wav2vec2-large-xlsr-53-polish
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Author | jonatasgrosman |
| Downloads | 339,090 |
| Test WER | 14.21% |
| Test CER | 3.49% |
What is wav2vec2-large-xlsr-53-polish?
This is a specialized speech recognition model fine-tuned from facebook/wav2vec2-large-xlsr-53 specifically for the Polish language. It's trained on Common Voice 6.1 dataset and represents a significant advancement in Polish automatic speech recognition (ASR) technology. The model requires 16kHz audio input and has demonstrated impressive performance metrics, especially when combined with a language model.
Implementation Details
The model is built upon the wav2vec2 architecture and has been carefully optimized for Polish language processing. It achieves a Word Error Rate (WER) of 14.21% and Character Error Rate (CER) of 3.49% on the test set, with even better results (10.98% WER, 2.93% CER) when enhanced with a language model.
- Supports both direct transcription and language model-enhanced processing
- Optimized for 16kHz audio input
- Implements the XLSR-53 architecture for robust speech recognition
- Trained using OVHcloud GPU resources
Core Capabilities
- High-accuracy Polish speech transcription
- Batch processing support for multiple audio files
- Compatible with popular audio processing libraries like librosa
- Flexible integration through Python APIs
- Support for both academic and commercial applications under Apache 2.0 license
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized optimization for Polish language processing, achieving impressive accuracy metrics and being backed by extensive training on the Common Voice dataset. Its combination of low error rates and practical implementation makes it particularly valuable for Polish ASR applications.
Q: What are the recommended use cases?
The model is ideal for Polish speech transcription tasks, including automated subtitling, voice command systems, and speech-to-text applications. It's particularly effective when integrated with a language model for enhanced accuracy.
