Wav2Vec2-Large
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch |
| Paper | View Paper |
| Downloads | 3,986 |
What is wav2vec2-large?
Wav2vec2-large is a powerful speech recognition model developed by Facebook that learns representations from raw audio data. It's designed to work with 16kHz sampled speech audio and employs a unique approach of masking speech input in the latent space while solving contrastive tasks over quantized latent representations.
Implementation Details
The model utilizes a transformer-based architecture and is pretrained on unlabeled speech data. It demonstrates remarkable performance even with limited labeled data - using just 10 minutes of labeled data and pretraining on 53k hours of unlabeled data achieves 4.8/8.2 WER on clean/other test sets.
- Pretrained on 16kHz sampled speech audio
- Employs masking in latent space
- Uses joint learning of quantized representations
- Achieves 1.8/3.3 WER on Librispeech clean/other test sets
Core Capabilities
- Speech recognition with minimal labeled data
- Robust representation learning from raw audio
- Fine-tuning capability for specific ASR tasks
- State-of-the-art performance on standard benchmarks
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to learn powerful representations from speech audio alone and achieve state-of-the-art results with minimal labeled data sets it apart. It can match or exceed semi-supervised methods while being conceptually simpler.
Q: What are the recommended use cases?
The model is best suited for automatic speech recognition tasks after fine-tuning. It's particularly valuable in scenarios with limited labeled data but access to large amounts of unlabeled speech data.
