wav2vec2-xlsr-multilingual-56
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Model Type | Automatic Speech Recognition |
| Languages Supported | 56 languages |
| Parent Model | wav2vec2-large-xlsr-53 |
What is wav2vec2-xlsr-multilingual-56?
wav2vec2-xlsr-multilingual-56 is a powerful multilingual automatic speech recognition (ASR) model that supports 56 different languages. It's built upon the wav2vec architecture and fine-tuned on the Common Voice dataset. The model is designed to process audio input at 16kHz sampling rate and can handle speech recognition tasks across diverse language families.
Implementation Details
The model is fine-tuned from facebook/wav2vec2-large-xlsr-53 and demonstrates varying performance across different languages. It shows particularly strong results for Spanish (WER 19.63%, CER 5.41%) and Esperanto (CER 6.23%). The implementation requires audio input to be sampled at 16kHz for optimal performance.
- Built on wav2vec architecture
- Supports 56 languages in a single model
- Fine-tuned on Common Voice dataset
- Implements CTC (Connectionist Temporal Classification) for speech recognition
Core Capabilities
- Multilingual speech recognition with single model deployment
- Language-specific recognition optimization
- Handles diverse phonetic systems across languages
- Real-time audio processing capabilities
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 56 languages in a single implementation makes it highly versatile for multilingual applications. It's particularly notable for its strong performance in several European languages while maintaining reasonable accuracy across diverse language families.
Q: What are the recommended use cases?
The model is ideal for multilingual speech recognition applications, particularly in scenarios requiring support for multiple languages without switching between different models. It's especially effective for Spanish, German, English, and French language processing, though performance varies significantly across languages.
