MuazAhmad7/Surah_Ikhlas-Labeled_Dataset

Surah Al-Ikhlas Quran Recitation Error Detection Dataset

Dataset Description

This dataset contains audio recordings of Quran recitations of Surah Al-Ikhlas (Chapter 112 - The Sincerity) with labels indicating whether each recitation contains errors in Tajweed (Quran recitation rules).

Dataset Summary

Statistic	Value
Total Samples	1,506
Error Recitations	851 (56.5%)
Correct Recitations	655 (43.5%)
Verses	4
Audio Format	WAV
Language	Arabic

Surah Al-Ikhlas Text

Verse	Arabic	Transliteration	Translation
1	قُلْ هُوَ اللَّهُ أَحَدٌ	Qul huwa Allahu ahad	Say, "He is Allah, [who is] One"
2	اللَّهُ الصَّمَدُ	Allahu assamad	"Allah, the Eternal Refuge"
3	لَمْ يَلِدْ وَلَمْ يُولَدْ	Lam yalid walam yulad	"He neither begets nor is born"
4	وَلَمْ يَكُن لَّهُ كُفُوًا أَحَدٌ	Walam yakun lahu kufuwan ahad	"Nor is there to Him any equivalent"

Dataset Structure

Files

• data/ - Folder containing all WAV audio files
• metadata.csv - CSV file with labels and metadata for each audio file

Metadata Fields

Field	Type	Description
file_name	string	Path to audio file (e.g., data/ID1V1F.wav)
label	int	Binary label: 0 = error, 1 = correct
label_name	string	Label as text: "error" or "correct"
verse_number	int	Verse number (1-4)
verse_text	string	Arabic text of the verse
error_type	string	Type of Tajweed error (Arabic, if applicable)
error_location	string	Location of error in the verse
error_explanation	string	Explanation of the error (Arabic)
error_count	int	Error category number

File Naming Convention

Audio files follow the pattern: ID{participant}V{verse}{T/F}.wav

• ID prefix followed by participant number
• V followed by verse number (1-4)
• T = True (correct recitation) / F = False (contains error)

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("MuazAhmad7/Surah_Ikhlas-Labeled_Dataset")

# Access the data
for sample in dataset['train']:
    print(f"File: {sample['file_name']}")
    print(f"Label: {sample['label_name']}")
    print(f"Verse: {sample['verse_number']}")
    break

Loading Audio

import pandas as pd
from datasets import Dataset, Audio

# Load metadata
df = pd.read_csv("metadata.csv")

# Create dataset with audio
dataset = Dataset.from_pandas(df)
dataset = dataset.cast_column("file_name", Audio(sampling_rate=16000))

Applications

This dataset can be used for:

• 🎯 Training audio classification models for Tajweed error detection
• 📱 Building Quran recitation assessment applications
• 🔬 Research in Arabic speech processing
• 📚 Educational tools for learning proper Quran recitation
• 🤖 Developing AI-assisted Quran tutoring systems

Error Types

The dataset includes various Tajweed errors including:

• Errors in Qalqalah (قلقلة) - echoing/bouncing sounds
• Errors in letter pronunciation
• Errors in elongation (Madd)
• And other Tajweed rule violations

License

This dataset is released under the CC BY 4.0 license.