whisper-medium-gl

Whisper Medium Galician

Model summary

Whisper Medium Galician is an automatic speech recognition (ASR) model for Galician (gl) speech. It is fine-tuned from [openai/whisper-medium] on the Galician portion of Mozilla Common Voice 13.0, achieving a Word Error Rate (WER) of 7.12% on the Common Voice evaluation split.

This model provides high-accuracy transcription while remaining computationally manageable, suitable for medium-scale Galician ASR tasks.

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Model description

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

Leveraging Whisper’s multilingual pretraining, this medium model is fine-tuned on Galician speech data to deliver highly accurate transcription for low-resource language applications.

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Intended use

Primary use cases

• High-accuracy transcription of Galician audio recordings
• Offline or batch ASR pipelines
• Research and development in Galician ASR
• Media, educational, and archival transcription tasks

Intended users

• Researchers working on Galician or low-resource ASR
• Developers building Galician speech applications
• Academic or institutional users

Out-of-scope use

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

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Limitations and known issues

• Performance may degrade on:
  • Noisy or low-quality recordings
  • Conversational or spontaneous speech
  • Accents underrepresented in Common Voice
• Transcription errors may still occur under challenging acoustic conditions
• Dataset biases from Common Voice may be reflected in outputs

Users are encouraged to evaluate the model on their own data before deployment.

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Training and evaluation data

Training data

• Dataset: Mozilla Common Voice 13.0 (Galician 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 data

• Dataset: Mozilla Common Voice 13.0 (Galician evaluation split)
• Metric: Word Error Rate (WER)

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Evaluation results

This reflects the expected performance of a medium Whisper model fine-tuned for Galician.

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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: 10,000
• Train batch size: 64
• Evaluation batch size: 32
• Seed: 42

Training results (summary)

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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-medium-gl"  # 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.