This dataset contains verse-by-verse (ayah) Quranic recitations from 4 professional reciters, with fully diacritized Arabic text aligned to each audio segment. The dataset is designed for automatic speech recognition (ASR), speaker identification, and other speech processing tasks focused on Classical Arabic and Quranic recitation.

Dataset Summary

Total Audio Files: 24,944 ayahs
Number of Reciters: 4
Audio Format: MP3, 64 kbps
Sampling Rate: 16 kHz
Text: Fully diacritized Arabic (with Tashkeel)
Total Duration: Approximately 174 hours
Coverage: Complete Quran (114 Surahs, 6,236 verses per reciter)

Supported Tasks

Automatic Speech Recognition (ASR): Train and evaluate models for transcribing Quranic Arabic audio
Speaker Identification: Classify reciters based on their voice characteristics
Speech Synthesis: Generate natural-sounding Quranic recitations
Diacritization: Develop models for automatic Arabic text diacritization
Pronunciation Analysis: Study Quranic recitation rules (Tajweed)

Languages

Arabic (Classical/Quranic Arabic)

Dataset Structure

Data Instances

Each data instance contains the following fields:

{
    'audio': {
        'path': 'ayah_audio/dukhain/001_001.mp3',
        'array': array([...]),  # Audio waveform as numpy array
        'sampling_rate': 16000
    },
    'text': 'بِسْمِ ٱللَّهِ ٱلرَّحْمَٰنِ ٱلرَّحِيمِ',
    'duration': 4.52,
    'reciter': 'dukhain'
}

Data Fields

audio (Audio): Audio file containing the recitation, automatically resampled to 16kHz
- path: Original file path
- array: Audio waveform as a 1-dimensional numpy array
- sampling_rate: Sample rate (16000 Hz)
text (string): Diacritized Arabic transcription of the verse
duration (float): Duration of the audio clip in seconds
reciter (string): Identifier for the reciter

Data Splits

The dataset is randomly split into three subsets:

Split	Number of Examples	Percentage
Train	17,460	70%
Validation	3,741	15%
Test	3,743	15%

All splits maintain a balanced distribution of reciters and surahs.

Reciters

This dataset includes recordings from four professional Quran reciters:

Reciter ID	Reciter Name	Number of Ayahs	Source
dukhain	Abdullah Matroud	6,236	EveryAyah.com
luhaidan	Abdullah Awad Al-Juhani	6,236	EveryAyah.com
salmi	Yasser Salama	6,236	EveryAyah.com
shahin	Mahmoud Khalil Al-Hussary	6,236	EveryAyah.com

Dataset Creation

Source Data

Audio Sources

Audio recordings were obtained from EveryAyah.com, a platform providing high-quality Quran recitations by various reciters. Each reciter has a complete recording of the entire Quran.

Text Sources

The Arabic text with full diacritization (Tashkeel) was obtained from the Quran JSON repository, which provides structured access to Quranic text.

Data Collection and Processing

Collection Process

Downloaded full surah (chapter) recordings for each of the 4 reciters from EveryAyah.com
Obtained verse-level timing information in JSON format for precise segmentation
Downloaded diacritized Quranic text for all 114 surahs

Processing Pipeline

Audio Segmentation: Used ffmpeg to segment full surah recordings into individual verse-level audio files based on timing information
Quality Control: Verified that each audio segment meets minimum duration (0.5 seconds) and file size (1KB) thresholds
Text Alignment: Matched each audio segment with its corresponding diacritized Arabic text
Audio Normalization: Converted all audio files to 16kHz sampling rate and 64kbps MP3 format
Dataset Splitting: Randomly shuffled and split data into train/validation/test sets with fixed random seed for reproducibility

Who are the source data producers?

Audio: Professional Quran reciters whose recordings are available on EveryAyah.com
Text: The Quran JSON repository maintainers who structured the Quranic text
Processing: Dataset compilation and processing by Rdyh

Annotations

Annotation Process

The text annotations (Arabic transcriptions) are not manual annotations but rather the canonical Quranic text matched to each audio segment. The Quranic text is standardized and obtained from authoritative sources.

Who are the annotators?

Not applicable. The text is the canonical Quranic text, not manual transcriptions.

Considerations for Using the Data

Social Impact of Dataset

This dataset is designed to support:

Development of Arabic speech recognition systems specialized in Quranic recitation
Building educational applications for Quran memorization and learning
Research in classical Arabic phonetics and pronunciation
Preservation of different recitation styles (Qira'at)

Discussion of Biases

Reciter Diversity: Limited to 4 male reciters; does not represent all recitation styles or genders
Audio Quality: Recording quality may vary by reciter and original recording conditions
Dialect: Contains only Classical Quranic Arabic, not modern dialects
Speaking Rate: Recitation speed varies by reciter, which may affect model training

Other Known Limitations

Very short verses (under 1 second) may be challenging for ASR models
Audio quality is dependent on original recordings from EveryAyah.com
Limited to specific recitation styles; may not generalize to all Quranic recitations
Does not include recitation rules (Tajweed) annotations beyond the diacritized text

Additional Information

Dataset Curators

This dataset was curated and compiled by Rdyh.

Licensing Information

This dataset is released under the Creative Commons Attribution 4.0 International License (CC-BY-4.0).

Citation Information

If you use this dataset in your research, please cite:

@dataset{everyayah_quran_2024,
  title={EveryAyah Quran Recitations Dataset},
  author={Rdyh},
  year={2024},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/Rdyh/everyayah}}
}

Contributions

Audio recordings courtesy of EveryAyah.com. Quranic text from Quran JSON repository.

Usage Examples

Loading the Dataset

from datasets import load_dataset

# Load the complete dataset
dataset = load_dataset("Rdyh/everyayah")

# Load a specific split
train_dataset = load_dataset("Rdyh/everyayah", split="train")
test_dataset = load_dataset("Rdyh/everyayah", split="test")

# Access a sample
sample = dataset['train'][0]
print(f"Text: {sample['text']}")
print(f"Reciter: {sample['reciter']}")
print(f"Duration: {sample['duration']} seconds")

Audio Processing

import librosa
import numpy as np

# Access audio data
audio_array = sample['audio']['array']
sampling_rate = sample['audio']['sampling_rate']

# Process with librosa
mfcc = librosa.feature.mfcc(y=audio_array, sr=sampling_rate, n_mfcc=13)

Training an ASR Model

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

# Load pre-trained model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-xlsr-53-arabic")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-xlsr-53-arabic")

# Fine-tune on this dataset
# (training code here)

Dataset Statistics

Duration Distribution

Total Duration: Approximately 174 hours
Average Duration per Ayah: 25.1 seconds
Shortest Ayah: 0.5 seconds
Longest Ayah: 126 seconds (Ayat Al-Kursi and similar long verses)

Coverage

Complete coverage of the Holy Quran:

114 Surahs (chapters)
6,236 unique verses
4 complete recitations
24,944 total audio-text pairs

Text Characteristics

Fully diacritized Classical Arabic
Average verse length: ~65 characters
Complete Tashkeel marks (Fatha, Damma, Kasra, Sukun, Shadda, Tanween)
Includes special Quranic symbols (Hamza, Alif variations, etc.)

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