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

Muhammad Umar Salman, Mohammad Areeb Qazi, Mohammed Talha Alam

Keywords: Arabic linguistics, natural language processing (NLP), text-to-speech (TTS), speech recognition

Abstract: We present Quran-MD, a comprehensive multimodal dataset of the Qur’an 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 Qur’anic 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 Qur’anic recitation and study. Beyond enabling tasks such as ASR, tajweed detection, and Qur’anic 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

Track: Track 1: ML on Islamic Content / ML for Muslim Communities

Submission Number: 11