|
|
--- |
|
|
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. |
|
|
|
|
|
|
