---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Large-V3 Spanish

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the mozilla-foundation/common_voice_13_0 es dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3245
- Wer: 4.9295

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| 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

## Citation

If you use these models 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},
      url={https://arxiv.org/abs/2503.23542}, 
}
```

Please, check the related paper preprint in
[arXiv:2503.23542](https://arxiv.org/abs/2503.23542)
for more details.

## Licensing

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.