|
|
--- |
|
|
language: |
|
|
- es |
|
|
license: apache-2.0 |
|
|
base_model: openai/whisper-tiny |
|
|
tags: |
|
|
- whisper-event |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- mozilla-foundation/common_voice_13_0 |
|
|
metrics: |
|
|
- wer |
|
|
model-index: |
|
|
- name: Whisper Tiny 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: 19.59044631343944 |
|
|
--- |
|
|
|
|
|
# Whisper Tiny Spanish |
|
|
|
|
|
## Model summary |
|
|
|
|
|
**Whisper Tiny Spanish** is an automatic speech recognition (ASR) model for **Spanish (es)** speech. It is fine-tuned from [openai/whisper-tiny] on the **Spanish subset of Mozilla Common Voice 13.0**, achieving a **Word Error Rate (WER) of 19.5904%** on the evaluation split. |
|
|
|
|
|
This variant is optimized for low-latency and lightweight ASR applications on Spanish audio. |
|
|
|
|
|
--- |
|
|
|
|
|
## Model description |
|
|
|
|
|
* **Architecture:** Transformer-based encoder–decoder (Whisper Tiny) |
|
|
* **Base model:** openai/whisper-tiny |
|
|
* **Language:** Spanish (es) |
|
|
* **Task:** Automatic Speech Recognition (ASR) |
|
|
* **Output:** Text transcription in Spanish |
|
|
* **Decoding:** Autoregressive sequence-to-sequence decoding |
|
|
|
|
|
Fine-tuned to improve transcription quality while maintaining a small model footprint. |
|
|
|
|
|
--- |
|
|
|
|
|
## Intended use |
|
|
|
|
|
### Primary use cases |
|
|
|
|
|
* Lightweight Spanish speech transcription |
|
|
* Research and experimentation with Spanish ASR |
|
|
* Applications on devices with limited compute resources |
|
|
|
|
|
### Out-of-scope use |
|
|
|
|
|
* High-accuracy or professional transcription (WER ~20%) |
|
|
* Real-time transcription without latency optimization |
|
|
* Safety-critical applications |
|
|
|
|
|
--- |
|
|
|
|
|
## Limitations and known issues |
|
|
|
|
|
* Performance may be limited on: |
|
|
* Noisy recordings or overlapping speech |
|
|
* Rapid or conversational Spanish |
|
|
* Regional dialects not well-represented in Common Voice |
|
|
|
|
|
* Occasional errors due to small model capacity and low parameter count. |
|
|
|
|
|
--- |
|
|
|
|
|
## 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 samples removed |
|
|
|
|
|
* **Evaluation metric:** Word Error Rate (WER) on held-out evaluation set |
|
|
|
|
|
--- |
|
|
|
|
|
## Evaluation results |
|
|
|
|
|
| Metric | Value | |
|
|
| ---------- | ---------- | |
|
|
| WER (eval) | **19.5904%** | |
|
|
|
|
|
--- |
|
|
|
|
|
## Training procedure |
|
|
|
|
|
### Training hyperparameters |
|
|
|
|
|
* Learning rate: 3.75e-5 |
|
|
* Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-8) |
|
|
* LR scheduler: Linear |
|
|
* Warmup steps: 500 |
|
|
* Training steps: 5000 |
|
|
* Train batch size: 256 |
|
|
* Eval batch size: 128 |
|
|
* Seed: 42 |
|
|
|
|
|
### Training results (summary) |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | WER | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:-------:| |
|
|
| 0.1801 | 8.0 | 1000 | 0.4318 | 22.1861 | |
|
|
| 0.1627 | 16.01 | 2000 | 0.4218 | 19.5904 | |
|
|
| 0.0691 | 24.01 | 3000 | 0.4374 | 20.7170 | |
|
|
| 0.0124 | 32.01 | 4000 | 0.4635 | 20.0459 | |
|
|
| 0.0129 | 40.02 | 5000 | 0.4568 | 20.4135 | |
|
|
|
|
|
--- |
|
|
|
|
|
## Framework versions |
|
|
|
|
|
- Transformers 4.33.0.dev0 |
|
|
- PyTorch 2.0.1+cu117 |
|
|
- Datasets 2.14.4 |
|
|
- Tokenizers 0.13.3 |
|
|
|
|
|
--- |
|
|
|
|
|
## How to use |
|
|
|
|
|
```python |
|
|
from transformers import pipeline |
|
|
|
|
|
hf_model = "HiTZ/whisper-tiny-es" # replace with actual repo ID |
|
|
device = 0 # set to -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. |
|
|
|
