metadata
language:
- sk
tags:
- speech
- asr
- whisper
- slovak
- parliament
- legal
- politics
base_model: openai/whisper-small
datasets:
- erikbozik/slovak-plenary-asr-corpus
metrics:
- wer
model-index:
- name: whisper-small-sk
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 21 (Slovak test set)
type: common_voice
metrics:
- name: WER
type: wer
value: 25.7
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: FLEURS (Slovak test set)
type: fleurs
metrics:
- name: WER
type: wer
value: 10.6
license: mit
Whisper Small — Fine-tuned on Slovak Plenary ASR Corpus
This model is a fine-tuned version of openai/whisper-small.
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) | 58.4 | 25.7 | -32.7 |
| FLEURS (sk) | 36.1 | 10.6 | -25.5 |
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:
@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.