This model is a fine-tuned version of 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

fawzanaramam/Whisper-Small-Finetuned-on-Surah-Fatiha

Whisper Small Finetuned on Surah Fatiha