wav2vec2-large-xlsr-53-dutch
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Downloads | 2.5M+ |
| Base Architecture | XLSR-53 |
| Test WER | 15.72% (12.84% with LM) |
What is wav2vec2-large-xlsr-53-dutch?
This is a fine-tuned version of Facebook's wav2vec2-large-xlsr-53 model, specifically optimized for Dutch speech recognition. Developed by Jonatas Grosman, it leverages the Common Voice 6.1 and CSS10 datasets to achieve state-of-the-art performance in Dutch ASR tasks. The model requires 16kHz audio input and demonstrates impressive accuracy with a Word Error Rate (WER) of 15.72%, which improves to 12.84% when combined with a language model.
Implementation Details
The model builds upon the robust XLSR-53 architecture and has been fine-tuned using OVHcloud's GPU infrastructure. It supports both direct transcription and enhanced performance through language model integration.
- Built on wav2vec2-large-xlsr-53 architecture
- Requires 16kHz audio sampling rate
- Supports batch processing and streaming input
- Includes pre-trained processor for audio handling
Core Capabilities
- Direct speech-to-text transcription in Dutch
- Batch processing of multiple audio files
- Integration with HuggingSound library for easy implementation
- Support for both WAV and MP3 input formats
- Enhanced performance with language model integration
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful XLSR-53 architecture with specialized Dutch language training, achieving impressive accuracy levels while maintaining processing efficiency. The dual compatibility with both standard and language model-enhanced operations makes it versatile for various applications.
Q: What are the recommended use cases?
The model is ideal for Dutch speech recognition tasks including transcription services, voice command systems, and audio content analysis. It's particularly effective for applications requiring high accuracy in Dutch language processing, such as automated subtitling or voice-based user interfaces.
