| --- |
| language: |
| - eu |
| license: apache-2.0 |
| base_model: openai/whisper-large |
| tags: |
| - whisper-event |
| - generated_from_trainer |
| datasets: |
| - mozilla-foundation/common_voice_13_0 |
| metrics: |
| - wer |
| model-index: |
| - name: Whisper Large 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: 12.234193365466401 |
| --- |
| |
| # Whisper Large Basque |
|
|
| ## Model summary |
|
|
| **Whisper Large Basque** is an automatic speech recognition (ASR) model for **Basque (eu)** speech. It is fine-tuned from [openai/whisper-large] on the **Basque portion of Mozilla Common Voice 13.0**, achieving a **Word Error Rate (WER) of 12.23%** on the Common Voice evaluation split. |
|
|
| This model provides high-quality transcription for Basque speech, offering substantial improvements in accuracy over smaller Whisper variants while suitable for offline and batch processing tasks. |
|
|
| --- |
|
|
| ## Model description |
|
|
| * **Architecture:** Transformer-based encoder–decoder (Whisper) |
| * **Base model:** openai/whisper-large |
| * **Language:** Basque (eu) |
| * **Task:** Automatic Speech Recognition (ASR) |
| * **Output:** Text transcription in Basque |
| * **Decoding:** Autoregressive sequence-to-sequence decoding |
|
|
| Leveraging Whisper’s multilingual pretraining, this large model is fine-tuned on Basque speech data to deliver highly accurate transcription for a low-resource language, suitable for research, media, and archival use cases. |
|
|
| --- |
|
|
| ## Intended use |
|
|
| ### Primary use cases |
|
|
| * High-quality transcription of Basque audio recordings |
| * Offline or batch ASR pipelines |
| * Research and development in Basque ASR |
| * Media, educational, and archival transcription tasks |
|
|
| ### Intended users |
|
|
| * Researchers working on Basque or low-resource ASR |
| * Developers building Basque speech applications |
| * Academic and institutional users |
|
|
| ### Out-of-scope use |
|
|
| * Real-time or low-latency ASR without optimization |
| * Speech translation tasks |
| * Safety-critical applications without validation |
|
|
| --- |
|
|
| ## Limitations and known issues |
|
|
| * Performance may degrade on: |
| * Noisy or low-quality recordings |
| * Conversational or spontaneous speech |
| * Accents underrepresented in Common Voice |
| * While highly accurate, transcription errors may still occur under challenging acoustic conditions |
| * 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 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) | **12.23%** | |
|
|
| These results indicate state-of-the-art performance for Basque ASR using a large Whisper model. |
|
|
| --- |
|
|
| ## 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: 20,000 |
| * Train batch size: 32 |
| * Gradient accumulation steps: 2 |
| * Total effective batch size: 64 |
| * Evaluation batch size: 16 |
| * Seed: 42 |
|
|
| ### Training results (summary) |
|
|
| | Training Loss | Epoch | Step | Validation Loss | WER | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:| |
| | 0.0196 | 4.01 | 1000 | 0.2825 | 15.4725 | |
| | 0.0039 | 9.01 | 2000 | 0.3072 | 14.2270 | |
| | 0.0031 | 14.01 | 3000 | 0.3170 | 13.7652 | |
| | 0.0023 | 19.0 | 4000 | 0.3310 | 13.6640 | |
| | 0.0014 | 24.0 | 5000 | 0.3384 | 13.5749 | |
| | 0.0034 | 29.0 | 6000 | 0.3425 | 13.7450 | |
| | 0.0011 | 33.01 | 7000 | 0.3476 | 13.0990 | |
| | 0.001 | 38.01 | 8000 | 0.3432 | 13.0990 | |
| | 0.0004 | 43.01 | 9000 | 0.3524 | 12.8033 | |
| | 0.0017 | 48.01 | 10000 | 0.3620 | 13.3946 | |
| | 0.0003 | 53.0 | 11000 | 0.3564 | 12.6190 | |
| | 0.0001 | 58.0 | 12000 | 0.3675 | 12.6352 | |
| | 0.0 | 63.0 | 13000 | 0.3878 | 12.4286 | |
| | 0.0 | 67.01 | 14000 | 0.3996 | 12.3577 | |
| | 0.0 | 72.01 | 15000 | 0.4088 | 12.3456 | |
| | 0.0 | 77.01 | 16000 | 0.4167 | 12.3091 | |
| | 0.0 | 82.01 | 17000 | 0.4241 | 12.3112 | |
| | 0.0 | 87.0 | 18000 | 0.4302 | 12.3193 | |
| | 0.0 | 92.0 | 19000 | 0.4351 | 12.2565 | |
| | 0.0 | 97.0 | 20000 | 0.4369 | 12.2342 | |
|
|
| --- |
|
|
| ## 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-large-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. |
|
|
|
|
