| --- |
| language: |
| - es |
| license: apache-2.0 |
| base_model: openai/whisper-medium |
| tags: |
| - whisper-event |
| - generated_from_trainer |
| datasets: |
| - mozilla-foundation/common_voice_13_0 |
| metrics: |
| - wer |
| model-index: |
| - name: Whisper Medium Spanish |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: mozilla-foundation/common_voice_13_0 es |
| type: mozilla-foundation/common_voice_13_0 |
| config: es |
| split: test |
| args: es |
| metrics: |
| - name: Wer |
| type: wer |
| value: 5.408751772230669 |
| --- |
| # Whisper Medium Spanish |
|
|
| ## Model summary |
|
|
| **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. |
|
|
| 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. |
|
|
| --- |
|
|
| ## Model description |
|
|
| * **Architecture:** Transformer-based encoder–decoder (Whisper Medium) |
| * **Base model:** openai/whisper-medium |
| * **Language:** Spanish (es) |
| * **Task:** Automatic Speech Recognition (ASR) |
| * **Output:** Text transcription in Spanish |
| * **Decoding:** Autoregressive sequence-to-sequence decoding |
|
|
| Medium-sized model balances accuracy and speed, handling conversational Spanish better than smaller models. |
|
|
| --- |
|
|
| ## Intended use |
|
|
| ### Primary use cases |
|
|
| * Batch or streaming transcription of Spanish speech |
| * Research on Spanish ASR |
| * Applications requiring moderate-to-high transcription accuracy without full-large model compute |
|
|
| ### Limitations |
|
|
| * Accuracy may drop for: |
| * Noisy environments or overlapping speakers |
| * Strong regional accents not well represented in Common Voice |
| * Extremely fast or slurred speech |
|
|
| * Not intended for legal, medical, or other safety-critical transcription. |
|
|
| --- |
|
|
| ## Training and evaluation data |
|
|
| * **Dataset:** Mozilla Common Voice 13.0 (Spanish subset) |
| * **Data type:** Crowd-sourced read speech |
| * **Preprocessing:** |
| * Audio resampled to 16 kHz |
| * Text tokenized with Whisper tokenizer |
| * Removal of invalid or corrupted samples |
|
|
| * **Evaluation metric:** Word Error Rate (WER) on held-out evaluation set |
|
|
| --- |
|
|
| ## Evaluation results |
|
|
| | Metric | Value | |
| | ---------- | ---------- | |
| | WER (eval) | **5.4088%** | |
|
|
| --- |
|
|
| ## 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: 10000 |
| * Train batch size: 64 |
| * Eval batch size: 32 |
| * Seed: 42 |
|
|
| ### Training results (summary) |
|
|
| | Training Loss | Epoch | Step | Validation Loss | WER | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:| |
| | 0.0917 | 2.0 | 1000 | 0.1944 | 6.8560 | |
| | 0.0927 | 4.0 | 2000 | 0.1817 | 6.1439 | |
| | 0.0456 | 6.01 | 3000 | 0.1805 | 6.2626 | |
| | 0.0343 | 8.01 | 4000 | 0.2097 | 6.1773 | |
| | 0.0046 | 10.01 | 5000 | 0.2292 | 5.9374 | |
| | 0.0829 | 12.01 | 6000 | 0.1814 | 6.0644 | |
| | 0.0021 | 14.01 | 7000 | 0.2318 | 5.7096 | |
| | 0.0288 | 16.01 | 8000 | 0.1871 | 5.5755 | |
| | 0.1297 | 18.02 | 9000 | 0.1831 | 5.6885 | |
| | 0.0377 | 20.02 | 10000 | 0.1915 | 5.4088 | |
|
|
| --- |
|
|
| ## Framework versions |
|
|
| - Transformers 4.33.0.dev0 |
| - PyTorch 2.0.1+cu117 |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
|
|
| --- |
|
|
| ## Example usage |
|
|
| ```python |
| from transformers import pipeline |
| |
| hf_model = "HiTZ/whisper-medium-es" # replace with actual repo ID |
| device = 0 # -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. |
|
|
|
|
