metadata

dataset_info:
  features:
    - name: audio
      struct:
        - name: bytes
          dtype: binary
        - name: path
          dtype: string
    - name: duration
      dtype: float64
    - name: text
      dtype: string
    - name: reciter
      dtype: string
  splits:
    - name: train
      num_bytes: 2315694478.08891
      num_examples: 4000
    - name: test
      num_bytes: 868385429.2833413
      num_examples: 1500
  download_size: 3081675303
  dataset_size: 3184079907.3722515
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
task_categories:
  - automatic-speech-recognition
language:
  - ar
tags:
  - quran
  - ASR
  - Islam
  - tarteel
  - verses
  - arabic
  - religion
size_categories:
  - 1K<n<10K
pretty_name: Quran_data_everyayah

Dataset Details

Part of (tarteel_ai_everyayah_dataset) with shuffle applaying on it This dataset is a collection of Quranic verses and their transcriptions, with diacritization, by different reciters.

NOTE

make sure you use the function dataset.cast_column("audio", Audio(sampling_rate=16_000)) on dataset

to convert audio from a Byte to structure in Data Instances.

Dataset Description

This data was created specifically because the original data (tarteel_ai_everyayah_dataset) is very large in size, which may cause problems during downloading and of course a large space, whether the device space or one of the cloud sites is used, and it is sufficient to train models such as ASR and reciter classification.

هذه البيانات قد تم عملها خصيصا بسب ان البيانات الاصلية

حجمها كبير جدا مما قد يسبب مشاكل اثناء تحميلها و بطبع مساحة كبيرة سواء تم استخدام مساحة الجهاز او احد مواقع السحابية

و هي كافية لتدريب النماذج

Curated by: [abo_salah , tarteel Ai company]
Language(s) (NLP): [Arabic]

Dataset Sources

https://huggingface.co/datasets/tarteel-ai/everyayah

Repository: [tarteel-ai/everyayah]

Data Instances

A typical data point comprises the audio file audio, and its transcription called text. The duration is in seconds, and the author is reciter.

An example from the dataset is:

{

'audio': {

'path': None,
'array': array([ 0.        ,  0.        ,  0.        , ..., -0.00057983,
   -0.00085449, -0.00061035]),
'sampling_rate': 16000

}

'duration': 6.478375,

'text': 'بِسْمِ اللَّهِ الرَّحْمَنِ الرَّحِيمِ',

'reciter': 'abdulsamad'

}

Data Fields

audio: A dictionary containing the path to the downloaded audio file, the decoded audio array,

and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].

sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time.

Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should

always be preferred over dataset["audio"][0].

text: The transcription of the audio file.

duration: The duration of the audio file.

reciter: The reciter of the verses.

Datasets:

abosalah
/

Part_of_tarteel_ai_everyayah_dataset