---
language:
- gl
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 Galician
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_13_0 gl
      type: mozilla-foundation/common_voice_13_0
      config: gl
      split: test
      args: gl
    metrics:
    - name: Wer
      type: wer
      value: 26.13307119205298
---

# Whisper Tiny Galician

## Model summary

**Whisper Tiny Galician** is an automatic speech recognition (ASR) model for **Galician (gl)** speech. It is fine-tuned from [openai/whisper-tiny] on the **Galician portion of Mozilla Common Voice 13.0**, achieving a **Word Error Rate (WER) of 26.13%** on the Common Voice evaluation split.

This model provides lightweight transcription capabilities for Galician speech, suitable for low-resource applications or devices with limited computational capacity.

---

## Model description

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

This tiny model leverages Whisper’s multilingual pretraining and is fine-tuned on Galician speech data to provide basic transcription functionality for a low-resource language, ideal for experimentation and lightweight applications.

---

## Intended use

### Primary use cases

* Lightweight transcription of Galician audio recordings
* Low-resource or offline ASR pipelines
* Educational and research purposes

### Intended users

* Researchers working on Galician or low-resource ASR
* Developers building Galician speech applications
* Academic or institutional users

### Out-of-scope use

* High-accuracy transcription requirements
* Real-time or low-latency ASR without optimization
* Speech translation tasks

---

## Limitations and known issues

* Performance may degrade on:
  * Noisy or low-quality recordings
  * Conversational or spontaneous speech
  * Accents underrepresented in Common Voice
* Transcription errors are expected due to the small model size
* Dataset biases from Common Voice may be reflected in outputs

Users are encouraged to evaluate the model on their own data before deployment.

---

## Training and evaluation data

### Training data

* **Dataset:** Mozilla Common Voice 13.0 (Galician subset)
* **Data type:** Crowd-sourced, read speech
* **Preprocessing:**
  * Audio resampled to 16 kHz
  * Text normalized using Whisper tokenizer
  * Filtering of invalid or problematic samples

### Evaluation data

* **Dataset:** Mozilla Common Voice 13.0 (Galician evaluation split)
* **Metric:** Word Error Rate (WER)

---

## Evaluation results

| Metric     | Value      |
| ---------- | ---------- |
| WER (eval) | **26.13%** |

This reflects the expected performance of a tiny Whisper model fine-tuned for Galician.

---

## 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: 5,000
* Train batch size: 256
* Evaluation batch size: 128
* Seed: 42
* Mixed precision training: Native AMP

### Training results (summary)

| Training Loss | Epoch | Step | Validation Loss | WER     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3626        | 20.0  | 1000 | 0.5407          | 30.8464 |
| 0.1103        | 40.0  | 2000 | 0.5370          | 27.0402 |
| 0.0473        | 60.0  | 3000 | 0.5769          | 26.7263 |
| 0.03          | 80.0  | 4000 | 0.5936          | 26.1382 |
| 0.0244        | 100.0 | 5000 | 0.6003          | 26.1331 |

---

## Framework versions

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

---

## How to use

```python
from transformers import pipeline

hf_model = "HiTZ/whisper-tiny-gl"  # 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.