---
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

<!-- Provide the basic links for the dataset. -->

  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.


## Data Splits
## Train	Test	
## 4000	    1500