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 |
|
---|---|---|---|
Development | master / nightly |
main / nightly |
>=3.7 , <=3.10 |
Latest versioned release | 1.13.1 |
0.13.1 |
>=3.7 , <=3.10 |
Previous versions
torch |
torchaudio |
python |
---|---|---|
1.13.0 |
0.13.0 |
>=3.7 , <=3.10 |
1.12.1 |
0.12.1 |
>=3.7 , <=3.10 |
1.12.0 |
0.12.0 |
>=3.7 , <=3.10 |
1.11.0 |
0.11.0 |
>=3.7 , <=3.9 |
1.10.0 |
0.10.0 |
>=3.6 , <=3.9 |
1.9.1 |
0.9.1 |
>=3.6 , <=3.9 |
1.9.0 |
0.9.0 |
>=3.6 , <=3.9 |
1.8.1 |
0.8.1 |
>=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 |
torchaudio
has binary distributions for PyPI (pip
) and Anaconda (conda
).
Please refer to https://pytorch.org/get-started/locally/ for the details.
Note Starting 0.10
, torchaudio has CPU-only and CUDA-enabled binary distributions, each of which requires a matching PyTorch version.
Note This software was compiled against an unmodified copy of FFmpeg (licensed under the LGPLv2.1), with the specific rpath removed so as to enable the use of system libraries. The LGPL source can be downloaded here.
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 and CTC beam search decoder. These functionalities can be disabled by setting the environment variable BUILD_RNNT=0
and BUILD_CTC_DECODER=0
, respectively.
# Linux
python setup.py install
# OSX
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 install
This 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 file
By 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 usual
Note
- SoundFile currently does not support mp3.
- "soundfile" backend is not supported by TorchScript.
API Reference is located here: http://pytorch.org/audio/main/
Please refer to CONTRIBUTING.md
If you find this package useful, please cite as:
@article{yang2021torchaudio,
title={TorchAudio: Building Blocks for Audio and Speech Processing},
author={Yao-Yuan Yang and Moto Hira and Zhaoheng Ni and Anjali Chourdia and Artyom Astafurov and Caroline Chen and Ching-Feng Yeh and Christian Puhrsch and David Pollack and Dmitriy Genzel and Donny Greenberg and Edward Z. Yang and Jason Lian and Jay Mahadeokar and Jeff Hwang and Ji Chen and Peter Goldsborough and Prabhat Roy and Sean Narenthiran and Shinji Watanabe and Soumith Chintala and Vincent Quenneville-Bélair and Yangyang Shi},
journal={arXiv preprint arXiv:2110.15018},
year={2021}
}
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!
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.