
﷽

# Dataset Card for Tarteel AI's EveryAyah Dataset

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Tarteel AI](https://www.tarteel.ai/)
- **Repository:** [Needs More Information]
- **Point of Contact:** [Mohamed Saad Ibn Seddik](mailto:ms.ibnseddik@tarteel.ai)

### Dataset Summary

This dataset is a collection of Quranic verses and their transcriptions, with diacritization, by different reciters.

### Supported Tasks and Leaderboards

[Needs More Information]

### Languages

The audio is in Arabic.

## Dataset Structure

### 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'
}
```

### Length:

Training:
Total duration: 2985111.2642479446 seconds
Total duration: 49751.85440413241 minutes
Total duration: 829.1975734022068 hours

Validation:
Total duration: 372720.43139099434 seconds
Total duration: 6212.007189849905 minutes
Total duration: 103.5334531641651 hours

Test:
Total duration: 375509.96909399604 seconds
Total duration: 6258.499484899934 minutes
Total duration: 104.30832474833224 hours

### 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 | Validation |
| ----- | ----- | ---- | ---------- |
| dataset | 187785 | 23473 | 23474 |

### reciters
- reciters_count: 36
- reciters: {'abdul_basit',
           'abdullah_basfar',
           'abdullah_matroud',
           'abdulsamad',
           'abdurrahmaan_as-sudais',
           'abu_bakr_ash-shaatree',
           'ahmed_ibn_ali_al_ajamy',
           'ahmed_neana',
           'akram_alalaqimy',
           'alafasy',
           'ali_hajjaj_alsuesy',
           'aziz_alili',
           'fares_abbad',
           'ghamadi',
           'hani_rifai',
           'husary',
           'karim_mansoori',
           'khaalid_abdullaah_al-qahtaanee',
           'khalefa_al_tunaiji',
           'maher_al_muaiqly',
           'mahmoud_ali_al_banna',
           'menshawi',
           'minshawi',
           'mohammad_al_tablaway',
           'muhammad_abdulkareem',
           'muhammad_ayyoub',
           'muhammad_jibreel',
           'muhsin_al_qasim',
           'mustafa_ismail',
           'nasser_alqatami',
           'parhizgar',
           'sahl_yassin',
           'salaah_abdulrahman_bukhatir',
           'saood_ash-shuraym',
           'yaser_salamah',
           'yasser_ad-dussary'}


## Dataset Creation

### Curation Rationale


### Source Data

#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations

#### Annotation process


#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators



### Licensing Information

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

### Citation Information

```
```

### Contributions

This dataset was created by:

task_categories:automatic-speech-recognition annotations_creators:expert-generated language_creators:crowdsourced multilinguality:monolingual size_categories:100K<n<1M source_datasets:original language:ar license:mit region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["ar"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "paperswithcode_id": "tarteel-everyayah", "pretty_name": "Tarteel AI - EveryAyah Dataset", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "duration", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "reciter", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 262627688145.3, "num_examples": 187785}, {"name": "test", "num_bytes": 25156009734.72, "num_examples": 23473}, {"name": "validation", "num_bytes": 23426886730.218, "num_examples": 23474}], "download_size": 117190597305, "dataset_size": 311210584610.23804}, "train-eval-index": [{"config": "clean", "task": "automatic-speech-recognition", "task_id": "speech_recognition", "splits": {"train_split": "train", "eval_split": "test", "validation_split": "validation"}, "col_mapping": {"audio": "audio", "text": "text", "reciter": "text"}, "metrics": [{"type": "wer", "name": "WER"}, {"type": "cer", "name": "CER"}]}]}
TAGS
#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Arabic #license-mit #region-us 

﷽


Dataset Card for Tarteel AI's EveryAyah Dataset
===============================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: Tarteel AI
* Repository:
* Point of Contact: Mohamed Saad Ibn Seddik


### Dataset Summary


This dataset is a collection of Quranic verses and their transcriptions, with diacritization, by different reciters.


### Supported Tasks and Leaderboards


### Languages


The audio is in Arabic.


Dataset Structure
-----------------


### 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:


### Length:


Training:
Total duration: 2985111.2642479446 seconds
Total duration: 49751.85440413241 minutes
Total duration: 829.1975734022068 hours


Validation:
Total duration: 372720.43139099434 seconds
Total duration: 6212.007189849905 minutes
Total duration: 103.5334531641651 hours


Test:
Total duration: 375509.96909399604 seconds
Total duration: 6258.499484899934 minutes
Total duration: 104.30832474833224 hours


### 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



### reciters


* reciters\_count: 36
* reciters: {'abdul\_basit',
'abdullah\_basfar',
'abdullah\_matroud',
'abdulsamad',
'abdurrahmaan\_as-sudais',
'abu\_bakr\_ash-shaatree',
'ahmed\_ibn\_ali\_al\_ajamy',
'ahmed\_neana',
'akram\_alalaqimy',
'alafasy',
'ali\_hajjaj\_alsuesy',
'aziz\_alili',
'fares\_abbad',
'ghamadi',
'hani\_rifai',
'husary',
'karim\_mansoori',
'khaalid\_abdullaah\_al-qahtaanee',
'khalefa\_al\_tunaiji',
'maher\_al\_muaiqly',
'mahmoud\_ali\_al\_banna',
'menshawi',
'minshawi',
'mohammad\_al\_tablaway',
'muhammad\_abdulkareem',
'muhammad\_ayyoub',
'muhammad\_jibreel',
'muhsin\_al\_qasim',
'mustafa\_ismail',
'nasser\_alqatami',
'parhizgar',
'sahl\_yassin',
'salaah\_abdulrahman\_bukhatir',
'saood\_ash-shuraym',
'yaser\_salamah',
'yasser\_ad-dussary'}


Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


CC BY 4.0


### Contributions


This dataset was created by:



### Dataset Summary


This dataset is a collection of Quranic verses and their transcriptions, with diacritization, by different reciters. ### Supported Tasks and Leaderboards ### Languages


The audio is in Arabic.


Dataset Structure
----------------- ### 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: ### Length:


Training:
Total duration: 2985111.2642479446 seconds
Total duration: 49751.85440413241 minutes
Total duration: 829.1975734022068 hours


Validation:
Total duration: 372720.43139099434 seconds
Total duration: 6212.007189849905 minutes
Total duration: 103.5334531641651 hours


Test:
Total duration: 375509.96909399604 seconds
Total duration: 6258.499484899934 minutes
Total duration: 104.30832474833224 hours ### 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 ### reciters


* reciters\_count: 36
* reciters: {'abdul\_basit',
'abdullah\_basfar',
'abdullah\_matroud',
'abdulsamad',
'abdurrahmaan\_as-sudais',
'abu\_bakr\_ash-shaatree',
'ahmed\_ibn\_ali\_al\_ajamy',
'ahmed\_neana',
'akram\_alalaqimy',
'alafasy',
'ali\_hajjaj\_alsuesy',
'aziz\_alili',
'fares\_abbad',
'ghamadi',
'hani\_rifai',
'husary',
'karim\_mansoori',
'khaalid\_abdullaah\_al-qahtaanee',
'khalefa\_al\_tunaiji',
'maher\_al\_muaiqly',
'mahmoud\_ali\_al\_banna',
'menshawi',
'minshawi',
'mohammad\_al\_tablaway',
'muhammad\_abdulkareem',
'muhammad\_ayyoub',
'muhammad\_jibreel',
'muhsin\_al\_qasim',
'mustafa\_ismail',
'nasser\_alqatami',
'parhizgar',
'sahl\_yassin',
'salaah\_abdulrahman\_bukhatir',
'saood\_ash-shuraym',
'yaser\_salamah',
'yasser\_ad-dussary'}


Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


CC BY 4.0 ### Contributions


This dataset was created by:
TAGS
#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Arabic #license-mit #region-us 
 ### Dataset Summary


This dataset is a collection of Quranic verses and their transcriptions, with diacritization, by different reciters. ### Supported Tasks and Leaderboards ### Languages


The audio is in Arabic.


Dataset Structure
----------------- ### 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: ### Length:


Training:
Total duration: 2985111.2642479446 seconds
Total duration: 49751.85440413241 minutes
Total duration: 829.1975734022068 hours


Validation:
Total duration: 372720.43139099434 seconds
Total duration: 6212.007189849905 minutes
Total duration: 103.5334531641651 hours


Test:
Total duration: 375509.96909399604 seconds
Total duration: 6258.499484899934 minutes
Total duration: 104.30832474833224 hours ### 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 ### reciters


* reciters\_count: 36
* reciters: {'abdul\_basit',
'abdullah\_basfar',
'abdullah\_matroud',
'abdulsamad',
'abdurrahmaan\_as-sudais',
'abu\_bakr\_ash-shaatree',
'ahmed\_ibn\_ali\_al\_ajamy',
'ahmed\_neana',
'akram\_alalaqimy',
'alafasy',
'ali\_hajjaj\_alsuesy',
'aziz\_alili',
'fares\_abbad',
'ghamadi',
'hani\_rifai',
'husary',
'karim\_mansoori',
'khaalid\_abdullaah\_al-qahtaanee',
'khalefa\_al\_tunaiji',
'maher\_al\_muaiqly',
'mahmoud\_ali\_al\_banna',
'menshawi',
'minshawi',
'mohammad\_al\_tablaway',
'muhammad\_abdulkareem',
'muhammad\_ayyoub',
'muhammad\_jibreel',
'muhsin\_al\_qasim',
'mustafa\_ismail',
'nasser\_alqatami',
'parhizgar',
'sahl\_yassin',
'salaah\_abdulrahman\_bukhatir',
'saood\_ash-shuraym',
'yaser\_salamah',
'yasser\_ad-dussary'}


Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


CC BY 4.0 ### Contributions


This dataset was created by:
passage: TAGS
#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Arabic #license-mit #region-us 
### Dataset Summary


This dataset is a collection of Quranic verses and their transcriptions, with diacritization, by different reciters.### Supported Tasks and Leaderboards### Languages


The audio is in Arabic.


Dataset Structure
-----------------### 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:### Length:


Training:
Total duration: 2985111.2642479446 seconds
Total duration: 49751.85440413241 minutes
Total duration: 829.1975734022068 hours


Validation:
Total duration: 372720.43139099434 seconds
Total duration: 6212.007189849905 minutes
Total duration: 103.5334531641651 hours


Test:
Total duration: 375509.96909399604 seconds
Total duration: 6258.499484899934 minutes
Total duration: 104.30832474833224 hours passage: ### 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### reciters


* reciters\_count: 36
* reciters: {'abdul\_basit',
'abdullah\_basfar',
'abdullah\_matroud',
'abdulsamad',
'abdurrahmaan\_as-sudais',
'abu\_bakr\_ash-shaatree',
'ahmed\_ibn\_ali\_al\_ajamy',
'ahmed\_neana',
'akram\_alalaqimy',
'alafasy',
'ali\_hajjaj\_alsuesy',
'aziz\_alili',
'fares\_abbad',
'ghamadi',
'hani\_rifai',
'husary',
'karim\_mansoori',
'khaalid\_abdullaah\_al-qahtaanee',
'khalefa\_al\_tunaiji',
'maher\_al\_muaiqly',
'mahmoud\_ali\_al\_banna',
'menshawi',
'minshawi',
'mohammad\_al\_tablaway',
'muhammad\_abdulkareem',
'muhammad\_ayyoub',
'muhammad\_jibreel',
'muhsin\_al\_qasim',
'mustafa\_ismail',
'nasser\_alqatami',
'parhizgar',
'sahl\_yassin',
'salaah\_abdulrahman\_bukhatir',
'saood\_ash-shuraym',
'yaser\_salamah',
'yasser\_ad-dussary'}


Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------### Social Impact of Dataset### Discussion of Biases