---
language:
- es
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Medium 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: 5.408751772230669
---
# Whisper Medium Spanish

## Model summary

**Whisper Medium Spanish** is an automatic speech recognition (ASR) model for **Spanish (es)**, fine-tuned from [openai/whisper-medium] on the **Spanish subset of Mozilla Common Voice 13.0**. It achieves a **Word Error Rate (WER) of 5.4088%** on the evaluation split.

This model offers higher accuracy than Whisper Small while remaining more efficient than Whisper Large variants, making it suitable for both batch and near real-time transcription of Spanish speech.

---

## Model description

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

Medium-sized model balances accuracy and speed, handling conversational Spanish better than smaller models.

---

## Intended use

### Primary use cases

* Batch or streaming transcription of Spanish speech  
* Research on Spanish ASR  
* Applications requiring moderate-to-high transcription accuracy without full-large model compute  

### Limitations

* Accuracy may drop for:
  * Noisy environments or overlapping speakers  
  * Strong regional accents not well represented in Common Voice  
  * Extremely fast or slurred speech  

* Not intended for legal, medical, or other safety-critical transcription.

---

## 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 with Whisper tokenizer  
  * Removal of invalid or corrupted samples  

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

---

## Evaluation results

| Metric     | Value      |
| ---------- | ---------- |
| WER (eval) | **5.4088%** |

---

## 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: 10000  
* Train batch size: 64  
* Eval batch size: 32  
* Seed: 42  

### Training results (summary)

| Training Loss | Epoch | Step  | Validation Loss | WER    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0917        | 2.0   | 1000  | 0.1944          | 6.8560 |
| 0.0927        | 4.0   | 2000  | 0.1817          | 6.1439 |
| 0.0456        | 6.01  | 3000  | 0.1805          | 6.2626 |
| 0.0343        | 8.01  | 4000  | 0.2097          | 6.1773 |
| 0.0046        | 10.01 | 5000  | 0.2292          | 5.9374 |
| 0.0829        | 12.01 | 6000  | 0.1814          | 6.0644 |
| 0.0021        | 14.01 | 7000  | 0.2318          | 5.7096 |
| 0.0288        | 16.01 | 8000  | 0.1871          | 5.5755 |
| 0.1297        | 18.02 | 9000  | 0.1831          | 5.6885 |
| 0.0377        | 20.02 | 10000 | 0.1915          | 5.4088 |

---

## 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-medium-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.