HiTZ/whisper-large-v3-eu

Whisper Large-V3 Basque

Model summary

Whisper Large-V3 Basque is an automatic speech recognition (ASR) model for Basque (eu) speech. It is fine-tuned from [openai/whisper-large-v3] on the Basque portion of Mozilla Common Voice 13.0, achieving a Word Error Rate (WER) of 10.62% on the Common Voice evaluation split.

This model offers state-of-the-art transcription quality for Basque speech, delivering improved accuracy and robustness over previous large Whisper variants while remaining suitable for offline and batch processing.

Model description

• —Architecture: Transformer-based encoder–decoder (Whisper)
• —Base model: openai/whisper-large-v3
• —Language: Basque (eu)
• —Task: Automatic Speech Recognition (ASR)
• —Output: Text transcription in Basque
• —Decoding: Autoregressive sequence-to-sequence decoding

Leveraging Whisper’s multilingual pretraining, this large-v3 model is fine-tuned on Basque speech data to provide highly accurate transcription for a low-resource language, suitable for research, media, and archival use cases.

Intended use

Primary use cases

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

Intended users

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

Out-of-scope use

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

Limitations and known issues

• —Performance may degrade on:
• —Noisy or low-quality recordings
• —Conversational or spontaneous speech
• —Accents underrepresented in Common Voice
• —While highly accurate, 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.

Training and evaluation data

Training data

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

Evaluation results

These results indicate state-of-the-art transcription performance for Basque ASR using a large-v3 Whisper model.

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: 20,000
• —Train batch size: 32
• —Gradient accumulation steps: 2
• —Total effective batch size: 64
• —Evaluation batch size: 16
• —Seed: 42
• —Mixed precision training: Native AMP

Training results (summary)

Framework versions

• —Transformers 4.37.2
• —PyTorch 2.2.0+cu121
• —Datasets 2.16.1
• —Tokenizers 0.15.1

How to use

from transformers import pipeline

hf_model = "HiTZ/whisper-large-v3-eu"  # 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:

@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.

Funding

This project with reference 2022/TL22/00215335 has been parcially funded by the Ministerio de Transformación Digital and by the Plan de Recuperación, Transformación y Resiliencia – Funded by the European Union – NextGenerationEU ILENIA and by the project IkerGaitu funded by the Basque Government. This model was trained at Hyperion, one of the high-performance computing (HPC) systems hosted by the DIPC Supercomputing Center.