| Property | Value |
|---|---|
| License | Apache 2.0 |
| Author | Jonatas Grosman |
| Test WER | 11.62% |
| Test CER | 3.36% |
What is wav2vec2-large-xlsr-53-greek?
This is a fine-tuned version of Facebook's wav2vec2-large-xlsr-53 model specifically optimized for Greek speech recognition. The model was trained on Common Voice 6.1 and CSS10 datasets, making it particularly effective for Greek language audio processing tasks. It operates on 16kHz audio input and demonstrates strong performance with a Word Error Rate of 11.62%.
Implementation Details
The model leverages the wav2vec2 architecture and was fine-tuned using GPU resources provided by OVHcloud. It processes audio directly without requiring a language model, making it straightforward to implement for speech recognition tasks.
- Built on the wav2vec2-large-xlsr-53 architecture
- Trained on Common Voice and CSS10 datasets
- Requires 16kHz audio input
- Implements CTC (Connectionist Temporal Classification) for sequence modeling
Core Capabilities
- Direct speech-to-text transcription for Greek language
- Batch processing of audio files
- No language model required for inference
- Competitive performance metrics (11.62% WER, 3.36% CER)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized fine-tuning for Greek language processing, achieving competitive performance metrics compared to other Greek ASR models. It's particularly notable for its ease of use, requiring no additional language model for inference.
Q: What are the recommended use cases?
The model is ideal for Greek speech recognition tasks, including transcription services, voice command systems, and audio content analysis. It's particularly suitable for applications requiring 16kHz audio processing and those needing direct speech-to-text conversion without additional language modeling.