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@@ -65,47 +65,21 @@ This data was created specifically because the original data (tarteel_ai_everyay
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  which may cause problems during downloading and of course a large space, whether the device space or one of the cloud sites is used,
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  and it is sufficient to train models such as ASR and reciter classification.
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- حجمها كبير جدا (tarteel_ai_everyayah_dataset) هذه البيانات قد تم عملها خصيصا بسب ان البيانات الاصلية
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- cloud مما قد يسبب مشاكل اثناء تحميلها و بطبع مساحة كبيرة سواء تم استخدام مساحة الجهاز او احد مواقع ال
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- ASR و هي كافية لتدريب نماذج مثل
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  - **Curated by:** [abo_salah , tarteel Ai company]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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  - **Language(s) (NLP):** [Arabic]
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- - **License:** [More Information Needed]
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- ### Dataset Sources [optional]
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  <!-- Provide the basic links for the dataset. -->
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  https://huggingface.co/datasets/tarteel-ai/everyayah
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  - **Repository:** [tarteel-ai/everyayah]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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-
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- <!-- Address questions around how the dataset is intended to be used. -->
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-
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- ### Direct Use
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- <!-- This section describes suitable use cases for the dataset. -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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- [More Information Needed]
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- ## Dataset Structure
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- <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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  ## Data Instances
@@ -116,117 +90,32 @@ The duration is in seconds, and the author is reciter.
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  An example from the dataset is:
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  {
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- 'audio': {
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  'path': None,
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  'array': array([ 0. , 0. , 0. , ..., -0.00057983,
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  -0.00085449, -0.00061035]),
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  'sampling_rate': 16000
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- },
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- 'duration': 6.478375,
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- 'text': 'بِسْمِ اللَّهِ الرَّحْمَنِ الرَّحِيمِ',
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- 'reciter': 'abdulsamad'
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- }
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-
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-
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- ## Data Fields
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- 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].
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- text: The transcription of the audio file.
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- duration: The duration of the audio file.
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- reciter: The reciter of the verses.
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- ## Data Splits
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- Train Test
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- 4000 1500
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- [More Information Needed]
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- ## Dataset Creation
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- ### Curation Rationale
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- <!-- Motivation for the creation of this dataset. -->
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- [More Information Needed]
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- ### Source Data
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- <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
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- #### Data Collection and Processing
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- <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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- [More Information Needed]
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- #### Who are the source data producers?
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- <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
 
 
 
 
 
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- [More Information Needed]
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- ### Annotations [optional]
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- <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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- #### Annotation process
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- <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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- [More Information Needed]
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- #### Who are the annotators?
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- <!-- This section describes the people or systems who created the annotations. -->
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- [More Information Needed]
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- #### Personal and Sensitive Information
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- <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
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- ## Dataset Card Authors [optional]
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- [More Information Needed]
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- ## Dataset Card Contact
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- [More Information Needed]
 
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  which may cause problems during downloading and of course a large space, whether the device space or one of the cloud sites is used,
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  and it is sufficient to train models such as ASR and reciter classification.
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+ ## هذه البيانات قد تم عملها خصيصا بسب ان البيانات الاصلية
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+ ## حجمها كبير جدا مما قد يسبب مشاكل اثناء تحميلها و بطبع مساحة كبيرة سواء تم استخدام مساحة الجهاز او احد مواقع السحابية
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+ ## و هي كافية لتدريب النماذج
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+
 
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  - **Curated by:** [abo_salah , tarteel Ai company]
 
 
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  - **Language(s) (NLP):** [Arabic]
 
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+ ### Dataset Sources
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  <!-- Provide the basic links for the dataset. -->
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  https://huggingface.co/datasets/tarteel-ai/everyayah
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  - **Repository:** [tarteel-ai/everyayah]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Data Instances
 
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  An example from the dataset is:
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  {
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+ ## 'audio': {
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  'path': None,
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  'array': array([ 0. , 0. , 0. , ..., -0.00057983,
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  -0.00085449, -0.00061035]),
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  'sampling_rate': 16000
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+ }
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+ ## 'duration': 6.478375,
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+ ## 'text': 'بِسْمِ اللَّهِ الرَّحْمَنِ الرَّحِيمِ',
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+ ## 'reciter': 'abdulsamad'
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+ }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Data Fields
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+ # audio: A dictionary containing the path to the downloaded audio file, the decoded audio array,
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+ # 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"].
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+ # sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time.
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+ # Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should
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+ # always be preferred over dataset["audio"][0].
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+ # text: The transcription of the audio file.
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+ # duration: The duration of the audio file.
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+ # reciter: The reciter of the verses.
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+ ## Data Splits
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+ ## Train Test
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+ ## 4000 1500