torchaudio: an audio library for PyTorch

• Support audio I/O (Load files, Save files)
  • Load the following formats into a torch Tensor
    • 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, SpectrogramToDB, MelScale, MelSpectrogram, MFCC, MuLawEncoding, MuLawDecoding, Resample
• Compliance interfaces: Run code using PyTorch that align with other libraries
  • Kaldi: fbank, spectrogram, 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

# Linux
python setup.py install

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

Quick Usage

import torchaudio
sound, sample_rate = torchaudio.load('foo.mp3')
torchaudio.save('foo_save.mp3', sound, sample_rate) # saves tensor to file

API Reference

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

Conventions

Torchaudio is standardized around the following naming conventions.

• 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 the following dimensions. In particular, the input of all transforms and functions assumes channel first.

• 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)