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--- |
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language: |
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- es |
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license: apache-2.0 |
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base_model: openai/whisper-medium |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_13_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Medium Spanish |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: mozilla-foundation/common_voice_13_0 es |
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type: mozilla-foundation/common_voice_13_0 |
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config: es |
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split: test |
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args: es |
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metrics: |
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- name: Wer |
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type: wer |
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value: 5.408751772230669 |
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--- |
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# Whisper Medium Spanish |
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## Model summary |
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**Whisper Medium Spanish** is an automatic speech recognition (ASR) model for **Spanish (es)**, fine-tuned from [openai/whisper-medium] on the **Spanish subset of Mozilla Common Voice 13.0**. It achieves a **Word Error Rate (WER) of 5.4088%** on the evaluation split. |
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This model offers higher accuracy than Whisper Small while remaining more efficient than Whisper Large variants, making it suitable for both batch and near real-time transcription of Spanish speech. |
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--- |
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## Model description |
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* **Architecture:** Transformer-based encoder–decoder (Whisper Medium) |
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* **Base model:** openai/whisper-medium |
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* **Language:** Spanish (es) |
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* **Task:** Automatic Speech Recognition (ASR) |
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* **Output:** Text transcription in Spanish |
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* **Decoding:** Autoregressive sequence-to-sequence decoding |
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Medium-sized model balances accuracy and speed, handling conversational Spanish better than smaller models. |
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--- |
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## Intended use |
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### Primary use cases |
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* Batch or streaming transcription of Spanish speech |
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* Research on Spanish ASR |
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* Applications requiring moderate-to-high transcription accuracy without full-large model compute |
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### Limitations |
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* Accuracy may drop for: |
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* Noisy environments or overlapping speakers |
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* Strong regional accents not well represented in Common Voice |
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* Extremely fast or slurred speech |
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* Not intended for legal, medical, or other safety-critical transcription. |
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--- |
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## Training and evaluation data |
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* **Dataset:** Mozilla Common Voice 13.0 (Spanish subset) |
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* **Data type:** Crowd-sourced read speech |
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* **Preprocessing:** |
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* Audio resampled to 16 kHz |
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* Text tokenized with Whisper tokenizer |
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* Removal of invalid or corrupted samples |
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* **Evaluation metric:** Word Error Rate (WER) on held-out evaluation set |
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--- |
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## Evaluation results |
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| Metric | Value | |
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| ---------- | ---------- | |
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| WER (eval) | **5.4088%** | |
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--- |
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## Training procedure |
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### Training hyperparameters |
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* Learning rate: 1e-5 |
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* Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-8) |
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* LR scheduler: Linear |
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* Warmup steps: 500 |
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* Training steps: 10000 |
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* Train batch size: 64 |
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* Eval batch size: 32 |
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* Seed: 42 |
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### Training results (summary) |
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| Training Loss | Epoch | Step | Validation Loss | WER | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 0.0917 | 2.0 | 1000 | 0.1944 | 6.8560 | |
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| 0.0927 | 4.0 | 2000 | 0.1817 | 6.1439 | |
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| 0.0456 | 6.01 | 3000 | 0.1805 | 6.2626 | |
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| 0.0343 | 8.01 | 4000 | 0.2097 | 6.1773 | |
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| 0.0046 | 10.01 | 5000 | 0.2292 | 5.9374 | |
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| 0.0829 | 12.01 | 6000 | 0.1814 | 6.0644 | |
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| 0.0021 | 14.01 | 7000 | 0.2318 | 5.7096 | |
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| 0.0288 | 16.01 | 8000 | 0.1871 | 5.5755 | |
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| 0.1297 | 18.02 | 9000 | 0.1831 | 5.6885 | |
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| 0.0377 | 20.02 | 10000 | 0.1915 | 5.4088 | |
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--- |
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## Framework versions |
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- Transformers 4.33.0.dev0 |
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- PyTorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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--- |
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## Example usage |
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```python |
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from transformers import pipeline |
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hf_model = "HiTZ/whisper-medium-es" # replace with actual repo ID |
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device = 0 # -1 for CPU |
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pipe = pipeline( |
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task="automatic-speech-recognition", |
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model=hf_model, |
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device=device |
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) |
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result = pipe("audio.wav") |
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print(result["text"]) |
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``` |
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--- |
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## Ethical considerations and risks |
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* This model transcribes speech and may process personal data. |
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* Users should ensure compliance with applicable data protection laws (e.g., GDPR). |
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* The model should not be used for surveillance or non-consensual audio processing. |
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--- |
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## Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@misc{dezuazo2025whisperlmimprovingasrmodels, |
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title={Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages}, |
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author={Xabier de Zuazo and Eva Navas and Ibon Saratxaga and Inma Hernáez Rioja}, |
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year={2025}, |
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eprint={2503.23542}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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Please, check the related paper preprint in |
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[arXiv:2503.23542](https://arxiv.org/abs/2503.23542) |
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for more details. |
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--- |
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## License |
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This model is available under the |
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[Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0). |
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You are free to use, modify, and distribute this model as long as you credit |
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the original creators. |
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--- |
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## Contact and attribution |
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* Fine-tuning and evaluation: HiTZ/Aholab - Basque Center for Language Technology |
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* Base model: OpenAI Whisper |
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* Dataset: Mozilla Common Voice |
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For questions or issues, please open an issue in the model repository. |
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