Buraaq/quran-md-ayahs

Quran-MD - Ayah Level

This dataset is part of the complete Quran-MD dataset available here: Complete Quran-MD

Dataset Description

This dataset contains 187,080 verse-level audio recordings of the complete Quran with multiple recitations. Each sample includes a complete ayah (verse) recitation along with Arabic text, English translation, and transliteration metadata.

Dataset Summary

• 🎵 Audio Samples: 187,080 MP3 files (complete verse recitations)
• 🎤 Reciters: 30 renowned Quranic reciters
• 📚 Coverage: Complete Quran (114 surahs, 6,236 unique ayahs)
• 🔢 Reciter Coverage: Each ayah is recited by all 30 reciters (complete coverage).
• 🌐 Languages: Arabic (original), English (translation), Transliteration
• 🎧 Audio Quality: Various bitrates (32kbps to 192kbps)
• ⏱️ Duration: Approximately 450+ hours of verse-level audio

Supported Tasks

• 🎙️ Automatic Speech Recognition (ASR): Arabic speech recognition training
• 🗣️ Text-to-Speech (TTS): Arabic text-to-speech synthesis
• 👤 Speaker Recognition: Multi-speaker voice identification (30 reciters)
• 📖 Religious Studies: Quranic recitation research and analysis
• 🎓 Language Learning: Arabic pronunciation and recitation learning
• 📊 Audio Analysis: Verse-level prosody and intonation studies

Languages

• Arabic (Classical Arabic - Quranic Arabic)
• English (translations)
• Transliteration (romanized Arabic)

Dataset Structure

Each sample contains complete verse-level information:

{
    "surah_id": int32,           # Surah (chapter) number (1-114)
    "ayah_id": int32,            # Ayah (verse) number within surah
    "surah_name_ar": string,     # Surah name in Arabic
    "surah_name_en": string,     # Surah name in English  
    "surah_name_tr": string,     # Surah name transliterated
    "ayah_count": int32,         # Total ayahs in this surah
    "ayah_ar": string,           # Complete ayah text in Arabic
    "ayah_en": string,           # Ayah translation in English
    "ayah_tr": string,           # Ayah transliteration
    "reciter_id": string,        # Unique reciter identifier
    "reciter_name": string,      # Reciter's name and audio quality info
    "audio": Audio(),            # Audio file (MP3 format)
}

Sample Data Example

{
    "surah_id": 1,
    "ayah_id": 1, 
    "surah_name_ar": "الفاتحة",
    "surah_name_en": "Al-Fatiha",
    "surah_name_tr": "Al-Faatiha",
    "ayah_count": 7,
    "ayah_ar": "بِسْمِ اللَّهِ الرَّحْمَٰنِ الرَّحِيمِ",
    "ayah_en": "In the name of Allah, the Entirely Merciful, the Especially Merciful.",
    "ayah_tr": "Bismillaahi ar-rahmaani ar-raheem",
    "reciter_id": "alafasy",
    "reciter_name": "Mishary Alafasy - 64kbps",
    "audio": <Audio object>
}

Featured Reciters

The dataset includes complete Quran recitations from 30 renowned reciters:

Reciter	Style	Samples
Abdul Basit	Murattal	6,236
Abdullaah 3awwaad Al-Juhaynee	Murattal / Standard	6,236
Abdullah Basfar	Murattal / Standard	6,236
Abdul Samad	Murattal / Standard	6,236
Abdurrahmaan As-Sudais	Murattal / Standard	6,236
Abu Bakr Ash-Shaatree	Murattal / Standard	6,236
Alafasy	Murattal / Standard	6,236
Ali Jaber	Murattal / Standard	6,236
Ayman Sowaid	Murattal / Standard	6,236
Banna	Murattal / Standard	6,236
Fares Abbad	Murattal / Standard	6,236
Ghamadi	Murattal / Standard	6,236
Hani Rifai	Murattal / Standard	6,236
Hudhaify	Murattal / Standard	6,236
Husary	Murattal / Standard	6,236
Husary (Mujawwad)	Mujawwad	6,236
Husary (Teacher)	Teacher Recitation	6,236
Husary (Warsh)	Warsh	6,236
Ibrahim Akhdar	Murattal / Standard	6,236
Minshawy (Mujawwad)	Mujawwad	6,236
Minshawy (Murattal)	Murattal	6,236
Minshawy (Teacher)	Teacher Recitation	6,236
Mostafa Ismail	Murattal / Standard	6,236
Muhammad Jibreel	Murattal / Standard	6,236
Muhsin Al-Qasim	Murattal / Standard	6,236
Nasser Alqatami	Murattal	6,236
Saood Ash-Shuraym	Murattal / Standard	6,236
Tunaiji	Murattal / Standard	6,236
Yassin (Warsh)	Warsh	6,236
Yasser Ad-Dussary	Murattal	6,236

Total: 30 reciters × 6,236 ayahs = 187,080 audio samples

Usage Examples

Loading the Dataset

from datasets import load_dataset

# Load the Ayah-level dataset
dataset = load_dataset("Buraaq/quran-md-ayahs")

print(f"Dataset size: {len(dataset):,} ayah recordings")
print(f"Sample keys: {list(dataset[0].keys())}")

# Access first sample
first_sample = dataset[0]
print(f"Surah: {first_sample['surah_name_en']} ({first_sample['surah_name_ar']})")
print(f"Ayah: {first_sample['ayah_ar']}")
print(f"Translation: {first_sample['ayah_en']}")
print(f"Reciter: {first_sample['reciter_name']}")

Play Audio Sample

import IPython.display as ipd

# Play first ayah audio
first_ayah = dataset[0]
ipd.Audio(first_ayah["audio"]["array"], rate=first_ayah["audio"]["sampling_rate"])

Filter by Reciter

# Get all recitations by Mishary Alafasy
alafasy_recitations = dataset.filter(lambda x: x["reciter_id"] == "alafasy")
print(f"Alafasy has {len(alafasy_recitations):,} ayah recordings")

# Get unique reciters in dataset
reciters = set(dataset["reciter_id"])
print(f"Available reciters: {len(reciters)}")
for reciter in sorted(reciters):
    count = len(dataset.filter(lambda x: x["reciter_id"] == reciter))
    print(f"  {reciter}: {count:,} recordings")

Filter by Surah (Chapter)

# Get all ayahs from Surah Al-Fatiha (Chapter 1) across all reciters
al_fatiha = dataset.filter(lambda x: x["surah_id"] == 1)
print(f"Al-Fatiha: {len(al_fatiha)} recordings across {len(set(al_fatiha['reciter_id']))} reciters")

# Get specific surah with specific reciter
al_fatiha_alafasy = dataset.filter(
    lambda x: x["surah_id"] == 1 and x["reciter_id"] == "alafasy"
)
print(f"Al-Fatiha by Alafasy: {len(al_fatiha_alafasy)} ayahs")

Audio Duration Analysis

import librosa

# Analyze audio durations
durations = []
sample_size = 1000  # Analyze first 1000 samples for performance

for i in range(min(sample_size, len(dataset))):
    audio_array = dataset[i]["audio"]["array"]
    sample_rate = dataset[i]["audio"]["sampling_rate"]
    duration = len(audio_array) / sample_rate
    durations.append(duration)

print(f"Average duration: {sum(durations)/len(durations):.2f} seconds")
print(f"Min duration: {min(durations):.2f} seconds")
print(f"Max duration: {max(durations):.2f} seconds")

Create Training/Validation Splits

# Split by surah for train/validation
train_surahs = list(range(1, 100))  # Surahs 1-99 for training
val_surahs = list(range(100, 115))   # Surahs 100-114 for validation

train_data = dataset.filter(lambda x: x["surah_id"] in train_surahs)
val_data = dataset.filter(lambda x: x["surah_id"] in val_surahs)

print(f"Training samples: {len(train_data):,}")
print(f"Validation samples: {len(val_data):,}")

Citation

@inproceedings{salman2025quran,
  title={Quran-MD: A Fine-Grained Multimodal Dataset of the Quran},
  author={Salman, Muhammad Umar and Qazi, Mohammad Areeb and Alam, Mohammed Talha},
  booktitle={5th Muslims in ML Workshop co-located with NeurIPS 2025}
}

Related Datasets

• 📝 Quran-MD - Word: Individual word pronunciations (77,429 samples)
• 🏠 Complete Quran-MD Overview: Full dataset collection documentation

Contact

• Hugging Face Profile: umarsalman
• GitHub Profile: umar1997
• Dataset Issues: Please open an issue on the repository.

Feel free to reach out for dataset issues, research collaborations, or community contributions.

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"And We have certainly made the Quran easy for remembrance, so is there any who will remember?" - Quran 54:17