Wav2letter


Wav2letter is an end-to-end Automatic Speech Recognition (ASR) system for researchers and developers to transcribe speech.

Simple and efficient

Wav2letter implements the architecture proposed in Wav2Letter: an End-to-End ConvNet-based Speech Recognition System and Letter Based Speech Recognition with Gated ConvNets. It provides pre-trained models for the Librispeech dataset to help developers start transcribing speech right away.


Get Started

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  • If you plan to train on a CPU, install Intel MKL. For training on a GPU, install NVIDIA CUDA Toolkit. Install LuaJIT + LuaRocks, KenLM, OpenMPI, and TorchMPI as needed to support development.

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  • Install Torch and Torch packages.

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    luarocks install torch
    luarocks install cudnn # for GPU support
    luarocks install cunn # for GPU support
            

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  • Install wav2letter packages.

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    git clone https://github.com/facebookresearch/wav2letter.git
    cd wav2letter
    cd gtn && luarocks make rocks/gtn-scm-1.rockspec && cd ..
    cd speech && luarocks make rocks/speech-scm-1.rockspec && cd ..
    cd torchnet-optim && luarocks make rocks/torchnet-optim-scm-1.rockspec && cd ..
    cd wav2letter && luarocks make rocks/wav2letter-scm-1.rockspec && cd ..
    # Assuming here you got KenLM in $HOME/kenlm
    # And only if you plan to use the decoder:
    cd beamer && KENLM_INC=$HOME/kenlm luarocks make rocks/beamer-scm-1.rockspec && cd ..
            

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  • Download pre-trained models and iterate on them or build and train new models.

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