Skip to content
Facebook AI Research Automatic Speech Recognition Toolkit
C++ CMake Python Cuda C
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
cmake
docs
recipes
src
tutorials
.gitignore
CMakeLists.txt
CODE_OF_CONDUCT.md
CONTRIBUTING.md
Decode.cpp
Dockerfile-CPU
Dockerfile-CUDA
LICENSE
README.md
Test.cpp
Train.cpp

README.md

wav2letter++

wav2letter++ is a fast open source speech processing toolkit from the Speech Team at Facebook AI Research. 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.

The goal of this software is to facilitate research in end-to-end models for speech recognition.

The previous version of wav2letter (written in Lua) can be found in the "wav2letter-lua" branch under the repository.

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.

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.

You can’t perform that action at this time.