Testing audio_preprocessing_tutorial.py (DON

Author: brianjo

beginner_source/audio_preprocessing_tutorial.py

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+"""
+Torchaudio Tutorial
+===================
+PyTorch is an open source deep learning platform that provides a
+seamless path from research prototyping to production deployment with
+GPU support.
+Significant effort in solving machine learning problems goes into data
+preparation. Torchaudio leverages PyTorch’s GPU support, and provides
+many tools to make data loading easy and more readable. In this
+tutorial, we will see how to load and preprocess data from a simple
+dataset.
+For this tutorial, please make sure the ``matplotlib`` package is
+installed for easier visualization.
+"""
+
+import torch
+import torchaudio
+import matplotlib.pyplot as plt
+
+
+######################################################################
+# Opening a dataset
+# -----------------
+# 
+
+
+######################################################################
+# Torchaudio supports loading sound files in the wav and mp3 format. We
+# call waveform the resulting raw audio signal.
+# 
+
+filename = "_static/img/steam-train-whistle-daniel_simon-converted-from-mp3.wav"
+waveform, sample_rate = torchaudio.load(filename)
+
+print("Shape of waveform: {}".format(waveform.size()))
+print("Sample rate of waveform: {}".format(sample_rate))
+
+plt.figure()
+plt.plot(waveform.transpose(0,1).numpy())
+
+
+######################################################################
+# Transformations
+# ---------------
+# 
+# Torchaudio supports a growing list of
+# `transformations <https://pytorch.org/audio/transforms.html>`_.
+# 
+# -  **Resample**: Resample waveform to a different sample rate.
+# -  **Spectrogram**: Create a spectrogram from a waveform.
+# -  **MelScale**: This turns a normal STFT into a Mel-frequency STFT,
+#    using a conversion matrix.
+# -  **SpectrogramToDB**: This turns a spectrogram from the
+#    power/amplitude scale to the decibel scale.
+# -  **MFCC**: Create the Mel-frequency cepstrum coefficients from a
+#    waveform.
+# -  **MelSpectrogram**: Create MEL Spectrograms from a waveform using the
+#    STFT function in PyTorch.
+# -  **MuLawEncoding**: Encode waveform based on mu-law companding.
+# -  **MuLawDeconding**: Decode mu-law encoded waveform.
+# 
+# Since all transforms are nn.Modules or jit.ScriptModules, they can be
+# used as part of a neural network at any point.
+# 
+
+
+######################################################################
+# To start, we can look at the log of the spectrogram on a log scale.
+# 
+
+specgram = torchaudio.transforms.Spectrogram()(waveform)
+
+print("Shape of spectrogram: {}".format(specgram.size()))
+
+plt.figure()
+plt.imshow(specgram.log2()[0,:,:].numpy(), cmap='gray')
+
+
+######################################################################
+# Or we can look at the Mel Spectrogram on a log scale.
+# 
+
+specgram = torchaudio.transforms.MelSpectrogram()(waveform)
+
+print("Shape of spectrogram: {}".format(specgram.size()))
+
+plt.figure()
+p = plt.imshow(specgram.log2()[0,:,:].detach().numpy(), cmap='gray')
+
+
+######################################################################
+# We can resample the waveform, one channel at a time.
+# 
+
+new_sample_rate = sample_rate/10
+
+# Since Resample applies to a single channel, we resample first channel here
+channel = 0
+transformed = torchaudio.transforms.Resample(sample_rate, new_sample_rate)(waveform[channel,:].view(1,-1))
+
+print("Shape of transformed waveform: {}".format(transformed.size()))
+
+plt.figure()
+plt.plot(transformed[0,:].numpy())
+
+
+######################################################################
+# As another example of transformations, we can encode the signal based on
+# Mu-Law enconding. But to do so, we need the signal to be between -1 and
+# 1. Since the tensor is just a regular PyTorch tensor, we can apply
+# standard operators on it.
+# 
+
+# Let's check if the tensor is in the interval [-1,1]
+print("Min of waveform: {}\nMax of waveform: {}\nMean of waveform: {}".format(waveform.min(), waveform.max(), waveform.mean()))
+
+
+######################################################################
+# Since the waveform is already between -1 and 1, we do not need to
+# normalize it.
+# 
+
+def normalize(tensor):
+    # Subtract the mean, and scale to the interval [-1,1]
+    tensor_minusmean = tensor - tensor.mean()
+    return tensor_minusmean/tensor_minusmean.abs().max()
+
+# Let's normalize to the full interval [-1,1]
+# waveform = normalize(waveform)
+
+
+######################################################################
+# Let’s apply encode the waveform.
+# 
+
+transformed = torchaudio.transforms.MuLawEncoding()(waveform)
+
+print("Shape of transformed waveform: {}".format(transformed.size()))
+
+plt.figure()
+plt.plot(transformed[0,:].numpy())
+
+
+######################################################################
+# And now decode.
+# 
+
+reconstructed = torchaudio.transforms.MuLawDecoding()(transformed)
+
+print("Shape of recovered waveform: {}".format(reconstructed.size()))
+
+plt.figure()
+plt.plot(reconstructed[0,:].numpy())
+
+
+######################################################################
+# We can finally compare the original waveform with its reconstructed
+# version.
+# 
+
+# Compute median relative difference
+err = ((waveform-reconstructed).abs() / waveform.abs()).median()
+
+print("Median relative difference between original and MuLaw reconstucted signals: {:.2%}".format(err))
+
+
+######################################################################
+# Migrating to Torchaudio from Kaldi
+# ----------------------------------
+# 
+# Users may be familiar with
+# `Kaldi <http://github.com/kaldi-asr/kaldi>`_, a toolkit for speech
+# recognition. Torchaudio offers compatibility with it in
+# ``torchaudio.kaldi_io``. It can indeed read from kaldi scp, or ark file
+# or streams with:
+# 
+# -  read_vec_int_ark
+# -  read_vec_flt_scp
+# -  read_vec_flt_arkfile/stream
+# -  read_mat_scp
+# -  read_mat_ark
+# 
+# Torchaudio provides Kaldi-compatible transforms for ``spectrogram`` and
+# ``fbank`` with the benefit of GPU support, see
+# `here <compliance.kaldi.html>`__ for more information.
+# 
+
+n_fft = 400.0
+frame_length = n_fft / sample_rate * 1000.0
+frame_shift = frame_length / 2.0
+
+params = {
+    "channel": 0,
+    "dither": 0.0,
+    "window_type": "hanning",
+    "frame_length": frame_length,
+    "frame_shift": frame_shift,
+    "remove_dc_offset": False,
+    "round_to_power_of_two": False,
+    "sample_frequency": sample_rate,
+}
+
+specgram = torchaudio.compliance.kaldi.spectrogram(waveform, **params)
+
+print("Shape of spectrogram: {}".format(specgram.size()))
+
+plt.figure()
+plt.imshow(specgram.transpose(0,1).numpy(), cmap='gray')
+
+
+######################################################################
+# We also support computing the filterbank features from waveforms,
+# matching Kaldi’s implementation.
+# 
+
+fbank = torchaudio.compliance.kaldi.fbank(waveform, **params)
+
+print("Shape of fbank: {}".format(fbank.size()))
+
+plt.figure()
+plt.imshow(fbank.transpose(0,1).numpy(), cmap='gray')
+
+
+######################################################################
+# Conclusion
+# ----------
+# 
+# We used an example raw audio signal, or waveform, to illustrate how to
+# open an audio file using Torchaudio, and how to pre-process and
+# transform such waveform. Given that Torchaudio is built on PyTorch,
+# these techniques can be used as building blocks for more advanced audio
+# applications, such as speech recognition, while leveraging GPUs.
+#