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5.7K Stars 957 Forks Other 445 Commits 89 Opened issues


Facebook AI Research's Automatic Speech Recognition Toolkit

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Important Note:

wav2letter has been moved and consolidated into Flashlight in the ASR application.

Future wav2letter development will occur in Flashlight.

To build the old, pre-consolidation version of wav2letter, checkout the wav2letter v0.2 release, which depends on the old Flashlight v0.2 release. The

project can be found on the
, accordingly.

For more information on wav2letter++, see or cite this arXiv paper.


This repository includes recipes to reproduce the following research papers as well as pre-trained models: - Pratap et al. (2020): Scaling Online Speech Recognition Using ConvNets - Synnaeve et al. (2020): End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures - Kahn et al. (2020): Self-Training for End-to-End Speech Recognition - Likhomanenko et al. (2019): Who Needs Words? Lexicon-free Speech Recognition - Hannun et al. (2019): Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions

Data preparation for training and evaluation can be found in data directory.

Building the Recipes

First, install Flashlight with the ASR application. Then, after cloning the project source:

mkdir build && cd build
cmake .. && make -j8
If Flashlight or ArrayFire are installed in nonstandard paths via a custom
, they can be found by passing
-Dflashlight_DIR=[PREFIX]/usr/share/flashlight/cmake/ -DArrayFire_DIR=[PREFIX]/usr/share/ArrayFire/cmake
when running

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

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