From 8e5be4b3e066cb56644b8e2e9cbb96286ea144b2 Mon Sep 17 00:00:00 2001 From: Jason Lian Date: Tue, 30 Jul 2019 08:42:00 -0700 Subject: [PATCH] more --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 1a21b49a97..bd543cdd03 100644 --- a/README.md +++ b/README.md @@ -4,16 +4,16 @@ torchaudio: an audio library for PyTorch [![Build Status](https://travis-ci.org/pytorch/audio.svg?branch=master)](https://travis-ci.org/pytorch/audio) The aim of torchaudio is to apply [PyTorch](https://github.com/pytorch/pytorch) to -the audio domain. By supporting PyTorch, torchaudio will follow the same philosophy +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 will be primarily a machine learning library and not a general signal -processing library. The benefits of Pytorch will be seen in torchaudio through +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 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 + - 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. @@ -73,8 +73,8 @@ Conventions ----------- With torchaudio being a machine learning library and built on top of PyTorch, -torchaudio is standardized around the following naming conventions. In particular, -tensors are assumed to have channel as the first dimension and time as the last +torchaudio is standardized around the following naming conventions. Tensors are +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