data2vec-audio-base-960h
| Property | Value |
|---|---|
| Developer | |
| License | Apache 2.0 |
| Paper | View Research Paper |
| Best WER (Clean) | 2.77% |
What is data2vec-audio-base-960h?
data2vec-audio-base-960h is Facebook's innovative speech recognition model that implements the data2vec framework for self-supervised learning. Trained on 960 hours of LibriSpeech audio data, this model represents a significant advancement in unified self-supervised learning across different modalities.
Implementation Details
The model utilizes a Transformer architecture and employs a unique approach to self-supervised learning where it predicts contextualized latent representations rather than modality-specific targets. It's specifically designed to process 16kHz sampled speech audio and implements CTC (Connectionist Temporal Classification) for speech recognition tasks.
- Optimized for 16kHz audio input
- Transformer-based architecture with self-distillation
- Achieves 2.77 WER on LibriSpeech clean test and 7.08 WER on other test sets
- Implements context-aware latent representation prediction
Core Capabilities
- High-accuracy speech recognition for clean audio (2.77% WER)
- Robust performance on challenging audio (7.08% WER on "other" test set)
- Easy integration with HuggingFace Transformers library
- Batch processing support for efficient inference
Frequently Asked Questions
Q: What makes this model unique?
This model is part of Facebook's data2vec framework, which uniquely applies the same learning method across speech, vision, and NLP tasks. Instead of focusing on modality-specific targets, it predicts contextualized latent representations, making it more versatile and powerful.
Q: What are the recommended use cases?
The model is best suited for automatic speech recognition tasks, particularly for clean audio sampled at 16kHz. It's ideal for transcription services, voice command systems, and any application requiring high-accuracy speech-to-text conversion.
