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Datasets:
rabah2026
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Quran-Ayah-Corpus

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Text
Formats:
parquet
Languages:
Arabic
Size:
100K - 1M
Tags:
quran
coran
everyayah
ASR
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
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metadata
task_categories:
  - automatic-speech-recognition
language:
  - ar
tags:
  - quran
  - coran
  - everyayah
  - ASR
pretty_name: Quran-Ayah-Corpus
size_categories:
  - 100K<n<1M
license: apache-2.0

Quran-Ayah-Corpus: A Multi-Reciter Arabic Quranic Speech Dataset

Dataset Description:

Ayah-Corpus is a large-scale, multi-reciter Arabic speech dataset meticulously curated for Automatic Speech Recognition (ASR) tasks. It consists of high-quality audio recordings of Quranic verses (Ayahs) paired with their corresponding exact transcriptions. The audio is sourced from two primary repositories: Al-Quran.cloud and EveryAyah.com.

This dataset is specifically designed to facilitate the development of ASR models for Quranic Arabic, which features a distinct vocabulary, phonetic structure, and recitation style (Tajweed) compared to Modern Standard Arabic or colloquial dialects. All audio files have been standardized to a 16kHz sampling rate to be compatible with most modern ASR pipelines.

Dataset Structure:

The dataset is divided into train, validation, and test splits, ensuring a strict separation of reciters between the sets to evaluate model generalization to unseen voices.

Data Splits:

Split Number of Samples Number of Reciters
train 230,254 38
validation 14,416 3
test 18,593 3
Total 263,263 44

📊 Analyzing 230254 durations (rows) from the Excel file... Analyzing durations: 100%|██████████| 230254/230254 [00:00<00:00, 326999.12it/s]

🎯 RESULTS BY THRESHOLD (Excel-based) 🎯

--- Rows (files) shorter than 500 seconds --- 📁 Number of rows: 230254 🎯 Total duration: 3968417.59 seconds ⏰ Which is: 1102h 20m 17.59s

--- Rows (files) shorter than 400 seconds --- 📁 Number of rows: 230253 🎯 Total duration: 3967958.12 seconds ⏰ Which is: 1102h 12m 38.12s

--- Rows (files) shorter than 300 seconds --- 📁 Number of rows: 230249 🎯 Total duration: 3966514.47 seconds ⏰ Which is: 1101h 48m 34.47s

--- Rows (files) shorter than 200 seconds --- 📁 Number of rows: 230219 🎯 Total duration: 3959690.55 seconds ⏰ Which is: 1099h 54m 50.55s

--- Rows (files) shorter than 100 seconds --- 📁 Number of rows: 229479 🎯 Total duration: 3866971.27 seconds ⏰ Which is: 1074h 9m 31.27s

--- Rows (files) shorter than 90 seconds --- 📁 Number of rows: 229066 🎯 Total duration: 3827934.93 seconds ⏰ Which is: 1063h 18m 54.93s

--- Rows (files) shorter than 80 seconds --- 📁 Number of rows: 228390 🎯 Total duration: 3770780.24 seconds ⏰ Which is: 1047h 26m 20.24s

--- Rows (files) shorter than 70 seconds --- 📁 Number of rows: 227276 🎯 Total duration: 3687626.85 seconds ⏰ Which is: 1024h 20m 26.85s

--- Rows (files) shorter than 60 seconds --- 📁 Number of rows: 225348 🎯 Total duration: 3563535.47 seconds ⏰ Which is: 989h 52m 15.47s

--- Rows (files) shorter than 50 seconds --- 📁 Number of rows: 221755 🎯 Total duration: 3367880.79 seconds ⏰ Which is: 935h 31m 20.79s

--- Rows (files) shorter than 40 seconds --- 📁 Number of rows: 215071 🎯 Total duration: 3071315.05 seconds ⏰ Which is: 853h 8m 35.05s

--- Rows (files) shorter than 30 seconds --- 📁 Number of rows: 199514 🎯 Total duration: 2538879.34 seconds ⏰ Which is: 705h 14m 39.34s

--- Rows (files) shorter than 20 seconds --- 📁 Number of rows: 163816 🎯 Total duration: 1669293.66 seconds ⏰ Which is: 463h 41m 33.66s

--- Rows (files) shorter than 10 seconds --- 📁 Number of rows: 85129 🎯 Total duration: 535014.06 seconds ⏰ Which is: 148h 36m 54.06s

--- Rows (files) shorter than 5 seconds --- 📁 Number of rows: 26516 🎯 Total duration: 101191.18 seconds ⏰ Which is: 28h 6m 31.18s

========================================

Data Fields:

Each instance in the dataset consists of the following fields:

  • audio: A dictionary containing the raw audio data (bytes) and its sampling rate
  • duration: The duration of the audio file in seconds (float)
  • text: The ground-truth transcription of the Quranic verse (string)
  • reciter: The name of the reciter (Qari) (string)

Data Instance Example: 

{
  "audio": {
    "path": null,
    "bytes": "...",
    "sampling_rate": 16000
  },
  "duration": 12.17,
  "text": "الْحَمْدُ لِلَّهِ رَبِّ الْعَالَمِينَ",
  "reciter": "Karim Mansoori"
}

How to Use The dataset can be easily loaded using the datasets library:

from datasets import load_dataset
# Load the dataset
dataset = load_dataset("rabah2026/Quran-Ayah-Corpus")

# Accessing a split
train_dataset = dataset["train"]

# Printing the first example
print(train_dataset[0])

Reciters in each Split:

To ensure robustness and prevent data leakage, reciters are exclusively assigned to one split.

Train Set Reciters (38):

  • Abdul Basit
  • Abdullah Basfar
  • Abdurrahmaan As-Sudais
  • Abdul Samad
  • Abu Bakr Ash-Shaatree
  • Ahmed ibn Ali al-Ajamy
  • Alafasy
  • Hani Rifai
  • Husary
  • Husary (Mujawwad)
  • Hudhaify
  • Ibrahim Akhdar
  • Maher Al Muaiqly
  • Minshawi
  • Minshawy (Mujawwad)
  • Muhammad Ayyoub
  • Muhammad Jibreel
  • Parhizgar
  • Ayman Sowaid
  • Abdullaah Awaad Al-Juhaynee
  • Abdullah Matroud
  • Ahmed Neana
  • Akram AlAlaqimy
  • Ali Hajjaj AlSuesy
  • Ali Jaber
  • Fares Abbad
  • Ghamadi
  • Khaalid Abdullaah Al-Qahtaanee
  • Mohammad Al Tablaway
  • Muhammad AbdulKareem
  • Muhsin Al Qasim
  • Nabil Rifa3i
  • Nasser Alqatami
  • Sahl Yassin
  • Salaah AbdulRahman Bukhatir
  • Salah Al Budair
  • Saood Ash-Shuraym
  • Yaser Salamah

Validation Set Reciters (3):

  • Mustafa Ismail
  • Yasser Ad-Dussary
  • Aziz Alili

Test Set Reciters (3):

  • Karim Mansoori
  • Khalefa Al Tunaiji
  • Mahmoud Ali Al Banna

Dataset Creation:

The dataset was curated through a multi-step process:

  • CSV files containing metadata about audio URLs and transcriptions were collected from alquran.cloud and everyayah.com
  • The corresponding audio files were downloaded and converted to .wav format at a 16kHz sampling rate
  • A cleaning script ensured data integrity by verifying that every audio file had a corresponding metadata entry
  • A processing pipeline packaged the data with metadata into Apache Parquet files
  • The data was uploaded to the Hugging Face Hub, chunked into 3 Parquet files per reciter for memory efficiency

Licensing Information:

This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to share and adapt the material for non-commercial purposes as long as you give appropriate credit, provide a link to the license, and distribute your contributions under the same license.

Citation:

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

@dataset{rabah2026_ayah_corpus,
  author    = {Rabah},
  title     = {Quran-Ayah-Corpus: A Multi-Reciter Arabic Quranic Speech Dataset},
  year      = {2025},
  url       = {https://huggingface.co/datasets/rabah2026/Quran-Ayah-Corpus}
}

Curated by:

This dataset was curated by Rabah.