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---
license: cc-by-4.0
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 3567628186
num_examples: 10000
- name: test
num_bytes: 119042383
num_examples: 334
download_size: 3467057358
dataset_size: 3686670569
pretty_name: ClArTTS
---
# Dataset Card for ClArTTS
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
## Dataset Details
ClArTTS corpus contains about 12 hours of speech from a single male speaker sampled at 40100 kHz
### Dataset Description
At present, Text-to-speech (TTS) systems that are trained with high-quality transcribed speech data using end-to-end neural models can generate speech that is intelligible, natural, and closely resembles human speech. These models are trained with relatively large single-speaker professionally recorded audio, typically extracted from audiobooks. Meanwhile, due to the scarcity of freely available speech corpora of this kind, a larger gap exists in Arabic TTS research and development. Most of the existing freely available Arabic speech corpora are not suitable for TTS training as they contain multi-speaker casual speech with variations in recording conditions and quality, whereas the corpus curated for speech synthesis are generally small in size and not suitable for training state-of-the-art end-to-end models. In a move towards filling this gap in resources, 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. In this paper, we describe the process of corpus creation and provide details of corpus statistics and a comparison with existing resources. Furthermore, we develop two TTS systems based on Grad-TTS and Glow-TTS and illustrate the performance of the resulting systems via subjective and objective evaluations. The corpus will be made publicly available at this http URL for research purposes, along with the baseline TTS systems demo
## Usage
```python
from datasets import load_dataset
dataset = load_dataset('AtharvA7k/ClArTTS')
```
- **Language:** Classical Arabic
- **License:** cc-by-4.0
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- Paper: https://arxiv.org/abs/2303.00069
- Demo: http://www.clartts.com/
## Citation [optional]
```
@inproceedings{kulkarni23_interspeech,
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={Proc. INTERSPEECH 2023},
pages={5511--5515},
doi={10.21437/Interspeech.2023-2224}
}
```
## Dataset Card Contact
atharva7kulkarni@gmail.com |