wav2vec2-large-xlsr-53-english
| Property | Value |
|---|---|
| Parameter Count | 315M |
| License | Apache 2.0 |
| Downloads | 21.7M+ |
| WER (Test) | 19.06% (14.81% with LM) |
What is wav2vec2-large-xlsr-53-english?
This is a fine-tuned version of Facebook's wav2vec2-large-xlsr-53 model, specifically optimized for English speech recognition. Developed by Jonatas Grosman, it's trained on Common Voice 6.1 dataset and represents a significant advancement in automatic speech recognition (ASR) technology.
Implementation Details
The model is built on the XLSR-53 architecture and requires 16kHz audio input. It achieves impressive performance metrics with a Word Error Rate (WER) of 19.06% on the Common Voice test set, which improves to 14.81% when combined with a language model.
- Pytorch-based implementation with Transformers architecture
- Supports both basic inference and language model integration
- Optimized for production use with Safetensors support
- 315M parameters for robust feature extraction
Core Capabilities
- Direct speech-to-text transcription
- Handles various English accents and speaking styles
- Batch processing support for multiple audio files
- Integration with popular audio processing libraries
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its balance of accuracy and practicality, achieving strong performance metrics while maintaining usability across different English speech recognition tasks. The combination of XLSR-53 architecture with specific English optimization makes it particularly effective for real-world applications.
Q: What are the recommended use cases?
The model is ideal for automatic speech recognition tasks requiring high accuracy, such as transcription services, voice command systems, and automated subtitling. It's particularly well-suited for applications where 16kHz audio input can be guaranteed and where language model integration might be beneficial for improved accuracy.
