Facebook AI Research Automatic Speech Recognition Toolkit
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jcai1 and facebook-github-bot CUDA FCC truncate long targets
Previous behavior of ASG was to filter out targets that were longer than input length. Earlier this was changed to truncate the targets instead. CUDA FCC wasn't updated to reflect this. This diff updates CUDA FCC to truncate the target.

Since FCC doesn't need to perform any filtering after this change, this diff also removes `filter_idxs` array logic.

Reviewed By: vineelpratap

Differential Revision: D14171264

fbshipit-source-id: bc24dc6a1730419c77f061d6d759a1686c6ee9f5
Latest commit 1565f95 Feb 21, 2019



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.


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

  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.


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