Added description and "how to use" example

README.md

@@ -28,45 +28,109 @@ model-index:
       value: 18.417108833893636
 ---
 
-<!-- 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 Basque
 
-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 eu dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3390
-- Wer: 18.4171
+## Model summary
+
+**Whisper Small Basque** is an automatic speech recognition (ASR) model for **Basque (eu)** speech. It is fine-tuned from [openai/whisper-small] on the **Basque portion of Mozilla Common Voice 13.0**, achieving a **Word Error Rate (WER) of 18.42%** on the Common Voice evaluation split.
+
+The model provides a balance between transcription accuracy and computational efficiency, offering substantially improved performance over tiny models while remaining suitable for offline and batch ASR use.
+
+---
 
 ## Model description
 
-More information needed
+* **Architecture:** Transformer-based encoder–decoder (Whisper)
+* **Base model:** openai/whisper-small
+* **Language:** Basque (eu)
+* **Task:** Automatic Speech Recognition (ASR)
+* **Output:** Text transcription in Basque
+* **Decoding:** Autoregressive sequence-to-sequence decoding
+
+This model builds on Whisper’s multilingual pretraining and is further fine-tuned on Basque speech data to improve recognition quality for a low-resource language while maintaining moderate computational requirements.
+
+---
+
+## Intended use
+
+### Primary use cases
+
+* Transcription of Basque audio recordings
+* Offline or batch ASR pipelines
+* Research on Basque and low-resource speech recognition
+* Media, educational, and archival transcription tasks
+
+### Intended users
+
+* Researchers working on Basque ASR
+* Developers building Basque speech applications
+* Academic and institutional users
 
-## Intended uses & limitations
+### Out-of-scope use
 
-More information needed
+* Real-time or low-latency ASR without further optimization
+* Speech translation tasks
+* Safety-critical or high-risk applications without additional validation
+
+---
+
+## Limitations and known issues
+
+* Performance may degrade on:
+  * Highly noisy or low-quality recordings
+  * Conversational or spontaneous speech
+  * Accents underrepresented in Common Voice
+* While significantly more accurate than tiny models, it may still produce errors in challenging acoustic conditions
+* Biases present in the Common Voice dataset may be reflected in outputs
+
+Users are encouraged to evaluate the model on their own data before deployment.
+
+---
 
 ## Training and evaluation data
 
-More information needed
+### Training data
+
+* **Dataset:** Mozilla Common Voice 13.0 (Basque 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 data
+
+* **Dataset:** Mozilla Common Voice 13.0 (Basque evaluation split)
+* **Metric:** Word Error Rate (WER)
+
+---
+
+## Evaluation results
+
+| Metric     | Value      |
+| ---------- | ---------- |
+| WER (eval) | **18.42%** |
+
+These results demonstrate a strong improvement over smaller Whisper variants for Basque ASR.
+
+---
 
 ## Training procedure
 
 ### Training hyperparameters
 
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 16
-- eval_batch_size: 8
-- 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: 16
+* Evaluation batch size: 8
+* Seed: 42
 
-### Training results
+### Training results (summary)
 
-| Training Loss | Epoch | Step | Validation Loss | Wer     |
+| Training Loss | Epoch | Step | Validation Loss | WER     |
 |:-------------:|:-----:|:----:|:---------------:|:-------:|
 | 0.2826        | 1.04  | 1000 | 0.3472          | 24.9342 |
 | 0.0872        | 2.07  | 2000 | 0.3012          | 20.2661 |
@@ -74,27 +138,57 @@ The following hyperparameters were used during training:
 | 0.0086        | 4.14  | 4000 | 0.3297          | 18.7513 |
 | 0.0051        | 6.01  | 5000 | 0.3390          | 18.4171 |
 
+---
+
+## 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 = "ORG_OR_USER/whisper-small-eu"  # replace with actual repo ID
+device = 0  # set to -1 for CPU
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=hf_model,
+    device=device
+)
 
-### Framework versions
+result = pipe("audio.wav")
+print(result["text"])
+```
+
+---
+
+## Ethical considerations and risks
 
-- Transformers 4.33.0.dev0
-- Pytorch 2.0.1+cu117
-- Datasets 2.14.4
-- Tokenizers 0.13.3
+* 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 +196,21 @@ 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.