Datasetpublic

MahtaFetrat/Mana-TTS

ManaTTS-Persian-Speech-Dataset ManaTTS is the largest publicly available single-speaker Persian corpus, comprising over 114 hours of high-quality audio (sampled at 44.1 kHz). Released under the permissive CC-0 license, this dataset is freely usable for both educational and commercial purposes. Collected from Nasl-e-Mana magazine, the dataset covers a diverse range of topics, making it ideal for training robust text-to-speech (TTS) models. The release includes a fully… See the full description on the dataset page: https://huggingface.co/datasets/MahtaFetrat/Mana-TTS.

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Dataset Card

ManaTTS-Persian-Speech-Dataset

ManaTTS is the largest publicly available single-speaker Persian corpus, comprising over 114 hours of high-quality audio (sampled at 44.1 kHz). Released under the permissive CC-0 license, this dataset is freely usable for both educational and commercial purposes.

Collected from [Nasl-e-Mana](https://naslemana.com/) magazine, the dataset covers a diverse range of topics, making it ideal for training robust text-to-speech (TTS) models. The release includes a fully transparent, open-source pipeline for data collection and processing, featuring tools for audio segmentation and forced alignment. For the full codebase, visit the [ManaTTS GitHub repository](https://github.com/MahtaFetrat/ManaTTS-Persian-Speech-Dataset).

Dataset Columns

Column Name	Description
file_name	Unique identifier for the audio file.
transcript	Ground-truth text transcription of the audio chunk.
duration	Duration of the audio chunk (in seconds).
match_quality	Quality of alignment between the approximate transcript and ground truth (`HIGH` or `MIDDLE`). Reflects confidence in transcript accuracy (see paper for details).
hypothesis	Approximate transcript used to search for the ground-truth text.
CER	Character Error Rate between the hypothesis and accepted transcript.
search_type	Indicates whether the transcript was matched continuously in the source text (`type 1`) or with gaps (`type 2`).
ASRs	Ordered list of ASRs used until a match was found.
audio	Audio file as a numerical array.
sample_rate	Sampling rate of the audio file (44.1 kHz).

Usage

Python (Hugging Face)

First install the required package:

bash

pip install datasets

Then load the data:

python

from datasets import load_dataset

# Load a specific partition (e.g., part 001)
dataset = load_dataset("MahtaFetrat/Mana-TTS", 
                      data_files="dataset/dataset_part_001.parquet", 
                      split="train")

# Inspect the data
print(dataset)
print(dataset[0])  # View first sample

Command Line (wget)

Download individual files directly:

bash

# Download single file (e.g., part 001)
wget https://huggingface.co/datasets/MahtaFetrat/Mana-TTS/resolve/main/dataset/dataset_part_001.parquet

Trained TTS Model

![Hugging Face](https://huggingface.co/MahtaFetrat/Persian-Tacotron2-on-ManaTTS)

A Tacotron2-based TTS model trained on ManaTTS is available on Hugging Face. For inference and weights, visit the model repository.

Contributing

Contributions to this project are welcome! If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request.

License

This dataset is released under the [CC-0 1.0 license](https://creativecommons.org/publicdomain/zero/1.0/).

Ethical Use Notice

The ManaTTS dataset is intended exclusively for ethical research and development. Misuse—including voice impersonation, identity theft, or fraudulent activities—is strictly prohibited. By using this dataset, you agree to uphold integrity and privacy standards. Violations may result in legal consequences.

For questions, contact the maintainers.

Acknowledgments

We extend our deepest gratitude to [Nasl-e-Mana](https://naslemana.com/), the monthly magazine of Iran’s blind community, for their generosity in releasing this data under CC-0. Their commitment to open collaboration has been pivotal in advancing Persian speech synthesis.

Community Impact

We encourage researchers and developers to leverage this resource for assistive technologies, such as screen readers, to benefit the Iranian blind community. Open-source collaboration is key to driving accessibility innovation.

Citation

If you use ManaTTS in your work, cite our paper:

bibtex

@inproceedings{qharabagh-etal-2025-manatts,
    title = "{M}ana{TTS} {P}ersian: A Recipe for Creating {TTS} Datasets for Lower-Resource Languages",
    author = "Qharabagh, Mahta Fetrat and Dehghanian, Zahra and Rabiee, Hamid R.",
    booktitle = "Proceedings of the 2025 Conference of the North American Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    pages = "9177--9206",
    url = "https://aclanthology.org/2025.naacl-long.464/",
}

Aditional Links

—ManaTTS Github Repository
—ManaTTS Paper
—Nasl-e-Mana Magazine
—Tacotron2 Trained on ManaTTS Huggingface | Github