added description and "how to use" example

README.md

@@ -28,45 +28,92 @@ model-index:
       value: 10.025150042869392
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # Whisper Small Catalan
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_13_0 ca dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2169
-- Wer: 10.0252
+## Model summary
+
+**Whisper Small Catalan** is an automatic speech recognition (ASR) model for **Catalan (ca)** speech. It is fine-tuned from [openai/whisper-small] on the **Catalan subset of Mozilla Common Voice 13.0**, achieving a **Word Error Rate (WER) of 10.03%** on the evaluation split.
+
+This model offers higher transcription accuracy than the tiny variant while being smaller and faster than the base variant.
+
+---
 
 ## Model description
 
-More information needed
+* **Architecture:** Transformer-based encoder–decoder (Whisper)  
+* **Base model:** openai/whisper-small  
+* **Language:** Catalan (ca)  
+* **Task:** Automatic Speech Recognition (ASR)  
+* **Output:** Text transcription in Catalan  
+* **Decoding:** Autoregressive sequence-to-sequence decoding  
+
+Fine-tuned to improve transcription quality on Catalan audio.
 
-## Intended uses & limitations
+---
+
+## Intended use
+
+### Primary use cases
 
-More information needed
+* Accurate transcription of Catalan audio  
+* Research and development in Catalan ASR  
+* Media, educational, or accessibility applications  
+
+### Out-of-scope use
+
+* Real-time transcription without optimization  
+* Speech translation  
+* Safety-critical applications without further validation  
+
+---
+
+## Limitations and known issues
+
+* Performance may degrade on:
+  * Noisy or low-quality recordings  
+  * Conversational or spontaneous speech  
+  * Regional dialects not well represented in Common Voice  
+* Occasional transcription errors on difficult audio  
+
+---
 
 ## Training and evaluation data
 
-More information needed
+* **Dataset:** Mozilla Common Voice 13.0 (Catalan subset)  
+* **Data type:** Crowd-sourced, read speech  
+* **Preprocessing:**  
+  * Audio resampled to 16 kHz  
+  * Text normalized using Whisper tokenizer  
+  * Filtering of invalid or problematic samples  
+
+* **Evaluation metric:** Word Error Rate (WER) on held-out evaluation set  
+
+---
+
+## Evaluation results
+
+| Metric     | Value      |
+| ---------- | ---------- |
+| WER (eval) | **10.03%** |
+
+---
 
 ## Training procedure
 
 ### Training hyperparameters
 
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 64
-- eval_batch_size: 32
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 500
-- training_steps: 5000
+* Learning rate: 1e-5  
+* Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-8)  
+* LR scheduler: Linear  
+* Warmup steps: 500  
+* Training steps: 5,000  
+* Train batch size: 64  
+* Eval batch size: 32  
+* Seed: 42  
 
-### Training results
+### Training results (summary)
 
-| Training Loss | Epoch | Step | Validation Loss | Wer     |
+| Training Loss | Epoch | Step | Validation Loss | WER     |
 |:-------------:|:-----:|:----:|:---------------:|:-------:|
 | 0.1708        | 1.1   | 1000 | 0.2494          | 12.1846 |
 | 0.0421        | 3.09  | 2000 | 0.2458          | 11.2689 |
@@ -74,27 +121,57 @@ The following hyperparameters were used during training:
 | 0.0928        | 7.08  | 4000 | 0.2150          | 10.0394 |
 | 0.0504        | 9.08  | 5000 | 0.2169          | 10.0252 |
 
+---
+
+## Framework versions
+
+- Transformers 4.33.0.dev0  
+- PyTorch 2.0.1+cu117  
+- Datasets 2.14.4  
+- Tokenizers 0.13.3  
+
+---
+
+## How to use
+
+```python
+from transformers import pipeline
+
+hf_model = "HiTZ/whisper-small-ca"  # replace with actual repo ID
+device = 0  # set to -1 for CPU
 
-### Framework versions
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=hf_model,
+    device=device
+)
 
-- Transformers 4.33.0.dev0
-- Pytorch 2.0.1+cu117
-- Datasets 2.14.4
-- Tokenizers 0.13.3
+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 these models in your research, please cite:
+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},
-      url={https://arxiv.org/abs/2503.23542}, 
+  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}
 }
 ```
 
@@ -102,9 +179,21 @@ Please, check the related paper preprint in
 [arXiv:2503.23542](https://arxiv.org/abs/2503.23542)
 for more details.
 
-## Licensing
+---
+
+## 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.
+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.