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--- |
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dataset_info: |
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features: |
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- name: audio |
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struct: |
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- name: bytes |
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dtype: binary |
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- name: path |
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dtype: string |
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- name: duration |
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dtype: float64 |
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- name: text |
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dtype: string |
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- name: reciter |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2315694478.08891 |
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num_examples: 4000 |
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- name: test |
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num_bytes: 868385429.2833413 |
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num_examples: 1500 |
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download_size: 3081675303 |
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dataset_size: 3184079907.3722515 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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task_categories: |
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- automatic-speech-recognition |
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language: |
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- ar |
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tags: |
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- quran |
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- ASR |
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- Islam |
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- tarteel |
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- verses |
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- arabic |
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- religion |
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size_categories: |
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- 1K<n<10K |
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pretty_name: Quran_data_everyayah |
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--- |
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## Dataset Details |
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Part of (tarteel_ai_everyayah_dataset) with shuffle applaying on it |
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This dataset is a collection of Quranic verses and their transcriptions, |
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with diacritization, by different reciters. |
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## NOTE |
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# make sure you use the function dataset.cast_column("audio", Audio(sampling_rate=16_000)) on dataset |
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# to convert audio from a Byte to structure in Data Instances. |
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### Dataset Description |
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This data was created specifically because the original data (tarteel_ai_everyayah_dataset) is very large in size, |
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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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- **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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A typical data point comprises the audio file audio, and its transcription called text. |
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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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# 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 |
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