Repository files navigation

The aim of torchaudio is to applyPyTorch tothe audio domain. By supporting PyTorch, torchaudio follows the same philosophyof providing strong GPU acceleration, having a focus on trainable features throughthe autograd system, and having consistent style (tensor names and dimension names).Therefore, it is primarily a machine learning library and not a general signalprocessing library. The benefits of PyTorch can be seen in torchaudio throughhaving all the computations be through PyTorch operations which makes it easyto use and feel like a natural extension.

• 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

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

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

Please refer toCONTRIBUTING.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}}

@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}}

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!

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. Seethe link for additional details.

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