| | --- |
| | 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 |
| |
|
| | ```python |
| | 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: |
| |
|
| | ```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. |
| |
|
| |
|
