--- 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.