Annotated Quranic Qira’at Dataset: AQQD v1.0

Abstract AQQD v1.0 is an open dataset of Quranic recitations annotated across multiple Qira’at to exclusively cover the ten canonical reading styles of the Quran. The dataset is developed to support a wide range of machine learning and audio analysis frameworks by providing carefully selected audio samples with rich, structured annotations suitable for classification, representation learning, and interpretability-focused modeling. The dataset encompasses recordings from 308 reciters, covering 70 Quranic Surahs segmented into representative verses, with each segment recited in 6–8 distinct Qira’at styles. Each audio file is accompanied by structured metadata embedded within its filename, indicating the reciter, recitation style, surah, ayah, and clip number. The first version of AQQD v1.0 fills a critical gap in computational Quranic studies by bridging traditional Qira’at scholarship with modern machine learning. It provides a high-quality and interpretable resource for researchers in audio processing, Islamic studies, and educational technology, contributing to the analysis and preservation of the diverse recitation heritage.