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--- |
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language: |
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- es |
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license: apache-2.0 |
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base_model: openai/whisper-base |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_13_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Base Spanish |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: mozilla-foundation/common_voice_13_0 es |
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type: mozilla-foundation/common_voice_13_0 |
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config: es |
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split: test |
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args: es |
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metrics: |
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- name: Wer |
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type: wer |
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value: 13.531181636803376 |
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--- |
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# Whisper Base Spanish |
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## Model summary |
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**Whisper Base Spanish** is an automatic speech recognition (ASR) model for **Spanish (es)** speech. It is fine-tuned from [openai/whisper-base] on the **Spanish subset of Mozilla Common Voice 13.0**, achieving a **Word Error Rate (WER) of 13.5312%** on the evaluation split. |
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This variant balances transcription quality and model size, suitable for general-purpose Spanish ASR tasks. |
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--- |
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## Model description |
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* **Architecture:** Transformer-based encoder–decoder (Whisper Base) |
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* **Base model:** openai/whisper-base |
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* **Language:** Spanish (es) |
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* **Task:** Automatic Speech Recognition (ASR) |
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* **Output:** Text transcription in Spanish |
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* **Decoding:** Autoregressive sequence-to-sequence decoding |
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Fine-tuned for improved transcription quality over Whisper Tiny while remaining lightweight compared to larger Whisper models. |
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--- |
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## Intended use |
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### Primary use cases |
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* General-purpose Spanish speech transcription |
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* Research and experimentation with Spanish ASR |
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* Moderate resource environments where Whisper Large is too heavy |
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### Out-of-scope use |
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* Professional transcription requiring near-zero WER |
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* Very noisy or heavily accented Spanish |
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* Safety-critical applications |
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--- |
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## Limitations and known issues |
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* Performance may vary on: |
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* Noisy audio or multi-speaker recordings |
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* Regional dialects not well represented in Common Voice |
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* Rapid conversational speech |
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* While better than Whisper Tiny, it may still produce transcription errors in difficult conditions. |
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--- |
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## Training and evaluation data |
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* **Dataset:** Mozilla Common Voice 13.0 (Spanish subset) |
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* **Data type:** Crowd-sourced read speech |
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* **Preprocessing:** |
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* Audio resampled to 16 kHz |
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* Text normalized using Whisper tokenizer |
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* Invalid samples removed |
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* **Evaluation metric:** Word Error Rate (WER) on held-out evaluation set |
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--- |
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## Evaluation results |
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| Metric | Value | |
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| ---------- | ---------- | |
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| WER (eval) | **13.5312%** | |
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--- |
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## Training procedure |
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### Training hyperparameters |
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* Learning rate: 2.5e-5 |
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* Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-8) |
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* LR scheduler: Linear |
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* Warmup steps: 500 |
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* Training steps: 5000 |
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* Train batch size: 128 |
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* Eval batch size: 64 |
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* Seed: 42 |
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### Training results (summary) |
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| Training Loss | Epoch | Step | Validation Loss | WER | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:| |
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| 0.2173 | 4.0 | 1000 | 0.3409 | 14.8123 | |
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| 0.0955 | 8.01 | 2000 | 0.3377 | 15.4269 | |
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| 0.1647 | 12.01 | 3000 | 0.3393 | 14.5602 | |
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| 0.0986 | 16.01 | 4000 | 0.3281 | 13.5312 | |
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| 0.1272 | 20.02 | 5000 | 0.3423 | 13.7596 | |
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--- |
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## Framework versions |
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- Transformers 4.33.0.dev0 |
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- PyTorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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--- |
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## How to use |
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```python |
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from transformers import pipeline |
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hf_model = "HiTZ/whisper-base-es" # replace with actual repo ID |
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device = 0 # set to -1 for CPU |
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pipe = pipeline( |
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task="automatic-speech-recognition", |
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model=hf_model, |
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device=device |
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) |
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result = pipe("audio.wav") |
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print(result["text"]) |
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``` |
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--- |
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## Ethical considerations and risks |
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* This model transcribes speech and may process personal data. |
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* Users should ensure compliance with applicable data protection laws (e.g., GDPR). |
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* The model should not be used for surveillance or non-consensual audio processing. |
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--- |
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## Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@misc{dezuazo2025whisperlmimprovingasrmodels, |
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title={Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages}, |
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author={Xabier de Zuazo and Eva Navas and Ibon Saratxaga and Inma Hernáez Rioja}, |
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year={2025}, |
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eprint={2503.23542}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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Please, check the related paper preprint in |
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[arXiv:2503.23542](https://arxiv.org/abs/2503.23542) |
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for more details. |
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--- |
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## License |
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This model is available under the |
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[Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0). |
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You are free to use, modify, and distribute this model as long as you credit |
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the original creators. |
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--- |
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## Contact and attribution |
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* Fine-tuning and evaluation: HiTZ/Aholab - Basque Center for Language Technology |
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* Base model: OpenAI Whisper |
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* Dataset: Mozilla Common Voice |
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For questions or issues, please open an issue in the model repository. |
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