The model was fine-tuned using Full Fine-tuning on a dataset of professional and non professional Quran recitations from MP3Quran and tarteel AI, making it highly effective for transcribing Quranic Arabic speech.

Developed by: Fine-tuned model
Model type: Automatic Speech Recognition (ASR)
Language: Arabic (ar)
License: Apache 2.0
Base model: openai/whisper-medium
Fine-tuning method: Full Fine-tuning

Training Details

Training Data

Source Dataset: yousifgamalo/mp3quran
Processed Dataset: yousifgamalo/quran-cleaned-nonprofessional
Training samples: 1250527
Validation samples: 12760
Test samples: 12761
Total samples: 1276048

The dataset consists of Quran recitations by professional reciters from MP3Quran, preprocessed with:

Audio normalized to 16kHz mono
Text without diacritics (tashkeel removed)
Log-mel spectrograms extracted
Shuffled to ensure diverse train/val/test splits

Training Hyperparameters

Training Arguments:

Batch size per device: 32
Gradient accumulation steps: 1
Effective batch size: 32
Learning rate: 1e-06
Warmup steps: 500
Number of epochs: 0.01
Precision: bf16
Optimizer: AdamW (default)
Learning rate scheduler: linear with warmup
Max generation length: 256

Generation Configuration:

Task: Transcription
Language: Arabic (forced)
No repeat n-gram size: 3
Repetition penalty: 2.0

Training Infrastructure

Gradient checkpointing: Enabled
Mixed precision training: bf16
Early stopping: WER threshold 0.03

Evaluation

Test Set Metrics

Metric	Value
Word Error Rate (WER)	0.1162
Test Loss	0.0317
Runtime (seconds)	1300.45
Samples per second	9.81

Evaluation Data

The model was evaluated on a held-out test set of 12761 samples from the same distribution as the training data (professional Quran recitations from MP3Quran).

Use limitations and license

not allowed for commercial use , only nonprofit is allowed .

Installation

pip install transformers torch torchaudio

Inference Example

python

import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from datasets import load_dataset, Audio

# Load model and processor
model_id = "yousifgamalo/quran-s-finetuned"
processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)

# Move to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Load and preprocess audio
# Example: Load from a file
import librosa
audio_array, sampling_rate = librosa.load("quran_recitation.wav", sr=16000)

# Process audio
input_features = processor(
    audio_array, 
    sampling_rate=16000, 
    return_tensors="pt"
).input_features.to(device)

# Generate transcription
# The model is configured to output Arabic text automatically
predicted_ids = model.generate(input_features)

# Decode prediction
transcription = processor.batch_decode(
    predicted_ids, 
    skip_special_tokens=True
)[0]

print(f"Transcription: {transcription}")

Using with Pipeline

python

from transformers import pipeline

# Create ASR pipeline
pipe = pipeline(
    "automatic-speech-recognition",
    model="yousifgamalo/quran-s-finetuned",
    device=0 if torch.cuda.is_available() else -1
)

# Transcribe audio
result = pipe("quran_recitation.wav")
print(result["text"])

Limitations and Bias

Domain-specific: This model is optimized for Quran recitation and may not perform well on general Arabic speech
Professional recordings: Trained on professional reciters from MP3Quran, performance may vary on non-professional recordings
No diacritics: The model outputs Arabic text without diacritical marks (tashkeel)
Classical Arabic: Optimized for Classical/Quranic Arabic, not Modern Standard Arabic or dialects

Training Procedure Details

Preprocessing

Audio files resampled to 16kHz mono
Log-mel spectrograms extracted using Whisper's feature extractor
Text normalized (Arabic diacritics removed)
Dataset shuffled before splitting to ensure representative distributions
Train/validation/test split: 98%/1%/1%

Full Fine-tuning

This model was trained using full fine-tuning, where all model parameters are updated during training. This provides maximum flexibility but requires more memory and compute resources.

Citation

If you use this model, please cite:

bibtex

  author = {Yousif H A },
  title = {Whisper Medium - Quran Fine-tuned },
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/yousifgamalo/quran-s-finetuned}}
}

Also cite the original Whisper paper:

bibtex

@article{radford2022whisper,
  title={Robust Speech Recognition via Large-Scale Weak Supervision},
  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  journal={arXiv preprint arXiv:2212.04356},
  year={2022}
}

Model Card Contact

For questions or issues, please open an issue in the model repository.