This model is a fine-tuned version of openai/whisper-medium.
It is adapted for Slovak ASR using SloPalSpeech: 2,806 hours of aligned, ≤30 s speech–text pairs from official plenary sessions of the Slovak National Council.

Language: Slovak
Domain: Parliamentary / formal speech
Training data: 2,806 h
Intended use: Slovak speech recognition; strongest in formal/public-speaking contexts

🧪 Evaluation

Dataset	Base WER	Fine-tuned WER	Δ (abs)
Common Voice 21 (sk)	38.0	18.0	-20.0
FLEURS (sk)	18.7	7.6	-11.1

Numbers from the paper’s final benchmark runs.

🔧 Training Details

Framework: Hugging Face Transformers
Hardware: NVIDIA A10 GPUs
Epochs: up to 3 with early stopping on validation WER
Learning rate: ~40× smaller than Whisper pretraining LR

⚠️ Limitations

Domain bias toward parliamentary speech (e.g., political vocabulary, formal register).
As with Whisper models generally, occasional hallucinations may appear; consider temperature fallback / compression-ratio checks at inference time.
Multilingual performance is not guaranteed (full-parameter finetuning emphasized Slovak).

📝 Citation & Paper

For more details, please see our paper on arXiv. If you use this model in your work, please cite it as:

bibtex

@misc{božík2025slopalspeech2800hourslovakspeech,
      title={SloPalSpeech: A 2,800-Hour Slovak Speech Corpus from Parliamentary Data}, 
      author={Erik Božík and Marek Šuppa},
      year={2025},
      eprint={2509.19270},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.19270}, 
}

🙏 Acknowledgements

This work was supported by VÚB Banka who provided the GPU resources and backing necessary to accomplish it, enabling progress in Slovak ASR research.

NaiveNeuron/whisper-medium-sk

Whisper Medium — Fine-tuned on SloPalSpeech

🧪 Evaluation

🔧 Training Details

⚠️ Limitations

📝 Citation & Paper

🙏 Acknowledgements

NaiveNeuron/
whisper-medium-sk