| | --- |
| | 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. |
| |
|
