MBZUAI/ArVoice

ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis

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) professionally recorded audio by 2 male and 2 female voice artists from diacritized transcripts, (2) professionally recorded audio by 1 male and 1 female voice artists from undiacritized transcripts, (3) a modified subset of the Arabic Speech Corpus, and (4) synthesized speech using commercial TTS 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.

This repo consists of only Parts (3), ASC subset, and (4) synthetic subset ; to access the main subset, part (1,2), which consists of six professional speakers, please sign this agreement and email it to us.

If you use the dataset or transcriptions provided in Huggingface, place cite the paper.

Usage Example

df = load_dataset("MBZUAI/ArVoice", "Human_3")  #data_dir options: Human_3, Synthetic,
print(df)

DatasetDict({
    train: Dataset({
        features: ['original_wav', 'normalized_wav', 'speaker_id', 'transcription'],
        num_rows: 907
    })
    test: Dataset({
        features: ['original_wav', 'normalized_wav', 'speaker_id', 'transcription'],
        num_rows: 100
    })
})

Data Statistics

Type	Part	Gender	Speaker Origin	Duration (hrs)	Text Source
Human	ArVoice Part 1	M	Egypt	1.17	Tashkeela
		F	Jordan	1.45
		M	Egypt	1.58
		F	Morocco	1.23
	ArVoice Part 2	M	Palestine	0.93	Khaleej
		F	Egypt	0.93
	ArVoice Part 3	M	Syria	2.69	ASC
Synthetic	ArVoice Part 4	2×M, 2×F	-	73.5	Tashkeela, Khaleej, ASC

License: https://creativecommons.org/licenses/by/4.0/

Citation

@misc{toyin2025arvoicemultispeakerdatasetarabic,
      title={ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis}, 
      author={Hawau Olamide Toyin and Rufael Marew and Humaid Alblooshi and Samar M. Magdy and Hanan Aldarmaki},
      year={2025},
      eprint={2505.20506},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.20506}, 
}