Datasets:

hetchyy
/

quranic-universal-ayahs

license: apache-2.0
task_categories:
  - automatic-speech-recognition
language:
  - ar
tags:
  - quran
  - recitation
  - forced-alignment
  - word-timestamps
  - letter-timestamps
  - audio-segmentation
  - speech-recognition
  - asr
  - vad
  - phoneme
version: v0.3.10
pretty_name: Qur'anic Universal Ayahs
size_categories:
  - 1K<n<10K
configs:
  - config_name: hafs_an_asim
    data_files:
      - split: mohammed_siddiq_al_minshawi
        path: hafs_an_asim/mohammed_siddiq_al_minshawi-*
  - config_name: reciters
    data_files:
      - split: all
        path: reciters/all-*
dataset_info:
  - config_name: hafs_an_asim
    features:
      - name: audio
        dtype: audio
      - name: surah
        dtype: int32
      - name: ayah
        dtype: int32
      - name: duration_ms
        dtype: int32
      - name: text_uthmani
        dtype: string
      - name: segments
        sequence:
          sequence: int32
      - name: word_timestamps
        sequence:
          sequence: int32
      - name: letter_timestamps
        sequence:
          - name: word_idx
            dtype: int32
          - name: char
            dtype: string
          - name: start_ms
            dtype: int32
          - name: end_ms
            dtype: int32
      - name: source_url
        dtype: string
      - name: source_offset_ms
        dtype: int32
    splits:
      - name: mohammed_siddiq_al_minshawi
        num_bytes: 0
        num_examples: 6236
    download_size: 1569809077
    dataset_size: 1571705367
  - config_name: reciters
    features:
      - name: reciter
        dtype: string
      - name: name_en
        dtype: string
      - name: name_ar
        dtype: string
      - name: riwayah
        dtype: string
      - name: style
        dtype: string
      - name: country
        dtype: string
      - name: source
        dtype: string
      - name: audio_category
        dtype: string
      - name: url_template
        dtype: string
      - name: coverage_surahs
        dtype: int32
      - name: coverage_ayahs
        dtype: int32
      - name: is_timestamped
        dtype: bool
    splits:
      - name: all
        num_bytes: 69253
        num_examples: 338
    download_size: 26962
    dataset_size: 69253

Qur'anic Universal Ayahs

Word-level and letter-level aligned Qur'an recitation audio with precise timestamps derived from phoneme-level forced alignment.
A community-verified dataset of 300+ reciters across 14 riwayat.

Dataset Description

Each row is one verse (ayah) of the Qur'an, with:

Audio clip of the verse recitation, trimmed to speech boundaries
Word-level timestamps in milliseconds, relative to the audio clip. The word_idx refers to the canonical word position in the verse, not the position in the text_uthmani field. When the reciter repeats a word, the same word_idx may appear multiple times and indices may go backward.
Letter-level timestamps for individual Arabic characters, tokenized as follows.
Pause-based segments showing how the recitation was naturally divided by silences
Uthmani text from alignment matching (reflects what was actually recited, including any repetitions). Non-recited markers (waqf signs, hizb, sajdah) are stripped.

Usage

from datasets import load_dataset

# Load a specific reciter (subset = riwayah, split = reciter)
ds = load_dataset("hetchyy/quranic-universal-ayahs", "hafs_an_asim", split="minshawy_murattal")

# Access a verse
verse = ds[0]
print(verse["surah"], verse["ayah"])       # 1 1
print(verse["duration_ms"])                 # Duration in ms
print(verse["text_uthmani"])                # Uthmani Arabic text
print(verse["word_timestamps"])             # [[1, 0, 400], [2, 400, 800], ...]

# Letter-level timestamps (if available)
lt = verse["letter_timestamps"]
if lt["word_idx"]:
    for w, ch, s, e in zip(lt["word_idx"], lt["char"], lt["start_ms"], lt["end_ms"]):
        print(f"  Word {w}: {ch} [{s}-{e}]ms")

# Play audio (in a notebook)
from IPython.display import Audio
Audio(verse["audio"]["array"], rate=verse["audio"]["sampling_rate"])

Schema

Column	Type	Description
`audio`	`Audio`	Verse audio (MP3), trimmed to speech boundaries
`surah`	`int32`	Surah number (1-114)
`ayah`	`int32`	Verse number within surah
`duration_ms`	`int32`	Audio clip duration in milliseconds
`text_uthmani`	`string`	Uthmani Arabic text of the verse from alignment
`segments`	`[[int, int, int, int]]`	Pause-based segments (ms, relative to clip)
`word_timestamps`	`[[int, int, int]]`	Word-level timestamps (ms, relative to clip)
`letter_timestamps`	`[[int, str, int, int]]`	Per-letter timestamps: `[word_idx, char, start_ms, end_ms]`. Empty if not available.
`source_url`	`string`	Original audio file URL (chapter or verse)
`source_offset_ms`	`int32`	Offset in source audio where this verse starts (ms)

Column Details

segments — Each segment is [word_from, word_to, start_ms, end_ms]. A continuous speech region between pauses.

Segments capture the natural pausing points in a recitation. A gap between consecutive segments is a pause. The word ranges tell you whether the reciter continued from where they left off or went back and repeated:

Sequential word ranges (next word_from = previous word_to + 1) — the reciter paused and continued.
Overlapping word ranges (next word_from ≤ previous word_to) — the reciter paused and repeated those words before continuing. The text_uthmani field includes the repeated words.

word_timestamps — Each entry is [word_index, start_ms, end_ms]. When a verse contains repeated segments, the same word index appears multiple times.

letter_timestamps — Per-letter character timestamps from phoneme-level forced alignment. Each entry is [word_idx, char, start_ms, end_ms] where word_idx matches the word_timestamps word index and char is the Arabic character. Multiple entries share the same word_idx for multi-letter words. Empty for reciters without letter-level alignment data.

duration_ms — Duration of the audio clip in milliseconds. Equal to the span from first word start to last word end.

text_uthmani — Uthmani script Arabic text from alignment. Non-recited markers (waqf stop signs, hizb markers, sajdah marks) are stripped. Preserves diacritics, silent markers, and tajweed annotations. Reflects what was actually recited, including any repetitions.

source_url — URL of the original audio file. For by-surah reciters, all verses in a surah share one URL; for by-ayah reciters, each verse has its own.

source_offset_ms — Millisecond offset in source_url where this verse begins. Convert clip-relative timestamps to source-relative: source_ms = clip_ms + source_offset_ms.

Example: Segments & Repetitions

Verse 21:73

#	Words	Time (ms)	Text
1	1–8	0–15,030	وَجَعَلْنَـٰهُمْ أَئِمَّةً يَهْدُونَ بِأَمْرِنَا وَأَوْحَيْنَآ إِلَيْهِمْ فِعْلَ ٱلْخَيْرَٰتِ
		pause (repeat back)
2	5–12	16,310–30,125	وَأَوْحَيْنَآ إِلَيْهِمْ فِعْلَ ٱلْخَيْرَٰتِ وَإِقَامَ ٱلصَّلَوٰةِ وَإِيتَآءَ ٱلزَّكَوٰةِ
		pause (continue)
3	13–15	30,345–34,725	وَكَانُوا۟ لَنَا عَـٰبِدِينَ

The text field contains 19 words (the 4 repeated words appear twice), and word_timestamps has entries for words 5–8 twice.

Gapless Playback

For by-surah reciters, combine source_url + source_offset_ms to seek within the original chapter audio:

fatiha = ds.filter(lambda x: x["surah"] == 1)
chapter_url = fatiha[0]["source_url"]  # all verses share the same chapter URL
for verse in fatiha:
    offset = verse["source_offset_ms"]
    for word_idx, start, end in verse["word_timestamps"]:
        source_start = start + offset  # seek position in chapter audio

Notes

All timestamps are in milliseconds, relative to the start of the audio clip
Word indices are 1-based
Word timestamps are padded forward within each segment so there are no gaps between consecutive words. Gaps only occur across segment boundaries (pauses in recitation).
For gapless chapter playback, use source_url directly (don't concatenate clips). Compute source-relative timestamps with source_ms = clip_ms + source_offset_ms. Content not covered by timestamps (e.g. basmalas, cross-verse transitions) plays naturally in the original audio without word highlighting.

Reciters Catalog

The reciters config is a lightweight index of all available reciters. Use it to discover reciters, filter by riwayah/style, and construct audio URLs:

from datasets import load_dataset

reciters = load_dataset("hetchyy/quranic-universal-ayahs", "reciters", split="all")

# All Hafs murattal reciters with full coverage
hafs = reciters.filter(lambda r: r["riwayah"] == "hafs_an_asim" and r["coverage_surahs"] == 114)

# Construct a direct audio URL (add https:// prefix to url_template)
r = hafs[0]
url = "https://" + r["url_template"].format(surah=2)  # Al-Baqarah chapter audio

Column	Type	Description
`reciter`	`string`	Reciter slug
`name_en`	`string`	English display name
`name_ar`	`string`	Arabic name
`riwayah`	`string`	Riwayah slug (e.g. `hafs_an_asim`)
`style`	`string`	Recitation style (`murattal`, `mujawwad`, `muallim`)
`country`	`string`	Country of origin
`source`	`string`	Audio source (e.g. `mp3quran`, `everyayah`)
`audio_category`	`string`	`by_surah` or `by_ayah`
`url_template`	`string`	URL pattern (without `https://`)
`coverage_surahs`	`int32`	Number of surahs with audio (max 114)
`coverage_ayahs`	`int32`	Number of ayahs with audio (max 6,236)
`is_timestamped`	`bool`	Whether word-level timestamps are available in the dataset

Configs

Subset (config) is the riwayah, split is the reciter.

`hafs_an_asim`

Reciter	Style	Verses	Audio Source
Mohammed Siddiq Al-Minshawi	murattal	6,237	mp3quran.net

Pipeline

Audio is processed through a multi-stage pipeline:

VAD segmentation — Detect speech regions using a recitation-specific VAD model
Phoneme-level ASR — CTC-based recognition with wav2vec2
Dynamic programming alignment — Match recognized phonemes against known Qur'anic reference text
MFA forced alignment — Montreal Forced Aligner produces phoneme-level timestamps, from which word boundaries are derived

License

Apache 2.0