README.md

---
language:
- eu
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 Basque
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_13_0 eu
      type: mozilla-foundation/common_voice_13_0
      config: eu
      split: test
      args: eu
    metrics:
    - name: Wer
      type: wer
      value: 32.26943173073028
---

# Whisper Tiny Basque

## Model summary

**Whisper Tiny Basque** is an automatic speech recognition (ASR) model for **Basque (eu)** speech. It is fine-tuned from [openai/whisper-tiny] on the **Basque portion of Mozilla Common Voice 13.0**, achieving a **Word Error Rate (WER) of 32.27%** on the Common Voice evaluation split.

The model is designed for lightweight transcription of Basque speech, prioritizing low computational cost over transcription accuracy.

---

## Model description

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

This model leverages Whisper’s multilingual pretraining and is further fine-tuned on Basque speech data to enable ASR for a low-resource language, using a compact model size suitable for constrained environments.

---

## Intended use

### Primary use cases

* Basque speech transcription in low-resource or experimental settings
* Lightweight ASR pipelines with limited computational resources
* Research on Basque ASR and low-resource speech recognition
* Dataset exploration and preprocessing

### Intended users

* Researchers working on Basque or low-resource ASR
* Developers experimenting with compact ASR models
* Academic and educational use

### Out-of-scope use

* High-accuracy transcription requirements
* Real-time or production-grade ASR without further optimization
* Speech translation tasks
* Safety-critical applications

---

## Limitations and known issues

* Relatively **high WER** compared to larger Whisper variants
* Performance may degrade significantly on:
  * Noisy audio
  * Conversational or spontaneous speech
  * Accents underrepresented in Common Voice
* As a tiny model, it may:
  * Miss words
  * Produce incomplete or inaccurate transcriptions
* 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 (Basque subset)
* **Data type:** Crowd-sourced, read speech
* **Preprocessing:**
  * Audio resampled to 16 kHz
  * Text normalized using Whisper tokenizer
  * Filtering of invalid samples

### Evaluation data

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

---

## Evaluation results

| Metric     | Value      |
| ---------- | ---------- |
| WER (eval) | **32.27%** |

These results reflect the expected performance of a tiny Whisper model fine-tuned for a low-resource language.

---

## 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

### Training results (summary)

| Training Loss | Epoch | Step | Validation Loss | WER     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0096        | 19.0  | 1000 | 0.5796          | 32.8952 |
| 0.0011        | 38.0  | 2000 | 0.6522          | 32.2694 |
| 0.0005        | 57.01 | 3000 | 0.6949          | 33.1403 |
| 0.0003        | 76.01 | 4000 | 0.7217          | 33.0734 |
| 0.0003        | 96.0  | 5000 | 0.7321          | 33.1585 |

---

## 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-eu"  # 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.

## Funding
This project with reference 2022/TL22/00215335 has been parcially funded by the Ministerio de Transformación Digital and by the Plan de Recuperación, Transformación y Resiliencia – Funded by the European Union – NextGenerationEU [ILENIA](https://proyectoilenia.es/) and by the project [IkerGaitu](https://www.hitz.eus/iker-gaitu/) funded by the Basque Government.
This model was trained at [Hyperion](https://scc.dipc.org/docs/systems/hyperion/overview/), one of the high-performance computing (HPC) systems hosted by the DIPC Supercomputing Center.