whisper-large-v3-es

Whisper Large-V3 Spanish

Model summary

Whisper Large-V3 Spanish is a cutting-edge automatic speech recognition (ASR) model for Spanish (es), fine-tuned from [openai/whisper-large-v3] on the Spanish subset of Mozilla Common Voice 13.0. It achieves a Word Error Rate (WER) of 4.9295% on the evaluation set, making it one of the most accurate Whisper models for Spanish.

This model incorporates improvements from the Large-V3 architecture, including better noise robustness, enhanced multilingual pretraining, and mixed precision training for efficiency.

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Model description

• Architecture: Transformer-based encoder–decoder (Whisper Large-V3)
• Base model: openai/whisper-large-v3
• Language: Spanish (es)
• Task: Automatic Speech Recognition (ASR)
• Output: Text transcription in Spanish
• Decoding: Autoregressive sequence-to-sequence decoding

Large-V3 builds upon Large-V2, offering lower WER and improved generalization across accents and audio conditions.

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Intended use

Primary use cases

• High-accuracy transcription of Spanish audio
• Podcasts, interviews, lectures, and long-form audio
• Research or commercial applications requiring top-tier ASR performance in Spanish

Limitations

• Performance may drop on heavily accented or extremely noisy audio
• High memory and compute requirements, particularly for real-time use
• Not suitable for critical domains (medical, legal) without human verification

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Training and evaluation data

• Dataset: Mozilla Common Voice 13.0 (Spanish subset)

• Data type: Crowd-sourced read speech

• Preprocessing:

  • Audio resampled to 16 kHz
  • Text tokenized using Whisper tokenizer
  • Filtering of corrupted or invalid samples

• Evaluation metric: Word Error Rate (WER) on held-out evaluation set

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Evaluation results

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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: 20000
• Train batch size: 32 (gradient accumulation 2 → effective batch size 64)
• Eval batch size: 16
• Seed: 42
• Mixed precision training: Native AMP

Training results (summary)

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Framework versions

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

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Example usage

from transformers import pipeline

hf_model = "HiTZ/whisper-large-v3-es"  # replace with actual repo ID
device = 0  # -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.