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 wav2letter-lua project can be found on the wav2letter-lua branch, 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. All results reproduction must use Flashlight <= 0.3.2 for exact reproducability. Papers contained here include:

• 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.

First, install Flashlight (using the 0.3 branch is required) with the ASR application.

mkdir build && cd build
cmake .. && make -j8

If Flashlight or ArrayFire are installed in nonstandard paths via a custom CMAKE_INSTALL_PREFIX, they can be found by passing

-Dflashlight_DIR=[PREFIX]/usr/share/flashlight/cmake/ -DArrayFire_DIR=[PREFIX]/usr/share/ArrayFire/cmake

when running cmake.

• Facebook page: https://www.facebook.com/groups/717232008481207/
• Google group: https://groups.google.com/forum/#!forum/wav2letter-users
• Contact: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]

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