Whisper Small Catalan is an automatic speech recognition (ASR) model for Catalan (ca) speech. It is fine-tuned from [openai/whisper-small] on the Catalan subset of Mozilla Common Voice 13.0, achieving a Word Error Rate (WER) of 10.03% on the evaluation split.

This model offers higher transcription accuracy than the tiny variant while being smaller and faster than the base variant.

Model description

Architecture: Transformer-based encoder–decoder (Whisper)
Base model: openai/whisper-small
Language: Catalan (ca)
Task: Automatic Speech Recognition (ASR)
Output: Text transcription in Catalan
Decoding: Autoregressive sequence-to-sequence decoding

Fine-tuned to improve transcription quality on Catalan audio.

Intended use

Primary use cases

Accurate transcription of Catalan audio
Research and development in Catalan ASR
Media, educational, or accessibility applications

Out-of-scope use

Real-time transcription without optimization
Speech translation
Safety-critical applications without further validation

Limitations and known issues

Performance may degrade on:
- Noisy or low-quality recordings
- Conversational or spontaneous speech
- Regional dialects not well represented in Common Voice
Occasional transcription errors on difficult audio

Training and evaluation data

Dataset: Mozilla Common Voice 13.0 (Catalan subset)
Data type: Crowd-sourced, read speech
Preprocessing:
- Audio resampled to 16 kHz
- Text normalized using Whisper tokenizer
- Filtering of invalid or problematic samples
Evaluation metric: Word Error Rate (WER) on held-out evaluation set

Evaluation results

Metric	Value
WER (eval)	10.03%

Training procedure

Training hyperparameters

Learning rate: 1e-5
Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-8)
LR scheduler: Linear
Warmup steps: 500
Training steps: 5,000
Train batch size: 64
Eval batch size: 32
Seed: 42

Training results (summary)

Training Loss	Epoch	Step	Validation Loss	WER
0.1708	1.1	1000	0.2494	12.1846
0.0421	3.09	2000	0.2458	11.2689
0.0761	5.09	3000	0.2340	10.9231
0.0928	7.08	4000	0.2150	10.0394
0.0504	9.08	5000	0.2169	10.0252

Framework versions

Transformers 4.33.0.dev0
PyTorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3

How to use

python

from transformers import pipeline

hf_model = "HiTZ/whisper-small-ca"  # replace with actual repo ID
device = 0  # set to -1 for CPU

pipe = pipeline(
    task="automatic-speech-recognition",
    model=hf_model,
    device=device
)

result = pipe("audio.wav")
print(result["text"])

Ethical considerations and risks

This model transcribes speech and may process personal data.
Users should ensure compliance with applicable data protection laws (e.g., GDPR).
The model should not be used for surveillance or non-consensual audio processing.

Citation

If you use this model in your research, please cite:

bibtex

@misc{dezuazo2025whisperlmimprovingasrmodels,
  title={Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages},
  author={Xabier de Zuazo and Eva Navas and Ibon Saratxaga and Inma Hernáez Rioja},
  year={2025},
  eprint={2503.23542},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

Please, check the related paper preprint in arXiv:2503.23542 for more details.

License

This model is available under the Apache-2.0 License. You are free to use, modify, and distribute this model as long as you credit the original creators.

Contact and attribution

Fine-tuning and evaluation: HiTZ/Aholab - Basque Center for Language Technology
Base model: OpenAI Whisper
Dataset: Mozilla Common Voice

For questions or issues, please open an issue in the model repository.