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Facebook AI Research's Automatic Speech Recognition Toolkit
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vineelpratap and facebook-github-bot Better exception handling for warp-ctc
Summary: warp-ctc works only for label length < 639 and throws "unknown_error" if the label length is larger than that. Throw a more readable error in this case.

Reviewed By: xuqiantong

Differential Revision: D18343656

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README.md

wav2letter++

CircleCI

wav2letter++ is a fast, open source speech processing toolkit from the Speech team at Facebook AI Research built to facilitate research in end-to-end models for speech recognition. It is written entirely in C++ and uses the ArrayFire tensor library and the flashlight machine learning library for maximum efficiency. Our approach is detailed in this arXiv paper.

This repository also contains pre-trained models and implementations for various ASR results including:

The previous iteration of wav2letter (written in Lua) can be found in the wav2letter-lua branch.

Building wav2letter++

See Building Instructions for details.

Full documentation

To get started with wav2letter++, checkout the tutorials section.

We also provide complete recipes for WSJ, Timit and Librispeech and they can be found in recipes folder.

Finally, we provide Python bindings for a subset of wav2letter++ (featurization, decoder, and ASG criterion).

Citation

If you use the code in your paper, then please cite it as:

@article{pratap2018w2l,
  author          = {Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert},
  title           = {wav2letter++: The Fastest Open-source Speech Recognition System},
  journal         = {CoRR},
  volume          = {abs/1812.07625},
  year            = {2018},
  url             = {https://arxiv.org/abs/1812.07625},
}

Join the wav2letter community

See the CONTRIBUTING file for how to help out.

License

wav2letter++ is BSD-licensed, as found in the LICENSE file.

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