---
configs:
- config_name: default
  data_files:
  - split: train
    path:
    - data/recitation_0/train/*.parquet
    - data/recitation_1/train/*.parquet
    - data/recitation_2/train/*.parquet
    - data/recitation_3/train/*.parquet
    - data/recitation_5/train/*.parquet
    - data/recitation_6/train/*.parquet
    - data/recitation_7/train/*.parquet
  - split: validation
    path:
    - data/recitation_0/validation/*.parquet
    - data/recitation_1/validation/*.parquet
    - data/recitation_2/validation/*.parquet
    - data/recitation_3/validation/*.parquet
    - data/recitation_5/validation/*.parquet
    - data/recitation_6/validation/*.parquet
    - data/recitation_7/validation/*.parquet
  - split: test
    path:
    - data/recitation_8/train/*.parquet
    - data/recitation_8/validation/*.parquet
dataset_info:
  splits:
  - name: train
    num_examples: 54823
  - name: test
    num_examples: 8787
  - name: validation
    num_examples: 7175
  featrues:
  - dtype: string
    name: aya_name
  - dtype: string
    name: aya_id
  - dtype: string
    name: reciter_name
  - dtype: int32
    name: recitation_id
  - dtype: string
    name: url
  - dtype:
      audio:
        decode: false
        sampling_rate: 16000
    name: audio
  - dtype: float32
    name: duration
  - dtype: float32
    name: speed
  - dtype:
      array2_d:
        dtype: float32
        shape:
        - null
        - 2
    name: speech_intervals
  - dtype: bool
    name: is_interval_complete
  - dtype: bool
    name: is_augmented
  - dtype:
      array2_d:
        dtype: float32
        shape:
        - null
        - 2
    name: input_features
  - dtype:
      array2_d:
        dtype: int32
        shape:
        - null
        - 1
    name: attention_mask
  - dtype:
      array2_d:
        dtype: int32
        shape:
        - null
        - 1
    name: labels
language:
- ar
license: mit
task_categories:
- automatic-speech-recognition
tags:
- quran
- arabic
- speech-segmentation
- audio-segmentation
- audio
---

# Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning

[Paper](https://huggingface.co/papers/2509.00094) | [Project Page](https://obadx.github.io/prepare-quran-dataset/) | [Code](https://github.com/obadx/recitations-segmenter)

## Introduction
This dataset is developed as part of the research presented in the paper "Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning". The work introduces a 98% automated pipeline to produce high-quality Quranic datasets, comprising over 850 hours of audio (~300K annotated utterances). This dataset supports a novel ASR-based approach for pronunciation error detection, utilizing a custom Quran Phonetic Script (QPS) designed to encode Tajweed rules.

## Recitation Segmentations Dataset

This is a modified version of [this dataset](https://huggingface.co/datasets/obadx/recitation-segmentation) with these modifications:
* adding augmentation to the speed of the recitations utterance with column `speed` reflects the speed from 0.8 to 1.5 on 40% of the dataset using [audumentations](https://iver56.github.io/audiomentations/).
* adding data augmentation with [audiomentations](https://iver56.github.io/audiomentations/) on 40% of the dataset to prepare it for training the recitations spliter.

The codes for building this dataset is available at [github](https://github.com/obadx/recitations-segmenter)

## Results
The model trained with this dataset achieved the following results on an unseen test set:

| Metric    | Value  |
|-----------|--------|
| Accuracy  | 0.9958 |
| F1        | 0.9964 |
| Loss      | 0.0132 |
| Precision | 0.9976 |
| Recall    | 0.9951 |

## Sample Usage

Below is a Python example demonstrating how to use the `recitations-segmenter` library (developed alongside this dataset) to segment Holy Quran recitations.

First, ensure you have the necessary Python packages and `ffmpeg`/`libsndfile` installed:

#### Linux

```bash
sudo apt-get update
sudo apt-get install -y ffmpeg libsndfile1 portaudio19-dev
```

#### Winodws & Mac

You can create an `anaconda` environment and then download these two libraries:

```bash
conda create -n segment python=3.12
conda activate segment
conda install -c conda-forge ffmpeg libsndfile
```

Install the library using pip:
```bash
pip install recitations-segmenter
```

Then, you can run the following Python script:

```python
from pathlib import Path

from recitations_segmenter import segment_recitations, read_audio, clean_speech_intervals
from transformers import AutoFeatureExtractor, AutoModelForAudioFrameClassification
import torch

if __name__ == '__main__':
    device = torch.device('cuda')
    dtype = torch.bfloat16

    processor = AutoFeatureExtractor.from_pretrained(
        "obadx/recitation-segmenter-v2")
    model = AutoModelForAudioFrameClassification.from_pretrained(
        "obadx/recitation-segmenter-v2",
    )

    model.to(device, dtype=dtype)

    # Change this to the file pathes of Holy Quran recitations
    # File pathes with the Holy Quran Recitations
    file_pathes = [
        './assets/dussary_002282.mp3',
        './assets/hussary_053001.mp3',
    ]
    waves = [read_audio(p) for p in file_pathes]

    # Extracting speech inervals in samples according to 16000 Sample rate
    sampled_outputs = segment_recitations(
        waves,
        model,
        processor,
        device=device,
        dtype=dtype,
        batch_size=8,
    )

    for out, path in zip(sampled_outputs, file_pathes):
        # Clean The speech intervals by:
        # * merging small silence durations
        # * remove small speech durations
        # * add padding to each speech duration
        # Raises:
        # * NoSpeechIntervals: if the wav is complete silence
        # * TooHighMinSpeechDruation: if `min_speech_duration` is too high which
        # resuls for deleting all speech intervals
        clean_out = clean_speech_intervals(
            out.speech_intervals,
            out.is_complete,
            min_silence_duration_ms=30,
            min_speech_duration_ms=30,
            pad_duration_ms=30,
            return_seconds=True,
        )

        print(f'Speech Intervals of: {Path(path).name}: ')
        print(clean_out.clean_speech_intervals)
        print(f'Is Recitation Complete: {clean_out.is_complete}')
        print('-' * 40)
```

## License

This dataset is licensed under the [MIT](https://mit-license.org/)