Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/flashlight/wav2letter

Facebook AI Research's Automatic Speech Recognition Toolkit
https://github.com/flashlight/wav2letter

cpp deep-learning end-to-end speech-recognition wav2letter

Last synced: about 1 month ago
JSON representation

Facebook AI Research's Automatic Speech Recognition Toolkit

Lists

README 

        
# wav2letter++



[![CircleCI](https://circleci.com/gh/flashlight/wav2letter.svg?style=svg)](https://app.circleci.com/pipelines/github/flashlight/wav2letter)

[![Join the chat at https://gitter.im/wav2letter/community](https://badges.gitter.im/wav2letter/community.svg)](https://gitter.im/wav2letter/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)



## Important Note:

### wav2letter has been moved and consolidated [into Flashlight](https://github.com/flashlight/flashlight) in the [ASR application](https://github.com/flashlight/flashlight/tree/master/flashlight/app/asr).



Future wav2letter development will occur in Flashlight.



*To build the old, pre-consolidation version of wav2letter*, checkout the [wav2letter v0.2](https://github.com/flashlight/wav2letter/releases/tag/v0.2) release, which depends on the old [Flashlight v0.2](https://github.com/flashlight/flashlight/releases/tag/v0.2) release. The [`wav2letter-lua`](https://github.com/flashlight/wav2letter/tree/wav2letter-lua) project can be found on the [`wav2letter-lua` branch](https://github.com/flashlight/wav2letter/tree/wav2letter-lua), accordingly.



For more information on wav2letter++, see or cite [this arXiv paper](https://arxiv.org/abs/1812.07625).



## Recipes

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](recipes/streaming_convnets/)

- [Synnaeve et al. (2020): End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures](recipes/sota/2019)

- [Kahn et al. (2020): Self-Training for End-to-End Speech Recognition](recipes/self_training)

- [Likhomanenko et al. (2019): Who Needs Words? Lexicon-free Speech Recognition](recipes/lexicon_free/)

- [Hannun et al. (2019): Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions](recipes/seq2seq_tds/)



Data preparation for training and evaluation can be found in [data](data) directory.



### Building the Recipes



First, install [Flashlight](https://github.com/flashlight/flashlight/tree/0.3) **(using the [0.3 branch](https://github.com/flashlight/flashlight/tree/0.3) is required)** with the [ASR application](https://github.com/flashlight/flashlight/tree/master/flashlight/app/asr).

```shell

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

```shell

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

```

when running `cmake`.



## Join the wav2letter community

* 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]



## License

wav2letter++ is MIT-licensed, as found in the [LICENSE](LICENSE) file.