# ClArTTS: An Open-Source Classical Arabic Text-to-Speech Corpus

arXiv: https://arxiv.org/abs/2303.00069 · HTML: https://mlsanity.com/paper/2303.00069/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech](https://mlsanity.com/paper/2105.06337/) (2021)
- [Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search](https://mlsanity.com/paper/2005.11129/) (2020)
- [Hi-Fi Multi-Speaker English TTS Dataset](https://mlsanity.com/paper/2104.01497/) (2021)
- [Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis](https://mlsanity.com/paper/2011.03568/) (2020)
- [Masader: Metadata Sourcing for Arabic Text and Speech Data Resources](https://mlsanity.com/paper/2110.06744/) (2021)
- [NatiQ: An End-to-end Text-to-Speech System for Arabic](https://mlsanity.com/paper/2206.07373/) (2022)
- [Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study](https://mlsanity.com/paper/2301.09099/) (2023)

## Built on by (most cited first, 14 of 14)

- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [ArTST: Arabic Text and Speech Transformer](https://mlsanity.com/paper/2310.16621/) (2023)
- [Meta Learning Text-to-Speech Synthesis in over 7000 Languages](https://mlsanity.com/paper/2406.06403/) (2024)
- [Automatic Restoration of Diacritics for Speech Data Sets](https://mlsanity.com/paper/2311.10771/) (2023)
- [NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2509.02038/) (2025)
- [To Distill or Not to Distill? On the Robustness of Robust Knowledge Distillation](https://mlsanity.com/paper/2406.04512/) (2024)
- [ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis](https://mlsanity.com/paper/2505.20506/) (2025)
- [Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology](https://mlsanity.com/paper/2601.13319/) (2026)
- [Towards Zero-Shot Text-To-Speech for Arabic Dialects](https://mlsanity.com/paper/2406.16751/) (2024)
- [A multilingual training strategy for low resource Text to Speech](https://mlsanity.com/paper/2409.01217/) (2024)
- [Habibi: Laying the Open-Source Foundation of Unified-Dialectal Arabic Speech Synthesis](https://mlsanity.com/paper/2601.13802/) (2026)
- [Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices](https://mlsanity.com/paper/2509.02523/) (2025)
- [More Data, Fewer Diacritics: Scaling Arabic TTS](https://mlsanity.com/paper/2603.01622/) (2026)
- [Constrained CTC Decoding for Efficient Diacritic Restoration](https://mlsanity.com/paper/2607.18946/) (2026)

## Abstract (arXiv)

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 www.clartts.com for research purposes, along with the baseline TTS systems demo.