Polish Whisper
At bards.ai, we fine-tuned OpenAI's Whisper model specifically for the Polish language as part of Hugging Face's global Whisper Fine-Tuning Sprint.
Our model took first place in the Polish category, standing out for its exceptional transcription accuracy and robustness across diverse Polish audio sources.
We focused on minimizing word error rate while preserving the natural flow of spoken Polish, ensuring the model performs well across interviews, podcasts, casual speech, and formal recordings. The result is a state-of-the-art Polish ASR model ready for real-world use in transcription, accessibility, and voice-driven applications.
Performance Metrics
| Metric | Value |
|---|---|
| Loss | 0.3684 |
| WER | 7.2802% |
Training Params
| Parameter | Value |
|---|---|
| Learning rate | 1e-05 |
| Train batch size | 8 |
| Eval batch size | 4 |
| Seed | 42 |
| Gradient accumulation steps | 8 |
| Total train batch size | 64 |
| Optimizer | Adam (betas=(0.9, 0.999), epsilon=1e-08) |
| LR scheduler type | linear |
| LR scheduler warmup steps | 500 |
| Training steps | 2100 |
| Mixed precision training | Native AMP |
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