https://github.com/amazon-science/proteno
This repository contains data used in the NAACL 2021 Paper - Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems (https://arxiv.org/abs/2104.07777)
https://github.com/amazon-science/proteno
Last synced: 4 months ago
JSON representation
This repository contains data used in the NAACL 2021 Paper - Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems (https://arxiv.org/abs/2104.07777)
- Host: GitHub
- URL: https://github.com/amazon-science/proteno
- Owner: amazon-science
- License: other
- Created: 2021-04-13T16:24:21.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-05-25T13:35:48.000Z (about 5 years ago)
- Last Synced: 2025-03-01T11:32:55.802Z (over 1 year ago)
- Homepage:
- Size: 7.67 MB
- Stars: 44
- Watchers: 4
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
## Proteno
This is the data release associated with the corresponding NAACL 2021 Paper - Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems (https://arxiv.org/abs/2104.07777)
## Security
See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
## License
This project is released under CC-BY-NC-4.0 and other licenses:
- English: CC-BY-SA
- Spanish: CC-BY-SA
- Tamil: CC-BY-NC-SA
## Citation
If you use our data, please cite the following paper:
```
@inproceedings{tyagi-etal-2021-proteno,
title = "Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems",
author = "Tyagi, Shubhi and
Bonafonte, Antonio and
Lorenzo-Trueba, Jaime and
Latorre, Javier",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.naacl-industry.10",
pages = "72--79",
}
```