---
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