facebookresearch/wav2letter
Wav2letter
wav2letter is an automatic speech recognition toolkit and deep learning framework designed to convert audio speech signals into written text. It functions as a distributed training system and an inference engine for building and deploying neural network architectures.
The system enables the training of large-scale speech models across multiple compute nodes using custom architecture files and structured recipes. It includes an inference engine that allows these trained models to be executed within Python workflows to transform audio sequences into text.
The framework covers the full speech recognition pipeline, including model training, audio sequence decoding, and the conversion of speech to text.
Features
- Speech-to-Text Conversion - Converts spoken audio signals into written text using deep learning models and neural network architectures.
- ASR Frameworks - Serves as a deep learning framework for building neural network architectures specialized for speech recognition and sequence decoding.
- Distributed Training - Implements a system for configuring data and model parallelism to train large-scale speech models across multiple compute nodes.
- Speech Model Training - Provides specialized training infrastructure for building automatic speech recognition models using custom architectures.
- Automatic Speech Recognition - Functions as a comprehensive system for converting spoken audio recordings into text using large-scale speech recognition technology.
- Model Inference APIs - Provides a Python API to execute trained speech recognition models for transforming audio sequences into text.
- Speech-to-Text Engines - Provides an inference engine that executes trained speech recognition models to transform audio sequences into text.
- Speech-to-Text Inference - Integrates speech recognition inference capabilities into Python workflows to transform audio files into text.
- Automatic Speech Recognition Toolkits - Provides a complete toolkit for training and deploying deep learning models that convert audio speech signals into written text.
- Connectionist Temporal Classification - Implements the Connectionist Temporal Classification loss function to map audio sequences to characters without pre-aligned time labels.
- Data-Parallel Training - Provides distributed data-parallel training to synchronize gradients across multiple GPU nodes for large-scale speech models.
- Beam Search Decoders - Includes beam search decoders to find the most probable character sequences from model probability distributions.
- Recurrent Neural Networks - Employs recurrent neural network architectures with gated loops to maintain temporal context in long speech signals.
- Sequence Decoders - Implements sequence decoders to determine the most accurate sequence of words for a given audio input.
- Training Recipes - Uses structured training recipes to standardize data preparation and hyperparameters for reproducible research.
- Computer Vision and Signal Processing - End-to-end automatic speech recognition system.
- Natural Language Processing - Automatic speech recognition toolkit.
- Audio - Toolkit for speech processing and recognition.
- Audio Processing - High-performance speech processing toolkit.
Star history
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