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wav2letter++

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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++ and full documentation

All details and documentation can be found on the wiki.

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) and a standalone inference framework for running online ASR.

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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Facebook AI Research's Automatic Speech Recognition Toolkit

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  • C++ 74.0%
  • Python 12.0%
  • CMake 8.7%
  • Cuda 2.5%
  • Shell 2.2%
  • C 0.4%
  • Dockerfile 0.2%