Tadabur: A Large-Scale Quran Audio Dataset

• URL: http://arxiv.org/abs/2604.18932v1
• Date: Tue, 21 Apr 2026 00:13:30 GMT
• Title: Tadabur: A Large-Scale Quran Audio Dataset
• Abstract summary: Tadabur is a large-scale Quran audio dataset.<n>It comprises more than 1400+ hours of audio recitation from over 600 distinct reciters.<n>This diversity makes Tadabur a comprehensive and representative resource for Quranic speech research and analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite growing interest in Quranic data research, existing Quran datasets remain limited in both scale and diversity. To address this gap, we present Tadabur, a large-scale Quran audio dataset. Tadabur comprises more than 1400+ hours of recitation audio from over 600 distinct reciters, providing substantial variation in recitation styles, vocal characteristics, and recording conditions. This diversity makes Tadabur a comprehensive and representative resource for Quranic speech research and analysis. By significantly expanding both the total duration and variability of available Quran data, Tadabur aims to support future research and facilitate the development of standardized Quranic speech benchmarks.

Related papers

• A Comparative Study of Pretrained Transformer Models for Quranic ASR: Speech Representations, Label Formats, and Dataset Composition [0.0]
  Quran Automatic Speech Recognition (ASR) aims to convert Quranic recitation into text, enabling applications such as aided memorisation tools and Quranic search engines.<n>Existing ASR models often exhibit high Word Error Rates (WER) on user-recited verses and lack full coverage of the Quranic corpus.<n>This paper presents a systematic empirical study of domain-specific fine-tuning of pretrained Transformer-based models for Quranic ASR.
  arXiv  Detail & Related papers  (2026-06-18T03:22:04Z)
• ATIR: Towards Audio-Text Interleaved Contextual Retrieval [63.68521448682396]
  We introduce the Audio-Text Interleaved contextual Retrieval (ATIR) task, where queries can alternate between audio and text modalities.<n>This benchmark substantially addresses the limitations of existing audio retrieval datasets in semantic retrieval.
  arXiv  Detail & Related papers  (2026-04-22T07:11:58Z)
• Beyond Transcripts: A Renewed Perspective on Audio Chaptering [66.61445564139052]
  We show that a novel audio-only architecture (AudioSeg) outperforms text-based approaches for segmenting long-form audio into coherent sections.<n>Our experiments on YTSeg reveal that AudioSeg substantially outperforms text-based approaches, pauses provide the largest acoustic gains, and MLLMs remain limited by context length and weak instruction following.
  arXiv  Detail & Related papers  (2026-02-09T18:28:10Z)
• Quran-MD: A Fine-Grained Multilingual Multimodal Dataset of the Quran [1.3481884955361023]
  Quran MD is a comprehensive dataset of the Quran that integrates textual, linguistic, and audio dimensions at the verse and word levels.<n>This dataset supports various applications, including natural language processing, speech recognition, text-to-speech synthesis, linguistic analysis, and digital Islamic studies.
  arXiv  Detail & Related papers  (2026-01-25T15:23:37Z)
• A Cocktail-Party Benchmark: Multi-Modal dataset and Comparative Evaluation Results [62.01871490859886]
  We introduce the task of Multi-Modal Context-Aware Recognition (MCoRec) in the ninth CHiME Challenge.<n>MCoRec captures natural multi-party conversations where the recordings focus on unscripted, casual group chats.<n>The task requires systems to answer the question "Who speaks when, what, and with whom?" by jointly transcribing each speaker's speech and clustering them into their respective conversations from audio-visual recordings.
  arXiv  Detail & Related papers  (2025-10-27T12:36:43Z)
• Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning [0.0]
  We build a 98% automated pipeline to produce high-quality Quranic datasets.<n>We use our custom Quran Phonetic Script to encode Tajweed rules.<n>We release all code, data, and models as open-source.
  arXiv  Detail & Related papers  (2025-08-27T15:28:46Z)
• Summarizing Speech: A Comprehensive Survey [89.69929708257744]
  Speech summarization has become an essential tool for efficiently managing and accessing the growing volume of spoken and audiovisual content.<n>This survey examines existing datasets and evaluation protocols, which are crucial for assessing the quality of summarization approaches.
  arXiv  Detail & Related papers  (2025-04-10T17:50:53Z)
• A Benchmark Dataset with Larger Context for Non-Factoid Question Answering over Islamic Text [0.16385815610837165]
  We introduce a comprehensive dataset meticulously crafted for Question-Answering purposes within the domain of Quranic Tafsir and Ahadith. This dataset comprises a robust collection of over 73,000 question-answer pairs, standing as the largest reported dataset in this specialized domain. While this paper highlights the dataset's contributions, our subsequent human evaluation uncovered critical insights regarding the limitations of existing automatic evaluation techniques.
  arXiv  Detail & Related papers  (2024-09-15T19:50:00Z)
• Quranic Audio Dataset: Crowdsourced and Labeled Recitation from Non-Arabic Speakers [1.2124551005857038]
  This paper addresses the challenge of learning to recite the Quran for non-Arabic speakers. We use the volunteer-based crowdsourcing genre and implement a crowdsourcing API to gather audio assets. We have collected around 7000 Quranic recitations from a pool of 1287 participants across more than 11 non-Arabic countries.
  arXiv  Detail & Related papers  (2024-05-04T14:29:05Z)
• Quran Recitation Recognition using End-to-End Deep Learning [0.0]
  The Quran is the holy scripture of Islam, and its recitation is an important aspect of the religion. Recognizing the recitation of the Holy Quran automatically is a challenging task due to its unique rules. We propose a novel end-to-end deep learning model for recognizing the recitation of the Holy Quran.
  arXiv  Detail & Related papers  (2023-05-10T18:40:01Z)
• WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research [82.42802570171096]
  We introduce WavCaps, the first large-scale weakly-labelled audio captioning dataset, comprising approximately 400k audio clips with paired captions. Online-harvested raw descriptions are highly noisy and unsuitable for direct use in tasks such as automated audio captioning. We propose a three-stage processing pipeline for filtering noisy data and generating high-quality captions, where ChatGPT, a large language model, is leveraged to filter and transform raw descriptions automatically.
  arXiv  Detail & Related papers  (2023-03-30T14:07:47Z)
• A Corpus for Large-Scale Phonetic Typology [112.19288631037055]
  We present VoxClamantis v1.0, the first large-scale corpus for phonetic typology. aligned segments and estimated phoneme-level labels in 690 readings spanning 635 languages, along with acoustic-phonetic measures of vowels and sibilants.
  arXiv  Detail & Related papers  (2020-05-28T13:03:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.