Quran-MD: A Fine-Grained Multilingual Multimodal Dataset of the Quran
Muhammad Umar Salman, Mohammad Areeb Qazi, Mohammed Talha Alam
TL;DR
Quran-MD addresses the lack of a unified, fine-grained multimodal Qur’an resource by combining verse- and word-level Arabic text, English translations, transliterations, and aligned audio from 30 reciters. The authors harmonize three data sources into a hierarchical JSON schema with explicit cross-modal alignments at both verse and word granularity, enabling end-to-end and multimodal analyses. Key contributions include the first holistic integration of all modalities across 114 surahs, 6,236 ayahs, and ~77.8k words with ~665 hours of verse-level and ~22 hours of word-level audio from 30 reciters, plus validation and an accessible Hugging Face release. These resources support applications in ASR, tajweed detection, Qur’anic TTS, multimodal embeddings, and style-aware retrieval, benefitting researchers, educators, and communities.
Abstract
We present Quran MD, a comprehensive multimodal dataset of the Quran that integrates textual, linguistic, and audio dimensions at the verse and word levels. For each verse (ayah), the dataset provides its original Arabic text, English translation, and phonetic transliteration. To capture the rich oral tradition of Quranic recitation, we include verse-level audio from 32 distinct reciters, reflecting diverse recitation styles and dialectical nuances. At the word level, each token is paired with its corresponding Arabic script, English translation, transliteration, and an aligned audio recording, allowing fine-grained analysis of pronunciation, phonology, and semantic context. This dataset supports various applications, including natural language processing, speech recognition, text-to-speech synthesis, linguistic analysis, and digital Islamic studies. Bridging text and audio modalities across multiple reciters, this dataset provides a unique resource to advance computational approaches to Quranic recitation and study. Beyond enabling tasks such as ASR, tajweed detection, and Quranic TTS, it lays the foundation for multimodal embeddings, semantic retrieval, style transfer, and personalized tutoring systems that can support both research and community applications. The dataset is available at https://huggingface.co/datasets/Buraaq/quran-audio-text-dataset