Quran-MD: A Fine-Grained Multilingual Multimodal Dataset of the Quran

TL;DR

Quran-MD is a detailed multilingual multimodal dataset combining text, audio, and linguistic data of the Quran at verse and word levels, supporting advanced NLP and speech applications.

Contribution

It introduces a comprehensive, fine-grained dataset with diverse recitation styles, enabling new research in Quranic recitation, linguistic analysis, and multimodal AI applications.

Findings

Supports tasks like ASR, TTS, and tajweed detection

Provides diverse recitation audio from 32 reciters

Facilitates multimodal embeddings and semantic retrieval

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