HiTZ/whisper-tiny-ca

Whisper Tiny Catalan

Model summary

Whisper Tiny Catalan is an automatic speech recognition (ASR) model for Catalan (ca) speech. It is fine-tuned from [openai/whisper-tiny] on the Catalan subset of Mozilla Common Voice 13.0, achieving a Word Error Rate (WER) of 16.90% on the evaluation split.

This model is intended for general-purpose transcription of Catalan audio.

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Model description

• Architecture: Transformer-based encoder–decoder (Whisper)
• Base model: openai/whisper-tiny
• 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, leveraging Whisper’s multilingual pretraining.

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Intended use

Primary use cases

• Transcription of Catalan audio recordings
• Offline or batch ASR pipelines
• Research and development in Catalan ASR
• Educational and media applications

Out-of-scope use

• Real-time or low-latency ASR without optimization
• Speech translation tasks
• Safety-critical applications without further validation

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Limitations and known issues

• Performance may degrade on:
  • Noisy or low-quality recordings
  • Conversational or spontaneous speech
  • Dialects underrepresented in Common Voice
• Dataset biases may be reflected in outputs
• Occasional transcription errors can occur under difficult acoustic conditions

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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

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Evaluation results

Metric	Value
WER (eval)	16.90%

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Training procedure

Training hyperparameters

• Learning rate: 3.75e-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: 256
• Eval batch size: 128
• Seed: 42

Training results (summary)

Training Loss	Epoch	Step	Validation Loss	WER
0.2098	7.02	1000	0.3994	22.5047
0.162	15.02	2000	0.3454	19.4181
0.0662	23.01	3000	0.3526	18.5687
0.0934	31.01	4000	0.3312	18.1600
0.1167	39.0	5000	0.3180	16.9043

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Framework versions

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

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How to use

from transformers import pipeline

hf_model = "HiTZ/whisper-tiny-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"])

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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.

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Citation

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

@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.

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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.

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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.