wav2vec2-base-960h
| Property | Value |
|---|---|
| Parameter Count | 94.4M parameters |
| License | Apache 2.0 |
| Paper | View Research Paper |
| WER (Clean/Other) | 3.4% / 8.6% |
What is wav2vec2-base-960h?
wav2vec2-base-960h is Facebook's state-of-the-art speech recognition model that has been pretrained and fine-tuned on 960 hours of LibriSpeech audio data. This model represents a significant breakthrough in speech recognition technology, designed to work with 16kHz sampled speech audio input.
Implementation Details
The model utilizes a contrastive learning approach where it masks speech input in the latent space and learns from quantized latent representations. It's implemented using PyTorch and can be easily deployed using the Transformers library.
- Achieves 3.4% WER on LibriSpeech clean test set
- Performs at 8.6% WER on LibriSpeech other test set
- Requires 16kHz audio sampling rate
- Implements CTC (Connectionist Temporal Classification) for speech recognition
Core Capabilities
- Automatic speech recognition for English language
- Raw audio processing without preprocessing requirements
- Efficient performance with limited labeled data
- Real-time transcription capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model demonstrates that learning representations from speech audio alone, followed by fine-tuning on transcribed speech, can outperform semi-supervised methods while maintaining simplicity. It's particularly effective with limited labeled data, achieving impressive results with as little as one hour of labeled speech.
Q: What are the recommended use cases?
The model is ideal for English speech recognition tasks, particularly in scenarios requiring accurate transcription of clean audio. It's well-suited for applications in transcription services, voice assistants, and audio content analysis where 16kHz audio input is available.
