wav2letter++ is a fast, open source speech processing toolkit from the Speech team at Facebook AI Research built to facilitate research in end-to-end models for speech recognition. It’s intended as a platform for rapid research in end-to-end speech recognition.
It follows a completely convolutional approach, and uses Convolutional Neural Networks (CNN) for acoustic modelling as well as language modelling.
wav2letter++ uses the ArrayFire tensor library and the flashlight machine learning library for maximum efficiency. ArrayFire is a highly optimized tensor library that can execute on multiple backends including a CUDA GPU back-end and a CPU backend. ArrayFire also uses just-in-time code generation to combine series of simple operations into a single kernel call. This results in faster execution for memory bandwidth bound operations and can reduce peak memory use.
wav2letter has been moved and consolidated into Flashlight in the ASR application.
wav2letter++ is free and open source software.
- Supports three main modes of training: train (flat-start training), continue (continuing with a checkpoint state), and fork (for e.g. transfer learning). The training pipeline scales seamlessly using data-parallel, synchronous stochastic-gradient-descent as well as inter process communications powered by NVIDIA Collective Communication Library. Training times scale linearly to 64 GPUs with 100 million parameters.
- Supports several end-to-end sequence models as well as a wide range of network architectures and activation functions.
- Supports feature extraction across different audio formats. The framework computes features on the fly prior to each network evaluation and enforces asynchrony and parallelization in order to maximize efficiency during the training of models.
- Training pipeline gives maximum flexibility for the user to experiment with different features, architectures and optimization parameters.
- Decoder is a beam-search decoder with several optimizations to improve efficiency.
- Written entirely in C++ which makes it efficient to train models and perform real-time decoding.
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