An audio package for PyTorch

These details have been verified by PyPI

Maintainers

atalman cpuhrsch ezyang malfet seemethere soumith

Project description

torchaudio: an audio library for PyTorch

TorchAudio Logo

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)
Dataloaders for common audio datasets
Audio and speech processing functions
- forced_align
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

Installation

Please refer to https://pytorch.org/audio/main/installation.html for installation and build process of TorchAudio.

API Reference

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

Contributing Guidelines

Please refer to CONTRIBUTING.md

Citation

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-B��lair and Yangyang Shi},
  journal={arXiv preprint arXiv:2110.15018},
  year={2021}
}

@misc{hwang2023torchaudio,
      title={TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch}, 
      author={Jeff Hwang and Moto Hira and Caroline Chen and Xiaohui Zhang and Zhaoheng Ni and Guangzhi Sun and Pingchuan Ma and Ruizhe Huang and Vineel Pratap and Yuekai Zhang and Anurag Kumar and Chin-Yun Yu and Chuang Zhu and Chunxi Liu and Jacob Kahn and Mirco Ravanelli and Peng Sun and Shinji Watanabe and Yangyang Shi and Yumeng Tao and Robin Scheibler and Samuele Cornell and Sean Kim and Stavros Petridis},
      year={2023},
      eprint={2310.17864},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}

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!

Pre-trained Model License

The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.

For instance, SquimSubjective model is released under the Creative Commons Attribution Non Commercial 4.0 International (CC-BY-NC 4.0) license. See the link for additional details.

Other pre-trained models that have different license are noted in documentation. Please checkout the documentation page.

Project details

These details have been verified by PyPI

Maintainers

atalman cpuhrsch ezyang malfet seemethere soumith

Release history Release notifications | RSS feed

2.5.1

Oct 29, 2024

This version

2.5.0

Oct 17, 2024

2.4.1

Sep 4, 2024

2.4.0

Jul 24, 2024

2.3.1

Jun 5, 2024

2.3.0

Apr 24, 2024

2.2.2

Mar 27, 2024

2.2.1

Feb 22, 2024

2.2.0

Jan 30, 2024

2.1.2

Dec 14, 2023

2.1.1

Nov 15, 2023

2.1.0

Oct 4, 2023

2.0.2

May 8, 2023

2.0.1

Mar 15, 2023

2.0.0 yanked

Mar 15, 2023

Reason this release was yanked:

Contains an incorrect torch dependency

0.13.1

Dec 15, 2022

0.13.0

Oct 28, 2022

0.12.1

Aug 5, 2022

0.12.0

Jun 28, 2022

0.11.0

Mar 10, 2022

0.10.2

Jan 27, 2022

0.10.1

Dec 15, 2021

0.10.0

Oct 21, 2021

0.9.1

Sep 20, 2021

0.9.0

Jun 15, 2021

0.8.1

Mar 25, 2021

0.8.0

Mar 4, 2021

0.7.2

Dec 10, 2020

0.7.0

Oct 27, 2020

0.6.0

Jul 28, 2020

0.5.1

Jun 18, 2020

0.5.0

Apr 21, 2020

0.4.0

Jan 16, 2020

0.3.2

Jan 9, 2020

0.3.1

Oct 17, 2019

0.3.0

Aug 8, 2019

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

torchaudio-2.5.0-cp312-cp312-win_amd64.whl (2.4 MB view details)

Uploaded Oct 17, 2024 CPython 3.12 Windows x86-64

torchaudio-2.5.0-cp312-cp312-manylinux2014_aarch64.whl (3.3 MB view details)

Uploaded Oct 17, 2024 CPython 3.12

torchaudio-2.5.0-cp312-cp312-manylinux1_x86_64.whl (3.4 MB view details)

Uploaded Oct 17, 2024 CPython 3.12

torchaudio-2.5.0-cp312-cp312-macosx_11_0_arm64.whl (1.8 MB view details)

Uploaded Oct 17, 2024 CPython 3.12 macOS 11.0+ ARM64

torchaudio-2.5.0-cp311-cp311-win_amd64.whl (2.4 MB view details)

Uploaded Oct 17, 2024 CPython 3.11 Windows x86-64

torchaudio-2.5.0-cp311-cp311-manylinux2014_aarch64.whl (3.3 MB view details)

Uploaded Oct 17, 2024 CPython 3.11

torchaudio-2.5.0-cp311-cp311-manylinux1_x86_64.whl (3.4 MB view details)

Uploaded Oct 17, 2024 CPython 3.11

torchaudio-2.5.0-cp311-cp311-macosx_11_0_arm64.whl (1.8 MB view details)

Uploaded Oct 17, 2024 CPython 3.11 macOS 11.0+ ARM64

torchaudio-2.5.0-cp310-cp310-win_amd64.whl (2.4 MB view details)

Uploaded Oct 17, 2024 CPython 3.10 Windows x86-64

torchaudio-2.5.0-cp310-cp310-manylinux2014_aarch64.whl (3.3 MB view details)

Uploaded Oct 17, 2024 CPython 3.10

torchaudio-2.5.0-cp310-cp310-manylinux1_x86_64.whl (3.4 MB view details)

Uploaded Oct 17, 2024 CPython 3.10

torchaudio-2.5.0-cp310-cp310-macosx_11_0_arm64.whl (1.8 MB view details)

Uploaded Oct 17, 2024 CPython 3.10 macOS 11.0+ ARM64

torchaudio-2.5.0-cp39-cp39-win_amd64.whl (2.4 MB view details)

Uploaded Oct 17, 2024 CPython 3.9 Windows x86-64

torchaudio-2.5.0-cp39-cp39-manylinux2014_aarch64.whl (3.3 MB view details)

Uploaded Oct 17, 2024 CPython 3.9

torchaudio-2.5.0-cp39-cp39-manylinux1_x86_64.whl (3.4 MB view details)

Uploaded Oct 17, 2024 CPython 3.9

torchaudio-2.5.0-cp39-cp39-macosx_11_0_arm64.whl (1.8 MB view details)

Uploaded Oct 17, 2024 CPython 3.9 macOS 11.0+ ARM64

torchaudio-2.5.0-1-cp312-cp312-manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded Oct 17, 2024 CPython 3.12

torchaudio-2.5.0-1-cp311-cp311-manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded Oct 17, 2024 CPython 3.11

torchaudio-2.5.0-1-cp310-cp310-manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded Oct 17, 2024 CPython 3.10

torchaudio-2.5.0-1-cp39-cp39-manylinux2014_aarch64.whl (1.7 MB view details)

Uploaded Oct 17, 2024 CPython 3.9

File details

Details for the file torchaudio-2.5.0-cp312-cp312-win_amd64.whl.

File metadata

Download URL: torchaudio-2.5.0-cp312-cp312-win_amd64.whl
Upload date: Oct 17, 2024
Size: 2.4 MB
Tags: CPython 3.12, Windows x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.11.7

File hashes

Hashes for torchaudio-2.5.0-cp312-cp312-win_amd64.whl
Algorithm	Hash digest
SHA256	`e98ad102e0e574a397759078bc9809afc05de6e6f8ac0cb26d2245e0d3817adf`
MD5	`b449c223e29a139ee27a418e1bd9078c`
BLAKE2b-256	`6329c86f39b9896d2ca03e14be736d7e38b6a77f3c37cf36fba7fff4de15d24e`

Algorithm	Hash digest
SHA256	`1971302bfc321be5ea18ba60dbc3ffdc6ae53cb47bb6427db25d5b69e4e932ec`
MD5	`8425641d772992fe9ce54373359bc362`
BLAKE2b-256	`a5be60092f279ac86fe2920464ee7540c8715fc621462e06de38915a8f935e60`

Algorithm	Hash digest
SHA256	`3009ca3e095825911917ab865b2ea48abbf3e66f948c4ffd64916fe6e476bfec`
MD5	`a0e9fa876e8a9e7c151bf400054a52d8`
BLAKE2b-256	`6db830849d2fa7ce60acc95df9c04ab42b6c12ba1ca968cb9fa6f64010cdc420`

Algorithm	Hash digest
SHA256	`c835da771701b06fbe8e19ce643d5e587fd385e5f4d8f140551ce04900b1b96b`
MD5	`5b56a8828f9cfd36e1316c38ad9c4df0`
BLAKE2b-256	`7126f1ac135bfb26d5073660c6e93e271046a2dd42cc7b79875dc0b6459a3aae`

Algorithm	Hash digest
SHA256	`168d9d2a8216a5f1888713c13914edf410d2e28d39c6bfd9e1211baf6f2c76d8`
MD5	`c2fc7b25a986cc03e90aeaadb0b296c1`
BLAKE2b-256	`092f62c8925ffdcac92ea64f00ccc77e6262f3212a0cc22c21473a9fd98966cd`

Algorithm	Hash digest
SHA256	`ee844aa12fa25f521f64ec86c835acf925d194ed4fb66a9b442436f80b39e8da`
MD5	`4ba57ae1fc31b071978a175fbc86d84a`
BLAKE2b-256	`320fe0104705503fd5fa738faff6b940aec2440cfd809e45792255e69165631d`

Algorithm	Hash digest
SHA256	`470dd171a2e44a4c1aa89c5cdd4a0ba9f99650b68228f3a63b20ceaafd553567`
MD5	`8e8c76657008de42edbd653d95a186ba`
BLAKE2b-256	`17edd6e73554726a4aea054353093f3dffe68ea18f006d14c0604e651f3e6ee0`

Algorithm	Hash digest
SHA256	`982ec0494a27c7f3e7e68c91cc92e6e1ad6f86fedeeec627096051309632b149`
MD5	`1165876e68ccd618d69ec66d73716d2e`
BLAKE2b-256	`bbe1fd1fa394941d8f1b093a8b8842b9a31a17e313b4a29c3f53b6cbcfb75bb7`

Algorithm	Hash digest
SHA256	`763bf99b2def4681b1e760883849e0e85fa172eac4a12d1870380d5b7d1149c2`
MD5	`cbb336d0a7790decc8cbe84be328f47d`
BLAKE2b-256	`cf9d6852b89ac387d2343aae2b92202d658656bef6691f44c1ae6b9875e48b08`

Algorithm	Hash digest
SHA256	`7e2f596b0e8909924cdf46acc579481132f5c0341824957f1cca8385c61db5b5`
MD5	`4bb25b7e61ff7a4c4459c926e97effd1`
BLAKE2b-256	`fb5130b26fde90d71d0a07009ad02d05388feccc3b01ebcf9919437e01f26b1b`

Algorithm	Hash digest
SHA256	`70fe426ecae408e9a7019cfcbcd4e81b6f084920ffffac2520f1d28a23e145fe`
MD5	`95954f16ffb29b7884c5d6525ed5a8c9`
BLAKE2b-256	`5d3b6813dd90c940ceb7b0ac8960e6bcb2109007b5dc643b00a2f73036ed2dde`

Algorithm	Hash digest
SHA256	`ab69bdfeb4434159e168a4a2c1618d1d65a5a14a91d17d21256ea960f33405fd`
MD5	`548f3e1d9bbc313452dcb7352acb0b97`
BLAKE2b-256	`b3a8a485ba8015794aa6a5e042469e0be1d6ca4930c391e78945d25d7a9c8310`

Algorithm	Hash digest
SHA256	`242b3c9b1abf212b3a1f28eae9814db4daf4507f74b63c7ec7161d35b3c37147`
MD5	`1c9ac64cc993aa817077055e8f27c631`
BLAKE2b-256	`528a0aadc983301d26dda7a6d3729e9078259db6089bd0fb603f9893affd8dab`

Algorithm	Hash digest
SHA256	`74a3de13ec0a5024999aec75b3fafa97891d617ce5566818d3094857d1e0229d`
MD5	`c4567a2f76f4141f189fe3e0a6ef3bec`
BLAKE2b-256	`fd6cedd1704d75dbbe4553bf8ca94dd1990a7286560781e44a1cbe0fd22a496c`

Algorithm	Hash digest
SHA256	`877706453e7329844382d06ffee31cb11b602c6991afefb594086ecdd739a5cf`
MD5	`45935ef4f41e444812f4d1137ea64b21`
BLAKE2b-256	`ff975b21f79f6d8c62c65f0b4a68b5ca8e33b135c69ebd0a8d0274a565e6a45a`

Algorithm	Hash digest
SHA256	`85573f66dc09497d282bbf406e8f2b03e5929eb3bdc1f76a9d9bc46644d407b1`
MD5	`e6e0d3e73600f18ebd4492615614afe4`
BLAKE2b-256	`9d5670d5e62d1d8f3743bc0c7e67ac601ce03014e2586041c4a871007fc968a3`

Algorithm	Hash digest
SHA256	`593258f33de1fa16ebe5718c9717daf3695460d48a0188194a5c574a710838cb`
MD5	`00725532ebf291df39c761dd9de5dfb4`
BLAKE2b-256	`928182dbbd19b7ed35cf247da5f5c9904979585f11b74165b15f3a3f5573342f`

Algorithm	Hash digest
SHA256	`c35201efd28244152d6edbde92775c10f39f5a5d9346202f07b1554dc78d25a2`
MD5	`798abcd3d35edd1837b77705ab5363c9`
BLAKE2b-256	`285060c694225f2b92f9f1ae1285db3207aee90c5271d911fed3a7b9c7665815`

Algorithm	Hash digest
SHA256	`9dfeedcef3e43010f3ec2d804c8f62fe49ab09ef1c19e6736325939661a293bd`
MD5	`4e20f40896f4fd8775da33bf2a2b3e6c`
BLAKE2b-256	`c1b47dd6321a957cd2a7faeba52fb3477d9fad70c517dcf198a0395d021a0651`

Algorithm	Hash digest
SHA256	`aabf8c4ce919c2e24ace49641ea429360018816371a3d470427fc02ab11156c5`
MD5	`ef6344ea1005575bd6ff3db195facd27`
BLAKE2b-256	`e567a3745d1ac6780c655a4bfe2bb260f827066428695e1dcf3df9796c734fa7`

torchaudio 2.5.0

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

torchaudio: an audio library for PyTorch

Installation

API Reference

Contributing Guidelines

Citation

Disclaimer on Datasets

Pre-trained Model License

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distributions

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details