README.md

metadata

dataset_info:
  - config_name: Human_3
    features:
      - name: original_wav
        dtype: audio
      - name: normalized_wav
        dtype: audio
      - name: speaker_id
        dtype: string
      - name: transcription
        dtype: string
    splits:
      - name: train
        num_bytes: 554661238
        num_examples: 907
      - name: test
        num_bytes: 65929372
        num_examples: 100
    download_size: 601830149
    dataset_size: 620590610
  - config_name: Synthetic
    features:
      - name: original_wav
        dtype: audio
      - name: normalized_wav
        dtype: audio
      - name: speaker_id
        dtype: string
      - name: transcription
        dtype: string
    splits:
      - name: train
        num_bytes: 16298373533.296
        num_examples: 20056
      - name: test
        num_bytes: 1735872207.904
        num_examples: 2048
    download_size: 15976481639
    dataset_size: 18034245741.2
  - config_name: default
    features:
      - name: original_wav
        dtype: audio
      - name: normalized_wav
        dtype: audio
      - name: speaker_id
        dtype: string
      - name: transcription
        dtype: string
    splits:
      - name: train
        num_bytes: 17035340160.108
        num_examples: 20963
      - name: test
        num_bytes: 1820617200.704
        num_examples: 2148
    download_size: 16584190993
    dataset_size: 18855957360.812
configs:
  - config_name: Human_3
    data_files:
      - split: train
        path: Human_3/train-*
      - split: test
        path: Human_3/test-*
  - config_name: Synthetic
    data_files:
      - split: train
        path: Synthetic/train-*
      - split: test
        path: Synthetic/test-*
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
license: cc-by-4.0
task_categories:
  - text-to-speech
  - text-to-audio
  - automatic-speech-recognition
language:
  - ar
pretty_name: arvoice
size_categories:
  - 10K<n<100K

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}, 
}