Wav2Vec2-XLS-R-2B
| Property | Value |
|---|---|
| Parameters | 2 Billion |
| License | Apache 2.0 |
| Author | |
| Paper | Research Paper |
| Languages Supported | 128 languages |
What is wav2vec2-xls-r-2b?
Wav2Vec2-XLS-R-2B is Facebook's groundbreaking multilingual speech model that represents a significant advancement in cross-lingual speech processing. This massive model, with 2 billion parameters, was pretrained on an unprecedented 436,000 hours of speech data across 128 languages, making it one of the most comprehensive multilingual speech models available.
Implementation Details
The model is built on the wav2vec 2.0 architecture and requires speech input sampled at 16kHz. It leverages data from multiple sources including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107 to achieve robust cross-lingual speech representations.
- Pretrained on 436K hours of unlabeled speech data
- Supports 128 different languages
- Uses wav2vec 2.0 objective for training
- Requires 16kHz audio input
Core Capabilities
- Automatic Speech Recognition (ASR) with 20-33% lower error rates
- Speech Translation with 7.4 BLEU score improvement
- State-of-the-art performance on VoxLingua107 language identification
- Cross-lingual speech processing that outperforms English-only pretraining
Frequently Asked Questions
Q: What makes this model unique?
The model's massive scale (2B parameters), extensive language coverage (128 languages), and the sheer volume of training data (436K hours) make it unique. It demonstrates that with sufficient model size, cross-lingual pretraining can outperform monolingual approaches.
Q: What are the recommended use cases?
The model is best suited for fine-tuning on downstream tasks such as Automatic Speech Recognition, Speech Translation, and Language Classification. It's particularly valuable for applications requiring multilingual speech processing capabilities.
