| --- |
| language: |
| - es |
| 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 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: 8.266774443952604 |
| --- |
| |
| # Whisper Small Spanish |
|
|
| ## Model summary |
|
|
| **Whisper Small Spanish** is an automatic speech recognition (ASR) model for **Spanish (es)**, fine-tuned from [openai/whisper-small] on the **Spanish subset of Mozilla Common Voice 13.0**. It achieves a **Word Error Rate (WER) of 8.2668%** on the evaluation split. |
|
|
| This model provides a good balance between transcription accuracy and computational efficiency, suitable for applications requiring relatively low-latency ASR with decent quality. |
|
|
| --- |
|
|
| ## Model description |
|
|
| * **Architecture:** Transformer-based encoder–decoder (Whisper Small) |
| * **Base model:** openai/whisper-small |
| * **Language:** Spanish (es) |
| * **Task:** Automatic Speech Recognition (ASR) |
| * **Output:** Text transcription in Spanish |
| * **Decoding:** Autoregressive sequence-to-sequence decoding |
|
|
| Compared to Whisper Base, this model is slightly larger and generally more accurate, particularly for standard read Spanish. |
|
|
| --- |
|
|
| ## Intended use |
|
|
| ### Primary use cases |
|
|
| * Real-time or batch transcription of Spanish speech |
| * Research or experimentation with Spanish ASR |
| * Applications with moderate hardware resources where Whisper Medium or Large is too heavy |
|
|
| ### Limitations |
|
|
| * Performance may degrade for: |
| * Noisy or overlapping speech |
| * Regional accents or dialects not well represented in Common Voice |
| * Very fast conversational speech |
|
|
| * Not recommended for safety-critical or professional-level transcription tasks. |
|
|
| --- |
|
|
| ## 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 normalized using Whisper tokenizer |
| * Invalid or corrupted samples removed |
|
|
| * **Evaluation metric:** Word Error Rate (WER) on held-out evaluation set |
|
|
| --- |
|
|
| ## Evaluation results |
|
|
| | Metric | Value | |
| | ---------- | ---------- | |
| | WER (eval) | **8.2668%** | |
|
|
| --- |
|
|
| ## 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: 5000 |
| * Train batch size: 64 |
| * Eval batch size: 32 |
| * Seed: 42 |
|
|
| ### Training results (summary) |
|
|
| | Training Loss | Epoch | Step | Validation Loss | WER | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| |
| | 0.1320 | 2.0 | 1000 | 0.2461 | 9.5267 | |
| | 0.1288 | 4.01 | 2000 | 0.2251 | 8.5215 | |
| | 0.0814 | 6.01 | 3000 | 0.2212 | 8.2668 | |
| | 0.0905 | 8.01 | 4000 | 0.2310 | 8.4997 | |
| | 0.0319 | 10.02 | 5000 | 0.2358 | 8.5343 | |
|
|
| --- |
|
|
| ## 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-small-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. |
|
|
