README.md

---
license: other
license_name: npl-1.1
license_link: LICENSE
language:
  - ar
task_categories:
  - text-to-speech
  - audio-to-audio
  - automatic-speech-recognition
pretty_name: QuranTTS
tags:
  - quran
  - recitation
  - speech-restoration
  - tts
  - arabic
  - 48khz
  - studio-quality
  - phonemes
  - ayah-aligned
size_categories:
  - 10K<n<100K
configs:
  - config_name: v2_clean
    default: true
    data_files:
      - split: train
        path: v2_clean/**
  - config_name: v2_raw
    data_files:
      - split: train
        path: v2_raw/**
  - config_name: v1_chunks
    data_files:
      - split: train
        path: data/**
---

<p align="center">
  <img src="banner.png" alt="QuranTTS" width="100%">
</p>

# QuranTTS

**An ear-verified Quranic recitation corpus for speech restoration and TTS.**
Three configurations ship in this repository: the same ayah-aligned segments in
cleaned and uncleaned form, plus the larger raw pause-cut pool they were cut
from.

```python
from datasets import load_dataset

# ayah-aligned, cleaned, with text and phonemes  (default)
ds = load_dataset("Quran-Lab/QuranTTS", "v2_clean", split="train")

# the SAME segments and labels, before any cleaning
raw = load_dataset("Quran-Lab/QuranTTS", "v2_raw", split="train")

# the larger raw pause-cut pool, audio and source labels only
pool = load_dataset("Quran-Lab/QuranTTS", "v1_chunks", split="train")
```

## Which configuration do I want?

| | `v2_clean` | `v2_raw` | `v1_chunks` |
|---|---|---|---|
| Segments | 1,615 | 1,615 | 14,091 |
| Duration | 7.7 h | 7.7 h | 58.4 h |
| Segment unit | **one complete ayah** | **one complete ayah** | pause-to-pause chunk |
| Arabic text | **yes** | **yes** | no |
| Phonemes | **yes** | **yes** | no |
| Surah and ayah ids | **yes** | **yes** | no |
| Acoustic cleaning | **yes** | no | no |
| Sample rate | 48 kHz mono FLAC | 48 kHz mono FLAC | 48 kHz mono FLAC |

`v2_raw` and `v2_clean` are **matched pairs**: identical segments, identical
boundaries, identical labels, differing only in whether the cleaning chain was
applied. That gives directly usable (degraded, clean) training pairs for speech
restoration, and it lets you reject our cleaning choices and redo them from the
aligned audio.

Use `v2_clean` for anything needing labels and clean targets. Use `v2_raw` when
you want the original acoustics or your own processing. Use `v1_chunks` when you
want maximum audio and intend to do your own segmentation.

### On the size difference

`v1_chunks` holds 58.4 h, of which the ayah-aligned configs currently cover only
7.7 h. That gap is a limitation of the alignment stage, not a judgement about
the remaining audio. The locator works on ten-minute spans and discards a span
it cannot confidently place, and it resolves each span to a single surah, so
recordings that cross surah boundaries lose material. Sources that sit inside
one surah converted at 60 to 77 percent; the longest continuous recitations
converted at under 5 percent. The unconverted audio is all present in
`v1_chunks`. Improving this is the main open work on the dataset.

## v2_clean and v2_raw

### Fields

| field | description |
|---|---|
| `audio` | 48 kHz mono FLAC, one complete ayah |
| `source_id` | originating recording |
| `surah`, `ayah` | canonical location, 1-indexed |
| `text_uthmani` | Uthmani script, as recited |
| `text_imlaei` | imlaei (simplified) orthography |
| `phonemes` | Quran Phonetic Script, whole-ayah phonetisation |
| `duration_s` | seconds |
| `reciter`, `grade`, `tier` | source-level provenance |
| `cleaning` | the exact processing chain applied |

Phonemes are produced with `quran-transcript`'s Hafs phonetiser, applied to the
**whole ayah** rather than word by word. Per-word phonetisation measures 20.5%
PER against whole-ayah 1.6%, because cross-word tajweed rules (idgham, madd at
word boundaries) are invisible when words are processed in isolation.

### How the audio was segmented

Rather than cutting on silence and hoping, each recording is transcribed with a
CTC model, the decoded text is located inside the canonical Quran with a
sequence matcher over a normalised 6,236-ayah index, and only the ayat actually
present are force-aligned. Boundaries are then refined against signal onsets.
Spans that locate nothing, such as introductions or nasheed beds, are dropped
rather than guessed at.

### Cleaning chain

```
de-hum notches (50/60 Hz and harmonics, Q=30)
  -> iZotope RX Dialogue Isolate (dialogue 0, reverb -30, noise -20)
  -> dereverb-echo mel-band roformer
  -> iZotope RX Voice De-noise (reduction 6)
  -> high-pass 80 Hz
  -> resample to 48 kHz
  -> normalise to -1 dBFS true peak
```

Every stage is **subtractive**. No generative model touches this audio, so
nothing can be hallucinated into a recitation. That was a deliberate constraint:
a generative enhancer can smooth or lengthen a vowel, and in Quranic recitation
an altered vowel length is an altered madd.

Measured effect across the corpus:

| | before | after |
|---|---|---|
| noise floor | -18.2 dB | **-21.3 dB** |
| 4-8 kHz energy share | 0.234 % | **0.299 %** |
| spectral edge | 17.9 kHz | **19.1 kHz** |

The 4-8 kHz band rising matters more than the floor: that region carries
sibilants and the emphatic consonants, and most denoisers erode it. The band
edge rising rather than falling is why a second, more aggressive dereverb model
was rejected during development, since it silently brickwalled everything above
17.5 kHz.

All 1,615 files were verified after processing for sample rate, clipping,
dynamics, and duration drift against their source segment, so the text and
phoneme labels still align with the audio.

## Known limitations

- **Hafs only.** Other qira'at are not represented.
- **Small speaker pool.** This is a clean-target corpus, not a speaker-diversity
  corpus. `v2_clean` draws on 21 recordings.
- **Coverage is partial**, 923 unique ayat across 19 surahs. It is not a
  complete mushaf.
- **Cleaning is not uniform.** Roughly 7% of segments came out with a slightly
  worse noise floor than they started with, scattered across sources rather than
  concentrated in any one.
- Waqf (pause) rules are not modelled in the phoneme layer.
- `v1_chunks` segments are cut at energy minima, which occasionally fall
  mid-breath. Do not treat those boundaries as phrase boundaries.

## Provenance and licensing

Audio originates from publicly posted recitations, each auditioned and approved
by a human listener before inclusion. Sources that measured well but sounded
processed were rejected. Full per-source provenance is maintained offline by the
maintainers.

Released under NPL 1.1 (see `LICENSE`). If you believe a recording of yours is
included and you want it removed, open a discussion on this repository and it
will be taken down.