---
license: cc-by-4.0
dataset_info:
  features:
  - name: text
    dtype: string
  - name: file
    dtype: string
  - name: audio
    sequence: float64
  - name: sampling_rate
    dtype: int64
  - name: duration
    dtype: float64
  splits:
  - name: train
    num_bytes: 12889189484
    num_examples: 9500
  - name: test
    num_bytes: 283646282
    num_examples: 205
  download_size: 3201049372
  dataset_size: 13172835766
task_categories:
- text-to-speech
- text-to-audio
language:
- ar
pretty_name: ClArTTS
size_categories:
- 1K<n<10K
multiliguality: monolingual
tags:
- audio
---




## 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 Description
- **Homepage:** [ClArTTS](http://www.clartts.com/)
- **Paper:** [ClARTTS: An Open-Source Classical Arabic Text-to-Speech Corpus](https://www.isca-archive.org/interspeech_2023/kulkarni23_interspeech.pdf)

## 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}
}

```