---
language:
- es
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Large-V3 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: 4.9295277686894154
---

# 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**.

---

## 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.

---

## 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  

---

## 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  

---

## Evaluation results

| Metric     | Value      |
| ---------- | ---------- |
| WER (eval) | **4.9295%** |

---

## 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)

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.058         | 2.04  | 1000  | 0.1540          | 4.6851 |
| 0.0124        | 4.07  | 2000  | 0.1829          | 4.6787 |
| 0.0052        | 6.11  | 3000  | 0.2190          | 4.8096 |
| 0.0024        | 8.15  | 4000  | 0.2289          | 4.8776 |
| 0.0024        | 10.18 | 5000  | 0.2341          | 4.8923 |
| 0.0015        | 12.22 | 6000  | 0.2459          | 4.9340 |
| 0.0021        | 14.26 | 7000  | 0.2558          | 4.9276 |
| 0.0011        | 16.29 | 8000  | 0.2540          | 5.1015 |
| 0.0013        | 18.33 | 9000  | 0.2611          | 5.1855 |
| 0.0005        | 20.37 | 10000 | 0.2720          | 4.9379 |
| 0.0028        | 22.4  | 11000 | 0.2614          | 5.0110 |
| 0.0004        | 24.44 | 12000 | 0.2652          | 4.9898 |
| 0.0004        | 26.48 | 13000 | 0.2850          | 4.9776 |
| 0.0006        | 28.51 | 14000 | 0.2736          | 4.9732 |
| 0.0002        | 30.55 | 15000 | 0.2944          | 5.1566 |
| 0.0002        | 32.59 | 16000 | 0.2949          | 5.0007 |
| 0.0001        | 34.62 | 17000 | 0.3094          | 4.9552 |
| 0.0           | 36.66 | 18000 | 0.3185          | 4.9622 |
| 0.0           | 38.7  | 19000 | 0.3229          | 4.9462 |
| 0.0           | 40.73 | 20000 | 0.3245          | 4.9295 |

---

## Framework versions

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

---

## Example usage

```python
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"])
```

---

## 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.