---
language:
- es
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Spanish
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_13_0 es
      type: mozilla-foundation/common_voice_13_0
      config: es
      split: test
      args: es
    metrics:
    - name: Wer
      type: wer
      value: 8.266774443952604
---

# Whisper Small Spanish

## Model summary

**Whisper Small Spanish** is an automatic speech recognition (ASR) model for **Spanish (es)**, fine-tuned from [openai/whisper-small] on the **Spanish subset of Mozilla Common Voice 13.0**. It achieves a **Word Error Rate (WER) of 8.2668%** on the evaluation split.

This model provides a good balance between transcription accuracy and computational efficiency, suitable for applications requiring relatively low-latency ASR with decent quality.

---

## Model description

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

Compared to Whisper Base, this model is slightly larger and generally more accurate, particularly for standard read Spanish.

---

## Intended use

### Primary use cases

* Real-time or batch transcription of Spanish speech  
* Research or experimentation with Spanish ASR  
* Applications with moderate hardware resources where Whisper Medium or Large is too heavy  

### Limitations

* Performance may degrade for:
  * Noisy or overlapping speech  
  * Regional accents or dialects not well represented in Common Voice  
  * Very fast conversational speech  

* Not recommended for safety-critical or professional-level transcription tasks.

---

## 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 normalized using Whisper tokenizer  
  * Invalid or corrupted samples removed  

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

---

## Evaluation results

| Metric     | Value      |
| ---------- | ---------- |
| WER (eval) | **8.2668%** |

---

## 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: 5000  
* Train batch size: 64  
* Eval batch size: 32  
* Seed: 42  

### Training results (summary)

| Training Loss | Epoch | Step | Validation Loss | WER     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1320        | 2.0   | 1000 | 0.2461          | 9.5267  |
| 0.1288        | 4.01  | 2000 | 0.2251          | 8.5215  |
| 0.0814        | 6.01  | 3000 | 0.2212          | 8.2668  |
| 0.0905        | 8.01  | 4000 | 0.2310          | 8.4997  |
| 0.0319        | 10.02 | 5000 | 0.2358          | 8.5343  |

---

## Framework versions

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

---

## Example usage

```python
from transformers import pipeline

hf_model = "HiTZ/whisper-small-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"])
```

---

## 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:

```bibtex
@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](https://arxiv.org/abs/2503.23542)
for more details.

---

## License

This model is available under the
[Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
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.