy_test.py b/test/torchaudio_unittest/backend/legacy_test.py deleted file mode 100644 index 3dbc419bde..0000000000 --- a/test/torchaudio_unittest/backend/legacy_test.py +++ /dev/null @@ -1,290 +0,0 @@ -import os -import math -import shutil -import tempfile -import unittest - -import torch -import torchaudio -from torchaudio.utils import sox_utils -from torchaudio._internal.module_utils import is_module_available - -from torchaudio_unittest.common_utils import get_asset_path - -BACKENDS = [] -BACKENDS_MP3 = [] - -if is_module_available('soundfile'): - BACKENDS.append('soundfile') - -if is_module_available('torchaudio._torchaudio'): - BACKENDS.append('sox') - - if ( - 'mp3' in sox_utils.list_read_formats() and - 'mp3' in sox_utils.list_write_formats() - ): - BACKENDS_MP3 = ['sox'] - - -def create_temp_assets_dir(): - """ - Creates a temporary directory and moves all files from test/assets there. - Returns a Tuple[string, TemporaryDirectory] which is the folder path - and object. - """ - tmp_dir = tempfile.TemporaryDirectory() - shutil.copytree(get_asset_path(), os.path.join(tmp_dir.name, "assets")) - return tmp_dir.name, tmp_dir - - -class Test_LoadSave(unittest.TestCase): - test_dirpath, test_dir = create_temp_assets_dir() - test_filepath = os.path.join(test_dirpath, "assets", - "steam-train-whistle-daniel_simon.mp3") - test_filepath_wav = os.path.join(test_dirpath, "assets", - "steam-train-whistle-daniel_simon.wav") - - def setUp(self): - torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = True - - def test_1_save(self): - for backend in BACKENDS_MP3: - with self.subTest(): - torchaudio.set_audio_backend(backend) - self._test_1_save(self.test_filepath, False) - - for backend in BACKENDS: - with self.subTest(): - torchaudio.set_audio_backend(backend) - self._test_1_save(self.test_filepath_wav, True) - - def _test_1_save(self, test_filepath, normalization): - # load signal - x, sr = torchaudio.load(test_filepath, normalization=normalization) - - # check save - new_filepath = os.path.join(self.test_dirpath, "test.wav") - torchaudio.save(new_filepath, x, sr) - self.assertTrue(os.path.isfile(new_filepath)) - os.unlink(new_filepath) - - # check automatic normalization - x /= 1 << 31 - torchaudio.save(new_filepath, x, sr) - self.assertTrue(os.path.isfile(new_filepath)) - os.unlink(new_filepath) - - # test save 1d tensor - x = x[0, :] # get mono signal - x.squeeze_() # remove channel dim - torchaudio.save(new_filepath, x, sr) - self.assertTrue(os.path.isfile(new_filepath)) - os.unlink(new_filepath) - - # don't allow invalid sizes as inputs - with self.assertRaises(ValueError): - x.unsqueeze_(1) # L x C not C x L - torchaudio.save(new_filepath, x, sr) - - with self.assertRaises(ValueError): - x.squeeze_() - x.unsqueeze_(1) - x.unsqueeze_(0) # 1 x L x 1 - torchaudio.save(new_filepath, x, sr) - - # don't save to folders that don't exist - with self.assertRaises(OSError): - new_filepath = os.path.join(self.test_dirpath, "no-path", - "test.wav") - torchaudio.save(new_filepath, x, sr) - - def test_1_save_sine(self): - for backend in BACKENDS: - with self.subTest(): - torchaudio.set_audio_backend(backend) - self._test_1_save_sine() - - def _test_1_save_sine(self): - - # save created file - sinewave_filepath = os.path.join(self.test_dirpath, "assets", - "sinewave.wav") - sr = 16000 - freq = 440 - volume = 0.3 - - y = (torch.cos( - 2 * math.pi * torch.arange(0, 4 * sr).float() * freq / sr)) - y.unsqueeze_(0) - # y is between -1 and 1, so must scale - y = (y * volume * (2**31)).long() - torchaudio.save(sinewave_filepath, y, sr) - self.assertTrue(os.path.isfile(sinewave_filepath)) - - # test precision - new_precision = 32 - new_filepath = os.path.join(self.test_dirpath, "test.wav") - si, ei = torchaudio.info(sinewave_filepath) - torchaudio.save(new_filepath, y, sr, new_precision) - si32, ei32 = torchaudio.info(new_filepath) - self.assertEqual(si.precision, 16) - self.assertEqual(si32.precision, new_precision) - os.unlink(new_filepath) - - def test_2_load(self): - for backend in BACKENDS_MP3: - with self.subTest(): - torchaudio.set_audio_backend(backend) - self._test_2_load(self.test_filepath, 278756) - - for backend in BACKENDS: - with self.subTest(): - torchaudio.set_audio_backend(backend) - self._test_2_load(self.test_filepath_wav, 276858) - - def _test_2_load(self, test_filepath, length): - # check normal loading - x, sr = torchaudio.load(test_filepath) - self.assertEqual(sr, 44100) - self.assertEqual(x.size(), (2, length)) - - # check offset - offset = 15 - x, _ = torchaudio.load(test_filepath) - x_offset, _ = torchaudio.load(test_filepath, offset=offset) - self.assertTrue(x[:, offset:].allclose(x_offset)) - - # check number of frames - n = 201 - x, _ = torchaudio.load(test_filepath, num_frames=n) - self.assertTrue(x.size(), (2, n)) - - # check channels first - x, _ = torchaudio.load(test_filepath, channels_first=False) - self.assertEqual(x.size(), (length, 2)) - - # check raising errors - with self.assertRaises(OSError): - torchaudio.load("file-does-not-exist.mp3") - - with self.assertRaises(OSError): - tdir = os.path.join( - os.path.dirname(self.test_dirpath), "torchaudio") - torchaudio.load(tdir) - - def test_2_load_nonormalization(self): - for backend in BACKENDS_MP3: - if backend == 'sox_io': - continue - with self.subTest(): - torchaudio.set_audio_backend(backend) - self._test_2_load_nonormalization(self.test_filepath, 278756) - - def _test_2_load_nonormalization(self, test_filepath, length): - - # check no normalizing - x, _ = torchaudio.load(test_filepath, normalization=False) - self.assertTrue(x.min() <= -1.0) - self.assertTrue(x.max() >= 1.0) - - # check different input tensor type - x, _ = torchaudio.load(test_filepath, torch.LongTensor(), normalization=False) - self.assertTrue(isinstance(x, torch.LongTensor)) - - def test_3_load_and_save_is_identity(self): - for backend in BACKENDS: - if backend == 'sox_io': - continue - with self.subTest(): - torchaudio.set_audio_backend(backend) - self._test_3_load_and_save_is_identity() - - def _test_3_load_and_save_is_identity(self): - input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') - tensor, sample_rate = torchaudio.load(input_path) - output_path = os.path.join(self.test_dirpath, 'test.wav') - torchaudio.save(output_path, tensor, sample_rate) - tensor2, sample_rate2 = torchaudio.load(output_path) - self.assertTrue(tensor.allclose(tensor2)) - self.assertEqual(sample_rate, sample_rate2) - os.unlink(output_path) - - @unittest.skipIf(any(be not in BACKENDS for be in ["sox", "soundfile"]), "sox and soundfile are not available") - def test_3_load_and_save_is_identity_across_backend(self): - with self.subTest(): - self._test_3_load_and_save_is_identity_across_backend("sox", "soundfile") - with self.subTest(): - self._test_3_load_and_save_is_identity_across_backend("soundfile", "sox") - - def _test_3_load_and_save_is_identity_across_backend(self, backend1, backend2): - torchaudio.set_audio_backend(backend1) - input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') - tensor1, sample_rate1 = torchaudio.load(input_path) - - output_path = os.path.join(self.test_dirpath, 'test.wav') - torchaudio.save(output_path, tensor1, sample_rate1) - - torchaudio.set_audio_backend(backend2) - tensor2, sample_rate2 = torchaudio.load(output_path) - - self.assertTrue(tensor1.allclose(tensor2)) - self.assertEqual(sample_rate1, sample_rate2) - os.unlink(output_path) - - def test_4_load_partial(self): - for backend in BACKENDS_MP3: - with self.subTest(): - torchaudio.set_audio_backend(backend) - self._test_4_load_partial() - - def _test_4_load_partial(self): - num_frames = 101 - offset = 201 - # load entire mono sinewave wav file, load a partial copy and then compare - input_sine_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') - x_sine_full, sr_sine = torchaudio.load(input_sine_path) - x_sine_part, _ = torchaudio.load(input_sine_path, num_frames=num_frames, offset=offset) - l1_error = x_sine_full[:, offset:(num_frames + offset)].sub(x_sine_part).abs().sum().item() - # test for the correct number of samples and that the correct portion was loaded - self.assertEqual(x_sine_part.size(1), num_frames) - self.assertEqual(l1_error, 0.) - # create a two channel version of this wavefile - x_2ch_sine = x_sine_full.repeat(1, 2) - out_2ch_sine_path = os.path.join(self.test_dirpath, 'assets', '2ch_sinewave.wav') - torchaudio.save(out_2ch_sine_path, x_2ch_sine, sr_sine) - x_2ch_sine_load, _ = torchaudio.load(out_2ch_sine_path, num_frames=num_frames, offset=offset) - os.unlink(out_2ch_sine_path) - l1_error = x_2ch_sine_load.sub(x_2ch_sine[:, offset:(offset + num_frames)]).abs().sum().item() - self.assertEqual(l1_error, 0.) - - # test with two channel mp3 - x_2ch_full, sr_2ch = torchaudio.load(self.test_filepath, normalization=True) - x_2ch_part, _ = torchaudio.load(self.test_filepath, normalization=True, num_frames=num_frames, offset=offset) - l1_error = x_2ch_full[:, offset:(offset + num_frames)].sub(x_2ch_part).abs().sum().item() - self.assertEqual(x_2ch_part.size(1), num_frames) - self.assertEqual(l1_error, 0.) - - # check behavior if number of samples would exceed file length - offset_ns = 300 - x_ns, _ = torchaudio.load(input_sine_path, num_frames=100000, offset=offset_ns) - self.assertEqual(x_ns.size(1), x_sine_full.size(1) - offset_ns) - - # check when offset is beyond the end of the file - with self.assertRaises(RuntimeError): - torchaudio.load(input_sine_path, offset=100000) - - def test_5_get_info(self): - for backend in BACKENDS: - with self.subTest(): - torchaudio.set_audio_backend(backend) - self._test_5_get_info() - - def _test_5_get_info(self): - input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') - channels, samples, rate, precision = (1, 64000, 16000, 16) - si, ei = torchaudio.info(input_path) - self.assertEqual(si.channels, channels) - self.assertEqual(si.length, samples) - self.assertEqual(si.rate, rate) - self.assertEqual(ei.bits_per_sample, precision) diff --git a/test/torchaudio_unittest/backend/soundfile/common.py b/test/torchaudio_unittest/backend/soundfile/common.py index 8f991fb0f8..c6b014dd4c 100644 --- a/test/torchaudio_unittest/backend/soundfile/common.py +++ b/test/torchaudio_unittest/backend/soundfile/common.py @@ -32,3 +32,26 @@ def skipIfFormatNotSupported(fmt): def parameterize(*params): return parameterized.expand(list(itertools.product(*params)), name_func=name_func) + + +def fetch_wav_subtype(dtype, encoding, bits_per_sample): + subtype = { + (None, None): dtype2subtype(dtype), + (None, 8): "PCM_U8", + ('PCM_U', None): "PCM_U8", + ('PCM_U', 8): "PCM_U8", + ('PCM_S', None): "PCM_32", + ('PCM_S', 16): "PCM_16", + ('PCM_S', 32): "PCM_32", + ('PCM_F', None): "FLOAT", + ('PCM_F', 32): "FLOAT", + ('PCM_F', 64): "DOUBLE", + ('ULAW', None): "ULAW", + ('ULAW', 8): "ULAW", + ('ALAW', None): "ALAW", + ('ALAW', 8): "ALAW", + }.get((encoding, bits_per_sample)) + if subtype: + return subtype + raise ValueError( + f"wav does not support ({encoding}, {bits_per_sample}).") diff --git a/test/torchaudio_unittest/backend/soundfile/info_test.py b/test/torchaudio_unittest/backend/soundfile/info_test.py index 3b3f792281..b6b8722410 100644 --- a/test/torchaudio_unittest/backend/soundfile/info_test.py +++ b/test/torchaudio_unittest/backend/soundfile/info_test.py @@ -3,7 +3,7 @@ import tarfile import torch -from torchaudio.backend import _soundfile_backend as soundfile_backend +from torchaudio.backend import soundfile_backend from torchaudio._internal import module_utils as _mod_utils from torchaudio_unittest.common_utils import ( @@ -12,6 +12,7 @@ skipIfNoModule, get_wav_data, save_wav, + nested_params, ) from torchaudio_unittest.backend.common import ( get_bits_per_sample, @@ -77,7 +78,14 @@ def test_ogg(self, sample_rate, num_channels): assert info.bits_per_sample == 0 assert info.encoding == "VORBIS" - @parameterize([8000, 16000], [1, 2], [('PCM_24', 24), ('PCM_32', 32)]) + @nested_params( + [8000, 16000], + [1, 2], + [ + ('PCM_24', 24), + ('PCM_32', 32) + ], + ) @skipIfFormatNotSupported("NIST") def test_sphere(self, sample_rate, num_channels, subtype_and_bit_depth): """`soundfile_backend.info` can check sph file correctly""" diff --git a/test/torchaudio_unittest/backend/soundfile/load_test.py b/test/torchaudio_unittest/backend/soundfile/load_test.py index 399266de8f..0e3a240d26 100644 --- a/test/torchaudio_unittest/backend/soundfile/load_test.py +++ b/test/torchaudio_unittest/backend/soundfile/load_test.py @@ -4,7 +4,7 @@ import torch from torchaudio._internal import module_utils as _mod_utils -from torchaudio.backend import _soundfile_backend as soundfile_backend +from torchaudio.backend import soundfile_backend from parameterized import parameterized from torchaudio_unittest.common_utils import ( diff --git a/test/torchaudio_unittest/backend/soundfile/save_test.py b/test/torchaudio_unittest/backend/soundfile/save_test.py index 2f2741c303..e4e8c93631 100644 --- a/test/torchaudio_unittest/backend/soundfile/save_test.py +++ b/test/torchaudio_unittest/backend/soundfile/save_test.py @@ -2,7 +2,7 @@ from unittest.mock import patch from torchaudio._internal import module_utils as _mod_utils -from torchaudio.backend import _soundfile_backend as soundfile_backend +from torchaudio.backend import soundfile_backend from torchaudio_unittest.common_utils import ( TempDirMixin, @@ -10,38 +10,65 @@ skipIfNoModule, get_wav_data, load_wav, + nested_params, +) +from .common import ( + fetch_wav_subtype, + parameterize, + skipIfFormatNotSupported, ) -from .common import parameterize, dtype2subtype, skipIfFormatNotSupported if _mod_utils.is_module_available("soundfile"): import soundfile class MockedSaveTest(PytorchTestCase): - @parameterize( - ["float32", "int32", "int16", "uint8"], [8000, 16000], [1, 2], [False, True], + @nested_params( + ["float32", "int32", "int16", "uint8"], + [8000, 16000], + [1, 2], + [False, True], + [ + (None, None), + ('PCM_U', None), + ('PCM_U', 8), + ('PCM_S', None), + ('PCM_S', 16), + ('PCM_S', 32), + ('PCM_F', None), + ('PCM_F', 32), + ('PCM_F', 64), + ('ULAW', None), + ('ULAW', 8), + ('ALAW', None), + ('ALAW', 8), + ], ) @patch("soundfile.write") - def test_wav(self, dtype, sample_rate, num_channels, channels_first, mocked_write): + def test_wav(self, dtype, sample_rate, num_channels, channels_first, + enc_params, mocked_write): """soundfile_backend.save passes correct subtype to soundfile.write when WAV""" filepath = "foo.wav" input_tensor = get_wav_data( dtype, num_channels, num_frames=3 * sample_rate, - normalize=dtype == "flaot32", + normalize=dtype == "float32", channels_first=channels_first, ).t() + encoding, bits_per_sample = enc_params soundfile_backend.save( - filepath, input_tensor, sample_rate, channels_first=channels_first + filepath, input_tensor, sample_rate, channels_first=channels_first, + encoding=encoding, bits_per_sample=bits_per_sample ) # on +Py3.8 call_args.kwargs is more descreptive args = mocked_write.call_args[1] assert args["file"] == filepath assert args["samplerate"] == sample_rate - assert args["subtype"] == dtype2subtype(dtype) + assert args["subtype"] == fetch_wav_subtype( + dtype, encoding, bits_per_sample) assert args["format"] is None self.assertEqual( args["data"], input_tensor.t() if channels_first else input_tensor @@ -49,7 +76,8 @@ def test_wav(self, dtype, sample_rate, num_channels, channels_first, mocked_writ @patch("soundfile.write") def assert_non_wav( - self, fmt, dtype, sample_rate, num_channels, channels_first, mocked_write + self, fmt, dtype, sample_rate, num_channels, channels_first, mocked_write, + encoding=None, bits_per_sample=None, ): """soundfile_backend.save passes correct subtype and format to soundfile.write when SPHERE""" filepath = f"foo.{fmt}" @@ -63,39 +91,56 @@ def assert_non_wav( expected_data = input_tensor.t() if channels_first else input_tensor soundfile_backend.save( - filepath, input_tensor, sample_rate, channels_first=channels_first + filepath, input_tensor, sample_rate, channels_first, + encoding=encoding, bits_per_sample=bits_per_sample, ) # on +Py3.8 call_args.kwargs is more descreptive args = mocked_write.call_args[1] assert args["file"] == filepath assert args["samplerate"] == sample_rate - assert args["subtype"] is None if fmt in ["sph", "nist", "nis"]: assert args["format"] == "NIST" else: assert args["format"] is None self.assertEqual(args["data"], expected_data) - @parameterize( + @nested_params( ["sph", "nist", "nis"], ["int32", "int16"], [8000, 16000], [1, 2], [False, True], + [ + ('PCM_S', 8), + ('PCM_S', 16), + ('PCM_S', 24), + ('PCM_S', 32), + ('ULAW', 8), + ('ALAW', 8), + ('ALAW', 16), + ('ALAW', 24), + ('ALAW', 32), + ], ) - def test_sph(self, fmt, dtype, sample_rate, num_channels, channels_first): + def test_sph(self, fmt, dtype, sample_rate, num_channels, channels_first, enc_params): """soundfile_backend.save passes default format and subtype (None-s) to soundfile.write when not WAV""" - self.assert_non_wav(fmt, dtype, sample_rate, num_channels, channels_first) + encoding, bits_per_sample = enc_params + self.assert_non_wav(fmt, dtype, sample_rate, num_channels, + channels_first, encoding=encoding, + bits_per_sample=bits_per_sample) @parameterize( ["int32", "int16"], [8000, 16000], [1, 2], [False, True], + [8, 16, 24], ) - def test_flac(self, dtype, sample_rate, num_channels, channels_first): + def test_flac(self, dtype, sample_rate, num_channels, + channels_first, bits_per_sample): """soundfile_backend.save passes default format and subtype (None-s) to soundfile.write when not WAV""" - self.assert_non_wav("flac", dtype, sample_rate, num_channels, channels_first) + self.assert_non_wav("flac", dtype, sample_rate, num_channels, + channels_first, bits_per_sample=bits_per_sample) @parameterize( ["int32", "int16"], [8000, 16000], [1, 2], [False, True], @@ -199,7 +244,7 @@ def test_ogg(self, sample_rate, num_channels): class TestSaveParams(TempDirMixin, PytorchTestCase): """Test the correctness of optional parameters of `soundfile_backend.save`""" - @parameterize([(True,), (False,)]) + @parameterize([True, False]) def test_channels_first(self, channels_first): """channels_first swaps axes""" path = self.get_temp_path("data.wav") @@ -228,7 +273,7 @@ def _test_fileobj(self, ext): found, sr = soundfile.read(fileobj, dtype='float32') assert sr == sample_rate - self.assertEqual(expected, found) + self.assertEqual(expected, found, atol=1e-4, rtol=1e-8) def test_fileobj_wav(self): """Saving audio via file-like object works""" diff --git a/test/torchaudio_unittest/backend/sox_io/common.py b/test/torchaudio_unittest/backend/sox_io/common.py index eb85937236..c2538b2bc4 100644 --- a/test/torchaudio_unittest/backend/sox_io/common.py +++ b/test/torchaudio_unittest/backend/sox_io/common.py @@ -1,2 +1,14 @@ def name_func(func, _, params): return f'{func.__name__}_{"_".join(str(arg) for arg in params.args)}' + + +def get_enc_params(dtype): + if dtype == 'float32': + return 'PCM_F', 32 + if dtype == 'int32': + return 'PCM_S', 32 + if dtype == 'int16': + return 'PCM_S', 16 + if dtype == 'uint8': + return 'PCM_U', 8 + raise ValueError(f'Unexpected dtype: {dtype}') diff --git a/test/torchaudio_unittest/backend/sox_io/info_test.py b/test/torchaudio_unittest/backend/sox_io/info_test.py index a2a93648a1..d459061eee 100644 --- a/test/torchaudio_unittest/backend/sox_io/info_test.py +++ b/test/torchaudio_unittest/backend/sox_io/info_test.py @@ -16,8 +16,8 @@ HttpServerMixin, PytorchTestCase, skipIfNoExec, - skipIfNoExtension, skipIfNoModule, + skipIfNoSox, get_asset_path, get_wav_data, save_wav, @@ -33,7 +33,7 @@ @skipIfNoExec('sox') -@skipIfNoExtension +@skipIfNoSox class TestInfo(TempDirMixin, PytorchTestCase): @parameterized.expand(list(itertools.product( ['float32', 'int32', 'int16', 'uint8'], @@ -205,7 +205,7 @@ def test_ulaw(self): assert info.encoding == "ULAW" def test_alaw(self): - """`sox_io_backend.info` can check ulaw file correctly""" + """`sox_io_backend.info` can check alaw file correctly""" duration = 1 num_channels = 1 sample_rate = 8000 @@ -221,8 +221,39 @@ def test_alaw(self): assert info.bits_per_sample == 8 assert info.encoding == "ALAW" + def test_gsm(self): + """`sox_io_backend.info` can check gsm file correctly""" + duration = 1 + num_channels = 1 + sample_rate = 8000 + path = self.get_temp_path('data.gsm') + sox_utils.gen_audio_file( + path, sample_rate=sample_rate, num_channels=num_channels, + duration=duration) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_channels == num_channels + assert info.bits_per_sample == 0 + assert info.encoding == "GSM" + + def test_htk(self): + """`sox_io_backend.info` can check HTK file correctly""" + duration = 1 + num_channels = 1 + sample_rate = 8000 + path = self.get_temp_path('data.htk') + sox_utils.gen_audio_file( + path, sample_rate=sample_rate, num_channels=num_channels, + bit_depth=16, duration=duration) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + assert info.bits_per_sample == 16 + assert info.encoding == "PCM_S" + -@skipIfNoExtension +@skipIfNoSox class TestInfoOpus(PytorchTestCase): @parameterized.expand(list(itertools.product( ['96k'], @@ -240,7 +271,7 @@ def test_opus(self, bitrate, num_channels, compression_level): assert info.encoding == "OPUS" -@skipIfNoExtension +@skipIfNoSox class TestLoadWithoutExtension(PytorchTestCase): def test_mp3(self): """Providing `format` allows to read mp3 without extension @@ -275,7 +306,7 @@ def _gen_file(self, ext, dtype, sample_rate, num_channels, num_frames): return path -@skipIfNoExtension +@skipIfNoSox @skipIfNoExec('sox') class TestFileObject(FileObjTestBase, PytorchTestCase): def _query_fileobj(self, ext, dtype, sample_rate, num_channels, num_frames): @@ -407,7 +438,7 @@ def test_tarfile(self, ext, dtype): assert sinfo.encoding == get_encoding(ext, dtype) -@skipIfNoExtension +@skipIfNoSox @skipIfNoExec('sox') @skipIfNoModule("requests") class TestFileObjectHttp(HttpServerMixin, FileObjTestBase, PytorchTestCase): diff --git a/test/torchaudio_unittest/backend/sox_io/load_test.py b/test/torchaudio_unittest/backend/sox_io/load_test.py index f2a514ea61..f8d06a4da9 100644 --- a/test/torchaudio_unittest/backend/sox_io/load_test.py +++ b/test/torchaudio_unittest/backend/sox_io/load_test.py @@ -11,8 +11,8 @@ HttpServerMixin, PytorchTestCase, skipIfNoExec, - skipIfNoExtension, skipIfNoModule, + skipIfNoSox, get_asset_path, get_wav_data, load_wav, @@ -29,178 +29,80 @@ class LoadTestBase(TempDirMixin, PytorchTestCase): - def assert_wav(self, dtype, sample_rate, num_channels, normalize, duration): - """`sox_io_backend.load` can load wav format correctly. - - Wav data loaded with sox_io backend should match those with scipy - """ - path = self.get_temp_path('reference.wav') - data = get_wav_data(dtype, num_channels, normalize=normalize, num_frames=duration * sample_rate) - save_wav(path, data, sample_rate) - expected = load_wav(path, normalize=normalize)[0] - data, sr = sox_io_backend.load(path, normalize=normalize) - assert sr == sample_rate - self.assertEqual(data, expected) - - def assert_mp3(self, sample_rate, num_channels, bit_rate, duration): - """`sox_io_backend.load` can load mp3 format. - - mp3 encoding introduces delay and boundary effects so - we create reference wav file from mp3 + def assert_format( + self, + format: str, + sample_rate: float, + num_channels: int, + compression: float = None, + bit_depth: int = None, + duration: float = 1, + normalize: bool = True, + encoding: str = None, + atol: float = 4e-05, + rtol: float = 1.3e-06, + ): + """`sox_io_backend.load` can load given format correctly. + + file encodings introduce delay and boundary effects so + we create a reference wav file from the original file format x | - | 1. Generate mp3 with Sox + | 1. Generate given format with Sox | v 2. Convert to wav with Sox - mp3 ------------------------------> wav + given format ----------------------> wav | | - | 3. Load with torchaudio | 4. Load with scipy + | 3. Load with torchaudio | 4. Load with scipy | | v v tensor ----------> x <----------- tensor 5. Compare Underlying assumptions are; - i. Conversion of mp3 to wav with Sox preserves data. + i. Conversion of given format to wav with Sox preserves data. ii. Loading wav file with scipy is correct. - By combining i & ii, step 2. and 4. allows to load reference mp3 data - without using torchaudio - """ - path = self.get_temp_path('1.original.mp3') - ref_path = self.get_temp_path('2.reference.wav') - - # 1. Generate mp3 with sox - sox_utils.gen_audio_file( - path, sample_rate, num_channels, - compression=bit_rate, duration=duration) - # 2. Convert to wav with sox - sox_utils.convert_audio_file(path, ref_path) - # 3. Load mp3 with torchaudio - data, sr = sox_io_backend.load(path) - # 4. Load wav with scipy - data_ref = load_wav(ref_path)[0] - # 5. Compare - assert sr == sample_rate - self.assertEqual(data, data_ref, atol=3e-03, rtol=1.3e-06) - - def assert_flac(self, sample_rate, num_channels, compression_level, duration): - """`sox_io_backend.load` can load flac format. - - This test takes the same strategy as mp3 to compare the result - """ - path = self.get_temp_path('1.original.flac') - ref_path = self.get_temp_path('2.reference.wav') - - # 1. Generate flac with sox - sox_utils.gen_audio_file( - path, sample_rate, num_channels, - compression=compression_level, bit_depth=16, duration=duration) - # 2. Convert to wav with sox - sox_utils.convert_audio_file(path, ref_path) - # 3. Load flac with torchaudio - data, sr = sox_io_backend.load(path) - # 4. Load wav with scipy - data_ref = load_wav(ref_path)[0] - # 5. Compare - assert sr == sample_rate - self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) - - def assert_vorbis(self, sample_rate, num_channels, quality_level, duration): - """`sox_io_backend.load` can load vorbis format. - - This test takes the same strategy as mp3 to compare the result - """ - path = self.get_temp_path('1.original.vorbis') - ref_path = self.get_temp_path('2.reference.wav') - - # 1. Generate vorbis with sox - sox_utils.gen_audio_file( - path, sample_rate, num_channels, - compression=quality_level, bit_depth=16, duration=duration) - # 2. Convert to wav with sox - sox_utils.convert_audio_file(path, ref_path) - # 3. Load vorbis with torchaudio - data, sr = sox_io_backend.load(path) - # 4. Load wav with scipy - data_ref = load_wav(ref_path)[0] - # 5. Compare - assert sr == sample_rate - self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) - - def assert_sphere(self, sample_rate, num_channels, duration): - """`sox_io_backend.load` can load sph format. - - This test takes the same strategy as mp3 to compare the result + By combining i & ii, step 2. and 4. allows to load reference given format + data without using torchaudio """ - path = self.get_temp_path('1.original.sph') - ref_path = self.get_temp_path('2.reference.wav') - # 1. Generate sph with sox - sox_utils.gen_audio_file( - path, sample_rate, num_channels, - bit_depth=32, duration=duration) - # 2. Convert to wav with sox - sox_utils.convert_audio_file(path, ref_path) - # 3. Load sph with torchaudio - data, sr = sox_io_backend.load(path) - # 4. Load wav with scipy - data_ref = load_wav(ref_path)[0] - # 5. Compare - assert sr == sample_rate - self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) - - def assert_amb(self, dtype, sample_rate, num_channels, normalize, duration): - """`sox_io_backend.load` can load amb format. - - This test takes the same strategy as mp3 to compare the result - """ - path = self.get_temp_path('1.original.amb') + path = self.get_temp_path(f'1.original.{format}') ref_path = self.get_temp_path('2.reference.wav') - # 1. Generate amb with sox + # 1. Generate the given format with sox sox_utils.gen_audio_file( - path, sample_rate, num_channels, - encoding=sox_utils.get_encoding(dtype), - bit_depth=sox_utils.get_bit_depth(dtype), duration=duration) + path, sample_rate, num_channels, encoding=encoding, + compression=compression, bit_depth=bit_depth, duration=duration, + ) # 2. Convert to wav with sox - sox_utils.convert_audio_file(path, ref_path) - # 3. Load amb with torchaudio + wav_bit_depth = 32 if bit_depth == 24 else None # for 24-bit wav + sox_utils.convert_audio_file(path, ref_path, bit_depth=wav_bit_depth) + # 3. Load the given format with torchaudio data, sr = sox_io_backend.load(path, normalize=normalize) # 4. Load wav with scipy data_ref = load_wav(ref_path, normalize=normalize)[0] # 5. Compare assert sr == sample_rate - self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + self.assertEqual(data, data_ref, atol=atol, rtol=rtol) - def assert_amr_nb(self, duration): - """`sox_io_backend.load` can load amr-nb format. + def assert_wav(self, dtype, sample_rate, num_channels, normalize, duration): + """`sox_io_backend.load` can load wav format correctly. - This test takes the same strategy as mp3 to compare the result + Wav data loaded with sox_io backend should match those with scipy """ - sample_rate = 8000 - num_channels = 1 - path = self.get_temp_path('1.original.amr-nb') - ref_path = self.get_temp_path('2.reference.wav') - - # 1. Generate amr-nb with sox - sox_utils.gen_audio_file( - path, sample_rate, num_channels, - bit_depth=32, duration=duration) - # 2. Convert to wav with sox - sox_utils.convert_audio_file(path, ref_path) - # 3. Load amr-nb with torchaudio - data, sr = sox_io_backend.load(path) - # 4. Load wav with scipy - data_ref = load_wav(ref_path)[0] - # 5. Compare + path = self.get_temp_path('reference.wav') + data = get_wav_data(dtype, num_channels, normalize=normalize, num_frames=duration * sample_rate) + save_wav(path, data, sample_rate) + expected = load_wav(path, normalize=normalize)[0] + data, sr = sox_io_backend.load(path, normalize=normalize) assert sr == sample_rate - self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + self.assertEqual(data, expected) @skipIfNoExec('sox') -@skipIfNoExtension +@skipIfNoSox class TestLoad(LoadTestBase): """Test the correctness of `sox_io_backend.load` for various formats""" @parameterized.expand(list(itertools.product( @@ -213,6 +115,15 @@ def test_wav(self, dtype, sample_rate, num_channels, normalize): """`sox_io_backend.load` can load wav format correctly.""" self.assert_wav(dtype, sample_rate, num_channels, normalize, duration=1) + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [False, True], + )), name_func=name_func) + def test_24bit_wav(self, sample_rate, num_channels, normalize): + """`sox_io_backend.load` can load 24bit wav format correctly. Corectly casts it to ``int32`` tensor dtype.""" + self.assert_format("wav", sample_rate, num_channels, bit_depth=24, normalize=normalize, duration=1) + @parameterized.expand(list(itertools.product( ['int16'], [16000], @@ -241,7 +152,7 @@ def test_multiple_channels(self, dtype, num_channels): )), name_func=name_func) def test_mp3(self, sample_rate, num_channels, bit_rate): """`sox_io_backend.load` can load mp3 format correctly.""" - self.assert_mp3(sample_rate, num_channels, bit_rate, duration=1) + self.assert_format("mp3", sample_rate, num_channels, compression=bit_rate, duration=1, atol=5e-05) @parameterized.expand(list(itertools.product( [16000], @@ -251,7 +162,7 @@ def test_mp3(self, sample_rate, num_channels, bit_rate): def test_mp3_large(self, sample_rate, num_channels, bit_rate): """`sox_io_backend.load` can load large mp3 file correctly.""" two_hours = 2 * 60 * 60 - self.assert_mp3(sample_rate, num_channels, bit_rate, two_hours) + self.assert_format("mp3", sample_rate, num_channels, compression=bit_rate, duration=two_hours, atol=5e-05) @parameterized.expand(list(itertools.product( [8000, 16000], @@ -260,7 +171,7 @@ def test_mp3_large(self, sample_rate, num_channels, bit_rate): )), name_func=name_func) def test_flac(self, sample_rate, num_channels, compression_level): """`sox_io_backend.load` can load flac format correctly.""" - self.assert_flac(sample_rate, num_channels, compression_level, duration=1) + self.assert_format("flac", sample_rate, num_channels, compression=compression_level, bit_depth=16, duration=1) @parameterized.expand(list(itertools.product( [16000], @@ -270,7 +181,8 @@ def test_flac(self, sample_rate, num_channels, compression_level): def test_flac_large(self, sample_rate, num_channels, compression_level): """`sox_io_backend.load` can load large flac file correctly.""" two_hours = 2 * 60 * 60 - self.assert_flac(sample_rate, num_channels, compression_level, two_hours) + self.assert_format( + "flac", sample_rate, num_channels, compression=compression_level, bit_depth=16, duration=two_hours) @parameterized.expand(list(itertools.product( [8000, 16000], @@ -279,7 +191,7 @@ def test_flac_large(self, sample_rate, num_channels, compression_level): )), name_func=name_func) def test_vorbis(self, sample_rate, num_channels, quality_level): """`sox_io_backend.load` can load vorbis format correctly.""" - self.assert_vorbis(sample_rate, num_channels, quality_level, duration=1) + self.assert_format("vorbis", sample_rate, num_channels, compression=quality_level, bit_depth=16, duration=1) @parameterized.expand(list(itertools.product( [16000], @@ -289,7 +201,8 @@ def test_vorbis(self, sample_rate, num_channels, quality_level): def test_vorbis_large(self, sample_rate, num_channels, quality_level): """`sox_io_backend.load` can load large vorbis file correctly.""" two_hours = 2 * 60 * 60 - self.assert_vorbis(sample_rate, num_channels, quality_level, two_hours) + self.assert_format( + "vorbis", sample_rate, num_channels, compression=quality_level, bit_depth=16, duration=two_hours) @parameterized.expand(list(itertools.product( ['96k'], @@ -314,7 +227,7 @@ def test_opus(self, bitrate, num_channels, compression_level): )), name_func=name_func) def test_sphere(self, sample_rate, num_channels): """`sox_io_backend.load` can load sph format correctly.""" - self.assert_sphere(sample_rate, num_channels, duration=1) + self.assert_format("sph", sample_rate, num_channels, bit_depth=32, duration=1) @parameterized.expand(list(itertools.product( ['float32', 'int32', 'int16'], @@ -323,16 +236,19 @@ def test_sphere(self, sample_rate, num_channels): [False, True], )), name_func=name_func) def test_amb(self, dtype, sample_rate, num_channels, normalize): - """`sox_io_backend.load` can load sph format correctly.""" - self.assert_amb(dtype, sample_rate, num_channels, normalize, duration=1) + """`sox_io_backend.load` can load amb format correctly.""" + bit_depth = sox_utils.get_bit_depth(dtype) + encoding = sox_utils.get_encoding(dtype) + self.assert_format( + "amb", sample_rate, num_channels, bit_depth=bit_depth, duration=1, encoding=encoding, normalize=normalize) def test_amr_nb(self): """`sox_io_backend.load` can load amr_nb format correctly.""" - self.assert_amr_nb(duration=1) + self.assert_format("amr-nb", sample_rate=8000, num_channels=1, bit_depth=32, duration=1) @skipIfNoExec('sox') -@skipIfNoExtension +@skipIfNoSox class TestLoadParams(TempDirMixin, PytorchTestCase): """Test the correctness of frame parameters of `sox_io_backend.load`""" original = None @@ -363,7 +279,7 @@ def test_channels_first(self, channels_first): self.assertEqual(found, expected) -@skipIfNoExtension +@skipIfNoSox class TestLoadWithoutExtension(PytorchTestCase): def test_mp3(self): """Providing format allows to read mp3 without extension @@ -393,7 +309,7 @@ def read(self, n): return ret -@skipIfNoExtension +@skipIfNoSox @skipIfNoExec('sox') class TestFileObject(TempDirMixin, PytorchTestCase): """ @@ -553,7 +469,7 @@ def test_tarfile(self, ext, compression): self.assertEqual(expected, found) -@skipIfNoExtension +@skipIfNoSox @skipIfNoExec('sox') @skipIfNoModule("requests") class TestFileObjectHttp(HttpServerMixin, PytorchTestCase): diff --git a/test/torchaudio_unittest/backend/sox_io/roundtrip_test.py b/test/torchaudio_unittest/backend/sox_io/roundtrip_test.py index 85aa3019e0..32c920eea1 100644 --- a/test/torchaudio_unittest/backend/sox_io/roundtrip_test.py +++ b/test/torchaudio_unittest/backend/sox_io/roundtrip_test.py @@ -7,16 +7,17 @@ TempDirMixin, PytorchTestCase, skipIfNoExec, - skipIfNoExtension, + skipIfNoSox, get_wav_data, ) from .common import ( name_func, + get_enc_params, ) @skipIfNoExec('sox') -@skipIfNoExtension +@skipIfNoSox class TestRoundTripIO(TempDirMixin, PytorchTestCase): """save/load round trip should not degrade data for lossless formats""" @parameterized.expand(list(itertools.product( @@ -27,10 +28,11 @@ class TestRoundTripIO(TempDirMixin, PytorchTestCase): def test_wav(self, dtype, sample_rate, num_channels): """save/load round trip should not degrade data for wav formats""" original = get_wav_data(dtype, num_channels, normalize=False) + enc, bps = get_enc_params(dtype) data = original for i in range(10): path = self.get_temp_path(f'{i}.wav') - sox_io_backend.save(path, data, sample_rate) + sox_io_backend.save(path, data, sample_rate, encoding=enc, bits_per_sample=bps) data, sr = sox_io_backend.load(path, normalize=False) assert sr == sample_rate self.assertEqual(original, data) diff --git a/test/torchaudio_unittest/backend/sox_io/save_test.py b/test/torchaudio_unittest/backend/sox_io/save_test.py index fc378f9c70..4356972342 100644 --- a/test/torchaudio_unittest/backend/sox_io/save_test.py +++ b/test/torchaudio_unittest/backend/sox_io/save_test.py @@ -1,53 +1,79 @@ import io -import itertools +import unittest -import torch from torchaudio.backend import sox_io_backend from parameterized import parameterized from torchaudio_unittest.common_utils import ( TempDirMixin, + TorchaudioTestCase, PytorchTestCase, skipIfNoExec, - skipIfNoExtension, + skipIfNoSox, get_wav_data, load_wav, save_wav, sox_utils, + nested_params, ) from .common import ( name_func, + get_enc_params, ) -class SaveTestBase(TempDirMixin, PytorchTestCase): - def assert_wav(self, dtype, sample_rate, num_channels, num_frames): - """`sox_io_backend.save` can save wav format.""" - path = self.get_temp_path('data.wav') - expected = get_wav_data(dtype, num_channels, num_frames=num_frames) - sox_io_backend.save(path, expected, sample_rate, dtype=None) - found, sr = load_wav(path) - assert sample_rate == sr - self.assertEqual(found, expected) - - def assert_mp3(self, sample_rate, num_channels, bit_rate, duration): - """`sox_io_backend.save` can save mp3 format. - - mp3 encoding introduces delay and boundary effects so - we convert the resulting mp3 to wav and compare the results there - - | - | 1. Generate original wav file with SciPy +def _get_sox_encoding(encoding): + encodings = { + 'PCM_F': 'floating-point', + 'PCM_S': 'signed-integer', + 'PCM_U': 'unsigned-integer', + 'ULAW': 'u-law', + 'ALAW': 'a-law', + } + return encodings.get(encoding) + + +class SaveTestBase(TempDirMixin, TorchaudioTestCase): + def assert_save_consistency( + self, + format: str, + *, + compression: float = None, + encoding: str = None, + bits_per_sample: int = None, + sample_rate: float = 8000, + num_channels: int = 2, + num_frames: float = 3 * 8000, + src_dtype: str = 'int32', + test_mode: str = "path", + ): + """`save` function produces file that is comparable with `sox` command + + To compare that the file produced by `save` function agains the file produced by + the equivalent `sox` command, we need to load both files. + But there are many formats that cannot be opened with common Python modules (like + SciPy). + So we use `sox` command to prepare the original data and convert the saved files + into a format that SciPy can read (PCM wav). + The following diagram illustrates this process. The difference is 2.1. and 3.1. + + This assumes that + - loading data with SciPy preserves the data well. + - converting the resulting files into WAV format with `sox` preserve the data well. + + x + | 1. Generate source wav file with SciPy | v -------------- wav ---------------- | | - | 2.1. load with scipy | 3.1. Convert to mp3 with Sox - | then save with torchaudio | + | 2.1. load with scipy | 3.1. Convert to the target + | then save it into the target | format depth with sox + | format with torchaudio | v v - mp3 mp3 + target format target format | | - | 2.2. Convert to wav with Sox | 3.2. Convert to wav with Sox + | 2.2. Convert to wav with sox | 3.2. Convert to wav with sox | | v v wav wav @@ -58,463 +84,306 @@ def assert_mp3(self, sample_rate, num_channels, bit_rate, duration): tensor -------> compare <--------- tensor """ - src_path = self.get_temp_path('1.reference.wav') - mp3_path = self.get_temp_path('2.1.torchaudio.mp3') - wav_path = self.get_temp_path('2.2.torchaudio.wav') - mp3_path_sox = self.get_temp_path('3.1.sox.mp3') - wav_path_sox = self.get_temp_path('3.2.sox.wav') + cmp_encoding = 'floating-point' + cmp_bit_depth = 32 - # 1. Generate original wav - data = get_wav_data('float32', num_channels, normalize=True, num_frames=duration * sample_rate) - save_wav(src_path, data, sample_rate) - # 2.1. Convert the original wav to mp3 with torchaudio - sox_io_backend.save( - mp3_path, load_wav(src_path)[0], sample_rate, compression=bit_rate, dtype=None) - # 2.2. Convert the mp3 to wav with Sox - sox_utils.convert_audio_file(mp3_path, wav_path) - # 2.3. Load - found = load_wav(wav_path)[0] - - # 3.1. Convert the original wav to mp3 with SoX - sox_utils.convert_audio_file(src_path, mp3_path_sox, compression=bit_rate) - # 3.2. Convert the mp3 to wav with Sox - sox_utils.convert_audio_file(mp3_path_sox, wav_path_sox) - # 3.3. Load - expected = load_wav(wav_path_sox)[0] - - self.assertEqual(found, expected) - - def assert_flac(self, sample_rate, num_channels, compression_level, duration): - """`sox_io_backend.save` can save flac format. - - This test takes the same strategy as mp3 to compare the result - """ - src_path = self.get_temp_path('1.reference.wav') - flc_path = self.get_temp_path('2.1.torchaudio.flac') - wav_path = self.get_temp_path('2.2.torchaudio.wav') - flc_path_sox = self.get_temp_path('3.1.sox.flac') - wav_path_sox = self.get_temp_path('3.2.sox.wav') + src_path = self.get_temp_path('1.source.wav') + tgt_path = self.get_temp_path(f'2.1.torchaudio.{format}') + tst_path = self.get_temp_path('2.2.result.wav') + sox_path = self.get_temp_path(f'3.1.sox.{format}') + ref_path = self.get_temp_path('3.2.ref.wav') # 1. Generate original wav - data = get_wav_data('float32', num_channels, normalize=True, num_frames=duration * sample_rate) + data = get_wav_data(src_dtype, num_channels, normalize=False, num_frames=num_frames) save_wav(src_path, data, sample_rate) - # 2.1. Convert the original wav to flac with torchaudio - sox_io_backend.save( - flc_path, load_wav(src_path)[0], sample_rate, compression=compression_level, dtype=None) - # 2.2. Convert the flac to wav with Sox - # converting to 32 bit because flac file has 24 bit depth which scipy cannot handle. - sox_utils.convert_audio_file(flc_path, wav_path, bit_depth=32) - # 2.3. Load - found = load_wav(wav_path)[0] - - # 3.1. Convert the original wav to flac with SoX - sox_utils.convert_audio_file(src_path, flc_path_sox, compression=compression_level) - # 3.2. Convert the flac to wav with Sox - # converting to 32 bit because flac file has 24 bit depth which scipy cannot handle. - sox_utils.convert_audio_file(flc_path_sox, wav_path_sox, bit_depth=32) - # 3.3. Load - expected = load_wav(wav_path_sox)[0] - - self.assertEqual(found, expected) - - def _assert_vorbis(self, sample_rate, num_channels, quality_level, duration): - """`sox_io_backend.save` can save vorbis format. - - This test takes the same strategy as mp3 to compare the result - """ - src_path = self.get_temp_path('1.reference.wav') - vbs_path = self.get_temp_path('2.1.torchaudio.vorbis') - wav_path = self.get_temp_path('2.2.torchaudio.wav') - vbs_path_sox = self.get_temp_path('3.1.sox.vorbis') - wav_path_sox = self.get_temp_path('3.2.sox.wav') - # 1. Generate original wav - data = get_wav_data('int16', num_channels, normalize=False, num_frames=duration * sample_rate) - save_wav(src_path, data, sample_rate) - # 2.1. Convert the original wav to vorbis with torchaudio - sox_io_backend.save( - vbs_path, load_wav(src_path)[0], sample_rate, compression=quality_level, dtype=None) - # 2.2. Convert the vorbis to wav with Sox - sox_utils.convert_audio_file(vbs_path, wav_path) - # 2.3. Load - found = load_wav(wav_path)[0] - - # 3.1. Convert the original wav to vorbis with SoX - sox_utils.convert_audio_file(src_path, vbs_path_sox, compression=quality_level) - # 3.2. Convert the vorbis to wav with Sox - sox_utils.convert_audio_file(vbs_path_sox, wav_path_sox) - # 3.3. Load - expected = load_wav(wav_path_sox)[0] - - # sox's vorbis encoding has some random boundary effect, which cause small number of - # samples yields higher descrepency than the others. - # so we allow small portions of data to be outside of absolute torelance. - # make sure to pass somewhat long duration - atol = 1.0e-4 - max_failure_allowed = 0.01 # this percent of samples are allowed to outside of atol. - failure_ratio = ((found - expected).abs() > atol).sum().item() / found.numel() - if failure_ratio > max_failure_allowed: - # it's failed and this will give a better error message. - self.assertEqual(found, expected, atol=atol, rtol=1.3e-6) - - def assert_vorbis(self, *args, **kwargs): - # sox's vorbis encoding has some randomness, so we run tests multiple time - max_retry = 5 - error = None - for _ in range(max_retry): - try: - self._assert_vorbis(*args, **kwargs) - break - except AssertionError as e: - error = e + # 2.1. Convert the original wav to target format with torchaudio + data = load_wav(src_path, normalize=False)[0] + if test_mode == "path": + sox_io_backend.save( + tgt_path, data, sample_rate, + compression=compression, encoding=encoding, bits_per_sample=bits_per_sample) + elif test_mode == "fileobj": + with open(tgt_path, 'bw') as file_: + sox_io_backend.save( + file_, data, sample_rate, + format=format, compression=compression, + encoding=encoding, bits_per_sample=bits_per_sample) + elif test_mode == "bytesio": + file_ = io.BytesIO() + sox_io_backend.save( + file_, data, sample_rate, + format=format, compression=compression, + encoding=encoding, bits_per_sample=bits_per_sample) + file_.seek(0) + with open(tgt_path, 'bw') as f: + f.write(file_.read()) else: - raise error - - def assert_sphere(self, sample_rate, num_channels, duration): - """`sox_io_backend.save` can save sph format. - - This test takes the same strategy as mp3 to compare the result - """ - src_path = self.get_temp_path('1.reference.wav') - flc_path = self.get_temp_path('2.1.torchaudio.sph') - wav_path = self.get_temp_path('2.2.torchaudio.wav') - flc_path_sox = self.get_temp_path('3.1.sox.sph') - wav_path_sox = self.get_temp_path('3.2.sox.wav') - - # 1. Generate original wav - data = get_wav_data('float32', num_channels, normalize=True, num_frames=duration * sample_rate) - save_wav(src_path, data, sample_rate) - # 2.1. Convert the original wav to sph with torchaudio - sox_io_backend.save(flc_path, load_wav(src_path)[0], sample_rate, dtype=None) - # 2.2. Convert the sph to wav with Sox - # converting to 32 bit because sph file has 24 bit depth which scipy cannot handle. - sox_utils.convert_audio_file(flc_path, wav_path, bit_depth=32) - # 2.3. Load - found = load_wav(wav_path)[0] - - # 3.1. Convert the original wav to sph with SoX - sox_utils.convert_audio_file(src_path, flc_path_sox) - # 3.2. Convert the sph to wav with Sox - # converting to 32 bit because sph file has 24 bit depth which scipy cannot handle. - sox_utils.convert_audio_file(flc_path_sox, wav_path_sox, bit_depth=32) - # 3.3. Load - expected = load_wav(wav_path_sox)[0] + raise ValueError(f"Unexpected test mode: {test_mode}") + # 2.2. Convert the target format to wav with sox + sox_utils.convert_audio_file( + tgt_path, tst_path, encoding=cmp_encoding, bit_depth=cmp_bit_depth) + # 2.3. Load with SciPy + found = load_wav(tst_path, normalize=False)[0] + + # 3.1. Convert the original wav to target format with sox + sox_encoding = _get_sox_encoding(encoding) + sox_utils.convert_audio_file( + src_path, sox_path, + compression=compression, encoding=sox_encoding, bit_depth=bits_per_sample) + # 3.2. Convert the target format to wav with sox + sox_utils.convert_audio_file( + sox_path, ref_path, encoding=cmp_encoding, bit_depth=cmp_bit_depth) + # 3.3. Load with SciPy + expected = load_wav(ref_path, normalize=False)[0] self.assertEqual(found, expected) - def assert_amb(self, dtype, sample_rate, num_channels, duration): - """`sox_io_backend.save` can save amb format. - - This test takes the same strategy as mp3 to compare the result - """ - src_path = self.get_temp_path('1.reference.wav') - amb_path = self.get_temp_path('2.1.torchaudio.amb') - wav_path = self.get_temp_path('2.2.torchaudio.wav') - amb_path_sox = self.get_temp_path('3.1.sox.amb') - wav_path_sox = self.get_temp_path('3.2.sox.wav') - - # 1. Generate original wav - data = get_wav_data(dtype, num_channels, normalize=False, num_frames=duration * sample_rate) - save_wav(src_path, data, sample_rate) - # 2.1. Convert the original wav to amb with torchaudio - sox_io_backend.save(amb_path, load_wav(src_path, normalize=False)[0], sample_rate, dtype=None) - # 2.2. Convert the amb to wav with Sox - sox_utils.convert_audio_file(amb_path, wav_path) - # 2.3. Load - found = load_wav(wav_path)[0] - - # 3.1. Convert the original wav to amb with SoX - sox_utils.convert_audio_file(src_path, amb_path_sox) - # 3.2. Convert the amb to wav with Sox - sox_utils.convert_audio_file(amb_path_sox, wav_path_sox) - # 3.3. Load - expected = load_wav(wav_path_sox)[0] - - self.assertEqual(found, expected) - def assert_amr_nb(self, duration): - """`sox_io_backend.save` can save amr_nb format. +@skipIfNoExec('sox') +@skipIfNoSox +class SaveTest(SaveTestBase): + @nested_params( + ["path", "fileobj", "bytesio"], + [ + ('PCM_U', 8), + ('PCM_S', 16), + ('PCM_S', 32), + ('PCM_F', 32), + ('PCM_F', 64), + ('ULAW', 8), + ('ALAW', 8), + ], + ) + def test_save_wav(self, test_mode, enc_params): + encoding, bits_per_sample = enc_params + self.assert_save_consistency( + "wav", encoding=encoding, bits_per_sample=bits_per_sample, test_mode=test_mode) + + @nested_params( + ["path", "fileobj", "bytesio"], + [ + ('float32', ), + ('int32', ), + ('int16', ), + ('uint8', ), + ], + ) + def test_save_wav_dtype(self, test_mode, params): + dtype, = params + self.assert_save_consistency( + "wav", src_dtype=dtype, test_mode=test_mode) + + @nested_params( + ["path", "fileobj", "bytesio"], + [ + None, + -4.2, + -0.2, + 0, + 0.2, + 96, + 128, + 160, + 192, + 224, + 256, + 320, + ], + ) + def test_save_mp3(self, test_mode, bit_rate): + if test_mode in ["fileobj", "bytesio"]: + if bit_rate is not None and bit_rate < 1: + raise unittest.SkipTest( + "mp3 format with variable bit rate is known to " + "not yield the exact same result as sox command.") + self.assert_save_consistency( + "mp3", compression=bit_rate, test_mode=test_mode) + + @nested_params( + ["path", "fileobj", "bytesio"], + [8, 16, 24], + [ + None, + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + ], + ) + def test_save_flac(self, test_mode, bits_per_sample, compression_level): + self.assert_save_consistency( + "flac", compression=compression_level, + bits_per_sample=bits_per_sample, test_mode=test_mode) + + @nested_params( + ["path", "fileobj", "bytesio"], + ) + def test_save_htk(self, test_mode): + self.assert_save_consistency("htk", test_mode=test_mode, num_channels=1) + + @nested_params( + ["path", "fileobj", "bytesio"], + [ + None, + -1, + 0, + 1, + 2, + 3, + 3.6, + 5, + 10, + ], + ) + def test_save_vorbis(self, test_mode, quality_level): + self.assert_save_consistency( + "vorbis", compression=quality_level, test_mode=test_mode) + + @nested_params( + ["path", "fileobj", "bytesio"], + [ + ('PCM_S', 8, ), + ('PCM_S', 16, ), + ('PCM_S', 24, ), + ('PCM_S', 32, ), + ('ULAW', 8), + ('ALAW', 8), + ('ALAW', 16), + ('ALAW', 24), + ('ALAW', 32), + ], + ) + def test_save_sphere(self, test_mode, enc_params): + encoding, bits_per_sample = enc_params + self.assert_save_consistency( + "sph", encoding=encoding, bits_per_sample=bits_per_sample, test_mode=test_mode) + + @nested_params( + ["path", "fileobj", "bytesio"], + [ + ('PCM_U', 8, ), + ('PCM_S', 16, ), + ('PCM_S', 24, ), + ('PCM_S', 32, ), + ('PCM_F', 32, ), + ('PCM_F', 64, ), + ('ULAW', 8, ), + ('ALAW', 8, ), + ], + ) + def test_save_amb(self, test_mode, enc_params): + encoding, bits_per_sample = enc_params + self.assert_save_consistency( + "amb", encoding=encoding, bits_per_sample=bits_per_sample, test_mode=test_mode) + + @nested_params( + ["path", "fileobj", "bytesio"], + [ + None, + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + ], + ) + def test_save_amr_nb(self, test_mode, bit_rate): + self.assert_save_consistency( + "amr-nb", compression=bit_rate, num_channels=1, test_mode=test_mode) + + @nested_params( + ["path", "fileobj", "bytesio"], + ) + def test_save_gsm(self, test_mode): + self.assert_save_consistency( + "gsm", num_channels=1, test_mode=test_mode) + with self.assertRaises( + RuntimeError, msg="gsm format only supports single channel audio."): + self.assert_save_consistency( + "gsm", num_channels=2, test_mode=test_mode) + with self.assertRaises( + RuntimeError, msg="gsm format only supports a sampling rate of 8kHz."): + self.assert_save_consistency( + "gsm", sample_rate=16000, test_mode=test_mode) - This test takes the same strategy as mp3 to compare the result - """ + @parameterized.expand([ + ("wav", "PCM_S", 16), + ("mp3", ), + ("flac", ), + ("vorbis", ), + ("sph", "PCM_S", 16), + ("amr-nb", ), + ("amb", "PCM_S", 16), + ], name_func=name_func) + def test_save_large(self, format, encoding=None, bits_per_sample=None): + """`sox_io_backend.save` can save large files.""" sample_rate = 8000 - num_channels = 1 - src_path = self.get_temp_path('1.reference.wav') - amr_path = self.get_temp_path('2.1.torchaudio.amr-nb') - wav_path = self.get_temp_path('2.2.torchaudio.wav') - amr_path_sox = self.get_temp_path('3.1.sox.amr-nb') - wav_path_sox = self.get_temp_path('3.2.sox.wav') - - # 1. Generate original wav - data = get_wav_data('int16', num_channels, normalize=False, num_frames=duration * sample_rate) - save_wav(src_path, data, sample_rate) - # 2.1. Convert the original wav to amr_nb with torchaudio - sox_io_backend.save(amr_path, load_wav(src_path, normalize=False)[0], sample_rate, dtype=None) - # 2.2. Convert the amr_nb to wav with Sox - sox_utils.convert_audio_file(amr_path, wav_path) - # 2.3. Load - found = load_wav(wav_path)[0] - - # 3.1. Convert the original wav to amr_nb with SoX - sox_utils.convert_audio_file(src_path, amr_path_sox) - # 3.2. Convert the amr_nb to wav with Sox - sox_utils.convert_audio_file(amr_path_sox, wav_path_sox) - # 3.3. Load - expected = load_wav(wav_path_sox)[0] - - self.assertEqual(found, expected) - + one_hour = 60 * 60 * sample_rate + self.assert_save_consistency( + format, num_channels=1, sample_rate=8000, num_frames=one_hour, + encoding=encoding, bits_per_sample=bits_per_sample) -@skipIfNoExec('sox') -@skipIfNoExtension -class TestSave(SaveTestBase): - @parameterized.expand(list(itertools.product( - ['float32', 'int32', 'int16', 'uint8'], - [8000, 16000], - [1, 2], - )), name_func=name_func) - def test_wav(self, dtype, sample_rate, num_channels): - """`sox_io_backend.save` can save wav format.""" - self.assert_wav(dtype, sample_rate, num_channels, num_frames=None) - - @parameterized.expand(list(itertools.product( - ['float32'], - [16000], - [2], - )), name_func=name_func) - def test_wav_large(self, dtype, sample_rate, num_channels): - """`sox_io_backend.save` can save large wav file.""" - two_hours = 2 * 60 * 60 * sample_rate - self.assert_wav(dtype, sample_rate, num_channels, num_frames=two_hours) - - @parameterized.expand(list(itertools.product( - ['float32', 'int32', 'int16', 'uint8'], - [4, 8, 16, 32], - )), name_func=name_func) - def test_multiple_channels(self, dtype, num_channels): - """`sox_io_backend.save` can save wav with more than 2 channels.""" - sample_rate = 8000 - self.assert_wav(dtype, sample_rate, num_channels, num_frames=None) - - @parameterized.expand(list(itertools.product( - [8000, 16000], - [1, 2], - [-4.2, -0.2, 0, 0.2, 96, 128, 160, 192, 224, 256, 320], - )), name_func=name_func) - def test_mp3(self, sample_rate, num_channels, bit_rate): - """`sox_io_backend.save` can save mp3 format.""" - self.assert_mp3(sample_rate, num_channels, bit_rate, duration=1) - - @parameterized.expand(list(itertools.product( - [16000], - [2], - [128], - )), name_func=name_func) - def test_mp3_large(self, sample_rate, num_channels, bit_rate): - """`sox_io_backend.save` can save large mp3 file.""" - two_hours = 2 * 60 * 60 - self.assert_mp3(sample_rate, num_channels, bit_rate, duration=two_hours) - - @parameterized.expand(list(itertools.product( - [8000, 16000], - [1, 2], - [None] + list(range(9)), - )), name_func=name_func) - def test_flac(self, sample_rate, num_channels, compression_level): - """`sox_io_backend.save` can save flac format.""" - self.assert_flac(sample_rate, num_channels, compression_level, duration=1) - - @parameterized.expand(list(itertools.product( - [16000], - [2], - [0], - )), name_func=name_func) - def test_flac_large(self, sample_rate, num_channels, compression_level): - """`sox_io_backend.save` can save large flac file.""" - two_hours = 2 * 60 * 60 - self.assert_flac(sample_rate, num_channels, compression_level, duration=two_hours) - - @parameterized.expand(list(itertools.product( - [8000, 16000], - [1, 2], - [None, -1, 0, 1, 2, 3, 3.6, 5, 10], - )), name_func=name_func) - def test_vorbis(self, sample_rate, num_channels, quality_level): - """`sox_io_backend.save` can save vorbis format.""" - self.assert_vorbis(sample_rate, num_channels, quality_level, duration=20) - - # note: torchaudio can load large vorbis file, but cannot save large volbis file - # the following test causes Segmentation fault - # - ''' - @parameterized.expand(list(itertools.product( - [16000], - [2], - [10], - )), name_func=name_func) - def test_vorbis_large(self, sample_rate, num_channels, quality_level): - """`sox_io_backend.save` can save large vorbis file correctly.""" - two_hours = 2 * 60 * 60 - self.assert_vorbis(sample_rate, num_channels, quality_level, two_hours) - ''' - - @parameterized.expand(list(itertools.product( - [8000, 16000], - [1, 2], - )), name_func=name_func) - def test_sphere(self, sample_rate, num_channels): - """`sox_io_backend.save` can save sph format.""" - self.assert_sphere(sample_rate, num_channels, duration=1) - - @parameterized.expand(list(itertools.product( - ['float32', 'int32', 'int16', 'uint8'], - [8000, 16000], - [1, 2], - )), name_func=name_func) - def test_amb(self, dtype, sample_rate, num_channels): - """`sox_io_backend.save` can save amb format.""" - self.assert_amb(dtype, sample_rate, num_channels, duration=1) - - def test_amr_nb(self): - """`sox_io_backend.save` can save amr-nb format.""" - self.assert_amr_nb(duration=1) + @parameterized.expand([ + (32, ), + (64, ), + (128, ), + (256, ), + ], name_func=name_func) + def test_save_multi_channels(self, num_channels): + """`sox_io_backend.save` can save audio with many channels""" + self.assert_save_consistency( + "wav", encoding="PCM_S", bits_per_sample=16, + num_channels=num_channels) @skipIfNoExec('sox') -@skipIfNoExtension +@skipIfNoSox class TestSaveParams(TempDirMixin, PytorchTestCase): """Test the correctness of optional parameters of `sox_io_backend.save`""" @parameterized.expand([(True, ), (False, )], name_func=name_func) - def test_channels_first(self, channels_first): + def test_save_channels_first(self, channels_first): """channels_first swaps axes""" path = self.get_temp_path('data.wav') - data = get_wav_data('int32', 2, channels_first=channels_first) + data = get_wav_data( + 'int16', 2, channels_first=channels_first, normalize=False) sox_io_backend.save( - path, data, 8000, channels_first=channels_first, dtype=None) - found = load_wav(path)[0] + path, data, 8000, channels_first=channels_first) + found = load_wav(path, normalize=False)[0] expected = data if channels_first else data.transpose(1, 0) self.assertEqual(found, expected) @parameterized.expand([ 'float32', 'int32', 'int16', 'uint8' ], name_func=name_func) - def test_noncontiguous(self, dtype): + def test_save_noncontiguous(self, dtype): """Noncontiguous tensors are saved correctly""" path = self.get_temp_path('data.wav') - expected = get_wav_data(dtype, 4)[::2, ::2] + enc, bps = get_enc_params(dtype) + expected = get_wav_data(dtype, 4, normalize=False)[::2, ::2] assert not expected.is_contiguous() - sox_io_backend.save(path, expected, 8000, dtype=None) - found = load_wav(path)[0] + sox_io_backend.save( + path, expected, 8000, encoding=enc, bits_per_sample=bps) + found = load_wav(path, normalize=False)[0] self.assertEqual(found, expected) @parameterized.expand([ 'float32', 'int32', 'int16', 'uint8', ]) - def test_tensor_preserve(self, dtype): + def test_save_tensor_preserve(self, dtype): """save function should not alter Tensor""" path = self.get_temp_path('data.wav') - expected = get_wav_data(dtype, 4)[::2, ::2] + expected = get_wav_data(dtype, 4, normalize=False)[::2, ::2] data = expected.clone() - sox_io_backend.save(path, data, 8000, dtype=None) + sox_io_backend.save(path, data, 8000) self.assertEqual(data, expected) - - @parameterized.expand([ - ('float32', torch.tensor([-1.0, -0.5, 0, 0.5, 1.0]).to(torch.float32)), - ('int32', torch.tensor([-2147483648, -1073741824, 0, 1073741824, 2147483647]).to(torch.int32)), - ('int16', torch.tensor([-32768, -16384, 0, 16384, 32767]).to(torch.int16)), - ('uint8', torch.tensor([0, 64, 128, 192, 255]).to(torch.uint8)), - ]) - def test_dtype_conversion(self, dtype, expected): - """`save` performs dtype conversion on float32 src tensors only.""" - path = self.get_temp_path("data.wav") - data = torch.tensor([-1.0, -0.5, 0, 0.5, 1.0]).to(torch.float32).view(-1, 1) - sox_io_backend.save(path, data, 8000, dtype=dtype) - found = load_wav(path, normalize=False)[0] - self.assertEqual(found, expected.view(-1, 1)) - - -@skipIfNoExtension -@skipIfNoExec('sox') -class TestFileObject(SaveTestBase): - """ - We campare the result of file-like object input against file path input because - `save` function is rigrously tested for file path inputs to match libsox's result, - """ - @parameterized.expand([ - ('wav', None), - ('mp3', 128), - ('mp3', 320), - ('flac', 0), - ('flac', 5), - ('flac', 8), - ('vorbis', -1), - ('vorbis', 10), - ('amb', None), - ]) - def test_fileobj(self, ext, compression): - """Saving audio to file object returns the same result as via file path.""" - sample_rate = 16000 - dtype = 'float32' - num_channels = 2 - num_frames = 16000 - channels_first = True - - data = get_wav_data(dtype, num_channels, num_frames=num_frames) - - ref_path = self.get_temp_path(f'reference.{ext}') - res_path = self.get_temp_path(f'test.{ext}') - sox_io_backend.save( - ref_path, data, channels_first=channels_first, - sample_rate=sample_rate, compression=compression, dtype=None) - with open(res_path, 'wb') as fileobj: - sox_io_backend.save( - fileobj, data, channels_first=channels_first, - sample_rate=sample_rate, compression=compression, format=ext, dtype=None) - - expected_data, _ = sox_io_backend.load(ref_path) - data, sr = sox_io_backend.load(res_path) - - assert sample_rate == sr - self.assertEqual(expected_data, data) - - @parameterized.expand([ - ('wav', None), - ('mp3', 128), - ('mp3', 320), - ('flac', 0), - ('flac', 5), - ('flac', 8), - ('vorbis', -1), - ('vorbis', 10), - ('amb', None), - ]) - def test_bytesio(self, ext, compression): - """Saving audio to BytesIO object returns the same result as via file path.""" - sample_rate = 16000 - dtype = 'float32' - num_channels = 2 - num_frames = 16000 - channels_first = True - - data = get_wav_data(dtype, num_channels, num_frames=num_frames) - - ref_path = self.get_temp_path(f'reference.{ext}') - res_path = self.get_temp_path(f'test.{ext}') - sox_io_backend.save( - ref_path, data, channels_first=channels_first, - sample_rate=sample_rate, compression=compression, dtype=None) - fileobj = io.BytesIO() - sox_io_backend.save( - fileobj, data, channels_first=channels_first, - sample_rate=sample_rate, compression=compression, format=ext, dtype=None) - fileobj.seek(0) - with open(res_path, 'wb') as file_: - file_.write(fileobj.read()) - - expected_data, _ = sox_io_backend.load(ref_path) - data, sr = sox_io_backend.load(res_path) - - assert sample_rate == sr - self.assertEqual(expected_data, data) diff --git a/test/torchaudio_unittest/backend/sox_io/smoke_test.py b/test/torchaudio_unittest/backend/sox_io/smoke_test.py index 598a98bd3d..656cfd9fbe 100644 --- a/test/torchaudio_unittest/backend/sox_io/smoke_test.py +++ b/test/torchaudio_unittest/backend/sox_io/smoke_test.py @@ -4,26 +4,26 @@ from torchaudio.utils import sox_utils from torchaudio.backend import sox_io_backend -from torchaudio._internal.module_utils import is_module_available +from torchaudio._internal.module_utils import is_sox_available from parameterized import parameterized from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, - skipIfNoExtension, + skipIfNoSox, get_wav_data, ) from .common import name_func skipIfNoMP3 = unittest.skipIf( - not is_module_available('torchaudio._torchaudio') or + not is_sox_available() or 'mp3' not in sox_utils.list_read_formats() or 'mp3' not in sox_utils.list_write_formats(), '"sox_io" backend does not support MP3') -@skipIfNoExtension +@skipIfNoSox class SmokeTest(TempDirMixin, TorchaudioTestCase): """Run smoke test on various audio format @@ -88,7 +88,7 @@ def test_flac(self, sample_rate, num_channels, compression_level): self.run_smoke_test('flac', sample_rate, num_channels, compression=compression_level) -@skipIfNoExtension +@skipIfNoSox class SmokeTestFileObj(TorchaudioTestCase): """Run smoke test on various audio format diff --git a/test/torchaudio_unittest/backend/sox_io/torchscript_test.py b/test/torchaudio_unittest/backend/sox_io/torchscript_test.py index dad678cbc0..7fefd2b182 100644 --- a/test/torchaudio_unittest/backend/sox_io/torchscript_test.py +++ b/test/torchaudio_unittest/backend/sox_io/torchscript_test.py @@ -9,7 +9,7 @@ TempDirMixin, TorchaudioTestCase, skipIfNoExec, - skipIfNoExtension, + skipIfNoSox, get_wav_data, save_wav, load_wav, @@ -17,6 +17,7 @@ ) from .common import ( name_func, + get_enc_params, ) @@ -35,12 +36,16 @@ def py_save_func( sample_rate: int, channels_first: bool = True, compression: Optional[float] = None, + encoding: Optional[str] = None, + bits_per_sample: Optional[int] = None, ): - torchaudio.save(filepath, tensor, sample_rate, channels_first, compression) + torchaudio.save( + filepath, tensor, sample_rate, channels_first, + compression, None, encoding, bits_per_sample) @skipIfNoExec('sox') -@skipIfNoExtension +@skipIfNoSox class SoxIO(TempDirMixin, TorchaudioTestCase): """TorchScript-ability Test suite for `sox_io_backend`""" backend = 'sox_io' @@ -102,15 +107,16 @@ def test_save_wav(self, dtype, sample_rate, num_channels): torch.jit.script(py_save_func).save(script_path) ts_save_func = torch.jit.load(script_path) - expected = get_wav_data(dtype, num_channels) + expected = get_wav_data(dtype, num_channels, normalize=False) py_path = self.get_temp_path(f'test_save_py_{dtype}_{sample_rate}_{num_channels}.wav') ts_path = self.get_temp_path(f'test_save_ts_{dtype}_{sample_rate}_{num_channels}.wav') + enc, bps = get_enc_params(dtype) - py_save_func(py_path, expected, sample_rate, True, None) - ts_save_func(ts_path, expected, sample_rate, True, None) + py_save_func(py_path, expected, sample_rate, True, None, enc, bps) + ts_save_func(ts_path, expected, sample_rate, True, None, enc, bps) - py_data, py_sr = load_wav(py_path) - ts_data, ts_sr = load_wav(ts_path) + py_data, py_sr = load_wav(py_path, normalize=False) + ts_data, ts_sr = load_wav(ts_path, normalize=False) self.assertEqual(sample_rate, py_sr) self.assertEqual(sample_rate, ts_sr) @@ -131,8 +137,8 @@ def test_save_flac(self, sample_rate, num_channels, compression_level): py_path = self.get_temp_path(f'test_save_py_{sample_rate}_{num_channels}_{compression_level}.flac') ts_path = self.get_temp_path(f'test_save_ts_{sample_rate}_{num_channels}_{compression_level}.flac') - py_save_func(py_path, expected, sample_rate, True, compression_level) - ts_save_func(ts_path, expected, sample_rate, True, compression_level) + py_save_func(py_path, expected, sample_rate, True, compression_level, None, None) + ts_save_func(ts_path, expected, sample_rate, True, compression_level, None, None) # converting to 32 bit because flac file has 24 bit depth which scipy cannot handle. py_path_wav = f'{py_path}.wav' diff --git a/test/torchaudio_unittest/backend/utils_test.py b/test/torchaudio_unittest/backend/utils_test.py index 3f355be0cb..c89f1e9788 100644 --- a/test/torchaudio_unittest/backend/utils_test.py +++ b/test/torchaudio_unittest/backend/utils_test.py @@ -15,7 +15,6 @@ def test_switch(self): else: assert torchaudio.get_audio_backend() == self.backend assert torchaudio.load == self.backend_module.load - assert torchaudio.load_wav == self.backend_module.load_wav assert torchaudio.save == self.backend_module.save assert torchaudio.info == self.backend_module.info @@ -25,31 +24,13 @@ class TestBackendSwitch_NoBackend(BackendSwitchMixin, common_utils.TorchaudioTes backend_module = torchaudio.backend.no_backend -@common_utils.skipIfNoExtension -class TestBackendSwitch_SoX(BackendSwitchMixin, common_utils.TorchaudioTestCase): - backend = 'sox' - backend_module = torchaudio.backend.sox_backend - - -@common_utils.skipIfNoExtension +@common_utils.skipIfNoSox class TestBackendSwitch_SoXIO(BackendSwitchMixin, common_utils.TorchaudioTestCase): backend = 'sox_io' backend_module = torchaudio.backend.sox_io_backend -@common_utils.skipIfNoModule('soundfile') -class TestBackendSwitch_soundfile_legacy(BackendSwitchMixin, common_utils.TorchaudioTestCase): - backend = 'soundfile' - backend_module = torchaudio.backend.soundfile_backend - - def setUp(self): - torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = True - - def tearDown(self): - torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = None - - @common_utils.skipIfNoModule('soundfile') class TestBackendSwitch_soundfile(BackendSwitchMixin, common_utils.TorchaudioTestCase): backend = 'soundfile' - backend_module = torchaudio.backend._soundfile_backend + backend_module = torchaudio.backend.soundfile_backend diff --git a/test/torchaudio_unittest/common_utils/__init__.py b/test/torchaudio_unittest/common_utils/__init__.py index 8cc44c3529..22cb18a08e 100644 --- a/test/torchaudio_unittest/common_utils/__init__.py +++ b/test/torchaudio_unittest/common_utils/__init__.py @@ -15,7 +15,8 @@ skipIfNoCuda, skipIfNoExec, skipIfNoModule, - skipIfNoExtension, + skipIfNoKaldi, + skipIfNoSox, skipIfNoSoxBackend, ) from .wav_utils import ( @@ -26,9 +27,11 @@ ) from .parameterized_utils import ( load_params, + nested_params ) __all__ = ['get_asset_path', 'get_whitenoise', 'get_sinusoid', 'set_audio_backend', 'TempDirMixin', 'HttpServerMixin', 'TestBaseMixin', 'PytorchTestCase', 'TorchaudioTestCase', - 'skipIfNoCuda', 'skipIfNoExec', 'skipIfNoModule', 'skipIfNoExtension', 'skipIfNoSoxBackend', - 'get_wav_data', 'normalize_wav', 'load_wav', 'save_wav', 'load_params'] + 'skipIfNoCuda', 'skipIfNoExec', 'skipIfNoModule', 'skipIfNoKaldi', 'skipIfNoSox', + 'skipIfNoSoxBackend', 'get_wav_data', 'normalize_wav', 'load_wav', 'save_wav', 'load_params', + 'nested_params'] diff --git a/test/torchaudio_unittest/common_utils/backend_utils.py b/test/torchaudio_unittest/common_utils/backend_utils.py index 7e519c80eb..84dd73ed2e 100644 --- a/test/torchaudio_unittest/common_utils/backend_utils.py +++ b/test/torchaudio_unittest/common_utils/backend_utils.py @@ -6,15 +6,13 @@ def set_audio_backend(backend): """Allow additional backend value, 'default'""" backends = torchaudio.list_audio_backends() - if backend == 'soundfile-new': + if backend == 'soundfile': be = 'soundfile' - torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = False elif backend == 'default': if 'sox_io' in backends: be = 'sox_io' elif 'soundfile' in backends: be = 'soundfile' - torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = True else: raise unittest.SkipTest('No default backend available') else: diff --git a/test/torchaudio_unittest/common_utils/case_utils.py b/test/torchaudio_unittest/common_utils/case_utils.py index 9a9d491541..5d476072bb 100644 --- a/test/torchaudio_unittest/common_utils/case_utils.py +++ b/test/torchaudio_unittest/common_utils/case_utils.py @@ -8,7 +8,11 @@ import torch from torch.testing._internal.common_utils import TestCase as PytorchTestCase import torchaudio -from torchaudio._internal.module_utils import is_module_available +from torchaudio._internal.module_utils import ( + is_module_available, + is_sox_available, + is_kaldi_available +) from .backend_utils import set_audio_backend @@ -95,11 +99,5 @@ def skipIfNoModule(module, display_name=None): skipIfNoSoxBackend = unittest.skipIf( 'sox' not in torchaudio.list_audio_backends(), 'Sox backend not available') skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') - - -def skipIfNoExtension(test_item): - if is_module_available('torchaudio._torchaudio'): - return test_item - if 'TORCHAUDIO_TEST_FAIL_IF_NO_EXTENSION' in os.environ: - raise RuntimeError('torchaudio C++ extension is not available.') - return unittest.skip('torchaudio C++ extension is not available')(test_item) +skipIfNoSox = unittest.skipIf(not is_sox_available(), reason='Sox not available') +skipIfNoKaldi = unittest.skipIf(not is_kaldi_available(), reason='Kaldi not available') diff --git a/test/torchaudio_unittest/common_utils/data_utils.py b/test/torchaudio_unittest/common_utils/data_utils.py index de19d8174f..9bbafe4f36 100644 --- a/test/torchaudio_unittest/common_utils/data_utils.py +++ b/test/torchaudio_unittest/common_utils/data_utils.py @@ -68,11 +68,11 @@ def get_whitenoise( # so we only fork on CPU, generate values and move the data to the given device with torch.random.fork_rng([]): torch.random.manual_seed(seed) - tensor = torch.randn([int(sample_rate * duration)], dtype=torch.float32, device='cpu') + tensor = torch.randn([n_channels, int(sample_rate * duration)], + dtype=torch.float32, device='cpu') tensor /= 2.0 tensor *= scale_factor tensor.clamp_(-1.0, 1.0) - tensor = tensor.repeat([n_channels, 1]) if not channels_first: tensor = tensor.t() return convert_tensor_encoding(tensor, dtype) diff --git a/test/torchaudio_unittest/common_utils/kaldi_utils.py b/test/torchaudio_unittest/common_utils/kaldi_utils.py new file mode 100644 index 0000000000..71053bd003 --- /dev/null +++ b/test/torchaudio_unittest/common_utils/kaldi_utils.py @@ -0,0 +1,38 @@ +import subprocess + +import torch + + +def convert_args(**kwargs): + args = [] + for key, value in kwargs.items(): + if key == 'sample_rate': + key = 'sample_frequency' + key = '--' + key.replace('_', '-') + value = str(value).lower() if value in [True, False] else str(value) + args.append('%s=%s' % (key, value)) + return args + + +def run_kaldi(command, input_type, input_value): + """Run provided Kaldi command, pass a tensor and get the resulting tensor + + Args: + command (list of str): The command with arguments + input_type (str): 'ark' or 'scp' + input_value (Tensor for 'ark', string for 'scp'): The input to pass. + Must be a path to an audio file for 'scp'. + """ + import kaldi_io + + key = 'foo' + process = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) + if input_type == 'ark': + kaldi_io.write_mat(process.stdin, input_value.cpu().numpy(), key=key) + elif input_type == 'scp': + process.stdin.write(f'{key} {input_value}'.encode('utf8')) + else: + raise NotImplementedError('Unexpected type') + process.stdin.close() + result = dict(kaldi_io.read_mat_ark(process.stdout))['foo'] + return torch.from_numpy(result.copy()) # copy supresses some torch warning diff --git a/test/torchaudio_unittest/common_utils/parameterized_utils.py b/test/torchaudio_unittest/common_utils/parameterized_utils.py index 24404a6edd..412ee82554 100644 --- a/test/torchaudio_unittest/common_utils/parameterized_utils.py +++ b/test/torchaudio_unittest/common_utils/parameterized_utils.py @@ -1,6 +1,7 @@ import json +from itertools import product -from parameterized import param +from parameterized import param, parameterized from .data_utils import get_asset_path @@ -8,3 +9,19 @@ def load_params(*paths): with open(get_asset_path(*paths), 'r') as file: return [param(json.loads(line)) for line in file] + + +def nested_params(*params): + def _name_func(func, _, params): + strs = [] + for arg in params.args: + if isinstance(arg, tuple): + strs.append("_".join(str(a) for a in arg)) + else: + strs.append(str(arg)) + return f'{func.__name__}_{"_".join(strs)}' + + return parameterized.expand( + list(product(*params)), + name_func=_name_func + ) diff --git a/test/torchaudio_unittest/common_utils/sox_utils.py b/test/torchaudio_unittest/common_utils/sox_utils.py index 0267017393..fd0949f31a 100644 --- a/test/torchaudio_unittest/common_utils/sox_utils.py +++ b/test/torchaudio_unittest/common_utils/sox_utils.py @@ -1,3 +1,4 @@ +import sys import subprocess import warnings @@ -32,6 +33,7 @@ def gen_audio_file( command = [ 'sox', '-V3', # verbose + '--no-dither', # disable automatic dithering '-R', # -R is supposed to be repeatable, though the implementation looks suspicious # and not setting the seed to a fixed value. @@ -61,21 +63,23 @@ def gen_audio_file( ] if attenuation is not None: command += ['vol', f'-{attenuation}dB'] - print(' '.join(command)) + print(' '.join(command), file=sys.stderr) subprocess.run(command, check=True) def convert_audio_file( src_path, dst_path, - *, bit_depth=None, compression=None): + *, encoding=None, bit_depth=None, compression=None): """Convert audio file with `sox` command.""" - command = ['sox', '-V3', '-R', str(src_path)] + command = ['sox', '-V3', '--no-dither', '-R', str(src_path)] + if encoding is not None: + command += ['--encoding', str(encoding)] if bit_depth is not None: command += ['--bits', str(bit_depth)] if compression is not None: command += ['--compression', str(compression)] command += [dst_path] - print(' '.join(command)) + print(' '.join(command), file=sys.stderr) subprocess.run(command, check=True) diff --git a/test/torchaudio_unittest/compliance/generate_fbank_data.py b/test/torchaudio_unittest/compliance/generate_fbank_data.py index 96a935d116..f1935564dc 100644 --- a/test/torchaudio_unittest/compliance/generate_fbank_data.py +++ b/test/torchaudio_unittest/compliance/generate_fbank_data.py @@ -6,6 +6,7 @@ import torch import torchaudio import utils +from torchaudio_unittest import common_utils def run(exe_path, scp_path, out_dir, wave_len, num_outputs, remove_files, log_level): @@ -102,7 +103,7 @@ def decode(fn, sound_path, exe_path, scp_path, out_dir): # print args for python inputs['dither'] = 0.0 logging.info(inputs) - sound, sample_rate = torchaudio.load_wav(sound_path) + sound, sample_rate = common_utils.load_wav(sound_path, normalize=False) kaldi_output_dict = {k: v for k, v in torchaudio.kaldi_io.read_mat_ark(out_fn)} res = torchaudio.compliance.kaldi.fbank(sound, **inputs) torch.set_printoptions(precision=10, sci_mode=False) diff --git a/test/torchaudio_unittest/compliance_kaldi_test.py b/test/torchaudio_unittest/compliance_kaldi_test.py index c7b4149819..a98240a9b8 100644 --- a/test/torchaudio_unittest/compliance_kaldi_test.py +++ b/test/torchaudio_unittest/compliance_kaldi_test.py @@ -45,9 +45,9 @@ def first_sample_of_frame(frame, window_size, window_shift, snip_edges): window[f, s] = wave[s_in_wave] -@common_utils.skipIfNoSoxBackend +@common_utils.skipIfNoSox class Test_Kaldi(common_utils.TempDirMixin, common_utils.TorchaudioTestCase): - backend = 'sox' + backend = 'sox_io' kaldi_output_dir = common_utils.get_asset_path('kaldi') test_filepath = common_utils.get_asset_path('kaldi_file.wav') @@ -91,7 +91,7 @@ def _test_get_strided_helper(self, num_samples, window_size, window_shift, snip_ for r in range(m): extract_window(window, waveform, r, window_size, window_shift, snip_edges) - torch.testing.assert_allclose(window, output) + self.assertEqual(window, output) def test_get_strided(self): # generate any combination where 0 < window_size <= num_samples and @@ -116,7 +116,7 @@ def _create_data_set(self): sound, sample_rate = torchaudio.load(self.test_filepath, normalization=False) print(y >> 16) self.assertTrue(sample_rate == sr) - torch.testing.assert_allclose(y, sound) + self.assertEqual(y, sound) def _print_diagnostic(self, output, expect_output): # given an output and expected output, it will print the absolute/relative errors (max and mean squared) @@ -144,7 +144,7 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil atol (float): absolute tolerance rtol (float): relative tolerance """ - sound, sr = torchaudio.load_wav(sound_filepath) + sound, sr = common_utils.load_wav(sound_filepath, normalize=False) files = self.test_filepaths[filepath_key] assert len(files) == expected_num_files, \ @@ -156,7 +156,7 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil # Read kaldi's output from file kaldi_output_path = os.path.join(self.kaldi_output_dir, f) - kaldi_output_dict = {k: v for k, v in torchaudio.kaldi_io.read_mat_ark(kaldi_output_path)} + kaldi_output_dict = dict(torchaudio.kaldi_io.read_mat_ark(kaldi_output_path)) assert len(kaldi_output_dict) == 1 and 'my_id' in kaldi_output_dict, 'invalid test kaldi ark file' kaldi_output = kaldi_output_dict['my_id'] @@ -170,7 +170,7 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil output = get_output_fn(sound, args) self._print_diagnostic(output, kaldi_output) - torch.testing.assert_allclose(output, kaldi_output, atol=atol, rtol=rtol) + self.assertEqual(output, kaldi_output, atol=atol, rtol=rtol) def test_mfcc_empty(self): # Passing in an empty tensor should result in an error @@ -178,7 +178,7 @@ def test_mfcc_empty(self): def test_resample_waveform(self): def get_output_fn(sound, args): - output = kaldi.resample_waveform(sound, args[1], args[2]) + output = kaldi.resample_waveform(sound.to(torch.float32), args[1], args[2]) return output self._compliance_test_helper(self.test2_filepath, 'resample', 32, 3, get_output_fn, atol=1e-2, rtol=1e-5) @@ -221,7 +221,7 @@ def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_fact ground_truth = ground_truth[..., n_to_trim:-n_to_trim] estimate = estimate[..., n_to_trim:-n_to_trim] - torch.testing.assert_allclose(estimate, ground_truth, atol=atol, rtol=rtol) + self.assertEqual(estimate, ground_truth, atol=atol, rtol=rtol) def test_resample_waveform_downsample_accuracy(self): for i in range(1, 20): @@ -246,4 +246,4 @@ def test_resample_waveform_multi_channel(self): single_channel = self.test1_signal * (i + 1) * 1.5 single_channel_sampled = kaldi.resample_waveform(single_channel, self.test1_signal_sr, self.test1_signal_sr // 2) - torch.testing.assert_allclose(multi_sound_sampled[i, :], single_channel_sampled[0], rtol=1e-4, atol=1e-7) + self.assertEqual(multi_sound_sampled[i, :], single_channel_sampled[0], rtol=1e-4, atol=1e-7) diff --git a/test/torchaudio_unittest/datasets/tedlium_test.py b/test/torchaudio_unittest/datasets/tedlium_test.py index abb6ea5b5c..00c3e1748e 100644 --- a/test/torchaudio_unittest/datasets/tedlium_test.py +++ b/test/torchaudio_unittest/datasets/tedlium_test.py @@ -7,6 +7,7 @@ TorchaudioTestCase, get_whitenoise, save_wav, + skipIfNoSox ) from torchaudio.datasets import tedlium @@ -143,13 +144,7 @@ class TestTedliumSoundfile(Tedlium, TorchaudioTestCase): backend = "soundfile" -class TestTedliumSoundfileNew(Tedlium, TorchaudioTestCase): - backend = "soundfile-new" - - if platform.system() != "Windows": - class TestTedliumSox(Tedlium, TorchaudioTestCase): - backend = "sox" - + @skipIfNoSox class TestTedliumSoxIO(Tedlium, TorchaudioTestCase): backend = "sox_io" diff --git a/test/torchaudio_unittest/functional/autograd_cpu_test.py b/test/torchaudio_unittest/functional/autograd_cpu_test.py new file mode 100644 index 0000000000..a33e82fa56 --- /dev/null +++ b/test/torchaudio_unittest/functional/autograd_cpu_test.py @@ -0,0 +1,8 @@ +import torch +from .autograd_impl import Autograd +from torchaudio_unittest import common_utils + + +class TestAutogradLfilterCPU(Autograd, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') diff --git a/test/torchaudio_unittest/functional/autograd_cuda_test.py b/test/torchaudio_unittest/functional/autograd_cuda_test.py new file mode 100644 index 0000000000..09ac74d721 --- /dev/null +++ b/test/torchaudio_unittest/functional/autograd_cuda_test.py @@ -0,0 +1,9 @@ +import torch +from .autograd_impl import Autograd +from torchaudio_unittest import common_utils + + +@common_utils.skipIfNoCuda +class TestAutogradLfilterCUDA(Autograd, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/torchaudio_unittest/functional/autograd_impl.py b/test/torchaudio_unittest/functional/autograd_impl.py new file mode 100644 index 0000000000..02477d0d33 --- /dev/null +++ b/test/torchaudio_unittest/functional/autograd_impl.py @@ -0,0 +1,40 @@ +import torch +import torchaudio.functional as F +from torch.autograd import gradcheck +from torchaudio_unittest import common_utils + + +class Autograd(common_utils.TestBaseMixin): + def test_x_grad(self): + torch.random.manual_seed(2434) + x = torch.rand(2, 4, 256 * 2, dtype=self.dtype, device=self.device) + a = torch.tensor([0.7, 0.2, 0.6], dtype=self.dtype, device=self.device) + b = torch.tensor([0.4, 0.2, 0.9], dtype=self.dtype, device=self.device) + x.requires_grad = True + assert gradcheck(F.lfilter, (x, a, b), eps=1e-10) + + def test_a_grad(self): + torch.random.manual_seed(2434) + x = torch.rand(2, 4, 256 * 2, dtype=self.dtype, device=self.device) + a = torch.tensor([0.7, 0.2, 0.6], dtype=self.dtype, device=self.device) + b = torch.tensor([0.4, 0.2, 0.9], dtype=self.dtype, device=self.device) + a.requires_grad = True + assert gradcheck(F.lfilter, (x, a, b), eps=1e-10) + + def test_b_grad(self): + torch.random.manual_seed(2434) + x = torch.rand(2, 4, 256 * 2, dtype=self.dtype, device=self.device) + a = torch.tensor([0.7, 0.2, 0.6], dtype=self.dtype, device=self.device) + b = torch.tensor([0.4, 0.2, 0.9], dtype=self.dtype, device=self.device) + b.requires_grad = True + assert gradcheck(F.lfilter, (x, a, b), eps=1e-10) + + def test_all_grad(self): + torch.random.manual_seed(2434) + x = torch.rand(2, 4, 256 * 2, dtype=self.dtype, device=self.device) + a = torch.tensor([0.7, 0.2, 0.6], dtype=self.dtype, device=self.device) + b = torch.tensor([0.4, 0.2, 0.9], dtype=self.dtype, device=self.device) + b.requires_grad = True + a.requires_grad = True + x.requires_grad = True + assert gradcheck(F.lfilter, (x, a, b), eps=1e-10) diff --git a/test/torchaudio_unittest/functional/batch_consistency_test.py b/test/torchaudio_unittest/functional/batch_consistency_test.py new file mode 100644 index 0000000000..44a9580fe8 --- /dev/null +++ b/test/torchaudio_unittest/functional/batch_consistency_test.py @@ -0,0 +1,224 @@ +"""Test numerical consistency among single input and batched input.""" +import itertools +import math + +from parameterized import parameterized, parameterized_class +import torch +import torchaudio.functional as F + +from torchaudio_unittest import common_utils + + +def _name_from_args(func, _, params): + """Return a parameterized test name, based on parameter values.""" + return "{}_{}".format( + func.__name__, + "_".join(str(arg) for arg in params.args)) + + +@parameterized_class([ + # Single-item batch isolates problems that come purely from adding a + # dimension (rather than processing multiple items) + {"batch_size": 1}, + {"batch_size": 3}, +]) +class TestFunctional(common_utils.TorchaudioTestCase): + """Test functions defined in `functional` module""" + backend = 'default' + + def assert_batch_consistency( + self, functional, batch, *args, atol=1e-8, rtol=1e-5, seed=42, + **kwargs): + n = batch.size(0) + + # Compute items separately, then batch the result + torch.random.manual_seed(seed) + items_input = batch.clone() + items_result = torch.stack([ + functional(items_input[i], *args, **kwargs) for i in range(n) + ]) + + # Batch the input and run + torch.random.manual_seed(seed) + batch_input = batch.clone() + batch_result = functional(batch_input, *args, **kwargs) + + self.assertEqual(items_input, batch_input, rtol=rtol, atol=atol) + self.assertEqual(items_result, batch_result, rtol=rtol, atol=atol) + + def test_griffinlim(self): + n_fft = 400 + ws = 400 + hop = 200 + window = torch.hann_window(ws) + power = 2 + momentum = 0.99 + n_iter = 32 + length = 1000 + torch.random.manual_seed(0) + batch = torch.rand(self.batch_size, 1, 201, 6) + self.assert_batch_consistency( + F.griffinlim, batch, window, n_fft, hop, ws, power, + n_iter, momentum, length, 0, atol=5e-5) + + @parameterized.expand(list(itertools.product( + [8000, 16000, 44100], + [1, 2], + )), name_func=_name_from_args) + def test_detect_pitch_frequency(self, sample_rate, n_channels): + # Use different frequencies to ensure each item in the batch returns a + # different answer. + torch.manual_seed(0) + frequencies = torch.randint(100, 1000, [self.batch_size]) + waveforms = torch.stack([ + common_utils.get_sinusoid( + frequency=frequency, sample_rate=sample_rate, + n_channels=n_channels, duration=5) + for frequency in frequencies + ]) + self.assert_batch_consistency( + F.detect_pitch_frequency, waveforms, sample_rate) + + def test_amplitude_to_DB(self): + torch.manual_seed(0) + spec = torch.rand(self.batch_size, 2, 100, 100) * 200 + + amplitude_mult = 20. + amin = 1e-10 + ref = 1.0 + db_mult = math.log10(max(amin, ref)) + + # Test with & without a `top_db` clamp + self.assert_batch_consistency( + F.amplitude_to_DB, spec, amplitude_mult, + amin, db_mult, top_db=None) + self.assert_batch_consistency( + F.amplitude_to_DB, spec, amplitude_mult, + amin, db_mult, top_db=40.) + + def test_amplitude_to_DB_itemwise_clamps(self): + """Ensure that the clamps are separate for each spectrogram in a batch. + + The clamp was determined per-batch in a prior implementation, which + meant it was determined by the loudest item, thus items weren't + independent. See: + + https://github.com/pytorch/audio/issues/994 + + """ + amplitude_mult = 20. + amin = 1e-10 + ref = 1.0 + db_mult = math.log10(max(amin, ref)) + top_db = 20. + + # Make a batch of noise + torch.manual_seed(0) + spec = torch.rand([2, 2, 100, 100]) * 200 + # Make one item blow out the other + spec[0] += 50 + + batchwise_dbs = F.amplitude_to_DB(spec, amplitude_mult, amin, + db_mult, top_db=top_db) + itemwise_dbs = torch.stack([ + F.amplitude_to_DB(item, amplitude_mult, amin, + db_mult, top_db=top_db) + for item in spec + ]) + + self.assertEqual(batchwise_dbs, itemwise_dbs) + + def test_amplitude_to_DB_not_channelwise_clamps(self): + """Check that clamps are applied per-item, not per channel.""" + amplitude_mult = 20. + amin = 1e-10 + ref = 1.0 + db_mult = math.log10(max(amin, ref)) + top_db = 40. + + torch.manual_seed(0) + spec = torch.rand([1, 2, 100, 100]) * 200 + # Make one channel blow out the other + spec[:, 0] += 50 + + specwise_dbs = F.amplitude_to_DB(spec, amplitude_mult, amin, + db_mult, top_db=top_db) + channelwise_dbs = torch.stack([ + F.amplitude_to_DB(spec[:, i], amplitude_mult, amin, + db_mult, top_db=top_db) + for i in range(spec.size(-3)) + ]) + + # Just check channelwise gives a different answer. + difference = (specwise_dbs - channelwise_dbs).abs() + assert (difference >= 1e-5).any() + + def test_contrast(self): + torch.random.manual_seed(0) + waveforms = torch.rand(self.batch_size, 2, 100) - 0.5 + self.assert_batch_consistency( + F.contrast, waveforms, enhancement_amount=80.) + + def test_dcshift(self): + torch.random.manual_seed(0) + waveforms = torch.rand(self.batch_size, 2, 100) - 0.5 + self.assert_batch_consistency( + F.dcshift, waveforms, shift=0.5, limiter_gain=0.05) + + def test_overdrive(self): + torch.random.manual_seed(0) + waveforms = torch.rand(self.batch_size, 2, 100) - 0.5 + self.assert_batch_consistency( + F.overdrive, waveforms, gain=45, colour=30) + + def test_phaser(self): + sample_rate = 44100 + n_channels = 2 + waveform = common_utils.get_whitenoise( + sample_rate=sample_rate, n_channels=self.batch_size * n_channels, + duration=1) + batch = waveform.view(self.batch_size, n_channels, waveform.size(-1)) + self.assert_batch_consistency(F.phaser, batch, sample_rate) + + def test_flanger(self): + torch.random.manual_seed(0) + waveforms = torch.rand(self.batch_size, 2, 100) - 0.5 + sample_rate = 44100 + self.assert_batch_consistency(F.flanger, waveforms, sample_rate) + + @parameterized.expand(list(itertools.product( + [True, False], # center + [True, False], # norm_vars + )), name_func=_name_from_args) + def test_sliding_window_cmn(self, center, norm_vars): + torch.manual_seed(0) + spectrogram = torch.rand(self.batch_size, 2, 1024, 1024) * 200 + self.assert_batch_consistency( + F.sliding_window_cmn, spectrogram, center=center, + norm_vars=norm_vars) + + def test_vad_from_file(self): + filepath = common_utils.get_asset_path("vad-go-stereo-44100.wav") + waveform, sample_rate = common_utils.load_wav(filepath) + # Each channel is slightly offset - we can use this to create a batch + # with different items. + batch = waveform.view(2, 1, -1) + self.assert_batch_consistency(F.vad, batch, sample_rate=sample_rate) + + def test_vad_different_items(self): + """Separate test to ensure VAD consistency with differing items.""" + sample_rate = 44100 + torch.manual_seed(0) + waveforms = torch.rand(self.batch_size, 2, 100) - 0.5 + self.assert_batch_consistency( + F.vad, waveforms, sample_rate=sample_rate) + + @common_utils.skipIfNoKaldi + def test_compute_kaldi_pitch(self): + sample_rate = 44100 + n_channels = 2 + waveform = common_utils.get_whitenoise( + sample_rate=sample_rate, n_channels=self.batch_size * n_channels) + batch = waveform.view(self.batch_size, n_channels, waveform.size(-1)) + self.assert_batch_consistency( + F.compute_kaldi_pitch, batch, sample_rate=sample_rate) diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py index 21e5a3e1b7..7669d96744 100644 --- a/test/torchaudio_unittest/functional/functional_cpu_test.py +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -6,8 +6,14 @@ import torchaudio.functional as F from parameterized import parameterized import itertools +import unittest from torchaudio_unittest import common_utils +from torchaudio_unittest.common_utils import ( + TorchaudioTestCase, + skipIfNoSox, +) + from .functional_impl import Lfilter, Spectrogram @@ -15,6 +21,10 @@ class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): dtype = torch.float32 device = torch.device('cpu') + @unittest.expectedFailure + def test_9th_order_filter_stability(self): + super().test_9th_order_filter_stability() + class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): dtype = torch.float64 @@ -53,6 +63,7 @@ def test_warning(self): class TestComputeDeltas(common_utils.TorchaudioTestCase): """Test suite for correctness of compute_deltas""" + def test_one_channel(self): specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) @@ -211,3 +222,45 @@ def test_mask_along_axis_iid(self, mask_param, mask_value, axis): assert mask_specgrams.size() == specgrams.size() assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() + + +@skipIfNoSox +class TestApplyCodec(TorchaudioTestCase): + backend = "sox_io" + + def _smoke_test(self, format, compression, check_num_frames): + """ + The purpose of this test suite is to verify that apply_codec functionalities do not exhibit + abnormal behaviors. + """ + torch.random.manual_seed(42) + sample_rate = 8000 + num_frames = 3 * sample_rate + num_channels = 2 + waveform = torch.rand(num_channels, num_frames) + + augmented = F.apply_codec(waveform, + sample_rate, + format, + True, + compression + ) + assert augmented.dtype == waveform.dtype + assert augmented.shape[0] == num_channels + if check_num_frames: + assert augmented.shape[1] == num_frames + + def test_wave(self): + self._smoke_test("wav", compression=None, check_num_frames=True) + + @parameterized.expand([(96,), (128,), (160,), (192,), (224,), (256,), (320,)]) + def test_mp3(self, compression): + self._smoke_test("mp3", compression, check_num_frames=False) + + @parameterized.expand([(0,), (1,), (2,), (3,), (4,), (5,), (6,), (7,), (8,)]) + def test_flac(self, compression): + self._smoke_test("flac", compression, check_num_frames=False) + + @parameterized.expand([(-1,), (0,), (1,), (2,), (3,), (3.6,), (5,), (10,)]) + def test_vorbis(self, compression): + self._smoke_test("vorbis", compression, check_num_frames=False) diff --git a/test/torchaudio_unittest/functional/functional_cuda_test.py b/test/torchaudio_unittest/functional/functional_cuda_test.py index c89795be01..f4db6ca3a0 100644 --- a/test/torchaudio_unittest/functional/functional_cuda_test.py +++ b/test/torchaudio_unittest/functional/functional_cuda_test.py @@ -1,4 +1,5 @@ import torch +import unittest from torchaudio_unittest import common_utils from .functional_impl import Lfilter, Spectrogram @@ -9,6 +10,10 @@ class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): dtype = torch.float32 device = torch.device('cuda') + @unittest.expectedFailure + def test_9th_order_filter_stability(self): + super().test_9th_order_filter_stability() + @common_utils.skipIfNoCuda class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): diff --git a/test/torchaudio_unittest/functional/functional_impl.py b/test/torchaudio_unittest/functional/functional_impl.py index 1ba5e21fbb..f7f8c17ecb 100644 --- a/test/torchaudio_unittest/functional/functional_impl.py +++ b/test/torchaudio_unittest/functional/functional_impl.py @@ -2,6 +2,7 @@ import torch import torchaudio.functional as F from parameterized import parameterized +from scipy import signal from torchaudio_unittest import common_utils @@ -31,6 +32,42 @@ def test_clamp(self): output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=False) assert output_signal.max() > 1 + @parameterized.expand([ + ((44100,),), + ((3, 44100),), + ((2, 3, 44100),), + ((1, 2, 3, 44100),) + ]) + def test_shape(self, shape): + torch.random.manual_seed(42) + waveform = torch.rand(*shape, dtype=self.dtype, device=self.device) + b_coeffs = torch.tensor([0, 0, 0, 1], dtype=self.dtype, device=self.device) + a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device) + output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) + assert shape == waveform.size() == output_waveform.size() + + def test_9th_order_filter_stability(self): + """ + Validate the precision of lfilter against reference scipy implementation when using high order filter. + The reference implementation use cascaded second-order filters so is more numerically accurate. + """ + # create an impulse signal + x = torch.zeros(1024, dtype=self.dtype, device=self.device) + x[0] = 1 + + # get target impulse response + sos = signal.butter(9, 850, 'hp', fs=22050, output='sos') + y = torch.from_numpy(signal.sosfilt(sos, x.cpu().numpy())).to(self.dtype).to(self.device) + + # get lfilter coefficients + b, a = signal.butter(9, 850, 'hp', fs=22050, output='ba') + b, a = torch.from_numpy(b).to(self.dtype).to(self.device), torch.from_numpy( + a).to(self.dtype).to(self.device) + + # predict impulse response + yhat = F.lfilter(x, a, b, False) + self.assertEqual(yhat, y, atol=1e-4, rtol=1e-5) + class Spectrogram(common_utils.TestBaseMixin): @parameterized.expand([(0., ), (1., ), (2., ), (3., )]) diff --git a/test/torchaudio_unittest/functional/kaldi_compatibility_cpu_test.py b/test/torchaudio_unittest/functional/kaldi_compatibility_cpu_test.py new file mode 100644 index 0000000000..90a634746c --- /dev/null +++ b/test/torchaudio_unittest/functional/kaldi_compatibility_cpu_test.py @@ -0,0 +1,19 @@ +import torch + +from torchaudio_unittest.common_utils import PytorchTestCase +from .kaldi_compatibility_test_impl import Kaldi, KaldiCPUOnly + + +class TestKaldiCPUOnly(KaldiCPUOnly, PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') + + +class TestKaldiFloat32(Kaldi, PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') + + +class TestKaldiFloat64(Kaldi, PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') diff --git a/test/torchaudio_unittest/functional/kaldi_compatibility_cuda_test.py b/test/torchaudio_unittest/functional/kaldi_compatibility_cuda_test.py new file mode 100644 index 0000000000..47a1bea338 --- /dev/null +++ b/test/torchaudio_unittest/functional/kaldi_compatibility_cuda_test.py @@ -0,0 +1,16 @@ +import torch + +from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda +from .kaldi_compatibility_test_impl import Kaldi + + +@skipIfNoCuda +class TestKaldiFloat32(Kaldi, PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@skipIfNoCuda +class TestKaldiFloat64(Kaldi, PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/torchaudio_unittest/functional/kaldi_compatibility_test_impl.py b/test/torchaudio_unittest/functional/kaldi_compatibility_test_impl.py new file mode 100644 index 0000000000..bad30afd9a --- /dev/null +++ b/test/torchaudio_unittest/functional/kaldi_compatibility_test_impl.py @@ -0,0 +1,60 @@ +from parameterized import parameterized +import torch +import torchaudio.functional as F + +from torchaudio_unittest.common_utils import ( + get_sinusoid, + load_params, + save_wav, + skipIfNoExec, + TempDirMixin, + TestBaseMixin, +) +from torchaudio_unittest.common_utils.kaldi_utils import ( + convert_args, + run_kaldi, +) + + +class Kaldi(TempDirMixin, TestBaseMixin): + def assert_equal(self, output, *, expected, rtol=None, atol=None): + expected = expected.to(dtype=self.dtype, device=self.device) + self.assertEqual(output, expected, rtol=rtol, atol=atol) + + @skipIfNoExec('apply-cmvn-sliding') + def test_sliding_window_cmn(self): + """sliding_window_cmn should be numerically compatible with apply-cmvn-sliding""" + kwargs = { + 'cmn_window': 600, + 'min_cmn_window': 100, + 'center': False, + 'norm_vars': False, + } + + tensor = torch.randn(40, 10, dtype=self.dtype, device=self.device) + result = F.sliding_window_cmn(tensor, **kwargs) + command = ['apply-cmvn-sliding'] + convert_args(**kwargs) + ['ark:-', 'ark:-'] + kaldi_result = run_kaldi(command, 'ark', tensor) + self.assert_equal(result, expected=kaldi_result) + + +class KaldiCPUOnly(TempDirMixin, TestBaseMixin): + def assert_equal(self, output, *, expected, rtol=None, atol=None): + expected = expected.to(dtype=self.dtype, device=self.device) + self.assertEqual(output, expected, rtol=rtol, atol=atol) + + @parameterized.expand(load_params('kaldi_test_pitch_args.jsonl')) + @skipIfNoExec('compute-kaldi-pitch-feats') + def test_pitch_feats(self, kwargs): + """compute_kaldi_pitch produces numerically compatible result with compute-kaldi-pitch-feats""" + sample_rate = kwargs['sample_rate'] + waveform = get_sinusoid(dtype='float32', sample_rate=sample_rate) + result = F.compute_kaldi_pitch(waveform[0], **kwargs) + + waveform = get_sinusoid(dtype='int16', sample_rate=sample_rate) + wave_file = self.get_temp_path('test.wav') + save_wav(wave_file, waveform, sample_rate) + + command = ['compute-kaldi-pitch-feats'] + convert_args(**kwargs) + ['scp:-', 'ark:-'] + kaldi_result = run_kaldi(command, 'scp', wave_file) + self.assert_equal(result, expected=kaldi_result) diff --git a/test/torchaudio_unittest/functional/librosa_compatibility_test.py b/test/torchaudio_unittest/functional/librosa_compatibility_test.py new file mode 100644 index 0000000000..c88d9f3ccd --- /dev/null +++ b/test/torchaudio_unittest/functional/librosa_compatibility_test.py @@ -0,0 +1,155 @@ +import itertools +import unittest +from distutils.version import StrictVersion + +import torch +from parameterized import parameterized, param + +import torchaudio.functional as F +from torchaudio._internal.module_utils import is_module_available + +LIBROSA_AVAILABLE = is_module_available('librosa') + +if LIBROSA_AVAILABLE: + import numpy as np + import librosa + +from torchaudio_unittest import common_utils + + +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") +class TestFunctional(common_utils.TorchaudioTestCase): + """Test suite for functions in `functional` module.""" + def test_griffinlim(self): + # NOTE: This test is flaky without a fixed random seed + # See https://github.com/pytorch/audio/issues/382 + torch.random.manual_seed(42) + tensor = torch.rand((1, 1000)) + + n_fft = 400 + ws = 400 + hop = 100 + window = torch.hann_window(ws) + normalize = False + momentum = 0.99 + n_iter = 8 + length = 1000 + rand_init = False + init = 'random' if rand_init else None + + specgram = F.spectrogram(tensor, 0, window, n_fft, hop, ws, 2, normalize).sqrt() + ta_out = F.griffinlim(specgram, window, n_fft, hop, ws, 1, + n_iter, momentum, length, rand_init) + lr_out = librosa.griffinlim(specgram.squeeze(0).numpy(), n_iter=n_iter, hop_length=hop, + momentum=momentum, init=init, length=length) + lr_out = torch.from_numpy(lr_out).unsqueeze(0) + + self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) + + @parameterized.expand([ + param(norm=norm, mel_scale=mel_scale, **p.kwargs) + for p in [ + param(), + param(n_mels=128, sample_rate=44100), + param(n_mels=128, fmin=2000.0, fmax=5000.0), + param(n_mels=56, fmin=100.0, fmax=9000.0), + param(n_mels=56, fmin=800.0, fmax=900.0), + param(n_mels=56, fmin=1900.0, fmax=900.0), + param(n_mels=10, fmin=1900.0, fmax=900.0), + ] + for norm in [None, 'slaney'] + for mel_scale in ['htk', 'slaney'] + ]) + def test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, + fmin=0.0, fmax=8000.0, norm=None, mel_scale="htk"): + if (norm == "slaney" and StrictVersion(librosa.__version__) < StrictVersion("0.7.2")): + self.skipTest('Test is known to fail with older versions of librosa.') + + librosa_fb = librosa.filters.mel(sr=sample_rate, + n_fft=n_fft, + n_mels=n_mels, + fmax=fmax, + fmin=fmin, + htk=mel_scale == "htk", + norm=norm) + fb = F.create_fb_matrix(sample_rate=sample_rate, + n_mels=n_mels, + f_max=fmax, + f_min=fmin, + n_freqs=(n_fft // 2 + 1), + norm=norm, + mel_scale=mel_scale) + + for i_mel_bank in range(n_mels): + self.assertEqual( + fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4, rtol=1e-5) + + def test_amplitude_to_DB(self): + spec = torch.rand((6, 201)) + + amin = 1e-10 + db_multiplier = 0.0 + top_db = 80.0 + + # Power to DB + multiplier = 10.0 + + ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) + lr_out = librosa.core.power_to_db(spec.numpy()) + lr_out = torch.from_numpy(lr_out) + + self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) + + # Amplitude to DB + multiplier = 20.0 + + ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) + lr_out = librosa.core.amplitude_to_db(spec.numpy()) + lr_out = torch.from_numpy(lr_out) + + self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) + + +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") +class TestPhaseVocoder(common_utils.TorchaudioTestCase): + @parameterized.expand(list(itertools.product( + [(2, 1025, 400, 2)], + [0.5, 1.01, 1.3], + [256] + ))) + def test_phase_vocoder(self, shape, rate, hop_length): + # Due to cummulative sum, numerical error in using torch.float32 will + # result in bottom right values of the stretched sectrogram to not + # match with librosa. + torch.random.manual_seed(42) + complex_specgrams = torch.randn(*shape) + complex_specgrams = complex_specgrams.type(torch.float64) + phase_advance = torch.linspace( + 0, + np.pi * hop_length, + complex_specgrams.shape[-3], + dtype=torch.float64)[..., None] + + complex_specgrams_stretch = F.phase_vocoder(complex_specgrams, rate=rate, phase_advance=phase_advance) + + # == Test shape + expected_size = list(complex_specgrams.size()) + expected_size[-2] = int(np.ceil(expected_size[-2] / rate)) + + assert complex_specgrams.dim() == complex_specgrams_stretch.dim() + assert complex_specgrams_stretch.size() == torch.Size(expected_size) + + # == Test values + index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3 + mono_complex_specgram = complex_specgrams[index].numpy() + mono_complex_specgram = mono_complex_specgram[..., 0] + \ + mono_complex_specgram[..., 1] * 1j + expected_complex_stretch = librosa.phase_vocoder( + mono_complex_specgram, + rate=rate, + hop_length=hop_length) + + complex_stretch = complex_specgrams_stretch[index].numpy() + complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1] + + self.assertEqual(complex_stretch, torch.from_numpy(expected_complex_stretch), atol=1e-5, rtol=1e-5) diff --git a/test/torchaudio_unittest/sox_compatibility_test.py b/test/torchaudio_unittest/functional/sox_compatibility_test.py similarity index 90% rename from test/torchaudio_unittest/sox_compatibility_test.py rename to test/torchaudio_unittest/functional/sox_compatibility_test.py index c68e529427..21f082683c 100644 --- a/test/torchaudio_unittest/sox_compatibility_test.py +++ b/test/torchaudio_unittest/functional/sox_compatibility_test.py @@ -1,7 +1,5 @@ import torch import torchaudio.functional as F -import torchaudio.transforms as T -from parameterized import parameterized from torchaudio_unittest.common_utils import ( skipIfNoSoxBackend, @@ -299,31 +297,3 @@ def test_perf_biquad_filtering(self): data, path = self.get_whitenoise() result = F.lfilter(data, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2])) self.assert_sox_effect(result, path, ['biquad', b0, b1, b2, a0, a1, a2]) - - @parameterized.expand([ - ('q', 'quarter_sine'), - ('h', 'half_sine'), - ('t', 'linear'), - ]) - def test_fade(self, fade_shape_sox, fade_shape): - fade_in_len, fade_out_len = 44100, 44100 - data, path = self.get_whitenoise(sample_rate=44100) - result = T.Fade(fade_in_len, fade_out_len, fade_shape)(data) - self.assert_sox_effect(result, path, ['fade', fade_shape_sox, '1', '0', '1']) - - @parameterized.expand([ - ('amplitude', 1.1), - ('db', 2), - ('power', 2), - ]) - def test_vol(self, gain_type, gain): - data, path = self.get_whitenoise() - result = T.Vol(gain, gain_type)(data) - self.assert_sox_effect(result, path, ['vol', f'{gain}', gain_type]) - - @parameterized.expand(['vad-go-stereo-44100.wav', 'vad-go-mono-32000.wav']) - def test_vad(self, filename): - path = get_asset_path(filename) - data, sample_rate = load_wav(path) - result = T.Vad(sample_rate)(data) - self.assert_sox_effect(result, path, ['vad']) diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py index 83f7e8e95d..b6aec04d9c 100644 --- a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py @@ -41,12 +41,11 @@ def func(tensor): hop = 200 window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) power = 2. - normalize = False momentum = 0.99 n_iter = 32 length = 1000 rand_int = False - return F.griffinlim(tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, rand_int) + return F.griffinlim(tensor, window, n_fft, hop, ws, power, n_iter, momentum, length, rand_int) tensor = torch.rand((1, 201, 6)) self._assert_consistency(func, tensor) @@ -547,3 +546,30 @@ def func(tensor): tensor = common_utils.get_whitenoise(sample_rate=44100) self._assert_consistency(func, tensor) + + def test_phase_vocoder(self): + def func(tensor, device: torch.device = self.device): + rate = 0.5 + hop_length = 256 + phase_advance = torch.linspace( + 0, + 3.14 * hop_length, + tensor.shape[-3], + dtype=torch.float64, + ).to(device)[..., None] + return F.phase_vocoder(tensor, rate, phase_advance) + + tensor = torch.randn(2, 1025, 400, 2) + self._assert_consistency(func, tensor) + + @common_utils.skipIfNoKaldi + def test_compute_kaldi_pitch(self): + if self.dtype != torch.float32 or self.device != torch.device('cpu'): + raise unittest.SkipTest("Only float32, cpu is supported.") + + def func(tensor): + sample_rate: float = 44100. + return F.compute_kaldi_pitch(tensor, sample_rate) + + tensor = common_utils.get_whitenoise(sample_rate=44100) + self._assert_consistency(func, tensor) diff --git a/test/torchaudio_unittest/kaldi_compatibility_impl.py b/test/torchaudio_unittest/kaldi_compatibility_impl.py deleted file mode 100644 index 983805fff5..0000000000 --- a/test/torchaudio_unittest/kaldi_compatibility_impl.py +++ /dev/null @@ -1,103 +0,0 @@ -"""Test suites for checking numerical compatibility against Kaldi""" -import subprocess - -import kaldi_io -import torch -import torchaudio.functional as F -import torchaudio.compliance.kaldi -from parameterized import parameterized - -from torchaudio_unittest.common_utils import ( - TestBaseMixin, - load_params, - skipIfNoExec, - get_asset_path, - load_wav -) - - -def _convert_args(**kwargs): - args = [] - for key, value in kwargs.items(): - key = '--' + key.replace('_', '-') - value = str(value).lower() if value in [True, False] else str(value) - args.append('%s=%s' % (key, value)) - return args - - -def _run_kaldi(command, input_type, input_value): - """Run provided Kaldi command, pass a tensor and get the resulting tensor - - Args: - input_type: str - 'ark' or 'scp' - input_value: - Tensor for 'ark' - string for 'scp' (path to an audio file) - """ - key = 'foo' - process = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) - if input_type == 'ark': - kaldi_io.write_mat(process.stdin, input_value.cpu().numpy(), key=key) - elif input_type == 'scp': - process.stdin.write(f'{key} {input_value}'.encode('utf8')) - else: - raise NotImplementedError('Unexpected type') - process.stdin.close() - result = dict(kaldi_io.read_mat_ark(process.stdout))['foo'] - return torch.from_numpy(result.copy()) # copy supresses some torch warning - - -class Kaldi(TestBaseMixin): - def assert_equal(self, output, *, expected, rtol=None, atol=None): - expected = expected.to(dtype=self.dtype, device=self.device) - self.assertEqual(output, expected, rtol=rtol, atol=atol) - - @skipIfNoExec('apply-cmvn-sliding') - def test_sliding_window_cmn(self): - """sliding_window_cmn should be numerically compatible with apply-cmvn-sliding""" - kwargs = { - 'cmn_window': 600, - 'min_cmn_window': 100, - 'center': False, - 'norm_vars': False, - } - - tensor = torch.randn(40, 10, dtype=self.dtype, device=self.device) - result = F.sliding_window_cmn(tensor, **kwargs) - command = ['apply-cmvn-sliding'] + _convert_args(**kwargs) + ['ark:-', 'ark:-'] - kaldi_result = _run_kaldi(command, 'ark', tensor) - self.assert_equal(result, expected=kaldi_result) - - @parameterized.expand(load_params('kaldi_test_fbank_args.json')) - @skipIfNoExec('compute-fbank-feats') - def test_fbank(self, kwargs): - """fbank should be numerically compatible with compute-fbank-feats""" - wave_file = get_asset_path('kaldi_file.wav') - waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) - result = torchaudio.compliance.kaldi.fbank(waveform, **kwargs) - command = ['compute-fbank-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] - kaldi_result = _run_kaldi(command, 'scp', wave_file) - self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) - - @parameterized.expand(load_params('kaldi_test_spectrogram_args.json')) - @skipIfNoExec('compute-spectrogram-feats') - def test_spectrogram(self, kwargs): - """spectrogram should be numerically compatible with compute-spectrogram-feats""" - wave_file = get_asset_path('kaldi_file.wav') - waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) - result = torchaudio.compliance.kaldi.spectrogram(waveform, **kwargs) - command = ['compute-spectrogram-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] - kaldi_result = _run_kaldi(command, 'scp', wave_file) - self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) - - @parameterized.expand(load_params('kaldi_test_mfcc_args.json')) - @skipIfNoExec('compute-mfcc-feats') - def test_mfcc(self, kwargs): - """mfcc should be numerically compatible with compute-mfcc-feats""" - wave_file = get_asset_path('kaldi_file.wav') - waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) - result = torchaudio.compliance.kaldi.mfcc(waveform, **kwargs) - command = ['compute-mfcc-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] - kaldi_result = _run_kaldi(command, 'scp', wave_file) - self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) diff --git a/test/torchaudio_unittest/librosa_compatibility_test.py b/test/torchaudio_unittest/librosa_compatibility_test.py deleted file mode 100644 index 9e070b141a..0000000000 --- a/test/torchaudio_unittest/librosa_compatibility_test.py +++ /dev/null @@ -1,366 +0,0 @@ -"""Test suites for numerical compatibility with librosa""" -import os -import unittest -from distutils.version import StrictVersion - -import torch -import torchaudio -import torchaudio.functional as F -from torchaudio._internal.module_utils import is_module_available -from parameterized import parameterized -import itertools - -LIBROSA_AVAILABLE = is_module_available('librosa') - -if LIBROSA_AVAILABLE: - import numpy as np - import librosa - import scipy - -from torchaudio_unittest import common_utils - - -@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") -class TestFunctional(common_utils.TorchaudioTestCase): - """Test suite for functions in `functional` module.""" - def test_griffinlim(self): - # NOTE: This test is flaky without a fixed random seed - # See https://github.com/pytorch/audio/issues/382 - torch.random.manual_seed(42) - tensor = torch.rand((1, 1000)) - - n_fft = 400 - ws = 400 - hop = 100 - window = torch.hann_window(ws) - normalize = False - momentum = 0.99 - n_iter = 8 - length = 1000 - rand_init = False - init = 'random' if rand_init else None - - specgram = F.spectrogram(tensor, 0, window, n_fft, hop, ws, 2, normalize).sqrt() - ta_out = F.griffinlim(specgram, window, n_fft, hop, ws, 1, normalize, - n_iter, momentum, length, rand_init) - lr_out = librosa.griffinlim(specgram.squeeze(0).numpy(), n_iter=n_iter, hop_length=hop, - momentum=momentum, init=init, length=length) - lr_out = torch.from_numpy(lr_out).unsqueeze(0) - - self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) - - def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0, norm=None): - librosa_fb = librosa.filters.mel(sr=sample_rate, - n_fft=n_fft, - n_mels=n_mels, - fmax=fmax, - fmin=fmin, - htk=True, - norm=norm) - fb = F.create_fb_matrix(sample_rate=sample_rate, - n_mels=n_mels, - f_max=fmax, - f_min=fmin, - n_freqs=(n_fft // 2 + 1), - norm=norm) - - for i_mel_bank in range(n_mels): - self.assertEqual( - fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4, rtol=1e-5) - - def test_create_fb(self): - self._test_create_fb() - self._test_create_fb(n_mels=128, sample_rate=44100) - self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0) - self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0) - self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0) - self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0) - self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0) - if StrictVersion(librosa.__version__) < StrictVersion("0.7.2"): - return - self._test_create_fb(n_mels=128, sample_rate=44100, norm="slaney") - self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0, norm="slaney") - self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0, norm="slaney") - self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0, norm="slaney") - self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0, norm="slaney") - self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0, norm="slaney") - - def test_amplitude_to_DB(self): - spec = torch.rand((6, 201)) - - amin = 1e-10 - db_multiplier = 0.0 - top_db = 80.0 - - # Power to DB - multiplier = 10.0 - - ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) - lr_out = librosa.core.power_to_db(spec.numpy()) - lr_out = torch.from_numpy(lr_out) - - self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) - - # Amplitude to DB - multiplier = 20.0 - - ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) - lr_out = librosa.core.amplitude_to_db(spec.numpy()) - lr_out = torch.from_numpy(lr_out) - - self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) - - -@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") -class TestPhaseVocoder(common_utils.TorchaudioTestCase): - @parameterized.expand(list(itertools.product( - [(2, 1025, 400, 2)], - [0.5, 1.01, 1.3], - [256] - ))) - def test_phase_vocoder(self, shape, rate, hop_length): - # Due to cummulative sum, numerical error in using torch.float32 will - # result in bottom right values of the stretched sectrogram to not - # match with librosa. - torch.random.manual_seed(42) - complex_specgrams = torch.randn(*shape) - complex_specgrams = complex_specgrams.type(torch.float64) - phase_advance = torch.linspace( - 0, - np.pi * hop_length, - complex_specgrams.shape[-3], - dtype=torch.float64)[..., None] - - complex_specgrams_stretch = F.phase_vocoder(complex_specgrams, rate=rate, phase_advance=phase_advance) - - # == Test shape - expected_size = list(complex_specgrams.size()) - expected_size[-2] = int(np.ceil(expected_size[-2] / rate)) - - assert complex_specgrams.dim() == complex_specgrams_stretch.dim() - assert complex_specgrams_stretch.size() == torch.Size(expected_size) - - # == Test values - index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3 - mono_complex_specgram = complex_specgrams[index].numpy() - mono_complex_specgram = mono_complex_specgram[..., 0] + \ - mono_complex_specgram[..., 1] * 1j - expected_complex_stretch = librosa.phase_vocoder( - mono_complex_specgram, - rate=rate, - hop_length=hop_length) - - complex_stretch = complex_specgrams_stretch[index].numpy() - complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1] - - self.assertEqual(complex_stretch, torch.from_numpy(expected_complex_stretch), atol=1e-5, rtol=1e-5) - - -def _load_audio_asset(*asset_paths, **kwargs): - file_path = common_utils.get_asset_path(*asset_paths) - sound, sample_rate = torchaudio.load(file_path, **kwargs) - return sound, sample_rate - - -@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") -class TestTransforms(common_utils.TorchaudioTestCase): - """Test suite for functions in `transforms` module.""" - def assert_compatibilities(self, n_fft, hop_length, power, n_mels, n_mfcc, sample_rate): - common_utils.set_audio_backend('default') - path = common_utils.get_asset_path('sinewave.wav') - sound, sample_rate = common_utils.load_wav(path) - sound_librosa = sound.cpu().numpy().squeeze() # (64000) - - # test core spectrogram - spect_transform = torchaudio.transforms.Spectrogram( - n_fft=n_fft, hop_length=hop_length, power=power) - out_librosa, _ = librosa.core.spectrum._spectrogram( - y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) - - out_torch = spect_transform(sound).squeeze().cpu() - self.assertEqual(out_torch, torch.from_numpy(out_librosa), atol=1e-5, rtol=1e-5) - - # test mel spectrogram - melspect_transform = torchaudio.transforms.MelSpectrogram( - sample_rate=sample_rate, window_fn=torch.hann_window, - hop_length=hop_length, n_mels=n_mels, n_fft=n_fft) - librosa_mel = librosa.feature.melspectrogram( - y=sound_librosa, sr=sample_rate, n_fft=n_fft, - hop_length=hop_length, n_mels=n_mels, htk=True, norm=None) - librosa_mel_tensor = torch.from_numpy(librosa_mel) - torch_mel = melspect_transform(sound).squeeze().cpu() - self.assertEqual( - torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3, rtol=1e-5) - - # test s2db - power_to_db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.) - power_to_db_torch = power_to_db_transform(spect_transform(sound)).squeeze().cpu() - power_to_db_librosa = librosa.core.spectrum.power_to_db(out_librosa) - self.assertEqual(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3, rtol=1e-5) - - mag_to_db_transform = torchaudio.transforms.AmplitudeToDB('magnitude', 80.) - mag_to_db_torch = mag_to_db_transform(torch.abs(sound)).squeeze().cpu() - mag_to_db_librosa = librosa.core.spectrum.amplitude_to_db(sound_librosa) - self.assertEqual(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3, rtol=1e-5) - - power_to_db_torch = power_to_db_transform(melspect_transform(sound)).squeeze().cpu() - db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) - db_librosa_tensor = torch.from_numpy(db_librosa) - self.assertEqual( - power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3, rtol=1e-5) - - # test MFCC - melkwargs = {'hop_length': hop_length, 'n_fft': n_fft} - mfcc_transform = torchaudio.transforms.MFCC( - sample_rate=sample_rate, n_mfcc=n_mfcc, norm='ortho', melkwargs=melkwargs) - - # librosa.feature.mfcc doesn't pass kwargs properly since some of the - # kwargs for melspectrogram and mfcc are the same. We just follow the - # function body in - # https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram - # to mirror this function call with correct args: - # - # librosa_mfcc = librosa.feature.mfcc( - # y=sound_librosa, sr=sample_rate, n_mfcc = n_mfcc, - # hop_length=hop_length, n_fft=n_fft, htk=True, norm=None, n_mels=n_mels) - - librosa_mfcc = scipy.fftpack.dct(db_librosa, axis=0, type=2, norm='ortho')[:n_mfcc] - librosa_mfcc_tensor = torch.from_numpy(librosa_mfcc) - torch_mfcc = mfcc_transform(sound).squeeze().cpu() - - self.assertEqual( - torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3, rtol=1e-5) - - self.assert_compatibilities_spectral_centroid(sample_rate, n_fft, hop_length, sound, sound_librosa) - - def assert_compatibilities_spectral_centroid(self, sample_rate, n_fft, hop_length, sound, sound_librosa): - spect_centroid = torchaudio.transforms.SpectralCentroid( - sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length) - out_torch = spect_centroid(sound).squeeze().cpu() - - out_librosa = librosa.feature.spectral_centroid( - y=sound_librosa, sr=sample_rate, n_fft=n_fft, hop_length=hop_length) - out_librosa = torch.from_numpy(out_librosa)[0] - - self.assertEqual(out_torch.type(out_librosa.dtype), out_librosa, atol=1e-5, rtol=1e-5) - - def test_basics1(self): - kwargs = { - 'n_fft': 400, - 'hop_length': 200, - 'power': 2.0, - 'n_mels': 128, - 'n_mfcc': 40, - 'sample_rate': 16000 - } - self.assert_compatibilities(**kwargs) - - def test_basics2(self): - kwargs = { - 'n_fft': 600, - 'hop_length': 100, - 'power': 2.0, - 'n_mels': 128, - 'n_mfcc': 20, - 'sample_rate': 16000 - } - self.assert_compatibilities(**kwargs) - - # NOTE: Test passes offline, but fails on TravisCI (and CircleCI), see #372. - @unittest.skipIf('CI' in os.environ, 'Test is known to fail on CI') - def test_basics3(self): - kwargs = { - 'n_fft': 200, - 'hop_length': 50, - 'power': 2.0, - 'n_mels': 128, - 'n_mfcc': 50, - 'sample_rate': 24000 - } - self.assert_compatibilities(**kwargs) - - def test_basics4(self): - kwargs = { - 'n_fft': 400, - 'hop_length': 200, - 'power': 3.0, - 'n_mels': 128, - 'n_mfcc': 40, - 'sample_rate': 16000 - } - self.assert_compatibilities(**kwargs) - - def test_MelScale(self): - """MelScale transform is comparable to that of librosa""" - n_fft = 2048 - n_mels = 256 - hop_length = n_fft // 4 - sample_rate = 44100 - sound = common_utils.get_whitenoise(sample_rate=sample_rate, duration=60) - sound = sound.mean(dim=0, keepdim=True) - spec_ta = F.spectrogram( - sound, pad=0, window=torch.hann_window(n_fft), n_fft=n_fft, - hop_length=hop_length, win_length=n_fft, power=2, normalized=False) - spec_lr = spec_ta.cpu().numpy().squeeze() - # Perform MelScale with torchaudio and librosa - melspec_ta = torchaudio.transforms.MelScale(n_mels=n_mels, sample_rate=sample_rate)(spec_ta) - melspec_lr = librosa.feature.melspectrogram( - S=spec_lr, sr=sample_rate, n_fft=n_fft, hop_length=hop_length, - win_length=n_fft, center=True, window='hann', n_mels=n_mels, htk=True, norm=None) - # Note: Using relaxed rtol instead of atol - self.assertEqual(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), atol=1e-8, rtol=1e-3) - - def test_InverseMelScale(self): - """InverseMelScale transform is comparable to that of librosa""" - n_fft = 2048 - n_mels = 256 - n_stft = n_fft // 2 + 1 - hop_length = n_fft // 4 - - # Prepare mel spectrogram input. We use torchaudio to compute one. - path = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - sound, sample_rate = common_utils.load_wav(path) - sound = sound[:, 2**10:2**10 + 2**14] - sound = sound.mean(dim=0, keepdim=True) - spec_orig = F.spectrogram( - sound, pad=0, window=torch.hann_window(n_fft), n_fft=n_fft, - hop_length=hop_length, win_length=n_fft, power=2, normalized=False) - melspec_ta = torchaudio.transforms.MelScale(n_mels=n_mels, sample_rate=sample_rate)(spec_orig) - melspec_lr = melspec_ta.cpu().numpy().squeeze() - # Perform InverseMelScale with torch audio and librosa - spec_ta = torchaudio.transforms.InverseMelScale( - n_stft, n_mels=n_mels, sample_rate=sample_rate)(melspec_ta) - spec_lr = librosa.feature.inverse.mel_to_stft( - melspec_lr, sr=sample_rate, n_fft=n_fft, power=2.0, htk=True, norm=None) - spec_lr = torch.from_numpy(spec_lr[None, ...]) - - # Align dimensions - # librosa does not return power spectrogram while torchaudio returns power spectrogram - spec_orig = spec_orig.sqrt() - spec_ta = spec_ta.sqrt() - - threshold = 2.0 - # This threshold was choosen empirically, based on the following observation - # - # torch.dist(spec_lr, spec_ta, p=float('inf')) - # >>> tensor(1.9666) - # - # The spectrograms reconstructed by librosa and torchaudio are not comparable elementwise. - # This is because they use different approximation algorithms and resulting values can live - # in different magnitude. (although most of them are very close) - # See - # https://github.com/pytorch/audio/pull/366 for the discussion of the choice of algorithm - # https://github.com/pytorch/audio/pull/448/files#r385747021 for the distribution of P-inf - # distance over frequencies. - self.assertEqual(spec_ta, spec_lr, atol=threshold, rtol=1e-5) - - threshold = 1700.0 - # This threshold was choosen empirically, based on the following observations - # - # torch.dist(spec_orig, spec_ta, p=1) - # >>> tensor(1644.3516) - # torch.dist(spec_orig, spec_lr, p=1) - # >>> tensor(1420.7103) - # torch.dist(spec_lr, spec_ta, p=1) - # >>> tensor(943.2759) - assert torch.dist(spec_orig, spec_ta, p=1) < threshold diff --git a/test/torchaudio_unittest/sox_effect/dataset_test.py b/test/torchaudio_unittest/sox_effect/dataset_test.py index 7e89960186..0b8cc9c4d8 100644 --- a/test/torchaudio_unittest/sox_effect/dataset_test.py +++ b/test/torchaudio_unittest/sox_effect/dataset_test.py @@ -11,7 +11,7 @@ from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, - skipIfNoExtension, + skipIfNoSox, get_whitenoise, save_wav, ) @@ -71,7 +71,7 @@ def init_random_seed(worker_id): dataset.rng = np.random.RandomState(worker_id) -@skipIfNoExtension +@skipIfNoSox @skipIf( platform.system() == 'Darwin' and sys.version_info.major == 3 and @@ -134,7 +134,7 @@ def speed(path): return torchaudio.sox_effects.apply_effects_tensor(wav, sample_rate, effects)[0] -@skipIfNoExtension +@skipIfNoSox class TestProcessPoolExecutor(TempDirMixin, PytorchTestCase): backend = "sox_io" diff --git a/test/torchaudio_unittest/sox_effect/smoke_test.py b/test/torchaudio_unittest/sox_effect/smoke_test.py index 3b2b34d3ec..70a6a346ea 100644 --- a/test/torchaudio_unittest/sox_effect/smoke_test.py +++ b/test/torchaudio_unittest/sox_effect/smoke_test.py @@ -4,7 +4,7 @@ from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, - skipIfNoExtension, + skipIfNoSox, get_wav_data, get_sinusoid, save_wav, @@ -14,7 +14,7 @@ ) -@skipIfNoExtension +@skipIfNoSox class SmokeTest(TempDirMixin, TorchaudioTestCase): """Run smoke test on various effects @@ -25,7 +25,7 @@ class SmokeTest(TempDirMixin, TorchaudioTestCase): however without such tools, the correctness of each function cannot be verified. """ @parameterized.expand( - load_params("sox_effect_test_args.json"), + load_params("sox_effect_test_args.jsonl"), name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', ) def test_apply_effects_tensor(self, args): @@ -39,7 +39,7 @@ def test_apply_effects_tensor(self, args): _found, _sr = sox_effects.apply_effects_tensor(original, input_sr, effects) @parameterized.expand( - load_params("sox_effect_test_args.json"), + load_params("sox_effect_test_args.jsonl"), name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', ) def test_apply_effects_file(self, args): @@ -58,7 +58,7 @@ def test_apply_effects_file(self, args): input_path, effects, normalize=False, channels_first=channels_first) @parameterized.expand( - load_params("sox_effect_test_args.json"), + load_params("sox_effect_test_args.jsonl"), name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', ) def test_apply_effects_fileobj(self, args): diff --git a/test/torchaudio_unittest/sox_effect/sox_effect_test.py b/test/torchaudio_unittest/sox_effect/sox_effect_test.py index ab334613e3..ca93e7d41b 100644 --- a/test/torchaudio_unittest/sox_effect/sox_effect_test.py +++ b/test/torchaudio_unittest/sox_effect/sox_effect_test.py @@ -11,7 +11,7 @@ TempDirMixin, HttpServerMixin, PytorchTestCase, - skipIfNoExtension, + skipIfNoSox, skipIfNoModule, skipIfNoExec, get_asset_path, @@ -31,7 +31,7 @@ import requests -@skipIfNoExtension +@skipIfNoSox class TestSoxEffects(PytorchTestCase): def test_init(self): """Calling init_sox_effects multiple times does not crush""" @@ -39,7 +39,7 @@ def test_init(self): sox_effects.init_sox_effects() -@skipIfNoExtension +@skipIfNoSox class TestSoxEffectsTensor(TempDirMixin, PytorchTestCase): """Test suite for `apply_effects_tensor` function""" @parameterized.expand(list(itertools.product( @@ -64,7 +64,7 @@ def test_apply_no_effect(self, dtype, sample_rate, num_channels, channels_first) self.assertEqual(expected, found) @parameterized.expand( - load_params("sox_effect_test_args.json"), + load_params("sox_effect_test_args.jsonl"), name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', ) def test_apply_effects(self, args): @@ -91,7 +91,7 @@ def test_apply_effects(self, args): self.assertEqual(expected, found) -@skipIfNoExtension +@skipIfNoSox class TestSoxEffectsFile(TempDirMixin, PytorchTestCase): """Test suite for `apply_effects_file` function""" @parameterized.expand(list(itertools.product( @@ -113,7 +113,7 @@ def test_apply_no_effect(self, dtype, sample_rate, num_channels, channels_first) self.assertEqual(expected, found) @parameterized.expand( - load_params("sox_effect_test_args.json"), + load_params("sox_effect_test_args.jsonl"), name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', ) def test_apply_effects_str(self, args): @@ -163,7 +163,7 @@ def test_apply_effects_path(self): self.assertEqual(found, expected) -@skipIfNoExtension +@skipIfNoSox class TestFileFormats(TempDirMixin, PytorchTestCase): """`apply_effects_file` gives the same result as sox on various file formats""" @parameterized.expand(list(itertools.product( @@ -256,7 +256,7 @@ def test_vorbis(self, sample_rate, num_channels): self.assertEqual(found, expected) -@skipIfNoExtension +@skipIfNoSox class TestApplyEffectFileWithoutExtension(PytorchTestCase): def test_mp3(self): """Providing format allows to read mp3 without extension @@ -275,7 +275,7 @@ def test_mp3(self): @skipIfNoExec('sox') -@skipIfNoExtension +@skipIfNoSox class TestFileObject(TempDirMixin, PytorchTestCase): @parameterized.expand([ ('wav', None), @@ -384,7 +384,7 @@ def test_tarfile(self, ext, compression): self.assertEqual(found, expected) -@skipIfNoExtension +@skipIfNoSox @skipIfNoExec('sox') @skipIfNoModule("requests") class TestFileObjectHttp(HttpServerMixin, PytorchTestCase): diff --git a/test/torchaudio_unittest/sox_effect/torchscript_test.py b/test/torchaudio_unittest/sox_effect/torchscript_test.py index 8e1a9a3980..3940e72931 100644 --- a/test/torchaudio_unittest/sox_effect/torchscript_test.py +++ b/test/torchaudio_unittest/sox_effect/torchscript_test.py @@ -7,7 +7,7 @@ from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, - skipIfNoExtension, + skipIfNoSox, get_sinusoid, save_wav, ) @@ -43,10 +43,10 @@ def forward(self, path: str): return sox_effects.apply_effects_file(path, self.effects, self.channels_first) -@skipIfNoExtension +@skipIfNoSox class TestTorchScript(TempDirMixin, PytorchTestCase): @parameterized.expand( - load_params("sox_effect_test_args.json"), + load_params("sox_effect_test_args.jsonl"), name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', ) def test_apply_effects_tensor(self, args): @@ -72,7 +72,7 @@ def test_apply_effects_tensor(self, args): self.assertEqual(expected, found) @parameterized.expand( - load_params("sox_effect_test_args.json"), + load_params("sox_effect_test_args.jsonl"), name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', ) def test_apply_effects_file(self, args): diff --git a/test/torchaudio_unittest/transducer_test.py b/test/torchaudio_unittest/transducer_test.py index 3d8dc0683d..209794a5b5 100644 --- a/test/torchaudio_unittest/transducer_test.py +++ b/test/torchaudio_unittest/transducer_test.py @@ -1,7 +1,8 @@ +import unittest import torch from torchaudio.prototype.transducer import RNNTLoss -from torchaudio_unittest import common_utils +from torchaudio_unittest.common_utils import TorchaudioTestCase def get_data_basic(device): @@ -241,6 +242,14 @@ def compute_with_pytorch_transducer(data): return costs, gradients +def skipIfNoTransducer(test_item): + try: + torch.ops.torchaudio.rnnt_loss + return test_item + except RuntimeError: + return unittest.skip("torchaudio C++ extension is not compiled with RNN transducer loss")(test_item) + + class TransducerTester: def test_basic_fp16_error(self): rnnt_loss = RNNTLoss() @@ -271,6 +280,6 @@ def test_costs_and_gradients_B2_T4_U3_D3_fp32(self): self.assertEqual(gradients, ref_gradients, atol=atol, rtol=rtol) -@common_utils.skipIfNoExtension -class CPUTransducerTester(TransducerTester, common_utils.PytorchTestCase): +@skipIfNoTransducer +class CPUTransducerTester(TransducerTester, TorchaudioTestCase): device = "cpu" diff --git a/test/torchaudio_unittest/transforms/__init__.py b/test/torchaudio_unittest/transforms/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/torchaudio_unittest/batch_consistency_test.py b/test/torchaudio_unittest/transforms/batch_consistency_test.py similarity index 54% rename from test/torchaudio_unittest/batch_consistency_test.py rename to test/torchaudio_unittest/transforms/batch_consistency_test.py index 4644fe7dd3..745401c328 100644 --- a/test/torchaudio_unittest/batch_consistency_test.py +++ b/test/torchaudio_unittest/transforms/batch_consistency_test.py @@ -1,176 +1,10 @@ """Test numerical consistency among single input and batched input.""" -import itertools -from parameterized import parameterized - -import math - import torch import torchaudio -import torchaudio.functional as F from torchaudio_unittest import common_utils -class TestFunctional(common_utils.TorchaudioTestCase): - backend = 'default' - """Test functions defined in `functional` module""" - def assert_batch_consistency( - self, functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, seed=42, **kwargs): - # run then batch the result - torch.random.manual_seed(seed) - expected = functional(tensor.clone(), *args, **kwargs) - expected = expected.repeat([batch_size] + [1] * expected.dim()) - - # batch the input and run - torch.random.manual_seed(seed) - pattern = [batch_size] + [1] * tensor.dim() - computed = functional(tensor.repeat(pattern), *args, **kwargs) - - self.assertEqual(computed, expected, rtol=rtol, atol=atol) - - def assert_batch_consistencies( - self, functional, tensor, *args, atol=1e-8, rtol=1e-5, seed=42, **kwargs): - self.assert_batch_consistency( - functional, tensor, *args, batch_size=1, atol=atol, rtol=rtol, seed=seed, **kwargs) - self.assert_batch_consistency( - functional, tensor, *args, batch_size=3, atol=atol, rtol=rtol, seed=seed, **kwargs) - - def test_griffinlim(self): - n_fft = 400 - ws = 400 - hop = 200 - window = torch.hann_window(ws) - power = 2 - normalize = False - momentum = 0.99 - n_iter = 32 - length = 1000 - tensor = torch.rand((1, 201, 6)) - self.assert_batch_consistencies( - F.griffinlim, tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, 0, atol=5e-5 - ) - - @parameterized.expand(list(itertools.product( - [100, 440], - [8000, 16000, 44100], - [1, 2], - )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') - def test_detect_pitch_frequency(self, frequency, sample_rate, n_channels): - waveform = common_utils.get_sinusoid(frequency=frequency, sample_rate=sample_rate, - n_channels=n_channels, duration=5) - self.assert_batch_consistencies(F.detect_pitch_frequency, waveform, sample_rate) - - def test_amplitude_to_DB(self): - torch.manual_seed(0) - spec = torch.rand(2, 100, 100) * 200 - - amplitude_mult = 20. - amin = 1e-10 - ref = 1.0 - db_mult = math.log10(max(amin, ref)) - - # Test with & without a `top_db` clamp - self.assert_batch_consistencies(F.amplitude_to_DB, spec, amplitude_mult, - amin, db_mult, top_db=None) - self.assert_batch_consistencies(F.amplitude_to_DB, spec, amplitude_mult, - amin, db_mult, top_db=40.) - - def test_amplitude_to_DB_itemwise_clamps(self): - """Ensure that the clamps are separate for each spectrogram in a batch. - - The clamp was determined per-batch in a prior implementation, which - meant it was determined by the loudest item, thus items weren't - independent. See: - - https://github.com/pytorch/audio/issues/994 - - """ - amplitude_mult = 20. - amin = 1e-10 - ref = 1.0 - db_mult = math.log10(max(amin, ref)) - top_db = 20. - - # Make a batch of noise - torch.manual_seed(0) - spec = torch.rand([2, 2, 100, 100]) * 200 - # Make one item blow out the other - spec[0] += 50 - - batchwise_dbs = F.amplitude_to_DB(spec, amplitude_mult, amin, - db_mult, top_db=top_db) - itemwise_dbs = torch.stack([ - F.amplitude_to_DB(item, amplitude_mult, amin, - db_mult, top_db=top_db) - for item in spec - ]) - - self.assertEqual(batchwise_dbs, itemwise_dbs) - - def test_amplitude_to_DB_not_channelwise_clamps(self): - """Check that clamps are applied per-item, not per channel.""" - amplitude_mult = 20. - amin = 1e-10 - ref = 1.0 - db_mult = math.log10(max(amin, ref)) - top_db = 40. - - torch.manual_seed(0) - spec = torch.rand([1, 2, 100, 100]) * 200 - # Make one channel blow out the other - spec[:, 0] += 50 - - specwise_dbs = F.amplitude_to_DB(spec, amplitude_mult, amin, - db_mult, top_db=top_db) - channelwise_dbs = torch.stack([ - F.amplitude_to_DB(spec[:, i], amplitude_mult, amin, - db_mult, top_db=top_db) - for i in range(spec.size(-3)) - ]) - - # Just check channelwise gives a different answer. - difference = (specwise_dbs - channelwise_dbs).abs() - assert (difference >= 1e-5).any() - - def test_contrast(self): - waveform = torch.rand(2, 100) - 0.5 - self.assert_batch_consistencies(F.contrast, waveform, enhancement_amount=80.) - - def test_dcshift(self): - waveform = torch.rand(2, 100) - 0.5 - self.assert_batch_consistencies(F.dcshift, waveform, shift=0.5, limiter_gain=0.05) - - def test_overdrive(self): - waveform = torch.rand(2, 100) - 0.5 - self.assert_batch_consistencies(F.overdrive, waveform, gain=45, colour=30) - - def test_phaser(self): - sample_rate = 44100 - waveform = common_utils.get_whitenoise( - sample_rate=sample_rate, duration=5, - ) - self.assert_batch_consistencies(F.phaser, waveform, sample_rate) - - def test_flanger(self): - torch.random.manual_seed(40) - waveform = torch.rand(2, 100) - 0.5 - sample_rate = 44100 - self.assert_batch_consistencies(F.flanger, waveform, sample_rate) - - def test_sliding_window_cmn(self): - waveform = torch.randn(2, 1024) - 0.5 - self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=True, norm_vars=True) - self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=True, norm_vars=False) - self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=False, norm_vars=True) - self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=False, norm_vars=False) - - def test_vad(self): - common_utils.set_audio_backend('default') - filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") - waveform, sample_rate = torchaudio.load(filepath) - self.assert_batch_consistencies(F.vad, waveform, sample_rate=sample_rate) - - class TestTransforms(common_utils.TorchaudioTestCase): backend = 'default' diff --git a/test/torchaudio_unittest/kaldi_compatibility_cpu_test.py b/test/torchaudio_unittest/transforms/kaldi_compatibility_cpu_test.py similarity index 100% rename from test/torchaudio_unittest/kaldi_compatibility_cpu_test.py rename to test/torchaudio_unittest/transforms/kaldi_compatibility_cpu_test.py diff --git a/test/torchaudio_unittest/kaldi_compatibility_cuda_test.py b/test/torchaudio_unittest/transforms/kaldi_compatibility_cuda_test.py similarity index 100% rename from test/torchaudio_unittest/kaldi_compatibility_cuda_test.py rename to test/torchaudio_unittest/transforms/kaldi_compatibility_cuda_test.py diff --git a/test/torchaudio_unittest/transforms/kaldi_compatibility_impl.py b/test/torchaudio_unittest/transforms/kaldi_compatibility_impl.py new file mode 100644 index 0000000000..9a0e6ab81d --- /dev/null +++ b/test/torchaudio_unittest/transforms/kaldi_compatibility_impl.py @@ -0,0 +1,55 @@ +"""Test suites for checking numerical compatibility against Kaldi""" +import torchaudio.compliance.kaldi +from parameterized import parameterized + +from torchaudio_unittest.common_utils import ( + TestBaseMixin, + TempDirMixin, + load_params, + skipIfNoExec, + get_asset_path, + load_wav, +) +from torchaudio_unittest.common_utils.kaldi_utils import ( + convert_args, + run_kaldi, +) + + +class Kaldi(TempDirMixin, TestBaseMixin): + def assert_equal(self, output, *, expected, rtol=None, atol=None): + expected = expected.to(dtype=self.dtype, device=self.device) + self.assertEqual(output, expected, rtol=rtol, atol=atol) + + @parameterized.expand(load_params('kaldi_test_fbank_args.jsonl')) + @skipIfNoExec('compute-fbank-feats') + def test_fbank(self, kwargs): + """fbank should be numerically compatible with compute-fbank-feats""" + wave_file = get_asset_path('kaldi_file.wav') + waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) + result = torchaudio.compliance.kaldi.fbank(waveform, **kwargs) + command = ['compute-fbank-feats'] + convert_args(**kwargs) + ['scp:-', 'ark:-'] + kaldi_result = run_kaldi(command, 'scp', wave_file) + self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) + + @parameterized.expand(load_params('kaldi_test_spectrogram_args.jsonl')) + @skipIfNoExec('compute-spectrogram-feats') + def test_spectrogram(self, kwargs): + """spectrogram should be numerically compatible with compute-spectrogram-feats""" + wave_file = get_asset_path('kaldi_file.wav') + waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) + result = torchaudio.compliance.kaldi.spectrogram(waveform, **kwargs) + command = ['compute-spectrogram-feats'] + convert_args(**kwargs) + ['scp:-', 'ark:-'] + kaldi_result = run_kaldi(command, 'scp', wave_file) + self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) + + @parameterized.expand(load_params('kaldi_test_mfcc_args.jsonl')) + @skipIfNoExec('compute-mfcc-feats') + def test_mfcc(self, kwargs): + """mfcc should be numerically compatible with compute-mfcc-feats""" + wave_file = get_asset_path('kaldi_file.wav') + waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) + result = torchaudio.compliance.kaldi.mfcc(waveform, **kwargs) + command = ['compute-mfcc-feats'] + convert_args(**kwargs) + ['scp:-', 'ark:-'] + kaldi_result = run_kaldi(command, 'scp', wave_file) + self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) diff --git a/test/torchaudio_unittest/transforms/librosa_compatibility_test.py b/test/torchaudio_unittest/transforms/librosa_compatibility_test.py new file mode 100644 index 0000000000..c954100f35 --- /dev/null +++ b/test/torchaudio_unittest/transforms/librosa_compatibility_test.py @@ -0,0 +1,245 @@ +"""Test suites for numerical compatibility with librosa""" +import os +import unittest + +import torch +import torchaudio +import torchaudio.functional as F +from torchaudio._internal.module_utils import is_module_available +from parameterized import parameterized, param + +LIBROSA_AVAILABLE = is_module_available('librosa') + +if LIBROSA_AVAILABLE: + import librosa + import scipy + +from torchaudio_unittest import common_utils + + +def _load_audio_asset(*asset_paths, **kwargs): + file_path = common_utils.get_asset_path(*asset_paths) + sound, sample_rate = torchaudio.load(file_path, **kwargs) + return sound, sample_rate + + +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") +class TestTransforms(common_utils.TorchaudioTestCase): + """Test suite for functions in `transforms` module.""" + + @parameterized.expand([ + param(n_fft=400, hop_length=200, power=2.0), + param(n_fft=600, hop_length=100, power=2.0), + param(n_fft=400, hop_length=200, power=3.0), + param(n_fft=200, hop_length=50, power=2.0), + ]) + def test_spectrogram(self, n_fft, hop_length, power): + sample_rate = 16000 + sound = common_utils.get_sinusoid(n_channels=1, sample_rate=sample_rate) + sound_librosa = sound.cpu().numpy().squeeze() + spect_transform = torchaudio.transforms.Spectrogram( + n_fft=n_fft, hop_length=hop_length, power=power) + out_librosa, _ = librosa.core.spectrum._spectrogram( + y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) + + out_torch = spect_transform(sound).squeeze().cpu() + self.assertEqual(out_torch, torch.from_numpy(out_librosa), atol=1e-5, rtol=1e-5) + + @parameterized.expand([ + param(norm=norm, mel_scale=mel_scale, **p.kwargs) + for p in [ + param(n_fft=400, hop_length=200, n_mels=128), + param(n_fft=600, hop_length=100, n_mels=128), + param(n_fft=200, hop_length=50, n_mels=128), + ] + for norm in [None, 'slaney'] + for mel_scale in ['htk', 'slaney'] + ]) + def test_mel_spectrogram(self, n_fft, hop_length, n_mels, norm, mel_scale): + sample_rate = 16000 + sound = common_utils.get_sinusoid(n_channels=1, sample_rate=sample_rate) + sound_librosa = sound.cpu().numpy().squeeze() + melspect_transform = torchaudio.transforms.MelSpectrogram( + sample_rate=sample_rate, window_fn=torch.hann_window, + hop_length=hop_length, n_mels=n_mels, n_fft=n_fft, norm=norm, mel_scale=mel_scale) + librosa_mel = librosa.feature.melspectrogram( + y=sound_librosa, sr=sample_rate, n_fft=n_fft, + hop_length=hop_length, n_mels=n_mels, htk=mel_scale == "htk", norm=norm) + librosa_mel_tensor = torch.from_numpy(librosa_mel) + torch_mel = melspect_transform(sound).squeeze().cpu() + self.assertEqual( + torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3, rtol=1e-5) + + @parameterized.expand([ + param(norm=norm, mel_scale=mel_scale, **p.kwargs) + for p in [ + param(n_fft=400, hop_length=200, power=2.0, n_mels=128), + param(n_fft=600, hop_length=100, power=2.0, n_mels=128), + param(n_fft=400, hop_length=200, power=3.0, n_mels=128), + # NOTE: Test passes offline, but fails on TravisCI (and CircleCI), see #372. + param(n_fft=200, hop_length=50, power=2.0, n_mels=128, skip_ci=True), + ] + for norm in [None, 'slaney'] + for mel_scale in ['htk', 'slaney'] + ]) + def test_s2db(self, n_fft, hop_length, power, n_mels, norm, mel_scale, skip_ci=False): + if skip_ci and 'CI' in os.environ: + self.skipTest('Test is known to fail on CI') + sample_rate = 16000 + sound = common_utils.get_sinusoid(n_channels=1, sample_rate=sample_rate) + sound_librosa = sound.cpu().numpy().squeeze() + spect_transform = torchaudio.transforms.Spectrogram( + n_fft=n_fft, hop_length=hop_length, power=power) + out_librosa, _ = librosa.core.spectrum._spectrogram( + y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) + melspect_transform = torchaudio.transforms.MelSpectrogram( + sample_rate=sample_rate, window_fn=torch.hann_window, + hop_length=hop_length, n_mels=n_mels, n_fft=n_fft, norm=norm, mel_scale=mel_scale) + librosa_mel = librosa.feature.melspectrogram( + y=sound_librosa, sr=sample_rate, n_fft=n_fft, + hop_length=hop_length, n_mels=n_mels, htk=mel_scale == "htk", norm=norm) + + power_to_db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.) + power_to_db_torch = power_to_db_transform(spect_transform(sound)).squeeze().cpu() + power_to_db_librosa = librosa.core.spectrum.power_to_db(out_librosa) + self.assertEqual(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3, rtol=1e-5) + + mag_to_db_transform = torchaudio.transforms.AmplitudeToDB('magnitude', 80.) + mag_to_db_torch = mag_to_db_transform(torch.abs(sound)).squeeze().cpu() + mag_to_db_librosa = librosa.core.spectrum.amplitude_to_db(sound_librosa) + self.assertEqual(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3, rtol=1e-5) + + power_to_db_torch = power_to_db_transform(melspect_transform(sound)).squeeze().cpu() + db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) + db_librosa_tensor = torch.from_numpy(db_librosa) + self.assertEqual( + power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3, rtol=1e-5) + + @parameterized.expand([ + param(n_fft=400, hop_length=200, n_mels=128, n_mfcc=40), + param(n_fft=600, hop_length=100, n_mels=128, n_mfcc=20), + param(n_fft=200, hop_length=50, n_mels=128, n_mfcc=50), + ]) + def test_mfcc(self, n_fft, hop_length, n_mels, n_mfcc): + sample_rate = 16000 + sound = common_utils.get_sinusoid(n_channels=1, sample_rate=sample_rate) + sound_librosa = sound.cpu().numpy().squeeze() + librosa_mel = librosa.feature.melspectrogram( + y=sound_librosa, sr=sample_rate, n_fft=n_fft, + hop_length=hop_length, n_mels=n_mels, htk=True, norm=None) + db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) + + # librosa.feature.mfcc doesn't pass kwargs properly since some of the + # kwargs for melspectrogram and mfcc are the same. We just follow the + # function body in + # https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram + # to mirror this function call with correct args: + # + # librosa_mfcc = librosa.feature.mfcc( + # y=sound_librosa, sr=sample_rate, n_mfcc = n_mfcc, + # hop_length=hop_length, n_fft=n_fft, htk=True, norm=None, n_mels=n_mels) + + librosa_mfcc = scipy.fftpack.dct(db_librosa, axis=0, type=2, norm='ortho')[:n_mfcc] + librosa_mfcc_tensor = torch.from_numpy(librosa_mfcc) + + melkwargs = {'hop_length': hop_length, 'n_fft': n_fft} + mfcc_transform = torchaudio.transforms.MFCC( + sample_rate=sample_rate, n_mfcc=n_mfcc, norm='ortho', melkwargs=melkwargs) + torch_mfcc = mfcc_transform(sound).squeeze().cpu() + + self.assertEqual( + torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3, rtol=1e-5) + + @parameterized.expand([ + param(n_fft=400, hop_length=200), + param(n_fft=600, hop_length=100), + param(n_fft=200, hop_length=50), + ]) + def test_spectral_centroid(self, n_fft, hop_length): + sample_rate = 16000 + sound = common_utils.get_sinusoid(n_channels=1, sample_rate=sample_rate) + sound_librosa = sound.cpu().numpy().squeeze() + spect_centroid = torchaudio.transforms.SpectralCentroid( + sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length) + out_torch = spect_centroid(sound).squeeze().cpu() + + out_librosa = librosa.feature.spectral_centroid( + y=sound_librosa, sr=sample_rate, n_fft=n_fft, hop_length=hop_length) + out_librosa = torch.from_numpy(out_librosa)[0] + + self.assertEqual(out_torch.type(out_librosa.dtype), out_librosa, atol=1e-5, rtol=1e-5) + + def test_MelScale(self): + """MelScale transform is comparable to that of librosa""" + n_fft = 2048 + n_mels = 256 + hop_length = n_fft // 4 + sample_rate = 44100 + sound = common_utils.get_whitenoise(sample_rate=sample_rate, duration=60) + sound = sound.mean(dim=0, keepdim=True) + spec_ta = F.spectrogram( + sound, pad=0, window=torch.hann_window(n_fft), n_fft=n_fft, + hop_length=hop_length, win_length=n_fft, power=2, normalized=False) + spec_lr = spec_ta.cpu().numpy().squeeze() + # Perform MelScale with torchaudio and librosa + melspec_ta = torchaudio.transforms.MelScale(n_mels=n_mels, sample_rate=sample_rate)(spec_ta) + melspec_lr = librosa.feature.melspectrogram( + S=spec_lr, sr=sample_rate, n_fft=n_fft, hop_length=hop_length, + win_length=n_fft, center=True, window='hann', n_mels=n_mels, htk=True, norm=None) + # Note: Using relaxed rtol instead of atol + self.assertEqual(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), atol=1e-8, rtol=1e-3) + + def test_InverseMelScale(self): + """InverseMelScale transform is comparable to that of librosa""" + n_fft = 2048 + n_mels = 256 + n_stft = n_fft // 2 + 1 + hop_length = n_fft // 4 + + # Prepare mel spectrogram input. We use torchaudio to compute one. + path = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') + sound, sample_rate = common_utils.load_wav(path) + sound = sound[:, 2**10:2**10 + 2**14] + sound = sound.mean(dim=0, keepdim=True) + spec_orig = F.spectrogram( + sound, pad=0, window=torch.hann_window(n_fft), n_fft=n_fft, + hop_length=hop_length, win_length=n_fft, power=2, normalized=False) + melspec_ta = torchaudio.transforms.MelScale(n_mels=n_mels, sample_rate=sample_rate)(spec_orig) + melspec_lr = melspec_ta.cpu().numpy().squeeze() + # Perform InverseMelScale with torch audio and librosa + spec_ta = torchaudio.transforms.InverseMelScale( + n_stft, n_mels=n_mels, sample_rate=sample_rate)(melspec_ta) + spec_lr = librosa.feature.inverse.mel_to_stft( + melspec_lr, sr=sample_rate, n_fft=n_fft, power=2.0, htk=True, norm=None) + spec_lr = torch.from_numpy(spec_lr[None, ...]) + + # Align dimensions + # librosa does not return power spectrogram while torchaudio returns power spectrogram + spec_orig = spec_orig.sqrt() + spec_ta = spec_ta.sqrt() + + threshold = 2.0 + # This threshold was chosen empirically, based on the following observation + # + # torch.dist(spec_lr, spec_ta, p=float('inf')) + # >>> tensor(1.9666) + # + # The spectrograms reconstructed by librosa and torchaudio are not comparable elementwise. + # This is because they use different approximation algorithms and resulting values can live + # in different magnitude. (although most of them are very close) + # See + # https://github.com/pytorch/audio/pull/366 for the discussion of the choice of algorithm + # https://github.com/pytorch/audio/pull/448/files#r385747021 for the distribution of P-inf + # distance over frequencies. + self.assertEqual(spec_ta, spec_lr, atol=threshold, rtol=1e-5) + + threshold = 1700.0 + # This threshold was chosen empirically, based on the following observations + # + # torch.dist(spec_orig, spec_ta, p=1) + # >>> tensor(1644.3516) + # torch.dist(spec_orig, spec_lr, p=1) + # >>> tensor(1420.7103) + # torch.dist(spec_lr, spec_ta, p=1) + # >>> tensor(943.2759) + assert torch.dist(spec_orig, spec_ta, p=1) < threshold diff --git a/test/torchaudio_unittest/transforms/sox_compatibility_test.py b/test/torchaudio_unittest/transforms/sox_compatibility_test.py new file mode 100644 index 0000000000..4bf6bf7884 --- /dev/null +++ b/test/torchaudio_unittest/transforms/sox_compatibility_test.py @@ -0,0 +1,53 @@ +import torchaudio.transforms as T +from parameterized import parameterized + +from torchaudio_unittest.common_utils import ( + skipIfNoSoxBackend, + skipIfNoExec, + TempDirMixin, + TorchaudioTestCase, + get_asset_path, + sox_utils, + load_wav, +) + + +@skipIfNoSoxBackend +@skipIfNoExec('sox') +class TestFunctionalFiltering(TempDirMixin, TorchaudioTestCase): + def run_sox_effect(self, input_file, effect): + output_file = self.get_temp_path('expected.wav') + sox_utils.run_sox_effect(input_file, output_file, [str(e) for e in effect]) + return load_wav(output_file) + + def assert_sox_effect(self, result, input_path, effects, atol=1e-04, rtol=1e-5): + expected, _ = self.run_sox_effect(input_path, effects) + self.assertEqual(result, expected, atol=atol, rtol=rtol) + + @parameterized.expand([ + ('q', 'quarter_sine'), + ('h', 'half_sine'), + ('t', 'linear'), + ]) + def test_fade(self, fade_shape_sox, fade_shape): + fade_in_len, fade_out_len = 44100, 44100 + data, path = self.get_whitenoise(sample_rate=44100) + result = T.Fade(fade_in_len, fade_out_len, fade_shape)(data) + self.assert_sox_effect(result, path, ['fade', fade_shape_sox, '1', '0', '1']) + + @parameterized.expand([ + ('amplitude', 1.1), + ('db', 2), + ('power', 2), + ]) + def test_vol(self, gain_type, gain): + data, path = self.get_whitenoise() + result = T.Vol(gain, gain_type)(data) + self.assert_sox_effect(result, path, ['vol', f'{gain}', gain_type]) + + @parameterized.expand(['vad-go-stereo-44100.wav', 'vad-go-mono-32000.wav']) + def test_vad(self, filename): + path = get_asset_path(filename) + data, sample_rate = load_wav(path) + result = T.Vad(sample_rate)(data) + self.assert_sox_effect(result, path, ['vad']) diff --git a/test/torchaudio_unittest/torchscript_consistency_cpu_test.py b/test/torchaudio_unittest/transforms/torchscript_consistency_cpu_test.py similarity index 100% rename from test/torchaudio_unittest/torchscript_consistency_cpu_test.py rename to test/torchaudio_unittest/transforms/torchscript_consistency_cpu_test.py diff --git a/test/torchaudio_unittest/torchscript_consistency_cuda_test.py b/test/torchaudio_unittest/transforms/torchscript_consistency_cuda_test.py similarity index 100% rename from test/torchaudio_unittest/torchscript_consistency_cuda_test.py rename to test/torchaudio_unittest/transforms/torchscript_consistency_cuda_test.py diff --git a/test/torchaudio_unittest/torchscript_consistency_impl.py b/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py similarity index 100% rename from test/torchaudio_unittest/torchscript_consistency_impl.py rename to test/torchaudio_unittest/transforms/torchscript_consistency_impl.py diff --git a/test/torchaudio_unittest/transforms_test.py b/test/torchaudio_unittest/transforms/transforms_test.py similarity index 100% rename from test/torchaudio_unittest/transforms_test.py rename to test/torchaudio_unittest/transforms/transforms_test.py diff --git a/test/torchaudio_unittest/utils/sox_utils_test.py b/test/torchaudio_unittest/utils/sox_utils_test.py index 7c206fdc0b..7bd9018437 100644 --- a/test/torchaudio_unittest/utils/sox_utils_test.py +++ b/test/torchaudio_unittest/utils/sox_utils_test.py @@ -2,11 +2,11 @@ from torchaudio_unittest.common_utils import ( PytorchTestCase, - skipIfNoExtension, + skipIfNoSox, ) -@skipIfNoExtension +@skipIfNoSox class TestSoxUtils(PytorchTestCase): """Smoke tests for sox_util module""" def test_set_seed(self): diff --git a/third_party/CMakeLists.txt b/third_party/CMakeLists.txt index 3e1ab962fe..bb3a469055 100644 --- a/third_party/CMakeLists.txt +++ b/third_party/CMakeLists.txt @@ -10,12 +10,16 @@ if (BUILD_SOX) add_subdirectory(sox) target_include_directories(libsox INTERFACE ${SOX_INCLUDE_DIR}) target_link_libraries(libsox INTERFACE ${SOX_LIBRARIES}) -else() - # If not building and linking libsox statically, then we expect that - # sox library and header are found in search path - target_link_libraries(libsox INTERFACE -lsox) + list(APPEND TORCHAUDIO_THIRD_PARTIES libsox) +endif() + +################################################################################ +# kaldi +################################################################################ +if (BUILD_KALDI) + add_subdirectory(kaldi) + list(APPEND TORCHAUDIO_THIRD_PARTIES kaldi) endif() -list(APPEND TORCHAUDIO_THIRD_PARTIES libsox) ################################################################################ # transducer diff --git a/third_party/kaldi/CMakeLists.txt b/third_party/kaldi/CMakeLists.txt new file mode 100644 index 0000000000..6f891f8d1a --- /dev/null +++ b/third_party/kaldi/CMakeLists.txt @@ -0,0 +1,30 @@ +set(KALDI_REPO ${CMAKE_CURRENT_SOURCE_DIR}/submodule) + +# Apply custom patch +execute_process( + WORKING_DIRECTORY ${KALDI_REPO} + COMMAND "git" "checkout" "." + ) +execute_process( + WORKING_DIRECTORY ${KALDI_REPO} + COMMAND git apply ../kaldi.patch + ) +# Update the version string +execute_process( + WORKING_DIRECTORY ${KALDI_REPO}/src/base + COMMAND sh get_version.sh + ) + +set(KALDI_SOURCES + src/matrix/kaldi-vector.cc + src/matrix/kaldi-matrix.cc + submodule/src/base/kaldi-error.cc + submodule/src/base/kaldi-math.cc + submodule/src/feat/feature-functions.cc + submodule/src/feat/pitch-functions.cc + submodule/src/feat/resample.cc + ) + +add_library(kaldi STATIC ${KALDI_SOURCES}) +target_include_directories(kaldi PUBLIC src submodule/src) +target_link_libraries(kaldi ${TORCH_LIBRARIES}) diff --git a/third_party/kaldi/README.md b/third_party/kaldi/README.md new file mode 100644 index 0000000000..58c48747a8 --- /dev/null +++ b/third_party/kaldi/README.md @@ -0,0 +1,6 @@ +# Custom Kaldi build + +This directory contains original Kaldi repository (as submodule), [the custom implementation of Kaldi's vector/matrix](./src) and the build script. + +We use the custom build process so that the resulting library only contains what torchaudio needs. +We use the custom vector/matrix implementation so that we can use the same BLAS library that PyTorch is compiled with, and so that we can (hopefully, in future) take advantage of other PyTorch features (such as differentiability and GPU support). The down side of this approach is that it adds a lot of overhead compared to the original Kaldi (operator dispatch and element-wise processing, which PyTorch is not efficient at). We can improve this gradually, and if you are interested in helping, please let us know by opening an issue. \ No newline at end of file diff --git a/third_party/kaldi/kaldi.patch b/third_party/kaldi/kaldi.patch new file mode 100644 index 0000000000..40667bced8 --- /dev/null +++ b/third_party/kaldi/kaldi.patch @@ -0,0 +1,76 @@ +diff --git a/src/base/kaldi-types.h b/src/base/kaldi-types.h +index 7ebf4f853..c15b288b2 100644 +--- a/src/base/kaldi-types.h ++++ b/src/base/kaldi-types.h +@@ -41,6 +41,7 @@ typedef float BaseFloat; + + // for discussion on what to do if you need compile kaldi + // without OpenFST, see the bottom of this this file ++/* + #include + + namespace kaldi { +@@ -53,10 +54,10 @@ namespace kaldi { + typedef float float32; + typedef double double64; + } // end namespace kaldi ++*/ + + // In a theoretical case you decide compile Kaldi without the OpenFST + // comment the previous namespace statement and uncomment the following +-/* + namespace kaldi { + typedef int8_t int8; + typedef int16_t int16; +@@ -70,6 +71,5 @@ namespace kaldi { + typedef float float32; + typedef double double64; + } // end namespace kaldi +-*/ + + #endif // KALDI_BASE_KALDI_TYPES_H_ +diff --git a/src/matrix/matrix-lib.h b/src/matrix/matrix-lib.h +index b6059b06c..4fb9e1b16 100644 +--- a/src/matrix/matrix-lib.h ++++ b/src/matrix/matrix-lib.h +@@ -25,14 +25,14 @@ + #include "base/kaldi-common.h" + #include "matrix/kaldi-vector.h" + #include "matrix/kaldi-matrix.h" +-#include "matrix/sp-matrix.h" +-#include "matrix/tp-matrix.h" ++// #include "matrix/sp-matrix.h" ++// #include "matrix/tp-matrix.h" + #include "matrix/matrix-functions.h" + #include "matrix/srfft.h" + #include "matrix/compressed-matrix.h" +-#include "matrix/sparse-matrix.h" ++// #include "matrix/sparse-matrix.h" + #include "matrix/optimization.h" +-#include "matrix/numpy-array.h" ++// #include "matrix/numpy-array.h" + + #endif + +diff --git a/src/util/common-utils.h b/src/util/common-utils.h +index cfb0c255c..48d199e97 100644 +--- a/src/util/common-utils.h ++++ b/src/util/common-utils.h +@@ -21,11 +21,11 @@ + + #include "base/kaldi-common.h" + #include "util/parse-options.h" +-#include "util/kaldi-io.h" +-#include "util/simple-io-funcs.h" +-#include "util/kaldi-holder.h" +-#include "util/kaldi-table.h" +-#include "util/table-types.h" +-#include "util/text-utils.h" ++// #include "util/kaldi-io.h" ++// #include "util/simple-io-funcs.h" ++// #include "util/kaldi-holder.h" ++// #include "util/kaldi-table.h" ++// #include "util/table-types.h" ++// #include "util/text-utils.h" + + #endif // KALDI_UTIL_COMMON_UTILS_H_ diff --git a/third_party/kaldi/src/matrix/kaldi-matrix.cc b/third_party/kaldi/src/matrix/kaldi-matrix.cc new file mode 100644 index 0000000000..a89c3809c9 --- /dev/null +++ b/third_party/kaldi/src/matrix/kaldi-matrix.cc @@ -0,0 +1,39 @@ +#include "matrix/kaldi-matrix.h" +#include + +namespace { + +template +void assert_matrix_shape(const torch::Tensor& tensor_); + +template <> +void assert_matrix_shape(const torch::Tensor& tensor_) { + TORCH_INTERNAL_ASSERT(tensor_.ndimension() == 2); + TORCH_INTERNAL_ASSERT(tensor_.dtype() == torch::kFloat32); + TORCH_CHECK(tensor_.device().is_cpu(), "Input tensor has to be on CPU."); +} + +template <> +void assert_matrix_shape(const torch::Tensor& tensor_) { + TORCH_INTERNAL_ASSERT(tensor_.ndimension() == 2); + TORCH_INTERNAL_ASSERT(tensor_.dtype() == torch::kFloat64); + TORCH_CHECK(tensor_.device().is_cpu(), "Input tensor has to be on CPU."); +} + +} // namespace + +namespace kaldi { + +template +MatrixBase::MatrixBase(torch::Tensor tensor) : tensor_(tensor) { + assert_matrix_shape(tensor_); +}; + +template class Matrix; +template class Matrix; +template class MatrixBase; +template class MatrixBase; +template class SubMatrix; +template class SubMatrix; + +} // namespace kaldi diff --git a/third_party/kaldi/src/matrix/kaldi-matrix.h b/third_party/kaldi/src/matrix/kaldi-matrix.h new file mode 100644 index 0000000000..f64828b84f --- /dev/null +++ b/third_party/kaldi/src/matrix/kaldi-matrix.h @@ -0,0 +1,178 @@ +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h + +#ifndef KALDI_MATRIX_KALDI_MATRIX_H_ +#define KALDI_MATRIX_KALDI_MATRIX_H_ + +#include +#include "matrix/kaldi-vector.h" +#include "matrix/matrix-common.h" + +using namespace torch::indexing; + +namespace kaldi { + +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L44-L48 +template +class MatrixBase { + public: + //////////////////////////////////////////////////////////////////////////////// + // PyTorch-specific items + //////////////////////////////////////////////////////////////////////////////// + torch::Tensor tensor_; + /// Construct VectorBase which is an interface to an existing torch::Tensor + /// object. + MatrixBase(torch::Tensor tensor); + + //////////////////////////////////////////////////////////////////////////////// + // Kaldi-compatible items + //////////////////////////////////////////////////////////////////////////////// + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L62-L63 + inline MatrixIndexT NumRows() const { + return tensor_.size(0); + }; + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L65-L66 + inline MatrixIndexT NumCols() const { + return tensor_.size(1); + }; + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L177-L178 + void CopyColFromVec(const VectorBase& v, const MatrixIndexT col) { + tensor_.index_put_({Slice(), col}, v.tensor_); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L99-L107 + inline Real& operator()(MatrixIndexT r, MatrixIndexT c) { + // CPU only + return tensor_.accessor()[r][c]; + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L112-L120 + inline const Real operator()(MatrixIndexT r, MatrixIndexT c) const { + return tensor_.index({Slice(r), Slice(c)}).item().template to(); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L138-L141 + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.cc#L859-L898 + template + void CopyFromMat( + const MatrixBase& M, + MatrixTransposeType trans = kNoTrans) { + auto src = M.tensor_; + if (trans == kTrans) + src = src.transpose(1, 0); + tensor_.index_put_({Slice(), Slice()}, src); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L186-L191 + inline const SubVector Row(MatrixIndexT i) const { + return SubVector(*this, i); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L208-L211 + inline SubMatrix RowRange( + const MatrixIndexT row_offset, + const MatrixIndexT num_rows) const { + return SubMatrix(*this, row_offset, num_rows, 0, NumCols()); + } + + protected: + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L749-L753 + explicit MatrixBase() : tensor_(torch::empty({0, 0})) { + KALDI_ASSERT_IS_FLOATING_TYPE(Real); + } +}; + +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L781-L784 +template +class Matrix : public MatrixBase { + public: + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L786-L787 + Matrix() : MatrixBase() {} + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L789-L793 + Matrix( + const MatrixIndexT r, + const MatrixIndexT c, + MatrixResizeType resize_type = kSetZero, + MatrixStrideType stride_type = kDefaultStride) + : MatrixBase() { + Resize(r, c, resize_type, stride_type); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L808-L811 + explicit Matrix( + const MatrixBase& M, + MatrixTransposeType trans = kNoTrans) + : MatrixBase( + trans == kNoTrans ? M.tensor_ : M.tensor_.transpose(1, 0)) {} + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L816-L819 + template + explicit Matrix( + const MatrixBase& M, + MatrixTransposeType trans = kNoTrans) + : MatrixBase( + trans == kNoTrans ? M.tensor_ : M.tensor_.transpose(1, 0)) {} + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L859-L874 + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.cc#L817-L857 + void Resize( + const MatrixIndexT r, + const MatrixIndexT c, + MatrixResizeType resize_type = kSetZero, + MatrixStrideType stride_type = kDefaultStride) { + auto& tensor_ = MatrixBase::tensor_; + switch (resize_type) { + case kSetZero: + tensor_.resize_({r, c}).zero_(); + break; + case kUndefined: + tensor_.resize_({r, c}); + break; + case kCopyData: + auto tmp = tensor_; + auto tmp_rows = tmp.size(0); + auto tmp_cols = tmp.size(1); + tensor_.resize_({r, c}).zero_(); + auto rows = Slice(None, r < tmp_rows ? r : tmp_rows); + auto cols = Slice(None, c < tmp_cols ? c : tmp_cols); + tensor_.index_put_({rows, cols}, tmp.index({rows, cols})); + break; + } + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L876-L883 + Matrix& operator=(const MatrixBase& other) { + if (MatrixBase::NumRows() != other.NumRows() || + MatrixBase::NumCols() != other.NumCols()) + Resize(other.NumRows(), other.NumCols(), kUndefined); + MatrixBase::CopyFromMat(other); + return *this; + } +}; + +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L940-L948 +template +class SubMatrix : public MatrixBase { + public: + SubMatrix( + const MatrixBase& T, + const MatrixIndexT ro, // row offset, 0 < ro < NumRows() + const MatrixIndexT r, // number of rows, r > 0 + const MatrixIndexT co, // column offset, 0 < co < NumCols() + const MatrixIndexT c) // number of columns, c > 0 + : MatrixBase( + T.tensor_.index({Slice(ro, ro + r), Slice(co, co + c)})) {} +}; + +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-matrix.h#L1059-L1060 +template +std::ostream& operator<<(std::ostream& Out, const MatrixBase& M) { + Out << M.tensor_; + return Out; +} + +} // namespace kaldi + +#endif diff --git a/third_party/kaldi/src/matrix/kaldi-vector.cc b/third_party/kaldi/src/matrix/kaldi-vector.cc new file mode 100644 index 0000000000..df59f13a36 --- /dev/null +++ b/third_party/kaldi/src/matrix/kaldi-vector.cc @@ -0,0 +1,42 @@ +#include "matrix/kaldi-vector.h" +#include +#include "matrix/kaldi-matrix.h" + +namespace { + +template +void assert_vector_shape(const torch::Tensor& tensor_); + +template <> +void assert_vector_shape(const torch::Tensor& tensor_) { + TORCH_INTERNAL_ASSERT(tensor_.ndimension() == 1); + TORCH_INTERNAL_ASSERT(tensor_.dtype() == torch::kFloat32); + TORCH_CHECK(tensor_.device().is_cpu(), "Input tensor has to be on CPU."); +} + +template <> +void assert_vector_shape(const torch::Tensor& tensor_) { + TORCH_INTERNAL_ASSERT(tensor_.ndimension() == 1); + TORCH_INTERNAL_ASSERT(tensor_.dtype() == torch::kFloat64); + TORCH_CHECK(tensor_.device().is_cpu(), "Input tensor has to be on CPU."); +} + +} // namespace + +namespace kaldi { + +template +VectorBase::VectorBase(torch::Tensor tensor) + : tensor_(tensor), data_(tensor.data_ptr()) { + assert_vector_shape(tensor_); +}; + +template +VectorBase::VectorBase() : VectorBase(torch::empty({0})) {} + +template class Vector; +template class Vector; +template class VectorBase; +template class VectorBase; + +} // namespace kaldi diff --git a/third_party/kaldi/src/matrix/kaldi-vector.h b/third_party/kaldi/src/matrix/kaldi-vector.h new file mode 100644 index 0000000000..620f3676d3 --- /dev/null +++ b/third_party/kaldi/src/matrix/kaldi-vector.h @@ -0,0 +1,313 @@ +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h + +#ifndef KALDI_MATRIX_KALDI_VECTOR_H_ +#define KALDI_MATRIX_KALDI_VECTOR_H_ + +#include +#include "matrix/matrix-common.h" + +using namespace torch::indexing; + +namespace kaldi { + +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L36-L40 +template +class VectorBase { + public: + //////////////////////////////////////////////////////////////////////////////// + // PyTorch-specific things + //////////////////////////////////////////////////////////////////////////////// + torch::Tensor tensor_; + + /// Construct VectorBase which is an interface to an existing torch::Tensor + /// object. + VectorBase(torch::Tensor tensor); + + //////////////////////////////////////////////////////////////////////////////// + // Kaldi-compatible methods + //////////////////////////////////////////////////////////////////////////////// + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L42-L43 + void SetZero() { + Set(0); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L48-L49 + void Set(Real f) { + tensor_.index_put_({"..."}, f); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L62-L63 + inline MatrixIndexT Dim() const { + return tensor_.numel(); + }; + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L68-L69 + inline Real* Data() { + return data_; + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L71-L72 + inline const Real* Data() const { + return data_; + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L74-L79 + inline Real operator()(MatrixIndexT i) const { + return data_[i]; + }; + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L81-L86 + inline Real& operator()(MatrixIndexT i) { + return tensor_.accessor()[i]; + }; + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L88-L95 + SubVector Range(const MatrixIndexT o, const MatrixIndexT l) { + return SubVector(*this, o, l); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L97-L105 + const SubVector Range(const MatrixIndexT o, const MatrixIndexT l) + const { + return SubVector(*this, o, l); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L107-L108 + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.cc#L226-L233 + void CopyFromVec(const VectorBase& v) { + TORCH_INTERNAL_ASSERT(tensor_.sizes() == v.tensor_.sizes()); + tensor_.copy_(v.tensor_); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L137-L139 + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.cc#L816-L832 + void ApplyFloor(Real floor_val, MatrixIndexT* floored_count = nullptr) { + auto index = tensor_ < floor_val; + auto tmp = tensor_.index_put_({index}, floor_val); + if (floored_count) { + *floored_count = index.sum().item().template to(); + } + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L164-L165 + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.cc#L449-L479 + void ApplyPow(Real power) { + tensor_.pow_(power); + TORCH_INTERNAL_ASSERT(!tensor_.isnan().sum().item().template to()); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L181-L184 + template + void AddVec(const Real alpha, const VectorBase& v) { + tensor_ += alpha * v.tensor_; + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L186-L187 + void AddVec2(const Real alpha, const VectorBase& v) { + tensor_ += alpha * (v.tensor_.square()); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L196-L198 + void AddMatVec( + const Real alpha, + const MatrixBase& M, + const MatrixTransposeType trans, + const VectorBase& v, + const Real beta) { // **beta previously defaulted to 0.0** + auto mat = M.tensor_; + if (trans == kTrans) { + mat = mat.transpose(1, 0); + } + tensor_.addmv_(mat, v.tensor_, beta, alpha); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L221-L222 + void MulElements(const VectorBase& v) { + tensor_ *= v.tensor_; + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L233-L234 + void Add(Real c) { + tensor_ += c; + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L236-L239 + void AddVecVec( + Real alpha, + const VectorBase& v, + const VectorBase& r, + Real beta) { + tensor_ = beta * tensor_ + alpha * v.tensor_ * r.tensor_; + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L246-L247 + void Scale(Real alpha) { + tensor_ *= alpha; + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L305-L306 + Real Min() const { + if (tensor_.numel()) { + return tensor_.min().item().template to(); + } + return std::numeric_limits::infinity(); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L308-L310 + Real Min(MatrixIndexT* index) const { + TORCH_INTERNAL_ASSERT(tensor_.numel()); + torch::Tensor value, ind; + std::tie(value, ind) = tensor_.min(0); + *index = ind.item().to(); + return value.item().to(); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L312-L313 + Real Sum() const { + return tensor_.sum().item().template to(); + }; + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L320-L321 + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.cc#L718-L736 + void AddRowSumMat(Real alpha, const MatrixBase& M, Real beta = 1.0) { + Vector ones(M.NumRows()); + ones.Set(1.0); + this->AddMatVec(alpha, M, kTrans, ones, beta); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L323-L324 + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.cc#L738-L757 + void AddColSumMat(Real alpha, const MatrixBase& M, Real beta = 1.0) { + Vector ones(M.NumCols()); + ones.Set(1.0); + this->AddMatVec(alpha, M, kNoTrans, ones, beta); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L326-L330 + void AddDiagMat2( + Real alpha, + const MatrixBase& M, + MatrixTransposeType trans = kNoTrans, + Real beta = 1.0) { + auto mat = M.tensor_; + if (trans == kNoTrans) { + tensor_ = + beta * tensor_ + torch::diag(torch::mm(mat, mat.transpose(1, 0))); + } else { + tensor_ = + beta * tensor_ + torch::diag(torch::mm(mat.transpose(1, 0), mat)); + } + } + + protected: + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L362-L365 + explicit VectorBase(); + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L378-L379 + Real* data_; + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L382 + KALDI_DISALLOW_COPY_AND_ASSIGN(VectorBase); +}; + +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L385-L390 +template +class Vector : public VectorBase { + public: + //////////////////////////////////////////////////////////////////////////////// + // PyTorch-compatibility things + //////////////////////////////////////////////////////////////////////////////// + /// Construct VectorBase which is an interface to an existing torch::Tensor + /// object. + Vector(torch::Tensor tensor) : VectorBase(tensor){}; + + //////////////////////////////////////////////////////////////////////////////// + // Kaldi-compatible methods + //////////////////////////////////////////////////////////////////////////////// + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L392-L393 + Vector() : VectorBase(){}; + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L395-L399 + explicit Vector(const MatrixIndexT s, MatrixResizeType resize_type = kSetZero) + : VectorBase() { + Resize(s, resize_type); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L406-L410 + // Note: unlike the original implementation, this is "explicit". + explicit Vector(const Vector& v) + : VectorBase(v.tensor_.clone()) {} + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L412-L416 + explicit Vector(const VectorBase& v) + : VectorBase(v.tensor_.clone()) {} + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L434-L435 + void Swap(Vector* other) { + auto tmp = VectorBase::tensor_; + this->tensor_ = other->tensor_; + other->tensor_ = tmp; + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L444-L451 + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.cc#L189-L223 + void Resize(MatrixIndexT length, MatrixResizeType resize_type = kSetZero) { + auto& tensor_ = this->tensor_; + switch (resize_type) { + case kSetZero: + tensor_.resize_({length}).zero_(); + break; + case kUndefined: + tensor_.resize_({length}); + break; + case kCopyData: + auto tmp = tensor_; + auto tmp_numel = tensor_.numel(); + tensor_.resize_({length}).zero_(); + auto numel = Slice(length < tmp_numel ? length : tmp_numel); + tensor_.index_put_({numel}, tmp.index({numel})); + break; + } + // data_ptr() causes compiler error + this->data_ = static_cast(tensor_.data_ptr()); + } + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L463-L468 + Vector& operator=(const VectorBase& other) { + Resize(other.Dim(), kUndefined); + this->CopyFromVec(other); + return *this; + } +}; + +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L482-L485 +template +class SubVector : public VectorBase { + public: + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L487-L499 + SubVector( + const VectorBase& t, + const MatrixIndexT origin, + const MatrixIndexT length) + : VectorBase(t.tensor_.index({Slice(origin, origin + length)})) {} + + // https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L524-L528 + SubVector(const MatrixBase& matrix, MatrixIndexT row) + : VectorBase(matrix.tensor_.index({row})) {} +}; + +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L540-L543 +template +std::ostream& operator<<(std::ostream& out, const VectorBase& v) { + out << v.tensor_; + return out; +} + +// https://github.com/kaldi-asr/kaldi/blob/7fb716aa0f56480af31514c7e362db5c9f787fd4/src/matrix/kaldi-vector.h#L573-L575 +template +Real VecVec(const VectorBase& v1, const VectorBase& v2) { + return torch::dot(v1.tensor_, v2.tensor_).item().template to(); +} + +} // namespace kaldi + +#endif diff --git a/third_party/sox/CMakeLists.txt b/third_party/sox/CMakeLists.txt index 840130f055..7bbad0e99e 100644 --- a/third_party/sox/CMakeLists.txt +++ b/third_party/sox/CMakeLists.txt @@ -197,6 +197,7 @@ ExternalProject_Add(sox URL https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2 URL_HASH SHA256=81a6956d4330e75b5827316e44ae381e6f1e8928003c6aa45896da9041ea149c CONFIGURE_COMMAND ${CMAKE_COMMAND} -E env ${envs} ${CMAKE_CURRENT_BINARY_DIR}/src/sox/configure ${COMMON_ARGS} ${SOX_OPTIONS} + PATCH_COMMAND patch -p1 < ${CMAKE_CURRENT_SOURCE_DIR}/patch/sox.patch BUILD_BYPRODUCTS ${SOX_LIBRARIES} DOWNLOAD_NO_PROGRESS ON LOG_DOWNLOAD ON diff --git a/third_party/sox/patch/sox.patch b/third_party/sox/patch/sox.patch new file mode 100644 index 0000000000..fe8df945c0 --- /dev/null +++ b/third_party/sox/patch/sox.patch @@ -0,0 +1,16 @@ +See https://github.com/pytorch/audio/pull/1297 +diff -ru sox/src/formats.c sox/src/formats.c +--- sox/src/formats.c 2014-10-26 19:55:50.000000000 -0700 ++++ sox/src/formats.c 2021-02-22 16:01:02.833144070 -0800 +@@ -333,6 +333,10 @@ + assert(ft); + if (!ft->fp) + return sox_false; +- fstat(fileno((FILE*)ft->fp), &st); ++ int fd = fileno((FILE*)ft->fp); ++ if (fd < 0) ++ return sox_false; ++ if (fstat(fd, &st) < 0) ++ return sox_false; + return ((st.st_mode & S_IFMT) == S_IFREG); + } diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index 35f5fd41d5..e72ecac4cb 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -10,20 +10,10 @@ transforms, ) -USE_SOUNDFILE_LEGACY_INTERFACE = None - from torchaudio.backend import ( list_audio_backends, get_audio_backend, set_audio_backend, - save_encinfo, - sox_signalinfo_t, - sox_encodinginfo_t, - get_sox_option_t, - get_sox_encoding_t, - get_sox_bool, - SignalInfo, - EncodingInfo, ) try: diff --git a/torchaudio/_internal/module_utils.py b/torchaudio/_internal/module_utils.py index 31bc2f1a31..67e11e6c5f 100644 --- a/torchaudio/_internal/module_utils.py +++ b/torchaudio/_internal/module_utils.py @@ -3,6 +3,8 @@ from typing import Optional from functools import wraps +import torch + def is_module_available(*modules: str) -> bool: r"""Returns if a top-level module with :attr:`name` exists *without** @@ -41,9 +43,11 @@ def deprecated(direction: str, version: Optional[str] = None): """Decorator to add deprecation message Args: - direction: Migration steps to be given to users. + direction (str): Migration steps to be given to users. + version (str or int): The version when the object will be removed """ def decorator(func): + @wraps(func) def wrapped(*args, **kwargs): message = ( @@ -54,3 +58,37 @@ def wrapped(*args, **kwargs): return func(*args, **kwargs) return wrapped return decorator + + +def is_kaldi_available(): + return is_module_available('torchaudio._torchaudio') and torch.ops.torchaudio.is_kaldi_available() + + +def requires_kaldi(): + if is_kaldi_available(): + def decorator(func): + return func + else: + def decorator(func): + @wraps(func) + def wrapped(*args, **kwargs): + raise RuntimeError(f'{func.__module__}.{func.__name__} requires kaldi') + return wrapped + return decorator + + +def is_sox_available(): + return is_module_available('torchaudio._torchaudio') and torch.ops.torchaudio.is_sox_available() + + +def requires_sox(): + if is_sox_available(): + def decorator(func): + return func + else: + def decorator(func): + @wraps(func) + def wrapped(*args, **kwargs): + raise RuntimeError(f'{func.__module__}.{func.__name__} requires sox') + return wrapped + return decorator diff --git a/torchaudio/backend/__init__.py b/torchaudio/backend/__init__.py index 361935229f..c3fdf0b439 100644 --- a/torchaudio/backend/__init__.py +++ b/torchaudio/backend/__init__.py @@ -5,18 +5,6 @@ get_audio_backend, set_audio_backend, ) -from .sox_backend import ( - save_encinfo, - sox_signalinfo_t, - sox_encodinginfo_t, - get_sox_option_t, - get_sox_encoding_t, - get_sox_bool, -) -from .common import ( - SignalInfo, - EncodingInfo, -) utils._init_audio_backend() diff --git a/torchaudio/backend/_soundfile_backend.py b/torchaudio/backend/_soundfile_backend.py deleted file mode 100644 index e3c992b6fd..0000000000 --- a/torchaudio/backend/_soundfile_backend.py +++ /dev/null @@ -1,316 +0,0 @@ -"""The new soundfile backend which will become default in 0.8.0 onward""" -from typing import Tuple, Optional -import warnings - -import torch -from torchaudio._internal import module_utils as _mod_utils -from .common import AudioMetaData - - -if _mod_utils.is_module_available("soundfile"): - import soundfile - - -# Mapping from soundfile subtype to number of bits per sample. -# This is mostly heuristical and the value is set to 0 when it is irrelevant -# (lossy formats) or when it can't be inferred. -# For ADPCM (and G72X) subtypes, it's hard to infer the bit depth because it's not part of the standard: -# According to https://en.wikipedia.org/wiki/Adaptive_differential_pulse-code_modulation#In_telephony, -# the default seems to be 8 bits but it can be compressed further to 4 bits. -# The dict is inspired from -# https://github.com/bastibe/python-soundfile/blob/744efb4b01abc72498a96b09115b42a4cabd85e4/soundfile.py#L66-L94 -_SUBTYPE_TO_BITS_PER_SAMPLE = { - 'PCM_S8': 8, # Signed 8 bit data - 'PCM_16': 16, # Signed 16 bit data - 'PCM_24': 24, # Signed 24 bit data - 'PCM_32': 32, # Signed 32 bit data - 'PCM_U8': 8, # Unsigned 8 bit data (WAV and RAW only) - 'FLOAT': 32, # 32 bit float data - 'DOUBLE': 64, # 64 bit float data - 'ULAW': 8, # U-Law encoded. See https://en.wikipedia.org/wiki/G.711#Types - 'ALAW': 8, # A-Law encoded. See https://en.wikipedia.org/wiki/G.711#Types - 'IMA_ADPCM': 0, # IMA ADPCM. - 'MS_ADPCM': 0, # Microsoft ADPCM. - 'GSM610': 0, # GSM 6.10 encoding. (Wikipedia says 1.625 bit depth?? https://en.wikipedia.org/wiki/Full_Rate) - 'VOX_ADPCM': 0, # OKI / Dialogix ADPCM - 'G721_32': 0, # 32kbs G721 ADPCM encoding. - 'G723_24': 0, # 24kbs G723 ADPCM encoding. - 'G723_40': 0, # 40kbs G723 ADPCM encoding. - 'DWVW_12': 12, # 12 bit Delta Width Variable Word encoding. - 'DWVW_16': 16, # 16 bit Delta Width Variable Word encoding. - 'DWVW_24': 24, # 24 bit Delta Width Variable Word encoding. - 'DWVW_N': 0, # N bit Delta Width Variable Word encoding. - 'DPCM_8': 8, # 8 bit differential PCM (XI only) - 'DPCM_16': 16, # 16 bit differential PCM (XI only) - 'VORBIS': 0, # Xiph Vorbis encoding. (lossy) - 'ALAC_16': 16, # Apple Lossless Audio Codec (16 bit). - 'ALAC_20': 20, # Apple Lossless Audio Codec (20 bit). - 'ALAC_24': 24, # Apple Lossless Audio Codec (24 bit). - 'ALAC_32': 32, # Apple Lossless Audio Codec (32 bit). -} - - -def _get_bit_depth(subtype): - if subtype not in _SUBTYPE_TO_BITS_PER_SAMPLE: - warnings.warn( - f"The {subtype} subtype is unknown to TorchAudio. As a result, the bits_per_sample " - "attribute will be set to 0. If you are seeing this warning, please " - "report by opening an issue on github (after checking for existing/closed ones). " - "You may otherwise ignore this warning." - ) - return _SUBTYPE_TO_BITS_PER_SAMPLE.get(subtype, 0) - - -_SUBTYPE_TO_ENCODING = { - 'PCM_S8': 'PCM_S', - 'PCM_16': 'PCM_S', - 'PCM_24': 'PCM_S', - 'PCM_32': 'PCM_S', - 'PCM_U8': 'PCM_U', - 'FLOAT': 'PCM_F', - 'DOUBLE': 'PCM_F', - 'ULAW': 'ULAW', - 'ALAW': 'ALAW', - 'VORBIS': 'VORBIS', -} - - -def _get_encoding(format: str, subtype: str): - if format == 'FLAC': - return 'FLAC' - return _SUBTYPE_TO_ENCODING.get(subtype, 'UNKNOWN') - - -@_mod_utils.requires_module("soundfile") -def info(filepath: str, format: Optional[str] = None) -> AudioMetaData: - """Get signal information of an audio file. - - Args: - filepath (path-like object or file-like object): - Source of audio data. - Note: - * This argument is intentionally annotated as ``str`` only, - for the consistency with "sox_io" backend, which has a restriction - on type annotation due to TorchScript compiler compatiblity. - format (str, optional): - Not used. PySoundFile does not accept format hint. - - Returns: - AudioMetaData: meta data of the given audio. - """ - sinfo = soundfile.info(filepath) - return AudioMetaData( - sinfo.samplerate, - sinfo.frames, - sinfo.channels, - bits_per_sample=_get_bit_depth(sinfo.subtype), - encoding=_get_encoding(sinfo.format, sinfo.subtype), - ) - - -_SUBTYPE2DTYPE = { - "PCM_S8": "int8", - "PCM_U8": "uint8", - "PCM_16": "int16", - "PCM_32": "int32", - "FLOAT": "float32", - "DOUBLE": "float64", -} - - -@_mod_utils.requires_module("soundfile") -def load( - filepath: str, - frame_offset: int = 0, - num_frames: int = -1, - normalize: bool = True, - channels_first: bool = True, - format: Optional[str] = None, -) -> Tuple[torch.Tensor, int]: - """Load audio data from file. - - Note: - The formats this function can handle depend on the soundfile installation. - This function is tested on the following formats; - - * WAV - - * 32-bit floating-point - * 32-bit signed integer - * 16-bit signed integer - * 8-bit unsigned integer - - * FLAC - * OGG/VORBIS - * SPHERE - - By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with - ``float32`` dtype and the shape of ``[channel, time]``. - The samples are normalized to fit in the range of ``[-1.0, 1.0]``. - - When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit - signed integer and 8-bit unsigned integer (24-bit signed integer is not supported), - by providing ``normalize=False``, this function can return integer Tensor, where the samples - are expressed within the whole range of the corresponding dtype, that is, ``int32`` tensor - for 32-bit signed PCM, ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. - - ``normalize`` parameter has no effect on 32-bit floating-point WAV and other formats, such as - ``flac`` and ``mp3``. - For these formats, this function always returns ``float32`` Tensor with values normalized to - ``[-1.0, 1.0]``. - - Args: - filepath (path-like object or file-like object): - Source of audio data. - Note: - * This argument is intentionally annotated as ``str`` only, - for the consistency with "sox_io" backend, which has a restriction - on type annotation due to TorchScript compiler compatiblity. - frame_offset (int): - Number of frames to skip before start reading data. - num_frames (int): - Maximum number of frames to read. ``-1`` reads all the remaining samples, - starting from ``frame_offset``. - This function may return the less number of frames if there is not enough - frames in the given file. - normalize (bool): - When ``True``, this function always return ``float32``, and sample values are - normalized to ``[-1.0, 1.0]``. - If input file is integer WAV, giving ``False`` will change the resulting Tensor type to - integer type. - This argument has no effect for formats other than integer WAV type. - channels_first (bool): - When True, the returned Tensor has dimension ``[channel, time]``. - Otherwise, the returned Tensor's dimension is ``[time, channel]``. - format (str, optional): - Not used. PySoundFile does not accept format hint. - - Returns: - Tuple[torch.Tensor, int]: Resulting Tensor and sample rate. - If the input file has integer wav format and normalization is off, then it has - integer type, else ``float32`` type. If ``channels_first=True``, it has - ``[channel, time]`` else ``[time, channel]``. - """ - with soundfile.SoundFile(filepath, "r") as file_: - if file_.format != "WAV" or normalize: - dtype = "float32" - elif file_.subtype not in _SUBTYPE2DTYPE: - raise ValueError(f"Unsupported subtype: {file_.subtype}") - else: - dtype = _SUBTYPE2DTYPE[file_.subtype] - - frames = file_._prepare_read(frame_offset, None, num_frames) - waveform = file_.read(frames, dtype, always_2d=True) - sample_rate = file_.samplerate - - waveform = torch.from_numpy(waveform) - if channels_first: - waveform = waveform.t() - return waveform, sample_rate - - -@_mod_utils.requires_module("soundfile") -def save( - filepath: str, - src: torch.Tensor, - sample_rate: int, - channels_first: bool = True, - compression: Optional[float] = None, - format: Optional[str] = None, -): - """Save audio data to file. - - Note: - The formats this function can handle depend on the soundfile installation. - This function is tested on the following formats; - - * WAV - - * 32-bit floating-point - * 32-bit signed integer - * 16-bit signed integer - * 8-bit unsigned integer - - * FLAC - * OGG/VORBIS - * SPHERE - - Args: - filepath (str or pathlib.Path): Path to audio file. - This functionalso handles ``pathlib.Path`` objects, but is annotated as ``str`` - for the consistency with "sox_io" backend, which has a restriction on type annotation - for TorchScript compiler compatiblity. - tensor (torch.Tensor): Audio data to save. must be 2D tensor. - sample_rate (int): sampling rate - channels_first (bool): - If ``True``, the given tensor is interpreted as ``[channel, time]``, - otherwise ``[time, channel]``. - compression (Optional[float]): - Not used. It is here only for interface compatibility reson with "sox_io" backend. - format (str, optional): - Output audio format. This is required when the output audio format cannot be infered from - ``filepath``, (such as file extension or ``name`` attribute of the given file object). - """ - if src.ndim != 2: - raise ValueError(f"Expected 2D Tensor, got {src.ndim}D.") - if compression is not None: - warnings.warn( - '`save` function of "soundfile" backend does not support "compression" parameter. ' - "The argument is silently ignored." - ) - if hasattr(filepath, 'write'): - if format is None: - raise RuntimeError('`format` is required when saving to file object.') - ext = format - else: - ext = str(filepath).split(".")[-1].lower() - - if ext != "wav": - subtype = None - elif src.dtype == torch.uint8: - subtype = "PCM_U8" - elif src.dtype == torch.int16: - subtype = "PCM_16" - elif src.dtype == torch.int32: - subtype = "PCM_32" - elif src.dtype == torch.float32: - subtype = "FLOAT" - elif src.dtype == torch.float64: - subtype = "DOUBLE" - else: - raise ValueError(f"Unsupported dtype for WAV: {src.dtype}") - - # sph is a extension used in TED-LIUM but soundfile does not recognize it as NIST format, - # so we extend the extensions manually here - if ext in ["nis", "nist", "sph"] and format is None: - format = "NIST" - - if channels_first: - src = src.t() - - soundfile.write( - file=filepath, data=src, samplerate=sample_rate, subtype=subtype, format=format - ) - - -@_mod_utils.requires_module("soundfile") -@_mod_utils.deprecated('Please use "torchaudio.load".', "0.9.0") -def load_wav( - filepath: str, - frame_offset: int = 0, - num_frames: int = -1, - channels_first: bool = True, -) -> Tuple[torch.Tensor, int]: - """Load wave file. - - This function is defined only for the purpose of compatibility against other backend - for simple usecases, such as ``torchaudio.load_wav(filepath)``. - The implementation is same as :py:func:`load`. - """ - return load( - filepath, - frame_offset, - num_frames, - normalize=False, - channels_first=channels_first, - ) diff --git a/torchaudio/backend/common.py b/torchaudio/backend/common.py index c0eb90c994..f944b23d74 100644 --- a/torchaudio/backend/common.py +++ b/torchaudio/backend/common.py @@ -1,8 +1,3 @@ -from typing import Any, Optional - -from torchaudio._internal import module_utils as _mod_utils - - class AudioMetaData: """Return type of ``torchaudio.info`` function. @@ -14,7 +9,21 @@ class AudioMetaData: :ivar int num_channels: The number of channels :ivar int bits_per_sample: The number of bits per sample. This is 0 for lossy formats, or when it cannot be accurately inferred. - :ivar str encoding: Audio encoding. + :ivar str encoding: Audio encoding + The values encoding can take are one of the following: + + * ``PCM_S``: Signed integer linear PCM + * ``PCM_U``: Unsigned integer linear PCM + * ``PCM_F``: Floating point linear PCM + * ``FLAC``: Flac, Free Lossless Audio Codec + * ``ULAW``: Mu-law + * ``ALAW``: A-law + * ``MP3`` : MP3, MPEG-1 Audio Layer III + * ``VORBIS``: OGG Vorbis + * ``AMR_WB``: Adaptive Multi-Rate + * ``AMR_NB``: Adaptive Multi-Rate Wideband + * ``OPUS``: Opus + * ``UNKNOWN`` : None of above """ def __init__( self, @@ -30,174 +39,13 @@ def __init__( self.bits_per_sample = bits_per_sample self.encoding = encoding - -@_mod_utils.deprecated('Please migrate to `AudioMetaData`.', '0.9.0') -class SignalInfo: - """One of return types of ``torchaudio.info`` functions. - - This class is used by :ref:`"sox" backend (deprecated)` and - :ref:`"soundfile" backend with the legacy interface (deprecated)`. - - See https://fossies.org/dox/sox-14.4.2/structsox__signalinfo__t.html - - :ivar Optional[int] channels: The number of channels - :ivar Optional[float] rate: Sampleing rate - :ivar Optional[int] precision: Bit depth - :ivar Optional[int] length: For :ref:`sox backend`, the number of samples. - (frames * channels). For :ref:`soundfile backend`, the number of frames. - """ - def __init__(self, - channels: Optional[int] = None, - rate: Optional[float] = None, - precision: Optional[int] = None, - length: Optional[int] = None) -> None: - self.channels = channels - self.rate = rate - self.precision = precision - self.length = length - - -@_mod_utils.deprecated('Please migrate to `AudioMetaData`.', '0.9.0') -class EncodingInfo: - """One of return types of ``torchaudio.info`` functions. - - This class is used by :ref:`"sox" backend (deprecated)` and - :ref:`"soundfile" backend with the legacy interface (deprecated)`. - - See https://fossies.org/dox/sox-14.4.2/structsox__encodinginfo__t.html - - :ivar Optional[int] encoding: sox_encoding_t - :ivar Optional[int] bits_per_sample: bit depth - :ivar Optional[float] compression: Compression option - :ivar Any reverse_bytes: - :ivar Any reverse_nibbles: - :ivar Any reverse_bits: - :ivar Optional[bool] opposite_endian: - """ - def __init__(self, - encoding: Any = None, - bits_per_sample: Optional[int] = None, - compression: Optional[float] = None, - reverse_bytes: Any = None, - reverse_nibbles: Any = None, - reverse_bits: Any = None, - opposite_endian: Optional[bool] = None) -> None: - self.encoding = encoding - self.bits_per_sample = bits_per_sample - self.compression = compression - self.reverse_bytes = reverse_bytes - self.reverse_nibbles = reverse_nibbles - self.reverse_bits = reverse_bits - self.opposite_endian = opposite_endian - - -_LOAD_DOCSTRING = r"""Loads an audio file from disk into a tensor - -Args: - filepath: Path to audio file - - out: An optional output tensor to use instead of creating one. (Default: ``None``) - - normalization: Optional normalization. - If boolean `True`, then output is divided by `1 << 31`. - Assuming the input is signed 32-bit audio, this normalizes to `[-1, 1]`. - If `float`, then output is divided by that number. - If `Callable`, then the output is passed as a paramete to the given function, - then the output is divided by the result. (Default: ``True``) - - channels_first: Set channels first or length first in result. (Default: ``True``) - - num_frames: Number of frames to load. 0 to load everything after the offset. - (Default: ``0``) - - offset: Number of frames from the start of the file to begin data loading. - (Default: ``0``) - - signalinfo: A sox_signalinfo_t type, which could be helpful if the - audio type cannot be automatically determined. (Default: ``None``) - - encodinginfo: A sox_encodinginfo_t type, which could be set if the - audio type cannot be automatically determined. (Default: ``None``) - - filetype: A filetype or extension to be set if sox cannot determine it - automatically. (Default: ``None``) - -Returns: - (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where - L is the number of audio frames and - C is the number of channels. - An integer which is the sample rate of the audio (as listed in the metadata of the file) - -Example - >>> data, sample_rate = torchaudio.load('foo.mp3') - >>> print(data.size()) - torch.Size([2, 278756]) - >>> print(sample_rate) - 44100 - >>> data_vol_normalized, _ = torchaudio.load('foo.mp3', normalization=lambda x: torch.abs(x).max()) - >>> print(data_vol_normalized.abs().max()) - 1. -""" - - -_LOAD_WAV_DOCSTRING = r""" Loads a wave file. - -It assumes that the wav file uses 16 bit per sample that needs normalization by -shifting the input right by 16 bits. - -Args: - filepath: Path to audio file - -Returns: - (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number - of audio frames and C is the number of channels. An integer which is the sample rate of the - audio (as listed in the metadata of the file) -""" - -_SAVE_DOCSTRING = r"""Saves a Tensor on file as an audio file - -Args: - filepath: Path to audio file - src: An input 2D tensor of shape `[C x L]` or `[L x C]` where L is - the number of audio frames, C is the number of channels - sample_rate: An integer which is the sample rate of the - audio (as listed in the metadata of the file) - precision Bit precision (Default: ``16``) - channels_first (bool, optional): Set channels first or length first in result. ( - Default: ``True``) -""" - - -_INFO_DOCSTRING = r"""Gets metadata from an audio file without loading the signal. - -Args: - filepath: Path to audio file - -Returns: - (sox_signalinfo_t, sox_encodinginfo_t): A si (sox_signalinfo_t) signal - info as a python object. An ei (sox_encodinginfo_t) encoding info - -Example - >>> si, ei = torchaudio.info('foo.wav') - >>> rate, channels, encoding = si.rate, si.channels, ei.encoding -""" - - -def _impl_load(func): - setattr(func, '__doc__', _LOAD_DOCSTRING) - return func - - -def _impl_load_wav(func): - setattr(func, '__doc__', _LOAD_WAV_DOCSTRING) - return func - - -def _impl_save(func): - setattr(func, '__doc__', _SAVE_DOCSTRING) - return func - - -def _impl_info(func): - setattr(func, '__doc__', _INFO_DOCSTRING) - return func + def __str__(self): + return ( + f"AudioMetaData(" + f"sample_rate={self.sample_rate}, " + f"num_frames={self.num_frames}, " + f"num_channels={self.num_channels}, " + f"bits_per_sample={self.bits_per_sample}, " + f"encoding={self.encoding}" + f")" + ) diff --git a/torchaudio/backend/no_backend.py b/torchaudio/backend/no_backend.py index 60571c34a9..7160c89f69 100644 --- a/torchaudio/backend/no_backend.py +++ b/torchaudio/backend/no_backend.py @@ -1,35 +1,22 @@ from pathlib import Path -from typing import Any, Callable, Optional, Tuple, Union +from typing import Callable, Optional, Tuple, Union from torch import Tensor -from . import common -from .common import SignalInfo, EncodingInfo - -@common._impl_load def load(filepath: Union[str, Path], out: Optional[Tensor] = None, normalization: Union[bool, float, Callable] = True, channels_first: bool = True, num_frames: int = 0, offset: int = 0, - signalinfo: Optional[SignalInfo] = None, - encodinginfo: Optional[EncodingInfo] = None, filetype: Optional[str] = None) -> Tuple[Tensor, int]: raise RuntimeError('No audio I/O backend is available.') -@common._impl_load_wav -def load_wav(filepath: Union[str, Path], **kwargs: Any) -> Tuple[Tensor, int]: - raise RuntimeError('No audio I/O backend is available.') - - -@common._impl_save def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: raise RuntimeError('No audio I/O backend is available.') -@common._impl_info -def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: +def info(filepath: str) -> None: raise RuntimeError('No audio I/O backend is available.') diff --git a/torchaudio/backend/soundfile_backend.py b/torchaudio/backend/soundfile_backend.py index 49cada21b0..8ad28a6fcf 100644 --- a/torchaudio/backend/soundfile_backend.py +++ b/torchaudio/backend/soundfile_backend.py @@ -1,127 +1,426 @@ -import os -from typing import Optional, Tuple +"""The new soundfile backend which will become default in 0.8.0 onward""" +from typing import Tuple, Optional +import warnings import torch -from torch import Tensor +from torchaudio._internal import module_utils as _mod_utils +from .common import AudioMetaData -from torchaudio._internal import ( - module_utils as _mod_utils, - misc_ops as _misc_ops, -) -from . import common -from .common import SignalInfo, EncodingInfo -if _mod_utils.is_module_available('soundfile'): +if _mod_utils.is_module_available("soundfile"): import soundfile -_subtype_to_precision = { - 'PCM_S8': 8, - 'PCM_16': 16, - 'PCM_24': 24, - 'PCM_32': 32, - 'PCM_U8': 8 +# Mapping from soundfile subtype to number of bits per sample. +# This is mostly heuristical and the value is set to 0 when it is irrelevant +# (lossy formats) or when it can't be inferred. +# For ADPCM (and G72X) subtypes, it's hard to infer the bit depth because it's not part of the standard: +# According to https://en.wikipedia.org/wiki/Adaptive_differential_pulse-code_modulation#In_telephony, +# the default seems to be 8 bits but it can be compressed further to 4 bits. +# The dict is inspired from +# https://github.com/bastibe/python-soundfile/blob/744efb4b01abc72498a96b09115b42a4cabd85e4/soundfile.py#L66-L94 +_SUBTYPE_TO_BITS_PER_SAMPLE = { + 'PCM_S8': 8, # Signed 8 bit data + 'PCM_16': 16, # Signed 16 bit data + 'PCM_24': 24, # Signed 24 bit data + 'PCM_32': 32, # Signed 32 bit data + 'PCM_U8': 8, # Unsigned 8 bit data (WAV and RAW only) + 'FLOAT': 32, # 32 bit float data + 'DOUBLE': 64, # 64 bit float data + 'ULAW': 8, # U-Law encoded. See https://en.wikipedia.org/wiki/G.711#Types + 'ALAW': 8, # A-Law encoded. See https://en.wikipedia.org/wiki/G.711#Types + 'IMA_ADPCM': 0, # IMA ADPCM. + 'MS_ADPCM': 0, # Microsoft ADPCM. + 'GSM610': 0, # GSM 6.10 encoding. (Wikipedia says 1.625 bit depth?? https://en.wikipedia.org/wiki/Full_Rate) + 'VOX_ADPCM': 0, # OKI / Dialogix ADPCM + 'G721_32': 0, # 32kbs G721 ADPCM encoding. + 'G723_24': 0, # 24kbs G723 ADPCM encoding. + 'G723_40': 0, # 40kbs G723 ADPCM encoding. + 'DWVW_12': 12, # 12 bit Delta Width Variable Word encoding. + 'DWVW_16': 16, # 16 bit Delta Width Variable Word encoding. + 'DWVW_24': 24, # 24 bit Delta Width Variable Word encoding. + 'DWVW_N': 0, # N bit Delta Width Variable Word encoding. + 'DPCM_8': 8, # 8 bit differential PCM (XI only) + 'DPCM_16': 16, # 16 bit differential PCM (XI only) + 'VORBIS': 0, # Xiph Vorbis encoding. (lossy) + 'ALAC_16': 16, # Apple Lossless Audio Codec (16 bit). + 'ALAC_20': 20, # Apple Lossless Audio Codec (20 bit). + 'ALAC_24': 24, # Apple Lossless Audio Codec (24 bit). + 'ALAC_32': 32, # Apple Lossless Audio Codec (32 bit). } -@_mod_utils.requires_module('soundfile') -@common._impl_load -def load(filepath: str, - out: Optional[Tensor] = None, - normalization: Optional[bool] = True, - channels_first: Optional[bool] = True, - num_frames: int = 0, - offset: int = 0, - signalinfo: SignalInfo = None, - encodinginfo: EncodingInfo = None, - filetype: Optional[str] = None) -> Tuple[Tensor, int]: - r"""See torchaudio.load""" - - assert out is None - assert normalization - assert signalinfo is None - assert encodinginfo is None - - # stringify if `pathlib.Path` (noop if already `str`) - filepath = str(filepath) - - # check if valid file - if not os.path.isfile(filepath): - raise OSError("{} not found or is a directory".format(filepath)) - - if num_frames < -1: - raise ValueError("Expected value for num_samples -1 (entire file) or >=0") - if num_frames == 0: - num_frames = -1 - if offset < 0: - raise ValueError("Expected positive offset value") - - # initialize output tensor - # TODO call libsoundfile directly to avoid numpy - out, sample_rate = soundfile.read( - filepath, frames=num_frames, start=offset, dtype="float32", always_2d=True +def _get_bit_depth(subtype): + if subtype not in _SUBTYPE_TO_BITS_PER_SAMPLE: + warnings.warn( + f"The {subtype} subtype is unknown to TorchAudio. As a result, the bits_per_sample " + "attribute will be set to 0. If you are seeing this warning, please " + "report by opening an issue on github (after checking for existing/closed ones). " + "You may otherwise ignore this warning." + ) + return _SUBTYPE_TO_BITS_PER_SAMPLE.get(subtype, 0) + + +_SUBTYPE_TO_ENCODING = { + 'PCM_S8': 'PCM_S', + 'PCM_16': 'PCM_S', + 'PCM_24': 'PCM_S', + 'PCM_32': 'PCM_S', + 'PCM_U8': 'PCM_U', + 'FLOAT': 'PCM_F', + 'DOUBLE': 'PCM_F', + 'ULAW': 'ULAW', + 'ALAW': 'ALAW', + 'VORBIS': 'VORBIS', +} + + +def _get_encoding(format: str, subtype: str): + if format == 'FLAC': + return 'FLAC' + return _SUBTYPE_TO_ENCODING.get(subtype, 'UNKNOWN') + + +@_mod_utils.requires_module("soundfile") +def info(filepath: str, format: Optional[str] = None) -> AudioMetaData: + """Get signal information of an audio file. + + Args: + filepath (path-like object or file-like object): + Source of audio data. + Note: + * This argument is intentionally annotated as ``str`` only, + for the consistency with "sox_io" backend, which has a restriction + on type annotation due to TorchScript compiler compatiblity. + format (str, optional): + Not used. PySoundFile does not accept format hint. + + Returns: + AudioMetaData: meta data of the given audio. + """ + sinfo = soundfile.info(filepath) + return AudioMetaData( + sinfo.samplerate, + sinfo.frames, + sinfo.channels, + bits_per_sample=_get_bit_depth(sinfo.subtype), + encoding=_get_encoding(sinfo.format, sinfo.subtype), ) - out = torch.from_numpy(out).t() - if not channels_first: - out = out.t() - # normalize if needed - # _audio_normalization(out, normalization) +_SUBTYPE2DTYPE = { + "PCM_S8": "int8", + "PCM_U8": "uint8", + "PCM_16": "int16", + "PCM_32": "int32", + "FLOAT": "float32", + "DOUBLE": "float64", +} + + +@_mod_utils.requires_module("soundfile") +def load( + filepath: str, + frame_offset: int = 0, + num_frames: int = -1, + normalize: bool = True, + channels_first: bool = True, + format: Optional[str] = None, +) -> Tuple[torch.Tensor, int]: + """Load audio data from file. + + Note: + The formats this function can handle depend on the soundfile installation. + This function is tested on the following formats; + + * WAV + + * 32-bit floating-point + * 32-bit signed integer + * 16-bit signed integer + * 8-bit unsigned integer - return out, sample_rate + * FLAC + * OGG/VORBIS + * SPHERE + By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with + ``float32`` dtype and the shape of ``[channel, time]``. + The samples are normalized to fit in the range of ``[-1.0, 1.0]``. -@_mod_utils.requires_module('soundfile') -@_mod_utils.deprecated('Please use "torchaudio.load".', '0.9.0') -@common._impl_load_wav -def load_wav(filepath, **kwargs): - kwargs['normalization'] = 1 << 16 - return load(filepath, **kwargs) + When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit + signed integer and 8-bit unsigned integer (24-bit signed integer is not supported), + by providing ``normalize=False``, this function can return integer Tensor, where the samples + are expressed within the whole range of the corresponding dtype, that is, ``int32`` tensor + for 32-bit signed PCM, ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. + ``normalize`` parameter has no effect on 32-bit floating-point WAV and other formats, such as + ``flac`` and ``mp3``. + For these formats, this function always returns ``float32`` Tensor with values normalized to + ``[-1.0, 1.0]``. -@_mod_utils.requires_module('soundfile') -@common._impl_save -def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: - r"""See torchaudio.save""" + Args: + filepath (path-like object or file-like object): + Source of audio data. + Note: + * This argument is intentionally annotated as ``str`` only, + for the consistency with "sox_io" backend, which has a restriction + on type annotation due to TorchScript compiler compatiblity. + frame_offset (int): + Number of frames to skip before start reading data. + num_frames (int): + Maximum number of frames to read. ``-1`` reads all the remaining samples, + starting from ``frame_offset``. + This function may return the less number of frames if there is not enough + frames in the given file. + normalize (bool): + When ``True``, this function always return ``float32``, and sample values are + normalized to ``[-1.0, 1.0]``. + If input file is integer WAV, giving ``False`` will change the resulting Tensor type to + integer type. + This argument has no effect for formats other than integer WAV type. + channels_first (bool): + When True, the returned Tensor has dimension ``[channel, time]``. + Otherwise, the returned Tensor's dimension is ``[time, channel]``. + format (str, optional): + Not used. PySoundFile does not accept format hint. - ch_idx, len_idx = (0, 1) if channels_first else (1, 0) + Returns: + Tuple[torch.Tensor, int]: Resulting Tensor and sample rate. + If the input file has integer wav format and normalization is off, then it has + integer type, else ``float32`` type. If ``channels_first=True``, it has + ``[channel, time]`` else ``[time, channel]``. + """ + with soundfile.SoundFile(filepath, "r") as file_: + if file_.format != "WAV" or normalize: + dtype = "float32" + elif file_.subtype not in _SUBTYPE2DTYPE: + raise ValueError(f"Unsupported subtype: {file_.subtype}") + else: + dtype = _SUBTYPE2DTYPE[file_.subtype] - # check if save directory exists - abs_dirpath = os.path.dirname(os.path.abspath(filepath)) - if not os.path.isdir(abs_dirpath): - raise OSError("Directory does not exist: {}".format(abs_dirpath)) - # check that src is a CPU tensor - _misc_ops.check_input(src) - # Check/Fix shape of source data - if src.dim() == 1: - # 1d tensors as assumed to be mono signals - src.unsqueeze_(ch_idx) - elif src.dim() > 2 or src.size(ch_idx) > 16: - # assumes num_channels < 16 - raise ValueError( - "Expected format where C < 16, but found {}".format(src.size())) + frames = file_._prepare_read(frame_offset, None, num_frames) + waveform = file_.read(frames, dtype, always_2d=True) + sample_rate = file_.samplerate + waveform = torch.from_numpy(waveform) if channels_first: - src = src.t() + waveform = waveform.t() + return waveform, sample_rate + + +def _get_subtype_for_wav( + dtype: torch.dtype, + encoding: str, + bits_per_sample: int): + if not encoding: + if not bits_per_sample: + subtype = { + torch.uint8: "PCM_U8", + torch.int16: "PCM_16", + torch.int32: "PCM_32", + torch.float32: "FLOAT", + torch.float64: "DOUBLE", + }.get(dtype) + if not subtype: + raise ValueError(f"Unsupported dtype for wav: {dtype}") + return subtype + if bits_per_sample == 8: + return "PCM_U8" + return f"PCM_{bits_per_sample}" + if encoding == "PCM_S": + if not bits_per_sample: + return "PCM_32" + if bits_per_sample == 8: + raise ValueError("wav does not support 8-bit signed PCM encoding.") + return f"PCM_{bits_per_sample}" + if encoding == "PCM_U": + if bits_per_sample in (None, 8): + return "PCM_U8" + raise ValueError("wav only supports 8-bit unsigned PCM encoding.") + if encoding == "PCM_F": + if bits_per_sample in (None, 32): + return "FLOAT" + if bits_per_sample == 64: + return "DOUBLE" + raise ValueError("wav only supports 32/64-bit float PCM encoding.") + if encoding == "ULAW": + if bits_per_sample in (None, 8): + return "ULAW" + raise ValueError("wav only supports 8-bit mu-law encoding.") + if encoding == "ALAW": + if bits_per_sample in (None, 8): + return "ALAW" + raise ValueError("wav only supports 8-bit a-law encoding.") + raise ValueError(f"wav does not support {encoding}.") + + +def _get_subtype_for_sphere(encoding: str, bits_per_sample: int): + if encoding in (None, "PCM_S"): + return f"PCM_{bits_per_sample}" if bits_per_sample else "PCM_32" + if encoding in ("PCM_U", "PCM_F"): + raise ValueError(f"sph does not support {encoding} encoding.") + if encoding == "ULAW": + if bits_per_sample in (None, 8): + return "ULAW" + raise ValueError("sph only supports 8-bit for mu-law encoding.") + if encoding == "ALAW": + return "ALAW" + raise ValueError(f"sph does not support {encoding}.") - if src.dtype == torch.int64: - # Soundfile doesn't support int64 - src = src.type(torch.int32) - precision = "PCM_S8" if precision == 8 else "PCM_" + str(precision) +def _get_subtype( + dtype: torch.dtype, + format: str, + encoding: str, + bits_per_sample: int): + if format == "wav": + return _get_subtype_for_wav(dtype, encoding, bits_per_sample) + if format == "flac": + if encoding: + raise ValueError("flac does not support encoding.") + if not bits_per_sample: + return "PCM_24" + if bits_per_sample > 24: + raise ValueError("flac does not support bits_per_sample > 24.") + return "PCM_S8" if bits_per_sample == 8 else f"PCM_{bits_per_sample}" + if format in ("ogg", "vorbis"): + if encoding or bits_per_sample: + raise ValueError( + "ogg/vorbis does not support encoding/bits_per_sample.") + return "VORBIS" + if format == "sph": + return _get_subtype_for_sphere(encoding, bits_per_sample) + if format in ("nis", "nist"): + return "PCM_16" + raise ValueError(f"Unsupported format: {format}") - return soundfile.write(filepath, src, sample_rate, precision) +@_mod_utils.requires_module("soundfile") +def save( + filepath: str, + src: torch.Tensor, + sample_rate: int, + channels_first: bool = True, + compression: Optional[float] = None, + format: Optional[str] = None, + encoding: Optional[str] = None, + bits_per_sample: Optional[int] = None, +): + """Save audio data to file. -@_mod_utils.requires_module('soundfile') -@common._impl_info -def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: - r"""See torchaudio.info""" + Note: + The formats this function can handle depend on the soundfile installation. + This function is tested on the following formats; - sfi = soundfile.info(filepath) + * WAV - precision = _subtype_to_precision[sfi.subtype] - si = SignalInfo(sfi.channels, sfi.samplerate, precision, sfi.frames) - ei = EncodingInfo(bits_per_sample=precision) - return si, ei + * 32-bit floating-point + * 32-bit signed integer + * 16-bit signed integer + * 8-bit unsigned integer + + * FLAC + * OGG/VORBIS + * SPHERE + + Args: + filepath (str or pathlib.Path): Path to audio file. + This functionalso handles ``pathlib.Path`` objects, but is annotated as ``str`` + for the consistency with "sox_io" backend, which has a restriction on type annotation + for TorchScript compiler compatiblity. + src (torch.Tensor): Audio data to save. must be 2D tensor. + sample_rate (int): sampling rate + channels_first (bool): If ``True``, the given tensor is interpreted as ``[channel, time]``, + otherwise ``[time, channel]``. + compression (Optional[float]): Not used. + It is here only for interface compatibility reson with "sox_io" backend. + format (str, optional): Override the audio format. + When ``filepath`` argument is path-like object, audio format is + inferred from file extension. If the file extension is missing or + different, you can specify the correct format with this argument. + + When ``filepath`` argument is file-like object, + this argument is required. + + Valid values are ``"wav"``, ``"ogg"``, ``"vorbis"``, + ``"flac"`` and ``"sph"``. + encoding (str, optional): Changes the encoding for supported formats. + This argument is effective only for supported formats, sush as + ``"wav"``, ``""flac"`` and ``"sph"``. Valid values are; + + - ``"PCM_S"`` (signed integer Linear PCM) + - ``"PCM_U"`` (unsigned integer Linear PCM) + - ``"PCM_F"`` (floating point PCM) + - ``"ULAW"`` (mu-law) + - ``"ALAW"`` (a-law) + + bits_per_sample (int, optional): Changes the bit depth for the + supported formats. + When ``format`` is one of ``"wav"``, ``"flac"`` or ``"sph"``, + you can change the bit depth. + Valid values are ``8``, ``16``, ``24``, ``32`` and ``64``. + + Supported formats/encodings/bit depth/compression are: + + ``"wav"`` + - 32-bit floating-point PCM + - 32-bit signed integer PCM + - 24-bit signed integer PCM + - 16-bit signed integer PCM + - 8-bit unsigned integer PCM + - 8-bit mu-law + - 8-bit a-law + + Note: Default encoding/bit depth is determined by the dtype of + the input Tensor. + + ``"flac"`` + - 8-bit + - 16-bit + - 24-bit (default) + + ``"ogg"``, ``"vorbis"`` + - Doesn't accept changing configuration. + + ``"sph"`` + - 8-bit signed integer PCM + - 16-bit signed integer PCM + - 24-bit signed integer PCM + - 32-bit signed integer PCM (default) + - 8-bit mu-law + - 8-bit a-law + - 16-bit a-law + - 24-bit a-law + - 32-bit a-law + + """ + if src.ndim != 2: + raise ValueError(f"Expected 2D Tensor, got {src.ndim}D.") + if compression is not None: + warnings.warn( + '`save` function of "soundfile" backend does not support "compression" parameter. ' + "The argument is silently ignored." + ) + if hasattr(filepath, 'write'): + if format is None: + raise RuntimeError('`format` is required when saving to file object.') + ext = format.lower() + else: + ext = str(filepath).split(".")[-1].lower() + + if bits_per_sample not in (None, 8, 16, 24, 32, 64): + raise ValueError("Invalid bits_per_sample.") + subtype = _get_subtype(src.dtype, ext, encoding, bits_per_sample) + + # sph is a extension used in TED-LIUM but soundfile does not recognize it as NIST format, + # so we extend the extensions manually here + if ext in ["nis", "nist", "sph"] and format is None: + format = "NIST" + + if channels_first: + src = src.t() + + soundfile.write( + file=filepath, data=src, samplerate=sample_rate, subtype=subtype, format=format + ) diff --git a/torchaudio/backend/sox_backend.py b/torchaudio/backend/sox_backend.py deleted file mode 100644 index bf167dd195..0000000000 --- a/torchaudio/backend/sox_backend.py +++ /dev/null @@ -1,294 +0,0 @@ -import os.path -from typing import Any, Optional, Tuple - -import torch -from torch import Tensor - -from torchaudio._internal import ( - module_utils as _mod_utils, - misc_ops as _misc_ops, -) -from . import common -from .common import SignalInfo, EncodingInfo - -if _mod_utils.is_module_available('torchaudio._torchaudio'): - from torchaudio import _torchaudio - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@common._impl_load -def load(filepath: str, - out: Optional[Tensor] = None, - normalization: bool = True, - channels_first: bool = True, - num_frames: int = 0, - offset: int = 0, - signalinfo: SignalInfo = None, - encodinginfo: EncodingInfo = None, - filetype: Optional[str] = None) -> Tuple[Tensor, int]: - r"""See torchaudio.load""" - - # stringify if `pathlib.Path` (noop if already `str`) - filepath = str(filepath) - # check if valid file - if not os.path.isfile(filepath): - raise OSError("{} not found or is a directory".format(filepath)) - - # initialize output tensor - if out is not None: - _misc_ops.check_input(out) - else: - out = torch.FloatTensor() - - if num_frames < -1: - raise ValueError("Expected value for num_samples -1 (entire file) or >=0") - if offset < 0: - raise ValueError("Expected positive offset value") - - sample_rate = _torchaudio.read_audio_file( - filepath, - out, - channels_first, - num_frames, - offset, - signalinfo, - encodinginfo, - filetype - ) - - # normalize if needed - _misc_ops.normalize_audio(out, normalization) - - return out, sample_rate - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@_mod_utils.deprecated('Please use "torchaudio.load".', '0.9.0') -@common._impl_load_wav -def load_wav(filepath, **kwargs): - kwargs['normalization'] = 1 << 16 - return load(filepath, **kwargs) - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@common._impl_save -def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: - r"""See torchaudio.save""" - - si = sox_signalinfo_t() - ch_idx = 0 if channels_first else 1 - si.rate = sample_rate - si.channels = 1 if src.dim() == 1 else src.size(ch_idx) - si.length = src.numel() - si.precision = precision - return save_encinfo(filepath, src, channels_first, si) - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@common._impl_info -def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: - r"""See torchaudio.info""" - return _torchaudio.get_info(filepath) - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@_mod_utils.deprecated( - 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', - '0.9.0') -def save_encinfo(filepath: str, - src: Tensor, - channels_first: bool = True, - signalinfo: Optional[SignalInfo] = None, - encodinginfo: Optional[EncodingInfo] = None, - filetype: Optional[str] = None) -> None: - r"""Saves a tensor of an audio signal to disk as a standard format like mp3, wav, etc. - - Args: - filepath (str): Path to audio file - src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is - the number of audio frames, C is the number of channels - channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) - signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the - audio type cannot be automatically determined (Default: ``None``). - encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the - audio type cannot be automatically determined (Default: ``None``). - filetype (str, optional): A filetype or extension to be set if sox cannot determine it - automatically. (Default: ``None``) - - Example - >>> data, sample_rate = torchaudio.load('foo.mp3') - >>> torchaudio.save('foo.wav', data, sample_rate) - - """ - ch_idx, len_idx = (0, 1) if channels_first else (1, 0) - - # check if save directory exists - abs_dirpath = os.path.dirname(os.path.abspath(filepath)) - if not os.path.isdir(abs_dirpath): - raise OSError("Directory does not exist: {}".format(abs_dirpath)) - # check that src is a CPU tensor - _misc_ops.check_input(src) - # Check/Fix shape of source data - if src.dim() == 1: - # 1d tensors as assumed to be mono signals - src.unsqueeze_(ch_idx) - elif src.dim() > 2 or src.size(ch_idx) > 16: - # assumes num_channels < 16 - raise ValueError( - "Expected format where C < 16, but found {}".format(src.size())) - # sox stores the sample rate as a float, though practically sample rates are almost always integers - # convert integers to floats - if signalinfo: - if signalinfo.rate and not isinstance(signalinfo.rate, float): - if float(signalinfo.rate) == signalinfo.rate: - signalinfo.rate = float(signalinfo.rate) - else: - raise TypeError('Sample rate should be a float or int') - # check if the bit precision (i.e. bits per sample) is an integer - if signalinfo.precision and not isinstance(signalinfo.precision, int): - if int(signalinfo.precision) == signalinfo.precision: - signalinfo.precision = int(signalinfo.precision) - else: - raise TypeError('Bit precision should be an integer') - # programs such as librosa normalize the signal, unnormalize if detected - if src.min() >= -1.0 and src.max() <= 1.0: - src = src * (1 << 31) - src = src.long() - # set filetype and allow for files with no extensions - extension = os.path.splitext(filepath)[1] - filetype = extension[1:] if len(extension) > 0 else filetype - # transpose from C x L -> L x C - if channels_first: - src = src.transpose(1, 0) - # save data to file - src = src.contiguous() - _torchaudio.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@_mod_utils.deprecated( - 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', - '0.9.0') -def sox_signalinfo_t() -> SignalInfo: - r"""Create a sox_signalinfo_t object. This object can be used to set the sample - rate, number of channels, length, bit precision and headroom multiplier - primarily for effects - - Returns: sox_signalinfo_t(object) - - rate (float), sample rate as a float, practically will likely be an integer float - - channel (int), number of audio channels - - precision (int), bit precision - - length (int), length of audio in samples * channels, 0 for unspecified and -1 for unknown - - mult (float, optional), headroom multiplier for effects and ``None`` for no multiplier - - Example - >>> si = torchaudio.sox_signalinfo_t() - >>> si.channels = 1 - >>> si.rate = 16000. - >>> si.precision = 16 - >>> si.length = 0 - """ - return _torchaudio.sox_signalinfo_t() - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@_mod_utils.deprecated( - 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', - '0.9.0') -def sox_encodinginfo_t() -> EncodingInfo: - r"""Create a sox_encodinginfo_t object. This object can be used to set the encoding - type, bit precision, compression factor, reverse bytes, reverse nibbles, - reverse bits and endianness. This can be used in an effects chain to encode the - final output or to save a file with a specific encoding. For example, one could - use the sox ulaw encoding to do 8-bit ulaw encoding. Note in a tensor output - the result will be a 32-bit number, but number of unique values will be determined by - the bit precision. - - Returns: sox_encodinginfo_t(object) - - encoding (sox_encoding_t), output encoding - - bits_per_sample (int), bit precision, same as `precision` in sox_signalinfo_t - - compression (float), compression for lossy formats, 0.0 for default compression - - reverse_bytes (sox_option_t), reverse bytes, use sox_option_default - - reverse_nibbles (sox_option_t), reverse nibbles, use sox_option_default - - reverse_bits (sox_option_t), reverse bytes, use sox_option_default - - opposite_endian (sox_bool), change endianness, use sox_false - - Example - >>> ei = torchaudio.sox_encodinginfo_t() - >>> ei.encoding = torchaudio.get_sox_encoding_t(1) - >>> ei.bits_per_sample = 16 - >>> ei.compression = 0 - >>> ei.reverse_bytes = torchaudio.get_sox_option_t(2) - >>> ei.reverse_nibbles = torchaudio.get_sox_option_t(2) - >>> ei.reverse_bits = torchaudio.get_sox_option_t(2) - >>> ei.opposite_endian = torchaudio.get_sox_bool(0) - - """ - ei = _torchaudio.sox_encodinginfo_t() - sdo = get_sox_option_t(2) # sox_default_option - ei.reverse_bytes = sdo - ei.reverse_nibbles = sdo - ei.reverse_bits = sdo - return ei - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@_mod_utils.deprecated( - 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', - '0.9.0') -def get_sox_encoding_t(i: int = None) -> EncodingInfo: - r"""Get enum of sox_encoding_t for sox encodings. - - Args: - i (int, optional): Choose type or get a dict with all possible options - use ``__members__`` to see all options when not specified. (Default: ``None``) - - Returns: - sox_encoding_t: A sox_encoding_t type for output encoding - """ - if i is None: - # one can see all possible values using the .__members__ attribute - return _torchaudio.sox_encoding_t - else: - return _torchaudio.sox_encoding_t(i) - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@_mod_utils.deprecated( - 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', - '0.9.0') -def get_sox_option_t(i: int = 2) -> Any: - r"""Get enum of sox_option_t for sox encodinginfo options. - - Args: - i (int, optional): Choose type or get a dict with all possible options - use ``__members__`` to see all options when not specified. - (Default: ``sox_option_default`` or ``2``) - Returns: - sox_option_t: A sox_option_t type - """ - if i is None: - return _torchaudio.sox_option_t - else: - return _torchaudio.sox_option_t(i) - - -@_mod_utils.requires_module('torchaudio._torchaudio') -@_mod_utils.deprecated( - 'Please migrate to "sox_io" backend. See https://github.com/pytorch/audio/issues/903 for the detail', - '0.9.0') -def get_sox_bool(i: int = 0) -> Any: - r"""Get enum of sox_bool for sox encodinginfo options. - - Args: - i (int, optional): Choose type or get a dict with all possible options - use ``__members__`` to see all options when not specified. (Default: - ``sox_false`` or ``0``) - - Returns: - sox_bool: A sox_bool type - """ - if i is None: - return _torchaudio.sox_bool - else: - return _torchaudio.sox_bool(i) diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 0eaf02118a..65b3e8bd50 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -1,5 +1,4 @@ import os -import warnings from typing import Tuple, Optional import torch @@ -11,7 +10,7 @@ from .common import AudioMetaData -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def info( filepath: str, format: Optional[str] = None, @@ -22,18 +21,22 @@ def info( filepath (path-like object or file-like object): Source of audio data. When the function is not compiled by TorchScript, (e.g. ``torch.jit.script``), the following types are accepted; + * ``path-like``: file path * ``file-like``: Object with ``read(size: int) -> bytes`` method, which returns byte string of at most ``size`` length. + When the function is compiled by TorchScript, only ``str`` type is allowed. Note: + * When the input type is file-like object, this function cannot get the correct length (``num_samples``) for certain formats, such as ``mp3`` and ``vorbis``. In this case, the value of ``num_samples`` is ``0``. * This argument is intentionally annotated as ``str`` only due to TorchScript compiler compatibility. + format (str, optional): Override the format detection with the given format. Providing the argument might help when libsox can not infer the format @@ -51,7 +54,7 @@ def info( return AudioMetaData(*sinfo) -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def load( filepath: str, frame_offset: int = 0, @@ -70,6 +73,7 @@ def load( * 32-bit floating-point * 32-bit signed integer + * 24-bit signed integer * 16-bit signed integer * 8-bit unsigned integer (WAV only) @@ -89,10 +93,11 @@ def load( The samples are normalized to fit in the range of ``[-1.0, 1.0]``. When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit - signed integer and 8-bit unsigned integer (24-bit signed integer is not supported), - by providing ``normalize=False``, this function can return integer Tensor, where the samples - are expressed within the whole range of the corresponding dtype, that is, ``int32`` tensor - for 32-bit signed PCM, ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. + signed integer, 24-bit signed integer, and 8-bit unsigned integer, by providing ``normalize=False``, + this function can return integer Tensor, where the samples are expressed within the whole range + of the corresponding dtype, that is, ``int32`` tensor for 32-bit signed PCM, + ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. Since torch does not + support ``int24`` dtype, 24-bit signed PCM are converted to ``int32`` tensors. ``normalize`` parameter has no effect on 32-bit floating-point WAV and other formats, such as ``flac`` and ``mp3``. @@ -103,14 +108,15 @@ def load( filepath (path-like object or file-like object): Source of audio data. When the function is not compiled by TorchScript, (e.g. ``torch.jit.script``), the following types are accepted; + * ``path-like``: file path * ``file-like``: Object with ``read(size: int) -> bytes`` method, which returns byte string of at most ``size`` length. + When the function is compiled by TorchScript, only ``str`` type is allowed. - Note: - * This argument is intentionally annotated as ``str`` only due to - TorchScript compiler compatibility. + Note: This argument is intentionally annotated as ``str`` only due to + TorchScript compiler compatibility. frame_offset (int): Number of frames to skip before start reading data. num_frames (int): @@ -147,27 +153,7 @@ def load( filepath, frame_offset, num_frames, normalize, channels_first, format) -@torch.jit.unused -def _save( - filepath: str, - src: torch.Tensor, - sample_rate: int, - channels_first: bool = True, - compression: Optional[float] = None, - format: Optional[str] = None, - dtype: Optional[str] = None, -): - if hasattr(filepath, 'write'): - if format is None: - raise RuntimeError('`format` is required when saving to file object.') - torchaudio._torchaudio.save_audio_fileobj( - filepath, src, sample_rate, channels_first, compression, format, dtype) - else: - torch.ops.torchaudio.sox_io_save_audio_file( - os.fspath(filepath), src, sample_rate, channels_first, compression, format, dtype) - - -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def save( filepath: str, src: torch.Tensor, @@ -175,83 +161,157 @@ def save( channels_first: bool = True, compression: Optional[float] = None, format: Optional[str] = None, - dtype: Optional[str] = None, + encoding: Optional[str] = None, + bits_per_sample: Optional[int] = None, ): """Save audio data to file. - Note: - Supported formats are; - - * WAV, AMB - - * 32-bit floating-point - * 32-bit signed integer - * 16-bit signed integer - * 8-bit unsigned integer - - * MP3 - * FLAC - * OGG/VORBIS - * SPHERE - * AMR-NB - - To save ``MP3``, ``FLAC``, ``OGG/VORBIS``, and other codecs ``libsox`` does not - handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` - and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc. - Args: - filepath (str or pathlib.Path): - Path to save file. This function also handles ``pathlib.Path`` objects, but is annotated + filepath (str or pathlib.Path): Path to save file. + This function also handles ``pathlib.Path`` objects, but is annotated as ``str`` for TorchScript compiler compatibility. - tensor (torch.Tensor): Audio data to save. must be 2D tensor. + src (torch.Tensor): Audio data to save. must be 2D tensor. sample_rate (int): sampling rate - channels_first (bool): - If ``True``, the given tensor is interpreted as ``[channel, time]``, + channels_first (bool): If ``True``, the given tensor is interpreted as ``[channel, time]``, otherwise ``[time, channel]``. - compression (Optional[float]): - Used for formats other than WAV. This corresponds to ``-C`` option of ``sox`` command. + compression (Optional[float]): Used for formats other than WAV. + This corresponds to ``-C`` option of ``sox`` command. - * | ``MP3``: Either bitrate (in ``kbps``) with quality factor, such as ``128.2``, or - | VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5``. - * | ``FLAC``: compression level. Whole number from ``0`` to ``8``. - | ``8`` is default and highest compression. - * | ``OGG/VORBIS``: number from ``-1`` to ``10``; ``-1`` is the highest compression - | and lowest quality. Default: ``3``. + ``"mp3"`` + Either bitrate (in ``kbps``) with quality factor, such as ``128.2``, or + VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5``. - See the detail at http://sox.sourceforge.net/soxformat.html. - format (str, optional): - Output audio format. This is required when the output audio format cannot be infered from - ``filepath``, (such as file extension or ``name`` attribute of the given file object). - dtype (str, optional) - Output tensor dtype. - Valid values: ``"uint8", "int16", "int32", "float32", "float64", None`` - ``dtype=None`` means no conversion is performed. - ``dtype`` parameter is only effective for ``float32`` Tensor. - """ - if src.dtype == torch.float32 and dtype is None: - warnings.warn( - '`dtype` default value will be changed to `int16` in 0.9 release.' - 'Specify `dtype` to suppress this warning.' - ) - if not torch.jit.is_scripting(): - _save(filepath, src, sample_rate, channels_first, compression, format, dtype) - return - torch.ops.torchaudio.sox_io_save_audio_file( - filepath, src, sample_rate, channels_first, compression, format, dtype) + ``"flac"`` + Whole number from ``0`` to ``8``. ``8`` is default and highest compression. + ``"ogg"``, ``"vorbis"`` + Number from ``-1`` to ``10``; ``-1`` is the highest compression + and lowest quality. Default: ``3``. -@_mod_utils.requires_module('torchaudio._torchaudio') -@_mod_utils.deprecated('Please use "torchaudio.load".', '0.9.0') -def load_wav( - filepath: str, - frame_offset: int = 0, - num_frames: int = -1, - channels_first: bool = True, -) -> Tuple[torch.Tensor, int]: - """Load wave file. + See the detail at http://sox.sourceforge.net/soxformat.html. + format (str, optional): Override the audio format. + When ``filepath`` argument is path-like object, audio format is infered from + file extension. If file extension is missing or different, you can specify the + correct format with this argument. + + When ``filepath`` argument is file-like object, this argument is required. + + Valid values are ``"wav"``, ``"mp3"``, ``"ogg"``, ``"vorbis"``, ``"amr-nb"``, + ``"amb"``, ``"flac"``, ``"sph"``, ``"gsm"``, and ``"htk"``. + + encoding (str, optional): Changes the encoding for the supported formats. + This argument is effective only for supported formats, such as ``"wav"``, ``""amb"`` + and ``"sph"``. Valid values are; + + - ``"PCM_S"`` (signed integer Linear PCM) + - ``"PCM_U"`` (unsigned integer Linear PCM) + - ``"PCM_F"`` (floating point PCM) + - ``"ULAW"`` (mu-law) + - ``"ALAW"`` (a-law) + + Default values + If not provided, the default value is picked based on ``format`` and ``bits_per_sample``. + + ``"wav"``, ``"amb"`` + - | If both ``encoding`` and ``bits_per_sample`` are not provided, the ``dtype`` of the + | Tensor is used to determine the default value. + - ``"PCM_U"`` if dtype is ``uint8`` + - ``"PCM_S"`` if dtype is ``int16`` or ``int32` + - ``"PCM_F"`` if dtype is ``float32`` + + - ``"PCM_U"`` if ``bits_per_sample=8`` + - ``"PCM_S"`` otherwise + + ``"sph"`` format; + - the default value is ``"PCM_S"`` + + bits_per_sample (int, optional): Changes the bit depth for the supported formats. + When ``format`` is one of ``"wav"``, ``"flac"``, ``"sph"``, or ``"amb"``, you can change the + bit depth. Valid values are ``8``, ``16``, ``32`` and ``64``. + + Default Value; + If not provided, the default values are picked based on ``format`` and ``"encoding"``; + + ``"wav"``, ``"amb"``; + - | If both ``encoding`` and ``bits_per_sample`` are not provided, the ``dtype`` of the + | Tensor is used. + - ``8`` if dtype is ``uint8`` + - ``16`` if dtype is ``int16`` + - ``32`` if dtype is ``int32`` or ``float32`` + + - ``8`` if ``encoding`` is ``"PCM_U"``, ``"ULAW"`` or ``"ALAW"`` + - ``16`` if ``encoding`` is ``"PCM_S"`` + - ``32`` if ``encoding`` is ``"PCM_F"`` + + ``"flac"`` format; + - the default value is ``24`` + + ``"sph"`` format; + - ``16`` if ``encoding`` is ``"PCM_U"``, ``"PCM_S"``, ``"PCM_F"`` or not provided. + - ``8`` if ``encoding`` is ``"ULAW"`` or ``"ALAW"`` + + ``"amb"`` format; + - ``8`` if ``encoding`` is ``"PCM_U"``, ``"ULAW"`` or ``"ALAW"`` + - ``16`` if ``encoding`` is ``"PCM_S"`` or not provided. + - ``32`` if ``encoding`` is ``"PCM_F"`` + + Supported formats/encodings/bit depth/compression are; + + ``"wav"``, ``"amb"`` + - 32-bit floating-point PCM + - 32-bit signed integer PCM + - 24-bit signed integer PCM + - 16-bit signed integer PCM + - 8-bit unsigned integer PCM + - 8-bit mu-law + - 8-bit a-law + + Note: Default encoding/bit depth is determined by the dtype of the input Tensor. + + ``"mp3"`` + Fixed bit rate (such as 128kHz) and variable bit rate compression. + Default: VBR with high quality. + + ``"flac"`` + - 8-bit + - 16-bit + - 24-bit (default) + + ``"ogg"``, ``"vorbis"`` + - Different quality level. Default: approx. 112kbps + + ``"sph"`` + - 8-bit signed integer PCM + - 16-bit signed integer PCM + - 24-bit signed integer PCM + - 32-bit signed integer PCM (default) + - 8-bit mu-law + - 8-bit a-law + - 16-bit a-law + - 24-bit a-law + - 32-bit a-law + + ``"amr-nb"`` + Bitrate ranging from 4.75 kbit/s to 12.2 kbit/s. Default: 4.75 kbit/s + + ``"gsm"`` + Lossy Speech Compression, CPU intensive. + + ``"htk"`` + Uses a default single-channel 16-bit PCM format. - This function is defined only for the purpose of compatibility against other backend - for simple usecases, such as ``torchaudio.load_wav(filepath)``. - The implementation is same as :py:func:`load`. + Note: + To save into formats that ``libsox`` does not handle natively, (such as ``"mp3"``, + ``"flac"``, ``"ogg"`` and ``"vorbis"``), your installation of ``torchaudio`` has + to be linked to ``libsox`` and corresponding codec libraries such as ``libmad`` + or ``libmp3lame`` etc. """ - return load(filepath, frame_offset, num_frames, normalize=False, channels_first=channels_first) + if not torch.jit.is_scripting(): + if hasattr(filepath, 'write'): + torchaudio._torchaudio.save_audio_fileobj( + filepath, src, sample_rate, channels_first, compression, + format, encoding, bits_per_sample) + return + filepath = os.fspath(filepath) + torch.ops.torchaudio.sox_io_save_audio_file( + filepath, src, sample_rate, channels_first, compression, format, encoding, bits_per_sample) diff --git a/torchaudio/backend/utils.py b/torchaudio/backend/utils.py index b888c04c48..8aa3612dfc 100644 --- a/torchaudio/backend/utils.py +++ b/torchaudio/backend/utils.py @@ -3,13 +3,11 @@ from typing import Optional, List import torchaudio -from torchaudio._internal.module_utils import is_module_available +from torchaudio._internal import module_utils as _mod_utils from . import ( no_backend, - sox_backend, sox_io_backend, soundfile_backend, - _soundfile_backend, ) __all__ = [ @@ -26,10 +24,9 @@ def list_audio_backends() -> List[str]: List[str]: The list of available backends. """ backends = [] - if is_module_available('soundfile'): + if _mod_utils.is_module_available('soundfile'): backends.append('soundfile') - if is_module_available('torchaudio._torchaudio'): - backends.append('sox') + if _mod_utils.is_sox_available(): backends.append('sox_io') return backends @@ -39,15 +36,9 @@ def set_audio_backend(backend: Optional[str]): Args: backend (Optional[str]): Name of the backend. - One of ``"sox"``, ``"sox_io"`` or ``"soundfile"`` based on availability + One of ``"sox_io"`` or ``"soundfile"`` based on availability of the system. If ``None`` is provided the current backend is unassigned. """ - if torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE is not None: - warnings.warn( - '"torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE" flag is depredated and will be removed in 0.9.0. ' - 'Please remove the use of flag.' - ) - if backend is not None and backend not in list_audio_backends(): raise RuntimeError( f'Backend "{backend}" is not one of ' @@ -55,33 +46,20 @@ def set_audio_backend(backend: Optional[str]): if backend is None: module = no_backend - elif backend == 'sox': - warnings.warn( - '"sox" backend is depredated and will be removed in 0.9.0. ' - 'Please use "sox_io" backend.' - ) - module = sox_backend elif backend == 'sox_io': module = sox_io_backend elif backend == 'soundfile': - if torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE: - warnings.warn( - 'The legacy interface of "soundfile" backend is depredated and will be removed in 0.9.0. ' - 'Please migrate to the new interface.' - ) - module = soundfile_backend - else: - module = _soundfile_backend + module = soundfile_backend else: raise NotImplementedError(f'Unexpected backend "{backend}"') - for func in ['save', 'load', 'load_wav', 'info']: + for func in ['save', 'load', 'info']: setattr(torchaudio, func, getattr(module, func)) def _init_audio_backend(): backends = list_audio_backends() - if 'sox' in backends: + if 'sox_io' in backends: set_audio_backend('sox_io') elif 'soundfile' in backends: set_audio_backend('soundfile') @@ -98,10 +76,8 @@ def get_audio_backend() -> Optional[str]: """ if torchaudio.load == no_backend.load: return None - if torchaudio.load == sox_backend.load: - return 'sox' if torchaudio.load == sox_io_backend.load: return 'sox_io' - if torchaudio.load in [soundfile_backend.load, _soundfile_backend.load]: + if torchaudio.load == soundfile_backend.load: return 'soundfile' raise ValueError('Unknown backend.') diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index 5b47ef3350..f1e1ddf5b5 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -793,7 +793,7 @@ def _get_sinc_resample_kernel(orig_freq: int, new_freq: int, lowpass_filter_widt t = t.clamp_(-lowpass_filter_width, lowpass_filter_width) t *= math.pi # we do not use torch.hann_window here as we need to evaluate the window - # at spectifics positions, not over a regular grid. + # at specific positions, not over a regular grid. window = torch.cos(t / lowpass_filter_width / 2)**2 kernel = torch.where(t == 0, torch.tensor(1.).to(t), torch.sin(t) / t) kernel.mul_(window) diff --git a/torchaudio/csrc/CMakeLists.txt b/torchaudio/csrc/CMakeLists.txt index f9c3a4d08f..fc033699b2 100644 --- a/torchaudio/csrc/CMakeLists.txt +++ b/torchaudio/csrc/CMakeLists.txt @@ -5,16 +5,31 @@ get_property(TORCHAUDIO_THIRD_PARTIES GLOBAL PROPERTY TORCHAUDIO_THIRD_PARTIES) ################################################################################ set( LIBTORCHAUDIO_SOURCES - sox/io.cpp - sox/utils.cpp - sox/effects.cpp - sox/effects_chain.cpp + lfilter.cpp + overdrive.cpp + utils.cpp ) if(BUILD_TRANSDUCER) list(APPEND LIBTORCHAUDIO_SOURCES transducer.cpp) endif() +if(BUILD_KALDI) + list(APPEND LIBTORCHAUDIO_SOURCES kaldi.cpp) +endif() + +if(BUILD_SOX) + set( + SOX_SOURCES + sox/io.cpp + sox/utils.cpp + sox/effects.cpp + sox/effects_chain.cpp + sox/types.cpp + ) + list(APPEND LIBTORCHAUDIO_SOURCES ${SOX_SOURCES}) +endif() + ################################################################################ # libtorchaudio.so ################################################################################ @@ -34,6 +49,7 @@ if(BUILD_LIBTORCHAUDIO) target_link_libraries( libtorchaudio + ${TORCH_LIBRARIES} ${TORCHAUDIO_THIRD_PARTIES} ) @@ -54,11 +70,13 @@ if (BUILD_TORCHAUDIO_PYTHON_EXTENSION) _torchaudio SHARED pybind.cpp - sox/legacy.cpp ${LIBTORCHAUDIO_SOURCES} ) set_target_properties(_torchaudio PROPERTIES PREFIX "") + if (MSVC) + set_target_properties(_torchaudio PROPERTIES SUFFIX ".pyd") + endif(MSVC) if (APPLE) # https://github.com/facebookarchive/caffe2/issues/854#issuecomment-364538485 @@ -70,6 +88,14 @@ if (BUILD_TORCHAUDIO_PYTHON_EXTENSION) _torchaudio PRIVATE TORCH_API_INCLUDE_EXTENSION_H ) + if (BUILD_SOX) + target_compile_definitions(_torchaudio PRIVATE INCLUDE_SOX) + endif() + + if (BUILD_KALDI) + target_compile_definitions(_torchaudio PRIVATE INCLUDE_KALDI) + endif() + target_include_directories( _torchaudio PRIVATE @@ -80,12 +106,22 @@ if (BUILD_TORCHAUDIO_PYTHON_EXTENSION) # See https://github.com/pytorch/pytorch/issues/38122 find_library(TORCH_PYTHON_LIBRARY torch_python PATHS "${TORCH_INSTALL_PREFIX}/lib") + if (WIN32) + find_package(Python3 ${PYTHON_VERSION} EXACT COMPONENTS Development) + set(ADDITIONAL_ITEMS Python3::Python) + endif() + target_link_libraries( _torchaudio ${TORCH_LIBRARIES} ${TORCH_PYTHON_LIBRARY} ${TORCHAUDIO_THIRD_PARTIES} + ${ADDITIONAL_ITEMS} ) - install(TARGETS _torchaudio LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}) + install( + TARGETS _torchaudio + LIBRARY DESTINATION . + RUNTIME DESTINATION . # For Windows + ) endif() diff --git a/torchaudio/csrc/kaldi.cpp b/torchaudio/csrc/kaldi.cpp new file mode 100644 index 0000000000..6f2b36c28f --- /dev/null +++ b/torchaudio/csrc/kaldi.cpp @@ -0,0 +1,93 @@ +#include +#include "feat/pitch-functions.h" + +namespace torchaudio { +namespace kaldi { + +namespace { + +torch::Tensor denormalize(const torch::Tensor& t) { + auto ret = t; + auto pos = t > 0, neg = t < 0; + ret.index_put({pos}, t.index({pos}) * 32767); + ret.index_put({neg}, t.index({neg}) * 32768); + return ret; +} + +torch::Tensor compute_kaldi_pitch( + const torch::Tensor& wave, + const ::kaldi::PitchExtractionOptions& opts) { + ::kaldi::VectorBase<::kaldi::BaseFloat> input(wave); + ::kaldi::Matrix<::kaldi::BaseFloat> output; + ::kaldi::ComputeKaldiPitch(opts, input, &output); + return output.tensor_; +} + +} // namespace + +torch::Tensor ComputeKaldiPitch( + const torch::Tensor& wave, + double sample_frequency, + double frame_length, + double frame_shift, + double min_f0, + double max_f0, + double soft_min_f0, + double penalty_factor, + double lowpass_cutoff, + double resample_frequency, + double delta_pitch, + double nccf_ballast, + int64_t lowpass_filter_width, + int64_t upsample_filter_width, + int64_t max_frames_latency, + int64_t frames_per_chunk, + bool simulate_first_pass_online, + int64_t recompute_frame, + bool snip_edges) { + TORCH_CHECK(wave.ndimension() == 2, "Input tensor must be 2 dimentional."); + TORCH_CHECK(wave.device().is_cpu(), "Input tensor must be on CPU."); + TORCH_CHECK( + wave.dtype() == torch::kFloat32, "Input tensor must be float32 type."); + + ::kaldi::PitchExtractionOptions opts; + opts.samp_freq = static_cast<::kaldi::BaseFloat>(sample_frequency); + opts.frame_shift_ms = static_cast<::kaldi::BaseFloat>(frame_shift); + opts.frame_length_ms = static_cast<::kaldi::BaseFloat>(frame_length); + opts.min_f0 = static_cast<::kaldi::BaseFloat>(min_f0); + opts.max_f0 = static_cast<::kaldi::BaseFloat>(max_f0); + opts.soft_min_f0 = static_cast<::kaldi::BaseFloat>(soft_min_f0); + opts.penalty_factor = static_cast<::kaldi::BaseFloat>(penalty_factor); + opts.lowpass_cutoff = static_cast<::kaldi::BaseFloat>(lowpass_cutoff); + opts.resample_freq = static_cast<::kaldi::BaseFloat>(resample_frequency); + opts.delta_pitch = static_cast<::kaldi::BaseFloat>(delta_pitch); + opts.lowpass_filter_width = static_cast<::kaldi::int32>(lowpass_filter_width); + opts.upsample_filter_width = + static_cast<::kaldi::int32>(upsample_filter_width); + opts.max_frames_latency = static_cast<::kaldi::int32>(max_frames_latency); + opts.frames_per_chunk = static_cast<::kaldi::int32>(frames_per_chunk); + opts.simulate_first_pass_online = simulate_first_pass_online; + opts.recompute_frame = static_cast<::kaldi::int32>(recompute_frame); + opts.snip_edges = snip_edges; + + // Kaldi's float type expects value range of int16 expressed as float + torch::Tensor wave_ = denormalize(wave); + + auto batch_size = wave_.size(0); + std::vector results(batch_size); + at::parallel_for(0, batch_size, 1, [&](int64_t begin, int64_t end) { + for (auto i = begin; i < end; ++i) { + results[i] = compute_kaldi_pitch(wave_.index({i}), opts); + } + }); + return torch::stack(results, 0); +} + +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def( + "torchaudio::kaldi_ComputeKaldiPitch", + &torchaudio::kaldi::ComputeKaldiPitch); +} + +} // namespace kaldi +} // namespace torchaudio diff --git a/torchaudio/csrc/lfilter.cpp b/torchaudio/csrc/lfilter.cpp new file mode 100644 index 0000000000..56540c33f6 --- /dev/null +++ b/torchaudio/csrc/lfilter.cpp @@ -0,0 +1,268 @@ +#include +#include + +namespace { + +template +void host_lfilter_core_loop( + const torch::Tensor& input_signal_windows, + const torch::Tensor& a_coeff_flipped, + torch::Tensor& padded_output_waveform) { + int64_t n_channel = input_signal_windows.size(0); + int64_t n_samples_input = input_signal_windows.size(1); + int64_t n_samples_output = padded_output_waveform.size(1); + int64_t n_order = a_coeff_flipped.size(0); + scalar_t* output_data = padded_output_waveform.data_ptr(); + const scalar_t* input_data = input_signal_windows.data_ptr(); + const scalar_t* a_coeff_flipped_data = a_coeff_flipped.data_ptr(); + for (int64_t i_channel = 0; i_channel < n_channel; i_channel++) { + for (int64_t i_sample = 0; i_sample < n_samples_input; i_sample++) { + int64_t offset_input = i_channel * n_samples_input; + int64_t offset_output = i_channel * n_samples_output; + scalar_t a0 = input_data[offset_input + i_sample]; + for (int64_t i_coeff = 0; i_coeff < n_order; i_coeff++) { + a0 -= output_data[offset_output + i_sample + i_coeff] * + a_coeff_flipped_data[i_coeff]; + } + output_data[offset_output + i_sample + n_order - 1] = a0; + } + } +} + +void cpu_lfilter_core_loop( + const torch::Tensor& input_signal_windows, + const torch::Tensor& a_coeff_flipped, + torch::Tensor& padded_output_waveform) { + TORCH_CHECK( + input_signal_windows.device().is_cpu() && + a_coeff_flipped.device().is_cpu() && + padded_output_waveform.device().is_cpu()); + + TORCH_CHECK( + input_signal_windows.is_contiguous() && a_coeff_flipped.is_contiguous() && + padded_output_waveform.is_contiguous()); + + TORCH_CHECK( + (input_signal_windows.dtype() == torch::kFloat32 || + input_signal_windows.dtype() == torch::kFloat64) && + (a_coeff_flipped.dtype() == torch::kFloat32 || + a_coeff_flipped.dtype() == torch::kFloat64) && + (padded_output_waveform.dtype() == torch::kFloat32 || + padded_output_waveform.dtype() == torch::kFloat64)); + + TORCH_CHECK(input_signal_windows.size(0) == padded_output_waveform.size(0)); + + TORCH_CHECK( + input_signal_windows.size(1) + a_coeff_flipped.size(0) - 1 == + padded_output_waveform.size(1)); + + AT_DISPATCH_FLOATING_TYPES( + input_signal_windows.scalar_type(), "lfilter_core_loop", [&] { + host_lfilter_core_loop( + input_signal_windows, a_coeff_flipped, padded_output_waveform); + }); +} + +void lfilter_core_generic_loop( + const torch::Tensor& input_signal_windows, + const torch::Tensor& a_coeff_flipped, + torch::Tensor& padded_output_waveform) { + int64_t n_samples_input = input_signal_windows.size(1); + int64_t n_order = a_coeff_flipped.size(0); + for (int64_t i_sample = 0; i_sample < n_samples_input; i_sample++) { + auto windowed_output_signal = padded_output_waveform.index( + {torch::indexing::Slice(), + torch::indexing::Slice(i_sample, i_sample + n_order)}); + auto o0 = input_signal_windows.index({torch::indexing::Slice(), i_sample}) + .addmv(windowed_output_signal, a_coeff_flipped, 1, -1); + padded_output_waveform.index_put_( + {torch::indexing::Slice(), i_sample + n_order - 1}, o0); + } +} + +std::tuple lfilter_core( + const torch::Tensor& waveform, + const torch::Tensor& a_coeffs, + const torch::Tensor& b_coeffs) { + TORCH_CHECK(waveform.device() == a_coeffs.device()); + TORCH_CHECK(b_coeffs.device() == a_coeffs.device()); + TORCH_CHECK(a_coeffs.size(0) == b_coeffs.size(0)); + + TORCH_INTERNAL_ASSERT(waveform.sizes().size() == 2); + + auto device = waveform.device(); + int64_t n_order = a_coeffs.size(0); + + TORCH_INTERNAL_ASSERT(n_order > 0); + + namespace F = torch::nn::functional; + + auto padded_waveform = F::pad(waveform, F::PadFuncOptions({n_order - 1, 0})); + auto padded_output_waveform = torch::zeros_like(padded_waveform); + + auto a_coeff_flipped = a_coeffs.flip(0).contiguous(); + auto b_coeff_flipped = b_coeffs.flip(0).contiguous(); + + auto input_signal_windows = + F::conv1d( + padded_waveform.unsqueeze(1), b_coeff_flipped.view({1, 1, n_order})) + .squeeze(1); + + input_signal_windows.div_(a_coeffs[0]); + a_coeff_flipped.div_(a_coeffs[0]); + + if (device.is_cpu()) { + cpu_lfilter_core_loop( + input_signal_windows, a_coeff_flipped, padded_output_waveform); + } else { + lfilter_core_generic_loop( + input_signal_windows, a_coeff_flipped, padded_output_waveform); + } + + auto output = padded_output_waveform.index( + {torch::indexing::Slice(), + torch::indexing::Slice(n_order - 1, torch::indexing::None)}); + + return {output, input_signal_windows}; +} + +torch::Tensor lfilter_simple( + const torch::Tensor& waveform, + const torch::Tensor& a_coeffs, + const torch::Tensor& b_coeffs) { + return std::get<0>(lfilter_core(waveform, a_coeffs, b_coeffs)); +} + +class DifferentiableLfilter + : public torch::autograd::Function { + public: + static torch::Tensor forward( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& waveform, + const torch::Tensor& a_coeffs, + const torch::Tensor& b_coeffs) { + at::AutoNonVariableTypeMode g; + auto result = lfilter_core(waveform, a_coeffs, b_coeffs); + ctx->save_for_backward( + {waveform, + a_coeffs, + b_coeffs, + std::get<0>(result), + std::get<1>(result)}); + return std::get<0>(result); + } + + static torch::autograd::tensor_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::tensor_list grad_outputs) { + auto saved = ctx->get_saved_variables(); + auto waveform = saved[0]; + auto a_coeffs = saved[1]; + auto b_coeffs = saved[2]; + auto y = saved[3]; + auto xh = saved[4]; + + auto device = waveform.device(); + auto dtype = waveform.dtype(); + int64_t n_channel = waveform.size(0); + int64_t n_sample = waveform.size(1); + int64_t n_order = a_coeffs.size(0); + int64_t n_sample_padded = n_sample + n_order - 1; + + auto a_coeff_flipped = a_coeffs.flip(0).contiguous(); + auto b_coeff_flipped = b_coeffs.flip(0).contiguous(); + b_coeff_flipped.div_(a_coeffs[0]); + a_coeff_flipped.div_(a_coeffs[0]); + + auto dx = torch::Tensor(); + auto da = torch::Tensor(); + auto db = torch::Tensor(); + auto dy = grad_outputs[0]; + + at::AutoNonVariableTypeMode g; + namespace F = torch::nn::functional; + auto options = torch::TensorOptions().dtype(dtype).device(device); + + if (a_coeffs.requires_grad()) { + auto dyda = torch::zeros({n_channel, n_sample_padded}, options); + if (device.is_cpu()) { + cpu_lfilter_core_loop(-y, a_coeff_flipped, dyda); + } else { + lfilter_core_generic_loop(-y, a_coeff_flipped, dyda); + } + + da = F::conv1d( + dyda.unsqueeze(0), + dy.unsqueeze(1), + F::Conv1dFuncOptions().groups(n_channel)) + .sum(1) + .squeeze(0) + .flip(0); + da.div_(a_coeffs[0]); + } + + if (b_coeffs.requires_grad() || waveform.requires_grad()) { + auto dxh = torch::zeros({n_channel, n_sample_padded}, options); + if (device.is_cpu()) { + cpu_lfilter_core_loop(dy.flip(1), a_coeff_flipped, dxh); + } else { + lfilter_core_generic_loop(dy.flip(1), a_coeff_flipped, dxh); + } + + dxh = dxh.index( + {torch::indexing::Slice(), + torch::indexing::Slice(n_order - 1, torch::indexing::None)}) + .flip(1); + + if (waveform.requires_grad()) { + dx = F::conv1d( + F::pad(dxh.unsqueeze(1), F::PadFuncOptions({0, n_order - 1})), + b_coeffs.view({1, 1, n_order})) + .squeeze(1); + dx.div_(a_coeffs[0]); + } + if (b_coeffs.requires_grad()) { + db = + F::conv1d( + F::pad( + waveform.unsqueeze(0), F::PadFuncOptions({n_order - 1, 0})), + dxh.unsqueeze(1), + F::Conv1dFuncOptions().groups(n_channel)) + .sum(1) + .squeeze(0) + .flip(0); + db.div_(a_coeffs[0]); + } + } + + return {dx, da, db}; + } +}; + +torch::Tensor lfilter_autograd( + const torch::Tensor& waveform, + const torch::Tensor& a_coeffs, + const torch::Tensor& b_coeffs) { + return DifferentiableLfilter::apply(waveform, a_coeffs, b_coeffs); +} + +} // namespace + +// Note: We want to avoid using "catch-all" kernel. +// The following registration should be replaced with CPU specific registration. +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("torchaudio::_lfilter_core_loop", &cpu_lfilter_core_loop); +} + +TORCH_LIBRARY(torchaudio, m) { + m.def( + "torchaudio::_lfilter(Tensor waveform, Tensor a_coeffs, Tensor b_coeffs) -> Tensor"); +} + +TORCH_LIBRARY_IMPL(torchaudio, DefaultBackend, m) { + m.impl("torchaudio::_lfilter", lfilter_simple); +} + +TORCH_LIBRARY_IMPL(torchaudio, Autograd, m) { + m.impl("torchaudio::_lfilter", lfilter_autograd); +} diff --git a/torchaudio/csrc/overdrive.cpp b/torchaudio/csrc/overdrive.cpp new file mode 100644 index 0000000000..4954271e41 --- /dev/null +++ b/torchaudio/csrc/overdrive.cpp @@ -0,0 +1,52 @@ +#include +#include + +namespace { + +template +void overdrive_cpu_kernel( + at::TensorAccessor waveform_accessor, + at::TensorAccessor temp_accessor, + at::TensorAccessor last_in_accessor, + at::TensorAccessor last_out_accessor, + at::TensorAccessor output_waveform_accessor) { + int64_t n_frames = waveform_accessor.size(1); + int64_t n_channels = waveform_accessor.size(0); + + at::parallel_for(0, n_channels, 1, [&](int64_t begin, int64_t end) { + for (int64_t i_channel = begin; i_channel < end; ++i_channel) { + for (int64_t i_frame = 0; i_frame < n_frames; ++i_frame) { + last_out_accessor[i_channel] = temp_accessor[i_channel][i_frame] - + last_in_accessor[i_channel] + 0.995 * last_out_accessor[i_channel]; + last_in_accessor[i_channel] = temp_accessor[i_channel][i_frame]; + output_waveform_accessor[i_channel][i_frame] = + waveform_accessor[i_channel][i_frame] * 0.5 + + last_out_accessor[i_channel] * 0.75; + } + } + }); +} + +void overdrive_core_loop_cpu( + at::Tensor& waveform, + at::Tensor& temp, + at::Tensor& last_in, + at::Tensor& last_out, + at::Tensor& output_waveform) { + AT_DISPATCH_FLOATING_TYPES(waveform.scalar_type(), "overdrive_cpu", ([&] { + overdrive_cpu_kernel( + waveform.accessor(), + temp.accessor(), + last_in.accessor(), + last_out.accessor(), + output_waveform.accessor()); + })); +} + +} // namespace + +// Note: We want to avoid using "catch-all" kernel. +// The following registration should be replaced with CPU specific registration. +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("torchaudio::_overdrive_core_loop", &overdrive_core_loop_cpu); +} diff --git a/torchaudio/csrc/pybind.cpp b/torchaudio/csrc/pybind.cpp index 373b9d0d96..81ccf459b1 100644 --- a/torchaudio/csrc/pybind.cpp +++ b/torchaudio/csrc/pybind.cpp @@ -1,106 +1,12 @@ #include + +#ifdef INCLUDE_SOX #include #include -#include +#endif PYBIND11_MODULE(_torchaudio, m) { - py::class_(m, "sox_signalinfo_t") - .def(py::init<>()) - .def( - "__repr__", - [](const sox_signalinfo_t& self) { - std::stringstream ss; - ss << "sox_signalinfo_t {\n" - << " rate-> " << self.rate << "\n" - << " channels-> " << self.channels << "\n" - << " precision-> " << self.precision << "\n" - << " length-> " << self.length << "\n" - << " mult-> " << self.mult << "\n" - << "}\n"; - return ss.str(); - }) - .def_readwrite("rate", &sox_signalinfo_t::rate) - .def_readwrite("channels", &sox_signalinfo_t::channels) - .def_readwrite("precision", &sox_signalinfo_t::precision) - .def_readwrite("length", &sox_signalinfo_t::length) - .def_readwrite("mult", &sox_signalinfo_t::mult); - py::class_(m, "sox_encodinginfo_t") - .def(py::init<>()) - .def( - "__repr__", - [](const sox_encodinginfo_t& self) { - std::stringstream ss; - ss << "sox_encodinginfo_t {\n" - << " encoding-> " << self.encoding << "\n" - << " bits_per_sample-> " << self.bits_per_sample << "\n" - << " compression-> " << self.compression << "\n" - << " reverse_bytes-> " << self.reverse_bytes << "\n" - << " reverse_nibbles-> " << self.reverse_nibbles << "\n" - << " reverse_bits-> " << self.reverse_bits << "\n" - << " opposite_endian-> " << self.opposite_endian << "\n" - << "}\n"; - return ss.str(); - }) - .def_readwrite("encoding", &sox_encodinginfo_t::encoding) - .def_readwrite("bits_per_sample", &sox_encodinginfo_t::bits_per_sample) - .def_readwrite("compression", &sox_encodinginfo_t::compression) - .def_readwrite("reverse_bytes", &sox_encodinginfo_t::reverse_bytes) - .def_readwrite("reverse_nibbles", &sox_encodinginfo_t::reverse_nibbles) - .def_readwrite("reverse_bits", &sox_encodinginfo_t::reverse_bits) - .def_readwrite("opposite_endian", &sox_encodinginfo_t::opposite_endian); - py::enum_(m, "sox_encoding_t") - .value("SOX_ENCODING_UNKNOWN", sox_encoding_t::SOX_ENCODING_UNKNOWN) - .value("SOX_ENCODING_SIGN2", sox_encoding_t::SOX_ENCODING_SIGN2) - .value("SOX_ENCODING_UNSIGNED", sox_encoding_t::SOX_ENCODING_UNSIGNED) - .value("SOX_ENCODING_FLOAT", sox_encoding_t::SOX_ENCODING_FLOAT) - .value("SOX_ENCODING_FLOAT_TEXT", sox_encoding_t::SOX_ENCODING_FLOAT_TEXT) - .value("SOX_ENCODING_FLAC", sox_encoding_t::SOX_ENCODING_FLAC) - .value("SOX_ENCODING_HCOM", sox_encoding_t::SOX_ENCODING_HCOM) - .value("SOX_ENCODING_WAVPACK", sox_encoding_t::SOX_ENCODING_WAVPACK) - .value("SOX_ENCODING_WAVPACKF", sox_encoding_t::SOX_ENCODING_WAVPACKF) - .value("SOX_ENCODING_ULAW", sox_encoding_t::SOX_ENCODING_ULAW) - .value("SOX_ENCODING_ALAW", sox_encoding_t::SOX_ENCODING_ALAW) - .value("SOX_ENCODING_G721", sox_encoding_t::SOX_ENCODING_G721) - .value("SOX_ENCODING_G723", sox_encoding_t::SOX_ENCODING_G723) - .value("SOX_ENCODING_CL_ADPCM", sox_encoding_t::SOX_ENCODING_CL_ADPCM) - .value("SOX_ENCODING_CL_ADPCM16", sox_encoding_t::SOX_ENCODING_CL_ADPCM16) - .value("SOX_ENCODING_MS_ADPCM", sox_encoding_t::SOX_ENCODING_MS_ADPCM) - .value("SOX_ENCODING_IMA_ADPCM", sox_encoding_t::SOX_ENCODING_IMA_ADPCM) - .value("SOX_ENCODING_OKI_ADPCM", sox_encoding_t::SOX_ENCODING_OKI_ADPCM) - .value("SOX_ENCODING_DPCM", sox_encoding_t::SOX_ENCODING_DPCM) - .value("SOX_ENCODING_DWVW", sox_encoding_t::SOX_ENCODING_DWVW) - .value("SOX_ENCODING_DWVWN", sox_encoding_t::SOX_ENCODING_DWVWN) - .value("SOX_ENCODING_GSM", sox_encoding_t::SOX_ENCODING_GSM) - .value("SOX_ENCODING_MP3", sox_encoding_t::SOX_ENCODING_MP3) - .value("SOX_ENCODING_VORBIS", sox_encoding_t::SOX_ENCODING_VORBIS) - .value("SOX_ENCODING_AMR_WB", sox_encoding_t::SOX_ENCODING_AMR_WB) - .value("SOX_ENCODING_AMR_NB", sox_encoding_t::SOX_ENCODING_AMR_NB) - .value("SOX_ENCODING_LPC10", sox_encoding_t::SOX_ENCODING_LPC10) - //.value("SOX_ENCODING_OPUS", sox_encoding_t::SOX_ENCODING_OPUS) // - // creates a compile error - .value("SOX_ENCODINGS", sox_encoding_t::SOX_ENCODINGS) - .export_values(); - py::enum_(m, "sox_option_t") - .value("sox_option_no", sox_option_t::sox_option_no) - .value("sox_option_yes", sox_option_t::sox_option_yes) - .value("sox_option_default", sox_option_t::sox_option_default) - .export_values(); - py::enum_(m, "sox_bool") - .value("sox_false", sox_bool::sox_false) - .value("sox_true", sox_bool::sox_true) - .export_values(); - m.def( - "read_audio_file", - &torch::audio::read_audio_file, - "Reads an audio file into a tensor"); - m.def( - "write_audio_file", - &torch::audio::write_audio_file, - "Writes data from a tensor into an audio file"); - m.def( - "get_info", - &torch::audio::get_info, - "Gets information about an audio file"); +#ifdef INCLUDE_SOX m.def( "get_info_fileobj", &torchaudio::sox_io::get_info_fileobj, @@ -117,4 +23,5 @@ PYBIND11_MODULE(_torchaudio, m) { "apply_effects_fileobj", &torchaudio::sox_effects::apply_effects_fileobj, "Decode audio data from file-like obj and apply effects."); +#endif } diff --git a/torchaudio/csrc/sox/effects.cpp b/torchaudio/csrc/sox/effects.cpp index d2d2a0f5ad..8ffc6327ac 100644 --- a/torchaudio/csrc/sox/effects.cpp +++ b/torchaudio/csrc/sox/effects.cpp @@ -161,9 +161,9 @@ std::tuple apply_effects_file( std::tuple apply_effects_fileobj( py::object fileobj, std::vector> effects, - c10::optional& normalize, - c10::optional& channels_first, - c10::optional& format) { + c10::optional normalize, + c10::optional channels_first, + c10::optional format) { // Prepare the buffer used throughout the lifecycle of SoxEffectChain. // // For certain format (such as FLAC), libsox keeps reading the content at @@ -204,7 +204,7 @@ std::tuple apply_effects_fileobj( /*filetype=*/format.has_value() ? format.value().c_str() : nullptr)); // In case of streamed data, length can be 0 - validate_input_file(sf, /*check_length=*/false); + validate_input_file(sf); // Prepare output buffer std::vector out_buffer; diff --git a/torchaudio/csrc/sox/effects.h b/torchaudio/csrc/sox/effects.h index efec06971c..fef8847214 100644 --- a/torchaudio/csrc/sox/effects.h +++ b/torchaudio/csrc/sox/effects.h @@ -33,9 +33,9 @@ std::tuple apply_effects_file( std::tuple apply_effects_fileobj( py::object fileobj, std::vector> effects, - c10::optional& normalize, - c10::optional& channels_first, - c10::optional& format); + c10::optional normalize, + c10::optional channels_first, + c10::optional format); #endif // TORCH_API_INCLUDE_EXTENSION_H diff --git a/torchaudio/csrc/sox/effects_chain.cpp b/torchaudio/csrc/sox/effects_chain.cpp index b98010f67b..3028220c3f 100644 --- a/torchaudio/csrc/sox/effects_chain.cpp +++ b/torchaudio/csrc/sox/effects_chain.cpp @@ -68,21 +68,48 @@ int tensor_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { // Ensure that it's a multiple of the number of channels *osamp -= *osamp % num_channels; - // Slice the input Tensor and unnormalize the values - const auto tensor_ = [&]() { + // Slice the input Tensor + auto chunk = [&]() { auto i_frame = index / num_channels; auto num_frames = *osamp / num_channels; auto t = (priv->channels_first) ? tensor.index({Slice(), Slice(i_frame, i_frame + num_frames)}).t() : tensor.index({Slice(i_frame, i_frame + num_frames), Slice()}); - return unnormalize_wav(t.reshape({-1})).contiguous(); + return t.reshape({-1}); }(); - priv->index += *osamp; - - // Write data to SoxEffectsChain buffer. - auto ptr = tensor_.data_ptr(); - std::copy(ptr, ptr + *osamp, obuf); + // Convert to sox_sample_t (int32_t) + switch (chunk.dtype().toScalarType()) { + case c10::ScalarType::Float: { + // Need to convert to 64-bit precision so that + // values around INT32_MIN/MAX are handled correctly. + chunk = chunk.to(c10::ScalarType::Double); + chunk *= 2147483648.; + chunk.clamp_(INT32_MIN, INT32_MAX); + chunk = chunk.to(c10::ScalarType::Int); + break; + } + case c10::ScalarType::Int: { + break; + } + case c10::ScalarType::Short: { + chunk = chunk.to(c10::ScalarType::Int); + chunk *= 65536; + break; + } + case c10::ScalarType::Byte: { + chunk = chunk.to(c10::ScalarType::Int); + chunk -= 128; + chunk *= 16777216; + break; + } + default: + throw std::runtime_error("Unexpected dtype."); + } + // Write to buffer + chunk = chunk.contiguous(); + memcpy(obuf, chunk.data_ptr(), *osamp * 4); + priv->index += *osamp; return (priv->index == num_samples) ? SOX_EOF : SOX_SUCCESS; } @@ -430,7 +457,7 @@ int fileobj_output_flow( fflush(fp); // Copy the encoded chunk to python object. - fileobj->attr("write")(py::bytes(*buffer, *buffer_size)); + fileobj->attr("write")(py::bytes(*buffer, ftell(fp))); // Reset FILE* sf->tell_off = 0; diff --git a/torchaudio/csrc/sox/io.cpp b/torchaudio/csrc/sox/io.cpp index 90532dd3ad..8e2ebce4e2 100644 --- a/torchaudio/csrc/sox/io.cpp +++ b/torchaudio/csrc/sox/io.cpp @@ -1,7 +1,7 @@ -#include #include #include #include +#include #include using namespace torch::indexing; @@ -10,41 +10,6 @@ using namespace torchaudio::sox_utils; namespace torchaudio { namespace sox_io { -namespace { - -std::string get_encoding(sox_encoding_t encoding) { - switch (encoding) { - case SOX_ENCODING_UNKNOWN: - return "UNKNOWN"; - case SOX_ENCODING_SIGN2: - return "PCM_S"; - case SOX_ENCODING_UNSIGNED: - return "PCM_U"; - case SOX_ENCODING_FLOAT: - return "PCM_F"; - case SOX_ENCODING_FLAC: - return "FLAC"; - case SOX_ENCODING_ULAW: - return "ULAW"; - case SOX_ENCODING_ALAW: - return "ALAW"; - case SOX_ENCODING_MP3: - return "MP3"; - case SOX_ENCODING_VORBIS: - return "VORBIS"; - case SOX_ENCODING_AMR_WB: - return "AMR_WB"; - case SOX_ENCODING_AMR_NB: - return "AMR_NB"; - case SOX_ENCODING_OPUS: - return "OPUS"; - default: - return "UNKNOWN"; - } -} - -} // namespace - std::tuple get_info_file( const std::string& path, const c10::optional& format) { @@ -118,33 +83,36 @@ void save_audio_file( bool channels_first, c10::optional compression, c10::optional format, - c10::optional dtype) { + c10::optional encoding, + c10::optional bits_per_sample) { validate_input_tensor(tensor); - if (tensor.dtype() != torch::kFloat32 && dtype.has_value()) { - throw std::runtime_error( - "dtype conversion only supported for float32 tensors"); - } - const auto tgt_dtype = - (tensor.dtype() == torch::kFloat32 && dtype.has_value()) - ? get_dtype_from_str(dtype.value()) - : tensor.dtype(); - const auto filetype = [&]() { if (format.has_value()) return format.value(); return get_filetype(path); }(); + if (filetype == "amr-nb") { const auto num_channels = tensor.size(channels_first ? 0 : 1); TORCH_CHECK( num_channels == 1, "amr-nb format only supports single channel audio."); - tensor = (unnormalize_wav(tensor) / 65536).to(torch::kInt16); + } else if (filetype == "htk") { + const auto num_channels = tensor.size(channels_first ? 0 : 1); + TORCH_CHECK( + num_channels == 1, "htk format only supports single channel audio."); + } else if (filetype == "gsm") { + const auto num_channels = tensor.size(channels_first ? 0 : 1); + TORCH_CHECK( + num_channels == 1, "gsm format only supports single channel audio."); + TORCH_CHECK( + sample_rate == 8000, + "gsm format only supports a sampling rate of 8kHz."); } const auto signal_info = get_signalinfo(&tensor, sample_rate, filetype, channels_first); - const auto encoding_info = - get_encodinginfo_for_save(filetype, tgt_dtype, compression); + const auto encoding_info = get_encodinginfo_for_save( + filetype, tensor.dtype(), compression, encoding, bits_per_sample); SoxFormat sf(sox_open_write( path.c_str(), @@ -170,7 +138,7 @@ void save_audio_file( std::tuple get_info_fileobj( py::object fileobj, - c10::optional& format) { + c10::optional format) { // Prepare in-memory file object // When libsox opens a file, it also reads the header. // When opening a file there are two functions that might touch FILE* (and the @@ -209,7 +177,7 @@ std::tuple get_info_fileobj( /*filetype=*/format.has_value() ? format.value().c_str() : nullptr)); // In case of streamed data, length can be 0 - validate_input_file(sf, /*check_length=*/false); + validate_input_file(sf); return std::make_tuple( static_cast(sf->signal.rate), @@ -221,11 +189,11 @@ std::tuple get_info_fileobj( std::tuple load_audio_fileobj( py::object fileobj, - c10::optional& frame_offset, - c10::optional& num_frames, - c10::optional& normalize, - c10::optional& channels_first, - c10::optional& format) { + c10::optional frame_offset, + c10::optional num_frames, + c10::optional normalize, + c10::optional channels_first, + c10::optional format) { auto effects = get_effects(frame_offset, num_frames); return torchaudio::sox_effects::apply_effects_fileobj( fileobj, effects, normalize, channels_first, format); @@ -259,18 +227,16 @@ void save_audio_fileobj( int64_t sample_rate, bool channels_first, c10::optional compression, - std::string filetype, - c10::optional dtype) { + c10::optional format, + c10::optional encoding, + c10::optional bits_per_sample) { validate_input_tensor(tensor); - if (tensor.dtype() != torch::kFloat32 && dtype.has_value()) { + if (!format.has_value()) { throw std::runtime_error( - "dtype conversion only supported for float32 tensors"); + "`format` is required when saving to file object."); } - const auto tgt_dtype = - (tensor.dtype() == torch::kFloat32 && dtype.has_value()) - ? get_dtype_from_str(dtype.value()) - : tensor.dtype(); + const auto filetype = format.value(); if (filetype == "amr-nb") { const auto num_channels = tensor.size(channels_first ? 0 : 1); @@ -278,12 +244,27 @@ void save_audio_fileobj( throw std::runtime_error( "amr-nb format only supports single channel audio."); } - tensor = (unnormalize_wav(tensor) / 65536).to(torch::kInt16); + } else if (filetype == "htk") { + const auto num_channels = tensor.size(channels_first ? 0 : 1); + if (num_channels != 1) { + throw std::runtime_error( + "htk format only supports single channel audio."); + } + } else if (filetype == "gsm") { + const auto num_channels = tensor.size(channels_first ? 0 : 1); + if (num_channels != 1) { + throw std::runtime_error( + "gsm format only supports single channel audio."); + } + if (sample_rate != 8000) { + throw std::runtime_error( + "gsm format only supports a sampling rate of 8kHz."); + } } const auto signal_info = get_signalinfo(&tensor, sample_rate, filetype, channels_first); - const auto encoding_info = - get_encodinginfo_for_save(filetype, tgt_dtype, compression); + const auto encoding_info = get_encodinginfo_for_save( + filetype, tensor.dtype(), compression, encoding, bits_per_sample); AutoReleaseBuffer buffer; diff --git a/torchaudio/csrc/sox/io.h b/torchaudio/csrc/sox/io.h index cd203e9c5c..8c18a64dd1 100644 --- a/torchaudio/csrc/sox/io.h +++ b/torchaudio/csrc/sox/io.h @@ -30,21 +30,22 @@ void save_audio_file( bool channels_first, c10::optional compression, c10::optional format, - c10::optional dtype); + c10::optional encoding, + c10::optional bits_per_sample); #ifdef TORCH_API_INCLUDE_EXTENSION_H std::tuple get_info_fileobj( py::object fileobj, - c10::optional& format); + c10::optional format); std::tuple load_audio_fileobj( py::object fileobj, - c10::optional& frame_offset, - c10::optional& num_frames, - c10::optional& normalize, - c10::optional& channels_first, - c10::optional& format); + c10::optional frame_offset, + c10::optional num_frames, + c10::optional normalize, + c10::optional channels_first, + c10::optional format); void save_audio_fileobj( py::object fileobj, @@ -52,8 +53,9 @@ void save_audio_fileobj( int64_t sample_rate, bool channels_first, c10::optional compression, - std::string filetype, - c10::optional dtype); + c10::optional format, + c10::optional encoding, + c10::optional bits_per_sample); #endif // TORCH_API_INCLUDE_EXTENSION_H diff --git a/torchaudio/csrc/sox/legacy.cpp b/torchaudio/csrc/sox/legacy.cpp deleted file mode 100644 index 858ad15149..0000000000 --- a/torchaudio/csrc/sox/legacy.cpp +++ /dev/null @@ -1,170 +0,0 @@ -#include - -namespace torch { -namespace audio { -namespace { -/// Helper struct to safely close the sox_format_t descriptor. -struct SoxDescriptor { - explicit SoxDescriptor(sox_format_t* fd) noexcept : fd_(fd) {} - SoxDescriptor(const SoxDescriptor& other) = delete; - SoxDescriptor(SoxDescriptor&& other) = delete; - SoxDescriptor& operator=(const SoxDescriptor& other) = delete; - SoxDescriptor& operator=(SoxDescriptor&& other) = delete; - ~SoxDescriptor() { - if (fd_ != nullptr) { - sox_close(fd_); - } - } - sox_format_t* operator->() noexcept { - return fd_; - } - sox_format_t* get() noexcept { - return fd_; - } - - private: - sox_format_t* fd_; -}; - -int64_t write_audio(SoxDescriptor& fd, at::Tensor tensor) { - std::vector buffer(tensor.numel()); - - AT_DISPATCH_ALL_TYPES(tensor.scalar_type(), "write_audio_buffer", [&] { - auto* data = tensor.data_ptr(); - std::copy(data, data + tensor.numel(), buffer.begin()); - }); - - const auto samples_written = - sox_write(fd.get(), buffer.data(), buffer.size()); - - return samples_written; -} - -void read_audio(SoxDescriptor& fd, at::Tensor output, int64_t buffer_length) { - std::vector buffer(buffer_length); - - int number_of_channels = fd->signal.channels; - const int64_t samples_read = sox_read(fd.get(), buffer.data(), buffer_length); - if (samples_read == 0) { - throw std::runtime_error( - "Error reading audio file: empty file or read failed in sox_read"); - } - - output.resize_({samples_read / number_of_channels, number_of_channels}); - output = output.contiguous(); - - AT_DISPATCH_ALL_TYPES(output.scalar_type(), "read_audio_buffer", [&] { - auto* data = output.data_ptr(); - std::copy(buffer.begin(), buffer.begin() + samples_read, data); - }); -} -} // namespace - -std::tuple get_info( - const std::string& file_name) { - SoxDescriptor fd(sox_open_read( - file_name.c_str(), - /*signal=*/nullptr, - /*encoding=*/nullptr, - /*filetype=*/nullptr)); - if (fd.get() == nullptr) { - throw std::runtime_error("Error opening audio file"); - } - return std::make_tuple(fd->signal, fd->encoding); -} - -int read_audio_file( - const std::string& file_name, - at::Tensor output, - bool ch_first, - int64_t nframes, - int64_t offset, - sox_signalinfo_t* si, - sox_encodinginfo_t* ei, - const char* ft) { - SoxDescriptor fd(sox_open_read(file_name.c_str(), si, ei, ft)); - if (fd.get() == nullptr) { - throw std::runtime_error("Error opening audio file"); - } - - // signal info - - const int number_of_channels = fd->signal.channels; - const int sample_rate = fd->signal.rate; - const int64_t total_length = fd->signal.length; - - // multiply offset and number of frames by number of channels - offset *= number_of_channels; - nframes *= number_of_channels; - - if (total_length == 0) { - throw std::runtime_error("Error reading audio file: unknown length"); - } - if (offset > total_length) { - throw std::runtime_error("Offset past EOF"); - } - - // calculate buffer length - int64_t buffer_length = total_length; - if (offset > 0) { - buffer_length -= offset; - } - if (nframes > 0 && buffer_length > nframes) { - buffer_length = nframes; - } - - // seek to offset point before reading data - if (sox_seek(fd.get(), offset, 0) == SOX_EOF) { - throw std::runtime_error( - "sox_seek reached EOF, try reducing offset or num_samples"); - } - - // read data and fill output tensor - read_audio(fd, output, buffer_length); - - // L x C -> C x L, if desired - if (ch_first) { - output.transpose_(1, 0); - } - - return sample_rate; -} - -void write_audio_file( - const std::string& file_name, - const at::Tensor& tensor, - sox_signalinfo_t* si, - sox_encodinginfo_t* ei, - const char* file_type) { - if (!tensor.is_contiguous()) { - throw std::runtime_error( - "Error writing audio file: input tensor must be contiguous"); - } - -#if SOX_LIB_VERSION_CODE >= 918272 // >= 14.3.0 - si->mult = nullptr; -#endif - - SoxDescriptor fd(sox_open_write( - file_name.c_str(), - si, - ei, - file_type, - /*oob=*/nullptr, - /*overwrite=*/nullptr)); - - if (fd.get() == nullptr) { - throw std::runtime_error( - "Error writing audio file: could not open file for writing"); - } - - const auto samples_written = write_audio(fd, tensor); - - if (samples_written != tensor.numel()) { - throw std::runtime_error( - "Error writing audio file: could not write entire buffer"); - } -} - -} // namespace audio -} // namespace torch diff --git a/torchaudio/csrc/sox/legacy.h b/torchaudio/csrc/sox/legacy.h deleted file mode 100644 index 5869695bfe..0000000000 --- a/torchaudio/csrc/sox/legacy.h +++ /dev/null @@ -1,40 +0,0 @@ -#include -#include - -namespace torch { -namespace audio { - -/// Reads an audio file from the given `path` into the `output` `Tensor` and -/// returns the sample rate of the audio file. -/// Throws `std::runtime_error` if the audio file could not be opened, or an -/// error occurred during reading of the audio data. -int read_audio_file( - const std::string& file_name, - at::Tensor output, - bool ch_first, - int64_t nframes, - int64_t offset, - sox_signalinfo_t* si, - sox_encodinginfo_t* ei, - const char* ft); - -/// Writes the data of a `Tensor` into an audio file at the given `path`, with -/// a certain extension (e.g. `wav`or `mp3`) and sample rate. -/// Throws `std::runtime_error` when the audio file could not be opened for -/// writing, or an error occurred during writing of the audio data. -void write_audio_file( - const std::string& file_name, - const at::Tensor& tensor, - sox_signalinfo_t* si, - sox_encodinginfo_t* ei, - const char* file_type); - -/// Reads an audio file from the given `path` and returns a tuple of -/// sox_signalinfo_t and sox_encodinginfo_t, which contain information about -/// the audio file such as sample rate, length, bit precision, encoding and -/// more. Throws `std::runtime_error` if the audio file could not be opened, or -/// an error occurred during reading of the audio data. -std::tuple get_info( - const std::string& file_name); -} // namespace audio -} // namespace torch diff --git a/torchaudio/csrc/sox/types.cpp b/torchaudio/csrc/sox/types.cpp new file mode 100644 index 0000000000..b4bbf710f6 --- /dev/null +++ b/torchaudio/csrc/sox/types.cpp @@ -0,0 +1,139 @@ +#include + +namespace torchaudio { +namespace sox_utils { + +Format get_format_from_string(const std::string& format) { + if (format == "wav") + return Format::WAV; + if (format == "mp3") + return Format::MP3; + if (format == "flac") + return Format::FLAC; + if (format == "ogg" || format == "vorbis") + return Format::VORBIS; + if (format == "amr-nb") + return Format::AMR_NB; + if (format == "amr-wb") + return Format::AMR_WB; + if (format == "amb") + return Format::AMB; + if (format == "sph") + return Format::SPHERE; + if (format == "htk") + return Format::HTK; + if (format == "gsm") + return Format::GSM; + std::ostringstream stream; + stream << "Internal Error: unexpected format value: " << format; + throw std::runtime_error(stream.str()); +} + +std::string to_string(Encoding v) { + switch (v) { + case Encoding::UNKNOWN: + return "UNKNOWN"; + case Encoding::PCM_SIGNED: + return "PCM_S"; + case Encoding::PCM_UNSIGNED: + return "PCM_U"; + case Encoding::PCM_FLOAT: + return "PCM_F"; + case Encoding::FLAC: + return "FLAC"; + case Encoding::ULAW: + return "ULAW"; + case Encoding::ALAW: + return "ALAW"; + case Encoding::MP3: + return "MP3"; + case Encoding::VORBIS: + return "VORBIS"; + case Encoding::AMR_WB: + return "AMR_WB"; + case Encoding::AMR_NB: + return "AMR_NB"; + case Encoding::OPUS: + return "OPUS"; + default: + throw std::runtime_error("Internal Error: unexpected encoding."); + } +} + +Encoding get_encoding_from_option(const c10::optional encoding) { + if (!encoding.has_value()) + return Encoding::NOT_PROVIDED; + std::string v = encoding.value(); + if (v == "PCM_S") + return Encoding::PCM_SIGNED; + if (v == "PCM_U") + return Encoding::PCM_UNSIGNED; + if (v == "PCM_F") + return Encoding::PCM_FLOAT; + if (v == "ULAW") + return Encoding::ULAW; + if (v == "ALAW") + return Encoding::ALAW; + std::ostringstream stream; + stream << "Internal Error: unexpected encoding value: " << v; + throw std::runtime_error(stream.str()); +} + +BitDepth get_bit_depth_from_option(const c10::optional bit_depth) { + if (!bit_depth.has_value()) + return BitDepth::NOT_PROVIDED; + int64_t v = bit_depth.value(); + switch (v) { + case 8: + return BitDepth::B8; + case 16: + return BitDepth::B16; + case 24: + return BitDepth::B24; + case 32: + return BitDepth::B32; + case 64: + return BitDepth::B64; + default: { + std::ostringstream s; + s << "Internal Error: unexpected bit depth value: " << v; + throw std::runtime_error(s.str()); + } + } +} + +std::string get_encoding(sox_encoding_t encoding) { + switch (encoding) { + case SOX_ENCODING_UNKNOWN: + return "UNKNOWN"; + case SOX_ENCODING_SIGN2: + return "PCM_S"; + case SOX_ENCODING_UNSIGNED: + return "PCM_U"; + case SOX_ENCODING_FLOAT: + return "PCM_F"; + case SOX_ENCODING_FLAC: + return "FLAC"; + case SOX_ENCODING_ULAW: + return "ULAW"; + case SOX_ENCODING_ALAW: + return "ALAW"; + case SOX_ENCODING_MP3: + return "MP3"; + case SOX_ENCODING_VORBIS: + return "VORBIS"; + case SOX_ENCODING_AMR_WB: + return "AMR_WB"; + case SOX_ENCODING_AMR_NB: + return "AMR_NB"; + case SOX_ENCODING_OPUS: + return "OPUS"; + case SOX_ENCODING_GSM: + return "GSM"; + default: + return "UNKNOWN"; + } +} + +} // namespace sox_utils +} // namespace torchaudio diff --git a/torchaudio/csrc/sox/types.h b/torchaudio/csrc/sox/types.h new file mode 100644 index 0000000000..afd84791a6 --- /dev/null +++ b/torchaudio/csrc/sox/types.h @@ -0,0 +1,60 @@ +#ifndef TORCHAUDIO_SOX_TYPES_H +#define TORCHAUDIO_SOX_TYPES_H + +#include +#include + +namespace torchaudio { +namespace sox_utils { + +enum class Format { + WAV, + MP3, + FLAC, + VORBIS, + AMR_NB, + AMR_WB, + AMB, + SPHERE, + GSM, + HTK, +}; + +Format get_format_from_string(const std::string& format); + +enum class Encoding { + NOT_PROVIDED, + UNKNOWN, + PCM_SIGNED, + PCM_UNSIGNED, + PCM_FLOAT, + FLAC, + ULAW, + ALAW, + MP3, + VORBIS, + AMR_WB, + AMR_NB, + OPUS, +}; + +std::string to_string(Encoding v); +Encoding get_encoding_from_option(const c10::optional encoding); + +enum class BitDepth : unsigned { + NOT_PROVIDED = 0, + B8 = 8, + B16 = 16, + B24 = 24, + B32 = 32, + B64 = 64, +}; + +BitDepth get_bit_depth_from_option(const c10::optional bit_depth); + +std::string get_encoding(sox_encoding_t encoding); + +} // namespace sox_utils +} // namespace torchaudio + +#endif diff --git a/torchaudio/csrc/sox/utils.cpp b/torchaudio/csrc/sox/utils.cpp index 983b7829fe..b46067626c 100644 --- a/torchaudio/csrc/sox/utils.cpp +++ b/torchaudio/csrc/sox/utils.cpp @@ -1,5 +1,6 @@ #include #include +#include #include namespace torchaudio { @@ -80,16 +81,13 @@ void SoxFormat::close() { } } -void validate_input_file(const SoxFormat& sf, bool check_length) { +void validate_input_file(const SoxFormat& sf) { if (static_cast(sf) == nullptr) { throw std::runtime_error("Error loading audio file: failed to open file."); } if (sf->encoding.encoding == SOX_ENCODING_UNKNOWN) { throw std::runtime_error("Error loading audio file: unknown encoding."); } - if (check_length && sf->signal.length == 0) { - throw std::runtime_error("Error reading audio file: unknown length."); - } } void validate_input_tensor(const torch::Tensor tensor) { @@ -101,11 +99,15 @@ void validate_input_tensor(const torch::Tensor tensor) { throw std::runtime_error("Input tensor has to be 2D."); } - const auto dtype = tensor.dtype(); - if (!(dtype == torch::kFloat32 || dtype == torch::kInt32 || - dtype == torch::kInt16 || dtype == torch::kUInt8)) { - throw std::runtime_error( - "Input tensor has to be one of float32, int32, int16 or uint8 type."); + switch (tensor.dtype().toScalarType()) { + case c10::ScalarType::Byte: + case c10::ScalarType::Short: + case c10::ScalarType::Int: + case c10::ScalarType::Float: + break; + default: + throw std::runtime_error( + "Input tensor has to be one of float32, int32, int16 or uint8 type."); } } @@ -116,15 +118,16 @@ caffe2::TypeMeta get_dtype( switch (encoding) { case SOX_ENCODING_UNSIGNED: // 8-bit PCM WAV return torch::kUInt8; - case SOX_ENCODING_SIGN2: // 16-bit or 32-bit PCM WAV + case SOX_ENCODING_SIGN2: // 16-bit, 24-bit, or 32-bit PCM WAV switch (precision) { case 16: return torch::kInt16; + case 24: // Cast 24-bit to 32-bit. case 32: return torch::kInt32; default: throw std::runtime_error( - "Only 16 and 32 bits are supported for signed PCM."); + "Only 16, 24, and 32 bits are supported for signed PCM."); } default: // default to float32 for the other formats, including @@ -138,24 +141,6 @@ caffe2::TypeMeta get_dtype( return c10::scalarTypeToTypeMeta(dtype); } -caffe2::TypeMeta get_dtype_from_str(const std::string dtype) { - const auto tgt_dtype = [&]() { - if (dtype == "uint8") - return torch::kUInt8; - else if (dtype == "int16") - return torch::kInt16; - else if (dtype == "int32") - return torch::kInt32; - else if (dtype == "float32") - return torch::kFloat32; - else if (dtype == "float64") - return torch::kFloat64; - else - throw std::runtime_error("Unsupported dtype"); - }(); - return c10::scalarTypeToTypeMeta(tgt_dtype); -} - torch::Tensor convert_to_tensor( sox_sample_t* buffer, const int32_t num_samples, @@ -163,22 +148,32 @@ torch::Tensor convert_to_tensor( const caffe2::TypeMeta dtype, const bool normalize, const bool channels_first) { - auto t = torch::from_blob( - buffer, {num_samples / num_channels, num_channels}, torch::kInt32); - // Note: Tensor created from_blob does not own data but borrwos - // So make sure to create a new copy after processing samples. + torch::Tensor t; + uint64_t dummy = 0; + SOX_SAMPLE_LOCALS; if (normalize || dtype == torch::kFloat32) { - t = t.to(torch::kFloat32); - t *= (t > 0) / 2147483647. + (t < 0) / 2147483648.; + t = torch::empty( + {num_samples / num_channels, num_channels}, torch::kFloat32); + auto ptr = t.data_ptr(); + for (int32_t i = 0; i < num_samples; ++i) { + ptr[i] = SOX_SAMPLE_TO_FLOAT_32BIT(buffer[i], dummy); + } } else if (dtype == torch::kInt32) { - t = t.clone(); + t = torch::from_blob( + buffer, {num_samples / num_channels, num_channels}, torch::kInt32) + .clone(); } else if (dtype == torch::kInt16) { - t.floor_divide_(1 << 16); - t = t.to(torch::kInt16); + t = torch::empty({num_samples / num_channels, num_channels}, torch::kInt16); + auto ptr = t.data_ptr(); + for (int32_t i = 0; i < num_samples; ++i) { + ptr[i] = SOX_SAMPLE_TO_SIGNED_16BIT(buffer[i], dummy); + } } else if (dtype == torch::kUInt8) { - t.floor_divide_(1 << 24); - t += 128; - t = t.to(torch::kUInt8); + t = torch::empty({num_samples / num_channels, num_channels}, torch::kUInt8); + auto ptr = t.data_ptr(); + for (int32_t i = 0; i < num_samples; ++i) { + ptr[i] = SOX_SAMPLE_TO_UNSIGNED_8BIT(buffer[i], dummy); + } } else { throw std::runtime_error("Unsupported dtype."); } @@ -188,68 +183,202 @@ torch::Tensor convert_to_tensor( return t.contiguous(); } -torch::Tensor unnormalize_wav(const torch::Tensor input_tensor) { - const auto dtype = input_tensor.dtype(); - auto tensor = input_tensor; - if (dtype == torch::kFloat32) { - double multi_pos = 2147483647.; - double multi_neg = -2147483648.; - auto mult = (tensor > 0) * multi_pos - (tensor < 0) * multi_neg; - tensor = tensor.to(torch::dtype(torch::kFloat64)); - tensor *= mult; - tensor.clamp_(multi_neg, multi_pos); - tensor = tensor.to(torch::dtype(torch::kInt32)); - } else if (dtype == torch::kInt32) { - // already denormalized - } else if (dtype == torch::kInt16) { - tensor = tensor.to(torch::dtype(torch::kInt32)); - tensor *= ((tensor != 0) * 65536); - } else if (dtype == torch::kUInt8) { - tensor = tensor.to(torch::dtype(torch::kInt32)); - tensor -= 128; - tensor *= 16777216; - } else { - throw std::runtime_error("Unexpected dtype."); - } - return tensor; -} - const std::string get_filetype(const std::string path) { std::string ext = path.substr(path.find_last_of(".") + 1); std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower); return ext; } -sox_encoding_t get_encoding( - const std::string filetype, - const caffe2::TypeMeta dtype) { - if (filetype == "mp3") - return SOX_ENCODING_MP3; - if (filetype == "flac") - return SOX_ENCODING_FLAC; - if (filetype == "ogg" || filetype == "vorbis") - return SOX_ENCODING_VORBIS; - if (filetype == "wav" || filetype == "amb") { - if (dtype == torch::kUInt8) - return SOX_ENCODING_UNSIGNED; - if (dtype == torch::kInt16) - return SOX_ENCODING_SIGN2; - if (dtype == torch::kInt32) - return SOX_ENCODING_SIGN2; - if (dtype == torch::kFloat32) - return SOX_ENCODING_FLOAT; - throw std::runtime_error("Unsupported dtype."); +namespace { + +std::tuple get_save_encoding_for_wav( + const std::string format, + caffe2::TypeMeta dtype, + const Encoding& encoding, + const BitDepth& bits_per_sample) { + switch (encoding) { + case Encoding::NOT_PROVIDED: + switch (bits_per_sample) { + case BitDepth::NOT_PROVIDED: + switch (dtype.toScalarType()) { + case c10::ScalarType::Float: + return std::make_tuple<>(SOX_ENCODING_FLOAT, 32); + case c10::ScalarType::Int: + return std::make_tuple<>(SOX_ENCODING_SIGN2, 32); + case c10::ScalarType::Short: + return std::make_tuple<>(SOX_ENCODING_SIGN2, 16); + case c10::ScalarType::Byte: + return std::make_tuple<>(SOX_ENCODING_UNSIGNED, 8); + default: + throw std::runtime_error("Internal Error: Unexpected dtype."); + } + case BitDepth::B8: + return std::make_tuple<>(SOX_ENCODING_UNSIGNED, 8); + default: + return std::make_tuple<>( + SOX_ENCODING_SIGN2, static_cast(bits_per_sample)); + } + case Encoding::PCM_SIGNED: + switch (bits_per_sample) { + case BitDepth::NOT_PROVIDED: + return std::make_tuple<>(SOX_ENCODING_SIGN2, 32); + case BitDepth::B8: + throw std::runtime_error( + format + " does not support 8-bit signed PCM encoding."); + default: + return std::make_tuple<>( + SOX_ENCODING_SIGN2, static_cast(bits_per_sample)); + } + case Encoding::PCM_UNSIGNED: + switch (bits_per_sample) { + case BitDepth::NOT_PROVIDED: + case BitDepth::B8: + return std::make_tuple<>(SOX_ENCODING_UNSIGNED, 8); + default: + throw std::runtime_error( + format + " only supports 8-bit for unsigned PCM encoding."); + } + case Encoding::PCM_FLOAT: + switch (bits_per_sample) { + case BitDepth::NOT_PROVIDED: + case BitDepth::B32: + return std::make_tuple<>(SOX_ENCODING_FLOAT, 32); + case BitDepth::B64: + return std::make_tuple<>(SOX_ENCODING_FLOAT, 64); + default: + throw std::runtime_error( + format + + " only supports 32-bit or 64-bit for floating-point PCM encoding."); + } + case Encoding::ULAW: + switch (bits_per_sample) { + case BitDepth::NOT_PROVIDED: + case BitDepth::B8: + return std::make_tuple<>(SOX_ENCODING_ULAW, 8); + default: + throw std::runtime_error( + format + " only supports 8-bit for mu-law encoding."); + } + case Encoding::ALAW: + switch (bits_per_sample) { + case BitDepth::NOT_PROVIDED: + case BitDepth::B8: + return std::make_tuple<>(SOX_ENCODING_ALAW, 8); + default: + throw std::runtime_error( + format + " only supports 8-bit for a-law encoding."); + } + default: + throw std::runtime_error( + format + " does not support encoding: " + to_string(encoding)); } - if (filetype == "sph") - return SOX_ENCODING_SIGN2; - if (filetype == "amr-nb") - return SOX_ENCODING_AMR_NB; - throw std::runtime_error("Unsupported file type: " + filetype); } -unsigned get_precision( - const std::string filetype, - const caffe2::TypeMeta dtype) { +std::tuple get_save_encoding( + const std::string& format, + const caffe2::TypeMeta dtype, + const c10::optional encoding, + const c10::optional bits_per_sample) { + const Format fmt = get_format_from_string(format); + const Encoding enc = get_encoding_from_option(encoding); + const BitDepth bps = get_bit_depth_from_option(bits_per_sample); + + switch (fmt) { + case Format::WAV: + case Format::AMB: + return get_save_encoding_for_wav(format, dtype, enc, bps); + case Format::MP3: + if (enc != Encoding::NOT_PROVIDED) + throw std::runtime_error("mp3 does not support `encoding` option."); + if (bps != BitDepth::NOT_PROVIDED) + throw std::runtime_error( + "mp3 does not support `bits_per_sample` option."); + return std::make_tuple<>(SOX_ENCODING_MP3, 16); + case Format::HTK: + if (enc != Encoding::NOT_PROVIDED) + throw std::runtime_error("htk does not support `encoding` option."); + if (bps != BitDepth::NOT_PROVIDED) + throw std::runtime_error( + "htk does not support `bits_per_sample` option."); + return std::make_tuple<>(SOX_ENCODING_SIGN2, 16); + case Format::VORBIS: + if (enc != Encoding::NOT_PROVIDED) + throw std::runtime_error("vorbis does not support `encoding` option."); + if (bps != BitDepth::NOT_PROVIDED) + throw std::runtime_error( + "vorbis does not support `bits_per_sample` option."); + return std::make_tuple<>(SOX_ENCODING_VORBIS, 16); + case Format::AMR_NB: + if (enc != Encoding::NOT_PROVIDED) + throw std::runtime_error("amr-nb does not support `encoding` option."); + if (bps != BitDepth::NOT_PROVIDED) + throw std::runtime_error( + "amr-nb does not support `bits_per_sample` option."); + return std::make_tuple<>(SOX_ENCODING_AMR_NB, 16); + case Format::FLAC: + if (enc != Encoding::NOT_PROVIDED) + throw std::runtime_error("flac does not support `encoding` option."); + switch (bps) { + case BitDepth::B32: + case BitDepth::B64: + throw std::runtime_error( + "flac does not support `bits_per_sample` larger than 24."); + default: + return std::make_tuple<>( + SOX_ENCODING_FLAC, static_cast(bps)); + } + case Format::SPHERE: + switch (enc) { + case Encoding::NOT_PROVIDED: + case Encoding::PCM_SIGNED: + switch (bps) { + case BitDepth::NOT_PROVIDED: + return std::make_tuple<>(SOX_ENCODING_SIGN2, 32); + default: + return std::make_tuple<>( + SOX_ENCODING_SIGN2, static_cast(bps)); + } + case Encoding::PCM_UNSIGNED: + throw std::runtime_error( + "sph does not support unsigned integer PCM."); + case Encoding::PCM_FLOAT: + throw std::runtime_error("sph does not support floating point PCM."); + case Encoding::ULAW: + switch (bps) { + case BitDepth::NOT_PROVIDED: + case BitDepth::B8: + return std::make_tuple<>(SOX_ENCODING_ULAW, 8); + default: + throw std::runtime_error( + "sph only supports 8-bit for mu-law encoding."); + } + case Encoding::ALAW: + switch (bps) { + case BitDepth::NOT_PROVIDED: + case BitDepth::B8: + return std::make_tuple<>(SOX_ENCODING_ALAW, 8); + default: + return std::make_tuple<>( + SOX_ENCODING_ALAW, static_cast(bps)); + } + default: + throw std::runtime_error( + "sph does not support encoding: " + encoding.value()); + } + case Format::GSM: + if (enc != Encoding::NOT_PROVIDED) + throw std::runtime_error("gsm does not support `encoding` option."); + if (bps != BitDepth::NOT_PROVIDED) + throw std::runtime_error( + "gsm does not support `bits_per_sample` option."); + return std::make_tuple<>(SOX_ENCODING_GSM, 16); + + default: + throw std::runtime_error("Unsupported format: " + format); + } +} + +unsigned get_precision(const std::string filetype, caffe2::TypeMeta dtype) { if (filetype == "mp3") return SOX_UNSPEC; if (filetype == "flac") @@ -257,27 +386,35 @@ unsigned get_precision( if (filetype == "ogg" || filetype == "vorbis") return SOX_UNSPEC; if (filetype == "wav" || filetype == "amb") { - if (dtype == torch::kUInt8) - return 8; - if (dtype == torch::kInt16) - return 16; - if (dtype == torch::kInt32) - return 32; - if (dtype == torch::kFloat32) - return 32; - throw std::runtime_error("Unsupported dtype."); + switch (dtype.toScalarType()) { + case c10::ScalarType::Byte: + return 8; + case c10::ScalarType::Short: + return 16; + case c10::ScalarType::Int: + return 32; + case c10::ScalarType::Float: + return 32; + default: + throw std::runtime_error("Unsupported dtype."); + } } if (filetype == "sph") return 32; if (filetype == "amr-nb") { - TORCH_INTERNAL_ASSERT( - dtype == torch::kInt16, - "When saving to AMR-NB format, the input tensor must be int16 type."); + return 16; + } + if (filetype == "gsm") { + return 16; + } + if (filetype == "htk") { return 16; } throw std::runtime_error("Unsupported file type: " + filetype); } +} // namespace + sox_signalinfo_t get_signalinfo( const torch::Tensor* waveform, const int64_t sample_rate, @@ -291,28 +428,34 @@ sox_signalinfo_t get_signalinfo( /*length=*/static_cast(waveform->numel())}; } -sox_encodinginfo_t get_tensor_encodinginfo(const caffe2::TypeMeta dtype) { +sox_encodinginfo_t get_tensor_encodinginfo(caffe2::TypeMeta dtype) { sox_encoding_t encoding = [&]() { - if (dtype == torch::kUInt8) - return SOX_ENCODING_UNSIGNED; - if (dtype == torch::kInt16) - return SOX_ENCODING_SIGN2; - if (dtype == torch::kInt32) - return SOX_ENCODING_SIGN2; - if (dtype == torch::kFloat32) - return SOX_ENCODING_FLOAT; - throw std::runtime_error("Unsupported dtype."); + switch (dtype.toScalarType()) { + case c10::ScalarType::Byte: + return SOX_ENCODING_UNSIGNED; + case c10::ScalarType::Short: + return SOX_ENCODING_SIGN2; + case c10::ScalarType::Int: + return SOX_ENCODING_SIGN2; + case c10::ScalarType::Float: + return SOX_ENCODING_FLOAT; + default: + throw std::runtime_error("Unsupported dtype."); + } }(); unsigned bits_per_sample = [&]() { - if (dtype == torch::kUInt8) - return 8; - if (dtype == torch::kInt16) - return 16; - if (dtype == torch::kInt32) - return 32; - if (dtype == torch::kFloat32) - return 32; - throw std::runtime_error("Unsupported dtype."); + switch (dtype.toScalarType()) { + case c10::ScalarType::Byte: + return 8; + case c10::ScalarType::Short: + return 16; + case c10::ScalarType::Int: + return 32; + case c10::ScalarType::Float: + return 32; + default: + throw std::runtime_error("Unsupported dtype."); + } }(); return sox_encodinginfo_t{ /*encoding=*/encoding, @@ -325,12 +468,15 @@ sox_encodinginfo_t get_tensor_encodinginfo(const caffe2::TypeMeta dtype) { } sox_encodinginfo_t get_encodinginfo_for_save( - const std::string filetype, + const std::string& format, const caffe2::TypeMeta dtype, - c10::optional& compression) { + const c10::optional compression, + const c10::optional encoding, + const c10::optional bits_per_sample) { + auto enc = get_save_encoding(format, dtype, encoding, bits_per_sample); return sox_encodinginfo_t{ - /*encoding=*/get_encoding(filetype, dtype), - /*bits_per_sample=*/get_precision(filetype, dtype), + /*encoding=*/std::get<0>(enc), + /*bits_per_sample=*/std::get<1>(enc), /*compression=*/compression.value_or(HUGE_VAL), /*reverse_bytes=*/sox_option_default, /*reverse_nibbles=*/sox_option_default, diff --git a/torchaudio/csrc/sox/utils.h b/torchaudio/csrc/sox/utils.h index ea2a6a2953..7e736dbe8d 100644 --- a/torchaudio/csrc/sox/utils.h +++ b/torchaudio/csrc/sox/utils.h @@ -56,7 +56,7 @@ struct SoxFormat { /// /// Verify that input file is found, has known encoding, and not empty -void validate_input_file(const SoxFormat& sf, bool check_length = true); +void validate_input_file(const SoxFormat& sf); /// /// Verify that input Tensor is 2D, CPU and either uin8, int16, int32 or float32 @@ -68,8 +68,6 @@ caffe2::TypeMeta get_dtype( const sox_encoding_t encoding, const unsigned precision); -caffe2::TypeMeta get_dtype_from_str(const std::string dtype); - /// /// Convert sox_sample_t buffer to uint8/int16/int32/float32 Tensor /// NOTE: This function might modify the values in the input buffer to @@ -93,11 +91,6 @@ torch::Tensor convert_to_tensor( const bool normalize, const bool channels_first); -/// -/// Convert float32/int32/int16/uint8 Tensor to int32 for Torch -> Sox -/// conversion. -torch::Tensor unnormalize_wav(const torch::Tensor); - /// Extract extension from file path const std::string get_filetype(const std::string path); @@ -113,9 +106,11 @@ sox_encodinginfo_t get_tensor_encodinginfo(const caffe2::TypeMeta dtype); /// Get sox_encodinginfo_t for saving to file/file object sox_encodinginfo_t get_encodinginfo_for_save( - const std::string filetype, + const std::string& format, const caffe2::TypeMeta dtype, - c10::optional& compression); + const c10::optional compression, + const c10::optional encoding, + const c10::optional bits_per_sample); #ifdef TORCH_API_INCLUDE_EXTENSION_H diff --git a/torchaudio/csrc/transducer.cpp b/torchaudio/csrc/transducer.cpp index f5d06c1ba9..00fa49b4b5 100644 --- a/torchaudio/csrc/transducer.cpp +++ b/torchaudio/csrc/transducer.cpp @@ -17,11 +17,40 @@ int64_t cpu_rnnt_loss( torch::Tensor grads, int64_t blank_label, int64_t num_threads) { + TORCH_CHECK(labels.dtype() == torch::kInt32, "labels must be int32 type"); + TORCH_CHECK( + label_lengths.dtype() == torch::kInt32, + "label_lengths must be int32 type"); + TORCH_CHECK( + input_lengths.dtype() == torch::kInt32, "lengths must be int32 type"); + TORCH_CHECK(acts.is_contiguous(), "acts must be contiguous"); + TORCH_CHECK(labels.is_contiguous(), "labels must be contiguous"); + TORCH_CHECK( + label_lengths.is_contiguous(), "label_lengths must be contiguous"); + TORCH_CHECK(input_lengths.is_contiguous(), "lengths must be contiguous"); + TORCH_CHECK( + input_lengths.size(0) == acts.size(0), + "batch dimension mismatch between acts and input_lengths: each example must have a length"); + TORCH_CHECK( + label_lengths.size(0) == acts.size(0), + "batch dimension mismatch between acts and label_lengths: each example must have a label length"); + TORCH_CHECK(acts.dim() == 4, "acts must be 4-D (batch, time, label, class)"); + TORCH_CHECK( + labels.dim() == 2, "labels must be 2-D (batch, max label length)"); + TORCH_CHECK(input_lengths.dim() == 1, "input_lengths must be 1-D"); + TORCH_CHECK(label_lengths.dim() == 1, "label_lengths must be 1-D"); + int maxT = acts.size(1); int maxU = acts.size(2); int minibatch_size = acts.size(0); int alphabet_size = acts.size(3); + TORCH_CHECK( + at::max(input_lengths).item().toInt() == maxT, "input length mismatch"); + TORCH_CHECK( + at::max(label_lengths).item().toInt() + 1 == maxU, + "output length mismatch"); + rnntOptions options; memset(&options, 0, sizeof(options)); options.maxT = maxT; diff --git a/torchaudio/csrc/utils.cpp b/torchaudio/csrc/utils.cpp new file mode 100644 index 0000000000..e8050bb69a --- /dev/null +++ b/torchaudio/csrc/utils.cpp @@ -0,0 +1,30 @@ +#include + +namespace torchaudio { + +namespace { + +bool is_sox_available() { +#ifdef INCLUDE_SOX + return true; +#else + return false; +#endif +} + +bool is_kaldi_available() { +#ifdef INCLUDE_KALDI + return true; +#else + return false; +#endif +} + +} // namespace + +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("torchaudio::is_sox_available", &is_sox_available); + m.def("torchaudio::is_kaldi_available", &is_kaldi_available); +} + +} // namespace torchaudio diff --git a/torchaudio/datasets/speechcommands.py b/torchaudio/datasets/speechcommands.py index 5264ea24de..fdde2855ba 100644 --- a/torchaudio/datasets/speechcommands.py +++ b/torchaudio/datasets/speechcommands.py @@ -34,7 +34,15 @@ def _load_list(root, *filenames): def load_speechcommands_item(filepath: str, path: str) -> Tuple[Tensor, int, str, str, int]: relpath = os.path.relpath(filepath, path) label, filename = os.path.split(relpath) + # Besides the officially supported split method for datasets defined by "validation_list.txt" + # and "testing_list.txt" over "speech_commands_v0.0x.tar.gz" archives, an alternative split + # method referred to in paragraph 2-3 of Section 7.1, references 13 and 14 of the original + # paper, and the checksums file from the tensorflow_datasets package [1] is also supported. + # Some filenames in those "speech_commands_test_set_v0.0x.tar.gz" archives have the form + # "xxx.wav.wav", so file extensions twice needs to be stripped twice. + # [1] https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/url_checksums/speech_commands.txt speaker, _ = os.path.splitext(filename) + speaker, _ = os.path.splitext(speaker) speaker_id, utterance_number = speaker.split(HASH_DIVIDER) utterance_number = int(utterance_number) @@ -60,7 +68,10 @@ class SPEECHCOMMANDS(Dataset): subset (Optional[str]): Select a subset of the dataset [None, "training", "validation", "testing"]. None means the whole dataset. "validation" and "testing" are defined in "validation_list.txt" and - "testing_list.txt", respectively, and "training" is the rest. (default: ``None``) + "testing_list.txt", respectively, and "training" is the rest. Details for the files + "validation_list.txt" and "testing_list.txt" are explained in the README of the dataset + and in the introduction of Section 7 of the original paper and its reference 12. The + original paper can be found `here `_. (Default: ``None``) """ def __init__(self, diff --git a/torchaudio/datasets/tedlium.py b/torchaudio/datasets/tedlium.py index e8d1d1cefc..fb96ca5789 100644 --- a/torchaudio/datasets/tedlium.py +++ b/torchaudio/datasets/tedlium.py @@ -153,11 +153,7 @@ def _load_audio(self, path: str, start_time: float, end_time: float, sample_rate start_time = int(float(start_time) * sample_rate) end_time = int(float(end_time) * sample_rate) - backend = torchaudio.get_audio_backend() - if backend == "sox" or (backend == "soundfile" and torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE): - kwargs = {"offset": start_time, "num_frames": end_time - start_time} - else: - kwargs = {"frame_offset": start_time, "num_frames": end_time - start_time} + kwargs = {"frame_offset": start_time, "num_frames": end_time - start_time} return torchaudio.load(path, **kwargs) diff --git a/torchaudio/extension/extension.py b/torchaudio/extension/extension.py index 5875f41023..5af62f19be 100644 --- a/torchaudio/extension/extension.py +++ b/torchaudio/extension/extension.py @@ -9,6 +9,7 @@ def _init_extension(): ext = 'torchaudio._torchaudio' if _mod_utils.is_module_available(ext): _init_script_module(ext) + import torchaudio._torchaudio # noqa else: warnings.warn('torchaudio C++ extension is not available.') diff --git a/torchaudio/functional/__init__.py b/torchaudio/functional/__init__.py index d59400e82e..88fc4d545f 100644 --- a/torchaudio/functional/__init__.py +++ b/torchaudio/functional/__init__.py @@ -3,6 +3,7 @@ angle, complex_norm, compute_deltas, + compute_kaldi_pitch, create_dct, create_fb_matrix, DB_to_amplitude, @@ -17,6 +18,7 @@ sliding_window_cmn, spectrogram, spectral_centroid, + apply_codec, ) from .filtering import ( allpass_biquad, @@ -47,6 +49,7 @@ 'angle', 'complex_norm', 'compute_deltas', + 'compute_kaldi_pitch', 'create_dct', 'create_fb_matrix', 'DB_to_amplitude', @@ -82,4 +85,5 @@ 'riaa_biquad', 'treble_biquad', 'vad', + 'apply_codec' ] diff --git a/torchaudio/functional/filtering.py b/torchaudio/functional/filtering.py index 55cf05a51f..82d9727876 100644 --- a/torchaudio/functional/filtering.py +++ b/torchaudio/functional/filtering.py @@ -20,7 +20,7 @@ def _generate_wave_table( phase: float, device: torch.device, ) -> Tensor: - r"""A helper fucntion for phaser. Generates a table with given parameters + r"""A helper function for phaser. Generates a table with given parameters. Args: wave_type (str): SINE or TRIANGULAR @@ -446,7 +446,7 @@ def _apply_probability_distribution( or Gaussian curve, typical of dither generated by analog sources. Args: waveform (Tensor): Tensor of audio of dimension (..., time) - probability_density_function (str, optional): The density function of a + density_function (str, optional): The density function of a continuous random variable (Default: ``"TPDF"``) Options: Triangular Probability Density Function - `TPDF` Rectangular Probability Density Function - `RPDF` @@ -625,7 +625,7 @@ def flanger( Allowed range of values are 0 to 30 depth (float): desired delay depth in milliseconds(ms) Allowed range of values are 0 to 10 - regen (float): desired regen(feeback gain) in dB + regen (float): desired regen(feedback gain) in dB Allowed range of values are -95 to 95 width (float): desired width(delay gain) in dB Allowed range of values are 0 to 100 @@ -808,83 +808,104 @@ def highpass_biquad( return biquad(waveform, b0, b1, b2, a0, a1, a2) -def lfilter( +def _lfilter_core_generic_loop(input_signal_windows: Tensor, a_coeffs_flipped: Tensor, padded_output_waveform: Tensor): + n_order = a_coeffs_flipped.size(0) + for i_sample, o0 in enumerate(input_signal_windows.t()): + windowed_output_signal = padded_output_waveform[ + :, i_sample:i_sample + n_order + ] + o0.addmv_(windowed_output_signal, a_coeffs_flipped, alpha=-1) + padded_output_waveform[:, i_sample + n_order - 1] = o0 + + +try: + _lfilter_core_cpu_loop = torch.ops.torchaudio._lfilter_core_loop +except RuntimeError as err: + assert str(err) == 'No such operator torchaudio::_lfilter_core_loop' + _lfilter_core_cpu_loop = _lfilter_core_generic_loop + + +def _lfilter_core( waveform: Tensor, a_coeffs: Tensor, b_coeffs: Tensor, - clamp: bool = True, ) -> Tensor: - r"""Perform an IIR filter by evaluating difference equation. - - Args: - waveform (Tensor): audio waveform of dimension of ``(..., time)``. Must be normalized to -1 to 1. - a_coeffs (Tensor): denominator coefficients of difference equation of dimension of ``(n_order + 1)``. - Lower delays coefficients are first, e.g. ``[a0, a1, a2, ...]``. - Must be same size as b_coeffs (pad with 0's as necessary). - b_coeffs (Tensor): numerator coefficients of difference equation of dimension of ``(n_order + 1)``. - Lower delays coefficients are first, e.g. ``[b0, b1, b2, ...]``. - Must be same size as a_coeffs (pad with 0's as necessary). - clamp (bool, optional): If ``True``, clamp the output signal to be in the range [-1, 1] (Default: ``True``) - - Returns: - Tensor: Waveform with dimension of ``(..., time)``. - """ - # pack batch - shape = waveform.size() - waveform = waveform.reshape(-1, shape[-1]) assert a_coeffs.size(0) == b_coeffs.size(0) assert len(waveform.size()) == 2 assert waveform.device == a_coeffs.device assert b_coeffs.device == a_coeffs.device - device = waveform.device - dtype = waveform.dtype n_channel, n_sample = waveform.size() n_order = a_coeffs.size(0) - n_sample_padded = n_sample + n_order - 1 assert n_order > 0 # Pad the input and create output - padded_waveform = torch.zeros( - n_channel, n_sample_padded, dtype=dtype, device=device - ) - padded_waveform[:, n_order - 1:] = waveform - padded_output_waveform = torch.zeros( - n_channel, n_sample_padded, dtype=dtype, device=device - ) + + padded_waveform = torch.nn.functional.pad(waveform, [n_order - 1, 0]) + padded_output_waveform = torch.zeros_like(padded_waveform) # Set up the coefficients matrix # Flip coefficients' order a_coeffs_flipped = a_coeffs.flip(0) b_coeffs_flipped = b_coeffs.flip(0) - # calculate windowed_input_signal in parallel - # create indices of original with shape (n_channel, n_order, n_sample) - window_idxs = torch.arange(n_sample, device=device).unsqueeze(0) + torch.arange( - n_order, device=device - ).unsqueeze(1) - window_idxs = window_idxs.repeat(n_channel, 1, 1) - window_idxs += ( - torch.arange(n_channel, device=device).unsqueeze(-1).unsqueeze(-1) - * n_sample_padded - ) - window_idxs = window_idxs.long() - # (n_order, ) matmul (n_channel, n_order, n_sample) -> (n_channel, n_sample) - input_signal_windows = torch.matmul( - b_coeffs_flipped, torch.take(padded_waveform, window_idxs) - ) + # calculate windowed_input_signal in parallel using convolution + input_signal_windows = torch.nn.functional.conv1d( + padded_waveform.unsqueeze(1), + b_coeffs_flipped.view(1, 1, -1) + ).squeeze(1) input_signal_windows.div_(a_coeffs[0]) a_coeffs_flipped.div_(a_coeffs[0]) - for i_sample, o0 in enumerate(input_signal_windows.t()): - windowed_output_signal = padded_output_waveform[ - :, i_sample:i_sample + n_order - ] - o0.addmv_(windowed_output_signal, a_coeffs_flipped, alpha=-1) - padded_output_waveform[:, i_sample + n_order - 1] = o0 + + if input_signal_windows.device == torch.device('cpu') and\ + a_coeffs_flipped.device == torch.device('cpu') and\ + padded_output_waveform.device == torch.device('cpu'): + _lfilter_core_cpu_loop(input_signal_windows, a_coeffs_flipped, padded_output_waveform) + else: + _lfilter_core_generic_loop(input_signal_windows, a_coeffs_flipped, padded_output_waveform) output = padded_output_waveform[:, n_order - 1:] + return output + +try: + _lfilter = torch.ops.torchaudio._lfilter +except RuntimeError as err: + assert str(err) == 'No such operator torchaudio::_lfilter' + _lfilter = _lfilter_core + + +def lfilter( + waveform: Tensor, + a_coeffs: Tensor, + b_coeffs: Tensor, + clamp: bool = True, +) -> Tensor: + r"""Perform an IIR filter by evaluating difference equation. + + Note: + To avoid numerical problems, small filter order is preferred. + Using double precision could also minimize numerical precision errors. + + Args: + waveform (Tensor): audio waveform of dimension of ``(..., time)``. Must be normalized to -1 to 1. + a_coeffs (Tensor): denominator coefficients of difference equation of dimension of ``(n_order + 1)``. + Lower delays coefficients are first, e.g. ``[a0, a1, a2, ...]``. + Must be same size as b_coeffs (pad with 0's as necessary). + b_coeffs (Tensor): numerator coefficients of difference equation of dimension of ``(n_order + 1)``. + Lower delays coefficients are first, e.g. ``[b0, b1, b2, ...]``. + Must be same size as a_coeffs (pad with 0's as necessary). + clamp (bool, optional): If ``True``, clamp the output signal to be in the range [-1, 1] (Default: ``True``) + + Returns: + Tensor: Waveform with dimension of ``(..., time)``. + """ + # pack batch + shape = waveform.size() + waveform = waveform.reshape(-1, shape[-1]) + + output = _lfilter(waveform, a_coeffs, b_coeffs) if clamp: output = torch.clamp(output, min=-1.0, max=1.0) @@ -921,6 +942,26 @@ def lowpass_biquad( return biquad(waveform, b0, b1, b2, a0, a1, a2) +def _overdrive_core_loop_generic( + waveform: Tensor, + temp: Tensor, + last_in: Tensor, + last_out: Tensor, + output_waveform: Tensor +): + for i in range(waveform.shape[-1]): + last_out = temp[:, i] - last_in + 0.995 * last_out + last_in = temp[:, i] + output_waveform[:, i] = waveform[:, i] * 0.5 + last_out * 0.75 + + +try: + _overdrive_core_loop_cpu = torch.ops.torchaudio._overdrive_core_loop +except RuntimeError as err: + assert str(err) == 'No such operator torchaudio::_overdrive_core_loop' + _overdrive_core_loop_cpu = _overdrive_core_loop_generic + + def overdrive(waveform: Tensor, gain: float = 20, colour: float = 20) -> Tensor: r"""Apply a overdrive effect to the audio. Similar to SoX implementation. This effect applies a non linear distortion to the audio signal. @@ -963,11 +1004,11 @@ def overdrive(waveform: Tensor, gain: float = 20, colour: float = 20) -> Tensor: output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) - # TODO: Implement a torch CPP extension - for i in range(waveform.shape[-1]): - last_out = temp[:, i] - last_in + 0.995 * last_out - last_in = temp[:, i] - output_waveform[:, i] = waveform[:, i] * 0.5 + last_out * 0.75 + # Uses CPU optimized loop function if available for CPU device + if device == torch.device('cpu'): + _overdrive_core_loop_cpu(waveform, temp, last_in, last_out, output_waveform) + else: + _overdrive_core_loop_generic(waveform, temp, last_in, last_out, output_waveform) return output_waveform.clamp(min=-1, max=1).view(actual_shape) @@ -991,7 +1032,7 @@ def phaser( Allowed range of values are 0 to 1 gain_out (float): desired output gain at the boost (or attenuation) in dB Allowed range of values are 0 to 1e9 - delay_ms (float): desired delay in milli seconds + delay_ms (float): desired delay in milliseconds Allowed range of values are 0 to 5.0 decay (float): desired decay relative to gain-in Allowed range of values are 0 to 0.99 @@ -1064,7 +1105,7 @@ def phaser( def riaa_biquad(waveform: Tensor, sample_rate: int) -> Tensor: - r"""Apply RIAA vinyl playback equalisation. Similar to SoX implementation. + r"""Apply RIAA vinyl playback equalization. Similar to SoX implementation. Args: waveform (Tensor): audio waveform of dimension of `(..., time)` @@ -1108,7 +1149,7 @@ def riaa_biquad(waveform: Tensor, sample_rate: int) -> Tensor: a1 = -(poles[0] + poles[1]) a2 = poles[0] * poles[1] - # Normalise to 0dB at 1kHz + # Normalize to 0dB at 1kHz y = 2 * math.pi * 1000 / sample_rate b_re = b0 + b1 * math.cos(-y) + b2 * math.cos(-2 * y) a_re = a0 + a1 * math.cos(-y) + a2 * math.cos(-2 * y) @@ -1288,7 +1329,7 @@ def vad( to search for quieter/shorter bursts of audio to include prior to the detected trigger point. (Default: 1.0) allowed_gap (float, optional): The allowed gap (in seconds) between - quiteter/shorter bursts of audio to include prior + quieter/shorter bursts of audio to include prior to the detected trigger point. (Default: 0.25) pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve before the trigger point and any found quieter/shorter bursts. (Default: 0.0) diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index 4dde324a31..b114b9aacf 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -1,11 +1,14 @@ # -*- coding: utf-8 -*- +import io import math -from typing import Optional, Tuple import warnings +from typing import Optional, Tuple import torch from torch import Tensor +from torchaudio._internal import module_utils as _mod_utils +import torchaudio __all__ = [ "spectrogram", @@ -13,6 +16,7 @@ "amplitude_to_DB", "DB_to_amplitude", "compute_deltas", + "compute_kaldi_pitch", "create_fb_matrix", "create_dct", "compute_deltas", @@ -28,6 +32,7 @@ 'mask_along_axis_iid', 'sliding_window_cmn', "spectral_centroid", + "apply_codec", ] @@ -113,7 +118,6 @@ def griffinlim( hop_length: int, win_length: int, power: float, - normalized: bool, n_iter: int, momentum: float, length: Optional[int], @@ -143,7 +147,6 @@ def griffinlim( win_length (int): Window size. (Default: ``n_fft``) power (float): Exponent for the magnitude spectrogram, (must be > 0) e.g., 1 for energy, 2 for power, etc. - normalized (bool): Whether to normalize by magnitude after stft. n_iter (int): Number of iteration for phase recovery process. momentum (float): The momentum parameter for fast Griffin-Lim. Setting this to 0 recovers the original Griffin-Lim method. @@ -157,12 +160,6 @@ def griffinlim( assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum) assert momentum >= 0, 'momentum={} < 0'.format(momentum) - if normalized: - warnings.warn( - "The argument normalized is not used in Griffin-Lim, " - "and will be removed in v0.9.0 release. To suppress this warning, " - "please use `normalized=False`.") - # pack batch shape = specgram.size() specgram = specgram.reshape([-1] + list(shape[-2:])) @@ -291,13 +288,81 @@ def DB_to_amplitude( return ref * torch.pow(torch.pow(10.0, 0.1 * x), power) +def _hz_to_mel(freq: float, mel_scale: str = "htk") -> float: + r"""Convert Hz to Mels. + + Args: + freqs (float): Frequencies in Hz + mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) + + Returns: + mels (float): Frequency in Mels + """ + + if mel_scale not in ['slaney', 'htk']: + raise ValueError('mel_scale should be one of "htk" or "slaney".') + + if mel_scale == "htk": + return 2595.0 * math.log10(1.0 + (freq / 700.0)) + + # Fill in the linear part + f_min = 0.0 + f_sp = 200.0 / 3 + + mels = (freq - f_min) / f_sp + + # Fill in the log-scale part + min_log_hz = 1000.0 + min_log_mel = (min_log_hz - f_min) / f_sp + logstep = math.log(6.4) / 27.0 + + if freq >= min_log_hz: + mels = min_log_mel + math.log(freq / min_log_hz) / logstep + + return mels + + +def _mel_to_hz(mels: Tensor, mel_scale: str = "htk") -> Tensor: + """Convert mel bin numbers to frequencies. + + Args: + mels (Tensor): Mel frequencies + mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) + + Returns: + freqs (Tensor): Mels converted in Hz + """ + + if mel_scale not in ['slaney', 'htk']: + raise ValueError('mel_scale should be one of "htk" or "slaney".') + + if mel_scale == "htk": + return 700.0 * (10.0**(mels / 2595.0) - 1.0) + + # Fill in the linear scale + f_min = 0.0 + f_sp = 200.0 / 3 + freqs = f_min + f_sp * mels + + # And now the nonlinear scale + min_log_hz = 1000.0 + min_log_mel = (min_log_hz - f_min) / f_sp + logstep = math.log(6.4) / 27.0 + + log_t = (mels >= min_log_mel) + freqs[log_t] = min_log_hz * torch.exp(logstep * (mels[log_t] - min_log_mel)) + + return freqs + + def create_fb_matrix( n_freqs: int, f_min: float, f_max: float, n_mels: int, sample_rate: int, - norm: Optional[str] = None + norm: Optional[str] = None, + mel_scale: str = "htk", ) -> Tensor: r"""Create a frequency bin conversion matrix. @@ -309,6 +374,7 @@ def create_fb_matrix( sample_rate (int): Sample rate of the audio waveform norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization). (Default: ``None``) + mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) Returns: Tensor: Triangular filter banks (fb matrix) of size (``n_freqs``, ``n_mels``) @@ -326,12 +392,12 @@ def create_fb_matrix( all_freqs = torch.linspace(0, sample_rate // 2, n_freqs) # calculate mel freq bins - # hertz to mel(f) is 2595. * math.log10(1. + (f / 700.)) - m_min = 2595.0 * math.log10(1.0 + (f_min / 700.0)) - m_max = 2595.0 * math.log10(1.0 + (f_max / 700.0)) + m_min = _hz_to_mel(f_min, mel_scale=mel_scale) + m_max = _hz_to_mel(f_max, mel_scale=mel_scale) + m_pts = torch.linspace(m_min, m_max, n_mels + 2) - # mel to hertz(mel) is 700. * (10**(mel / 2595.) - 1.) - f_pts = 700.0 * (10 ** (m_pts / 2595.0) - 1.0) + f_pts = _mel_to_hz(m_pts, mel_scale=mel_scale) + # calculate the difference between each mel point and each stft freq point in hertz f_diff = f_pts[1:] - f_pts[:-1] # (n_mels + 1) slopes = f_pts.unsqueeze(0) - all_freqs.unsqueeze(1) # (n_freqs, n_mels + 2) @@ -868,7 +934,7 @@ def detect_pitch_frequency( def sliding_window_cmn( - waveform: Tensor, + specgram: Tensor, cmn_window: int = 600, min_cmn_window: int = 100, center: bool = False, @@ -878,7 +944,7 @@ def sliding_window_cmn( Apply sliding-window cepstral mean (and optionally variance) normalization per utterance. Args: - waveform (Tensor): Tensor of audio of dimension (..., freq, time) + specgram (Tensor): Tensor of audio of dimension (..., time, freq) cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600) min_cmn_window (int, optional): Minimum CMN window used at start of decoding (adds latency only at start). Only applicable if center == false, ignored if center==true (int, default = 100) @@ -887,19 +953,19 @@ def sliding_window_cmn( norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false) Returns: - Tensor: Tensor of freq of dimension (..., frame) + Tensor: Tensor matching input shape (..., freq, time) """ - input_shape = waveform.shape + input_shape = specgram.shape num_frames, num_feats = input_shape[-2:] - waveform = waveform.view(-1, num_frames, num_feats) - num_channels = waveform.shape[0] + specgram = specgram.view(-1, num_frames, num_feats) + num_channels = specgram.shape[0] - dtype = waveform.dtype - device = waveform.device + dtype = specgram.dtype + device = specgram.device last_window_start = last_window_end = -1 cur_sum = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) cur_sumsq = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) - cmn_waveform = torch.zeros( + cmn_specgram = torch.zeros( num_channels, num_frames, num_feats, dtype=dtype, device=device) for t in range(num_frames): window_start = 0 @@ -922,40 +988,40 @@ def sliding_window_cmn( if window_start < 0: window_start = 0 if last_window_start == -1: - input_part = waveform[:, window_start: window_end - window_start, :] + input_part = specgram[:, window_start: window_end - window_start, :] cur_sum += torch.sum(input_part, 1) if norm_vars: cur_sumsq += torch.cumsum(input_part ** 2, 1)[:, -1, :] else: if window_start > last_window_start: - frame_to_remove = waveform[:, last_window_start, :] + frame_to_remove = specgram[:, last_window_start, :] cur_sum -= frame_to_remove if norm_vars: cur_sumsq -= (frame_to_remove ** 2) if window_end > last_window_end: - frame_to_add = waveform[:, last_window_end, :] + frame_to_add = specgram[:, last_window_end, :] cur_sum += frame_to_add if norm_vars: cur_sumsq += (frame_to_add ** 2) window_frames = window_end - window_start last_window_start = window_start last_window_end = window_end - cmn_waveform[:, t, :] = waveform[:, t, :] - cur_sum / window_frames + cmn_specgram[:, t, :] = specgram[:, t, :] - cur_sum / window_frames if norm_vars: if window_frames == 1: - cmn_waveform[:, t, :] = torch.zeros( + cmn_specgram[:, t, :] = torch.zeros( num_channels, num_feats, dtype=dtype, device=device) else: variance = cur_sumsq variance = variance / window_frames variance -= ((cur_sum ** 2) / (window_frames ** 2)) variance = torch.pow(variance, -0.5) - cmn_waveform[:, t, :] *= variance + cmn_specgram[:, t, :] *= variance - cmn_waveform = cmn_waveform.view(input_shape[:-2] + (num_frames, num_feats)) + cmn_specgram = cmn_specgram.view(input_shape[:-2] + (num_frames, num_feats)) if len(input_shape) == 2: - cmn_waveform = cmn_waveform.squeeze(0) - return cmn_waveform + cmn_specgram = cmn_specgram.squeeze(0) + return cmn_specgram def spectral_centroid( @@ -991,3 +1057,155 @@ def spectral_centroid( device=specgram.device).reshape((-1, 1)) freq_dim = -2 return (freqs * specgram).sum(dim=freq_dim) / specgram.sum(dim=freq_dim) + + +@_mod_utils.requires_sox() +def apply_codec( + waveform: Tensor, + sample_rate: int, + format: str, + channels_first: bool = True, + compression: Optional[float] = None, + encoding: Optional[str] = None, + bits_per_sample: Optional[int] = None, +) -> Tensor: + r""" + Apply codecs as a form of augmentation. + + Args: + waveform (Tensor): Audio data. Must be 2 dimensional. See also ```channels_first```. + sample_rate (int): Sample rate of the audio waveform. + format (str): File format. + channels_first (bool): + When True, both the input and output Tensor have dimension ``[channel, time]``. + Otherwise, they have dimension ``[time, channel]``. + compression (float): Used for formats other than WAV. + For mor details see :py:func:`torchaudio.backend.sox_io_backend.save`. + encoding (str, optional): Changes the encoding for the supported formats. + For more details see :py:func:`torchaudio.backend.sox_io_backend.save`. + bits_per_sample (int, optional): Changes the bit depth for the supported formats. + For more details see :py:func:`torchaudio.backend.sox_io_backend.save`. + + Returns: + torch.Tensor: Resulting Tensor. + If ``channels_first=True``, it has ``[channel, time]`` else ``[time, channel]``. + """ + bytes = io.BytesIO() + torchaudio.backend.sox_io_backend.save(bytes, + waveform, + sample_rate, + channels_first, + compression, + format, + encoding, + bits_per_sample + ) + bytes.seek(0) + augmented, _ = torchaudio.sox_effects.sox_effects.apply_effects_file( + bytes, effects=[["rate", f"{sample_rate}"]], channels_first=channels_first, format=format) + return augmented + + +@_mod_utils.requires_kaldi() +def compute_kaldi_pitch( + waveform: torch.Tensor, + sample_rate: float, + frame_length: float = 25.0, + frame_shift: float = 10.0, + min_f0: float = 50, + max_f0: float = 400, + soft_min_f0: float = 10.0, + penalty_factor: float = 0.1, + lowpass_cutoff: float = 1000, + resample_frequency: float = 4000, + delta_pitch: float = 0.005, + nccf_ballast: float = 7000, + lowpass_filter_width: int = 1, + upsample_filter_width: int = 5, + max_frames_latency: int = 0, + frames_per_chunk: int = 0, + simulate_first_pass_online: bool = False, + recompute_frame: int = 500, + snip_edges: bool = True, +) -> torch.Tensor: + """Extract pitch based on method described in [1]. + + This function computes the equivalent of `compute-kaldi-pitch-feats` from Kaldi. + + Args: + waveform (Tensor): + The input waveform of shape `(..., time)`. + sample_rate (float): + Sample rate of `waveform`. + frame_length (float, optional): + Frame length in milliseconds. (default: 25.0) + frame_shift (float, optional): + Frame shift in milliseconds. (default: 10.0) + min_f0 (float, optional): + Minimum F0 to search for (Hz) (default: 50.0) + max_f0 (float, optional): + Maximum F0 to search for (Hz) (default: 400.0) + soft_min_f0 (float, optional): + Minimum f0, applied in soft way, must not exceed min-f0 (default: 10.0) + penalty_factor (float, optional): + Cost factor for FO change. (default: 0.1) + lowpass_cutoff (float, optional): + Cutoff frequency for LowPass filter (Hz) (default: 1000) + resample_frequency (float, optional): + Frequency that we down-sample the signal to. Must be more than twice lowpass-cutoff. + (default: 4000) + delta_pitch( float, optional): + Smallest relative change in pitch that our algorithm measures. (default: 0.005) + nccf_ballast (float, optional): + Increasing this factor reduces NCCF for quiet frames (default: 7000) + lowpass_filter_width (int, optional): + Integer that determines filter width of lowpass filter, more gives sharper filter. + (default: 1) + upsample_filter_width (int, optional): + Integer that determines filter width when upsampling NCCF. (default: 5) + max_frames_latency (int, optional): + Maximum number of frames of latency that we allow pitch tracking to introduce into + the feature processing (affects output only if ``frames_per_chunk > 0`` and + ``simulate_first_pass_online=True``) (default: 0) + frames_per_chunk (int, optional): + The number of frames used for energy normalization. (default: 0) + simulate_first_pass_online (bool, optional): + If true, the function will output features that correspond to what an online decoder + would see in the first pass of decoding -- not the final version of the features, + which is the default. (default: False) + Relevant if ``frames_per_chunk > 0``. + recompute_frame (int, optional): + Only relevant for compatibility with online pitch extraction. + A non-critical parameter; the frame at which we recompute some of the forward pointers, + after revising our estimate of the signal energy. + Relevant if ``frames_per_chunk > 0``. (default: 500) + snip_edges (bool, optional): + If this is set to false, the incomplete frames near the ending edge won't be snipped, + so that the number of frames is the file size divided by the frame-shift. + This makes different types of features give the same number of frames. (default: True) + + Returns: + Tensor: Pitch feature. Shape: ``(batch, frames 2)`` where the last dimension + corresponds to pitch and NCCF. + + Reference: + - A pitch extraction algorithm tuned for automatic speech recognition + + P. Ghahremani, B. BabaAli, D. Povey, K. Riedhammer, J. Trmal and S. Khudanpur + + 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), + + Florence, 2014, pp. 2494-2498, doi: 10.1109/ICASSP.2014.6854049. + """ + shape = waveform.shape + waveform = waveform.reshape(-1, shape[-1]) + result = torch.ops.torchaudio.kaldi_ComputeKaldiPitch( + waveform, sample_rate, frame_length, frame_shift, + min_f0, max_f0, soft_min_f0, penalty_factor, lowpass_cutoff, + resample_frequency, delta_pitch, nccf_ballast, + lowpass_filter_width, upsample_filter_width, max_frames_latency, + frames_per_chunk, simulate_first_pass_online, recompute_frame, + snip_edges, + ) + result = result.reshape(shape[:-1] + result.shape[-2:]) + return result diff --git a/torchaudio/kaldi_io.py b/torchaudio/kaldi_io.py index 7f14f48e2c..ba1689da2b 100644 --- a/torchaudio/kaldi_io.py +++ b/torchaudio/kaldi_io.py @@ -57,7 +57,7 @@ def read_vec_int_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: >>> d = { u:d for u,d in torchaudio.kaldi_io.read_vec_int_ark(file) } """ # Requires convert_contiguous to be True because elements from int32 vector are - # sored in tuples: (sizeof(int32), value) so strides are (5,) instead of (4,) which will throw an error + # sorted in tuples: (sizeof(int32), value) so strides are (5,) instead of (4,) which will throw an error # in from_numpy as it expects strides to be a multiple of 4 (int32). return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_int_ark, convert_contiguous=True) diff --git a/torchaudio/prototype/transducer.py b/torchaudio/prototype/transducer.py index 843cf5f9a4..99c1e9877e 100644 --- a/torchaudio/prototype/transducer.py +++ b/torchaudio/prototype/transducer.py @@ -19,7 +19,6 @@ def forward(ctx, acts, labels, act_lens, label_lens, blank, reduction): """ device = acts.device - check_inputs(acts, labels, act_lens, label_lens) acts = acts.to("cpu") labels = labels.to("cpu") @@ -118,45 +117,3 @@ def forward(self, acts, labels, act_lens, label_lens): # log_softmax is computed within GPU version. acts = torch.nn.functional.log_softmax(acts, -1) return self.loss(acts, labels, act_lens, label_lens, self.blank, self.reduction) - - -def check_type(var, t, name): - if var.dtype is not t: - raise TypeError("{} must be {}".format(name, t)) - - -def check_contiguous(var, name): - if not var.is_contiguous(): - raise ValueError("{} must be contiguous".format(name)) - - -def check_dim(var, dim, name): - if len(var.shape) != dim: - raise ValueError("{} must be {}D".format(name, dim)) - - -def check_inputs(log_probs, labels, lengths, label_lengths): - check_type(labels, torch.int32, "labels") - check_type(label_lengths, torch.int32, "label_lengths") - check_type(lengths, torch.int32, "lengths") - check_contiguous(log_probs, "log_probs") - check_contiguous(labels, "labels") - check_contiguous(label_lengths, "label_lengths") - check_contiguous(lengths, "lengths") - - if lengths.shape[0] != log_probs.shape[0]: - raise ValueError("must have a length per example.") - if label_lengths.shape[0] != log_probs.shape[0]: - raise ValueError("must have a label length per example.") - - check_dim(log_probs, 4, "log_probs") - check_dim(labels, 2, "labels") - check_dim(lengths, 1, "lengths") - check_dim(label_lengths, 1, "label_lengths") - max_T = torch.max(lengths) - max_U = torch.max(label_lengths) - T, U = log_probs.shape[1:3] - if T != max_T: - raise ValueError("Input length mismatch") - if U != max_U + 1: - raise ValueError("Output length mismatch") diff --git a/torchaudio/sox_effects/__init__.py b/torchaudio/sox_effects/__init__.py index b3e2708ad1..3c46cebdd2 100644 --- a/torchaudio/sox_effects/__init__.py +++ b/torchaudio/sox_effects/__init__.py @@ -8,7 +8,7 @@ ) -if _mod_utils.is_module_available('torchaudio._torchaudio'): +if _mod_utils.is_sox_available(): import atexit init_sox_effects() atexit.register(shutdown_sox_effects) diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index e183932788..26fc0659e6 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -8,7 +8,7 @@ from torchaudio.utils.sox_utils import list_effects -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def init_sox_effects(): """Initialize resources required to use sox effects. @@ -23,7 +23,7 @@ def init_sox_effects(): torch.ops.torchaudio.sox_effects_initialize_sox_effects() -@_mod_utils.requires_module("torchaudio._torchaudio") +@_mod_utils.requires_sox() def shutdown_sox_effects(): """Clean up resources required to use sox effects. @@ -37,7 +37,7 @@ def shutdown_sox_effects(): torch.ops.torchaudio.sox_effects_shutdown_sox_effects() -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def effect_names() -> List[str]: """Gets list of valid sox effect names @@ -51,7 +51,7 @@ def effect_names() -> List[str]: return list(list_effects().keys()) -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def apply_effects_tensor( tensor: torch.Tensor, sample_rate: int, @@ -152,7 +152,7 @@ def apply_effects_tensor( tensor, sample_rate, effects, channels_first) -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def apply_effects_file( path: str, effects: List[List[str]], @@ -173,14 +173,16 @@ def apply_effects_file( Args: path (path-like object or file-like object): Source of audio data. When the function is not compiled by TorchScript, - (e.g. ``torch.jit.script``), the following types are accepted; + (e.g. ``torch.jit.script``), the following types are accepted: + * ``path-like``: file path * ``file-like``: Object with ``read(size: int) -> bytes`` method, which returns byte string of at most ``size`` length. + When the function is compiled by TorchScript, only ``str`` type is allowed. - Note: - * This argument is intentionally annotated as ``str`` only for - TorchScript compiler compatibility. + + Note: This argument is intentionally annotated as ``str`` only for + TorchScript compiler compatibility. effects (List[List[str]]): List of effects. normalize (bool): When ``True``, this function always return ``float32``, and sample values are diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index af89d5b250..d57b3eb35c 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -71,7 +71,7 @@ def __init__(self, super(Spectrogram, self).__init__() self.n_fft = n_fft # number of FFT bins. the returned STFT result will have n_fft // 2 + 1 - # number of frequecies due to onesided=True in torch.stft + # number of frequencies due to onesided=True in torch.stft self.win_length = win_length if win_length is not None else n_fft self.hop_length = hop_length if hop_length is not None else self.win_length // 2 window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs) @@ -122,7 +122,6 @@ class GriffinLim(torch.nn.Module): that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``) power (float, optional): Exponent for the magnitude spectrogram, (must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: ``2``) - normalized (bool, optional): Whether to normalize by magnitude after stft. (Default: ``False``) wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``) momentum (float, optional): The momentum parameter for fast Griffin-Lim. Setting this to 0 recovers the original Griffin-Lim method. @@ -158,7 +157,6 @@ def __init__(self, hop_length: Optional[int] = None, window_fn: Callable[..., Tensor] = torch.hann_window, power: float = 2., - normalized: bool = False, wkwargs: Optional[dict] = None, momentum: float = 0.99, length: Optional[int] = None, @@ -174,7 +172,6 @@ def __init__(self, self.hop_length = hop_length if hop_length is not None else self.win_length // 2 window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs) self.register_buffer('window', window) - self.normalized = normalized self.length = length self.power = power self.momentum = momentum / (1 + momentum) @@ -191,7 +188,7 @@ def forward(self, specgram: Tensor) -> Tensor: Tensor: waveform of (..., time), where time equals the ``length`` parameter if given. """ return F.griffinlim(specgram, self.window, self.n_fft, self.hop_length, self.win_length, self.power, - self.normalized, self.n_iter, self.momentum, self.length, self.rand_init) + self.n_iter, self.momentum, self.length, self.rand_init) class AmplitudeToDB(torch.nn.Module): @@ -249,6 +246,7 @@ class MelScale(torch.nn.Module): if None is given. See ``n_fft`` in :class:`Spectrogram`. (Default: ``None``) norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization). (Default: ``None``) + mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) """ __constants__ = ['n_mels', 'sample_rate', 'f_min', 'f_max'] @@ -258,18 +256,21 @@ def __init__(self, f_min: float = 0., f_max: Optional[float] = None, n_stft: Optional[int] = None, - norm: Optional[str] = None) -> None: + norm: Optional[str] = None, + mel_scale: str = "htk") -> None: super(MelScale, self).__init__() self.n_mels = n_mels self.sample_rate = sample_rate self.f_max = f_max if f_max is not None else float(sample_rate // 2) self.f_min = f_min self.norm = norm + self.mel_scale = mel_scale assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max) fb = torch.empty(0) if n_stft is None else F.create_fb_matrix( - n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, self.norm) + n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, self.norm, + self.mel_scale) self.register_buffer('fb', fb) def forward(self, specgram: Tensor) -> Tensor: @@ -287,7 +288,8 @@ def forward(self, specgram: Tensor) -> Tensor: if self.fb.numel() == 0: tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max, - self.n_mels, self.sample_rate, self.norm) + self.n_mels, self.sample_rate, self.norm, + self.mel_scale) # Attributes cannot be reassigned outside __init__ so workaround self.fb.resize_(tmp_fb.size()) self.fb.copy_(tmp_fb) @@ -321,6 +323,7 @@ class InverseMelScale(torch.nn.Module): sgdargs (dict or None, optional): Arguments for the SGD optimizer. (Default: ``None``) norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization). (Default: ``None``) + mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) """ __constants__ = ['n_stft', 'n_mels', 'sample_rate', 'f_min', 'f_max', 'max_iter', 'tolerance_loss', 'tolerance_change', 'sgdargs'] @@ -335,7 +338,8 @@ def __init__(self, tolerance_loss: float = 1e-5, tolerance_change: float = 1e-8, sgdargs: Optional[dict] = None, - norm: Optional[str] = None) -> None: + norm: Optional[str] = None, + mel_scale: str = "htk") -> None: super(InverseMelScale, self).__init__() self.n_mels = n_mels self.sample_rate = sample_rate @@ -348,7 +352,8 @@ def __init__(self, assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max) - fb = F.create_fb_matrix(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, norm) + fb = F.create_fb_matrix(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, norm, + mel_scale) self.register_buffer('fb', fb) def forward(self, melspec: Tensor) -> Tensor: @@ -427,6 +432,7 @@ class MelSpectrogram(torch.nn.Module): avoid redundancy. Default: ``True`` norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization). (Default: ``None``) + mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalization=True) @@ -450,7 +456,8 @@ def __init__(self, center: bool = True, pad_mode: str = "reflect", onesided: bool = True, - norm: Optional[str] = None) -> None: + norm: Optional[str] = None, + mel_scale: str = "htk") -> None: super(MelSpectrogram, self).__init__() self.sample_rate = sample_rate self.n_fft = n_fft @@ -467,7 +474,15 @@ def __init__(self, pad=self.pad, window_fn=window_fn, power=self.power, normalized=self.normalized, wkwargs=wkwargs, center=center, pad_mode=pad_mode, onesided=onesided) - self.mel_scale = MelScale(self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1, norm) + self.mel_scale = MelScale( + self.n_mels, + self.sample_rate, + self.f_min, + self.f_max, + self.n_fft // 2 + 1, + norm, + mel_scale + ) def forward(self, waveform: Tensor) -> Tensor: r""" @@ -547,8 +562,8 @@ def forward(self, waveform: Tensor) -> Tensor: else: mel_specgram = self.amplitude_to_DB(mel_specgram) - # (..., channel, n_mels, time).tranpose(...) dot (n_mels, n_mfcc) - # -> (..., channel, time, n_mfcc).tranpose(...) + # (..., channel, n_mels, time).transpose(...) dot (n_mels, n_mfcc) + # -> (..., channel, time, n_mfcc).transpose(...) mfcc = torch.matmul(mel_specgram.transpose(-2, -1), self.dct_mat).transpose(-2, -1) return mfcc diff --git a/torchaudio/utils/__init__.py b/torchaudio/utils/__init__.py index bc11f86893..578a51925a 100644 --- a/torchaudio/utils/__init__.py +++ b/torchaudio/utils/__init__.py @@ -1,9 +1,8 @@ from . import ( sox_utils, ) - from torchaudio._internal import module_utils as _mod_utils -if _mod_utils.is_module_available('torchaudio._torchaudio'): +if _mod_utils.is_sox_available(): sox_utils.set_verbosity(1) diff --git a/torchaudio/utils/sox_utils.py b/torchaudio/utils/sox_utils.py index 3f9ad0b5ec..ed576a83a3 100644 --- a/torchaudio/utils/sox_utils.py +++ b/torchaudio/utils/sox_utils.py @@ -1,13 +1,10 @@ from typing import List, Dict import torch +from torchaudio._internal import module_utils as _mod_utils -from torchaudio._internal import ( - module_utils as _mod_utils, -) - -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def set_seed(seed: int): """Set libsox's PRNG @@ -20,7 +17,7 @@ def set_seed(seed: int): torch.ops.torchaudio.sox_utils_set_seed(seed) -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def set_verbosity(verbosity: int): """Set libsox's verbosity @@ -38,7 +35,7 @@ def set_verbosity(verbosity: int): torch.ops.torchaudio.sox_utils_set_verbosity(verbosity) -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def set_buffer_size(buffer_size: int): """Set buffer size for sox effect chain @@ -51,7 +48,7 @@ def set_buffer_size(buffer_size: int): torch.ops.torchaudio.sox_utils_set_buffer_size(buffer_size) -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def set_use_threads(use_threads: bool): """Set multithread option for sox effect chain @@ -65,7 +62,7 @@ def set_use_threads(use_threads: bool): torch.ops.torchaudio.sox_utils_set_use_threads(use_threads) -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def list_effects() -> Dict[str, str]: """List the available sox effect names @@ -75,7 +72,7 @@ def list_effects() -> Dict[str, str]: return dict(torch.ops.torchaudio.sox_utils_list_effects()) -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def list_read_formats() -> List[str]: """List the supported audio formats for read @@ -85,7 +82,7 @@ def list_read_formats() -> List[str]: return torch.ops.torchaudio.sox_utils_list_read_formats() -@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.requires_sox() def list_write_formats() -> List[str]: """List the supported audio formats for write From f88723cd3c1f5e30478a9d1d8d25f1b1e22863f3 Mon Sep 17 00:00:00 2001 From: Dmitry Polukhin Date: Tue, 30 Mar 2021 02:18:05 -0700 Subject: [PATCH 34/73] Fix broken list of checks (#1401) Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/1401 Extra spaces broke list of checks. Reviewed By: mthrok Differential Revision: D27125520 fbshipit-source-id: 506924f9b73266b3f3ac174a020830f33b0c7489 --- .clang-tidy | 57 ++++++++++++++++++++++++++++------------------------- 1 file changed, 30 insertions(+), 27 deletions(-) diff --git a/.clang-tidy b/.clang-tidy index a3a40b5907..e2d7eb387d 100644 --- a/.clang-tidy +++ b/.clang-tidy @@ -1,32 +1,35 @@ --- -# NOTE there must be no spaces before the '-', so put the comma first. +# NOTE there must be no spaces before the '-' and check name. +# If you edit this list, please verify list of enabled check with +# clang-tidy --list-checks +InheritParentConfig: true Checks: ' - -* - ,bugprone-* - ,-bugprone-forward-declaration-namespace - ,-bugprone-macro-parentheses - ,cppcoreguidelines-* - ,-cppcoreguidelines-interfaces-global-init - ,-cppcoreguidelines-owning-memory - ,-cppcoreguidelines-pro-bounds-array-to-pointer-decay - ,-cppcoreguidelines-pro-bounds-constant-array-index - ,-cppcoreguidelines-pro-bounds-pointer-arithmetic - ,-cppcoreguidelines-pro-type-cstyle-cast - ,-cppcoreguidelines-pro-type-reinterpret-cast - ,-cppcoreguidelines-pro-type-static-cast-downcast - ,-cppcoreguidelines-pro-type-union-access - ,-cppcoreguidelines-pro-type-vararg - ,-cppcoreguidelines-special-member-functions - ,hicpp-exception-baseclass - ,hicpp-avoid-goto - ,modernize-* - ,-modernize-return-braced-init-list - ,-modernize-use-auto - ,-modernize-use-default-member-init - ,-modernize-use-using - ,performance-unnecessary-value-param - ' -WarningsAsErrors: '*' +bugprone-*, +-bugprone-forward-declaration-namespace, +-bugprone-macro-parentheses, +-clang-analyzer-*, +cppcoreguidelines-*, +-cppcoreguidelines-interfaces-global-init, +-cppcoreguidelines-owning-memory, +-cppcoreguidelines-pro-bounds-array-to-pointer-decay, +-cppcoreguidelines-pro-bounds-constant-array-index, +-cppcoreguidelines-pro-bounds-pointer-arithmetic, +-cppcoreguidelines-pro-type-cstyle-cast, +-cppcoreguidelines-pro-type-reinterpret-cast, +-cppcoreguidelines-pro-type-static-cast-downcast, +-cppcoreguidelines-pro-type-union-access, +-cppcoreguidelines-pro-type-vararg, +-cppcoreguidelines-special-member-functions, +-facebook-hte-RelativeInclude, +hicpp-exception-baseclass, +hicpp-avoid-goto, +modernize-*, +-modernize-return-braced-init-list, +-modernize-use-auto, +-modernize-use-default-member-init, +-modernize-use-using, +performance-unnecessary-value-param, +' HeaderFilterRegex: 'torchaudio/.*' AnalyzeTemporaryDtors: false CheckOptions: From d48089a05fef44aad03a0a9bc428d3c93ffac63d Mon Sep 17 00:00:00 2001 From: Parmeet Singh Bhatia Date: Wed, 31 Mar 2021 00:03:31 -0700 Subject: [PATCH 35/73] Import torchaudio #1412 c0bfb03 Summary: Import latest from github to fbcode Pass: 951 Skip: 19 Omit: 1 ListingSuccess: 26 Result available at: https://www.internalfb.com/intern/testinfra/testrun/8444249336935844 Reviewed By: mthrok Differential Revision: D27448988 fbshipit-source-id: 61f63ffa1295a31b4452abaf2c74ebfefb827dcf --- docs/source/functional.rst | 7 +- .../common_utils/__init__.py | 1 - .../common_utils/case_utils.py | 3 - .../compliance_kaldi_test.py | 3 +- .../functional/librosa_compatibility_test.py | 19 ++++ .../functional/sox_compatibility_test.py | 4 +- .../transforms/sox_compatibility_test.py | 14 ++- torchaudio/compliance/kaldi.py | 81 +------------- torchaudio/datasets/yesno.py | 4 +- torchaudio/functional/__init__.py | 4 +- torchaudio/functional/functional.py | 103 ++++++++++++++++++ torchaudio/transforms.py | 13 +-- 12 files changed, 153 insertions(+), 103 deletions(-) diff --git a/docs/source/functional.rst b/docs/source/functional.rst index e52e83ca42..98517929ea 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -55,7 +55,7 @@ apply_codec ----------- .. autofunction:: apply_codec - + :hidden:`Complex Utility` ~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -230,3 +230,8 @@ vad --------------------------- .. autofunction:: spectral_centroid + +:hidden:`resample` +--------------------------- + +.. autofunction:: resample diff --git a/test/torchaudio_unittest/common_utils/__init__.py b/test/torchaudio_unittest/common_utils/__init__.py index 22cb18a08e..736ec82855 100644 --- a/test/torchaudio_unittest/common_utils/__init__.py +++ b/test/torchaudio_unittest/common_utils/__init__.py @@ -17,7 +17,6 @@ skipIfNoModule, skipIfNoKaldi, skipIfNoSox, - skipIfNoSoxBackend, ) from .wav_utils import ( get_wav_data, diff --git a/test/torchaudio_unittest/common_utils/case_utils.py b/test/torchaudio_unittest/common_utils/case_utils.py index 5d476072bb..69b9a795cd 100644 --- a/test/torchaudio_unittest/common_utils/case_utils.py +++ b/test/torchaudio_unittest/common_utils/case_utils.py @@ -7,7 +7,6 @@ import torch from torch.testing._internal.common_utils import TestCase as PytorchTestCase -import torchaudio from torchaudio._internal.module_utils import ( is_module_available, is_sox_available, @@ -96,8 +95,6 @@ def skipIfNoModule(module, display_name=None): return unittest.skipIf(not is_module_available(module), f'"{display_name}" is not available') -skipIfNoSoxBackend = unittest.skipIf( - 'sox' not in torchaudio.list_audio_backends(), 'Sox backend not available') skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') skipIfNoSox = unittest.skipIf(not is_sox_available(), reason='Sox not available') skipIfNoKaldi = unittest.skipIf(not is_kaldi_available(), reason='Kaldi not available') diff --git a/test/torchaudio_unittest/compliance_kaldi_test.py b/test/torchaudio_unittest/compliance_kaldi_test.py index a98240a9b8..17d73fbf2f 100644 --- a/test/torchaudio_unittest/compliance_kaldi_test.py +++ b/test/torchaudio_unittest/compliance_kaldi_test.py @@ -47,7 +47,6 @@ def first_sample_of_frame(frame, window_size, window_shift, snip_edges): @common_utils.skipIfNoSox class Test_Kaldi(common_utils.TempDirMixin, common_utils.TorchaudioTestCase): - backend = 'sox_io' kaldi_output_dir = common_utils.get_asset_path('kaldi') test_filepath = common_utils.get_asset_path('kaldi_file.wav') @@ -113,7 +112,7 @@ def _create_data_set(self): # clear the last 16 bits because they aren't used anyways y = ((y >> 16) << 16).float() torchaudio.save(self.test_filepath, y, sr) - sound, sample_rate = torchaudio.load(self.test_filepath, normalization=False) + sound, sample_rate = common_utils.load_wav(self.test_filepath, normalize=False) print(y >> 16) self.assertTrue(sample_rate == sr) self.assertEqual(y, sound) diff --git a/test/torchaudio_unittest/functional/librosa_compatibility_test.py b/test/torchaudio_unittest/functional/librosa_compatibility_test.py index c88d9f3ccd..5905c239d3 100644 --- a/test/torchaudio_unittest/functional/librosa_compatibility_test.py +++ b/test/torchaudio_unittest/functional/librosa_compatibility_test.py @@ -109,6 +109,25 @@ def test_amplitude_to_DB(self): self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) + def test_resample(self): + input_path = common_utils.get_asset_path('sinewave.wav') + waveform, sample_rate = common_utils.load_wav(input_path) + + upsample_rate = sample_rate * 2 + downsample_rate = sample_rate // 2 + + ta_upsampled = F.resample(waveform, sample_rate, upsample_rate) + lr_upsampled = librosa.resample(waveform.squeeze(0).numpy(), sample_rate, upsample_rate) + lr_upsampled = torch.from_numpy(lr_upsampled).unsqueeze(0) + + self.assertEqual(ta_upsampled, lr_upsampled, atol=1e-2, rtol=1e-5) + + ta_downsampled = F.resample(waveform, sample_rate, downsample_rate) + lr_downsampled = librosa.resample(waveform.squeeze(0).numpy(), sample_rate, downsample_rate) + lr_downsampled = torch.from_numpy(lr_downsampled).unsqueeze(0) + + self.assertEqual(ta_downsampled, lr_downsampled, atol=1e-2, rtol=1e-5) + @unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") class TestPhaseVocoder(common_utils.TorchaudioTestCase): diff --git a/test/torchaudio_unittest/functional/sox_compatibility_test.py b/test/torchaudio_unittest/functional/sox_compatibility_test.py index 21f082683c..fe9744f22a 100644 --- a/test/torchaudio_unittest/functional/sox_compatibility_test.py +++ b/test/torchaudio_unittest/functional/sox_compatibility_test.py @@ -2,7 +2,7 @@ import torchaudio.functional as F from torchaudio_unittest.common_utils import ( - skipIfNoSoxBackend, + skipIfNoSox, skipIfNoExec, TempDirMixin, TorchaudioTestCase, @@ -14,7 +14,7 @@ ) -@skipIfNoSoxBackend +@skipIfNoSox @skipIfNoExec('sox') class TestFunctionalFiltering(TempDirMixin, TorchaudioTestCase): def run_sox_effect(self, input_file, effect): diff --git a/test/torchaudio_unittest/transforms/sox_compatibility_test.py b/test/torchaudio_unittest/transforms/sox_compatibility_test.py index 4bf6bf7884..81582c8393 100644 --- a/test/torchaudio_unittest/transforms/sox_compatibility_test.py +++ b/test/torchaudio_unittest/transforms/sox_compatibility_test.py @@ -2,17 +2,19 @@ from parameterized import parameterized from torchaudio_unittest.common_utils import ( - skipIfNoSoxBackend, + skipIfNoSox, skipIfNoExec, TempDirMixin, TorchaudioTestCase, get_asset_path, sox_utils, load_wav, + save_wav, + get_whitenoise, ) -@skipIfNoSoxBackend +@skipIfNoSox @skipIfNoExec('sox') class TestFunctionalFiltering(TempDirMixin, TorchaudioTestCase): def run_sox_effect(self, input_file, effect): @@ -24,6 +26,14 @@ def assert_sox_effect(self, result, input_path, effects, atol=1e-04, rtol=1e-5): expected, _ = self.run_sox_effect(input_path, effects) self.assertEqual(result, expected, atol=atol, rtol=rtol) + def get_whitenoise(self, sample_rate=8000): + noise = get_whitenoise( + sample_rate=sample_rate, duration=3, scale_factor=0.9, + ) + path = self.get_temp_path("whitenoise.wav") + save_wav(path, noise, sample_rate) + return noise, path + @parameterized.expand([ ('q', 'quarter_sine'), ('h', 'half_sine'), diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index f1e1ddf5b5..a5c3354c9b 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -3,7 +3,6 @@ import math import torch from torch import Tensor -from torch.nn import functional as F import torchaudio import torchaudio._internal.fft @@ -753,71 +752,16 @@ def mfcc( return feature -def _get_sinc_resample_kernel(orig_freq: int, new_freq: int, lowpass_filter_width: int, - device: torch.device, dtype: torch.dtype): - assert lowpass_filter_width > 0 - kernels = [] - base_freq = min(orig_freq, new_freq) - # This will perform antialiasing filtering by removing the highest frequencies. - # At first I thought I only needed this when downsampling, but when upsampling - # you will get edge artifacts without this, as the edge is equivalent to zero padding, - # which will add high freq artifacts. - base_freq *= 0.99 - - # The key idea of the algorithm is that x(t) can be exactly reconstructed from x[i] (tensor) - # using the sinc interpolation formula: - # x(t) = sum_i x[i] sinc(pi * orig_freq * (i / orig_freq - t)) - # We can then sample the function x(t) with a different sample rate: - # y[j] = x(j / new_freq) - # or, - # y[j] = sum_i x[i] sinc(pi * orig_freq * (i / orig_freq - j / new_freq)) - - # We see here that y[j] is the convolution of x[i] with a specific filter, for which - # we take an FIR approximation, stopping when we see at least `lowpass_filter_width` zeros crossing. - # But y[j+1] is going to have a different set of weights and so on, until y[j + new_freq]. - # Indeed: - # y[j + new_freq] = sum_i x[i] sinc(pi * orig_freq * ((i / orig_freq - (j + new_freq) / new_freq)) - # = sum_i x[i] sinc(pi * orig_freq * ((i - orig_freq) / orig_freq - j / new_freq)) - # = sum_i x[i + orig_freq] sinc(pi * orig_freq * (i / orig_freq - j / new_freq)) - # so y[j+new_freq] uses the same filter as y[j], but on a shifted version of x by `orig_freq`. - # This will explain the F.conv1d after, with a stride of orig_freq. - width = math.ceil(lowpass_filter_width * orig_freq / base_freq) - # If orig_freq is still big after GCD reduction, most filters will be very unbalanced, i.e., - # they will have a lot of almost zero values to the left or to the right... - # There is probably a way to evaluate those filters more efficiently, but this is kept for - # future work. - idx = torch.arange(-width, width + orig_freq, device=device, dtype=dtype) - - for i in range(new_freq): - t = (-i / new_freq + idx / orig_freq) * base_freq - t = t.clamp_(-lowpass_filter_width, lowpass_filter_width) - t *= math.pi - # we do not use torch.hann_window here as we need to evaluate the window - # at specific positions, not over a regular grid. - window = torch.cos(t / lowpass_filter_width / 2)**2 - kernel = torch.where(t == 0, torch.tensor(1.).to(t), torch.sin(t) / t) - kernel.mul_(window) - kernels.append(kernel) - - scale = base_freq / orig_freq - return torch.stack(kernels).view(new_freq, 1, -1).mul_(scale), width - - def resample_waveform(waveform: Tensor, orig_freq: float, new_freq: float, lowpass_filter_width: int = 6) -> Tensor: - r"""Resamples the waveform at the new frequency. This matches Kaldi's OfflineFeatureTpl ResampleWaveform - which uses a LinearResample (resample a signal at linearly spaced intervals to upsample/downsample - a signal). LinearResample (LR) means that the output signal is at linearly spaced intervals (i.e - the output signal has a frequency of ``new_freq``). It uses sinc/bandlimited interpolation to - upsample/downsample the signal. + r"""Resamples the waveform at the new frequency. - https://ccrma.stanford.edu/~jos/resample/Theory_Ideal_Bandlimited_Interpolation.html - https://github.com/kaldi-asr/kaldi/blob/master/src/feat/resample.h#L56 + This is a wrapper around ``torchaudio.functional.resample``. Args: - waveform (Tensor): The input signal of size (c, n) + waveform (Tensor): The input signal of size (..., time) orig_freq (float): The original frequency of the signal new_freq (float): The desired frequency lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper @@ -826,21 +770,4 @@ def resample_waveform(waveform: Tensor, Returns: Tensor: The waveform at the new frequency """ - assert waveform.dim() == 2 - assert orig_freq > 0.0 and new_freq > 0.0 - - orig_freq = int(orig_freq) - new_freq = int(new_freq) - gcd = math.gcd(orig_freq, new_freq) - orig_freq = orig_freq // gcd - new_freq = new_freq // gcd - - kernel, width = _get_sinc_resample_kernel(orig_freq, new_freq, lowpass_filter_width, - waveform.device, waveform.dtype) - - num_wavs, length = waveform.shape - waveform = F.pad(waveform, (width, width + orig_freq)) - resampled = F.conv1d(waveform[:, None], kernel, stride=orig_freq) - resampled = resampled.transpose(1, 2).reshape(num_wavs, -1) - target_length = int(math.ceil(new_freq * length / orig_freq)) - return resampled[..., :target_length] + return torchaudio.functional.resample(waveform, orig_freq, new_freq, lowpass_filter_width) diff --git a/torchaudio/datasets/yesno.py b/torchaudio/datasets/yesno.py index 6d0d2c0a5f..10b38a1260 100644 --- a/torchaudio/datasets/yesno.py +++ b/torchaudio/datasets/yesno.py @@ -16,7 +16,7 @@ "release1": { "folder_in_archive": "waves_yesno", "url": "http://www.openslr.org/resources/1/waves_yesno.tar.gz", - "checksum": "30301975fd8c5cac4040c261c0852f57cfa8adbbad2ce78e77e4986957445f27", + "checksum": "c3f49e0cca421f96b75b41640749167b52118f232498667ca7a5f9416aef8e73", } } @@ -54,7 +54,7 @@ def _parse_filesystem(self, root: str, url: str, folder_in_archive: str, downloa if not os.path.isdir(self._path): if not os.path.isfile(archive): checksum = _RELEASE_CONFIGS["release1"]["checksum"] - download_url(url, root, hash_value=checksum, hash_type="md5") + download_url(url, root, hash_value=checksum) extract_archive(archive) if not os.path.isdir(self._path): diff --git a/torchaudio/functional/__init__.py b/torchaudio/functional/__init__.py index 88fc4d545f..39f2fb8f53 100644 --- a/torchaudio/functional/__init__.py +++ b/torchaudio/functional/__init__.py @@ -19,6 +19,7 @@ spectrogram, spectral_centroid, apply_codec, + resample, ) from .filtering import ( allpass_biquad, @@ -85,5 +86,6 @@ 'riaa_biquad', 'treble_biquad', 'vad', - 'apply_codec' + 'apply_codec', + 'resample', ] diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index b114b9aacf..73496bf4cc 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -33,6 +33,7 @@ 'sliding_window_cmn', "spectral_centroid", "apply_codec", + "resample", ] @@ -1209,3 +1210,105 @@ def compute_kaldi_pitch( ) result = result.reshape(shape[:-1] + result.shape[-2:]) return result + + +def _get_sinc_resample_kernel(orig_freq: int, new_freq: int, lowpass_filter_width: int, + device: torch.device, dtype: torch.dtype): + assert lowpass_filter_width > 0 + kernels = [] + base_freq = min(orig_freq, new_freq) + # This will perform antialiasing filtering by removing the highest frequencies. + # At first I thought I only needed this when downsampling, but when upsampling + # you will get edge artifacts without this, as the edge is equivalent to zero padding, + # which will add high freq artifacts. + base_freq *= 0.99 + + # The key idea of the algorithm is that x(t) can be exactly reconstructed from x[i] (tensor) + # using the sinc interpolation formula: + # x(t) = sum_i x[i] sinc(pi * orig_freq * (i / orig_freq - t)) + # We can then sample the function x(t) with a different sample rate: + # y[j] = x(j / new_freq) + # or, + # y[j] = sum_i x[i] sinc(pi * orig_freq * (i / orig_freq - j / new_freq)) + + # We see here that y[j] is the convolution of x[i] with a specific filter, for which + # we take an FIR approximation, stopping when we see at least `lowpass_filter_width` zeros crossing. + # But y[j+1] is going to have a different set of weights and so on, until y[j + new_freq]. + # Indeed: + # y[j + new_freq] = sum_i x[i] sinc(pi * orig_freq * ((i / orig_freq - (j + new_freq) / new_freq)) + # = sum_i x[i] sinc(pi * orig_freq * ((i - orig_freq) / orig_freq - j / new_freq)) + # = sum_i x[i + orig_freq] sinc(pi * orig_freq * (i / orig_freq - j / new_freq)) + # so y[j+new_freq] uses the same filter as y[j], but on a shifted version of x by `orig_freq`. + # This will explain the F.conv1d after, with a stride of orig_freq. + width = math.ceil(lowpass_filter_width * orig_freq / base_freq) + # If orig_freq is still big after GCD reduction, most filters will be very unbalanced, i.e., + # they will have a lot of almost zero values to the left or to the right... + # There is probably a way to evaluate those filters more efficiently, but this is kept for + # future work. + idx = torch.arange(-width, width + orig_freq, device=device, dtype=dtype) + + for i in range(new_freq): + t = (-i / new_freq + idx / orig_freq) * base_freq + t = t.clamp_(-lowpass_filter_width, lowpass_filter_width) + t *= math.pi + # we do not use torch.hann_window here as we need to evaluate the window + # at specific positions, not over a regular grid. + window = torch.cos(t / lowpass_filter_width / 2)**2 + kernel = torch.where(t == 0, torch.tensor(1.).to(t), torch.sin(t) / t) + kernel.mul_(window) + kernels.append(kernel) + + scale = base_freq / orig_freq + return torch.stack(kernels).view(new_freq, 1, -1).mul_(scale), width + + +def resample( + waveform: Tensor, + orig_freq: float, + new_freq: float, + lowpass_filter_width: int = 6 +) -> Tensor: + r"""Resamples the waveform at the new frequency. This matches Kaldi's OfflineFeatureTpl ResampleWaveform + which uses a LinearResample (resample a signal at linearly spaced intervals to upsample/downsample + a signal). LinearResample (LR) means that the output signal is at linearly spaced intervals (i.e + the output signal has a frequency of ``new_freq``). It uses sinc/bandlimited interpolation to + upsample/downsample the signal. + + https://ccrma.stanford.edu/~jos/resample/Theory_Ideal_Bandlimited_Interpolation.html + https://github.com/kaldi-asr/kaldi/blob/master/src/feat/resample.h#L56 + + Args: + waveform (Tensor): The input signal of dimension (..., time) + orig_freq (float): The original frequency of the signal + new_freq (float): The desired frequency + lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper + but less efficient. We suggest around 4 to 10 for normal use. (Default: ``6``) + + Returns: + Tensor: The waveform at the new frequency of dimension (..., time). + """ + # pack batch + shape = waveform.size() + waveform = waveform.view(-1, shape[-1]) + + assert orig_freq > 0.0 and new_freq > 0.0 + + orig_freq = int(orig_freq) + new_freq = int(new_freq) + gcd = math.gcd(orig_freq, new_freq) + orig_freq = orig_freq // gcd + new_freq = new_freq // gcd + + kernel, width = _get_sinc_resample_kernel(orig_freq, new_freq, lowpass_filter_width, + waveform.device, waveform.dtype) + + num_wavs, length = waveform.shape + waveform = torch.nn.functional.pad(waveform, (width, width + orig_freq)) + resampled = torch.nn.functional.conv1d(waveform[:, None], kernel, stride=orig_freq) + resampled = resampled.transpose(1, 2).reshape(num_wavs, -1) + target_length = int(math.ceil(new_freq * length / orig_freq)) + resampled = resampled[..., :target_length] + + # unpack batch + resampled = resampled.view(shape[:-1] + resampled.shape[-1:]) + return resampled diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index d57b3eb35c..6f3ea1b6e1 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -6,7 +6,6 @@ import torch from torch import Tensor from torchaudio import functional as F -from torchaudio.compliance import kaldi __all__ = [ @@ -649,17 +648,7 @@ def forward(self, waveform: Tensor) -> Tensor: Tensor: Output signal of dimension (..., time). """ if self.resampling_method == 'sinc_interpolation': - - # pack batch - shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) - - waveform = kaldi.resample_waveform(waveform, self.orig_freq, self.new_freq) - - # unpack batch - waveform = waveform.view(shape[:-1] + waveform.shape[-1:]) - - return waveform + return F.resample(waveform, self.orig_freq, self.new_freq) raise ValueError('Invalid resampling method: {}'.format(self.resampling_method)) From 561f357d76aecf80b74639ce65ed6a65c1ebdbec Mon Sep 17 00:00:00 2001 From: Christian Puhrsch Date: Fri, 2 Apr 2021 10:47:25 -0700 Subject: [PATCH 36/73] fbshipit-source-id: 5ee15b601a9c5c08836e4a7198401d54aa50aa3f --- test/torchscript_bc_test/assets/.gitignore | 1 - 1 file changed, 1 deletion(-) delete mode 100644 test/torchscript_bc_test/assets/.gitignore diff --git a/test/torchscript_bc_test/assets/.gitignore b/test/torchscript_bc_test/assets/.gitignore deleted file mode 100644 index 72e8ffc0db..0000000000 --- a/test/torchscript_bc_test/assets/.gitignore +++ /dev/null @@ -1 +0,0 @@ -* From 5b7417bc6085d1677ea4b07dc11ccb2ec31289d0 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Thu, 15 Apr 2021 17:57:24 -0700 Subject: [PATCH 37/73] Import torchaudio #1420 ad534c1 Summary: Import torchaudio from github Reviewed By: carolineechen Differential Revision: D27770998 fbshipit-source-id: 0b4a4a143769ae49cc30478dd9f8e075594074e8 --- .circleci/regenerate.py | 3 +- .circleci/unittest/linux/scripts/run_test.sh | 4 +- .github/workflows/bandit.yml | 23 ++ .github/workflows/codeql.yml | 43 +++ CMakeLists.txt | 8 + README.md | 9 + build_tools/setup_helpers/extension.py | 2 + cmake/LoadHIP.cmake | 234 +++++++++++++++++ .../backend/soundfile/save_test.py | 2 + .../common_utils/__init__.py | 32 ++- .../common_utils/case_utils.py | 2 + .../common_utils/data_utils.py | 44 +++- .../common_utils/parameterized_utils.py | 54 +++- .../functional/autograd_impl.py | 184 +++++++++++-- .../functional/batch_consistency_test.py | 16 -- .../functional/functional_cpu_test.py | 20 +- .../functional/functional_cuda_test.py | 20 +- .../functional/functional_impl.py | 52 +++- .../librosa_compatibility_cpu_test.py | 10 + .../librosa_compatibility_cuda_test.py | 12 + .../functional/librosa_compatibility_test.py | 174 ------------- .../librosa_compatibility_test_impl.py | 161 ++++++++++++ .../torchscript_consistency_cpu_test.py | 14 +- .../torchscript_consistency_cuda_test.py | 16 +- .../torchscript_consistency_impl.py | 68 +++-- .../transforms/autograd_cpu_test.py | 6 + .../transforms/autograd_cuda_test.py | 10 + .../transforms/autograd_test_impl.py | 183 +++++++++++++ .../transforms/batch_consistency_test.py | 36 +-- .../librosa_compatibility_cpu_test.py | 9 + .../librosa_compatibility_cuda_test.py | 10 + .../transforms/librosa_compatibility_test.py | 245 ------------------ .../librosa_compatibility_test_impl.py | 141 ++++++++++ .../torchscript_consistency_cpu_test.py | 14 +- .../torchscript_consistency_cuda_test.py | 16 +- .../torchscript_consistency_impl.py | 66 ++++- .../transforms/transforms_cpu_test.py | 14 + .../transforms/transforms_cuda_test.py | 19 ++ .../transforms/transforms_test_impl.py | 61 +++++ torchaudio/datasets/utils.py | 3 +- torchaudio/functional/filtering.py | 184 ++++++++----- torchaudio/functional/functional.py | 183 +++++++++---- torchaudio/transforms.py | 40 +-- 43 files changed, 1747 insertions(+), 700 deletions(-) create mode 100644 .github/workflows/bandit.yml create mode 100644 .github/workflows/codeql.yml create mode 100644 cmake/LoadHIP.cmake create mode 100644 test/torchaudio_unittest/functional/librosa_compatibility_cpu_test.py create mode 100644 test/torchaudio_unittest/functional/librosa_compatibility_cuda_test.py delete mode 100644 test/torchaudio_unittest/functional/librosa_compatibility_test.py create mode 100644 test/torchaudio_unittest/functional/librosa_compatibility_test_impl.py create mode 100644 test/torchaudio_unittest/transforms/autograd_cpu_test.py create mode 100644 test/torchaudio_unittest/transforms/autograd_cuda_test.py create mode 100644 test/torchaudio_unittest/transforms/autograd_test_impl.py create mode 100644 test/torchaudio_unittest/transforms/librosa_compatibility_cpu_test.py create mode 100644 test/torchaudio_unittest/transforms/librosa_compatibility_cuda_test.py delete mode 100644 test/torchaudio_unittest/transforms/librosa_compatibility_test.py create mode 100644 test/torchaudio_unittest/transforms/librosa_compatibility_test_impl.py create mode 100644 test/torchaudio_unittest/transforms/transforms_cpu_test.py create mode 100644 test/torchaudio_unittest/transforms/transforms_cuda_test.py create mode 100644 test/torchaudio_unittest/transforms/transforms_test_impl.py diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index 0985c1f9f6..137fed87c4 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -15,6 +15,7 @@ """ import jinja2 +from jinja2 import select_autoescape import yaml import os.path @@ -213,7 +214,7 @@ def unittest_workflows(indentation=6): env = jinja2.Environment( loader=jinja2.FileSystemLoader(d), lstrip_blocks=True, - autoescape=False, + autoescape=select_autoescape(enabled_extensions=('html', 'xml')), ) with open(os.path.join(d, 'config.yml'), 'w') as f: diff --git a/.circleci/unittest/linux/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh index cbd96f79a8..e083743630 100755 --- a/.circleci/unittest/linux/scripts/run_test.sh +++ b/.circleci/unittest/linux/scripts/run_test.sh @@ -11,9 +11,7 @@ case "$(uname -s)" in esac python -m torch.utils.collect_env -if [ "${os}" == Linux ]; then - cat /proc/cpuinfo -fi + export TORCHAUDIO_TEST_FAIL_IF_NO_EXTENSION=1 export PATH="${PWD}/third_party/install/bin/:${PATH}" diff --git a/.github/workflows/bandit.yml b/.github/workflows/bandit.yml new file mode 100644 index 0000000000..93bae80f9b --- /dev/null +++ b/.github/workflows/bandit.yml @@ -0,0 +1,23 @@ +# GitHub Actions Bandit Workflow + +name: Bandit + +on: + pull_request: + branches: [ master ] + + workflow_dispatch: + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + + # Task will fail if any high-severity issues are found + # Ignoring submodules + - name: Run Bandit Security Analysis + run: | + python -m pip install bandit + python -m bandit -r . -x ./third_party -lll diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml new file mode 100644 index 0000000000..f0ea58f309 --- /dev/null +++ b/.github/workflows/codeql.yml @@ -0,0 +1,43 @@ +# GitHub Actions CodeQL Workflow + +name: CodeQL + +on: + pull_request: + branches: [ master ] + + workflow_dispatch: + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + + - name: Initialize CodeQL + uses: github/codeql-action/init@v1 + with: + languages: python, cpp + + - name: Install Ninja + run: | + sudo apt-get update -y + sudo apt-get install -y ninja-build + + - name: Update submodules + run: git submodule update --init --recursive + + - name: Install Torch + run: | + python -m pip install cmake + python -m pip install torch==1.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html + sudo ln -s /usr/bin/ninja /usr/bin/ninja-build + + - name: Build TorchAudio + run: BUILD_SOX=1 USE_CUDA=0 python setup.py develop --user + + # If any code scanning alerts are found, they will be under Security -> CodeQL + # Link: https://github.com/pytorch/audio/security/code-scanning + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v1 diff --git a/CMakeLists.txt b/CMakeLists.txt index 87abef1262..96360532f2 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -18,6 +18,14 @@ endif() project(torchaudio) +# Find the HIP package, set the HIP paths, load the HIP CMake. +if(USE_ROCM) + include(cmake/LoadHIP.cmake) + if(NOT PYTORCH_FOUND_HIP) + set(USE_ROCM OFF) + endif() +endif() + # check and set CMAKE_CXX_STANDARD string(FIND "${CMAKE_CXX_FLAGS}" "-std=c++" env_cxx_standard) if(env_cxx_standard GREATER -1) diff --git a/README.md b/README.md index 2cff4bf076..820301c041 100644 --- a/README.md +++ b/README.md @@ -93,6 +93,15 @@ BUILD_SOX=1 python setup.py install # OSX BUILD_SOX=1 MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install + +# Windows +# We need to use the MSVC x64 toolset for compilation, with Visual Studio's vcvarsall.bat or directly with vcvars64.bat. +# These batch files are under Visual Studio's installation folder, under 'VC\Auxiliary\Build\'. +# More information available at: +# https://docs.microsoft.com/en-us/cpp/build/how-to-enable-a-64-bit-visual-cpp-toolset-on-the-command-line?view=msvc-160#use-vcvarsallbat-to-set-a-64-bit-hosted-build-architecture +call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x64 && set BUILD_SOX=0 && python setup.py install +# or +call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvars64.bat" && set BUILD_SOX=0 && python setup.py install ``` This is known to work on linux and unix distributions such as Ubuntu and CentOS 7 and macOS. diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 4e04097716..430ff168c3 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -37,6 +37,7 @@ def _get_build(var, default=False): _BUILD_SOX = False if platform.system() == 'Windows' else _get_build("BUILD_SOX") _BUILD_KALDI = False if platform.system() == 'Windows' else _get_build("BUILD_KALDI", True) _BUILD_TRANSDUCER = _get_build("BUILD_TRANSDUCER") +_USE_ROCM = _get_build("USE_ROCM") def get_ext_modules(): @@ -74,6 +75,7 @@ def build_extension(self, ext): f"-DBUILD_TRANSDUCER:BOOL={'ON' if _BUILD_TRANSDUCER else 'OFF'}", "-DBUILD_TORCHAUDIO_PYTHON_EXTENSION:BOOL=ON", "-DBUILD_LIBTORCHAUDIO:BOOL=OFF", + f"-DUSE_ROCM:BOOL={'ON' if _USE_ROCM else 'OFF'}", ] build_args = [ '--target', 'install' diff --git a/cmake/LoadHIP.cmake b/cmake/LoadHIP.cmake new file mode 100644 index 0000000000..09ae6385cf --- /dev/null +++ b/cmake/LoadHIP.cmake @@ -0,0 +1,234 @@ +set(PYTORCH_FOUND_HIP FALSE) + +if(NOT DEFINED ENV{ROCM_PATH}) + set(ROCM_PATH /opt/rocm) +else() + set(ROCM_PATH $ENV{ROCM_PATH}) +endif() + +# HIP_PATH +if(NOT DEFINED ENV{HIP_PATH}) + set(HIP_PATH ${ROCM_PATH}/hip) +else() + set(HIP_PATH $ENV{HIP_PATH}) +endif() + +if(NOT EXISTS ${HIP_PATH}) + return() +endif() + +# HCC_PATH +if(NOT DEFINED ENV{HCC_PATH}) + set(HCC_PATH ${ROCM_PATH}/hcc) +else() + set(HCC_PATH $ENV{HCC_PATH}) +endif() + +# HSA_PATH +if(NOT DEFINED ENV{HSA_PATH}) + set(HSA_PATH ${ROCM_PATH}/hsa) +else() + set(HSA_PATH $ENV{HSA_PATH}) +endif() + +# ROCBLAS_PATH +if(NOT DEFINED ENV{ROCBLAS_PATH}) + set(ROCBLAS_PATH ${ROCM_PATH}/rocblas) +else() + set(ROCBLAS_PATH $ENV{ROCBLAS_PATH}) +endif() + +# ROCFFT_PATH +if(NOT DEFINED ENV{ROCFFT_PATH}) + set(ROCFFT_PATH ${ROCM_PATH}/rocfft) +else() + set(ROCFFT_PATH $ENV{ROCFFT_PATH}) +endif() + +# HIPFFT_PATH +if(NOT DEFINED ENV{HIPFFT_PATH}) + set(HIPFFT_PATH ${ROCM_PATH}/hipfft) +else() + set(HIPFFT_PATH $ENV{HIPFFT_PATH}) +endif() + +# HIPSPARSE_PATH +if(NOT DEFINED ENV{HIPSPARSE_PATH}) + set(HIPSPARSE_PATH ${ROCM_PATH}/hipsparse) +else() + set(HIPSPARSE_PATH $ENV{HIPSPARSE_PATH}) +endif() + +# THRUST_PATH +if(DEFINED ENV{THRUST_PATH}) + set(THRUST_PATH $ENV{THRUST_PATH}) +else() + set(THRUST_PATH ${ROCM_PATH}/include) +endif() + +# HIPRAND_PATH +if(NOT DEFINED ENV{HIPRAND_PATH}) + set(HIPRAND_PATH ${ROCM_PATH}/hiprand) +else() + set(HIPRAND_PATH $ENV{HIPRAND_PATH}) +endif() + +# ROCRAND_PATH +if(NOT DEFINED ENV{ROCRAND_PATH}) + set(ROCRAND_PATH ${ROCM_PATH}/rocrand) +else() + set(ROCRAND_PATH $ENV{ROCRAND_PATH}) +endif() + +# MIOPEN_PATH +if(NOT DEFINED ENV{MIOPEN_PATH}) + set(MIOPEN_PATH ${ROCM_PATH}/miopen) +else() + set(MIOPEN_PATH $ENV{MIOPEN_PATH}) +endif() + +# RCCL_PATH +if(NOT DEFINED ENV{RCCL_PATH}) + set(RCCL_PATH ${ROCM_PATH}/rccl) +else() + set(RCCL_PATH $ENV{RCCL_PATH}) +endif() + +# ROCPRIM_PATH +if(NOT DEFINED ENV{ROCPRIM_PATH}) + set(ROCPRIM_PATH ${ROCM_PATH}/rocprim) +else() + set(ROCPRIM_PATH $ENV{ROCPRIM_PATH}) +endif() + +# HIPCUB_PATH +if(NOT DEFINED ENV{HIPCUB_PATH}) + set(HIPCUB_PATH ${ROCM_PATH}/hipcub) +else() + set(HIPCUB_PATH $ENV{HIPCUB_PATH}) +endif() + +# ROCTHRUST_PATH +if(NOT DEFINED ENV{ROCTHRUST_PATH}) + set(ROCTHRUST_PATH ${ROCM_PATH}/rocthrust) +else() + set(ROCTHRUST_PATH $ENV{ROCTHRUST_PATH}) +endif() + +# ROCTRACER_PATH +if(NOT DEFINED ENV{ROCTRACER_PATH}) + set(ROCTRACER_PATH ${ROCM_PATH}/roctracer) +else() + set(ROCTRACER_PATH $ENV{ROCTRACER_PATH}) +endif() + +if(NOT DEFINED ENV{PYTORCH_ROCM_ARCH}) + set(PYTORCH_ROCM_ARCH gfx803;gfx900;gfx906;gfx908) +else() + set(PYTORCH_ROCM_ARCH $ENV{PYTORCH_ROCM_ARCH}) +endif() + +# Add HIP to the CMAKE Module Path +set(CMAKE_MODULE_PATH ${HIP_PATH}/cmake ${CMAKE_MODULE_PATH}) + +# Disable Asserts In Code (Can't use asserts on HIP stack.) +add_definitions(-DNDEBUG) + +macro(find_package_and_print_version PACKAGE_NAME) + find_package("${PACKAGE_NAME}" ${ARGN}) + message("${PACKAGE_NAME} VERSION: ${${PACKAGE_NAME}_VERSION}") +endmacro() + +# Find the HIP Package +find_package_and_print_version(HIP 1.0) + +if(HIP_FOUND) + set(PYTORCH_FOUND_HIP TRUE) + + # Find ROCM version for checks + file(READ "${ROCM_PATH}/.info/version-dev" ROCM_VERSION_DEV_RAW) + string(REGEX MATCH "^([0-9]+)\.([0-9]+)\.([0-9]+)-.*$" ROCM_VERSION_DEV_MATCH ${ROCM_VERSION_DEV_RAW}) + if(ROCM_VERSION_DEV_MATCH) + set(ROCM_VERSION_DEV_MAJOR ${CMAKE_MATCH_1}) + set(ROCM_VERSION_DEV_MINOR ${CMAKE_MATCH_2}) + set(ROCM_VERSION_DEV_PATCH ${CMAKE_MATCH_3}) + set(ROCM_VERSION_DEV "${ROCM_VERSION_DEV_MAJOR}.${ROCM_VERSION_DEV_MINOR}.${ROCM_VERSION_DEV_PATCH}") + endif() + message("\n***** ROCm version from ${ROCM_PATH}/.info/version-dev ****\n") + message("ROCM_VERSION_DEV: ${ROCM_VERSION_DEV}") + message("ROCM_VERSION_DEV_MAJOR: ${ROCM_VERSION_DEV_MAJOR}") + message("ROCM_VERSION_DEV_MINOR: ${ROCM_VERSION_DEV_MINOR}") + message("ROCM_VERSION_DEV_PATCH: ${ROCM_VERSION_DEV_PATCH}") + + message("\n***** Library versions from dpkg *****\n") + execute_process(COMMAND dpkg -l COMMAND grep rocm-dev COMMAND awk "{print $2 \" VERSION: \" $3}") + execute_process(COMMAND dpkg -l COMMAND grep rocm-libs COMMAND awk "{print $2 \" VERSION: \" $3}") + execute_process(COMMAND dpkg -l COMMAND grep hsakmt-roct COMMAND awk "{print $2 \" VERSION: \" $3}") + execute_process(COMMAND dpkg -l COMMAND grep rocr-dev COMMAND awk "{print $2 \" VERSION: \" $3}") + execute_process(COMMAND dpkg -l COMMAND grep -w hcc COMMAND awk "{print $2 \" VERSION: \" $3}") + execute_process(COMMAND dpkg -l COMMAND grep hip_base COMMAND awk "{print $2 \" VERSION: \" $3}") + execute_process(COMMAND dpkg -l COMMAND grep hip_hcc COMMAND awk "{print $2 \" VERSION: \" $3}") + + message("\n***** Library versions from cmake find_package *****\n") + + set(CMAKE_HCC_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) + set(CMAKE_HCC_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) + ### Remove setting of Flags when FindHIP.CMake PR #558 is accepted.### + + set(hip_DIR ${HIP_PATH}/lib/cmake/hip) + set(hsa-runtime64_DIR ${ROCM_PATH}/lib/cmake/hsa-runtime64) + set(AMDDeviceLibs_DIR ${ROCM_PATH}/lib/cmake/AMDDeviceLibs) + set(amd_comgr_DIR ${ROCM_PATH}/lib/cmake/amd_comgr) + set(rocrand_DIR ${ROCRAND_PATH}/lib/cmake/rocrand) + set(hiprand_DIR ${HIPRAND_PATH}/lib/cmake/hiprand) + set(rocblas_DIR ${ROCBLAS_PATH}/lib/cmake/rocblas) + set(miopen_DIR ${MIOPEN_PATH}/lib/cmake/miopen) + set(rocfft_DIR ${ROCFFT_PATH}/lib/cmake/rocfft) + set(hipfft_DIR ${HIPFFT_PATH}/lib/cmake/hipfft) + set(hipsparse_DIR ${HIPSPARSE_PATH}/lib/cmake/hipsparse) + set(rccl_DIR ${RCCL_PATH}/lib/cmake/rccl) + set(rocprim_DIR ${ROCPRIM_PATH}/lib/cmake/rocprim) + set(hipcub_DIR ${HIPCUB_PATH}/lib/cmake/hipcub) + set(rocthrust_DIR ${ROCTHRUST_PATH}/lib/cmake/rocthrust) + + find_package_and_print_version(hip REQUIRED) + find_package_and_print_version(hsa-runtime64 REQUIRED) + find_package_and_print_version(amd_comgr REQUIRED) + find_package_and_print_version(rocrand REQUIRED) + find_package_and_print_version(hiprand REQUIRED) + find_package_and_print_version(rocblas REQUIRED) + find_package_and_print_version(miopen REQUIRED) + find_package_and_print_version(rocfft REQUIRED) + if(ROCM_VERSION_DEV VERSION_GREATER_EQUAL "4.1.0") + find_package_and_print_version(hipfft REQUIRED) + endif() + find_package_and_print_version(hipsparse REQUIRED) + find_package_and_print_version(rccl) + find_package_and_print_version(rocprim REQUIRED) + find_package_and_print_version(hipcub REQUIRED) + find_package_and_print_version(rocthrust REQUIRED) + + if(HIP_COMPILER STREQUAL clang) + set(hip_library_name amdhip64) + else() + set(hip_library_name hip_hcc) + endif() + message("HIP library name: ${hip_library_name}") + + # TODO: hip_hcc has an interface include flag "-hc" which is only + # recognizable by hcc, but not gcc and clang. Right now in our + # setup, hcc is only used for linking, but it should be used to + # compile the *_hip.cc files as well. + find_library(PYTORCH_HIP_HCC_LIBRARIES ${hip_library_name} HINTS ${HIP_PATH}/lib) + # TODO: miopen_LIBRARIES should return fullpath to the library file, + # however currently it's just the lib name + find_library(PYTORCH_MIOPEN_LIBRARIES ${miopen_LIBRARIES} HINTS ${MIOPEN_PATH}/lib) + # TODO: rccl_LIBRARIES should return fullpath to the library file, + # however currently it's just the lib name + find_library(PYTORCH_RCCL_LIBRARIES ${rccl_LIBRARIES} HINTS ${RCCL_PATH}/lib) + # hiprtc is part of HIP + find_library(ROCM_HIPRTC_LIB ${hip_library_name} HINTS ${HIP_PATH}/lib) + # roctx is part of roctracer + find_library(ROCM_ROCTX_LIB roctx64 HINTS ${ROCTRACER_PATH}/lib) + set(roctracer_INCLUDE_DIRS ${ROCTRACER_PATH}/include) +endif() diff --git a/test/torchaudio_unittest/backend/soundfile/save_test.py b/test/torchaudio_unittest/backend/soundfile/save_test.py index e4e8c93631..c8c97436e5 100644 --- a/test/torchaudio_unittest/backend/soundfile/save_test.py +++ b/test/torchaudio_unittest/backend/soundfile/save_test.py @@ -11,6 +11,7 @@ get_wav_data, load_wav, nested_params, + skipIfRocm, ) from .common import ( fetch_wav_subtype, @@ -280,6 +281,7 @@ def test_fileobj_wav(self): self._test_fileobj('wav') @skipIfFormatNotSupported("FLAC") + @skipIfRocm def test_fileobj_flac(self): """Saving audio via file-like object works""" self._test_fileobj('flac') diff --git a/test/torchaudio_unittest/common_utils/__init__.py b/test/torchaudio_unittest/common_utils/__init__.py index 736ec82855..f7e48b878f 100644 --- a/test/torchaudio_unittest/common_utils/__init__.py +++ b/test/torchaudio_unittest/common_utils/__init__.py @@ -2,6 +2,7 @@ get_asset_path, get_whitenoise, get_sinusoid, + get_spectrogram, ) from .backend_utils import ( set_audio_backend, @@ -17,6 +18,7 @@ skipIfNoModule, skipIfNoKaldi, skipIfNoSox, + skipIfRocm, ) from .wav_utils import ( get_wav_data, @@ -29,8 +31,28 @@ nested_params ) -__all__ = ['get_asset_path', 'get_whitenoise', 'get_sinusoid', 'set_audio_backend', - 'TempDirMixin', 'HttpServerMixin', 'TestBaseMixin', 'PytorchTestCase', 'TorchaudioTestCase', - 'skipIfNoCuda', 'skipIfNoExec', 'skipIfNoModule', 'skipIfNoKaldi', 'skipIfNoSox', - 'skipIfNoSoxBackend', 'get_wav_data', 'normalize_wav', 'load_wav', 'save_wav', 'load_params', - 'nested_params'] +__all__ = [ + 'get_asset_path', + 'get_whitenoise', + 'get_sinusoid', + 'get_spectrogram', + 'set_audio_backend', + 'TempDirMixin', + 'HttpServerMixin', + 'TestBaseMixin', + 'PytorchTestCase', + 'TorchaudioTestCase', + 'skipIfNoCuda', + 'skipIfNoExec', + 'skipIfNoModule', + 'skipIfNoKaldi', + 'skipIfNoSox', + 'skipIfNoSoxBackend', + 'skipIfRocm', + 'get_wav_data', + 'normalize_wav', + 'load_wav', + 'save_wav', + 'load_params', + 'nested_params', +] diff --git a/test/torchaudio_unittest/common_utils/case_utils.py b/test/torchaudio_unittest/common_utils/case_utils.py index 69b9a795cd..7357cec4c4 100644 --- a/test/torchaudio_unittest/common_utils/case_utils.py +++ b/test/torchaudio_unittest/common_utils/case_utils.py @@ -98,3 +98,5 @@ def skipIfNoModule(module, display_name=None): skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') skipIfNoSox = unittest.skipIf(not is_sox_available(), reason='Sox not available') skipIfNoKaldi = unittest.skipIf(not is_kaldi_available(), reason='Kaldi not available') +skipIfRocm = unittest.skipIf(os.getenv('TORCHAUDIO_TEST_WITH_ROCM', '0') == '1', + reason="test doesn't currently work on the ROCm stack") diff --git a/test/torchaudio_unittest/common_utils/data_utils.py b/test/torchaudio_unittest/common_utils/data_utils.py index 9bbafe4f36..3facfbc6d7 100644 --- a/test/torchaudio_unittest/common_utils/data_utils.py +++ b/test/torchaudio_unittest/common_utils/data_utils.py @@ -1,5 +1,5 @@ import os.path -from typing import Union +from typing import Union, Optional import torch @@ -62,7 +62,7 @@ def get_whitenoise( """ if isinstance(dtype, str): dtype = getattr(torch, dtype) - if dtype not in [torch.float32, torch.int32, torch.int16, torch.uint8]: + if dtype not in [torch.float64, torch.float32, torch.int32, torch.int16, torch.uint8]: raise NotImplementedError(f'dtype {dtype} is not supported.') # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices, # so we only fork on CPU, generate values and move the data to the given device @@ -110,3 +110,43 @@ def get_sinusoid( if not channels_first: tensor = tensor.t() return convert_tensor_encoding(tensor, dtype) + + +def get_spectrogram( + waveform, + *, + n_fft: int = 2048, + hop_length: Optional[int] = None, + win_length: Optional[int] = None, + window: Optional[torch.Tensor] = None, + center: bool = True, + pad_mode: str = 'reflect', + power: Optional[float] = None, +): + """Generate a spectrogram of the given Tensor + + Args: + n_fft: The number of FFT bins. + hop_length: Stride for sliding window. default: ``n_fft // 4``. + win_length: The size of window frame and STFT filter. default: ``n_fft``. + winwdow: Window function. default: Hann window + center: Pad the input sequence if True. See ``torch.stft`` for the detail. + pad_mode: Padding method used when center is True. Default: "reflect". + power: If ``None``, raw spectrogram with complex values are returned, + otherwise the norm of the spectrogram is returned. + """ + hop_length = hop_length or n_fft // 4 + win_length = win_length or n_fft + window = torch.hann_window(win_length) if window is None else window + spec = torch.stft( + waveform, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + center=center, + window=window, + pad_mode=pad_mode, + return_complex=True) + if power is not None: + spec = spec.abs() ** power + return spec diff --git a/test/torchaudio_unittest/common_utils/parameterized_utils.py b/test/torchaudio_unittest/common_utils/parameterized_utils.py index 412ee82554..000dccaf12 100644 --- a/test/torchaudio_unittest/common_utils/parameterized_utils.py +++ b/test/torchaudio_unittest/common_utils/parameterized_utils.py @@ -11,17 +11,43 @@ def load_params(*paths): return [param(json.loads(line)) for line in file] -def nested_params(*params): - def _name_func(func, _, params): - strs = [] - for arg in params.args: - if isinstance(arg, tuple): - strs.append("_".join(str(a) for a in arg)) - else: - strs.append(str(arg)) - return f'{func.__name__}_{"_".join(strs)}' - - return parameterized.expand( - list(product(*params)), - name_func=_name_func - ) +def _name_func(func, _, params): + strs = [] + for arg in params.args: + if isinstance(arg, tuple): + strs.append("_".join(str(a) for a in arg)) + else: + strs.append(str(arg)) + # sanitize the test name + name = "_".join(strs).replace(".", "_") + return f'{func.__name__}_{name}' + + +def nested_params(*params_set): + """Generate the cartesian product of the given list of parameters. + + Args: + params_set (list of parameters): Parameters. When using ``parameterized.param`` class, + all the parameters have to be specified with the class, only using kwargs. + """ + flatten = [p for params in params_set for p in params] + + # Parameters to be nested are given as list of plain objects + if all(not isinstance(p, param) for p in flatten): + args = list(product(*params_set)) + return parameterized.expand(args, name_func=_name_func) + + # Parameters to be nested are given as list of `parameterized.param` + if not all(isinstance(p, param) for p in flatten): + raise TypeError( + "When using ``parameterized.param``, " + "all the parameters have to be of the ``param`` type.") + if any(p.args for p in flatten): + raise ValueError( + "When using ``parameterized.param``, " + "all the parameters have to be provided as keyword argument." + ) + args = [param()] + for params in params_set: + args = [param(**x.kwargs, **y.kwargs) for x in args for y in params] + return parameterized.expand(args) diff --git a/test/torchaudio_unittest/functional/autograd_impl.py b/test/torchaudio_unittest/functional/autograd_impl.py index 02477d0d33..e273369ea6 100644 --- a/test/torchaudio_unittest/functional/autograd_impl.py +++ b/test/torchaudio_unittest/functional/autograd_impl.py @@ -1,40 +1,174 @@ +from typing import Callable, Tuple import torch +from parameterized import parameterized +from torch import Tensor import torchaudio.functional as F from torch.autograd import gradcheck -from torchaudio_unittest import common_utils +from torchaudio_unittest.common_utils import ( + TestBaseMixin, + get_whitenoise, +) -class Autograd(common_utils.TestBaseMixin): - def test_x_grad(self): +class Autograd(TestBaseMixin): + def assert_grad( + self, + transform: Callable[..., Tensor], + inputs: Tuple[torch.Tensor], + *, + enable_all_grad: bool = True, + ): + inputs_ = [] + for i in inputs: + if torch.is_tensor(i): + i = i.to(dtype=self.dtype, device=self.device) + if enable_all_grad: + i.requires_grad = True + inputs_.append(i) + assert gradcheck(transform, inputs_) + + def test_lfilter_x(self): torch.random.manual_seed(2434) - x = torch.rand(2, 4, 256 * 2, dtype=self.dtype, device=self.device) - a = torch.tensor([0.7, 0.2, 0.6], dtype=self.dtype, device=self.device) - b = torch.tensor([0.4, 0.2, 0.9], dtype=self.dtype, device=self.device) + x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2) + a = torch.tensor([0.7, 0.2, 0.6]) + b = torch.tensor([0.4, 0.2, 0.9]) x.requires_grad = True - assert gradcheck(F.lfilter, (x, a, b), eps=1e-10) + self.assert_grad(F.lfilter, (x, a, b), enable_all_grad=False) - def test_a_grad(self): + def test_lfilter_a(self): torch.random.manual_seed(2434) - x = torch.rand(2, 4, 256 * 2, dtype=self.dtype, device=self.device) - a = torch.tensor([0.7, 0.2, 0.6], dtype=self.dtype, device=self.device) - b = torch.tensor([0.4, 0.2, 0.9], dtype=self.dtype, device=self.device) + x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2) + a = torch.tensor([0.7, 0.2, 0.6]) + b = torch.tensor([0.4, 0.2, 0.9]) a.requires_grad = True - assert gradcheck(F.lfilter, (x, a, b), eps=1e-10) + self.assert_grad(F.lfilter, (x, a, b), enable_all_grad=False) - def test_b_grad(self): + def test_lfilter_b(self): torch.random.manual_seed(2434) - x = torch.rand(2, 4, 256 * 2, dtype=self.dtype, device=self.device) - a = torch.tensor([0.7, 0.2, 0.6], dtype=self.dtype, device=self.device) - b = torch.tensor([0.4, 0.2, 0.9], dtype=self.dtype, device=self.device) + x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2) + a = torch.tensor([0.7, 0.2, 0.6]) + b = torch.tensor([0.4, 0.2, 0.9]) b.requires_grad = True - assert gradcheck(F.lfilter, (x, a, b), eps=1e-10) + self.assert_grad(F.lfilter, (x, a, b), enable_all_grad=False) - def test_all_grad(self): + def test_lfilter_all_inputs(self): torch.random.manual_seed(2434) - x = torch.rand(2, 4, 256 * 2, dtype=self.dtype, device=self.device) - a = torch.tensor([0.7, 0.2, 0.6], dtype=self.dtype, device=self.device) - b = torch.tensor([0.4, 0.2, 0.9], dtype=self.dtype, device=self.device) - b.requires_grad = True - a.requires_grad = True - x.requires_grad = True - assert gradcheck(F.lfilter, (x, a, b), eps=1e-10) + x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2) + a = torch.tensor([0.7, 0.2, 0.6]) + b = torch.tensor([0.4, 0.2, 0.9]) + self.assert_grad(F.lfilter, (x, a, b)) + + def test_biquad(self): + torch.random.manual_seed(2434) + x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=1) + a = torch.tensor([0.7, 0.2, 0.6]) + b = torch.tensor([0.4, 0.2, 0.9]) + self.assert_grad(F.biquad, (x, b[0], b[1], b[2], a[0], a[1], a[2])) + + @parameterized.expand([ + (800, 0.7, True), + (800, 0.7, False), + ]) + def test_band_biquad(self, central_freq, Q, noise): + torch.random.manual_seed(2434) + sr = 22050 + x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1) + central_freq = torch.tensor(central_freq) + Q = torch.tensor(Q) + self.assert_grad(F.band_biquad, (x, sr, central_freq, Q, noise)) + + @parameterized.expand([ + (800, 0.7, 10), + (800, 0.7, -10), + ]) + def test_bass_biquad(self, central_freq, Q, gain): + torch.random.manual_seed(2434) + sr = 22050 + x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1) + central_freq = torch.tensor(central_freq) + Q = torch.tensor(Q) + gain = torch.tensor(gain) + self.assert_grad(F.bass_biquad, (x, sr, gain, central_freq, Q)) + + @parameterized.expand([ + (3000, 0.7, 10), + (3000, 0.7, -10), + + ]) + def test_treble_biquad(self, central_freq, Q, gain): + torch.random.manual_seed(2434) + sr = 22050 + x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1) + central_freq = torch.tensor(central_freq) + Q = torch.tensor(Q) + gain = torch.tensor(gain) + self.assert_grad(F.treble_biquad, (x, sr, gain, central_freq, Q)) + + @parameterized.expand([ + (800, 0.7, ), + ]) + def test_allpass_biquad(self, central_freq, Q): + torch.random.manual_seed(2434) + sr = 22050 + x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1) + central_freq = torch.tensor(central_freq) + Q = torch.tensor(Q) + self.assert_grad(F.allpass_biquad, (x, sr, central_freq, Q)) + + @parameterized.expand([ + (800, 0.7, ), + ]) + def test_lowpass_biquad(self, cutoff_freq, Q): + torch.random.manual_seed(2434) + sr = 22050 + x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1) + cutoff_freq = torch.tensor(cutoff_freq) + Q = torch.tensor(Q) + self.assert_grad(F.lowpass_biquad, (x, sr, cutoff_freq, Q)) + + @parameterized.expand([ + (800, 0.7, ), + ]) + def test_highpass_biquad(self, cutoff_freq, Q): + torch.random.manual_seed(2434) + sr = 22050 + x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1) + cutoff_freq = torch.tensor(cutoff_freq) + Q = torch.tensor(Q) + self.assert_grad(F.highpass_biquad, (x, sr, cutoff_freq, Q)) + + @parameterized.expand([ + (800, 0.7, True), + (800, 0.7, False), + ]) + def test_bandpass_biquad(self, central_freq, Q, const_skirt_gain): + torch.random.manual_seed(2434) + sr = 22050 + x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1) + central_freq = torch.tensor(central_freq) + Q = torch.tensor(Q) + self.assert_grad(F.bandpass_biquad, (x, sr, central_freq, Q, const_skirt_gain)) + + @parameterized.expand([ + (800, 0.7, 10), + (800, 0.7, -10), + ]) + def test_equalizer_biquad(self, central_freq, Q, gain): + torch.random.manual_seed(2434) + sr = 22050 + x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1) + central_freq = torch.tensor(central_freq) + Q = torch.tensor(Q) + gain = torch.tensor(gain) + self.assert_grad(F.equalizer_biquad, (x, sr, central_freq, gain, Q)) + + @parameterized.expand([ + (800, 0.7, ), + ]) + def test_bandreject_biquad(self, central_freq, Q): + torch.random.manual_seed(2434) + sr = 22050 + x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1) + central_freq = torch.tensor(central_freq) + Q = torch.tensor(Q) + self.assert_grad(F.bandreject_biquad, (x, sr, central_freq, Q)) diff --git a/test/torchaudio_unittest/functional/batch_consistency_test.py b/test/torchaudio_unittest/functional/batch_consistency_test.py index 44a9580fe8..d9b49dfa61 100644 --- a/test/torchaudio_unittest/functional/batch_consistency_test.py +++ b/test/torchaudio_unittest/functional/batch_consistency_test.py @@ -197,22 +197,6 @@ def test_sliding_window_cmn(self, center, norm_vars): F.sliding_window_cmn, spectrogram, center=center, norm_vars=norm_vars) - def test_vad_from_file(self): - filepath = common_utils.get_asset_path("vad-go-stereo-44100.wav") - waveform, sample_rate = common_utils.load_wav(filepath) - # Each channel is slightly offset - we can use this to create a batch - # with different items. - batch = waveform.view(2, 1, -1) - self.assert_batch_consistency(F.vad, batch, sample_rate=sample_rate) - - def test_vad_different_items(self): - """Separate test to ensure VAD consistency with differing items.""" - sample_rate = 44100 - torch.manual_seed(0) - waveforms = torch.rand(self.batch_size, 2, 100) - 0.5 - self.assert_batch_consistency( - F.vad, waveforms, sample_rate=sample_rate) - @common_utils.skipIfNoKaldi def test_compute_kaldi_pitch(self): sample_rate = 44100 diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py index 7669d96744..b9c5a6f64e 100644 --- a/test/torchaudio_unittest/functional/functional_cpu_test.py +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -14,30 +14,32 @@ skipIfNoSox, ) -from .functional_impl import Lfilter, Spectrogram +from .functional_impl import Functional, FunctionalComplex -class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): +class TestFunctionalFloat32(Functional, common_utils.PytorchTestCase): dtype = torch.float32 device = torch.device('cpu') @unittest.expectedFailure - def test_9th_order_filter_stability(self): - super().test_9th_order_filter_stability() + def test_lfilter_9th_order_filter_stability(self): + super().test_lfilter_9th_order_filter_stability() -class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): +class TestFunctionalFloat64(Functional, common_utils.PytorchTestCase): dtype = torch.float64 device = torch.device('cpu') -class TestSpectrogramFloat32(Spectrogram, common_utils.PytorchTestCase): - dtype = torch.float32 +class TestFunctionalComplex64(FunctionalComplex, common_utils.PytorchTestCase): + complex_dtype = torch.complex64 + real_dtype = torch.float32 device = torch.device('cpu') -class TestSpectrogramFloat64(Spectrogram, common_utils.PytorchTestCase): - dtype = torch.float64 +class TestFunctionalComplex128(FunctionalComplex, common_utils.PytorchTestCase): + complex_dtype = torch.complex128 + real_dtype = torch.float64 device = torch.device('cpu') diff --git a/test/torchaudio_unittest/functional/functional_cuda_test.py b/test/torchaudio_unittest/functional/functional_cuda_test.py index f4db6ca3a0..ebf8cd8326 100644 --- a/test/torchaudio_unittest/functional/functional_cuda_test.py +++ b/test/torchaudio_unittest/functional/functional_cuda_test.py @@ -2,32 +2,34 @@ import unittest from torchaudio_unittest import common_utils -from .functional_impl import Lfilter, Spectrogram +from .functional_impl import Functional, FunctionalComplex @common_utils.skipIfNoCuda -class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): +class TestFunctionalloat32(Functional, common_utils.PytorchTestCase): dtype = torch.float32 device = torch.device('cuda') @unittest.expectedFailure - def test_9th_order_filter_stability(self): - super().test_9th_order_filter_stability() + def test_lfilter_9th_order_filter_stability(self): + super().test_lfilter_9th_order_filter_stability() @common_utils.skipIfNoCuda -class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): +class TestLFilterFloat64(Functional, common_utils.PytorchTestCase): dtype = torch.float64 device = torch.device('cuda') @common_utils.skipIfNoCuda -class TestSpectrogramFloat32(Spectrogram, common_utils.PytorchTestCase): - dtype = torch.float32 +class TestFunctionalComplex64(FunctionalComplex, common_utils.PytorchTestCase): + complex_dtype = torch.complex64 + real_dtype = torch.float32 device = torch.device('cuda') @common_utils.skipIfNoCuda -class TestSpectrogramFloat64(Spectrogram, common_utils.PytorchTestCase): - dtype = torch.float64 +class TestFunctionalComplex128(FunctionalComplex, common_utils.PytorchTestCase): + complex_dtype = torch.complex64 + real_dtype = torch.float32 device = torch.device('cuda') diff --git a/test/torchaudio_unittest/functional/functional_impl.py b/test/torchaudio_unittest/functional/functional_impl.py index f7f8c17ecb..8b890e9563 100644 --- a/test/torchaudio_unittest/functional/functional_impl.py +++ b/test/torchaudio_unittest/functional/functional_impl.py @@ -2,13 +2,15 @@ import torch import torchaudio.functional as F from parameterized import parameterized +import numpy as np from scipy import signal from torchaudio_unittest import common_utils +from torchaudio_unittest.common_utils import nested_params -class Lfilter(common_utils.TestBaseMixin): - def test_simple(self): +class Functional(common_utils.TestBaseMixin): + def test_lfilter_simple(self): """ Create a very basic signal, Then make a simple 4th order delay @@ -23,7 +25,7 @@ def test_simple(self): self.assertEqual(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) - def test_clamp(self): + def test_lfilter_clamp(self): input_signal = torch.ones(1, 44100 * 1, dtype=self.dtype, device=self.device) b_coeffs = torch.tensor([1, 0], dtype=self.dtype, device=self.device) a_coeffs = torch.tensor([1, -0.95], dtype=self.dtype, device=self.device) @@ -38,7 +40,7 @@ def test_clamp(self): ((2, 3, 44100),), ((1, 2, 3, 44100),) ]) - def test_shape(self, shape): + def test_lfilter_shape(self, shape): torch.random.manual_seed(42) waveform = torch.rand(*shape, dtype=self.dtype, device=self.device) b_coeffs = torch.tensor([0, 0, 0, 1], dtype=self.dtype, device=self.device) @@ -46,7 +48,7 @@ def test_shape(self, shape): output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) assert shape == waveform.size() == output_waveform.size() - def test_9th_order_filter_stability(self): + def test_lfilter_9th_order_filter_stability(self): """ Validate the precision of lfilter against reference scipy implementation when using high order filter. The reference implementation use cascaded second-order filters so is more numerically accurate. @@ -68,10 +70,8 @@ def test_9th_order_filter_stability(self): yhat = F.lfilter(x, a, b, False) self.assertEqual(yhat, y, atol=1e-4, rtol=1e-5) - -class Spectrogram(common_utils.TestBaseMixin): @parameterized.expand([(0., ), (1., ), (2., ), (3., )]) - def test_grad_at_zero(self, power): + def test_spectogram_grad_at_zero(self, power): """The gradient of power spectrogram should not be nan but zero near x=0 https://github.com/pytorch/audio/issues/993 @@ -89,3 +89,39 @@ def test_grad_at_zero(self, power): ) spec.sum().backward() assert not x.grad.isnan().sum() + + +class FunctionalComplex(common_utils.TestBaseMixin): + complex_dtype = None + real_dtype = None + device = None + + @nested_params( + [0.5, 1.01, 1.3], + [True, False], + ) + def test_phase_vocoder_shape(self, rate, test_pseudo_complex): + """Verify the output shape of phase vocoder""" + hop_length = 256 + num_freq = 1025 + num_frames = 400 + batch_size = 2 + + torch.random.manual_seed(42) + spec = torch.randn( + batch_size, num_freq, num_frames, dtype=self.complex_dtype, device=self.device) + if test_pseudo_complex: + spec = torch.view_as_real(spec) + + phase_advance = torch.linspace( + 0, + np.pi * hop_length, + num_freq, + dtype=self.real_dtype, device=self.device)[..., None] + + spec_stretch = F.phase_vocoder(spec, rate=rate, phase_advance=phase_advance) + + assert spec.dim() == spec_stretch.dim() + expected_shape = torch.Size([batch_size, num_freq, int(np.ceil(num_frames / rate))]) + output_shape = (torch.view_as_complex(spec_stretch) if test_pseudo_complex else spec_stretch).shape + assert output_shape == expected_shape diff --git a/test/torchaudio_unittest/functional/librosa_compatibility_cpu_test.py b/test/torchaudio_unittest/functional/librosa_compatibility_cpu_test.py new file mode 100644 index 0000000000..dcfe6fcf4d --- /dev/null +++ b/test/torchaudio_unittest/functional/librosa_compatibility_cpu_test.py @@ -0,0 +1,10 @@ +from torchaudio_unittest.common_utils import PytorchTestCase +from .librosa_compatibility_test_impl import Functional, FunctionalComplex + + +class TestFunctionalCPU(Functional, PytorchTestCase): + device = 'cpu' + + +class TestFunctionalComplexCPU(FunctionalComplex, PytorchTestCase): + device = 'cpu' diff --git a/test/torchaudio_unittest/functional/librosa_compatibility_cuda_test.py b/test/torchaudio_unittest/functional/librosa_compatibility_cuda_test.py new file mode 100644 index 0000000000..9f42b4dc84 --- /dev/null +++ b/test/torchaudio_unittest/functional/librosa_compatibility_cuda_test.py @@ -0,0 +1,12 @@ +from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda +from .librosa_compatibility_test_impl import Functional, FunctionalComplex + + +@skipIfNoCuda +class TestFunctionalCUDA(Functional, PytorchTestCase): + device = 'cuda' + + +@skipIfNoCuda +class TestFunctionalComplexCUDA(FunctionalComplex, PytorchTestCase): + device = 'cuda' diff --git a/test/torchaudio_unittest/functional/librosa_compatibility_test.py b/test/torchaudio_unittest/functional/librosa_compatibility_test.py deleted file mode 100644 index 5905c239d3..0000000000 --- a/test/torchaudio_unittest/functional/librosa_compatibility_test.py +++ /dev/null @@ -1,174 +0,0 @@ -import itertools -import unittest -from distutils.version import StrictVersion - -import torch -from parameterized import parameterized, param - -import torchaudio.functional as F -from torchaudio._internal.module_utils import is_module_available - -LIBROSA_AVAILABLE = is_module_available('librosa') - -if LIBROSA_AVAILABLE: - import numpy as np - import librosa - -from torchaudio_unittest import common_utils - - -@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") -class TestFunctional(common_utils.TorchaudioTestCase): - """Test suite for functions in `functional` module.""" - def test_griffinlim(self): - # NOTE: This test is flaky without a fixed random seed - # See https://github.com/pytorch/audio/issues/382 - torch.random.manual_seed(42) - tensor = torch.rand((1, 1000)) - - n_fft = 400 - ws = 400 - hop = 100 - window = torch.hann_window(ws) - normalize = False - momentum = 0.99 - n_iter = 8 - length = 1000 - rand_init = False - init = 'random' if rand_init else None - - specgram = F.spectrogram(tensor, 0, window, n_fft, hop, ws, 2, normalize).sqrt() - ta_out = F.griffinlim(specgram, window, n_fft, hop, ws, 1, - n_iter, momentum, length, rand_init) - lr_out = librosa.griffinlim(specgram.squeeze(0).numpy(), n_iter=n_iter, hop_length=hop, - momentum=momentum, init=init, length=length) - lr_out = torch.from_numpy(lr_out).unsqueeze(0) - - self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) - - @parameterized.expand([ - param(norm=norm, mel_scale=mel_scale, **p.kwargs) - for p in [ - param(), - param(n_mels=128, sample_rate=44100), - param(n_mels=128, fmin=2000.0, fmax=5000.0), - param(n_mels=56, fmin=100.0, fmax=9000.0), - param(n_mels=56, fmin=800.0, fmax=900.0), - param(n_mels=56, fmin=1900.0, fmax=900.0), - param(n_mels=10, fmin=1900.0, fmax=900.0), - ] - for norm in [None, 'slaney'] - for mel_scale in ['htk', 'slaney'] - ]) - def test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, - fmin=0.0, fmax=8000.0, norm=None, mel_scale="htk"): - if (norm == "slaney" and StrictVersion(librosa.__version__) < StrictVersion("0.7.2")): - self.skipTest('Test is known to fail with older versions of librosa.') - - librosa_fb = librosa.filters.mel(sr=sample_rate, - n_fft=n_fft, - n_mels=n_mels, - fmax=fmax, - fmin=fmin, - htk=mel_scale == "htk", - norm=norm) - fb = F.create_fb_matrix(sample_rate=sample_rate, - n_mels=n_mels, - f_max=fmax, - f_min=fmin, - n_freqs=(n_fft // 2 + 1), - norm=norm, - mel_scale=mel_scale) - - for i_mel_bank in range(n_mels): - self.assertEqual( - fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4, rtol=1e-5) - - def test_amplitude_to_DB(self): - spec = torch.rand((6, 201)) - - amin = 1e-10 - db_multiplier = 0.0 - top_db = 80.0 - - # Power to DB - multiplier = 10.0 - - ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) - lr_out = librosa.core.power_to_db(spec.numpy()) - lr_out = torch.from_numpy(lr_out) - - self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) - - # Amplitude to DB - multiplier = 20.0 - - ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) - lr_out = librosa.core.amplitude_to_db(spec.numpy()) - lr_out = torch.from_numpy(lr_out) - - self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) - - def test_resample(self): - input_path = common_utils.get_asset_path('sinewave.wav') - waveform, sample_rate = common_utils.load_wav(input_path) - - upsample_rate = sample_rate * 2 - downsample_rate = sample_rate // 2 - - ta_upsampled = F.resample(waveform, sample_rate, upsample_rate) - lr_upsampled = librosa.resample(waveform.squeeze(0).numpy(), sample_rate, upsample_rate) - lr_upsampled = torch.from_numpy(lr_upsampled).unsqueeze(0) - - self.assertEqual(ta_upsampled, lr_upsampled, atol=1e-2, rtol=1e-5) - - ta_downsampled = F.resample(waveform, sample_rate, downsample_rate) - lr_downsampled = librosa.resample(waveform.squeeze(0).numpy(), sample_rate, downsample_rate) - lr_downsampled = torch.from_numpy(lr_downsampled).unsqueeze(0) - - self.assertEqual(ta_downsampled, lr_downsampled, atol=1e-2, rtol=1e-5) - - -@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") -class TestPhaseVocoder(common_utils.TorchaudioTestCase): - @parameterized.expand(list(itertools.product( - [(2, 1025, 400, 2)], - [0.5, 1.01, 1.3], - [256] - ))) - def test_phase_vocoder(self, shape, rate, hop_length): - # Due to cummulative sum, numerical error in using torch.float32 will - # result in bottom right values of the stretched sectrogram to not - # match with librosa. - torch.random.manual_seed(42) - complex_specgrams = torch.randn(*shape) - complex_specgrams = complex_specgrams.type(torch.float64) - phase_advance = torch.linspace( - 0, - np.pi * hop_length, - complex_specgrams.shape[-3], - dtype=torch.float64)[..., None] - - complex_specgrams_stretch = F.phase_vocoder(complex_specgrams, rate=rate, phase_advance=phase_advance) - - # == Test shape - expected_size = list(complex_specgrams.size()) - expected_size[-2] = int(np.ceil(expected_size[-2] / rate)) - - assert complex_specgrams.dim() == complex_specgrams_stretch.dim() - assert complex_specgrams_stretch.size() == torch.Size(expected_size) - - # == Test values - index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3 - mono_complex_specgram = complex_specgrams[index].numpy() - mono_complex_specgram = mono_complex_specgram[..., 0] + \ - mono_complex_specgram[..., 1] * 1j - expected_complex_stretch = librosa.phase_vocoder( - mono_complex_specgram, - rate=rate, - hop_length=hop_length) - - complex_stretch = complex_specgrams_stretch[index].numpy() - complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1] - - self.assertEqual(complex_stretch, torch.from_numpy(expected_complex_stretch), atol=1e-5, rtol=1e-5) diff --git a/test/torchaudio_unittest/functional/librosa_compatibility_test_impl.py b/test/torchaudio_unittest/functional/librosa_compatibility_test_impl.py new file mode 100644 index 0000000000..f0ac4cdd75 --- /dev/null +++ b/test/torchaudio_unittest/functional/librosa_compatibility_test_impl.py @@ -0,0 +1,161 @@ +import unittest +from distutils.version import StrictVersion + +import torch +from parameterized import param + +import torchaudio.functional as F +from torchaudio._internal.module_utils import is_module_available + +LIBROSA_AVAILABLE = is_module_available('librosa') + +if LIBROSA_AVAILABLE: + import numpy as np + import librosa + + +from torchaudio_unittest.common_utils import ( + TestBaseMixin, + nested_params, + get_whitenoise, + get_spectrogram, +) + + +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") +class Functional(TestBaseMixin): + """Test suite for functions in `functional` module.""" + dtype = torch.float64 + + @nested_params([0, 0.99]) + def test_griffinlim(self, momentum): + # FFT params + n_fft = 400 + win_length = n_fft + hop_length = n_fft // 4 + window = torch.hann_window(win_length) + power = 1 + # GriffinLim params + n_iter = 8 + + waveform = get_whitenoise(device=self.device, dtype=self.dtype) + specgram = get_spectrogram( + waveform, n_fft=n_fft, hop_length=hop_length, power=power, + win_length=win_length, window=window) + + result = F.griffinlim( + specgram, + window=window, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + power=power, + n_iter=n_iter, + momentum=momentum, + length=waveform.size(1), + rand_init=False) + expected = librosa.griffinlim( + specgram[0].cpu().numpy(), + n_iter=n_iter, + hop_length=hop_length, + momentum=momentum, + init=None, + length=waveform.size(1))[None, ...] + self.assertEqual(result, torch.from_numpy(expected), atol=5e-5, rtol=1e-07) + + @nested_params( + [ + param(), + param(n_mels=128, sample_rate=44100), + param(n_mels=128, fmin=2000.0, fmax=5000.0), + param(n_mels=56, fmin=100.0, fmax=9000.0), + param(n_mels=56, fmin=800.0, fmax=900.0), + param(n_mels=56, fmin=1900.0, fmax=900.0), + param(n_mels=10, fmin=1900.0, fmax=900.0), + ], + [param(norm=n) for n in [None, 'slaney']], + [param(mel_scale=s) for s in ['htk', 'slaney']], + ) + def test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, + fmin=0.0, fmax=8000.0, norm=None, mel_scale="htk"): + if (norm == "slaney" and StrictVersion(librosa.__version__) < StrictVersion("0.7.2")): + self.skipTest('Test is known to fail with older versions of librosa.') + if self.device != 'cpu': + self.skipTest('No need to run this test on CUDA') + + expected = librosa.filters.mel( + sr=sample_rate, + n_fft=n_fft, + n_mels=n_mels, + fmax=fmax, + fmin=fmin, + htk=mel_scale == "htk", + norm=norm).T + result = F.create_fb_matrix( + sample_rate=sample_rate, + n_mels=n_mels, + f_max=fmax, + f_min=fmin, + n_freqs=(n_fft // 2 + 1), + norm=norm, + mel_scale=mel_scale) + self.assertEqual(result, torch.from_numpy(expected), atol=7e-5, rtol=1.3e-6) + + def test_amplitude_to_DB_power(self): + amin = 1e-10 + db_multiplier = 0.0 + top_db = 80.0 + multiplier = 10.0 + + spec = get_spectrogram(get_whitenoise(device=self.device, dtype=self.dtype), power=2) + result = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) + expected = librosa.core.power_to_db(spec[0].cpu().numpy())[None, ...] + self.assertEqual(result, torch.from_numpy(expected)) + + def test_amplitude_to_DB(self): + amin = 1e-10 + db_multiplier = 0.0 + top_db = 80.0 + multiplier = 20.0 + + spec = get_spectrogram(get_whitenoise(device=self.device, dtype=self.dtype), power=1) + result = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) + expected = librosa.core.amplitude_to_db(spec[0].cpu().numpy())[None, ...] + self.assertEqual(result, torch.from_numpy(expected)) + + +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") +class FunctionalComplex(TestBaseMixin): + @nested_params( + [0.5, 1.01, 1.3], + [True, False], + ) + def test_phase_vocoder(self, rate, test_pseudo_complex): + hop_length = 256 + num_freq = 1025 + num_frames = 400 + torch.random.manual_seed(42) + + # Due to cummulative sum, numerical error in using torch.float32 will + # result in bottom right values of the stretched sectrogram to not + # match with librosa. + spec = torch.randn(num_freq, num_frames, device=self.device, dtype=torch.complex128) + phase_advance = torch.linspace( + 0, + np.pi * hop_length, + num_freq, + device=self.device, + dtype=torch.float64)[..., None] + + stretched = F.phase_vocoder( + torch.view_as_real(spec) if test_pseudo_complex else spec, + rate=rate, phase_advance=phase_advance) + + expected_stretched = librosa.phase_vocoder( + spec.cpu().numpy(), + rate=rate, + hop_length=hop_length) + + self.assertEqual( + torch.view_as_complex(stretched) if test_pseudo_complex else stretched, + torch.from_numpy(expected_stretched)) diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py b/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py index 06871f9bde..d9d34eff76 100644 --- a/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py +++ b/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py @@ -1,7 +1,7 @@ import torch from torchaudio_unittest.common_utils import PytorchTestCase -from .torchscript_consistency_impl import Functional +from .torchscript_consistency_impl import Functional, FunctionalComplex class TestFunctionalFloat32(Functional, PytorchTestCase): @@ -12,3 +12,15 @@ class TestFunctionalFloat32(Functional, PytorchTestCase): class TestFunctionalFloat64(Functional, PytorchTestCase): dtype = torch.float64 device = torch.device('cpu') + + +class TestFunctionalComplex64(FunctionalComplex, PytorchTestCase): + complex_dtype = torch.complex64 + real_dtype = torch.float32 + device = torch.device('cpu') + + +class TestFunctionalComplex128(FunctionalComplex, PytorchTestCase): + complex_dtype = torch.complex128 + real_dtype = torch.float64 + device = torch.device('cpu') diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py b/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py index 53ccbad3c9..026e09abfa 100644 --- a/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py +++ b/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py @@ -1,7 +1,7 @@ import torch from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase -from .torchscript_consistency_impl import Functional +from .torchscript_consistency_impl import Functional, FunctionalComplex @skipIfNoCuda @@ -14,3 +14,17 @@ class TestFunctionalFloat32(Functional, PytorchTestCase): class TestFunctionalFloat64(Functional, PytorchTestCase): dtype = torch.float64 device = torch.device('cuda') + + +@skipIfNoCuda +class TestFunctionalComplex64(FunctionalComplex, PytorchTestCase): + complex_dtype = torch.complex64 + real_dtype = torch.float32 + device = torch.device('cuda') + + +@skipIfNoCuda +class TestFunctionalComplex128(FunctionalComplex, PytorchTestCase): + complex_dtype = torch.complex128 + real_dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py index b6aec04d9c..9c34846b9e 100644 --- a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py @@ -3,16 +3,24 @@ import torch import torchaudio.functional as F +from parameterized import parameterized from torchaudio_unittest import common_utils +from torchaudio_unittest.common_utils import TempDirMixin, TestBaseMixin +from torchaudio_unittest.common_utils import ( + skipIfRocm, +) -class Functional(common_utils.TestBaseMixin): +class Functional(TempDirMixin, TestBaseMixin): """Implements test for `functinoal` modul that are performed for different devices""" def _assert_consistency(self, func, tensor, shape_only=False): tensor = tensor.to(device=self.device, dtype=self.dtype) - ts_func = torch.jit.script(func) + path = self.get_temp_path('func.zip') + torch.jit.script(func).save(path) + ts_func = torch.jit.load(path) + output = func(tensor) ts_output = ts_func(tensor) if shape_only: @@ -34,6 +42,7 @@ def func(tensor): tensor = common_utils.get_whitenoise() self._assert_consistency(func, tensor) + @skipIfRocm def test_griffinlim(self): def func(tensor): n_fft = 400 @@ -547,21 +556,6 @@ def func(tensor): tensor = common_utils.get_whitenoise(sample_rate=44100) self._assert_consistency(func, tensor) - def test_phase_vocoder(self): - def func(tensor, device: torch.device = self.device): - rate = 0.5 - hop_length = 256 - phase_advance = torch.linspace( - 0, - 3.14 * hop_length, - tensor.shape[-3], - dtype=torch.float64, - ).to(device)[..., None] - return F.phase_vocoder(tensor, rate, phase_advance) - - tensor = torch.randn(2, 1025, 400, 2) - self._assert_consistency(func, tensor) - @common_utils.skipIfNoKaldi def test_compute_kaldi_pitch(self): if self.dtype != torch.float32 or self.device != torch.device('cpu'): @@ -573,3 +567,43 @@ def func(tensor): tensor = common_utils.get_whitenoise(sample_rate=44100) self._assert_consistency(func, tensor) + + +class FunctionalComplex(TempDirMixin, TestBaseMixin): + complex_dtype = None + real_dtype = None + device = None + + def _assert_consistency(self, func, tensor, test_pseudo_complex=False): + assert tensor.is_complex() + tensor = tensor.to(device=self.device, dtype=self.complex_dtype) + + path = self.get_temp_path('func.zip') + torch.jit.script(func).save(path) + ts_func = torch.jit.load(path) + + if test_pseudo_complex: + tensor = torch.view_as_real(tensor) + output = func(tensor) + ts_output = ts_func(tensor) + self.assertEqual(ts_output, output) + + @parameterized.expand([(True, ), (False, )]) + def test_phase_vocoder(self, test_paseudo_complex): + def func(tensor): + is_complex = tensor.is_complex() + + n_freq = tensor.size(-2 if is_complex else -3) + rate = 0.5 + hop_length = 256 + phase_advance = torch.linspace( + 0, + 3.14 * hop_length, + n_freq, + dtype=(torch.real(tensor) if is_complex else tensor).dtype, + device=tensor.device, + )[..., None] + return F.phase_vocoder(tensor, rate, phase_advance) + + tensor = torch.view_as_complex(torch.randn(2, 1025, 400, 2)) + self._assert_consistency(func, tensor, test_paseudo_complex) diff --git a/test/torchaudio_unittest/transforms/autograd_cpu_test.py b/test/torchaudio_unittest/transforms/autograd_cpu_test.py new file mode 100644 index 0000000000..e4abfb237d --- /dev/null +++ b/test/torchaudio_unittest/transforms/autograd_cpu_test.py @@ -0,0 +1,6 @@ +from torchaudio_unittest.common_utils import PytorchTestCase +from .autograd_test_impl import AutogradTestMixin + + +class AutogradCPUTest(AutogradTestMixin, PytorchTestCase): + device = 'cpu' diff --git a/test/torchaudio_unittest/transforms/autograd_cuda_test.py b/test/torchaudio_unittest/transforms/autograd_cuda_test.py new file mode 100644 index 0000000000..ecccb9d897 --- /dev/null +++ b/test/torchaudio_unittest/transforms/autograd_cuda_test.py @@ -0,0 +1,10 @@ +from torchaudio_unittest.common_utils import ( + PytorchTestCase, + skipIfNoCuda, +) +from .autograd_test_impl import AutogradTestMixin + + +@skipIfNoCuda +class AutogradCUDATest(AutogradTestMixin, PytorchTestCase): + device = 'cuda' diff --git a/test/torchaudio_unittest/transforms/autograd_test_impl.py b/test/torchaudio_unittest/transforms/autograd_test_impl.py new file mode 100644 index 0000000000..438f623855 --- /dev/null +++ b/test/torchaudio_unittest/transforms/autograd_test_impl.py @@ -0,0 +1,183 @@ +from typing import List +import unittest + +from parameterized import parameterized +import torch +from torch.autograd import gradcheck, gradgradcheck +import torchaudio.transforms as T + +from torchaudio_unittest.common_utils import ( + TestBaseMixin, + get_whitenoise, + get_spectrogram, + nested_params, +) + + +class _DeterministicWrapper(torch.nn.Module): + """Helper transform wrapper to make the given transform deterministic""" + def __init__(self, transform, seed=0): + super().__init__() + self.seed = seed + self.transform = transform + + def forward(self, input: torch.Tensor): + torch.random.manual_seed(self.seed) + return self.transform(input) + + +class AutogradTestMixin(TestBaseMixin): + def assert_grad( + self, + transform: torch.nn.Module, + inputs: List[torch.Tensor], + *, + nondet_tol: float = 0.0, + ): + transform = transform.to(dtype=torch.float64, device=self.device) + + # gradcheck and gradgradcheck only pass if the input tensors are of dtype `torch.double` or + # `torch.cdouble`, when the default eps and tolerance values are used. + inputs_ = [] + for i in inputs: + if torch.is_tensor(i): + i = i.to( + dtype=torch.cdouble if i.is_complex() else torch.double, + device=self.device) + i.requires_grad = True + inputs_.append(i) + assert gradcheck(transform, inputs_) + assert gradgradcheck(transform, inputs_, nondet_tol=nondet_tol) + + @parameterized.expand([ + ({'pad': 0, 'normalized': False, 'power': None, 'return_complex': True}, ), + ({'pad': 3, 'normalized': False, 'power': None, 'return_complex': True}, ), + ({'pad': 0, 'normalized': True, 'power': None, 'return_complex': True}, ), + ({'pad': 3, 'normalized': True, 'power': None, 'return_complex': True}, ), + ({'pad': 0, 'normalized': False, 'power': None}, ), + ({'pad': 3, 'normalized': False, 'power': None}, ), + ({'pad': 0, 'normalized': True, 'power': None}, ), + ({'pad': 3, 'normalized': True, 'power': None}, ), + ({'pad': 0, 'normalized': False, 'power': 1.0}, ), + ({'pad': 3, 'normalized': False, 'power': 1.0}, ), + ({'pad': 0, 'normalized': True, 'power': 1.0}, ), + ({'pad': 3, 'normalized': True, 'power': 1.0}, ), + ({'pad': 0, 'normalized': False, 'power': 2.0}, ), + ({'pad': 3, 'normalized': False, 'power': 2.0}, ), + ({'pad': 0, 'normalized': True, 'power': 2.0}, ), + ({'pad': 3, 'normalized': True, 'power': 2.0}, ), + ]) + def test_spectrogram(self, kwargs): + # replication_pad1d_backward_cuda is not deteministic and + # gives very small (~2.7756e-17) difference. + # + # See https://github.com/pytorch/pytorch/issues/54093 + transform = T.Spectrogram(**kwargs) + waveform = get_whitenoise(sample_rate=8000, duration=0.05, n_channels=2) + self.assert_grad(transform, [waveform], nondet_tol=1e-10) + + def test_melspectrogram(self): + # replication_pad1d_backward_cuda is not deteministic and + # gives very small (~2.7756e-17) difference. + # + # See https://github.com/pytorch/pytorch/issues/54093 + sample_rate = 8000 + transform = T.MelSpectrogram(sample_rate=sample_rate) + waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2) + self.assert_grad(transform, [waveform], nondet_tol=1e-10) + + @nested_params( + [0, 0.99], + [False, True], + ) + def test_griffinlim(self, momentum, rand_init): + n_fft = 400 + power = 1 + n_iter = 3 + spec = get_spectrogram( + get_whitenoise(sample_rate=8000, duration=0.05, n_channels=2), + n_fft=n_fft, power=power) + transform = _DeterministicWrapper( + T.GriffinLim(n_fft=n_fft, n_iter=n_iter, momentum=momentum, rand_init=rand_init, power=power)) + self.assert_grad(transform, [spec]) + + @parameterized.expand([(False, ), (True, )]) + def test_mfcc(self, log_mels): + sample_rate = 8000 + transform = T.MFCC(sample_rate=sample_rate, log_mels=log_mels) + waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2) + self.assert_grad(transform, [waveform]) + + def test_compute_deltas(self): + transform = T.ComputeDeltas() + spec = torch.rand(10, 20) + self.assert_grad(transform, [spec]) + + @parameterized.expand([(8000, 8000), (8000, 4000), (4000, 8000)]) + def test_resample(self, orig_freq, new_freq): + transform = T.Resample(orig_freq=orig_freq, new_freq=new_freq) + waveform = get_whitenoise(sample_rate=8000, duration=0.05, n_channels=2) + self.assert_grad(transform, [waveform]) + + @parameterized.expand([("linear", ), ("exponential", ), ("logarithmic", ), ("quarter_sine", ), ("half_sine", )]) + def test_fade(self, fade_shape): + transform = T.Fade(fade_shape=fade_shape) + waveform = get_whitenoise(sample_rate=8000, duration=0.05, n_channels=2) + self.assert_grad(transform, [waveform], nondet_tol=1e-10) + + def test_spectral_centroid(self): + sample_rate = 8000 + transform = T.SpectralCentroid(sample_rate=sample_rate) + waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2) + self.assert_grad(transform, [waveform], nondet_tol=1e-10) + + def test_amplitude_to_db(self): + sample_rate = 8000 + transform = T.AmplitudeToDB() + waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2) + self.assert_grad(transform, [waveform]) + + @unittest.expectedFailure + def test_timestretch_zeros_fail(self): + """Test that ``T.TimeStretch`` fails gradcheck at 0 + + This is because ``F.phase_vocoder`` converts data from cartesian to polar coordinate, + which performs ``atan2(img, real)``, and gradient is not defined at 0. + """ + n_fft = 16 + transform = T.TimeStretch(n_freq=n_fft // 2 + 1, fixed_rate=0.99) + waveform = torch.zeros(2, 40) + spectrogram = get_spectrogram(waveform, n_fft=n_fft, power=None) + self.assert_grad(transform, [spectrogram]) + + @nested_params( + [0.7, 0.8, 0.9, 1.0, 1.3], + [False, True], + ) + def test_timestretch_non_zero(self, rate, test_pseudo_complex): + """Verify that ``T.TimeStretch`` does not fail if it's not close to 0 + + ``T.TimeStrech`` is not differentiable around 0, so this test checks the differentiability + for cases where input is not zero. + + As tested above, when spectrogram contains values close to zero, the gradients are unstable + and gradcheck fails. + + In this test, we generate spectrogram from random signal, then we push the points around + zero away from the origin. + + This process does not reflect the real use-case, and it is not practical for users, but + this helps us understand to what degree the function is differentiable and when not. + """ + n_fft = 16 + transform = T.TimeStretch(n_freq=n_fft // 2 + 1, fixed_rate=rate) + waveform = get_whitenoise(sample_rate=40, duration=1, n_channels=2) + spectrogram = get_spectrogram(waveform, n_fft=n_fft, power=None) + + # 1e-3 is too small (on CPU) + epsilon = 1e-2 + too_close = spectrogram.abs() < epsilon + spectrogram[too_close] = epsilon * spectrogram[too_close] / spectrogram[too_close].abs() + if test_pseudo_complex: + spectrogram = torch.view_as_real(spectrogram) + self.assert_grad(transform, [spectrogram]) diff --git a/test/torchaudio_unittest/transforms/batch_consistency_test.py b/test/torchaudio_unittest/transforms/batch_consistency_test.py index 745401c328..cab68e0152 100644 --- a/test/torchaudio_unittest/transforms/batch_consistency_test.py +++ b/test/torchaudio_unittest/transforms/batch_consistency_test.py @@ -1,6 +1,7 @@ """Test numerical consistency among single input and batched input.""" import torch import torchaudio +from parameterized import parameterized from torchaudio_unittest import common_utils @@ -130,40 +131,31 @@ def test_batch_mfcc(self): computed = torchaudio.transforms.MFCC()(waveform.repeat(3, 1, 1)) self.assertEqual(computed, expected, atol=1e-4, rtol=1e-5) - def test_batch_TimeStretch(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 - + @parameterized.expand([(True, ), (False, )]) + def test_batch_TimeStretch(self, test_pseudo_complex): rate = 2 + num_freq = 1025 + num_frames = 400 - complex_specgrams = torch.view_as_real( - torch.stft( - input=waveform, - n_fft=2048, - hop_length=512, - win_length=2048, - window=torch.hann_window(2048), - center=True, - pad_mode='reflect', - normalized=True, - onesided=True, - return_complex=True, - ) - ) + spec = torch.randn(num_freq, num_frames, dtype=torch.complex64) + pattern = [3, 1, 1, 1] + if test_pseudo_complex: + spec = torch.view_as_real(spec) + pattern += [1] # Single then transform then batch expected = torchaudio.transforms.TimeStretch( fixed_rate=rate, - n_freq=1025, + n_freq=num_freq, hop_length=512, - )(complex_specgrams).repeat(3, 1, 1, 1, 1) + )(spec).repeat(*pattern) # Batch then transform computed = torchaudio.transforms.TimeStretch( fixed_rate=rate, - n_freq=1025, + n_freq=num_freq, hop_length=512, - )(complex_specgrams.repeat(3, 1, 1, 1, 1)) + )(spec.repeat(*pattern)) self.assertEqual(computed, expected, atol=1e-5, rtol=1e-5) diff --git a/test/torchaudio_unittest/transforms/librosa_compatibility_cpu_test.py b/test/torchaudio_unittest/transforms/librosa_compatibility_cpu_test.py new file mode 100644 index 0000000000..300f4a3f71 --- /dev/null +++ b/test/torchaudio_unittest/transforms/librosa_compatibility_cpu_test.py @@ -0,0 +1,9 @@ +import torch + +from torchaudio_unittest.common_utils import PytorchTestCase +from .librosa_compatibility_test_impl import TransformsTestBase + + +class TestTransforms(TransformsTestBase, PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') diff --git a/test/torchaudio_unittest/transforms/librosa_compatibility_cuda_test.py b/test/torchaudio_unittest/transforms/librosa_compatibility_cuda_test.py new file mode 100644 index 0000000000..d553458aca --- /dev/null +++ b/test/torchaudio_unittest/transforms/librosa_compatibility_cuda_test.py @@ -0,0 +1,10 @@ +import torch + +from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda +from .librosa_compatibility_test_impl import TransformsTestBase + + +@skipIfNoCuda +class TestTransforms(TransformsTestBase, PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/torchaudio_unittest/transforms/librosa_compatibility_test.py b/test/torchaudio_unittest/transforms/librosa_compatibility_test.py deleted file mode 100644 index c954100f35..0000000000 --- a/test/torchaudio_unittest/transforms/librosa_compatibility_test.py +++ /dev/null @@ -1,245 +0,0 @@ -"""Test suites for numerical compatibility with librosa""" -import os -import unittest - -import torch -import torchaudio -import torchaudio.functional as F -from torchaudio._internal.module_utils import is_module_available -from parameterized import parameterized, param - -LIBROSA_AVAILABLE = is_module_available('librosa') - -if LIBROSA_AVAILABLE: - import librosa - import scipy - -from torchaudio_unittest import common_utils - - -def _load_audio_asset(*asset_paths, **kwargs): - file_path = common_utils.get_asset_path(*asset_paths) - sound, sample_rate = torchaudio.load(file_path, **kwargs) - return sound, sample_rate - - -@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") -class TestTransforms(common_utils.TorchaudioTestCase): - """Test suite for functions in `transforms` module.""" - - @parameterized.expand([ - param(n_fft=400, hop_length=200, power=2.0), - param(n_fft=600, hop_length=100, power=2.0), - param(n_fft=400, hop_length=200, power=3.0), - param(n_fft=200, hop_length=50, power=2.0), - ]) - def test_spectrogram(self, n_fft, hop_length, power): - sample_rate = 16000 - sound = common_utils.get_sinusoid(n_channels=1, sample_rate=sample_rate) - sound_librosa = sound.cpu().numpy().squeeze() - spect_transform = torchaudio.transforms.Spectrogram( - n_fft=n_fft, hop_length=hop_length, power=power) - out_librosa, _ = librosa.core.spectrum._spectrogram( - y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) - - out_torch = spect_transform(sound).squeeze().cpu() - self.assertEqual(out_torch, torch.from_numpy(out_librosa), atol=1e-5, rtol=1e-5) - - @parameterized.expand([ - param(norm=norm, mel_scale=mel_scale, **p.kwargs) - for p in [ - param(n_fft=400, hop_length=200, n_mels=128), - param(n_fft=600, hop_length=100, n_mels=128), - param(n_fft=200, hop_length=50, n_mels=128), - ] - for norm in [None, 'slaney'] - for mel_scale in ['htk', 'slaney'] - ]) - def test_mel_spectrogram(self, n_fft, hop_length, n_mels, norm, mel_scale): - sample_rate = 16000 - sound = common_utils.get_sinusoid(n_channels=1, sample_rate=sample_rate) - sound_librosa = sound.cpu().numpy().squeeze() - melspect_transform = torchaudio.transforms.MelSpectrogram( - sample_rate=sample_rate, window_fn=torch.hann_window, - hop_length=hop_length, n_mels=n_mels, n_fft=n_fft, norm=norm, mel_scale=mel_scale) - librosa_mel = librosa.feature.melspectrogram( - y=sound_librosa, sr=sample_rate, n_fft=n_fft, - hop_length=hop_length, n_mels=n_mels, htk=mel_scale == "htk", norm=norm) - librosa_mel_tensor = torch.from_numpy(librosa_mel) - torch_mel = melspect_transform(sound).squeeze().cpu() - self.assertEqual( - torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3, rtol=1e-5) - - @parameterized.expand([ - param(norm=norm, mel_scale=mel_scale, **p.kwargs) - for p in [ - param(n_fft=400, hop_length=200, power=2.0, n_mels=128), - param(n_fft=600, hop_length=100, power=2.0, n_mels=128), - param(n_fft=400, hop_length=200, power=3.0, n_mels=128), - # NOTE: Test passes offline, but fails on TravisCI (and CircleCI), see #372. - param(n_fft=200, hop_length=50, power=2.0, n_mels=128, skip_ci=True), - ] - for norm in [None, 'slaney'] - for mel_scale in ['htk', 'slaney'] - ]) - def test_s2db(self, n_fft, hop_length, power, n_mels, norm, mel_scale, skip_ci=False): - if skip_ci and 'CI' in os.environ: - self.skipTest('Test is known to fail on CI') - sample_rate = 16000 - sound = common_utils.get_sinusoid(n_channels=1, sample_rate=sample_rate) - sound_librosa = sound.cpu().numpy().squeeze() - spect_transform = torchaudio.transforms.Spectrogram( - n_fft=n_fft, hop_length=hop_length, power=power) - out_librosa, _ = librosa.core.spectrum._spectrogram( - y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) - melspect_transform = torchaudio.transforms.MelSpectrogram( - sample_rate=sample_rate, window_fn=torch.hann_window, - hop_length=hop_length, n_mels=n_mels, n_fft=n_fft, norm=norm, mel_scale=mel_scale) - librosa_mel = librosa.feature.melspectrogram( - y=sound_librosa, sr=sample_rate, n_fft=n_fft, - hop_length=hop_length, n_mels=n_mels, htk=mel_scale == "htk", norm=norm) - - power_to_db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.) - power_to_db_torch = power_to_db_transform(spect_transform(sound)).squeeze().cpu() - power_to_db_librosa = librosa.core.spectrum.power_to_db(out_librosa) - self.assertEqual(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3, rtol=1e-5) - - mag_to_db_transform = torchaudio.transforms.AmplitudeToDB('magnitude', 80.) - mag_to_db_torch = mag_to_db_transform(torch.abs(sound)).squeeze().cpu() - mag_to_db_librosa = librosa.core.spectrum.amplitude_to_db(sound_librosa) - self.assertEqual(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3, rtol=1e-5) - - power_to_db_torch = power_to_db_transform(melspect_transform(sound)).squeeze().cpu() - db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) - db_librosa_tensor = torch.from_numpy(db_librosa) - self.assertEqual( - power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3, rtol=1e-5) - - @parameterized.expand([ - param(n_fft=400, hop_length=200, n_mels=128, n_mfcc=40), - param(n_fft=600, hop_length=100, n_mels=128, n_mfcc=20), - param(n_fft=200, hop_length=50, n_mels=128, n_mfcc=50), - ]) - def test_mfcc(self, n_fft, hop_length, n_mels, n_mfcc): - sample_rate = 16000 - sound = common_utils.get_sinusoid(n_channels=1, sample_rate=sample_rate) - sound_librosa = sound.cpu().numpy().squeeze() - librosa_mel = librosa.feature.melspectrogram( - y=sound_librosa, sr=sample_rate, n_fft=n_fft, - hop_length=hop_length, n_mels=n_mels, htk=True, norm=None) - db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) - - # librosa.feature.mfcc doesn't pass kwargs properly since some of the - # kwargs for melspectrogram and mfcc are the same. We just follow the - # function body in - # https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram - # to mirror this function call with correct args: - # - # librosa_mfcc = librosa.feature.mfcc( - # y=sound_librosa, sr=sample_rate, n_mfcc = n_mfcc, - # hop_length=hop_length, n_fft=n_fft, htk=True, norm=None, n_mels=n_mels) - - librosa_mfcc = scipy.fftpack.dct(db_librosa, axis=0, type=2, norm='ortho')[:n_mfcc] - librosa_mfcc_tensor = torch.from_numpy(librosa_mfcc) - - melkwargs = {'hop_length': hop_length, 'n_fft': n_fft} - mfcc_transform = torchaudio.transforms.MFCC( - sample_rate=sample_rate, n_mfcc=n_mfcc, norm='ortho', melkwargs=melkwargs) - torch_mfcc = mfcc_transform(sound).squeeze().cpu() - - self.assertEqual( - torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3, rtol=1e-5) - - @parameterized.expand([ - param(n_fft=400, hop_length=200), - param(n_fft=600, hop_length=100), - param(n_fft=200, hop_length=50), - ]) - def test_spectral_centroid(self, n_fft, hop_length): - sample_rate = 16000 - sound = common_utils.get_sinusoid(n_channels=1, sample_rate=sample_rate) - sound_librosa = sound.cpu().numpy().squeeze() - spect_centroid = torchaudio.transforms.SpectralCentroid( - sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length) - out_torch = spect_centroid(sound).squeeze().cpu() - - out_librosa = librosa.feature.spectral_centroid( - y=sound_librosa, sr=sample_rate, n_fft=n_fft, hop_length=hop_length) - out_librosa = torch.from_numpy(out_librosa)[0] - - self.assertEqual(out_torch.type(out_librosa.dtype), out_librosa, atol=1e-5, rtol=1e-5) - - def test_MelScale(self): - """MelScale transform is comparable to that of librosa""" - n_fft = 2048 - n_mels = 256 - hop_length = n_fft // 4 - sample_rate = 44100 - sound = common_utils.get_whitenoise(sample_rate=sample_rate, duration=60) - sound = sound.mean(dim=0, keepdim=True) - spec_ta = F.spectrogram( - sound, pad=0, window=torch.hann_window(n_fft), n_fft=n_fft, - hop_length=hop_length, win_length=n_fft, power=2, normalized=False) - spec_lr = spec_ta.cpu().numpy().squeeze() - # Perform MelScale with torchaudio and librosa - melspec_ta = torchaudio.transforms.MelScale(n_mels=n_mels, sample_rate=sample_rate)(spec_ta) - melspec_lr = librosa.feature.melspectrogram( - S=spec_lr, sr=sample_rate, n_fft=n_fft, hop_length=hop_length, - win_length=n_fft, center=True, window='hann', n_mels=n_mels, htk=True, norm=None) - # Note: Using relaxed rtol instead of atol - self.assertEqual(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), atol=1e-8, rtol=1e-3) - - def test_InverseMelScale(self): - """InverseMelScale transform is comparable to that of librosa""" - n_fft = 2048 - n_mels = 256 - n_stft = n_fft // 2 + 1 - hop_length = n_fft // 4 - - # Prepare mel spectrogram input. We use torchaudio to compute one. - path = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - sound, sample_rate = common_utils.load_wav(path) - sound = sound[:, 2**10:2**10 + 2**14] - sound = sound.mean(dim=0, keepdim=True) - spec_orig = F.spectrogram( - sound, pad=0, window=torch.hann_window(n_fft), n_fft=n_fft, - hop_length=hop_length, win_length=n_fft, power=2, normalized=False) - melspec_ta = torchaudio.transforms.MelScale(n_mels=n_mels, sample_rate=sample_rate)(spec_orig) - melspec_lr = melspec_ta.cpu().numpy().squeeze() - # Perform InverseMelScale with torch audio and librosa - spec_ta = torchaudio.transforms.InverseMelScale( - n_stft, n_mels=n_mels, sample_rate=sample_rate)(melspec_ta) - spec_lr = librosa.feature.inverse.mel_to_stft( - melspec_lr, sr=sample_rate, n_fft=n_fft, power=2.0, htk=True, norm=None) - spec_lr = torch.from_numpy(spec_lr[None, ...]) - - # Align dimensions - # librosa does not return power spectrogram while torchaudio returns power spectrogram - spec_orig = spec_orig.sqrt() - spec_ta = spec_ta.sqrt() - - threshold = 2.0 - # This threshold was chosen empirically, based on the following observation - # - # torch.dist(spec_lr, spec_ta, p=float('inf')) - # >>> tensor(1.9666) - # - # The spectrograms reconstructed by librosa and torchaudio are not comparable elementwise. - # This is because they use different approximation algorithms and resulting values can live - # in different magnitude. (although most of them are very close) - # See - # https://github.com/pytorch/audio/pull/366 for the discussion of the choice of algorithm - # https://github.com/pytorch/audio/pull/448/files#r385747021 for the distribution of P-inf - # distance over frequencies. - self.assertEqual(spec_ta, spec_lr, atol=threshold, rtol=1e-5) - - threshold = 1700.0 - # This threshold was chosen empirically, based on the following observations - # - # torch.dist(spec_orig, spec_ta, p=1) - # >>> tensor(1644.3516) - # torch.dist(spec_orig, spec_lr, p=1) - # >>> tensor(1420.7103) - # torch.dist(spec_lr, spec_ta, p=1) - # >>> tensor(943.2759) - assert torch.dist(spec_orig, spec_ta, p=1) < threshold diff --git a/test/torchaudio_unittest/transforms/librosa_compatibility_test_impl.py b/test/torchaudio_unittest/transforms/librosa_compatibility_test_impl.py new file mode 100644 index 0000000000..9bc8edbb20 --- /dev/null +++ b/test/torchaudio_unittest/transforms/librosa_compatibility_test_impl.py @@ -0,0 +1,141 @@ +import unittest + +import torch +import torchaudio.transforms as T +from torchaudio._internal.module_utils import is_module_available +from parameterized import param, parameterized + +from torchaudio_unittest.common_utils import ( + TestBaseMixin, + get_whitenoise, + get_sinusoid, + get_spectrogram, + nested_params, +) + +LIBROSA_AVAILABLE = is_module_available('librosa') + +if LIBROSA_AVAILABLE: + import librosa + + +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") +class TransformsTestBase(TestBaseMixin): + @parameterized.expand([ + param(n_fft=400, hop_length=200, power=2.0), + param(n_fft=600, hop_length=100, power=2.0), + param(n_fft=400, hop_length=200, power=3.0), + param(n_fft=200, hop_length=50, power=2.0), + ]) + def test_Spectrogram(self, n_fft, hop_length, power): + sample_rate = 16000 + waveform = get_whitenoise( + sample_rate=sample_rate, n_channels=1, + ).to(self.device, self.dtype) + + expected = librosa.core.spectrum._spectrogram( + y=waveform[0].cpu().numpy(), + n_fft=n_fft, hop_length=hop_length, power=power)[0] + + result = T.Spectrogram( + n_fft=n_fft, hop_length=hop_length, power=power, + ).to(self.device, self.dtype)(waveform)[0] + self.assertEqual(result, torch.from_numpy(expected), atol=1e-5, rtol=1e-5) + + def test_Spectrogram_complex(self): + n_fft = 400 + hop_length = 200 + sample_rate = 16000 + waveform = get_whitenoise( + sample_rate=sample_rate, n_channels=1, + ).to(self.device, self.dtype) + + expected = librosa.core.spectrum._spectrogram( + y=waveform[0].cpu().numpy(), + n_fft=n_fft, hop_length=hop_length, power=1)[0] + + result = T.Spectrogram( + n_fft=n_fft, hop_length=hop_length, power=None, return_complex=True, + ).to(self.device, self.dtype)(waveform)[0] + self.assertEqual(result.abs(), torch.from_numpy(expected), atol=1e-5, rtol=1e-5) + + @nested_params( + [ + param(n_fft=400, hop_length=200, n_mels=64), + param(n_fft=600, hop_length=100, n_mels=128), + param(n_fft=200, hop_length=50, n_mels=32), + ], + [param(norm=norm) for norm in [None, 'slaney']], + [param(mel_scale=mel_scale) for mel_scale in ['htk', 'slaney']], + ) + def test_MelSpectrogram(self, n_fft, hop_length, n_mels, norm, mel_scale): + sample_rate = 16000 + waveform = get_sinusoid( + sample_rate=sample_rate, n_channels=1, + ).to(self.device, self.dtype) + + expected = librosa.feature.melspectrogram( + y=waveform[0].cpu().numpy(), + sr=sample_rate, n_fft=n_fft, + hop_length=hop_length, n_mels=n_mels, norm=norm, + htk=mel_scale == "htk") + result = T.MelSpectrogram( + sample_rate=sample_rate, window_fn=torch.hann_window, + hop_length=hop_length, n_mels=n_mels, + n_fft=n_fft, norm=norm, mel_scale=mel_scale, + ).to(self.device, self.dtype)(waveform)[0] + self.assertEqual(result, torch.from_numpy(expected), atol=5e-4, rtol=1e-5) + + def test_magnitude_to_db(self): + spectrogram = get_spectrogram( + get_whitenoise(), n_fft=400, power=2).to(self.device, self.dtype) + result = T.AmplitudeToDB('magnitude', 80.).to(self.device, self.dtype)(spectrogram)[0] + expected = librosa.core.spectrum.amplitude_to_db(spectrogram[0].cpu().numpy()) + self.assertEqual(result, torch.from_numpy(expected)) + + def test_power_to_db(self): + spectrogram = get_spectrogram( + get_whitenoise(), n_fft=400, power=2).to(self.device, self.dtype) + result = T.AmplitudeToDB('power', 80.).to(self.device, self.dtype)(spectrogram)[0] + expected = librosa.core.spectrum.power_to_db(spectrogram[0].cpu().numpy()) + self.assertEqual(result, torch.from_numpy(expected)) + + @nested_params([ + param(n_fft=400, hop_length=200, n_mels=64, n_mfcc=40), + param(n_fft=600, hop_length=100, n_mels=128, n_mfcc=20), + param(n_fft=200, hop_length=50, n_mels=32, n_mfcc=25), + ]) + def test_mfcc(self, n_fft, hop_length, n_mels, n_mfcc): + sample_rate = 16000 + waveform = get_whitenoise( + sample_rate=sample_rate, n_channels=1).to(self.device, self.dtype) + result = T.MFCC( + sample_rate=sample_rate, n_mfcc=n_mfcc, norm='ortho', + melkwargs={'hop_length': hop_length, 'n_fft': n_fft, 'n_mels': n_mels}, + ).to(self.device, self.dtype)(waveform)[0] + + melspec = librosa.feature.melspectrogram( + y=waveform[0].cpu().numpy(), sr=sample_rate, n_fft=n_fft, + win_length=n_fft, hop_length=hop_length, + n_mels=n_mels, htk=True, norm=None) + expected = librosa.feature.mfcc( + S=librosa.core.spectrum.power_to_db(melspec), + n_mfcc=n_mfcc, dct_type=2, norm='ortho') + self.assertEqual(result, torch.from_numpy(expected), atol=5e-4, rtol=1e-5) + + @parameterized.expand([ + param(n_fft=400, hop_length=200), + param(n_fft=600, hop_length=100), + param(n_fft=200, hop_length=50), + ]) + def test_spectral_centroid(self, n_fft, hop_length): + sample_rate = 16000 + waveform = get_whitenoise( + sample_rate=sample_rate, n_channels=1).to(self.device, self.dtype) + + result = T.SpectralCentroid( + sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length, + ).to(self.device, self.dtype)(waveform) + expected = librosa.feature.spectral_centroid( + y=waveform[0].cpu().numpy(), sr=sample_rate, n_fft=n_fft, hop_length=hop_length) + self.assertEqual(result, torch.from_numpy(expected), atol=5e-4, rtol=1e-5) diff --git a/test/torchaudio_unittest/transforms/torchscript_consistency_cpu_test.py b/test/torchaudio_unittest/transforms/torchscript_consistency_cpu_test.py index 9092f3a64b..4de32a57bb 100644 --- a/test/torchaudio_unittest/transforms/torchscript_consistency_cpu_test.py +++ b/test/torchaudio_unittest/transforms/torchscript_consistency_cpu_test.py @@ -1,7 +1,7 @@ import torch from torchaudio_unittest.common_utils import PytorchTestCase -from .torchscript_consistency_impl import Transforms +from .torchscript_consistency_impl import Transforms, TransformsComplex class TestTransformsFloat32(Transforms, PytorchTestCase): @@ -12,3 +12,15 @@ class TestTransformsFloat32(Transforms, PytorchTestCase): class TestTransformsFloat64(Transforms, PytorchTestCase): dtype = torch.float64 device = torch.device('cpu') + + +class TestTransformsComplex64(TransformsComplex, PytorchTestCase): + complex_dtype = torch.complex64 + real_dtype = torch.float32 + device = torch.device('cpu') + + +class TestTransformsComplex128(TransformsComplex, PytorchTestCase): + complex_dtype = torch.complex128 + real_dtype = torch.float64 + device = torch.device('cpu') diff --git a/test/torchaudio_unittest/transforms/torchscript_consistency_cuda_test.py b/test/torchaudio_unittest/transforms/torchscript_consistency_cuda_test.py index 7425647bab..2435b82589 100644 --- a/test/torchaudio_unittest/transforms/torchscript_consistency_cuda_test.py +++ b/test/torchaudio_unittest/transforms/torchscript_consistency_cuda_test.py @@ -1,7 +1,7 @@ import torch from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase -from .torchscript_consistency_impl import Transforms +from .torchscript_consistency_impl import Transforms, TransformsComplex @skipIfNoCuda @@ -14,3 +14,17 @@ class TestTransformsFloat32(Transforms, PytorchTestCase): class TestTransformsFloat64(Transforms, PytorchTestCase): dtype = torch.float64 device = torch.device('cuda') + + +@skipIfNoCuda +class TestTransformsComplex64(TransformsComplex, PytorchTestCase): + complex_dtype = torch.complex64 + real_dtype = torch.float32 + device = torch.device('cuda') + + +@skipIfNoCuda +class TestTransformsComplex128(TransformsComplex, PytorchTestCase): + complex_dtype = torch.complex128 + real_dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py b/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py index 492e1e4a92..64ecbe53f7 100644 --- a/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py @@ -2,17 +2,26 @@ import torch import torchaudio.transforms as T +from parameterized import parameterized from torchaudio_unittest import common_utils +from torchaudio_unittest.common_utils import ( + skipIfRocm, + TempDirMixin, + TestBaseMixin, +) -class Transforms(common_utils.TestBaseMixin): +class Transforms(TempDirMixin, TestBaseMixin): """Implements test for Transforms that are performed for different devices""" def _assert_consistency(self, transform, tensor): tensor = tensor.to(device=self.device, dtype=self.dtype) transform = transform.to(device=self.device, dtype=self.dtype) - ts_transform = torch.jit.script(transform) + path = self.get_temp_path('transform.zip') + torch.jit.script(transform).save(path) + ts_transform = torch.jit.load(path) + output = transform(tensor) ts_output = ts_transform(tensor) self.assertEqual(ts_output, output) @@ -21,6 +30,11 @@ def test_Spectrogram(self): tensor = torch.rand((1, 1000)) self._assert_consistency(T.Spectrogram(), tensor) + def test_Spectrogram_return_complex(self): + tensor = torch.rand((1, 1000)) + self._assert_consistency(T.Spectrogram(power=None, return_complex=True), tensor) + + @skipIfRocm def test_GriffinLim(self): tensor = torch.rand((1, 201, 6)) self._assert_consistency(T.GriffinLim(length=1000, rand_init=False), tensor) @@ -30,8 +44,8 @@ def test_AmplitudeToDB(self): self._assert_consistency(T.AmplitudeToDB(), spec) def test_MelScale(self): - spec_f = torch.rand((1, 6, 201)) - self._assert_consistency(T.MelScale(), spec_f) + spec_f = torch.rand((1, 201, 6)) + self._assert_consistency(T.MelScale(n_stft=201), spec_f) def test_MelSpectrogram(self): tensor = torch.rand((1, 1000)) @@ -58,16 +72,6 @@ def test_MuLawDecoding(self): tensor = torch.rand((1, 10)) self._assert_consistency(T.MuLawDecoding(), tensor) - def test_TimeStretch(self): - n_freq = 400 - hop_length = 512 - fixed_rate = 1.3 - tensor = torch.rand((10, 2, n_freq, 10, 2)) - self._assert_consistency( - T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate), - tensor, - ) - def test_Fade(self): waveform = common_utils.get_whitenoise() fade_in_len = 3000 @@ -99,3 +103,37 @@ def test_SpectralCentroid(self): sample_rate = 44100 waveform = common_utils.get_whitenoise(sample_rate=sample_rate) self._assert_consistency(T.SpectralCentroid(sample_rate=sample_rate), waveform) + + +class TransformsComplex(TempDirMixin, TestBaseMixin): + complex_dtype = None + real_dtype = None + device = None + + def _assert_consistency(self, transform, tensor, test_pseudo_complex=False): + assert tensor.is_complex() + tensor = tensor.to(device=self.device, dtype=self.complex_dtype) + transform = transform.to(device=self.device, dtype=self.real_dtype) + + path = self.get_temp_path('transform.zip') + torch.jit.script(transform).save(path) + ts_transform = torch.jit.load(path) + + if test_pseudo_complex: + tensor = torch.view_as_real(tensor) + + output = transform(tensor) + ts_output = ts_transform(tensor) + self.assertEqual(ts_output, output) + + @parameterized.expand([(True, ), (False, )]) + def test_TimeStretch(self, test_pseudo_complex): + n_freq = 400 + hop_length = 512 + fixed_rate = 1.3 + tensor = torch.view_as_complex(torch.rand((10, 2, n_freq, 10, 2))) + self._assert_consistency( + T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate), + tensor, + test_pseudo_complex + ) diff --git a/test/torchaudio_unittest/transforms/transforms_cpu_test.py b/test/torchaudio_unittest/transforms/transforms_cpu_test.py new file mode 100644 index 0000000000..5177b9a455 --- /dev/null +++ b/test/torchaudio_unittest/transforms/transforms_cpu_test.py @@ -0,0 +1,14 @@ +import torch + +from torchaudio_unittest.common_utils import PytorchTestCase +from . transforms_test_impl import TransformsTestBase + + +class TransformsCPUFloat32Test(TransformsTestBase, PytorchTestCase): + device = 'cpu' + dtype = torch.float32 + + +class TransformsCPUFloat64Test(TransformsTestBase, PytorchTestCase): + device = 'cpu' + dtype = torch.float64 diff --git a/test/torchaudio_unittest/transforms/transforms_cuda_test.py b/test/torchaudio_unittest/transforms/transforms_cuda_test.py new file mode 100644 index 0000000000..9489660384 --- /dev/null +++ b/test/torchaudio_unittest/transforms/transforms_cuda_test.py @@ -0,0 +1,19 @@ +import torch + +from torchaudio_unittest.common_utils import ( + PytorchTestCase, + skipIfNoCuda, +) +from . transforms_test_impl import TransformsTestBase + + +@skipIfNoCuda +class TransformsCUDAFloat32Test(TransformsTestBase, PytorchTestCase): + device = 'cuda' + dtype = torch.float32 + + +@skipIfNoCuda +class TransformsCUDAFloat64Test(TransformsTestBase, PytorchTestCase): + device = 'cuda' + dtype = torch.float64 diff --git a/test/torchaudio_unittest/transforms/transforms_test_impl.py b/test/torchaudio_unittest/transforms/transforms_test_impl.py new file mode 100644 index 0000000000..8a92662200 --- /dev/null +++ b/test/torchaudio_unittest/transforms/transforms_test_impl.py @@ -0,0 +1,61 @@ +import torch +import torchaudio.transforms as T + +from torchaudio_unittest.common_utils import ( + TestBaseMixin, + get_whitenoise, + get_spectrogram, +) + + +def _get_ratio(mat): + return (mat.sum() / mat.numel()).item() + + +class TransformsTestBase(TestBaseMixin): + def test_InverseMelScale(self): + """Gauge the quality of InverseMelScale transform. + + As InverseMelScale is currently implemented with + random initialization + iterative optimization, + it is not practically possible to assert the difference between + the estimated spectrogram and the original spectrogram as a whole. + Estimated spectrogram has very huge descrepency locally. + Thus in this test we gauge what percentage of elements are bellow + certain tolerance. + At the moment, the quality of estimated spectrogram is not good. + When implementation is changed in a way it makes the quality even worse, + this test will fail. + """ + n_fft = 400 + power = 1 + n_mels = 64 + sample_rate = 8000 + + n_stft = n_fft // 2 + 1 + + # Generate reference spectrogram and input mel-scaled spectrogram + expected = get_spectrogram( + get_whitenoise(sample_rate=sample_rate, duration=1, n_channels=2), + n_fft=n_fft, power=power).to(self.device, self.dtype) + input = T.MelScale( + n_mels=n_mels, sample_rate=sample_rate + ).to(self.device, self.dtype)(expected) + + # Run transform + transform = T.InverseMelScale( + n_stft, n_mels=n_mels, sample_rate=sample_rate).to(self.device, self.dtype) + torch.random.manual_seed(0) + result = transform(input) + + # Compare + epsilon = 1e-60 + relative_diff = torch.abs((result - expected) / (expected + epsilon)) + + for tol in [1e-1, 1e-3, 1e-5, 1e-10]: + print( + f"Ratio of relative diff smaller than {tol:e} is " + f"{_get_ratio(relative_diff < tol)}") + assert _get_ratio(relative_diff < 1e-1) > 0.2 + assert _get_ratio(relative_diff < 1e-3) > 5e-3 + assert _get_ratio(relative_diff < 1e-5) > 1e-5 diff --git a/torchaudio/datasets/utils.py b/torchaudio/datasets/utils.py index 189866374f..b9f3b14360 100644 --- a/torchaudio/datasets/utils.py +++ b/torchaudio/datasets/utils.py @@ -29,7 +29,8 @@ def stream_url(url: str, # If we already have the whole file, there is no need to download it again req = urllib.request.Request(url, method="HEAD") - url_size = int(urllib.request.urlopen(req).info().get("Content-Length", -1)) + with urllib.request.urlopen(req) as response: + url_size = int(response.info().get("Content-Length", -1)) if url_size == start_byte: return diff --git a/torchaudio/functional/filtering.py b/torchaudio/functional/filtering.py index 82d9727876..ad594350d3 100644 --- a/torchaudio/functional/filtering.py +++ b/torchaudio/functional/filtering.py @@ -73,8 +73,8 @@ def allpass_biquad( Args: waveform(torch.Tensor): audio waveform of dimension of `(..., time)` sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - central_freq (float): central frequency (in Hz) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + central_freq (float or torch.Tensor): central frequency (in Hz) + Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) Returns: Tensor: Waveform of dimension of `(..., time)` @@ -83,14 +83,20 @@ def allpass_biquad( http://sox.sourceforge.net/sox.html https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF """ + dtype = waveform.dtype + device = waveform.device + central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device) + Q = torch.as_tensor(Q, dtype=dtype, device=device) + w0 = 2 * math.pi * central_freq / sample_rate - alpha = math.sin(w0) / 2 / Q + + alpha = torch.sin(w0) / 2 / Q b0 = 1 - alpha - b1 = -2 * math.cos(w0) + b1 = -2 * torch.cos(w0) b2 = 1 + alpha a0 = 1 + alpha - a1 = -2 * math.cos(w0) + a1 = -2 * torch.cos(w0) a2 = 1 - alpha return biquad(waveform, b0, b1, b2, a0, a1, a2) @@ -107,8 +113,8 @@ def band_biquad( Args: waveform (Tensor): audio waveform of dimension of `(..., time)` sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - central_freq (float): central frequency (in Hz) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). + central_freq (float or torch.Tensor): central frequency (in Hz) + Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). noise (bool, optional) : If ``True``, uses the alternate mode for un-pitched audio (e.g. percussion). If ``False``, uses mode oriented to pitched audio, i.e. voice, singing, or instrumental music (Default: ``False``). @@ -120,18 +126,23 @@ def band_biquad( http://sox.sourceforge.net/sox.html https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF """ + dtype = waveform.dtype + device = waveform.device + central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device) + Q = torch.as_tensor(Q, dtype=dtype, device=device) + w0 = 2 * math.pi * central_freq / sample_rate bw_Hz = central_freq / Q a0 = 1.0 - a2 = math.exp(-2 * math.pi * bw_Hz / sample_rate) - a1 = -4 * a2 / (1 + a2) * math.cos(w0) + a2 = torch.exp(-2 * math.pi * bw_Hz / sample_rate) + a1 = -4 * a2 / (1 + a2) * torch.cos(w0) - b0 = math.sqrt(1 - a1 * a1 / (4 * a2)) * (1 - a2) + b0 = torch.sqrt(1 - a1 * a1 / (4 * a2)) * (1 - a2) if noise: - mult = math.sqrt(((1 + a2) * (1 + a2) - a1 * a1) * (1 - a2) / (1 + a2)) / b0 - b0 *= mult + mult = torch.sqrt(((1 + a2) * (1 + a2) - a1 * a1) * (1 - a2) / (1 + a2)) / b0 + b0 = mult * b0 b1 = 0.0 b2 = 0.0 @@ -151,8 +162,8 @@ def bandpass_biquad( Args: waveform (Tensor): audio waveform of dimension of `(..., time)` sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - central_freq (float): central frequency (in Hz) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + central_freq (float or torch.Tensor): central frequency (in Hz) + Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) const_skirt_gain (bool, optional) : If ``True``, uses a constant skirt gain (peak gain = Q). If ``False``, uses a constant 0dB peak gain. (Default: ``False``) @@ -163,15 +174,20 @@ def bandpass_biquad( http://sox.sourceforge.net/sox.html https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF """ + dtype = waveform.dtype + device = waveform.device + central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device) + Q = torch.as_tensor(Q, dtype=dtype, device=device) + w0 = 2 * math.pi * central_freq / sample_rate - alpha = math.sin(w0) / 2 / Q + alpha = torch.sin(w0) / 2 / Q - temp = math.sin(w0) / 2 if const_skirt_gain else alpha + temp = torch.sin(w0) / 2 if const_skirt_gain else alpha b0 = temp b1 = 0.0 b2 = -temp a0 = 1 + alpha - a1 = -2 * math.cos(w0) + a1 = -2 * torch.cos(w0) a2 = 1 - alpha return biquad(waveform, b0, b1, b2, a0, a1, a2) @@ -184,8 +200,8 @@ def bandreject_biquad( Args: waveform (Tensor): audio waveform of dimension of `(..., time)` sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - central_freq (float): central frequency (in Hz) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + central_freq (float or torch.Tensor): central frequency (in Hz) + Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) Returns: Tensor: Waveform of dimension of `(..., time)` @@ -194,14 +210,19 @@ def bandreject_biquad( http://sox.sourceforge.net/sox.html https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF """ + dtype = waveform.dtype + device = waveform.device + central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device) + Q = torch.as_tensor(Q, dtype=dtype, device=device) + w0 = 2 * math.pi * central_freq / sample_rate - alpha = math.sin(w0) / 2 / Q + alpha = torch.sin(w0) / 2 / Q b0 = 1.0 - b1 = -2 * math.cos(w0) + b1 = -2 * torch.cos(w0) b2 = 1.0 a0 = 1 + alpha - a1 = -2 * math.cos(w0) + a1 = -2 * torch.cos(w0) a2 = 1 - alpha return biquad(waveform, b0, b1, b2, a0, a1, a2) @@ -218,9 +239,9 @@ def bass_biquad( Args: waveform (Tensor): audio waveform of dimension of `(..., time)` sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - gain (float): desired gain at the boost (or attenuation) in dB. - central_freq (float, optional): central frequency (in Hz). (Default: ``100``) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). + gain (float or torch.Tensor): desired gain at the boost (or attenuation) in dB. + central_freq (float or torch.Tensor, optional): central frequency (in Hz). (Default: ``100``) + Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). Returns: Tensor: Waveform of dimension of `(..., time)` @@ -229,13 +250,19 @@ def bass_biquad( http://sox.sourceforge.net/sox.html https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF """ + dtype = waveform.dtype + device = waveform.device + central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device) + Q = torch.as_tensor(Q, dtype=dtype, device=device) + gain = torch.as_tensor(gain, dtype=dtype, device=device) + w0 = 2 * math.pi * central_freq / sample_rate - alpha = math.sin(w0) / 2 / Q - A = math.exp(gain / 40 * math.log(10)) + alpha = torch.sin(w0) / 2 / Q + A = torch.exp(gain / 40 * math.log(10)) - temp1 = 2 * math.sqrt(A) * alpha - temp2 = (A - 1) * math.cos(w0) - temp3 = (A + 1) * math.cos(w0) + temp1 = 2 * torch.sqrt(A) * alpha + temp2 = (A - 1) * torch.cos(w0) + temp3 = (A + 1) * torch.cos(w0) b0 = A * ((A + 1) - temp2 + temp1) b1 = 2 * A * ((A - 1) - temp3) @@ -255,12 +282,12 @@ def biquad( Args: waveform (Tensor): audio waveform of dimension of `(..., time)` - b0 (float): numerator coefficient of current input, x[n] - b1 (float): numerator coefficient of input one time step ago x[n-1] - b2 (float): numerator coefficient of input two time steps ago x[n-2] - a0 (float): denominator coefficient of current output y[n], typically 1 - a1 (float): denominator coefficient of current output y[n-1] - a2 (float): denominator coefficient of current output y[n-2] + b0 (float or torch.Tensor): numerator coefficient of current input, x[n] + b1 (float or torch.Tensor): numerator coefficient of input one time step ago x[n-1] + b2 (float or torch.Tensor): numerator coefficient of input two time steps ago x[n-2] + a0 (float or torch.Tensor): denominator coefficient of current output y[n], typically 1 + a1 (float or torch.Tensor): denominator coefficient of current output y[n-1] + a2 (float or torch.Tensor): denominator coefficient of current output y[n-2] Returns: Tensor: Waveform with dimension of `(..., time)` @@ -269,10 +296,17 @@ def biquad( device = waveform.device dtype = waveform.dtype + b0 = torch.as_tensor(b0, dtype=dtype, device=device).view(1) + b1 = torch.as_tensor(b1, dtype=dtype, device=device).view(1) + b2 = torch.as_tensor(b2, dtype=dtype, device=device).view(1) + a0 = torch.as_tensor(a0, dtype=dtype, device=device).view(1) + a1 = torch.as_tensor(a1, dtype=dtype, device=device).view(1) + a2 = torch.as_tensor(a2, dtype=dtype, device=device).view(1) + output_waveform = lfilter( waveform, - torch.tensor([a0, a1, a2], dtype=dtype, device=device), - torch.tensor([b0, b1, b2], dtype=dtype, device=device), + torch.cat([a0, a1, a2]), + torch.cat([b0, b1, b2]), ) return output_waveform @@ -584,21 +618,27 @@ def equalizer_biquad( waveform (Tensor): audio waveform of dimension of `(..., time)` sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) center_freq (float): filter's central frequency - gain (float): desired gain at the boost (or attenuation) in dB - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + gain (float or torch.Tensor): desired gain at the boost (or attenuation) in dB + Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) Returns: Tensor: Waveform of dimension of `(..., time)` """ + dtype = waveform.dtype + device = waveform.device + center_freq = torch.as_tensor(center_freq, dtype=dtype, device=device) + Q = torch.as_tensor(Q, dtype=dtype, device=device) + gain = torch.as_tensor(gain, dtype=dtype, device=device) + w0 = 2 * math.pi * center_freq / sample_rate - A = math.exp(gain / 40.0 * math.log(10)) - alpha = math.sin(w0) / 2 / Q + A = torch.exp(gain / 40.0 * math.log(10)) + alpha = torch.sin(w0) / 2 / Q b0 = 1 + alpha * A - b1 = -2 * math.cos(w0) + b1 = -2 * torch.cos(w0) b2 = 1 - alpha * A a0 = 1 + alpha / A - a1 = -2 * math.cos(w0) + a1 = -2 * torch.cos(w0) a2 = 1 - alpha / A return biquad(waveform, b0, b1, b2, a0, a1, a2) @@ -790,20 +830,25 @@ def highpass_biquad( Args: waveform (Tensor): audio waveform of dimension of `(..., time)` sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - cutoff_freq (float): filter cutoff frequency - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + cutoff_freq (float or torch.Tensor): filter cutoff frequency + Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) Returns: Tensor: Waveform dimension of `(..., time)` """ + dtype = waveform.dtype + device = waveform.device + cutoff_freq = torch.as_tensor(cutoff_freq, dtype=dtype, device=device) + Q = torch.as_tensor(Q, dtype=dtype, device=device) + w0 = 2 * math.pi * cutoff_freq / sample_rate - alpha = math.sin(w0) / 2.0 / Q + alpha = torch.sin(w0) / 2.0 / Q - b0 = (1 + math.cos(w0)) / 2 - b1 = -1 - math.cos(w0) + b0 = (1 + torch.cos(w0)) / 2 + b1 = -1 - torch.cos(w0) b2 = b0 a0 = 1 + alpha - a1 = -2 * math.cos(w0) + a1 = -2 * torch.cos(w0) a2 = 1 - alpha return biquad(waveform, b0, b1, b2, a0, a1, a2) @@ -924,20 +969,25 @@ def lowpass_biquad( Args: waveform (torch.Tensor): audio waveform of dimension of `(..., time)` sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - cutoff_freq (float): filter cutoff frequency - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) + cutoff_freq (float or torch.Tensor): filter cutoff frequency + Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``) Returns: Tensor: Waveform of dimension of `(..., time)` """ + dtype = waveform.dtype + device = waveform.device + cutoff_freq = torch.as_tensor(cutoff_freq, dtype=dtype, device=device) + Q = torch.as_tensor(Q, dtype=dtype, device=device) + w0 = 2 * math.pi * cutoff_freq / sample_rate - alpha = math.sin(w0) / 2 / Q + alpha = torch.sin(w0) / 2 / Q - b0 = (1 - math.cos(w0)) / 2 - b1 = 1 - math.cos(w0) + b0 = (1 - torch.cos(w0)) / 2 + b1 = 1 - torch.cos(w0) b2 = b0 a0 = 1 + alpha - a1 = -2 * math.cos(w0) + a1 = -2 * torch.cos(w0) a2 = 1 - alpha return biquad(waveform, b0, b1, b2, a0, a1, a2) @@ -1176,9 +1226,9 @@ def treble_biquad( Args: waveform (Tensor): audio waveform of dimension of `(..., time)` sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) - gain (float): desired gain at the boost (or attenuation) in dB. - central_freq (float, optional): central frequency (in Hz). (Default: ``3000``) - Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). + gain (float or torch.Tensor): desired gain at the boost (or attenuation) in dB. + central_freq (float or torch.Tensor, optional): central frequency (in Hz). (Default: ``3000``) + Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). Returns: Tensor: Waveform of dimension of `(..., time)` @@ -1187,13 +1237,19 @@ def treble_biquad( http://sox.sourceforge.net/sox.html https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF """ + dtype = waveform.dtype + device = waveform.device + central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device) + Q = torch.as_tensor(Q, dtype=dtype, device=device) + gain = torch.as_tensor(gain, dtype=dtype, device=device) + w0 = 2 * math.pi * central_freq / sample_rate - alpha = math.sin(w0) / 2 / Q - A = math.exp(gain / 40 * math.log(10)) + alpha = torch.sin(w0) / 2 / Q + A = torch.exp(gain / 40 * math.log(10)) - temp1 = 2 * math.sqrt(A) * alpha - temp2 = (A - 1) * math.cos(w0) - temp3 = (A + 1) * math.cos(w0) + temp1 = 2 * torch.sqrt(A) * alpha + temp2 = (A - 1) * torch.cos(w0) + temp3 = (A + 1) * torch.cos(w0) b0 = A * ((A + 1) + temp2 + temp1) b1 = -2 * A * ((A - 1) + temp3) diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index 73496bf4cc..9d23a2cace 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -48,7 +48,8 @@ def spectrogram( normalized: bool, center: bool = True, pad_mode: str = "reflect", - onesided: bool = True + onesided: bool = True, + return_complex: bool = False, ) -> Tensor: r"""Create a spectrogram or a batch of spectrograms from a raw audio signal. The spectrogram can be either magnitude-only or complex. @@ -71,12 +72,30 @@ def spectrogram( :attr:`center` is ``True``. Default: ``"reflect"`` onesided (bool, optional): controls whether to return half of results to avoid redundancy. Default: ``True`` + return_complex (bool, optional): + ``return_complex = True``, this function returns the resulting Tensor in + complex dtype, otherwise it returns the resulting Tensor in real dtype with extra + dimension for real and imaginary parts. (see ``torch.view_as_real``). + When ``power`` is provided, the value must be False, as the resulting + Tensor represents real-valued power. Returns: Tensor: Dimension (..., freq, time), freq is ``n_fft // 2 + 1`` and ``n_fft`` is the number of Fourier bins, and time is the number of window hops (n_frame). """ + if power is None and not return_complex: + warnings.warn( + "The use of pseudo complex type in spectrogram is now deprecated." + "Please migrate to native complex type by providing `return_complex=True`. " + "Please refer to https://github.com/pytorch/audio/issues/1337 " + "for more details about torchaudio's plan to migrate to native complex type." + ) + + if power is not None and return_complex: + raise ValueError( + 'When `power` is provided, the return value is real-valued. ' + 'Therefore, `return_complex` must be False.') if pad > 0: # TODO add "with torch.no_grad():" back when JIT supports it @@ -109,7 +128,19 @@ def spectrogram( if power == 1.0: return spec_f.abs() return spec_f.abs().pow(power) - return torch.view_as_real(spec_f) + if not return_complex: + return torch.view_as_real(spec_f) + return spec_f + + +def _get_complex_dtype(real_dtype: torch.dtype): + if real_dtype == torch.double: + return torch.cdouble + if real_dtype == torch.float: + return torch.cfloat + if real_dtype == torch.half: + return torch.complex32 + raise ValueError(f'Unexpected dtype {real_dtype}') def griffinlim( @@ -167,52 +198,49 @@ def griffinlim( specgram = specgram.pow(1 / power) - # randomly initialize the phase - batch, freq, frames = specgram.size() + # initialize the phase if rand_init: - angles = 2 * math.pi * torch.rand(batch, freq, frames) + angles = torch.rand( + specgram.size(), + dtype=_get_complex_dtype(specgram.dtype), device=specgram.device) else: - angles = torch.zeros(batch, freq, frames) - angles = torch.stack([angles.cos(), angles.sin()], dim=-1) \ - .to(dtype=specgram.dtype, device=specgram.device) - specgram = specgram.unsqueeze(-1).expand_as(angles) + angles = torch.full( + specgram.size(), 1, + dtype=_get_complex_dtype(specgram.dtype), device=specgram.device) # And initialize the previous iterate to 0 - rebuilt = torch.tensor(0.) - + tprev = torch.tensor(0., dtype=specgram.dtype, device=specgram.device) for _ in range(n_iter): - # Store the previous iterate - tprev = rebuilt - # Invert with our current estimate of the phases inverse = torch.istft(specgram * angles, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, - length=length).float() + length=length) # Rebuild the spectrogram - rebuilt = torch.view_as_real( - torch.stft( - input=inverse, - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - window=window, - center=True, - pad_mode='reflect', - normalized=False, - onesided=True, - return_complex=True, - ) + rebuilt = torch.stft( + input=inverse, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + center=True, + pad_mode='reflect', + normalized=False, + onesided=True, + return_complex=True, ) # Update our phase estimates angles = rebuilt if momentum: angles = angles - tprev.mul_(momentum / (1 + momentum)) - angles = angles.div(complex_norm(angles).add(1e-16).unsqueeze(-1).expand_as(angles)) + angles = angles.div(angles.abs().add(1e-16)) + + # Store the previous iterate + tprev = rebuilt # Return the final phase estimates waveform = torch.istft(specgram * angles, @@ -505,6 +533,12 @@ def mu_law_decoding( return x +@_mod_utils.deprecated( + "Please convert the input Tensor to complex type with `torch.view_as_complex` then " + "use `torch.abs`. " + "Please refer to https://github.com/pytorch/audio/issues/1337 " + "for more details about torchaudio's plan to migrate to native complex type." +) def complex_norm( complex_tensor: Tensor, power: float = 1.0 @@ -524,6 +558,12 @@ def complex_norm( return complex_tensor.pow(2.).sum(-1).pow(0.5 * power) +@_mod_utils.deprecated( + "Please convert the input Tensor to complex type with `torch.view_as_complex` then " + "use `torch.angle`. " + "Please refer to https://github.com/pytorch/audio/issues/1337 " + "for more details about torchaudio's plan to migrate to native complex type." +) def angle( complex_tensor: Tensor ) -> Tensor: @@ -565,14 +605,29 @@ def phase_vocoder( factor of ``rate``. Args: - complex_specgrams (Tensor): Dimension of `(..., freq, time, complex=2)` + complex_specgrams (Tensor): + Either a real tensor of dimension of ``(..., freq, num_frame, complex=2)`` + or a tensor of dimension ``(..., freq, num_frame)`` with complex dtype. rate (float): Speed-up factor phase_advance (Tensor): Expected phase advance in each bin. Dimension of (freq, 1) Returns: - Tensor: Complex Specgrams Stretch with dimension of `(..., freq, ceil(time/rate), complex=2)` + Tensor: + Stretched spectrogram. The resulting tensor is of the same dtype as the input + spectrogram, but the number of frames is changed to ``ceil(num_frame / rate)``. - Example + Example - With Tensor of complex dtype + >>> freq, hop_length = 1025, 512 + >>> # (channel, freq, time) + >>> complex_specgrams = torch.randn(2, freq, 300, dtype=torch.cfloat) + >>> rate = 1.3 # Speed up by 30% + >>> phase_advance = torch.linspace( + >>> 0, math.pi * hop_length, freq)[..., None] + >>> x = phase_vocoder(complex_specgrams, rate, phase_advance) + >>> x.shape # with 231 == ceil(300 / 1.3) + torch.Size([2, 1025, 231]) + + Example - With Tensor of real dtype and extra dimension for complex field >>> freq, hop_length = 1025, 512 >>> # (channel, freq, time, complex=2) >>> complex_specgrams = torch.randn(2, freq, 300, 2) @@ -583,32 +638,57 @@ def phase_vocoder( >>> x.shape # with 231 == ceil(300 / 1.3) torch.Size([2, 1025, 231, 2]) """ + if rate == 1.0: + return complex_specgrams + + if not complex_specgrams.is_complex(): + warnings.warn( + "The use of pseudo complex type in `torchaudio.functional.phase_vocoder` and " + "`torchaudio.transforms.TimeStretch` is now deprecated." + "Please migrate to native complex type by converting the input tensor with " + "`torch.view_as_complex`. " + "Please refer to https://github.com/pytorch/audio/issues/1337 " + "for more details about torchaudio's plan to migrate to native complex type." + ) + if complex_specgrams.size(-1) != 2: + raise ValueError( + "complex_specgrams must be either native complex tensors or " + "real valued tensors with shape (..., 2)") + + is_complex = complex_specgrams.is_complex() + + if not is_complex: + complex_specgrams = torch.view_as_complex(complex_specgrams) # pack batch shape = complex_specgrams.size() - complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-3:])) - - time_steps = torch.arange(0, - complex_specgrams.size(-2), - rate, - device=complex_specgrams.device, - dtype=complex_specgrams.dtype) + complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-2:])) + + # Figures out the corresponding real dtype, i.e. complex128 -> float64, complex64 -> float32 + # Note torch.real is a view so it does not incur any memory copy. + real_dtype = torch.real(complex_specgrams).dtype + time_steps = torch.arange( + 0, + complex_specgrams.size(-1), + rate, + device=complex_specgrams.device, + dtype=real_dtype) alphas = time_steps % 1.0 - phase_0 = angle(complex_specgrams[..., :1, :]) + phase_0 = complex_specgrams[..., :1].angle() # Time Padding - complex_specgrams = torch.nn.functional.pad(complex_specgrams, [0, 0, 0, 2]) + complex_specgrams = torch.nn.functional.pad(complex_specgrams, [0, 2]) # (new_bins, freq, 2) - complex_specgrams_0 = complex_specgrams.index_select(-2, time_steps.long()) - complex_specgrams_1 = complex_specgrams.index_select(-2, (time_steps + 1).long()) + complex_specgrams_0 = complex_specgrams.index_select(-1, time_steps.long()) + complex_specgrams_1 = complex_specgrams.index_select(-1, (time_steps + 1).long()) - angle_0 = angle(complex_specgrams_0) - angle_1 = angle(complex_specgrams_1) + angle_0 = complex_specgrams_0.angle() + angle_1 = complex_specgrams_1.angle() - norm_0 = torch.norm(complex_specgrams_0, p=2, dim=-1) - norm_1 = torch.norm(complex_specgrams_1, p=2, dim=-1) + norm_0 = complex_specgrams_0.abs() + norm_1 = complex_specgrams_1.abs() phase = angle_1 - angle_0 - phase_advance phase = phase - 2 * math.pi * torch.round(phase / (2 * math.pi)) @@ -620,14 +700,13 @@ def phase_vocoder( mag = alphas * norm_1 + (1 - alphas) * norm_0 - real_stretch = mag * torch.cos(phase_acc) - imag_stretch = mag * torch.sin(phase_acc) - - complex_specgrams_stretch = torch.stack([real_stretch, imag_stretch], dim=-1) + complex_specgrams_stretch = torch.polar(mag, phase_acc) # unpack batch - complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-3] + complex_specgrams_stretch.shape[1:]) + complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-2] + complex_specgrams_stretch.shape[1:]) + if not is_complex: + return torch.view_as_real(complex_specgrams_stretch) return complex_specgrams_stretch diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 6f3ea1b6e1..96832167d2 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -52,6 +52,12 @@ class Spectrogram(torch.nn.Module): :attr:`center` is ``True``. Default: ``"reflect"`` onesided (bool, optional): controls whether to return half of results to avoid redundancy Default: ``True`` + return_complex (bool, optional): + ``return_complex = True``, this function returns the resulting Tensor in + complex dtype, otherwise it returns the resulting Tensor in real dtype with extra + dimension for real and imaginary parts. (see ``torch.view_as_real``). + When ``power`` is provided, the value must be False, as the resulting + Tensor represents real-valued power. """ __constants__ = ['n_fft', 'win_length', 'hop_length', 'pad', 'power', 'normalized'] @@ -66,7 +72,8 @@ def __init__(self, wkwargs: Optional[dict] = None, center: bool = True, pad_mode: str = "reflect", - onesided: bool = True) -> None: + onesided: bool = True, + return_complex: bool = False) -> None: super(Spectrogram, self).__init__() self.n_fft = n_fft # number of FFT bins. the returned STFT result will have n_fft // 2 + 1 @@ -81,6 +88,7 @@ def __init__(self, self.center = center self.pad_mode = pad_mode self.onesided = onesided + self.return_complex = return_complex def forward(self, waveform: Tensor) -> Tensor: r""" @@ -103,7 +111,8 @@ def forward(self, waveform: Tensor) -> Tensor: self.normalized, self.center, self.pad_mode, - self.onesided + self.onesided, + self.return_complex, ) @@ -146,7 +155,7 @@ class GriffinLim(torch.nn.Module): | "Signal estimation from modified short-time Fourier transform," | IEEE Trans. ASSP, vol.32, no.2, pp.236โ€“243, Apr. 1984. """ - __constants__ = ['n_fft', 'n_iter', 'win_length', 'hop_length', 'power', 'normalized', + __constants__ = ['n_fft', 'n_iter', 'win_length', 'hop_length', 'power', 'length', 'momentum', 'rand_init'] def __init__(self, @@ -163,7 +172,7 @@ def __init__(self, super(GriffinLim, self).__init__() assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum) - assert momentum > 0, 'momentum={} < 0'.format(momentum) + assert momentum >= 0, 'momentum={} < 0'.format(momentum) self.n_fft = n_fft self.n_iter = n_iter @@ -729,26 +738,24 @@ def __init__(self, def forward(self, complex_specgrams: Tensor, overriding_rate: Optional[float] = None) -> Tensor: r""" Args: - complex_specgrams (Tensor): complex spectrogram (..., freq, time, complex=2). + complex_specgrams (Tensor): + Either a real tensor of dimension of ``(..., freq, num_frame, complex=2)`` + or a tensor of dimension ``(..., freq, num_frame)`` with complex dtype. overriding_rate (float or None, optional): speed up to apply to this batch. If no rate is passed, use ``self.fixed_rate``. (Default: ``None``) Returns: - Tensor: Stretched complex spectrogram of dimension (..., freq, ceil(time/rate), complex=2). + Tensor: + Stretched spectrogram. The resulting tensor is of the same dtype as the input + spectrogram, but the number of frames is changed to ``ceil(num_frame / rate)``. """ - assert complex_specgrams.size(-1) == 2, "complex_specgrams should be a complex tensor, shape (..., complex=2)" - if overriding_rate is None: + if self.fixed_rate is None: + raise ValueError( + "If no fixed_rate is specified, must pass a valid rate to the forward method.") rate = self.fixed_rate - if rate is None: - raise ValueError("If no fixed_rate is specified" - ", must pass a valid rate to the forward method.") else: rate = overriding_rate - - if rate == 1.0: - return complex_specgrams - return F.phase_vocoder(complex_specgrams, rate, self.phase_advance) @@ -761,6 +768,7 @@ class Fade(torch.nn.Module): fade_shape (str, optional): Shape of fade. Must be one of: "quarter_sine", "half_sine", "linear", "logarithmic", "exponential". (Default: ``"linear"``) """ + def __init__(self, fade_in_len: int = 0, fade_out_len: int = 0, @@ -870,6 +878,7 @@ class FrequencyMasking(_AxisMasking): example/channel in the batch. (Default: ``False``) This option is applicable only when the input tensor is 4D. """ + def __init__(self, freq_mask_param: int, iid_masks: bool = False) -> None: super(FrequencyMasking, self).__init__(freq_mask_param, 1, iid_masks) @@ -884,6 +893,7 @@ class TimeMasking(_AxisMasking): example/channel in the batch. (Default: ``False``) This option is applicable only when the input tensor is 4D. """ + def __init__(self, time_mask_param: int, iid_masks: bool = False) -> None: super(TimeMasking, self).__init__(time_mask_param, 2, iid_masks) From 93fb018a30cbb761deecf7fc83c1e2f0b5f8ee8d Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Fri, 16 Apr 2021 07:44:03 -0700 Subject: [PATCH 38/73] Remove CI script that is removed in GitHub Summary: `run-clang-format.py` has been renamed to `run_clang_format.py` in github.com/pytorch/audio but there exists two of them in fbcode. Removing the unneeded one. Reviewed By: carolineechen Differential Revision: D27822084 fbshipit-source-id: 132de34b85b866342757bf4648cc1b6b81ff12be --- .../linux/scripts/run-clang-format.py | 340 ------------------ 1 file changed, 340 deletions(-) delete mode 100755 .circleci/unittest/linux/scripts/run-clang-format.py diff --git a/.circleci/unittest/linux/scripts/run-clang-format.py b/.circleci/unittest/linux/scripts/run-clang-format.py deleted file mode 100755 index fd2913bd70..0000000000 --- a/.circleci/unittest/linux/scripts/run-clang-format.py +++ /dev/null @@ -1,340 +0,0 @@ -#!/usr/bin/env python -"""A wrapper script around clang-format, suitable for linting multiple files -and to use for continuous integration. - -This is an alternative API for the clang-format command line. -It runs over multiple files and directories in parallel. -A diff output is produced and a sensible exit code is returned. - -""" - -import argparse -import codecs -import difflib -import fnmatch -import io -import multiprocessing -import os -import signal -import subprocess -import sys -import traceback - -from functools import partial - -try: - from subprocess import DEVNULL # py3k -except ImportError: - DEVNULL = open(os.devnull, "wb") - - -DEFAULT_EXTENSIONS = 'c,h,C,H,cpp,hpp,cc,hh,c++,h++,cxx,hxx,cu' - - -class ExitStatus: - SUCCESS = 0 - DIFF = 1 - TROUBLE = 2 - - -def list_files(files, recursive=False, extensions=None, exclude=None): - if extensions is None: - extensions = [] - if exclude is None: - exclude = [] - - out = [] - for file in files: - if recursive and os.path.isdir(file): - for dirpath, dnames, fnames in os.walk(file): - fpaths = [os.path.join(dirpath, fname) for fname in fnames] - for pattern in exclude: - # os.walk() supports trimming down the dnames list - # by modifying it in-place, - # to avoid unnecessary directory listings. - dnames[:] = [ - x for x in dnames - if - not fnmatch.fnmatch(os.path.join(dirpath, x), pattern) - ] - fpaths = [ - x for x in fpaths if not fnmatch.fnmatch(x, pattern) - ] - for f in fpaths: - ext = os.path.splitext(f)[1][1:] - if ext in extensions: - out.append(f) - else: - out.append(file) - return out - - -def make_diff(file, original, reformatted): - return list( - difflib.unified_diff( - original, - reformatted, - fromfile='{}\t(original)'.format(file), - tofile='{}\t(reformatted)'.format(file), - n=3)) - - -class DiffError(Exception): - def __init__(self, message, errs=None): - super(DiffError, self).__init__(message) - self.errs = errs or [] - - -class UnexpectedError(Exception): - def __init__(self, message, exc=None): - super(UnexpectedError, self).__init__(message) - self.formatted_traceback = traceback.format_exc() - self.exc = exc - - -def run_clang_format_diff_wrapper(args, file): - try: - ret = run_clang_format_diff(args, file) - return ret - except DiffError: - raise - except Exception as e: - raise UnexpectedError('{}: {}: {}'.format(file, e.__class__.__name__, - e), e) - - -def run_clang_format_diff(args, file): - try: - with io.open(file, 'r', encoding='utf-8') as f: - original = f.readlines() - except IOError as exc: - raise DiffError(str(exc)) - invocation = [args.clang_format_executable, file] - - # Use of utf-8 to decode the process output. - # - # Hopefully, this is the correct thing to do. - # - # It's done due to the following assumptions (which may be incorrect): - # - clang-format will returns the bytes read from the files as-is, - # without conversion, and it is already assumed that the files use utf-8. - # - if the diagnostics were internationalized, they would use utf-8: - # > Adding Translations to Clang - # > - # > Not possible yet! - # > Diagnostic strings should be written in UTF-8, - # > the client can translate to the relevant code page if needed. - # > Each translation completely replaces the format string - # > for the diagnostic. - # > -- http://clang.llvm.org/docs/InternalsManual.html#internals-diag-translation - - try: - proc = subprocess.Popen( - invocation, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - universal_newlines=True, - encoding='utf-8') - except OSError as exc: - raise DiffError( - "Command '{}' failed to start: {}".format( - subprocess.list2cmdline(invocation), exc - ) - ) - proc_stdout = proc.stdout - proc_stderr = proc.stderr - - # hopefully the stderr pipe won't get full and block the process - outs = list(proc_stdout.readlines()) - errs = list(proc_stderr.readlines()) - proc.wait() - if proc.returncode: - raise DiffError( - "Command '{}' returned non-zero exit status {}".format( - subprocess.list2cmdline(invocation), proc.returncode - ), - errs, - ) - return make_diff(file, original, outs), errs - - -def bold_red(s): - return '\x1b[1m\x1b[31m' + s + '\x1b[0m' - - -def colorize(diff_lines): - def bold(s): - return '\x1b[1m' + s + '\x1b[0m' - - def cyan(s): - return '\x1b[36m' + s + '\x1b[0m' - - def green(s): - return '\x1b[32m' + s + '\x1b[0m' - - def red(s): - return '\x1b[31m' + s + '\x1b[0m' - - for line in diff_lines: - if line[:4] in ['--- ', '+++ ']: - yield bold(line) - elif line.startswith('@@ '): - yield cyan(line) - elif line.startswith('+'): - yield green(line) - elif line.startswith('-'): - yield red(line) - else: - yield line - - -def print_diff(diff_lines, use_color): - if use_color: - diff_lines = colorize(diff_lines) - sys.stdout.writelines(diff_lines) - - -def print_trouble(prog, message, use_colors): - error_text = 'error:' - if use_colors: - error_text = bold_red(error_text) - print("{}: {} {}".format(prog, error_text, message), file=sys.stderr) - - -def main(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - '--clang-format-executable', - metavar='EXECUTABLE', - help='path to the clang-format executable', - default='clang-format') - parser.add_argument( - '--extensions', - help='comma separated list of file extensions (default: {})'.format( - DEFAULT_EXTENSIONS), - default=DEFAULT_EXTENSIONS) - parser.add_argument( - '-r', - '--recursive', - action='store_true', - help='run recursively over directories') - parser.add_argument('files', metavar='file', nargs='+') - parser.add_argument( - '-q', - '--quiet', - action='store_true') - parser.add_argument( - '-j', - metavar='N', - type=int, - default=0, - help='run N clang-format jobs in parallel' - ' (default number of cpus + 1)') - parser.add_argument( - '--color', - default='auto', - choices=['auto', 'always', 'never'], - help='show colored diff (default: auto)') - parser.add_argument( - '-e', - '--exclude', - metavar='PATTERN', - action='append', - default=[], - help='exclude paths matching the given glob-like pattern(s)' - ' from recursive search') - - args = parser.parse_args() - - # use default signal handling, like diff return SIGINT value on ^C - # https://bugs.python.org/issue14229#msg156446 - signal.signal(signal.SIGINT, signal.SIG_DFL) - try: - signal.SIGPIPE - except AttributeError: - # compatibility, SIGPIPE does not exist on Windows - pass - else: - signal.signal(signal.SIGPIPE, signal.SIG_DFL) - - colored_stdout = False - colored_stderr = False - if args.color == 'always': - colored_stdout = True - colored_stderr = True - elif args.color == 'auto': - colored_stdout = sys.stdout.isatty() - colored_stderr = sys.stderr.isatty() - - version_invocation = [args.clang_format_executable, str("--version")] - try: - subprocess.check_call(version_invocation, stdout=DEVNULL) - except subprocess.CalledProcessError as e: - print_trouble(parser.prog, str(e), use_colors=colored_stderr) - return ExitStatus.TROUBLE - except OSError as e: - print_trouble( - parser.prog, - "Command '{}' failed to start: {}".format( - subprocess.list2cmdline(version_invocation), e - ), - use_colors=colored_stderr, - ) - return ExitStatus.TROUBLE - - retcode = ExitStatus.SUCCESS - files = list_files( - args.files, - recursive=args.recursive, - exclude=args.exclude, - extensions=args.extensions.split(',')) - - if not files: - return - - njobs = args.j - if njobs == 0: - njobs = multiprocessing.cpu_count() + 1 - njobs = min(len(files), njobs) - - if njobs == 1: - # execute directly instead of in a pool, - # less overhead, simpler stacktraces - it = (run_clang_format_diff_wrapper(args, file) for file in files) - pool = None - else: - pool = multiprocessing.Pool(njobs) - it = pool.imap_unordered( - partial(run_clang_format_diff_wrapper, args), files) - while True: - try: - outs, errs = next(it) - except StopIteration: - break - except DiffError as e: - print_trouble(parser.prog, str(e), use_colors=colored_stderr) - retcode = ExitStatus.TROUBLE - sys.stderr.writelines(e.errs) - except UnexpectedError as e: - print_trouble(parser.prog, str(e), use_colors=colored_stderr) - sys.stderr.write(e.formatted_traceback) - retcode = ExitStatus.TROUBLE - # stop at the first unexpected error, - # something could be very wrong, - # don't process all files unnecessarily - if pool: - pool.terminate() - break - else: - sys.stderr.writelines(errs) - if outs == []: - continue - if not args.quiet: - print_diff(outs, use_color=colored_stdout) - if retcode == ExitStatus.SUCCESS: - retcode = ExitStatus.DIFF - return retcode - - -if __name__ == '__main__': - sys.exit(main()) From dfb4943515480be512a717491d08ca839d860767 Mon Sep 17 00:00:00 2001 From: Artyom Astafurov Date: Thu, 22 Apr 2021 08:39:10 -0700 Subject: [PATCH 39/73] Import torchaudio #1466 9d50acf Reviewed By: vincentqb, mthrok Differential Revision: D27922742 fbshipit-source-id: 6fa96728171687089abe6d734c23fc98bd29430b --- .circleci/config.yml | 26 +-- .circleci/config.yml.in | 26 +-- .circleci/unittest/linux/scripts/install.sh | 2 +- .circleci/unittest/linux/scripts/run_test.sh | 1 + .circleci/unittest/windows/scripts/install.sh | 2 +- .../unittest/windows/scripts/run_test.sh | 1 + docs/source/conf.py | 3 +- examples/pipeline_wav2letter/README.md | 6 +- examples/pipeline_wavernn/main.py | 10 +- examples/pipeline_wavernn/processing.py | 27 +-- .../functional/functional_cpu_test.py | 207 +----------------- .../functional/functional_cuda_test.py | 18 +- .../functional/functional_impl.py | 169 +++++++++++++- .../transforms/autograd_test_impl.py | 7 + third_party/sox/CMakeLists.txt | 1 + torchaudio/_internal/misc_ops.py | 30 --- torchaudio/functional/filtering.py | 2 +- torchaudio/transforms.py | 2 + 18 files changed, 227 insertions(+), 313 deletions(-) delete mode 100644 torchaudio/_internal/misc_ops.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 3bf72c3a6d..2cbddf2e3c 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -424,12 +424,10 @@ jobs: - run: name: Run tests command: .circleci/unittest/linux/scripts/run_test.sh - - run: - name: Post process - command: .circleci/unittest/linux/scripts/post_process.sh - store_test_results: path: test-results - + - store_artifacts: + path: test/htmlcov unittest_linux_gpu: <<: *binary_common machine: @@ -456,11 +454,10 @@ jobs: - run: name: Run tests command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh - - run: - name: Post Process - command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/post_process.sh - store_test_results: path: test-results + - store_artifacts: + path: test/htmlcov unittest_windows_cpu: <<: *binary_common @@ -479,11 +476,10 @@ jobs: - run: name: Run tests command: .circleci/unittest/windows/scripts/run_test.sh - - run: - name: Post process - command: .circleci/unittest/windows/scripts/post_process.sh - store_test_results: path: test-results + - store_artifacts: + path: test/htmlcov unittest_windows_gpu: <<: *binary_common @@ -505,11 +501,10 @@ jobs: - run: name: Run tests command: .circleci/unittest/windows/scripts/run_test.sh - - run: - name: Post process - command: .circleci/unittest/windows/scripts/post_process.sh - store_test_results: path: test-results + - store_artifacts: + path: test/htmlcov unittest_macos_cpu: <<: *binary_common @@ -532,11 +527,10 @@ jobs: - run: name: Run tests command: .circleci/unittest/linux/scripts/run_test.sh - - run: - name: Post process - command: .circleci/unittest/linux/scripts/post_process.sh - store_test_results: path: test-results + - store_artifacts: + path: test/htmlcov stylecheck: <<: *binary_common diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 8e01b1da56..f15767041e 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -424,12 +424,10 @@ jobs: - run: name: Run tests command: .circleci/unittest/linux/scripts/run_test.sh - - run: - name: Post process - command: .circleci/unittest/linux/scripts/post_process.sh - store_test_results: path: test-results - + - store_artifacts: + path: test/htmlcov unittest_linux_gpu: <<: *binary_common machine: @@ -456,11 +454,10 @@ jobs: - run: name: Run tests command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh - - run: - name: Post Process - command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/post_process.sh - store_test_results: path: test-results + - store_artifacts: + path: test/htmlcov unittest_windows_cpu: <<: *binary_common @@ -479,11 +476,10 @@ jobs: - run: name: Run tests command: .circleci/unittest/windows/scripts/run_test.sh - - run: - name: Post process - command: .circleci/unittest/windows/scripts/post_process.sh - store_test_results: path: test-results + - store_artifacts: + path: test/htmlcov unittest_windows_gpu: <<: *binary_common @@ -505,11 +501,10 @@ jobs: - run: name: Run tests command: .circleci/unittest/windows/scripts/run_test.sh - - run: - name: Post process - command: .circleci/unittest/windows/scripts/post_process.sh - store_test_results: path: test-results + - store_artifacts: + path: test/htmlcov unittest_macos_cpu: <<: *binary_common @@ -532,11 +527,10 @@ jobs: - run: name: Run tests command: .circleci/unittest/linux/scripts/run_test.sh - - run: - name: Post process - command: .circleci/unittest/linux/scripts/post_process.sh - store_test_results: path: test-results + - store_artifacts: + path: test/htmlcov stylecheck: <<: *binary_common diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh index 5638a28850..3b0cd4bc4c 100755 --- a/.circleci/unittest/linux/scripts/install.sh +++ b/.circleci/unittest/linux/scripts/install.sh @@ -56,5 +56,5 @@ fi ( set -x conda install -y -c conda-forge ${NUMBA_DEV_CHANNEL} 'librosa>=0.8.0' parameterized 'requests>=2.20' - pip install kaldi-io SoundFile codecov pytest pytest-cov scipy + pip install kaldi-io SoundFile coverage pytest pytest-cov scipy ) diff --git a/.circleci/unittest/linux/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh index e083743630..4e8d24748b 100755 --- a/.circleci/unittest/linux/scripts/run_test.sh +++ b/.circleci/unittest/linux/scripts/run_test.sh @@ -24,3 +24,4 @@ declare -a args=( cd test pytest "${args[@]}" torchaudio_unittest +coverage html diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh index be0c890614..9ed4f203ad 100644 --- a/.circleci/unittest/windows/scripts/install.sh +++ b/.circleci/unittest/windows/scripts/install.sh @@ -44,5 +44,5 @@ fi ( set -x conda install -y -c conda-forge ${NUMBA_DEV_CHANNEL} 'librosa>=0.8.0' parameterized 'requests>=2.20' - pip install kaldi-io SoundFile codecov pytest pytest-cov scipy + pip install kaldi-io SoundFile coverage pytest pytest-cov scipy ) diff --git a/.circleci/unittest/windows/scripts/run_test.sh b/.circleci/unittest/windows/scripts/run_test.sh index 7fdd1d4c6d..c6e7ffd37f 100644 --- a/.circleci/unittest/windows/scripts/run_test.sh +++ b/.circleci/unittest/windows/scripts/run_test.sh @@ -8,3 +8,4 @@ conda activate ./env python -m torch.utils.collect_env cd test pytest --cov=torchaudio --junitxml=../test-results/junit.xml -v --durations 20 torchaudio_unittest +coverage html diff --git a/docs/source/conf.py b/docs/source/conf.py index 3bc89e999d..f78858e381 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -123,7 +123,8 @@ 'collapse_navigation': False, 'display_version': True, 'logo_only': True, - 'navigation_with_keys': True + 'navigation_with_keys': True, + 'analytics_id': 'UA-117752657-2', } html_logo = '_static/img/pytorch-logo-dark.svg' diff --git a/examples/pipeline_wav2letter/README.md b/examples/pipeline_wav2letter/README.md index 8117d19c91..49cca3c83d 100644 --- a/examples/pipeline_wav2letter/README.md +++ b/examples/pipeline_wav2letter/README.md @@ -4,8 +4,13 @@ This is an example pipeline for speech recognition using a greedy or Viterbi CTC More information about each command line parameters is available with the `--help` option. An example can be invoked as follows. ``` +DATASET_ROOT = // +DATASET_FOLDER_IN_ARCHIVE = 'LibriSpeech' + python main.py \ --reduce-lr-valid \ + --dataset-root "${DATASET_ROOT}" \ + --dataset-folder-in-archive "${DATASET_FOLDER_IN_ARCHIVE}" \ --dataset-train train-clean-100 train-clean-360 train-other-500 \ --dataset-valid dev-clean \ --batch-size 128 \ @@ -15,7 +20,6 @@ python main.py \ --clip-grad 0. \ --gamma .99 \ --hop-length 160 \ - --n-hidden-channels 2000 \ --win-length 400 \ --n-bins 13 \ --normalize \ diff --git a/examples/pipeline_wavernn/main.py b/examples/pipeline_wavernn/main.py index 28ecb76fb9..2c67059cae 100644 --- a/examples/pipeline_wavernn/main.py +++ b/examples/pipeline_wavernn/main.py @@ -17,7 +17,7 @@ from datasets import collate_factory, split_process_dataset from losses import LongCrossEntropyLoss, MoLLoss -from processing import LinearToMel, NormalizeDB +from processing import NormalizeDB from utils import MetricLogger, count_parameters, save_checkpoint @@ -269,12 +269,12 @@ def main(args): } transforms = torch.nn.Sequential( - torchaudio.transforms.Spectrogram(**melkwargs), - LinearToMel( + torchaudio.transforms.MelSpectrogram( sample_rate=args.sample_rate, - n_fft=args.n_fft, n_mels=args.n_freq, - fmin=args.f_min, + f_min=args.f_min, + mel_scale='slaney', + **melkwargs, ), NormalizeDB(min_level_db=args.min_level_db, normalization=args.normalization), ) diff --git a/examples/pipeline_wavernn/processing.py b/examples/pipeline_wavernn/processing.py index dca45e97f4..9b06ea6d48 100644 --- a/examples/pipeline_wavernn/processing.py +++ b/examples/pipeline_wavernn/processing.py @@ -1,32 +1,7 @@ -import librosa import torch import torch.nn as nn -# TODO Replace by torchaudio, once https://github.com/pytorch/audio/pull/593 is resolved -class LinearToMel(nn.Module): - def __init__(self, sample_rate, n_fft, n_mels, fmin, htk=False, norm="slaney"): - super().__init__() - self.sample_rate = sample_rate - self.n_fft = n_fft - self.n_mels = n_mels - self.fmin = fmin - self.htk = htk - self.norm = norm - - def forward(self, specgram): - specgram = librosa.feature.melspectrogram( - S=specgram.squeeze(0).numpy(), - sr=self.sample_rate, - n_fft=self.n_fft, - n_mels=self.n_mels, - fmin=self.fmin, - htk=self.htk, - norm=self.norm, - ) - return torch.from_numpy(specgram) - - class NormalizeDB(nn.Module): r"""Normalize the spectrogram with a minimum db value """ @@ -37,7 +12,7 @@ def __init__(self, min_level_db, normalization): self.normalization = normalization def forward(self, specgram): - specgram = torch.log10(torch.clamp(specgram, min=1e-5)) + specgram = torch.log10(torch.clamp(specgram.squeeze(0), min=1e-5)) if self.normalization: return torch.clamp( (self.min_level_db - 20 * specgram) / self.min_level_db, min=0, max=1 diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py index b9c5a6f64e..6f497a3193 100644 --- a/test/torchaudio_unittest/functional/functional_cpu_test.py +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -1,23 +1,13 @@ -import math -import warnings - import torch -import torchaudio import torchaudio.functional as F -from parameterized import parameterized -import itertools import unittest +from parameterized import parameterized -from torchaudio_unittest import common_utils -from torchaudio_unittest.common_utils import ( - TorchaudioTestCase, - skipIfNoSox, -) - -from .functional_impl import Functional, FunctionalComplex +from torchaudio_unittest.common_utils import PytorchTestCase, TorchaudioTestCase, skipIfNoSox +from .functional_impl import Functional, FunctionalComplex, FunctionalCPUOnly -class TestFunctionalFloat32(Functional, common_utils.PytorchTestCase): +class TestFunctionalFloat32(Functional, FunctionalCPUOnly, PytorchTestCase): dtype = torch.float32 device = torch.device('cpu') @@ -26,206 +16,23 @@ def test_lfilter_9th_order_filter_stability(self): super().test_lfilter_9th_order_filter_stability() -class TestFunctionalFloat64(Functional, common_utils.PytorchTestCase): +class TestFunctionalFloat64(Functional, FunctionalCPUOnly, PytorchTestCase): dtype = torch.float64 device = torch.device('cpu') -class TestFunctionalComplex64(FunctionalComplex, common_utils.PytorchTestCase): +class TestFunctionalComplex64(FunctionalComplex, FunctionalCPUOnly, PytorchTestCase): complex_dtype = torch.complex64 real_dtype = torch.float32 device = torch.device('cpu') -class TestFunctionalComplex128(FunctionalComplex, common_utils.PytorchTestCase): +class TestFunctionalComplex128(FunctionalComplex, FunctionalCPUOnly, PytorchTestCase): complex_dtype = torch.complex128 real_dtype = torch.float64 device = torch.device('cpu') -class TestCreateFBMatrix(common_utils.TorchaudioTestCase): - def test_no_warning_high_n_freq(self): - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter("always") - F.create_fb_matrix(288, 0, 8000, 128, 16000) - assert len(w) == 0 - - def test_no_warning_low_n_mels(self): - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter("always") - F.create_fb_matrix(201, 0, 8000, 89, 16000) - assert len(w) == 0 - - def test_warning(self): - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter("always") - F.create_fb_matrix(201, 0, 8000, 128, 16000) - assert len(w) == 1 - - -class TestComputeDeltas(common_utils.TorchaudioTestCase): - """Test suite for correctness of compute_deltas""" - - def test_one_channel(self): - specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) - expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) - computed = F.compute_deltas(specgram, win_length=3) - self.assertEqual(computed, expected) - - def test_two_channels(self): - specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], - [1.0, 2.0, 3.0, 4.0]]]) - expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], - [0.5, 1.0, 1.0, 0.5]]]) - computed = F.compute_deltas(specgram, win_length=3) - self.assertEqual(computed, expected) - - -class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): - @parameterized.expand([(100,), (440,)]) - def test_pitch(self, frequency): - sample_rate = 44100 - test_sine_waveform = common_utils.get_sinusoid( - frequency=frequency, sample_rate=sample_rate, duration=5, - ) - - freq = torchaudio.functional.detect_pitch_frequency(test_sine_waveform, sample_rate) - - threshold = 1 - s = ((freq - frequency).abs() > threshold).sum() - self.assertFalse(s) - - -class Testamplitude_to_DB(common_utils.TorchaudioTestCase): - @parameterized.expand([ - ([100, 100],), - ([2, 100, 100],), - ([2, 2, 100, 100],), - ]) - def test_reversible(self, shape): - """Round trip between amplitude and db should return the original for various shape - - This implicitly also tests `DB_to_amplitude`. - - """ - amplitude_mult = 20. - power_mult = 10. - amin = 1e-10 - ref = 1.0 - db_mult = math.log10(max(amin, ref)) - - torch.manual_seed(0) - spec = torch.rand(*shape) * 200 - - # Spectrogram amplitude -> DB -> amplitude - db = F.amplitude_to_DB(spec, amplitude_mult, amin, db_mult, top_db=None) - x2 = F.DB_to_amplitude(db, ref, 0.5) - - self.assertEqual(x2, spec, atol=5e-5, rtol=1e-5) - - # Spectrogram power -> DB -> power - db = F.amplitude_to_DB(spec, power_mult, amin, db_mult, top_db=None) - x2 = F.DB_to_amplitude(db, ref, 1.) - - self.assertEqual(x2, spec) - - @parameterized.expand([ - ([100, 100],), - ([2, 100, 100],), - ([2, 2, 100, 100],), - ]) - def test_top_db_clamp(self, shape): - """Ensure values are properly clamped when `top_db` is supplied.""" - amplitude_mult = 20. - amin = 1e-10 - ref = 1.0 - db_mult = math.log10(max(amin, ref)) - top_db = 40. - - torch.manual_seed(0) - # A random tensor is used for increased entropy, but the max and min for - # each spectrogram still need to be predictable. The max determines the - # decibel cutoff, and the distance from the min must be large enough - # that it triggers a clamp. - spec = torch.rand(*shape) - # Ensure each spectrogram has a min of 0 and a max of 1. - spec -= spec.amin([-2, -1])[..., None, None] - spec /= spec.amax([-2, -1])[..., None, None] - # Expand the range to (0, 200) - wide enough to properly test clamping. - spec *= 200 - - decibels = F.amplitude_to_DB(spec, amplitude_mult, amin, - db_mult, top_db=top_db) - # Ensure the clamp was applied - below_limit = decibels < 6.0205 - assert not below_limit.any(), ( - "{} decibel values were below the expected cutoff:\n{}".format( - below_limit.sum().item(), decibels - ) - ) - # Ensure it didn't over-clamp - close_to_limit = decibels < 6.0207 - assert close_to_limit.any(), ( - f"No values were close to the limit. Did it over-clamp?\n{decibels}" - ) - - -class TestComplexNorm(common_utils.TorchaudioTestCase): - @parameterized.expand(list(itertools.product( - [(1, 2, 1025, 400, 2), (1025, 400, 2)], - [1, 2, 0.7] - ))) - def test_complex_norm(self, shape, power): - torch.random.manual_seed(42) - complex_tensor = torch.randn(*shape) - expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) - norm_tensor = F.complex_norm(complex_tensor, power) - self.assertEqual(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) - - -class TestMaskAlongAxis(common_utils.TorchaudioTestCase): - @parameterized.expand(list(itertools.product( - [(2, 1025, 400), (1, 201, 100)], - [100], - [0., 30.], - [1, 2] - ))) - def test_mask_along_axis(self, shape, mask_param, mask_value, axis): - torch.random.manual_seed(42) - specgram = torch.randn(*shape) - mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis) - - other_axis = 1 if axis == 2 else 2 - - masked_columns = (mask_specgram == mask_value).sum(other_axis) - num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum() - num_masked_columns //= mask_specgram.size(0) - - assert mask_specgram.size() == specgram.size() - assert num_masked_columns < mask_param - - -class TestMaskAlongAxisIID(common_utils.TorchaudioTestCase): - @parameterized.expand(list(itertools.product( - [100], - [0., 30.], - [2, 3] - ))) - def test_mask_along_axis_iid(self, mask_param, mask_value, axis): - torch.random.manual_seed(42) - specgrams = torch.randn(4, 2, 1025, 400) - - mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) - - other_axis = 2 if axis == 3 else 3 - - masked_columns = (mask_specgrams == mask_value).sum(other_axis) - num_masked_columns = (masked_columns == mask_specgrams.size(other_axis)).sum(-1) - - assert mask_specgrams.size() == specgrams.size() - assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() - - @skipIfNoSox class TestApplyCodec(TorchaudioTestCase): backend = "sox_io" diff --git a/test/torchaudio_unittest/functional/functional_cuda_test.py b/test/torchaudio_unittest/functional/functional_cuda_test.py index ebf8cd8326..054e2c3e4f 100644 --- a/test/torchaudio_unittest/functional/functional_cuda_test.py +++ b/test/torchaudio_unittest/functional/functional_cuda_test.py @@ -1,12 +1,12 @@ import torch import unittest -from torchaudio_unittest import common_utils +from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda from .functional_impl import Functional, FunctionalComplex -@common_utils.skipIfNoCuda -class TestFunctionalloat32(Functional, common_utils.PytorchTestCase): +@skipIfNoCuda +class TestFunctionalFloat32(Functional, PytorchTestCase): dtype = torch.float32 device = torch.device('cuda') @@ -15,21 +15,21 @@ def test_lfilter_9th_order_filter_stability(self): super().test_lfilter_9th_order_filter_stability() -@common_utils.skipIfNoCuda -class TestLFilterFloat64(Functional, common_utils.PytorchTestCase): +@skipIfNoCuda +class TestLFilterFloat64(Functional, PytorchTestCase): dtype = torch.float64 device = torch.device('cuda') -@common_utils.skipIfNoCuda -class TestFunctionalComplex64(FunctionalComplex, common_utils.PytorchTestCase): +@skipIfNoCuda +class TestFunctionalComplex64(FunctionalComplex, PytorchTestCase): complex_dtype = torch.complex64 real_dtype = torch.float32 device = torch.device('cuda') -@common_utils.skipIfNoCuda -class TestFunctionalComplex128(FunctionalComplex, common_utils.PytorchTestCase): +@skipIfNoCuda +class TestFunctionalComplex128(FunctionalComplex, PytorchTestCase): complex_dtype = torch.complex64 real_dtype = torch.float32 device = torch.device('cuda') diff --git a/test/torchaudio_unittest/functional/functional_impl.py b/test/torchaudio_unittest/functional/functional_impl.py index 8b890e9563..3c78ee3e90 100644 --- a/test/torchaudio_unittest/functional/functional_impl.py +++ b/test/torchaudio_unittest/functional/functional_impl.py @@ -1,15 +1,18 @@ -"""Test defintion common to CPU and CUDA""" +"""Test definition common to CPU and CUDA""" +import math +import itertools +import warnings + +import numpy as np import torch import torchaudio.functional as F from parameterized import parameterized -import numpy as np from scipy import signal -from torchaudio_unittest import common_utils -from torchaudio_unittest.common_utils import nested_params +from torchaudio_unittest.common_utils import TestBaseMixin, get_sinusoid, nested_params -class Functional(common_utils.TestBaseMixin): +class Functional(TestBaseMixin): def test_lfilter_simple(self): """ Create a very basic signal, @@ -91,7 +94,7 @@ def test_spectogram_grad_at_zero(self, power): assert not x.grad.isnan().sum() -class FunctionalComplex(common_utils.TestBaseMixin): +class FunctionalComplex(TestBaseMixin): complex_dtype = None real_dtype = None device = None @@ -125,3 +128,157 @@ def test_phase_vocoder_shape(self, rate, test_pseudo_complex): expected_shape = torch.Size([batch_size, num_freq, int(np.ceil(num_frames / rate))]) output_shape = (torch.view_as_complex(spec_stretch) if test_pseudo_complex else spec_stretch).shape assert output_shape == expected_shape + + +class FunctionalCPUOnly(TestBaseMixin): + def test_create_fb_matrix_no_warning_high_n_freq(self): + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + F.create_fb_matrix(288, 0, 8000, 128, 16000) + assert len(w) == 0 + + def test_create_fb_matrix_no_warning_low_n_mels(self): + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + F.create_fb_matrix(201, 0, 8000, 89, 16000) + assert len(w) == 0 + + def test_create_fb_matrix_warning(self): + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + F.create_fb_matrix(201, 0, 8000, 128, 16000) + assert len(w) == 1 + + def test_compute_deltas_one_channel(self): + specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) + expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) + computed = F.compute_deltas(specgram, win_length=3) + self.assertEqual(computed, expected) + + def test_compute_deltas_two_channels(self): + specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], + [1.0, 2.0, 3.0, 4.0]]]) + expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], + [0.5, 1.0, 1.0, 0.5]]]) + computed = F.compute_deltas(specgram, win_length=3) + self.assertEqual(computed, expected) + + @parameterized.expand([(100,), (440,)]) + def test_detect_pitch_frequency_pitch(self, frequency): + sample_rate = 44100 + test_sine_waveform = get_sinusoid( + frequency=frequency, sample_rate=sample_rate, duration=5 + ) + + freq = F.detect_pitch_frequency(test_sine_waveform, sample_rate) + + threshold = 1 + s = ((freq - frequency).abs() > threshold).sum() + self.assertFalse(s) + + @parameterized.expand([([100, 100],), ([2, 100, 100],), ([2, 2, 100, 100],)]) + def test_amplitude_to_DB_reversible(self, shape): + """Round trip between amplitude and db should return the original for various shape + + This implicitly also tests `DB_to_amplitude`. + + """ + amplitude_mult = 20. + power_mult = 10. + amin = 1e-10 + ref = 1.0 + db_mult = math.log10(max(amin, ref)) + + torch.manual_seed(0) + spec = torch.rand(*shape) * 200 + + # Spectrogram amplitude -> DB -> amplitude + db = F.amplitude_to_DB(spec, amplitude_mult, amin, db_mult, top_db=None) + x2 = F.DB_to_amplitude(db, ref, 0.5) + + self.assertEqual(x2, spec, atol=5e-5, rtol=1e-5) + + # Spectrogram power -> DB -> power + db = F.amplitude_to_DB(spec, power_mult, amin, db_mult, top_db=None) + x2 = F.DB_to_amplitude(db, ref, 1.) + + self.assertEqual(x2, spec) + + @parameterized.expand([([100, 100],), ([2, 100, 100],), ([2, 2, 100, 100],)]) + def test_amplitude_to_DB_top_db_clamp(self, shape): + """Ensure values are properly clamped when `top_db` is supplied.""" + amplitude_mult = 20. + amin = 1e-10 + ref = 1.0 + db_mult = math.log10(max(amin, ref)) + top_db = 40. + + torch.manual_seed(0) + # A random tensor is used for increased entropy, but the max and min for + # each spectrogram still need to be predictable. The max determines the + # decibel cutoff, and the distance from the min must be large enough + # that it triggers a clamp. + spec = torch.rand(*shape) + # Ensure each spectrogram has a min of 0 and a max of 1. + spec -= spec.amin([-2, -1])[..., None, None] + spec /= spec.amax([-2, -1])[..., None, None] + # Expand the range to (0, 200) - wide enough to properly test clamping. + spec *= 200 + + decibels = F.amplitude_to_DB(spec, amplitude_mult, amin, + db_mult, top_db=top_db) + # Ensure the clamp was applied + below_limit = decibels < 6.0205 + assert not below_limit.any(), ( + "{} decibel values were below the expected cutoff:\n{}".format( + below_limit.sum().item(), decibels + ) + ) + # Ensure it didn't over-clamp + close_to_limit = decibels < 6.0207 + assert close_to_limit.any(), ( + f"No values were close to the limit. Did it over-clamp?\n{decibels}" + ) + + @parameterized.expand( + list(itertools.product([(1, 2, 1025, 400, 2), (1025, 400, 2)], [1, 2, 0.7])) + ) + def test_complex_norm(self, shape, power): + torch.random.manual_seed(42) + complex_tensor = torch.randn(*shape) + expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) + norm_tensor = F.complex_norm(complex_tensor, power) + self.assertEqual(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) + + @parameterized.expand( + list(itertools.product([(2, 1025, 400), (1, 201, 100)], [100], [0., 30.], [1, 2])) + ) + def test_mask_along_axis(self, shape, mask_param, mask_value, axis): + torch.random.manual_seed(42) + specgram = torch.randn(*shape) + mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis) + + other_axis = 1 if axis == 2 else 2 + + masked_columns = (mask_specgram == mask_value).sum(other_axis) + num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum() + num_masked_columns = torch.div( + num_masked_columns, mask_specgram.size(0), rounding_mode='floor') + + assert mask_specgram.size() == specgram.size() + assert num_masked_columns < mask_param + + @parameterized.expand(list(itertools.product([100], [0., 30.], [2, 3]))) + def test_mask_along_axis_iid(self, mask_param, mask_value, axis): + torch.random.manual_seed(42) + specgrams = torch.randn(4, 2, 1025, 400) + + mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) + + other_axis = 2 if axis == 3 else 3 + + masked_columns = (mask_specgrams == mask_value).sum(other_axis) + num_masked_columns = (masked_columns == mask_specgrams.size(other_axis)).sum(-1) + + assert mask_specgrams.size() == specgrams.size() + assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() diff --git a/test/torchaudio_unittest/transforms/autograd_test_impl.py b/test/torchaudio_unittest/transforms/autograd_test_impl.py index 438f623855..7c368445a8 100644 --- a/test/torchaudio_unittest/transforms/autograd_test_impl.py +++ b/test/torchaudio_unittest/transforms/autograd_test_impl.py @@ -137,6 +137,13 @@ def test_amplitude_to_db(self): waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2) self.assert_grad(transform, [waveform]) + @parameterized.expand([(1.5, "amplitude"), (2, "power"), (10, "db")]) + def test_vol(self, gain, gain_type): + sample_rate = 8000 + transform = T.Vol(gain=gain, gain_type=gain_type) + waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2) + self.assert_grad(transform, [waveform]) + @unittest.expectedFailure def test_timestretch_zeros_fail(self): """Test that ``T.TimeStretch`` fails gradcheck at 0 diff --git a/third_party/sox/CMakeLists.txt b/third_party/sox/CMakeLists.txt index 7bbad0e99e..42d9d06bfb 100644 --- a/third_party/sox/CMakeLists.txt +++ b/third_party/sox/CMakeLists.txt @@ -172,6 +172,7 @@ set(SOX_OPTIONS --without-sndio --without-sunaudio --without-waveaudio + --without-wavpack --without-twolame ) diff --git a/torchaudio/_internal/misc_ops.py b/torchaudio/_internal/misc_ops.py deleted file mode 100644 index b67b8d1598..0000000000 --- a/torchaudio/_internal/misc_ops.py +++ /dev/null @@ -1,30 +0,0 @@ -from typing import Union, Callable - -import torch -from torch import Tensor - - -def normalize_audio(signal: Tensor, normalization: Union[bool, float, Callable]) -> None: - """Audio normalization of a tensor in-place. The normalization can be a bool, - a number, or a callable that takes the audio tensor as an input. SoX uses - 32-bit signed integers internally, thus bool normalizes based on that assumption. - """ - - if not normalization: - return - - if isinstance(normalization, bool): - normalization = 1 << 31 - - if isinstance(normalization, (float, int)): - # normalize with custom value - signal /= normalization - elif callable(normalization): - signal /= normalization(signal) - - -def check_input(src: Tensor) -> None: - if not torch.is_tensor(src): - raise TypeError('Expected a tensor, got %s' % type(src)) - if src.is_cuda: - raise TypeError('Expected a CPU based tensor, got %s' % type(src)) diff --git a/torchaudio/functional/filtering.py b/torchaudio/functional/filtering.py index ad594350d3..93da27493b 100644 --- a/torchaudio/functional/filtering.py +++ b/torchaudio/functional/filtering.py @@ -46,7 +46,7 @@ def _generate_wave_table( d = (torch.sin(point.to(torch.float64) / table_size * 2 * math.pi) + 1) / 2 elif wave_type == "TRIANGLE": d = point.to(torch.float64) * 2 / table_size - value = 4 * point // table_size + value = torch.div(4 * point, table_size, rounding_mode='floor') d[value == 0] = d[value == 0] + 0.5 d[value == 1] = 1.5 - d[value == 1] d[value == 2] = 1.5 - d[value == 2] diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 96832167d2..a04592f2c4 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -27,6 +27,8 @@ 'SlidingWindowCmn', 'Vad', 'SpectralCentroid', + 'Vol', + 'ComputeDeltas', ] From b866c06dc8047e42cd7813453ea188d112d01a68 Mon Sep 17 00:00:00 2001 From: Christian Puhrsch Date: Thu, 29 Apr 2021 15:24:02 -0700 Subject: [PATCH 40/73] Import torchaudio #1475 b5d8027 Reviewed By: mthrok Differential Revision: D28098981 fbshipit-source-id: 48231fc919f3fda2bf946a9a6f0c666f9a417017 --- .circleci/config.yml | 2 +- .circleci/config.yml.in | 2 +- examples/pipeline_wav2letter/README.md | 21 +-- examples/pipeline_wav2letter/main.py | 20 +-- .../functional/functional_cpu_test.py | 6 +- .../functional/functional_impl.py | 130 +++++++++--------- .../transforms/autograd_test_impl.py | 10 ++ torchaudio/functional/functional.py | 2 +- torchaudio/sox_effects/sox_effects.py | 5 +- 9 files changed, 107 insertions(+), 91 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 2cbddf2e3c..7bba9977fd 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -16,7 +16,7 @@ executors: windows-gpu: machine: resource_class: windows.gpu.nvidia.medium - image: windows-server-2019-nvidia:stable + image: windows-server-2019-nvidia:previous shell: bash.exe commands: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index f15767041e..cc4c391ea1 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -16,7 +16,7 @@ executors: windows-gpu: machine: resource_class: windows.gpu.nvidia.medium - image: windows-server-2019-nvidia:stable + image: windows-server-2019-nvidia:previous shell: bash.exe commands: diff --git a/examples/pipeline_wav2letter/README.md b/examples/pipeline_wav2letter/README.md index 49cca3c83d..afecf5c204 100644 --- a/examples/pipeline_wav2letter/README.md +++ b/examples/pipeline_wav2letter/README.md @@ -3,7 +3,7 @@ This is an example pipeline for speech recognition using a greedy or Viterbi CTC ### Usage More information about each command line parameters is available with the `--help` option. An example can be invoked as follows. -``` +```bash DATASET_ROOT = // DATASET_FOLDER_IN_ARCHIVE = 'LibriSpeech' @@ -25,19 +25,20 @@ python main.py \ --normalize \ --optimizer adadelta \ --scheduler reduceonplateau \ - --epochs 30 + --epochs 40 ``` -With these default parameters, we get a character error rate of 13.8% on dev-clean after 30 epochs. -### Output +With these default parameters, we get 13.3 %CER and 41.9 %WER on dev-clean after 40 epochs (character and word error rates, respectively) while training on train-clean. The tail of the output is the following. -The information reported at each iteration and epoch (e.g. loss, character error rate, word error rate) is printed to standard output in the form of one json per line, e.g. -```python -{"name": "train", "epoch": 0, "cer over target length": 1.0, "cumulative cer": 23317.0, "total chars": 23317.0, "cer": 0.0, "cumulative cer over target length": 0.0, "wer over target length": 1.0, "cumulative wer": 4446.0, "total words": 4446.0, "wer": 0.0, "cumulative wer over target length": 0.0, "lr": 0.6, "batch size": 128, "n_channel": 13, "n_time": 2453, "dataset length": 128.0, "iteration": 1.0, "loss": 8.712121963500977, "cumulative loss": 8.712121963500977, "average loss": 8.712121963500977, "iteration time": 41.46276903152466, "epoch time": 41.46276903152466} -{"name": "train", "epoch": 0, "cer over target length": 1.0, "cumulative cer": 46005.0, "total chars": 46005.0, "cer": 0.0, "cumulative cer over target length": 0.0, "wer over target length": 1.0, "cumulative wer": 8762.0, "total words": 8762.0, "wer": 0.0, "cumulative wer over target length": 0.0, "lr": 0.6, "batch size": 128, "n_channel": 13, "n_time": 1703, "dataset length": 256.0, "iteration": 2.0, "loss": 8.918599128723145, "cumulative loss": 17.63072109222412, "average loss": 8.81536054611206, "iteration time": 1.2905676364898682, "epoch time": 42.753336668014526} -{"name": "train", "epoch": 0, "cer over target length": 1.0, "cumulative cer": 70030.0, "total chars": 70030.0, "cer": 0.0, "cumulative cer over target length": 0.0, "wer over target length": 1.0, "cumulative wer": 13348.0, "total words": 13348.0, "wer": 0.0, "cumulative wer over target length": 0.0, "lr": 0.6, "batch size": 128, "n_channel": 13, "n_time": 1713, "dataset length": 384.0, "iteration": 3.0, "loss": 8.550191879272461, "cumulative loss": 26.180912971496582, "average loss": 8.726970990498861, "iteration time": 1.2109291553497314, "epoch time": 43.96426582336426} +```json +... +{"name": "train", "epoch": 40, "batch char error": 925, "batch char total": 22563, "batch char error rate": 0.040996321411159865, "epoch char error": 1135098.0, "epoch char total": 23857713.0, "epoch char error rate": 0.047577821059378154, "batch word error": 791, "batch word total": 4308, "batch word error rate": 0.18361188486536675, "epoch word error": 942906.0, "epoch word total": 4569507.0, "epoch word error rate": 0.20634742435015418, "lr": 0.06, "batch size": 128, "n_channel": 13, "n_time": 1685, "dataset length": 132096.0, "iteration": 1032.0, "loss": 0.07428030669689178, "cumulative loss": 90.47326805442572, "average loss": 0.08766789540157531, "iteration time": 1.9895553588867188, "epoch time": 2036.8874564170837} +{"name": "train", "epoch": 40, "batch char error": 1131, "batch char total": 24260, "batch char error rate": 0.0466199505358615, "epoch char error": 1136229.0, "epoch char total": 23881973.0, "epoch char error rate": 0.04757684802675223, "batch word error": 957, "batch word total": 4657, "batch word error rate": 0.2054971011380717, "epoch word error": 943863.0, "epoch word total": 4574164.0, "epoch word error rate": 0.20634655862798099, "lr": 0.06, "batch size": 128, "n_channel": 13, "n_time": 1641, "dataset length": 132224.0, "iteration": 1033.0, "loss": 0.08775319904088974, "cumulative loss": 90.5610212534666, "average loss": 0.08766797798012256, "iteration time": 2.108018159866333, "epoch time": 2038.99547457695} +{"name": "train", "epoch": 40, "batch char error": 1099, "batch char total": 23526, "batch char error rate": 0.0467142735696676, "epoch char error": 1137328.0, "epoch char total": 23905499.0, "epoch char error rate": 0.04757599914563591, "batch word error": 936, "batch word total": 4544, "batch word error rate": 0.20598591549295775, "epoch word error": 944799.0, "epoch word total": 4578708.0, "epoch word error rate": 0.20634620071863066, "lr": 0.06, "batch size": 128, "n_channel": 13, "n_time": 1682, "dataset length": 132352.0, "iteration": 1034.0, "loss": 0.0791337713599205, "cumulative loss": 90.64015502482653, "average loss": 0.08765972439538348, "iteration time": 2.0329701900482178, "epoch time": 2041.0284447669983} +{"name": "train", "epoch": 40, "batch char error": 1023, "batch char total": 22399, "batch char error rate": 0.045671681771507655, "epoch char error": 1138351.0, "epoch char total": 23927898.0, "epoch char error rate": 0.04757421650660664, "batch word error": 863, "batch word total": 4318, "batch word error rate": 0.1998610467809171, "epoch word error": 945662.0, "epoch word total": 4583026.0, "epoch word error rate": 0.20634009058643787, "lr": 0.06, "batch size": 128, "n_channel": 13, "n_time": 1644, "dataset length": 132480.0, "iteration": 1035.0, "loss": 0.07874362915754318, "cumulative loss": 90.71889865398407, "average loss": 0.08765110981061262, "iteration time": 1.9106628894805908, "epoch time": 2042.9391076564789} +{"name": "validation", "epoch": 40, "cumulative loss": 12.095281183719635, "dataset length": 2688.0, "iteration": 21.0, "batch char error": 1867, "batch char total": 14792, "batch char error rate": 0.12621687398593834, "epoch char error": 37119.0, "epoch char total": 280923.0, "epoch char error rate": 0.13213229247872194, "batch word error": 1155, "batch word total": 2841, "batch word error rate": 0.4065469904963041, "epoch word error": 22601.0, "epoch word total": 54008.0, "epoch word error rate": 0.418475040734706, "average loss": 0.575965770653316, "validation time": 24.185853481292725} ``` -One way to import the output in python with pandas is by saving the standard output to a file, and then using `pandas.read_json(filename, lines=True)`. +As can be seen in the output above, the information reported at each iteration and epoch (e.g. loss, character error rate, word error rate) is printed to standard output in the form of one json per line. One way to import the output in python with pandas is by saving the standard output to a file, and then using `pandas.read_json(filename, lines=True)`. ## Structure of pipeline diff --git a/examples/pipeline_wav2letter/main.py b/examples/pipeline_wav2letter/main.py index 549f3dcdeb..f771fa8fce 100644 --- a/examples/pipeline_wav2letter/main.py +++ b/examples/pipeline_wav2letter/main.py @@ -220,10 +220,12 @@ def compute_error_rates(outputs, targets, decoder, language_model, metric): cers = [levenshtein_distance(t, o) for t, o in zip(target, output)] cers = sum(cers) n = sum(len(t) for t in target) - metric["cer over target length"] = cers / n - metric["cumulative cer"] += cers - metric["total chars"] += n - metric["cumulative cer over target length"] = metric["cer"] / metric["total chars"] + metric["batch char error"] = cers + metric["batch char total"] = n + metric["batch char error rate"] = cers / n + metric["epoch char error"] += cers + metric["epoch char total"] += n + metric["epoch char error rate"] = metric["epoch char error"] / metric["epoch char total"] # Compute WER @@ -233,10 +235,12 @@ def compute_error_rates(outputs, targets, decoder, language_model, metric): wers = [levenshtein_distance(t, o) for t, o in zip(target, output)] wers = sum(wers) n = sum(len(t) for t in target) - metric["wer over target length"] = wers / n - metric["cumulative wer"] += wers - metric["total words"] += n - metric["cumulative wer over target length"] = metric["wer"] / metric["total words"] + metric["batch word error"] = wers + metric["batch word total"] = n + metric["batch word error rate"] = wers / n + metric["epoch word error"] += wers + metric["epoch word total"] += n + metric["epoch word error rate"] = metric["epoch word error"] / metric["epoch word total"] def train_one_epoch( diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py index 6f497a3193..73cc8662fa 100644 --- a/test/torchaudio_unittest/functional/functional_cpu_test.py +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -16,18 +16,18 @@ def test_lfilter_9th_order_filter_stability(self): super().test_lfilter_9th_order_filter_stability() -class TestFunctionalFloat64(Functional, FunctionalCPUOnly, PytorchTestCase): +class TestFunctionalFloat64(Functional, PytorchTestCase): dtype = torch.float64 device = torch.device('cpu') -class TestFunctionalComplex64(FunctionalComplex, FunctionalCPUOnly, PytorchTestCase): +class TestFunctionalComplex64(FunctionalComplex, PytorchTestCase): complex_dtype = torch.complex64 real_dtype = torch.float32 device = torch.device('cpu') -class TestFunctionalComplex128(FunctionalComplex, FunctionalCPUOnly, PytorchTestCase): +class TestFunctionalComplex128(FunctionalComplex, PytorchTestCase): complex_dtype = torch.complex128 real_dtype = torch.float64 device = torch.device('cpu') diff --git a/test/torchaudio_unittest/functional/functional_impl.py b/test/torchaudio_unittest/functional/functional_impl.py index 3c78ee3e90..f4cc94f806 100644 --- a/test/torchaudio_unittest/functional/functional_impl.py +++ b/test/torchaudio_unittest/functional/functional_impl.py @@ -93,73 +93,17 @@ def test_spectogram_grad_at_zero(self, power): spec.sum().backward() assert not x.grad.isnan().sum() - -class FunctionalComplex(TestBaseMixin): - complex_dtype = None - real_dtype = None - device = None - - @nested_params( - [0.5, 1.01, 1.3], - [True, False], - ) - def test_phase_vocoder_shape(self, rate, test_pseudo_complex): - """Verify the output shape of phase vocoder""" - hop_length = 256 - num_freq = 1025 - num_frames = 400 - batch_size = 2 - - torch.random.manual_seed(42) - spec = torch.randn( - batch_size, num_freq, num_frames, dtype=self.complex_dtype, device=self.device) - if test_pseudo_complex: - spec = torch.view_as_real(spec) - - phase_advance = torch.linspace( - 0, - np.pi * hop_length, - num_freq, - dtype=self.real_dtype, device=self.device)[..., None] - - spec_stretch = F.phase_vocoder(spec, rate=rate, phase_advance=phase_advance) - - assert spec.dim() == spec_stretch.dim() - expected_shape = torch.Size([batch_size, num_freq, int(np.ceil(num_frames / rate))]) - output_shape = (torch.view_as_complex(spec_stretch) if test_pseudo_complex else spec_stretch).shape - assert output_shape == expected_shape - - -class FunctionalCPUOnly(TestBaseMixin): - def test_create_fb_matrix_no_warning_high_n_freq(self): - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter("always") - F.create_fb_matrix(288, 0, 8000, 128, 16000) - assert len(w) == 0 - - def test_create_fb_matrix_no_warning_low_n_mels(self): - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter("always") - F.create_fb_matrix(201, 0, 8000, 89, 16000) - assert len(w) == 0 - - def test_create_fb_matrix_warning(self): - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter("always") - F.create_fb_matrix(201, 0, 8000, 128, 16000) - assert len(w) == 1 - def test_compute_deltas_one_channel(self): - specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) - expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) + specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]], dtype=self.dtype, device=self.device) + expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]], dtype=self.dtype, device=self.device) computed = F.compute_deltas(specgram, win_length=3) self.assertEqual(computed, expected) def test_compute_deltas_two_channels(self): specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], - [1.0, 2.0, 3.0, 4.0]]]) + [1.0, 2.0, 3.0, 4.0]]], dtype=self.dtype, device=self.device) expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], - [0.5, 1.0, 1.0, 0.5]]]) + [0.5, 1.0, 1.0, 0.5]]], dtype=self.dtype, device=self.device) computed = F.compute_deltas(specgram, win_length=3) self.assertEqual(computed, expected) @@ -190,7 +134,7 @@ def test_amplitude_to_DB_reversible(self, shape): db_mult = math.log10(max(amin, ref)) torch.manual_seed(0) - spec = torch.rand(*shape) * 200 + spec = torch.rand(*shape, dtype=self.dtype, device=self.device) * 200 # Spectrogram amplitude -> DB -> amplitude db = F.amplitude_to_DB(spec, amplitude_mult, amin, db_mult, top_db=None) @@ -218,7 +162,7 @@ def test_amplitude_to_DB_top_db_clamp(self, shape): # each spectrogram still need to be predictable. The max determines the # decibel cutoff, and the distance from the min must be large enough # that it triggers a clamp. - spec = torch.rand(*shape) + spec = torch.rand(*shape, dtype=self.dtype, device=self.device) # Ensure each spectrogram has a min of 0 and a max of 1. spec -= spec.amin([-2, -1])[..., None, None] spec /= spec.amax([-2, -1])[..., None, None] @@ -245,7 +189,7 @@ def test_amplitude_to_DB_top_db_clamp(self, shape): ) def test_complex_norm(self, shape, power): torch.random.manual_seed(42) - complex_tensor = torch.randn(*shape) + complex_tensor = torch.randn(*shape, dtype=self.dtype, device=self.device) expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) norm_tensor = F.complex_norm(complex_tensor, power) self.assertEqual(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) @@ -255,7 +199,7 @@ def test_complex_norm(self, shape, power): ) def test_mask_along_axis(self, shape, mask_param, mask_value, axis): torch.random.manual_seed(42) - specgram = torch.randn(*shape) + specgram = torch.randn(*shape, dtype=self.dtype, device=self.device) mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis) other_axis = 1 if axis == 2 else 2 @@ -271,7 +215,7 @@ def test_mask_along_axis(self, shape, mask_param, mask_value, axis): @parameterized.expand(list(itertools.product([100], [0., 30.], [2, 3]))) def test_mask_along_axis_iid(self, mask_param, mask_value, axis): torch.random.manual_seed(42) - specgrams = torch.randn(4, 2, 1025, 400) + specgrams = torch.randn(4, 2, 1025, 400, dtype=self.dtype, device=self.device) mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) @@ -282,3 +226,59 @@ def test_mask_along_axis_iid(self, mask_param, mask_value, axis): assert mask_specgrams.size() == specgrams.size() assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() + + +class FunctionalComplex(TestBaseMixin): + complex_dtype = None + real_dtype = None + device = None + + @nested_params( + [0.5, 1.01, 1.3], + [True, False], + ) + def test_phase_vocoder_shape(self, rate, test_pseudo_complex): + """Verify the output shape of phase vocoder""" + hop_length = 256 + num_freq = 1025 + num_frames = 400 + batch_size = 2 + + torch.random.manual_seed(42) + spec = torch.randn( + batch_size, num_freq, num_frames, dtype=self.complex_dtype, device=self.device) + if test_pseudo_complex: + spec = torch.view_as_real(spec) + + phase_advance = torch.linspace( + 0, + np.pi * hop_length, + num_freq, + dtype=self.real_dtype, device=self.device)[..., None] + + spec_stretch = F.phase_vocoder(spec, rate=rate, phase_advance=phase_advance) + + assert spec.dim() == spec_stretch.dim() + expected_shape = torch.Size([batch_size, num_freq, int(np.ceil(num_frames / rate))]) + output_shape = (torch.view_as_complex(spec_stretch) if test_pseudo_complex else spec_stretch).shape + assert output_shape == expected_shape + + +class FunctionalCPUOnly(TestBaseMixin): + def test_create_fb_matrix_no_warning_high_n_freq(self): + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + F.create_fb_matrix(288, 0, 8000, 128, 16000) + assert len(w) == 0 + + def test_create_fb_matrix_no_warning_low_n_mels(self): + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + F.create_fb_matrix(201, 0, 8000, 89, 16000) + assert len(w) == 0 + + def test_create_fb_matrix_warning(self): + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + F.create_fb_matrix(201, 0, 8000, 128, 16000) + assert len(w) == 1 diff --git a/test/torchaudio_unittest/transforms/autograd_test_impl.py b/test/torchaudio_unittest/transforms/autograd_test_impl.py index 7c368445a8..b8fe6c1863 100644 --- a/test/torchaudio_unittest/transforms/autograd_test_impl.py +++ b/test/torchaudio_unittest/transforms/autograd_test_impl.py @@ -137,6 +137,16 @@ def test_amplitude_to_db(self): waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2) self.assert_grad(transform, [waveform]) + def test_melscale(self): + sample_rate = 8000 + n_fft = 400 + n_mels = n_fft // 2 + 1 + transform = T.MelScale(sample_rate=sample_rate, n_mels=n_mels) + spec = get_spectrogram( + get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2), + n_fft=n_fft, power=1) + self.assert_grad(transform, [spec]) + @parameterized.expand([(1.5, "amplitude"), (2, "power"), (10, "db")]) def test_vol(self, gain, gain_type): sample_rate = 8000 diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index 9d23a2cace..cef2553d82 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -1160,7 +1160,7 @@ def apply_codec( When True, both the input and output Tensor have dimension ``[channel, time]``. Otherwise, they have dimension ``[time, channel]``. compression (float): Used for formats other than WAV. - For mor details see :py:func:`torchaudio.backend.sox_io_backend.save`. + For more details see :py:func:`torchaudio.backend.sox_io_backend.save`. encoding (str, optional): Changes the encoding for the supported formats. For more details see :py:func:`torchaudio.backend.sox_io_backend.save`. bits_per_sample (int, optional): Changes the bit depth for the supported formats. diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 26fc0659e6..05cd860a7f 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -61,14 +61,15 @@ def apply_effects_tensor( """Apply sox effects to given Tensor Note: + This function only works on CPU Tensors. This function works in the way very similar to ``sox`` command, however there are slight differences. For example, ``sox`` command adds certain effects automatically (such as ``rate`` effect after ``speed`` and ``pitch`` and other effects), but this function does only applies the given effects. (Therefore, to actually apply ``speed`` effect, you also - need to give ``rate`` effect with desired sampling rate.) + need to give ``rate`` effect with desired sampling rate.). Args: - tensor (torch.Tensor): Input 2D Tensor. + tensor (torch.Tensor): Input 2D CPU Tensor. sample_rate (int): Sample rate effects (List[List[str]]): List of effects. channels_first (bool): Indicates if the input Tensor's dimension is From 1f9b7e7d3e2ed9339b010505d5eb4e96fabe60e3 Mon Sep 17 00:00:00 2001 From: Caroline Chen Date: Fri, 30 Apr 2021 12:53:14 -0700 Subject: [PATCH 41/73] Replace prototype RNNTL with PySpeech RNNTL Summary: - bind RNNTL to `torchaudio` - remove previous version of RNNTL, including submodule - replace references to previous RNNTL with new PySpeech RNNTL Reviewed By: vincentqb Differential Revision: D27973417 fbshipit-source-id: 992eab9f82edc7fdec18851c7a393c9bb3169f30 --- .gitmodules | 4 - test/torchaudio_unittest/transducer_test.py | 26 ++-- third_party/CMakeLists.txt | 8 -- third_party/transducer/CMakeLists.txt | 3 - torchaudio/csrc/transducer.cpp | 132 -------------------- torchaudio/prototype/transducer.py | 119 ------------------ 6 files changed, 12 insertions(+), 280 deletions(-) delete mode 100755 third_party/transducer/CMakeLists.txt delete mode 100644 torchaudio/csrc/transducer.cpp delete mode 100644 torchaudio/prototype/transducer.py diff --git a/.gitmodules b/.gitmodules index dd47fc1e91..724846120c 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,7 +1,3 @@ -[submodule "third_party/warp_transducer/submodule"] - path = third_party/transducer/submodule - url = https://github.com/HawkAaron/warp-transducer - ignore = dirty [submodule "kaldi"] path = third_party/kaldi/submodule url = https://github.com/kaldi-asr/kaldi diff --git a/test/torchaudio_unittest/transducer_test.py b/test/torchaudio_unittest/transducer_test.py index 209794a5b5..305da970ed 100644 --- a/test/torchaudio_unittest/transducer_test.py +++ b/test/torchaudio_unittest/transducer_test.py @@ -1,6 +1,6 @@ import unittest import torch -from torchaudio.prototype.transducer import RNNTLoss +from torchaudio.facebook.transducer import TransducerLoss from torchaudio_unittest.common_utils import TorchaudioTestCase @@ -228,11 +228,11 @@ def grad_hook(grad): def compute_with_pytorch_transducer(data): - costs = RNNTLoss(blank=data["blank"], reduction="none")( - acts=data["logits"], - labels=data["targets"], - act_lens=data["src_lengths"], - label_lens=data["tgt_lengths"], + costs = TransducerLoss(blank=data["blank"])( + logits=data["logits"], + targets=data["targets"], + src_lengths=data["src_lengths"], + tgt_lengths=data["tgt_lengths"], ) loss = torch.sum(costs) @@ -244,24 +244,22 @@ def compute_with_pytorch_transducer(data): def skipIfNoTransducer(test_item): try: - torch.ops.torchaudio.rnnt_loss + torch.ops.torchaudio.compute return test_item except RuntimeError: return unittest.skip("torchaudio C++ extension is not compiled with RNN transducer loss")(test_item) class TransducerTester: - def test_basic_fp16_error(self): - rnnt_loss = RNNTLoss() + def test_basic_fp16_no_error(self): + # Test that fp16 is supported + rnnt_loss = TransducerLoss() acts, labels, act_length, label_length = get_data_basic(self.device) acts = acts.to(torch.float16) - # RuntimeError raised by log_softmax before reaching transducer's bindings - self.assertRaises( - RuntimeError, rnnt_loss, acts, labels, act_length, label_length - ) + rnnt_loss(acts, labels, act_length, label_length) def test_basic_backward(self): - rnnt_loss = RNNTLoss() + rnnt_loss = TransducerLoss() acts, labels, act_length, label_length = get_data_basic(self.device) loss = rnnt_loss(acts, labels, act_length, label_length) loss.backward() diff --git a/third_party/CMakeLists.txt b/third_party/CMakeLists.txt index bb3a469055..ef984d57b6 100644 --- a/third_party/CMakeLists.txt +++ b/third_party/CMakeLists.txt @@ -21,12 +21,4 @@ if (BUILD_KALDI) list(APPEND TORCHAUDIO_THIRD_PARTIES kaldi) endif() -################################################################################ -# transducer -################################################################################ -if (BUILD_TRANSDUCER) - add_subdirectory(transducer) - list(APPEND TORCHAUDIO_THIRD_PARTIES warprnnt) -endif() - set_property(GLOBAL PROPERTY TORCHAUDIO_THIRD_PARTIES "${TORCHAUDIO_THIRD_PARTIES}") diff --git a/third_party/transducer/CMakeLists.txt b/third_party/transducer/CMakeLists.txt deleted file mode 100755 index 359af3e3e3..0000000000 --- a/third_party/transducer/CMakeLists.txt +++ /dev/null @@ -1,3 +0,0 @@ -add_library(warprnnt STATIC submodule/src/rnnt_entrypoint.cpp) -target_compile_definitions(warprnnt PRIVATE RNNT_DISABLE_OMP) -target_include_directories(warprnnt PUBLIC submodule/include) diff --git a/torchaudio/csrc/transducer.cpp b/torchaudio/csrc/transducer.cpp deleted file mode 100644 index 00fa49b4b5..0000000000 --- a/torchaudio/csrc/transducer.cpp +++ /dev/null @@ -1,132 +0,0 @@ -#include -#include -#include -#include - -#include -#include "rnnt.h" - -namespace { - -int64_t cpu_rnnt_loss( - torch::Tensor acts, - torch::Tensor labels, - torch::Tensor input_lengths, - torch::Tensor label_lengths, - torch::Tensor costs, - torch::Tensor grads, - int64_t blank_label, - int64_t num_threads) { - TORCH_CHECK(labels.dtype() == torch::kInt32, "labels must be int32 type"); - TORCH_CHECK( - label_lengths.dtype() == torch::kInt32, - "label_lengths must be int32 type"); - TORCH_CHECK( - input_lengths.dtype() == torch::kInt32, "lengths must be int32 type"); - TORCH_CHECK(acts.is_contiguous(), "acts must be contiguous"); - TORCH_CHECK(labels.is_contiguous(), "labels must be contiguous"); - TORCH_CHECK( - label_lengths.is_contiguous(), "label_lengths must be contiguous"); - TORCH_CHECK(input_lengths.is_contiguous(), "lengths must be contiguous"); - TORCH_CHECK( - input_lengths.size(0) == acts.size(0), - "batch dimension mismatch between acts and input_lengths: each example must have a length"); - TORCH_CHECK( - label_lengths.size(0) == acts.size(0), - "batch dimension mismatch between acts and label_lengths: each example must have a label length"); - TORCH_CHECK(acts.dim() == 4, "acts must be 4-D (batch, time, label, class)"); - TORCH_CHECK( - labels.dim() == 2, "labels must be 2-D (batch, max label length)"); - TORCH_CHECK(input_lengths.dim() == 1, "input_lengths must be 1-D"); - TORCH_CHECK(label_lengths.dim() == 1, "label_lengths must be 1-D"); - - int maxT = acts.size(1); - int maxU = acts.size(2); - int minibatch_size = acts.size(0); - int alphabet_size = acts.size(3); - - TORCH_CHECK( - at::max(input_lengths).item().toInt() == maxT, "input length mismatch"); - TORCH_CHECK( - at::max(label_lengths).item().toInt() + 1 == maxU, - "output length mismatch"); - - rnntOptions options; - memset(&options, 0, sizeof(options)); - options.maxT = maxT; - options.maxU = maxU; - options.blank_label = blank_label; - options.batch_first = true; - options.loc = RNNT_CPU; - options.num_threads = num_threads; - - // have to use at least one - options.num_threads = std::max(options.num_threads, (unsigned int)1); - - size_t cpu_size_bytes = 0; - switch (acts.scalar_type()) { - case torch::ScalarType::Float: { - get_workspace_size(maxT, maxU, minibatch_size, false, &cpu_size_bytes); - - std::vector cpu_workspace(cpu_size_bytes / sizeof(float), 0); - - compute_rnnt_loss( - acts.data_ptr(), - grads.data_ptr(), - labels.data_ptr(), - label_lengths.data_ptr(), - input_lengths.data_ptr(), - alphabet_size, - minibatch_size, - costs.data_ptr(), - cpu_workspace.data(), - options); - - return 0; - } - case torch::ScalarType::Double: { - get_workspace_size( - maxT, maxU, minibatch_size, false, &cpu_size_bytes, sizeof(double)); - - std::vector cpu_workspace(cpu_size_bytes / sizeof(double), 0); - - compute_rnnt_loss_fp64( - acts.data_ptr(), - grads.data_ptr(), - labels.data_ptr(), - label_lengths.data_ptr(), - input_lengths.data_ptr(), - alphabet_size, - minibatch_size, - costs.data_ptr(), - cpu_workspace.data(), - options); - - return 0; - } - default: - TORCH_CHECK( - false, - std::string(__func__) + " not implemented for '" + - toString(acts.scalar_type()) + "'"); - } - return -1; -} - -} // namespace - -TORCH_LIBRARY_IMPL(torchaudio, CPU, m) { - m.impl("rnnt_loss", &cpu_rnnt_loss); -} - -TORCH_LIBRARY_FRAGMENT(torchaudio, m) { - m.def( - "rnnt_loss(Tensor acts," - "Tensor labels," - "Tensor input_lengths," - "Tensor label_lengths," - "Tensor costs," - "Tensor grads," - "int blank_label," - "int num_threads) -> int"); -} diff --git a/torchaudio/prototype/transducer.py b/torchaudio/prototype/transducer.py deleted file mode 100644 index 99c1e9877e..0000000000 --- a/torchaudio/prototype/transducer.py +++ /dev/null @@ -1,119 +0,0 @@ -import torch -from torch.autograd import Function -from torch.nn import Module -from torchaudio._internal import ( - module_utils as _mod_utils, -) - -__all__ = [ - "rnnt_loss", - "RNNTLoss", -] - - -class _RNNT(Function): - @staticmethod - def forward(ctx, acts, labels, act_lens, label_lens, blank, reduction): - """ - See documentation for RNNTLoss. - """ - - device = acts.device - - acts = acts.to("cpu") - labels = labels.to("cpu") - act_lens = act_lens.to("cpu") - label_lens = label_lens.to("cpu") - - loss_func = torch.ops.torchaudio.rnnt_loss - - grads = torch.zeros_like(acts) - minibatch_size = acts.size(0) - costs = torch.zeros(minibatch_size, dtype=acts.dtype) - - loss_func(acts, labels, act_lens, label_lens, costs, grads, blank, 0) - - if reduction in ["sum", "mean"]: - costs = costs.sum().unsqueeze_(-1) - if reduction == "mean": - costs /= minibatch_size - grads /= minibatch_size - - costs = costs.to(device) - ctx.grads = grads.to(device) - - return costs - - @staticmethod - def backward(ctx, grad_output): - grad_output = grad_output.view(-1, 1, 1, 1).to(ctx.grads) - return ctx.grads.mul_(grad_output), None, None, None, None, None - - -@_mod_utils.requires_module("torchaudio._torchaudio") -def rnnt_loss(acts, labels, act_lens, label_lens, blank=0, reduction="mean"): - """Compute the RNN Transducer Loss. - - The RNN Transducer loss (`Graves 2012 `__) extends the CTC loss by defining - a distribution over output sequences of all lengths, and by jointly modelling both input-output and output-output - dependencies. - - The implementation uses `warp-transducer `__. - - Args: - acts (Tensor): Tensor of dimension (batch, time, label, class) containing output from network - before applying ``torch.nn.functional.log_softmax``. - labels (Tensor): Tensor of dimension (batch, max label length) containing the labels padded by zero - act_lens (Tensor): Tensor of dimension (batch) containing the length of each output sequence - label_lens (Tensor): Tensor of dimension (batch) containing the length of each output sequence - blank (int): blank label. (Default: ``0``) - reduction (string): If ``'sum'``, the output losses will be summed. - If ``'mean'``, the output losses will be divided by the target lengths and - then the mean over the batch is taken. If ``'none'``, no reduction will be applied. - (Default: ``'mean'``) - """ - - # NOTE manually done log_softmax for CPU version, - # log_softmax is computed within GPU version. - acts = torch.nn.functional.log_softmax(acts, -1) - return _RNNT.apply(acts, labels, act_lens, label_lens, blank, reduction) - - -@_mod_utils.requires_module("torchaudio._torchaudio") -class RNNTLoss(Module): - """Compute the RNN Transducer Loss. - - The RNN Transducer loss (`Graves 2012 `__) extends the CTC loss by defining - a distribution over output sequences of all lengths, and by jointly modelling both input-output and output-output - dependencies. - - The implementation uses `warp-transducer `__. - - Args: - blank (int): blank label. (Default: ``0``) - reduction (string): If ``'sum'``, the output losses will be summed. - If ``'mean'``, the output losses will be divided by the target lengths and - then the mean over the batch is taken. If ``'none'``, no reduction will be applied. - (Default: ``'mean'``) - """ - - def __init__(self, blank=0, reduction="mean"): - super(RNNTLoss, self).__init__() - self.blank = blank - self.reduction = reduction - self.loss = _RNNT.apply - - def forward(self, acts, labels, act_lens, label_lens): - """ - Args: - acts (Tensor): Tensor of dimension (batch, time, label, class) containing output from network - before applying ``torch.nn.functional.log_softmax``. - labels (Tensor): Tensor of dimension (batch, max label length) containing the labels padded by zero - act_lens (Tensor): Tensor of dimension (batch) containing the length of each output sequence - label_lens (Tensor): Tensor of dimension (batch) containing the length of each output sequence - """ - - # NOTE manually done log_softmax for CPU version, - # log_softmax is computed within GPU version. - acts = torch.nn.functional.log_softmax(acts, -1) - return self.loss(acts, labels, act_lens, label_lens, self.blank, self.reduction) From 97f354e0e2c4977d5ed3521dc9535719fb208ad1 Mon Sep 17 00:00:00 2001 From: Caroline Chen Date: Fri, 30 Apr 2021 12:53:14 -0700 Subject: [PATCH 42/73] Combine old and new RNNTL tests Summary: - remove unused code from `numpy_transducer` - merge prototype transducer loss tests into internal transducer loss tests Reviewed By: vincentqb Differential Revision: D27973416 fbshipit-source-id: cc8f3b566c48dd584cd0400dceb406f3c84471ac --- test/torchaudio_unittest/transducer_test.py | 283 -------------------- 1 file changed, 283 deletions(-) delete mode 100644 test/torchaudio_unittest/transducer_test.py diff --git a/test/torchaudio_unittest/transducer_test.py b/test/torchaudio_unittest/transducer_test.py deleted file mode 100644 index 305da970ed..0000000000 --- a/test/torchaudio_unittest/transducer_test.py +++ /dev/null @@ -1,283 +0,0 @@ -import unittest -import torch -from torchaudio.facebook.transducer import TransducerLoss - -from torchaudio_unittest.common_utils import TorchaudioTestCase - - -def get_data_basic(device): - # Example provided - # in 6f73a2513dc784c59eec153a45f40bc528355b18 - # of https://github.com/HawkAaron/warp-transducer - - acts = torch.tensor( - [ - [ - [ - [0.1, 0.6, 0.1, 0.1, 0.1], - [0.1, 0.1, 0.6, 0.1, 0.1], - [0.1, 0.1, 0.2, 0.8, 0.1], - ], - [ - [0.1, 0.6, 0.1, 0.1, 0.1], - [0.1, 0.1, 0.2, 0.1, 0.1], - [0.7, 0.1, 0.2, 0.1, 0.1], - ], - ] - ], - dtype=torch.float, - ) - labels = torch.tensor([[1, 2]], dtype=torch.int) - act_length = torch.tensor([2], dtype=torch.int) - label_length = torch.tensor([2], dtype=torch.int) - - acts = acts.to(device) - labels = labels.to(device) - act_length = act_length.to(device) - label_length = label_length.to(device) - - acts.requires_grad_(True) - - return acts, labels, act_length, label_length - - -def get_data_B2_T4_U3_D3(dtype=torch.float32, device="cpu"): - # Test from D21322854 - - logits = torch.tensor( - [ - 0.065357, - 0.787530, - 0.081592, - 0.529716, - 0.750675, - 0.754135, - 0.609764, - 0.868140, - 0.622532, - 0.668522, - 0.858039, - 0.164539, - 0.989780, - 0.944298, - 0.603168, - 0.946783, - 0.666203, - 0.286882, - 0.094184, - 0.366674, - 0.736168, - 0.166680, - 0.714154, - 0.399400, - 0.535982, - 0.291821, - 0.612642, - 0.324241, - 0.800764, - 0.524106, - 0.779195, - 0.183314, - 0.113745, - 0.240222, - 0.339470, - 0.134160, - 0.505562, - 0.051597, - 0.640290, - 0.430733, - 0.829473, - 0.177467, - 0.320700, - 0.042883, - 0.302803, - 0.675178, - 0.569537, - 0.558474, - 0.083132, - 0.060165, - 0.107958, - 0.748615, - 0.943918, - 0.486356, - 0.418199, - 0.652408, - 0.024243, - 0.134582, - 0.366342, - 0.295830, - 0.923670, - 0.689929, - 0.741898, - 0.250005, - 0.603430, - 0.987289, - 0.592606, - 0.884672, - 0.543450, - 0.660770, - 0.377128, - 0.358021, - ], - dtype=dtype, - ).reshape(2, 4, 3, 3) - - targets = torch.tensor([[1, 2], [1, 1]], dtype=torch.int32) - src_lengths = torch.tensor([4, 4], dtype=torch.int32) - tgt_lengths = torch.tensor([2, 2], dtype=torch.int32) - - blank = 0 - - ref_costs = torch.tensor([4.2806528590890736, 3.9384369822503591], dtype=dtype) - - ref_gradients = torch.tensor( - [ - -0.186844, - -0.062555, - 0.249399, - -0.203377, - 0.202399, - 0.000977, - -0.141016, - 0.079123, - 0.061893, - -0.011552, - -0.081280, - 0.092832, - -0.154257, - 0.229433, - -0.075176, - -0.246593, - 0.146405, - 0.100188, - -0.012918, - -0.061593, - 0.074512, - -0.055986, - 0.219831, - -0.163845, - -0.497627, - 0.209240, - 0.288387, - 0.013605, - -0.030220, - 0.016615, - 0.113925, - 0.062781, - -0.176706, - -0.667078, - 0.367659, - 0.299419, - -0.356344, - -0.055347, - 0.411691, - -0.096922, - 0.029459, - 0.067463, - -0.063518, - 0.027654, - 0.035863, - -0.154499, - -0.073942, - 0.228441, - -0.166790, - -0.000088, - 0.166878, - -0.172370, - 0.105565, - 0.066804, - 0.023875, - -0.118256, - 0.094381, - -0.104707, - -0.108934, - 0.213642, - -0.369844, - 0.180118, - 0.189726, - 0.025714, - -0.079462, - 0.053748, - 0.122328, - -0.238789, - 0.116460, - -0.598687, - 0.302203, - 0.296484, - ], - dtype=dtype, - ).reshape(2, 4, 3, 3) - - logits.requires_grad_(True) - logits = logits.to(device) - - def grad_hook(grad): - logits.saved_grad = grad.clone() - - logits.register_hook(grad_hook) - - data = { - "logits": logits, - "targets": targets, - "src_lengths": src_lengths, - "tgt_lengths": tgt_lengths, - "blank": blank, - } - - return data, ref_costs, ref_gradients - - -def compute_with_pytorch_transducer(data): - costs = TransducerLoss(blank=data["blank"])( - logits=data["logits"], - targets=data["targets"], - src_lengths=data["src_lengths"], - tgt_lengths=data["tgt_lengths"], - ) - - loss = torch.sum(costs) - loss.backward() - costs = costs.cpu() - gradients = data["logits"].saved_grad.cpu() - return costs, gradients - - -def skipIfNoTransducer(test_item): - try: - torch.ops.torchaudio.compute - return test_item - except RuntimeError: - return unittest.skip("torchaudio C++ extension is not compiled with RNN transducer loss")(test_item) - - -class TransducerTester: - def test_basic_fp16_no_error(self): - # Test that fp16 is supported - rnnt_loss = TransducerLoss() - acts, labels, act_length, label_length = get_data_basic(self.device) - acts = acts.to(torch.float16) - rnnt_loss(acts, labels, act_length, label_length) - - def test_basic_backward(self): - rnnt_loss = TransducerLoss() - acts, labels, act_length, label_length = get_data_basic(self.device) - loss = rnnt_loss(acts, labels, act_length, label_length) - loss.backward() - - def test_costs_and_gradients_B2_T4_U3_D3_fp32(self): - - data, ref_costs, ref_gradients = get_data_B2_T4_U3_D3( - dtype=torch.float32, device=self.device - ) - logits_shape = data["logits"].shape - costs, gradients = compute_with_pytorch_transducer(data=data) - - atol, rtol = 1e-6, 1e-2 - self.assertEqual(costs, ref_costs, atol=atol, rtol=rtol) - self.assertEqual(logits_shape, gradients.shape) - self.assertEqual(gradients, ref_gradients, atol=atol, rtol=rtol) - - -@skipIfNoTransducer -class CPUTransducerTester(TransducerTester, TorchaudioTestCase): - device = "cpu" From e1c06ebdb0adabee34d6570921d8b1a514b305bc Mon Sep 17 00:00:00 2001 From: Caroline Chen Date: Fri, 30 Apr 2021 12:53:14 -0700 Subject: [PATCH 43/73] Move rnnt files externally Summary: move rnnt files out of internal-only folders - `csrc/facebook/transducer` --> `csrc/rnnt` - `torchaudio_unittest/facebook/transducer` --> `torchaudio_unittest/rnnt` - `torchaudio/facebook/transducer` --> `torchaudio/rnnt` Reviewed By: vincentqb Differential Revision: D28072192 fbshipit-source-id: 9d01736d37a3eb7110fb2adc8bda5544d3340e7d --- test/torchaudio_unittest/rnnt/__init__.py | 0 .../rnnt/numpy_transducer.py | 406 +++++++++ .../rnnt/rnnt_loss_cpu_test.py | 9 + .../rnnt/rnnt_loss_cuda_test.py | 16 + .../rnnt/rnnt_loss_impl.py | 535 +++++++++++ test/torchaudio_unittest/rnnt/utils.py | 542 ++++++++++++ torchaudio/csrc/rnnt/compute.cpp | 15 + torchaudio/csrc/rnnt/compute_alphas.cpp | 13 + torchaudio/csrc/rnnt/compute_betas.cpp | 13 + .../csrc/rnnt/cpu/alignment_restrictions.cpp | 98 +++ .../csrc/rnnt/cpu/alignment_restrictions.h | 117 +++ torchaudio/csrc/rnnt/cpu/compute.cpp | 108 +++ torchaudio/csrc/rnnt/cpu/compute_alphas.cpp | 76 ++ torchaudio/csrc/rnnt/cpu/compute_betas.cpp | 81 ++ torchaudio/csrc/rnnt/cpu/cpu_kernels.h | 677 ++++++++++++++ torchaudio/csrc/rnnt/cpu/cpu_transducer.h | 192 ++++ torchaudio/csrc/rnnt/cpu/kernel_utils.h | 117 +++ torchaudio/csrc/rnnt/cpu/kernels.h | 242 +++++ torchaudio/csrc/rnnt/cpu/math.h | 35 + torchaudio/csrc/rnnt/gpu/compute.cu | 242 +++++ torchaudio/csrc/rnnt/gpu/compute_alphas.cu | 168 ++++ torchaudio/csrc/rnnt/gpu/compute_betas.cu | 178 ++++ .../rnnt/gpu/gpu_alignment_restrictions.cuh | 551 ++++++++++++ torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh | 99 +++ torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh | 488 +++++++++++ torchaudio/csrc/rnnt/gpu/gpu_transducer.h | 829 ++++++++++++++++++ torchaudio/csrc/rnnt/gpu/half.cuh | 38 + torchaudio/csrc/rnnt/gpu/kernel_utils.h | 117 +++ torchaudio/csrc/rnnt/gpu/kernels.h | 215 +++++ torchaudio/csrc/rnnt/gpu/math.cuh | 41 + torchaudio/csrc/rnnt/macros.cpp | 21 + torchaudio/csrc/rnnt/macros.h | 47 + torchaudio/csrc/rnnt/options.h | 99 +++ torchaudio/csrc/rnnt/transducer.h | 239 +++++ torchaudio/csrc/rnnt/types.cpp | 41 + torchaudio/csrc/rnnt/types.h | 23 + torchaudio/csrc/rnnt/workspace.cpp | 20 + torchaudio/csrc/rnnt/workspace.h | 210 +++++ torchaudio/prototype/rnnt_loss.py | 520 +++++++++++ 39 files changed, 7478 insertions(+) create mode 100644 test/torchaudio_unittest/rnnt/__init__.py create mode 100644 test/torchaudio_unittest/rnnt/numpy_transducer.py create mode 100644 test/torchaudio_unittest/rnnt/rnnt_loss_cpu_test.py create mode 100644 test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py create mode 100644 test/torchaudio_unittest/rnnt/rnnt_loss_impl.py create mode 100644 test/torchaudio_unittest/rnnt/utils.py create mode 100644 torchaudio/csrc/rnnt/compute.cpp create mode 100644 torchaudio/csrc/rnnt/compute_alphas.cpp create mode 100644 torchaudio/csrc/rnnt/compute_betas.cpp create mode 100644 torchaudio/csrc/rnnt/cpu/alignment_restrictions.cpp create mode 100644 torchaudio/csrc/rnnt/cpu/alignment_restrictions.h create mode 100644 torchaudio/csrc/rnnt/cpu/compute.cpp create mode 100644 torchaudio/csrc/rnnt/cpu/compute_alphas.cpp create mode 100644 torchaudio/csrc/rnnt/cpu/compute_betas.cpp create mode 100644 torchaudio/csrc/rnnt/cpu/cpu_kernels.h create mode 100644 torchaudio/csrc/rnnt/cpu/cpu_transducer.h create mode 100644 torchaudio/csrc/rnnt/cpu/kernel_utils.h create mode 100644 torchaudio/csrc/rnnt/cpu/kernels.h create mode 100644 torchaudio/csrc/rnnt/cpu/math.h create mode 100644 torchaudio/csrc/rnnt/gpu/compute.cu create mode 100644 torchaudio/csrc/rnnt/gpu/compute_alphas.cu create mode 100644 torchaudio/csrc/rnnt/gpu/compute_betas.cu create mode 100644 torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh create mode 100644 torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh create mode 100644 torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh create mode 100644 torchaudio/csrc/rnnt/gpu/gpu_transducer.h create mode 100644 torchaudio/csrc/rnnt/gpu/half.cuh create mode 100644 torchaudio/csrc/rnnt/gpu/kernel_utils.h create mode 100644 torchaudio/csrc/rnnt/gpu/kernels.h create mode 100644 torchaudio/csrc/rnnt/gpu/math.cuh create mode 100644 torchaudio/csrc/rnnt/macros.cpp create mode 100644 torchaudio/csrc/rnnt/macros.h create mode 100644 torchaudio/csrc/rnnt/options.h create mode 100644 torchaudio/csrc/rnnt/transducer.h create mode 100644 torchaudio/csrc/rnnt/types.cpp create mode 100644 torchaudio/csrc/rnnt/types.h create mode 100644 torchaudio/csrc/rnnt/workspace.cpp create mode 100644 torchaudio/csrc/rnnt/workspace.h create mode 100644 torchaudio/prototype/rnnt_loss.py diff --git a/test/torchaudio_unittest/rnnt/__init__.py b/test/torchaudio_unittest/rnnt/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/torchaudio_unittest/rnnt/numpy_transducer.py b/test/torchaudio_unittest/rnnt/numpy_transducer.py new file mode 100644 index 0000000000..7a26013dd7 --- /dev/null +++ b/test/torchaudio_unittest/rnnt/numpy_transducer.py @@ -0,0 +1,406 @@ +import numpy as np +import torch + + +class _NumpyTransducer(torch.autograd.Function): + @staticmethod + def forward( + ctx, + log_probs, + logit_lengths, + target_lengths, + targets, + blank=-1, + wordpiece_ends=None, + left_buffer=0, + right_buffer=0, + ): + device = log_probs.device + log_probs = log_probs.cpu().data.numpy() + logit_lengths = logit_lengths.cpu().data.numpy() + target_lengths = target_lengths.cpu().data.numpy() + targets = targets.cpu().data.numpy() + + gradients, costs, _, _ = __class__.compute( + log_probs=log_probs, + logit_lengths=logit_lengths, + target_lengths=target_lengths, + targets=targets, + blank=blank, + wordpiece_ends=wordpiece_ends, + left_buffer=left_buffer, + right_buffer=right_buffer, + ) + + costs = torch.FloatTensor(costs).to(device=device) + gradients = torch.FloatTensor(gradients).to(device=device) + ctx.grads = torch.autograd.Variable(gradients) + + return costs + + @staticmethod + def backward(ctx, output_gradients): + return ctx.grads, None, None, None, None, None, None, None, None + + @staticmethod + def compute_alpha_one_sequence(log_probs, targets, blank=-1): + max_T, max_U, D = log_probs.shape + alpha = np.zeros((max_T, max_U), dtype=np.float32) + for t in range(1, max_T): + alpha[t, 0] = alpha[t - 1, 0] + log_probs[t - 1, 0, blank] + + for u in range(1, max_U): + alpha[0, u] = alpha[0, u - 1] + log_probs[0, u - 1, targets[u - 1]] + + for t in range(1, max_T): + for u in range(1, max_U): + skip = alpha[t - 1, u] + log_probs[t - 1, u, blank] + emit = alpha[t, u - 1] + log_probs[t, u - 1, targets[u - 1]] + alpha[t, u] = np.logaddexp(skip, emit) + + cost = -(alpha[-1, -1] + log_probs[-1, -1, blank]) + return alpha, cost + + @staticmethod + def compute_alpha_one_sequence_restricted( + log_probs, targets, wordpiece_ends, blank=-1, left_buffer=0, right_buffer=0 + ): + # Note - this is a more generic case + # If we set left_buffer = right_buffer = inf this becomes same as + # `compute_alpha_one_sequence`. However keeping both versions, + # since without the checks, compute_alpha_one_sequence may be tiny + # bit faster than `compute_alpha_one_sequence_restricted` + + max_T, max_U, D = log_probs.shape + check = AlignmentRestrictionCheck( + max_U, max_T, wordpiece_ends, left_buffer, right_buffer + ) + + alpha = np.zeros((max_T, max_U), dtype=np.float32) + alpha.fill(float("-inf")) + alpha[0, 0] = 0.0 + for t in range(1, max_T): + if not check.alpha_blank_transition(t, 0): + break + alpha[t, 0] = log_probs[t - 1, 0, blank] + alpha[t - 1, 0] + + for u in range(1, max_U): + if not check.alpha_emit_transition(0, u): + break + alpha[0, u] = log_probs[0, u - 1, targets[u - 1]] + alpha[0, u - 1] + + for u in range(1, max_U): + start_t, end_t = check.valid_time_ranges(u) + for t in range(start_t, end_t + 1): + skip, emit = float("-inf"), float("-inf") + + if check.alpha_blank_transition(t, u): + skip = alpha[t - 1, u] + log_probs[t - 1, u, blank] + + if check.alpha_emit_transition(t, u): + emit = alpha[t, u - 1] + log_probs[t, u - 1, targets[u - 1]] + + if emit != float("-inf") or skip != float("-inf"): + alpha[t, u] = np.logaddexp(skip, emit) + + cost = -(alpha[-1, -1] + log_probs[-1, -1, blank]) + return alpha, cost + + @staticmethod + def compute_beta_one_sequence(log_probs, targets, blank=-1): + max_T, max_U, D = log_probs.shape + beta = np.zeros((max_T, max_U), dtype=np.float32) + beta[-1, -1] = log_probs[-1, -1, blank] + + for t in reversed(range(max_T - 1)): + beta[t, -1] = beta[t + 1, -1] + log_probs[t, -1, blank] + + for u in reversed(range(max_U - 1)): + beta[-1, u] = beta[-1, u + 1] + log_probs[-1, u, targets[u]] + + for t in reversed(range(max_T - 1)): + for u in reversed(range(max_U - 1)): + skip = beta[t + 1, u] + log_probs[t, u, blank] + emit = beta[t, u + 1] + log_probs[t, u, targets[u]] + beta[t, u] = np.logaddexp(skip, emit) + + cost = -beta[0, 0] + return beta, cost + + @staticmethod + def compute_beta_one_sequence_restricted( + log_probs, targets, wordpiece_ends, blank=-1, left_buffer=0, right_buffer=0 + ): + # Note - this is a more generic case + # If we set left_buffer = right_buffer = inf this becomes same as + # `compute_beta_one_sequence`. However keeping both versions, + # since without the checks, compute_beta_one_sequence may be tiny + # bit faster than `compute_beta_one_sequence_restricted` + max_T, max_U, D = log_probs.shape + + check = AlignmentRestrictionCheck( + max_U, max_T, wordpiece_ends, left_buffer, right_buffer + ) + beta = np.zeros((max_T, max_U), dtype=np.float32) + beta.fill(float("-inf")) + beta[-1, -1] = log_probs[-1, -1, blank] + + for t in reversed(range(max_T - 1)): + if not check.beta_blank_transition(t, max_U - 1): + break + beta[t, -1] = beta[t + 1, -1] + log_probs[t, -1, blank] + + for u in reversed(range(max_U - 1)): + if not check.beta_emit_transition(max_T - 1, u): + break + beta[-1, u] = beta[-1, u + 1] + log_probs[-1, u, targets[u]] + + for u in reversed(range(max_U - 1)): + start_t, end_t = check.valid_time_ranges(u) + for t in reversed(range(start_t, end_t + 1)): + skip, emit = float("-inf"), float("-inf") + + if check.beta_emit_transition(t, u): + emit = beta[t, u + 1] + log_probs[t, u, targets[u]] + + if check.beta_blank_transition(t, u): + skip = beta[t + 1, u] + log_probs[t, u, blank] + + if emit != float("-inf") or skip != float("-inf"): + beta[t, u] = np.logaddexp(skip, emit) + + cost = -beta[0, 0] + return beta, cost + + @staticmethod + def compute_gradients_one_sequence( + log_probs, alpha, beta, targets, blank=-1 + ): + max_T, max_U, D = log_probs.shape + gradients = np.full(log_probs.shape, float("-inf")) + cost = -beta[0, 0] + + gradients[-1, -1, blank] = alpha[-1, -1] + + gradients[:-1, :, blank] = alpha[:-1, :] + beta[1:, :] + + for u, l in enumerate(targets): + gradients[:, u, l] = alpha[:, u] + beta[:, u + 1] + + gradients = -(np.exp(gradients + log_probs + cost)) + return gradients + + @staticmethod + def compute( + log_probs, + logit_lengths, + target_lengths, + targets, + blank=-1, + wordpiece_ends=None, + left_buffer=0, + right_buffer=0, + ): + gradients = np.zeros_like(log_probs) + B_tgt, max_T, max_U, D = log_probs.shape + B_src = logit_lengths.shape[0] + + H = int(B_tgt / B_src) + + alphas = np.zeros((B_tgt, max_T, max_U)) + betas = np.zeros((B_tgt, max_T, max_U)) + betas.fill(float("-inf")) + alphas.fill(float("-inf")) + costs = np.zeros(B_tgt) + for b_tgt in range(B_tgt): + b_src = int(b_tgt / H) + T = int(logit_lengths[b_src]) + # NOTE: see https://arxiv.org/pdf/1211.3711.pdf Section 2.1 + U = int(target_lengths[b_tgt]) + 1 + + seq_log_probs = log_probs[b_tgt, :T, :U, :] + seq_targets = targets[b_tgt, : int(target_lengths[b_tgt])] + if wordpiece_ends is not None: + seq_wordpiece_ends = wordpiece_ends[b_tgt, : int(target_lengths[b_tgt]) + 1] + alpha, alpha_cost = __class__.compute_alpha_one_sequence_restricted( + log_probs=seq_log_probs, + targets=seq_targets, + blank=blank, + wordpiece_ends=seq_wordpiece_ends, + left_buffer=left_buffer, + right_buffer=right_buffer, + ) + else: + alpha, alpha_cost = __class__.compute_alpha_one_sequence( + log_probs=seq_log_probs, targets=seq_targets, blank=blank + ) + + if wordpiece_ends is not None: + seq_wordpiece_ends = wordpiece_ends[b_tgt, : int(target_lengths[b_tgt]) + 1] + beta, beta_cost = __class__.compute_beta_one_sequence_restricted( + log_probs=seq_log_probs, + targets=seq_targets, + blank=blank, + wordpiece_ends=seq_wordpiece_ends, + left_buffer=left_buffer, + right_buffer=right_buffer, + ) + else: + beta, beta_cost = __class__.compute_beta_one_sequence( + log_probs=seq_log_probs, targets=seq_targets, blank=blank + ) + + seq_gradients = __class__.compute_gradients_one_sequence( + log_probs=seq_log_probs, + alpha=alpha, + beta=beta, + targets=seq_targets, + blank=blank, + ) + np.testing.assert_almost_equal(alpha_cost, beta_cost, decimal=2) + gradients[b_tgt, :T, :U, :] = seq_gradients + costs[b_tgt] = beta_cost + alphas[b_tgt, :T, :U] = alpha + betas[b_tgt, :T, :U] = beta + + return gradients, costs, alphas, betas + + +class NumpyTransducerLoss(torch.nn.Module): + def __init__(self, blank=-1): + super().__init__() + self.blank = blank + + def forward( + self, + logits, + logit_lengths, + target_lengths, + targets, + wordpiece_ends=None, + left_buffer=0, + right_buffer=0, + ): + log_probs = torch.nn.functional.log_softmax(logits, dim=-1) + return _NumpyTransducer.apply( + log_probs, + logit_lengths, + target_lengths, + targets, + self.blank, + wordpiece_ends, + left_buffer, + right_buffer, + ) + + +class AlignmentRestrictionCheck(object): + """ + Helper class which returns locations where + blank / emit transitions are valid while + updating alpha / betas for Alignment + Restricted RNN Transducer loss. This class + will help test c++ code, the correctness has been + verified by thorough manual inspections in n268104 + + """ + + def __init__(self, U, T, wordpiece_ends, left_buffer, right_buffer): + self.U = U + self.T = T + # assume word_ends contains the ground truth end + # timestep of every word piece. + # assume 0th wordpiece_ends is and its endtime is 0 + assert wordpiece_ends[0] == 0 + assert len(wordpiece_ends) == U + self.wordpiece_ends = wordpiece_ends + self.left_buffer = left_buffer + self.right_buffer = right_buffer + + def valid_time_ranges(self, u): + # Returns ranges of valid paths which satisfy + # word boundary constraints + # For bos ranges are valid from 0 until end time of + # 1st symbol + right_buffer + start = max(self.wordpiece_ends[u] - self.left_buffer, 0) + if u == self.U - 1: + end = self.T - 1 + else: + end = min(self.wordpiece_ends[u + 1] + self.right_buffer, self.T - 1) + + return start, end + + def alpha_blank_transition(self, t, u): + # Examine if doing blank transition into (t, u) + # is allowed while updating alphas. + if u == 0 and t == 0: + return False + + # For alphas blank transitions are valid from: + # start time when current symbol is emitted + start = max(self.wordpiece_ends[u] - self.left_buffer + 1, 1) + + # For alphas blank transitions are valid until + # last timestep we can emit next symbol + if u == self.U - 1: + # for last symbol, allow blank transitions till end + end = self.T - 1 + else: + end = min(self.wordpiece_ends[u + 1] + self.right_buffer, self.T - 1) + + return start <= t and t <= end + + def alpha_emit_transition(self, t, u): + # Examine if doing emit transition into (t, u) + # is allowed while updating alphas. + if u == 0: + # emit transitions into (t, 0) for alpha are invalid + return False + + # For alphas emit are valid starting from: + # first time when current symbol can be emitted + start = max(self.wordpiece_ends[u] - self.left_buffer, 0) + + # For alphas emit are valid until: + # last timestep when current symbol can be emitted + end = min(self.wordpiece_ends[u] + self.right_buffer, self.T - 1) + + return start <= t and t <= end + + def beta_blank_transition(self, t, u): + # Examine if doing blank transition into (t, u) + # is allowed while updating betas. + + # for beta, blanks transitions are can start + # first timestep when we emit previous symbol + start = max(self.wordpiece_ends[u] - self.left_buffer, 0) + + # for beta, blanks transitions are valid until + # last timestep we can emit current symbol + # note: T-2, we init beta[-1, -1] by log_prob[-1, -1, blank] + if u == self.U - 1: + end = self.T - 2 + else: + # we cannot transition into beta for T-1 + end = min(self.wordpiece_ends[u + 1] + self.right_buffer - 1, self.T - 2) + + return start <= t and t <= end + + def beta_emit_transition(self, t, u): + # Examine if doing emit transition into (t, u) + # is allowed while updating betas + + # for last symbol, we do not allow emit transition, + # so beta into u-1, t is invalid + if u == self.U - 1: + return False + + # For betas we allow emit transition starting from + # first time we can emit next symbol + start = max(0, self.wordpiece_ends[u + 1] - self.left_buffer) + + # For betas we allow emit transitions to end with + # last time we can emit next symbol + end = min(self.wordpiece_ends[u + 1] + self.right_buffer, self.T - 1) + + return start <= t and t <= end diff --git a/test/torchaudio_unittest/rnnt/rnnt_loss_cpu_test.py b/test/torchaudio_unittest/rnnt/rnnt_loss_cpu_test.py new file mode 100644 index 0000000000..938ed7b210 --- /dev/null +++ b/test/torchaudio_unittest/rnnt/rnnt_loss_cpu_test.py @@ -0,0 +1,9 @@ +import torch +from torchaudio_unittest import common_utils +from .utils import skipIfNoTransducer +from .rnnt_loss_impl import RNNTLossTest + + +@skipIfNoTransducer +class TestRNNTLoss(RNNTLossTest, common_utils.PytorchTestCase): + device = torch.device('cpu') diff --git a/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py b/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py new file mode 100644 index 0000000000..ea6e8eb513 --- /dev/null +++ b/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py @@ -0,0 +1,16 @@ +import torch +from .rnnt_loss_impl import RNNTLossTest, RNNTLossCUDAOnly +from torchaudio_unittest import common_utils +from .utils import skipIfNoTransducer + + +@skipIfNoTransducer +@common_utils.skipIfNoCuda +class TestRNNTLoss(RNNTLossTest, common_utils.PytorchTestCase): + device = torch.device('cuda') + + +@skipIfNoTransducer +@common_utils.skipIfNoCuda +class TestRNNTLossCUDAOnly(RNNTLossCUDAOnly, common_utils.PytorchTestCase): + device = torch.device('cuda') diff --git a/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py b/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py new file mode 100644 index 0000000000..6b6e4d1d20 --- /dev/null +++ b/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py @@ -0,0 +1,535 @@ +import numpy as np +import torch +from torchaudio.prototype.rnnt_loss import ( + RNNTLoss, + _rnnt_loss_alphas, + _rnnt_loss_betas, +) + +from .numpy_transducer import _NumpyTransducer + +from .utils import ( + assert_sparse_all_close, + compute_with_numpy_transducer, + compute_with_pytorch_transducer, + get_B1_T10_U3_D4_data, + get_data_basic, + get_numpy_data_B1_T2_U3_D5, + get_numpy_data_B2_T4_U3_D3, + get_numpy_random_data, + numpy_to_torch, +) + +from torchaudio_unittest.common_utils import skipIfNoCuda + + +class RNNTLossTest: + def _test_costs_and_gradients( + self, data, ref_costs, ref_gradients, atol=1e-6, rtol=1e-2 + ): + logits_shape = data["logits"].shape + for reuse_logits_for_grads in [False, True]: + with self.subTest(reuse_logits_for_grads=reuse_logits_for_grads): + costs, gradients = compute_with_pytorch_transducer( + data=data, reuse_logits_for_grads=reuse_logits_for_grads + ) + np.testing.assert_allclose(costs, ref_costs, atol=atol, rtol=rtol) + if "logits_sparse" in data: + assert_sparse_all_close( + data, gradients, ref_gradients, atol=atol, rtol=rtol + ) + else: + self.assertEqual(logits_shape, gradients.shape) + if not np.allclose(gradients, ref_gradients, atol=atol, rtol=rtol): + for b in range(len(gradients)): + T = data["logit_lengths"][b] + U = data["target_lengths"][b] + for t in range(gradients.shape[1]): + for u in range(gradients.shape[2]): + np.testing.assert_allclose( + gradients[b, t, u], + ref_gradients[b, t, u], + atol=atol, + rtol=rtol, + err_msg=f"failed on b={b}, t={t}/T={T}, u={u}/U={U}", + ) + + def test_basic_backward(self): + rnnt_loss = RNNTLoss() + logits, targets, logit_lengths, target_lengths = get_data_basic(self.device) + loss = rnnt_loss(logits, targets, logit_lengths, target_lengths) + loss.backward() + + def test_costs_and_gradients_B1_T2_U3_D5_fp32(self): + data, ref_costs, ref_gradients = get_numpy_data_B1_T2_U3_D5( + dtype=np.float32 + ) + data = numpy_to_torch(data=data, device=self.device, requires_grad=True) + self._test_costs_and_gradients( + data=data, ref_costs=ref_costs, ref_gradients=ref_gradients + ) + + def test_costs_and_gradients_B1_T2_U3_D5_fp16(self): + data, ref_costs, ref_gradients = get_numpy_data_B1_T2_U3_D5( + dtype=np.float16 + ) + data = numpy_to_torch(data=data, device=self.device, requires_grad=True) + self._test_costs_and_gradients( + data=data, + ref_costs=ref_costs, + ref_gradients=ref_gradients, + atol=1e-3, + rtol=1e-2, + ) + + def test_costs_and_gradients_B2_T4_U3_D3_fp32(self): + data, ref_costs, ref_gradients = get_numpy_data_B2_T4_U3_D3( + dtype=np.float32 + ) + data = numpy_to_torch(data=data, device=self.device, requires_grad=True) + self._test_costs_and_gradients( + data=data, ref_costs=ref_costs, ref_gradients=ref_gradients + ) + + def test_costs_and_gradients_B2_T4_U3_D3_fp16(self): + data, ref_costs, ref_gradients = get_numpy_data_B2_T4_U3_D3( + dtype=np.float16 + ) + data = numpy_to_torch(data=data, device=self.device, requires_grad=True) + self._test_costs_and_gradients( + data=data, + ref_costs=ref_costs, + ref_gradients=ref_gradients, + atol=1e-3, + rtol=1e-2, + ) + + def test_costs_and_gradients_random_data_with_numpy_fp32(self): + seed = 777 + for i in range(5): + data = get_numpy_random_data(dtype=np.float32, seed=(seed + i)) + data = numpy_to_torch(data=data, device=self.device, requires_grad=True) + ref_costs, ref_gradients = compute_with_numpy_transducer(data=data) + self._test_costs_and_gradients( + data=data, ref_costs=ref_costs, ref_gradients=ref_gradients + ) + + def test_alphas_restricted_B1_T10_U3_D4(self): + for random in [True, False]: + for left_buffer in [0, 2, 10]: + for right_buffer in [0, 1, 2, 10]: + data = get_B1_T10_U3_D4_data(random=random) + logits = torch.tensor(data["logits"]) + log_probs = torch.nn.functional.log_softmax(logits, dim=-1).numpy() + ( + np_gradients, + np_costs, + np_alphas, + np_betas, + ) = _NumpyTransducer.compute( + log_probs=log_probs, + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + blank=data["blank"], + wordpiece_ends=data["wordpiece_ends"], + left_buffer=left_buffer, + right_buffer=right_buffer, + ) + + data = numpy_to_torch( + data=data, device=self.device, requires_grad=True + ) + alphas = _rnnt_loss_alphas( + logits=data["logits"], + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + wordpiece_ends=data.get("wordpiece_ends", None), + blank=data["blank"], + left_buffer=left_buffer, + right_buffer=right_buffer, + ) + np.testing.assert_allclose( + alphas.cpu().data.numpy(), np_alphas, atol=1e-6, rtol=1e-2 + ) + + def test_betas_restricted_B1_T10_U3_D4(self): + for random in [True, False]: + for left_buffer in [0, 2, 10]: + for right_buffer in [0, 1, 2, 10]: + data = get_B1_T10_U3_D4_data(random=random) + logits = torch.tensor(data["logits"]) + log_probs = torch.nn.functional.log_softmax(logits, dim=-1).numpy() + ( + np_gradients, + np_costs, + np_alphas, + np_betas, + ) = _NumpyTransducer.compute( + log_probs=log_probs, + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + blank=data["blank"], + wordpiece_ends=data["wordpiece_ends"], + left_buffer=left_buffer, + right_buffer=right_buffer, + ) + + data = numpy_to_torch( + data=data, device=self.device, requires_grad=True + ) + betas = _rnnt_loss_betas( + logits=data["logits"], + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + wordpiece_ends=data.get("wordpiece_ends", None), + blank=data["blank"], + left_buffer=left_buffer, + right_buffer=right_buffer, + ) + np.testing.assert_allclose( + betas.cpu().data.numpy(), np_betas, atol=1e-6, rtol=1e-2 + ) + + def test_rnnt_restricted_B1_T10_U3_D4(self): + for left_buffer in [0, 1, 10]: + for right_buffer in [0, 1, 2, 5, 10]: + data = get_B1_T10_U3_D4_data(random=False) + data = numpy_to_torch( + data=data, device=self.device, requires_grad=True + ) + data["left_buffer"] = left_buffer + data["right_buffer"] = right_buffer + ref_costs, ref_gradients = compute_with_numpy_transducer( + data=data + ) + self._test_costs_and_gradients( + data=data, ref_costs=ref_costs, ref_gradients=ref_gradients + ) + + def test_rnnt_restricted_B2_T4_U3_D3(self): + # Note - this test just ensures that the numpy and c++ implementations match. + # Probably a hand constructed test for gradients will be more thorough + data, _, _ = get_numpy_data_B2_T4_U3_D3(dtype=np.float32) + data = numpy_to_torch(data=data, device=self.device, requires_grad=True) + data["wordpiece_ends"] = torch.tensor([[0, 1, 2], [0, 1, 2]]).int() + + for left_buffer in [0]: + for right_buffer in [0, 1, 2]: + data["left_buffer"] = left_buffer + data["right_buffer"] = right_buffer + ref_costs, ref_gradients = compute_with_numpy_transducer( + data=data + ) + + self._test_costs_and_gradients( + data=data, ref_costs=ref_costs, ref_gradients=ref_gradients + ) + + def test_restricted_parity_with_unrestricted_B1_T10_U3_D4(self): + for random in [False, True]: + data = get_B1_T10_U3_D4_data(random=random) + data = numpy_to_torch(data=data, device=self.device, requires_grad=True) + wordpiece_ends = data["wordpiece_ends"] + del data["wordpiece_ends"] + ref_costs, ref_gradients = compute_with_pytorch_transducer(data=data) + data["wordpiece_ends"] = wordpiece_ends + data["left_buffer"] = 100 + data["right_buffer"] = 100 + wp_costs, wp_gradients = compute_with_pytorch_transducer(data=data) + np.testing.assert_allclose(ref_costs, wp_costs, atol=1e-2, rtol=1e-2) + np.testing.assert_allclose( + ref_gradients, wp_gradients, atol=1e-2, rtol=1e-2 + ) + + def test_restricted_parity_with_unrestricted_alpha_B1_T10_U3_D4(self): + for random in [False, True]: + data = get_B1_T10_U3_D4_data(random=random) + data = numpy_to_torch(data=data, device=self.device, requires_grad=True) + + ref_alphas = _rnnt_loss_alphas( + blank=data["blank"], + logits=data["logits"], + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + ) + + wp_alphas = _rnnt_loss_alphas( + blank=data["blank"], + left_buffer=100, + right_buffer=100, + logits=data["logits"], + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + wordpiece_ends=data["wordpiece_ends"], + ) + np.testing.assert_allclose( + ref_alphas.cpu().data.numpy(), + wp_alphas.cpu().data.numpy(), + atol=1e-2, + rtol=1e-2, + ) + + def test_restricted_parity_with_unrestricted_beta_B1_T10_U3_D4(self): + for random in [False, True]: + data = get_B1_T10_U3_D4_data(random=random) + data = numpy_to_torch(data=data, device=self.device, requires_grad=True) + + ref_betas = _rnnt_loss_betas( + blank=data["blank"], + logits=data["logits"], + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + ) + wp_betas = _rnnt_loss_betas( + blank=data["blank"], + left_buffer=100, + right_buffer=100, + logits=data["logits"], + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + wordpiece_ends=data["wordpiece_ends"], + ) + np.testing.assert_allclose( + ref_betas.cpu().data.numpy(), + wp_betas.cpu().data.numpy(), + atol=1e-2, + rtol=1e-2, + ) + + +@skipIfNoCuda +class RNNTLossCUDAOnly: + def _test_costs_and_gradients( + self, data, ref_costs, ref_gradients, atol=1e-6, rtol=1e-2 + ): + logits_shape = data["logits"].shape + for reuse_logits_for_grads in [False, True]: + with self.subTest(reuse_logits_for_grads=reuse_logits_for_grads): + costs, gradients = compute_with_pytorch_transducer( + data=data, reuse_logits_for_grads=reuse_logits_for_grads + ) + np.testing.assert_allclose(costs, ref_costs, atol=atol, rtol=rtol) + if "logits_sparse" in data: + assert_sparse_all_close( + data, gradients, ref_gradients, atol=atol, rtol=rtol + ) + else: + self.assertEqual(logits_shape, gradients.shape) + if not np.allclose(gradients, ref_gradients, atol=atol, rtol=rtol): + for b in range(len(gradients)): + T = data["logit_lengths"][b] + U = data["target_lengths"][b] + for t in range(gradients.shape[1]): + for u in range(gradients.shape[2]): + np.testing.assert_allclose( + gradients[b, t, u], + ref_gradients[b, t, u], + atol=atol, + rtol=rtol, + err_msg=f"failed on b={b}, t={t}/T={T}, u={u}/U={U}", + ) + + def test_nan_logits(self): + for sparse in [False, True]: + for reuse_logits_for_grads in [False, True]: + data = get_B1_T10_U3_D4_data( + random=True, left_buffer=10, right_buffer=10, sparse=sparse, nan=True + ) + data = numpy_to_torch( + data=data, device=self.device, requires_grad=True + ) + data["left_buffer"] = 10 + data["right_buffer"] = 10 + costs, gradients = compute_with_pytorch_transducer( + data=data, reuse_logits_for_grads=reuse_logits_for_grads + ) + self.assertTrue(np.all(costs == 0)) + self.assertTrue(np.all(gradients == 0)) + + def test_rnnt_restricted_betas_with_random_data(self): + for u in range(50, 60): + data = get_numpy_random_data(max_B=5, max_T=499, max_U=u, max_D=4096) + data = numpy_to_torch( + data=data, device=self.device, requires_grad=True + ) + targets = data["targets"] + target_lengths = data["target_lengths"] + B, max_U = targets.size() + wordpiece_ends = np.zeros((B, max_U + 1), dtype=np.int32) + for b in range(B): + u = target_lengths[b] + wordpiece_ends[b, : u + 1] = np.array([0] + list(range(u)), dtype=np.int32) + wordpiece_ends = torch.from_numpy(wordpiece_ends) + _ = _rnnt_loss_betas( + logits=data["logits"], + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + wordpiece_ends=wordpiece_ends, + blank=4095, + sparse=False, + ) + + def test_rnnt_nonfused_log_softmax(self): + for random in [False, True]: + for sparse in [False, True]: + for left_buffer in [1, 2, 10]: + for right_buffer in [1, 2, 5, 10]: + data = get_B1_T10_U3_D4_data( + random=random, + left_buffer=left_buffer, + right_buffer=right_buffer, + sparse=sparse, + ) + data = numpy_to_torch( + data=data, device=self.device, requires_grad=True + ) + data["left_buffer"] = left_buffer + data["right_buffer"] = right_buffer + data["fused_log_softmax"] = False + ref_costs, ref_gradients = compute_with_numpy_transducer( + data=data + ) + self._test_costs_and_gradients( + data=data, ref_costs=ref_costs, ref_gradients=ref_gradients + ) + + def test_rnnt_sparse_B1_T10_U3_D4(self): + for random in [False, True]: + for left_buffer in [1, 2, 10]: + for right_buffer in [1, 2, 5, 10]: + data = get_B1_T10_U3_D4_data( + random=random, left_buffer=left_buffer, right_buffer=right_buffer, sparse=True + ) + data = numpy_to_torch( + data=data, device=self.device, requires_grad=True + ) + data["left_buffer"] = left_buffer + data["right_buffer"] = right_buffer + ref_costs, ref_gradients = compute_with_numpy_transducer( + data=data + ) + self._test_costs_and_gradients( + data=data, ref_costs=ref_costs, ref_gradients=ref_gradients + ) + + def test_rnnt_sparse_B1_T10_U3_D4_fp16(self): + for random in [False, True]: + for left_buffer in [1, 2, 10]: + for right_buffer in [1, 2, 5, 10]: + data = get_B1_T10_U3_D4_data( + random=random, + left_buffer=left_buffer, + right_buffer=right_buffer, + sparse=True, + dtype=np.float16, + ) + data = numpy_to_torch( + data=data, device=self.device, requires_grad=True + ) + data["left_buffer"] = left_buffer + data["right_buffer"] = right_buffer + ref_costs, ref_gradients = compute_with_numpy_transducer( + data=data + ) + self._test_costs_and_gradients( + data=data, + ref_costs=ref_costs, + ref_gradients=ref_gradients, + atol=1e-3, + rtol=10, + ) + + def test_rnnt_sparse_alpha_beta_B1_T10_U3_D4(self): + for random in [False, True]: + for left_buffer in [1, 2, 10]: + for right_buffer in [1, 2, 10]: + data = get_B1_T10_U3_D4_data( + random=random, left_buffer=left_buffer, right_buffer=right_buffer, sparse=True + ) + data = numpy_to_torch( + data=data, device=self.device, requires_grad=True + ) + data["left_buffer"] = left_buffer + data["right_buffer"] = right_buffer + logits = torch.tensor(data["logits"]) + log_probs = ( + torch.nn.functional.log_softmax(logits, dim=-1).cpu().numpy() + ) + ( + np_gradients, + np_costs, + np_alphas, + np_betas, + ) = _NumpyTransducer.compute( + log_probs=log_probs, + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + blank=data["blank"], + wordpiece_ends=data["wordpiece_ends"], + left_buffer=left_buffer, + right_buffer=right_buffer, + ) + pt_alphas = _rnnt_loss_alphas( + logits=data["logits_sparse"], + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + wordpiece_ends=data["wordpiece_ends"], + blank=data["blank"], + left_buffer=left_buffer, + right_buffer=right_buffer, + sparse=True, + valid_ranges=data["valid_ranges"], + cells_per_sample=data["cells_per_sample"], + ) + total_valid = torch.sum(data["cells_per_sample"]) + sparse_np_alphas = np.zeros((total_valid)).astype(np.float32) + valid_ranges = data["valid_ranges"] + offset = 0 + for b in range(2): + for u in range(3): + st, en = valid_ranges[b][u][0], valid_ranges[b][u][1] + sparse_np_alphas[ + offset : offset + (en - st) + 1 + ] = np_alphas[b, st : en + 1, u] + offset += (en - st) + 1 + np.testing.assert_allclose( + pt_alphas.cpu(), sparse_np_alphas, atol=1e-6, rtol=1e-2 + ) + + pt_betas = _rnnt_loss_betas( + logits=data["logits_sparse"], + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + wordpiece_ends=data["wordpiece_ends"], + blank=data["blank"], + left_buffer=left_buffer, + right_buffer=right_buffer, + sparse=True, + valid_ranges=data["valid_ranges"], + cells_per_sample=data["cells_per_sample"], + ) + sparse_np_betas = np.zeros((total_valid)).astype(np.float32) + valid_ranges = data["valid_ranges"] + offset = 0 + for b in range(2): + for u in range(3): + st, en = valid_ranges[b][u][0], valid_ranges[b][u][1] + sparse_np_betas[offset : offset + (en - st) + 1] = np_betas[ + b, st : en + 1, u + ] + offset += (en - st) + 1 + np.testing.assert_allclose( + pt_betas.cpu(), sparse_np_betas, atol=1e-6, rtol=1e-2 + ) diff --git a/test/torchaudio_unittest/rnnt/utils.py b/test/torchaudio_unittest/rnnt/utils.py new file mode 100644 index 0000000000..5763e72688 --- /dev/null +++ b/test/torchaudio_unittest/rnnt/utils.py @@ -0,0 +1,542 @@ +import unittest + +import numpy as np +import torch +from torchaudio.prototype.rnnt_loss import RNNTLoss + +from .numpy_transducer import ( + AlignmentRestrictionCheck, + NumpyTransducerLoss, +) + + +def _get_sparse(data, dense_tensor, left_buffer, right_buffer, H=1): + B, _, U, D = 2, 10, 3, 4 + total_valid = 0 + valid_ranges = np.zeros((B * H, U, 2), dtype=np.int32) + cells_per_sample = np.zeros(B * H, dtype=np.int32) + wordpiece_ends = data["wordpiece_ends"] + for b_tgt in range(B * H): + b_src = int(b_tgt / H) + src_len = int(data["logit_lengths"][b_src]) + tgt_len = int(data["target_lengths"][b_tgt]) + 1 + ar_check = AlignmentRestrictionCheck( + tgt_len, src_len, wordpiece_ends[b_tgt][:tgt_len], left_buffer, right_buffer + ) + sample_cells = 0 + for u in range(tgt_len): + v_range = ar_check.valid_time_ranges(u) + valid_ranges[b_tgt, u, 0] = v_range[0] + valid_ranges[b_tgt, u, 1] = v_range[1] + total_valid += v_range[1] - v_range[0] + 1 + sample_cells += v_range[1] - v_range[0] + 1 + cells_per_sample[b_tgt] = sample_cells + sparse_joint_enc = np.zeros((total_valid, D)).astype(dense_tensor.dtype) + offset = 0 + for b in range(B * H): + for u in range(U): + st, en = valid_ranges[b_tgt][u][0], valid_ranges[b_tgt][u][1] + sparse_joint_enc[offset : offset + (en - st) + 1, :] = dense_tensor[ + b, st : en + 1, u, : + ] + offset += (en - st) + 1 + return sparse_joint_enc, valid_ranges, cells_per_sample + + +def assert_sparse_all_close(data, gradients, ref_gradients, atol=1e-6, rtol=1e-2): + valid_ranges = data["valid_ranges"] + idx = 0 + for b in range(valid_ranges.size(0)): + for u in range(valid_ranges.size(1)): + st, en = valid_ranges[b, u, 0], valid_ranges[b, u, 1] + np.testing.assert_allclose( + gradients[idx : idx + (en - st + 1), :], + ref_gradients[b, st : en + 1, u, :], + atol=atol, + rtol=rtol, + ) + idx += (en - st) + 1 + + +def compute_with_numpy_transducer(data): + costs = NumpyTransducerLoss( + blank=data["blank"], + )( + logits=data["logits"], + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + wordpiece_ends=data.get("wordpiece_ends", None), + left_buffer=data.get("left_buffer", 0), + right_buffer=data.get("right_buffer", 0), + ) + + loss = torch.sum(costs) + loss.backward() + + costs = costs.cpu().data.numpy() + gradients = data["logits"].saved_grad.cpu().data.numpy() + + return costs, gradients + + +def compute_with_pytorch_transducer(data, reuse_logits_for_grads=False): + left_buffer = data.get("left_buffer", 0) + right_buffer = data.get("right_buffer", 0) + + costs = RNNTLoss( + blank=data["blank"], + left_buffer=left_buffer, + right_buffer=right_buffer, + sparse=True if "logits_sparse" in data else False, + fused_log_softmax=data.get("fused_log_softmax", True), + reuse_logits_for_grads=reuse_logits_for_grads, + )( + logits=data.get("logits_sparse", data["logits"]), + logit_lengths=data["logit_lengths"], + target_lengths=data["target_lengths"], + targets=data["targets"], + wordpiece_ends=data.get("wordpiece_ends", None), + valid_ranges=data.get("valid_ranges", None), + cells_per_sample=data["cells_per_sample"] + if "cells_per_sample" in data + else None, + ) + + loss = torch.sum(costs) + loss.backward() + costs = costs.cpu().data.numpy() + gradients = data["logits"].saved_grad.cpu().data.numpy() + return costs, gradients + + +def get_data_basic(device): + # Example provided + # in 6f73a2513dc784c59eec153a45f40bc528355b18 + # of https://github.com/HawkAaron/warp-transducer + + logits = torch.tensor( + [ + [ + [ + [0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.1, 0.6, 0.1, 0.1], + [0.1, 0.1, 0.2, 0.8, 0.1], + ], + [ + [0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.1, 0.2, 0.1, 0.1], + [0.7, 0.1, 0.2, 0.1, 0.1], + ], + ] + ], + dtype=torch.float, + ) + targets = torch.tensor([[1, 2]], dtype=torch.int) + logit_lengths = torch.tensor([2], dtype=torch.int) + target_lengths = torch.tensor([2], dtype=torch.int) + + logits = logits.to(device=device) + targets = targets.to(device=device) + logit_lengths = logit_lengths.to(device=device) + target_lengths = target_lengths.to(device=device) + + logits.requires_grad_(True) + + return logits, targets, logit_lengths, target_lengths + + +def get_B1_T10_U3_D4_data( + random=False, + left_buffer=0, + right_buffer=0, + sparse=False, + dtype=np.float32, + nan=False, +): + B, T, U, D = 2, 10, 3, 4 + data = {} + data["logits"] = np.random.rand(B, T, U, D).astype(dtype) + if not random: + data["logits"].fill(0.1) + if nan: + for i in range(B): + data["logits"][i][0][0][0] = np.nan + data["logit_lengths"] = np.array([10, 10], dtype=np.int32) + data["target_lengths"] = np.array([2, 2], dtype=np.int32) + data["targets"] = np.array([[1, 2], [1, 2]], dtype=np.int32) + data["blank"] = 0 + data["wordpiece_ends"] = np.array([[0, 2, 7], [0, 2, 7]], dtype=np.int32) + + if sparse: + sparse_joint_enc, valid_ranges, cells_per_sample = _get_sparse( + data, data["logits"], left_buffer, right_buffer + ) + data["logits_sparse"] = sparse_joint_enc + data["valid_ranges"] = valid_ranges + data["cells_per_sample"] = cells_per_sample + return data + + +def get_numpy_data_B1_T2_U3_D5(dtype=np.float32): + logits = np.array( + [ + 0.1, + 0.6, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.6, + 0.1, + 0.1, + 0.1, + 0.1, + 0.2, + 0.8, + 0.1, + 0.1, + 0.6, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.2, + 0.1, + 0.1, + 0.7, + 0.1, + 0.2, + 0.1, + 0.1, + ], + dtype=dtype, + ).reshape(1, 2, 3, 5) + targets = np.array([[1, 2]], dtype=np.int32) + logit_lengths = np.array([2], dtype=np.int32) + target_lengths = np.array([2], dtype=np.int32) + + blank = -1 + + ref_costs = np.array([5.09566688538], dtype=dtype) + ref_gradients = np.array( + [ + 0.17703132, + -0.39992708, + 0.17703132, + 0.17703132, + -0.13116692, + 0.12247062, + 0.12247062, + -0.181684, + 0.12247062, + -0.1857276, + 0.06269141, + 0.06269141, + 0.06928471, + 0.12624498, + -0.32091248, + 0.05456069, + -0.2182428, + 0.05456069, + 0.05456069, + 0.05456069, + 0.12073967, + 0.12073967, + -0.48295838, + 0.12073967, + 0.12073967, + 0.30741188, + 0.16871123, + 0.18645471, + 0.16871123, + -0.83128875, + ], + dtype=dtype, + ).reshape(1, 2, 3, 5) + + data = { + "logits": logits, + "targets": targets, + "logit_lengths": logit_lengths, + "target_lengths": target_lengths, + "blank": blank, + } + + return data, ref_costs, ref_gradients + + +def get_numpy_data_B2_T4_U3_D3(dtype=np.float32): + # Test from D21322854 + + logits = np.array( + [ + 0.065357, + 0.787530, + 0.081592, + 0.529716, + 0.750675, + 0.754135, + 0.609764, + 0.868140, + 0.622532, + 0.668522, + 0.858039, + 0.164539, + 0.989780, + 0.944298, + 0.603168, + 0.946783, + 0.666203, + 0.286882, + 0.094184, + 0.366674, + 0.736168, + 0.166680, + 0.714154, + 0.399400, + 0.535982, + 0.291821, + 0.612642, + 0.324241, + 0.800764, + 0.524106, + 0.779195, + 0.183314, + 0.113745, + 0.240222, + 0.339470, + 0.134160, + 0.505562, + 0.051597, + 0.640290, + 0.430733, + 0.829473, + 0.177467, + 0.320700, + 0.042883, + 0.302803, + 0.675178, + 0.569537, + 0.558474, + 0.083132, + 0.060165, + 0.107958, + 0.748615, + 0.943918, + 0.486356, + 0.418199, + 0.652408, + 0.024243, + 0.134582, + 0.366342, + 0.295830, + 0.923670, + 0.689929, + 0.741898, + 0.250005, + 0.603430, + 0.987289, + 0.592606, + 0.884672, + 0.543450, + 0.660770, + 0.377128, + 0.358021, + ], + dtype=dtype, + ).reshape(2, 4, 3, 3) + + targets = np.array([[1, 2], [1, 1]], dtype=np.int32) + logit_lengths = np.array([4, 4], dtype=np.int32) + target_lengths = np.array([2, 2], dtype=np.int32) + + blank = 0 + + ref_costs = np.array([4.2806528590890736, 3.9384369822503591], dtype=dtype) + + ref_gradients = np.array( + [ + -0.186844, + -0.062555, + 0.249399, + -0.203377, + 0.202399, + 0.000977, + -0.141016, + 0.079123, + 0.061893, + -0.011552, + -0.081280, + 0.092832, + -0.154257, + 0.229433, + -0.075176, + -0.246593, + 0.146405, + 0.100188, + -0.012918, + -0.061593, + 0.074512, + -0.055986, + 0.219831, + -0.163845, + -0.497627, + 0.209240, + 0.288387, + 0.013605, + -0.030220, + 0.016615, + 0.113925, + 0.062781, + -0.176706, + -0.667078, + 0.367659, + 0.299419, + -0.356344, + -0.055347, + 0.411691, + -0.096922, + 0.029459, + 0.067463, + -0.063518, + 0.027654, + 0.035863, + -0.154499, + -0.073942, + 0.228441, + -0.166790, + -0.000088, + 0.166878, + -0.172370, + 0.105565, + 0.066804, + 0.023875, + -0.118256, + 0.094381, + -0.104707, + -0.108934, + 0.213642, + -0.369844, + 0.180118, + 0.189726, + 0.025714, + -0.079462, + 0.053748, + 0.122328, + -0.238789, + 0.116460, + -0.598687, + 0.302203, + 0.296484, + ], + dtype=dtype, + ).reshape(2, 4, 3, 3) + + data = { + "logits": logits, + "targets": targets, + "logit_lengths": logit_lengths, + "target_lengths": target_lengths, + "blank": blank, + } + + return data, ref_costs, ref_gradients + + +def get_numpy_random_data( + max_B=8, max_T=128, max_U=32, max_D=40, blank=-1, dtype=np.float32, seed=None +): + if seed is not None: + np.random.seed(seed=seed) + + if blank != -1: + raise ValueError("blank != -1 is not supported yet.") + + B = np.random.randint(low=1, high=max_B) + T = np.random.randint(low=5, high=max_T) + U = np.random.randint(low=5, high=max_U) + D = np.random.randint(low=2, high=max_D) + + logit_lengths = np.random.randint(low=5, high=T + 1, size=(B,), dtype=np.int32) + target_lengths = np.random.randint(low=5, high=U + 1, size=(B,), dtype=np.int32) + max_src_length = np.max(logit_lengths) + max_tgt_length = np.max(target_lengths) + targets = np.random.randint( + low=0, high=D - 1, size=(B, max_tgt_length), dtype=np.int32 + ) + logits = np.random.random_sample( + size=(B, max_src_length, max_tgt_length + 1, D) + ).astype(dtype=dtype) + + return { + "logits": logits, + "targets": targets, + "logit_lengths": logit_lengths, + "target_lengths": target_lengths, + "blank": blank, + } + + +def numpy_to_torch(data, device, requires_grad=True): + logits = torch.from_numpy(data["logits"]) + targets = torch.from_numpy(data["targets"]) + logit_lengths = torch.from_numpy(data["logit_lengths"]) + target_lengths = torch.from_numpy(data["target_lengths"]) + + if "wordpiece_ends" in data: + data["wordpiece_ends"] = torch.from_numpy(data["wordpiece_ends"]).to(device=device) + if "nbest_wers" in data: + data["nbest_wers"] = torch.from_numpy(data["nbest_wers"]).to(device=device) + if "nbest_scores" in data: + data["nbest_scores"] = torch.from_numpy(data["nbest_scores"]).to( + device=device + ) + + logits = torch.autograd.Variable(logits, requires_grad=requires_grad) + logit_lengths = torch.autograd.Variable(logit_lengths) + target_lengths = torch.autograd.Variable(target_lengths) + targets = torch.autograd.Variable(targets) + + if device == torch.device("cpu"): + logits = logits.cpu() + elif device == torch.device("cuda"): + logits = logits.cuda() + else: + raise ValueError("unrecognized device = {}".format(device)) + + def grad_hook(grad): + logits.saved_grad = grad.clone() + + logits.register_hook(grad_hook) + + data["logits"] = logits + data["logit_lengths"] = logit_lengths + data["target_lengths"] = target_lengths + data["targets"] = targets + + if "logits_sparse" in data: + logits_sparse = torch.from_numpy(data["logits_sparse"]) + logits_sparse = torch.autograd.Variable( + logits_sparse, requires_grad=requires_grad + ) + logits_sparse = logits_sparse.to(device=logits.device) + logits_sparse.register_hook(grad_hook) + data["logits_sparse"] = logits_sparse + valid_ranges = torch.from_numpy(data["valid_ranges"]) + valid_ranges = valid_ranges.to(device=logits.device) + data["valid_ranges"] = valid_ranges + cells_per_sample = torch.from_numpy(data["cells_per_sample"]) + cells_per_sample = cells_per_sample.to(device=logits.device) + data["cells_per_sample"] = cells_per_sample + return data + + +def skipIfNoTransducer(test_item): + try: + torch.ops.torchaudio.rnnt_loss + return test_item + except RuntimeError: + return unittest.skip("torchaudio C++ extension is not compiled with RNN transducer loss") diff --git a/torchaudio/csrc/rnnt/compute.cpp b/torchaudio/csrc/rnnt/compute.cpp new file mode 100644 index 0000000000..5ca69a24c7 --- /dev/null +++ b/torchaudio/csrc/rnnt/compute.cpp @@ -0,0 +1,15 @@ +#include + +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("rnnt_loss(Tensor logits," + "Tensor targets," + "Tensor src_lengths," + "Tensor tgt_lengths," + "int blank," + "float clamp," + "Tensor? wp_ends=None," + "int l_buffer=0," + "int r_buffer=0," + "bool fused_log_smax=True," + "bool reuse_logits_for_grads=True) -> (Tensor, Tensor?)"); +} diff --git a/torchaudio/csrc/rnnt/compute_alphas.cpp b/torchaudio/csrc/rnnt/compute_alphas.cpp new file mode 100644 index 0000000000..80d53995cc --- /dev/null +++ b/torchaudio/csrc/rnnt/compute_alphas.cpp @@ -0,0 +1,13 @@ +#include + +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("rnnt_loss_alphas(Tensor logits," + "Tensor targets," + "Tensor src_lengths," + "Tensor tgt_lengths," + "int blank," + "float clamp," + "Tensor? wp_ends=None," + "int l_buffer=0," + "int r_buffer=0) -> Tensor"); +} diff --git a/torchaudio/csrc/rnnt/compute_betas.cpp b/torchaudio/csrc/rnnt/compute_betas.cpp new file mode 100644 index 0000000000..f4312c59cf --- /dev/null +++ b/torchaudio/csrc/rnnt/compute_betas.cpp @@ -0,0 +1,13 @@ +#include + +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("rnnt_loss_betas(Tensor logits," + "Tensor targets," + "Tensor src_lengths," + "Tensor tgt_lengths," + "int blank," + "float clamp," + "Tensor? wp_ends=None," + "int l_buffer=0," + "int r_buffer=0) -> Tensor"); +} diff --git a/torchaudio/csrc/rnnt/cpu/alignment_restrictions.cpp b/torchaudio/csrc/rnnt/cpu/alignment_restrictions.cpp new file mode 100644 index 0000000000..abef70409d --- /dev/null +++ b/torchaudio/csrc/rnnt/cpu/alignment_restrictions.cpp @@ -0,0 +1,98 @@ +#include + +namespace torchaudio { +namespace rnnt { +namespace cpu { + +void AlignmentRestrictionCheck::validTimeRanges( + const int u, + int& t_start, + int& t_end) { + t_start = std::max(wpEnds_[u] - lBuffer_, 0); + t_end = (u == U - 1) ? T - 1 : std::min(wpEnds_[u + 1] + rBuffer_, T - 1); + return; +} + +bool AlignmentRestrictionCheck::alphaBlankTransition( + const int t, + const int u) { + if (u == 0 && t == 0) { + return false; + } + + // for alpha blank updates we move from left to right + // blank transitions are valid from: + // start time when current symbol is emitted + // **offset to right by 1** + int start = std::max(wpEnds_[u] - lBuffer_ + 1, 1); + + // blank transitions are valid until: + // for U-1: last allowed timestep i.e. T - 1 + // for other cases: last time we may emit the next symbol + int end = (u == U - 1) ? T - 1 : std::min(wpEnds_[u + 1] + rBuffer_, T - 1); + + return start <= t && t <= end; +} + +bool AlignmentRestrictionCheck::alphaEmitTransition( + const int t, + const int u) { + // For alphas we move from bottom to top + // emit transitions into (t, 0) for alpha are invalid + if (u == 0) { + return false; + } + + // For alphas emit are valid starting from: + // first time when current symbol can be emitted + int start = std::max(wpEnds_[u] - lBuffer_, 0); + + // For alphas emit are valid until: + // last timestep when current symbol can be emitted + int end = std::min(wpEnds_[u] + rBuffer_, T - 1); + + return start <= t && t <= end; +} + +bool AlignmentRestrictionCheck::betaBlankTransition( + const int t, + const int u) { + // For updating betas with blank transition + // we move from right to left + + // for beta, blanks transitions are can start + // first timestep when we emit previous symbol + int start = std::max(wpEnds_[u] - lBuffer_, 0); + + // for beta, blanks transitions are valid until + // we can emit current symbol **offset to left by 1** + // note: T-2, we init beta[-1, -1] by log_prob[-1, -1, blank] + int end = + (u == U - 1) ? T - 2 : std::min(wpEnds_[u + 1] + rBuffer_ - 1, T - 2); + + return start <= t && t <= end; +} + +bool AlignmentRestrictionCheck::betaEmitTransition(const int t, const int u) { + // While updating betas we go from top to bottom. + + // for last symbol, we do not allow emit transition, + // so beta into u-1, t is invalid + if (u == U - 1) { + return false; + } + + // For betas we allow emit transition starting from + // first time we can emit next symbol + int start = std::max(0, wpEnds_[u + 1] - lBuffer_); + + // For betas we allow emit transitions to end with + // last time we can emit next symbol + int end = std::min(wpEnds_[u + 1] + rBuffer_, T - 1); + + return start <= t and t <= end; +} + +} // namespace cpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/alignment_restrictions.h b/torchaudio/csrc/rnnt/cpu/alignment_restrictions.h new file mode 100644 index 0000000000..0ecc6af3bb --- /dev/null +++ b/torchaudio/csrc/rnnt/cpu/alignment_restrictions.h @@ -0,0 +1,117 @@ +#pragma once +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace cpu { + +// Helper class which returns locations where +// blank / emit transitions are valid while +// updating alpha / betas for Alignment +// Restricted Transducer loss +class AlignmentRestrictionCheck { + public: + AlignmentRestrictionCheck( + const int* wpEnds, + int T, + int U, + int lBuffer, + int rBuffer) + : wpEnds_(wpEnds), T(T), U(U), lBuffer_(lBuffer), rBuffer_(rBuffer) { + } + + // Returns ranges of valid timesteps which satisfy + // alignment boundary constraints + void validTimeRanges(const int u, int& t_start, int& t_end); + + // Examine if doing blank transition into (t, u) + // is allowed while updating alphas + // Note that while doing blank transitions for alpha + // we move from left to right + bool alphaBlankTransition(const int t, const int u); + + // Examine if doing emit transition into (t, u) + // is allowed while updating alphas + // Note that while doing emit transitions for alpha + // we move from bottom to top + bool alphaEmitTransition(const int t, const int u); + + // Examine if doing blank transition into (t, u) + // is allowed while updating betas + // Note that while doing blank transitions for beta + // we move from right to left + bool betaBlankTransition(const int t, const int u); + + // Examine if doing emit transition into (t, u) + // is allowed while updating betas + // Note that while doing emit transitions for beta + // we move from top to bottom + bool betaEmitTransition(const int t, const int u); + + private: + const int* wpEnds_; + int T; + int U; + int lBuffer_; + int rBuffer_; +}; + +struct SparseIndexer { + + SparseIndexer( + const int& maxU, + const int* tgtLengths, + const int* validRanges, + const int* cellsPerSample): + maxU_(maxU), + tgtLengths_(tgtLengths), + validRanges_(validRanges), + cellsPerSample_(cellsPerSample) {}; + + int operator() (int bIdx, int tIdx, int uIdx){ + + // increment the idx for valid cells in previous samples + int idx = 0; + for(int b = 0 ; b < bIdx; b++) { + idx += cellsPerSample_[b]; + } + + // increment the index for valid cells in current sample + // using pre-computed valid ranges + const int* validRangesB = validRanges_ + (maxU_*2) * bIdx; + int U = tgtLengths_[bIdx] + 1; + for (int u = 0 ; u < U ; u++) { + int startT = validRangesB[2 * u]; + int endT = validRangesB[2 * u + 1]; + if (u == uIdx) { + if (tIdx < startT || tIdx > endT) { + return -1; + } + idx += (tIdx - startT); + return idx; + } else { + idx += (endT - startT) + 1; + } + } + return -1; + } + + private: + const int& maxU_; + + // (vector of size B) contains the lengths of U for each sample in batch + const int* tgtLengths_; + + // (vector of size B*2*U) contains the valid time ranges (start_t, end_t) + // for each u in every sample, as per alignment restrictions + const int* validRanges_; + + // The total valid timesteps for each sample (sumed over all u in the sample). + // This is a quick loopup so we dont need to iterate on all previous u*b values. + const int* cellsPerSample_; +}; + +} // namespace cpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/compute.cpp b/torchaudio/csrc/rnnt/cpu/compute.cpp new file mode 100644 index 0000000000..c8002c3bff --- /dev/null +++ b/torchaudio/csrc/rnnt/cpu/compute.cpp @@ -0,0 +1,108 @@ +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace cpu { + +// Entry point into RNNT Loss +std::tuple> compute( + torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + const c10::optional& wp_ends = c10::nullopt, + int64_t l_buffer = 0, + int64_t r_buffer = 0, + bool fused_log_smax = true, + bool reuse_logits_for_grads = true) { + + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = logits.size(1); + options.maxTgtLen_ = logits.size(2); + options.numTargets_ = logits.size(3); + options.blank_ = blank; + options.clamp_ = clamp; + options.lBuffer_ = l_buffer; + options.rBuffer_ = r_buffer; + options.fusedLogSmax_ = fused_log_smax; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CPU); + options.device_ = CPU; + + torch::Tensor costs = torch::empty( + options.batchSize_ * options.nHypos_, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + c10::optional gradients = c10::nullopt; + if (logits.requires_grad()) { + if (reuse_logits_for_grads) { + gradients = logits; + } else { + gradients = torch::zeros_like(logits); + } + } + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data(), + /*int_size=*/int_workspace.numel()); + + switch (logits.type().scalarType()) { + case torch::ScalarType::Float: + { + Compute( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*costs=*/costs.data(), + /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), + /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data()); + break; + } + case torch::ScalarType::Half: + { + Compute( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*costs=*/costs.data(), + /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), + /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data()); + break; + } + default: + { + LOG(ERROR) << "unsupported logits.type().scalarType() = " + << logits.type().scalarType(); + break; + } + }; + + return std::make_tuple(costs, gradients); +} + +TORCH_LIBRARY_IMPL(torchaudio, CPU, m) { + m.impl("rnnt_loss", &compute); +} + +} // namespace cpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp b/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp new file mode 100644 index 0000000000..7bc462c239 --- /dev/null +++ b/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp @@ -0,0 +1,76 @@ +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace cpu { + +torch::Tensor compute_alphas( + const torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + const c10::optional& wp_ends = c10::nullopt, + int64_t l_buffer = 0, + int64_t r_buffer = 0) { + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = logits.size(1); + options.maxTgtLen_ = logits.size(2); + options.numTargets_ = logits.size(3); + options.blank_ = blank; + options.clamp_ = clamp; + options.lBuffer_ = l_buffer; + options.rBuffer_ = r_buffer; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CPU); + options.device_ = CPU; + + torch::Tensor alphas = torch::zeros( + {options.batchSize_ * options.nHypos_, + options.maxSrcLen_, + options.maxTgtLen_}, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data(), + /*int_size=*/int_workspace.numel()); + + // Only support float, this is mainly to enable easy + // unit-testing + ComputeAlphas( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*alphas=*/alphas.data(), + /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data()); + return alphas; +} + +TORCH_LIBRARY_IMPL(torchaudio, CPU, m) { + m.impl("rnnt_loss_alphas", &compute_alphas); +} + +} // namespace cpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/compute_betas.cpp b/torchaudio/csrc/rnnt/cpu/compute_betas.cpp new file mode 100644 index 0000000000..1c7e3d187a --- /dev/null +++ b/torchaudio/csrc/rnnt/cpu/compute_betas.cpp @@ -0,0 +1,81 @@ +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace cpu { + +torch::Tensor compute_betas( + const torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + const c10::optional& wp_ends = c10::nullopt, + int64_t l_buffer = 0, + int64_t r_buffer = 0) { + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = logits.size(1); + options.maxTgtLen_ = logits.size(2); + options.numTargets_ = logits.size(3); + options.blank_ = blank; + options.clamp_ = clamp; + options.lBuffer_ = l_buffer; + options.rBuffer_ = r_buffer; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CPU); + options.device_ = CPU; + + torch::Tensor costs = torch::empty( + tgt_lengths.size(0), + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor betas = torch::zeros( + {options.batchSize_ * options.nHypos_, + options.maxSrcLen_, + options.maxTgtLen_}, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data(), + /*int_size=*/int_workspace.numel()); + + // Only support float, this is mainly to enable easy + // unit-testing + ComputeBetas( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*costs=*/costs.data(), + /*betas=*/betas.data(), + /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data()); + return betas; +} + +TORCH_LIBRARY_IMPL(torchaudio, CPU, m) { + m.impl("rnnt_loss_betas", &compute_betas); +} + +} // namespace cpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/cpu_kernels.h b/torchaudio/csrc/rnnt/cpu/cpu_kernels.h new file mode 100644 index 0000000000..6d2084d24d --- /dev/null +++ b/torchaudio/csrc/rnnt/cpu/cpu_kernels.h @@ -0,0 +1,677 @@ +#pragma once + +#include +#include +#include +#include + +#include +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace cpu { + +template +struct LogProbs { + DTYPE skip_; // blank. + DTYPE emit_; // target. + + LogProbs(DTYPE skip, DTYPE emit) : skip_(skip), emit_(emit) {} + + DTYPE& skip() { + return skip_; + } + DTYPE& emit() { + return emit_; + } + + const DTYPE& skip() const { + return skip_; + } + const DTYPE& emit() const { + return emit_; + } +}; + +// TensorView: view a block of allocated memory as a tensor. +template +class TensorView { + public: + TensorView(const std::vector& dims, DTYPE* data) + : dims_(dims), data_(data) { + strides_.resize(dims.size()); + strides_.back() = 1; + for (int i = dims.size() - 2; i >= 0; --i) { + strides_[i] = strides_[i + 1] * dims[i + 1]; + } + } + + DTYPE& operator()(const std::vector& indices) { + CHECK_EQ(indices.size(), dims_.size()); + int index = indices.back(); + for (int i = indices.size() - 2; i >= 0; --i) { + index += indices[i] * strides_[i]; + } + return data_[index]; + } + + void SetZero() { + int size = dims_[0] * strides_[0]; + std::memset(data_, 0, sizeof(DTYPE) * size); + } + + private: + std::vector dims_; + std::vector strides_; + DTYPE* data_; +}; + +template +status_t LogSumExp2D(int N, int D, const DTYPE* logits, CAST_DTYPE* outputs) { + for (int i = 0; i < N * D; i += D) { + CAST_DTYPE max = logits[i]; + for (int j = 1; j < D; ++j) { + max = std::max(max, CAST_DTYPE(logits[i + j])); + } + CAST_DTYPE sum = 0; + for (int j = 0; j < D; ++j) { + sum = sum + std::exp(CAST_DTYPE(logits[i + j]) - max); + } + outputs[i / D] = max + std::log(sum); + } + + return SUCCESS; +} + +template +void ComputeLogProbsOneSequence( + const Options& options, + TensorView& logits, + const int* targets, + int srcLen, + int tgtLen, + TensorView& denom, + TensorView>& logProbs) { + const int& T = srcLen; + const int& U = tgtLen; + const int& blank = options.blank_; + + for (int t = 0; t < T; ++t) { + for (int u = 0; u < U; ++u) { + if (u < U - 1) { + logProbs({t, u}).emit() = + CAST_DTYPE(logits({t, u, targets[u]})) - denom({t, u}); + } + logProbs({t, u}).skip() = + CAST_DTYPE(logits({t, u, blank})) - denom({t, u}); + } + } +} + +template +status_t ComputeLogProbs( + const Options& options, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + CAST_DTYPE* logProbs) { + std::vector> seqLogits; + std::vector seqTargets; + std::vector> seqDenoms; + std::vector>> seqlogProbs; + + const int& B = options.batchSize_; + const int& maxT = options.maxSrcLen_; + const int& maxU = options.maxTgtLen_; + const int& D = options.numTargets_; + for (int b = 0; b < B; ++b) { + seqLogits.push_back( + TensorView({maxT, maxU, D}, logits + b * maxT * maxU * D)); + seqTargets.push_back(targets + b * (maxU - 1)); + seqDenoms.push_back(TensorView( + {maxT, maxU}, denominators + b * maxT * maxU)); + seqlogProbs.push_back(TensorView>( + {maxT, maxU}, + reinterpret_cast*>(logProbs) + b * maxT * maxU)); + } + + //#pragma omp parallel for + for (int b = 0; b < B; ++b) { // use max 2 * B threads. + ComputeLogProbsOneSequence( + /*options=*/options, + /*logits=*/seqLogits[b], + /*targets=*/seqTargets[b], + /*srcLen=*/srcLengths[b], + /*tgtLen=*/tgtLengths[b] + 1, // with prepended blank. + /*denom=*/seqDenoms[b], + /*logProbs=*/seqlogProbs[b]); + } + + return SUCCESS; +} + +template +DTYPE ComputeAlphaOneSequence( + const Options& options, + TensorView>& logProbs, + int srcLen, + int tgtLen, + TensorView& alpha) { + const int& T = srcLen; + const int& U = tgtLen; + + alpha({0, 0}) = DTYPE(0); + + for (int t = 1; t < T; ++t) { // u == 0. + alpha({t, 0}) = alpha({t - 1, 0}) + logProbs({t - 1, 0}).skip(); + } + + for (int u = 1; u < U; ++u) { // t == 0. + alpha({0, u}) = alpha({0, u - 1}) + logProbs({0, u - 1}).emit(); + } + + for (int t = 1; t < T; ++t) { + for (int u = 1; u < U; ++u) { + alpha({t, u}) = math::lse( + alpha({t - 1, u}) + logProbs({t - 1, u}).skip(), + alpha({t, u - 1}) + logProbs({t, u - 1}).emit()); + } + } + + DTYPE forward_score = alpha({T - 1, U - 1}) + logProbs({T - 1, U - 1}).skip(); + + return forward_score; +} + +template +DTYPE ComputeAlphaOneSequenceRestricted( + const Options& options, + TensorView>& logProbs, + int srcLen, + int tgtLen, + TensorView& alpha, + const int* wpEnds) { + const int& T = srcLen; + const int& U = tgtLen; + + const int& lBuffer = options.lBuffer_; + const int& r_buffer = options.rBuffer_; + + AlignmentRestrictionCheck check(wpEnds, T, U, lBuffer, r_buffer); + + for (int t = 0; t < T; t++) { + for (int u = 0; u < U; u++) { + alpha({t, u}) = DTYPE(-INFINITY); + } + } + alpha({0, 0}) = DTYPE(0); + + for (int t = 1; t < T; ++t) { // u == 0. + if (!check.alphaBlankTransition(t, 0)) { + break; + } + alpha({t, 0}) = alpha({t - 1, 0}) + logProbs({t - 1, 0}).skip(); + } + + for (int u = 1; u < U; ++u) { // t == 0. + if (!check.alphaEmitTransition(0, u)) { + break; + } + alpha({0, u}) = alpha({0, u - 1}) + logProbs({0, u - 1}).emit(); + } + + for (int u = 1; u < U; ++u) { + int start_t = 0, end_t = 0; + check.validTimeRanges(u, start_t, end_t); + for (int t = start_t; t < end_t + 1; ++t) { + DTYPE skip = DTYPE(-INFINITY), emit = DTYPE(-INFINITY); + + if (check.alphaBlankTransition(t, u)) { + skip = alpha({t - 1, u}) + logProbs({t - 1, u}).skip(); + } + + if (check.alphaEmitTransition(t, u)) { + emit = alpha({t, u - 1}) + logProbs({t, u - 1}).emit(); + } + + if (skip != DTYPE(-INFINITY) || emit != DTYPE(-INFINITY)) { + alpha({t, u}) = math::lse(skip, emit); + } + } + } + DTYPE forward_score = alpha({T - 1, U - 1}) + logProbs({T - 1, U - 1}).skip(); + + return forward_score; +} + +template +DTYPE ComputeBetaOneSequence( + const Options& options, + TensorView>& logProbs, + int srcLen, + int tgtLen, + TensorView& beta) { + const int& T = srcLen; + const int& U = tgtLen; + + beta({T - 1, U - 1}) = logProbs({T - 1, U - 1}).skip(); + + for (int t = T - 2; t >= 0; --t) { // u == U - 1. + beta({t, U - 1}) = beta({t + 1, U - 1}) + logProbs({t, U - 1}).skip(); + } + + for (int u = U - 2; u >= 0; --u) { // t == T - 1. + beta({T - 1, u}) = beta({T - 1, u + 1}) + logProbs({T - 1, u}).emit(); + } + + for (int t = T - 2; t >= 0; --t) { + for (int u = U - 2; u >= 0; --u) { + beta({t, u}) = math::lse( + beta({t + 1, u}) + logProbs({t, u}).skip(), + beta({t, u + 1}) + logProbs({t, u}).emit()); + } + } + + DTYPE backward_score = beta({0, 0}); + + return backward_score; +} + +template +DTYPE ComputeBetaOneSequenceRestricted( + const Options& options, + TensorView>& logProbs, + int srcLen, + int tgtLen, + TensorView& beta, + const int* wpEnds = nullptr) { + const int& T = srcLen; + const int& U = tgtLen; + const int& lBuffer = options.lBuffer_; + const int& r_buffer = options.rBuffer_; + AlignmentRestrictionCheck check(wpEnds, T, U, lBuffer, r_buffer); + + for (int t = 0; t < T; t++) { + for (int u = 0; u < U; u++) { + beta({t, u}) = DTYPE(-INFINITY); + } + } + + beta({T - 1, U - 1}) = logProbs({T - 1, U - 1}).skip(); + + for (int t = T - 2; t >= 0; --t) { // u == U - 1. + if (!check.betaBlankTransition(t, U - 1)) { + break; + } + beta({t, U - 1}) = beta({t + 1, U - 1}) + logProbs({t, U - 1}).skip(); + } + + for (int u = U - 2; u >= 0; --u) { // t == T - 1. + if (!check.betaEmitTransition(T - 1, u)) { + break; + } + beta({T - 1, u}) = beta({T - 1, u + 1}) + logProbs({T - 1, u}).emit(); + } + for (int u = U - 2; u >= 0; --u) { + int start_t = 0, end_t = 0; + check.validTimeRanges(u, start_t, end_t); + for (int t = end_t; t >= start_t; --t) { + DTYPE skip = DTYPE(-INFINITY), emit = DTYPE(-INFINITY); + if (check.betaBlankTransition(t, u)) { + skip = beta({t + 1, u}) + logProbs({t, u}).skip(); + } + + if (check.betaEmitTransition(t, u)) { + emit = beta({t, u + 1}) + logProbs({t, u}).emit(); + } + + if (skip != DTYPE(-INFINITY) || emit != DTYPE(-INFINITY)) { + beta({t, u}) = math::lse(skip, emit); + } + } + } + DTYPE backward_score = beta({0, 0}); + + return backward_score; +} + +template +DTYPE ComputeAlphaOrBetaOneSequence( + int thread, + const Options& options, + TensorView>& logProbs, + int srcLen, + int tgtLen, + TensorView& alpha, + TensorView& beta, + const int* wpEnds = nullptr) { + if (wpEnds == nullptr) { + if (thread & 1) { + return ComputeAlphaOneSequence( + /*options=*/options, + /*logProbs=*/logProbs, + /*srcLen=*/srcLen, + /*tgtLen=*/tgtLen, + /*alpha=*/alpha); + } else { + return ComputeBetaOneSequence( + /*options=*/options, + /*logProbs=*/logProbs, + /*srcLen=*/srcLen, + /*tgtLen=*/tgtLen, + /*beta=*/beta); + } + } else { + if (thread & 1) { + return ComputeAlphaOneSequenceRestricted( + /*options=*/options, + /*logProbs=*/logProbs, + /*srcLen=*/srcLen, + /*tgtLen=*/tgtLen, + /*alpha=*/alpha, + /*wpEnds=*/wpEnds); + } else { + return ComputeBetaOneSequenceRestricted( + /*options=*/options, + /*logProbs=*/logProbs, + /*srcLen=*/srcLen, + /*tgtLen=*/tgtLen, + /*beta=*/beta, + /*wpEnds=*/wpEnds); + } + } +} + +template +void ComputeAlphasBetas( + const Options& options, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + CAST_DTYPE* alphas, + CAST_DTYPE* betas, + DTYPE* costs, + const int* wpEnds = nullptr) { + std::vector>> seqlogProbs; + std::vector> seq_alphas; + std::vector> seq_betas; + std::vector seq_wpEnds; + + const int& B = options.batchSize_; + const int& maxT = options.maxSrcLen_; + const int& maxU = options.maxTgtLen_; + + for (int b = 0; b < B; ++b) { + seqlogProbs.push_back(TensorView>( + {maxT, maxU}, + reinterpret_cast*>( + const_cast(logProbs)) + + b * maxT * maxU)); + seq_alphas.push_back( + TensorView({maxT, maxU}, alphas + b * maxT * maxU)); + seq_betas.push_back( + TensorView({maxT, maxU}, betas + b * maxT * maxU)); + + if (wpEnds != nullptr) { + seq_wpEnds.push_back(wpEnds + b * (maxU)); + } + } + + std::vector scores(B << 1); + //#pragma omp parallel for + for (int t = 0; t < (B << 1); ++t) { // use max 2 * B threads. + int i = (t >> 1); + scores[t] = ComputeAlphaOrBetaOneSequence( + /*thread=*/t, + /*options=*/options, + /*logProbs=*/seqlogProbs[i], + /*srcLen=*/srcLengths[i], + /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. + /*alpha=*/seq_alphas[i], + /*beta=*/seq_betas[i], + /*wpEnds=*/(wpEnds == nullptr) ? nullptr : seq_wpEnds[i]); + } + for (int b = 0; b < B; ++b) { + costs[b] = -scores[b << 1]; + } +} + +template +void ComputeGradientsOneSequence( + const Options& options, + TensorView& logits, + const int* targets, + int srcLen, + int tgtLen, + TensorView& denom, + TensorView& alpha, + TensorView& beta, + TensorView& gradients) { + // don't set gradients to zero to here as gradients might reuse memory from + // logits + + const int& T = srcLen; + const int& U = tgtLen; + const int& D = options.numTargets_; + const int& blank = options.blank_; + const CAST_DTYPE clamp = options.clamp_; + + CAST_DTYPE cost = -beta({0, 0}); + + // Note - below gradient is different from numpy_transducer, since we + // compute log_softmax more efficiently within the loss, to save memory The + // details of the below implementation / equations can be found in Sec 3.2 + // (function merging) in below paper: + // https://www.microsoft.com/en-us/research/uploads/prod/2019/10/RNNT.pdf + + for (int t = 0; t < T; ++t) { + for (int u = 0; u < U; ++u) { + CAST_DTYPE c = alpha({t, u}) + cost - denom({t, u}); + for (int d = 0; d < D; ++d) { + CAST_DTYPE g = CAST_DTYPE(logits({t, u, d})) + c; + if (d == blank && t == T - 1 && + u == U - 1) { // last blank transition. + gradients({t, u, d}) = std::exp(g + beta({t, u})) - std::exp(g); + } else if (d == blank && t < T - 1) { + gradients({t, u, d}) = + std::exp(g + beta({t, u})) - std::exp(g + beta({t + 1, u})); + } else if (u < U - 1 && d == targets[u]) { + gradients({t, u, d}) = + std::exp(g + beta({t, u})) - std::exp(g + beta({t, u + 1})); + } else { + gradients({t, u, d}) = std::exp(g + beta({t, u})); + } + + if (clamp > 0) { + gradients({t, u, d}) = + math::min(CAST_DTYPE(gradients({t, u, d})), clamp); + gradients({t, u, d}) = + math::max(CAST_DTYPE(gradients({t, u, d})), -clamp); + } + } + } + } + + // zero out the rest of the gradients, necessary when reusing logits memory + // check the memory location to see if it's necessary + if (&gradients({0, 0, 0}) == &logits({0, 0, 0})) { + const int& maxT = options.maxSrcLen_; + const int& maxU = options.maxTgtLen_; + for (int t = T; t < maxT; ++t) { + for (int u = 0; u < maxU; ++u) { + for (int d = 0; d < D; ++d) { + gradients({t, u, d}) = 0.; + } + } + } + for (int t = 0; t < T; ++t) { + for (int u = U; u < maxU; ++u) { + for (int d = 0; d < D; ++d) { + gradients({t, u, d}) = 0.; + } + } + } + } +} + +template +void ComputeGradients( + const Options& options, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + const CAST_DTYPE* alphas, + const CAST_DTYPE* betas, + DTYPE* gradients) { + std::vector> seqLogits; + std::vector seqTargets; + std::vector> seqDenoms; + std::vector> seq_alphas; + std::vector> seq_betas; + std::vector> seq_gradients; + + const int& B = options.batchSize_; + const int& maxT = options.maxSrcLen_; + const int& maxU = options.maxTgtLen_; + const int& D = options.numTargets_; + for (int b = 0; b < B; ++b) { + seqLogits.push_back( + TensorView({maxT, maxU, D}, logits + b * maxT * maxU * D)); + seqTargets.push_back(targets + b * (maxU - 1)); + seqDenoms.push_back(TensorView( + {maxT, maxU}, denominators + b * maxT * maxU)); + seq_alphas.push_back( + TensorView({maxT, maxU}, alphas + b * maxT * maxU)); + seq_betas.push_back( + TensorView({maxT, maxU}, betas + b * maxT * maxU)); + seq_gradients.push_back( + TensorView({maxT, maxU, D}, gradients + b * maxT * maxU * D)); + } + + //#pragma omp parallel for + for (int b = 0; b < B; ++b) { // use max 2 * B threads. + ComputeGradientsOneSequence( + /*options=*/options, + /*logits=*/seqLogits[b], + /*targets=*/seqTargets[b], + /*srcLen=*/srcLengths[b], + /*tgtLen=*/tgtLengths[b] + 1, // with prepended blank. + /*denom=*/seqDenoms[b], + /*alpha=*/seq_alphas[b], + /*beta=*/seq_betas[b], + /*gradients=*/seq_gradients[b]); + } +} + +template +void ComputeAlphas( + const Options& options, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + CAST_DTYPE* alphas, + const int* wpEnds = nullptr) { + std::vector>> seqlogProbs; + std::vector> seq_alphas; + std::vector seq_wpEnds; + + const int& B = options.batchSize_; + const int& maxT = options.maxSrcLen_; + const int& maxU = options.maxTgtLen_; + + for (int b = 0; b < B; ++b) { + seqlogProbs.push_back(TensorView>( + {maxT, maxU}, + reinterpret_cast*>( + const_cast(logProbs)) + + b * maxT * maxU)); + seq_alphas.push_back( + TensorView({maxT, maxU}, alphas + b * maxT * maxU)); + if (wpEnds != nullptr) { + seq_wpEnds.push_back(wpEnds + b * (maxU)); + } + } + + std::vector scores(B << 1); + //#pragma omp parallel for + for (int i = 0; i < B; ++i) { // use max 2 * B threads. + if (wpEnds == nullptr) { + ComputeAlphaOneSequence( + options, + /*logProbs=*/seqlogProbs[i], + /*srcLen=*/srcLengths[i], + /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. + /*alpha=*/seq_alphas[i]); + } else { + ComputeAlphaOneSequenceRestricted( + /*options=*/options, + /*logProbs=*/seqlogProbs[i], + /*srcLen=*/srcLengths[i], + /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. + /*alpha=*/seq_alphas[i], + /*wpEnds=*/seq_wpEnds[i]); + } + } +} + +template +void ComputeBetas( + const Options& options, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + CAST_DTYPE* costs, + CAST_DTYPE* betas, + const int* wpEnds = nullptr) { + std::vector>> seqlogProbs; + std::vector> seq_betas; + std::vector seq_wpEnds; + + const int& B = options.batchSize_; + const int& maxT = options.maxSrcLen_; + const int& maxU = options.maxTgtLen_; + + for (int b = 0; b < B; ++b) { + seqlogProbs.push_back(TensorView>( + {maxT, maxU}, + reinterpret_cast*>( + const_cast(logProbs)) + + b * maxT * maxU)); + seq_betas.push_back( + TensorView({maxT, maxU}, betas + b * maxT * maxU)); + if (wpEnds != nullptr) { + seq_wpEnds.push_back(wpEnds + b * (maxU)); + } + } + + std::vector scores(B << 1); + //#pragma omp parallel for + for (int i = 0; i < B; ++i) { // use max 2 * B threads. + if (wpEnds == nullptr) { + ComputeBetaOneSequence( + options, + /*logProbs=*/seqlogProbs[i], + /*srcLen=*/srcLengths[i], + /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. + /*betas=*/seq_betas[i]); + } else { + ComputeBetaOneSequenceRestricted( + /*options=*/options, + /*logProbs=*/seqlogProbs[i], + /*srcLen=*/srcLengths[i], + /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. + /*betas=*/seq_betas[i], + /*wpEnds=*/seq_wpEnds[i]); + } + } +} + +} // namespace cpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/cpu_transducer.h b/torchaudio/csrc/rnnt/cpu/cpu_transducer.h new file mode 100644 index 0000000000..72764325ee --- /dev/null +++ b/torchaudio/csrc/rnnt/cpu/cpu_transducer.h @@ -0,0 +1,192 @@ +#pragma once + +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace cpu { + +// Inputs: +// workspace: workspace. +// logits: pointer to (B, maxT, maxU, D) logits. +// targets: pointer to (B, maxU - 1) targets in the batch. +// srcLengths: pointer to (B, ) source lengths in the batch. +// tgtLengths: pointer to (B, ) target lengths in the batch. +// +// Outputs: +// costs: pointer to (B, ) costs in the batch. +// gradients: pointer to (B, maxT, maxU, D) gradients in the batch. +template +status_t Compute( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* gradients = nullptr, + const int* wpEnds = nullptr) { + const Options& options = workspace.GetOptions(); + + CHECK_EQ(options.device_, CPU); + + const int& B = options.batchSize_; + const int& maxT = options.maxSrcLen_; + const int& maxU = options.maxTgtLen_; + const int& D = options.numTargets_; + + { // compute denominators. + status_t status = LogSumExp2D( + /*N=*/B * maxT * maxU, + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + } + + { // compute log prob pairs. + status_t status = ComputeLogProbs( + /*options=*/options, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs()); + } + + { // compute alphas and betas. + ComputeAlphasBetas( + /*options=*/options, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alphas=*/workspace.GetPointerToAlphas(), + /*betas=*/workspace.GetPointerToBetas(), + /*costs=*/costs, + /*wpEnds=*/wpEnds); + } + + if (gradients != nullptr) { + ComputeGradients( + /*options=*/options, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*alphas=*/workspace.GetPointerToAlphas(), + /*betas=*/workspace.GetPointerToBetas(), + /*gradients=*/gradients); + } + + return SUCCESS; +} + +template +status_t ComputeAlphas( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* alphas, + const int* wpEnds = nullptr) { + const Options& options = workspace.GetOptions(); + + CHECK_EQ(options.device_, CPU); + + const int& B = options.batchSize_; + const int& maxT = options.maxSrcLen_; + const int& maxU = options.maxTgtLen_; + const int& D = options.numTargets_; + + { // compute denominators. + status_t status = LogSumExp2D( + /*N=*/B * maxT * maxU, + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + } + + { // compute log prob pairs. + status_t status = ComputeLogProbs( + /*options=*/options, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs()); + } + + { // compute alphas. + ComputeAlphas( + /*options=*/options, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alphas=*/alphas, + /*wpEnds=*/wpEnds); + } + + return SUCCESS; +} + + + +template +status_t ComputeBetas( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* betas, + const int* wpEnds = nullptr) { + const Options& options = workspace.GetOptions(); + + CHECK_EQ(options.device_, CPU); + + const int& B = options.batchSize_; + const int& maxT = options.maxSrcLen_; + const int& maxU = options.maxTgtLen_; + const int& D = options.numTargets_; + + { // compute denominators. + status_t status = LogSumExp2D( + /*N=*/B * maxT * maxU, + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + } + + { // compute log prob pairs. + status_t status = ComputeLogProbs( + /*options=*/options, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs()); + } + + { // compute betas. + ComputeBetas( + /*options=*/options, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*costs=*/costs, + /*betas=*/betas, + /*wpEnds=*/wpEnds); + } + + return SUCCESS; +} + +} // namespace cpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/kernel_utils.h b/torchaudio/csrc/rnnt/cpu/kernel_utils.h new file mode 100644 index 0000000000..4ba141503e --- /dev/null +++ b/torchaudio/csrc/rnnt/cpu/kernel_utils.h @@ -0,0 +1,117 @@ +#pragma once + +#include + +#include + +namespace torchaudio { +namespace rnnt { + +inline HOST_AND_DEVICE bool in_range( + int start, + int end, // inclusive + int val) { + return start <= val && val <= end; +} + +#define LOG_PROBS_SKIP_IDX 0 +#define LOG_PROBS_EMIT_IDX 1 + + +struct Indexer2D { + const int& size2_; + + FORCE_INLINE HOST_AND_DEVICE Indexer2D(const int& size2): size2_(size2) {} + + FORCE_INLINE HOST_AND_DEVICE int operator() (int index1, int index2) { + return index1 * size2_ + index2; + } +}; + + +struct Indexer3D { + const int& size2_; + const int& size3_; + + FORCE_INLINE HOST_AND_DEVICE Indexer3D(const int& size2, const int& size3) + : size2_(size2), size3_(size3) {} + + FORCE_INLINE HOST_AND_DEVICE int operator() (int index1, int index2, int index3) { + return (index1 * size2_ + index2) * size3_ + index3; + } +}; + + +struct Indexer4D { + const int& size2_; + const int& size3_; + const int& size4_; + + HOST_AND_DEVICE Indexer4D(const int& size2, const int& size3, const int& size4) + : size2_(size2), size3_(size3), size4_(size4) {} + + HOST_AND_DEVICE int operator() (int index1, int index2, int index3, int index4) { + return ((index1 * size2_ + index2) * size3_ + index3) * size4_ + index4; + } +}; + + +struct SparseIndexer { + HOST_AND_DEVICE SparseIndexer( + const int& maxU, + const int* tgtLengths, + const int* validRanges, + const int* cellsPerSample) : + maxU_(maxU), + tgtLengths_(tgtLengths), + validRanges_(validRanges), + cellsPerSample_(cellsPerSample) {}; + + HOST_AND_DEVICE int operator() (int bIdx, int tIdx, int uIdx) { + // Returns the sparse index given bIdx, tIdx, uIdx; or -1 if t out of band. + + // increment the idx for valid cells in previous samples + // TODO: Inefficient in the inner loop; precompute this? + int idx = 0; + for(int b = 0 ; b < bIdx; b++) { + idx += cellsPerSample_[b]; + } + + // increment the idx for valid cells in current sample + // using pre-computed valid ranges + const int* validRangesB = validRanges_ + (maxU_ * 2) * bIdx; + int U = tgtLengths_[bIdx] + 1; + for (int u = 0 ; u < U ; u++) { + int startT = validRangesB[2*u]; + int endT = validRangesB[2*u + 1]; + if (u == uIdx) { + if (tIdx < startT || tIdx > endT) { + return -1; + } + idx += (tIdx - startT); + return idx; + } else { + idx += (endT - startT) + 1; + } + } + return -1; + } + + private: + const int& maxU_; + + // (vector of size B) contains the lengths of U for each sample in batch + const int* tgtLengths_; + + // (vector of size B*2*U) contains the valid time ranges (start_t, end_t) + // for each u in every sample, as per alignment restrictions + const int* validRanges_; + + // The total valid timesteps for each sample (sumed over all u in the sample). + // This is a quick loopup so we dont need to iterate on all previous u*b values. + const int* cellsPerSample_; + +}; + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/kernels.h b/torchaudio/csrc/rnnt/cpu/kernels.h new file mode 100644 index 0000000000..66ffb6657e --- /dev/null +++ b/torchaudio/csrc/rnnt/cpu/kernels.h @@ -0,0 +1,242 @@ +#pragma once + +#include + +#include +#include + +namespace torchaudio { +namespace rnnt { + + +template +FORCE_INLINE HOST_AND_DEVICE void ComputeLogProbsSparseElement( + int bTgt, + int t, + int u, + int maxT, + int maxU, + int numTargets, + int blank, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + CAST_DTYPE* logProbs, + const int* wpEnds, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr, + int H=1, + bool fusedLogSmax=true) { + + const int& D = numTargets; + + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + if (t >= T || u >= U) { // out of boundary. + return; + } + const int* validRangesB = validRanges + (maxU*2) * bTgt; + int start = validRangesB[2*u]; + int end = validRangesB[2*u + 1]; + + // out of boundary for alignment restriction condition + if (t < start || t > end) { + return; + } + SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); + int idx = idxr(bTgt, t, u); + logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = + CAST_DTYPE(logits[idx * D + blank]) - denominators[idx]; + + if (!fusedLogSmax) { + logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = + CAST_DTYPE(logits[idx * D + blank]); + } + + if (u < U - 1) { + // emit: log_prob(b, t, u).emit() = logits(b, t, u, tgt[u]) - denom(b, t, u). + int target = targets[Indexer2D(maxU - 1)(bTgt, u)]; + logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = + CAST_DTYPE(logits[idx * D + target]) - denominators[idx]; + + if (!fusedLogSmax) { + logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = + CAST_DTYPE(logits[idx * D + target]); + } + } +} + + +template +HOST_AND_DEVICE void ComputeGradientsElement( + int bTgt, + int t, + int u, + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + CAST_DTYPE clamp, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + const CAST_DTYPE* alphas, + const CAST_DTYPE* betas, + DTYPE* gradients, + bool monotonic = false, + bool sparse = false, + const int* validRanges = nullptr, + const int* cellsPerSample = nullptr, + int H = 1, + bool fusedLogSmax = true) { + + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + const int& D = numTargets; + + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + if (t >= T || u >= U) { // out of boundary. + if (sparse) { + // no extra elements needs to be set to 0 + return; + } else if (gradients == logits && t < maxT && u < maxU) { + // gradients and logits are pointing to the same memory location + Indexer3D idxr3(maxT, maxU); + int idx_b_t_u_zero = idxr3(bTgt, t, u); + if (idx_b_t_u_zero != -1 ) { + int start = idx_b_t_u_zero * D; + for (int b_t_u_d = start; b_t_u_d < start + D; ++b_t_u_d) { + gradients[b_t_u_d] = 0; + } + } + } + return; + } + + int costIdx; + if (!sparse) { + costIdx = bTgt * maxT * maxU; + } else { + SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); + costIdx = idxr(bTgt, 0, 0); + } + CAST_DTYPE cost = -(betas[costIdx]); + + + Indexer2D idxr2(maxU - 1); + + int idx_b_t_u, idx_b_t_up1, idx_b_tp1_u, idx_b_tp1_up1; + if (sparse) { + SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); + idx_b_t_u = idxr(bTgt, t, u); + idx_b_t_up1 = idxr(bTgt, t, u+1); + idx_b_tp1_u = idxr(bTgt, t+1, u); + idx_b_tp1_up1 = idxr(bTgt, t+1, u+1); + } else { + Indexer3D idxr3(maxT, maxU); + idx_b_t_u = idxr3(bTgt, t, u); + idx_b_t_up1 = idxr3(bTgt, t, u+1); + idx_b_tp1_u = idxr3(bTgt, t+1, u); + idx_b_tp1_up1 = idxr3(bTgt, t+1, u+1); + } + + if (idx_b_t_u == -1 ) { + return; + } + + if (isinf(cost) || isnan(cost)) { + for (int d = 0; d < D; ++d) { + int b_t_u_d = idx_b_t_u * D + d; + gradients[b_t_u_d] = 0; + } + return; + } + + CAST_DTYPE c = alphas[idx_b_t_u] + cost - denominators[idx_b_t_u]; + for (int d = 0; d < D; ++d) { + int b_t_u_d = idx_b_t_u * D + d; + CAST_DTYPE g = CAST_DTYPE(logits[b_t_u_d]) + c; + + if (!monotonic) { + if (fusedLogSmax) { + if (d == blank && t == T - 1 && u == U - 1) { // last blank transition. + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]) - std::exp(g); + } else if (t < T - 1 && d == blank) { + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); + if (idx_b_tp1_u != -1) { + gradients[b_t_u_d] = gradients[b_t_u_d] - std::exp(g + betas[idx_b_tp1_u]); + } + + } else if (u < U - 1 && d == targets[idxr2(bTgt, u)]) { + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); + if (idx_b_t_up1 != -1) { + gradients[b_t_u_d] = gradients[b_t_u_d] - std::exp(g + betas[idx_b_t_up1]); + } + } else { + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); + } + } + else { // Non fused log softmax case + CAST_DTYPE g = cost + CAST_DTYPE(logits[b_t_u_d]); + if (d == blank && t == T - 1 && u == U - 1) { + gradients[b_t_u_d] = g + alphas[idx_b_t_u]; + } else if (t < T - 1 && d == blank) { + if (idx_b_tp1_u != -1) { + gradients[b_t_u_d] = g + alphas[idx_b_t_u] + betas[idx_b_tp1_u]; + } else { + gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); + } + } else if (u < U - 1 && d == targets[idxr2(bTgt, u)]) { + + if (idx_b_t_up1 != -1) { + gradients[b_t_u_d] = g + alphas[idx_b_t_u] + betas[idx_b_t_up1]; + } else { + gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); + } + + } + else { + gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); + } + gradients[b_t_u_d] = -std::exp(gradients[b_t_u_d]); + } + } else { // Monotonic RNNT case + CAST_DTYPE g = cost + CAST_DTYPE(logits[b_t_u_d]); + if (d == blank && t == T - 1 && u == U - 1) { // last blank transition. + g = g + alphas[idx_b_t_u]; + } else if (t < T - 1 && d == blank) { + g = g + alphas[idx_b_t_u]; + if (idx_b_tp1_u != -1) { + g = g + betas[idx_b_tp1_u]; + } + } else if (u < U - 1 && t < T - 1 && d == targets[idxr2(bTgt, u)]) { + g = g + alphas[idx_b_t_u]; + if (idx_b_tp1_up1 != -1) { + g = g + betas[idx_b_tp1_up1]; + } + } + else { + g = g + CAST_DTYPE(-INFINITY); + } + gradients[b_t_u_d] = -std::exp(g); + } // end of monotonic else + + if (clamp > 0) { + auto g = CAST_DTYPE(gradients[b_t_u_d]); + gradients[b_t_u_d] = math::min(g, clamp); + gradients[b_t_u_d] = math::max(g, -clamp); + } + } +} + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/math.h b/torchaudio/csrc/rnnt/cpu/math.h new file mode 100644 index 0000000000..4f1d7bc4dd --- /dev/null +++ b/torchaudio/csrc/rnnt/cpu/math.h @@ -0,0 +1,35 @@ +#pragma once + +#include + +namespace torchaudio { +namespace rnnt { + +namespace math { + +template +FORCE_INLINE HOST_AND_DEVICE DTYPE max(DTYPE x, DTYPE y) { + if (x > y) return x; + else return y; +} + +template +FORCE_INLINE HOST_AND_DEVICE DTYPE min(DTYPE x, DTYPE y) { + if (x > y) return y; + else return x; +} + +// log_sum_exp +template +FORCE_INLINE HOST_AND_DEVICE DTYPE lse(DTYPE x, DTYPE y); + +template <> +FORCE_INLINE HOST_AND_DEVICE float lse(float x, float y) { + if (y > x) { return y + log1pf(expf(x - y)); } + else { return x + log1pf(expf(y-x)); } +} + +} + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/compute.cu b/torchaudio/csrc/rnnt/gpu/compute.cu new file mode 100644 index 0000000000..53567c131d --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/compute.cu @@ -0,0 +1,242 @@ +#include +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace gpu { + +// Entry point into Sparse Alignment Restricted RNNT Loss. +std::tuple> +compute_sparse( + torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + int64_t max_T, + int64_t max_U, + const c10::optional& wp_ends = c10::nullopt, + int64_t l_buffer = 0, + int64_t r_buffer = 0, + const c10::optional& valid_ranges = c10::nullopt, + const c10::optional& cells_per_sample = c10::nullopt, + bool fused_log_smax = true, + bool reuse_logits_for_grads = true) { + + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = max_T; + options.maxTgtLen_ = max_U; + options.sparseCells_ = logits.size(0); + options.numTargets_ = logits.size(1); + options.fusedLogSmax_ = fused_log_smax; + + options.blank_ = blank; + options.clamp_ = clamp; + options.lBuffer_ = l_buffer; + options.rBuffer_ = r_buffer; + options.sparse_ = true; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); + options.stream_ = at::cuda::getCurrentCUDAStream(); + cudaSetDevice(logits.get_device()); + options.device_ = GPU; + + torch::Tensor costs = torch::empty( + options.batchSize_ * options.nHypos_, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + c10::optional gradients = c10::nullopt; + if (logits.requires_grad()) { + if (reuse_logits_for_grads) { + gradients = logits; + } else { + gradients = torch::zeros_like(logits); + } + } + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data(), + /*int_size=*/int_workspace.numel()); + + switch (logits.type().scalarType()) { + case torch::ScalarType::Float: + { + ComputeSparse( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*costs=*/costs.data(), + /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), + /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data(), + (valid_ranges == c10::nullopt)? nullptr : valid_ranges->data(), + (cells_per_sample == c10::nullopt)? nullptr : cells_per_sample->data()); + break; + } + case torch::ScalarType::Half: + { + ComputeSparse( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*costs=*/costs.data(), + /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), + /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data(), + (valid_ranges == c10::nullopt)? nullptr : valid_ranges->data(), + (cells_per_sample == c10::nullopt)? nullptr : cells_per_sample->data()); + break; + } + default: + { + LOG(ERROR) << "unsupported logits.type().scalarType() = " + << logits.type().scalarType(); + break; + } + }; + + + return std::make_tuple(costs, gradients); +} + + +// Entry point into RNNT Loss +std::tuple> +compute( + torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + const c10::optional& wp_ends = c10::nullopt, + int64_t l_buffer = 0, + int64_t r_buffer = 0, + bool fused_log_smax = true, + bool reuse_logits_for_grads = true) { + + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = logits.size(1); + options.maxTgtLen_ = logits.size(2); + options.numTargets_ = logits.size(3); + options.blank_ = blank; + options.clamp_ = clamp; + options.lBuffer_ = l_buffer; + options.rBuffer_ = r_buffer; + options.fusedLogSmax_ = fused_log_smax; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); + options.stream_ = at::cuda::getCurrentCUDAStream(); + cudaSetDevice(logits.get_device()); + options.device_ = GPU; + + torch::Tensor costs = torch::empty( + options.batchSize_ * options.nHypos_, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + c10::optional gradients = c10::nullopt; + if (logits.requires_grad()) { + if (reuse_logits_for_grads) { + gradients = logits; + } else { + gradients = torch::zeros_like(logits); + } + } + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data(), + /*int_size=*/int_workspace.numel()); + + switch (logits.type().scalarType()) { + case torch::ScalarType::Float: + { + Compute( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*costs=*/costs.data(), + /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), + /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data()); + break; + } + case torch::ScalarType::Half: + { + Compute( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*costs=*/costs.data(), + /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), + /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data()); + break; + } + default: + { + LOG(ERROR) << "unsupported logits.type().scalarType() = " + << logits.type().scalarType(); + break; + } + }; + + return std::make_tuple(costs, gradients); +} + +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("rnnt_loss_sparse(Tensor logits," + "Tensor targets," + "Tensor src_lengths," + "Tensor tgt_lengths," + "int blank," + "float clamp," + "int max_T," + "int max_U," + "Tensor? wp_ends=None," + "int l_buffer=0," + "int r_buffer=0," + "Tensor? valid_ranges=None," + "Tensor? cells_per_sample=None," + "bool fused_log_smax=True," + "bool reuse_logits_for_grads=True) -> (Tensor, Tensor?)", + &compute_sparse); +} + +TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { + m.impl("rnnt_loss", &compute); + m.impl("rnnt_loss_sparse", &compute_sparse); +} + +} // namespace gpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/compute_alphas.cu b/torchaudio/csrc/rnnt/gpu/compute_alphas.cu new file mode 100644 index 0000000000..f9bff447cd --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/compute_alphas.cu @@ -0,0 +1,168 @@ +#include +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace gpu { + +torch::Tensor compute_alphas( + const torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + const c10::optional& wp_ends = c10::nullopt, + int64_t l_buffer = 0, + int64_t r_buffer = 0) { + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = logits.size(1); + options.maxTgtLen_ = logits.size(2); + options.numTargets_ = logits.size(3); + options.blank_ = blank; + options.clamp_ = clamp; + options.lBuffer_ = l_buffer; + options.rBuffer_ = r_buffer; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); + options.stream_ = at::cuda::getCurrentCUDAStream(); + cudaSetDevice(logits.get_device()); + options.device_ = GPU; + + torch::Tensor alphas = torch::zeros( + {options.batchSize_ * options.nHypos_, + options.maxSrcLen_, + options.maxTgtLen_}, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data(), + /*int_size=*/int_workspace.numel()); + + // Only support float, this is mainly to enable easy + // unit-testing + ComputeAlphas( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*alphas=*/alphas.data(), + /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data()); + return alphas; +} + +torch::Tensor compute_alphas_sparse( + const torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + int64_t max_T, + int64_t max_U, + const c10::optional& wp_ends = c10::nullopt, + int64_t l_buffer = 0, + int64_t r_buffer = 0, + const c10::optional& valid_ranges = c10::nullopt, + const c10::optional& cells_per_sample = c10::nullopt) { + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = max_T; + options.maxTgtLen_ = max_U; + options.sparseCells_ = logits.size(0); + options.numTargets_ = logits.size(1); + + options.blank_ = blank; + options.clamp_ = clamp; + options.lBuffer_ = l_buffer; + options.rBuffer_ = r_buffer; + options.sparse_ = true; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); + options.stream_ = at::cuda::getCurrentCUDAStream(); + cudaSetDevice(logits.get_device()); + options.device_ = GPU; + + torch::Tensor alphas = torch::zeros( + {logits.size(0)}, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data(), + /*int_size=*/int_workspace.numel()); + + // Only support float, this is mainly to enable easy + // unit-testing + ComputeAlphasSparse( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*alphas=*/alphas.data(), + /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data(), + (valid_ranges == c10::nullopt) ? nullptr : valid_ranges->data(), + (cells_per_sample == c10::nullopt) ? nullptr + : cells_per_sample->data()); + return alphas; +} + +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("rnnt_loss_alphas_sparse(Tensor logits," + "Tensor targets," + "Tensor src_lengths," + "Tensor tgt_lengths," + "int blank," + "float clamp," + "int max_T," + "int max_U, Tensor? wp_ends=None," + "int l_buffer=0," + "int r_buffer=0," + "Tensor? valid_ranges=None," + "Tensor? cells_per_sample=None) -> Tensor", + &compute_alphas_sparse); +} + +TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { + m.impl("rnnt_loss_alphas", &compute_alphas); + m.impl("rnnt_loss_alphas_sparse", &compute_alphas_sparse); +} + +} // namespace gpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/compute_betas.cu b/torchaudio/csrc/rnnt/gpu/compute_betas.cu new file mode 100644 index 0000000000..ffdad7ccb7 --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/compute_betas.cu @@ -0,0 +1,178 @@ +#include +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace gpu { + +torch::Tensor compute_betas( + const torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + const c10::optional& wp_ends = c10::nullopt, + int64_t l_buffer = 0, + int64_t r_buffer = 0) { + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = logits.size(1); + options.maxTgtLen_ = logits.size(2); + options.numTargets_ = logits.size(3); + options.blank_ = blank; + options.clamp_ = clamp; + options.lBuffer_ = l_buffer; + options.rBuffer_ = r_buffer; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); + options.stream_ = at::cuda::getCurrentCUDAStream(); + cudaSetDevice(logits.get_device()); + options.device_ = GPU; + + torch::Tensor costs = torch::empty( + tgt_lengths.size(0), + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor betas = torch::zeros( + {options.batchSize_ * options.nHypos_, + options.maxSrcLen_, + options.maxTgtLen_}, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data(), + /*int_size=*/int_workspace.numel()); + + // Only support float, this is mainly to enable easy + // unit-testing + ComputeBetas( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*costs=*/costs.data(), + /*betas=*/betas.data(), + /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data()); + return betas; +} + +torch::Tensor compute_betas_sparse( + const torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + int64_t max_T, + int64_t max_U, + const c10::optional& wp_ends = c10::nullopt, + int64_t l_buffer = 0, + int64_t r_buffer = 0, + const c10::optional& valid_ranges = c10::nullopt, + const c10::optional& cells_per_sample = c10::nullopt) { + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = max_T; + options.maxTgtLen_ = max_U; + options.sparseCells_ = logits.size(0); + options.numTargets_ = logits.size(1); + + options.blank_ = blank; + options.clamp_ = clamp; + options.lBuffer_ = l_buffer; + options.rBuffer_ = r_buffer; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); + options.stream_ = at::cuda::getCurrentCUDAStream(); + cudaSetDevice(logits.get_device()); + options.device_ = GPU; + + torch::Tensor costs = torch::empty( + tgt_lengths.size(0), + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor betas = torch::zeros( + {logits.size(0)}, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data(), + /*int_size=*/int_workspace.numel()); + + // Only support float, this is mainly to enable easy + // unit-testing + ComputeBetasSparse( + /*workspace=*/workspace, + /*logits=*/logits.data(), + /*targets=*/targets.data(), + /*src_lengths=*/src_lengths.data(), + /*tgt_lengths=*/tgt_lengths.data(), + /*costs=*/costs.data(), + /*betas=*/betas.data(), + /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data(), + (valid_ranges == c10::nullopt) ? nullptr : valid_ranges->data(), + (cells_per_sample == c10::nullopt) ? nullptr + : cells_per_sample->data()); + return betas; +} + +TORCH_LIBRARY_FRAGMENT(torchaudio, m) { + m.def("rnnt_loss_betas_sparse(Tensor logits," + "Tensor targets," + "Tensor src_lengths," + "Tensor tgt_lengths," + "int blank," + "float clamp," + "int max_T," + "int max_U," + "Tensor? wp_ends=None," + "int l_buffer=0," + "int r_buffer=0," + "Tensor? valid_ranges=None," + "Tensor? cells_per_sample=None) -> Tensor", + &compute_betas_sparse); +} + +TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { + m.impl("rnnt_loss_betas", &compute_betas); + m.impl("rnnt_loss_betas_sparse", &compute_betas_sparse); +} + +} // namespace gpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh b/torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh new file mode 100644 index 0000000000..564eb0a3fb --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh @@ -0,0 +1,551 @@ +#pragma once + +#ifdef USE_CUDA + +#include + +#include +#include +#include + +namespace torchaudio { +namespace rnnt { + +template +__global__ void ComputeLogProbsSparse( + int maxT, + int maxU, + int numTargets, + int blank, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + CAST_DTYPE* logProbs, + const int* wpEnds, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr, + int H=1, + bool fusedLogSmax=true) { + + const int bTgt = blockIdx.z; // 0 <= b < B + const int t = blockIdx.x * blockDim.x + threadIdx.x; + const int u = blockIdx.y; + + ComputeLogProbsSparseElement( + bTgt, + t, + u, + maxT, + maxU, + numTargets, + blank, + logits, + targets, + srcLengths, + tgtLengths, + denominators, + logProbs, + wpEnds, + validRanges, + cellsPerSample, + H=1, + fusedLogSmax); +} + +template +__device__ void ComputeAlphasRestricted( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* alpha_counters, + volatile CAST_DTYPE* alphas, + const int* wpEnds, + int lBuffer, + int rBuffer, + int warp_size, + bool sparse=false, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr, + int H=1) { + + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + + // Block's 3d dim is batchsize + const int bTgt = blockIdx.z; // 0 <= b < B + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + assert(T <= maxT && "T must be < maxT"); + assert(U <= maxU && "U must be < maxU"); + + // Blocks 1st dim is T i.e. warpsize timesteps + // to find timestep, we index into correct block idx * dim + // then offset by the thread + const int t = blockIdx.x * blockDim.x + threadIdx.x; + + // Blocks 2nd dim is U. For u, we just offset by 2nd idx + const int u = blockIdx.y; + + // simply exit if we are out of bounds + // this happens typically when warp size is larger than T + if (t > T-1 || u > U-1 || t < 0 || u < 0) { + return; + } + + const int* wpEndsB = wpEnds + maxU * bTgt; + assert(wpEndsB[u] >= 0 && "wpEnds cannot be negative"); + if (u < U - 1) { + assert(wpEndsB[u+1] >= 0 && "wpEnds cannot be negative"); + } + + // below indexes are inclusive + // compute valid ranges for emit transitions in current warp + int emit_start = math::max(wpEndsB[u] - lBuffer, 0); + int emit_end = math::min(wpEndsB[u] + rBuffer, T - 1); + + // compute valid ranges for blank transitions in current warp + int blank_start = math::max(wpEndsB[u] - lBuffer + 1, 1); + int blank_end = (u == U - 1) ? T - 1 : math::min(wpEndsB[u + 1] + rBuffer, T - 1); + + // this is a useful pointer to track the last timestep + // for previous warp. + int prevWarpEndT = blockIdx.x * blockDim.x - 1; + + int* counter = alpha_counters + Indexer2D(maxU)(bTgt, blockIdx.y); + + + int idx_b_t_u = -1, idx_b_t_um1 = -1, idx_b_tm1_u = -1; + int idx_b_prevWarpEndT_u = -1; + + if (sparse) { + SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); + idx_b_t_u = idxr(bTgt, t, u); + idx_b_t_um1 = idxr(bTgt, t, u-1); + idx_b_tm1_u = idxr(bTgt, t-1, u); + idx_b_prevWarpEndT_u = idxr(bTgt, prevWarpEndT, u); + } else { + Indexer3D idxr(maxT, maxU); + idx_b_t_u = idxr(bTgt, t, u); + idx_b_t_um1 = idxr(bTgt, t, u - 1); + idx_b_tm1_u = idxr(bTgt, t - 1, u); + idx_b_prevWarpEndT_u = idxr(bTgt, prevWarpEndT, u); + } + + alphas[idx_b_t_u] = CAST_DTYPE(-INFINITY); + + // Initialization condition for alphas + if (t == 0 && u == 0) { + alphas[idx_b_t_u] = 0; + } + + // Synchronization routines: + // Below conditions make sure given t, u + // - threads in previous warp for u is complete + // - threads in the warp corresponding to t, u-1 is complete + // See https://github.com/1ytic/warp-rnnt for illustration + if (blockIdx.x > 0) { // wait for previous warp (in t-axis) is ready. + while (atomicAdd(counter, 0) < blockIdx.x) {} + } + + if (blockIdx.y > 0) { // wait for previous warp (in u-axis) is ready. + while (atomicAdd(counter - 1, 0) <= blockIdx.x) {} + } + + // This is initialization loop for t = 0, u + // i.e. emit condition while running alphas + if (t == 0 && u > 0) { + // Alignment Restriction check + if (in_range(emit_start, emit_end, t)) { + alphas[idx_b_t_u] = + alphas[idx_b_t_um1] + + logProbs[(idx_b_t_um1 << 1) + LOG_PROBS_EMIT_IDX]; + } + } + + // This is initialization loop for u = 0, t + if (u == 0 && t > 0) { + + // skip_prob stores the log probability of t, u + CAST_DTYPE skip_prob = logProbs[(idx_b_tm1_u << 1) + LOG_PROBS_SKIP_IDX]; + + #pragma unroll + // iterating on i = 1, 2, 4, 8.. + for (int i = 1; i < warp_size; i <<= 1) { + + // https://devblogs.nvidia.com/using-cuda-warp-level-primitives/ + // first param is a mask -1 indicates all threads in warp participate + // second param is the value being passed for current thread + // third param is the offset which will be used for addition of values + // copy from a thread with lower ID relative to the caller + CAST_DTYPE synced_val = __shfl_up_sync(0xffffffff, skip_prob, i); + + // Illustration with 8 threads in warp + // Initial: 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 + // i == 1: 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2 + // i == 2: 0.1, 0.2, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4 + // i == 4: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 + + // Alignment restriction check + // add to skip_prob only if *t-i* is within allowed range + if (i <= threadIdx.x && + in_range(blank_start, blank_end, t - i) ) { + skip_prob += synced_val; + } + } + + // update the values of alpha if *t* is within allowed range + if (in_range(blank_start, blank_end, t)) { + alphas[idx_b_t_u] = skip_prob; + } + + // Optionally add previous warp's end idx value if it is + // within range to get cumulative score for all previous timesteps, + // and not just within current warp + if (in_range(blank_start, blank_end, prevWarpEndT)) { + alphas[idx_b_t_u] += alphas[idx_b_prevWarpEndT_u]; + } + } + + // General case + if (t < T && u < U) { + + CAST_DTYPE skip_prob = CAST_DTYPE(-INFINITY); + if (in_range(blank_start, blank_end, t) && t > 0) { + skip_prob = logProbs[(idx_b_tm1_u << 1) + LOG_PROBS_SKIP_IDX]; + } + + // We check if the first index of current warp (prevWarpEndT+1) was within allowed + // timesteps, if so we add the alphas of previous warp's last timestep + // to skip score. + CAST_DTYPE skip = skip_prob; + if (in_range(blank_start, blank_end, prevWarpEndT+1) && prevWarpEndT >= 0) { + skip += alphas[idx_b_prevWarpEndT_u]; + } + + CAST_DTYPE emit = CAST_DTYPE(-INFINITY); + if (in_range(emit_start, emit_end, t) && u > 0) { + emit = alphas[idx_b_t_um1] + + logProbs[(idx_b_t_um1 << 1) + LOG_PROBS_EMIT_IDX]; + } + + CAST_DTYPE out_score = math::lse(skip, emit); + + + // Below we loop over warp_size updating the out score + // once per thread in each loop. + // shared memory is used to synchronize the values in threads + for(int i = 1; i < warp_size; i++) { + + CAST_DTYPE synced_val = __shfl_up_sync(0xffffffff, out_score, 1); + + // We will only update the out_score if t is within range + // of alignment constraints and it is the thread's turn in loop + if ((i == threadIdx.x) && in_range(blank_start, blank_end, t)) { + out_score = math::lse(synced_val + skip_prob, emit); + } + } + // update alphas if t was within range of blanks, or emits + if ( (in_range(blank_start, blank_end, t) || in_range(emit_start, emit_end, t) ) + && (t > 0 && u > 0)) { + alphas[idx_b_t_u] = out_score; + } + } + + // Synchronization mechanism + if (threadIdx.x == 0) { + __threadfence(); + atomicAdd(counter, 1); + } +} + + + +// This is a wrapper around ComputeAlphasRestricted +// kernel to enable unit testing +template +__global__ void ComputeAlphasRestrictedWrapper( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* alpha_counters, + volatile CAST_DTYPE* alphas, + const int* wpEnds, + int lBuffer, + int rBuffer, + int warp_size, + bool sparse=false, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr, + int H=1) { + ComputeAlphasRestricted( + maxSrcLen, + maxTgtLen, + numTargets, + blank, + logProbs, + srcLengths, + tgtLengths, + alpha_counters, + alphas, + wpEnds, + lBuffer, + rBuffer, + warp_size, + sparse, + validRanges, + cellsPerSample, + H); +} + + +template +__device__ void ComputeBetasCostsRestricted( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* beta_counters, + DTYPE* costs, + volatile CAST_DTYPE* betas, + const int* wpEnds, + int lBuffer, + int rBuffer, + int warp_size, + int num_warps, + bool sparse=false, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr, + int H=1) { + + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + + // Block's 3d dim is batchsize + const int bTgt = blockIdx.z; // 0 <= b < B + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + assert(T <= maxT); + assert(U <= maxU); + + const int t = T - 1 - blockIdx.x * blockDim.x - threadIdx.x; + const int u = U - 1 - blockIdx.y; + + if (t < 0 || u < 0) { // out of boundary. + return; + } + + const int* wpEndsB = wpEnds + maxU * bTgt; + + // below indexes are inclusive + // compute valid ranges for emit transitions in + // current warp + int emit_start = (u >= U - 1) ? -1 : math::max(0, wpEndsB[u + 1] - lBuffer); + int emit_end = (u >= U - 1) ? -1 : math::min(wpEndsB[u + 1] + rBuffer, T - 1); + + + // compute valid ranges for blank transitions in + // current warp + int blank_start = (u > U - 1) ? -1 : math::max(wpEndsB[u] - lBuffer, 0); + int blank_end = (u >= U - 1) ? T - 2 : math::min(wpEndsB[u + 1] + rBuffer - 1, T - 2); + + int rightBlockStartT = T - 1 - blockIdx.x * blockDim.x + 1; + int* counter = beta_counters + Indexer2D(maxU)(bTgt, blockIdx.y); + + int idx_b_t_u = -1, idx_b_t_up1 = -1; + int idx_b_tp1_up1 = -1, idx_b_rightBlockStartT_u = -1; + if (sparse) { + SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); + idx_b_t_u = idxr(bTgt, t, u); + idx_b_t_up1 = idxr(bTgt, t, u+1); + idx_b_tp1_up1 = idxr(bTgt, t+1, u+1); + idx_b_rightBlockStartT_u = idxr(bTgt, rightBlockStartT, u); + } else { + Indexer3D idxr(maxT, maxU); + idx_b_t_u = idxr(bTgt, t, u); + idx_b_t_up1 = idxr(bTgt, t, u+1); + idx_b_tp1_up1 = idxr(bTgt, t+1, u+1); + idx_b_rightBlockStartT_u = idxr(bTgt, rightBlockStartT, u); + } + + if (t < T && u < U) { + betas[idx_b_t_u] = CAST_DTYPE(-INFINITY); + } + + // Initialization condition + if (t == T - 1 && u == U - 1) { + betas[idx_b_t_u] = + logProbs[(idx_b_t_u << 1) + LOG_PROBS_SKIP_IDX]; + } + + // For betas, we process warps from right to left + // blockIdx.x specifies the warp id for a given u + // here we want to wait until the warp to the right is done + if (blockIdx.x > 0) { // wait for previous warp (in t-axis) is ready. + while (atomicAdd(counter, 0) < blockIdx.x) {} + } + + if (blockIdx.y > 0) { // wait for previous warp (in u-axis) is ready. + while (atomicAdd(counter - 1, 0) <= blockIdx.x) {} + } + + // edge condition for emits + if (t == T - 1 && u < U - 1) { + if (emit_start <= t && t <= emit_end) { + betas[idx_b_t_u] = + betas[idx_b_t_up1] + + logProbs[(idx_b_t_u << 1) + LOG_PROBS_EMIT_IDX]; + } + } + + if (u == U - 1 && t <= T - 1) { + CAST_DTYPE skip_prob = logProbs[(idx_b_t_u << 1) + LOG_PROBS_SKIP_IDX]; + + #pragma unroll + for(int i = 1; i < warp_size; i <<= 1) { + + CAST_DTYPE synced_val = __shfl_up_sync(0xffffffff, skip_prob, i); + if (i <= threadIdx.x && in_range(blank_start, blank_end, t)) { + skip_prob = skip_prob + synced_val; + } + } + + if (in_range(blank_start, blank_end, t) && t < T - 1) { + // we add skipprob and right block's first value + // and assign it to beta + betas[idx_b_t_u] = skip_prob; + if (in_range(blank_start, blank_end, rightBlockStartT) || + in_range(emit_start, emit_end, rightBlockStartT)) { + betas[idx_b_t_u] += betas[idx_b_rightBlockStartT_u]; + } + } + } + + // General case + if (t < T && u < U) { + + CAST_DTYPE skip_prob = CAST_DTYPE(-INFINITY); + if (in_range(blank_start, blank_end, t)) { + skip_prob = logProbs[(idx_b_t_u << 1) + LOG_PROBS_SKIP_IDX]; + } + // We check if the first index of right warp is within allowed + // timesteps, if so we add the betas of right warp's first timestep + // to skip score + CAST_DTYPE skip = skip_prob; + if (in_range(blank_start, blank_end, rightBlockStartT) || in_range(emit_start, emit_end, rightBlockStartT)) { + skip += betas[idx_b_rightBlockStartT_u]; + } + + CAST_DTYPE emit = CAST_DTYPE(-INFINITY); + if (in_range(emit_start, emit_end, t) && u < U - 1) { + emit = betas[idx_b_t_up1] + + logProbs[(idx_b_t_u << 1) + LOG_PROBS_EMIT_IDX]; + } + + CAST_DTYPE out_score = CAST_DTYPE(-INFINITY); + if (skip != CAST_DTYPE(-INFINITY) || emit != CAST_DTYPE(-INFINITY)) { + out_score = math::lse(skip, emit); + } + + for(int i = 1; i < warp_size; i++) { + + CAST_DTYPE synced_val = __shfl_up_sync(0xffffffff, out_score, 1); + + if (i == threadIdx.x && + (in_range(blank_start, blank_end, t) || + in_range(emit_start, emit_end, t)) + ) { + out_score = math::lse(synced_val + skip_prob, emit); + } + } + + // bounds check for last warps + if (t < T - 1 && u < U - 1) { + // bound checks for t + if (in_range(blank_start, blank_end, t) || + in_range(emit_start, emit_end, t)) { + betas[idx_b_t_u] = out_score; + } + } + } + + if (t == 0 && u == 0) { // use -beta(0, 0) as cost. + if (isinf(-betas[idx_b_t_u]) || isnan(-betas[idx_b_t_u])) { + costs[bTgt] = 0; + } else { + costs[bTgt] = -betas[idx_b_t_u]; + } + } + + if (threadIdx.x == 0) { + __threadfence(); + atomicAdd(counter, 1); + } +} + + +// This is a wrapper around ComputeAlphasRestricted +// kernel to enable unit testing +template +__global__ void ComputeBetasCostsRestrictedWrapper( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* beta_counters, + CAST_DTYPE* costs, + volatile CAST_DTYPE* betas, + const int* wpEnds, + int lBuffer, + int rBuffer, + int warp_size, + int num_warp, + bool sparse=false, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr, + int H=1) { + ComputeBetasCostsRestricted( + maxSrcLen, + maxTgtLen, + numTargets, + blank, + logProbs, + srcLengths, + tgtLengths, + beta_counters, + costs, + betas, + wpEnds, + lBuffer, + rBuffer, + warp_size, + num_warp, + sparse, + validRanges, + cellsPerSample, + H); +} + +} // namespace rnnt +} // namespace torchaudio + +#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh b/torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh new file mode 100644 index 0000000000..f42e35b46f --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh @@ -0,0 +1,99 @@ +#pragma once + +#ifdef USE_CUDA + +#include + +namespace torchaudio { +namespace rnnt { + +template +__global__ void ReduceMax2D( + int dim, + const DTYPE* inputs, // [N, dim] + CAST_DTYPE* outputs) { + + __shared__ CAST_DTYPE shared[NUM_THREADS]; + + // each thread reduces one matrix row + int offset = blockIdx.x * dim; // [n, 0] + CAST_DTYPE val = inputs[offset]; // default = inputs(n, 0) + for (int d = threadIdx.x; d < dim; d += NUM_THREADS) { + CAST_DTYPE next = inputs[offset + d]; + if (next > val) { + val = next; + } + } + + shared[threadIdx.x] = val; + __syncthreads(); + + for (int stride = (NUM_THREADS >> 1); stride >= WARP_SIZE; stride >>= 1) { + if (threadIdx.x < stride && threadIdx.x + stride < dim) { + if (shared[threadIdx.x + stride] > shared[threadIdx.x]) { + shared[threadIdx.x] = shared[threadIdx.x + stride]; + val = shared[threadIdx.x]; + } + } + __syncthreads(); + } + + CAST_DTYPE shf; + for (int stride = (WARP_SIZE >> 1); stride > 0; stride >>= 1) { + shf = __shfl_down_sync(0xFFFFFFFF, val, stride); + if (threadIdx.x < stride && threadIdx.x + stride < dim) { + if (shf > val) { + val = shf; + } + } + } + + if (threadIdx.x == 0) { + outputs[blockIdx.x] = val; + } +} + +template +__global__ void ReduceLogSumExpGivenMax2D( + int dim, + const DTYPE* inputs, // [N, dim] + CAST_DTYPE* outputs) { // in: max -> out: logsum + + __shared__ CAST_DTYPE shared[NUM_THREADS]; + + CAST_DTYPE max = outputs[blockIdx.x]; + CAST_DTYPE val = 0; + + int offset = blockIdx.x * dim; + for (int d = threadIdx.x; d < dim; d += NUM_THREADS) { + val = val + std::exp(CAST_DTYPE(inputs[offset + d]) - max); + } + + shared[threadIdx.x] = val; + __syncthreads(); + + for (int stride = (NUM_THREADS >> 1); stride >= WARP_SIZE; stride >>= 1) { + if (threadIdx.x < stride && threadIdx.x + stride < dim) { + val = shared[threadIdx.x] + shared[threadIdx.x + stride]; + shared[threadIdx.x] = val; + } + __syncthreads(); + } + + CAST_DTYPE shf; + for (int stride = (WARP_SIZE >> 1); stride > 0; stride >>= 1) { + shf = __shfl_down_sync(0xFFFFFFFF, val, stride); + if (threadIdx.x < stride && threadIdx.x + stride < dim) { + val = val + shf; + } + } + + if (threadIdx.x == 0) { + outputs[blockIdx.x] = max + std::log(val); + } +} + +} // namespace rnnt +} // namespace torchaudio + +#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh b/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh new file mode 100644 index 0000000000..88f625a251 --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh @@ -0,0 +1,488 @@ +#pragma once + +#ifdef USE_CUDA + +#include + +#include +#include +#include + +namespace torchaudio { +namespace rnnt { + + +template +__global__ void ComputeLogProbs( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + CAST_DTYPE* logProbs, + int H=1, + bool fusedLogSmax=true) { + + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + const int& D = numTargets; + + const int bTgt = blockIdx.z; // 0 <= b < B + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + const int t = blockIdx.x * blockDim.x + threadIdx.x; + const int u = blockIdx.y; + + if (t >= T || u >= U) { // out of boundary. + return; + } + + Indexer3D indexer(maxT, maxU); + + int idx = indexer(bTgt, t, u); + + // skip: log_prob(b, t, u).skip() = logits(b, t, u, blank) - denom(b, t, u). + logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = + CAST_DTYPE(logits[idx * D + blank]) - denominators[idx]; + + if (!fusedLogSmax) { + logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = + CAST_DTYPE(logits[idx * D + blank]); + } + + if (u < U - 1) { + // emit: log_prob(b, t, u).emit() = logits(b, t, u, tgt[u]) - denom(b, t, u). + int target = targets[Indexer2D(maxU - 1)(bTgt, u)]; + logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = + CAST_DTYPE(logits[idx * D + target]) - denominators[idx]; + + if (!fusedLogSmax) { + logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = + CAST_DTYPE(logits[idx * D + target]); + } + } + + +} + + +template +__device__ void ComputeAlphas( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* alpha_counters, + volatile CAST_DTYPE* alphas, + int H=1) { + + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + + const int bTgt = blockIdx.z; // 0 <= b < B + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + const int t = blockIdx.x * blockDim.x + threadIdx.x + 1; + const int u = blockIdx.y + 1; + + if (t >= T || u >= U) { // out of boundary. + return; + } + + int* counter = alpha_counters + Indexer2D(maxU)(bTgt, blockIdx.y); + + Indexer3D idxr(maxT, maxU); + + if (t == 1 && u == 1) { + alphas[idxr(bTgt, 0, 0)] = 0; + } + + if (blockIdx.x > 0) { // wait for previous warp (in t-axis) is ready. + while (atomicAdd(counter, 0) < blockIdx.x) {} + } + + if (blockIdx.y > 0) { // wait for previous warp (in u-axis) is ready. + while (atomicAdd(counter - 1, 0) <= blockIdx.x) {} + } + + if (t == 1 && u < U) { + + // alpha(0, u) = alpha(0, u - 1) + logProbs(0, u - 1).emit(). + alphas[idxr(bTgt, 0, u)] = + alphas[idxr(bTgt, 0, u - 1)] + + logProbs[(idxr(bTgt, 0, u - 1) << 1) + LOG_PROBS_EMIT_IDX]; + } + + if (blockIdx.y == 0 && t < T) { + CAST_DTYPE skip_prob = logProbs[(idxr(bTgt, t - 1, 0) << 1) + LOG_PROBS_SKIP_IDX]; + CAST_DTYPE val; + +#pragma unroll + for (int i = 1; i < warpSize; i <<= 1) { + val = __shfl_up_sync(0xffffffff, skip_prob, i); + if (i <= threadIdx.x) { + skip_prob = skip_prob + val; + } + } + + val = alphas[idxr(bTgt, blockIdx.x * blockDim.x, 0)]; + alphas[idxr(bTgt, t, 0)] = skip_prob + val; + } + + if (t < T && u < U) { + + CAST_DTYPE skip_prob = logProbs[(idxr(bTgt, t - 1, u) << 1) + LOG_PROBS_SKIP_IDX]; + CAST_DTYPE emit_prob = logProbs[(idxr(bTgt, t, u - 1) << 1) + LOG_PROBS_EMIT_IDX]; + + CAST_DTYPE skip = alphas[idxr(bTgt, blockIdx.x * blockDim.x, u)] + skip_prob; + CAST_DTYPE emit = alphas[idxr(bTgt, t, u - 1)] + emit_prob; + + CAST_DTYPE val = math::lse(skip, emit); + CAST_DTYPE out = val; + + for(int i = 1; i < warpSize; ++i) { + val = __shfl_up_sync(0xffffffff, val, 1); + if (i == threadIdx.x) { + val = math::lse(val + skip_prob, emit); + out = val; + } + } + + alphas[idxr(bTgt, t, u)] = out; + } + + if (threadIdx.x == 0) { + __threadfence(); + atomicAdd(counter, 1); + } +} + + +template +__device__ void ComputeBetasCosts( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* betaCounters, + volatile CAST_DTYPE* betas, + DTYPE* costs, + int H=1) { + + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + + const int bTgt = blockIdx.z; // 0 <= b < B + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + const int t = T - 2 - blockIdx.x * blockDim.x - threadIdx.x; + const int u = U - 2 - blockIdx.y; + + if (t < 0 || u < 0) { // out of boundary. + return; + } + + int* counter = betaCounters + Indexer2D(maxU)(bTgt, blockIdx.y); + + Indexer3D idxr(maxT, maxU); + + if (t == T - 2 && u == U - 2) { + betas[idxr(bTgt, T - 1, U - 1)] = + logProbs[(idxr(bTgt, T - 1, U - 1) << 1) + LOG_PROBS_SKIP_IDX]; + } + + if (blockIdx.x > 0) { // wait for previous warp (in t-axis) is ready. + while (atomicAdd(counter, 0) < blockIdx.x) {} + } + + if (blockIdx.y > 0) { // wait for previous warp (in u-axis) is ready. + while (atomicAdd(counter - 1, 0) <= blockIdx.x) {} + } + + if (t == T - 2 && u >= 0) { + + betas[idxr(bTgt, T - 1, u)] = + betas[idxr(bTgt, T - 1, u + 1)] + + logProbs[(idxr(bTgt, T - 1, u) << 1) + LOG_PROBS_EMIT_IDX]; + } + + if (blockIdx.y == 0 && t >= 0) { + CAST_DTYPE skip_prob = logProbs[(idxr(bTgt, t, U - 1) << 1) + LOG_PROBS_SKIP_IDX]; + CAST_DTYPE val; + +#pragma unroll + for(int i = 1; i < warpSize; i <<= 1) { + val = __shfl_up_sync(0xffffffff, skip_prob, i); + if (i <= threadIdx.x) { + skip_prob = skip_prob + val; + } + } + + betas[idxr(bTgt, t, U - 1)] = + betas[idxr(bTgt, T - 1 - blockIdx.x * blockDim.x, U - 1)] + skip_prob; + } + + if (t >= 0 && u >= 0) { + + CAST_DTYPE skip_prob = logProbs[(idxr(bTgt, t, u) << 1) + LOG_PROBS_SKIP_IDX]; + CAST_DTYPE emit_prob = logProbs[(idxr(bTgt, t, u) << 1) + LOG_PROBS_EMIT_IDX]; + + CAST_DTYPE skip = betas[idxr(bTgt, t + threadIdx.x + 1, u)] + skip_prob; + CAST_DTYPE emit = betas[idxr(bTgt, t, u + 1)] + emit_prob; + + CAST_DTYPE val = math::lse(skip, emit); + CAST_DTYPE out = val; + + for(int i = 1; i < warpSize; ++i) { + val = __shfl_up_sync(0xffffffff, val, 1); + if (i == threadIdx.x) { + val = math::lse(val + skip_prob, emit); + out = val; + } + } + + betas[idxr(bTgt, t, u)] = out; + + if (t == 0 && u == 0) { // use -beta(0, 0) as cost. + costs[bTgt] = DTYPE(-out); + } + } + + if (threadIdx.x == 0) { + __threadfence(); + atomicAdd(counter, 1); + } +} + + +template +__global__ void ComputeAlphasBetasCosts( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* alpha_counters, + volatile CAST_DTYPE* alphas, + int* betaCounters, + volatile CAST_DTYPE* betas, + DTYPE* costs, + const int* wpEnds=nullptr, + int lBuffer = 0, + int rBuffer = 0, + int warpSize = 0, + int numWarps=0, + bool sparse=false, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr, + int H = 1) { + + assert(threadIdx.y == 0 || threadIdx.y == 1); + + if (threadIdx.y == 0) { + if (wpEnds == nullptr) { + ComputeAlphas( + /*maxSrcLen=*/maxSrcLen, + /*maxTgtLen=*/maxTgtLen, + /*numTargets=*/numTargets, + /*blank=*/blank, + /*logProbs=*/logProbs, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/alpha_counters, + /*alphas=*/alphas, + H); + } + else { + ComputeAlphasRestricted( + /*maxSrcLen=*/maxSrcLen, + /*maxTgtLen=*/maxTgtLen, + /*numTargets=*/numTargets, + /*blank=*/blank, + /*logProbs=*/logProbs, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/alpha_counters, + /*alphas=*/(CAST_DTYPE*)alphas, + /*wpEnds=*/wpEnds, + /*lBuffer=*/lBuffer, + /*rBuffer=*/rBuffer, + /*warpSize=*/warpSize, + sparse, + validRanges, + cellsPerSample, + H); + } + } else { // threadIdx.y == 1 + if (wpEnds == nullptr) { + ComputeBetasCosts( + /*maxSrcLen=*/maxSrcLen, + /*maxTgtLen=*/maxTgtLen, + /*numTargets=*/numTargets, + /*blank=*/blank, + /*logProbs=*/logProbs, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*betaCounters=*/betaCounters, + /*beta=*/betas, + /*costs=*/costs, + H); + } else { + ComputeBetasCostsRestricted( + maxSrcLen, + maxTgtLen, + numTargets, + blank, + logProbs, + srcLengths, + tgtLengths, + betaCounters, + costs, + betas, + wpEnds, + lBuffer, + rBuffer, + warpSize, + numWarps, + sparse, + validRanges, + cellsPerSample, + H); + } + + } +} + + +template +__global__ void ComputeGradients( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + CAST_DTYPE clamp, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + const CAST_DTYPE* alphas, + const CAST_DTYPE* betas, + DTYPE* gradients, + bool sparse = false, + const int* validRanges = nullptr, + const int* cellsPerSample = nullptr, + int H = 1, + bool fusedLogSmax = true) { + + const int bTgt = blockIdx.z; // 0 <= b < B + const int t = blockIdx.x * blockDim.x + threadIdx.x; + const int u = blockIdx.y; + + ComputeGradientsElement( + bTgt, + t, + u, + maxSrcLen, + maxTgtLen, + numTargets, + blank, + clamp, + logits, + targets, + srcLengths, + tgtLengths, + denominators, + alphas, + betas, + gradients, + sparse, + validRanges, + cellsPerSample, + H, + fusedLogSmax); +} + + +// This is a __global__ wrapper around ComputeAlphas +// device kernel to enable unit testing +template +__global__ void ComputeAlphasWrapper( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* alpha_counters, + volatile CAST_DTYPE* alphas, + int H=1) { + ComputeAlphas( + maxSrcLen, + maxTgtLen, + numTargets, + blank, + logProbs, + srcLengths, + tgtLengths, + alpha_counters, + alphas, + H); +} + +// This is a __global__ wrapper around ComputeBetas +// device kernel to enable unit testing +template +__global__ void ComputeBetasWrapper( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* betaCounters, + volatile CAST_DTYPE* betas, + DTYPE* costs, + int H=1) { + ComputeBetasCosts( + maxSrcLen, + maxTgtLen, + numTargets, + blank, + logProbs, + srcLengths, + tgtLengths, + betaCounters, + betas, + costs, + H); +} + + +// #undef LOG_PROBS_SKIP_IDX +// #undef LOG_PROBS_EMIT_IDX + +} // namespace rnnt +} // namespace torchaudio + +#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/gpu_transducer.h b/torchaudio/csrc/rnnt/gpu/gpu_transducer.h new file mode 100644 index 0000000000..7db7fe8a78 --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/gpu_transducer.h @@ -0,0 +1,829 @@ +#pragma once + +#ifdef USE_CUDA + +#include +#include +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace gpu { + +#define gpuErrchk(ans) \ + { gpuAssert((ans), __FILE__, __LINE__); } + +inline void +gpuAssert(cudaError_t code, const char* file, int line, bool abort = true) { + if (code != cudaSuccess) { + fprintf( + stderr, + "\nGPUassert: %s %s %d\n", + cudaGetErrorString(code), + file, + line); + if (abort) + exit(code); + } +} + +template +status_t LogSumExp2D( + cudaStream_t stream, + int N, + int D, + const DTYPE* logits, // [N, D] + CAST_DTYPE* outputs) { + { // compute max among D. + dim3 block_dims(N); + dim3 thread_dims(REDUCE_THREADS); + + ReduceMax2D + <<>>( + /*dim=*/D, + /*inputs=*/logits, + /*outputs=*/outputs); + + // BUGBUG: These error codes are only accurate when launching with + // blocking. Otherwise they usually reflect earlier errors. + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_DENOMINATOR_REDUCE_MAX_FAILED; + } + } + + { // compute log(sum(exp(d_i - max))) + dim3 block_dims(N); + dim3 thread_dims(REDUCE_THREADS); + + ReduceLogSumExpGivenMax2D + <<>>( + /*dim=*/D, + /*inputs=*/logits, + /*outputs=*/outputs); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_DENOMINATOR_REDUCE_SUM_FAILED; + } + } + + return SUCCESS; +} + +// Inputs: +// workspace: workspace. +// logits: pointer to (B, max_T, max_U, D) logits. +// targets: pointer to (B, max_U - 1) targets in the batch. +// srcLengths: pointer to (B, ) source lengths in the batch. +// tgtLengths: pointer to (B, ) target lengths in the batch. +// +// Outputs: +// costs: pointer to (B, ) costs in the batch. +// gradients: pointer to (B, max_T, max_U, D) gradients in the batch. +template +status_t Compute( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* gradients = nullptr, + const int* wpEnds = nullptr) { + const Options& options = workspace.GetOptions(); + + const cudaStream_t& stream = options.stream_; + const int& B = options.batchSize_; + const int& H = options.nHypos_; + const int& max_T = options.maxSrcLen_; + const int& max_U = options.maxTgtLen_; + const int& D = options.numTargets_; + const int& blank = options.blank_; + const CAST_DTYPE clamp = options.clamp_; + + const int& l_buffer = options.lBuffer_; + const int& r_buffer = options.rBuffer_; + const bool& fusedLogSmax = options.fusedLogSmax_; + + { // compute denominators. + status_t status = LogSumExp2D( + /*stream=*/stream, + /*N=*/B * H * max_T * max_U, + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + + if (status != SUCCESS) { + return status; + } + } + + { // compute log probability pairs (blank and target). + int num_segments = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_segments, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeLogProbs<<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs(), + H, + fusedLogSmax); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_LOG_PROBS_FAILED; + } + } + + { // compute alphas, betas and costs. + // warp is usually a group of threads (32) + int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; + + // each block is identified by 3 d tuple. + // we are using num_warp * max_U * B * H blocks + // where num_warp is division among Time axis + dim3 block_dims(num_warps, max_U, B * H); + + // each thread is identified by a 2 d tuple + // 2nd dim is 2. 1 for alpha, 1 for beta + dim3 thread_dims(WARP_SIZE, 2); + + ComputeAlphasBetasCosts + <<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/workspace.GetPointerToAlphaCounters(), + /*alphas=*/workspace.GetPointerToAlphas(), + /*beta_counters=*/workspace.GetPointerToBetaCounters(), + /*betas=*/workspace.GetPointerToBetas(), + /*costs=*/costs, + /*wpEnds=*/wpEnds, + /*l_buffer=*/l_buffer, + /*r_buffer=*/r_buffer, + /*warp_size=*/WARP_SIZE, + /*num_warps=*/num_warps, + /*sparse=*/false, + /*validRanges=*/nullptr, + /*cellsPerSample=*/nullptr, + H); + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; + } + } + + if (gradients != nullptr) { // compute gradients. + // don't set gradients to zero to here as gradients might reuse memory from + // logits + + int num_blocks = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_blocks, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeGradients<<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*clamp=*/clamp, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*alphas=*/workspace.GetPointerToAlphas(), + /*betas=*/workspace.GetPointerToBetas(), + /*gradients=*/gradients, + /*sparse=*/false, + /*validRanges=*/nullptr, + /*cellsPerSample=*/nullptr, + H, + fusedLogSmax); + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_GRADIENTS_FAILED; + } + } + + return SUCCESS; +} + +// Inputs: +// workspace: workspace. +// logits: pointer to (sparseCells, D) logits. +// targets: pointer to (B, max_U - 1) targets in the batch. +// srcLengths: pointer to (B, ) source lengths in the batch. +// tgtLengths: pointer to (B, ) target lengths in the batch. +// +// Outputs: +// costs: pointer to (B, ) costs in the batch. +// gradients: pointer to (sparseCells, D) gradients in the batch. +template +status_t ComputeSparse( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* gradients = nullptr, + const int* wpEnds = nullptr, + const int* validRanges = nullptr, + const int* cellsPerSample = nullptr) { + const Options& options = workspace.GetOptions(); + + const cudaStream_t& stream = options.stream_; + const int& B = options.batchSize_; + const int& H = options.nHypos_; + const int& max_T = options.maxSrcLen_; + const int& max_U = options.maxTgtLen_; + const int& D = options.numTargets_; + const int& blank = options.blank_; + const CAST_DTYPE clamp = options.clamp_; + + const int& l_buffer = options.lBuffer_; + const int& r_buffer = options.rBuffer_; + const bool& fusedLogSmax = options.fusedLogSmax_; + + const int& sparseCells = options.sparseCells_; + { // compute denominators. + // for sparse kernel, we call kernel with sparseSize + status_t status = LogSumExp2D( + /*stream=*/stream, + /*N=*/sparseCells, // not B * max_T * max_U + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + gpuErrchk(cudaGetLastError()); + if (status != SUCCESS) { + return status; + } + } + { // compute log probability pairs (blank and target). + int num_segments = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_segments, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeLogProbsSparse + <<>>( + max_T, + max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs(), + wpEnds, + validRanges, + cellsPerSample, + H, + fusedLogSmax); + gpuErrchk(cudaGetLastError()); + } + { // compute alphas, betas and costs. + // warp is usually a group of threads (32) + int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; + + // each block is identified by 3 d tuple. + // we are using num_warp * max_U * B * H blocks + // where num_warp is division among Time axis + dim3 block_dims(num_warps, max_U, B * H); + + // each thread is identified by a 2 d tuple + // 2nd dim is 2. 1 for alpha, 1 for beta + dim3 thread_dims(WARP_SIZE, 2); + + ComputeAlphasBetasCosts + <<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/workspace.GetPointerToAlphaCounters(), + /*alphas=*/workspace.GetPointerToAlphas(), + /*beta_counters=*/workspace.GetPointerToBetaCounters(), + /*betas=*/workspace.GetPointerToBetas(), + /*costs=*/costs, + /*wpEnds=*/wpEnds, + /*l_buffer=*/l_buffer, + /*r_buffer=*/r_buffer, + /*warp_size=*/WARP_SIZE, + /*num_warps=*/num_warps, + true, + validRanges, + cellsPerSample, + H); + gpuErrchk(cudaGetLastError()); + } + + if (gradients != nullptr) { // compute gradients. + // don't zero gradients here as gradient might share memory with logits + + int num_blocks = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_blocks, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeGradients<<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*clamp=*/clamp, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*alphas=*/workspace.GetPointerToAlphas(), + /*betas=*/workspace.GetPointerToBetas(), + /*gradients=*/gradients, + /*sparse=*/true, + /*validRanges=*/validRanges, + /*cellsPerSample=*/cellsPerSample, + H, + fusedLogSmax); + gpuErrchk(cudaGetLastError()); + } + + return SUCCESS; +} + +template +status_t ComputeAlphas( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* alphas, + const int* wpEnds = nullptr) { + const Options& options = workspace.GetOptions(); + + const cudaStream_t& stream = options.stream_; + const int& B = options.batchSize_; + const int& H = options.nHypos_; + const int& max_T = options.maxSrcLen_; + const int& max_U = options.maxTgtLen_; + const int& D = options.numTargets_; + const int& blank = options.blank_; + + const int& l_buffer = options.lBuffer_; + const int& r_buffer = options.rBuffer_; + + { // compute denominators. + status_t status = LogSumExp2D( + /*stream=*/stream, + /*N=*/B * H * max_T * max_U, + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + + if (status != SUCCESS) { + return status; + } + } + + { // compute log probability pairs (blank and target). + int num_segments = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_segments, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeLogProbs<<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs(), + H); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_LOG_PROBS_FAILED; + } + } + { // compute alphas + // warp is usually a group of threads (32) + int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; + + // each block is identified by 3 d tuple. + // we are using num_warp * max_U * B blocks + // where num_warp is division among Time axis + dim3 block_dims(num_warps, max_U, B * H); + + // each thread is identified by a 2 d tuple + // 2nd dim is 1 for alpha only + dim3 thread_dims(WARP_SIZE, 1); + + if (wpEnds == nullptr) { + ComputeAlphasWrapper + <<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/workspace.GetPointerToAlphaCounters(), + /*alphas=*/(volatile DTYPE*)alphas, + H); + } else { + // cudaMemset(alphas, 0, B * max_T * max_U * sizeof(DTYPE)); + ComputeAlphasRestrictedWrapper + <<>>( + max_T, + max_U, + D, + blank, + workspace.GetPointerToLogProbs(), + srcLengths, + tgtLengths, + workspace.GetPointerToAlphaCounters(), + // workspace.GetPointerToAlphas(), + (volatile DTYPE*)alphas, + wpEnds, + l_buffer, + r_buffer, + WARP_SIZE, + false, + nullptr, + nullptr, + H); + } + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; + } + } + + return SUCCESS; +} + +template +status_t ComputeAlphasSparse( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* alphas, + const int* wpEnds = nullptr, + const int* validRanges = nullptr, + const int* cellsPerSample = nullptr) { + const Options& options = workspace.GetOptions(); + const cudaStream_t& stream = options.stream_; + const int& B = options.batchSize_; + const int& H = options.nHypos_; + const int& max_T = options.maxSrcLen_; + const int& max_U = options.maxTgtLen_; + const int& D = options.numTargets_; + const int& blank = options.blank_; + + const int& l_buffer = options.lBuffer_; + const int& r_buffer = options.rBuffer_; + + const int& sparseCells = options.sparseCells_; + + { // compute denominators. + // for sparse kernel, we would call kernel with sparseSize + status_t status = LogSumExp2D( + /*stream=*/stream, + /*N=*/sparseCells, // not B * max_T * max_U + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + + if (status != SUCCESS) { + return status; + } + } + + { // compute log probability pairs (blank and target). + int num_segments = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_segments, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeLogProbsSparse + <<>>( + max_T, + max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs(), + wpEnds, + validRanges, + cellsPerSample, + H); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_LOG_PROBS_FAILED; + } + } + { // compute alphas + // warp is usually a group of threads (32) + int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; + + // each block is identified by 3 d tuple. + // we are using num_warp * max_U * B blocks + // where num_warp is division among Time axis + dim3 block_dims(num_warps, max_U, B * H); + + // each thread is identified by a 2 d tuple + // 2nd dim is 1 for alpha only + dim3 thread_dims(WARP_SIZE, 1); + + // cudaMemset(alphas, 0, B * max_T * max_U * sizeof(DTYPE)); + ComputeAlphasRestrictedWrapper + <<>>( + max_T, + max_U, + D, + blank, + workspace.GetPointerToLogProbs(), + srcLengths, + tgtLengths, + workspace.GetPointerToAlphaCounters(), + // workspace.GetPointerToAlphas(), + (volatile DTYPE*)alphas, + wpEnds, + l_buffer, + r_buffer, + WARP_SIZE, + true, + validRanges, + cellsPerSample, + H); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; + } + } + + return SUCCESS; +} + +template +status_t ComputeBetas( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* betas, + const int* wpEnds = nullptr) { + const Options& options = workspace.GetOptions(); + + const cudaStream_t& stream = options.stream_; + const int& B = options.batchSize_; + const int& H = options.nHypos_; + const int& max_T = options.maxSrcLen_; + const int& max_U = options.maxTgtLen_; + const int& D = options.numTargets_; + const int& blank = options.blank_; + + const int& l_buffer = options.lBuffer_; + const int& r_buffer = options.rBuffer_; + + { // compute denominators. + status_t status = LogSumExp2D( + /*stream=*/stream, + /*N=*/B * H * max_T * max_U, + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + + if (status != SUCCESS) { + return status; + } + } + + { // compute log probability pairs (blank and target). + int num_segments = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_segments, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeLogProbs<<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs(), + H); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_LOG_PROBS_FAILED; + } + } + { // compute betas + // warp is usually a group of threads (32) + int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; + + // each block is identified by 3 d tuple. + // we are using num_warp * max_U * B blocks + // where num_warp is division among Time axis + dim3 block_dims(num_warps, max_U, B * H); + + // each thread is identified by a 2 d tuple + // 2nd dim is 1 for betas only + dim3 thread_dims(WARP_SIZE, 1); + + if (wpEnds == nullptr) { + ComputeBetasWrapper + <<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/workspace.GetPointerToBetaCounters(), + /*alphas=*/(volatile DTYPE*)betas, + costs, + H); + } else { + ComputeBetasCostsRestrictedWrapper + <<>>( + max_T, + max_U, + D, + blank, + workspace.GetPointerToLogProbs(), + srcLengths, + tgtLengths, + workspace.GetPointerToBetaCounters(), + costs, + (volatile DTYPE*)betas, + wpEnds, + l_buffer, + r_buffer, + WARP_SIZE, + num_warps, + false, + nullptr, + nullptr, + H); + cudaDeviceSynchronize(); + } + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; + } + } + + return SUCCESS; +} + +template +status_t ComputeBetasSparse( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* betas, + const int* wpEnds = nullptr, + const int* validRanges = nullptr, + const int* cellsPerSample = nullptr) { + const Options& options = workspace.GetOptions(); + const cudaStream_t& stream = options.stream_; + const int& B = options.batchSize_; + const int& H = options.nHypos_; + const int& max_T = options.maxSrcLen_; + const int& max_U = options.maxTgtLen_; + const int& D = options.numTargets_; + const int& blank = options.blank_; + + const int& l_buffer = options.lBuffer_; + const int& r_buffer = options.rBuffer_; + + const int& sparseCells = options.sparseCells_; + + { // compute denominators. + // for sparse kernel, we would call kernel with sparseSize + status_t status = LogSumExp2D( + /*stream=*/stream, + /*N=*/sparseCells, // not B * max_T * max_U + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + + if (status != SUCCESS) { + return status; + } + } + + { // compute log probability pairs (blank and target). + int num_segments = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_segments, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeLogProbsSparse + <<>>( + max_T, + max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs(), + wpEnds, + validRanges, + cellsPerSample, + H); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_LOG_PROBS_FAILED; + } + } + { // compute alphas + // warp is usually a group of threads (32) + int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; + + // each block is identified by 3 d tuple. + // we are using num_warp * max_U * B blocks + // where num_warp is division among Time axis + dim3 block_dims(num_warps, max_U, B * H); + + // each thread is identified by a 2 d tuple + // 2nd dim is 1 for alpha only + dim3 thread_dims(WARP_SIZE, 1); + + // cudaMemset(alphas, 0, B * max_T * max_U * sizeof(DTYPE)); + ComputeBetasCostsRestrictedWrapper + <<>>( + max_T, + max_U, + D, + blank, + workspace.GetPointerToLogProbs(), + srcLengths, + tgtLengths, + workspace.GetPointerToBetaCounters(), + costs, + (volatile DTYPE*)betas, + wpEnds, + l_buffer, + r_buffer, + WARP_SIZE, + num_warps, + true, + validRanges, + cellsPerSample, + H); + cudaDeviceSynchronize(); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; + } + } + + return SUCCESS; +} + +} // namespace gpu +} // namespace rnnt +} // namespace torchaudio + +#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/half.cuh b/torchaudio/csrc/rnnt/gpu/half.cuh new file mode 100644 index 0000000000..d49ac3bdcf --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/half.cuh @@ -0,0 +1,38 @@ +#pragma once + +#ifdef USE_C10_HALF +#include "c10/util/Half.h" +#endif // USE_C10_HALF + +#include + +namespace torchaudio { +namespace rnnt { + +struct alignas(sizeof(__half)) Half { + __half x; + + HOST_AND_DEVICE Half() = default; + + FORCE_INLINE HOST_AND_DEVICE Half(float f) { + x = __float2half_rn(f); + if (isinf(__half2float(x))) { + x = __float2half_rz(f); // round toward 0. + } + } + + FORCE_INLINE HOST_AND_DEVICE operator float() const { + return __half2float(x); + } + + FORCE_INLINE HOST_AND_DEVICE Half(__half f) { + x = f; + } + + FORCE_INLINE HOST_AND_DEVICE operator __half() const { + return x; + } +}; + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/kernel_utils.h b/torchaudio/csrc/rnnt/gpu/kernel_utils.h new file mode 100644 index 0000000000..89d681a22d --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/kernel_utils.h @@ -0,0 +1,117 @@ +#pragma once + +#include + +#include + +namespace torchaudio { +namespace rnnt { + +inline HOST_AND_DEVICE bool in_range( + int start, + int end, // inclusive + int val) { + return start <= val && val <= end; +} + +#define LOG_PROBS_SKIP_IDX 0 +#define LOG_PROBS_EMIT_IDX 1 + + +struct Indexer2D { + const int& size2_; + + FORCE_INLINE HOST_AND_DEVICE Indexer2D(const int& size2): size2_(size2) {} + + FORCE_INLINE HOST_AND_DEVICE int operator() (int index1, int index2) { + return index1 * size2_ + index2; + } +}; + + +struct Indexer3D { + const int& size2_; + const int& size3_; + + FORCE_INLINE HOST_AND_DEVICE Indexer3D(const int& size2, const int& size3) + : size2_(size2), size3_(size3) {} + + FORCE_INLINE HOST_AND_DEVICE int operator() (int index1, int index2, int index3) { + return (index1 * size2_ + index2) * size3_ + index3; + } +}; + + +struct Indexer4D { + const int& size2_; + const int& size3_; + const int& size4_; + + HOST_AND_DEVICE Indexer4D(const int& size2, const int& size3, const int& size4) + : size2_(size2), size3_(size3), size4_(size4) {} + + HOST_AND_DEVICE int operator() (int index1, int index2, int index3, int index4) { + return ((index1 * size2_ + index2) * size3_ + index3) * size4_ + index4; + } +}; + + +struct SparseIndexer { + HOST_AND_DEVICE SparseIndexer( + const int& maxU, + const int* tgtLengths, + const int* validRanges, + const int* cellsPerSample) : + maxU_(maxU), + tgtLengths_(tgtLengths), + validRanges_(validRanges), + cellsPerSample_(cellsPerSample) {}; + + HOST_AND_DEVICE int operator() (int bIdx, int tIdx, int uIdx) { + // Returns the sparse index given bIdx, tIdx, uIdx; or -1 if t out of band. + + // increment the idx for valid cells in previous samples + // TODO: Inefficient in the inner loop; precompute this? + int idx = 0; + for(int b = 0 ; b < bIdx; b++) { + idx += cellsPerSample_[b]; + } + + // increment the idx for valid cells in current sample + // using pre-computed valid ranges + const int* validRangesB = validRanges_ + (maxU_ * 2) * bIdx; + int U = tgtLengths_[bIdx] + 1; + for (int u = 0 ; u < U ; u++) { + int startT = validRangesB[2*u]; + int endT = validRangesB[2*u + 1]; + if (u == uIdx) { + if (tIdx < startT || tIdx > endT) { + return -1; + } + idx += (tIdx - startT); + return idx; + } else { + idx += (endT - startT) + 1; + } + } + return -1; + } + + private: + const int& maxU_; + + // (vector of size B) contains the lengths of U for each sample in batch + const int* tgtLengths_; + + // (vector of size B*2*U) contains the valid time ranges (start_t, end_t) + // for each u in every sample, as per alignment restrictions + const int* validRanges_; + + // The total valid timesteps for each sample (sumed over all u in the sample). + // This is a quick loopup so we dont need to iterate on all previous u*b values. + const int* cellsPerSample_; + +}; + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/kernels.h b/torchaudio/csrc/rnnt/gpu/kernels.h new file mode 100644 index 0000000000..2dbf6231b4 --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/kernels.h @@ -0,0 +1,215 @@ +#pragma once + +#include + +#include +#include + +namespace torchaudio { +namespace rnnt { + + +template +FORCE_INLINE HOST_AND_DEVICE void ComputeLogProbsSparseElement( + int bTgt, + int t, + int u, + int maxT, + int maxU, + int numTargets, + int blank, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + CAST_DTYPE* logProbs, + const int* wpEnds, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr, + int H=1, + bool fusedLogSmax=true) { + + const int& D = numTargets; + + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + if (t >= T || u >= U) { // out of boundary. + return; + } + const int* validRangesB = validRanges + (maxU*2) * bTgt; + int start = validRangesB[2*u]; + int end = validRangesB[2*u + 1]; + + // out of boundary for alignment restriction condition + if (t < start || t > end) { + return; + } + SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); + int idx = idxr(bTgt, t, u); + logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = + CAST_DTYPE(logits[idx * D + blank]) - denominators[idx]; + + if (!fusedLogSmax) { + logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = + CAST_DTYPE(logits[idx * D + blank]); + } + + if (u < U - 1) { + // emit: log_prob(b, t, u).emit() = logits(b, t, u, tgt[u]) - denom(b, t, u). + int target = targets[Indexer2D(maxU - 1)(bTgt, u)]; + logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = + CAST_DTYPE(logits[idx * D + target]) - denominators[idx]; + + if (!fusedLogSmax) { + logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = + CAST_DTYPE(logits[idx * D + target]); + } + } +} + + +template +HOST_AND_DEVICE void ComputeGradientsElement( + int bTgt, + int t, + int u, + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + CAST_DTYPE clamp, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + const CAST_DTYPE* alphas, + const CAST_DTYPE* betas, + DTYPE* gradients, + bool sparse = false, + const int* validRanges = nullptr, + const int* cellsPerSample = nullptr, + int H = 1, + bool fusedLogSmax = true) { + + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + const int& D = numTargets; + + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + if (t >= T || u >= U) { // out of boundary. + if (sparse) { + // no extra elements needs to be set to 0 + return; + } else if (gradients == logits && t < maxT && u < maxU) { + // gradients and logits are pointing to the same memory location + Indexer3D idxr3(maxT, maxU); + int idx_b_t_u_zero = idxr3(bTgt, t, u); + if (idx_b_t_u_zero != -1 ) { + int start = idx_b_t_u_zero * D; + for (int b_t_u_d = start; b_t_u_d < start + D; ++b_t_u_d) { + gradients[b_t_u_d] = 0; + } + } + } + return; + } + + int costIdx; + if (!sparse) { + costIdx = bTgt * maxT * maxU; + } else { + SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); + costIdx = idxr(bTgt, 0, 0); + } + CAST_DTYPE cost = -(betas[costIdx]); + + + Indexer2D idxr2(maxU - 1); + + int idx_b_t_u, idx_b_t_up1, idx_b_tp1_u, idx_b_tp1_up1; + if (sparse) { + SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); + idx_b_t_u = idxr(bTgt, t, u); + idx_b_t_up1 = idxr(bTgt, t, u+1); + idx_b_tp1_u = idxr(bTgt, t+1, u); + idx_b_tp1_up1 = idxr(bTgt, t+1, u+1); + } else { + Indexer3D idxr3(maxT, maxU); + idx_b_t_u = idxr3(bTgt, t, u); + idx_b_t_up1 = idxr3(bTgt, t, u+1); + idx_b_tp1_u = idxr3(bTgt, t+1, u); + idx_b_tp1_up1 = idxr3(bTgt, t+1, u+1); + } + + if (idx_b_t_u == -1 ) { + return; + } + + if (isinf(cost) || isnan(cost)) { + for (int d = 0; d < D; ++d) { + int b_t_u_d = idx_b_t_u * D + d; + gradients[b_t_u_d] = 0; + } + return; + } + + CAST_DTYPE c = alphas[idx_b_t_u] + cost - denominators[idx_b_t_u]; + for (int d = 0; d < D; ++d) { + int b_t_u_d = idx_b_t_u * D + d; + CAST_DTYPE g = CAST_DTYPE(logits[b_t_u_d]) + c; + + if (fusedLogSmax) { + if (d == blank && t == T - 1 && u == U - 1) { // last blank transition. + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]) - std::exp(g); + } else if (t < T - 1 && d == blank) { + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); + if (idx_b_tp1_u != -1) { + gradients[b_t_u_d] = gradients[b_t_u_d] - std::exp(g + betas[idx_b_tp1_u]); + } + } else if (u < U - 1 && d == targets[idxr2(bTgt, u)]) { + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); + if (idx_b_t_up1 != -1) { + gradients[b_t_u_d] = gradients[b_t_u_d] - std::exp(g + betas[idx_b_t_up1]); + } + } else { + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); + } + } else { // Non fused log softmax case + CAST_DTYPE g = cost + CAST_DTYPE(logits[b_t_u_d]); + if (d == blank && t == T - 1 && u == U - 1) { + gradients[b_t_u_d] = g + alphas[idx_b_t_u]; + } else if (t < T - 1 && d == blank) { + if (idx_b_tp1_u != -1) { + gradients[b_t_u_d] = g + alphas[idx_b_t_u] + betas[idx_b_tp1_u]; + } else { + gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); + } + } else if (u < U - 1 && d == targets[idxr2(bTgt, u)]) { + if (idx_b_t_up1 != -1) { + gradients[b_t_u_d] = g + alphas[idx_b_t_u] + betas[idx_b_t_up1]; + } else { + gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); + } + } else { + gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); + } + gradients[b_t_u_d] = -std::exp(gradients[b_t_u_d]); + } + + if (clamp > 0) { + auto g = CAST_DTYPE(gradients[b_t_u_d]); + gradients[b_t_u_d] = math::min(g, clamp); + gradients[b_t_u_d] = math::max(g, -clamp); + } + } +} + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/math.cuh b/torchaudio/csrc/rnnt/gpu/math.cuh new file mode 100644 index 0000000000..a2eaabff93 --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/math.cuh @@ -0,0 +1,41 @@ +#pragma once + +#ifdef USE_CUDA + +#include + +#endif // USE_CUDA + +#include + +namespace torchaudio { +namespace rnnt { + +namespace math { + +template +FORCE_INLINE HOST_AND_DEVICE DTYPE max(DTYPE x, DTYPE y) { + if (x > y) return x; + else return y; +} + +template +FORCE_INLINE HOST_AND_DEVICE DTYPE min(DTYPE x, DTYPE y) { + if (x > y) return y; + else return x; +} + +// log_sum_exp +template +FORCE_INLINE HOST_AND_DEVICE DTYPE lse(DTYPE x, DTYPE y); + +template <> +FORCE_INLINE HOST_AND_DEVICE float lse(float x, float y) { + if (y > x) { return y + log1pf(expf(x - y)); } + else { return x + log1pf(expf(y-x)); } +} + +} + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/macros.cpp b/torchaudio/csrc/rnnt/macros.cpp new file mode 100644 index 0000000000..da6e5045bc --- /dev/null +++ b/torchaudio/csrc/rnnt/macros.cpp @@ -0,0 +1,21 @@ +#include + +#ifdef USE_GLOG +#else + +const char* ToString(level_t level) { + switch (level) { + case INFO: + return "INFO"; + case WARNING: + return "WARNING"; + case ERROR: + return "ERROR"; + case FATAL: + return "FATAL"; + default: + return "UNKNOWN"; + } +} + +#endif // USE_GLOG diff --git a/torchaudio/csrc/rnnt/macros.h b/torchaudio/csrc/rnnt/macros.h new file mode 100644 index 0000000000..84a8db24f0 --- /dev/null +++ b/torchaudio/csrc/rnnt/macros.h @@ -0,0 +1,47 @@ +#pragma once + +#ifdef USE_CUDA +#define WARP_SIZE 32 +#define MAX_THREADS_PER_BLOCK 1024 +#define REDUCE_THREADS 256 +#define HOST_AND_DEVICE __host__ __device__ +#define FORCE_INLINE __forceinline__ +#include +#include +#else +#define HOST_AND_DEVICE +#define FORCE_INLINE inline +#endif // USE_CUDA + +#ifdef USE_GLOG +#include +#else +#include +#include + +typedef enum { INFO = 0, WARNING = 1, ERROR = 2, FATAL = 3 } level_t; + +const char* ToString(level_t level); + +struct LOG { + LOG(const level_t& level) { + ::std::cerr << "LOG(" << ToString(level) << "): "; + } + ~LOG() { + ::std::cerr << ::std::endl; + } +}; + +template +LOG&& operator<<(LOG&& log, const T& object) { + ::std::cerr << object; + return ::std::move(log); +} + +#define DCHECK(x) +#define DCHECK_EQ(x, y) +#define DCHECK_NE(x, y) +#define CHECK(x) +#define CHECK_EQ(x, y) +#define CHECK_NE(x, y) +#endif // USE_GLOG diff --git a/torchaudio/csrc/rnnt/options.h b/torchaudio/csrc/rnnt/options.h new file mode 100644 index 0000000000..4debce21df --- /dev/null +++ b/torchaudio/csrc/rnnt/options.h @@ -0,0 +1,99 @@ +#pragma once + +//#include + +#ifdef USE_CUDA +#include +#endif // USE_CUDA + +#include +#include + +namespace torchaudio { +namespace rnnt { + +typedef struct Options { + // the device to compute transducer loss. + device_t device_; +#ifdef USE_CUDA + // the stream to launch kernels in when using GPU. + cudaStream_t stream_; +#endif + // The maximum number of threads that can be used. + int numThreads_; + + // the index for "blank". + int blank_; + // whether to backtrack the best path. + bool backtrack_; + // gradient clamp value. + float clamp_; + + // alignment restricted loss l buffer + int lBuffer_; + + // alignment restricted loss r buffer + int rBuffer_; + + // batch size = B. + int batchSize_; + + // Number of hypos per sample = H + int nHypos_; + + // the maximum length of src encodings = max_T. + int maxSrcLen_; + // the maximum length of tgt encodings = max_U. + int maxTgtLen_; + // num_targets = D. + int numTargets_; + + // whether to use sparse layout for ar-rnnt + bool sparse_; + + int sparseCells_; + + // if set to true, inputs are logits and gradients are + // fused with logsoftmax gradients. + // if set to false, log_softmax is computed outside of loss + // True by default + bool fusedLogSmax_; + + Options() + : device_(UNDEFINED), + numThreads_(0), + blank_(-1), + backtrack_(false), + clamp_(-1), // negative for disabling clamping by default. + lBuffer_(0), + rBuffer_(0), + batchSize_(0), + nHypos_(1), + maxSrcLen_(0), + maxTgtLen_(0), + numTargets_(0), + sparse_(false), + sparseCells_(0), + fusedLogSmax_(true) {} + + int BU() const { + return batchSize_ * maxTgtLen_ * nHypos_; + } + + int BTU() const { + return batchSize_ * maxSrcLen_ * maxTgtLen_ * nHypos_; + } + + friend std::ostream& operator<<(std::ostream& os, const Options& options) { + os << "Options(" + << "batchSize_=" << options.batchSize_ << ", " + << "maxSrcLen_=" << options.maxSrcLen_ << ", " + << "maxTgtLen_=" << options.maxTgtLen_ << ", " + << "numTargets_=" << options.numTargets_ << ")"; + + return os; + } +} Options; + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/transducer.h b/torchaudio/csrc/rnnt/transducer.h new file mode 100644 index 0000000000..8384fd5a9d --- /dev/null +++ b/torchaudio/csrc/rnnt/transducer.h @@ -0,0 +1,239 @@ +#pragma once + +#include +#include + +namespace torchaudio { +namespace rnnt { + +template +status_t Compute( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* gradients = nullptr, + const int* wp_ends=nullptr) { + switch (workspace.GetOptions().device_) { + case CPU: { + status_t status = cpu::Compute( + /*workspace=*/workspace, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*costs=*/costs, + /*gradients=*/gradients, + /*wp_ends =*/wp_ends); + return status; + } + case GPU: { + status_t status = gpu::Compute( + /*workspace=*/workspace, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*costs=*/costs, + /*gradients=*/gradients, + /*wp_ends =*/wp_ends); + return status; + } + default: { + LOG(ERROR) << "unsupported workspace.GetOptions().device = " + << workspace.GetOptions().device_; + return FAILURE; + } + }; +} + +template +status_t ComputeSparse( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* gradients = nullptr, + const int* wp_ends=nullptr, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr) { + switch (workspace.GetOptions().device_) { + case GPU: { + status_t status = gpu::ComputeSparse( + /*workspace=*/workspace, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*costs=*/costs, + /*gradients=*/gradients, + /*wp_ends =*/wp_ends, + validRanges, + cellsPerSample); + return status; + } + default: { + LOG(ERROR) << "unsupported workspace.GetOptions().device = " + << workspace.GetOptions().device_; + return FAILURE; + } + }; +} + +template +status_t ComputeAlphas( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* alphas, + const int* wp_ends=nullptr) { + switch (workspace.GetOptions().device_) { + case CPU: { + status_t status = cpu::ComputeAlphas( + /*workspace=*/workspace, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alphas=*/alphas, + /*wp_ends =*/wp_ends); + return status; + } + case GPU: { + status_t status = gpu::ComputeAlphas( + /*workspace=*/workspace, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*costs=*/alphas, + /*wp_ends =*/wp_ends); + return status; + } + default: { + LOG(ERROR) << "unsupported workspace.GetOptions().device = " + << workspace.GetOptions().device_; + return FAILURE; + } + }; +} + +template +status_t ComputeAlphasSparse( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* alphas, + const int* wp_ends=nullptr, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr) { + switch (workspace.GetOptions().device_) { + case GPU: { + status_t status = gpu::ComputeAlphasSparse( + /*workspace=*/workspace, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*costs=*/alphas, + /*wp_ends =*/wp_ends, + validRanges, + cellsPerSample); + return status; + } + default: { + LOG(ERROR) << "unsupported workspace.GetOptions().device = " + << workspace.GetOptions().device_; + return FAILURE; + } + }; +} + +template +status_t ComputeBetas( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* betas, + const int* wp_ends=nullptr) { + switch (workspace.GetOptions().device_) { + case CPU: { + status_t status = cpu::ComputeBetas( + /*workspace=*/workspace, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*costs=*/costs, + /*betas=*/betas, + /*wp_ends =*/wp_ends); + return status; + } + case GPU: { + status_t status = gpu::ComputeBetas( + /*workspace=*/workspace, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*costs=*/costs, + /*betas=*/betas, + /*wp_ends =*/wp_ends); + return status; + } + default: { + LOG(ERROR) << "unsupported workspace.GetOptions().device = " + << workspace.GetOptions().device_; + return FAILURE; + } + }; +} + +template +status_t ComputeBetasSparse( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* betas, + const int* wp_ends=nullptr, + const int* validRanges=nullptr, + const int* cellsPerSample=nullptr) { + switch (workspace.GetOptions().device_) { + case GPU: { + status_t status = gpu::ComputeBetasSparse( + /*workspace=*/workspace, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*costs=*/costs, + /*betas=*/betas, + /*wp_ends =*/wp_ends, + validRanges, + cellsPerSample); + return status; + } + default: { + LOG(ERROR) << "unsupported workspace.GetOptions().device = " + << workspace.GetOptions().device_; + return FAILURE; + } + }; +} + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/types.cpp b/torchaudio/csrc/rnnt/types.cpp new file mode 100644 index 0000000000..3c08a7eeb3 --- /dev/null +++ b/torchaudio/csrc/rnnt/types.cpp @@ -0,0 +1,41 @@ +#include + +namespace torchaudio { +namespace rnnt { + +const char* toString(status_t status) { + switch (status) { + case SUCCESS: + return "success"; + case FAILURE: + return "failure"; + case COMPUTE_DENOMINATOR_REDUCE_MAX_FAILED: + return "compute_denominator_reduce_max_failed"; + case COMPUTE_DENOMINATOR_REDUCE_SUM_FAILED: + return "compute_denominator_reduce_sum_failed"; + case COMPUTE_LOG_PROBS_FAILED: + return "compute_log_probs_failed"; + case COMPUTE_ALPHAS_BETAS_COSTS_FAILED: + return "compute_alphas_betas_costs_failed"; + case COMPUTE_GRADIENTS_FAILED: + return "compute_gradients_failed"; + default: + return "unknown"; + } +} + +const char* toString(device_t device) { + switch (device) { + case UNDEFINED: + return "undefined"; + case CPU: + return "cpu"; + case GPU: + return "gpu"; + default: + return "unknown"; + } +} + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/types.h b/torchaudio/csrc/rnnt/types.h new file mode 100644 index 0000000000..34d2998cff --- /dev/null +++ b/torchaudio/csrc/rnnt/types.h @@ -0,0 +1,23 @@ +#pragma once + +namespace torchaudio { +namespace rnnt { + +typedef enum { + SUCCESS = 0, + FAILURE = 1, + COMPUTE_DENOMINATOR_REDUCE_MAX_FAILED = 2, + COMPUTE_DENOMINATOR_REDUCE_SUM_FAILED = 3, + COMPUTE_LOG_PROBS_FAILED = 4, + COMPUTE_ALPHAS_BETAS_COSTS_FAILED = 5, + COMPUTE_GRADIENTS_FAILED = 6 +} status_t; + +typedef enum { UNDEFINED = 0, CPU = 1, GPU = 2 } device_t; + +const char* toString(status_t status); + +const char* toString(device_t device); + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/workspace.cpp b/torchaudio/csrc/rnnt/workspace.cpp new file mode 100644 index 0000000000..2c8d0f6ab1 --- /dev/null +++ b/torchaudio/csrc/rnnt/workspace.cpp @@ -0,0 +1,20 @@ +#include + +namespace torchaudio { +namespace rnnt { + +void IntWorkspace::ResetAlphaBetaCounters() { + if (data_ != nullptr && options_.device_ == GPU) { + cudaMemset( + GetPointerToAlphaCounters(), + 0, + ComputeSizeForAlphaCounters(options_) * sizeof(int)); + cudaMemset( + GetPointerToBetaCounters(), + 0, + ComputeSizeForBetaCounters(options_) * sizeof(int)); + } +} + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/workspace.h b/torchaudio/csrc/rnnt/workspace.h new file mode 100644 index 0000000000..243b836a93 --- /dev/null +++ b/torchaudio/csrc/rnnt/workspace.h @@ -0,0 +1,210 @@ +#pragma once + +#include +#include + +#include + +namespace torchaudio { +namespace rnnt { + +// Since CUDA has strict memory alignment, it's better to keep allocated memory +// blocks separate for different data types. + +// DtypeWorkspace holds a "view" of workspace for: +// 1. softmax denominators (in log form), size = B * max_T * max_U +// 2. log probibility pairs for blank and target, size = B * max_T * max_U +// 3. alphas, size = B * max_T * max_U +// 4. betas, size = B * max_T * max_U +template +class DtypeWorkspace { + public: + DtypeWorkspace() : options_(), size_(0), data_(nullptr) {} + DtypeWorkspace(const Options& options, DTYPE* data, int size) + : DtypeWorkspace() { + Reset(options, data, size); + } + ~DtypeWorkspace() {} + + static int ComputeSizeFromOptions(const Options& options) { + CHECK_NE(options.device_, UNDEFINED); + return ComputeSizeForDenominators(options) + + ComputeSizeForLogProbs(options) + ComputeSizeForAlphas(options) + + ComputeSizeForBetas(options); + } + + void Free(); + void Reset(const Options& options, DTYPE* data, int size) { + int needed_size = ComputeSizeFromOptions(options); + CHECK_LE(needed_size, size); + options_ = options; + data_ = data; + size_ = size; + } + int Size() const { + return size_; + } + + DTYPE* GetPointerToDenominators() const { + return data_; + } + DTYPE* GetPointerToLogProbs() const { + return GetPointerToDenominators() + ComputeSizeForDenominators(options_); + } + DTYPE* GetPointerToAlphas() const { + return GetPointerToLogProbs() + ComputeSizeForLogProbs(options_); + } + DTYPE* GetPointerToBetas() const { + return GetPointerToAlphas() + ComputeSizeForAlphas(options_); + } + + private: + static int ComputeSizeForDenominators(const Options& options) { // B * T * U + return options.sparse_ ? options.sparseCells_ : options.BTU(); + } + + static int ComputeSizeForLogProbs(const Options& options) { // B * T * U * 2 + return options.sparse_ ? options.sparseCells_ * 2 : options.BTU() * 2; + } + + static int ComputeSizeForAlphas(const Options& options) { // B * T * U + return options.sparse_ ? options.sparseCells_ : options.BTU(); + } + + static int ComputeSizeForBetas(const Options& options) { // B * T * U + return options.sparse_ ? options.sparseCells_ : options.BTU(); + } + + Options options_; + int size_; // number of elements in allocated memory. + DTYPE* data_; // pointer to the allocated memory. +}; + +// IntWorkspace holds a "view" of workspace for: +// 1. alpha counters, size = B * max_U +// 2. beta counters, size = B * max_U +class IntWorkspace { + public: + IntWorkspace() : options_(), size_(0), data_(nullptr) {} + IntWorkspace(const Options& options, int* data, int size) : IntWorkspace() { + Reset(options, data, size); + } + ~IntWorkspace() {} + + static int ComputeSizeFromOptions(const Options& options) { + return ComputeSizeForAlphaCounters(options) + + ComputeSizeForBetaCounters(options); + } + + void Reset(const Options& options, int* data, int size) { + int needed_size = ComputeSizeFromOptions(options); + //CHECK_LE(needed_size, size); + options_ = options; + data_ = data; + size_ = size; + ResetAlphaBetaCounters(); + } + int Size() const { + return size_; + } + + int* GetPointerToAlphaCounters() const { + CHECK_EQ(options_.device_, GPU); + return data_; + } + int* GetPointerToBetaCounters() const { + CHECK_EQ(options_.device_, GPU); + return GetPointerToAlphaCounters() + ComputeSizeForAlphaCounters(options_); + } + + private: + void ResetAlphaBetaCounters(); + + static int ComputeSizeForAlphaCounters(const Options& options) { // B * U +#ifdef USE_CUDA + if (options.device_ == GPU) { + return options.BU(); + } else { + return 0; + } +#else + return 0; +#endif // USE_CUDA + } + static int ComputeSizeForBetaCounters(const Options& options) { // B * U +#ifdef USE_CUDA + if (options.device_ == GPU) { + return options.BU(); + } else { + return 0; + } +#else + return 0; +#endif // USE_CUDA + } + + Options options_; + int size_; // number of elements in allocated memory. + int* data_; // pointer to the allocated memory. +}; + +// Workspace holds: +// 1. DtypeWorkspace +// 2. IntWorkspace +template +class Workspace { + public: + Workspace() : options_(), dtype_workspace_(), int_workspace_() {} + Workspace( + const Options& options, + DTYPE* dtype_data, + int dtype_size, + int* int_data, + int int_size) + : Workspace() { + Reset(options, dtype_data, dtype_size, int_data, int_size); + } + ~Workspace() {} + + void Reset( + const Options& options, + DTYPE* dtype_data, + int dtype_size, + int* int_data, + int int_size) { + options_ = options; + dtype_workspace_.Reset(options_, dtype_data, dtype_size); + int_workspace_.Reset(options_, int_data, int_size); + } + + const Options& GetOptions() const { + return options_; + } + + DTYPE* GetPointerToDenominators() const { + return dtype_workspace_.GetPointerToDenominators(); + } + DTYPE* GetPointerToLogProbs() const { + return dtype_workspace_.GetPointerToLogProbs(); + } + DTYPE* GetPointerToAlphas() const { + return dtype_workspace_.GetPointerToAlphas(); + } + DTYPE* GetPointerToBetas() const { + return dtype_workspace_.GetPointerToBetas(); + } + int* GetPointerToAlphaCounters() const { + return int_workspace_.GetPointerToAlphaCounters(); + } + int* GetPointerToBetaCounters() const { + return int_workspace_.GetPointerToBetaCounters(); + } + + private: + Options options_; + DtypeWorkspace dtype_workspace_; + IntWorkspace int_workspace_; +}; + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/prototype/rnnt_loss.py b/torchaudio/prototype/rnnt_loss.py new file mode 100644 index 0000000000..0839290e0a --- /dev/null +++ b/torchaudio/prototype/rnnt_loss.py @@ -0,0 +1,520 @@ +import torch + +__all__ = [ + "RNNTLoss", + "rnnt_loss", +] + + +def _rnnt_loss_alphas( + logits, + targets, + logit_lengths, + target_lengths, + blank=-1, + clamp=-1, + wordpiece_ends=None, + left_buffer=0, + right_buffer=0, + sparse=False, + valid_ranges=None, + cells_per_sample=None, +): + """ + Compute alphas for RNN transducer loss. + + See documentation for RNNTLoss + """ + targets = targets.to(device=logits.device) + logit_lengths = logit_lengths.to(device=logits.device) + target_lengths = target_lengths.to(device=logits.device) + if wordpiece_ends is not None: + wordpiece_ends = wordpiece_ends.to(device=logits.device) + + # make sure all int tensors are of type int32. + targets = targets.int() + logit_lengths = logit_lengths.int() + target_lengths = target_lengths.int() + if not sparse: + return torch.ops.torchaudio.rnnt_loss_alphas( + logits, + targets, + logit_lengths, + target_lengths, + blank, + clamp, + wordpiece_ends, + left_buffer, + right_buffer, + ) + else: + try: + return torch.ops.torchaudio.rnnt_loss_alphas_sparse( + logits, + targets, + logit_lengths, + target_lengths, + blank, + clamp, + torch.max(logit_lengths).item(), + torch.max(target_lengths).item() + 1, + wordpiece_ends, + left_buffer, + right_buffer, + valid_ranges, + cells_per_sample, + ) + except RuntimeError: + raise RuntimeError("sparse is only supported on GPU with torchaudio compiled with GPU support") + + +def _rnnt_loss_betas( + logits, + targets, + logit_lengths, + target_lengths, + blank=-1, + clamp=-1, + wordpiece_ends=None, + left_buffer=0, + right_buffer=0, + sparse=False, + valid_ranges=None, + cells_per_sample=None, +): + """ + Compute betas for RNN transducer loss + + See documentation for RNNTLoss + """ + targets = targets.to(device=logits.device) + logit_lengths = logit_lengths.to(device=logits.device) + target_lengths = target_lengths.to(device=logits.device) + if wordpiece_ends is not None: + wordpiece_ends = wordpiece_ends.to(device=logits.device) + + # make sure all int tensors are of type int32. + targets = targets.int() + logit_lengths = logit_lengths.int() + target_lengths = target_lengths.int() + if not sparse: + return torch.ops.torchaudio.rnnt_loss_betas( + logits, + targets, + logit_lengths, + target_lengths, + blank, + clamp, + wordpiece_ends, + left_buffer, + right_buffer, + ) + else: + try: + return torch.ops.torchaudio.rnnt_loss_betas_sparse( + logits, + targets, + logit_lengths, + target_lengths, + blank, + clamp, + torch.max(logit_lengths).item(), + torch.max(target_lengths).item() + 1, + wordpiece_ends, + left_buffer, + right_buffer, + valid_ranges, + cells_per_sample, + ) + except RuntimeError: + raise RuntimeError("sparse is only supported on GPU with torchaudio compiled with GPU support") + + +class _RNNT(torch.autograd.Function): + @staticmethod + def forward( + ctx, + logits, + targets, + logit_lengths, + target_lengths, + blank=-1, + clamp=-1, + runtime_check=False, + wordpiece_ends=None, + left_buffer=0, + right_buffer=0, + sparse=False, + valid_ranges=None, + cells_per_sample=None, + fused_log_softmax=True, + reuse_logits_for_grads=True, + ): + """ + See documentation for RNNTLoss + """ + + # move everything to the same device. + targets = targets.to(device=logits.device) + logit_lengths = logit_lengths.to(device=logits.device) + target_lengths = target_lengths.to(device=logits.device) + if wordpiece_ends is not None: + wordpiece_ends = wordpiece_ends.to(device=logits.device) + if valid_ranges is not None: + valid_ranges = valid_ranges.to(device=logits.device) + if cells_per_sample is not None: + cells_per_sample = cells_per_sample.to(device=logits.device) + + # make sure all int tensors are of type int32. + targets = targets.int() + logit_lengths = logit_lengths.int() + target_lengths = target_lengths.int() + + if blank < 0: # reinterpret blank index if blank < 0. + blank = logits.shape[-1] + blank + + if runtime_check: + check_inputs( + logits=logits, + targets=targets, + logit_lengths=logit_lengths, + target_lengths=target_lengths, + blank=blank, + ) + if sparse: + try: + rnnt_loss_sparse = torch.ops.torchaudio.rnnt_loss_sparse + except RuntimeError: + raise RuntimeError("sparse is only supported on GPU with torchaudio compiled with GPU support") + + max_T = torch.max(logit_lengths).item() # note: not used for indexing + max_U = targets.shape[1] + 1 # used for indexing, e.g. wordpiece_ends + assert max_U >= torch.max(target_lengths).item() + 1 + assert max_U == wordpiece_ends.shape[1] and max_U == valid_ranges.shape[1] + costs, gradients = rnnt_loss_sparse( + logits=logits, + targets=targets, + src_lengths=logit_lengths, + tgt_lengths=target_lengths, + blank=blank, + clamp=clamp, + wp_ends=wordpiece_ends, + l_buffer=left_buffer, + r_buffer=right_buffer, + max_T=max_T, + max_U=max_U, + valid_ranges=valid_ranges, + cells_per_sample=cells_per_sample, + fused_log_smax=fused_log_softmax, + reuse_logits_for_grads=reuse_logits_for_grads, + ) + ctx.cells_per_sample = cells_per_sample + else: + costs, gradients = torch.ops.torchaudio.rnnt_loss( + logits=logits, + targets=targets, + src_lengths=logit_lengths, + tgt_lengths=target_lengths, + blank=blank, + clamp=clamp, + wp_ends=wordpiece_ends, + l_buffer=left_buffer, + r_buffer=right_buffer, + fused_log_smax=fused_log_softmax, + reuse_logits_for_grads=reuse_logits_for_grads, + ) + + ctx.grads = gradients + ctx.sparse = sparse + + return costs + + @staticmethod + def backward(ctx, output_gradients): + if ctx.sparse: + output_gradients = output_gradients.view(-1, 1).to(ctx.grads) + offset = 0 + count = 0 + for ncells in ctx.cells_per_sample: + ctx.grads[offset : offset + ncells, :].mul_(output_gradients[count]) + offset += ncells + count += 1 + else: + output_gradients = output_gradients.view(-1, 1, 1, 1).to(ctx.grads) + ctx.grads.mul_(output_gradients).to(ctx.grads) + + return ( + ctx.grads, # logits + None, # targets + None, # logit_lengths + None, # target_lengths + None, # blank + None, # clamp + None, # runtime_check + None, # wordpiece_ends + None, # left_buffer + None, # right_buffer + None, # sparse + None, # valid_ranges + None, # cells_per_sample + None, # fused_log_softmax + None, # reuse_logits_for_grads + ) + + +def rnnt_loss( + logits, + targets, + logit_lengths, + target_lengths, + blank=-1, + clamp=-1, + runtime_check=False, + wordpiece_ends=None, + left_buffer=0, + right_buffer=0, + sparse=False, + valid_ranges=None, + cells_per_sample=None, + fused_log_softmax=True, + reuse_logits_for_grads=True, +): + """ + Compute the RNN Transducer Loss. + + The RNN Transducer loss (`Graves 2012 `__) extends the CTC loss by defining + a distribution over output sequences of all lengths, and by jointly modelling both input-output and output-output + dependencies. + + Args: + logits (Tensor): Tensor of dimension (batch, time, target, class) containing output from joiner + targets (Tensor): Tensor of dimension (batch, max target length) containing targets with zero padded + logit_lengths (Tensor): Tensor of dimension (batch) containing lengths of each sequence from encoder + target_lengths (Tensor): Tensor of dimension (batch) containing lengths of targets for each sequence + blank (int, opt): blank label (Default: ``-1``) + clamp (float): clamp for gradients (Default: ``-1``) + runtime_check (bool): whether to do sanity check during runtime. (Default: ``False``) + wordpiece_ends (Tensor): Tensor of dimension (batch, target) containing the end frames of targets + for each sequence (including bos end_frame = 0) (Default: ``None``) + left_buffer (int): left buffer frames used for alignment restricted RNNT loss. + Loss will not be imposed on frames smaller than wordpiece_end - left_buffer. (Default: ``0``) + right_buffer (int): right buffer frames used for alignment restricted RNNT loss. + Loss will not be imposed on frames greater than wordpiece_end + right_buffer. (Default: ``0``) + sparse (bool): set to true for sparse alignment restricted RNNT. (Default: ``False``) + valid_ranges (Tensor): Tensor of dimension (batch, target, 2) containing valid ranges for sparse AR-RNNT + (Default: ``None``) + cells_per_sample (Tensor): Tensor of dimension (batch) containing total valid cells for each (Default: ``None``) + sample while doing sparse AR-RNNT (Default: ``None``) + fused_log_smax (bool): set to False if calling log_softmax outside loss (Default: ``True``) + reuse_logits_for_grads (bool): whether to save memory by reusing logits memory for grads (Default: ``True``) + """ + if not fused_log_softmax: + logits = torch.nn.functional.log_softmax(logits, dim=-1) + reuse_logits_for_grads = ( + False # softmax needs the original logits value + ) + + cost = _RNNT.apply( + logits, + targets, + logit_lengths, + target_lengths, + blank, + clamp, + runtime_check, + wordpiece_ends, + left_buffer, + right_buffer, + sparse, + valid_ranges, + cells_per_sample, + fused_log_softmax, + reuse_logits_for_grads, + ) + return cost + + +class RNNTLoss(torch.nn.Module): + """ + Compute the RNN Transducer Loss. + + The RNN Transducer loss (`Graves 2012 `__) extends the CTC loss by defining + a distribution over output sequences of all lengths, and by jointly modelling both input-output and output-output + dependencies. + + Args: + blank (int, opt): blank label (Default: ``-1``) + clamp (float): clamp for gradients (Default: ``-1``) + runtime_check (bool): whether to do sanity check during runtime. (Default: ``False``) + left_buffer (int): left buffer frames used for alignment restricted RNNT loss. + Loss will not be imposed on frames smaller than wordpiece_end - left_buffer. (Default: ``0``) + right_buffer (int): right buffer frames used for alignment restricted RNNT loss. + Loss will not be imposed on frames greater than wordpiece_end + right_buffer. (Default: ``0``) + sparse (bool): set to true for sparse alignment restricted RNNT. (Default: ``False``) + fused_log_smax (bool): set to False if calling log_softmax outside loss (Default: ``True``) + reuse_logits_for_grads (bool): whether to save memory by reusing logits memory for grads (Default: ``True``) + """ + + def __init__( + self, + blank=-1, + clamp=-1, + runtime_check=False, + left_buffer=0, + right_buffer=0, + sparse=False, + fused_log_softmax=True, + reuse_logits_for_grads=True, + ): + super().__init__() + self.blank = blank + self.clamp = clamp + self.runtime_check = runtime_check + self.left_buffer = left_buffer + self.right_buffer = right_buffer + self.sparse = sparse + self.fused_log_softmax = fused_log_softmax + self.reuse_logits_for_grads = reuse_logits_for_grads + + def forward( + self, + logits, + targets, + logit_lengths, + target_lengths, + wordpiece_ends=None, + valid_ranges=None, + cells_per_sample=None, + left_buffer=None, + right_buffer=None, + ): + """ + Args: + logits (Tensor): Tensor of dimension (batch, time, target, class) containing output from joiner + targets (Tensor): Tensor of dimension (batch, max target length) containing targets with zero padded + logit_lengths (Tensor): Tensor of dimension (batch) containing lengths of each sequence from encoder + target_lengths (Tensor): Tensor of dimension (batch) containing lengths of targets for each sequence + wordpiece_ends (Tensor): Tensor of dimension (batch, target) containing the end frames of targets + for each sequence (including bos end_frame = 0) (Default: ``None``) + valid_ranges (Tensor): Tensor of dimension (batch, target, 2) containing valid ranges for sparse AR-RNNT + (Default: ``None``) + cells_per_sample (Tensor): Tensor of dimension (batch) containing total valid cells for each + sample while doing sparse AR-RNNT (Default: ``None``) + left_buffer (int): left buffer frames used for alignment restricted RNNT loss. + Loss will not be imposed on frames smaller than wordpiece_end - left_buffer. (Default: ``None``) + right_buffer (int): right buffer frames used for alignment restricted RNNT loss. + Loss will not be imposed on frames greater than wordpiece_end + right_buffer. (Default: ``None``) + """ + # left_buffer / right_buffer is passed in forward, + # when we use dataset or sample specific left/right buffer + # self.left_buffer / right_buffer is used by default, or + # to preserve backward compatibility + if left_buffer is None: + left_buffer = self.left_buffer + if right_buffer is None: + right_buffer = self.right_buffer + + return rnnt_loss( + logits, + targets, + logit_lengths, + target_lengths, + self.blank, + self.clamp, + self.runtime_check, + wordpiece_ends, + left_buffer, + right_buffer, + self.sparse, + valid_ranges, + cells_per_sample, + self.fused_log_softmax, + self.reuse_logits_for_grads, + ) + + +def check_type(var, t, name): + if var.dtype is not t: + raise TypeError("{} must be {}".format(name, t)) + + +def check_contiguous(var, name): + if not var.is_contiguous(): + raise ValueError("{} must be contiguous".format(name)) + + +def check_dim(var, dim, name): + if len(var.shape) != dim: + raise ValueError("{} must be {}D".format(name, dim)) + + +def check_equal(var1, name1, var2, name2): + if var1 != var2: + raise ValueError( + "`{}` ({}) must equal to ".format(name1, var1) + + "`{}` ({})".format(name2, var2) + ) + + +def check_device(var1, name1, var2, name2): + if var1.device != var2.device: + raise ValueError( + "`{}` ({}) must be on the same ".format(name1, var1.device.type) + + "device as `{}` ({})".format(name2, var2.device.type) + ) + + +def check_inputs(logits, targets, logit_lengths, target_lengths, blank): + check_device(logits, "logits", targets, "targets") + check_device(logits, "logits", targets, "logit_lengths") + check_device(logits, "logits", targets, "target_lengths") + + check_type(logits, torch.float32, "logits") + check_type(targets, torch.int32, "targets") + check_type(logit_lengths, torch.int32, "logit_lengths") + check_type(target_lengths, torch.int32, "target_lengths") + + check_contiguous(logits, "logits") + check_contiguous(targets, "targets") + check_contiguous(target_lengths, "target_lengths") + check_contiguous(logit_lengths, "logit_lengths") + + check_dim(logits, 4, "logits") + check_dim(targets, 2, "targets") + check_dim(logit_lengths, 1, "logit_lengths") + check_dim(target_lengths, 1, "target_lengths") + + check_equal( + logit_lengths.shape[0], "logit_lengths.shape[0]", logits.shape[0], "logits.shape[0]" + ) + check_equal( + target_lengths.shape[0], "target_lengths.shape[0]", logits.shape[0], "logits.shape[0]" + ) + check_equal( + targets.shape[0], "targets.shape[0]", logits.shape[0], "logits.shape[0]" + ) + check_equal( + targets.shape[1], + "targets.shape[1]", + torch.max(target_lengths), + "torch.max(target_lengths)", + ) + check_equal( + logits.shape[1], + "logits.shape[1]", + torch.max(logit_lengths), + "torch.max(logit_lengths)", + ) + check_equal( + logits.shape[2], + "logits.shape[2]", + torch.max(target_lengths) + 1, + "torch.max(target_lengths) + 1", + ) + + if blank < 0 or blank >= logits.shape[-1]: + raise ValueError( + "blank ({}) must be within [0, logits.shape[-1]={})".format( + blank, logits.shape[-1] + ) + ) From d72165b08cd2d72c61ef5f6e47c1cccbc072ffbb Mon Sep 17 00:00:00 2001 From: Caroline Chen Date: Fri, 30 Apr 2021 12:53:14 -0700 Subject: [PATCH 44/73] Remove unused file Summary: `kernels.h` was copied into both cpu and gpu folders previously, but it is not actually necessary for cpu rnnt Reviewed By: vincentqb Differential Revision: D28072209 fbshipit-source-id: 74427ee7d0c81aafdb82d1151035e89e4faec359 --- torchaudio/csrc/rnnt/cpu/kernels.h | 242 ----------------------------- 1 file changed, 242 deletions(-) delete mode 100644 torchaudio/csrc/rnnt/cpu/kernels.h diff --git a/torchaudio/csrc/rnnt/cpu/kernels.h b/torchaudio/csrc/rnnt/cpu/kernels.h deleted file mode 100644 index 66ffb6657e..0000000000 --- a/torchaudio/csrc/rnnt/cpu/kernels.h +++ /dev/null @@ -1,242 +0,0 @@ -#pragma once - -#include - -#include -#include - -namespace torchaudio { -namespace rnnt { - - -template -FORCE_INLINE HOST_AND_DEVICE void ComputeLogProbsSparseElement( - int bTgt, - int t, - int u, - int maxT, - int maxU, - int numTargets, - int blank, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - const CAST_DTYPE* denominators, - CAST_DTYPE* logProbs, - const int* wpEnds, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr, - int H=1, - bool fusedLogSmax=true) { - - const int& D = numTargets; - - const int bSrc = bTgt / H; - const int T = srcLengths[bSrc]; - const int U = tgtLengths[bTgt] + 1; - - if (t >= T || u >= U) { // out of boundary. - return; - } - const int* validRangesB = validRanges + (maxU*2) * bTgt; - int start = validRangesB[2*u]; - int end = validRangesB[2*u + 1]; - - // out of boundary for alignment restriction condition - if (t < start || t > end) { - return; - } - SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); - int idx = idxr(bTgt, t, u); - logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = - CAST_DTYPE(logits[idx * D + blank]) - denominators[idx]; - - if (!fusedLogSmax) { - logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = - CAST_DTYPE(logits[idx * D + blank]); - } - - if (u < U - 1) { - // emit: log_prob(b, t, u).emit() = logits(b, t, u, tgt[u]) - denom(b, t, u). - int target = targets[Indexer2D(maxU - 1)(bTgt, u)]; - logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = - CAST_DTYPE(logits[idx * D + target]) - denominators[idx]; - - if (!fusedLogSmax) { - logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = - CAST_DTYPE(logits[idx * D + target]); - } - } -} - - -template -HOST_AND_DEVICE void ComputeGradientsElement( - int bTgt, - int t, - int u, - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - CAST_DTYPE clamp, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - const CAST_DTYPE* denominators, - const CAST_DTYPE* alphas, - const CAST_DTYPE* betas, - DTYPE* gradients, - bool monotonic = false, - bool sparse = false, - const int* validRanges = nullptr, - const int* cellsPerSample = nullptr, - int H = 1, - bool fusedLogSmax = true) { - - const int& maxT = maxSrcLen; - const int& maxU = maxTgtLen; - const int& D = numTargets; - - const int bSrc = bTgt / H; - const int T = srcLengths[bSrc]; - const int U = tgtLengths[bTgt] + 1; - - if (t >= T || u >= U) { // out of boundary. - if (sparse) { - // no extra elements needs to be set to 0 - return; - } else if (gradients == logits && t < maxT && u < maxU) { - // gradients and logits are pointing to the same memory location - Indexer3D idxr3(maxT, maxU); - int idx_b_t_u_zero = idxr3(bTgt, t, u); - if (idx_b_t_u_zero != -1 ) { - int start = idx_b_t_u_zero * D; - for (int b_t_u_d = start; b_t_u_d < start + D; ++b_t_u_d) { - gradients[b_t_u_d] = 0; - } - } - } - return; - } - - int costIdx; - if (!sparse) { - costIdx = bTgt * maxT * maxU; - } else { - SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); - costIdx = idxr(bTgt, 0, 0); - } - CAST_DTYPE cost = -(betas[costIdx]); - - - Indexer2D idxr2(maxU - 1); - - int idx_b_t_u, idx_b_t_up1, idx_b_tp1_u, idx_b_tp1_up1; - if (sparse) { - SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); - idx_b_t_u = idxr(bTgt, t, u); - idx_b_t_up1 = idxr(bTgt, t, u+1); - idx_b_tp1_u = idxr(bTgt, t+1, u); - idx_b_tp1_up1 = idxr(bTgt, t+1, u+1); - } else { - Indexer3D idxr3(maxT, maxU); - idx_b_t_u = idxr3(bTgt, t, u); - idx_b_t_up1 = idxr3(bTgt, t, u+1); - idx_b_tp1_u = idxr3(bTgt, t+1, u); - idx_b_tp1_up1 = idxr3(bTgt, t+1, u+1); - } - - if (idx_b_t_u == -1 ) { - return; - } - - if (isinf(cost) || isnan(cost)) { - for (int d = 0; d < D; ++d) { - int b_t_u_d = idx_b_t_u * D + d; - gradients[b_t_u_d] = 0; - } - return; - } - - CAST_DTYPE c = alphas[idx_b_t_u] + cost - denominators[idx_b_t_u]; - for (int d = 0; d < D; ++d) { - int b_t_u_d = idx_b_t_u * D + d; - CAST_DTYPE g = CAST_DTYPE(logits[b_t_u_d]) + c; - - if (!monotonic) { - if (fusedLogSmax) { - if (d == blank && t == T - 1 && u == U - 1) { // last blank transition. - gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]) - std::exp(g); - } else if (t < T - 1 && d == blank) { - gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); - if (idx_b_tp1_u != -1) { - gradients[b_t_u_d] = gradients[b_t_u_d] - std::exp(g + betas[idx_b_tp1_u]); - } - - } else if (u < U - 1 && d == targets[idxr2(bTgt, u)]) { - gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); - if (idx_b_t_up1 != -1) { - gradients[b_t_u_d] = gradients[b_t_u_d] - std::exp(g + betas[idx_b_t_up1]); - } - } else { - gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); - } - } - else { // Non fused log softmax case - CAST_DTYPE g = cost + CAST_DTYPE(logits[b_t_u_d]); - if (d == blank && t == T - 1 && u == U - 1) { - gradients[b_t_u_d] = g + alphas[idx_b_t_u]; - } else if (t < T - 1 && d == blank) { - if (idx_b_tp1_u != -1) { - gradients[b_t_u_d] = g + alphas[idx_b_t_u] + betas[idx_b_tp1_u]; - } else { - gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); - } - } else if (u < U - 1 && d == targets[idxr2(bTgt, u)]) { - - if (idx_b_t_up1 != -1) { - gradients[b_t_u_d] = g + alphas[idx_b_t_u] + betas[idx_b_t_up1]; - } else { - gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); - } - - } - else { - gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); - } - gradients[b_t_u_d] = -std::exp(gradients[b_t_u_d]); - } - } else { // Monotonic RNNT case - CAST_DTYPE g = cost + CAST_DTYPE(logits[b_t_u_d]); - if (d == blank && t == T - 1 && u == U - 1) { // last blank transition. - g = g + alphas[idx_b_t_u]; - } else if (t < T - 1 && d == blank) { - g = g + alphas[idx_b_t_u]; - if (idx_b_tp1_u != -1) { - g = g + betas[idx_b_tp1_u]; - } - } else if (u < U - 1 && t < T - 1 && d == targets[idxr2(bTgt, u)]) { - g = g + alphas[idx_b_t_u]; - if (idx_b_tp1_up1 != -1) { - g = g + betas[idx_b_tp1_up1]; - } - } - else { - g = g + CAST_DTYPE(-INFINITY); - } - gradients[b_t_u_d] = -std::exp(g); - } // end of monotonic else - - if (clamp > 0) { - auto g = CAST_DTYPE(gradients[b_t_u_d]); - gradients[b_t_u_d] = math::min(g, clamp); - gradients[b_t_u_d] = math::max(g, -clamp); - } - } -} - -} // namespace rnnt -} // namespace torchaudio From c3ff2cd445d40419353302d39c0ef2719a18febe Mon Sep 17 00:00:00 2001 From: Caroline Chen Date: Fri, 30 Apr 2021 12:53:14 -0700 Subject: [PATCH 45/73] Remove sparse support for A-R RNNT Summary: sparse support is a layer on top of alignment-restriction rnnt, but we do not wish to release either of them initially this diff removes sparse functionality along with sparse-related parameters (`valid_ranges`, `cells_per_sample`) Reviewed By: vincentqb Differential Revision: D28072213 fbshipit-source-id: 9a88368af1a730b4167ffb9cebdd5eddcc6e4bf9 --- .../rnnt/rnnt_loss_impl.py | 235 ++--------- test/torchaudio_unittest/rnnt/utils.py | 77 +--- .../csrc/rnnt/cpu/alignment_restrictions.h | 55 --- torchaudio/csrc/rnnt/cpu/kernel_utils.h | 58 --- torchaudio/csrc/rnnt/gpu/compute.cu | 130 ------ torchaudio/csrc/rnnt/gpu/compute_alphas.cu | 89 ---- torchaudio/csrc/rnnt/gpu/compute_betas.cu | 94 ----- .../rnnt/gpu/gpu_alignment_restrictions.cuh | 97 +---- torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh | 15 - torchaudio/csrc/rnnt/gpu/gpu_transducer.h | 381 ------------------ torchaudio/csrc/rnnt/gpu/kernel_utils.h | 58 --- torchaudio/csrc/rnnt/gpu/kernels.h | 96 +---- torchaudio/csrc/rnnt/options.h | 7 - torchaudio/csrc/rnnt/transducer.h | 103 ----- torchaudio/csrc/rnnt/workspace.h | 8 +- torchaudio/prototype/rnnt_loss.py | 195 ++------- 16 files changed, 107 insertions(+), 1591 deletions(-) diff --git a/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py b/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py index 6b6e4d1d20..dcab6def6f 100644 --- a/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py +++ b/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py @@ -9,7 +9,6 @@ from .numpy_transducer import _NumpyTransducer from .utils import ( - assert_sparse_all_close, compute_with_numpy_transducer, compute_with_pytorch_transducer, get_B1_T10_U3_D4_data, @@ -34,25 +33,20 @@ def _test_costs_and_gradients( data=data, reuse_logits_for_grads=reuse_logits_for_grads ) np.testing.assert_allclose(costs, ref_costs, atol=atol, rtol=rtol) - if "logits_sparse" in data: - assert_sparse_all_close( - data, gradients, ref_gradients, atol=atol, rtol=rtol - ) - else: - self.assertEqual(logits_shape, gradients.shape) - if not np.allclose(gradients, ref_gradients, atol=atol, rtol=rtol): - for b in range(len(gradients)): - T = data["logit_lengths"][b] - U = data["target_lengths"][b] - for t in range(gradients.shape[1]): - for u in range(gradients.shape[2]): - np.testing.assert_allclose( - gradients[b, t, u], - ref_gradients[b, t, u], - atol=atol, - rtol=rtol, - err_msg=f"failed on b={b}, t={t}/T={T}, u={u}/U={U}", - ) + self.assertEqual(logits_shape, gradients.shape) + if not np.allclose(gradients, ref_gradients, atol=atol, rtol=rtol): + for b in range(len(gradients)): + T = data["logit_lengths"][b] + U = data["target_lengths"][b] + for t in range(gradients.shape[1]): + for u in range(gradients.shape[2]): + np.testing.assert_allclose( + gradients[b, t, u], + ref_gradients[b, t, u], + atol=atol, + rtol=rtol, + err_msg=f"failed on b={b}, t={t}/T={T}, u={u}/U={U}", + ) def test_basic_backward(self): rnnt_loss = RNNTLoss() @@ -317,42 +311,36 @@ def _test_costs_and_gradients( data=data, reuse_logits_for_grads=reuse_logits_for_grads ) np.testing.assert_allclose(costs, ref_costs, atol=atol, rtol=rtol) - if "logits_sparse" in data: - assert_sparse_all_close( - data, gradients, ref_gradients, atol=atol, rtol=rtol - ) - else: - self.assertEqual(logits_shape, gradients.shape) - if not np.allclose(gradients, ref_gradients, atol=atol, rtol=rtol): - for b in range(len(gradients)): - T = data["logit_lengths"][b] - U = data["target_lengths"][b] - for t in range(gradients.shape[1]): - for u in range(gradients.shape[2]): - np.testing.assert_allclose( - gradients[b, t, u], - ref_gradients[b, t, u], - atol=atol, - rtol=rtol, - err_msg=f"failed on b={b}, t={t}/T={T}, u={u}/U={U}", - ) + self.assertEqual(logits_shape, gradients.shape) + if not np.allclose(gradients, ref_gradients, atol=atol, rtol=rtol): + for b in range(len(gradients)): + T = data["logit_lengths"][b] + U = data["target_lengths"][b] + for t in range(gradients.shape[1]): + for u in range(gradients.shape[2]): + np.testing.assert_allclose( + gradients[b, t, u], + ref_gradients[b, t, u], + atol=atol, + rtol=rtol, + err_msg=f"failed on b={b}, t={t}/T={T}, u={u}/U={U}", + ) def test_nan_logits(self): - for sparse in [False, True]: - for reuse_logits_for_grads in [False, True]: - data = get_B1_T10_U3_D4_data( - random=True, left_buffer=10, right_buffer=10, sparse=sparse, nan=True - ) - data = numpy_to_torch( - data=data, device=self.device, requires_grad=True - ) - data["left_buffer"] = 10 - data["right_buffer"] = 10 - costs, gradients = compute_with_pytorch_transducer( - data=data, reuse_logits_for_grads=reuse_logits_for_grads - ) - self.assertTrue(np.all(costs == 0)) - self.assertTrue(np.all(gradients == 0)) + for reuse_logits_for_grads in [False, True]: + data = get_B1_T10_U3_D4_data( + random=True, left_buffer=10, right_buffer=10, nan=True + ) + data = numpy_to_torch( + data=data, device=self.device, requires_grad=True + ) + data["left_buffer"] = 10 + data["right_buffer"] = 10 + costs, gradients = compute_with_pytorch_transducer( + data=data, reuse_logits_for_grads=reuse_logits_for_grads + ) + self.assertTrue(np.all(costs == 0)) + self.assertTrue(np.all(gradients == 0)) def test_rnnt_restricted_betas_with_random_data(self): for u in range(50, 60): @@ -375,53 +363,9 @@ def test_rnnt_restricted_betas_with_random_data(self): targets=data["targets"], wordpiece_ends=wordpiece_ends, blank=4095, - sparse=False, ) def test_rnnt_nonfused_log_softmax(self): - for random in [False, True]: - for sparse in [False, True]: - for left_buffer in [1, 2, 10]: - for right_buffer in [1, 2, 5, 10]: - data = get_B1_T10_U3_D4_data( - random=random, - left_buffer=left_buffer, - right_buffer=right_buffer, - sparse=sparse, - ) - data = numpy_to_torch( - data=data, device=self.device, requires_grad=True - ) - data["left_buffer"] = left_buffer - data["right_buffer"] = right_buffer - data["fused_log_softmax"] = False - ref_costs, ref_gradients = compute_with_numpy_transducer( - data=data - ) - self._test_costs_and_gradients( - data=data, ref_costs=ref_costs, ref_gradients=ref_gradients - ) - - def test_rnnt_sparse_B1_T10_U3_D4(self): - for random in [False, True]: - for left_buffer in [1, 2, 10]: - for right_buffer in [1, 2, 5, 10]: - data = get_B1_T10_U3_D4_data( - random=random, left_buffer=left_buffer, right_buffer=right_buffer, sparse=True - ) - data = numpy_to_torch( - data=data, device=self.device, requires_grad=True - ) - data["left_buffer"] = left_buffer - data["right_buffer"] = right_buffer - ref_costs, ref_gradients = compute_with_numpy_transducer( - data=data - ) - self._test_costs_and_gradients( - data=data, ref_costs=ref_costs, ref_gradients=ref_gradients - ) - - def test_rnnt_sparse_B1_T10_U3_D4_fp16(self): for random in [False, True]: for left_buffer in [1, 2, 10]: for right_buffer in [1, 2, 5, 10]: @@ -429,107 +373,16 @@ def test_rnnt_sparse_B1_T10_U3_D4_fp16(self): random=random, left_buffer=left_buffer, right_buffer=right_buffer, - sparse=True, - dtype=np.float16, ) data = numpy_to_torch( data=data, device=self.device, requires_grad=True ) data["left_buffer"] = left_buffer data["right_buffer"] = right_buffer + data["fused_log_softmax"] = False ref_costs, ref_gradients = compute_with_numpy_transducer( data=data ) self._test_costs_and_gradients( - data=data, - ref_costs=ref_costs, - ref_gradients=ref_gradients, - atol=1e-3, - rtol=10, - ) - - def test_rnnt_sparse_alpha_beta_B1_T10_U3_D4(self): - for random in [False, True]: - for left_buffer in [1, 2, 10]: - for right_buffer in [1, 2, 10]: - data = get_B1_T10_U3_D4_data( - random=random, left_buffer=left_buffer, right_buffer=right_buffer, sparse=True - ) - data = numpy_to_torch( - data=data, device=self.device, requires_grad=True - ) - data["left_buffer"] = left_buffer - data["right_buffer"] = right_buffer - logits = torch.tensor(data["logits"]) - log_probs = ( - torch.nn.functional.log_softmax(logits, dim=-1).cpu().numpy() - ) - ( - np_gradients, - np_costs, - np_alphas, - np_betas, - ) = _NumpyTransducer.compute( - log_probs=log_probs, - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - blank=data["blank"], - wordpiece_ends=data["wordpiece_ends"], - left_buffer=left_buffer, - right_buffer=right_buffer, - ) - pt_alphas = _rnnt_loss_alphas( - logits=data["logits_sparse"], - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - wordpiece_ends=data["wordpiece_ends"], - blank=data["blank"], - left_buffer=left_buffer, - right_buffer=right_buffer, - sparse=True, - valid_ranges=data["valid_ranges"], - cells_per_sample=data["cells_per_sample"], - ) - total_valid = torch.sum(data["cells_per_sample"]) - sparse_np_alphas = np.zeros((total_valid)).astype(np.float32) - valid_ranges = data["valid_ranges"] - offset = 0 - for b in range(2): - for u in range(3): - st, en = valid_ranges[b][u][0], valid_ranges[b][u][1] - sparse_np_alphas[ - offset : offset + (en - st) + 1 - ] = np_alphas[b, st : en + 1, u] - offset += (en - st) + 1 - np.testing.assert_allclose( - pt_alphas.cpu(), sparse_np_alphas, atol=1e-6, rtol=1e-2 - ) - - pt_betas = _rnnt_loss_betas( - logits=data["logits_sparse"], - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - wordpiece_ends=data["wordpiece_ends"], - blank=data["blank"], - left_buffer=left_buffer, - right_buffer=right_buffer, - sparse=True, - valid_ranges=data["valid_ranges"], - cells_per_sample=data["cells_per_sample"], - ) - sparse_np_betas = np.zeros((total_valid)).astype(np.float32) - valid_ranges = data["valid_ranges"] - offset = 0 - for b in range(2): - for u in range(3): - st, en = valid_ranges[b][u][0], valid_ranges[b][u][1] - sparse_np_betas[offset : offset + (en - st) + 1] = np_betas[ - b, st : en + 1, u - ] - offset += (en - st) + 1 - np.testing.assert_allclose( - pt_betas.cpu(), sparse_np_betas, atol=1e-6, rtol=1e-2 + data=data, ref_costs=ref_costs, ref_gradients=ref_gradients ) diff --git a/test/torchaudio_unittest/rnnt/utils.py b/test/torchaudio_unittest/rnnt/utils.py index 5763e72688..d8030c4989 100644 --- a/test/torchaudio_unittest/rnnt/utils.py +++ b/test/torchaudio_unittest/rnnt/utils.py @@ -10,54 +10,6 @@ ) -def _get_sparse(data, dense_tensor, left_buffer, right_buffer, H=1): - B, _, U, D = 2, 10, 3, 4 - total_valid = 0 - valid_ranges = np.zeros((B * H, U, 2), dtype=np.int32) - cells_per_sample = np.zeros(B * H, dtype=np.int32) - wordpiece_ends = data["wordpiece_ends"] - for b_tgt in range(B * H): - b_src = int(b_tgt / H) - src_len = int(data["logit_lengths"][b_src]) - tgt_len = int(data["target_lengths"][b_tgt]) + 1 - ar_check = AlignmentRestrictionCheck( - tgt_len, src_len, wordpiece_ends[b_tgt][:tgt_len], left_buffer, right_buffer - ) - sample_cells = 0 - for u in range(tgt_len): - v_range = ar_check.valid_time_ranges(u) - valid_ranges[b_tgt, u, 0] = v_range[0] - valid_ranges[b_tgt, u, 1] = v_range[1] - total_valid += v_range[1] - v_range[0] + 1 - sample_cells += v_range[1] - v_range[0] + 1 - cells_per_sample[b_tgt] = sample_cells - sparse_joint_enc = np.zeros((total_valid, D)).astype(dense_tensor.dtype) - offset = 0 - for b in range(B * H): - for u in range(U): - st, en = valid_ranges[b_tgt][u][0], valid_ranges[b_tgt][u][1] - sparse_joint_enc[offset : offset + (en - st) + 1, :] = dense_tensor[ - b, st : en + 1, u, : - ] - offset += (en - st) + 1 - return sparse_joint_enc, valid_ranges, cells_per_sample - - -def assert_sparse_all_close(data, gradients, ref_gradients, atol=1e-6, rtol=1e-2): - valid_ranges = data["valid_ranges"] - idx = 0 - for b in range(valid_ranges.size(0)): - for u in range(valid_ranges.size(1)): - st, en = valid_ranges[b, u, 0], valid_ranges[b, u, 1] - np.testing.assert_allclose( - gradients[idx : idx + (en - st + 1), :], - ref_gradients[b, st : en + 1, u, :], - atol=atol, - rtol=rtol, - ) - idx += (en - st) + 1 - - def compute_with_numpy_transducer(data): costs = NumpyTransducerLoss( blank=data["blank"], @@ -88,19 +40,14 @@ def compute_with_pytorch_transducer(data, reuse_logits_for_grads=False): blank=data["blank"], left_buffer=left_buffer, right_buffer=right_buffer, - sparse=True if "logits_sparse" in data else False, fused_log_softmax=data.get("fused_log_softmax", True), reuse_logits_for_grads=reuse_logits_for_grads, )( - logits=data.get("logits_sparse", data["logits"]), + logits=data["logits"], logit_lengths=data["logit_lengths"], target_lengths=data["target_lengths"], targets=data["targets"], wordpiece_ends=data.get("wordpiece_ends", None), - valid_ranges=data.get("valid_ranges", None), - cells_per_sample=data["cells_per_sample"] - if "cells_per_sample" in data - else None, ) loss = torch.sum(costs) @@ -150,7 +97,6 @@ def get_B1_T10_U3_D4_data( random=False, left_buffer=0, right_buffer=0, - sparse=False, dtype=np.float32, nan=False, ): @@ -168,13 +114,6 @@ def get_B1_T10_U3_D4_data( data["blank"] = 0 data["wordpiece_ends"] = np.array([[0, 2, 7], [0, 2, 7]], dtype=np.int32) - if sparse: - sparse_joint_enc, valid_ranges, cells_per_sample = _get_sparse( - data, data["logits"], left_buffer, right_buffer - ) - data["logits_sparse"] = sparse_joint_enc - data["valid_ranges"] = valid_ranges - data["cells_per_sample"] = cells_per_sample return data @@ -517,20 +456,6 @@ def grad_hook(grad): data["target_lengths"] = target_lengths data["targets"] = targets - if "logits_sparse" in data: - logits_sparse = torch.from_numpy(data["logits_sparse"]) - logits_sparse = torch.autograd.Variable( - logits_sparse, requires_grad=requires_grad - ) - logits_sparse = logits_sparse.to(device=logits.device) - logits_sparse.register_hook(grad_hook) - data["logits_sparse"] = logits_sparse - valid_ranges = torch.from_numpy(data["valid_ranges"]) - valid_ranges = valid_ranges.to(device=logits.device) - data["valid_ranges"] = valid_ranges - cells_per_sample = torch.from_numpy(data["cells_per_sample"]) - cells_per_sample = cells_per_sample.to(device=logits.device) - data["cells_per_sample"] = cells_per_sample return data diff --git a/torchaudio/csrc/rnnt/cpu/alignment_restrictions.h b/torchaudio/csrc/rnnt/cpu/alignment_restrictions.h index 0ecc6af3bb..0c265b10be 100644 --- a/torchaudio/csrc/rnnt/cpu/alignment_restrictions.h +++ b/torchaudio/csrc/rnnt/cpu/alignment_restrictions.h @@ -57,61 +57,6 @@ class AlignmentRestrictionCheck { int rBuffer_; }; -struct SparseIndexer { - - SparseIndexer( - const int& maxU, - const int* tgtLengths, - const int* validRanges, - const int* cellsPerSample): - maxU_(maxU), - tgtLengths_(tgtLengths), - validRanges_(validRanges), - cellsPerSample_(cellsPerSample) {}; - - int operator() (int bIdx, int tIdx, int uIdx){ - - // increment the idx for valid cells in previous samples - int idx = 0; - for(int b = 0 ; b < bIdx; b++) { - idx += cellsPerSample_[b]; - } - - // increment the index for valid cells in current sample - // using pre-computed valid ranges - const int* validRangesB = validRanges_ + (maxU_*2) * bIdx; - int U = tgtLengths_[bIdx] + 1; - for (int u = 0 ; u < U ; u++) { - int startT = validRangesB[2 * u]; - int endT = validRangesB[2 * u + 1]; - if (u == uIdx) { - if (tIdx < startT || tIdx > endT) { - return -1; - } - idx += (tIdx - startT); - return idx; - } else { - idx += (endT - startT) + 1; - } - } - return -1; - } - - private: - const int& maxU_; - - // (vector of size B) contains the lengths of U for each sample in batch - const int* tgtLengths_; - - // (vector of size B*2*U) contains the valid time ranges (start_t, end_t) - // for each u in every sample, as per alignment restrictions - const int* validRanges_; - - // The total valid timesteps for each sample (sumed over all u in the sample). - // This is a quick loopup so we dont need to iterate on all previous u*b values. - const int* cellsPerSample_; -}; - } // namespace cpu } // namespace rnnt } // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/kernel_utils.h b/torchaudio/csrc/rnnt/cpu/kernel_utils.h index 4ba141503e..08fc97b2e1 100644 --- a/torchaudio/csrc/rnnt/cpu/kernel_utils.h +++ b/torchaudio/csrc/rnnt/cpu/kernel_utils.h @@ -55,63 +55,5 @@ struct Indexer4D { } }; - -struct SparseIndexer { - HOST_AND_DEVICE SparseIndexer( - const int& maxU, - const int* tgtLengths, - const int* validRanges, - const int* cellsPerSample) : - maxU_(maxU), - tgtLengths_(tgtLengths), - validRanges_(validRanges), - cellsPerSample_(cellsPerSample) {}; - - HOST_AND_DEVICE int operator() (int bIdx, int tIdx, int uIdx) { - // Returns the sparse index given bIdx, tIdx, uIdx; or -1 if t out of band. - - // increment the idx for valid cells in previous samples - // TODO: Inefficient in the inner loop; precompute this? - int idx = 0; - for(int b = 0 ; b < bIdx; b++) { - idx += cellsPerSample_[b]; - } - - // increment the idx for valid cells in current sample - // using pre-computed valid ranges - const int* validRangesB = validRanges_ + (maxU_ * 2) * bIdx; - int U = tgtLengths_[bIdx] + 1; - for (int u = 0 ; u < U ; u++) { - int startT = validRangesB[2*u]; - int endT = validRangesB[2*u + 1]; - if (u == uIdx) { - if (tIdx < startT || tIdx > endT) { - return -1; - } - idx += (tIdx - startT); - return idx; - } else { - idx += (endT - startT) + 1; - } - } - return -1; - } - - private: - const int& maxU_; - - // (vector of size B) contains the lengths of U for each sample in batch - const int* tgtLengths_; - - // (vector of size B*2*U) contains the valid time ranges (start_t, end_t) - // for each u in every sample, as per alignment restrictions - const int* validRanges_; - - // The total valid timesteps for each sample (sumed over all u in the sample). - // This is a quick loopup so we dont need to iterate on all previous u*b values. - const int* cellsPerSample_; - -}; - } // namespace rnnt } // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/compute.cu b/torchaudio/csrc/rnnt/gpu/compute.cu index 53567c131d..98cf39e2cc 100644 --- a/torchaudio/csrc/rnnt/gpu/compute.cu +++ b/torchaudio/csrc/rnnt/gpu/compute.cu @@ -6,116 +6,6 @@ namespace torchaudio { namespace rnnt { namespace gpu { -// Entry point into Sparse Alignment Restricted RNNT Loss. -std::tuple> -compute_sparse( - torch::Tensor& logits, - const torch::Tensor& targets, - const torch::Tensor& src_lengths, - const torch::Tensor& tgt_lengths, - int64_t blank, - double clamp, - int64_t max_T, - int64_t max_U, - const c10::optional& wp_ends = c10::nullopt, - int64_t l_buffer = 0, - int64_t r_buffer = 0, - const c10::optional& valid_ranges = c10::nullopt, - const c10::optional& cells_per_sample = c10::nullopt, - bool fused_log_smax = true, - bool reuse_logits_for_grads = true) { - - Options options; - options.batchSize_ = src_lengths.size(0); - options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); - options.maxSrcLen_ = max_T; - options.maxTgtLen_ = max_U; - options.sparseCells_ = logits.size(0); - options.numTargets_ = logits.size(1); - options.fusedLogSmax_ = fused_log_smax; - - options.blank_ = blank; - options.clamp_ = clamp; - options.lBuffer_ = l_buffer; - options.rBuffer_ = r_buffer; - options.sparse_ = true; - - CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); - options.stream_ = at::cuda::getCurrentCUDAStream(); - cudaSetDevice(logits.get_device()); - options.device_ = GPU; - - torch::Tensor costs = torch::empty( - options.batchSize_ * options.nHypos_, - torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); - - c10::optional gradients = c10::nullopt; - if (logits.requires_grad()) { - if (reuse_logits_for_grads) { - gradients = logits; - } else { - gradients = torch::zeros_like(logits); - } - } - torch::Tensor int_workspace = torch::empty( - IntWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Int)); - - torch::Tensor float_workspace = torch::empty( - DtypeWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Float)); - - Workspace workspace( - /*options=*/options, - /*dtype_data=*/float_workspace.data(), - /*dtype_size=*/float_workspace.numel(), - /*int_data=*/int_workspace.data(), - /*int_size=*/int_workspace.numel()); - - switch (logits.type().scalarType()) { - case torch::ScalarType::Float: - { - ComputeSparse( - /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*costs=*/costs.data(), - /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), - /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data(), - (valid_ranges == c10::nullopt)? nullptr : valid_ranges->data(), - (cells_per_sample == c10::nullopt)? nullptr : cells_per_sample->data()); - break; - } - case torch::ScalarType::Half: - { - ComputeSparse( - /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*costs=*/costs.data(), - /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), - /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data(), - (valid_ranges == c10::nullopt)? nullptr : valid_ranges->data(), - (cells_per_sample == c10::nullopt)? nullptr : cells_per_sample->data()); - break; - } - default: - { - LOG(ERROR) << "unsupported logits.type().scalarType() = " - << logits.type().scalarType(); - break; - } - }; - - - return std::make_tuple(costs, gradients); -} - - // Entry point into RNNT Loss std::tuple> compute( @@ -213,28 +103,8 @@ compute( return std::make_tuple(costs, gradients); } -TORCH_LIBRARY_FRAGMENT(torchaudio, m) { - m.def("rnnt_loss_sparse(Tensor logits," - "Tensor targets," - "Tensor src_lengths," - "Tensor tgt_lengths," - "int blank," - "float clamp," - "int max_T," - "int max_U," - "Tensor? wp_ends=None," - "int l_buffer=0," - "int r_buffer=0," - "Tensor? valid_ranges=None," - "Tensor? cells_per_sample=None," - "bool fused_log_smax=True," - "bool reuse_logits_for_grads=True) -> (Tensor, Tensor?)", - &compute_sparse); -} - TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { m.impl("rnnt_loss", &compute); - m.impl("rnnt_loss_sparse", &compute_sparse); } } // namespace gpu diff --git a/torchaudio/csrc/rnnt/gpu/compute_alphas.cu b/torchaudio/csrc/rnnt/gpu/compute_alphas.cu index f9bff447cd..929434e19d 100644 --- a/torchaudio/csrc/rnnt/gpu/compute_alphas.cu +++ b/torchaudio/csrc/rnnt/gpu/compute_alphas.cu @@ -70,97 +70,8 @@ torch::Tensor compute_alphas( return alphas; } -torch::Tensor compute_alphas_sparse( - const torch::Tensor& logits, - const torch::Tensor& targets, - const torch::Tensor& src_lengths, - const torch::Tensor& tgt_lengths, - int64_t blank, - double clamp, - int64_t max_T, - int64_t max_U, - const c10::optional& wp_ends = c10::nullopt, - int64_t l_buffer = 0, - int64_t r_buffer = 0, - const c10::optional& valid_ranges = c10::nullopt, - const c10::optional& cells_per_sample = c10::nullopt) { - Options options; - options.batchSize_ = src_lengths.size(0); - options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); - options.maxSrcLen_ = max_T; - options.maxTgtLen_ = max_U; - options.sparseCells_ = logits.size(0); - options.numTargets_ = logits.size(1); - - options.blank_ = blank; - options.clamp_ = clamp; - options.lBuffer_ = l_buffer; - options.rBuffer_ = r_buffer; - options.sparse_ = true; - - CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); - options.stream_ = at::cuda::getCurrentCUDAStream(); - cudaSetDevice(logits.get_device()); - options.device_ = GPU; - - torch::Tensor alphas = torch::zeros( - {logits.size(0)}, - torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); - - torch::Tensor int_workspace = torch::empty( - IntWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions() - .device(logits.device()) - .dtype(torch::ScalarType::Int)); - - torch::Tensor float_workspace = torch::empty( - DtypeWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions() - .device(logits.device()) - .dtype(torch::ScalarType::Float)); - - Workspace workspace( - /*options=*/options, - /*dtype_data=*/float_workspace.data(), - /*dtype_size=*/float_workspace.numel(), - /*int_data=*/int_workspace.data(), - /*int_size=*/int_workspace.numel()); - - // Only support float, this is mainly to enable easy - // unit-testing - ComputeAlphasSparse( - /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*alphas=*/alphas.data(), - /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data(), - (valid_ranges == c10::nullopt) ? nullptr : valid_ranges->data(), - (cells_per_sample == c10::nullopt) ? nullptr - : cells_per_sample->data()); - return alphas; -} - -TORCH_LIBRARY_FRAGMENT(torchaudio, m) { - m.def("rnnt_loss_alphas_sparse(Tensor logits," - "Tensor targets," - "Tensor src_lengths," - "Tensor tgt_lengths," - "int blank," - "float clamp," - "int max_T," - "int max_U, Tensor? wp_ends=None," - "int l_buffer=0," - "int r_buffer=0," - "Tensor? valid_ranges=None," - "Tensor? cells_per_sample=None) -> Tensor", - &compute_alphas_sparse); -} - TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { m.impl("rnnt_loss_alphas", &compute_alphas); - m.impl("rnnt_loss_alphas_sparse", &compute_alphas_sparse); } } // namespace gpu diff --git a/torchaudio/csrc/rnnt/gpu/compute_betas.cu b/torchaudio/csrc/rnnt/gpu/compute_betas.cu index ffdad7ccb7..f906d1bbe3 100644 --- a/torchaudio/csrc/rnnt/gpu/compute_betas.cu +++ b/torchaudio/csrc/rnnt/gpu/compute_betas.cu @@ -75,102 +75,8 @@ torch::Tensor compute_betas( return betas; } -torch::Tensor compute_betas_sparse( - const torch::Tensor& logits, - const torch::Tensor& targets, - const torch::Tensor& src_lengths, - const torch::Tensor& tgt_lengths, - int64_t blank, - double clamp, - int64_t max_T, - int64_t max_U, - const c10::optional& wp_ends = c10::nullopt, - int64_t l_buffer = 0, - int64_t r_buffer = 0, - const c10::optional& valid_ranges = c10::nullopt, - const c10::optional& cells_per_sample = c10::nullopt) { - Options options; - options.batchSize_ = src_lengths.size(0); - options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); - options.maxSrcLen_ = max_T; - options.maxTgtLen_ = max_U; - options.sparseCells_ = logits.size(0); - options.numTargets_ = logits.size(1); - - options.blank_ = blank; - options.clamp_ = clamp; - options.lBuffer_ = l_buffer; - options.rBuffer_ = r_buffer; - - CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); - options.stream_ = at::cuda::getCurrentCUDAStream(); - cudaSetDevice(logits.get_device()); - options.device_ = GPU; - - torch::Tensor costs = torch::empty( - tgt_lengths.size(0), - torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); - - torch::Tensor betas = torch::zeros( - {logits.size(0)}, - torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); - - torch::Tensor int_workspace = torch::empty( - IntWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions() - .device(logits.device()) - .dtype(torch::ScalarType::Int)); - - torch::Tensor float_workspace = torch::empty( - DtypeWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions() - .device(logits.device()) - .dtype(torch::ScalarType::Float)); - - Workspace workspace( - /*options=*/options, - /*dtype_data=*/float_workspace.data(), - /*dtype_size=*/float_workspace.numel(), - /*int_data=*/int_workspace.data(), - /*int_size=*/int_workspace.numel()); - - // Only support float, this is mainly to enable easy - // unit-testing - ComputeBetasSparse( - /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*costs=*/costs.data(), - /*betas=*/betas.data(), - /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data(), - (valid_ranges == c10::nullopt) ? nullptr : valid_ranges->data(), - (cells_per_sample == c10::nullopt) ? nullptr - : cells_per_sample->data()); - return betas; -} - -TORCH_LIBRARY_FRAGMENT(torchaudio, m) { - m.def("rnnt_loss_betas_sparse(Tensor logits," - "Tensor targets," - "Tensor src_lengths," - "Tensor tgt_lengths," - "int blank," - "float clamp," - "int max_T," - "int max_U," - "Tensor? wp_ends=None," - "int l_buffer=0," - "int r_buffer=0," - "Tensor? valid_ranges=None," - "Tensor? cells_per_sample=None) -> Tensor", - &compute_betas_sparse); -} - TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { m.impl("rnnt_loss_betas", &compute_betas); - m.impl("rnnt_loss_betas_sparse", &compute_betas_sparse); } } // namespace gpu diff --git a/torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh b/torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh index 564eb0a3fb..d5d927992f 100644 --- a/torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh +++ b/torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh @@ -11,48 +11,6 @@ namespace torchaudio { namespace rnnt { -template -__global__ void ComputeLogProbsSparse( - int maxT, - int maxU, - int numTargets, - int blank, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - const CAST_DTYPE* denominators, - CAST_DTYPE* logProbs, - const int* wpEnds, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr, - int H=1, - bool fusedLogSmax=true) { - - const int bTgt = blockIdx.z; // 0 <= b < B - const int t = blockIdx.x * blockDim.x + threadIdx.x; - const int u = blockIdx.y; - - ComputeLogProbsSparseElement( - bTgt, - t, - u, - maxT, - maxU, - numTargets, - blank, - logits, - targets, - srcLengths, - tgtLengths, - denominators, - logProbs, - wpEnds, - validRanges, - cellsPerSample, - H=1, - fusedLogSmax); -} template __device__ void ComputeAlphasRestricted( @@ -69,9 +27,6 @@ __device__ void ComputeAlphasRestricted( int lBuffer, int rBuffer, int warp_size, - bool sparse=false, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr, int H=1) { const int& maxT = maxSrcLen; @@ -125,19 +80,11 @@ __device__ void ComputeAlphasRestricted( int idx_b_t_u = -1, idx_b_t_um1 = -1, idx_b_tm1_u = -1; int idx_b_prevWarpEndT_u = -1; - if (sparse) { - SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); - idx_b_t_u = idxr(bTgt, t, u); - idx_b_t_um1 = idxr(bTgt, t, u-1); - idx_b_tm1_u = idxr(bTgt, t-1, u); - idx_b_prevWarpEndT_u = idxr(bTgt, prevWarpEndT, u); - } else { - Indexer3D idxr(maxT, maxU); - idx_b_t_u = idxr(bTgt, t, u); - idx_b_t_um1 = idxr(bTgt, t, u - 1); - idx_b_tm1_u = idxr(bTgt, t - 1, u); - idx_b_prevWarpEndT_u = idxr(bTgt, prevWarpEndT, u); - } + Indexer3D idxr(maxT, maxU); + idx_b_t_u = idxr(bTgt, t, u); + idx_b_t_um1 = idxr(bTgt, t, u - 1); + idx_b_tm1_u = idxr(bTgt, t - 1, u); + idx_b_prevWarpEndT_u = idxr(bTgt, prevWarpEndT, u); alphas[idx_b_t_u] = CAST_DTYPE(-INFINITY); @@ -285,9 +232,6 @@ __global__ void ComputeAlphasRestrictedWrapper( int lBuffer, int rBuffer, int warp_size, - bool sparse=false, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr, int H=1) { ComputeAlphasRestricted( maxSrcLen, @@ -303,9 +247,6 @@ __global__ void ComputeAlphasRestrictedWrapper( lBuffer, rBuffer, warp_size, - sparse, - validRanges, - cellsPerSample, H); } @@ -327,9 +268,6 @@ __device__ void ComputeBetasCostsRestricted( int rBuffer, int warp_size, int num_warps, - bool sparse=false, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr, int H=1) { const int& maxT = maxSrcLen; @@ -370,19 +308,12 @@ __device__ void ComputeBetasCostsRestricted( int idx_b_t_u = -1, idx_b_t_up1 = -1; int idx_b_tp1_up1 = -1, idx_b_rightBlockStartT_u = -1; - if (sparse) { - SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); - idx_b_t_u = idxr(bTgt, t, u); - idx_b_t_up1 = idxr(bTgt, t, u+1); - idx_b_tp1_up1 = idxr(bTgt, t+1, u+1); - idx_b_rightBlockStartT_u = idxr(bTgt, rightBlockStartT, u); - } else { - Indexer3D idxr(maxT, maxU); - idx_b_t_u = idxr(bTgt, t, u); - idx_b_t_up1 = idxr(bTgt, t, u+1); - idx_b_tp1_up1 = idxr(bTgt, t+1, u+1); - idx_b_rightBlockStartT_u = idxr(bTgt, rightBlockStartT, u); - } + + Indexer3D idxr(maxT, maxU); + idx_b_t_u = idxr(bTgt, t, u); + idx_b_t_up1 = idxr(bTgt, t, u+1); + idx_b_tp1_up1 = idxr(bTgt, t+1, u+1); + idx_b_rightBlockStartT_u = idxr(bTgt, rightBlockStartT, u); if (t < T && u < U) { betas[idx_b_t_u] = CAST_DTYPE(-INFINITY); @@ -519,9 +450,6 @@ __global__ void ComputeBetasCostsRestrictedWrapper( int rBuffer, int warp_size, int num_warp, - bool sparse=false, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr, int H=1) { ComputeBetasCostsRestricted( maxSrcLen, @@ -539,9 +467,6 @@ __global__ void ComputeBetasCostsRestrictedWrapper( rBuffer, warp_size, num_warp, - sparse, - validRanges, - cellsPerSample, H); } diff --git a/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh b/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh index 88f625a251..2969a7f790 100644 --- a/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh +++ b/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh @@ -290,9 +290,6 @@ __global__ void ComputeAlphasBetasCosts( int rBuffer = 0, int warpSize = 0, int numWarps=0, - bool sparse=false, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr, int H = 1) { assert(threadIdx.y == 0 || threadIdx.y == 1); @@ -326,9 +323,6 @@ __global__ void ComputeAlphasBetasCosts( /*lBuffer=*/lBuffer, /*rBuffer=*/rBuffer, /*warpSize=*/warpSize, - sparse, - validRanges, - cellsPerSample, H); } } else { // threadIdx.y == 1 @@ -362,9 +356,6 @@ __global__ void ComputeAlphasBetasCosts( rBuffer, warpSize, numWarps, - sparse, - validRanges, - cellsPerSample, H); } @@ -387,9 +378,6 @@ __global__ void ComputeGradients( const CAST_DTYPE* alphas, const CAST_DTYPE* betas, DTYPE* gradients, - bool sparse = false, - const int* validRanges = nullptr, - const int* cellsPerSample = nullptr, int H = 1, bool fusedLogSmax = true) { @@ -414,9 +402,6 @@ __global__ void ComputeGradients( alphas, betas, gradients, - sparse, - validRanges, - cellsPerSample, H, fusedLogSmax); } diff --git a/torchaudio/csrc/rnnt/gpu/gpu_transducer.h b/torchaudio/csrc/rnnt/gpu/gpu_transducer.h index 7db7fe8a78..cde953a8ef 100644 --- a/torchaudio/csrc/rnnt/gpu/gpu_transducer.h +++ b/torchaudio/csrc/rnnt/gpu/gpu_transducer.h @@ -175,9 +175,6 @@ status_t Compute( /*r_buffer=*/r_buffer, /*warp_size=*/WARP_SIZE, /*num_warps=*/num_warps, - /*sparse=*/false, - /*validRanges=*/nullptr, - /*cellsPerSample=*/nullptr, H); if (cudaGetLastError() != cudaSuccess) { return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; @@ -207,9 +204,6 @@ status_t Compute( /*alphas=*/workspace.GetPointerToAlphas(), /*betas=*/workspace.GetPointerToBetas(), /*gradients=*/gradients, - /*sparse=*/false, - /*validRanges=*/nullptr, - /*cellsPerSample=*/nullptr, H, fusedLogSmax); if (cudaGetLastError() != cudaSuccess) { @@ -220,154 +214,6 @@ status_t Compute( return SUCCESS; } -// Inputs: -// workspace: workspace. -// logits: pointer to (sparseCells, D) logits. -// targets: pointer to (B, max_U - 1) targets in the batch. -// srcLengths: pointer to (B, ) source lengths in the batch. -// tgtLengths: pointer to (B, ) target lengths in the batch. -// -// Outputs: -// costs: pointer to (B, ) costs in the batch. -// gradients: pointer to (sparseCells, D) gradients in the batch. -template -status_t ComputeSparse( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* costs, - DTYPE* gradients = nullptr, - const int* wpEnds = nullptr, - const int* validRanges = nullptr, - const int* cellsPerSample = nullptr) { - const Options& options = workspace.GetOptions(); - - const cudaStream_t& stream = options.stream_; - const int& B = options.batchSize_; - const int& H = options.nHypos_; - const int& max_T = options.maxSrcLen_; - const int& max_U = options.maxTgtLen_; - const int& D = options.numTargets_; - const int& blank = options.blank_; - const CAST_DTYPE clamp = options.clamp_; - - const int& l_buffer = options.lBuffer_; - const int& r_buffer = options.rBuffer_; - const bool& fusedLogSmax = options.fusedLogSmax_; - - const int& sparseCells = options.sparseCells_; - { // compute denominators. - // for sparse kernel, we call kernel with sparseSize - status_t status = LogSumExp2D( - /*stream=*/stream, - /*N=*/sparseCells, // not B * max_T * max_U - /*D=*/D, - /*logits=*/logits, - /*denominators=*/workspace.GetPointerToDenominators()); - gpuErrchk(cudaGetLastError()); - if (status != SUCCESS) { - return status; - } - } - { // compute log probability pairs (blank and target). - int num_segments = - (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; - dim3 block_dims(num_segments, max_U, B * H); - dim3 thread_dims(MAX_THREADS_PER_BLOCK); - - ComputeLogProbsSparse - <<>>( - max_T, - max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*denominators=*/workspace.GetPointerToDenominators(), - /*log_probs=*/workspace.GetPointerToLogProbs(), - wpEnds, - validRanges, - cellsPerSample, - H, - fusedLogSmax); - gpuErrchk(cudaGetLastError()); - } - { // compute alphas, betas and costs. - // warp is usually a group of threads (32) - int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; - - // each block is identified by 3 d tuple. - // we are using num_warp * max_U * B * H blocks - // where num_warp is division among Time axis - dim3 block_dims(num_warps, max_U, B * H); - - // each thread is identified by a 2 d tuple - // 2nd dim is 2. 1 for alpha, 1 for beta - dim3 thread_dims(WARP_SIZE, 2); - - ComputeAlphasBetasCosts - <<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*log_probs=*/workspace.GetPointerToLogProbs(), - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*alpha_counters=*/workspace.GetPointerToAlphaCounters(), - /*alphas=*/workspace.GetPointerToAlphas(), - /*beta_counters=*/workspace.GetPointerToBetaCounters(), - /*betas=*/workspace.GetPointerToBetas(), - /*costs=*/costs, - /*wpEnds=*/wpEnds, - /*l_buffer=*/l_buffer, - /*r_buffer=*/r_buffer, - /*warp_size=*/WARP_SIZE, - /*num_warps=*/num_warps, - true, - validRanges, - cellsPerSample, - H); - gpuErrchk(cudaGetLastError()); - } - - if (gradients != nullptr) { // compute gradients. - // don't zero gradients here as gradient might share memory with logits - - int num_blocks = - (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; - dim3 block_dims(num_blocks, max_U, B * H); - dim3 thread_dims(MAX_THREADS_PER_BLOCK); - - ComputeGradients<<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*clamp=*/clamp, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*denominators=*/workspace.GetPointerToDenominators(), - /*alphas=*/workspace.GetPointerToAlphas(), - /*betas=*/workspace.GetPointerToBetas(), - /*gradients=*/gradients, - /*sparse=*/true, - /*validRanges=*/validRanges, - /*cellsPerSample=*/cellsPerSample, - H, - fusedLogSmax); - gpuErrchk(cudaGetLastError()); - } - - return SUCCESS; -} - template status_t ComputeAlphas( const Workspace& workspace, @@ -470,9 +316,6 @@ status_t ComputeAlphas( l_buffer, r_buffer, WARP_SIZE, - false, - nullptr, - nullptr, H); } if (cudaGetLastError() != cudaSuccess) { @@ -483,115 +326,6 @@ status_t ComputeAlphas( return SUCCESS; } -template -status_t ComputeAlphasSparse( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* alphas, - const int* wpEnds = nullptr, - const int* validRanges = nullptr, - const int* cellsPerSample = nullptr) { - const Options& options = workspace.GetOptions(); - const cudaStream_t& stream = options.stream_; - const int& B = options.batchSize_; - const int& H = options.nHypos_; - const int& max_T = options.maxSrcLen_; - const int& max_U = options.maxTgtLen_; - const int& D = options.numTargets_; - const int& blank = options.blank_; - - const int& l_buffer = options.lBuffer_; - const int& r_buffer = options.rBuffer_; - - const int& sparseCells = options.sparseCells_; - - { // compute denominators. - // for sparse kernel, we would call kernel with sparseSize - status_t status = LogSumExp2D( - /*stream=*/stream, - /*N=*/sparseCells, // not B * max_T * max_U - /*D=*/D, - /*logits=*/logits, - /*denominators=*/workspace.GetPointerToDenominators()); - - if (status != SUCCESS) { - return status; - } - } - - { // compute log probability pairs (blank and target). - int num_segments = - (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; - dim3 block_dims(num_segments, max_U, B * H); - dim3 thread_dims(MAX_THREADS_PER_BLOCK); - - ComputeLogProbsSparse - <<>>( - max_T, - max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*denominators=*/workspace.GetPointerToDenominators(), - /*log_probs=*/workspace.GetPointerToLogProbs(), - wpEnds, - validRanges, - cellsPerSample, - H); - - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_LOG_PROBS_FAILED; - } - } - { // compute alphas - // warp is usually a group of threads (32) - int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; - - // each block is identified by 3 d tuple. - // we are using num_warp * max_U * B blocks - // where num_warp is division among Time axis - dim3 block_dims(num_warps, max_U, B * H); - - // each thread is identified by a 2 d tuple - // 2nd dim is 1 for alpha only - dim3 thread_dims(WARP_SIZE, 1); - - // cudaMemset(alphas, 0, B * max_T * max_U * sizeof(DTYPE)); - ComputeAlphasRestrictedWrapper - <<>>( - max_T, - max_U, - D, - blank, - workspace.GetPointerToLogProbs(), - srcLengths, - tgtLengths, - workspace.GetPointerToAlphaCounters(), - // workspace.GetPointerToAlphas(), - (volatile DTYPE*)alphas, - wpEnds, - l_buffer, - r_buffer, - WARP_SIZE, - true, - validRanges, - cellsPerSample, - H); - - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; - } - } - - return SUCCESS; -} - template status_t ComputeBetas( const Workspace& workspace, @@ -696,9 +430,6 @@ status_t ComputeBetas( r_buffer, WARP_SIZE, num_warps, - false, - nullptr, - nullptr, H); cudaDeviceSynchronize(); } @@ -710,118 +441,6 @@ status_t ComputeBetas( return SUCCESS; } -template -status_t ComputeBetasSparse( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* costs, - DTYPE* betas, - const int* wpEnds = nullptr, - const int* validRanges = nullptr, - const int* cellsPerSample = nullptr) { - const Options& options = workspace.GetOptions(); - const cudaStream_t& stream = options.stream_; - const int& B = options.batchSize_; - const int& H = options.nHypos_; - const int& max_T = options.maxSrcLen_; - const int& max_U = options.maxTgtLen_; - const int& D = options.numTargets_; - const int& blank = options.blank_; - - const int& l_buffer = options.lBuffer_; - const int& r_buffer = options.rBuffer_; - - const int& sparseCells = options.sparseCells_; - - { // compute denominators. - // for sparse kernel, we would call kernel with sparseSize - status_t status = LogSumExp2D( - /*stream=*/stream, - /*N=*/sparseCells, // not B * max_T * max_U - /*D=*/D, - /*logits=*/logits, - /*denominators=*/workspace.GetPointerToDenominators()); - - if (status != SUCCESS) { - return status; - } - } - - { // compute log probability pairs (blank and target). - int num_segments = - (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; - dim3 block_dims(num_segments, max_U, B * H); - dim3 thread_dims(MAX_THREADS_PER_BLOCK); - - ComputeLogProbsSparse - <<>>( - max_T, - max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*denominators=*/workspace.GetPointerToDenominators(), - /*log_probs=*/workspace.GetPointerToLogProbs(), - wpEnds, - validRanges, - cellsPerSample, - H); - - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_LOG_PROBS_FAILED; - } - } - { // compute alphas - // warp is usually a group of threads (32) - int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; - - // each block is identified by 3 d tuple. - // we are using num_warp * max_U * B blocks - // where num_warp is division among Time axis - dim3 block_dims(num_warps, max_U, B * H); - - // each thread is identified by a 2 d tuple - // 2nd dim is 1 for alpha only - dim3 thread_dims(WARP_SIZE, 1); - - // cudaMemset(alphas, 0, B * max_T * max_U * sizeof(DTYPE)); - ComputeBetasCostsRestrictedWrapper - <<>>( - max_T, - max_U, - D, - blank, - workspace.GetPointerToLogProbs(), - srcLengths, - tgtLengths, - workspace.GetPointerToBetaCounters(), - costs, - (volatile DTYPE*)betas, - wpEnds, - l_buffer, - r_buffer, - WARP_SIZE, - num_warps, - true, - validRanges, - cellsPerSample, - H); - cudaDeviceSynchronize(); - - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; - } - } - - return SUCCESS; -} - } // namespace gpu } // namespace rnnt } // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/kernel_utils.h b/torchaudio/csrc/rnnt/gpu/kernel_utils.h index 89d681a22d..03eb3d33b4 100644 --- a/torchaudio/csrc/rnnt/gpu/kernel_utils.h +++ b/torchaudio/csrc/rnnt/gpu/kernel_utils.h @@ -55,63 +55,5 @@ struct Indexer4D { } }; - -struct SparseIndexer { - HOST_AND_DEVICE SparseIndexer( - const int& maxU, - const int* tgtLengths, - const int* validRanges, - const int* cellsPerSample) : - maxU_(maxU), - tgtLengths_(tgtLengths), - validRanges_(validRanges), - cellsPerSample_(cellsPerSample) {}; - - HOST_AND_DEVICE int operator() (int bIdx, int tIdx, int uIdx) { - // Returns the sparse index given bIdx, tIdx, uIdx; or -1 if t out of band. - - // increment the idx for valid cells in previous samples - // TODO: Inefficient in the inner loop; precompute this? - int idx = 0; - for(int b = 0 ; b < bIdx; b++) { - idx += cellsPerSample_[b]; - } - - // increment the idx for valid cells in current sample - // using pre-computed valid ranges - const int* validRangesB = validRanges_ + (maxU_ * 2) * bIdx; - int U = tgtLengths_[bIdx] + 1; - for (int u = 0 ; u < U ; u++) { - int startT = validRangesB[2*u]; - int endT = validRangesB[2*u + 1]; - if (u == uIdx) { - if (tIdx < startT || tIdx > endT) { - return -1; - } - idx += (tIdx - startT); - return idx; - } else { - idx += (endT - startT) + 1; - } - } - return -1; - } - - private: - const int& maxU_; - - // (vector of size B) contains the lengths of U for each sample in batch - const int* tgtLengths_; - - // (vector of size B*2*U) contains the valid time ranges (start_t, end_t) - // for each u in every sample, as per alignment restrictions - const int* validRanges_; - - // The total valid timesteps for each sample (sumed over all u in the sample). - // This is a quick loopup so we dont need to iterate on all previous u*b values. - const int* cellsPerSample_; - -}; - } // namespace rnnt } // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/kernels.h b/torchaudio/csrc/rnnt/gpu/kernels.h index 2dbf6231b4..2cbef51327 100644 --- a/torchaudio/csrc/rnnt/gpu/kernels.h +++ b/torchaudio/csrc/rnnt/gpu/kernels.h @@ -9,68 +9,6 @@ namespace torchaudio { namespace rnnt { -template -FORCE_INLINE HOST_AND_DEVICE void ComputeLogProbsSparseElement( - int bTgt, - int t, - int u, - int maxT, - int maxU, - int numTargets, - int blank, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - const CAST_DTYPE* denominators, - CAST_DTYPE* logProbs, - const int* wpEnds, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr, - int H=1, - bool fusedLogSmax=true) { - - const int& D = numTargets; - - const int bSrc = bTgt / H; - const int T = srcLengths[bSrc]; - const int U = tgtLengths[bTgt] + 1; - - if (t >= T || u >= U) { // out of boundary. - return; - } - const int* validRangesB = validRanges + (maxU*2) * bTgt; - int start = validRangesB[2*u]; - int end = validRangesB[2*u + 1]; - - // out of boundary for alignment restriction condition - if (t < start || t > end) { - return; - } - SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); - int idx = idxr(bTgt, t, u); - logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = - CAST_DTYPE(logits[idx * D + blank]) - denominators[idx]; - - if (!fusedLogSmax) { - logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = - CAST_DTYPE(logits[idx * D + blank]); - } - - if (u < U - 1) { - // emit: log_prob(b, t, u).emit() = logits(b, t, u, tgt[u]) - denom(b, t, u). - int target = targets[Indexer2D(maxU - 1)(bTgt, u)]; - logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = - CAST_DTYPE(logits[idx * D + target]) - denominators[idx]; - - if (!fusedLogSmax) { - logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = - CAST_DTYPE(logits[idx * D + target]); - } - } -} - - template HOST_AND_DEVICE void ComputeGradientsElement( int bTgt, @@ -89,9 +27,6 @@ HOST_AND_DEVICE void ComputeGradientsElement( const CAST_DTYPE* alphas, const CAST_DTYPE* betas, DTYPE* gradients, - bool sparse = false, - const int* validRanges = nullptr, - const int* cellsPerSample = nullptr, int H = 1, bool fusedLogSmax = true) { @@ -104,10 +39,7 @@ HOST_AND_DEVICE void ComputeGradientsElement( const int U = tgtLengths[bTgt] + 1; if (t >= T || u >= U) { // out of boundary. - if (sparse) { - // no extra elements needs to be set to 0 - return; - } else if (gradients == logits && t < maxT && u < maxU) { + if (gradients == logits && t < maxT && u < maxU) { // gradients and logits are pointing to the same memory location Indexer3D idxr3(maxT, maxU); int idx_b_t_u_zero = idxr3(bTgt, t, u); @@ -121,32 +53,18 @@ HOST_AND_DEVICE void ComputeGradientsElement( return; } - int costIdx; - if (!sparse) { - costIdx = bTgt * maxT * maxU; - } else { - SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); - costIdx = idxr(bTgt, 0, 0); - } + int costIdx = bTgt * maxT * maxU; CAST_DTYPE cost = -(betas[costIdx]); Indexer2D idxr2(maxU - 1); int idx_b_t_u, idx_b_t_up1, idx_b_tp1_u, idx_b_tp1_up1; - if (sparse) { - SparseIndexer idxr(maxU, tgtLengths, validRanges, cellsPerSample); - idx_b_t_u = idxr(bTgt, t, u); - idx_b_t_up1 = idxr(bTgt, t, u+1); - idx_b_tp1_u = idxr(bTgt, t+1, u); - idx_b_tp1_up1 = idxr(bTgt, t+1, u+1); - } else { - Indexer3D idxr3(maxT, maxU); - idx_b_t_u = idxr3(bTgt, t, u); - idx_b_t_up1 = idxr3(bTgt, t, u+1); - idx_b_tp1_u = idxr3(bTgt, t+1, u); - idx_b_tp1_up1 = idxr3(bTgt, t+1, u+1); - } + Indexer3D idxr3(maxT, maxU); + idx_b_t_u = idxr3(bTgt, t, u); + idx_b_t_up1 = idxr3(bTgt, t, u+1); + idx_b_tp1_u = idxr3(bTgt, t+1, u); + idx_b_tp1_up1 = idxr3(bTgt, t+1, u+1); if (idx_b_t_u == -1 ) { return; diff --git a/torchaudio/csrc/rnnt/options.h b/torchaudio/csrc/rnnt/options.h index 4debce21df..c7ea63eda9 100644 --- a/torchaudio/csrc/rnnt/options.h +++ b/torchaudio/csrc/rnnt/options.h @@ -48,11 +48,6 @@ typedef struct Options { // num_targets = D. int numTargets_; - // whether to use sparse layout for ar-rnnt - bool sparse_; - - int sparseCells_; - // if set to true, inputs are logits and gradients are // fused with logsoftmax gradients. // if set to false, log_softmax is computed outside of loss @@ -72,8 +67,6 @@ typedef struct Options { maxSrcLen_(0), maxTgtLen_(0), numTargets_(0), - sparse_(false), - sparseCells_(0), fusedLogSmax_(true) {} int BU() const { diff --git a/torchaudio/csrc/rnnt/transducer.h b/torchaudio/csrc/rnnt/transducer.h index 8384fd5a9d..c69067a000 100644 --- a/torchaudio/csrc/rnnt/transducer.h +++ b/torchaudio/csrc/rnnt/transducer.h @@ -49,41 +49,6 @@ status_t Compute( }; } -template -status_t ComputeSparse( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* costs, - DTYPE* gradients = nullptr, - const int* wp_ends=nullptr, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr) { - switch (workspace.GetOptions().device_) { - case GPU: { - status_t status = gpu::ComputeSparse( - /*workspace=*/workspace, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*costs=*/costs, - /*gradients=*/gradients, - /*wp_ends =*/wp_ends, - validRanges, - cellsPerSample); - return status; - } - default: { - LOG(ERROR) << "unsupported workspace.GetOptions().device = " - << workspace.GetOptions().device_; - return FAILURE; - } - }; -} - template status_t ComputeAlphas( const Workspace& workspace, @@ -124,39 +89,6 @@ status_t ComputeAlphas( }; } -template -status_t ComputeAlphasSparse( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* alphas, - const int* wp_ends=nullptr, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr) { - switch (workspace.GetOptions().device_) { - case GPU: { - status_t status = gpu::ComputeAlphasSparse( - /*workspace=*/workspace, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*costs=*/alphas, - /*wp_ends =*/wp_ends, - validRanges, - cellsPerSample); - return status; - } - default: { - LOG(ERROR) << "unsupported workspace.GetOptions().device = " - << workspace.GetOptions().device_; - return FAILURE; - } - }; -} - template status_t ComputeBetas( const Workspace& workspace, @@ -200,40 +132,5 @@ status_t ComputeBetas( }; } -template -status_t ComputeBetasSparse( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* costs, - DTYPE* betas, - const int* wp_ends=nullptr, - const int* validRanges=nullptr, - const int* cellsPerSample=nullptr) { - switch (workspace.GetOptions().device_) { - case GPU: { - status_t status = gpu::ComputeBetasSparse( - /*workspace=*/workspace, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*costs=*/costs, - /*betas=*/betas, - /*wp_ends =*/wp_ends, - validRanges, - cellsPerSample); - return status; - } - default: { - LOG(ERROR) << "unsupported workspace.GetOptions().device = " - << workspace.GetOptions().device_; - return FAILURE; - } - }; -} - } // namespace rnnt } // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/workspace.h b/torchaudio/csrc/rnnt/workspace.h index 243b836a93..95a1fcf0c4 100644 --- a/torchaudio/csrc/rnnt/workspace.h +++ b/torchaudio/csrc/rnnt/workspace.h @@ -60,19 +60,19 @@ class DtypeWorkspace { private: static int ComputeSizeForDenominators(const Options& options) { // B * T * U - return options.sparse_ ? options.sparseCells_ : options.BTU(); + return options.BTU(); } static int ComputeSizeForLogProbs(const Options& options) { // B * T * U * 2 - return options.sparse_ ? options.sparseCells_ * 2 : options.BTU() * 2; + return options.BTU() * 2; } static int ComputeSizeForAlphas(const Options& options) { // B * T * U - return options.sparse_ ? options.sparseCells_ : options.BTU(); + return options.BTU(); } static int ComputeSizeForBetas(const Options& options) { // B * T * U - return options.sparse_ ? options.sparseCells_ : options.BTU(); + return options.BTU(); } Options options_; diff --git a/torchaudio/prototype/rnnt_loss.py b/torchaudio/prototype/rnnt_loss.py index 0839290e0a..f69d328f53 100644 --- a/torchaudio/prototype/rnnt_loss.py +++ b/torchaudio/prototype/rnnt_loss.py @@ -16,9 +16,6 @@ def _rnnt_loss_alphas( wordpiece_ends=None, left_buffer=0, right_buffer=0, - sparse=False, - valid_ranges=None, - cells_per_sample=None, ): """ Compute alphas for RNN transducer loss. @@ -35,37 +32,18 @@ def _rnnt_loss_alphas( targets = targets.int() logit_lengths = logit_lengths.int() target_lengths = target_lengths.int() - if not sparse: - return torch.ops.torchaudio.rnnt_loss_alphas( - logits, - targets, - logit_lengths, - target_lengths, - blank, - clamp, - wordpiece_ends, - left_buffer, - right_buffer, - ) - else: - try: - return torch.ops.torchaudio.rnnt_loss_alphas_sparse( - logits, - targets, - logit_lengths, - target_lengths, - blank, - clamp, - torch.max(logit_lengths).item(), - torch.max(target_lengths).item() + 1, - wordpiece_ends, - left_buffer, - right_buffer, - valid_ranges, - cells_per_sample, - ) - except RuntimeError: - raise RuntimeError("sparse is only supported on GPU with torchaudio compiled with GPU support") + + return torch.ops.torchaudio.rnnt_loss_alphas( + logits, + targets, + logit_lengths, + target_lengths, + blank, + clamp, + wordpiece_ends, + left_buffer, + right_buffer, + ) def _rnnt_loss_betas( @@ -78,9 +56,6 @@ def _rnnt_loss_betas( wordpiece_ends=None, left_buffer=0, right_buffer=0, - sparse=False, - valid_ranges=None, - cells_per_sample=None, ): """ Compute betas for RNN transducer loss @@ -97,37 +72,18 @@ def _rnnt_loss_betas( targets = targets.int() logit_lengths = logit_lengths.int() target_lengths = target_lengths.int() - if not sparse: - return torch.ops.torchaudio.rnnt_loss_betas( - logits, - targets, - logit_lengths, - target_lengths, - blank, - clamp, - wordpiece_ends, - left_buffer, - right_buffer, - ) - else: - try: - return torch.ops.torchaudio.rnnt_loss_betas_sparse( - logits, - targets, - logit_lengths, - target_lengths, - blank, - clamp, - torch.max(logit_lengths).item(), - torch.max(target_lengths).item() + 1, - wordpiece_ends, - left_buffer, - right_buffer, - valid_ranges, - cells_per_sample, - ) - except RuntimeError: - raise RuntimeError("sparse is only supported on GPU with torchaudio compiled with GPU support") + + return torch.ops.torchaudio.rnnt_loss_betas( + logits, + targets, + logit_lengths, + target_lengths, + blank, + clamp, + wordpiece_ends, + left_buffer, + right_buffer, + ) class _RNNT(torch.autograd.Function): @@ -144,9 +100,6 @@ def forward( wordpiece_ends=None, left_buffer=0, right_buffer=0, - sparse=False, - valid_ranges=None, - cells_per_sample=None, fused_log_softmax=True, reuse_logits_for_grads=True, ): @@ -160,10 +113,6 @@ def forward( target_lengths = target_lengths.to(device=logits.device) if wordpiece_ends is not None: wordpiece_ends = wordpiece_ends.to(device=logits.device) - if valid_ranges is not None: - valid_ranges = valid_ranges.to(device=logits.device) - if cells_per_sample is not None: - cells_per_sample = cells_per_sample.to(device=logits.device) # make sure all int tensors are of type int32. targets = targets.int() @@ -181,67 +130,29 @@ def forward( target_lengths=target_lengths, blank=blank, ) - if sparse: - try: - rnnt_loss_sparse = torch.ops.torchaudio.rnnt_loss_sparse - except RuntimeError: - raise RuntimeError("sparse is only supported on GPU with torchaudio compiled with GPU support") - - max_T = torch.max(logit_lengths).item() # note: not used for indexing - max_U = targets.shape[1] + 1 # used for indexing, e.g. wordpiece_ends - assert max_U >= torch.max(target_lengths).item() + 1 - assert max_U == wordpiece_ends.shape[1] and max_U == valid_ranges.shape[1] - costs, gradients = rnnt_loss_sparse( - logits=logits, - targets=targets, - src_lengths=logit_lengths, - tgt_lengths=target_lengths, - blank=blank, - clamp=clamp, - wp_ends=wordpiece_ends, - l_buffer=left_buffer, - r_buffer=right_buffer, - max_T=max_T, - max_U=max_U, - valid_ranges=valid_ranges, - cells_per_sample=cells_per_sample, - fused_log_smax=fused_log_softmax, - reuse_logits_for_grads=reuse_logits_for_grads, - ) - ctx.cells_per_sample = cells_per_sample - else: - costs, gradients = torch.ops.torchaudio.rnnt_loss( - logits=logits, - targets=targets, - src_lengths=logit_lengths, - tgt_lengths=target_lengths, - blank=blank, - clamp=clamp, - wp_ends=wordpiece_ends, - l_buffer=left_buffer, - r_buffer=right_buffer, - fused_log_smax=fused_log_softmax, - reuse_logits_for_grads=reuse_logits_for_grads, - ) + + costs, gradients = torch.ops.torchaudio.rnnt_loss( + logits=logits, + targets=targets, + src_lengths=logit_lengths, + tgt_lengths=target_lengths, + blank=blank, + clamp=clamp, + wp_ends=wordpiece_ends, + l_buffer=left_buffer, + r_buffer=right_buffer, + fused_log_smax=fused_log_softmax, + reuse_logits_for_grads=reuse_logits_for_grads, + ) ctx.grads = gradients - ctx.sparse = sparse return costs @staticmethod def backward(ctx, output_gradients): - if ctx.sparse: - output_gradients = output_gradients.view(-1, 1).to(ctx.grads) - offset = 0 - count = 0 - for ncells in ctx.cells_per_sample: - ctx.grads[offset : offset + ncells, :].mul_(output_gradients[count]) - offset += ncells - count += 1 - else: - output_gradients = output_gradients.view(-1, 1, 1, 1).to(ctx.grads) - ctx.grads.mul_(output_gradients).to(ctx.grads) + output_gradients = output_gradients.view(-1, 1, 1, 1).to(ctx.grads) + ctx.grads.mul_(output_gradients).to(ctx.grads) return ( ctx.grads, # logits @@ -254,9 +165,6 @@ def backward(ctx, output_gradients): None, # wordpiece_ends None, # left_buffer None, # right_buffer - None, # sparse - None, # valid_ranges - None, # cells_per_sample None, # fused_log_softmax None, # reuse_logits_for_grads ) @@ -273,9 +181,6 @@ def rnnt_loss( wordpiece_ends=None, left_buffer=0, right_buffer=0, - sparse=False, - valid_ranges=None, - cells_per_sample=None, fused_log_softmax=True, reuse_logits_for_grads=True, ): @@ -300,11 +205,6 @@ def rnnt_loss( Loss will not be imposed on frames smaller than wordpiece_end - left_buffer. (Default: ``0``) right_buffer (int): right buffer frames used for alignment restricted RNNT loss. Loss will not be imposed on frames greater than wordpiece_end + right_buffer. (Default: ``0``) - sparse (bool): set to true for sparse alignment restricted RNNT. (Default: ``False``) - valid_ranges (Tensor): Tensor of dimension (batch, target, 2) containing valid ranges for sparse AR-RNNT - (Default: ``None``) - cells_per_sample (Tensor): Tensor of dimension (batch) containing total valid cells for each (Default: ``None``) - sample while doing sparse AR-RNNT (Default: ``None``) fused_log_smax (bool): set to False if calling log_softmax outside loss (Default: ``True``) reuse_logits_for_grads (bool): whether to save memory by reusing logits memory for grads (Default: ``True``) """ @@ -325,9 +225,6 @@ def rnnt_loss( wordpiece_ends, left_buffer, right_buffer, - sparse, - valid_ranges, - cells_per_sample, fused_log_softmax, reuse_logits_for_grads, ) @@ -350,7 +247,6 @@ class RNNTLoss(torch.nn.Module): Loss will not be imposed on frames smaller than wordpiece_end - left_buffer. (Default: ``0``) right_buffer (int): right buffer frames used for alignment restricted RNNT loss. Loss will not be imposed on frames greater than wordpiece_end + right_buffer. (Default: ``0``) - sparse (bool): set to true for sparse alignment restricted RNNT. (Default: ``False``) fused_log_smax (bool): set to False if calling log_softmax outside loss (Default: ``True``) reuse_logits_for_grads (bool): whether to save memory by reusing logits memory for grads (Default: ``True``) """ @@ -362,7 +258,6 @@ def __init__( runtime_check=False, left_buffer=0, right_buffer=0, - sparse=False, fused_log_softmax=True, reuse_logits_for_grads=True, ): @@ -372,7 +267,6 @@ def __init__( self.runtime_check = runtime_check self.left_buffer = left_buffer self.right_buffer = right_buffer - self.sparse = sparse self.fused_log_softmax = fused_log_softmax self.reuse_logits_for_grads = reuse_logits_for_grads @@ -383,8 +277,6 @@ def forward( logit_lengths, target_lengths, wordpiece_ends=None, - valid_ranges=None, - cells_per_sample=None, left_buffer=None, right_buffer=None, ): @@ -396,10 +288,6 @@ def forward( target_lengths (Tensor): Tensor of dimension (batch) containing lengths of targets for each sequence wordpiece_ends (Tensor): Tensor of dimension (batch, target) containing the end frames of targets for each sequence (including bos end_frame = 0) (Default: ``None``) - valid_ranges (Tensor): Tensor of dimension (batch, target, 2) containing valid ranges for sparse AR-RNNT - (Default: ``None``) - cells_per_sample (Tensor): Tensor of dimension (batch) containing total valid cells for each - sample while doing sparse AR-RNNT (Default: ``None``) left_buffer (int): left buffer frames used for alignment restricted RNNT loss. Loss will not be imposed on frames smaller than wordpiece_end - left_buffer. (Default: ``None``) right_buffer (int): right buffer frames used for alignment restricted RNNT loss. @@ -425,9 +313,6 @@ def forward( wordpiece_ends, left_buffer, right_buffer, - self.sparse, - valid_ranges, - cells_per_sample, self.fused_log_softmax, self.reuse_logits_for_grads, ) From 66f3db297951f014a9479f89fbcccafb81d7e9ad Mon Sep 17 00:00:00 2001 From: Caroline Chen Date: Fri, 30 Apr 2021 12:53:14 -0700 Subject: [PATCH 46/73] Remove alignment restriction support Summary: we do not wish to support alignment-restriction for the first release. this diff removes alignment restriction support along with relevant parameters (`wordpiece_ends`, `left_buffer`, `right_buffer`) and unit tests Reviewed By: vincentqb Differential Revision: D28072228 fbshipit-source-id: daf62b10a1e004ab4c22d498811c8bee3f0a22e0 --- .../rnnt/numpy_transducer.py | 251 +-------- .../rnnt/rnnt_loss_cuda_test.py | 8 +- .../rnnt/rnnt_loss_impl.py | 288 +---------- test/torchaudio_unittest/rnnt/utils.py | 19 +- torchaudio/csrc/rnnt/compute.cpp | 3 - torchaudio/csrc/rnnt/compute_alphas.cpp | 5 +- torchaudio/csrc/rnnt/compute_betas.cpp | 5 +- .../csrc/rnnt/cpu/alignment_restrictions.cpp | 98 ---- .../csrc/rnnt/cpu/alignment_restrictions.h | 62 --- torchaudio/csrc/rnnt/cpu/compute.cpp | 11 +- torchaudio/csrc/rnnt/cpu/compute_alphas.cpp | 10 +- torchaudio/csrc/rnnt/cpu/compute_betas.cpp | 10 +- torchaudio/csrc/rnnt/cpu/cpu_kernels.h | 238 ++------- torchaudio/csrc/rnnt/cpu/cpu_transducer.h | 18 +- torchaudio/csrc/rnnt/gpu/compute.cu | 11 +- torchaudio/csrc/rnnt/gpu/compute_alphas.cu | 10 +- torchaudio/csrc/rnnt/gpu/compute_betas.cu | 10 +- .../rnnt/gpu/gpu_alignment_restrictions.cuh | 476 ------------------ torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh | 46 +- torchaudio/csrc/rnnt/gpu/gpu_transducer.h | 117 ++--- torchaudio/csrc/rnnt/options.h | 8 - torchaudio/csrc/rnnt/transducer.h | 27 +- torchaudio/prototype/rnnt_loss.py | 68 --- 23 files changed, 108 insertions(+), 1691 deletions(-) delete mode 100644 torchaudio/csrc/rnnt/cpu/alignment_restrictions.cpp delete mode 100644 torchaudio/csrc/rnnt/cpu/alignment_restrictions.h delete mode 100644 torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh diff --git a/test/torchaudio_unittest/rnnt/numpy_transducer.py b/test/torchaudio_unittest/rnnt/numpy_transducer.py index 7a26013dd7..a284bc1a8d 100644 --- a/test/torchaudio_unittest/rnnt/numpy_transducer.py +++ b/test/torchaudio_unittest/rnnt/numpy_transducer.py @@ -11,9 +11,6 @@ def forward( target_lengths, targets, blank=-1, - wordpiece_ends=None, - left_buffer=0, - right_buffer=0, ): device = log_probs.device log_probs = log_probs.cpu().data.numpy() @@ -27,9 +24,6 @@ def forward( target_lengths=target_lengths, targets=targets, blank=blank, - wordpiece_ends=wordpiece_ends, - left_buffer=left_buffer, - right_buffer=right_buffer, ) costs = torch.FloatTensor(costs).to(device=device) @@ -61,51 +55,6 @@ def compute_alpha_one_sequence(log_probs, targets, blank=-1): cost = -(alpha[-1, -1] + log_probs[-1, -1, blank]) return alpha, cost - @staticmethod - def compute_alpha_one_sequence_restricted( - log_probs, targets, wordpiece_ends, blank=-1, left_buffer=0, right_buffer=0 - ): - # Note - this is a more generic case - # If we set left_buffer = right_buffer = inf this becomes same as - # `compute_alpha_one_sequence`. However keeping both versions, - # since without the checks, compute_alpha_one_sequence may be tiny - # bit faster than `compute_alpha_one_sequence_restricted` - - max_T, max_U, D = log_probs.shape - check = AlignmentRestrictionCheck( - max_U, max_T, wordpiece_ends, left_buffer, right_buffer - ) - - alpha = np.zeros((max_T, max_U), dtype=np.float32) - alpha.fill(float("-inf")) - alpha[0, 0] = 0.0 - for t in range(1, max_T): - if not check.alpha_blank_transition(t, 0): - break - alpha[t, 0] = log_probs[t - 1, 0, blank] + alpha[t - 1, 0] - - for u in range(1, max_U): - if not check.alpha_emit_transition(0, u): - break - alpha[0, u] = log_probs[0, u - 1, targets[u - 1]] + alpha[0, u - 1] - - for u in range(1, max_U): - start_t, end_t = check.valid_time_ranges(u) - for t in range(start_t, end_t + 1): - skip, emit = float("-inf"), float("-inf") - - if check.alpha_blank_transition(t, u): - skip = alpha[t - 1, u] + log_probs[t - 1, u, blank] - - if check.alpha_emit_transition(t, u): - emit = alpha[t, u - 1] + log_probs[t, u - 1, targets[u - 1]] - - if emit != float("-inf") or skip != float("-inf"): - alpha[t, u] = np.logaddexp(skip, emit) - - cost = -(alpha[-1, -1] + log_probs[-1, -1, blank]) - return alpha, cost - @staticmethod def compute_beta_one_sequence(log_probs, targets, blank=-1): max_T, max_U, D = log_probs.shape @@ -127,51 +76,6 @@ def compute_beta_one_sequence(log_probs, targets, blank=-1): cost = -beta[0, 0] return beta, cost - @staticmethod - def compute_beta_one_sequence_restricted( - log_probs, targets, wordpiece_ends, blank=-1, left_buffer=0, right_buffer=0 - ): - # Note - this is a more generic case - # If we set left_buffer = right_buffer = inf this becomes same as - # `compute_beta_one_sequence`. However keeping both versions, - # since without the checks, compute_beta_one_sequence may be tiny - # bit faster than `compute_beta_one_sequence_restricted` - max_T, max_U, D = log_probs.shape - - check = AlignmentRestrictionCheck( - max_U, max_T, wordpiece_ends, left_buffer, right_buffer - ) - beta = np.zeros((max_T, max_U), dtype=np.float32) - beta.fill(float("-inf")) - beta[-1, -1] = log_probs[-1, -1, blank] - - for t in reversed(range(max_T - 1)): - if not check.beta_blank_transition(t, max_U - 1): - break - beta[t, -1] = beta[t + 1, -1] + log_probs[t, -1, blank] - - for u in reversed(range(max_U - 1)): - if not check.beta_emit_transition(max_T - 1, u): - break - beta[-1, u] = beta[-1, u + 1] + log_probs[-1, u, targets[u]] - - for u in reversed(range(max_U - 1)): - start_t, end_t = check.valid_time_ranges(u) - for t in reversed(range(start_t, end_t + 1)): - skip, emit = float("-inf"), float("-inf") - - if check.beta_emit_transition(t, u): - emit = beta[t, u + 1] + log_probs[t, u, targets[u]] - - if check.beta_blank_transition(t, u): - skip = beta[t + 1, u] + log_probs[t, u, blank] - - if emit != float("-inf") or skip != float("-inf"): - beta[t, u] = np.logaddexp(skip, emit) - - cost = -beta[0, 0] - return beta, cost - @staticmethod def compute_gradients_one_sequence( log_probs, alpha, beta, targets, blank=-1 @@ -197,9 +101,6 @@ def compute( target_lengths, targets, blank=-1, - wordpiece_ends=None, - left_buffer=0, - right_buffer=0, ): gradients = np.zeros_like(log_probs) B_tgt, max_T, max_U, D = log_probs.shape @@ -220,35 +121,13 @@ def compute( seq_log_probs = log_probs[b_tgt, :T, :U, :] seq_targets = targets[b_tgt, : int(target_lengths[b_tgt])] - if wordpiece_ends is not None: - seq_wordpiece_ends = wordpiece_ends[b_tgt, : int(target_lengths[b_tgt]) + 1] - alpha, alpha_cost = __class__.compute_alpha_one_sequence_restricted( - log_probs=seq_log_probs, - targets=seq_targets, - blank=blank, - wordpiece_ends=seq_wordpiece_ends, - left_buffer=left_buffer, - right_buffer=right_buffer, - ) - else: - alpha, alpha_cost = __class__.compute_alpha_one_sequence( - log_probs=seq_log_probs, targets=seq_targets, blank=blank - ) + alpha, alpha_cost = __class__.compute_alpha_one_sequence( + log_probs=seq_log_probs, targets=seq_targets, blank=blank + ) - if wordpiece_ends is not None: - seq_wordpiece_ends = wordpiece_ends[b_tgt, : int(target_lengths[b_tgt]) + 1] - beta, beta_cost = __class__.compute_beta_one_sequence_restricted( - log_probs=seq_log_probs, - targets=seq_targets, - blank=blank, - wordpiece_ends=seq_wordpiece_ends, - left_buffer=left_buffer, - right_buffer=right_buffer, - ) - else: - beta, beta_cost = __class__.compute_beta_one_sequence( - log_probs=seq_log_probs, targets=seq_targets, blank=blank - ) + beta, beta_cost = __class__.compute_beta_one_sequence( + log_probs=seq_log_probs, targets=seq_targets, blank=blank + ) seq_gradients = __class__.compute_gradients_one_sequence( log_probs=seq_log_probs, @@ -277,9 +156,6 @@ def forward( logit_lengths, target_lengths, targets, - wordpiece_ends=None, - left_buffer=0, - right_buffer=0, ): log_probs = torch.nn.functional.log_softmax(logits, dim=-1) return _NumpyTransducer.apply( @@ -288,119 +164,4 @@ def forward( target_lengths, targets, self.blank, - wordpiece_ends, - left_buffer, - right_buffer, ) - - -class AlignmentRestrictionCheck(object): - """ - Helper class which returns locations where - blank / emit transitions are valid while - updating alpha / betas for Alignment - Restricted RNN Transducer loss. This class - will help test c++ code, the correctness has been - verified by thorough manual inspections in n268104 - - """ - - def __init__(self, U, T, wordpiece_ends, left_buffer, right_buffer): - self.U = U - self.T = T - # assume word_ends contains the ground truth end - # timestep of every word piece. - # assume 0th wordpiece_ends is and its endtime is 0 - assert wordpiece_ends[0] == 0 - assert len(wordpiece_ends) == U - self.wordpiece_ends = wordpiece_ends - self.left_buffer = left_buffer - self.right_buffer = right_buffer - - def valid_time_ranges(self, u): - # Returns ranges of valid paths which satisfy - # word boundary constraints - # For bos ranges are valid from 0 until end time of - # 1st symbol + right_buffer - start = max(self.wordpiece_ends[u] - self.left_buffer, 0) - if u == self.U - 1: - end = self.T - 1 - else: - end = min(self.wordpiece_ends[u + 1] + self.right_buffer, self.T - 1) - - return start, end - - def alpha_blank_transition(self, t, u): - # Examine if doing blank transition into (t, u) - # is allowed while updating alphas. - if u == 0 and t == 0: - return False - - # For alphas blank transitions are valid from: - # start time when current symbol is emitted - start = max(self.wordpiece_ends[u] - self.left_buffer + 1, 1) - - # For alphas blank transitions are valid until - # last timestep we can emit next symbol - if u == self.U - 1: - # for last symbol, allow blank transitions till end - end = self.T - 1 - else: - end = min(self.wordpiece_ends[u + 1] + self.right_buffer, self.T - 1) - - return start <= t and t <= end - - def alpha_emit_transition(self, t, u): - # Examine if doing emit transition into (t, u) - # is allowed while updating alphas. - if u == 0: - # emit transitions into (t, 0) for alpha are invalid - return False - - # For alphas emit are valid starting from: - # first time when current symbol can be emitted - start = max(self.wordpiece_ends[u] - self.left_buffer, 0) - - # For alphas emit are valid until: - # last timestep when current symbol can be emitted - end = min(self.wordpiece_ends[u] + self.right_buffer, self.T - 1) - - return start <= t and t <= end - - def beta_blank_transition(self, t, u): - # Examine if doing blank transition into (t, u) - # is allowed while updating betas. - - # for beta, blanks transitions are can start - # first timestep when we emit previous symbol - start = max(self.wordpiece_ends[u] - self.left_buffer, 0) - - # for beta, blanks transitions are valid until - # last timestep we can emit current symbol - # note: T-2, we init beta[-1, -1] by log_prob[-1, -1, blank] - if u == self.U - 1: - end = self.T - 2 - else: - # we cannot transition into beta for T-1 - end = min(self.wordpiece_ends[u + 1] + self.right_buffer - 1, self.T - 2) - - return start <= t and t <= end - - def beta_emit_transition(self, t, u): - # Examine if doing emit transition into (t, u) - # is allowed while updating betas - - # for last symbol, we do not allow emit transition, - # so beta into u-1, t is invalid - if u == self.U - 1: - return False - - # For betas we allow emit transition starting from - # first time we can emit next symbol - start = max(0, self.wordpiece_ends[u + 1] - self.left_buffer) - - # For betas we allow emit transitions to end with - # last time we can emit next symbol - end = min(self.wordpiece_ends[u + 1] + self.right_buffer, self.T - 1) - - return start <= t and t <= end diff --git a/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py b/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py index ea6e8eb513..c64fd687fe 100644 --- a/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py +++ b/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py @@ -1,5 +1,5 @@ import torch -from .rnnt_loss_impl import RNNTLossTest, RNNTLossCUDAOnly +from .rnnt_loss_impl import RNNTLossTest from torchaudio_unittest import common_utils from .utils import skipIfNoTransducer @@ -8,9 +8,3 @@ @common_utils.skipIfNoCuda class TestRNNTLoss(RNNTLossTest, common_utils.PytorchTestCase): device = torch.device('cuda') - - -@skipIfNoTransducer -@common_utils.skipIfNoCuda -class TestRNNTLossCUDAOnly(RNNTLossCUDAOnly, common_utils.PytorchTestCase): - device = torch.device('cuda') diff --git a/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py b/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py index dcab6def6f..2ac4d0836f 100644 --- a/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py +++ b/test/torchaudio_unittest/rnnt/rnnt_loss_impl.py @@ -1,12 +1,5 @@ import numpy as np -import torch -from torchaudio.prototype.rnnt_loss import ( - RNNTLoss, - _rnnt_loss_alphas, - _rnnt_loss_betas, -) - -from .numpy_transducer import _NumpyTransducer +from torchaudio.prototype.rnnt_loss import RNNTLoss from .utils import ( compute_with_numpy_transducer, @@ -19,8 +12,6 @@ numpy_to_torch, ) -from torchaudio_unittest.common_utils import skipIfNoCuda - class RNNTLossTest: def _test_costs_and_gradients( @@ -108,281 +99,18 @@ def test_costs_and_gradients_random_data_with_numpy_fp32(self): data=data, ref_costs=ref_costs, ref_gradients=ref_gradients ) - def test_alphas_restricted_B1_T10_U3_D4(self): - for random in [True, False]: - for left_buffer in [0, 2, 10]: - for right_buffer in [0, 1, 2, 10]: - data = get_B1_T10_U3_D4_data(random=random) - logits = torch.tensor(data["logits"]) - log_probs = torch.nn.functional.log_softmax(logits, dim=-1).numpy() - ( - np_gradients, - np_costs, - np_alphas, - np_betas, - ) = _NumpyTransducer.compute( - log_probs=log_probs, - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - blank=data["blank"], - wordpiece_ends=data["wordpiece_ends"], - left_buffer=left_buffer, - right_buffer=right_buffer, - ) - - data = numpy_to_torch( - data=data, device=self.device, requires_grad=True - ) - alphas = _rnnt_loss_alphas( - logits=data["logits"], - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - wordpiece_ends=data.get("wordpiece_ends", None), - blank=data["blank"], - left_buffer=left_buffer, - right_buffer=right_buffer, - ) - np.testing.assert_allclose( - alphas.cpu().data.numpy(), np_alphas, atol=1e-6, rtol=1e-2 - ) - - def test_betas_restricted_B1_T10_U3_D4(self): - for random in [True, False]: - for left_buffer in [0, 2, 10]: - for right_buffer in [0, 1, 2, 10]: - data = get_B1_T10_U3_D4_data(random=random) - logits = torch.tensor(data["logits"]) - log_probs = torch.nn.functional.log_softmax(logits, dim=-1).numpy() - ( - np_gradients, - np_costs, - np_alphas, - np_betas, - ) = _NumpyTransducer.compute( - log_probs=log_probs, - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - blank=data["blank"], - wordpiece_ends=data["wordpiece_ends"], - left_buffer=left_buffer, - right_buffer=right_buffer, - ) - - data = numpy_to_torch( - data=data, device=self.device, requires_grad=True - ) - betas = _rnnt_loss_betas( - logits=data["logits"], - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - wordpiece_ends=data.get("wordpiece_ends", None), - blank=data["blank"], - left_buffer=left_buffer, - right_buffer=right_buffer, - ) - np.testing.assert_allclose( - betas.cpu().data.numpy(), np_betas, atol=1e-6, rtol=1e-2 - ) - - def test_rnnt_restricted_B1_T10_U3_D4(self): - for left_buffer in [0, 1, 10]: - for right_buffer in [0, 1, 2, 5, 10]: - data = get_B1_T10_U3_D4_data(random=False) - data = numpy_to_torch( - data=data, device=self.device, requires_grad=True - ) - data["left_buffer"] = left_buffer - data["right_buffer"] = right_buffer - ref_costs, ref_gradients = compute_with_numpy_transducer( - data=data - ) - self._test_costs_and_gradients( - data=data, ref_costs=ref_costs, ref_gradients=ref_gradients - ) - - def test_rnnt_restricted_B2_T4_U3_D3(self): - # Note - this test just ensures that the numpy and c++ implementations match. - # Probably a hand constructed test for gradients will be more thorough - data, _, _ = get_numpy_data_B2_T4_U3_D3(dtype=np.float32) - data = numpy_to_torch(data=data, device=self.device, requires_grad=True) - data["wordpiece_ends"] = torch.tensor([[0, 1, 2], [0, 1, 2]]).int() - - for left_buffer in [0]: - for right_buffer in [0, 1, 2]: - data["left_buffer"] = left_buffer - data["right_buffer"] = right_buffer - ref_costs, ref_gradients = compute_with_numpy_transducer( - data=data - ) - - self._test_costs_and_gradients( - data=data, ref_costs=ref_costs, ref_gradients=ref_gradients - ) - - def test_restricted_parity_with_unrestricted_B1_T10_U3_D4(self): - for random in [False, True]: - data = get_B1_T10_U3_D4_data(random=random) - data = numpy_to_torch(data=data, device=self.device, requires_grad=True) - wordpiece_ends = data["wordpiece_ends"] - del data["wordpiece_ends"] - ref_costs, ref_gradients = compute_with_pytorch_transducer(data=data) - data["wordpiece_ends"] = wordpiece_ends - data["left_buffer"] = 100 - data["right_buffer"] = 100 - wp_costs, wp_gradients = compute_with_pytorch_transducer(data=data) - np.testing.assert_allclose(ref_costs, wp_costs, atol=1e-2, rtol=1e-2) - np.testing.assert_allclose( - ref_gradients, wp_gradients, atol=1e-2, rtol=1e-2 - ) - - def test_restricted_parity_with_unrestricted_alpha_B1_T10_U3_D4(self): - for random in [False, True]: - data = get_B1_T10_U3_D4_data(random=random) - data = numpy_to_torch(data=data, device=self.device, requires_grad=True) - - ref_alphas = _rnnt_loss_alphas( - blank=data["blank"], - logits=data["logits"], - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - ) - - wp_alphas = _rnnt_loss_alphas( - blank=data["blank"], - left_buffer=100, - right_buffer=100, - logits=data["logits"], - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - wordpiece_ends=data["wordpiece_ends"], - ) - np.testing.assert_allclose( - ref_alphas.cpu().data.numpy(), - wp_alphas.cpu().data.numpy(), - atol=1e-2, - rtol=1e-2, - ) - - def test_restricted_parity_with_unrestricted_beta_B1_T10_U3_D4(self): + def test_rnnt_nonfused_log_softmax(self): for random in [False, True]: - data = get_B1_T10_U3_D4_data(random=random) - data = numpy_to_torch(data=data, device=self.device, requires_grad=True) - - ref_betas = _rnnt_loss_betas( - blank=data["blank"], - logits=data["logits"], - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - ) - wp_betas = _rnnt_loss_betas( - blank=data["blank"], - left_buffer=100, - right_buffer=100, - logits=data["logits"], - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - wordpiece_ends=data["wordpiece_ends"], - ) - np.testing.assert_allclose( - ref_betas.cpu().data.numpy(), - wp_betas.cpu().data.numpy(), - atol=1e-2, - rtol=1e-2, - ) - - -@skipIfNoCuda -class RNNTLossCUDAOnly: - def _test_costs_and_gradients( - self, data, ref_costs, ref_gradients, atol=1e-6, rtol=1e-2 - ): - logits_shape = data["logits"].shape - for reuse_logits_for_grads in [False, True]: - with self.subTest(reuse_logits_for_grads=reuse_logits_for_grads): - costs, gradients = compute_with_pytorch_transducer( - data=data, reuse_logits_for_grads=reuse_logits_for_grads - ) - np.testing.assert_allclose(costs, ref_costs, atol=atol, rtol=rtol) - self.assertEqual(logits_shape, gradients.shape) - if not np.allclose(gradients, ref_gradients, atol=atol, rtol=rtol): - for b in range(len(gradients)): - T = data["logit_lengths"][b] - U = data["target_lengths"][b] - for t in range(gradients.shape[1]): - for u in range(gradients.shape[2]): - np.testing.assert_allclose( - gradients[b, t, u], - ref_gradients[b, t, u], - atol=atol, - rtol=rtol, - err_msg=f"failed on b={b}, t={t}/T={T}, u={u}/U={U}", - ) - - def test_nan_logits(self): - for reuse_logits_for_grads in [False, True]: data = get_B1_T10_U3_D4_data( - random=True, left_buffer=10, right_buffer=10, nan=True + random=random, ) data = numpy_to_torch( data=data, device=self.device, requires_grad=True ) - data["left_buffer"] = 10 - data["right_buffer"] = 10 - costs, gradients = compute_with_pytorch_transducer( - data=data, reuse_logits_for_grads=reuse_logits_for_grads + data["fused_log_softmax"] = False + ref_costs, ref_gradients = compute_with_numpy_transducer( + data=data ) - self.assertTrue(np.all(costs == 0)) - self.assertTrue(np.all(gradients == 0)) - - def test_rnnt_restricted_betas_with_random_data(self): - for u in range(50, 60): - data = get_numpy_random_data(max_B=5, max_T=499, max_U=u, max_D=4096) - data = numpy_to_torch( - data=data, device=self.device, requires_grad=True - ) - targets = data["targets"] - target_lengths = data["target_lengths"] - B, max_U = targets.size() - wordpiece_ends = np.zeros((B, max_U + 1), dtype=np.int32) - for b in range(B): - u = target_lengths[b] - wordpiece_ends[b, : u + 1] = np.array([0] + list(range(u)), dtype=np.int32) - wordpiece_ends = torch.from_numpy(wordpiece_ends) - _ = _rnnt_loss_betas( - logits=data["logits"], - logit_lengths=data["logit_lengths"], - target_lengths=data["target_lengths"], - targets=data["targets"], - wordpiece_ends=wordpiece_ends, - blank=4095, + self._test_costs_and_gradients( + data=data, ref_costs=ref_costs, ref_gradients=ref_gradients ) - - def test_rnnt_nonfused_log_softmax(self): - for random in [False, True]: - for left_buffer in [1, 2, 10]: - for right_buffer in [1, 2, 5, 10]: - data = get_B1_T10_U3_D4_data( - random=random, - left_buffer=left_buffer, - right_buffer=right_buffer, - ) - data = numpy_to_torch( - data=data, device=self.device, requires_grad=True - ) - data["left_buffer"] = left_buffer - data["right_buffer"] = right_buffer - data["fused_log_softmax"] = False - ref_costs, ref_gradients = compute_with_numpy_transducer( - data=data - ) - self._test_costs_and_gradients( - data=data, ref_costs=ref_costs, ref_gradients=ref_gradients - ) diff --git a/test/torchaudio_unittest/rnnt/utils.py b/test/torchaudio_unittest/rnnt/utils.py index d8030c4989..8e93d28032 100644 --- a/test/torchaudio_unittest/rnnt/utils.py +++ b/test/torchaudio_unittest/rnnt/utils.py @@ -4,10 +4,7 @@ import torch from torchaudio.prototype.rnnt_loss import RNNTLoss -from .numpy_transducer import ( - AlignmentRestrictionCheck, - NumpyTransducerLoss, -) +from .numpy_transducer import NumpyTransducerLoss def compute_with_numpy_transducer(data): @@ -18,9 +15,6 @@ def compute_with_numpy_transducer(data): logit_lengths=data["logit_lengths"], target_lengths=data["target_lengths"], targets=data["targets"], - wordpiece_ends=data.get("wordpiece_ends", None), - left_buffer=data.get("left_buffer", 0), - right_buffer=data.get("right_buffer", 0), ) loss = torch.sum(costs) @@ -33,13 +27,8 @@ def compute_with_numpy_transducer(data): def compute_with_pytorch_transducer(data, reuse_logits_for_grads=False): - left_buffer = data.get("left_buffer", 0) - right_buffer = data.get("right_buffer", 0) - costs = RNNTLoss( blank=data["blank"], - left_buffer=left_buffer, - right_buffer=right_buffer, fused_log_softmax=data.get("fused_log_softmax", True), reuse_logits_for_grads=reuse_logits_for_grads, )( @@ -47,7 +36,6 @@ def compute_with_pytorch_transducer(data, reuse_logits_for_grads=False): logit_lengths=data["logit_lengths"], target_lengths=data["target_lengths"], targets=data["targets"], - wordpiece_ends=data.get("wordpiece_ends", None), ) loss = torch.sum(costs) @@ -95,8 +83,6 @@ def get_data_basic(device): def get_B1_T10_U3_D4_data( random=False, - left_buffer=0, - right_buffer=0, dtype=np.float32, nan=False, ): @@ -112,7 +98,6 @@ def get_B1_T10_U3_D4_data( data["target_lengths"] = np.array([2, 2], dtype=np.int32) data["targets"] = np.array([[1, 2], [1, 2]], dtype=np.int32) data["blank"] = 0 - data["wordpiece_ends"] = np.array([[0, 2, 7], [0, 2, 7]], dtype=np.int32) return data @@ -425,8 +410,6 @@ def numpy_to_torch(data, device, requires_grad=True): logit_lengths = torch.from_numpy(data["logit_lengths"]) target_lengths = torch.from_numpy(data["target_lengths"]) - if "wordpiece_ends" in data: - data["wordpiece_ends"] = torch.from_numpy(data["wordpiece_ends"]).to(device=device) if "nbest_wers" in data: data["nbest_wers"] = torch.from_numpy(data["nbest_wers"]).to(device=device) if "nbest_scores" in data: diff --git a/torchaudio/csrc/rnnt/compute.cpp b/torchaudio/csrc/rnnt/compute.cpp index 5ca69a24c7..91ce8cceb6 100644 --- a/torchaudio/csrc/rnnt/compute.cpp +++ b/torchaudio/csrc/rnnt/compute.cpp @@ -7,9 +7,6 @@ TORCH_LIBRARY_FRAGMENT(torchaudio, m) { "Tensor tgt_lengths," "int blank," "float clamp," - "Tensor? wp_ends=None," - "int l_buffer=0," - "int r_buffer=0," "bool fused_log_smax=True," "bool reuse_logits_for_grads=True) -> (Tensor, Tensor?)"); } diff --git a/torchaudio/csrc/rnnt/compute_alphas.cpp b/torchaudio/csrc/rnnt/compute_alphas.cpp index 80d53995cc..a52d49b8a1 100644 --- a/torchaudio/csrc/rnnt/compute_alphas.cpp +++ b/torchaudio/csrc/rnnt/compute_alphas.cpp @@ -6,8 +6,5 @@ TORCH_LIBRARY_FRAGMENT(torchaudio, m) { "Tensor src_lengths," "Tensor tgt_lengths," "int blank," - "float clamp," - "Tensor? wp_ends=None," - "int l_buffer=0," - "int r_buffer=0) -> Tensor"); + "float clamp) -> Tensor"); } diff --git a/torchaudio/csrc/rnnt/compute_betas.cpp b/torchaudio/csrc/rnnt/compute_betas.cpp index f4312c59cf..234dd909b5 100644 --- a/torchaudio/csrc/rnnt/compute_betas.cpp +++ b/torchaudio/csrc/rnnt/compute_betas.cpp @@ -6,8 +6,5 @@ TORCH_LIBRARY_FRAGMENT(torchaudio, m) { "Tensor src_lengths," "Tensor tgt_lengths," "int blank," - "float clamp," - "Tensor? wp_ends=None," - "int l_buffer=0," - "int r_buffer=0) -> Tensor"); + "float clamp) -> Tensor"); } diff --git a/torchaudio/csrc/rnnt/cpu/alignment_restrictions.cpp b/torchaudio/csrc/rnnt/cpu/alignment_restrictions.cpp deleted file mode 100644 index abef70409d..0000000000 --- a/torchaudio/csrc/rnnt/cpu/alignment_restrictions.cpp +++ /dev/null @@ -1,98 +0,0 @@ -#include - -namespace torchaudio { -namespace rnnt { -namespace cpu { - -void AlignmentRestrictionCheck::validTimeRanges( - const int u, - int& t_start, - int& t_end) { - t_start = std::max(wpEnds_[u] - lBuffer_, 0); - t_end = (u == U - 1) ? T - 1 : std::min(wpEnds_[u + 1] + rBuffer_, T - 1); - return; -} - -bool AlignmentRestrictionCheck::alphaBlankTransition( - const int t, - const int u) { - if (u == 0 && t == 0) { - return false; - } - - // for alpha blank updates we move from left to right - // blank transitions are valid from: - // start time when current symbol is emitted - // **offset to right by 1** - int start = std::max(wpEnds_[u] - lBuffer_ + 1, 1); - - // blank transitions are valid until: - // for U-1: last allowed timestep i.e. T - 1 - // for other cases: last time we may emit the next symbol - int end = (u == U - 1) ? T - 1 : std::min(wpEnds_[u + 1] + rBuffer_, T - 1); - - return start <= t && t <= end; -} - -bool AlignmentRestrictionCheck::alphaEmitTransition( - const int t, - const int u) { - // For alphas we move from bottom to top - // emit transitions into (t, 0) for alpha are invalid - if (u == 0) { - return false; - } - - // For alphas emit are valid starting from: - // first time when current symbol can be emitted - int start = std::max(wpEnds_[u] - lBuffer_, 0); - - // For alphas emit are valid until: - // last timestep when current symbol can be emitted - int end = std::min(wpEnds_[u] + rBuffer_, T - 1); - - return start <= t && t <= end; -} - -bool AlignmentRestrictionCheck::betaBlankTransition( - const int t, - const int u) { - // For updating betas with blank transition - // we move from right to left - - // for beta, blanks transitions are can start - // first timestep when we emit previous symbol - int start = std::max(wpEnds_[u] - lBuffer_, 0); - - // for beta, blanks transitions are valid until - // we can emit current symbol **offset to left by 1** - // note: T-2, we init beta[-1, -1] by log_prob[-1, -1, blank] - int end = - (u == U - 1) ? T - 2 : std::min(wpEnds_[u + 1] + rBuffer_ - 1, T - 2); - - return start <= t && t <= end; -} - -bool AlignmentRestrictionCheck::betaEmitTransition(const int t, const int u) { - // While updating betas we go from top to bottom. - - // for last symbol, we do not allow emit transition, - // so beta into u-1, t is invalid - if (u == U - 1) { - return false; - } - - // For betas we allow emit transition starting from - // first time we can emit next symbol - int start = std::max(0, wpEnds_[u + 1] - lBuffer_); - - // For betas we allow emit transitions to end with - // last time we can emit next symbol - int end = std::min(wpEnds_[u + 1] + rBuffer_, T - 1); - - return start <= t and t <= end; -} - -} // namespace cpu -} // namespace rnnt -} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/alignment_restrictions.h b/torchaudio/csrc/rnnt/cpu/alignment_restrictions.h deleted file mode 100644 index 0c265b10be..0000000000 --- a/torchaudio/csrc/rnnt/cpu/alignment_restrictions.h +++ /dev/null @@ -1,62 +0,0 @@ -#pragma once -#include -#include - -namespace torchaudio { -namespace rnnt { -namespace cpu { - -// Helper class which returns locations where -// blank / emit transitions are valid while -// updating alpha / betas for Alignment -// Restricted Transducer loss -class AlignmentRestrictionCheck { - public: - AlignmentRestrictionCheck( - const int* wpEnds, - int T, - int U, - int lBuffer, - int rBuffer) - : wpEnds_(wpEnds), T(T), U(U), lBuffer_(lBuffer), rBuffer_(rBuffer) { - } - - // Returns ranges of valid timesteps which satisfy - // alignment boundary constraints - void validTimeRanges(const int u, int& t_start, int& t_end); - - // Examine if doing blank transition into (t, u) - // is allowed while updating alphas - // Note that while doing blank transitions for alpha - // we move from left to right - bool alphaBlankTransition(const int t, const int u); - - // Examine if doing emit transition into (t, u) - // is allowed while updating alphas - // Note that while doing emit transitions for alpha - // we move from bottom to top - bool alphaEmitTransition(const int t, const int u); - - // Examine if doing blank transition into (t, u) - // is allowed while updating betas - // Note that while doing blank transitions for beta - // we move from right to left - bool betaBlankTransition(const int t, const int u); - - // Examine if doing emit transition into (t, u) - // is allowed while updating betas - // Note that while doing emit transitions for beta - // we move from top to bottom - bool betaEmitTransition(const int t, const int u); - - private: - const int* wpEnds_; - int T; - int U; - int lBuffer_; - int rBuffer_; -}; - -} // namespace cpu -} // namespace rnnt -} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/compute.cpp b/torchaudio/csrc/rnnt/cpu/compute.cpp index c8002c3bff..1cd9801d18 100644 --- a/torchaudio/csrc/rnnt/cpu/compute.cpp +++ b/torchaudio/csrc/rnnt/cpu/compute.cpp @@ -13,9 +13,6 @@ std::tuple> compute( const torch::Tensor& tgt_lengths, int64_t blank, double clamp, - const c10::optional& wp_ends = c10::nullopt, - int64_t l_buffer = 0, - int64_t r_buffer = 0, bool fused_log_smax = true, bool reuse_logits_for_grads = true) { @@ -27,8 +24,6 @@ std::tuple> compute( options.numTargets_ = logits.size(3); options.blank_ = blank; options.clamp_ = clamp; - options.lBuffer_ = l_buffer; - options.rBuffer_ = r_buffer; options.fusedLogSmax_ = fused_log_smax; CHECK_EQ(logits.device().type(), torch::DeviceType::CPU); @@ -71,8 +66,7 @@ std::tuple> compute( /*src_lengths=*/src_lengths.data(), /*tgt_lengths=*/tgt_lengths.data(), /*costs=*/costs.data(), - /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), - /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data()); + /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data()); break; } case torch::ScalarType::Half: @@ -84,8 +78,7 @@ std::tuple> compute( /*src_lengths=*/src_lengths.data(), /*tgt_lengths=*/tgt_lengths.data(), /*costs=*/costs.data(), - /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), - /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data()); + /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data()); break; } default: diff --git a/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp b/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp index 7bc462c239..fed1dec5cd 100644 --- a/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp +++ b/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp @@ -11,10 +11,7 @@ torch::Tensor compute_alphas( const torch::Tensor& src_lengths, const torch::Tensor& tgt_lengths, int64_t blank, - double clamp, - const c10::optional& wp_ends = c10::nullopt, - int64_t l_buffer = 0, - int64_t r_buffer = 0) { + double clamp) { Options options; options.batchSize_ = src_lengths.size(0); options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); @@ -23,8 +20,6 @@ torch::Tensor compute_alphas( options.numTargets_ = logits.size(3); options.blank_ = blank; options.clamp_ = clamp; - options.lBuffer_ = l_buffer; - options.rBuffer_ = r_buffer; CHECK_EQ(logits.device().type(), torch::DeviceType::CPU); options.device_ = CPU; @@ -62,8 +57,7 @@ torch::Tensor compute_alphas( /*targets=*/targets.data(), /*src_lengths=*/src_lengths.data(), /*tgt_lengths=*/tgt_lengths.data(), - /*alphas=*/alphas.data(), - /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data()); + /*alphas=*/alphas.data()); return alphas; } diff --git a/torchaudio/csrc/rnnt/cpu/compute_betas.cpp b/torchaudio/csrc/rnnt/cpu/compute_betas.cpp index 1c7e3d187a..3789dec895 100644 --- a/torchaudio/csrc/rnnt/cpu/compute_betas.cpp +++ b/torchaudio/csrc/rnnt/cpu/compute_betas.cpp @@ -11,10 +11,7 @@ torch::Tensor compute_betas( const torch::Tensor& src_lengths, const torch::Tensor& tgt_lengths, int64_t blank, - double clamp, - const c10::optional& wp_ends = c10::nullopt, - int64_t l_buffer = 0, - int64_t r_buffer = 0) { + double clamp) { Options options; options.batchSize_ = src_lengths.size(0); options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); @@ -23,8 +20,6 @@ torch::Tensor compute_betas( options.numTargets_ = logits.size(3); options.blank_ = blank; options.clamp_ = clamp; - options.lBuffer_ = l_buffer; - options.rBuffer_ = r_buffer; CHECK_EQ(logits.device().type(), torch::DeviceType::CPU); options.device_ = CPU; @@ -67,8 +62,7 @@ torch::Tensor compute_betas( /*src_lengths=*/src_lengths.data(), /*tgt_lengths=*/tgt_lengths.data(), /*costs=*/costs.data(), - /*betas=*/betas.data(), - /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data()); + /*betas=*/betas.data()); return betas; } diff --git a/torchaudio/csrc/rnnt/cpu/cpu_kernels.h b/torchaudio/csrc/rnnt/cpu/cpu_kernels.h index 6d2084d24d..bb63b97ce2 100644 --- a/torchaudio/csrc/rnnt/cpu/cpu_kernels.h +++ b/torchaudio/csrc/rnnt/cpu/cpu_kernels.h @@ -1,6 +1,5 @@ #pragma once -#include #include #include #include @@ -187,67 +186,6 @@ DTYPE ComputeAlphaOneSequence( return forward_score; } -template -DTYPE ComputeAlphaOneSequenceRestricted( - const Options& options, - TensorView>& logProbs, - int srcLen, - int tgtLen, - TensorView& alpha, - const int* wpEnds) { - const int& T = srcLen; - const int& U = tgtLen; - - const int& lBuffer = options.lBuffer_; - const int& r_buffer = options.rBuffer_; - - AlignmentRestrictionCheck check(wpEnds, T, U, lBuffer, r_buffer); - - for (int t = 0; t < T; t++) { - for (int u = 0; u < U; u++) { - alpha({t, u}) = DTYPE(-INFINITY); - } - } - alpha({0, 0}) = DTYPE(0); - - for (int t = 1; t < T; ++t) { // u == 0. - if (!check.alphaBlankTransition(t, 0)) { - break; - } - alpha({t, 0}) = alpha({t - 1, 0}) + logProbs({t - 1, 0}).skip(); - } - - for (int u = 1; u < U; ++u) { // t == 0. - if (!check.alphaEmitTransition(0, u)) { - break; - } - alpha({0, u}) = alpha({0, u - 1}) + logProbs({0, u - 1}).emit(); - } - - for (int u = 1; u < U; ++u) { - int start_t = 0, end_t = 0; - check.validTimeRanges(u, start_t, end_t); - for (int t = start_t; t < end_t + 1; ++t) { - DTYPE skip = DTYPE(-INFINITY), emit = DTYPE(-INFINITY); - - if (check.alphaBlankTransition(t, u)) { - skip = alpha({t - 1, u}) + logProbs({t - 1, u}).skip(); - } - - if (check.alphaEmitTransition(t, u)) { - emit = alpha({t, u - 1}) + logProbs({t, u - 1}).emit(); - } - - if (skip != DTYPE(-INFINITY) || emit != DTYPE(-INFINITY)) { - alpha({t, u}) = math::lse(skip, emit); - } - } - } - DTYPE forward_score = alpha({T - 1, U - 1}) + logProbs({T - 1, U - 1}).skip(); - - return forward_score; -} - template DTYPE ComputeBetaOneSequence( const Options& options, @@ -281,64 +219,6 @@ DTYPE ComputeBetaOneSequence( return backward_score; } -template -DTYPE ComputeBetaOneSequenceRestricted( - const Options& options, - TensorView>& logProbs, - int srcLen, - int tgtLen, - TensorView& beta, - const int* wpEnds = nullptr) { - const int& T = srcLen; - const int& U = tgtLen; - const int& lBuffer = options.lBuffer_; - const int& r_buffer = options.rBuffer_; - AlignmentRestrictionCheck check(wpEnds, T, U, lBuffer, r_buffer); - - for (int t = 0; t < T; t++) { - for (int u = 0; u < U; u++) { - beta({t, u}) = DTYPE(-INFINITY); - } - } - - beta({T - 1, U - 1}) = logProbs({T - 1, U - 1}).skip(); - - for (int t = T - 2; t >= 0; --t) { // u == U - 1. - if (!check.betaBlankTransition(t, U - 1)) { - break; - } - beta({t, U - 1}) = beta({t + 1, U - 1}) + logProbs({t, U - 1}).skip(); - } - - for (int u = U - 2; u >= 0; --u) { // t == T - 1. - if (!check.betaEmitTransition(T - 1, u)) { - break; - } - beta({T - 1, u}) = beta({T - 1, u + 1}) + logProbs({T - 1, u}).emit(); - } - for (int u = U - 2; u >= 0; --u) { - int start_t = 0, end_t = 0; - check.validTimeRanges(u, start_t, end_t); - for (int t = end_t; t >= start_t; --t) { - DTYPE skip = DTYPE(-INFINITY), emit = DTYPE(-INFINITY); - if (check.betaBlankTransition(t, u)) { - skip = beta({t + 1, u}) + logProbs({t, u}).skip(); - } - - if (check.betaEmitTransition(t, u)) { - emit = beta({t, u + 1}) + logProbs({t, u}).emit(); - } - - if (skip != DTYPE(-INFINITY) || emit != DTYPE(-INFINITY)) { - beta({t, u}) = math::lse(skip, emit); - } - } - } - DTYPE backward_score = beta({0, 0}); - - return backward_score; -} - template DTYPE ComputeAlphaOrBetaOneSequence( int thread, @@ -347,42 +227,21 @@ DTYPE ComputeAlphaOrBetaOneSequence( int srcLen, int tgtLen, TensorView& alpha, - TensorView& beta, - const int* wpEnds = nullptr) { - if (wpEnds == nullptr) { - if (thread & 1) { - return ComputeAlphaOneSequence( - /*options=*/options, - /*logProbs=*/logProbs, - /*srcLen=*/srcLen, - /*tgtLen=*/tgtLen, - /*alpha=*/alpha); - } else { - return ComputeBetaOneSequence( - /*options=*/options, - /*logProbs=*/logProbs, - /*srcLen=*/srcLen, - /*tgtLen=*/tgtLen, - /*beta=*/beta); - } + TensorView& beta) { + if (thread & 1) { + return ComputeAlphaOneSequence( + /*options=*/options, + /*logProbs=*/logProbs, + /*srcLen=*/srcLen, + /*tgtLen=*/tgtLen, + /*alpha=*/alpha); } else { - if (thread & 1) { - return ComputeAlphaOneSequenceRestricted( - /*options=*/options, - /*logProbs=*/logProbs, - /*srcLen=*/srcLen, - /*tgtLen=*/tgtLen, - /*alpha=*/alpha, - /*wpEnds=*/wpEnds); - } else { - return ComputeBetaOneSequenceRestricted( - /*options=*/options, - /*logProbs=*/logProbs, - /*srcLen=*/srcLen, - /*tgtLen=*/tgtLen, - /*beta=*/beta, - /*wpEnds=*/wpEnds); - } + return ComputeBetaOneSequence( + /*options=*/options, + /*logProbs=*/logProbs, + /*srcLen=*/srcLen, + /*tgtLen=*/tgtLen, + /*beta=*/beta); } } @@ -394,12 +253,10 @@ void ComputeAlphasBetas( const int* tgtLengths, CAST_DTYPE* alphas, CAST_DTYPE* betas, - DTYPE* costs, - const int* wpEnds = nullptr) { + DTYPE* costs) { std::vector>> seqlogProbs; std::vector> seq_alphas; std::vector> seq_betas; - std::vector seq_wpEnds; const int& B = options.batchSize_; const int& maxT = options.maxSrcLen_; @@ -415,10 +272,6 @@ void ComputeAlphasBetas( TensorView({maxT, maxU}, alphas + b * maxT * maxU)); seq_betas.push_back( TensorView({maxT, maxU}, betas + b * maxT * maxU)); - - if (wpEnds != nullptr) { - seq_wpEnds.push_back(wpEnds + b * (maxU)); - } } std::vector scores(B << 1); @@ -432,8 +285,7 @@ void ComputeAlphasBetas( /*srcLen=*/srcLengths[i], /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. /*alpha=*/seq_alphas[i], - /*beta=*/seq_betas[i], - /*wpEnds=*/(wpEnds == nullptr) ? nullptr : seq_wpEnds[i]); + /*beta=*/seq_betas[i]); } for (int b = 0; b < B; ++b) { costs[b] = -scores[b << 1]; @@ -575,11 +427,9 @@ void ComputeAlphas( const CAST_DTYPE* logProbs, const int* srcLengths, const int* tgtLengths, - CAST_DTYPE* alphas, - const int* wpEnds = nullptr) { + CAST_DTYPE* alphas) { std::vector>> seqlogProbs; std::vector> seq_alphas; - std::vector seq_wpEnds; const int& B = options.batchSize_; const int& maxT = options.maxSrcLen_; @@ -593,30 +443,17 @@ void ComputeAlphas( b * maxT * maxU)); seq_alphas.push_back( TensorView({maxT, maxU}, alphas + b * maxT * maxU)); - if (wpEnds != nullptr) { - seq_wpEnds.push_back(wpEnds + b * (maxU)); - } } std::vector scores(B << 1); //#pragma omp parallel for for (int i = 0; i < B; ++i) { // use max 2 * B threads. - if (wpEnds == nullptr) { - ComputeAlphaOneSequence( - options, - /*logProbs=*/seqlogProbs[i], - /*srcLen=*/srcLengths[i], - /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. - /*alpha=*/seq_alphas[i]); - } else { - ComputeAlphaOneSequenceRestricted( - /*options=*/options, - /*logProbs=*/seqlogProbs[i], - /*srcLen=*/srcLengths[i], - /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. - /*alpha=*/seq_alphas[i], - /*wpEnds=*/seq_wpEnds[i]); - } + ComputeAlphaOneSequence( + options, + /*logProbs=*/seqlogProbs[i], + /*srcLen=*/srcLengths[i], + /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. + /*alpha=*/seq_alphas[i]); } } @@ -627,11 +464,9 @@ void ComputeBetas( const int* srcLengths, const int* tgtLengths, CAST_DTYPE* costs, - CAST_DTYPE* betas, - const int* wpEnds = nullptr) { + CAST_DTYPE* betas) { std::vector>> seqlogProbs; std::vector> seq_betas; - std::vector seq_wpEnds; const int& B = options.batchSize_; const int& maxT = options.maxSrcLen_; @@ -645,30 +480,17 @@ void ComputeBetas( b * maxT * maxU)); seq_betas.push_back( TensorView({maxT, maxU}, betas + b * maxT * maxU)); - if (wpEnds != nullptr) { - seq_wpEnds.push_back(wpEnds + b * (maxU)); - } } std::vector scores(B << 1); //#pragma omp parallel for for (int i = 0; i < B; ++i) { // use max 2 * B threads. - if (wpEnds == nullptr) { - ComputeBetaOneSequence( - options, - /*logProbs=*/seqlogProbs[i], - /*srcLen=*/srcLengths[i], - /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. - /*betas=*/seq_betas[i]); - } else { - ComputeBetaOneSequenceRestricted( - /*options=*/options, - /*logProbs=*/seqlogProbs[i], - /*srcLen=*/srcLengths[i], - /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. - /*betas=*/seq_betas[i], - /*wpEnds=*/seq_wpEnds[i]); - } + ComputeBetaOneSequence( + options, + /*logProbs=*/seqlogProbs[i], + /*srcLen=*/srcLengths[i], + /*tgtLen=*/tgtLengths[i] + 1, // with prepended blank. + /*betas=*/seq_betas[i]); } } diff --git a/torchaudio/csrc/rnnt/cpu/cpu_transducer.h b/torchaudio/csrc/rnnt/cpu/cpu_transducer.h index 72764325ee..47a058dc31 100644 --- a/torchaudio/csrc/rnnt/cpu/cpu_transducer.h +++ b/torchaudio/csrc/rnnt/cpu/cpu_transducer.h @@ -25,8 +25,7 @@ status_t Compute( const int* srcLengths, const int* tgtLengths, DTYPE* costs, - DTYPE* gradients = nullptr, - const int* wpEnds = nullptr) { + DTYPE* gradients = nullptr) { const Options& options = workspace.GetOptions(); CHECK_EQ(options.device_, CPU); @@ -63,8 +62,7 @@ status_t Compute( /*tgtLengths=*/tgtLengths, /*alphas=*/workspace.GetPointerToAlphas(), /*betas=*/workspace.GetPointerToBetas(), - /*costs=*/costs, - /*wpEnds=*/wpEnds); + /*costs=*/costs); } if (gradients != nullptr) { @@ -90,8 +88,7 @@ status_t ComputeAlphas( const int* targets, const int* srcLengths, const int* tgtLengths, - DTYPE* alphas, - const int* wpEnds = nullptr) { + DTYPE* alphas) { const Options& options = workspace.GetOptions(); CHECK_EQ(options.device_, CPU); @@ -126,8 +123,7 @@ status_t ComputeAlphas( /*log_probs=*/workspace.GetPointerToLogProbs(), /*srcLengths=*/srcLengths, /*tgtLengths=*/tgtLengths, - /*alphas=*/alphas, - /*wpEnds=*/wpEnds); + /*alphas=*/alphas); } return SUCCESS; @@ -143,8 +139,7 @@ status_t ComputeBetas( const int* srcLengths, const int* tgtLengths, DTYPE* costs, - DTYPE* betas, - const int* wpEnds = nullptr) { + DTYPE* betas) { const Options& options = workspace.GetOptions(); CHECK_EQ(options.device_, CPU); @@ -180,8 +175,7 @@ status_t ComputeBetas( /*srcLengths=*/srcLengths, /*tgtLengths=*/tgtLengths, /*costs=*/costs, - /*betas=*/betas, - /*wpEnds=*/wpEnds); + /*betas=*/betas); } return SUCCESS; diff --git a/torchaudio/csrc/rnnt/gpu/compute.cu b/torchaudio/csrc/rnnt/gpu/compute.cu index 98cf39e2cc..8582f36d48 100644 --- a/torchaudio/csrc/rnnt/gpu/compute.cu +++ b/torchaudio/csrc/rnnt/gpu/compute.cu @@ -15,9 +15,6 @@ compute( const torch::Tensor& tgt_lengths, int64_t blank, double clamp, - const c10::optional& wp_ends = c10::nullopt, - int64_t l_buffer = 0, - int64_t r_buffer = 0, bool fused_log_smax = true, bool reuse_logits_for_grads = true) { @@ -29,8 +26,6 @@ compute( options.numTargets_ = logits.size(3); options.blank_ = blank; options.clamp_ = clamp; - options.lBuffer_ = l_buffer; - options.rBuffer_ = r_buffer; options.fusedLogSmax_ = fused_log_smax; CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); @@ -75,8 +70,7 @@ compute( /*src_lengths=*/src_lengths.data(), /*tgt_lengths=*/tgt_lengths.data(), /*costs=*/costs.data(), - /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), - /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data()); + /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data()); break; } case torch::ScalarType::Half: @@ -88,8 +82,7 @@ compute( /*src_lengths=*/src_lengths.data(), /*tgt_lengths=*/tgt_lengths.data(), /*costs=*/costs.data(), - /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data(), - /*wp_ends=*/(wp_ends == c10::nullopt)? nullptr : wp_ends->data()); + /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data()); break; } default: diff --git a/torchaudio/csrc/rnnt/gpu/compute_alphas.cu b/torchaudio/csrc/rnnt/gpu/compute_alphas.cu index 929434e19d..dc101b0ecb 100644 --- a/torchaudio/csrc/rnnt/gpu/compute_alphas.cu +++ b/torchaudio/csrc/rnnt/gpu/compute_alphas.cu @@ -12,10 +12,7 @@ torch::Tensor compute_alphas( const torch::Tensor& src_lengths, const torch::Tensor& tgt_lengths, int64_t blank, - double clamp, - const c10::optional& wp_ends = c10::nullopt, - int64_t l_buffer = 0, - int64_t r_buffer = 0) { + double clamp) { Options options; options.batchSize_ = src_lengths.size(0); options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); @@ -24,8 +21,6 @@ torch::Tensor compute_alphas( options.numTargets_ = logits.size(3); options.blank_ = blank; options.clamp_ = clamp; - options.lBuffer_ = l_buffer; - options.rBuffer_ = r_buffer; CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); options.stream_ = at::cuda::getCurrentCUDAStream(); @@ -65,8 +60,7 @@ torch::Tensor compute_alphas( /*targets=*/targets.data(), /*src_lengths=*/src_lengths.data(), /*tgt_lengths=*/tgt_lengths.data(), - /*alphas=*/alphas.data(), - /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data()); + /*alphas=*/alphas.data()); return alphas; } diff --git a/torchaudio/csrc/rnnt/gpu/compute_betas.cu b/torchaudio/csrc/rnnt/gpu/compute_betas.cu index f906d1bbe3..63b8fd636a 100644 --- a/torchaudio/csrc/rnnt/gpu/compute_betas.cu +++ b/torchaudio/csrc/rnnt/gpu/compute_betas.cu @@ -12,10 +12,7 @@ torch::Tensor compute_betas( const torch::Tensor& src_lengths, const torch::Tensor& tgt_lengths, int64_t blank, - double clamp, - const c10::optional& wp_ends = c10::nullopt, - int64_t l_buffer = 0, - int64_t r_buffer = 0) { + double clamp) { Options options; options.batchSize_ = src_lengths.size(0); options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); @@ -24,8 +21,6 @@ torch::Tensor compute_betas( options.numTargets_ = logits.size(3); options.blank_ = blank; options.clamp_ = clamp; - options.lBuffer_ = l_buffer; - options.rBuffer_ = r_buffer; CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); options.stream_ = at::cuda::getCurrentCUDAStream(); @@ -70,8 +65,7 @@ torch::Tensor compute_betas( /*src_lengths=*/src_lengths.data(), /*tgt_lengths=*/tgt_lengths.data(), /*costs=*/costs.data(), - /*betas=*/betas.data(), - /*wp_ends=*/(wp_ends == c10::nullopt) ? nullptr : wp_ends->data()); + /*betas=*/betas.data()); return betas; } diff --git a/torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh b/torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh deleted file mode 100644 index d5d927992f..0000000000 --- a/torchaudio/csrc/rnnt/gpu/gpu_alignment_restrictions.cuh +++ /dev/null @@ -1,476 +0,0 @@ -#pragma once - -#ifdef USE_CUDA - -#include - -#include -#include -#include - -namespace torchaudio { -namespace rnnt { - - -template -__device__ void ComputeAlphasRestricted( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - const CAST_DTYPE* logProbs, - const int* srcLengths, - const int* tgtLengths, - int* alpha_counters, - volatile CAST_DTYPE* alphas, - const int* wpEnds, - int lBuffer, - int rBuffer, - int warp_size, - int H=1) { - - const int& maxT = maxSrcLen; - const int& maxU = maxTgtLen; - - // Block's 3d dim is batchsize - const int bTgt = blockIdx.z; // 0 <= b < B - const int bSrc = bTgt / H; - const int T = srcLengths[bSrc]; - const int U = tgtLengths[bTgt] + 1; - - assert(T <= maxT && "T must be < maxT"); - assert(U <= maxU && "U must be < maxU"); - - // Blocks 1st dim is T i.e. warpsize timesteps - // to find timestep, we index into correct block idx * dim - // then offset by the thread - const int t = blockIdx.x * blockDim.x + threadIdx.x; - - // Blocks 2nd dim is U. For u, we just offset by 2nd idx - const int u = blockIdx.y; - - // simply exit if we are out of bounds - // this happens typically when warp size is larger than T - if (t > T-1 || u > U-1 || t < 0 || u < 0) { - return; - } - - const int* wpEndsB = wpEnds + maxU * bTgt; - assert(wpEndsB[u] >= 0 && "wpEnds cannot be negative"); - if (u < U - 1) { - assert(wpEndsB[u+1] >= 0 && "wpEnds cannot be negative"); - } - - // below indexes are inclusive - // compute valid ranges for emit transitions in current warp - int emit_start = math::max(wpEndsB[u] - lBuffer, 0); - int emit_end = math::min(wpEndsB[u] + rBuffer, T - 1); - - // compute valid ranges for blank transitions in current warp - int blank_start = math::max(wpEndsB[u] - lBuffer + 1, 1); - int blank_end = (u == U - 1) ? T - 1 : math::min(wpEndsB[u + 1] + rBuffer, T - 1); - - // this is a useful pointer to track the last timestep - // for previous warp. - int prevWarpEndT = blockIdx.x * blockDim.x - 1; - - int* counter = alpha_counters + Indexer2D(maxU)(bTgt, blockIdx.y); - - - int idx_b_t_u = -1, idx_b_t_um1 = -1, idx_b_tm1_u = -1; - int idx_b_prevWarpEndT_u = -1; - - Indexer3D idxr(maxT, maxU); - idx_b_t_u = idxr(bTgt, t, u); - idx_b_t_um1 = idxr(bTgt, t, u - 1); - idx_b_tm1_u = idxr(bTgt, t - 1, u); - idx_b_prevWarpEndT_u = idxr(bTgt, prevWarpEndT, u); - - alphas[idx_b_t_u] = CAST_DTYPE(-INFINITY); - - // Initialization condition for alphas - if (t == 0 && u == 0) { - alphas[idx_b_t_u] = 0; - } - - // Synchronization routines: - // Below conditions make sure given t, u - // - threads in previous warp for u is complete - // - threads in the warp corresponding to t, u-1 is complete - // See https://github.com/1ytic/warp-rnnt for illustration - if (blockIdx.x > 0) { // wait for previous warp (in t-axis) is ready. - while (atomicAdd(counter, 0) < blockIdx.x) {} - } - - if (blockIdx.y > 0) { // wait for previous warp (in u-axis) is ready. - while (atomicAdd(counter - 1, 0) <= blockIdx.x) {} - } - - // This is initialization loop for t = 0, u - // i.e. emit condition while running alphas - if (t == 0 && u > 0) { - // Alignment Restriction check - if (in_range(emit_start, emit_end, t)) { - alphas[idx_b_t_u] = - alphas[idx_b_t_um1] - + logProbs[(idx_b_t_um1 << 1) + LOG_PROBS_EMIT_IDX]; - } - } - - // This is initialization loop for u = 0, t - if (u == 0 && t > 0) { - - // skip_prob stores the log probability of t, u - CAST_DTYPE skip_prob = logProbs[(idx_b_tm1_u << 1) + LOG_PROBS_SKIP_IDX]; - - #pragma unroll - // iterating on i = 1, 2, 4, 8.. - for (int i = 1; i < warp_size; i <<= 1) { - - // https://devblogs.nvidia.com/using-cuda-warp-level-primitives/ - // first param is a mask -1 indicates all threads in warp participate - // second param is the value being passed for current thread - // third param is the offset which will be used for addition of values - // copy from a thread with lower ID relative to the caller - CAST_DTYPE synced_val = __shfl_up_sync(0xffffffff, skip_prob, i); - - // Illustration with 8 threads in warp - // Initial: 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 - // i == 1: 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2 - // i == 2: 0.1, 0.2, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4 - // i == 4: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 - - // Alignment restriction check - // add to skip_prob only if *t-i* is within allowed range - if (i <= threadIdx.x && - in_range(blank_start, blank_end, t - i) ) { - skip_prob += synced_val; - } - } - - // update the values of alpha if *t* is within allowed range - if (in_range(blank_start, blank_end, t)) { - alphas[idx_b_t_u] = skip_prob; - } - - // Optionally add previous warp's end idx value if it is - // within range to get cumulative score for all previous timesteps, - // and not just within current warp - if (in_range(blank_start, blank_end, prevWarpEndT)) { - alphas[idx_b_t_u] += alphas[idx_b_prevWarpEndT_u]; - } - } - - // General case - if (t < T && u < U) { - - CAST_DTYPE skip_prob = CAST_DTYPE(-INFINITY); - if (in_range(blank_start, blank_end, t) && t > 0) { - skip_prob = logProbs[(idx_b_tm1_u << 1) + LOG_PROBS_SKIP_IDX]; - } - - // We check if the first index of current warp (prevWarpEndT+1) was within allowed - // timesteps, if so we add the alphas of previous warp's last timestep - // to skip score. - CAST_DTYPE skip = skip_prob; - if (in_range(blank_start, blank_end, prevWarpEndT+1) && prevWarpEndT >= 0) { - skip += alphas[idx_b_prevWarpEndT_u]; - } - - CAST_DTYPE emit = CAST_DTYPE(-INFINITY); - if (in_range(emit_start, emit_end, t) && u > 0) { - emit = alphas[idx_b_t_um1] + - logProbs[(idx_b_t_um1 << 1) + LOG_PROBS_EMIT_IDX]; - } - - CAST_DTYPE out_score = math::lse(skip, emit); - - - // Below we loop over warp_size updating the out score - // once per thread in each loop. - // shared memory is used to synchronize the values in threads - for(int i = 1; i < warp_size; i++) { - - CAST_DTYPE synced_val = __shfl_up_sync(0xffffffff, out_score, 1); - - // We will only update the out_score if t is within range - // of alignment constraints and it is the thread's turn in loop - if ((i == threadIdx.x) && in_range(blank_start, blank_end, t)) { - out_score = math::lse(synced_val + skip_prob, emit); - } - } - // update alphas if t was within range of blanks, or emits - if ( (in_range(blank_start, blank_end, t) || in_range(emit_start, emit_end, t) ) - && (t > 0 && u > 0)) { - alphas[idx_b_t_u] = out_score; - } - } - - // Synchronization mechanism - if (threadIdx.x == 0) { - __threadfence(); - atomicAdd(counter, 1); - } -} - - - -// This is a wrapper around ComputeAlphasRestricted -// kernel to enable unit testing -template -__global__ void ComputeAlphasRestrictedWrapper( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - const CAST_DTYPE* logProbs, - const int* srcLengths, - const int* tgtLengths, - int* alpha_counters, - volatile CAST_DTYPE* alphas, - const int* wpEnds, - int lBuffer, - int rBuffer, - int warp_size, - int H=1) { - ComputeAlphasRestricted( - maxSrcLen, - maxTgtLen, - numTargets, - blank, - logProbs, - srcLengths, - tgtLengths, - alpha_counters, - alphas, - wpEnds, - lBuffer, - rBuffer, - warp_size, - H); -} - - -template -__device__ void ComputeBetasCostsRestricted( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - const CAST_DTYPE* logProbs, - const int* srcLengths, - const int* tgtLengths, - int* beta_counters, - DTYPE* costs, - volatile CAST_DTYPE* betas, - const int* wpEnds, - int lBuffer, - int rBuffer, - int warp_size, - int num_warps, - int H=1) { - - const int& maxT = maxSrcLen; - const int& maxU = maxTgtLen; - - // Block's 3d dim is batchsize - const int bTgt = blockIdx.z; // 0 <= b < B - const int bSrc = bTgt / H; - const int T = srcLengths[bSrc]; - const int U = tgtLengths[bTgt] + 1; - - assert(T <= maxT); - assert(U <= maxU); - - const int t = T - 1 - blockIdx.x * blockDim.x - threadIdx.x; - const int u = U - 1 - blockIdx.y; - - if (t < 0 || u < 0) { // out of boundary. - return; - } - - const int* wpEndsB = wpEnds + maxU * bTgt; - - // below indexes are inclusive - // compute valid ranges for emit transitions in - // current warp - int emit_start = (u >= U - 1) ? -1 : math::max(0, wpEndsB[u + 1] - lBuffer); - int emit_end = (u >= U - 1) ? -1 : math::min(wpEndsB[u + 1] + rBuffer, T - 1); - - - // compute valid ranges for blank transitions in - // current warp - int blank_start = (u > U - 1) ? -1 : math::max(wpEndsB[u] - lBuffer, 0); - int blank_end = (u >= U - 1) ? T - 2 : math::min(wpEndsB[u + 1] + rBuffer - 1, T - 2); - - int rightBlockStartT = T - 1 - blockIdx.x * blockDim.x + 1; - int* counter = beta_counters + Indexer2D(maxU)(bTgt, blockIdx.y); - - int idx_b_t_u = -1, idx_b_t_up1 = -1; - int idx_b_tp1_up1 = -1, idx_b_rightBlockStartT_u = -1; - - Indexer3D idxr(maxT, maxU); - idx_b_t_u = idxr(bTgt, t, u); - idx_b_t_up1 = idxr(bTgt, t, u+1); - idx_b_tp1_up1 = idxr(bTgt, t+1, u+1); - idx_b_rightBlockStartT_u = idxr(bTgt, rightBlockStartT, u); - - if (t < T && u < U) { - betas[idx_b_t_u] = CAST_DTYPE(-INFINITY); - } - - // Initialization condition - if (t == T - 1 && u == U - 1) { - betas[idx_b_t_u] = - logProbs[(idx_b_t_u << 1) + LOG_PROBS_SKIP_IDX]; - } - - // For betas, we process warps from right to left - // blockIdx.x specifies the warp id for a given u - // here we want to wait until the warp to the right is done - if (blockIdx.x > 0) { // wait for previous warp (in t-axis) is ready. - while (atomicAdd(counter, 0) < blockIdx.x) {} - } - - if (blockIdx.y > 0) { // wait for previous warp (in u-axis) is ready. - while (atomicAdd(counter - 1, 0) <= blockIdx.x) {} - } - - // edge condition for emits - if (t == T - 1 && u < U - 1) { - if (emit_start <= t && t <= emit_end) { - betas[idx_b_t_u] = - betas[idx_b_t_up1] - + logProbs[(idx_b_t_u << 1) + LOG_PROBS_EMIT_IDX]; - } - } - - if (u == U - 1 && t <= T - 1) { - CAST_DTYPE skip_prob = logProbs[(idx_b_t_u << 1) + LOG_PROBS_SKIP_IDX]; - - #pragma unroll - for(int i = 1; i < warp_size; i <<= 1) { - - CAST_DTYPE synced_val = __shfl_up_sync(0xffffffff, skip_prob, i); - if (i <= threadIdx.x && in_range(blank_start, blank_end, t)) { - skip_prob = skip_prob + synced_val; - } - } - - if (in_range(blank_start, blank_end, t) && t < T - 1) { - // we add skipprob and right block's first value - // and assign it to beta - betas[idx_b_t_u] = skip_prob; - if (in_range(blank_start, blank_end, rightBlockStartT) || - in_range(emit_start, emit_end, rightBlockStartT)) { - betas[idx_b_t_u] += betas[idx_b_rightBlockStartT_u]; - } - } - } - - // General case - if (t < T && u < U) { - - CAST_DTYPE skip_prob = CAST_DTYPE(-INFINITY); - if (in_range(blank_start, blank_end, t)) { - skip_prob = logProbs[(idx_b_t_u << 1) + LOG_PROBS_SKIP_IDX]; - } - // We check if the first index of right warp is within allowed - // timesteps, if so we add the betas of right warp's first timestep - // to skip score - CAST_DTYPE skip = skip_prob; - if (in_range(blank_start, blank_end, rightBlockStartT) || in_range(emit_start, emit_end, rightBlockStartT)) { - skip += betas[idx_b_rightBlockStartT_u]; - } - - CAST_DTYPE emit = CAST_DTYPE(-INFINITY); - if (in_range(emit_start, emit_end, t) && u < U - 1) { - emit = betas[idx_b_t_up1] + - logProbs[(idx_b_t_u << 1) + LOG_PROBS_EMIT_IDX]; - } - - CAST_DTYPE out_score = CAST_DTYPE(-INFINITY); - if (skip != CAST_DTYPE(-INFINITY) || emit != CAST_DTYPE(-INFINITY)) { - out_score = math::lse(skip, emit); - } - - for(int i = 1; i < warp_size; i++) { - - CAST_DTYPE synced_val = __shfl_up_sync(0xffffffff, out_score, 1); - - if (i == threadIdx.x && - (in_range(blank_start, blank_end, t) || - in_range(emit_start, emit_end, t)) - ) { - out_score = math::lse(synced_val + skip_prob, emit); - } - } - - // bounds check for last warps - if (t < T - 1 && u < U - 1) { - // bound checks for t - if (in_range(blank_start, blank_end, t) || - in_range(emit_start, emit_end, t)) { - betas[idx_b_t_u] = out_score; - } - } - } - - if (t == 0 && u == 0) { // use -beta(0, 0) as cost. - if (isinf(-betas[idx_b_t_u]) || isnan(-betas[idx_b_t_u])) { - costs[bTgt] = 0; - } else { - costs[bTgt] = -betas[idx_b_t_u]; - } - } - - if (threadIdx.x == 0) { - __threadfence(); - atomicAdd(counter, 1); - } -} - - -// This is a wrapper around ComputeAlphasRestricted -// kernel to enable unit testing -template -__global__ void ComputeBetasCostsRestrictedWrapper( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - const CAST_DTYPE* logProbs, - const int* srcLengths, - const int* tgtLengths, - int* beta_counters, - CAST_DTYPE* costs, - volatile CAST_DTYPE* betas, - const int* wpEnds, - int lBuffer, - int rBuffer, - int warp_size, - int num_warp, - int H=1) { - ComputeBetasCostsRestricted( - maxSrcLen, - maxTgtLen, - numTargets, - blank, - logProbs, - srcLengths, - tgtLengths, - beta_counters, - costs, - betas, - wpEnds, - lBuffer, - rBuffer, - warp_size, - num_warp, - H); -} - -} // namespace rnnt -} // namespace torchaudio - -#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh b/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh index 2969a7f790..a2d724bf8f 100644 --- a/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh +++ b/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh @@ -5,8 +5,8 @@ #include #include +#include #include -#include namespace torchaudio { namespace rnnt { @@ -285,9 +285,6 @@ __global__ void ComputeAlphasBetasCosts( int* betaCounters, volatile CAST_DTYPE* betas, DTYPE* costs, - const int* wpEnds=nullptr, - int lBuffer = 0, - int rBuffer = 0, int warpSize = 0, int numWarps=0, int H = 1) { @@ -295,7 +292,7 @@ __global__ void ComputeAlphasBetasCosts( assert(threadIdx.y == 0 || threadIdx.y == 1); if (threadIdx.y == 0) { - if (wpEnds == nullptr) { + ComputeAlphas( /*maxSrcLen=*/maxSrcLen, /*maxTgtLen=*/maxTgtLen, @@ -307,26 +304,7 @@ __global__ void ComputeAlphasBetasCosts( /*alpha_counters=*/alpha_counters, /*alphas=*/alphas, H); - } - else { - ComputeAlphasRestricted( - /*maxSrcLen=*/maxSrcLen, - /*maxTgtLen=*/maxTgtLen, - /*numTargets=*/numTargets, - /*blank=*/blank, - /*logProbs=*/logProbs, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*alpha_counters=*/alpha_counters, - /*alphas=*/(CAST_DTYPE*)alphas, - /*wpEnds=*/wpEnds, - /*lBuffer=*/lBuffer, - /*rBuffer=*/rBuffer, - /*warpSize=*/warpSize, - H); - } } else { // threadIdx.y == 1 - if (wpEnds == nullptr) { ComputeBetasCosts( /*maxSrcLen=*/maxSrcLen, /*maxTgtLen=*/maxTgtLen, @@ -339,26 +317,6 @@ __global__ void ComputeAlphasBetasCosts( /*beta=*/betas, /*costs=*/costs, H); - } else { - ComputeBetasCostsRestricted( - maxSrcLen, - maxTgtLen, - numTargets, - blank, - logProbs, - srcLengths, - tgtLengths, - betaCounters, - costs, - betas, - wpEnds, - lBuffer, - rBuffer, - warpSize, - numWarps, - H); - } - } } diff --git a/torchaudio/csrc/rnnt/gpu/gpu_transducer.h b/torchaudio/csrc/rnnt/gpu/gpu_transducer.h index cde953a8ef..1720b55645 100644 --- a/torchaudio/csrc/rnnt/gpu/gpu_transducer.h +++ b/torchaudio/csrc/rnnt/gpu/gpu_transducer.h @@ -4,7 +4,6 @@ #include #include -#include #include namespace torchaudio { @@ -88,8 +87,7 @@ status_t Compute( const int* srcLengths, const int* tgtLengths, DTYPE* costs, - DTYPE* gradients = nullptr, - const int* wpEnds = nullptr) { + DTYPE* gradients = nullptr) { const Options& options = workspace.GetOptions(); const cudaStream_t& stream = options.stream_; @@ -101,8 +99,6 @@ status_t Compute( const int& blank = options.blank_; const CAST_DTYPE clamp = options.clamp_; - const int& l_buffer = options.lBuffer_; - const int& r_buffer = options.rBuffer_; const bool& fusedLogSmax = options.fusedLogSmax_; { // compute denominators. @@ -170,9 +166,6 @@ status_t Compute( /*beta_counters=*/workspace.GetPointerToBetaCounters(), /*betas=*/workspace.GetPointerToBetas(), /*costs=*/costs, - /*wpEnds=*/wpEnds, - /*l_buffer=*/l_buffer, - /*r_buffer=*/r_buffer, /*warp_size=*/WARP_SIZE, /*num_warps=*/num_warps, H); @@ -221,8 +214,7 @@ status_t ComputeAlphas( const int* targets, const int* srcLengths, const int* tgtLengths, - DTYPE* alphas, - const int* wpEnds = nullptr) { + DTYPE* alphas) { const Options& options = workspace.GetOptions(); const cudaStream_t& stream = options.stream_; @@ -233,9 +225,6 @@ status_t ComputeAlphas( const int& D = options.numTargets_; const int& blank = options.blank_; - const int& l_buffer = options.lBuffer_; - const int& r_buffer = options.rBuffer_; - { // compute denominators. status_t status = LogSumExp2D( /*stream=*/stream, @@ -285,39 +274,20 @@ status_t ComputeAlphas( // 2nd dim is 1 for alpha only dim3 thread_dims(WARP_SIZE, 1); - if (wpEnds == nullptr) { - ComputeAlphasWrapper - <<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*log_probs=*/workspace.GetPointerToLogProbs(), - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*alpha_counters=*/workspace.GetPointerToAlphaCounters(), - /*alphas=*/(volatile DTYPE*)alphas, - H); - } else { - // cudaMemset(alphas, 0, B * max_T * max_U * sizeof(DTYPE)); - ComputeAlphasRestrictedWrapper - <<>>( - max_T, - max_U, - D, - blank, - workspace.GetPointerToLogProbs(), - srcLengths, - tgtLengths, - workspace.GetPointerToAlphaCounters(), - // workspace.GetPointerToAlphas(), - (volatile DTYPE*)alphas, - wpEnds, - l_buffer, - r_buffer, - WARP_SIZE, - H); - } + + ComputeAlphasWrapper + <<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/workspace.GetPointerToAlphaCounters(), + /*alphas=*/(volatile DTYPE*)alphas, + H); + if (cudaGetLastError() != cudaSuccess) { return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; } @@ -334,8 +304,7 @@ status_t ComputeBetas( const int* srcLengths, const int* tgtLengths, DTYPE* costs, - DTYPE* betas, - const int* wpEnds = nullptr) { + DTYPE* betas) { const Options& options = workspace.GetOptions(); const cudaStream_t& stream = options.stream_; @@ -346,9 +315,6 @@ status_t ComputeBetas( const int& D = options.numTargets_; const int& blank = options.blank_; - const int& l_buffer = options.lBuffer_; - const int& r_buffer = options.rBuffer_; - { // compute denominators. status_t status = LogSumExp2D( /*stream=*/stream, @@ -398,41 +364,20 @@ status_t ComputeBetas( // 2nd dim is 1 for betas only dim3 thread_dims(WARP_SIZE, 1); - if (wpEnds == nullptr) { - ComputeBetasWrapper - <<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*log_probs=*/workspace.GetPointerToLogProbs(), - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*alpha_counters=*/workspace.GetPointerToBetaCounters(), - /*alphas=*/(volatile DTYPE*)betas, - costs, - H); - } else { - ComputeBetasCostsRestrictedWrapper - <<>>( - max_T, - max_U, - D, - blank, - workspace.GetPointerToLogProbs(), - srcLengths, - tgtLengths, - workspace.GetPointerToBetaCounters(), - costs, - (volatile DTYPE*)betas, - wpEnds, - l_buffer, - r_buffer, - WARP_SIZE, - num_warps, - H); - cudaDeviceSynchronize(); - } + ComputeBetasWrapper + <<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/workspace.GetPointerToBetaCounters(), + /*alphas=*/(volatile DTYPE*)betas, + costs, + H); + if (cudaGetLastError() != cudaSuccess) { return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; } diff --git a/torchaudio/csrc/rnnt/options.h b/torchaudio/csrc/rnnt/options.h index c7ea63eda9..f70a3c8c07 100644 --- a/torchaudio/csrc/rnnt/options.h +++ b/torchaudio/csrc/rnnt/options.h @@ -29,12 +29,6 @@ typedef struct Options { // gradient clamp value. float clamp_; - // alignment restricted loss l buffer - int lBuffer_; - - // alignment restricted loss r buffer - int rBuffer_; - // batch size = B. int batchSize_; @@ -60,8 +54,6 @@ typedef struct Options { blank_(-1), backtrack_(false), clamp_(-1), // negative for disabling clamping by default. - lBuffer_(0), - rBuffer_(0), batchSize_(0), nHypos_(1), maxSrcLen_(0), diff --git a/torchaudio/csrc/rnnt/transducer.h b/torchaudio/csrc/rnnt/transducer.h index c69067a000..82b84ce0ea 100644 --- a/torchaudio/csrc/rnnt/transducer.h +++ b/torchaudio/csrc/rnnt/transducer.h @@ -14,8 +14,7 @@ status_t Compute( const int* srcLengths, const int* tgtLengths, DTYPE* costs, - DTYPE* gradients = nullptr, - const int* wp_ends=nullptr) { + DTYPE* gradients = nullptr) { switch (workspace.GetOptions().device_) { case CPU: { status_t status = cpu::Compute( @@ -25,8 +24,7 @@ status_t Compute( /*srcLengths=*/srcLengths, /*tgtLengths=*/tgtLengths, /*costs=*/costs, - /*gradients=*/gradients, - /*wp_ends =*/wp_ends); + /*gradients=*/gradients); return status; } case GPU: { @@ -37,8 +35,7 @@ status_t Compute( /*srcLengths=*/srcLengths, /*tgtLengths=*/tgtLengths, /*costs=*/costs, - /*gradients=*/gradients, - /*wp_ends =*/wp_ends); + /*gradients=*/gradients); return status; } default: { @@ -56,8 +53,7 @@ status_t ComputeAlphas( const int* targets, const int* srcLengths, const int* tgtLengths, - DTYPE* alphas, - const int* wp_ends=nullptr) { + DTYPE* alphas) { switch (workspace.GetOptions().device_) { case CPU: { status_t status = cpu::ComputeAlphas( @@ -66,8 +62,7 @@ status_t ComputeAlphas( /*targets=*/targets, /*srcLengths=*/srcLengths, /*tgtLengths=*/tgtLengths, - /*alphas=*/alphas, - /*wp_ends =*/wp_ends); + /*alphas=*/alphas); return status; } case GPU: { @@ -77,8 +72,7 @@ status_t ComputeAlphas( /*targets=*/targets, /*srcLengths=*/srcLengths, /*tgtLengths=*/tgtLengths, - /*costs=*/alphas, - /*wp_ends =*/wp_ends); + /*costs=*/alphas); return status; } default: { @@ -97,8 +91,7 @@ status_t ComputeBetas( const int* srcLengths, const int* tgtLengths, DTYPE* costs, - DTYPE* betas, - const int* wp_ends=nullptr) { + DTYPE* betas) { switch (workspace.GetOptions().device_) { case CPU: { status_t status = cpu::ComputeBetas( @@ -108,8 +101,7 @@ status_t ComputeBetas( /*srcLengths=*/srcLengths, /*tgtLengths=*/tgtLengths, /*costs=*/costs, - /*betas=*/betas, - /*wp_ends =*/wp_ends); + /*betas=*/betas); return status; } case GPU: { @@ -120,8 +112,7 @@ status_t ComputeBetas( /*srcLengths=*/srcLengths, /*tgtLengths=*/tgtLengths, /*costs=*/costs, - /*betas=*/betas, - /*wp_ends =*/wp_ends); + /*betas=*/betas); return status; } default: { diff --git a/torchaudio/prototype/rnnt_loss.py b/torchaudio/prototype/rnnt_loss.py index f69d328f53..1da03bfb2a 100644 --- a/torchaudio/prototype/rnnt_loss.py +++ b/torchaudio/prototype/rnnt_loss.py @@ -13,9 +13,6 @@ def _rnnt_loss_alphas( target_lengths, blank=-1, clamp=-1, - wordpiece_ends=None, - left_buffer=0, - right_buffer=0, ): """ Compute alphas for RNN transducer loss. @@ -25,8 +22,6 @@ def _rnnt_loss_alphas( targets = targets.to(device=logits.device) logit_lengths = logit_lengths.to(device=logits.device) target_lengths = target_lengths.to(device=logits.device) - if wordpiece_ends is not None: - wordpiece_ends = wordpiece_ends.to(device=logits.device) # make sure all int tensors are of type int32. targets = targets.int() @@ -40,9 +35,6 @@ def _rnnt_loss_alphas( target_lengths, blank, clamp, - wordpiece_ends, - left_buffer, - right_buffer, ) @@ -53,9 +45,6 @@ def _rnnt_loss_betas( target_lengths, blank=-1, clamp=-1, - wordpiece_ends=None, - left_buffer=0, - right_buffer=0, ): """ Compute betas for RNN transducer loss @@ -65,8 +54,6 @@ def _rnnt_loss_betas( targets = targets.to(device=logits.device) logit_lengths = logit_lengths.to(device=logits.device) target_lengths = target_lengths.to(device=logits.device) - if wordpiece_ends is not None: - wordpiece_ends = wordpiece_ends.to(device=logits.device) # make sure all int tensors are of type int32. targets = targets.int() @@ -80,9 +67,6 @@ def _rnnt_loss_betas( target_lengths, blank, clamp, - wordpiece_ends, - left_buffer, - right_buffer, ) @@ -97,9 +81,6 @@ def forward( blank=-1, clamp=-1, runtime_check=False, - wordpiece_ends=None, - left_buffer=0, - right_buffer=0, fused_log_softmax=True, reuse_logits_for_grads=True, ): @@ -111,8 +92,6 @@ def forward( targets = targets.to(device=logits.device) logit_lengths = logit_lengths.to(device=logits.device) target_lengths = target_lengths.to(device=logits.device) - if wordpiece_ends is not None: - wordpiece_ends = wordpiece_ends.to(device=logits.device) # make sure all int tensors are of type int32. targets = targets.int() @@ -138,9 +117,6 @@ def forward( tgt_lengths=target_lengths, blank=blank, clamp=clamp, - wp_ends=wordpiece_ends, - l_buffer=left_buffer, - r_buffer=right_buffer, fused_log_smax=fused_log_softmax, reuse_logits_for_grads=reuse_logits_for_grads, ) @@ -162,9 +138,6 @@ def backward(ctx, output_gradients): None, # blank None, # clamp None, # runtime_check - None, # wordpiece_ends - None, # left_buffer - None, # right_buffer None, # fused_log_softmax None, # reuse_logits_for_grads ) @@ -178,9 +151,6 @@ def rnnt_loss( blank=-1, clamp=-1, runtime_check=False, - wordpiece_ends=None, - left_buffer=0, - right_buffer=0, fused_log_softmax=True, reuse_logits_for_grads=True, ): @@ -199,12 +169,6 @@ def rnnt_loss( blank (int, opt): blank label (Default: ``-1``) clamp (float): clamp for gradients (Default: ``-1``) runtime_check (bool): whether to do sanity check during runtime. (Default: ``False``) - wordpiece_ends (Tensor): Tensor of dimension (batch, target) containing the end frames of targets - for each sequence (including bos end_frame = 0) (Default: ``None``) - left_buffer (int): left buffer frames used for alignment restricted RNNT loss. - Loss will not be imposed on frames smaller than wordpiece_end - left_buffer. (Default: ``0``) - right_buffer (int): right buffer frames used for alignment restricted RNNT loss. - Loss will not be imposed on frames greater than wordpiece_end + right_buffer. (Default: ``0``) fused_log_smax (bool): set to False if calling log_softmax outside loss (Default: ``True``) reuse_logits_for_grads (bool): whether to save memory by reusing logits memory for grads (Default: ``True``) """ @@ -222,9 +186,6 @@ def rnnt_loss( blank, clamp, runtime_check, - wordpiece_ends, - left_buffer, - right_buffer, fused_log_softmax, reuse_logits_for_grads, ) @@ -243,10 +204,6 @@ class RNNTLoss(torch.nn.Module): blank (int, opt): blank label (Default: ``-1``) clamp (float): clamp for gradients (Default: ``-1``) runtime_check (bool): whether to do sanity check during runtime. (Default: ``False``) - left_buffer (int): left buffer frames used for alignment restricted RNNT loss. - Loss will not be imposed on frames smaller than wordpiece_end - left_buffer. (Default: ``0``) - right_buffer (int): right buffer frames used for alignment restricted RNNT loss. - Loss will not be imposed on frames greater than wordpiece_end + right_buffer. (Default: ``0``) fused_log_smax (bool): set to False if calling log_softmax outside loss (Default: ``True``) reuse_logits_for_grads (bool): whether to save memory by reusing logits memory for grads (Default: ``True``) """ @@ -256,8 +213,6 @@ def __init__( blank=-1, clamp=-1, runtime_check=False, - left_buffer=0, - right_buffer=0, fused_log_softmax=True, reuse_logits_for_grads=True, ): @@ -265,8 +220,6 @@ def __init__( self.blank = blank self.clamp = clamp self.runtime_check = runtime_check - self.left_buffer = left_buffer - self.right_buffer = right_buffer self.fused_log_softmax = fused_log_softmax self.reuse_logits_for_grads = reuse_logits_for_grads @@ -276,9 +229,6 @@ def forward( targets, logit_lengths, target_lengths, - wordpiece_ends=None, - left_buffer=None, - right_buffer=None, ): """ Args: @@ -286,22 +236,7 @@ def forward( targets (Tensor): Tensor of dimension (batch, max target length) containing targets with zero padded logit_lengths (Tensor): Tensor of dimension (batch) containing lengths of each sequence from encoder target_lengths (Tensor): Tensor of dimension (batch) containing lengths of targets for each sequence - wordpiece_ends (Tensor): Tensor of dimension (batch, target) containing the end frames of targets - for each sequence (including bos end_frame = 0) (Default: ``None``) - left_buffer (int): left buffer frames used for alignment restricted RNNT loss. - Loss will not be imposed on frames smaller than wordpiece_end - left_buffer. (Default: ``None``) - right_buffer (int): right buffer frames used for alignment restricted RNNT loss. - Loss will not be imposed on frames greater than wordpiece_end + right_buffer. (Default: ``None``) """ - # left_buffer / right_buffer is passed in forward, - # when we use dataset or sample specific left/right buffer - # self.left_buffer / right_buffer is used by default, or - # to preserve backward compatibility - if left_buffer is None: - left_buffer = self.left_buffer - if right_buffer is None: - right_buffer = self.right_buffer - return rnnt_loss( logits, targets, @@ -310,9 +245,6 @@ def forward( self.blank, self.clamp, self.runtime_check, - wordpiece_ends, - left_buffer, - right_buffer, self.fused_log_softmax, self.reuse_logits_for_grads, ) From d25de70f61bfc483ac4f17e466fbd71ca801c239 Mon Sep 17 00:00:00 2001 From: Caroline Chen Date: Fri, 30 Apr 2021 12:53:14 -0700 Subject: [PATCH 47/73] Remove GPU code Summary: to make fixing the CI build easier, we want to first remove the CPU code and export only the CPU code. we will add back GPU code after CPU code is merged into open source Reviewed By: vincentqb Differential Revision: D28076934 fbshipit-source-id: 9e12298b0ba8733853999c1127f0ee9d9368e25f --- .../rnnt/rnnt_loss_cuda_test.py | 10 - torchaudio/csrc/rnnt/gpu/compute.cu | 105 ----- torchaudio/csrc/rnnt/gpu/compute_alphas.cu | 73 --- torchaudio/csrc/rnnt/gpu/compute_betas.cu | 78 ---- torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh | 99 ---- torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh | 431 ------------------ torchaudio/csrc/rnnt/gpu/gpu_transducer.h | 393 ---------------- torchaudio/csrc/rnnt/gpu/half.cuh | 38 -- torchaudio/csrc/rnnt/gpu/kernel_utils.h | 59 --- torchaudio/csrc/rnnt/gpu/kernels.h | 133 ------ torchaudio/csrc/rnnt/gpu/math.cuh | 41 -- 11 files changed, 1460 deletions(-) delete mode 100644 test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py delete mode 100644 torchaudio/csrc/rnnt/gpu/compute.cu delete mode 100644 torchaudio/csrc/rnnt/gpu/compute_alphas.cu delete mode 100644 torchaudio/csrc/rnnt/gpu/compute_betas.cu delete mode 100644 torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh delete mode 100644 torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh delete mode 100644 torchaudio/csrc/rnnt/gpu/gpu_transducer.h delete mode 100644 torchaudio/csrc/rnnt/gpu/half.cuh delete mode 100644 torchaudio/csrc/rnnt/gpu/kernel_utils.h delete mode 100644 torchaudio/csrc/rnnt/gpu/kernels.h delete mode 100644 torchaudio/csrc/rnnt/gpu/math.cuh diff --git a/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py b/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py deleted file mode 100644 index c64fd687fe..0000000000 --- a/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py +++ /dev/null @@ -1,10 +0,0 @@ -import torch -from .rnnt_loss_impl import RNNTLossTest -from torchaudio_unittest import common_utils -from .utils import skipIfNoTransducer - - -@skipIfNoTransducer -@common_utils.skipIfNoCuda -class TestRNNTLoss(RNNTLossTest, common_utils.PytorchTestCase): - device = torch.device('cuda') diff --git a/torchaudio/csrc/rnnt/gpu/compute.cu b/torchaudio/csrc/rnnt/gpu/compute.cu deleted file mode 100644 index 8582f36d48..0000000000 --- a/torchaudio/csrc/rnnt/gpu/compute.cu +++ /dev/null @@ -1,105 +0,0 @@ -#include -#include -#include - -namespace torchaudio { -namespace rnnt { -namespace gpu { - -// Entry point into RNNT Loss -std::tuple> -compute( - torch::Tensor& logits, - const torch::Tensor& targets, - const torch::Tensor& src_lengths, - const torch::Tensor& tgt_lengths, - int64_t blank, - double clamp, - bool fused_log_smax = true, - bool reuse_logits_for_grads = true) { - - Options options; - options.batchSize_ = src_lengths.size(0); - options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); - options.maxSrcLen_ = logits.size(1); - options.maxTgtLen_ = logits.size(2); - options.numTargets_ = logits.size(3); - options.blank_ = blank; - options.clamp_ = clamp; - options.fusedLogSmax_ = fused_log_smax; - - CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); - options.stream_ = at::cuda::getCurrentCUDAStream(); - cudaSetDevice(logits.get_device()); - options.device_ = GPU; - - torch::Tensor costs = torch::empty( - options.batchSize_ * options.nHypos_, - torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); - c10::optional gradients = c10::nullopt; - if (logits.requires_grad()) { - if (reuse_logits_for_grads) { - gradients = logits; - } else { - gradients = torch::zeros_like(logits); - } - } - - torch::Tensor int_workspace = torch::empty( - IntWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Int)); - - torch::Tensor float_workspace = torch::empty( - DtypeWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Float)); - - Workspace workspace( - /*options=*/options, - /*dtype_data=*/float_workspace.data(), - /*dtype_size=*/float_workspace.numel(), - /*int_data=*/int_workspace.data(), - /*int_size=*/int_workspace.numel()); - - switch (logits.type().scalarType()) { - case torch::ScalarType::Float: - { - Compute( - /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*costs=*/costs.data(), - /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data()); - break; - } - case torch::ScalarType::Half: - { - Compute( - /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*costs=*/costs.data(), - /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data()); - break; - } - default: - { - LOG(ERROR) << "unsupported logits.type().scalarType() = " - << logits.type().scalarType(); - break; - } - }; - - return std::make_tuple(costs, gradients); -} - -TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { - m.impl("rnnt_loss", &compute); -} - -} // namespace gpu -} // namespace rnnt -} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/compute_alphas.cu b/torchaudio/csrc/rnnt/gpu/compute_alphas.cu deleted file mode 100644 index dc101b0ecb..0000000000 --- a/torchaudio/csrc/rnnt/gpu/compute_alphas.cu +++ /dev/null @@ -1,73 +0,0 @@ -#include -#include -#include - -namespace torchaudio { -namespace rnnt { -namespace gpu { - -torch::Tensor compute_alphas( - const torch::Tensor& logits, - const torch::Tensor& targets, - const torch::Tensor& src_lengths, - const torch::Tensor& tgt_lengths, - int64_t blank, - double clamp) { - Options options; - options.batchSize_ = src_lengths.size(0); - options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); - options.maxSrcLen_ = logits.size(1); - options.maxTgtLen_ = logits.size(2); - options.numTargets_ = logits.size(3); - options.blank_ = blank; - options.clamp_ = clamp; - - CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); - options.stream_ = at::cuda::getCurrentCUDAStream(); - cudaSetDevice(logits.get_device()); - options.device_ = GPU; - - torch::Tensor alphas = torch::zeros( - {options.batchSize_ * options.nHypos_, - options.maxSrcLen_, - options.maxTgtLen_}, - torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); - - torch::Tensor int_workspace = torch::empty( - IntWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions() - .device(logits.device()) - .dtype(torch::ScalarType::Int)); - - torch::Tensor float_workspace = torch::empty( - DtypeWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions() - .device(logits.device()) - .dtype(torch::ScalarType::Float)); - - Workspace workspace( - /*options=*/options, - /*dtype_data=*/float_workspace.data(), - /*dtype_size=*/float_workspace.numel(), - /*int_data=*/int_workspace.data(), - /*int_size=*/int_workspace.numel()); - - // Only support float, this is mainly to enable easy - // unit-testing - ComputeAlphas( - /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*alphas=*/alphas.data()); - return alphas; -} - -TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { - m.impl("rnnt_loss_alphas", &compute_alphas); -} - -} // namespace gpu -} // namespace rnnt -} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/compute_betas.cu b/torchaudio/csrc/rnnt/gpu/compute_betas.cu deleted file mode 100644 index 63b8fd636a..0000000000 --- a/torchaudio/csrc/rnnt/gpu/compute_betas.cu +++ /dev/null @@ -1,78 +0,0 @@ -#include -#include -#include - -namespace torchaudio { -namespace rnnt { -namespace gpu { - -torch::Tensor compute_betas( - const torch::Tensor& logits, - const torch::Tensor& targets, - const torch::Tensor& src_lengths, - const torch::Tensor& tgt_lengths, - int64_t blank, - double clamp) { - Options options; - options.batchSize_ = src_lengths.size(0); - options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); - options.maxSrcLen_ = logits.size(1); - options.maxTgtLen_ = logits.size(2); - options.numTargets_ = logits.size(3); - options.blank_ = blank; - options.clamp_ = clamp; - - CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); - options.stream_ = at::cuda::getCurrentCUDAStream(); - cudaSetDevice(logits.get_device()); - options.device_ = GPU; - - torch::Tensor costs = torch::empty( - tgt_lengths.size(0), - torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); - - torch::Tensor betas = torch::zeros( - {options.batchSize_ * options.nHypos_, - options.maxSrcLen_, - options.maxTgtLen_}, - torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); - - torch::Tensor int_workspace = torch::empty( - IntWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions() - .device(logits.device()) - .dtype(torch::ScalarType::Int)); - - torch::Tensor float_workspace = torch::empty( - DtypeWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions() - .device(logits.device()) - .dtype(torch::ScalarType::Float)); - - Workspace workspace( - /*options=*/options, - /*dtype_data=*/float_workspace.data(), - /*dtype_size=*/float_workspace.numel(), - /*int_data=*/int_workspace.data(), - /*int_size=*/int_workspace.numel()); - - // Only support float, this is mainly to enable easy - // unit-testing - ComputeBetas( - /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*costs=*/costs.data(), - /*betas=*/betas.data()); - return betas; -} - -TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { - m.impl("rnnt_loss_betas", &compute_betas); -} - -} // namespace gpu -} // namespace rnnt -} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh b/torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh deleted file mode 100644 index f42e35b46f..0000000000 --- a/torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh +++ /dev/null @@ -1,99 +0,0 @@ -#pragma once - -#ifdef USE_CUDA - -#include - -namespace torchaudio { -namespace rnnt { - -template -__global__ void ReduceMax2D( - int dim, - const DTYPE* inputs, // [N, dim] - CAST_DTYPE* outputs) { - - __shared__ CAST_DTYPE shared[NUM_THREADS]; - - // each thread reduces one matrix row - int offset = blockIdx.x * dim; // [n, 0] - CAST_DTYPE val = inputs[offset]; // default = inputs(n, 0) - for (int d = threadIdx.x; d < dim; d += NUM_THREADS) { - CAST_DTYPE next = inputs[offset + d]; - if (next > val) { - val = next; - } - } - - shared[threadIdx.x] = val; - __syncthreads(); - - for (int stride = (NUM_THREADS >> 1); stride >= WARP_SIZE; stride >>= 1) { - if (threadIdx.x < stride && threadIdx.x + stride < dim) { - if (shared[threadIdx.x + stride] > shared[threadIdx.x]) { - shared[threadIdx.x] = shared[threadIdx.x + stride]; - val = shared[threadIdx.x]; - } - } - __syncthreads(); - } - - CAST_DTYPE shf; - for (int stride = (WARP_SIZE >> 1); stride > 0; stride >>= 1) { - shf = __shfl_down_sync(0xFFFFFFFF, val, stride); - if (threadIdx.x < stride && threadIdx.x + stride < dim) { - if (shf > val) { - val = shf; - } - } - } - - if (threadIdx.x == 0) { - outputs[blockIdx.x] = val; - } -} - -template -__global__ void ReduceLogSumExpGivenMax2D( - int dim, - const DTYPE* inputs, // [N, dim] - CAST_DTYPE* outputs) { // in: max -> out: logsum - - __shared__ CAST_DTYPE shared[NUM_THREADS]; - - CAST_DTYPE max = outputs[blockIdx.x]; - CAST_DTYPE val = 0; - - int offset = blockIdx.x * dim; - for (int d = threadIdx.x; d < dim; d += NUM_THREADS) { - val = val + std::exp(CAST_DTYPE(inputs[offset + d]) - max); - } - - shared[threadIdx.x] = val; - __syncthreads(); - - for (int stride = (NUM_THREADS >> 1); stride >= WARP_SIZE; stride >>= 1) { - if (threadIdx.x < stride && threadIdx.x + stride < dim) { - val = shared[threadIdx.x] + shared[threadIdx.x + stride]; - shared[threadIdx.x] = val; - } - __syncthreads(); - } - - CAST_DTYPE shf; - for (int stride = (WARP_SIZE >> 1); stride > 0; stride >>= 1) { - shf = __shfl_down_sync(0xFFFFFFFF, val, stride); - if (threadIdx.x < stride && threadIdx.x + stride < dim) { - val = val + shf; - } - } - - if (threadIdx.x == 0) { - outputs[blockIdx.x] = max + std::log(val); - } -} - -} // namespace rnnt -} // namespace torchaudio - -#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh b/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh deleted file mode 100644 index a2d724bf8f..0000000000 --- a/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh +++ /dev/null @@ -1,431 +0,0 @@ -#pragma once - -#ifdef USE_CUDA - -#include - -#include -#include -#include - -namespace torchaudio { -namespace rnnt { - - -template -__global__ void ComputeLogProbs( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - const CAST_DTYPE* denominators, - CAST_DTYPE* logProbs, - int H=1, - bool fusedLogSmax=true) { - - const int& maxT = maxSrcLen; - const int& maxU = maxTgtLen; - const int& D = numTargets; - - const int bTgt = blockIdx.z; // 0 <= b < B - const int bSrc = bTgt / H; - const int T = srcLengths[bSrc]; - const int U = tgtLengths[bTgt] + 1; - - const int t = blockIdx.x * blockDim.x + threadIdx.x; - const int u = blockIdx.y; - - if (t >= T || u >= U) { // out of boundary. - return; - } - - Indexer3D indexer(maxT, maxU); - - int idx = indexer(bTgt, t, u); - - // skip: log_prob(b, t, u).skip() = logits(b, t, u, blank) - denom(b, t, u). - logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = - CAST_DTYPE(logits[idx * D + blank]) - denominators[idx]; - - if (!fusedLogSmax) { - logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = - CAST_DTYPE(logits[idx * D + blank]); - } - - if (u < U - 1) { - // emit: log_prob(b, t, u).emit() = logits(b, t, u, tgt[u]) - denom(b, t, u). - int target = targets[Indexer2D(maxU - 1)(bTgt, u)]; - logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = - CAST_DTYPE(logits[idx * D + target]) - denominators[idx]; - - if (!fusedLogSmax) { - logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = - CAST_DTYPE(logits[idx * D + target]); - } - } - - -} - - -template -__device__ void ComputeAlphas( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - const CAST_DTYPE* logProbs, - const int* srcLengths, - const int* tgtLengths, - int* alpha_counters, - volatile CAST_DTYPE* alphas, - int H=1) { - - const int& maxT = maxSrcLen; - const int& maxU = maxTgtLen; - - const int bTgt = blockIdx.z; // 0 <= b < B - const int bSrc = bTgt / H; - const int T = srcLengths[bSrc]; - const int U = tgtLengths[bTgt] + 1; - - const int t = blockIdx.x * blockDim.x + threadIdx.x + 1; - const int u = blockIdx.y + 1; - - if (t >= T || u >= U) { // out of boundary. - return; - } - - int* counter = alpha_counters + Indexer2D(maxU)(bTgt, blockIdx.y); - - Indexer3D idxr(maxT, maxU); - - if (t == 1 && u == 1) { - alphas[idxr(bTgt, 0, 0)] = 0; - } - - if (blockIdx.x > 0) { // wait for previous warp (in t-axis) is ready. - while (atomicAdd(counter, 0) < blockIdx.x) {} - } - - if (blockIdx.y > 0) { // wait for previous warp (in u-axis) is ready. - while (atomicAdd(counter - 1, 0) <= blockIdx.x) {} - } - - if (t == 1 && u < U) { - - // alpha(0, u) = alpha(0, u - 1) + logProbs(0, u - 1).emit(). - alphas[idxr(bTgt, 0, u)] = - alphas[idxr(bTgt, 0, u - 1)] - + logProbs[(idxr(bTgt, 0, u - 1) << 1) + LOG_PROBS_EMIT_IDX]; - } - - if (blockIdx.y == 0 && t < T) { - CAST_DTYPE skip_prob = logProbs[(idxr(bTgt, t - 1, 0) << 1) + LOG_PROBS_SKIP_IDX]; - CAST_DTYPE val; - -#pragma unroll - for (int i = 1; i < warpSize; i <<= 1) { - val = __shfl_up_sync(0xffffffff, skip_prob, i); - if (i <= threadIdx.x) { - skip_prob = skip_prob + val; - } - } - - val = alphas[idxr(bTgt, blockIdx.x * blockDim.x, 0)]; - alphas[idxr(bTgt, t, 0)] = skip_prob + val; - } - - if (t < T && u < U) { - - CAST_DTYPE skip_prob = logProbs[(idxr(bTgt, t - 1, u) << 1) + LOG_PROBS_SKIP_IDX]; - CAST_DTYPE emit_prob = logProbs[(idxr(bTgt, t, u - 1) << 1) + LOG_PROBS_EMIT_IDX]; - - CAST_DTYPE skip = alphas[idxr(bTgt, blockIdx.x * blockDim.x, u)] + skip_prob; - CAST_DTYPE emit = alphas[idxr(bTgt, t, u - 1)] + emit_prob; - - CAST_DTYPE val = math::lse(skip, emit); - CAST_DTYPE out = val; - - for(int i = 1; i < warpSize; ++i) { - val = __shfl_up_sync(0xffffffff, val, 1); - if (i == threadIdx.x) { - val = math::lse(val + skip_prob, emit); - out = val; - } - } - - alphas[idxr(bTgt, t, u)] = out; - } - - if (threadIdx.x == 0) { - __threadfence(); - atomicAdd(counter, 1); - } -} - - -template -__device__ void ComputeBetasCosts( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - const CAST_DTYPE* logProbs, - const int* srcLengths, - const int* tgtLengths, - int* betaCounters, - volatile CAST_DTYPE* betas, - DTYPE* costs, - int H=1) { - - const int& maxT = maxSrcLen; - const int& maxU = maxTgtLen; - - const int bTgt = blockIdx.z; // 0 <= b < B - const int bSrc = bTgt / H; - const int T = srcLengths[bSrc]; - const int U = tgtLengths[bTgt] + 1; - - const int t = T - 2 - blockIdx.x * blockDim.x - threadIdx.x; - const int u = U - 2 - blockIdx.y; - - if (t < 0 || u < 0) { // out of boundary. - return; - } - - int* counter = betaCounters + Indexer2D(maxU)(bTgt, blockIdx.y); - - Indexer3D idxr(maxT, maxU); - - if (t == T - 2 && u == U - 2) { - betas[idxr(bTgt, T - 1, U - 1)] = - logProbs[(idxr(bTgt, T - 1, U - 1) << 1) + LOG_PROBS_SKIP_IDX]; - } - - if (blockIdx.x > 0) { // wait for previous warp (in t-axis) is ready. - while (atomicAdd(counter, 0) < blockIdx.x) {} - } - - if (blockIdx.y > 0) { // wait for previous warp (in u-axis) is ready. - while (atomicAdd(counter - 1, 0) <= blockIdx.x) {} - } - - if (t == T - 2 && u >= 0) { - - betas[idxr(bTgt, T - 1, u)] = - betas[idxr(bTgt, T - 1, u + 1)] - + logProbs[(idxr(bTgt, T - 1, u) << 1) + LOG_PROBS_EMIT_IDX]; - } - - if (blockIdx.y == 0 && t >= 0) { - CAST_DTYPE skip_prob = logProbs[(idxr(bTgt, t, U - 1) << 1) + LOG_PROBS_SKIP_IDX]; - CAST_DTYPE val; - -#pragma unroll - for(int i = 1; i < warpSize; i <<= 1) { - val = __shfl_up_sync(0xffffffff, skip_prob, i); - if (i <= threadIdx.x) { - skip_prob = skip_prob + val; - } - } - - betas[idxr(bTgt, t, U - 1)] = - betas[idxr(bTgt, T - 1 - blockIdx.x * blockDim.x, U - 1)] + skip_prob; - } - - if (t >= 0 && u >= 0) { - - CAST_DTYPE skip_prob = logProbs[(idxr(bTgt, t, u) << 1) + LOG_PROBS_SKIP_IDX]; - CAST_DTYPE emit_prob = logProbs[(idxr(bTgt, t, u) << 1) + LOG_PROBS_EMIT_IDX]; - - CAST_DTYPE skip = betas[idxr(bTgt, t + threadIdx.x + 1, u)] + skip_prob; - CAST_DTYPE emit = betas[idxr(bTgt, t, u + 1)] + emit_prob; - - CAST_DTYPE val = math::lse(skip, emit); - CAST_DTYPE out = val; - - for(int i = 1; i < warpSize; ++i) { - val = __shfl_up_sync(0xffffffff, val, 1); - if (i == threadIdx.x) { - val = math::lse(val + skip_prob, emit); - out = val; - } - } - - betas[idxr(bTgt, t, u)] = out; - - if (t == 0 && u == 0) { // use -beta(0, 0) as cost. - costs[bTgt] = DTYPE(-out); - } - } - - if (threadIdx.x == 0) { - __threadfence(); - atomicAdd(counter, 1); - } -} - - -template -__global__ void ComputeAlphasBetasCosts( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - const CAST_DTYPE* logProbs, - const int* srcLengths, - const int* tgtLengths, - int* alpha_counters, - volatile CAST_DTYPE* alphas, - int* betaCounters, - volatile CAST_DTYPE* betas, - DTYPE* costs, - int warpSize = 0, - int numWarps=0, - int H = 1) { - - assert(threadIdx.y == 0 || threadIdx.y == 1); - - if (threadIdx.y == 0) { - - ComputeAlphas( - /*maxSrcLen=*/maxSrcLen, - /*maxTgtLen=*/maxTgtLen, - /*numTargets=*/numTargets, - /*blank=*/blank, - /*logProbs=*/logProbs, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*alpha_counters=*/alpha_counters, - /*alphas=*/alphas, - H); - } else { // threadIdx.y == 1 - ComputeBetasCosts( - /*maxSrcLen=*/maxSrcLen, - /*maxTgtLen=*/maxTgtLen, - /*numTargets=*/numTargets, - /*blank=*/blank, - /*logProbs=*/logProbs, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*betaCounters=*/betaCounters, - /*beta=*/betas, - /*costs=*/costs, - H); - } -} - - -template -__global__ void ComputeGradients( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - CAST_DTYPE clamp, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - const CAST_DTYPE* denominators, - const CAST_DTYPE* alphas, - const CAST_DTYPE* betas, - DTYPE* gradients, - int H = 1, - bool fusedLogSmax = true) { - - const int bTgt = blockIdx.z; // 0 <= b < B - const int t = blockIdx.x * blockDim.x + threadIdx.x; - const int u = blockIdx.y; - - ComputeGradientsElement( - bTgt, - t, - u, - maxSrcLen, - maxTgtLen, - numTargets, - blank, - clamp, - logits, - targets, - srcLengths, - tgtLengths, - denominators, - alphas, - betas, - gradients, - H, - fusedLogSmax); -} - - -// This is a __global__ wrapper around ComputeAlphas -// device kernel to enable unit testing -template -__global__ void ComputeAlphasWrapper( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - const CAST_DTYPE* logProbs, - const int* srcLengths, - const int* tgtLengths, - int* alpha_counters, - volatile CAST_DTYPE* alphas, - int H=1) { - ComputeAlphas( - maxSrcLen, - maxTgtLen, - numTargets, - blank, - logProbs, - srcLengths, - tgtLengths, - alpha_counters, - alphas, - H); -} - -// This is a __global__ wrapper around ComputeBetas -// device kernel to enable unit testing -template -__global__ void ComputeBetasWrapper( - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - const CAST_DTYPE* logProbs, - const int* srcLengths, - const int* tgtLengths, - int* betaCounters, - volatile CAST_DTYPE* betas, - DTYPE* costs, - int H=1) { - ComputeBetasCosts( - maxSrcLen, - maxTgtLen, - numTargets, - blank, - logProbs, - srcLengths, - tgtLengths, - betaCounters, - betas, - costs, - H); -} - - -// #undef LOG_PROBS_SKIP_IDX -// #undef LOG_PROBS_EMIT_IDX - -} // namespace rnnt -} // namespace torchaudio - -#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/gpu_transducer.h b/torchaudio/csrc/rnnt/gpu/gpu_transducer.h deleted file mode 100644 index 1720b55645..0000000000 --- a/torchaudio/csrc/rnnt/gpu/gpu_transducer.h +++ /dev/null @@ -1,393 +0,0 @@ -#pragma once - -#ifdef USE_CUDA - -#include -#include -#include - -namespace torchaudio { -namespace rnnt { -namespace gpu { - -#define gpuErrchk(ans) \ - { gpuAssert((ans), __FILE__, __LINE__); } - -inline void -gpuAssert(cudaError_t code, const char* file, int line, bool abort = true) { - if (code != cudaSuccess) { - fprintf( - stderr, - "\nGPUassert: %s %s %d\n", - cudaGetErrorString(code), - file, - line); - if (abort) - exit(code); - } -} - -template -status_t LogSumExp2D( - cudaStream_t stream, - int N, - int D, - const DTYPE* logits, // [N, D] - CAST_DTYPE* outputs) { - { // compute max among D. - dim3 block_dims(N); - dim3 thread_dims(REDUCE_THREADS); - - ReduceMax2D - <<>>( - /*dim=*/D, - /*inputs=*/logits, - /*outputs=*/outputs); - - // BUGBUG: These error codes are only accurate when launching with - // blocking. Otherwise they usually reflect earlier errors. - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_DENOMINATOR_REDUCE_MAX_FAILED; - } - } - - { // compute log(sum(exp(d_i - max))) - dim3 block_dims(N); - dim3 thread_dims(REDUCE_THREADS); - - ReduceLogSumExpGivenMax2D - <<>>( - /*dim=*/D, - /*inputs=*/logits, - /*outputs=*/outputs); - - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_DENOMINATOR_REDUCE_SUM_FAILED; - } - } - - return SUCCESS; -} - -// Inputs: -// workspace: workspace. -// logits: pointer to (B, max_T, max_U, D) logits. -// targets: pointer to (B, max_U - 1) targets in the batch. -// srcLengths: pointer to (B, ) source lengths in the batch. -// tgtLengths: pointer to (B, ) target lengths in the batch. -// -// Outputs: -// costs: pointer to (B, ) costs in the batch. -// gradients: pointer to (B, max_T, max_U, D) gradients in the batch. -template -status_t Compute( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* costs, - DTYPE* gradients = nullptr) { - const Options& options = workspace.GetOptions(); - - const cudaStream_t& stream = options.stream_; - const int& B = options.batchSize_; - const int& H = options.nHypos_; - const int& max_T = options.maxSrcLen_; - const int& max_U = options.maxTgtLen_; - const int& D = options.numTargets_; - const int& blank = options.blank_; - const CAST_DTYPE clamp = options.clamp_; - - const bool& fusedLogSmax = options.fusedLogSmax_; - - { // compute denominators. - status_t status = LogSumExp2D( - /*stream=*/stream, - /*N=*/B * H * max_T * max_U, - /*D=*/D, - /*logits=*/logits, - /*denominators=*/workspace.GetPointerToDenominators()); - - if (status != SUCCESS) { - return status; - } - } - - { // compute log probability pairs (blank and target). - int num_segments = - (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; - dim3 block_dims(num_segments, max_U, B * H); - dim3 thread_dims(MAX_THREADS_PER_BLOCK); - - ComputeLogProbs<<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*denominators=*/workspace.GetPointerToDenominators(), - /*log_probs=*/workspace.GetPointerToLogProbs(), - H, - fusedLogSmax); - - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_LOG_PROBS_FAILED; - } - } - - { // compute alphas, betas and costs. - // warp is usually a group of threads (32) - int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; - - // each block is identified by 3 d tuple. - // we are using num_warp * max_U * B * H blocks - // where num_warp is division among Time axis - dim3 block_dims(num_warps, max_U, B * H); - - // each thread is identified by a 2 d tuple - // 2nd dim is 2. 1 for alpha, 1 for beta - dim3 thread_dims(WARP_SIZE, 2); - - ComputeAlphasBetasCosts - <<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*log_probs=*/workspace.GetPointerToLogProbs(), - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*alpha_counters=*/workspace.GetPointerToAlphaCounters(), - /*alphas=*/workspace.GetPointerToAlphas(), - /*beta_counters=*/workspace.GetPointerToBetaCounters(), - /*betas=*/workspace.GetPointerToBetas(), - /*costs=*/costs, - /*warp_size=*/WARP_SIZE, - /*num_warps=*/num_warps, - H); - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; - } - } - - if (gradients != nullptr) { // compute gradients. - // don't set gradients to zero to here as gradients might reuse memory from - // logits - - int num_blocks = - (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; - dim3 block_dims(num_blocks, max_U, B * H); - dim3 thread_dims(MAX_THREADS_PER_BLOCK); - - ComputeGradients<<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*clamp=*/clamp, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*denominators=*/workspace.GetPointerToDenominators(), - /*alphas=*/workspace.GetPointerToAlphas(), - /*betas=*/workspace.GetPointerToBetas(), - /*gradients=*/gradients, - H, - fusedLogSmax); - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_GRADIENTS_FAILED; - } - } - - return SUCCESS; -} - -template -status_t ComputeAlphas( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* alphas) { - const Options& options = workspace.GetOptions(); - - const cudaStream_t& stream = options.stream_; - const int& B = options.batchSize_; - const int& H = options.nHypos_; - const int& max_T = options.maxSrcLen_; - const int& max_U = options.maxTgtLen_; - const int& D = options.numTargets_; - const int& blank = options.blank_; - - { // compute denominators. - status_t status = LogSumExp2D( - /*stream=*/stream, - /*N=*/B * H * max_T * max_U, - /*D=*/D, - /*logits=*/logits, - /*denominators=*/workspace.GetPointerToDenominators()); - - if (status != SUCCESS) { - return status; - } - } - - { // compute log probability pairs (blank and target). - int num_segments = - (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; - dim3 block_dims(num_segments, max_U, B * H); - dim3 thread_dims(MAX_THREADS_PER_BLOCK); - - ComputeLogProbs<<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*denominators=*/workspace.GetPointerToDenominators(), - /*log_probs=*/workspace.GetPointerToLogProbs(), - H); - - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_LOG_PROBS_FAILED; - } - } - { // compute alphas - // warp is usually a group of threads (32) - int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; - - // each block is identified by 3 d tuple. - // we are using num_warp * max_U * B blocks - // where num_warp is division among Time axis - dim3 block_dims(num_warps, max_U, B * H); - - // each thread is identified by a 2 d tuple - // 2nd dim is 1 for alpha only - dim3 thread_dims(WARP_SIZE, 1); - - - ComputeAlphasWrapper - <<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*log_probs=*/workspace.GetPointerToLogProbs(), - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*alpha_counters=*/workspace.GetPointerToAlphaCounters(), - /*alphas=*/(volatile DTYPE*)alphas, - H); - - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; - } - } - - return SUCCESS; -} - -template -status_t ComputeBetas( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* costs, - DTYPE* betas) { - const Options& options = workspace.GetOptions(); - - const cudaStream_t& stream = options.stream_; - const int& B = options.batchSize_; - const int& H = options.nHypos_; - const int& max_T = options.maxSrcLen_; - const int& max_U = options.maxTgtLen_; - const int& D = options.numTargets_; - const int& blank = options.blank_; - - { // compute denominators. - status_t status = LogSumExp2D( - /*stream=*/stream, - /*N=*/B * H * max_T * max_U, - /*D=*/D, - /*logits=*/logits, - /*denominators=*/workspace.GetPointerToDenominators()); - - if (status != SUCCESS) { - return status; - } - } - - { // compute log probability pairs (blank and target). - int num_segments = - (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; - dim3 block_dims(num_segments, max_U, B * H); - dim3 thread_dims(MAX_THREADS_PER_BLOCK); - - ComputeLogProbs<<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*denominators=*/workspace.GetPointerToDenominators(), - /*log_probs=*/workspace.GetPointerToLogProbs(), - H); - - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_LOG_PROBS_FAILED; - } - } - { // compute betas - // warp is usually a group of threads (32) - int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; - - // each block is identified by 3 d tuple. - // we are using num_warp * max_U * B blocks - // where num_warp is division among Time axis - dim3 block_dims(num_warps, max_U, B * H); - - // each thread is identified by a 2 d tuple - // 2nd dim is 1 for betas only - dim3 thread_dims(WARP_SIZE, 1); - - ComputeBetasWrapper - <<>>( - /*max_src_len=*/max_T, - /*max_tgt_len=*/max_U, - /*num_targets=*/D, - /*blank=*/blank, - /*log_probs=*/workspace.GetPointerToLogProbs(), - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*alpha_counters=*/workspace.GetPointerToBetaCounters(), - /*alphas=*/(volatile DTYPE*)betas, - costs, - H); - - if (cudaGetLastError() != cudaSuccess) { - return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; - } - } - - return SUCCESS; -} - -} // namespace gpu -} // namespace rnnt -} // namespace torchaudio - -#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/half.cuh b/torchaudio/csrc/rnnt/gpu/half.cuh deleted file mode 100644 index d49ac3bdcf..0000000000 --- a/torchaudio/csrc/rnnt/gpu/half.cuh +++ /dev/null @@ -1,38 +0,0 @@ -#pragma once - -#ifdef USE_C10_HALF -#include "c10/util/Half.h" -#endif // USE_C10_HALF - -#include - -namespace torchaudio { -namespace rnnt { - -struct alignas(sizeof(__half)) Half { - __half x; - - HOST_AND_DEVICE Half() = default; - - FORCE_INLINE HOST_AND_DEVICE Half(float f) { - x = __float2half_rn(f); - if (isinf(__half2float(x))) { - x = __float2half_rz(f); // round toward 0. - } - } - - FORCE_INLINE HOST_AND_DEVICE operator float() const { - return __half2float(x); - } - - FORCE_INLINE HOST_AND_DEVICE Half(__half f) { - x = f; - } - - FORCE_INLINE HOST_AND_DEVICE operator __half() const { - return x; - } -}; - -} // namespace rnnt -} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/kernel_utils.h b/torchaudio/csrc/rnnt/gpu/kernel_utils.h deleted file mode 100644 index 03eb3d33b4..0000000000 --- a/torchaudio/csrc/rnnt/gpu/kernel_utils.h +++ /dev/null @@ -1,59 +0,0 @@ -#pragma once - -#include - -#include - -namespace torchaudio { -namespace rnnt { - -inline HOST_AND_DEVICE bool in_range( - int start, - int end, // inclusive - int val) { - return start <= val && val <= end; -} - -#define LOG_PROBS_SKIP_IDX 0 -#define LOG_PROBS_EMIT_IDX 1 - - -struct Indexer2D { - const int& size2_; - - FORCE_INLINE HOST_AND_DEVICE Indexer2D(const int& size2): size2_(size2) {} - - FORCE_INLINE HOST_AND_DEVICE int operator() (int index1, int index2) { - return index1 * size2_ + index2; - } -}; - - -struct Indexer3D { - const int& size2_; - const int& size3_; - - FORCE_INLINE HOST_AND_DEVICE Indexer3D(const int& size2, const int& size3) - : size2_(size2), size3_(size3) {} - - FORCE_INLINE HOST_AND_DEVICE int operator() (int index1, int index2, int index3) { - return (index1 * size2_ + index2) * size3_ + index3; - } -}; - - -struct Indexer4D { - const int& size2_; - const int& size3_; - const int& size4_; - - HOST_AND_DEVICE Indexer4D(const int& size2, const int& size3, const int& size4) - : size2_(size2), size3_(size3), size4_(size4) {} - - HOST_AND_DEVICE int operator() (int index1, int index2, int index3, int index4) { - return ((index1 * size2_ + index2) * size3_ + index3) * size4_ + index4; - } -}; - -} // namespace rnnt -} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/kernels.h b/torchaudio/csrc/rnnt/gpu/kernels.h deleted file mode 100644 index 2cbef51327..0000000000 --- a/torchaudio/csrc/rnnt/gpu/kernels.h +++ /dev/null @@ -1,133 +0,0 @@ -#pragma once - -#include - -#include -#include - -namespace torchaudio { -namespace rnnt { - - -template -HOST_AND_DEVICE void ComputeGradientsElement( - int bTgt, - int t, - int u, - int maxSrcLen, - int maxTgtLen, - int numTargets, - int blank, - CAST_DTYPE clamp, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - const CAST_DTYPE* denominators, - const CAST_DTYPE* alphas, - const CAST_DTYPE* betas, - DTYPE* gradients, - int H = 1, - bool fusedLogSmax = true) { - - const int& maxT = maxSrcLen; - const int& maxU = maxTgtLen; - const int& D = numTargets; - - const int bSrc = bTgt / H; - const int T = srcLengths[bSrc]; - const int U = tgtLengths[bTgt] + 1; - - if (t >= T || u >= U) { // out of boundary. - if (gradients == logits && t < maxT && u < maxU) { - // gradients and logits are pointing to the same memory location - Indexer3D idxr3(maxT, maxU); - int idx_b_t_u_zero = idxr3(bTgt, t, u); - if (idx_b_t_u_zero != -1 ) { - int start = idx_b_t_u_zero * D; - for (int b_t_u_d = start; b_t_u_d < start + D; ++b_t_u_d) { - gradients[b_t_u_d] = 0; - } - } - } - return; - } - - int costIdx = bTgt * maxT * maxU; - CAST_DTYPE cost = -(betas[costIdx]); - - - Indexer2D idxr2(maxU - 1); - - int idx_b_t_u, idx_b_t_up1, idx_b_tp1_u, idx_b_tp1_up1; - Indexer3D idxr3(maxT, maxU); - idx_b_t_u = idxr3(bTgt, t, u); - idx_b_t_up1 = idxr3(bTgt, t, u+1); - idx_b_tp1_u = idxr3(bTgt, t+1, u); - idx_b_tp1_up1 = idxr3(bTgt, t+1, u+1); - - if (idx_b_t_u == -1 ) { - return; - } - - if (isinf(cost) || isnan(cost)) { - for (int d = 0; d < D; ++d) { - int b_t_u_d = idx_b_t_u * D + d; - gradients[b_t_u_d] = 0; - } - return; - } - - CAST_DTYPE c = alphas[idx_b_t_u] + cost - denominators[idx_b_t_u]; - for (int d = 0; d < D; ++d) { - int b_t_u_d = idx_b_t_u * D + d; - CAST_DTYPE g = CAST_DTYPE(logits[b_t_u_d]) + c; - - if (fusedLogSmax) { - if (d == blank && t == T - 1 && u == U - 1) { // last blank transition. - gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]) - std::exp(g); - } else if (t < T - 1 && d == blank) { - gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); - if (idx_b_tp1_u != -1) { - gradients[b_t_u_d] = gradients[b_t_u_d] - std::exp(g + betas[idx_b_tp1_u]); - } - } else if (u < U - 1 && d == targets[idxr2(bTgt, u)]) { - gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); - if (idx_b_t_up1 != -1) { - gradients[b_t_u_d] = gradients[b_t_u_d] - std::exp(g + betas[idx_b_t_up1]); - } - } else { - gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); - } - } else { // Non fused log softmax case - CAST_DTYPE g = cost + CAST_DTYPE(logits[b_t_u_d]); - if (d == blank && t == T - 1 && u == U - 1) { - gradients[b_t_u_d] = g + alphas[idx_b_t_u]; - } else if (t < T - 1 && d == blank) { - if (idx_b_tp1_u != -1) { - gradients[b_t_u_d] = g + alphas[idx_b_t_u] + betas[idx_b_tp1_u]; - } else { - gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); - } - } else if (u < U - 1 && d == targets[idxr2(bTgt, u)]) { - if (idx_b_t_up1 != -1) { - gradients[b_t_u_d] = g + alphas[idx_b_t_u] + betas[idx_b_t_up1]; - } else { - gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); - } - } else { - gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); - } - gradients[b_t_u_d] = -std::exp(gradients[b_t_u_d]); - } - - if (clamp > 0) { - auto g = CAST_DTYPE(gradients[b_t_u_d]); - gradients[b_t_u_d] = math::min(g, clamp); - gradients[b_t_u_d] = math::max(g, -clamp); - } - } -} - -} // namespace rnnt -} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/math.cuh b/torchaudio/csrc/rnnt/gpu/math.cuh deleted file mode 100644 index a2eaabff93..0000000000 --- a/torchaudio/csrc/rnnt/gpu/math.cuh +++ /dev/null @@ -1,41 +0,0 @@ -#pragma once - -#ifdef USE_CUDA - -#include - -#endif // USE_CUDA - -#include - -namespace torchaudio { -namespace rnnt { - -namespace math { - -template -FORCE_INLINE HOST_AND_DEVICE DTYPE max(DTYPE x, DTYPE y) { - if (x > y) return x; - else return y; -} - -template -FORCE_INLINE HOST_AND_DEVICE DTYPE min(DTYPE x, DTYPE y) { - if (x > y) return y; - else return x; -} - -// log_sum_exp -template -FORCE_INLINE HOST_AND_DEVICE DTYPE lse(DTYPE x, DTYPE y); - -template <> -FORCE_INLINE HOST_AND_DEVICE float lse(float x, float y) { - if (y > x) { return y + log1pf(expf(x - y)); } - else { return x + log1pf(expf(y-x)); } -} - -} - -} // namespace rnnt -} // namespace torchaudio From 7f6ac050cc24dc046197d64bf35d7bda784f3809 Mon Sep 17 00:00:00 2001 From: Nicolas Hug Date: Mon, 10 May 2021 09:58:15 -0700 Subject: [PATCH 48/73] Import torchaudio #1479 0c263a935f178490bda78c046e220e84b095447d Summary: This diff syncs torchaudio GH with fbcode Reviewed By: cpuhrsch Differential Revision: D28321222 fbshipit-source-id: 8c5b5ed87c5b7c3aa87495ccb68ccbf9eaaab152 --- .circleci/config.yml | 8 +- .circleci/config.yml.in | 8 +- CMakeLists.txt | 5 + build_tools/setup_helpers/extension.py | 2 + docs/source/index.rst | 8 +- docs/source/{transducer.rst => rnnt_loss.rst} | 6 +- examples/libtorchaudio/CMakeLists.txt | 7 +- examples/libtorchaudio/README.md | 30 +- .../libtorchaudio/augmentation/CMakeLists.txt | 3 + examples/libtorchaudio/augmentation/README.md | 36 ++ .../create_jittable_pipeline.py | 22 +- .../libtorchaudio/{ => augmentation}/main.cpp | 2 +- examples/libtorchaudio/{run.sh => build.sh} | 12 +- .../common_utils/case_utils.py | 8 + .../functional/autograd_impl.py | 3 +- .../functional/functional_cpu_test.py | 14 +- .../functional/functional_cuda_test.py | 16 +- .../functional/functional_impl.py | 36 +- .../torchscript_consistency_cpu_test.py | 14 +- .../torchscript_consistency_cuda_test.py | 16 +- .../torchscript_consistency_impl.py | 47 +- .../rnnt/rnnt_loss_cuda_test.py | 10 + .../transforms/autograd_test_impl.py | 17 + .../torchscript_consistency_cpu_test.py | 14 +- .../torchscript_consistency_cuda_test.py | 16 +- .../torchscript_consistency_impl.py | 40 +- torchaudio/csrc/CMakeLists.txt | 26 +- torchaudio/csrc/lfilter.cpp | 231 +++++----- torchaudio/csrc/rnnt/compute.cpp | 17 +- torchaudio/csrc/rnnt/compute_alphas.cpp | 13 +- torchaudio/csrc/rnnt/compute_betas.cpp | 13 +- torchaudio/csrc/rnnt/cpu/compute.cpp | 133 ++++-- torchaudio/csrc/rnnt/cpu/compute_alphas.cpp | 14 +- torchaudio/csrc/rnnt/cpu/compute_betas.cpp | 18 +- torchaudio/csrc/rnnt/cpu/cpu_kernels.h | 3 +- torchaudio/csrc/rnnt/cpu/cpu_transducer.h | 14 +- torchaudio/csrc/rnnt/cpu/kernel_utils.h | 33 +- torchaudio/csrc/rnnt/cpu/math.h | 25 +- torchaudio/csrc/rnnt/gpu/compute.cu | 161 +++++++ torchaudio/csrc/rnnt/gpu/compute_alphas.cu | 73 +++ torchaudio/csrc/rnnt/gpu/compute_betas.cu | 78 ++++ torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh | 98 ++++ torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh | 422 ++++++++++++++++++ torchaudio/csrc/rnnt/gpu/gpu_transducer.h | 395 ++++++++++++++++ torchaudio/csrc/rnnt/gpu/half.cuh | 38 ++ torchaudio/csrc/rnnt/gpu/kernel_utils.h | 66 +++ torchaudio/csrc/rnnt/gpu/kernels.h | 131 ++++++ torchaudio/csrc/rnnt/gpu/math.cuh | 48 ++ torchaudio/csrc/rnnt/macros.cpp | 5 - torchaudio/csrc/rnnt/macros.h | 26 -- torchaudio/csrc/rnnt/transducer.h | 6 - torchaudio/csrc/rnnt/workspace.cpp | 20 - torchaudio/csrc/rnnt/workspace.h | 17 +- torchaudio/functional/functional.py | 23 +- torchaudio/prototype/rnnt_loss.py | 108 +---- torchaudio/transforms.py | 9 + 56 files changed, 2077 insertions(+), 587 deletions(-) rename docs/source/{transducer.rst => rnnt_loss.rst} (69%) create mode 100644 examples/libtorchaudio/augmentation/CMakeLists.txt create mode 100644 examples/libtorchaudio/augmentation/README.md rename examples/libtorchaudio/{ => augmentation}/create_jittable_pipeline.py (80%) rename examples/libtorchaudio/{ => augmentation}/main.cpp (90%) rename examples/libtorchaudio/{run.sh => build.sh} (56%) create mode 100644 test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py create mode 100644 torchaudio/csrc/rnnt/gpu/compute.cu create mode 100644 torchaudio/csrc/rnnt/gpu/compute_alphas.cu create mode 100644 torchaudio/csrc/rnnt/gpu/compute_betas.cu create mode 100644 torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh create mode 100644 torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh create mode 100644 torchaudio/csrc/rnnt/gpu/gpu_transducer.h create mode 100644 torchaudio/csrc/rnnt/gpu/half.cuh create mode 100644 torchaudio/csrc/rnnt/gpu/kernel_utils.h create mode 100644 torchaudio/csrc/rnnt/gpu/kernels.h create mode 100644 torchaudio/csrc/rnnt/gpu/math.cuh delete mode 100644 torchaudio/csrc/rnnt/workspace.cpp diff --git a/.circleci/config.yml b/.circleci/config.yml index 7bba9977fd..64a6ae9f9f 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -42,7 +42,7 @@ commands: description: "installs tools required to build torchaudio" steps: - run: - name: Install pkg-config + name: Install build tools command: HOMEBREW_NO_AUTO_UPDATE=1 brew install pkg-config wget # Disable brew auto update which is very slow load_conda_channel_flags: @@ -162,7 +162,7 @@ jobs: binary_macos_wheel: <<: *binary_common macos: - xcode: "9.4.1" + xcode: "12.0" steps: - checkout - install_build_tools_macos @@ -188,7 +188,7 @@ jobs: binary_macos_conda: <<: *binary_common macos: - xcode: "9.4.1" + xcode: "12.0" steps: - checkout - install_build_tools_macos @@ -509,7 +509,7 @@ jobs: unittest_macos_cpu: <<: *binary_common macos: - xcode: "9.4.1" + xcode: "12.0" resource_class: large steps: - checkout diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index cc4c391ea1..16b47bde79 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -42,7 +42,7 @@ commands: description: "installs tools required to build torchaudio" steps: - run: - name: Install pkg-config + name: Install build tools command: HOMEBREW_NO_AUTO_UPDATE=1 brew install pkg-config wget # Disable brew auto update which is very slow load_conda_channel_flags: @@ -162,7 +162,7 @@ jobs: binary_macos_wheel: <<: *binary_common macos: - xcode: "9.4.1" + xcode: "12.0" steps: - checkout - install_build_tools_macos @@ -188,7 +188,7 @@ jobs: binary_macos_conda: <<: *binary_common macos: - xcode: "9.4.1" + xcode: "12.0" steps: - checkout - install_build_tools_macos @@ -509,7 +509,7 @@ jobs: unittest_macos_cpu: <<: *binary_common macos: - xcode: "9.4.1" + xcode: "12.0" resource_class: large steps: - checkout diff --git a/CMakeLists.txt b/CMakeLists.txt index 96360532f2..35b148c058 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -59,6 +59,11 @@ option(BUILD_KALDI "Build kaldi statically" ON) option(BUILD_TRANSDUCER "Enable transducer" OFF) option(BUILD_LIBTORCHAUDIO "Build C++ Library" ON) option(BUILD_TORCHAUDIO_PYTHON_EXTENSION "Build Python extension" OFF) +option(USE_CUDA "Enable CUDA support" OFF) + +if(USE_CUDA) + enable_language(CUDA) +endif() find_package(Torch REQUIRED) diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 430ff168c3..91002fa0e9 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -38,6 +38,7 @@ def _get_build(var, default=False): _BUILD_KALDI = False if platform.system() == 'Windows' else _get_build("BUILD_KALDI", True) _BUILD_TRANSDUCER = _get_build("BUILD_TRANSDUCER") _USE_ROCM = _get_build("USE_ROCM") +_USE_CUDA = torch.cuda.is_available() def get_ext_modules(): @@ -76,6 +77,7 @@ def build_extension(self, ext): "-DBUILD_TORCHAUDIO_PYTHON_EXTENSION:BOOL=ON", "-DBUILD_LIBTORCHAUDIO:BOOL=OFF", f"-DUSE_ROCM:BOOL={'ON' if _USE_ROCM else 'OFF'}", + f"-DUSE_CUDA:BOOL={'ON' if _USE_CUDA else 'OFF'}", ] build_args = [ '--target', 'install' diff --git a/docs/source/index.rst b/docs/source/index.rst index 49ffb9af22..5aacb87f54 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -21,7 +21,7 @@ Features described in this documentation are classified by release status: *Prototype:* These features are typically not available as part of binary distributions like PyPI or Conda, except sometimes behind run-time flags, and are at an early stage for feedback and testing. - + The :mod:`torchaudio` package consists of I/O, popular datasets and common audio transformations. @@ -39,9 +39,9 @@ The :mod:`torchaudio` package consists of I/O, popular datasets and common audio compliance.kaldi kaldi_io utils - transducer - - + rnnt_loss + + .. toctree:: :maxdepth: 1 :caption: PyTorch Libraries diff --git a/docs/source/transducer.rst b/docs/source/rnnt_loss.rst similarity index 69% rename from docs/source/transducer.rst rename to docs/source/rnnt_loss.rst index 505ab0187c..0c3b075d65 100644 --- a/docs/source/transducer.rst +++ b/docs/source/rnnt_loss.rst @@ -1,14 +1,14 @@ .. role:: hidden :class: hidden-section -torchaudio.prototype.transducer +torchaudio.prototype.rnnt_loss =============================== -.. currentmodule:: torchaudio.prototype.transducer +.. currentmodule:: torchaudio.prototype.rnnt_loss .. note:: - The RNN transducer loss is a prototype feature, see `here `_ to learn more about the nomenclature. It is only available within the nightlies, and also needs to be imported explicitly using: :code:`from torchaudio.prototype.transducer import rnnt_loss, RNNTLoss`. + The RNN transducer loss is a prototype feature, see `here `_ to learn more about the nomenclature. It is only available within the nightlies, and also needs to be imported explicitly using: :code:`from torchaudio.prototype.rnnt_loss import rnnt_loss, RNNTLoss`. rnnt_loss --------- diff --git a/examples/libtorchaudio/CMakeLists.txt b/examples/libtorchaudio/CMakeLists.txt index 264949bc12..6a359bd47e 100644 --- a/examples/libtorchaudio/CMakeLists.txt +++ b/examples/libtorchaudio/CMakeLists.txt @@ -6,7 +6,7 @@ SET(BUILD_LIBTORCHAUDIO ON CACHE BOOL "Build libtorchaudio") SET(BUILD_SOX ON CACHE BOOL "Build libsox into libtorchaudio") SET(BUILD_KALDI OFF CACHE BOOL "Build Kaldi into libtorchaudio") -SET(BUILD_TRANSDUCER OFF CACHE BOOL "Build Python binding") +SET(BUILD_TRANSDUCER OFF CACHE BOOL "Build transducer into libtorchaudio") SET(BUILD_TORCHAUDIO_PYTHON_EXTENSION OFF CACHE BOOL "Build Python binding") find_package(Torch REQUIRED) @@ -14,7 +14,4 @@ message("libtorchaudio CMakeLists: ${TORCH_CXX_FLAGS}") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}") add_subdirectory(../.. libtorchaudio) - -add_executable(main main.cpp) -target_link_libraries(main "${TORCH_LIBRARIES}" "${TORCHAUDIO_LIBRARY}") -set_property(TARGET main PROPERTY CXX_STANDARD 14) +add_subdirectory(augmentation) diff --git a/examples/libtorchaudio/README.md b/examples/libtorchaudio/README.md index 10f1228894..be6611ea85 100644 --- a/examples/libtorchaudio/README.md +++ b/examples/libtorchaudio/README.md @@ -1,11 +1,27 @@ -# Example usage: libtorchaudio +# Libtorchaudio Examples -This example demonstrates how you can use torchaudio's I/O features in C++ application, in addition to PyTorch's operations. +* [Augmentation](./augmentation) -To try this example, simply run `./run.sh`. This script will +## Build -1. Create an audio preprocessing pipeline with TorchScript and dump it to a file. -2. Build the application using `libtorch` and `libtorchaudio`. -3. Execute the preprocessing pipeline on an example audio. +The example applications in this directory depend on `libtorch` and `libtorchaudio`. +If you have a working `PyTorch`, you already have `libtorch`. +Please refer to [this tutorial](https://pytorch.org/tutorials/advanced/torch_script_custom_classes.html) for the use of `libtorch` and TorchScript. -The detail of the preprocessing pipeline can be found in [`create_jittable_pipeline.py`](./create_jittable_pipeline.py). +`libtorchaudio` is the library of torchaudio's C++ components without Python component. +It is currently not distributed, and it will be built alongside with the applications. + +The following commands will build `libtorchaudio` and applications. + +```bash +mkdir build +cd build +cmake -GNinja \ + -DCMAKE_PREFIX_PATH="$(python -c 'import torch;print(torch.utils.cmake_prefix_path)')" \ + -DBUILD_SOX=ON \ + -DBUILD_KALDI=OFF \ + .. +cmake --build . +``` + +For the usages of each application, refer to the corresponding application directory. diff --git a/examples/libtorchaudio/augmentation/CMakeLists.txt b/examples/libtorchaudio/augmentation/CMakeLists.txt new file mode 100644 index 0000000000..e9bfece93a --- /dev/null +++ b/examples/libtorchaudio/augmentation/CMakeLists.txt @@ -0,0 +1,3 @@ +add_executable(augment main.cpp) +target_link_libraries(augment "${TORCH_LIBRARIES}" "${TORCHAUDIO_LIBRARY}") +set_property(TARGET augment PROPERTY CXX_STANDARD 14) diff --git a/examples/libtorchaudio/augmentation/README.md b/examples/libtorchaudio/augmentation/README.md new file mode 100644 index 0000000000..b78248af6b --- /dev/null +++ b/examples/libtorchaudio/augmentation/README.md @@ -0,0 +1,36 @@ +# Augmentation + +This example demonstrates how you can use torchaudio's I/O features and augmentations in C++ application. + +**NOTE** +This example uses `"sox_io"` backend, thus does not work on Windows. + +## Steps +### 1. Create augmentation pipeline TorchScript file. + +First, we implement our data process pipeline as a regular Python, and save it as a TorchScript object. +We will load and execute it in our C++ application. The C++ code is found in [`main.cpp`](./main.cpp). + +```python +python create_jittable_pipeline.py \ + --rir-path "../data/rir.wav" \ + --output-path "./pipeline.zip" +``` + +### 2. Build the application + +Please refer to [the top level README.md](../README.md) + +### 3. Run the application + +Now we run the C++ application `augment`, with the TorchScript object we created in Step.1 and an input audio file. + +In [the top level directory](../) + +```bash +input_audio_file="./data/input.wav" +./build/augmentation/augment ./augmentation/pipeline.zip "${input_audio_file}" "output.wav" +``` + +When you give a clean speech file, the output audio sounds like it's a phone conversation. + diff --git a/examples/libtorchaudio/create_jittable_pipeline.py b/examples/libtorchaudio/augmentation/create_jittable_pipeline.py similarity index 80% rename from examples/libtorchaudio/create_jittable_pipeline.py rename to examples/libtorchaudio/augmentation/create_jittable_pipeline.py index 14a7eefd50..ac33a837da 100755 --- a/examples/libtorchaudio/create_jittable_pipeline.py +++ b/examples/libtorchaudio/augmentation/create_jittable_pipeline.py @@ -14,9 +14,9 @@ class Pipeline(torch.nn.Module): This example load waveform from a file then apply effects and save it to a file. """ - def __init__(self): + def __init__(self, rir_path: str): super().__init__() - rir, sample_rate = _load_rir() + rir, sample_rate = torchaudio.load(rir_path) self.register_buffer('rir', rir) self.rir_sample_rate: int = sample_rate @@ -44,8 +44,8 @@ def forward(self, input_path: str, output_path: str): torchaudio.save(output_path, waveform, sample_rate) -def _create_jit_pipeline(output_path): - module = torch.jit.script(Pipeline()) +def _create_jit_pipeline(rir_path, output_path): + module = torch.jit.script(Pipeline(rir_path)) print("*" * 40) print("* Pipeline code") print("*" * 40) @@ -59,16 +59,16 @@ def _get_path(*paths): return os.path.join(os.path.dirname(__file__), *paths) -def _load_rir(): - path = _get_path("data", "rir.wav") - return torchaudio.load(path) - - def _parse_args(): parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--rir-path", + default=_get_path("..", "data", "rir.wav"), + help="Audio dara for room impulse response." + ) parser.add_argument( "--output-path", - default=_get_path("data", "pipeline.zip"), + default=_get_path("pipeline.zip"), help="Output JIT file." ) return parser.parse_args() @@ -76,7 +76,7 @@ def _parse_args(): def _main(): args = _parse_args() - _create_jit_pipeline(args.output_path) + _create_jit_pipeline(args.rir_path, args.output_path) if __name__ == '__main__': diff --git a/examples/libtorchaudio/main.cpp b/examples/libtorchaudio/augmentation/main.cpp similarity index 90% rename from examples/libtorchaudio/main.cpp rename to examples/libtorchaudio/augmentation/main.cpp index 9f9a7b0d00..fe45d28aa9 100644 --- a/examples/libtorchaudio/main.cpp +++ b/examples/libtorchaudio/augmentation/main.cpp @@ -7,7 +7,7 @@ int main(int argc, char* argv[]) { } torch::jit::script::Module module; - std::cout << "Loading module from: " << argv[0] << std::endl; + std::cout << "Loading module from: " << argv[1] << std::endl; try { module = torch::jit::load(argv[1]); } catch (const c10::Error &error) { diff --git a/examples/libtorchaudio/run.sh b/examples/libtorchaudio/build.sh similarity index 56% rename from examples/libtorchaudio/run.sh rename to examples/libtorchaudio/build.sh index a55614e909..11ca65f751 100755 --- a/examples/libtorchaudio/run.sh +++ b/examples/libtorchaudio/build.sh @@ -3,22 +3,14 @@ set -eux this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" - build_dir="${this_dir}/build" -data_dir="${this_dir}/data" -jit_file="${data_dir}/pipeline.zip" -input_file="${data_dir}/input.wav" -output_file="${data_dir}/output.wav" - -cd "${this_dir}" -python create_jittable_pipeline.py mkdir -p "${build_dir}" cd "${build_dir}" + cmake -GNinja \ -DCMAKE_PREFIX_PATH="$(python -c 'import torch;print(torch.utils.cmake_prefix_path)')" \ -DBUILD_SOX=ON \ -DBUILD_KALDI=OFF \ .. -cmake --build . --target main -./main "${jit_file}" "${input_file}" "${output_file}" +cmake --build . diff --git a/test/torchaudio_unittest/common_utils/case_utils.py b/test/torchaudio_unittest/common_utils/case_utils.py index 7357cec4c4..773f1915b9 100644 --- a/test/torchaudio_unittest/common_utils/case_utils.py +++ b/test/torchaudio_unittest/common_utils/case_utils.py @@ -81,6 +81,14 @@ def setUp(self): super().setUp() set_audio_backend(self.backend) + @property + def complex_dtype(self): + if self.dtype in ['float32', 'float', torch.float, torch.float32]: + return torch.cfloat + if self.dtype in ['float64', 'double', torch.double, torch.float64]: + return torch.cdouble + raise ValueError(f'No corresponding complex dtype for {self.dtype}') + class TorchaudioTestCase(TestBaseMixin, PytorchTestCase): pass diff --git a/test/torchaudio_unittest/functional/autograd_impl.py b/test/torchaudio_unittest/functional/autograd_impl.py index e273369ea6..049ae25c90 100644 --- a/test/torchaudio_unittest/functional/autograd_impl.py +++ b/test/torchaudio_unittest/functional/autograd_impl.py @@ -3,7 +3,7 @@ from parameterized import parameterized from torch import Tensor import torchaudio.functional as F -from torch.autograd import gradcheck +from torch.autograd import gradcheck, gradgradcheck from torchaudio_unittest.common_utils import ( TestBaseMixin, get_whitenoise, @@ -26,6 +26,7 @@ def assert_grad( i.requires_grad = True inputs_.append(i) assert gradcheck(transform, inputs_) + assert gradgradcheck(transform, inputs_) def test_lfilter_x(self): torch.random.manual_seed(2434) diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py index 73cc8662fa..520ff86b20 100644 --- a/test/torchaudio_unittest/functional/functional_cpu_test.py +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -4,7 +4,7 @@ from parameterized import parameterized from torchaudio_unittest.common_utils import PytorchTestCase, TorchaudioTestCase, skipIfNoSox -from .functional_impl import Functional, FunctionalComplex, FunctionalCPUOnly +from .functional_impl import Functional, FunctionalCPUOnly class TestFunctionalFloat32(Functional, FunctionalCPUOnly, PytorchTestCase): @@ -21,18 +21,6 @@ class TestFunctionalFloat64(Functional, PytorchTestCase): device = torch.device('cpu') -class TestFunctionalComplex64(FunctionalComplex, PytorchTestCase): - complex_dtype = torch.complex64 - real_dtype = torch.float32 - device = torch.device('cpu') - - -class TestFunctionalComplex128(FunctionalComplex, PytorchTestCase): - complex_dtype = torch.complex128 - real_dtype = torch.float64 - device = torch.device('cpu') - - @skipIfNoSox class TestApplyCodec(TorchaudioTestCase): backend = "sox_io" diff --git a/test/torchaudio_unittest/functional/functional_cuda_test.py b/test/torchaudio_unittest/functional/functional_cuda_test.py index 054e2c3e4f..fd35547270 100644 --- a/test/torchaudio_unittest/functional/functional_cuda_test.py +++ b/test/torchaudio_unittest/functional/functional_cuda_test.py @@ -2,7 +2,7 @@ import unittest from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda -from .functional_impl import Functional, FunctionalComplex +from .functional_impl import Functional @skipIfNoCuda @@ -19,17 +19,3 @@ def test_lfilter_9th_order_filter_stability(self): class TestLFilterFloat64(Functional, PytorchTestCase): dtype = torch.float64 device = torch.device('cuda') - - -@skipIfNoCuda -class TestFunctionalComplex64(FunctionalComplex, PytorchTestCase): - complex_dtype = torch.complex64 - real_dtype = torch.float32 - device = torch.device('cuda') - - -@skipIfNoCuda -class TestFunctionalComplex128(FunctionalComplex, PytorchTestCase): - complex_dtype = torch.complex64 - real_dtype = torch.float32 - device = torch.device('cuda') diff --git a/test/torchaudio_unittest/functional/functional_impl.py b/test/torchaudio_unittest/functional/functional_impl.py index f4cc94f806..424ead2ca9 100644 --- a/test/torchaudio_unittest/functional/functional_impl.py +++ b/test/torchaudio_unittest/functional/functional_impl.py @@ -227,11 +227,37 @@ def test_mask_along_axis_iid(self, mask_param, mask_value, axis): assert mask_specgrams.size() == specgrams.size() assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() + @parameterized.expand( + list(itertools.product([(2, 1025, 400), (1, 201, 100)], [100], [0., 30.], [1, 2])) + ) + def test_mask_along_axis_preserve(self, shape, mask_param, mask_value, axis): + """mask_along_axis should not alter original input Tensor + + Test is run 5 times to bound the probability of no masking occurring to 1e-10 + See https://github.com/pytorch/audio/issues/1478 + """ + torch.random.manual_seed(42) + for _ in range(5): + specgram = torch.randn(*shape, dtype=self.dtype, device=self.device) + specgram_copy = specgram.clone() + F.mask_along_axis(specgram, mask_param, mask_value, axis) + + self.assertEqual(specgram, specgram_copy) + + @parameterized.expand(list(itertools.product([100], [0., 30.], [2, 3]))) + def test_mask_along_axis_iid_preserve(self, mask_param, mask_value, axis): + """mask_along_axis_iid should not alter original input Tensor + + Test is run 5 times to bound the probability of no masking occurring to 1e-10 + See https://github.com/pytorch/audio/issues/1478 + """ + torch.random.manual_seed(42) + for _ in range(5): + specgrams = torch.randn(4, 2, 1025, 400, dtype=self.dtype, device=self.device) + specgrams_copy = specgrams.clone() + F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) -class FunctionalComplex(TestBaseMixin): - complex_dtype = None - real_dtype = None - device = None + self.assertEqual(specgrams, specgrams_copy) @nested_params( [0.5, 1.01, 1.3], @@ -254,7 +280,7 @@ def test_phase_vocoder_shape(self, rate, test_pseudo_complex): 0, np.pi * hop_length, num_freq, - dtype=self.real_dtype, device=self.device)[..., None] + dtype=self.dtype, device=self.device)[..., None] spec_stretch = F.phase_vocoder(spec, rate=rate, phase_advance=phase_advance) diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py b/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py index d9d34eff76..06871f9bde 100644 --- a/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py +++ b/test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py @@ -1,7 +1,7 @@ import torch from torchaudio_unittest.common_utils import PytorchTestCase -from .torchscript_consistency_impl import Functional, FunctionalComplex +from .torchscript_consistency_impl import Functional class TestFunctionalFloat32(Functional, PytorchTestCase): @@ -12,15 +12,3 @@ class TestFunctionalFloat32(Functional, PytorchTestCase): class TestFunctionalFloat64(Functional, PytorchTestCase): dtype = torch.float64 device = torch.device('cpu') - - -class TestFunctionalComplex64(FunctionalComplex, PytorchTestCase): - complex_dtype = torch.complex64 - real_dtype = torch.float32 - device = torch.device('cpu') - - -class TestFunctionalComplex128(FunctionalComplex, PytorchTestCase): - complex_dtype = torch.complex128 - real_dtype = torch.float64 - device = torch.device('cpu') diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py b/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py index 026e09abfa..53ccbad3c9 100644 --- a/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py +++ b/test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py @@ -1,7 +1,7 @@ import torch from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase -from .torchscript_consistency_impl import Functional, FunctionalComplex +from .torchscript_consistency_impl import Functional @skipIfNoCuda @@ -14,17 +14,3 @@ class TestFunctionalFloat32(Functional, PytorchTestCase): class TestFunctionalFloat64(Functional, PytorchTestCase): dtype = torch.float64 device = torch.device('cuda') - - -@skipIfNoCuda -class TestFunctionalComplex64(FunctionalComplex, PytorchTestCase): - complex_dtype = torch.complex64 - real_dtype = torch.float32 - device = torch.device('cuda') - - -@skipIfNoCuda -class TestFunctionalComplex128(FunctionalComplex, PytorchTestCase): - complex_dtype = torch.complex128 - real_dtype = torch.float64 - device = torch.device('cuda') diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py index 9c34846b9e..a39973f867 100644 --- a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py @@ -13,7 +13,7 @@ class Functional(TempDirMixin, TestBaseMixin): - """Implements test for `functinoal` modul that are performed for different devices""" + """Implements test for `functional` module that are performed for different devices""" def _assert_consistency(self, func, tensor, shape_only=False): tensor = tensor.to(device=self.device, dtype=self.dtype) @@ -21,13 +21,36 @@ def _assert_consistency(self, func, tensor, shape_only=False): torch.jit.script(func).save(path) ts_func = torch.jit.load(path) + torch.random.manual_seed(40) output = func(tensor) + + torch.random.manual_seed(40) ts_output = ts_func(tensor) + if shape_only: ts_output = ts_output.shape output = output.shape self.assertEqual(ts_output, output) + def _assert_consistency_complex(self, func, tensor, test_pseudo_complex=False): + assert tensor.is_complex() + tensor = tensor.to(device=self.device, dtype=self.complex_dtype) + + path = self.get_temp_path('func.zip') + torch.jit.script(func).save(path) + ts_func = torch.jit.load(path) + + if test_pseudo_complex: + tensor = torch.view_as_real(tensor) + + torch.random.manual_seed(40) + output = func(tensor) + + torch.random.manual_seed(40) + ts_output = ts_func(tensor) + + self.assertEqual(ts_output, output) + def test_spectrogram(self): def func(tensor): n_fft = 400 @@ -568,26 +591,6 @@ def func(tensor): tensor = common_utils.get_whitenoise(sample_rate=44100) self._assert_consistency(func, tensor) - -class FunctionalComplex(TempDirMixin, TestBaseMixin): - complex_dtype = None - real_dtype = None - device = None - - def _assert_consistency(self, func, tensor, test_pseudo_complex=False): - assert tensor.is_complex() - tensor = tensor.to(device=self.device, dtype=self.complex_dtype) - - path = self.get_temp_path('func.zip') - torch.jit.script(func).save(path) - ts_func = torch.jit.load(path) - - if test_pseudo_complex: - tensor = torch.view_as_real(tensor) - output = func(tensor) - ts_output = ts_func(tensor) - self.assertEqual(ts_output, output) - @parameterized.expand([(True, ), (False, )]) def test_phase_vocoder(self, test_paseudo_complex): def func(tensor): @@ -606,4 +609,4 @@ def func(tensor): return F.phase_vocoder(tensor, rate, phase_advance) tensor = torch.view_as_complex(torch.randn(2, 1025, 400, 2)) - self._assert_consistency(func, tensor, test_paseudo_complex) + self._assert_consistency_complex(func, tensor, test_paseudo_complex) diff --git a/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py b/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py new file mode 100644 index 0000000000..c64fd687fe --- /dev/null +++ b/test/torchaudio_unittest/rnnt/rnnt_loss_cuda_test.py @@ -0,0 +1,10 @@ +import torch +from .rnnt_loss_impl import RNNTLossTest +from torchaudio_unittest import common_utils +from .utils import skipIfNoTransducer + + +@skipIfNoTransducer +@common_utils.skipIfNoCuda +class TestRNNTLoss(RNNTLossTest, common_utils.PytorchTestCase): + device = torch.device('cuda') diff --git a/test/torchaudio_unittest/transforms/autograd_test_impl.py b/test/torchaudio_unittest/transforms/autograd_test_impl.py index b8fe6c1863..717a7bc87b 100644 --- a/test/torchaudio_unittest/transforms/autograd_test_impl.py +++ b/test/torchaudio_unittest/transforms/autograd_test_impl.py @@ -154,6 +154,23 @@ def test_vol(self, gain, gain_type): waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2) self.assert_grad(transform, [waveform]) + @parameterized.expand([ + ({'cmn_window': 100, 'min_cmn_window': 50, 'center': False, 'norm_vars': False}, ), + ({'cmn_window': 100, 'min_cmn_window': 50, 'center': True, 'norm_vars': False}, ), + ({'cmn_window': 100, 'min_cmn_window': 50, 'center': False, 'norm_vars': True}, ), + ({'cmn_window': 100, 'min_cmn_window': 50, 'center': True, 'norm_vars': True}, ), + ]) + def test_sliding_window_cmn(self, kwargs): + n_fft = 10 + power = 1 + spec = get_spectrogram( + get_whitenoise(sample_rate=200, duration=0.05, n_channels=2), + n_fft=n_fft, power=power) + spec_reshaped = spec.transpose(-1, -2) + + transform = T.SlidingWindowCmn(**kwargs) + self.assert_grad(transform, [spec_reshaped]) + @unittest.expectedFailure def test_timestretch_zeros_fail(self): """Test that ``T.TimeStretch`` fails gradcheck at 0 diff --git a/test/torchaudio_unittest/transforms/torchscript_consistency_cpu_test.py b/test/torchaudio_unittest/transforms/torchscript_consistency_cpu_test.py index 4de32a57bb..9092f3a64b 100644 --- a/test/torchaudio_unittest/transforms/torchscript_consistency_cpu_test.py +++ b/test/torchaudio_unittest/transforms/torchscript_consistency_cpu_test.py @@ -1,7 +1,7 @@ import torch from torchaudio_unittest.common_utils import PytorchTestCase -from .torchscript_consistency_impl import Transforms, TransformsComplex +from .torchscript_consistency_impl import Transforms class TestTransformsFloat32(Transforms, PytorchTestCase): @@ -12,15 +12,3 @@ class TestTransformsFloat32(Transforms, PytorchTestCase): class TestTransformsFloat64(Transforms, PytorchTestCase): dtype = torch.float64 device = torch.device('cpu') - - -class TestTransformsComplex64(TransformsComplex, PytorchTestCase): - complex_dtype = torch.complex64 - real_dtype = torch.float32 - device = torch.device('cpu') - - -class TestTransformsComplex128(TransformsComplex, PytorchTestCase): - complex_dtype = torch.complex128 - real_dtype = torch.float64 - device = torch.device('cpu') diff --git a/test/torchaudio_unittest/transforms/torchscript_consistency_cuda_test.py b/test/torchaudio_unittest/transforms/torchscript_consistency_cuda_test.py index 2435b82589..7425647bab 100644 --- a/test/torchaudio_unittest/transforms/torchscript_consistency_cuda_test.py +++ b/test/torchaudio_unittest/transforms/torchscript_consistency_cuda_test.py @@ -1,7 +1,7 @@ import torch from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase -from .torchscript_consistency_impl import Transforms, TransformsComplex +from .torchscript_consistency_impl import Transforms @skipIfNoCuda @@ -14,17 +14,3 @@ class TestTransformsFloat32(Transforms, PytorchTestCase): class TestTransformsFloat64(Transforms, PytorchTestCase): dtype = torch.float64 device = torch.device('cuda') - - -@skipIfNoCuda -class TestTransformsComplex64(TransformsComplex, PytorchTestCase): - complex_dtype = torch.complex64 - real_dtype = torch.float32 - device = torch.device('cuda') - - -@skipIfNoCuda -class TestTransformsComplex128(TransformsComplex, PytorchTestCase): - complex_dtype = torch.complex128 - real_dtype = torch.float64 - device = torch.device('cuda') diff --git a/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py b/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py index 64ecbe53f7..506ca9af6c 100644 --- a/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py @@ -26,6 +26,22 @@ def _assert_consistency(self, transform, tensor): ts_output = ts_transform(tensor) self.assertEqual(ts_output, output) + def _assert_consistency_complex(self, transform, tensor, test_pseudo_complex=False): + assert tensor.is_complex() + tensor = tensor.to(device=self.device, dtype=self.complex_dtype) + transform = transform.to(device=self.device, dtype=self.dtype) + + path = self.get_temp_path('transform.zip') + torch.jit.script(transform).save(path) + ts_transform = torch.jit.load(path) + + if test_pseudo_complex: + tensor = torch.view_as_real(tensor) + + output = transform(tensor) + ts_output = ts_transform(tensor) + self.assertEqual(ts_output, output) + def test_Spectrogram(self): tensor = torch.rand((1, 1000)) self._assert_consistency(T.Spectrogram(), tensor) @@ -104,35 +120,13 @@ def test_SpectralCentroid(self): waveform = common_utils.get_whitenoise(sample_rate=sample_rate) self._assert_consistency(T.SpectralCentroid(sample_rate=sample_rate), waveform) - -class TransformsComplex(TempDirMixin, TestBaseMixin): - complex_dtype = None - real_dtype = None - device = None - - def _assert_consistency(self, transform, tensor, test_pseudo_complex=False): - assert tensor.is_complex() - tensor = tensor.to(device=self.device, dtype=self.complex_dtype) - transform = transform.to(device=self.device, dtype=self.real_dtype) - - path = self.get_temp_path('transform.zip') - torch.jit.script(transform).save(path) - ts_transform = torch.jit.load(path) - - if test_pseudo_complex: - tensor = torch.view_as_real(tensor) - - output = transform(tensor) - ts_output = ts_transform(tensor) - self.assertEqual(ts_output, output) - @parameterized.expand([(True, ), (False, )]) def test_TimeStretch(self, test_pseudo_complex): n_freq = 400 hop_length = 512 fixed_rate = 1.3 tensor = torch.view_as_complex(torch.rand((10, 2, n_freq, 10, 2))) - self._assert_consistency( + self._assert_consistency_complex( T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate), tensor, test_pseudo_complex diff --git a/torchaudio/csrc/CMakeLists.txt b/torchaudio/csrc/CMakeLists.txt index fc033699b2..ebf577eb64 100644 --- a/torchaudio/csrc/CMakeLists.txt +++ b/torchaudio/csrc/CMakeLists.txt @@ -11,7 +11,27 @@ set( ) if(BUILD_TRANSDUCER) - list(APPEND LIBTORCHAUDIO_SOURCES transducer.cpp) + set( + TRANSDUCER_SOURCES + rnnt/cpu/compute_alphas.cpp + rnnt/cpu/compute_betas.cpp + rnnt/cpu/compute.cpp + rnnt/compute_alphas.cpp + rnnt/compute_betas.cpp + rnnt/compute.cpp + ) + + if (USE_CUDA) + set( + CUDA_TRANSDUCER_SOURCES + rnnt/gpu/compute_alphas.cu + rnnt/gpu/compute_betas.cu + rnnt/gpu/compute.cu + ) + list(APPEND TRANSDUCER_SOURCES ${CUDA_TRANSDUCER_SOURCES}) + endif() + + list(APPEND LIBTORCHAUDIO_SOURCES ${TRANSDUCER_SOURCES}) endif() if(BUILD_KALDI) @@ -96,6 +116,10 @@ if (BUILD_TORCHAUDIO_PYTHON_EXTENSION) target_compile_definitions(_torchaudio PRIVATE INCLUDE_KALDI) endif() + if (USE_CUDA) + target_compile_definitions(_torchaudio PRIVATE USE_CUDA) + endif() + target_include_directories( _torchaudio PRIVATE diff --git a/torchaudio/csrc/lfilter.cpp b/torchaudio/csrc/lfilter.cpp index 56540c33f6..dcc5dbe442 100644 --- a/torchaudio/csrc/lfilter.cpp +++ b/torchaudio/csrc/lfilter.cpp @@ -80,170 +80,159 @@ void lfilter_core_generic_loop( } } -std::tuple lfilter_core( - const torch::Tensor& waveform, - const torch::Tensor& a_coeffs, - const torch::Tensor& b_coeffs) { - TORCH_CHECK(waveform.device() == a_coeffs.device()); - TORCH_CHECK(b_coeffs.device() == a_coeffs.device()); - TORCH_CHECK(a_coeffs.size(0) == b_coeffs.size(0)); +class DifferentiableIIR : public torch::autograd::Function { + public: + static torch::Tensor forward( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& waveform, + const torch::Tensor& a_coeffs_normalized) { + auto device = waveform.device(); + auto dtype = waveform.dtype(); + int64_t n_channel = waveform.size(0); + int64_t n_sample = waveform.size(1); + int64_t n_order = a_coeffs_normalized.size(0); + int64_t n_sample_padded = n_sample + n_order - 1; - TORCH_INTERNAL_ASSERT(waveform.sizes().size() == 2); + auto a_coeff_flipped = a_coeffs_normalized.flip(0).contiguous(); - auto device = waveform.device(); - int64_t n_order = a_coeffs.size(0); + auto options = torch::TensorOptions().dtype(dtype).device(device); + auto padded_output_waveform = + torch::zeros({n_channel, n_sample_padded}, options); + + if (device.is_cpu()) { + cpu_lfilter_core_loop(waveform, a_coeff_flipped, padded_output_waveform); + } else { + lfilter_core_generic_loop( + waveform, a_coeff_flipped, padded_output_waveform); + } - TORCH_INTERNAL_ASSERT(n_order > 0); + auto output = padded_output_waveform.index( + {torch::indexing::Slice(), + torch::indexing::Slice(n_order - 1, torch::indexing::None)}); - namespace F = torch::nn::functional; + ctx->save_for_backward({waveform, a_coeffs_normalized, output}); + return output; + } - auto padded_waveform = F::pad(waveform, F::PadFuncOptions({n_order - 1, 0})); - auto padded_output_waveform = torch::zeros_like(padded_waveform); + static torch::autograd::tensor_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::tensor_list grad_outputs) { + auto saved = ctx->get_saved_variables(); + auto x = saved[0]; + auto a_coeffs_normalized = saved[1]; + auto y = saved[2]; - auto a_coeff_flipped = a_coeffs.flip(0).contiguous(); - auto b_coeff_flipped = b_coeffs.flip(0).contiguous(); + int64_t n_channel = x.size(0); + int64_t n_order = a_coeffs_normalized.size(0); - auto input_signal_windows = - F::conv1d( - padded_waveform.unsqueeze(1), b_coeff_flipped.view({1, 1, n_order})) - .squeeze(1); + auto dx = torch::Tensor(); + auto da = torch::Tensor(); + auto dy = grad_outputs[0]; - input_signal_windows.div_(a_coeffs[0]); - a_coeff_flipped.div_(a_coeffs[0]); + namespace F = torch::nn::functional; - if (device.is_cpu()) { - cpu_lfilter_core_loop( - input_signal_windows, a_coeff_flipped, padded_output_waveform); - } else { - lfilter_core_generic_loop( - input_signal_windows, a_coeff_flipped, padded_output_waveform); - } + if (a_coeffs_normalized.requires_grad()) { + auto dyda = F::pad( + DifferentiableIIR::apply(-y, a_coeffs_normalized), + F::PadFuncOptions({n_order - 1, 0})); - auto output = padded_output_waveform.index( - {torch::indexing::Slice(), - torch::indexing::Slice(n_order - 1, torch::indexing::None)}); + da = F::conv1d( + dyda.unsqueeze(0), + dy.unsqueeze(1), + F::Conv1dFuncOptions().groups(n_channel)) + .sum(1) + .squeeze(0) + .flip(0); + } - return {output, input_signal_windows}; -} + if (x.requires_grad()) { + dx = DifferentiableIIR::apply(dy.flip(1), a_coeffs_normalized).flip(1); + } -torch::Tensor lfilter_simple( - const torch::Tensor& waveform, - const torch::Tensor& a_coeffs, - const torch::Tensor& b_coeffs) { - return std::get<0>(lfilter_core(waveform, a_coeffs, b_coeffs)); -} + return {dx, da}; + } +}; -class DifferentiableLfilter - : public torch::autograd::Function { +class DifferentiableFIR : public torch::autograd::Function { public: static torch::Tensor forward( torch::autograd::AutogradContext* ctx, const torch::Tensor& waveform, - const torch::Tensor& a_coeffs, const torch::Tensor& b_coeffs) { - at::AutoNonVariableTypeMode g; - auto result = lfilter_core(waveform, a_coeffs, b_coeffs); - ctx->save_for_backward( - {waveform, - a_coeffs, - b_coeffs, - std::get<0>(result), - std::get<1>(result)}); - return std::get<0>(result); + int64_t n_order = b_coeffs.size(0); + + namespace F = torch::nn::functional; + auto b_coeff_flipped = b_coeffs.flip(0).contiguous(); + auto padded_waveform = + F::pad(waveform, F::PadFuncOptions({n_order - 1, 0})); + + auto output = + F::conv1d( + padded_waveform.unsqueeze(1), b_coeff_flipped.view({1, 1, n_order})) + .squeeze(1); + + ctx->save_for_backward({waveform, b_coeffs, output}); + return output; } static torch::autograd::tensor_list backward( torch::autograd::AutogradContext* ctx, torch::autograd::tensor_list grad_outputs) { auto saved = ctx->get_saved_variables(); - auto waveform = saved[0]; - auto a_coeffs = saved[1]; - auto b_coeffs = saved[2]; - auto y = saved[3]; - auto xh = saved[4]; - - auto device = waveform.device(); - auto dtype = waveform.dtype(); - int64_t n_channel = waveform.size(0); - int64_t n_sample = waveform.size(1); - int64_t n_order = a_coeffs.size(0); - int64_t n_sample_padded = n_sample + n_order - 1; + auto x = saved[0]; + auto b_coeffs = saved[1]; + auto y = saved[2]; - auto a_coeff_flipped = a_coeffs.flip(0).contiguous(); - auto b_coeff_flipped = b_coeffs.flip(0).contiguous(); - b_coeff_flipped.div_(a_coeffs[0]); - a_coeff_flipped.div_(a_coeffs[0]); + int64_t n_channel = x.size(0); + int64_t n_order = b_coeffs.size(0); auto dx = torch::Tensor(); - auto da = torch::Tensor(); auto db = torch::Tensor(); auto dy = grad_outputs[0]; - at::AutoNonVariableTypeMode g; namespace F = torch::nn::functional; - auto options = torch::TensorOptions().dtype(dtype).device(device); - if (a_coeffs.requires_grad()) { - auto dyda = torch::zeros({n_channel, n_sample_padded}, options); - if (device.is_cpu()) { - cpu_lfilter_core_loop(-y, a_coeff_flipped, dyda); - } else { - lfilter_core_generic_loop(-y, a_coeff_flipped, dyda); - } - - da = F::conv1d( - dyda.unsqueeze(0), + if (b_coeffs.requires_grad()) { + db = F::conv1d( + F::pad(x.unsqueeze(0), F::PadFuncOptions({n_order - 1, 0})), dy.unsqueeze(1), F::Conv1dFuncOptions().groups(n_channel)) .sum(1) .squeeze(0) .flip(0); - da.div_(a_coeffs[0]); } - if (b_coeffs.requires_grad() || waveform.requires_grad()) { - auto dxh = torch::zeros({n_channel, n_sample_padded}, options); - if (device.is_cpu()) { - cpu_lfilter_core_loop(dy.flip(1), a_coeff_flipped, dxh); - } else { - lfilter_core_generic_loop(dy.flip(1), a_coeff_flipped, dxh); - } - - dxh = dxh.index( - {torch::indexing::Slice(), - torch::indexing::Slice(n_order - 1, torch::indexing::None)}) - .flip(1); - - if (waveform.requires_grad()) { - dx = F::conv1d( - F::pad(dxh.unsqueeze(1), F::PadFuncOptions({0, n_order - 1})), - b_coeffs.view({1, 1, n_order})) - .squeeze(1); - dx.div_(a_coeffs[0]); - } - if (b_coeffs.requires_grad()) { - db = - F::conv1d( - F::pad( - waveform.unsqueeze(0), F::PadFuncOptions({n_order - 1, 0})), - dxh.unsqueeze(1), - F::Conv1dFuncOptions().groups(n_channel)) - .sum(1) - .squeeze(0) - .flip(0); - db.div_(a_coeffs[0]); - } + if (x.requires_grad()) { + dx = F::conv1d( + F::pad(dy.unsqueeze(1), F::PadFuncOptions({0, n_order - 1})), + b_coeffs.view({1, 1, n_order})) + .squeeze(1); } - return {dx, da, db}; + return {dx, db}; } }; -torch::Tensor lfilter_autograd( +torch::Tensor lfilter_core( const torch::Tensor& waveform, const torch::Tensor& a_coeffs, const torch::Tensor& b_coeffs) { - return DifferentiableLfilter::apply(waveform, a_coeffs, b_coeffs); + TORCH_CHECK(waveform.device() == a_coeffs.device()); + TORCH_CHECK(b_coeffs.device() == a_coeffs.device()); + TORCH_CHECK(a_coeffs.size(0) == b_coeffs.size(0)); + + TORCH_INTERNAL_ASSERT(waveform.sizes().size() == 2); + + int64_t n_order = b_coeffs.size(0); + + TORCH_INTERNAL_ASSERT(n_order > 0); + + auto filtered_waveform = + DifferentiableFIR::apply(waveform, b_coeffs / a_coeffs[0]); + + auto output = + DifferentiableIIR::apply(filtered_waveform, a_coeffs / a_coeffs[0]); + return output; } } // namespace @@ -259,10 +248,6 @@ TORCH_LIBRARY(torchaudio, m) { "torchaudio::_lfilter(Tensor waveform, Tensor a_coeffs, Tensor b_coeffs) -> Tensor"); } -TORCH_LIBRARY_IMPL(torchaudio, DefaultBackend, m) { - m.impl("torchaudio::_lfilter", lfilter_simple); -} - -TORCH_LIBRARY_IMPL(torchaudio, Autograd, m) { - m.impl("torchaudio::_lfilter", lfilter_autograd); +TORCH_LIBRARY_IMPL(torchaudio, CompositeImplicitAutograd, m) { + m.impl("torchaudio::_lfilter", lfilter_core); } diff --git a/torchaudio/csrc/rnnt/compute.cpp b/torchaudio/csrc/rnnt/compute.cpp index 91ce8cceb6..bce803fffa 100644 --- a/torchaudio/csrc/rnnt/compute.cpp +++ b/torchaudio/csrc/rnnt/compute.cpp @@ -1,12 +1,13 @@ #include TORCH_LIBRARY_FRAGMENT(torchaudio, m) { - m.def("rnnt_loss(Tensor logits," - "Tensor targets," - "Tensor src_lengths," - "Tensor tgt_lengths," - "int blank," - "float clamp," - "bool fused_log_smax=True," - "bool reuse_logits_for_grads=True) -> (Tensor, Tensor?)"); + m.def( + "rnnt_loss(Tensor logits," + "Tensor targets," + "Tensor src_lengths," + "Tensor tgt_lengths," + "int blank," + "float clamp," + "bool fused_log_smax=True," + "bool reuse_logits_for_grads=True) -> (Tensor, Tensor?)"); } diff --git a/torchaudio/csrc/rnnt/compute_alphas.cpp b/torchaudio/csrc/rnnt/compute_alphas.cpp index a52d49b8a1..6460fa1ad5 100644 --- a/torchaudio/csrc/rnnt/compute_alphas.cpp +++ b/torchaudio/csrc/rnnt/compute_alphas.cpp @@ -1,10 +1,11 @@ #include TORCH_LIBRARY_FRAGMENT(torchaudio, m) { - m.def("rnnt_loss_alphas(Tensor logits," - "Tensor targets," - "Tensor src_lengths," - "Tensor tgt_lengths," - "int blank," - "float clamp) -> Tensor"); + m.def( + "rnnt_loss_alphas(Tensor logits," + "Tensor targets," + "Tensor src_lengths," + "Tensor tgt_lengths," + "int blank," + "float clamp) -> Tensor"); } diff --git a/torchaudio/csrc/rnnt/compute_betas.cpp b/torchaudio/csrc/rnnt/compute_betas.cpp index 234dd909b5..209f786d4e 100644 --- a/torchaudio/csrc/rnnt/compute_betas.cpp +++ b/torchaudio/csrc/rnnt/compute_betas.cpp @@ -1,10 +1,11 @@ #include TORCH_LIBRARY_FRAGMENT(torchaudio, m) { - m.def("rnnt_loss_betas(Tensor logits," - "Tensor targets," - "Tensor src_lengths," - "Tensor tgt_lengths," - "int blank," - "float clamp) -> Tensor"); + m.def( + "rnnt_loss_betas(Tensor logits," + "Tensor targets," + "Tensor src_lengths," + "Tensor tgt_lengths," + "int blank," + "float clamp) -> Tensor"); } diff --git a/torchaudio/csrc/rnnt/cpu/compute.cpp b/torchaudio/csrc/rnnt/cpu/compute.cpp index 1cd9801d18..bc8f8034db 100644 --- a/torchaudio/csrc/rnnt/cpu/compute.cpp +++ b/torchaudio/csrc/rnnt/cpu/compute.cpp @@ -15,6 +15,61 @@ std::tuple> compute( double clamp, bool fused_log_smax = true, bool reuse_logits_for_grads = true) { + TORCH_CHECK( + logits.device().type() == targets.device().type(), + "logits and targets must be on the same device"); + TORCH_CHECK( + logits.device().type() == src_lengths.device().type(), + "logits and logit_lengths must be on the same device"); + TORCH_CHECK( + logits.device().type() == tgt_lengths.device().type(), + "logits and target_lengths must be on the same device"); + + TORCH_CHECK( + logits.dtype() == torch::kFloat32 || logits.dtype() == torch::kFloat16, + "logits must be float32 or float16 (half) type"); + TORCH_CHECK(targets.dtype() == torch::kInt32, "targets must be int32 type"); + TORCH_CHECK( + src_lengths.dtype() == torch::kInt32, "logit_lengths must be int32 type"); + TORCH_CHECK( + tgt_lengths.dtype() == torch::kInt32, + "target_lengths must be int32 type"); + + TORCH_CHECK(logits.is_contiguous(), "logits must be contiguous"); + TORCH_CHECK(targets.is_contiguous(), "targets must be contiguous"); + TORCH_CHECK(src_lengths.is_contiguous(), "logit_lengths must be contiguous"); + TORCH_CHECK(tgt_lengths.is_contiguous(), "target_lengths must be contiguous"); + + TORCH_CHECK( + logits.dim() == 4, "logits must be 4-D (batch, time, target, class)"); + TORCH_CHECK( + targets.dim() == 2, "targets must be 2-D (batch, max target length)"); + TORCH_CHECK(src_lengths.dim() == 1, "logit_lengths must be 1-D"); + TORCH_CHECK(tgt_lengths.dim() == 1, "target_lengths must be 1-D"); + + TORCH_CHECK( + src_lengths.size(0) == logits.size(0), + "batch dimension mismatch between logits and logit_lengths"); + TORCH_CHECK( + tgt_lengths.size(0) == logits.size(0), + "batch dimension mismatch between logits and target_lengths"); + TORCH_CHECK( + targets.size(0) == logits.size(0), + "batch dimension mismatch between logits and targets"); + + TORCH_CHECK( + blank >= 0 && blank < logits.size(-1), + "blank must be within [0, logits.shape[-1])"); + + TORCH_CHECK( + logits.size(1) == at::max(src_lengths).item().toInt(), + "input length mismatch"); + TORCH_CHECK( + logits.size(2) == at::max(tgt_lengths).item().toInt() + 1, + "output length mismatch"); + TORCH_CHECK( + targets.size(1) == at::max(tgt_lengths).item().toInt(), + "target length mismatch"); Options options; options.batchSize_ = src_lengths.size(0); @@ -43,50 +98,52 @@ std::tuple> compute( torch::Tensor int_workspace = torch::empty( IntWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Int)); + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Int)); torch::Tensor float_workspace = torch::empty( DtypeWorkspace::ComputeSizeFromOptions(options), - torch::TensorOptions().device(logits.device()).dtype(torch::ScalarType::Float)); + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Float)); Workspace workspace( /*options=*/options, - /*dtype_data=*/float_workspace.data(), + /*dtype_data=*/float_workspace.data_ptr(), /*dtype_size=*/float_workspace.numel(), - /*int_data=*/int_workspace.data(), + /*int_data=*/int_workspace.data_ptr(), /*int_size=*/int_workspace.numel()); - switch (logits.type().scalarType()) { - case torch::ScalarType::Float: - { - Compute( - /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*costs=*/costs.data(), - /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data()); - break; - } - case torch::ScalarType::Half: - { - Compute( - /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*costs=*/costs.data(), - /*gradients=*/(gradients == c10::nullopt)? nullptr : gradients->data()); - break; - } - default: - { - LOG(ERROR) << "unsupported logits.type().scalarType() = " - << logits.type().scalarType(); - break; - } + switch (logits.scalar_type()) { + case torch::ScalarType::Float: { + Compute( + /*workspace=*/workspace, + /*logits=*/logits.data_ptr(), + /*targets=*/targets.data_ptr(), + /*src_lengths=*/src_lengths.data_ptr(), + /*tgt_lengths=*/tgt_lengths.data_ptr(), + /*costs=*/costs.data_ptr(), + /*gradients=*/ + (gradients == c10::nullopt) ? nullptr : gradients->data_ptr()); + break; + } + case torch::ScalarType::Half: { + Compute( + /*workspace=*/workspace, + /*logits=*/logits.data_ptr(), + /*targets=*/targets.data_ptr(), + /*src_lengths=*/src_lengths.data_ptr(), + /*tgt_lengths=*/tgt_lengths.data_ptr(), + /*costs=*/costs.data_ptr(), + /*gradients=*/ + (gradients == c10::nullopt) ? nullptr + : gradients->data_ptr()); + break; + } + default: { + break; + } }; return std::make_tuple(costs, gradients); @@ -96,6 +153,6 @@ TORCH_LIBRARY_IMPL(torchaudio, CPU, m) { m.impl("rnnt_loss", &compute); } -} // namespace cpu -} // namespace rnnt -} // namespace torchaudio +} // namespace cpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp b/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp index fed1dec5cd..5eb8528b15 100644 --- a/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp +++ b/torchaudio/csrc/rnnt/cpu/compute_alphas.cpp @@ -44,20 +44,20 @@ torch::Tensor compute_alphas( Workspace workspace( /*options=*/options, - /*dtype_data=*/float_workspace.data(), + /*dtype_data=*/float_workspace.data_ptr(), /*dtype_size=*/float_workspace.numel(), - /*int_data=*/int_workspace.data(), + /*int_data=*/int_workspace.data_ptr(), /*int_size=*/int_workspace.numel()); // Only support float, this is mainly to enable easy // unit-testing ComputeAlphas( /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*alphas=*/alphas.data()); + /*logits=*/logits.data_ptr(), + /*targets=*/targets.data_ptr(), + /*src_lengths=*/src_lengths.data_ptr(), + /*tgt_lengths=*/tgt_lengths.data_ptr(), + /*alphas=*/alphas.data_ptr()); return alphas; } diff --git a/torchaudio/csrc/rnnt/cpu/compute_betas.cpp b/torchaudio/csrc/rnnt/cpu/compute_betas.cpp index 3789dec895..228cd66c1b 100644 --- a/torchaudio/csrc/rnnt/cpu/compute_betas.cpp +++ b/torchaudio/csrc/rnnt/cpu/compute_betas.cpp @@ -48,21 +48,21 @@ torch::Tensor compute_betas( Workspace workspace( /*options=*/options, - /*dtype_data=*/float_workspace.data(), + /*dtype_data=*/float_workspace.data_ptr(), /*dtype_size=*/float_workspace.numel(), - /*int_data=*/int_workspace.data(), + /*int_data=*/int_workspace.data_ptr(), /*int_size=*/int_workspace.numel()); // Only support float, this is mainly to enable easy // unit-testing ComputeBetas( /*workspace=*/workspace, - /*logits=*/logits.data(), - /*targets=*/targets.data(), - /*src_lengths=*/src_lengths.data(), - /*tgt_lengths=*/tgt_lengths.data(), - /*costs=*/costs.data(), - /*betas=*/betas.data()); + /*logits=*/logits.data_ptr(), + /*targets=*/targets.data_ptr(), + /*src_lengths=*/src_lengths.data_ptr(), + /*tgt_lengths=*/tgt_lengths.data_ptr(), + /*costs=*/costs.data_ptr(), + /*betas=*/betas.data_ptr()); return betas; } @@ -70,6 +70,6 @@ TORCH_LIBRARY_IMPL(torchaudio, CPU, m) { m.impl("rnnt_loss_betas", &compute_betas); } -} // namespace cpu +} // namespace cpu } // namespace rnnt } // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/cpu_kernels.h b/torchaudio/csrc/rnnt/cpu/cpu_kernels.h index bb63b97ce2..468cb41887 100644 --- a/torchaudio/csrc/rnnt/cpu/cpu_kernels.h +++ b/torchaudio/csrc/rnnt/cpu/cpu_kernels.h @@ -325,8 +325,7 @@ void ComputeGradientsOneSequence( CAST_DTYPE c = alpha({t, u}) + cost - denom({t, u}); for (int d = 0; d < D; ++d) { CAST_DTYPE g = CAST_DTYPE(logits({t, u, d})) + c; - if (d == blank && t == T - 1 && - u == U - 1) { // last blank transition. + if (d == blank && t == T - 1 && u == U - 1) { // last blank transition. gradients({t, u, d}) = std::exp(g + beta({t, u})) - std::exp(g); } else if (d == blank && t < T - 1) { gradients({t, u, d}) = diff --git a/torchaudio/csrc/rnnt/cpu/cpu_transducer.h b/torchaudio/csrc/rnnt/cpu/cpu_transducer.h index 47a058dc31..9d1fc86789 100644 --- a/torchaudio/csrc/rnnt/cpu/cpu_transducer.h +++ b/torchaudio/csrc/rnnt/cpu/cpu_transducer.h @@ -36,7 +36,7 @@ status_t Compute( const int& D = options.numTargets_; { // compute denominators. - status_t status = LogSumExp2D( + LogSumExp2D( /*N=*/B * maxT * maxU, /*D=*/D, /*logits=*/logits, @@ -44,7 +44,7 @@ status_t Compute( } { // compute log prob pairs. - status_t status = ComputeLogProbs( + ComputeLogProbs( /*options=*/options, /*logits=*/logits, /*targets=*/targets, @@ -99,7 +99,7 @@ status_t ComputeAlphas( const int& D = options.numTargets_; { // compute denominators. - status_t status = LogSumExp2D( + LogSumExp2D( /*N=*/B * maxT * maxU, /*D=*/D, /*logits=*/logits, @@ -107,7 +107,7 @@ status_t ComputeAlphas( } { // compute log prob pairs. - status_t status = ComputeLogProbs( + ComputeLogProbs( /*options=*/options, /*logits=*/logits, /*targets=*/targets, @@ -129,8 +129,6 @@ status_t ComputeAlphas( return SUCCESS; } - - template status_t ComputeBetas( const Workspace& workspace, @@ -150,7 +148,7 @@ status_t ComputeBetas( const int& D = options.numTargets_; { // compute denominators. - status_t status = LogSumExp2D( + LogSumExp2D( /*N=*/B * maxT * maxU, /*D=*/D, /*logits=*/logits, @@ -158,7 +156,7 @@ status_t ComputeBetas( } { // compute log prob pairs. - status_t status = ComputeLogProbs( + ComputeLogProbs( /*options=*/options, /*logits=*/logits, /*targets=*/targets, diff --git a/torchaudio/csrc/rnnt/cpu/kernel_utils.h b/torchaudio/csrc/rnnt/cpu/kernel_utils.h index 08fc97b2e1..5a4b0fb887 100644 --- a/torchaudio/csrc/rnnt/cpu/kernel_utils.h +++ b/torchaudio/csrc/rnnt/cpu/kernel_utils.h @@ -17,43 +17,50 @@ inline HOST_AND_DEVICE bool in_range( #define LOG_PROBS_SKIP_IDX 0 #define LOG_PROBS_EMIT_IDX 1 - struct Indexer2D { const int& size2_; - FORCE_INLINE HOST_AND_DEVICE Indexer2D(const int& size2): size2_(size2) {} + FORCE_INLINE HOST_AND_DEVICE Indexer2D(const int& size2) : size2_(size2) {} - FORCE_INLINE HOST_AND_DEVICE int operator() (int index1, int index2) { + FORCE_INLINE HOST_AND_DEVICE int operator()(int index1, int index2) { return index1 * size2_ + index2; } }; - struct Indexer3D { const int& size2_; const int& size3_; FORCE_INLINE HOST_AND_DEVICE Indexer3D(const int& size2, const int& size3) - : size2_(size2), size3_(size3) {} + : size2_(size2), size3_(size3) {} - FORCE_INLINE HOST_AND_DEVICE int operator() (int index1, int index2, int index3) { + FORCE_INLINE HOST_AND_DEVICE int operator()( + int index1, + int index2, + int index3) { return (index1 * size2_ + index2) * size3_ + index3; } }; - struct Indexer4D { const int& size2_; const int& size3_; const int& size4_; - HOST_AND_DEVICE Indexer4D(const int& size2, const int& size3, const int& size4) - : size2_(size2), size3_(size3), size4_(size4) {} - - HOST_AND_DEVICE int operator() (int index1, int index2, int index3, int index4) { + HOST_AND_DEVICE Indexer4D( + const int& size2, + const int& size3, + const int& size4) + : size2_(size2), size3_(size3), size4_(size4) {} + + HOST_AND_DEVICE int operator()( + int index1, + int index2, + int index3, + int index4) { return ((index1 * size2_ + index2) * size3_ + index3) * size4_ + index4; } }; -} // namespace rnnt -} // namespace torchaudio +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/cpu/math.h b/torchaudio/csrc/rnnt/cpu/math.h index 4f1d7bc4dd..e630a65cd2 100644 --- a/torchaudio/csrc/rnnt/cpu/math.h +++ b/torchaudio/csrc/rnnt/cpu/math.h @@ -9,14 +9,18 @@ namespace math { template FORCE_INLINE HOST_AND_DEVICE DTYPE max(DTYPE x, DTYPE y) { - if (x > y) return x; - else return y; + if (x > y) + return x; + else + return y; } template FORCE_INLINE HOST_AND_DEVICE DTYPE min(DTYPE x, DTYPE y) { - if (x > y) return y; - else return x; + if (x > y) + return y; + else + return x; } // log_sum_exp @@ -25,11 +29,14 @@ FORCE_INLINE HOST_AND_DEVICE DTYPE lse(DTYPE x, DTYPE y); template <> FORCE_INLINE HOST_AND_DEVICE float lse(float x, float y) { - if (y > x) { return y + log1pf(expf(x - y)); } - else { return x + log1pf(expf(y-x)); } + if (y > x) { + return y + log1pf(expf(x - y)); + } else { + return x + log1pf(expf(y - x)); + } } -} +} // namespace math -} // namespace rnnt -} // namespace torchaudio +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/compute.cu b/torchaudio/csrc/rnnt/gpu/compute.cu new file mode 100644 index 0000000000..d04efaec7e --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/compute.cu @@ -0,0 +1,161 @@ +#include +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace gpu { + +// Entry point into RNNT Loss +std::tuple> compute( + torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + bool fused_log_smax = true, + bool reuse_logits_for_grads = true) { + TORCH_CHECK( + logits.device().type() == targets.device().type(), + "logits and targets must be on the same device"); + TORCH_CHECK( + logits.device().type() == src_lengths.device().type(), + "logits and logit_lengths must be on the same device"); + TORCH_CHECK( + logits.device().type() == tgt_lengths.device().type(), + "logits and target_lengths must be on the same device"); + + TORCH_CHECK( + logits.dtype() == torch::kFloat32 || logits.dtype() == torch::kFloat16, + "logits must be float32 or float16 (half) type"); + TORCH_CHECK(targets.dtype() == torch::kInt32, "targets must be int32 type"); + TORCH_CHECK( + src_lengths.dtype() == torch::kInt32, "logit_lengths must be int32 type"); + TORCH_CHECK( + tgt_lengths.dtype() == torch::kInt32, + "target_lengths must be int32 type"); + + TORCH_CHECK(logits.is_contiguous(), "logits must be contiguous"); + TORCH_CHECK(targets.is_contiguous(), "targets must be contiguous"); + TORCH_CHECK(src_lengths.is_contiguous(), "logit_lengths must be contiguous"); + TORCH_CHECK(tgt_lengths.is_contiguous(), "target_lengths must be contiguous"); + + TORCH_CHECK( + logits.dim() == 4, "logits must be 4-D (batch, time, target, class)"); + TORCH_CHECK( + targets.dim() == 2, "targets must be 2-D (batch, max target length)"); + TORCH_CHECK(src_lengths.dim() == 1, "logit_lengths must be 1-D"); + TORCH_CHECK(tgt_lengths.dim() == 1, "target_lengths must be 1-D"); + + TORCH_CHECK( + src_lengths.size(0) == logits.size(0), + "batch dimension mismatch between logits and logit_lengths"); + TORCH_CHECK( + tgt_lengths.size(0) == logits.size(0), + "batch dimension mismatch between logits and target_lengths"); + TORCH_CHECK( + targets.size(0) == logits.size(0), + "batch dimension mismatch between logits and targets"); + + TORCH_CHECK( + blank >= 0 && blank < logits.size(-1), + "blank must be within [0, logits.shape[-1])"); + + TORCH_CHECK( + logits.size(1) == at::max(src_lengths).item().toInt(), + "input length mismatch"); + TORCH_CHECK( + logits.size(2) == at::max(tgt_lengths).item().toInt() + 1, + "output length mismatch"); + TORCH_CHECK( + targets.size(1) == at::max(tgt_lengths).item().toInt(), + "target length mismatch"); + + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = logits.size(1); + options.maxTgtLen_ = logits.size(2); + options.numTargets_ = logits.size(3); + options.blank_ = blank; + options.clamp_ = clamp; + options.fusedLogSmax_ = fused_log_smax; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); + options.stream_ = at::cuda::getCurrentCUDAStream(); + cudaSetDevice(logits.get_device()); + options.device_ = GPU; + + torch::Tensor costs = torch::empty( + options.batchSize_ * options.nHypos_, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + c10::optional gradients = c10::nullopt; + if (logits.requires_grad()) { + if (reuse_logits_for_grads) { + gradients = logits; + } else { + gradients = torch::zeros_like(logits); + } + } + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data_ptr(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data_ptr(), + /*int_size=*/int_workspace.numel()); + + switch (logits.scalar_type()) { + case torch::ScalarType::Float: { + Compute( + /*workspace=*/workspace, + /*logits=*/logits.data_ptr(), + /*targets=*/targets.data_ptr(), + /*src_lengths=*/src_lengths.data_ptr(), + /*tgt_lengths=*/tgt_lengths.data_ptr(), + /*costs=*/costs.data_ptr(), + /*gradients=*/ + (gradients == c10::nullopt) ? nullptr : gradients->data_ptr()); + break; + } + case torch::ScalarType::Half: { + Compute( + /*workspace=*/workspace, + /*logits=*/logits.data_ptr(), + /*targets=*/targets.data_ptr(), + /*src_lengths=*/src_lengths.data_ptr(), + /*tgt_lengths=*/tgt_lengths.data_ptr(), + /*costs=*/costs.data_ptr(), + /*gradients=*/ + (gradients == c10::nullopt) ? nullptr + : gradients->data_ptr()); + break; + } + default: { + break; + } + }; + + return std::make_tuple(costs, gradients); +} + +TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { + m.impl("rnnt_loss", &compute); +} + +} // namespace gpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/compute_alphas.cu b/torchaudio/csrc/rnnt/gpu/compute_alphas.cu new file mode 100644 index 0000000000..f24c6a5b0f --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/compute_alphas.cu @@ -0,0 +1,73 @@ +#include +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace gpu { + +torch::Tensor compute_alphas( + const torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp) { + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = logits.size(1); + options.maxTgtLen_ = logits.size(2); + options.numTargets_ = logits.size(3); + options.blank_ = blank; + options.clamp_ = clamp; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); + options.stream_ = at::cuda::getCurrentCUDAStream(); + cudaSetDevice(logits.get_device()); + options.device_ = GPU; + + torch::Tensor alphas = torch::zeros( + {options.batchSize_ * options.nHypos_, + options.maxSrcLen_, + options.maxTgtLen_}, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data_ptr(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data_ptr(), + /*int_size=*/int_workspace.numel()); + + // Only support float, this is mainly to enable easy + // unit-testing + ComputeAlphas( + /*workspace=*/workspace, + /*logits=*/logits.data_ptr(), + /*targets=*/targets.data_ptr(), + /*src_lengths=*/src_lengths.data_ptr(), + /*tgt_lengths=*/tgt_lengths.data_ptr(), + /*alphas=*/alphas.data_ptr()); + return alphas; +} + +TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { + m.impl("rnnt_loss_alphas", &compute_alphas); +} + +} // namespace gpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/compute_betas.cu b/torchaudio/csrc/rnnt/gpu/compute_betas.cu new file mode 100644 index 0000000000..a225c9cf42 --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/compute_betas.cu @@ -0,0 +1,78 @@ +#include +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace gpu { + +torch::Tensor compute_betas( + const torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp) { + Options options; + options.batchSize_ = src_lengths.size(0); + options.nHypos_ = tgt_lengths.size(0) / src_lengths.size(0); + options.maxSrcLen_ = logits.size(1); + options.maxTgtLen_ = logits.size(2); + options.numTargets_ = logits.size(3); + options.blank_ = blank; + options.clamp_ = clamp; + + CHECK_EQ(logits.device().type(), torch::DeviceType::CUDA); + options.stream_ = at::cuda::getCurrentCUDAStream(); + cudaSetDevice(logits.get_device()); + options.device_ = GPU; + + torch::Tensor costs = torch::empty( + tgt_lengths.size(0), + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor betas = torch::zeros( + {options.batchSize_ * options.nHypos_, + options.maxSrcLen_, + options.maxTgtLen_}, + torch::TensorOptions().device(logits.device()).dtype(logits.dtype())); + + torch::Tensor int_workspace = torch::empty( + IntWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Int)); + + torch::Tensor float_workspace = torch::empty( + DtypeWorkspace::ComputeSizeFromOptions(options), + torch::TensorOptions() + .device(logits.device()) + .dtype(torch::ScalarType::Float)); + + Workspace workspace( + /*options=*/options, + /*dtype_data=*/float_workspace.data_ptr(), + /*dtype_size=*/float_workspace.numel(), + /*int_data=*/int_workspace.data_ptr(), + /*int_size=*/int_workspace.numel()); + + // Only support float, this is mainly to enable easy + // unit-testing + ComputeBetas( + /*workspace=*/workspace, + /*logits=*/logits.data_ptr(), + /*targets=*/targets.data_ptr(), + /*src_lengths=*/src_lengths.data_ptr(), + /*tgt_lengths=*/tgt_lengths.data_ptr(), + /*costs=*/costs.data_ptr(), + /*betas=*/betas.data_ptr()); + return betas; +} + +TORCH_LIBRARY_IMPL(torchaudio, CUDA, m) { + m.impl("rnnt_loss_betas", &compute_betas); +} + +} // namespace gpu +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh b/torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh new file mode 100644 index 0000000000..e5f1cfc2df --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/gpu_kernel_utils.cuh @@ -0,0 +1,98 @@ +#pragma once + +#ifdef USE_CUDA + +#include + +namespace torchaudio { +namespace rnnt { + +template +__global__ void ReduceMax2D( + int dim, + const DTYPE* inputs, // [N, dim] + CAST_DTYPE* outputs) { + __shared__ CAST_DTYPE shared[NUM_THREADS]; + + // each thread reduces one matrix row + int offset = blockIdx.x * dim; // [n, 0] + CAST_DTYPE val = inputs[offset]; // default = inputs(n, 0) + for (int d = threadIdx.x; d < dim; d += NUM_THREADS) { + CAST_DTYPE next = inputs[offset + d]; + if (next > val) { + val = next; + } + } + + shared[threadIdx.x] = val; + __syncthreads(); + + for (int stride = (NUM_THREADS >> 1); stride >= WARP_SIZE; stride >>= 1) { + if (threadIdx.x < stride && threadIdx.x + stride < dim) { + if (shared[threadIdx.x + stride] > shared[threadIdx.x]) { + shared[threadIdx.x] = shared[threadIdx.x + stride]; + val = shared[threadIdx.x]; + } + } + __syncthreads(); + } + + CAST_DTYPE shf; + for (int stride = (WARP_SIZE >> 1); stride > 0; stride >>= 1) { + shf = __shfl_down_sync(0xFFFFFFFF, val, stride); + if (threadIdx.x < stride && threadIdx.x + stride < dim) { + if (shf > val) { + val = shf; + } + } + } + + if (threadIdx.x == 0) { + outputs[blockIdx.x] = val; + } +} + +template +__global__ void ReduceLogSumExpGivenMax2D( + int dim, + const DTYPE* inputs, // [N, dim] + CAST_DTYPE* outputs) { // in: max -> out: logsum + + __shared__ CAST_DTYPE shared[NUM_THREADS]; + + CAST_DTYPE max = outputs[blockIdx.x]; + CAST_DTYPE val = 0; + + int offset = blockIdx.x * dim; + for (int d = threadIdx.x; d < dim; d += NUM_THREADS) { + val = val + std::exp(CAST_DTYPE(inputs[offset + d]) - max); + } + + shared[threadIdx.x] = val; + __syncthreads(); + + for (int stride = (NUM_THREADS >> 1); stride >= WARP_SIZE; stride >>= 1) { + if (threadIdx.x < stride && threadIdx.x + stride < dim) { + val = shared[threadIdx.x] + shared[threadIdx.x + stride]; + shared[threadIdx.x] = val; + } + __syncthreads(); + } + + CAST_DTYPE shf; + for (int stride = (WARP_SIZE >> 1); stride > 0; stride >>= 1) { + shf = __shfl_down_sync(0xFFFFFFFF, val, stride); + if (threadIdx.x < stride && threadIdx.x + stride < dim) { + val = val + shf; + } + } + + if (threadIdx.x == 0) { + outputs[blockIdx.x] = max + std::log(val); + } +} + +} // namespace rnnt +} // namespace torchaudio + +#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh b/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh new file mode 100644 index 0000000000..4ba04b68fc --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh @@ -0,0 +1,422 @@ +#pragma once + +#ifdef USE_CUDA + +#include + +#include +#include +#include + +namespace torchaudio { +namespace rnnt { + +template +__global__ void ComputeLogProbs( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + CAST_DTYPE* logProbs, + int H = 1, + bool fusedLogSmax = true) { + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + const int& D = numTargets; + + const int bTgt = blockIdx.z; // 0 <= b < B + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + const int t = blockIdx.x * blockDim.x + threadIdx.x; + const int u = blockIdx.y; + + if (t >= T || u >= U) { // out of boundary. + return; + } + + Indexer3D indexer(maxT, maxU); + + int idx = indexer(bTgt, t, u); + + // skip: log_prob(b, t, u).skip() = logits(b, t, u, blank) - denom(b, t, u). + logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = + CAST_DTYPE(logits[idx * D + blank]) - denominators[idx]; + + if (!fusedLogSmax) { + logProbs[(idx << 1) + LOG_PROBS_SKIP_IDX] = + CAST_DTYPE(logits[idx * D + blank]); + } + + if (u < U - 1) { + // emit: log_prob(b, t, u).emit() = logits(b, t, u, tgt[u]) - denom(b, t, + // u). + int target = targets[Indexer2D(maxU - 1)(bTgt, u)]; + logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = + CAST_DTYPE(logits[idx * D + target]) - denominators[idx]; + + if (!fusedLogSmax) { + logProbs[(idx << 1) + LOG_PROBS_EMIT_IDX] = + CAST_DTYPE(logits[idx * D + target]); + } + } +} + +template +__device__ void ComputeAlphas( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* alpha_counters, + volatile CAST_DTYPE* alphas, + int H = 1) { + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + + const int bTgt = blockIdx.z; // 0 <= b < B + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + const int t = blockIdx.x * blockDim.x + threadIdx.x + 1; + const int u = blockIdx.y + 1; + + if (t >= T || u >= U) { // out of boundary. + return; + } + + int* counter = alpha_counters + Indexer2D(maxU)(bTgt, blockIdx.y); + + Indexer3D idxr(maxT, maxU); + + if (t == 1 && u == 1) { + alphas[idxr(bTgt, 0, 0)] = 0; + } + + if (blockIdx.x > 0) { // wait for previous warp (in t-axis) is ready. + while (atomicAdd(counter, 0) < blockIdx.x) { + } + } + + if (blockIdx.y > 0) { // wait for previous warp (in u-axis) is ready. + while (atomicAdd(counter - 1, 0) <= blockIdx.x) { + } + } + + if (t == 1 && u < U) { + // alpha(0, u) = alpha(0, u - 1) + logProbs(0, u - 1).emit(). + alphas[idxr(bTgt, 0, u)] = alphas[idxr(bTgt, 0, u - 1)] + + logProbs[(idxr(bTgt, 0, u - 1) << 1) + LOG_PROBS_EMIT_IDX]; + } + + if (blockIdx.y == 0 && t < T) { + CAST_DTYPE skip_prob = + logProbs[(idxr(bTgt, t - 1, 0) << 1) + LOG_PROBS_SKIP_IDX]; + CAST_DTYPE val; + +#pragma unroll + for (int i = 1; i < warpSize; i <<= 1) { + val = __shfl_up_sync(0xffffffff, skip_prob, i); + if (i <= threadIdx.x) { + skip_prob = skip_prob + val; + } + } + + val = alphas[idxr(bTgt, blockIdx.x * blockDim.x, 0)]; + alphas[idxr(bTgt, t, 0)] = skip_prob + val; + } + + if (t < T && u < U) { + CAST_DTYPE skip_prob = + logProbs[(idxr(bTgt, t - 1, u) << 1) + LOG_PROBS_SKIP_IDX]; + CAST_DTYPE emit_prob = + logProbs[(idxr(bTgt, t, u - 1) << 1) + LOG_PROBS_EMIT_IDX]; + + CAST_DTYPE skip = + alphas[idxr(bTgt, blockIdx.x * blockDim.x, u)] + skip_prob; + CAST_DTYPE emit = alphas[idxr(bTgt, t, u - 1)] + emit_prob; + + CAST_DTYPE val = math::lse(skip, emit); + CAST_DTYPE out = val; + + for (int i = 1; i < warpSize; ++i) { + val = __shfl_up_sync(0xffffffff, val, 1); + if (i == threadIdx.x) { + val = math::lse(val + skip_prob, emit); + out = val; + } + } + + alphas[idxr(bTgt, t, u)] = out; + } + + if (threadIdx.x == 0) { + __threadfence(); + atomicAdd(counter, 1); + } +} + +template +__device__ void ComputeBetasCosts( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* betaCounters, + volatile CAST_DTYPE* betas, + DTYPE* costs, + int H = 1) { + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + + const int bTgt = blockIdx.z; // 0 <= b < B + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + const int t = T - 2 - blockIdx.x * blockDim.x - threadIdx.x; + const int u = U - 2 - blockIdx.y; + + if (t < 0 || u < 0) { // out of boundary. + return; + } + + int* counter = betaCounters + Indexer2D(maxU)(bTgt, blockIdx.y); + + Indexer3D idxr(maxT, maxU); + + if (t == T - 2 && u == U - 2) { + betas[idxr(bTgt, T - 1, U - 1)] = + logProbs[(idxr(bTgt, T - 1, U - 1) << 1) + LOG_PROBS_SKIP_IDX]; + } + + if (blockIdx.x > 0) { // wait for previous warp (in t-axis) is ready. + while (atomicAdd(counter, 0) < blockIdx.x) { + } + } + + if (blockIdx.y > 0) { // wait for previous warp (in u-axis) is ready. + while (atomicAdd(counter - 1, 0) <= blockIdx.x) { + } + } + + if (t == T - 2 && u >= 0) { + betas[idxr(bTgt, T - 1, u)] = betas[idxr(bTgt, T - 1, u + 1)] + + logProbs[(idxr(bTgt, T - 1, u) << 1) + LOG_PROBS_EMIT_IDX]; + } + + if (blockIdx.y == 0 && t >= 0) { + CAST_DTYPE skip_prob = + logProbs[(idxr(bTgt, t, U - 1) << 1) + LOG_PROBS_SKIP_IDX]; + CAST_DTYPE val; + +#pragma unroll + for (int i = 1; i < warpSize; i <<= 1) { + val = __shfl_up_sync(0xffffffff, skip_prob, i); + if (i <= threadIdx.x) { + skip_prob = skip_prob + val; + } + } + + betas[idxr(bTgt, t, U - 1)] = + betas[idxr(bTgt, T - 1 - blockIdx.x * blockDim.x, U - 1)] + skip_prob; + } + + if (t >= 0 && u >= 0) { + CAST_DTYPE skip_prob = + logProbs[(idxr(bTgt, t, u) << 1) + LOG_PROBS_SKIP_IDX]; + CAST_DTYPE emit_prob = + logProbs[(idxr(bTgt, t, u) << 1) + LOG_PROBS_EMIT_IDX]; + + CAST_DTYPE skip = betas[idxr(bTgt, t + threadIdx.x + 1, u)] + skip_prob; + CAST_DTYPE emit = betas[idxr(bTgt, t, u + 1)] + emit_prob; + + CAST_DTYPE val = math::lse(skip, emit); + CAST_DTYPE out = val; + + for (int i = 1; i < warpSize; ++i) { + val = __shfl_up_sync(0xffffffff, val, 1); + if (i == threadIdx.x) { + val = math::lse(val + skip_prob, emit); + out = val; + } + } + + betas[idxr(bTgt, t, u)] = out; + + if (t == 0 && u == 0) { // use -beta(0, 0) as cost. + costs[bTgt] = DTYPE(-out); + } + } + + if (threadIdx.x == 0) { + __threadfence(); + atomicAdd(counter, 1); + } +} + +template +__global__ void ComputeAlphasBetasCosts( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* alpha_counters, + volatile CAST_DTYPE* alphas, + int* betaCounters, + volatile CAST_DTYPE* betas, + DTYPE* costs, + int warpSize = 0, + int numWarps = 0, + int H = 1) { + assert(threadIdx.y == 0 || threadIdx.y == 1); + + if (threadIdx.y == 0) { + ComputeAlphas( + /*maxSrcLen=*/maxSrcLen, + /*maxTgtLen=*/maxTgtLen, + /*numTargets=*/numTargets, + /*blank=*/blank, + /*logProbs=*/logProbs, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/alpha_counters, + /*alphas=*/alphas, + H); + } else { // threadIdx.y == 1 + ComputeBetasCosts( + /*maxSrcLen=*/maxSrcLen, + /*maxTgtLen=*/maxTgtLen, + /*numTargets=*/numTargets, + /*blank=*/blank, + /*logProbs=*/logProbs, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*betaCounters=*/betaCounters, + /*beta=*/betas, + /*costs=*/costs, + H); + } +} + +template +__global__ void ComputeGradients( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + CAST_DTYPE clamp, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + const CAST_DTYPE* alphas, + const CAST_DTYPE* betas, + DTYPE* gradients, + int H = 1, + bool fusedLogSmax = true) { + const int bTgt = blockIdx.z; // 0 <= b < B + const int t = blockIdx.x * blockDim.x + threadIdx.x; + const int u = blockIdx.y; + + ComputeGradientsElement( + bTgt, + t, + u, + maxSrcLen, + maxTgtLen, + numTargets, + blank, + clamp, + logits, + targets, + srcLengths, + tgtLengths, + denominators, + alphas, + betas, + gradients, + H, + fusedLogSmax); +} + +// This is a __global__ wrapper around ComputeAlphas +// device kernel to enable unit testing +template +__global__ void ComputeAlphasWrapper( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* alpha_counters, + volatile CAST_DTYPE* alphas, + int H = 1) { + ComputeAlphas( + maxSrcLen, + maxTgtLen, + numTargets, + blank, + logProbs, + srcLengths, + tgtLengths, + alpha_counters, + alphas, + H); +} + +// This is a __global__ wrapper around ComputeBetas +// device kernel to enable unit testing +template +__global__ void ComputeBetasWrapper( + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + const CAST_DTYPE* logProbs, + const int* srcLengths, + const int* tgtLengths, + int* betaCounters, + volatile CAST_DTYPE* betas, + DTYPE* costs, + int H = 1) { + ComputeBetasCosts( + maxSrcLen, + maxTgtLen, + numTargets, + blank, + logProbs, + srcLengths, + tgtLengths, + betaCounters, + betas, + costs, + H); +} + +// #undef LOG_PROBS_SKIP_IDX +// #undef LOG_PROBS_EMIT_IDX + +} // namespace rnnt +} // namespace torchaudio + +#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/gpu_transducer.h b/torchaudio/csrc/rnnt/gpu/gpu_transducer.h new file mode 100644 index 0000000000..72759b39f4 --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/gpu_transducer.h @@ -0,0 +1,395 @@ +#pragma once + +#ifdef USE_CUDA + +#include +#include +#include + +namespace torchaudio { +namespace rnnt { +namespace gpu { + +#define gpuErrchk(ans) \ + { gpuAssert((ans), __FILE__, __LINE__); } + +inline void gpuAssert( + cudaError_t code, + const char* file, + int line, + bool abort = true) { + if (code != cudaSuccess) { + fprintf( + stderr, + "\nGPUassert: %s %s %d\n", + cudaGetErrorString(code), + file, + line); + if (abort) + exit(code); + } +} + +template +status_t LogSumExp2D( + cudaStream_t stream, + int N, + int D, + const DTYPE* logits, // [N, D] + CAST_DTYPE* outputs) { + { // compute max among D. + dim3 block_dims(N); + dim3 thread_dims(REDUCE_THREADS); + + ReduceMax2D + <<>>( + /*dim=*/D, + /*inputs=*/logits, + /*outputs=*/outputs); + + // BUGBUG: These error codes are only accurate when launching with + // blocking. Otherwise they usually reflect earlier errors. + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_DENOMINATOR_REDUCE_MAX_FAILED; + } + } + + { // compute log(sum(exp(d_i - max))) + dim3 block_dims(N); + dim3 thread_dims(REDUCE_THREADS); + + ReduceLogSumExpGivenMax2D + <<>>( + /*dim=*/D, + /*inputs=*/logits, + /*outputs=*/outputs); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_DENOMINATOR_REDUCE_SUM_FAILED; + } + } + + return SUCCESS; +} + +// Inputs: +// workspace: workspace. +// logits: pointer to (B, max_T, max_U, D) logits. +// targets: pointer to (B, max_U - 1) targets in the batch. +// srcLengths: pointer to (B, ) source lengths in the batch. +// tgtLengths: pointer to (B, ) target lengths in the batch. +// +// Outputs: +// costs: pointer to (B, ) costs in the batch. +// gradients: pointer to (B, max_T, max_U, D) gradients in the batch. +template +status_t Compute( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* gradients = nullptr) { + const Options& options = workspace.GetOptions(); + + const cudaStream_t& stream = options.stream_; + const int& B = options.batchSize_; + const int& H = options.nHypos_; + const int& max_T = options.maxSrcLen_; + const int& max_U = options.maxTgtLen_; + const int& D = options.numTargets_; + const int& blank = options.blank_; + const CAST_DTYPE clamp = options.clamp_; + + const bool& fusedLogSmax = options.fusedLogSmax_; + + { // compute denominators. + status_t status = LogSumExp2D( + /*stream=*/stream, + /*N=*/B * H * max_T * max_U, + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + + if (status != SUCCESS) { + return status; + } + } + + { // compute log probability pairs (blank and target). + int num_segments = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_segments, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeLogProbs<<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs(), + H, + fusedLogSmax); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_LOG_PROBS_FAILED; + } + } + + { // compute alphas, betas and costs. + // warp is usually a group of threads (32) + int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; + + // each block is identified by 3 d tuple. + // we are using num_warp * max_U * B * H blocks + // where num_warp is division among Time axis + dim3 block_dims(num_warps, max_U, B * H); + + // each thread is identified by a 2 d tuple + // 2nd dim is 2. 1 for alpha, 1 for beta + dim3 thread_dims(WARP_SIZE, 2); + + ComputeAlphasBetasCosts + <<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/workspace.GetPointerToAlphaCounters(), + /*alphas=*/workspace.GetPointerToAlphas(), + /*beta_counters=*/workspace.GetPointerToBetaCounters(), + /*betas=*/workspace.GetPointerToBetas(), + /*costs=*/costs, + /*warp_size=*/WARP_SIZE, + /*num_warps=*/num_warps, + H); + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; + } + } + + if (gradients != nullptr) { // compute gradients. + // don't set gradients to zero to here as gradients might reuse memory from + // logits + + int num_blocks = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_blocks, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeGradients<<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*clamp=*/clamp, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*alphas=*/workspace.GetPointerToAlphas(), + /*betas=*/workspace.GetPointerToBetas(), + /*gradients=*/gradients, + H, + fusedLogSmax); + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_GRADIENTS_FAILED; + } + } + + return SUCCESS; +} + +template +status_t ComputeAlphas( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* alphas) { + const Options& options = workspace.GetOptions(); + + const cudaStream_t& stream = options.stream_; + const int& B = options.batchSize_; + const int& H = options.nHypos_; + const int& max_T = options.maxSrcLen_; + const int& max_U = options.maxTgtLen_; + const int& D = options.numTargets_; + const int& blank = options.blank_; + + { // compute denominators. + status_t status = LogSumExp2D( + /*stream=*/stream, + /*N=*/B * H * max_T * max_U, + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + + if (status != SUCCESS) { + return status; + } + } + + { // compute log probability pairs (blank and target). + int num_segments = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_segments, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeLogProbs<<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs(), + H); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_LOG_PROBS_FAILED; + } + } + { // compute alphas + // warp is usually a group of threads (32) + int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; + + // each block is identified by 3 d tuple. + // we are using num_warp * max_U * B blocks + // where num_warp is division among Time axis + dim3 block_dims(num_warps, max_U, B * H); + + // each thread is identified by a 2 d tuple + // 2nd dim is 1 for alpha only + dim3 thread_dims(WARP_SIZE, 1); + + ComputeAlphasWrapper + <<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/workspace.GetPointerToAlphaCounters(), + /*alphas=*/(volatile DTYPE*)alphas, + H); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; + } + } + + return SUCCESS; +} + +template +status_t ComputeBetas( + const Workspace& workspace, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + DTYPE* costs, + DTYPE* betas) { + const Options& options = workspace.GetOptions(); + + const cudaStream_t& stream = options.stream_; + const int& B = options.batchSize_; + const int& H = options.nHypos_; + const int& max_T = options.maxSrcLen_; + const int& max_U = options.maxTgtLen_; + const int& D = options.numTargets_; + const int& blank = options.blank_; + + { // compute denominators. + status_t status = LogSumExp2D( + /*stream=*/stream, + /*N=*/B * H * max_T * max_U, + /*D=*/D, + /*logits=*/logits, + /*denominators=*/workspace.GetPointerToDenominators()); + + if (status != SUCCESS) { + return status; + } + } + + { // compute log probability pairs (blank and target). + int num_segments = + (max_T + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; + dim3 block_dims(num_segments, max_U, B * H); + dim3 thread_dims(MAX_THREADS_PER_BLOCK); + + ComputeLogProbs<<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*logits=*/logits, + /*targets=*/targets, + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*denominators=*/workspace.GetPointerToDenominators(), + /*log_probs=*/workspace.GetPointerToLogProbs(), + H); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_LOG_PROBS_FAILED; + } + } + { // compute betas + // warp is usually a group of threads (32) + int num_warps = (max_T + WARP_SIZE - 1) / WARP_SIZE; + + // each block is identified by 3 d tuple. + // we are using num_warp * max_U * B blocks + // where num_warp is division among Time axis + dim3 block_dims(num_warps, max_U, B * H); + + // each thread is identified by a 2 d tuple + // 2nd dim is 1 for betas only + dim3 thread_dims(WARP_SIZE, 1); + + ComputeBetasWrapper + <<>>( + /*max_src_len=*/max_T, + /*max_tgt_len=*/max_U, + /*num_targets=*/D, + /*blank=*/blank, + /*log_probs=*/workspace.GetPointerToLogProbs(), + /*srcLengths=*/srcLengths, + /*tgtLengths=*/tgtLengths, + /*alpha_counters=*/workspace.GetPointerToBetaCounters(), + /*alphas=*/(volatile DTYPE*)betas, + costs, + H); + + if (cudaGetLastError() != cudaSuccess) { + return COMPUTE_ALPHAS_BETAS_COSTS_FAILED; + } + } + + return SUCCESS; +} + +} // namespace gpu +} // namespace rnnt +} // namespace torchaudio + +#endif // USE_CUDA diff --git a/torchaudio/csrc/rnnt/gpu/half.cuh b/torchaudio/csrc/rnnt/gpu/half.cuh new file mode 100644 index 0000000000..72a2f37e04 --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/half.cuh @@ -0,0 +1,38 @@ +#pragma once + +#ifdef USE_C10_HALF +#include "c10/util/Half.h" +#endif // USE_C10_HALF + +#include + +namespace torchaudio { +namespace rnnt { + +struct alignas(sizeof(__half)) Half { + __half x; + + HOST_AND_DEVICE Half() = default; + + FORCE_INLINE HOST_AND_DEVICE Half(float f) { + x = __float2half_rn(f); + if (isinf(__half2float(x))) { + x = __float2half_rz(f); // round toward 0. + } + } + + FORCE_INLINE HOST_AND_DEVICE operator float() const { + return __half2float(x); + } + + FORCE_INLINE HOST_AND_DEVICE Half(__half f) { + x = f; + } + + FORCE_INLINE HOST_AND_DEVICE operator __half() const { + return x; + } +}; + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/kernel_utils.h b/torchaudio/csrc/rnnt/gpu/kernel_utils.h new file mode 100644 index 0000000000..3b2989b073 --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/kernel_utils.h @@ -0,0 +1,66 @@ +#pragma once + +#include + +#include + +namespace torchaudio { +namespace rnnt { + +inline HOST_AND_DEVICE bool in_range( + int start, + int end, // inclusive + int val) { + return start <= val && val <= end; +} + +#define LOG_PROBS_SKIP_IDX 0 +#define LOG_PROBS_EMIT_IDX 1 + +struct Indexer2D { + const int& size2_; + + FORCE_INLINE HOST_AND_DEVICE Indexer2D(const int& size2) : size2_(size2) {} + + FORCE_INLINE HOST_AND_DEVICE int operator()(int index1, int index2) { + return index1 * size2_ + index2; + } +}; + +struct Indexer3D { + const int& size2_; + const int& size3_; + + FORCE_INLINE HOST_AND_DEVICE Indexer3D(const int& size2, const int& size3) + : size2_(size2), size3_(size3) {} + + FORCE_INLINE HOST_AND_DEVICE int operator()( + int index1, + int index2, + int index3) { + return (index1 * size2_ + index2) * size3_ + index3; + } +}; + +struct Indexer4D { + const int& size2_; + const int& size3_; + const int& size4_; + + HOST_AND_DEVICE Indexer4D( + const int& size2, + const int& size3, + const int& size4) + : size2_(size2), size3_(size3), size4_(size4) {} + + HOST_AND_DEVICE int operator()( + int index1, + int index2, + int index3, + int index4) { + return ((index1 * size2_ + index2) * size3_ + index3) * size4_ + index4; + } +}; + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/kernels.h b/torchaudio/csrc/rnnt/gpu/kernels.h new file mode 100644 index 0000000000..db8bb5092b --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/kernels.h @@ -0,0 +1,131 @@ +#pragma once + +#include + +#include +#include + +namespace torchaudio { +namespace rnnt { + +template +HOST_AND_DEVICE void ComputeGradientsElement( + int bTgt, + int t, + int u, + int maxSrcLen, + int maxTgtLen, + int numTargets, + int blank, + CAST_DTYPE clamp, + const DTYPE* logits, + const int* targets, + const int* srcLengths, + const int* tgtLengths, + const CAST_DTYPE* denominators, + const CAST_DTYPE* alphas, + const CAST_DTYPE* betas, + DTYPE* gradients, + int H = 1, + bool fusedLogSmax = true) { + const int& maxT = maxSrcLen; + const int& maxU = maxTgtLen; + const int& D = numTargets; + + const int bSrc = bTgt / H; + const int T = srcLengths[bSrc]; + const int U = tgtLengths[bTgt] + 1; + + if (t >= T || u >= U) { // out of boundary. + if (gradients == logits && t < maxT && u < maxU) { + // gradients and logits are pointing to the same memory location + Indexer3D idxr3(maxT, maxU); + int idx_b_t_u_zero = idxr3(bTgt, t, u); + if (idx_b_t_u_zero != -1) { + int start = idx_b_t_u_zero * D; + for (int b_t_u_d = start; b_t_u_d < start + D; ++b_t_u_d) { + gradients[b_t_u_d] = 0; + } + } + } + return; + } + + int costIdx = bTgt * maxT * maxU; + CAST_DTYPE cost = -(betas[costIdx]); + + Indexer2D idxr2(maxU - 1); + + int idx_b_t_u, idx_b_t_up1, idx_b_tp1_u; + Indexer3D idxr3(maxT, maxU); + idx_b_t_u = idxr3(bTgt, t, u); + idx_b_t_up1 = idxr3(bTgt, t, u + 1); + idx_b_tp1_u = idxr3(bTgt, t + 1, u); + + if (idx_b_t_u == -1) { + return; + } + + if (isinf(cost) || isnan(cost)) { + for (int d = 0; d < D; ++d) { + int b_t_u_d = idx_b_t_u * D + d; + gradients[b_t_u_d] = 0; + } + return; + } + + CAST_DTYPE c = alphas[idx_b_t_u] + cost - denominators[idx_b_t_u]; + for (int d = 0; d < D; ++d) { + int b_t_u_d = idx_b_t_u * D + d; + CAST_DTYPE g = CAST_DTYPE(logits[b_t_u_d]) + c; + + if (fusedLogSmax) { + if (d == blank && t == T - 1 && u == U - 1) { // last blank transition. + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]) - std::exp(g); + } else if (t < T - 1 && d == blank) { + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); + if (idx_b_tp1_u != -1) { + gradients[b_t_u_d] = + gradients[b_t_u_d] - std::exp(g + betas[idx_b_tp1_u]); + } + } else if (u < U - 1 && d == targets[idxr2(bTgt, u)]) { + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); + if (idx_b_t_up1 != -1) { + gradients[b_t_u_d] = + gradients[b_t_u_d] - std::exp(g + betas[idx_b_t_up1]); + } + } else { + gradients[b_t_u_d] = std::exp(g + betas[idx_b_t_u]); + } + } else { // Non fused log softmax case + CAST_DTYPE g = cost + CAST_DTYPE(logits[b_t_u_d]); + if (d == blank && t == T - 1 && u == U - 1) { + gradients[b_t_u_d] = g + alphas[idx_b_t_u]; + } else if (t < T - 1 && d == blank) { + if (idx_b_tp1_u != -1) { + gradients[b_t_u_d] = g + alphas[idx_b_t_u] + betas[idx_b_tp1_u]; + } else { + gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); + } + } else if (u < U - 1 && d == targets[idxr2(bTgt, u)]) { + if (idx_b_t_up1 != -1) { + gradients[b_t_u_d] = g + alphas[idx_b_t_u] + betas[idx_b_t_up1]; + } else { + gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); + } + } else { + gradients[b_t_u_d] = g + CAST_DTYPE(-INFINITY); + } + gradients[b_t_u_d] = -std::exp(gradients[b_t_u_d]); + } + + if (clamp > 0) { + auto g = CAST_DTYPE(gradients[b_t_u_d]); + gradients[b_t_u_d] = math::min(g, clamp); + gradients[b_t_u_d] = math::max(g, -clamp); + } + } +} + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/gpu/math.cuh b/torchaudio/csrc/rnnt/gpu/math.cuh new file mode 100644 index 0000000000..643fa98300 --- /dev/null +++ b/torchaudio/csrc/rnnt/gpu/math.cuh @@ -0,0 +1,48 @@ +#pragma once + +#ifdef USE_CUDA + +#include + +#endif // USE_CUDA + +#include + +namespace torchaudio { +namespace rnnt { + +namespace math { + +template +FORCE_INLINE HOST_AND_DEVICE DTYPE max(DTYPE x, DTYPE y) { + if (x > y) + return x; + else + return y; +} + +template +FORCE_INLINE HOST_AND_DEVICE DTYPE min(DTYPE x, DTYPE y) { + if (x > y) + return y; + else + return x; +} + +// log_sum_exp +template +FORCE_INLINE HOST_AND_DEVICE DTYPE lse(DTYPE x, DTYPE y); + +template <> +FORCE_INLINE HOST_AND_DEVICE float lse(float x, float y) { + if (y > x) { + return y + log1pf(expf(x - y)); + } else { + return x + log1pf(expf(y - x)); + } +} + +} // namespace math + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/macros.cpp b/torchaudio/csrc/rnnt/macros.cpp index da6e5045bc..d2ea30a69b 100644 --- a/torchaudio/csrc/rnnt/macros.cpp +++ b/torchaudio/csrc/rnnt/macros.cpp @@ -1,8 +1,5 @@ #include -#ifdef USE_GLOG -#else - const char* ToString(level_t level) { switch (level) { case INFO: @@ -17,5 +14,3 @@ const char* ToString(level_t level) { return "UNKNOWN"; } } - -#endif // USE_GLOG diff --git a/torchaudio/csrc/rnnt/macros.h b/torchaudio/csrc/rnnt/macros.h index 84a8db24f0..abcbc39966 100644 --- a/torchaudio/csrc/rnnt/macros.h +++ b/torchaudio/csrc/rnnt/macros.h @@ -13,35 +13,9 @@ #define FORCE_INLINE inline #endif // USE_CUDA -#ifdef USE_GLOG -#include -#else #include #include typedef enum { INFO = 0, WARNING = 1, ERROR = 2, FATAL = 3 } level_t; const char* ToString(level_t level); - -struct LOG { - LOG(const level_t& level) { - ::std::cerr << "LOG(" << ToString(level) << "): "; - } - ~LOG() { - ::std::cerr << ::std::endl; - } -}; - -template -LOG&& operator<<(LOG&& log, const T& object) { - ::std::cerr << object; - return ::std::move(log); -} - -#define DCHECK(x) -#define DCHECK_EQ(x, y) -#define DCHECK_NE(x, y) -#define CHECK(x) -#define CHECK_EQ(x, y) -#define CHECK_NE(x, y) -#endif // USE_GLOG diff --git a/torchaudio/csrc/rnnt/transducer.h b/torchaudio/csrc/rnnt/transducer.h index 82b84ce0ea..0553616114 100644 --- a/torchaudio/csrc/rnnt/transducer.h +++ b/torchaudio/csrc/rnnt/transducer.h @@ -39,8 +39,6 @@ status_t Compute( return status; } default: { - LOG(ERROR) << "unsupported workspace.GetOptions().device = " - << workspace.GetOptions().device_; return FAILURE; } }; @@ -76,8 +74,6 @@ status_t ComputeAlphas( return status; } default: { - LOG(ERROR) << "unsupported workspace.GetOptions().device = " - << workspace.GetOptions().device_; return FAILURE; } }; @@ -116,8 +112,6 @@ status_t ComputeBetas( return status; } default: { - LOG(ERROR) << "unsupported workspace.GetOptions().device = " - << workspace.GetOptions().device_; return FAILURE; } }; diff --git a/torchaudio/csrc/rnnt/workspace.cpp b/torchaudio/csrc/rnnt/workspace.cpp deleted file mode 100644 index 2c8d0f6ab1..0000000000 --- a/torchaudio/csrc/rnnt/workspace.cpp +++ /dev/null @@ -1,20 +0,0 @@ -#include - -namespace torchaudio { -namespace rnnt { - -void IntWorkspace::ResetAlphaBetaCounters() { - if (data_ != nullptr && options_.device_ == GPU) { - cudaMemset( - GetPointerToAlphaCounters(), - 0, - ComputeSizeForAlphaCounters(options_) * sizeof(int)); - cudaMemset( - GetPointerToBetaCounters(), - 0, - ComputeSizeForBetaCounters(options_) * sizeof(int)); - } -} - -} // namespace rnnt -} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/workspace.h b/torchaudio/csrc/rnnt/workspace.h index 95a1fcf0c4..31b57647af 100644 --- a/torchaudio/csrc/rnnt/workspace.h +++ b/torchaudio/csrc/rnnt/workspace.h @@ -98,7 +98,7 @@ class IntWorkspace { void Reset(const Options& options, int* data, int size) { int needed_size = ComputeSizeFromOptions(options); - //CHECK_LE(needed_size, size); + CHECK_LE(needed_size, size); options_ = options; data_ = data; size_ = size; @@ -118,7 +118,20 @@ class IntWorkspace { } private: - void ResetAlphaBetaCounters(); + inline void ResetAlphaBetaCounters() { +#ifdef USE_CUDA + if (data_ != nullptr && options_.device_ == GPU) { + cudaMemset( + GetPointerToAlphaCounters(), + 0, + ComputeSizeForAlphaCounters(options_) * sizeof(int)); + cudaMemset( + GetPointerToBetaCounters(), + 0, + ComputeSizeForBetaCounters(options_) * sizeof(int)); + } +#endif // USE_CUDA + } static int ComputeSizeForAlphaCounters(const Options& options) { // B * U #ifdef USE_CUDA diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index cef2553d82..b7d9b112b8 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -578,6 +578,12 @@ def angle( return torch.atan2(complex_tensor[..., 1], complex_tensor[..., 0]) +@_mod_utils.deprecated( + "Please convert the input Tensor to complex type with `torch.view_as_complex` then " + "use `torch.abs` and `torch.angle`. " + "Please refer to https://github.com/pytorch/audio/issues/1337 " + "for more details about torchaudio's plan to migrate to native complex type." +) def magphase( complex_tensor: Tensor, power: float = 1.0 @@ -746,7 +752,7 @@ def mask_along_axis_iid( # Per batch example masking specgrams = specgrams.transpose(axis, -1) - specgrams.masked_fill_((mask >= mask_start) & (mask < mask_end), mask_value) + specgrams = specgrams.masked_fill((mask >= mask_start) & (mask < mask_end), mask_value) specgrams = specgrams.transpose(axis, -1) return specgrams @@ -772,24 +778,25 @@ def mask_along_axis( Returns: Tensor: Masked spectrogram of dimensions (channel, freq, time) """ + if axis != 1 and axis != 2: + raise ValueError('Only Frequency and Time masking are supported') # pack batch shape = specgram.size() specgram = specgram.reshape([-1] + list(shape[-2:])) - value = torch.rand(1) * mask_param min_value = torch.rand(1) * (specgram.size(axis) - value) mask_start = (min_value.long()).squeeze() mask_end = (min_value.long() + value.long()).squeeze() + mask = torch.arange(0, specgram.shape[axis], device=specgram.device, dtype=specgram.dtype) + mask = (mask >= mask_start) & (mask < mask_end) + if axis == 1: + mask = mask.unsqueeze(-1) assert mask_end - mask_start < mask_param - if axis == 1: - specgram[:, mask_start:mask_end] = mask_value - elif axis == 2: - specgram[:, :, mask_start:mask_end] = mask_value - else: - raise ValueError('Only Frequency and Time masking are supported') + + specgram = specgram.masked_fill(mask, mask_value) # unpack batch specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:]) diff --git a/torchaudio/prototype/rnnt_loss.py b/torchaudio/prototype/rnnt_loss.py index 1da03bfb2a..08c96ab0f2 100644 --- a/torchaudio/prototype/rnnt_loss.py +++ b/torchaudio/prototype/rnnt_loss.py @@ -80,7 +80,6 @@ def forward( target_lengths, blank=-1, clamp=-1, - runtime_check=False, fused_log_softmax=True, reuse_logits_for_grads=True, ): @@ -101,15 +100,6 @@ def forward( if blank < 0: # reinterpret blank index if blank < 0. blank = logits.shape[-1] + blank - if runtime_check: - check_inputs( - logits=logits, - targets=targets, - logit_lengths=logit_lengths, - target_lengths=target_lengths, - blank=blank, - ) - costs, gradients = torch.ops.torchaudio.rnnt_loss( logits=logits, targets=targets, @@ -137,7 +127,6 @@ def backward(ctx, output_gradients): None, # target_lengths None, # blank None, # clamp - None, # runtime_check None, # fused_log_softmax None, # reuse_logits_for_grads ) @@ -150,7 +139,6 @@ def rnnt_loss( target_lengths, blank=-1, clamp=-1, - runtime_check=False, fused_log_softmax=True, reuse_logits_for_grads=True, ): @@ -169,7 +157,7 @@ def rnnt_loss( blank (int, opt): blank label (Default: ``-1``) clamp (float): clamp for gradients (Default: ``-1``) runtime_check (bool): whether to do sanity check during runtime. (Default: ``False``) - fused_log_smax (bool): set to False if calling log_softmax outside loss (Default: ``True``) + fused_log_softmax (bool): set to False if calling log_softmax outside loss (Default: ``True``) reuse_logits_for_grads (bool): whether to save memory by reusing logits memory for grads (Default: ``True``) """ if not fused_log_softmax: @@ -185,7 +173,6 @@ def rnnt_loss( target_lengths, blank, clamp, - runtime_check, fused_log_softmax, reuse_logits_for_grads, ) @@ -203,8 +190,7 @@ class RNNTLoss(torch.nn.Module): Args: blank (int, opt): blank label (Default: ``-1``) clamp (float): clamp for gradients (Default: ``-1``) - runtime_check (bool): whether to do sanity check during runtime. (Default: ``False``) - fused_log_smax (bool): set to False if calling log_softmax outside loss (Default: ``True``) + fused_log_softmax (bool): set to False if calling log_softmax outside loss (Default: ``True``) reuse_logits_for_grads (bool): whether to save memory by reusing logits memory for grads (Default: ``True``) """ @@ -212,14 +198,12 @@ def __init__( self, blank=-1, clamp=-1, - runtime_check=False, fused_log_softmax=True, reuse_logits_for_grads=True, ): super().__init__() self.blank = blank self.clamp = clamp - self.runtime_check = runtime_check self.fused_log_softmax = fused_log_softmax self.reuse_logits_for_grads = reuse_logits_for_grads @@ -244,94 +228,6 @@ def forward( target_lengths, self.blank, self.clamp, - self.runtime_check, self.fused_log_softmax, self.reuse_logits_for_grads, ) - - -def check_type(var, t, name): - if var.dtype is not t: - raise TypeError("{} must be {}".format(name, t)) - - -def check_contiguous(var, name): - if not var.is_contiguous(): - raise ValueError("{} must be contiguous".format(name)) - - -def check_dim(var, dim, name): - if len(var.shape) != dim: - raise ValueError("{} must be {}D".format(name, dim)) - - -def check_equal(var1, name1, var2, name2): - if var1 != var2: - raise ValueError( - "`{}` ({}) must equal to ".format(name1, var1) - + "`{}` ({})".format(name2, var2) - ) - - -def check_device(var1, name1, var2, name2): - if var1.device != var2.device: - raise ValueError( - "`{}` ({}) must be on the same ".format(name1, var1.device.type) - + "device as `{}` ({})".format(name2, var2.device.type) - ) - - -def check_inputs(logits, targets, logit_lengths, target_lengths, blank): - check_device(logits, "logits", targets, "targets") - check_device(logits, "logits", targets, "logit_lengths") - check_device(logits, "logits", targets, "target_lengths") - - check_type(logits, torch.float32, "logits") - check_type(targets, torch.int32, "targets") - check_type(logit_lengths, torch.int32, "logit_lengths") - check_type(target_lengths, torch.int32, "target_lengths") - - check_contiguous(logits, "logits") - check_contiguous(targets, "targets") - check_contiguous(target_lengths, "target_lengths") - check_contiguous(logit_lengths, "logit_lengths") - - check_dim(logits, 4, "logits") - check_dim(targets, 2, "targets") - check_dim(logit_lengths, 1, "logit_lengths") - check_dim(target_lengths, 1, "target_lengths") - - check_equal( - logit_lengths.shape[0], "logit_lengths.shape[0]", logits.shape[0], "logits.shape[0]" - ) - check_equal( - target_lengths.shape[0], "target_lengths.shape[0]", logits.shape[0], "logits.shape[0]" - ) - check_equal( - targets.shape[0], "targets.shape[0]", logits.shape[0], "logits.shape[0]" - ) - check_equal( - targets.shape[1], - "targets.shape[1]", - torch.max(target_lengths), - "torch.max(target_lengths)", - ) - check_equal( - logits.shape[1], - "logits.shape[1]", - torch.max(logit_lengths), - "torch.max(logit_lengths)", - ) - check_equal( - logits.shape[2], - "logits.shape[2]", - torch.max(target_lengths) + 1, - "torch.max(target_lengths) + 1", - ) - - if blank < 0 or blank >= logits.shape[-1]: - raise ValueError( - "blank ({}) must be within [0, logits.shape[-1]={})".format( - blank, logits.shape[-1] - ) - ) diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index a04592f2c4..fa38118043 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- import math +import warnings from typing import Callable, Optional import torch @@ -673,6 +674,14 @@ class ComplexNorm(torch.nn.Module): __constants__ = ['power'] def __init__(self, power: float = 1.0) -> None: + warnings.warn( + 'torchaudio.transforms.ComplexNorm has been deprecated ' + 'and will be removed from future release.' + 'Please convert the input Tensor to complex type with `torch.view_as_complex` then ' + 'use `torch.abs` and `torch.angle`. ' + 'Please refer to https://github.com/pytorch/audio/issues/1337 ' + "for more details about torchaudio's plan to migrate to native complex type." + ) super(ComplexNorm, self).__init__() self.power = power From 81db19be529233ab7a91daa4c664089fbb654973 Mon Sep 17 00:00:00 2001 From: Parmeet Singh Bhatia Date: Fri, 21 May 2021 16:54:14 -0700 Subject: [PATCH 49/73] Import torchaudio #1513 08f2bde Summary: Import from github Reviewed By: mthrok Differential Revision: D28606124 fbshipit-source-id: 05dcb07efc5537d928bec682a68e6ccee7cc325e --- .github/workflows/codeql.yml | 10 +- docs/source/models.rst | 8 + .../common_utils/case_utils.py | 5 +- .../compliance_kaldi_test.py | 43 +++-- .../functional/functional_impl.py | 21 ++- .../torchscript_consistency_impl.py | 22 +++ test/torchaudio_unittest/models_test.py | 19 +- .../rnnt/torchscript_consistency_cpu_test.py | 10 + .../rnnt/torchscript_consistency_cuda_test.py | 11 ++ .../rnnt/torchscript_consistency_impl.py | 70 +++++++ test/torchaudio_unittest/rnnt/utils.py | 8 +- .../transforms/autograd_test_impl.py | 25 +++ .../transforms/sox_compatibility_test.py | 25 +++ .../torchscript_consistency_impl.py | 4 + .../transforms/transforms_test.py | 6 +- .../transforms/transforms_test_impl.py | 20 +- torchaudio/compliance/kaldi.py | 11 +- torchaudio/csrc/CMakeLists.txt | 1 + torchaudio/csrc/rnnt/autograd.cpp | 74 ++++++++ torchaudio/csrc/rnnt/compute.cpp | 24 +++ torchaudio/csrc/rnnt/compute.h | 13 ++ torchaudio/csrc/rnnt/transducer.h | 121 ------------ torchaudio/functional/filtering.py | 15 +- torchaudio/functional/functional.py | 129 +++++++++---- torchaudio/models/__init__.py | 2 + torchaudio/models/deepspeech.py | 92 +++++++++ torchaudio/prototype/rnnt_loss.py | 176 +++--------------- torchaudio/transforms.py | 69 ++++++- 28 files changed, 682 insertions(+), 352 deletions(-) create mode 100644 test/torchaudio_unittest/rnnt/torchscript_consistency_cpu_test.py create mode 100644 test/torchaudio_unittest/rnnt/torchscript_consistency_cuda_test.py create mode 100644 test/torchaudio_unittest/rnnt/torchscript_consistency_impl.py create mode 100644 torchaudio/csrc/rnnt/autograd.cpp create mode 100644 torchaudio/csrc/rnnt/compute.h delete mode 100644 torchaudio/csrc/rnnt/transducer.h create mode 100644 torchaudio/models/deepspeech.py diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index f0ea58f309..7d38c2c0e2 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -20,19 +20,13 @@ jobs: with: languages: python, cpp - - name: Install Ninja - run: | - sudo apt-get update -y - sudo apt-get install -y ninja-build - - name: Update submodules run: git submodule update --init --recursive - name: Install Torch run: | - python -m pip install cmake - python -m pip install torch==1.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html - sudo ln -s /usr/bin/ninja /usr/bin/ninja-build + python -m pip install cmake ninja + python -m pip install --pre torch -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html - name: Build TorchAudio run: BUILD_SOX=1 USE_CUDA=0 python setup.py develop --user diff --git a/docs/source/models.rst b/docs/source/models.rst index ea86d8b73b..2030eefd28 100644 --- a/docs/source/models.rst +++ b/docs/source/models.rst @@ -17,6 +17,14 @@ The models subpackage contains definitions of models for addressing common audio .. automethod:: forward +:hidden:`DeepSpeech` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: DeepSpeech + + .. automethod:: forward + + :hidden:`Wav2Letter` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/test/torchaudio_unittest/common_utils/case_utils.py b/test/torchaudio_unittest/common_utils/case_utils.py index 773f1915b9..fa3d5077bb 100644 --- a/test/torchaudio_unittest/common_utils/case_utils.py +++ b/test/torchaudio_unittest/common_utils/case_utils.py @@ -59,8 +59,9 @@ def setUpClass(cls): super().setUpClass() cls._proc = subprocess.Popen( ['python', '-m', 'http.server', f'{cls._port}'], - cwd=cls.get_base_temp_dir()) - time.sleep(1.0) + cwd=cls.get_base_temp_dir(), + stderr=subprocess.DEVNULL) # Disable server-side error log because it is confusing + time.sleep(2.0) @classmethod def tearDownClass(cls): diff --git a/test/torchaudio_unittest/compliance_kaldi_test.py b/test/torchaudio_unittest/compliance_kaldi_test.py index 17d73fbf2f..af093cea55 100644 --- a/test/torchaudio_unittest/compliance_kaldi_test.py +++ b/test/torchaudio_unittest/compliance_kaldi_test.py @@ -7,6 +7,7 @@ from torchaudio_unittest import common_utils from .compliance import utils as compliance_utils +from parameterized import parameterized def extract_window(window, wave, f, frame_length, frame_shift, snip_edges): @@ -182,20 +183,26 @@ def get_output_fn(sound, args): self._compliance_test_helper(self.test2_filepath, 'resample', 32, 3, get_output_fn, atol=1e-2, rtol=1e-5) - def test_resample_waveform_upsample_size(self): - upsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr * 2) + @parameterized.expand([("sinc_interpolation"), ("kaiser_window")]) + def test_resample_waveform_upsample_size(self, resampling_method): + upsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr * 2, + resampling_method=resampling_method) self.assertTrue(upsample_sound.size(-1) == self.test1_signal.size(-1) * 2) - def test_resample_waveform_downsample_size(self): - downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr // 2) + @parameterized.expand([("sinc_interpolation"), ("kaiser_window")]) + def test_resample_waveform_downsample_size(self, resampling_method): + downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr // 2, + resampling_method=resampling_method) self.assertTrue(downsample_sound.size(-1) == self.test1_signal.size(-1) // 2) - def test_resample_waveform_identity_size(self): - downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr) + @parameterized.expand([("sinc_interpolation"), ("kaiser_window")]) + def test_resample_waveform_identity_size(self, resampling_method): + downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr, + resampling_method=resampling_method) self.assertTrue(downsample_sound.size(-1) == self.test1_signal.size(-1)) def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_factor=None, - atol=1e-1, rtol=1e-4): + resampling_method="sinc_interpolation", atol=1e-1, rtol=1e-4): # resample the signal and compare it to the ground truth n_to_trim = 20 sample_rate = 1000 @@ -211,7 +218,8 @@ def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_fact original_timestamps = torch.arange(0, duration, 1.0 / sample_rate) sound = 123 * torch.cos(2 * math.pi * 3 * original_timestamps).unsqueeze(0) - estimate = kaldi.resample_waveform(sound, sample_rate, new_sample_rate).squeeze() + estimate = kaldi.resample_waveform(sound, sample_rate, new_sample_rate, + resampling_method=resampling_method).squeeze() new_timestamps = torch.arange(0, duration, 1.0 / new_sample_rate)[:estimate.size(0)] ground_truth = 123 * torch.cos(2 * math.pi * 3 * new_timestamps) @@ -222,15 +230,18 @@ def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_fact self.assertEqual(estimate, ground_truth, atol=atol, rtol=rtol) - def test_resample_waveform_downsample_accuracy(self): + @parameterized.expand([("sinc_interpolation"), ("kaiser_window")]) + def test_resample_waveform_downsample_accuracy(self, resampling_method): for i in range(1, 20): - self._test_resample_waveform_accuracy(down_scale_factor=i * 2) + self._test_resample_waveform_accuracy(down_scale_factor=i * 2, resampling_method=resampling_method) - def test_resample_waveform_upsample_accuracy(self): + @parameterized.expand([("sinc_interpolation"), ("kaiser_window")]) + def test_resample_waveform_upsample_accuracy(self, resampling_method): for i in range(1, 20): - self._test_resample_waveform_accuracy(up_scale_factor=1.0 + i / 20.0) + self._test_resample_waveform_accuracy(up_scale_factor=1.0 + i / 20.0, resampling_method=resampling_method) - def test_resample_waveform_multi_channel(self): + @parameterized.expand([("sinc_interpolation"), ("kaiser_window")]) + def test_resample_waveform_multi_channel(self, resampling_method): num_channels = 3 multi_sound = self.test1_signal.repeat(num_channels, 1) # (num_channels, 8000 smp) @@ -238,11 +249,13 @@ def test_resample_waveform_multi_channel(self): for i in range(num_channels): multi_sound[i, :] *= (i + 1) * 1.5 - multi_sound_sampled = kaldi.resample_waveform(multi_sound, self.test1_signal_sr, self.test1_signal_sr // 2) + multi_sound_sampled = kaldi.resample_waveform(multi_sound, self.test1_signal_sr, self.test1_signal_sr // 2, + resampling_method=resampling_method) # check that sampling is same whether using separately or in a tensor of size (c, n) for i in range(num_channels): single_channel = self.test1_signal * (i + 1) * 1.5 single_channel_sampled = kaldi.resample_waveform(single_channel, self.test1_signal_sr, - self.test1_signal_sr // 2) + self.test1_signal_sr // 2, + resampling_method=resampling_method) self.assertEqual(multi_sound_sampled[i, :], single_channel_sampled[0], rtol=1e-4, atol=1e-7) diff --git a/test/torchaudio_unittest/functional/functional_impl.py b/test/torchaudio_unittest/functional/functional_impl.py index 424ead2ca9..1a2026233d 100644 --- a/test/torchaudio_unittest/functional/functional_impl.py +++ b/test/torchaudio_unittest/functional/functional_impl.py @@ -9,7 +9,7 @@ from parameterized import parameterized from scipy import signal -from torchaudio_unittest.common_utils import TestBaseMixin, get_sinusoid, nested_params +from torchaudio_unittest.common_utils import TestBaseMixin, get_sinusoid, nested_params, get_whitenoise class Functional(TestBaseMixin): @@ -259,6 +259,25 @@ def test_mask_along_axis_iid_preserve(self, mask_param, mask_value, axis): self.assertEqual(specgrams, specgrams_copy) + def test_resample_no_warning(self): + sample_rate = 44100 + waveform = get_whitenoise(sample_rate=sample_rate, duration=0.1) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + F.resample(waveform, float(sample_rate), sample_rate / 2.) + assert len(w) == 0 + + def test_resample_warning(self): + """resample should throw a warning if an input frequency is not of an integer value""" + sample_rate = 44100 + waveform = get_whitenoise(sample_rate=sample_rate, duration=0.1) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + F.resample(waveform, sample_rate, 5512.5) + assert len(w) == 1 + @nested_params( [0.5, 1.01, 1.3], [True, False], diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py index a39973f867..3eb869c876 100644 --- a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py @@ -591,6 +591,28 @@ def func(tensor): tensor = common_utils.get_whitenoise(sample_rate=44100) self._assert_consistency(func, tensor) + def test_resample_sinc(self): + def func(tensor): + sr1, sr2 = 16000., 8000. + return F.resample(tensor, sr1, sr2, resampling_method="sinc_interpolation") + + tensor = common_utils.get_whitenoise(sample_rate=16000) + self._assert_consistency(func, tensor) + + def test_resample_kaiser(self): + def func(tensor): + sr1, sr2 = 16000., 8000. + return F.resample(tensor, sr1, sr2, resampling_method="kaiser_window") + + def func_beta(tensor): + sr1, sr2 = 16000., 8000. + beta = 6. + return F.resample(tensor, sr1, sr2, resampling_method="kaiser_window", beta=beta) + + tensor = common_utils.get_whitenoise(sample_rate=16000) + self._assert_consistency(func, tensor) + self._assert_consistency(func_beta, tensor) + @parameterized.expand([(True, ), (False, )]) def test_phase_vocoder(self, test_paseudo_complex): def func(tensor): diff --git a/test/torchaudio_unittest/models_test.py b/test/torchaudio_unittest/models_test.py index 484ec6c10c..4db4895b8f 100644 --- a/test/torchaudio_unittest/models_test.py +++ b/test/torchaudio_unittest/models_test.py @@ -3,7 +3,7 @@ import torch from parameterized import parameterized -from torchaudio.models import ConvTasNet, Wav2Letter, WaveRNN +from torchaudio.models import ConvTasNet, DeepSpeech, Wav2Letter, WaveRNN from torchaudio.models.wavernn import MelResNet, UpsampleNetwork from torchaudio_unittest import common_utils @@ -174,3 +174,20 @@ def test_paper_configuration(self, num_sources, model_params): output = model(tensor) assert output.shape == (batch_size, num_sources, num_frames) + + +class TestDeepSpeech(common_utils.TorchaudioTestCase): + + def test_deepspeech(self): + n_batch = 2 + n_feature = 1 + n_channel = 1 + n_class = 40 + n_time = 320 + + model = DeepSpeech(n_feature=n_feature, n_class=n_class) + + x = torch.rand(n_batch, n_channel, n_time, n_feature) + out = model(x) + + assert out.size() == (n_batch, n_time, n_class) diff --git a/test/torchaudio_unittest/rnnt/torchscript_consistency_cpu_test.py b/test/torchaudio_unittest/rnnt/torchscript_consistency_cpu_test.py new file mode 100644 index 0000000000..06b6baf5a1 --- /dev/null +++ b/test/torchaudio_unittest/rnnt/torchscript_consistency_cpu_test.py @@ -0,0 +1,10 @@ +import torch + +from torchaudio_unittest.common_utils import PytorchTestCase +from .utils import skipIfNoTransducer +from .torchscript_consistency_impl import RNNTLossTorchscript + + +@skipIfNoTransducer +class TestRNNTLoss(RNNTLossTorchscript, PytorchTestCase): + device = torch.device('cpu') diff --git a/test/torchaudio_unittest/rnnt/torchscript_consistency_cuda_test.py b/test/torchaudio_unittest/rnnt/torchscript_consistency_cuda_test.py new file mode 100644 index 0000000000..22b1713582 --- /dev/null +++ b/test/torchaudio_unittest/rnnt/torchscript_consistency_cuda_test.py @@ -0,0 +1,11 @@ +import torch + +from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda +from .utils import skipIfNoTransducer +from .torchscript_consistency_impl import RNNTLossTorchscript + + +@skipIfNoTransducer +@skipIfNoCuda +class TestRNNTLoss(RNNTLossTorchscript, PytorchTestCase): + device = torch.device('cuda') diff --git a/test/torchaudio_unittest/rnnt/torchscript_consistency_impl.py b/test/torchaudio_unittest/rnnt/torchscript_consistency_impl.py new file mode 100644 index 0000000000..aeba7e3ae4 --- /dev/null +++ b/test/torchaudio_unittest/rnnt/torchscript_consistency_impl.py @@ -0,0 +1,70 @@ +import torch +from torchaudio_unittest.common_utils import TempDirMixin, TestBaseMixin +from torchaudio.prototype.rnnt_loss import RNNTLoss, rnnt_loss + + +class RNNTLossTorchscript(TempDirMixin, TestBaseMixin): + """Implements test for RNNT Loss that are performed for different devices""" + def _assert_consistency(self, func, tensor, shape_only=False): + tensor = tensor.to(device=self.device, dtype=self.dtype) + + path = self.get_temp_path('func.zip') + torch.jit.script(func).save(path) + ts_func = torch.jit.load(path) + + torch.random.manual_seed(40) + input_tensor = tensor.clone().detach().requires_grad_(True) + output = func(input_tensor) + + torch.random.manual_seed(40) + input_tensor = tensor.clone().detach().requires_grad_(True) + ts_output = ts_func(input_tensor) + + self.assertEqual(ts_output, output) + + def test_rnnt_loss(self): + def func( + logits, + ): + targets = torch.tensor([[1, 2]], device=logits.device, dtype=torch.int32) + logit_lengths = torch.tensor([2], device=logits.device, dtype=torch.int32) + target_lengths = torch.tensor([2], device=logits.device, dtype=torch.int32) + return rnnt_loss(logits, targets, logit_lengths, target_lengths) + + logits = torch.tensor([[[[0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.1, 0.6, 0.1, 0.1], + [0.1, 0.1, 0.2, 0.8, 0.1]], + [[0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.1, 0.2, 0.1, 0.1], + [0.7, 0.1, 0.2, 0.1, 0.1]]]]) + + self._assert_consistency(func, logits) + + def test_RNNTLoss(self): + func = RNNTLoss() + + logits = torch.tensor([[[[0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.1, 0.6, 0.1, 0.1], + [0.1, 0.1, 0.2, 0.8, 0.1]], + [[0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.1, 0.2, 0.1, 0.1], + [0.7, 0.1, 0.2, 0.1, 0.1]]]]) + targets = torch.tensor([[1, 2]], device=self.device, dtype=torch.int32) + logit_lengths = torch.tensor([2], device=self.device, dtype=torch.int32) + target_lengths = torch.tensor([2], device=self.device, dtype=torch.int32) + + tensor = logits.to(device=self.device, dtype=self.dtype) + + path = self.get_temp_path('func.zip') + torch.jit.script(func).save(path) + ts_func = torch.jit.load(path) + + torch.random.manual_seed(40) + input_tensor = tensor.clone().detach().requires_grad_(True) + output = func(input_tensor, targets, logit_lengths, target_lengths) + + torch.random.manual_seed(40) + input_tensor = tensor.clone().detach().requires_grad_(True) + ts_output = ts_func(input_tensor, targets, logit_lengths, target_lengths) + + self.assertEqual(ts_output, output) diff --git a/test/torchaudio_unittest/rnnt/utils.py b/test/torchaudio_unittest/rnnt/utils.py index 8e93d28032..5f4c40379e 100644 --- a/test/torchaudio_unittest/rnnt/utils.py +++ b/test/torchaudio_unittest/rnnt/utils.py @@ -405,10 +405,10 @@ def get_numpy_random_data( def numpy_to_torch(data, device, requires_grad=True): - logits = torch.from_numpy(data["logits"]) - targets = torch.from_numpy(data["targets"]) - logit_lengths = torch.from_numpy(data["logit_lengths"]) - target_lengths = torch.from_numpy(data["target_lengths"]) + logits = torch.from_numpy(data["logits"]).to(device=device) + targets = torch.from_numpy(data["targets"]).to(device=device) + logit_lengths = torch.from_numpy(data["logit_lengths"]).to(device=device) + target_lengths = torch.from_numpy(data["target_lengths"]).to(device=device) if "nbest_wers" in data: data["nbest_wers"] = torch.from_numpy(data["nbest_wers"]).to(device=device) diff --git a/test/torchaudio_unittest/transforms/autograd_test_impl.py b/test/torchaudio_unittest/transforms/autograd_test_impl.py index 717a7bc87b..0f7b612482 100644 --- a/test/torchaudio_unittest/transforms/autograd_test_impl.py +++ b/test/torchaudio_unittest/transforms/autograd_test_impl.py @@ -125,6 +125,31 @@ def test_fade(self, fade_shape): waveform = get_whitenoise(sample_rate=8000, duration=0.05, n_channels=2) self.assert_grad(transform, [waveform], nondet_tol=1e-10) + @parameterized.expand([(T.TimeMasking,), (T.FrequencyMasking,)]) + def test_masking(self, masking_transform): + sample_rate = 8000 + n_fft = 400 + spectrogram = get_spectrogram( + get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2), + n_fft=n_fft, power=1) + deterministic_transform = _DeterministicWrapper(masking_transform(400)) + self.assert_grad(deterministic_transform, [spectrogram]) + + @parameterized.expand([(T.TimeMasking,), (T.FrequencyMasking,)]) + def test_masking_iid(self, masking_transform): + sample_rate = 8000 + n_fft = 400 + specs = [get_spectrogram( + get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2, seed=i), + n_fft=n_fft, power=1) + for i in range(3) + ] + + batch = torch.stack(specs) + assert batch.ndim == 4 + deterministic_transform = _DeterministicWrapper(masking_transform(400, True)) + self.assert_grad(deterministic_transform, [batch]) + def test_spectral_centroid(self): sample_rate = 8000 transform = T.SpectralCentroid(sample_rate=sample_rate) diff --git a/test/torchaudio_unittest/transforms/sox_compatibility_test.py b/test/torchaudio_unittest/transforms/sox_compatibility_test.py index 81582c8393..be6c9020ab 100644 --- a/test/torchaudio_unittest/transforms/sox_compatibility_test.py +++ b/test/torchaudio_unittest/transforms/sox_compatibility_test.py @@ -1,3 +1,6 @@ +import warnings + +import torch import torchaudio.transforms as T from parameterized import parameterized @@ -61,3 +64,25 @@ def test_vad(self, filename): data, sample_rate = load_wav(path) result = T.Vad(sample_rate)(data) self.assert_sox_effect(result, path, ['vad']) + + def test_vad_warning(self): + """vad should throw a warning if input dimension is greater than 2""" + sample_rate = 41100 + + data = torch.rand(5, 5, sample_rate) + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + T.Vad(sample_rate)(data) + assert len(w) == 1 + + data = torch.rand(5, sample_rate) + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + T.Vad(sample_rate)(data) + assert len(w) == 0 + + data = torch.rand(sample_rate) + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + T.Vad(sample_rate)(data) + assert len(w) == 0 diff --git a/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py b/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py index 506ca9af6c..5258343181 100644 --- a/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py @@ -59,6 +59,10 @@ def test_AmplitudeToDB(self): spec = torch.rand((6, 201)) self._assert_consistency(T.AmplitudeToDB(), spec) + def test_MelScale_invalid(self): + with self.assertRaises(ValueError): + torch.jit.script(T.MelScale()) + def test_MelScale(self): spec_f = torch.rand((1, 201, 6)) self._assert_consistency(T.MelScale(n_stft=201), spec_f) diff --git a/test/torchaudio_unittest/transforms/transforms_test.py b/test/torchaudio_unittest/transforms/transforms_test.py index ab7b2ed981..5aa88ead90 100644 --- a/test/torchaudio_unittest/transforms/transforms_test.py +++ b/test/torchaudio_unittest/transforms/transforms_test.py @@ -169,9 +169,11 @@ def test_resample_size(self): upsample_rate = sample_rate * 2 downsample_rate = sample_rate // 2 - invalid_resample = torchaudio.transforms.Resample(sample_rate, upsample_rate, resampling_method='foo') + invalid_resampling_method = 'foo' - self.assertRaises(ValueError, invalid_resample, waveform) + with self.assertRaises(ValueError): + torchaudio.transforms.Resample(sample_rate, upsample_rate, + resampling_method=invalid_resampling_method) upsample_resample = torchaudio.transforms.Resample( sample_rate, upsample_rate, resampling_method='sinc_interpolation') diff --git a/test/torchaudio_unittest/transforms/transforms_test_impl.py b/test/torchaudio_unittest/transforms/transforms_test_impl.py index 8a92662200..44f254e540 100644 --- a/test/torchaudio_unittest/transforms/transforms_test_impl.py +++ b/test/torchaudio_unittest/transforms/transforms_test_impl.py @@ -1,3 +1,5 @@ +import warnings + import torch import torchaudio.transforms as T @@ -39,7 +41,7 @@ def test_InverseMelScale(self): get_whitenoise(sample_rate=sample_rate, duration=1, n_channels=2), n_fft=n_fft, power=power).to(self.device, self.dtype) input = T.MelScale( - n_mels=n_mels, sample_rate=sample_rate + n_mels=n_mels, sample_rate=sample_rate, n_stft=n_stft ).to(self.device, self.dtype)(expected) # Run transform @@ -59,3 +61,19 @@ def test_InverseMelScale(self): assert _get_ratio(relative_diff < 1e-1) > 0.2 assert _get_ratio(relative_diff < 1e-3) > 5e-3 assert _get_ratio(relative_diff < 1e-5) > 1e-5 + + def test_melscale_unset_weight_warning(self): + """Issue a warning if MelScale initialized without a weight + + As part of the deprecation of lazy intialization behavior (#1510), + issue a warning if `n_stft` is not set. + """ + with warnings.catch_warnings(record=True) as caught_warnings: + warnings.simplefilter("always") + T.MelScale(n_mels=64, sample_rate=8000) + assert len(caught_warnings) == 1 + + with warnings.catch_warnings(record=True) as caught_warnings: + warnings.simplefilter("always") + T.MelScale(n_mels=64, sample_rate=8000, n_stft=201) + assert len(caught_warnings) == 0 diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index a5c3354c9b..22616038f5 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -755,7 +755,9 @@ def mfcc( def resample_waveform(waveform: Tensor, orig_freq: float, new_freq: float, - lowpass_filter_width: int = 6) -> Tensor: + lowpass_filter_width: int = 6, + rolloff: float = 0.99, + resampling_method: str = "sinc_interpolation") -> Tensor: r"""Resamples the waveform at the new frequency. This is a wrapper around ``torchaudio.functional.resample``. @@ -766,8 +768,13 @@ def resample_waveform(waveform: Tensor, new_freq (float): The desired frequency lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper but less efficient. We suggest around 4 to 10 for normal use. (Default: ``6``) + rolloff (float, optional): The roll-off frequency of the filter, as a fraction of the Nyquist. + Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: ``0.99``) + resampling_method (str, optional): The resampling method to use. + Options: [``sinc_interpolation``, ``kaiser_window``] (Default: ``'sinc_interpolation'``) Returns: Tensor: The waveform at the new frequency """ - return torchaudio.functional.resample(waveform, orig_freq, new_freq, lowpass_filter_width) + return torchaudio.functional.resample(waveform, orig_freq, new_freq, lowpass_filter_width, + rolloff, resampling_method) diff --git a/torchaudio/csrc/CMakeLists.txt b/torchaudio/csrc/CMakeLists.txt index ebf577eb64..64661f96a5 100644 --- a/torchaudio/csrc/CMakeLists.txt +++ b/torchaudio/csrc/CMakeLists.txt @@ -19,6 +19,7 @@ if(BUILD_TRANSDUCER) rnnt/compute_alphas.cpp rnnt/compute_betas.cpp rnnt/compute.cpp + rnnt/autograd.cpp ) if (USE_CUDA) diff --git a/torchaudio/csrc/rnnt/autograd.cpp b/torchaudio/csrc/rnnt/autograd.cpp new file mode 100644 index 0000000000..73ad9f9b3c --- /dev/null +++ b/torchaudio/csrc/rnnt/autograd.cpp @@ -0,0 +1,74 @@ +#include +#include + +namespace torchaudio { +namespace rnnt { + +class RNNTLossFunction : public torch::autograd::Function { + public: + static torch::autograd::tensor_list forward( + torch::autograd::AutogradContext* ctx, + torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + bool fused_log_smax = true, + bool reuse_logits_for_grads = true) { + at::AutoNonVariableTypeMode g; + torch::Tensor undef; + auto result = rnnt_loss( + logits, + targets, + src_lengths, + tgt_lengths, + blank, + clamp, + fused_log_smax, + reuse_logits_for_grads); + auto costs = std::get<0>(result); + auto grads = std::get<1>(result).value_or(undef); + ctx->save_for_backward({grads}); + return {costs, grads}; + } + + static torch::autograd::tensor_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::tensor_list grad_outputs) { + auto saved = ctx->get_saved_variables(); + auto grad = saved[0]; + auto grad_out = grad_outputs[0].view({-1, 1, 1, 1}); + auto result = grad * grad_out; + torch::Tensor undef; + return {result, undef, undef, undef, undef, undef, undef, undef}; + } +}; + +std::tuple> rnnt_loss_autograd( + torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + bool fused_log_smax = true, + bool reuse_logits_for_grads = true) { + auto results = RNNTLossFunction::apply( + logits, + targets, + src_lengths, + tgt_lengths, + blank, + clamp, + fused_log_smax, + reuse_logits_for_grads); + return std::make_tuple(results[0], results[1]); +} + +TORCH_LIBRARY_IMPL(torchaudio, Autograd, m) { + m.impl("rnnt_loss", rnnt_loss_autograd); +} + +} // namespace rnnt +} // namespace torchaudio diff --git a/torchaudio/csrc/rnnt/compute.cpp b/torchaudio/csrc/rnnt/compute.cpp index bce803fffa..f47f0f505d 100644 --- a/torchaudio/csrc/rnnt/compute.cpp +++ b/torchaudio/csrc/rnnt/compute.cpp @@ -1,4 +1,28 @@ #include +#include + +std::tuple> rnnt_loss( + torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + bool fused_log_smax = true, + bool reuse_logits_for_grads = true) { + static auto op = torch::Dispatcher::singleton() + .findSchemaOrThrow("torchaudio::rnnt_loss", "") + .typed(); + return op.call( + logits, + targets, + src_lengths, + tgt_lengths, + blank, + clamp, + fused_log_smax, + reuse_logits_for_grads); +} TORCH_LIBRARY_FRAGMENT(torchaudio, m) { m.def( diff --git a/torchaudio/csrc/rnnt/compute.h b/torchaudio/csrc/rnnt/compute.h new file mode 100644 index 0000000000..9616d45fc3 --- /dev/null +++ b/torchaudio/csrc/rnnt/compute.h @@ -0,0 +1,13 @@ +#pragma once + +#include + +std::tuple> rnnt_loss( + torch::Tensor& logits, + const torch::Tensor& targets, + const torch::Tensor& src_lengths, + const torch::Tensor& tgt_lengths, + int64_t blank, + double clamp, + bool fused_log_smax, + bool reuse_logits_for_grads); diff --git a/torchaudio/csrc/rnnt/transducer.h b/torchaudio/csrc/rnnt/transducer.h deleted file mode 100644 index 0553616114..0000000000 --- a/torchaudio/csrc/rnnt/transducer.h +++ /dev/null @@ -1,121 +0,0 @@ -#pragma once - -#include -#include - -namespace torchaudio { -namespace rnnt { - -template -status_t Compute( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* costs, - DTYPE* gradients = nullptr) { - switch (workspace.GetOptions().device_) { - case CPU: { - status_t status = cpu::Compute( - /*workspace=*/workspace, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*costs=*/costs, - /*gradients=*/gradients); - return status; - } - case GPU: { - status_t status = gpu::Compute( - /*workspace=*/workspace, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*costs=*/costs, - /*gradients=*/gradients); - return status; - } - default: { - return FAILURE; - } - }; -} - -template -status_t ComputeAlphas( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* alphas) { - switch (workspace.GetOptions().device_) { - case CPU: { - status_t status = cpu::ComputeAlphas( - /*workspace=*/workspace, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*alphas=*/alphas); - return status; - } - case GPU: { - status_t status = gpu::ComputeAlphas( - /*workspace=*/workspace, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*costs=*/alphas); - return status; - } - default: { - return FAILURE; - } - }; -} - -template -status_t ComputeBetas( - const Workspace& workspace, - const DTYPE* logits, - const int* targets, - const int* srcLengths, - const int* tgtLengths, - DTYPE* costs, - DTYPE* betas) { - switch (workspace.GetOptions().device_) { - case CPU: { - status_t status = cpu::ComputeBetas( - /*workspace=*/workspace, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*costs=*/costs, - /*betas=*/betas); - return status; - } - case GPU: { - status_t status = gpu::ComputeBetas( - /*workspace=*/workspace, - /*logits=*/logits, - /*targets=*/targets, - /*srcLengths=*/srcLengths, - /*tgtLengths=*/tgtLengths, - /*costs=*/costs, - /*betas=*/betas); - return status; - } - default: { - return FAILURE; - } - }; -} - -} // namespace rnnt -} // namespace torchaudio diff --git a/torchaudio/functional/filtering.py b/torchaudio/functional/filtering.py index 93da27493b..85abe81339 100644 --- a/torchaudio/functional/filtering.py +++ b/torchaudio/functional/filtering.py @@ -1,4 +1,5 @@ import math +import warnings from typing import Optional import torch @@ -1374,7 +1375,10 @@ def vad( so in order to trim from the back, the reverse effect must also be used. Args: - waveform (Tensor): Tensor of audio of dimension `(..., time)` + waveform (Tensor): Tensor of audio of dimension `(channels, time)` or `(time)` + Tensor of shape `(channels, time)` is treated as a multi-channel recording + of the same event and the resulting output will be trimmed to the earliest + voice activity in any channel. sample_rate (int): Sample rate of audio signal. trigger_level (float, optional): The measurement level used to trigger activity detection. This may need to be cahnged depending on the noise level, signal level, @@ -1420,6 +1424,15 @@ def vad( http://sox.sourceforge.net/sox.html """ + if waveform.ndim > 2: + warnings.warn( + "Expected input tensor dimension of 1 for single channel" + f" or 2 for multi-channel. Got {waveform.ndim} instead. " + "Batch semantics is not supported. " + "Please refer to https://github.com/pytorch/audio/issues/1348" + " and https://github.com/pytorch/audio/issues/1468." + ) + measure_duration: float = ( 2.0 / measure_freq if measure_duration is None else measure_duration ) diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py index b7d9b112b8..74271798eb 100644 --- a/torchaudio/functional/functional.py +++ b/torchaudio/functional/functional.py @@ -1298,8 +1298,36 @@ def compute_kaldi_pitch( return result -def _get_sinc_resample_kernel(orig_freq: int, new_freq: int, lowpass_filter_width: int, - device: torch.device, dtype: torch.dtype): +def _get_sinc_resample_kernel( + orig_freq: float, + new_freq: float, + gcd: int, + lowpass_filter_width: int, + rolloff: float, + resampling_method: str, + beta: Optional[float], + device: torch.device = torch.device("cpu"), + dtype: Optional[torch.dtype] = None): + + if not (int(orig_freq) == orig_freq and int(new_freq) == new_freq): + warnings.warn( + "Non-integer frequencies are being cast to ints and may result in poor resampling quality " + "because the underlying algorithm requires an integer ratio between `orig_freq` and `new_freq`. " + "Using non-integer valued frequencies will throw an error in the next release. " + "To work around this issue, manually convert both frequencies to integer values " + "that maintain their resampling rate ratio before passing them into the function " + "Example: To downsample a 44100 hz waveform by a factor of 8, use " + "`orig_freq=8` and `new_freq=1` instead of `orig_freq=44100` and `new_freq=5512.5` " + "For more information or to leave feedback about this change, please refer to " + "https://github.com/pytorch/audio/issues/1487." + ) + + if resampling_method not in ['sinc_interpolation', 'kaiser_window']: + raise ValueError('Invalid resampling method: {}'.format(resampling_method)) + + orig_freq = int(orig_freq) // gcd + new_freq = int(new_freq) // gcd + assert lowpass_filter_width > 0 kernels = [] base_freq = min(orig_freq, new_freq) @@ -1307,7 +1335,7 @@ def _get_sinc_resample_kernel(orig_freq: int, new_freq: int, lowpass_filter_widt # At first I thought I only needed this when downsampling, but when upsampling # you will get edge artifacts without this, as the edge is equivalent to zero padding, # which will add high freq artifacts. - base_freq *= 0.99 + base_freq *= rolloff # The key idea of the algorithm is that x(t) can be exactly reconstructed from x[i] (tensor) # using the sinc interpolation formula: @@ -1331,70 +1359,99 @@ def _get_sinc_resample_kernel(orig_freq: int, new_freq: int, lowpass_filter_widt # they will have a lot of almost zero values to the left or to the right... # There is probably a way to evaluate those filters more efficiently, but this is kept for # future work. - idx = torch.arange(-width, width + orig_freq, device=device, dtype=dtype) + idx_dtype = dtype if dtype is not None else torch.float64 + idx = torch.arange(-width, width + orig_freq, device=device, dtype=idx_dtype) for i in range(new_freq): t = (-i / new_freq + idx / orig_freq) * base_freq t = t.clamp_(-lowpass_filter_width, lowpass_filter_width) - t *= math.pi - # we do not use torch.hann_window here as we need to evaluate the window + + # we do not use built in torch windows here as we need to evaluate the window # at specific positions, not over a regular grid. - window = torch.cos(t / lowpass_filter_width / 2)**2 + if resampling_method == "sinc_interpolation": + window = torch.cos(t * math.pi / lowpass_filter_width / 2)**2 + else: + # kaiser_window + if beta is None: + beta = 14.769656459379492 + beta_tensor = torch.tensor(float(beta)) + window = torch.i0(beta_tensor * torch.sqrt(1 - (t / lowpass_filter_width) ** 2)) / torch.i0(beta_tensor) + t *= math.pi kernel = torch.where(t == 0, torch.tensor(1.).to(t), torch.sin(t) / t) kernel.mul_(window) kernels.append(kernel) scale = base_freq / orig_freq - return torch.stack(kernels).view(new_freq, 1, -1).mul_(scale), width + kernels = torch.stack(kernels).view(new_freq, 1, -1).mul_(scale) + if dtype is None: + kernels = kernels.to(dtype=torch.float32) + return kernels, width + + +def _apply_sinc_resample_kernel( + waveform: Tensor, + orig_freq: float, + new_freq: float, + gcd: int, + kernel: Tensor, + width: int, +): + orig_freq = int(orig_freq) // gcd + new_freq = int(new_freq) // gcd + + # pack batch + shape = waveform.size() + waveform = waveform.view(-1, shape[-1]) + + num_wavs, length = waveform.shape + waveform = torch.nn.functional.pad(waveform, (width, width + orig_freq)) + resampled = torch.nn.functional.conv1d(waveform[:, None], kernel, stride=orig_freq) + resampled = resampled.transpose(1, 2).reshape(num_wavs, -1) + target_length = int(math.ceil(new_freq * length / orig_freq)) + resampled = resampled[..., :target_length] + + # unpack batch + resampled = resampled.view(shape[:-1] + resampled.shape[-1:]) + return resampled def resample( waveform: Tensor, orig_freq: float, new_freq: float, - lowpass_filter_width: int = 6 + lowpass_filter_width: int = 6, + rolloff: float = 0.99, + resampling_method: str = "sinc_interpolation", + beta: Optional[float] = None, ) -> Tensor: - r"""Resamples the waveform at the new frequency. This matches Kaldi's OfflineFeatureTpl ResampleWaveform - which uses a LinearResample (resample a signal at linearly spaced intervals to upsample/downsample - a signal). LinearResample (LR) means that the output signal is at linearly spaced intervals (i.e - the output signal has a frequency of ``new_freq``). It uses sinc/bandlimited interpolation to - upsample/downsample the signal. + r"""Resamples the waveform at the new frequency using bandlimited interpolation. https://ccrma.stanford.edu/~jos/resample/Theory_Ideal_Bandlimited_Interpolation.html - https://github.com/kaldi-asr/kaldi/blob/master/src/feat/resample.h#L56 Args: waveform (Tensor): The input signal of dimension (..., time) orig_freq (float): The original frequency of the signal new_freq (float): The desired frequency lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper - but less efficient. We suggest around 4 to 10 for normal use. (Default: ``6``) + but less efficient. (Default: ``6``) + rolloff (float, optional): The roll-off frequency of the filter, as a fraction of the Nyquist. + Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: ``0.99``) + resampling_method (str, optional): The resampling method to use. + Options: [``sinc_interpolation``, ``kaiser_window``] (Default: ``'sinc_interpolation'``) + beta (float or None): The shape parameter used for kaiser window. Returns: Tensor: The waveform at the new frequency of dimension (..., time). + + Note: ``transforms.Resample`` precomputes and reuses the resampling kernel, so using it will result in + more efficient computation if resampling multiple waveforms with the same resampling parameters. """ - # pack batch - shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) assert orig_freq > 0.0 and new_freq > 0.0 - orig_freq = int(orig_freq) - new_freq = int(new_freq) - gcd = math.gcd(orig_freq, new_freq) - orig_freq = orig_freq // gcd - new_freq = new_freq // gcd - - kernel, width = _get_sinc_resample_kernel(orig_freq, new_freq, lowpass_filter_width, - waveform.device, waveform.dtype) + gcd = math.gcd(int(orig_freq), int(new_freq)) - num_wavs, length = waveform.shape - waveform = torch.nn.functional.pad(waveform, (width, width + orig_freq)) - resampled = torch.nn.functional.conv1d(waveform[:, None], kernel, stride=orig_freq) - resampled = resampled.transpose(1, 2).reshape(num_wavs, -1) - target_length = int(math.ceil(new_freq * length / orig_freq)) - resampled = resampled[..., :target_length] - - # unpack batch - resampled = resampled.view(shape[:-1] + resampled.shape[-1:]) + kernel, width = _get_sinc_resample_kernel(orig_freq, new_freq, gcd, lowpass_filter_width, rolloff, + resampling_method, beta, waveform.device, waveform.dtype) + resampled = _apply_sinc_resample_kernel(waveform, orig_freq, new_freq, gcd, kernel, width) return resampled diff --git a/torchaudio/models/__init__.py b/torchaudio/models/__init__.py index 5b134345af..6696d8ded2 100644 --- a/torchaudio/models/__init__.py +++ b/torchaudio/models/__init__.py @@ -1,9 +1,11 @@ from .wav2letter import Wav2Letter from .wavernn import WaveRNN from .conv_tasnet import ConvTasNet +from .deepspeech import DeepSpeech __all__ = [ 'Wav2Letter', 'WaveRNN', 'ConvTasNet', + 'DeepSpeech', ] diff --git a/torchaudio/models/deepspeech.py b/torchaudio/models/deepspeech.py new file mode 100644 index 0000000000..477993e411 --- /dev/null +++ b/torchaudio/models/deepspeech.py @@ -0,0 +1,92 @@ +import torch + +__all__ = ["DeepSpeech"] + + +class FullyConnected(torch.nn.Module): + """ + Args: + n_feature: Number of input features + n_hidden: Internal hidden unit size. + """ + + def __init__(self, + n_feature: int, + n_hidden: int, + dropout: float, + relu_max_clip: int = 20) -> None: + super(FullyConnected, self).__init__() + self.fc = torch.nn.Linear(n_feature, n_hidden, bias=True) + self.relu_max_clip = relu_max_clip + self.dropout = dropout + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.fc(x) + x = torch.nn.functional.relu(x) + x = torch.nn.functional.hardtanh(x, 0, self.relu_max_clip) + if self.dropout: + x = torch.nn.functional.dropout(x, self.dropout, self.training) + return x + + +class DeepSpeech(torch.nn.Module): + """ + DeepSpeech model architecture from + `"Deep Speech: Scaling up end-to-end speech recognition"` + paper. + + Args: + n_feature: Number of input features + n_hidden: Internal hidden unit size. + n_class: Number of output classes + """ + + def __init__( + self, + n_feature: int, + n_hidden: int = 2048, + n_class: int = 40, + dropout: float = 0.0, + ) -> None: + super(DeepSpeech, self).__init__() + self.n_hidden = n_hidden + self.fc1 = FullyConnected(n_feature, n_hidden, dropout) + self.fc2 = FullyConnected(n_hidden, n_hidden, dropout) + self.fc3 = FullyConnected(n_hidden, n_hidden, dropout) + self.bi_rnn = torch.nn.RNN( + n_hidden, n_hidden, num_layers=1, nonlinearity="relu", bidirectional=True + ) + self.fc4 = FullyConnected(n_hidden, n_hidden, dropout) + self.out = torch.nn.Linear(n_hidden, n_class) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x (torch.Tensor): Tensor of dimension (batch, channel, time, feature). + Returns: + Tensor: Predictor tensor of dimension (batch, time, class). + """ + # N x C x T x F + x = self.fc1(x) + # N x C x T x H + x = self.fc2(x) + # N x C x T x H + x = self.fc3(x) + # N x C x T x H + x = x.squeeze(1) + # N x T x H + x = x.transpose(0, 1) + # T x N x H + x, _ = self.bi_rnn(x) + # The fifth (non-recurrent) layer takes both the forward and backward units as inputs + x = x[:, :, :self.n_hidden] + x[:, :, self.n_hidden:] + # T x N x H + x = self.fc4(x) + # T x N x H + x = self.out(x) + # T x N x n_class + x = x.permute(1, 0, 2) + # N x T x n_class + x = torch.nn.functional.log_softmax(x, dim=2) + # N x T x n_class + return x diff --git a/torchaudio/prototype/rnnt_loss.py b/torchaudio/prototype/rnnt_loss.py index 08c96ab0f2..2bce60835b 100644 --- a/torchaudio/prototype/rnnt_loss.py +++ b/torchaudio/prototype/rnnt_loss.py @@ -1,4 +1,5 @@ import torch +from torch import Tensor __all__ = [ "RNNTLoss", @@ -6,141 +7,15 @@ ] -def _rnnt_loss_alphas( - logits, - targets, - logit_lengths, - target_lengths, - blank=-1, - clamp=-1, -): - """ - Compute alphas for RNN transducer loss. - - See documentation for RNNTLoss - """ - targets = targets.to(device=logits.device) - logit_lengths = logit_lengths.to(device=logits.device) - target_lengths = target_lengths.to(device=logits.device) - - # make sure all int tensors are of type int32. - targets = targets.int() - logit_lengths = logit_lengths.int() - target_lengths = target_lengths.int() - - return torch.ops.torchaudio.rnnt_loss_alphas( - logits, - targets, - logit_lengths, - target_lengths, - blank, - clamp, - ) - - -def _rnnt_loss_betas( - logits, - targets, - logit_lengths, - target_lengths, - blank=-1, - clamp=-1, -): - """ - Compute betas for RNN transducer loss - - See documentation for RNNTLoss - """ - targets = targets.to(device=logits.device) - logit_lengths = logit_lengths.to(device=logits.device) - target_lengths = target_lengths.to(device=logits.device) - - # make sure all int tensors are of type int32. - targets = targets.int() - logit_lengths = logit_lengths.int() - target_lengths = target_lengths.int() - - return torch.ops.torchaudio.rnnt_loss_betas( - logits, - targets, - logit_lengths, - target_lengths, - blank, - clamp, - ) - - -class _RNNT(torch.autograd.Function): - @staticmethod - def forward( - ctx, - logits, - targets, - logit_lengths, - target_lengths, - blank=-1, - clamp=-1, - fused_log_softmax=True, - reuse_logits_for_grads=True, - ): - """ - See documentation for RNNTLoss - """ - - # move everything to the same device. - targets = targets.to(device=logits.device) - logit_lengths = logit_lengths.to(device=logits.device) - target_lengths = target_lengths.to(device=logits.device) - - # make sure all int tensors are of type int32. - targets = targets.int() - logit_lengths = logit_lengths.int() - target_lengths = target_lengths.int() - - if blank < 0: # reinterpret blank index if blank < 0. - blank = logits.shape[-1] + blank - - costs, gradients = torch.ops.torchaudio.rnnt_loss( - logits=logits, - targets=targets, - src_lengths=logit_lengths, - tgt_lengths=target_lengths, - blank=blank, - clamp=clamp, - fused_log_smax=fused_log_softmax, - reuse_logits_for_grads=reuse_logits_for_grads, - ) - - ctx.grads = gradients - - return costs - - @staticmethod - def backward(ctx, output_gradients): - output_gradients = output_gradients.view(-1, 1, 1, 1).to(ctx.grads) - ctx.grads.mul_(output_gradients).to(ctx.grads) - - return ( - ctx.grads, # logits - None, # targets - None, # logit_lengths - None, # target_lengths - None, # blank - None, # clamp - None, # fused_log_softmax - None, # reuse_logits_for_grads - ) - - def rnnt_loss( - logits, - targets, - logit_lengths, - target_lengths, - blank=-1, - clamp=-1, - fused_log_softmax=True, - reuse_logits_for_grads=True, + logits: Tensor, + targets: Tensor, + logit_lengths: Tensor, + target_lengths: Tensor, + blank: int = -1, + clamp: float = -1, + fused_log_softmax: bool = True, + reuse_logits_for_grads: bool = True, ): """ Compute the RNN Transducer Loss. @@ -166,17 +41,20 @@ def rnnt_loss( False # softmax needs the original logits value ) - cost = _RNNT.apply( - logits, - targets, - logit_lengths, - target_lengths, - blank, - clamp, - fused_log_softmax, - reuse_logits_for_grads, - ) - return cost + if blank < 0: # reinterpret blank index if blank < 0. + blank = logits.shape[-1] + blank + + costs, gradients = torch.ops.torchaudio.rnnt_loss( + logits=logits, + targets=targets, + src_lengths=logit_lengths, + tgt_lengths=target_lengths, + blank=blank, + clamp=clamp, + fused_log_smax=fused_log_softmax, + reuse_logits_for_grads=reuse_logits_for_grads,) + + return costs class RNNTLoss(torch.nn.Module): @@ -196,10 +74,10 @@ class RNNTLoss(torch.nn.Module): def __init__( self, - blank=-1, - clamp=-1, - fused_log_softmax=True, - reuse_logits_for_grads=True, + blank: int = -1, + clamp: float = -1., + fused_log_softmax: bool = True, + reuse_logits_for_grads: bool = True, ): super().__init__() self.blank = blank diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index fa38118043..49d3ff599b 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -8,6 +8,10 @@ from torch import Tensor from torchaudio import functional as F +from .functional.functional import ( + _get_sinc_resample_kernel, + _apply_sinc_resample_kernel, +) __all__ = [ 'Spectrogram', @@ -279,11 +283,34 @@ def __init__(self, assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max) + if n_stft is None or n_stft == 0: + warnings.warn( + 'Initialization of torchaudio.transforms.MelScale with an unset weight ' + '`n_stft=None` is deprecated and will be removed from a future release. ' + 'Please set a proper `n_stft` value. Typically this is `n_fft // 2 + 1`. ' + 'Refer to https://github.com/pytorch/audio/issues/1510 ' + 'for more details.' + ) + fb = torch.empty(0) if n_stft is None else F.create_fb_matrix( n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, self.norm, self.mel_scale) self.register_buffer('fb', fb) + def __prepare_scriptable__(self): + r"""If `self.fb` is empty, the `forward` method will try to resize the parameter, + which does not work once the transform is scripted. However, this error does not happen + until the transform is executed. This is inconvenient especially if the resulting + TorchScript object is executed in other environments. Therefore, we check the + validity of `self.fb` here and fail if the resulting TS does not work. + + Returns: + MelScale: self + """ + if self.fb.numel() == 0: + raise ValueError("n_stft must be provided at construction") + return self + def forward(self, specgram: Tensor) -> Tensor: r""" Args: @@ -639,17 +666,40 @@ class Resample(torch.nn.Module): Args: orig_freq (float, optional): The original frequency of the signal. (Default: ``16000``) new_freq (float, optional): The desired frequency. (Default: ``16000``) - resampling_method (str, optional): The resampling method. (Default: ``'sinc_interpolation'``) + resampling_method (str, optional): The resampling method to use. + Options: [``sinc_interpolation``, ``kaiser_window``] (Default: ``'sinc_interpolation'``) + lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper + but less efficient. (Default: ``6``) + rolloff (float, optional): The roll-off frequency of the filter, as a fraction of the Nyquist. + Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: ``0.99``) + beta (float or None): The shape parameter used for kaiser window. + + Note: If resampling on waveforms of higher precision than float32, there may be a small loss of precision + because the kernel is cached once as float32. If high precision resampling is important for your application, + the functional form will retain higher precision, but run slower because it does not cache the kernel. + Alternatively, you could rewrite a transform that caches a higher precision kernel. """ def __init__(self, - orig_freq: int = 16000, - new_freq: int = 16000, - resampling_method: str = 'sinc_interpolation') -> None: + orig_freq: float = 16000, + new_freq: float = 16000, + resampling_method: str = 'sinc_interpolation', + lowpass_filter_width: int = 6, + rolloff: float = 0.99, + beta: Optional[float] = None) -> None: super(Resample, self).__init__() + self.orig_freq = orig_freq self.new_freq = new_freq + self.gcd = math.gcd(int(self.orig_freq), int(self.new_freq)) self.resampling_method = resampling_method + self.lowpass_filter_width = lowpass_filter_width + self.rolloff = rolloff + + kernel, self.width = _get_sinc_resample_kernel(self.orig_freq, self.new_freq, self.gcd, + self.lowpass_filter_width, self.rolloff, + self.resampling_method, beta) + self.register_buffer('kernel', kernel) def forward(self, waveform: Tensor) -> Tensor: r""" @@ -659,10 +709,8 @@ def forward(self, waveform: Tensor) -> Tensor: Returns: Tensor: Output signal of dimension (..., time). """ - if self.resampling_method == 'sinc_interpolation': - return F.resample(waveform, self.orig_freq, self.new_freq) - - raise ValueError('Invalid resampling method: {}'.format(self.resampling_method)) + return _apply_sinc_resample_kernel(waveform, self.orig_freq, self.new_freq, self.gcd, + self.kernel, self.width) class ComplexNorm(torch.nn.Module): @@ -1078,7 +1126,10 @@ def __init__(self, def forward(self, waveform: Tensor) -> Tensor: r""" Args: - waveform (Tensor): Tensor of audio of dimension `(..., time)` + waveform (Tensor): Tensor of audio of dimension `(channels, time)` or `(time)` + Tensor of shape `(channels, time)` is treated as a multi-channel recording + of the same event and the resulting output will be trimmed to the earliest + voice activity in any channel. """ return F.vad( waveform=waveform, From 7198cc6d7ed83be39b8dc8aab56d5022e73380d7 Mon Sep 17 00:00:00 2001 From: Vasilis Vryniotis Date: Tue, 25 May 2021 11:04:49 -0700 Subject: [PATCH 50/73] Import audio #1497 ffe735bacfa918e6e21a5d751fd07afab3faaa15 Reviewed By: mthrok Differential Revision: D28678814 fbshipit-source-id: 3356fd88dc33ad9f20294ca19b0c3958ce55f1ae --- .circleci/config.yml | 1875 +++++++++++++++-- .circleci/config.yml.in | 20 +- .circleci/regenerate.py | 48 +- packaging/pkg_helpers.bash | 51 +- .../backend/sox_io/info_test.py | 11 + .../backend/sox_io/load_test.py | 11 + .../backend/sox_io/save_test.py | 13 + .../transforms/batch_consistency_test.py | 18 +- torchaudio/csrc/sox/effects.cpp | 4 +- torchaudio/csrc/sox/io.cpp | 9 +- torchaudio/csrc/sox/utils.cpp | 9 +- torchaudio/csrc/sox/utils.h | 5 +- 12 files changed, 1801 insertions(+), 273 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 64a6ae9f9f..32c7effa9c 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -71,11 +71,23 @@ binary_common: &binary_common python_version: description: "Python version to build against (e.g., 3.8)" type: string + cuda_version: + description: "CUDA version to build against (e.g., cpu, cu101)" + type: string + default: "cpu" + wheel_docker_image: + description: "Wheel only: what docker image to use" + type: string + default: "pytorch/manylinux-cuda102" + conda_docker_image: + description: "Conda only: what docker image to use" + type: string + default: "pytorch/conda-builder:cuda102" environment: &environment PYTHON_VERSION: << parameters.python_version >> BUILD_VERSION: << parameters.build_version >> PYTORCH_VERSION: << parameters.pytorch_version >> - CU_VERSION: cpu + CU_VERSION: << parameters.cuda_version >> smoke_test_common: &smoke_test_common <<: *binary_common @@ -127,7 +139,7 @@ jobs: binary_linux_wheel: <<: *binary_common docker: - - image: "pytorch/manylinux-cuda102" + - image: << parameters.wheel_docker_image >> resource_class: 2xlarge+ steps: - checkout @@ -144,7 +156,7 @@ jobs: binary_linux_conda: <<: *binary_common docker: - - image: "pytorch/conda-cuda" + - image: "<< parameters.conda_docker_image >>" resource_class: 2xlarge+ steps: - checkout @@ -487,7 +499,7 @@ jobs: name: windows-gpu environment: <<: *environment - CUDA_VERSION: "10.1" + CUDA_VERSION: "10.2" steps: - checkout - designate_upload_channel @@ -619,114 +631,342 @@ workflows: - download_third_parties_nix: name: download_third_parties_nix - binary_linux_wheel: - name: binary_linux_wheel_py3.6 + cuda_version: cpu + name: binary_linux_wheel_py3.6_cpu + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_linux_wheel: + cuda_version: cu102 + name: binary_linux_wheel_py3.6_cu102 + python_version: '3.6' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda102 + - binary_linux_wheel: + cuda_version: cu111 + name: binary_linux_wheel_py3.6_cu111 python_version: '3.6' requires: - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_linux_wheel: + cuda_version: cpu + name: binary_linux_wheel_py3.7_cpu + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_linux_wheel: + cuda_version: cu102 + name: binary_linux_wheel_py3.7_cu102 + python_version: '3.7' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda102 - binary_linux_wheel: - name: binary_linux_wheel_py3.7 + cuda_version: cu111 + name: binary_linux_wheel_py3.7_cu111 python_version: '3.7' requires: - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_linux_wheel: + cuda_version: cpu + name: binary_linux_wheel_py3.8_cpu + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_linux_wheel: + cuda_version: cu102 + name: binary_linux_wheel_py3.8_cu102 + python_version: '3.8' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda102 - binary_linux_wheel: - name: binary_linux_wheel_py3.8 + cuda_version: cu111 + name: binary_linux_wheel_py3.8_cu111 python_version: '3.8' requires: - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_linux_wheel: + cuda_version: cpu + name: binary_linux_wheel_py3.9_cpu + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_linux_wheel: + cuda_version: cu102 + name: binary_linux_wheel_py3.9_cu102 + python_version: '3.9' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda102 - binary_linux_wheel: - name: binary_linux_wheel_py3.9 + cuda_version: cu111 + name: binary_linux_wheel_py3.9_cu111 python_version: '3.9' requires: - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda111 - binary_macos_wheel: - name: binary_macos_wheel_py3.6 + cuda_version: cpu + name: binary_macos_wheel_py3.6_cpu python_version: '3.6' requires: - download_third_parties_nix - binary_macos_wheel: - name: binary_macos_wheel_py3.7 + cuda_version: cpu + name: binary_macos_wheel_py3.7_cpu python_version: '3.7' requires: - download_third_parties_nix - binary_macos_wheel: - name: binary_macos_wheel_py3.8 + cuda_version: cpu + name: binary_macos_wheel_py3.8_cpu python_version: '3.8' requires: - download_third_parties_nix - binary_macos_wheel: - name: binary_macos_wheel_py3.9 + cuda_version: cpu + name: binary_macos_wheel_py3.9_cpu python_version: '3.9' requires: - download_third_parties_nix - binary_windows_wheel: - name: binary_windows_wheel_py3.6 + cuda_version: cpu + name: binary_windows_wheel_py3.6_cpu + python_version: '3.6' + - binary_windows_wheel: + cuda_version: cu102 + name: binary_windows_wheel_py3.6_cu102 + python_version: '3.6' + wheel_docker_image: pytorch/manylinux-cuda102 + - binary_windows_wheel: + cuda_version: cu111 + name: binary_windows_wheel_py3.6_cu111 python_version: '3.6' + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_windows_wheel: + cuda_version: cpu + name: binary_windows_wheel_py3.7_cpu + python_version: '3.7' + - binary_windows_wheel: + cuda_version: cu102 + name: binary_windows_wheel_py3.7_cu102 + python_version: '3.7' + wheel_docker_image: pytorch/manylinux-cuda102 - binary_windows_wheel: - name: binary_windows_wheel_py3.7 + cuda_version: cu111 + name: binary_windows_wheel_py3.7_cu111 python_version: '3.7' + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_windows_wheel: + cuda_version: cpu + name: binary_windows_wheel_py3.8_cpu + python_version: '3.8' + - binary_windows_wheel: + cuda_version: cu102 + name: binary_windows_wheel_py3.8_cu102 + python_version: '3.8' + wheel_docker_image: pytorch/manylinux-cuda102 - binary_windows_wheel: - name: binary_windows_wheel_py3.8 + cuda_version: cu111 + name: binary_windows_wheel_py3.8_cu111 python_version: '3.8' + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_windows_wheel: + cuda_version: cpu + name: binary_windows_wheel_py3.9_cpu + python_version: '3.9' + - binary_windows_wheel: + cuda_version: cu102 + name: binary_windows_wheel_py3.9_cu102 + python_version: '3.9' + wheel_docker_image: pytorch/manylinux-cuda102 - binary_windows_wheel: - name: binary_windows_wheel_py3.9 + cuda_version: cu111 + name: binary_windows_wheel_py3.9_cu111 python_version: '3.9' + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_linux_conda_py3.6_cpu + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + name: binary_linux_conda_py3.6_cu102 + python_version: '3.6' + requires: + - download_third_parties_nix - binary_linux_conda: - name: binary_linux_conda_py3.6 + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + name: binary_linux_conda_py3.6_cu111 python_version: '3.6' requires: - download_third_parties_nix - binary_linux_conda: - name: binary_linux_conda_py3.7 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_linux_conda_py3.7_cpu + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + name: binary_linux_conda_py3.7_cu102 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + name: binary_linux_conda_py3.7_cu111 python_version: '3.7' requires: - download_third_parties_nix - binary_linux_conda: - name: binary_linux_conda_py3.8 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_linux_conda_py3.8_cpu + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + name: binary_linux_conda_py3.8_cu102 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + name: binary_linux_conda_py3.8_cu111 python_version: '3.8' requires: - download_third_parties_nix - binary_linux_conda: - name: binary_linux_conda_py3.9 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_linux_conda_py3.9_cpu + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + name: binary_linux_conda_py3.9_cu102 + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + name: binary_linux_conda_py3.9_cu111 python_version: '3.9' requires: - download_third_parties_nix - binary_macos_conda: - name: binary_macos_conda_py3.6 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_macos_conda_py3.6_cpu python_version: '3.6' requires: - download_third_parties_nix - binary_macos_conda: - name: binary_macos_conda_py3.7 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_macos_conda_py3.7_cpu python_version: '3.7' requires: - download_third_parties_nix - binary_macos_conda: - name: binary_macos_conda_py3.8 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_macos_conda_py3.8_cpu python_version: '3.8' requires: - download_third_parties_nix - binary_macos_conda: - name: binary_macos_conda_py3.9 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_macos_conda_py3.9_cpu python_version: '3.9' requires: - download_third_parties_nix - binary_windows_conda: - name: binary_windows_conda_py3.6 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_windows_conda_py3.6_cpu + python_version: '3.6' + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + name: binary_windows_conda_py3.6_cu102 + python_version: '3.6' + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + name: binary_windows_conda_py3.6_cu111 python_version: '3.6' - binary_windows_conda: - name: binary_windows_conda_py3.7 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_windows_conda_py3.7_cpu + python_version: '3.7' + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + name: binary_windows_conda_py3.7_cu102 + python_version: '3.7' + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + name: binary_windows_conda_py3.7_cu111 python_version: '3.7' - binary_windows_conda: - name: binary_windows_conda_py3.8 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_windows_conda_py3.8_cpu + python_version: '3.8' + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + name: binary_windows_conda_py3.8_cu102 + python_version: '3.8' + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + name: binary_windows_conda_py3.8_cu111 python_version: '3.8' - binary_windows_conda: - name: binary_windows_conda_py3.9 + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + name: binary_windows_conda_py3.9_cpu + python_version: '3.9' + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + name: binary_windows_conda_py3.9_cu102 + python_version: '3.9' + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + name: binary_windows_conda_py3.9_cu111 python_version: '3.9' - build_docs: name: build_docs python_version: '3.8' requires: - - binary_linux_wheel_py3.8 + - binary_linux_wheel_py3.8_cpu - upload_docs: context: org-member filters: @@ -743,94 +983,115 @@ workflows: name: docstring_parameters_sync python_version: '3.8' requires: - - binary_linux_wheel_py3.8 + - binary_linux_wheel_py3.8_cpu unittest: jobs: - download_third_parties_nix: name: download_third_parties_nix - unittest_linux_cpu: + cuda_version: cpu name: unittest_linux_cpu_py3.6 python_version: '3.6' requires: - download_third_parties_nix - stylecheck: + cuda_version: cpu name: stylecheck_py3.6 python_version: '3.6' - unittest_linux_cpu: + cuda_version: cpu name: unittest_linux_cpu_py3.7 python_version: '3.7' requires: - download_third_parties_nix - unittest_linux_cpu: + cuda_version: cpu name: unittest_linux_cpu_py3.8 python_version: '3.8' requires: - download_third_parties_nix - unittest_linux_cpu: + cuda_version: cpu name: unittest_linux_cpu_py3.9 python_version: '3.9' requires: - download_third_parties_nix - unittest_linux_gpu: + cuda_version: cu102 name: unittest_linux_gpu_py3.6 python_version: '3.6' requires: - download_third_parties_nix - unittest_linux_gpu: + cuda_version: cu102 name: unittest_linux_gpu_py3.7 python_version: '3.7' requires: - download_third_parties_nix - unittest_linux_gpu: + cuda_version: cu102 name: unittest_linux_gpu_py3.8 python_version: '3.8' requires: - download_third_parties_nix - unittest_linux_gpu: + cuda_version: cu102 name: unittest_linux_gpu_py3.9 python_version: '3.9' requires: - download_third_parties_nix - unittest_windows_cpu: + cuda_version: cpu name: unittest_windows_cpu_py3.6 python_version: '3.6' - unittest_windows_cpu: + cuda_version: cpu name: unittest_windows_cpu_py3.7 python_version: '3.7' - unittest_windows_cpu: + cuda_version: cpu name: unittest_windows_cpu_py3.8 python_version: '3.8' - unittest_windows_cpu: + cuda_version: cpu name: unittest_windows_cpu_py3.9 python_version: '3.9' - unittest_windows_gpu: + cuda_version: cu102 name: unittest_windows_gpu_py3.6 python_version: '3.6' - unittest_windows_gpu: + cuda_version: cu102 name: unittest_windows_gpu_py3.7 python_version: '3.7' - unittest_windows_gpu: + cuda_version: cu102 name: unittest_windows_gpu_py3.8 python_version: '3.8' - unittest_windows_gpu: + cuda_version: cu102 name: unittest_windows_gpu_py3.9 python_version: '3.9' - unittest_macos_cpu: + cuda_version: cpu name: unittest_macos_cpu_py3.6 python_version: '3.6' requires: - download_third_parties_nix - unittest_macos_cpu: + cuda_version: cpu name: unittest_macos_cpu_py3.7 python_version: '3.7' requires: - download_third_parties_nix - unittest_macos_cpu: + cuda_version: cpu name: unittest_macos_cpu_py3.8 python_version: '3.8' requires: - download_third_parties_nix - unittest_macos_cpu: + cuda_version: cpu name: unittest_macos_cpu_py3.9 python_version: '3.9' requires: @@ -850,13 +1111,14 @@ workflows: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ name: download_third_parties_nix - binary_linux_wheel: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.6 + name: nightly_binary_linux_wheel_py3.6_cpu python_version: '3.6' requires: - download_third_parties_nix @@ -868,31 +1130,34 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.6_upload + name: nightly_binary_linux_wheel_py3.6_cpu_upload requires: - - nightly_binary_linux_wheel_py3.6 + - nightly_binary_linux_wheel_py3.6_cpu - smoke_test_linux_pip: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.6_smoke_test_pip + name: nightly_binary_linux_wheel_py3.6_cpu_smoke_test_pip python_version: '3.6' requires: - - nightly_binary_linux_wheel_py3.6_upload + - nightly_binary_linux_wheel_py3.6_cpu_upload - binary_linux_wheel: + cuda_version: cu102 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.7 - python_version: '3.7' + name: nightly_binary_linux_wheel_py3.6_cu102 + python_version: '3.6' requires: - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda102 - binary_wheel_upload: context: org-member filters: @@ -901,31 +1166,34 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.7_upload + name: nightly_binary_linux_wheel_py3.6_cu102_upload requires: - - nightly_binary_linux_wheel_py3.7 + - nightly_binary_linux_wheel_py3.6_cu102 - smoke_test_linux_pip: + cuda_version: cu102 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.7_smoke_test_pip - python_version: '3.7' + name: nightly_binary_linux_wheel_py3.6_cu102_smoke_test_pip + python_version: '3.6' requires: - - nightly_binary_linux_wheel_py3.7_upload + - nightly_binary_linux_wheel_py3.6_cu102_upload - binary_linux_wheel: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.8 - python_version: '3.8' + name: nightly_binary_linux_wheel_py3.6_cu111 + python_version: '3.6' requires: - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda111 - binary_wheel_upload: context: org-member filters: @@ -934,29 +1202,31 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.8_upload + name: nightly_binary_linux_wheel_py3.6_cu111_upload requires: - - nightly_binary_linux_wheel_py3.8 + - nightly_binary_linux_wheel_py3.6_cu111 - smoke_test_linux_pip: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.8_smoke_test_pip - python_version: '3.8' + name: nightly_binary_linux_wheel_py3.6_cu111_smoke_test_pip + python_version: '3.6' requires: - - nightly_binary_linux_wheel_py3.8_upload + - nightly_binary_linux_wheel_py3.6_cu111_upload - binary_linux_wheel: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.9 - python_version: '3.9' + name: nightly_binary_linux_wheel_py3.7_cpu + python_version: '3.7' requires: - download_third_parties_nix - binary_wheel_upload: @@ -967,31 +1237,34 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.9_upload + name: nightly_binary_linux_wheel_py3.7_cpu_upload requires: - - nightly_binary_linux_wheel_py3.9 + - nightly_binary_linux_wheel_py3.7_cpu - smoke_test_linux_pip: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_wheel_py3.9_smoke_test_pip - python_version: '3.9' + name: nightly_binary_linux_wheel_py3.7_cpu_smoke_test_pip + python_version: '3.7' requires: - - nightly_binary_linux_wheel_py3.9_upload - - binary_macos_wheel: + - nightly_binary_linux_wheel_py3.7_cpu_upload + - binary_linux_wheel: + cuda_version: cu102 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_wheel_py3.6 - python_version: '3.6' + name: nightly_binary_linux_wheel_py3.7_cu102 + python_version: '3.7' requires: - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda102 - binary_wheel_upload: context: org-member filters: @@ -1000,42 +1273,34 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_wheel_py3.6_upload + name: nightly_binary_linux_wheel_py3.7_cu102_upload requires: - - nightly_binary_macos_wheel_py3.6 - - binary_macos_wheel: + - nightly_binary_linux_wheel_py3.7_cu102 + - smoke_test_linux_pip: + cuda_version: cu102 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_wheel_py3.7 + name: nightly_binary_linux_wheel_py3.7_cu102_smoke_test_pip python_version: '3.7' requires: - - download_third_parties_nix - - binary_wheel_upload: - context: org-member - filters: - branches: - only: - - nightly - tags: - only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_wheel_py3.7_upload - requires: - - nightly_binary_macos_wheel_py3.7 - - binary_macos_wheel: + - nightly_binary_linux_wheel_py3.7_cu102_upload + - binary_linux_wheel: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_wheel_py3.8 - python_version: '3.8' + name: nightly_binary_linux_wheel_py3.7_cu111 + python_version: '3.7' requires: - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda111 - binary_wheel_upload: context: org-member filters: @@ -1044,40 +1309,33 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_wheel_py3.8_upload + name: nightly_binary_linux_wheel_py3.7_cu111_upload requires: - - nightly_binary_macos_wheel_py3.8 - - binary_macos_wheel: + - nightly_binary_linux_wheel_py3.7_cu111 + - smoke_test_linux_pip: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_wheel_py3.9 - python_version: '3.9' + name: nightly_binary_linux_wheel_py3.7_cu111_smoke_test_pip + python_version: '3.7' requires: - - download_third_parties_nix - - binary_wheel_upload: - context: org-member + - nightly_binary_linux_wheel_py3.7_cu111_upload + - binary_linux_wheel: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_wheel_py3.9_upload + name: nightly_binary_linux_wheel_py3.8_cpu + python_version: '3.8' requires: - - nightly_binary_macos_wheel_py3.9 - - binary_windows_wheel: - filters: - branches: - only: - - nightly - tags: - only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.6 - python_version: '3.6' + - download_third_parties_nix - binary_wheel_upload: context: org-member filters: @@ -1086,29 +1344,34 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.6_upload + name: nightly_binary_linux_wheel_py3.8_cpu_upload requires: - - nightly_binary_windows_wheel_py3.6 - - smoke_test_windows_pip: + - nightly_binary_linux_wheel_py3.8_cpu + - smoke_test_linux_pip: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.6_smoke_test_pip - python_version: '3.6' + name: nightly_binary_linux_wheel_py3.8_cpu_smoke_test_pip + python_version: '3.8' requires: - - nightly_binary_windows_wheel_py3.6_upload - - binary_windows_wheel: + - nightly_binary_linux_wheel_py3.8_cpu_upload + - binary_linux_wheel: + cuda_version: cu102 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.7 - python_version: '3.7' + name: nightly_binary_linux_wheel_py3.8_cu102 + python_version: '3.8' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda102 - binary_wheel_upload: context: org-member filters: @@ -1117,29 +1380,34 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.7_upload + name: nightly_binary_linux_wheel_py3.8_cu102_upload requires: - - nightly_binary_windows_wheel_py3.7 - - smoke_test_windows_pip: + - nightly_binary_linux_wheel_py3.8_cu102 + - smoke_test_linux_pip: + cuda_version: cu102 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.7_smoke_test_pip - python_version: '3.7' + name: nightly_binary_linux_wheel_py3.8_cu102_smoke_test_pip + python_version: '3.8' requires: - - nightly_binary_windows_wheel_py3.7_upload - - binary_windows_wheel: + - nightly_binary_linux_wheel_py3.8_cu102_upload + - binary_linux_wheel: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.8 + name: nightly_binary_linux_wheel_py3.8_cu111 python_version: '3.8' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-cuda111 - binary_wheel_upload: context: org-member filters: @@ -1148,29 +1416,33 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.8_upload + name: nightly_binary_linux_wheel_py3.8_cu111_upload requires: - - nightly_binary_windows_wheel_py3.8 - - smoke_test_windows_pip: + - nightly_binary_linux_wheel_py3.8_cu111 + - smoke_test_linux_pip: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.8_smoke_test_pip + name: nightly_binary_linux_wheel_py3.8_cu111_smoke_test_pip python_version: '3.8' requires: - - nightly_binary_windows_wheel_py3.8_upload - - binary_windows_wheel: + - nightly_binary_linux_wheel_py3.8_cu111_upload + - binary_linux_wheel: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.9 + name: nightly_binary_linux_wheel_py3.9_cpu python_version: '3.9' + requires: + - download_third_parties_nix - binary_wheel_upload: context: org-member filters: @@ -1179,32 +1451,35 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.9_upload + name: nightly_binary_linux_wheel_py3.9_cpu_upload requires: - - nightly_binary_windows_wheel_py3.9 - - smoke_test_windows_pip: + - nightly_binary_linux_wheel_py3.9_cpu + - smoke_test_linux_pip: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_wheel_py3.9_smoke_test_pip + name: nightly_binary_linux_wheel_py3.9_cpu_smoke_test_pip python_version: '3.9' requires: - - nightly_binary_windows_wheel_py3.9_upload - - binary_linux_conda: + - nightly_binary_linux_wheel_py3.9_cpu_upload + - binary_linux_wheel: + cuda_version: cu102 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.6 - python_version: '3.6' + name: nightly_binary_linux_wheel_py3.9_cu102 + python_version: '3.9' requires: - download_third_parties_nix - - binary_conda_upload: + wheel_docker_image: pytorch/manylinux-cuda102 + - binary_wheel_upload: context: org-member filters: branches: @@ -1212,32 +1487,35 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.6_upload + name: nightly_binary_linux_wheel_py3.9_cu102_upload requires: - - nightly_binary_linux_conda_py3.6 - - smoke_test_linux_conda: + - nightly_binary_linux_wheel_py3.9_cu102 + - smoke_test_linux_pip: + cuda_version: cu102 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.6_smoke_test_conda - python_version: '3.6' + name: nightly_binary_linux_wheel_py3.9_cu102_smoke_test_pip + python_version: '3.9' requires: - - nightly_binary_linux_conda_py3.6_upload - - binary_linux_conda: + - nightly_binary_linux_wheel_py3.9_cu102_upload + - binary_linux_wheel: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.7 - python_version: '3.7' + name: nightly_binary_linux_wheel_py3.9_cu111 + python_version: '3.9' requires: - download_third_parties_nix - - binary_conda_upload: + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_wheel_upload: context: org-member filters: branches: @@ -1245,32 +1523,34 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.7_upload + name: nightly_binary_linux_wheel_py3.9_cu111_upload requires: - - nightly_binary_linux_conda_py3.7 - - smoke_test_linux_conda: + - nightly_binary_linux_wheel_py3.9_cu111 + - smoke_test_linux_pip: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.7_smoke_test_conda - python_version: '3.7' + name: nightly_binary_linux_wheel_py3.9_cu111_smoke_test_pip + python_version: '3.9' requires: - - nightly_binary_linux_conda_py3.7_upload - - binary_linux_conda: + - nightly_binary_linux_wheel_py3.9_cu111_upload + - binary_macos_wheel: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.8 - python_version: '3.8' + name: nightly_binary_macos_wheel_py3.6_cpu + python_version: '3.6' requires: - download_third_parties_nix - - binary_conda_upload: + - binary_wheel_upload: context: org-member filters: branches: @@ -1278,65 +1558,68 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.8_upload + name: nightly_binary_macos_wheel_py3.6_cpu_upload requires: - - nightly_binary_linux_conda_py3.8 - - smoke_test_linux_conda: + - nightly_binary_macos_wheel_py3.6_cpu + - binary_macos_wheel: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.8_smoke_test_conda - python_version: '3.8' + name: nightly_binary_macos_wheel_py3.7_cpu + python_version: '3.7' requires: - - nightly_binary_linux_conda_py3.8_upload - - binary_linux_conda: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.9 - python_version: '3.9' + name: nightly_binary_macos_wheel_py3.7_cpu_upload requires: - - download_third_parties_nix - - binary_conda_upload: - context: org-member + - nightly_binary_macos_wheel_py3.7_cpu + - binary_macos_wheel: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.9_upload + name: nightly_binary_macos_wheel_py3.8_cpu + python_version: '3.8' requires: - - nightly_binary_linux_conda_py3.9 - - smoke_test_linux_conda: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_linux_conda_py3.9_smoke_test_conda - python_version: '3.9' + name: nightly_binary_macos_wheel_py3.8_cpu_upload requires: - - nightly_binary_linux_conda_py3.9_upload - - binary_macos_conda: + - nightly_binary_macos_wheel_py3.8_cpu + - binary_macos_wheel: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_conda_py3.6 - python_version: '3.6' + name: nightly_binary_macos_wheel_py3.9_cpu + python_version: '3.9' requires: - download_third_parties_nix - - binary_conda_upload: + - binary_wheel_upload: context: org-member filters: branches: @@ -1344,21 +1627,20 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_conda_py3.6_upload + name: nightly_binary_macos_wheel_py3.9_cpu_upload requires: - - nightly_binary_macos_conda_py3.6 - - binary_macos_conda: + - nightly_binary_macos_wheel_py3.9_cpu + - binary_windows_wheel: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_conda_py3.7 - python_version: '3.7' - requires: - - download_third_parties_nix - - binary_conda_upload: + name: nightly_binary_windows_wheel_py3.6_cpu + python_version: '3.6' + - binary_wheel_upload: context: org-member filters: branches: @@ -1366,63 +1648,1000 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_conda_py3.7_upload + name: nightly_binary_windows_wheel_py3.6_cpu_upload requires: - - nightly_binary_macos_conda_py3.7 - - binary_macos_conda: + - nightly_binary_windows_wheel_py3.6_cpu + - smoke_test_windows_pip: + cuda_version: cpu filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_conda_py3.8 - python_version: '3.8' + name: nightly_binary_windows_wheel_py3.6_cpu_smoke_test_pip + python_version: '3.6' requires: - - download_third_parties_nix - - binary_conda_upload: - context: org-member + - nightly_binary_windows_wheel_py3.6_cpu_upload + - binary_windows_wheel: + cuda_version: cu102 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_conda_py3.8_upload - requires: - - nightly_binary_macos_conda_py3.8 - - binary_macos_conda: + name: nightly_binary_windows_wheel_py3.6_cu102 + python_version: '3.6' + wheel_docker_image: pytorch/manylinux-cuda102 + - binary_wheel_upload: + context: org-member filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_conda_py3.9 - python_version: '3.9' + name: nightly_binary_windows_wheel_py3.6_cu102_upload requires: - - download_third_parties_nix - - binary_conda_upload: - context: org-member + - nightly_binary_windows_wheel_py3.6_cu102 + - smoke_test_windows_pip: + cuda_version: cu102 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_macos_conda_py3.9_upload + name: nightly_binary_windows_wheel_py3.6_cu102_smoke_test_pip + python_version: '3.6' requires: - - nightly_binary_macos_conda_py3.9 - - binary_windows_conda: + - nightly_binary_windows_wheel_py3.6_cu102_upload + - binary_windows_wheel: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.6 + name: nightly_binary_windows_wheel_py3.6_cu111 python_version: '3.6' - - binary_conda_upload: + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.6_cu111_upload + requires: + - nightly_binary_windows_wheel_py3.6_cu111 + - smoke_test_windows_pip: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.6_cu111_smoke_test_pip + python_version: '3.6' + requires: + - nightly_binary_windows_wheel_py3.6_cu111_upload + - binary_windows_wheel: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_cpu + python_version: '3.7' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_cpu_upload + requires: + - nightly_binary_windows_wheel_py3.7_cpu + - smoke_test_windows_pip: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_cpu_smoke_test_pip + python_version: '3.7' + requires: + - nightly_binary_windows_wheel_py3.7_cpu_upload + - binary_windows_wheel: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_cu102 + python_version: '3.7' + wheel_docker_image: pytorch/manylinux-cuda102 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_cu102_upload + requires: + - nightly_binary_windows_wheel_py3.7_cu102 + - smoke_test_windows_pip: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_cu102_smoke_test_pip + python_version: '3.7' + requires: + - nightly_binary_windows_wheel_py3.7_cu102_upload + - binary_windows_wheel: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_cu111 + python_version: '3.7' + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_cu111_upload + requires: + - nightly_binary_windows_wheel_py3.7_cu111 + - smoke_test_windows_pip: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_cu111_smoke_test_pip + python_version: '3.7' + requires: + - nightly_binary_windows_wheel_py3.7_cu111_upload + - binary_windows_wheel: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_cpu + python_version: '3.8' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_cpu_upload + requires: + - nightly_binary_windows_wheel_py3.8_cpu + - smoke_test_windows_pip: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_cpu_smoke_test_pip + python_version: '3.8' + requires: + - nightly_binary_windows_wheel_py3.8_cpu_upload + - binary_windows_wheel: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_cu102 + python_version: '3.8' + wheel_docker_image: pytorch/manylinux-cuda102 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_cu102_upload + requires: + - nightly_binary_windows_wheel_py3.8_cu102 + - smoke_test_windows_pip: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_cu102_smoke_test_pip + python_version: '3.8' + requires: + - nightly_binary_windows_wheel_py3.8_cu102_upload + - binary_windows_wheel: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_cu111 + python_version: '3.8' + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_cu111_upload + requires: + - nightly_binary_windows_wheel_py3.8_cu111 + - smoke_test_windows_pip: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_cu111_smoke_test_pip + python_version: '3.8' + requires: + - nightly_binary_windows_wheel_py3.8_cu111_upload + - binary_windows_wheel: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_cpu + python_version: '3.9' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_cpu_upload + requires: + - nightly_binary_windows_wheel_py3.9_cpu + - smoke_test_windows_pip: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_cpu_smoke_test_pip + python_version: '3.9' + requires: + - nightly_binary_windows_wheel_py3.9_cpu_upload + - binary_windows_wheel: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_cu102 + python_version: '3.9' + wheel_docker_image: pytorch/manylinux-cuda102 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_cu102_upload + requires: + - nightly_binary_windows_wheel_py3.9_cu102 + - smoke_test_windows_pip: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_cu102_smoke_test_pip + python_version: '3.9' + requires: + - nightly_binary_windows_wheel_py3.9_cu102_upload + - binary_windows_wheel: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_cu111 + python_version: '3.9' + wheel_docker_image: pytorch/manylinux-cuda111 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_cu111_upload + requires: + - nightly_binary_windows_wheel_py3.9_cu111 + - smoke_test_windows_pip: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.9_cu111_smoke_test_pip + python_version: '3.9' + requires: + - nightly_binary_windows_wheel_py3.9_cu111_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_cpu + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_cpu_upload + requires: + - nightly_binary_linux_conda_py3.6_cpu + - smoke_test_linux_conda: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_cpu_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_linux_conda_py3.6_cpu_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_cu102 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_cu102_upload + requires: + - nightly_binary_linux_conda_py3.6_cu102 + - smoke_test_linux_conda: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_cu102_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_linux_conda_py3.6_cu102_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_cu111 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_cu111_upload + requires: + - nightly_binary_linux_conda_py3.6_cu111 + - smoke_test_linux_conda: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_cu111_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_linux_conda_py3.6_cu111_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_cpu + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_cpu_upload + requires: + - nightly_binary_linux_conda_py3.7_cpu + - smoke_test_linux_conda: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_cpu_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_linux_conda_py3.7_cpu_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_cu102 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_cu102_upload + requires: + - nightly_binary_linux_conda_py3.7_cu102 + - smoke_test_linux_conda: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_cu102_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_linux_conda_py3.7_cu102_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_cu111 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_cu111_upload + requires: + - nightly_binary_linux_conda_py3.7_cu111 + - smoke_test_linux_conda: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_cu111_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_linux_conda_py3.7_cu111_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_cpu + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_cpu_upload + requires: + - nightly_binary_linux_conda_py3.8_cpu + - smoke_test_linux_conda: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_cpu_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_linux_conda_py3.8_cpu_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_cu102 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_cu102_upload + requires: + - nightly_binary_linux_conda_py3.8_cu102 + - smoke_test_linux_conda: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_cu102_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_linux_conda_py3.8_cu102_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_cu111 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_cu111_upload + requires: + - nightly_binary_linux_conda_py3.8_cu111 + - smoke_test_linux_conda: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_cu111_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_linux_conda_py3.8_cu111_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_cpu + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_cpu_upload + requires: + - nightly_binary_linux_conda_py3.9_cpu + - smoke_test_linux_conda: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_cpu_smoke_test_conda + python_version: '3.9' + requires: + - nightly_binary_linux_conda_py3.9_cpu_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_cu102 + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_cu102_upload + requires: + - nightly_binary_linux_conda_py3.9_cu102 + - smoke_test_linux_conda: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_cu102_smoke_test_conda + python_version: '3.9' + requires: + - nightly_binary_linux_conda_py3.9_cu102_upload + - binary_linux_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_cu111 + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_cu111_upload + requires: + - nightly_binary_linux_conda_py3.9_cu111 + - smoke_test_linux_conda: + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.9_cu111_smoke_test_conda + python_version: '3.9' + requires: + - nightly_binary_linux_conda_py3.9_cu111_upload + - binary_macos_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.6_cpu + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.6_cpu_upload + requires: + - nightly_binary_macos_conda_py3.6_cpu + - binary_macos_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.7_cpu + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.7_cpu_upload + requires: + - nightly_binary_macos_conda_py3.7_cpu + - binary_macos_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.8_cpu + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.8_cpu_upload + requires: + - nightly_binary_macos_conda_py3.8_cpu + - binary_macos_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.9_cpu + python_version: '3.9' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.9_cpu_upload + requires: + - nightly_binary_macos_conda_py3.9_cpu + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_cpu + python_version: '3.6' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_cpu_upload + requires: + - nightly_binary_windows_conda_py3.6_cpu + - smoke_test_windows_conda: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_cpu_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_windows_conda_py3.6_cpu_upload + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_cu102 + python_version: '3.6' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_cu102_upload + requires: + - nightly_binary_windows_conda_py3.6_cu102 + - smoke_test_windows_conda: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_cu102_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_windows_conda_py3.6_cu102_upload + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_cu111 + python_version: '3.6' + - binary_conda_upload: context: org-member filters: branches: @@ -1430,28 +2649,99 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.6_upload + name: nightly_binary_windows_conda_py3.6_cu111_upload requires: - - nightly_binary_windows_conda_py3.6 + - nightly_binary_windows_conda_py3.6_cu111 - smoke_test_windows_conda: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.6_smoke_test_conda + name: nightly_binary_windows_conda_py3.6_cu111_smoke_test_conda python_version: '3.6' requires: - - nightly_binary_windows_conda_py3.6_upload + - nightly_binary_windows_conda_py3.6_cu111_upload + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7_cpu + python_version: '3.7' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7_cpu_upload + requires: + - nightly_binary_windows_conda_py3.7_cpu + - smoke_test_windows_conda: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7_cpu_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_windows_conda_py3.7_cpu_upload + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7_cu102 + python_version: '3.7' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7_cu102_upload + requires: + - nightly_binary_windows_conda_py3.7_cu102 + - smoke_test_windows_conda: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7_cu102_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_windows_conda_py3.7_cu102_upload - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.7 + name: nightly_binary_windows_conda_py3.7_cu111 python_version: '3.7' - binary_conda_upload: context: org-member @@ -1461,28 +2751,99 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.7_upload + name: nightly_binary_windows_conda_py3.7_cu111_upload requires: - - nightly_binary_windows_conda_py3.7 + - nightly_binary_windows_conda_py3.7_cu111 - smoke_test_windows_conda: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.7_smoke_test_conda + name: nightly_binary_windows_conda_py3.7_cu111_smoke_test_conda python_version: '3.7' requires: - - nightly_binary_windows_conda_py3.7_upload + - nightly_binary_windows_conda_py3.7_cu111_upload + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8_cpu + python_version: '3.8' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8_cpu_upload + requires: + - nightly_binary_windows_conda_py3.8_cpu + - smoke_test_windows_conda: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8_cpu_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_windows_conda_py3.8_cpu_upload + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8_cu102 + python_version: '3.8' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8_cu102_upload + requires: + - nightly_binary_windows_conda_py3.8_cu102 + - smoke_test_windows_conda: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8_cu102_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_windows_conda_py3.8_cu102_upload - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.8 + name: nightly_binary_windows_conda_py3.8_cu111 python_version: '3.8' - binary_conda_upload: context: org-member @@ -1492,28 +2853,99 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.8_upload + name: nightly_binary_windows_conda_py3.8_cu111_upload requires: - - nightly_binary_windows_conda_py3.8 + - nightly_binary_windows_conda_py3.8_cu111 - smoke_test_windows_conda: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.8_smoke_test_conda + name: nightly_binary_windows_conda_py3.8_cu111_smoke_test_conda python_version: '3.8' requires: - - nightly_binary_windows_conda_py3.8_upload + - nightly_binary_windows_conda_py3.8_cu111_upload + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cpu + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.9_cpu + python_version: '3.9' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.9_cpu_upload + requires: + - nightly_binary_windows_conda_py3.9_cpu + - smoke_test_windows_conda: + cuda_version: cpu + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.9_cpu_smoke_test_conda + python_version: '3.9' + requires: + - nightly_binary_windows_conda_py3.9_cpu_upload + - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda102 + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.9_cu102 + python_version: '3.9' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.9_cu102_upload + requires: + - nightly_binary_windows_conda_py3.9_cu102 + - smoke_test_windows_conda: + cuda_version: cu102 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.9_cu102_smoke_test_conda + python_version: '3.9' + requires: + - nightly_binary_windows_conda_py3.9_cu102_upload - binary_windows_conda: + conda_docker_image: pytorch/conda-builder:cuda111 + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.9 + name: nightly_binary_windows_conda_py3.9_cu111 python_version: '3.9' - binary_conda_upload: context: org-member @@ -1523,20 +2955,21 @@ workflows: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.9_upload + name: nightly_binary_windows_conda_py3.9_cu111_upload requires: - - nightly_binary_windows_conda_py3.9 + - nightly_binary_windows_conda_py3.9_cu111 - smoke_test_windows_conda: + cuda_version: cu111 filters: branches: only: - nightly tags: only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ - name: nightly_binary_windows_conda_py3.9_smoke_test_conda + name: nightly_binary_windows_conda_py3.9_cu111_smoke_test_conda python_version: '3.9' requires: - - nightly_binary_windows_conda_py3.9_upload + - nightly_binary_windows_conda_py3.9_cu111_upload docker_build: triggers: - schedule: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 16b47bde79..aff66edf1d 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -71,11 +71,23 @@ binary_common: &binary_common python_version: description: "Python version to build against (e.g., 3.8)" type: string + cuda_version: + description: "CUDA version to build against (e.g., cpu, cu101)" + type: string + default: "cpu" + wheel_docker_image: + description: "Wheel only: what docker image to use" + type: string + default: "pytorch/manylinux-cuda102" + conda_docker_image: + description: "Conda only: what docker image to use" + type: string + default: "pytorch/conda-builder:cuda102" environment: &environment PYTHON_VERSION: << parameters.python_version >> BUILD_VERSION: << parameters.build_version >> PYTORCH_VERSION: << parameters.pytorch_version >> - CU_VERSION: cpu + CU_VERSION: << parameters.cuda_version >> smoke_test_common: &smoke_test_common <<: *binary_common @@ -127,7 +139,7 @@ jobs: binary_linux_wheel: <<: *binary_common docker: - - image: "pytorch/manylinux-cuda102" + - image: << parameters.wheel_docker_image >> resource_class: 2xlarge+ steps: - checkout @@ -144,7 +156,7 @@ jobs: binary_linux_conda: <<: *binary_common docker: - - image: "pytorch/conda-cuda" + - image: "<< parameters.conda_docker_image >>" resource_class: 2xlarge+ steps: - checkout @@ -487,7 +499,7 @@ jobs: name: windows-gpu environment: <<: *environment - CUDA_VERSION: "10.1" + CUDA_VERSION: "10.2" steps: - checkout - designate_upload_channel diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index 137fed87c4..f1720979fb 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -21,6 +21,10 @@ PYTHON_VERSIONS = ["3.6", "3.7", "3.8", "3.9"] +CU_VERSIONS_DICT = {"linux": ["cpu", "cu102", "cu111"], + "windows": ["cpu", "cu102", "cu111"], + "macos": ["cpu"]} + DOC_VERSION = ('linux', '3.8') @@ -31,7 +35,8 @@ def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): for btype in ["wheel", "conda"]: for os_type in ["linux", "macos", "windows"]: for python_version in PYTHON_VERSIONS: - w += build_workflow_pair(btype, os_type, python_version, filter_branch, prefix, upload) + for cu_version in CU_VERSIONS_DICT[os_type]: + w += build_workflow_pair(btype, os_type, python_version, cu_version, filter_branch, prefix, upload) if not filter_branch: # Build on every pull request, but upload only on nightly and tags @@ -53,27 +58,19 @@ def build_download_job(filter_branch): return [{"download_third_parties_nix": job}] -def build_workflow_pair(btype, os_type, python_version, filter_branch, prefix='', upload=False): +def build_workflow_pair(btype, os_type, python_version, cu_version, filter_branch, prefix='', upload=False): w = [] - base_workflow_name = "{prefix}binary_{os_type}_{btype}_py{python_version}".format( - prefix=prefix, - os_type=os_type, - btype=btype, - python_version=python_version, - ) - - w.append(generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype)) + base_workflow_name = f"{prefix}binary_{os_type}_{btype}_py{python_version}_{cu_version}" + w.append(generate_base_workflow(base_workflow_name, python_version, cu_version, filter_branch, os_type, btype)) if upload: - is_py3_linux = os_type in ['linux', "windows"] and not python_version.startswith("2.") - w.append(generate_upload_workflow(base_workflow_name, filter_branch, btype)) - if filter_branch == 'nightly' and is_py3_linux: + if filter_branch == 'nightly' and os_type != 'macos': pydistro = 'pip' if btype == 'wheel' else 'conda' - w.append(generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version, os_type)) + w.append(generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version, cu_version, os_type)) return w @@ -82,7 +79,7 @@ def build_doc_job(filter_branch): job = { "name": "build_docs", "python_version": "3.8", - "requires": ["binary_linux_wheel_py3.8", ], + "requires": ["binary_linux_wheel_py3.8_cpu", ], } if filter_branch: @@ -107,7 +104,7 @@ def docstring_parameters_sync_job(filter_branch): job = { "name": "docstring_parameters_sync", "python_version": "3.8", - "requires": ["binary_linux_wheel_py3.8", ], + "requires": ["binary_linux_wheel_py3.8_cpu", ], } if filter_branch: @@ -115,15 +112,20 @@ def docstring_parameters_sync_job(filter_branch): return [{"docstring_parameters_sync": job}] -def generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype): +def generate_base_workflow(base_workflow_name, python_version, cu_version, filter_branch, os_type, btype): d = { "name": base_workflow_name, "python_version": python_version, + "cuda_version": cu_version, } if os_type in ['linux', 'macos']: d['requires'] = ['download_third_parties_nix'] + if btype == 'conda': + d['conda_docker_image'] = f'pytorch/conda-builder:{cu_version.replace("cu1","cuda1")}' + elif cu_version != 'cpu': + d['wheel_docker_image'] = f'pytorch/manylinux-{cu_version.replace("cu1","cuda1")}' if filter_branch: d["filters"] = gen_filter_branch_tree(filter_branch) @@ -157,23 +159,23 @@ def generate_upload_workflow(base_workflow_name, filter_branch, btype): return {"binary_{btype}_upload".format(btype=btype): d} -def generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version, os_type): +def generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version, cu_version, os_type): required_build_suffix = "_upload" required_build_name = base_workflow_name + required_build_suffix - smoke_suffix = "smoke_test_{pydistro}".format(pydistro=pydistro) + smoke_suffix = f"smoke_test_{pydistro}".format(pydistro=pydistro) d = { - "name": "{base_workflow_name}_{smoke_suffix}".format( - base_workflow_name=base_workflow_name, smoke_suffix=smoke_suffix), + "name": f"{base_workflow_name}_{smoke_suffix}", "requires": [required_build_name], "python_version": python_version, + "cuda_version": cu_version, } if filter_branch: d["filters"] = gen_filter_branch_tree(filter_branch) - return {"smoke_test_{os_type}_{pydistro}".format(os_type=os_type, pydistro=pydistro): d} + return {f"smoke_test_{os_type}_{pydistro}": d} def indent(indentation, data_list): @@ -192,6 +194,7 @@ def unittest_workflows(indentation=6): job = { "name": f"unittest_{os_type}_{device_type}_py{python_version}", "python_version": python_version, + "cuda_version": 'cpu' if device_type=="cpu" else "cu102", } if os_type != "windows": @@ -204,6 +207,7 @@ def unittest_workflows(indentation=6): "stylecheck": { "name": f"stylecheck_py{python_version}", "python_version": python_version, + "cuda_version": 'cpu' if device_type=="cpu" else "cu102", } }) return indent(indentation, jobs) diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index 5dc5397042..173aa4ca07 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -52,17 +52,60 @@ setup_cuda() { # Now work out the CUDA settings case "$CU_VERSION" in + cu112) + if [[ "$OSTYPE" == "msys" ]]; then + export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2" + else + export CUDA_HOME=/usr/local/cuda-11.2/ + fi + export FORCE_CUDA=1 + export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0;8.6" + ;; + cu111) + if [[ "$OSTYPE" == "msys" ]]; then + export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1" + else + export CUDA_HOME=/usr/local/cuda-11.1/ + fi + export FORCE_CUDA=1 + export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0;8.6" + ;; + cu110) + if [[ "$OSTYPE" == "msys" ]]; then + export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.0" + else + export CUDA_HOME=/usr/local/cuda-11.0/ + fi + export FORCE_CUDA=1 + export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5;8.0" + ;; + cu102) + if [[ "$OSTYPE" == "msys" ]]; then + export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + else + export CUDA_HOME=/usr/local/cuda-10.2/ + fi + export FORCE_CUDA=1 + export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5" + ;; + cu101) + if [[ "$OSTYPE" == "msys" ]]; then + export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.1" + else + export CUDA_HOME=/usr/local/cuda-10.1/ + fi + export FORCE_CUDA=1 + export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5" + ;; cu100) export CUDA_HOME=/usr/local/cuda-10.0/ export FORCE_CUDA=1 - # Hard-coding gencode flags is temporary situation until - # https://github.com/pytorch/pytorch/pull/23408 lands - export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_50,code=compute_50" + export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0;7.5" ;; cu92) export CUDA_HOME=/usr/local/cuda-9.2/ export FORCE_CUDA=1 - export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_50,code=compute_50" + export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0" ;; cpu) ;; diff --git a/test/torchaudio_unittest/backend/sox_io/info_test.py b/test/torchaudio_unittest/backend/sox_io/info_test.py index d459061eee..a7f5bdf02e 100644 --- a/test/torchaudio_unittest/backend/sox_io/info_test.py +++ b/test/torchaudio_unittest/backend/sox_io/info_test.py @@ -476,3 +476,14 @@ def test_requests(self, ext, dtype): assert sinfo.num_frames == num_frames assert sinfo.bits_per_sample == bits_per_sample assert sinfo.encoding == get_encoding(ext, dtype) + + +@skipIfNoSox +class TestInfoNoSuchFile(PytorchTestCase): + def test_info_fail(self): + """ + When attempted to get info on a non-existing file, error message must contain the file path. + """ + path = "non_existing_audio.wav" + with self.assertRaisesRegex(RuntimeError, "^Error loading audio file: failed to open file {0}$".format(path)): + sox_io_backend.info(path) diff --git a/test/torchaudio_unittest/backend/sox_io/load_test.py b/test/torchaudio_unittest/backend/sox_io/load_test.py index f8d06a4da9..824d012cfd 100644 --- a/test/torchaudio_unittest/backend/sox_io/load_test.py +++ b/test/torchaudio_unittest/backend/sox_io/load_test.py @@ -522,3 +522,14 @@ def test_frame(self, frame_offset, num_frames): assert sr == sample_rate self.assertEqual(expected, found) + + +@skipIfNoSox +class TestLoadNoSuchFile(PytorchTestCase): + def test_load_fail(self): + """ + When attempted to load a non-existing file, error message must contain the file path. + """ + path = "non_existing_audio.wav" + with self.assertRaisesRegex(RuntimeError, "^Error loading audio file: failed to open file {0}$".format(path)): + sox_io_backend.load(path) diff --git a/test/torchaudio_unittest/backend/sox_io/save_test.py b/test/torchaudio_unittest/backend/sox_io/save_test.py index 4356972342..9592e78d2d 100644 --- a/test/torchaudio_unittest/backend/sox_io/save_test.py +++ b/test/torchaudio_unittest/backend/sox_io/save_test.py @@ -1,6 +1,8 @@ import io +import os import unittest +import torch from torchaudio.backend import sox_io_backend from parameterized import parameterized @@ -387,3 +389,14 @@ def test_save_tensor_preserve(self, dtype): sox_io_backend.save(path, data, 8000) self.assertEqual(data, expected) + + +@skipIfNoSox +class TestSaveNonExistingDirectory(PytorchTestCase): + def test_save_fail(self): + """ + When attempted to save into a non-existing dir, error message must contain the file path. + """ + path = os.path.join("non_existing_directory", "foo.wav") + with self.assertRaisesRegex(RuntimeError, "^Error saving audio file: failed to open file {0}$".format(path)): + sox_io_backend.save(path, torch.zeros(1, 1), 8000) diff --git a/test/torchaudio_unittest/transforms/batch_consistency_test.py b/test/torchaudio_unittest/transforms/batch_consistency_test.py index cab68e0152..609f1ee631 100644 --- a/test/torchaudio_unittest/transforms/batch_consistency_test.py +++ b/test/torchaudio_unittest/transforms/batch_consistency_test.py @@ -74,8 +74,7 @@ def test_batch_compute_deltas(self): self.assertEqual(computed, expected) def test_batch_mulaw(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 + waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=2) # Single then transform then batch waveform_encoded = torchaudio.transforms.MuLawEncoding()(waveform) @@ -99,8 +98,7 @@ def test_batch_mulaw(self): self.assertEqual(computed, expected) def test_batch_spectrogram(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 + waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=2) # Single then transform then batch expected = torchaudio.transforms.Spectrogram()(waveform).repeat(3, 1, 1, 1) @@ -110,8 +108,7 @@ def test_batch_spectrogram(self): self.assertEqual(computed, expected) def test_batch_melspectrogram(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 + waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=2) # Single then transform then batch expected = torchaudio.transforms.MelSpectrogram()(waveform).repeat(3, 1, 1, 1) @@ -121,8 +118,7 @@ def test_batch_melspectrogram(self): self.assertEqual(computed, expected) def test_batch_mfcc(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) + waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=2) # Single then transform then batch expected = torchaudio.transforms.MFCC()(waveform).repeat(3, 1, 1, 1) @@ -160,8 +156,7 @@ def test_batch_TimeStretch(self, test_pseudo_complex): self.assertEqual(computed, expected, atol=1e-5, rtol=1e-5) def test_batch_Fade(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 + waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=2) fade_in_len = 3000 fade_out_len = 3000 @@ -173,8 +168,7 @@ def test_batch_Fade(self): self.assertEqual(computed, expected) def test_batch_Vol(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 + waveform = common_utils.get_whitenoise(sample_rate=8000, duration=1, n_channels=2) # Single then transform then batch expected = torchaudio.transforms.Vol(gain=1.1)(waveform).repeat(3, 1, 1) diff --git a/torchaudio/csrc/sox/effects.cpp b/torchaudio/csrc/sox/effects.cpp index 8ffc6327ac..7568c46d08 100644 --- a/torchaudio/csrc/sox/effects.cpp +++ b/torchaudio/csrc/sox/effects.cpp @@ -101,7 +101,7 @@ std::tuple apply_effects_file( /*encoding=*/nullptr, /*filetype=*/format.has_value() ? format.value().c_str() : nullptr)); - validate_input_file(sf); + validate_input_file(sf, path); const auto dtype = get_dtype(sf->encoding.encoding, sf->signal.precision); @@ -204,7 +204,7 @@ std::tuple apply_effects_fileobj( /*filetype=*/format.has_value() ? format.value().c_str() : nullptr)); // In case of streamed data, length can be 0 - validate_input_file(sf); + validate_input_memfile(sf); // Prepare output buffer std::vector out_buffer; diff --git a/torchaudio/csrc/sox/io.cpp b/torchaudio/csrc/sox/io.cpp index 8e2ebce4e2..a075a23f74 100644 --- a/torchaudio/csrc/sox/io.cpp +++ b/torchaudio/csrc/sox/io.cpp @@ -19,9 +19,7 @@ std::tuple get_info_file( /*encoding=*/nullptr, /*filetype=*/format.has_value() ? format.value().c_str() : nullptr)); - if (static_cast(sf) == nullptr) { - throw std::runtime_error("Error opening audio file"); - } + validate_input_file(sf, path); return std::make_tuple( static_cast(sf->signal.rate), @@ -123,7 +121,8 @@ void save_audio_file( /*overwrite_permitted=*/nullptr)); if (static_cast(sf) == nullptr) { - throw std::runtime_error("Error saving audio file: failed to open file."); + throw std::runtime_error( + "Error saving audio file: failed to open file " + path); } torchaudio::sox_effects_chain::SoxEffectsChain chain( @@ -177,7 +176,7 @@ std::tuple get_info_fileobj( /*filetype=*/format.has_value() ? format.value().c_str() : nullptr)); // In case of streamed data, length can be 0 - validate_input_file(sf); + validate_input_memfile(sf); return std::make_tuple( static_cast(sf->signal.rate), diff --git a/torchaudio/csrc/sox/utils.cpp b/torchaudio/csrc/sox/utils.cpp index b46067626c..efb489a529 100644 --- a/torchaudio/csrc/sox/utils.cpp +++ b/torchaudio/csrc/sox/utils.cpp @@ -81,15 +81,20 @@ void SoxFormat::close() { } } -void validate_input_file(const SoxFormat& sf) { +void validate_input_file(const SoxFormat& sf, const std::string& path) { if (static_cast(sf) == nullptr) { - throw std::runtime_error("Error loading audio file: failed to open file."); + throw std::runtime_error( + "Error loading audio file: failed to open file " + path); } if (sf->encoding.encoding == SOX_ENCODING_UNKNOWN) { throw std::runtime_error("Error loading audio file: unknown encoding."); } } +void validate_input_memfile(const SoxFormat& sf) { + return validate_input_file(sf, ""); +} + void validate_input_tensor(const torch::Tensor tensor) { if (!tensor.device().is_cpu()) { throw std::runtime_error("Input tensor has to be on CPU."); diff --git a/torchaudio/csrc/sox/utils.h b/torchaudio/csrc/sox/utils.h index 7e736dbe8d..67624b8597 100644 --- a/torchaudio/csrc/sox/utils.h +++ b/torchaudio/csrc/sox/utils.h @@ -56,7 +56,10 @@ struct SoxFormat { /// /// Verify that input file is found, has known encoding, and not empty -void validate_input_file(const SoxFormat& sf); +void validate_input_file(const SoxFormat& sf, const std::string& path); + +/// Verify that input memory buffer has known encoding, and not empty +void validate_input_memfile(const SoxFormat& sf); /// /// Verify that input Tensor is 2D, CPU and either uin8, int16, int32 or float32 From e2049fe79ae433196e76c04b76c847b72f90ac99 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Wed, 9 Jun 2021 11:34:37 -0700 Subject: [PATCH 51/73] Import torchaudio #1554 afb6626 Summary: Import torchaudio #1554 afb6626 Reviewed By: NicolasHug Differential Revision: D28891382 fbshipit-source-id: 9b6e06ff94b2ec2f6d948049cc74046dee721471 --- .circleci/config.yml | 172 ++ .circleci/regenerate.py | 11 +- .circleci/unittest/linux/scripts/install.sh | 7 +- .circleci/unittest/windows/scripts/install.sh | 7 +- .gitmodules | 3 + README.md | 47 +- docs/requirements.txt | 1 + docs/source/_static/css/pytorch_theme.css | 118 -- docs/source/compliance.kaldi.rst | 5 - docs/source/conf.py | 22 +- docs/source/functional.rst | 11 +- docs/source/models.rst | 72 +- docs/source/refs.bib | 90 + docs/source/rnnt_loss.rst | 13 +- docs/source/transforms.rst | 34 +- examples/libtorchaudio/.gitignore | 3 +- examples/libtorchaudio/CMakeLists.txt | 2 + examples/libtorchaudio/README.md | 2 + examples/libtorchaudio/build.sh | 1 + .../speech_recognition/CMakeLists.txt | 6 + .../speech_recognition/README.md | 187 ++ .../build_pipeline_from_fairseq.py | 202 ++ ..._pipeline_from_huggingface_transformers.py | 142 ++ .../speech_recognition/parse_librispeech.py | 55 + .../speech_recognition/parse_voxforge.py | 67 + .../speech_recognition/transcribe.cpp | 38 + .../speech_recognition/transcribe_list.cpp | 66 + packaging/build_conda.sh | 2 +- packaging/build_wheel.sh | 2 +- packaging/pkg_helpers.bash | 4 + setup.py | 2 +- 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.../torchscript_consistency_impl.py | 18 +- .../{compliance => models}/__init__.py | 0 .../{ => models}/models_test.py | 0 .../models/wav2vec2/__init__.py | 0 .../wav2vec2/fairseq_integration_test.py | 167 ++ .../wav2vec2/huggingface_intergration_test.py | 192 ++ .../models/wav2vec2/model_test.py | 182 ++ .../rnnt/autograd_cpu_test.py | 10 + .../rnnt/autograd_cuda_test.py | 11 + .../torchaudio_unittest/rnnt/autograd_impl.py | 99 + .../rnnt/numpy_transducer.py | 5 +- .../transforms/transforms_test_impl.py | 13 + third_party/sox/CMakeLists.txt | 3 +- third_party/sox/patch/config.guess | 1700 +++++++++++++++++ torchaudio/backend/soundfile_backend.py | 27 +- torchaudio/compliance/kaldi.py | 29 - torchaudio/datasets/commonvoice.py | 31 +- torchaudio/functional/filtering.py | 80 +- torchaudio/functional/functional.py | 71 +- torchaudio/models/__init__.py | 11 + torchaudio/models/conv_tasnet.py | 21 +- torchaudio/models/deepspeech.py | 4 +- torchaudio/models/wav2letter.py | 4 +- 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test/torchaudio_unittest/compliance/utils.py rename test/torchaudio_unittest/{compliance => models}/__init__.py (100%) rename test/torchaudio_unittest/{ => models}/models_test.py (100%) create mode 100644 test/torchaudio_unittest/models/wav2vec2/__init__.py create mode 100644 test/torchaudio_unittest/models/wav2vec2/fairseq_integration_test.py create mode 100644 test/torchaudio_unittest/models/wav2vec2/huggingface_intergration_test.py create mode 100644 test/torchaudio_unittest/models/wav2vec2/model_test.py create mode 100644 test/torchaudio_unittest/rnnt/autograd_cpu_test.py create mode 100644 test/torchaudio_unittest/rnnt/autograd_cuda_test.py create mode 100644 test/torchaudio_unittest/rnnt/autograd_impl.py create mode 100644 third_party/sox/patch/config.guess create mode 100644 torchaudio/models/wav2vec2/__init__.py create mode 100644 torchaudio/models/wav2vec2/components.py create mode 100644 torchaudio/models/wav2vec2/model.py create mode 100644 torchaudio/models/wav2vec2/utils/__init__.py create mode 100644 torchaudio/models/wav2vec2/utils/import_fairseq.py create mode 100644 torchaudio/models/wav2vec2/utils/import_huggingface.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 32c7effa9c..cb796d03eb 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -650,6 +650,13 @@ workflows: requires: - download_third_parties_nix wheel_docker_image: pytorch/manylinux-cuda111 + - binary_linux_wheel: + cuda_version: rocm4.1 + name: binary_linux_wheel_py3.6_rocm4.1 + python_version: '3.6' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-rocm:4.1 - binary_linux_wheel: cuda_version: cpu name: binary_linux_wheel_py3.7_cpu @@ -670,6 +677,13 @@ workflows: requires: - download_third_parties_nix wheel_docker_image: pytorch/manylinux-cuda111 + - binary_linux_wheel: + cuda_version: rocm4.1 + name: binary_linux_wheel_py3.7_rocm4.1 + python_version: '3.7' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-rocm:4.1 - binary_linux_wheel: cuda_version: cpu name: binary_linux_wheel_py3.8_cpu @@ -690,6 +704,13 @@ workflows: requires: - download_third_parties_nix wheel_docker_image: pytorch/manylinux-cuda111 + - binary_linux_wheel: + cuda_version: rocm4.1 + name: binary_linux_wheel_py3.8_rocm4.1 + python_version: '3.8' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-rocm:4.1 - binary_linux_wheel: cuda_version: cpu name: binary_linux_wheel_py3.9_cpu @@ -710,6 +731,13 @@ workflows: requires: - download_third_parties_nix wheel_docker_image: pytorch/manylinux-cuda111 + - binary_linux_wheel: + cuda_version: rocm4.1 + name: binary_linux_wheel_py3.9_rocm4.1 + python_version: '3.9' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-rocm:4.1 - binary_macos_wheel: cuda_version: cpu name: binary_macos_wheel_py3.6_cpu @@ -1217,6 +1245,42 @@ workflows: python_version: '3.6' requires: - nightly_binary_linux_wheel_py3.6_cu111_upload + - binary_linux_wheel: + cuda_version: rocm4.1 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.6_rocm4.1 + python_version: '3.6' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-rocm:4.1 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.6_rocm4.1_upload + requires: + - nightly_binary_linux_wheel_py3.6_rocm4.1 + - smoke_test_linux_pip: + cuda_version: rocm4.1 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.6_rocm4.1_smoke_test_pip + python_version: '3.6' + requires: + - nightly_binary_linux_wheel_py3.6_rocm4.1_upload - binary_linux_wheel: cuda_version: cpu filters: @@ -1324,6 +1388,42 @@ workflows: python_version: '3.7' requires: - nightly_binary_linux_wheel_py3.7_cu111_upload + - binary_linux_wheel: + cuda_version: rocm4.1 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.7_rocm4.1 + python_version: '3.7' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-rocm:4.1 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.7_rocm4.1_upload + requires: + - nightly_binary_linux_wheel_py3.7_rocm4.1 + - smoke_test_linux_pip: + cuda_version: rocm4.1 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.7_rocm4.1_smoke_test_pip + python_version: '3.7' + requires: + - nightly_binary_linux_wheel_py3.7_rocm4.1_upload - binary_linux_wheel: cuda_version: cpu filters: @@ -1431,6 +1531,42 @@ workflows: python_version: '3.8' requires: - nightly_binary_linux_wheel_py3.8_cu111_upload + - binary_linux_wheel: + cuda_version: rocm4.1 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.8_rocm4.1 + python_version: '3.8' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-rocm:4.1 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.8_rocm4.1_upload + requires: + - nightly_binary_linux_wheel_py3.8_rocm4.1 + - smoke_test_linux_pip: + cuda_version: rocm4.1 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.8_rocm4.1_smoke_test_pip + python_version: '3.8' + requires: + - nightly_binary_linux_wheel_py3.8_rocm4.1_upload - binary_linux_wheel: cuda_version: cpu filters: @@ -1538,6 +1674,42 @@ workflows: python_version: '3.9' requires: - nightly_binary_linux_wheel_py3.9_cu111_upload + - binary_linux_wheel: + cuda_version: rocm4.1 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.9_rocm4.1 + python_version: '3.9' + requires: + - download_third_parties_nix + wheel_docker_image: pytorch/manylinux-rocm:4.1 + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.9_rocm4.1_upload + requires: + - nightly_binary_linux_wheel_py3.9_rocm4.1 + - smoke_test_linux_pip: + cuda_version: rocm4.1 + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.9_rocm4.1_smoke_test_pip + python_version: '3.9' + requires: + - nightly_binary_linux_wheel_py3.9_rocm4.1_upload - binary_macos_wheel: cuda_version: cpu filters: diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py index f1720979fb..0f64498754 100755 --- a/.circleci/regenerate.py +++ b/.circleci/regenerate.py @@ -21,7 +21,7 @@ PYTHON_VERSIONS = ["3.6", "3.7", "3.8", "3.9"] -CU_VERSIONS_DICT = {"linux": ["cpu", "cu102", "cu111"], +CU_VERSIONS_DICT = {"linux": ["cpu", "cu102", "cu111","rocm4.1"], "windows": ["cpu", "cu102", "cu111"], "macos": ["cpu"]} @@ -36,7 +36,10 @@ def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): for os_type in ["linux", "macos", "windows"]: for python_version in PYTHON_VERSIONS: for cu_version in CU_VERSIONS_DICT[os_type]: - w += build_workflow_pair(btype, os_type, python_version, cu_version, filter_branch, prefix, upload) + if cu_version.startswith("rocm") and btype=="conda": + pass + else: + w += build_workflow_pair(btype, os_type, python_version, cu_version, filter_branch, prefix, upload) if not filter_branch: # Build on every pull request, but upload only on nightly and tags @@ -124,8 +127,10 @@ def generate_base_workflow(base_workflow_name, python_version, cu_version, filte d['requires'] = ['download_third_parties_nix'] if btype == 'conda': d['conda_docker_image'] = f'pytorch/conda-builder:{cu_version.replace("cu1","cuda1")}' - elif cu_version != 'cpu': + elif cu_version.startswith('cu'): d['wheel_docker_image'] = f'pytorch/manylinux-{cu_version.replace("cu1","cuda1")}' + elif cu_version.startswith('rocm'): + d["wheel_docker_image"] = f"pytorch/manylinux-rocm:{cu_version[len('rocm'):]}" if filter_branch: d["filters"] = gen_filter_branch_tree(filter_branch) diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh index 3b0cd4bc4c..50a02f11e3 100755 --- a/.circleci/unittest/linux/scripts/install.sh +++ b/.circleci/unittest/linux/scripts/install.sh @@ -56,5 +56,10 @@ fi ( set -x conda install -y -c conda-forge ${NUMBA_DEV_CHANNEL} 'librosa>=0.8.0' parameterized 'requests>=2.20' - pip install kaldi-io SoundFile coverage pytest pytest-cov scipy + pip install kaldi-io SoundFile coverage pytest pytest-cov scipy transformers ) +# Install fairseq +git clone https://github.com/pytorch/fairseq +cd fairseq +git checkout e6eddd80 +pip install . diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh index 9ed4f203ad..770bbf31dd 100644 --- a/.circleci/unittest/windows/scripts/install.sh +++ b/.circleci/unittest/windows/scripts/install.sh @@ -44,5 +44,10 @@ fi ( set -x conda install -y -c conda-forge ${NUMBA_DEV_CHANNEL} 'librosa>=0.8.0' parameterized 'requests>=2.20' - pip install kaldi-io SoundFile coverage pytest pytest-cov scipy + pip install kaldi-io SoundFile coverage pytest pytest-cov scipy transformers ) +# Install fairseq +git clone https://github.com/pytorch/fairseq +cd fairseq +git checkout e6eddd80 +pip install . diff --git a/.gitmodules b/.gitmodules index 724846120c..90318aa60c 100644 --- a/.gitmodules +++ b/.gitmodules @@ -2,3 +2,6 @@ path = third_party/kaldi/submodule url = https://github.com/kaldi-asr/kaldi ignore = dirty +[submodule "examples/libtorchaudio/simplectc"] + path = examples/libtorchaudio/simplectc + url = https://github.com/mthrok/ctcdecode diff --git a/README.md b/README.md index 820301c041..2080b5b6fb 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,6 @@ torchaudio: an audio library for PyTorch ======================================== [![Build Status](https://circleci.com/gh/pytorch/audio.svg?style=svg)](https://app.circleci.com/pipelines/github/pytorch/audio) -[![Coverage](https://codecov.io/gh/pytorch/audio/branch/master/graph/badge.svg)](https://codecov.io/gh/pytorch/audio) [![Documentation](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchaudio%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/audio/) The aim of torchaudio is to apply [PyTorch](https://github.com/pytorch/pytorch) to @@ -15,16 +14,13 @@ having all the computations be through PyTorch operations which makes it easy to use and feel like a natural extension. - [Support audio I/O (Load files, Save files)](http://pytorch.org/audio/stable/) - - Load the following formats into a torch Tensor using SoX - - mp3, wav, aac, ogg, flac, avr, cdda, cvs/vms, - - aiff, au, amr, mp2, mp4, ac3, avi, wmv, - - mpeg, ircam and any other format supported by libsox. - - [Kaldi (ark/scp)](http://pytorch.org/audio/stable/kaldi_io.html) -- [Dataloaders for common audio datasets (VCTK, YesNo)](http://pytorch.org/audio/stable/datasets.html) + - Load a variety of audio formats, such as `wav`, `mp3`, `ogg`, `flac`, `opus`, `sphere`, into a torch Tensor using SoX + - [Kaldi (ark/scp)](http://pytorch.org/audio/stable/kaldi_io.html) +- [Dataloaders for common audio datasets](http://pytorch.org/audio/stable/datasets.html) - Common audio transforms - [Spectrogram, AmplitudeToDB, MelScale, MelSpectrogram, MFCC, MuLawEncoding, MuLawDecoding, Resample](http://pytorch.org/audio/stable/transforms.html) - Compliance interfaces: Run code using PyTorch that align with other libraries - - [Kaldi: spectrogram, fbank, mfcc, resample_waveform](https://pytorch.org/audio/stable/compliance.kaldi.html) + - [Kaldi: spectrogram, fbank, mfcc](https://pytorch.org/audio/stable/compliance.kaldi.html) Dependencies ------------ @@ -36,6 +32,7 @@ The following are the corresponding ``torchaudio`` versions and supported Python | ``torch`` | ``torchaudio`` | ``python`` | | ------------------------ | ------------------------ | ------------------------------- | | ``master`` / ``nightly`` | ``master`` / ``nightly`` | ``>=3.6``, ``<=3.9`` | +| ``1.9.0`` | ``0.9.0`` | ``>=3.6``, ``<=3.9`` | | ``1.8.0`` | ``0.8.0`` | ``>=3.6``, ``<=3.9`` | | ``1.7.1`` | ``0.7.2`` | ``>=3.6``, ``<=3.9`` | | ``1.7.0`` | ``0.7.0`` | ``>=3.6``, ``<=3.8`` | @@ -107,34 +104,6 @@ call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary This is known to work on linux and unix distributions such as Ubuntu and CentOS 7 and macOS. If you try this on a new system and find a solution to make it work, feel free to share it by opening an issue. -#### Troubleshooting - -
checking build system type... ./config.guess: unable to guess system type - -Since the configuration file for codecs are old, they cannot correctly detect the new environments, such as Jetson Aarch. You need to replace the `config.guess` file in `./third_party/tmp/lame-3.99.5/config.guess` and/or `./third_party/tmp/libmad-0.15.1b/config.guess` with [the latest one](https://github.com/gcc-mirror/gcc/blob/master/config.guess). - -See also: [#658](https://github.com/pytorch/audio/issues/658) - -
- -
Undefined reference to `tgetnum' when using `BUILD_SOX` - -If while building from within an anaconda environment you come across errors similar to the following: - -``` -../bin/ld: console.c:(.text+0xc1): undefined reference to `tgetnum' -``` - -Install `ncurses` from `conda-forge` before running `python setup.py install`: - -``` -# Install ncurses from conda-forge -conda install -c conda-forge ncurses -``` - -
- - Quick Usage ----------- @@ -161,7 +130,9 @@ waveform, sample_rate = torchaudio.load('foo.wav') # load tensor from file, as torchaudio.save('foo_save.wav', waveform, sample_rate) # save tensor to file, as usual ``` -Unlike SoX, SoundFile does not currently support mp3. +**Note** +- SoundFile currently does not support mp3. +- "soundfile" backend is not supported by TorchScript. API Reference ------------- @@ -205,7 +176,7 @@ Transforms expect and return the following dimensions. * `Fade`: (channel, time) -> (channel, time) * `Vol`: (channel, time) -> (channel, time) -Complex numbers are supported via tensors of dimension (..., 2), and torchaudio provides `complex_norm` and `angle` to convert such a tensor into its magnitude and phase. Here, and in the documentation, we use an ellipsis "..." as a placeholder for the rest of the dimensions of a tensor, e.g. optional batching and channel dimensions. +Here, and in the documentation, we use an ellipsis "..." as a placeholder for the rest of the dimensions of a tensor, e.g. optional batching and channel dimensions. Contributing Guidelines ----------------------- diff --git a/docs/requirements.txt b/docs/requirements.txt index 99a7811de6..fa5ea2b7e6 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,4 +1,5 @@ sphinx==2.4.4 -e git+git://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme sphinxcontrib.katex +sphinxcontrib.bibtex matplotlib diff --git a/docs/source/_static/css/pytorch_theme.css b/docs/source/_static/css/pytorch_theme.css deleted file mode 100644 index 0e54497643..0000000000 --- a/docs/source/_static/css/pytorch_theme.css +++ /dev/null @@ -1,118 +0,0 @@ -body { - font-family: "Lato","proxima-nova","Helvetica Neue",Arial,sans-serif; -} - -/* Default header fonts are ugly */ -h1, h2, .rst-content .toctree-wrapper p.caption, h3, h4, h5, h6, legend, p.caption { - font-family: "Lato","proxima-nova","Helvetica Neue",Arial,sans-serif; -} - -/* Use white for docs background */ -.wy-side-nav-search { - background-color: #fff; -} - -.wy-nav-content-wrap, .wy-menu li.current > a { - background-color: #fff; -} - -@media screen and (min-width: 1400px) { - .wy-nav-content-wrap { - background-color: rgba(0, 0, 0, 0.0470588); - } - - .wy-nav-content { - background-color: #fff; - } -} - -/* Fixes for mobile */ -.wy-nav-top { - background-color: #fff; - background-image: url('../img/pytorch-logo-dark.svg'); - background-repeat: no-repeat; - background-position: center; - padding: 0; - margin: 0.4045em 0.809em; - color: #333; -} - -.wy-nav-top > a { - display: none; -} - -@media screen and (max-width: 768px) { - .wy-side-nav-search>a img.logo { - height: 60px; - } -} - -/* This is needed to ensure that logo above search scales properly */ -.wy-side-nav-search a { - display: block; -} - -/* This ensures that multiple constructors will remain in separate lines. */ -.rst-content dl:not(.docutils) dt { - display: table; -} - -/* Use our red for literals (it's very similar to the original color) */ -.rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal { - color: #F05732; -} - -.rst-content tt.xref, a .rst-content tt, .rst-content tt.xref, -.rst-content code.xref, a .rst-content tt, a .rst-content code { - color: #404040; -} - -/* Change link colors (except for the menu) */ - -a { - color: #F05732; -} - -a:hover { - color: #F05732; -} - - -a:visited { - color: #D44D2C; -} - -.wy-menu a { - color: #b3b3b3; -} - -.wy-menu a:hover { - color: #b3b3b3; -} - -/* Default footer text is quite big */ -footer { - font-size: 80%; -} - -footer .rst-footer-buttons { - font-size: 125%; /* revert footer settings - 1/80% = 125% */ -} - -footer p { - font-size: 100%; -} - -/* For hidden headers that appear in TOC tree */ -/* see http://stackoverflow.com/a/32363545/3343043 */ -.rst-content .hidden-section { - display: none; -} - -nav .hidden-section { - display: inherit; -} - -.wy-side-nav-search>div.version { - color: #000; -} diff --git a/docs/source/compliance.kaldi.rst b/docs/source/compliance.kaldi.rst index cc75021d69..72827ca3fb 100644 --- a/docs/source/compliance.kaldi.rst +++ b/docs/source/compliance.kaldi.rst @@ -29,8 +29,3 @@ Functions ~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: mfcc - -:hidden:`resample_waveform` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autofunction:: resample_waveform diff --git a/docs/source/conf.py b/docs/source/conf.py index f78858e381..c3364d7fd7 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -41,6 +41,7 @@ 'sphinx.ext.napoleon', 'sphinx.ext.viewcode', 'sphinxcontrib.katex', + 'sphinxcontrib.bibtex', ] # katex options @@ -55,6 +56,8 @@ ] ''' +bibtex_bibfiles = ['refs.bib'] + napoleon_use_ivar = True napoleon_numpy_docstring = False napoleon_google_docstring = True @@ -133,22 +136,9 @@ # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] - - -def setup(app): - # NOTE: in Sphinx 1.8+ `html_css_files` is an official configuration value - # and can be moved outside of this function (and the setup(app) function - # can be deleted). - html_css_files = [ - 'https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css' - ] - - # In Sphinx 1.8 it was renamed to `add_css_file`, 1.7 and prior it is - # `add_stylesheet` (deprecated in 1.8). - add_css = getattr(app, 'add_css_file', app.add_stylesheet) - for css_file in html_css_files: - add_css(css_file) - +html_css_files = [ + 'https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css' +] # -- Options for HTMLHelp output ------------------------------------------ diff --git a/docs/source/functional.rst b/docs/source/functional.rst index 98517929ea..37593da3fc 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -56,6 +56,11 @@ apply_codec .. autofunction:: apply_codec +resample +-------- + +.. autofunction:: resample + :hidden:`Complex Utility` ~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -231,7 +236,7 @@ vad .. autofunction:: spectral_centroid -:hidden:`resample` ---------------------------- +References +~~~~~~~~~~ -.. autofunction:: resample +.. footbibliography:: diff --git a/docs/source/models.rst b/docs/source/models.rst index 2030eefd28..39e162baa0 100644 --- a/docs/source/models.rst +++ b/docs/source/models.rst @@ -2,40 +2,94 @@ :class: hidden-section torchaudio.models -====================== +================= .. currentmodule:: torchaudio.models The models subpackage contains definitions of models for addressing common audio tasks. -:hidden:`ConvTasNet` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +ConvTasNet +~~~~~~~~~~ .. autoclass:: ConvTasNet .. automethod:: forward -:hidden:`DeepSpeech` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +DeepSpeech +~~~~~~~~~~ .. autoclass:: DeepSpeech .. automethod:: forward -:hidden:`Wav2Letter` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Wav2Letter +~~~~~~~~~~ .. autoclass:: Wav2Letter .. automethod:: forward -:hidden:`WaveRNN` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Wav2Vec2.0 +~~~~~~~~~~ + +Wav2Vec2Model +------------- + +.. autoclass:: Wav2Vec2Model + + .. automethod:: extract_features + + .. automethod:: forward + +Factory Functions +----------------- + +wav2vec2_base +------------- + +.. autofunction:: wav2vec2_base + +wav2vec2_large +-------------- + +.. autofunction:: wav2vec2_large + +wav2vec2_large_lv60k +-------------------- + +.. autofunction:: wav2vec2_large_lv60k + +.. currentmodule:: torchaudio.models.wav2vec2.utils + +Utility Functions +----------------- + +import_huggingface_model +------------------------ + +.. autofunction:: import_huggingface_model + +import_fairseq_model +-------------------- + +.. autofunction:: import_fairseq_model + +.. currentmodule:: torchaudio.models + +WaveRNN +~~~~~~~ .. autoclass:: WaveRNN .. automethod:: forward + +References +~~~~~~~~~~ + +.. footbibliography:: + diff --git a/docs/source/refs.bib b/docs/source/refs.bib new file mode 100644 index 0000000000..c6ddb3b61a --- /dev/null +++ b/docs/source/refs.bib @@ -0,0 +1,90 @@ +@misc{baevski2020wav2vec, + title={wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations}, + author={Alexei Baevski and Henry Zhou and Abdelrahman Mohamed and Michael Auli}, + year={2020}, + eprint={2006.11477}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +@misc{hannun2014deep, + title={Deep Speech: Scaling up end-to-end speech recognition}, + author={Awni Hannun and Carl Case and Jared Casper and Bryan Catanzaro and Greg Diamos and Erich Elsen and Ryan Prenger and Sanjeev Satheesh and Shubho Sengupta and Adam Coates and Andrew Y. Ng}, + year={2014}, + eprint={1412.5567}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +@misc{graves2012sequence, + title={Sequence Transduction with Recurrent Neural Networks}, + author={Alex Graves}, + year={2012}, + eprint={1211.3711}, + archivePrefix={arXiv}, + primaryClass={cs.NE} +} +@misc{collobert2016wav2letter, + title={Wav2Letter: an End-to-End ConvNet-based Speech Recognition System}, + author={Ronan Collobert and Christian Puhrsch and Gabriel Synnaeve}, + year={2016}, + eprint={1609.03193}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +@misc{kalchbrenner2018efficient, + title={Efficient Neural Audio Synthesis}, + author={Nal Kalchbrenner and Erich Elsen and Karen Simonyan and Seb Noury and Norman Casagrande and Edward Lockhart and Florian Stimberg and Aaron van den Oord and Sander Dieleman and Koray Kavukcuoglu}, + year={2018}, + eprint={1802.08435}, + archivePrefix={arXiv}, + primaryClass={cs.SD} +} +@article{Luo_2019, + title={Conv-TasNet: Surpassing Ideal Timeโ€“Frequency Magnitude Masking for Speech Separation}, + volume={27}, + ISSN={2329-9304}, + url={http://dx.doi.org/10.1109/TASLP.2019.2915167}, + DOI={10.1109/taslp.2019.2915167}, + number={8}, + journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, + publisher={Institute of Electrical and Electronics Engineers (IEEE)}, + author={Luo, Yi and Mesgarani, Nima}, + year={2019}, + month={Aug}, + pages={1256โ€“1266} +} +@InProceedings{ brian_mcfee-proc-scipy-2015, + author = { {B}rian {M}c{F}ee and {C}olin {R}affel and {D}awen {L}iang and {D}aniel {P}.{W}. {E}llis and {M}att {M}c{V}icar and {E}ric {B}attenberg and {O}riol {N}ieto }, + title = { librosa: {A}udio and {M}usic {S}ignal {A}nalysis in {P}ython }, + booktitle = { {P}roceedings of the 14th {P}ython in {S}cience {C}onference }, + pages = { 18 - 24 }, + year = { 2015 }, + editor = { {K}athryn {H}uff and {J}ames {B}ergstra }, + doi = { 10.25080/Majora-7b98e3ed-003 } +} +@INPROCEEDINGS{6701851, + author={Perraudin, Nathanaรซl and Balazs, Peter and Sรธndergaard, Peter L.}, + booktitle={2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics}, + title={A fast Griffin-Lim algorithm}, + year={2013}, + volume={}, + number={}, + pages={1-4}, + doi={10.1109/WASPAA.2013.6701851}} +@INPROCEEDINGS{1172092, + author={Griffin, D. and Jae Lim}, + booktitle={ICASSP '83. IEEE International Conference on Acoustics, Speech, and Signal Processing}, + title={Signal estimation from modified short-time Fourier transform}, + year={1983}, + volume={8}, + number={}, + pages={804-807}, + doi={10.1109/ICASSP.1983.1172092}} +@INPROCEEDINGS{6854049, + author={Ghahremani, Pegah and BabaAli, Bagher and Povey, Daniel and Riedhammer, Korbinian and Trmal, Jan and Khudanpur, Sanjeev}, + booktitle={2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, + title={A pitch extraction algorithm tuned for automatic speech recognition}, + year={2014}, + volume={}, + number={}, + pages={2494-2498}, + doi={10.1109/ICASSP.2014.6854049}} diff --git a/docs/source/rnnt_loss.rst b/docs/source/rnnt_loss.rst index 0c3b075d65..6e8ac81909 100644 --- a/docs/source/rnnt_loss.rst +++ b/docs/source/rnnt_loss.rst @@ -2,7 +2,7 @@ :class: hidden-section torchaudio.prototype.rnnt_loss -=============================== +============================== .. currentmodule:: torchaudio.prototype.rnnt_loss @@ -11,13 +11,18 @@ torchaudio.prototype.rnnt_loss The RNN transducer loss is a prototype feature, see `here `_ to learn more about the nomenclature. It is only available within the nightlies, and also needs to be imported explicitly using: :code:`from torchaudio.prototype.rnnt_loss import rnnt_loss, RNNTLoss`. rnnt_loss ---------- +~~~~~~~~~ .. autofunction:: rnnt_loss -:hidden:`RNNTLoss` -~~~~~~~~~~~~~~~~~~ +RNNTLoss +~~~~~~~~ .. autoclass:: RNNTLoss .. automethod:: forward + +References +~~~~~~~~~~ + +.. footbibliography:: diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst index 8269193e22..787673f8df 100644 --- a/docs/source/transforms.rst +++ b/docs/source/transforms.rst @@ -53,99 +53,105 @@ Transforms are common audio transforms. They can be chained together using :clas .. automethod:: forward :hidden:`MFCC` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~ .. autoclass:: MFCC .. automethod:: forward :hidden:`MuLawEncoding` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: MuLawEncoding .. automethod:: forward :hidden:`MuLawDecoding` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: MuLawDecoding .. automethod:: forward :hidden:`Resample` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~ .. autoclass:: Resample .. automethod:: forward :hidden:`ComplexNorm` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: ComplexNorm .. automethod:: forward :hidden:`ComputeDeltas` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: ComputeDeltas .. automethod:: forward :hidden:`TimeStretch` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: TimeStretch .. automethod:: forward :hidden:`Fade` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~ .. autoclass:: Fade .. automethod:: forward :hidden:`FrequencyMasking` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: FrequencyMasking .. automethod:: forward :hidden:`TimeMasking` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: TimeMasking .. automethod:: forward :hidden:`Vol` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~ .. autoclass:: Vol .. automethod:: forward :hidden:`SlidingWindowCmn` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: SlidingWindowCmn .. automethod:: forward :hidden:`SpectralCentroid` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: SpectralCentroid .. automethod:: forward :hidden:`Vad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~ .. autoclass:: Vad .. automethod:: forward + + +References +~~~~~~~~~~ + +.. footbibliography:: diff --git a/examples/libtorchaudio/.gitignore b/examples/libtorchaudio/.gitignore index f03338dc7d..d2c4084e75 100644 --- a/examples/libtorchaudio/.gitignore +++ b/examples/libtorchaudio/.gitignore @@ -1,3 +1,4 @@ build data/output.wav -data/pipeline.zip +*.zip +output diff --git a/examples/libtorchaudio/CMakeLists.txt b/examples/libtorchaudio/CMakeLists.txt index 6a359bd47e..6d7607cc0e 100644 --- a/examples/libtorchaudio/CMakeLists.txt +++ b/examples/libtorchaudio/CMakeLists.txt @@ -14,4 +14,6 @@ message("libtorchaudio CMakeLists: ${TORCH_CXX_FLAGS}") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}") add_subdirectory(../.. libtorchaudio) +add_subdirectory(simplectc) add_subdirectory(augmentation) +add_subdirectory(speech_recognition) diff --git a/examples/libtorchaudio/README.md b/examples/libtorchaudio/README.md index be6611ea85..477a3d7e54 100644 --- a/examples/libtorchaudio/README.md +++ b/examples/libtorchaudio/README.md @@ -1,6 +1,7 @@ # Libtorchaudio Examples * [Augmentation](./augmentation) +* [Speech Recognition with wav2vec2.0](./speech_recognition) ## Build @@ -14,6 +15,7 @@ It is currently not distributed, and it will be built alongside with the applica The following commands will build `libtorchaudio` and applications. ```bash +git submodule update mkdir build cd build cmake -GNinja \ diff --git a/examples/libtorchaudio/build.sh b/examples/libtorchaudio/build.sh index 11ca65f751..ac51caf34c 100755 --- a/examples/libtorchaudio/build.sh +++ b/examples/libtorchaudio/build.sh @@ -8,6 +8,7 @@ build_dir="${this_dir}/build" mkdir -p "${build_dir}" cd "${build_dir}" +git submodule update cmake -GNinja \ -DCMAKE_PREFIX_PATH="$(python -c 'import torch;print(torch.utils.cmake_prefix_path)')" \ -DBUILD_SOX=ON \ diff --git a/examples/libtorchaudio/speech_recognition/CMakeLists.txt b/examples/libtorchaudio/speech_recognition/CMakeLists.txt new file mode 100644 index 0000000000..3771a33442 --- /dev/null +++ b/examples/libtorchaudio/speech_recognition/CMakeLists.txt @@ -0,0 +1,6 @@ +add_executable(transcribe transcribe.cpp) +add_executable(transcribe_list transcribe_list.cpp) +target_link_libraries(transcribe "${TORCH_LIBRARIES}" "${TORCHAUDIO_LIBRARY}" "${CTCDECODE_LIBRARY}") +target_link_libraries(transcribe_list "${TORCH_LIBRARIES}" "${TORCHAUDIO_LIBRARY}" "${CTCDECODE_LIBRARY}") +set_property(TARGET transcribe PROPERTY CXX_STANDARD 14) +set_property(TARGET transcribe_list PROPERTY CXX_STANDARD 14) diff --git a/examples/libtorchaudio/speech_recognition/README.md b/examples/libtorchaudio/speech_recognition/README.md new file mode 100644 index 0000000000..6a617a8ebf --- /dev/null +++ b/examples/libtorchaudio/speech_recognition/README.md @@ -0,0 +1,187 @@ +# Speech Recognition with wav2vec2.0 + +This example demonstarates how you can use torchaudio's I/O features and models to run speech recognition in C++ application. + +**NOTE** +This example uses `"sox_io"` backend for loading audio, which does not work on Windows. To make it work on +Windows, you need to replace the part of loading audio and converting it to Tensor object. + +## 1. Create a transcription pipeline TorchScript file + +We will create a TorchScript that performs the following processes; + +1. Load audio from a file. +1. Pass audio to encoder which produces the sequence of probability distribution on labels. +1. Pass the encoder output to decoder which generates transcripts. + +For building decoder, we borrow the pre-trained weights published by `fairseq` and/or Hugging Face Transformers, then convert it `torchaudio`'s format, which supports TorchScript. + +### 1.1. From `fairseq` + +For `fairseq` models, you can download pre-trained weights +You can download a model from [`fairseq` repository](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec). Here, we will use `Base / 960h` model. You also need to download [the letter dictionary file](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#evaluating-a-ctc-model). + +For the decoder part, we use [simple_ctc](https://github.com/mthrok/ctcdecode), which also supports TorchScript. + +```bash +mkdir -p pipeline-fairseq +python build_pipeline_from_fairseq.py \ + --model-file "wav2vec_small_960.pt" \ + --dict-dir \ + --output-path "./pipeline-fairseq/" +``` + +The above command should create the following TorchScript object files in the output directory. + +``` +decoder.zip encoder.zip loader.zip +``` + +* `loader.zip` loads audio file and generate waveform Tensor. +* `encoder.zip` receives waveform Tensor and generates the sequence of probability distribution over the label. +* `decoder.zip` receives the probability distribution over the label and generates a transcript. + +### 1.2. From Hugging Face Transformers + + +[Hugging Face Transformers](https://huggingface.co/transformers/index.html) and [Hugging Face Model Hub](https://huggingface.co/models) provides `wav2vec2.0` models fine-tuned on variety of datasets and languages. + +We can also import the model published on Hugging Face Hub and run it in our C++ application. +In the following example, we will try the Geremeny model, ([facebook/wav2vec2-large-xlsr-53-german](https://huggingface.co/facebook/wav2vec2-large-xlsr-53-german/tree/main)) on [VoxForge Germany dataset](http://www.voxforge.org/de/downloads). + +```bash +mkdir -p pipeline-hf +python build_pipeline_from_huggingface_transformers.py \ + --model facebook/wav2vec2-large-xlsr-53-german \ + --output-path ./pipeline-hf/ +``` + +The resulting TorchScript object files should be same as the `fairseq` example. + +## 2. Build the application + +Please refer to [the top level README.md](../README.md) + +## 3. Run the application + +Now we run the C++ application [`transcribe`](./transcribe.cpp), with the TorchScript object we created in Step.1.1. and an input audio file. + +```bash +../build/speech_recognition/transcribe ./pipeline-fairseq ../data/input.wav +``` + +This will output something like the following. + +``` +Loading module from: ./pipeline/loader.zip +Loading module from: ./pipeline/encoder.zip +Loading module from: ./pipeline/decoder.zip +Loading the audio +Running inference +Generating the transcription +I HAD THAT CURIOSITY BESIDE ME AT THIS MOMENT +Done. +``` + +## 4. Evaluate the pipeline on Librispeech dataset + +Let's evaluate this word error rate (WER) of this application using [Librispeech dataset](https://www.openslr.org/12). + +### 4.1. Create a list of audio paths + +For the sake of simplifying our C++ code, we will first parse the Librispeech dataset to get the list of audio path + +```bash +python parse_librispeech.py /LibriSpeech/test-clean ./flist.txt +``` + +The list should look like the following; + +```bash +head flist.txt + +1089-134691-0000 /LibriSpeech/test-clean/1089/134691/1089-134691-0000.flac HE COULD WAIT NO LONGER +``` + +### 4.2. Run the transcription + +[`transcribe_list`](./transcribe_list.cpp) processes the input flist list and feed the audio path one by one to the pipeline, then generate reference file and hypothesis file. + +```bash +../build/speech_recognition/transcribe_list ./pipeline-fairseq ./flist.txt +``` + +### 4.3. Score WER + +You need `sclite` for this step. You can download the code from [SCTK repository](https://github.com/usnistgov/SCTK). + +```bash +# in the output directory +sclite -r ref.trn -h hyp.trn -i wsj -o pralign -o sum +``` + +WER can be found in the resulting `hyp.trn.sys`. Check out the column that starts with `Sum/Avg` the first column of the third block is `100 - WER`. + +In our test, we got the following results. + +| model | Fine Tune | test-clean | test-other | +|:-----------------------------------------:|----------:|:----------:|:----------:| +| Base
`wav2vec_small_960` | 960h | 3.1 | 7.7 | +| Large
`wav2vec_big_960` | 960h | 2.6 | 5.9 | +| Large (LV-60)
`wav2vec2_vox_960h_new` | 960h | 2.9 | 6.2 | +| Large (LV-60) + Self Training
`wav2vec_vox_960h_pl` | 960h | 1.9 | 4.5 | + + +You can also check `hyp.trn.pra` file to see what errors were made. + +``` +id: (3528-168669-0005) +Scores: (#C #S #D #I) 7 1 0 0 +REF: there is a stone to be RAISED heavy +HYP: there is a stone to be RACED heavy +Eval: S +``` + +## 5. Evaluate the pipeline on VoxForge dataset + +Now we use the pipeline we created in step 1.2. This time with German language dataset from VoxForge. + +### 5.1. Create a list of audio paths + +Download an archive from http://www.repository.voxforge1.org/downloads/de/Trunk/Audio/Main/16kHz_16bit/, and extract it to your local file system, then run the following to generate the file list. + +```bash +python parse_voxforge.py > ./flist-de.txt +``` + +The list should look like + +```bash +head flist-de.txt +de5-001 /datasets/voxforge/de/guenter-20140214-afn/wav/de5-001.wav ES SOLL ETWA FรœNFZIGTAUSEND VERSCHIEDENE SORTEN GEBEN +``` + +### 5.2. Run the application and score WER + +This process is same as the Librispeech example. We just use the pipeline with the Germany model and file list of Germany dataset. Refer to the corresponding ssection in Librispeech evaluation.. + +```bash +../build/speech_recognition/transcribe_list ./pipeline-hf ./flist-de.txt +``` + +Then + +```bash +# in the output directory +sclite -r ref.trn -h hyp.trn -i wsj -o pralign -o sum +``` + +You can find the detail of evalauation result in PRA. + +``` +id: (guenter-20140214-afn/mfc/de5-012) +Scores: (#C #S #D #I) 4 1 1 0 +REF: die ausgaben kร–nnen gigantisch STEIGE N +HYP: die ausgaben kร–nnen gigantisch ****** STEIGEN +Eval: D S +``` diff --git a/examples/libtorchaudio/speech_recognition/build_pipeline_from_fairseq.py b/examples/libtorchaudio/speech_recognition/build_pipeline_from_fairseq.py new file mode 100644 index 0000000000..3b6dbafe41 --- /dev/null +++ b/examples/libtorchaudio/speech_recognition/build_pipeline_from_fairseq.py @@ -0,0 +1,202 @@ +#!/usr/bin/evn python3 +"""Build Speech Recognition pipeline based on fairseq's wav2vec2.0 and dump it to TorchScript file. + +To use this script, you need `fairseq`. +""" +import os +import argparse +import logging + +import torch +from torch.utils.mobile_optimizer import optimize_for_mobile +import torchaudio +from torchaudio.models.wav2vec2.utils.import_fairseq import import_fairseq_model +import fairseq +import simple_ctc + +_LG = logging.getLogger(__name__) + + +def _parse_args(): + parser = argparse.ArgumentParser( + description=__doc__, + ) + parser.add_argument( + '--model-file', + required=True, + help='Path to the input pretrained weight file.' + ) + parser.add_argument( + '--dict-dir', + help=( + 'Path to the directory in which `dict.ltr.txt` file is found. ' + 'Required only when the model is finetuned.' + ) + ) + parser.add_argument( + '--output-path', + help='Path to the directory, where the TorchScript-ed pipelines are saved.', + ) + parser.add_argument( + '--test-file', + help='Path to a test audio file.', + ) + parser.add_argument( + '--debug', + action='store_true', + help=( + 'When enabled, individual components are separately tested ' + 'for the numerical compatibility and TorchScript compatibility.' + ) + ) + parser.add_argument( + '--quantize', + action='store_true', + help='Apply quantization to model.' + ) + parser.add_argument( + '--optimize-for-mobile', + action='store_true', + help='Apply optmization for mobile.' + ) + return parser.parse_args() + + +class Loader(torch.nn.Module): + def forward(self, audio_path: str) -> torch.Tensor: + waveform, sample_rate = torchaudio.load(audio_path) + if sample_rate != 16000: + waveform = torchaudio.functional.resample(waveform, float(sample_rate), 16000.) + return waveform + + +class Encoder(torch.nn.Module): + def __init__(self, encoder: torch.nn.Module): + super().__init__() + self.encoder = encoder + + def forward(self, waveform: torch.Tensor) -> torch.Tensor: + result, _ = self.encoder(waveform) + return result + + +class Decoder(torch.nn.Module): + def __init__(self, decoder: torch.nn.Module): + super().__init__() + self.decoder = decoder + + def forward(self, emission: torch.Tensor) -> str: + result = self.decoder.decode(emission) + return ''.join(result.label_sequences[0][0]).replace('|', ' ') + + +def _get_decoder(): + labels = [ + "", + "", + "", + "", + "|", + "E", + "T", + "A", + "O", + "N", + "I", + "H", + "S", + "R", + "D", + "L", + "U", + "M", + "W", + "C", + "F", + "G", + "Y", + "P", + "B", + "V", + "K", + "'", + "X", + "J", + "Q", + "Z", + ] + + return Decoder( + simple_ctc.BeamSearchDecoder( + labels, + cutoff_top_n=40, + cutoff_prob=0.8, + beam_size=100, + num_processes=1, + blank_id=0, + is_nll=True, + ) + ) + + +def _load_fairseq_model(input_file, data_dir=None): + overrides = {} + if data_dir: + overrides['data'] = data_dir + + model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [input_file], arg_overrides=overrides + ) + model = model[0] + return model + + +def _get_model(model_file, dict_dir): + original = _load_fairseq_model(model_file, dict_dir) + model = import_fairseq_model(original.w2v_encoder) + return model + + +def _main(): + args = _parse_args() + _init_logging(args.debug) + loader = Loader() + model = _get_model(args.model_file, args.dict_dir).eval() + encoder = Encoder(model) + decoder = _get_decoder() + _LG.info(encoder) + + if args.quantize: + _LG.info('Quantizing the model') + model.encoder.transformer.pos_conv_embed.__prepare_scriptable__() + encoder = torch.quantization.quantize_dynamic( + encoder, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8) + _LG.info(encoder) + + # test + if args.test_file: + _LG.info('Testing with %s', args.test_file) + waveform = loader(args.test_file) + emission = encoder(waveform) + transcript = decoder(emission) + _LG.info(transcript) + + torch.jit.script(loader).save(os.path.join(args.output_path, 'loader.zip')) + torch.jit.script(decoder).save(os.path.join(args.output_path, 'decoder.zip')) + scripted = torch.jit.script(encoder) + if args.optimize_for_mobile: + scripted = optimize_for_mobile(scripted) + scripted.save(os.path.join(args.output_path, 'encoder.zip')) + + +def _init_logging(debug=False): + level = logging.DEBUG if debug else logging.INFO + format_ = ( + '%(message)s' if not debug else + '%(asctime)s: %(levelname)7s: %(funcName)10s: %(message)s' + ) + logging.basicConfig(level=level, format=format_) + + +if __name__ == '__main__': + _main() diff --git a/examples/libtorchaudio/speech_recognition/build_pipeline_from_huggingface_transformers.py b/examples/libtorchaudio/speech_recognition/build_pipeline_from_huggingface_transformers.py new file mode 100644 index 0000000000..5c6466f205 --- /dev/null +++ b/examples/libtorchaudio/speech_recognition/build_pipeline_from_huggingface_transformers.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +import argparse +import logging +import os + +import torch +import torchaudio +from torchaudio.models.wav2vec2.utils.import_huggingface import import_huggingface_model +import simple_ctc + + +_LG = logging.getLogger(__name__) + + +def _parse_args(): + parser = argparse.ArgumentParser( + description=__doc__, + ) + parser.add_argument( + '--model', + required=True, + help='Path to the input pretrained weight file.' + ) + parser.add_argument( + '--output-path', + help='Path to the directory, where the Torchscript-ed pipelines are saved.', + ) + parser.add_argument( + '--test-file', + help='Path to a test audio file.', + ) + parser.add_argument( + '--quantize', + action='store_true', + help='Quantize the model.', + ) + parser.add_argument( + '--debug', + action='store_true', + help=( + 'When enabled, individual components are separately tested ' + 'for the numerical compatibility and TorchScript compatibility.' + ) + ) + return parser.parse_args() + + +class Loader(torch.nn.Module): + def forward(self, audio_path: str) -> torch.Tensor: + waveform, sample_rate = torchaudio.load(audio_path) + if sample_rate != 16000: + waveform = torchaudio.functional.resample(waveform, float(sample_rate), 16000.) + return waveform + + +class Encoder(torch.nn.Module): + def __init__(self, encoder: torch.nn.Module): + super().__init__() + self.encoder = encoder + + def forward(self, waveform: torch.Tensor) -> torch.Tensor: + length = torch.tensor([waveform.shape[1]]) + result, length = self.encoder(waveform, length) + return result + + +class Decoder(torch.nn.Module): + def __init__(self, decoder: torch.nn.Module): + super().__init__() + self.decoder = decoder + + def forward(self, emission: torch.Tensor) -> str: + result = self.decoder.decode(emission) + return ''.join(result.label_sequences[0][0]).replace('|', ' ') + + +def _get_model(model_id): + from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor + tokenizer = Wav2Vec2Processor.from_pretrained(model_id).tokenizer + labels = [k for k, v in sorted(tokenizer.get_vocab().items(), key=lambda kv: kv[1])] + original = Wav2Vec2ForCTC.from_pretrained(model_id) + model = import_huggingface_model(original) + return model.eval(), labels + + +def _get_decoder(labels): + return Decoder( + simple_ctc.BeamSearchDecoder( + labels, + cutoff_top_n=40, + cutoff_prob=0.8, + beam_size=100, + num_processes=1, + blank_id=0, + is_nll=True, + ) + ) + + +def _main(): + args = _parse_args() + _init_logging(args.debug) + _LG.info('Loading model: %s', args.model) + model, labels = _get_model(args.model) + _LG.info('Labels: %s', labels) + _LG.info('Building pipeline') + loader = Loader() + encoder = Encoder(model) + decoder = _get_decoder(labels) + _LG.info(encoder) + + if args.quantize: + _LG.info('Quantizing the model') + model.encoder.transformer.pos_conv_embed.__prepare_scriptable__() + encoder = torch.quantization.quantize_dynamic( + encoder, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8) + _LG.info(encoder) + + # test + if args.test_file: + _LG.info('Testing with %s', args.test_file) + waveform = loader(args.test_file) + emission = encoder(waveform) + transcript = decoder(emission) + _LG.info(transcript) + + torch.jit.script(loader).save(os.path.join(args.output_path, 'loader.zip')) + torch.jit.script(encoder).save(os.path.join(args.output_path, 'encoder.zip')) + torch.jit.script(decoder).save(os.path.join(args.output_path, 'decoder.zip')) + + +def _init_logging(debug=False): + level = logging.DEBUG if debug else logging.INFO + format_ = ( + '%(message)s' if not debug else + '%(asctime)s: %(levelname)7s: %(funcName)10s: %(message)s' + ) + logging.basicConfig(level=level, format=format_) + + +if __name__ == '__main__': + _main() diff --git a/examples/libtorchaudio/speech_recognition/parse_librispeech.py b/examples/libtorchaudio/speech_recognition/parse_librispeech.py new file mode 100644 index 0000000000..dcd7aaf938 --- /dev/null +++ b/examples/libtorchaudio/speech_recognition/parse_librispeech.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 +"""Parse a directory contains Librispeech dataset. + +Recursively search for "*.trans.txt" file in the given directory and print out + +`\\t\\t` + +example: python parse_librispeech.py LibriSpeech/test-clean + + 1089-134691-0000\t/LibriSpeech/test-clean/1089/134691/1089-134691-0000.flac\tHE COULD WAIT NO LONGER + ... + +Dataset can be obtained from https://www.openslr.org/12 +""" +import argparse +from pathlib import Path + + +def _parse_args(): + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawTextHelpFormatter, + ) + parser.add_argument( + 'input_dir', + type=Path, + help='Directory where `*.trans.txt` files are searched.' + ) + return parser.parse_args() + + +def _parse_transcript(path): + with open(path) as trans_fileobj: + for line in trans_fileobj: + line = line.strip() + if line: + yield line.split(' ', maxsplit=1) + + +def _parse_directory(root_dir: Path): + for trans_file in root_dir.glob('**/*.trans.txt'): + trans_dir = trans_file.parent + for id_, transcription in _parse_transcript(trans_file): + audio_path = trans_dir / f'{id_}.flac' + yield id_, audio_path, transcription + + +def _main(): + args = _parse_args() + for id_, path, transcription in _parse_directory(args.input_dir): + print(f'{id_}\t{path}\t{transcription}') + + +if __name__ == '__main__': + _main() diff --git a/examples/libtorchaudio/speech_recognition/parse_voxforge.py b/examples/libtorchaudio/speech_recognition/parse_voxforge.py new file mode 100644 index 0000000000..1f5f8991e0 --- /dev/null +++ b/examples/libtorchaudio/speech_recognition/parse_voxforge.py @@ -0,0 +1,67 @@ +#!/usr/bin/env python +"""Parse a directory contains VoxForge dataset. + +Recursively search for "PROMPTS" file in the given directory and print out + +`\\t\\t` + +example: python parse_voxforge.py voxforge/de/Helge-20150608-aku + + de5-001\t/datasets/voxforge/de/guenter-20140214-afn/wav/de5-001.wav\tES SOLL ETWA FรœNFZIGTAUSEND VERSCHIEDENE SORTEN GEBEN + ... + +Dataset can be obtained from http://www.repository.voxforge1.org/downloads/de/Trunk/Audio/Main/16kHz_16bit/ +""" +import os +import argparse +from pathlib import Path + + +def _parse_args(): + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawTextHelpFormatter, + ) + parser.add_argument( + 'input_dir', + type=Path, + help='Directory where `*.trans.txt` files are searched.' + ) + return parser.parse_args() + + +def _parse_prompts(path): + base_dir = path.parent.parent + with open(path) as trans_fileobj: + for line in trans_fileobj: + line = line.strip() + if not line: + continue + + id_, transcript = line.split(' ', maxsplit=1) + if not transcript: + continue + + transcript = transcript.upper() + filename = id_.split('/')[-1] + audio_path = base_dir / 'wav' / f'{filename}.wav' + if os.path.exists(audio_path): + yield id_, audio_path, transcript + + +def _parse_directory(root_dir: Path): + for prompt_file in root_dir.glob('**/PROMPTS'): + try: + yield from _parse_prompts(prompt_file) + except UnicodeDecodeError: + pass + + +def _main(): + args = _parse_args() + for id_, path, transcription in _parse_directory(args.input_dir): + print(f'{id_}\t{path}\t{transcription}') + + +if __name__ == '__main__': + _main() diff --git a/examples/libtorchaudio/speech_recognition/transcribe.cpp b/examples/libtorchaudio/speech_recognition/transcribe.cpp new file mode 100644 index 0000000000..e6d65b17e4 --- /dev/null +++ b/examples/libtorchaudio/speech_recognition/transcribe.cpp @@ -0,0 +1,38 @@ +#include + +int main(int argc, char* argv[]) { + if (argc != 3) { + std::cerr << "Usage: " << argv[0] << " " << std::endl; + return -1; + } + + torch::jit::script::Module loader, encoder, decoder; + std::cout << "Loading module from: " << argv[1] << std::endl; + try { + loader = torch::jit::load(std::string(argv[1]) + "/loader.zip"); + } catch (const c10::Error &error) { + std::cerr << "Failed to load the module:" << error.what() << std::endl; + return -1; + } + try { + encoder = torch::jit::load(std::string(argv[1]) + "/encoder.zip"); + } catch (const c10::Error &error) { + std::cerr << "Failed to load the module:" << error.what() << std::endl; + return -1; + } + try { + decoder = torch::jit::load(std::string(argv[1]) + "/decoder.zip"); + } catch (const c10::Error &error) { + std::cerr << "Failed to load the module:" << error.what() << std::endl; + return -1; + } + + std::cout << "Loading the audio" << std::endl; + auto waveform = loader.forward({c10::IValue(argv[2])}); + std::cout << "Running inference" << std::endl; + auto emission = encoder.forward({waveform}); + std::cout << "Generating the transcription" << std::endl; + auto result = decoder.forward({emission}); + std::cout << result.toString()->string() << std::endl; + std::cout << "Done." << std::endl; +} diff --git a/examples/libtorchaudio/speech_recognition/transcribe_list.cpp b/examples/libtorchaudio/speech_recognition/transcribe_list.cpp new file mode 100644 index 0000000000..458a98f568 --- /dev/null +++ b/examples/libtorchaudio/speech_recognition/transcribe_list.cpp @@ -0,0 +1,66 @@ +#include +#include + + +int main(int argc, char* argv[]) { + if (argc != 4) { + std::cerr << "Usage: " << argv[0] << " \n" << std::endl; + std::cerr << " is `\t\t`" << std::endl; + return -1; + } + + torch::jit::script::Module loader, encoder, decoder; + std::cout << "Loading module from: " << argv[1] << std::endl; + try { + loader = torch::jit::load(std::string(argv[1]) + "/loader.zip"); + } catch (const c10::Error &error) { + std::cerr << "Failed to load the module:" << error.what() << std::endl; + return -1; + } + try { + encoder = torch::jit::load(std::string(argv[1]) + "/encoder.zip"); + } catch (const c10::Error &error) { + std::cerr << "Failed to load the module:" << error.what() << std::endl; + return -1; + } + try { + decoder = torch::jit::load(std::string(argv[1]) + "/decoder.zip"); + } catch (const c10::Error &error) { + std::cerr << "Failed to load the module:" << error.what() << std::endl; + return -1; + } + + std::ifstream input_file(argv[2]); + std::string output_dir(argv[3]); + std::ofstream output_ref(output_dir + "/ref.trn"); + std::ofstream output_hyp(output_dir + "/hyp.trn"); + std::string line; + std::chrono::milliseconds t_encode(0); + std::chrono::milliseconds t_decode(0); + while(std::getline(input_file, line)) { + std::istringstream iline(line); + std::string id; + std::string path; + std::string reference; + std::getline(iline, id, '\t'); + std::getline(iline, path, '\t'); + std::getline(iline, reference, '\t'); + + auto waveform = loader.forward({c10::IValue(path)}); + std::chrono::steady_clock::time_point t0 = std::chrono::steady_clock::now(); + auto emission = encoder.forward({waveform}); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); + auto result = decoder.forward({emission}); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); + + t_encode += std::chrono::duration_cast(t1 - t0); + t_decode += std::chrono::duration_cast(t2 - t1); + + auto hypothesis = result.toString()->string(); + output_hyp << hypothesis << " (" << id << ")" << std::endl; + output_ref << reference << " (" << id << ")" << std::endl; + std::cout << id << '\t' << hypothesis << std::endl; + } + std::cout << "Time (encode): " << t_encode.count() << " [ms]" << std::endl; + std::cout << "Time (decode): " << t_decode.count() << " [ms]" << std::endl; +} diff --git a/packaging/build_conda.sh b/packaging/build_conda.sh index 161c48ad43..c4a4a009b7 100755 --- a/packaging/build_conda.sh +++ b/packaging/build_conda.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="conda" export NO_CUDA_PACKAGE=1 -setup_env 0.9.0 +setup_env 0.10.0 export SOURCE_ROOT_DIR="$PWD" setup_conda_pytorch_constraint setup_visual_studio_constraint diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index 9b4194afda..24f544d167 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="wheel" export NO_CUDA_PACKAGE=1 -setup_env 0.9.0 +setup_env 0.10.0 setup_wheel_python pip_install numpy future cmake ninja setup_pip_pytorch_version diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index 173aa4ca07..1626938863 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -107,6 +107,10 @@ setup_cuda() { export FORCE_CUDA=1 export TORCH_CUDA_ARCH_LIST="3.5;5.0+PTX;6.0;7.0" ;; + rocm*) + export FORCE_CUDA=1 + export USE_ROCM=1 + ;; cpu) ;; *) diff --git a/setup.py b/setup.py index 255ac9894d..36f28f0eb1 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ # Creating the 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