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 can 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 a variety of audio formats, such as
wav,mp3,ogg,flac,opus,sphere, into a torch Tensor using SoX - Kaldi (ark/scp)
- Load a variety of audio formats, such as
- Dataloaders for common audio datasets
- Common audio transforms
- Compliance interfaces: Run code using PyTorch that align with other libraries
- PyTorch (See below for the compatible versions)
- [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 |
main / 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 |
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 |
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.)
Note that nightly build is built on PyTorch's nightly build. Therefore, you need to install the latest PyTorch when you use nightly build of torchaudio.
pip
pip install --pre torchaudio -f https://download.pytorch.org/whl/nightly/torch_nightly.html
conda
conda install -y -c pytorch-nightly torchaudio
On non-Windows platforms, the build process builds libsox and codecs that torchaudio need to link to. It will fetch and build libmad, lame, flac, vorbis, opus, and libsox before building extension. This process requires cmake and pkg-config. libsox-based features can be disabled with BUILD_SOX=0.
The build process also builds the RNN transducer loss. This functionality can be disabled by setting the environment variable BUILD_RNNT=0.
# Linux
python setup.py install
# OSX
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 installThis 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.
import torchaudio
waveform, sample_rate = torchaudio.load('foo.wav') # load tensor from file
torchaudio.save('foo_save.wav', waveform, sample_rate) # save tensor to fileBy default in OSX and Linux, torchaudio uses SoX as a backend to load and save files. The backend can be changed to SoundFile using the following. See SoundFile for installation instructions.
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 usualNote
- SoundFile currently does not support mp3.
- "soundfile" backend is not supported by TorchScript.
API Reference is located here: http://pytorch.org/audio/
Please refer to CONTRIBUTING.md
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!