| --- |
| language: |
| - es |
| 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 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: 5.126477928109984 |
| --- |
| |
| # Whisper Large Spanish |
|
|
| ## Model summary |
|
|
| **Whisper Large Spanish** is a high-accuracy automatic speech recognition (ASR) model for **Spanish (es)**, fine-tuned from [openai/whisper-large] on the **Spanish subset of Mozilla Common Voice 13.0**. It achieves a **Word Error Rate (WER) of 5.1265%** on the evaluation set. |
|
|
| This model is designed for applications that require near state-of-the-art transcription accuracy in Spanish, such as transcription of lectures, podcasts, and other high-quality recordings. |
|
|
| --- |
|
|
| ## Model description |
|
|
| * **Architecture:** Transformer-based encoder–decoder (Whisper Large) |
| * **Base model:** openai/whisper-large |
| * **Language:** Spanish (es) |
| * **Task:** Automatic Speech Recognition (ASR) |
| * **Output:** Text transcription in Spanish |
| * **Decoding:** Autoregressive sequence-to-sequence decoding |
|
|
| Large model offers very high accuracy at the cost of higher computational requirements compared to Medium or Small variants. |
|
|
| --- |
|
|
| ## Intended use |
|
|
| ### Primary use cases |
|
|
| * High-accuracy Spanish speech transcription |
| * Applications requiring transcription of long-form audio |
| * Research in Spanish ASR performance and benchmarking |
|
|
| ### Limitations |
|
|
| * May underperform in extremely noisy audio or with strong regional accents not well represented in the Common Voice dataset |
| * High computational cost for real-time inference |
| * Not suitable for legal, medical, or safety-critical applications without human review |
|
|
| --- |
|
|
| ## 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 using 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) | **5.1265%** | |
|
|
| --- |
|
|
| ## 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: 20000 |
| * Train batch size: 32 (gradient accumulation 2 → effective batch size 64) |
| * Eval batch size: 16 |
| * Seed: 42 |
|
|
| ### Training results (summary) |
|
|
| | Training Loss | Epoch | Step | Validation Loss | WER | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:| |
| | 0.0834 | 2.0 | 1000 | 0.1862 | 6.3852 | |
| | 0.0871 | 4.0 | 2000 | 0.1777 | 5.9175 | |
| | 0.039 | 6.0 | 3000 | 0.1780 | 5.7423 | |
| | 0.0265 | 8.0 | 4000 | 0.2121 | 5.7744 | |
| | 0.0059 | 10.0 | 5000 | 0.2219 | 5.8097 | |
| | 0.0855 | 12.01 | 6000 | 0.1839 | 5.9778 | |
| | 0.0037 | 14.01 | 7000 | 0.2273 | 5.8565 | |
| | 0.0293 | 16.01 | 8000 | 0.1965 | 5.8078 | |
| | 0.1174 | 18.01 | 9000 | 0.1984 | 5.8893 | |
| | 0.0355 | 20.01 | 10000 | 0.2136 | 5.8662 | |
| | 0.0279 | 22.01 | 11000 | 0.1882 | 5.4960 | |
| | 0.0043 | 24.01 | 12000 | 0.2444 | 5.3356 | |
| | 0.0302 | 26.01 | 13000 | 0.2223 | 5.4620 | |
| | 0.0011 | 28.01 | 14000 | 0.2603 | 5.5608 | |
| | 0.001 | 30.01 | 15000 | 0.2452 | 5.3087 | |
| | 0.0003 | 32.01 | 16000 | 0.2573 | 5.3523 | |
| | 0.0004 | 34.02 | 17000 | 0.2690 | 5.2952 | |
| | 0.0013 | 36.02 | 18000 | 0.2373 | 5.1438 | |
| | 0.0004 | 38.02 | 19000 | 0.2618 | 5.1361 | |
| | 0.0004 | 40.02 | 20000 | 0.2663 | 5.1265 | |
|
|
| --- |
|
|
| ## 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-large-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. |
|
|
