---
language:
- ar
license: apache-2.0
base_model: openai/whisper-small
tags:
- fine-tuned
- Quran
- automatic-speech-recognition
- arabic
- whisper
datasets:
- fawzanaramam/the-truth-1st-chapter
metrics:
- wer
model-index:
- name: Whisper Small Finetuned on Surah Fatiha
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: The Truth 2.0 - Surah Fatiha
      type: fawzanaramam/the-truth-1st-chapter
      args: 'config: ar, split: train'
    metrics:
    - name: Word Error Rate (WER)
      type: wer
      value: 0.0
---

# Whisper Small Finetuned on Surah Fatiha

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small), transcribing Surah Fatiha, the first chapter of the Quran. It has been trained using *The Truth 2.0 - Surah Fatiha* dataset and achieves excellent results with a Word Error Rate (WER) of **0.0**, indicating perfect transcription on the evaluation set.

## Model Description

Whisper Small is a transformer-based automatic speech recognition (ASR) model developed by OpenAI. By fine-tuning it on the *Surah Fatiha* dataset, this model becomes highly accurate in transcribing Quranic recitation. It is designed to assist in religious, educational, and research-oriented tasks that require precise Quranic transcription.

## Performance Metrics

On the evaluation set, the model achieved:
- **Loss**: 0.0088  
- **Word Error Rate (WER)**: 0.0  

These metrics showcase the model's exceptional performance and reliability in transcribing Surah Fatiha audio.

## Training Results

The following table summarizes the training process and results:

| **Training Loss** | **Epoch** | **Step** | **Validation Loss** | **WER**    |
|:------------------:|:---------:|:--------:|:-------------------:|:----------:|
| No log            | 0.5556    | 10       | 1.1057              | 96.2766    |
| No log            | 1.1111    | 20       | 0.3582              | 29.7872    |
| 0.6771            | 1.6667    | 30       | 0.1882              | 23.4043    |
| 0.6771            | 2.2222    | 40       | 0.0928              | 25.0       |
| 0.0289            | 2.7778    | 50       | 0.0660              | 34.0426    |
| 0.0289            | 3.3333    | 60       | 0.0484              | 32.9787    |
| 0.0289            | 3.8889    | 70       | 0.0241              | 25.5319    |
| 0.0056            | 4.4444    | 80       | 0.0184              | 28.7234    |
| 0.0056            | 5.0       | 90       | 0.0111              | 0.0        |
| 0.0019            | 5.5556    | 100      | 0.0088              | 0.0        |

## Intended Uses & Limitations

### Intended Uses
- **Speech-to-text transcription** of Quranic recitation for Surah Fatiha.  
- Educational tools to assist in learning and practicing Quranic recitation.  
- Research and analysis of Quranic audio transcription methods.  

### Limitations
- This model is fine-tuned specifically for Surah Fatiha and may not generalize well to other chapters or non-Quranic Arabic audio.  
- Variability in audio quality, accents, or recitation styles might affect performance.  
- Optimal performance is achieved with high-quality audio inputs.  

## Training and Evaluation Data

The model was trained on *The Truth 2.0 - Surah Fatiha* dataset, which comprises high-quality audio recordings of Surah Fatiha and their corresponding transcripts. The dataset was meticulously curated to ensure the accuracy and authenticity of Quranic content.

## 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  
- **Training Steps**: 100  
- **Mixed Precision Training**: Native AMP  

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