metadata

dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: Surah
      dtype: string
    - name: Aya
      dtype: string
    - name: duration_ms
      dtype: int64
    - name: create_date
      dtype: string
    - name: golden
      dtype: bool
    - name: final_label
      dtype: string
    - name: reciter_id
      dtype: string
    - name: reciter_country
      dtype: string
    - name: reciter_gender
      dtype: string
    - name: reciter_age
      dtype: string
    - name: reciter_qiraah
      dtype: string
    - name: judgments_num
      dtype: int64
    - name: annotation_metadata
      dtype: string
  splits:
    - name: train
      num_bytes: 1290351809.656
      num_examples: 6828
  download_size: 1258070687
  dataset_size: 1290351809.656
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
task_categories:
  - automatic-speech-recognition
  - audio-classification
language:
  - ar
tags:
  - Crowdsourcing
  - Quranic recitation
  - Non-Arabic Speakers
pretty_name: >-
  Quranic Audio Dataset - Crowdsourced and Labeled Recitation from Non-Arabic
  Speakers

Dataset Card for Quranic Audio Dataset : Crowdsourced and Labeled Recitation from Non-Arabic Speakers

Dataset Summary

We explore the possibility of crowdsourcing a carefully annotated Quranic dataset, on top of which AI models can be built to simplify the learning process. In particular, we use the volunteer-based crowdsourcing genre and implement a crowdsourcing API to gather audio assets. We developed a crowdsourcing platform called Quran Voice for annotating the gathered audio assets. As a result, we have collected around 7000 Quranic recitations from a pool of 1287 participants across more than 11 non-Arabic countries, and we have annotated 1166 recitations from the dataset in six categories. We have achieved a crowd accuracy of 0.77, an inter-rater agreement of 0.63 between the annotators, and 0.89 between the labels assigned by the algorithm and the expert judgments.

How to use

The dataset can be downloaded using the load_dataset function from datasets library.

!pip install datasets

from datasets import load_dataset

quranic_dataset = load_dataset("RetaSy/quranic_audio_dataset")

print(quranic_dataset)

Dataset Structure

Data Instances

{
  'audio': {
      'path': '0058a4f7-6a3a-4665-b43b-d6f67fd14dbf.wav',
      'array': array([0.00000000e+00, 0.00000000e+00, 3.05175781e-05, ...,9.15527344e-05, 0.00000000e+00, 1.83105469e-04]),
      'sampling_rate': 16000
  },
  'Surah': 'Al-Faatihah',
  'Aya': 'أَعُوذُ بِاللَّهِ مِنَ الشَّيْطَانِ الرَّجِيْمِ',
  'duration_ms': 3520,
  'create_date': '2023-03-15T19:57:35.027430+03:00',
  'golden': False,
  'final_label': 'in_correct',
  'reciter_id': 'ef1ada15-e225-4155-a81c-fc461d940a6d',
  'reciter_country': 'AT',
  'reciter_gender': 'female',
  'reciter_age': 'Unknown',
  'reciter_qiraah': 'hafs',
  'judgments_num': 3,
  'annotation_metadata': '{
          "label_1": "in_correct",
          "annotator1_id": "1",
          "annotator1_SCT": "257",
          "annotator1_MCC": "0.87",
          "annotator1_ACC": "0.92",
          "annotator1_F1": "0.91",
          "label_2": "correct",
          "annotator2_id": "10",
          "annotator2_SCT": "21",
          "annotator2_MCC": "0.52",
          "annotator2_ACC": "0.57",
          "annotator2_F1": "0.55",
          "label_3": "in_correct",
          "annotator3_id": "19",
          "annotator3_SCT": "12",
          "annotator3_MCC": "0.75",
          "annotator3_ACC": "0.83",
          "annotator3_F1": "0.78"
  }'
}

Data Fields

Citation Information

@inproceedings{quran_audio_dataset:2024,
  author      = {Salameh, R., Mdfaa, M. A., Askarbekuly, N., & Mazzara, M.},
  title       = {Quranic Audio Dataset: Crowdsourced and Labeled Recitation from Non-Arabic Speakers},
  year        = 2024,
  eprint      = {2405.02675},
  eprinttype  = {arxiv},
  eprintclass = {cs.SD},
  url         = {https://arxiv.org/abs/2405.02675},
  language    = {english}
}