torchaudio: an audio library for PyTorch

The aim of torchaudio is to apply PyTorch to 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 to use and feel like a natural extension.

Support audio I/O (Load files, Save files)
- 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)
Dataloaders for common audio datasets (VCTK, YesNo)
Common audio transforms
- Spectrogram, AmplitudeToDB, MelScale, MelSpectrogram, MFCC, MuLawEncoding, MuLawDecoding, Resample
Compliance interfaces: Run code using PyTorch that align with other libraries
- Kaldi: spectrogram, fbank, mfcc, resample_waveform

Dependencies

pytorch (nightly version needed for development)
libsox v14.3.2 or above
[optional] vesis84/kaldi-io-for-python commit cb46cb1f44318a5d04d4941cf39084c5b021241e or above

Quick install on OSX (Homebrew):

brew install sox

Linux (Ubuntu):

sudo apt-get install sox libsox-dev libsox-fmt-all

Anaconda

conda install -c conda-forge sox

Installation

Binaries

To install the latest version using anaconda, run:

conda install -c pytorch torchaudio

To install the latest pip wheels, run:

pip install torchaudio -f https://download.pytorch.org/whl/torch_stable.html

(If you do not have torch already installed, this will default to installing 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. To install the latest nightly via pip, run:

pip install numpy
pip install future # only on python 2.7
pip install six    # only on python 3.5
pip install torchaudio_nightly -f https://download.pytorch.org/whl/nightly/torch_nightly.html

To install the latest nightly via conda, run:

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.

# Linux
python setup.py install

# OSX
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install

Quick Usage

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

API Reference

API Reference is located here: http://pytorch.org/audio/

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 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 (channel, 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 (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
n_freq: the number of bins in a linear spectrogram
min_freq: the lowest frequency of the lowest band in a spectrogram
max_freq: the highest frequency of the highest band in a spectrogram
win_length: the length of the STFT window
window_fn: for functions that creates windows e.g. torch.hann_window

Transforms expect and return the following dimensions.

Spectrogram: (channel, time) -> (channel, freq, time)
AmplitudeToDB: (channel, freq, time) -> (channel, freq, time)
MelScale: (channel, 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)

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.

Contributing Guidelines

Please let us know if you encounter a bug by filing an issue.

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.

Disclaimer on Datasets

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!

wdwlinda/audio