MBZUAI/ClArTTS

Dataset Summary

We present a speech corpus for Classical Arabic Text-to-Speech (ClArTTS) to support the development of end-to-end TTS systems for Arabic. The speech is extracted from a LibriVox audiobook, which is then processed, segmented, and manually transcribed and annotated. The final ClArTTS corpus contains about 12 hours of speech from a single male speaker sampled at 40100 kHz.

Dataset Structure

A typical data point comprises the name of the audio file, called 'file', its transcription, called text, the audio as an array, called 'audio'. Some additional information; sampling rate and audio duration.

DatasetDict({
    train: Dataset({
        features: ['text', 'file', 'audio', 'sampling_rate', 'duration'],
        num_rows: 9500
    })
    test: Dataset({
        features: ['text', 'file', 'audio', 'sampling_rate', 'duration'],
        num_rows: 205
    })
})

Citation Information

@inproceedings{kulkarni2023clartts,
  author={Ajinkya Kulkarni and Atharva Kulkarni and Sara Abedalmon'em Mohammad Shatnawi and Hanan Aldarmaki},
  title={ClArTTS: An Open-Source Classical Arabic Text-to-Speech Corpus},
  year={2023},
  booktitle={2023 INTERSPEECH },
  pages={5511--5515},
  doi={10.21437/Interspeech.2023-2224}
}