Add model card with evaluation metrics

README.md

@@ -0,0 +1,64 @@
+---
+language:
+- ar
+- tn
+tags:
+- automatic-speech-recognition
+- whisper
+- tunisian
+- generated_from_trainer
+dataset:
+- fbougares/TEDxTN
+model-index:
+- name: Whisper Small Tunisian
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: TEDxTN
+      type: fbougares/TEDxTN
+      config: default
+      split: test
+    metrics:
+    - name: WER
+      type: wer
+      value: 37.99
+    - name: CER
+      type: cer
+      value: 18.77
+---
+
+# Whisper Small Fine-tuned on Tunisian Dialect (TEDxTN)
+
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [TEDxTN dataset](https://huggingface.co/datasets/fbougares/TEDxTN). It was trained to transcribe Tunisian dialect (Derja/Arabizi).
+
+## Model Description
+- **Model:** openai/whisper-small
+- **Language:** Tunisian Arabic (Derja)
+- **Dataset:** TEDxTN (~22 hours)
+
+## Evaluation Results
+| Metric | Score |
+| :--- | :--- |
+| **WER** | 37.99% |
+| **CER** | 18.77% |
+
+## Usage
+```python
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+
+model_id = "medfadiabaidi/whisper-small-tunisian-asr"
+processor = WhisperProcessor.from_pretrained(model_id)
+model = WhisperForConditionalGeneration.from_pretrained(model_id)
+
+# audio_input = ... # Load your audio here
+# inputs = processor(audio_input, return_tensors="pt")
+# generated_ids = model.generate(inputs.input_features)
+# transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+## Training Details
+- **Epochs:** 15
+- **Batch Size:** 64
+- **Learning Rate:** 1e-05