Datasets:
Update README.md
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README.md
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- name: train
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num_bytes: 277452582
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num_examples: 907
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- name: test
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num_bytes: 32977207
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num_examples: 100
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download_size: 302126264
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dataset_size: 310429789
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- config_name: Synthetic
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features:
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- name: file_name
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- automatic-speech-recognition
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language:
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- ar
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---
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dtype: string
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splits:
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- name: train
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num_bytes: 277452582
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num_examples: 907
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- name: test
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num_bytes: 32977207
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num_examples: 100
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download_size: 302126264
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dataset_size: 310429789
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- config_name: Synthetic
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features:
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- name: file_name
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- automatic-speech-recognition
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language:
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- ar
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pretty_name: arvoice
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size_categories:
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- 10K<n<100K
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---
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<h2 align="center">
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<b>ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis</b>
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</h2>
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<div style="font-size: 16px; text-align: justify;">
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<p>ArVoice is a multi-speaker Modern Standard Arabic (MSA) speech corpus with fully diacritized transcriptions, intended for multi-speaker speech synthesis, and can be useful for other tasks such as speech-based diacritic restoration, voice conversion, and deepfake detection. ArVoice comprises: (1) a new professionally recorded set from 6 voice talents with diverse demographics, (2) a modified subset of the Arabic Speech Corpus; and (3) high-quality synthetic speech from 2 commercial systems. The complete corpus consists of a total of 83.52 hours of speech across 11 voices; around 10 hours consist of human voices from 7 speakers.The modified subset and full synthetic subset are available in this repo. To access the new professionally recorded subset, <a href="/"> sign this agreement</a>. If you use the dataset or transcriptions provided in Huggingface, <u>place cite the paper</u>.
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</p>
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</div>
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Usage Example
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```python
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df = load_dataset(path="herwoww/ArVoice", data_dir="Human_3") #data_dir options: Human_3, Synthetic,
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print(df)
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DatasetDict({
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train: Dataset({
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features: ['audio', 'transcription', 'speaker_id'],
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num_rows: 907
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})
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test: Dataset({
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features: ['audio', 'transcription', 'speaker_id'],
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num_rows: 100
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})
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})
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```
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