language:
- eu
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small 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: 18.417108833893636
Whisper Small Basque
Model summary
Whisper Small Basque is an automatic speech recognition (ASR) model for Basque (eu) speech. It is fine-tuned from [openai/whisper-small] on the Basque portion of Mozilla Common Voice 13.0, achieving a Word Error Rate (WER) of 18.42% on the Common Voice evaluation split.
The model provides a balance between transcription accuracy and computational efficiency, offering substantially improved performance over tiny models while remaining suitable for offline and batch ASR use.
Model description
- Architecture: Transformer-based encoder–decoder (Whisper)
- Base model: openai/whisper-small
- Language: Basque (eu)
- Task: Automatic Speech Recognition (ASR)
- Output: Text transcription in Basque
- Decoding: Autoregressive sequence-to-sequence decoding
This model builds on Whisper’s multilingual pretraining and is further fine-tuned on Basque speech data to improve recognition quality for a low-resource language while maintaining moderate computational requirements.
Intended use
Primary use cases
- Transcription of Basque audio recordings
- Offline or batch ASR pipelines
- Research on Basque and low-resource speech recognition
- Media, educational, and archival transcription tasks
Intended users
- Researchers working on Basque ASR
- Developers building Basque speech applications
- Academic and institutional users
Out-of-scope use
- Real-time or low-latency ASR without further optimization
- Speech translation tasks
- Safety-critical or high-risk applications without additional validation
Limitations and known issues
- Performance may degrade on:
- Highly noisy or low-quality recordings
- Conversational or spontaneous speech
- Accents underrepresented in Common Voice
- While significantly more accurate than tiny models, it may still produce errors in challenging acoustic conditions
- Biases present in the Common Voice dataset 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 or problematic samples
Evaluation data
- Dataset: Mozilla Common Voice 13.0 (Basque evaluation split)
- Metric: Word Error Rate (WER)
Evaluation results
| Metric | Value |
|---|---|
| WER (eval) | 18.42% |
These results demonstrate a strong improvement over smaller Whisper variants for Basque ASR.
Training procedure
Training hyperparameters
- Learning rate: 1e-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: 16
- Evaluation batch size: 8
- Seed: 42
Training results (summary)
| Training Loss | Epoch | Step | Validation Loss | WER |
|---|---|---|---|---|
| 0.2826 | 1.04 | 1000 | 0.3472 | 24.9342 |
| 0.0872 | 2.07 | 2000 | 0.3012 | 20.2661 |
| 0.0275 | 3.11 | 3000 | 0.3085 | 19.3021 |
| 0.0086 | 4.14 | 4000 | 0.3297 | 18.7513 |
| 0.0051 | 6.01 | 5000 | 0.3390 | 18.4171 |
Framework versions
- Transformers 4.33.0.dev0
- PyTorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
How to use
from transformers import pipeline
hf_model = "HiTZ/whisper-small-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:
@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 for more details.
License
This model is available under the Apache-2.0 License. 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 and by the project IkerGaitu funded by the Basque Government. This model was trained at Hyperion, one of the high-performance computing (HPC) systems hosted by the DIPC Supercomputing Center.