README.md

---
language:
- ar
license: apache-2.0
base_model: openai/whisper-small
tags:
- fine-tuned
- Quran
- automatic-speech-recognition
- arabic
- whisper
datasets:
- fawzanaramam/the-amma-juz
model-index:
- name: Whisper small Finetuned on Amma Juz of Quran
  results:
  - task:
      type: automatic-speech-recognition
      name: Speech Recognition
    dataset:
      name: The Amma Juz Dataset
      type: fawzanaramam/the-amma-juz
    metrics:
      - type: eval_loss
        value: 0.0058
      - type: eval_wer
        value: 1.1494
---

# Whisper Small Finetuned on Amma Juz of Quran

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small), specialized in transcribing Arabic audio with a focus on Quranic recitation from the *Amma Juz* dataset. This fine-tuning makes the model highly effective for tasks involving accurate recognition of Arabic speech, especially in religious and Quranic contexts.

## Model Description

Whisper Small is a transformer-based model for automatic speech recognition (ASR), developed by OpenAI. By fine-tuning it on the *Amma Juz* dataset, this version achieves state-of-the-art results on transcribing Quranic recitations with minimal word error rates and high accuracy. The fine-tuned model retains the original capabilities of the Whisper architecture while being optimized for Arabic Quranic text.

## Performance Metrics

On the evaluation set, the model achieved:
- **Evaluation Loss**: 0.0058  
- **Word Error Rate (WER)**: 1.1494%  
- **Evaluation Runtime**: 44.2766 seconds  
- **Evaluation Samples per Second**: 2.259  
- **Evaluation Steps per Second**: 0.294  

These metrics demonstrate the model's efficiency and accuracy when processing Quranic recitations.

## Intended Uses & Limitations

### Intended Uses
- **Speech-to-text transcription** of Arabic Quranic recitation, specifically from the *Amma Juz*.  
- Research and educational purposes in the domain of Quranic studies.  
- Applications in tools for learning Quranic recitation.  

### Limitations
- The model is fine-tuned on Quranic recitation and may not perform as well on non-Quranic Arabic speech or general Arabic conversations.  
- Noise in audio inputs, variations in recitation style, or heavy accents might affect accuracy.  
- It is recommended to use clean and high-quality audio for optimal performance.  

## Training and Evaluation Data

The model was trained using the *Amma Juz* dataset, which comprises Quranic audio data and corresponding transcripts. This dataset was curated to ensure high-quality representation of Quranic recitations.

## Training Procedure

### Training Hyperparameters
The following hyperparameters were used during training:
- **Learning Rate**: 1e-05  
- **Training Batch Size**: 16  
- **Evaluation Batch Size**: 8  
- **Seed**: 42  
- **Optimizer**: Adam (betas=(0.9, 0.999), epsilon=1e-08)  
- **Learning Rate Scheduler**: Linear  
- **Warmup Steps**: 10  
- **Number of Epochs**: 3.0  
- **Mixed Precision Training**: Native AMP  

### Framework Versions
- **Transformers**: 4.41.1  
- **PyTorch**: 2.2.1+cu121  
- **Datasets**: 2.19.1  
- **Tokenizers**: 0.19.1