GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks

Yu Zhang, Changhao Pan, Wenxiang Guo, Ruiqi Li, Zhiyuan Zhu, Jialei Wang, Wenhao Xu, Jingyu Lu, Zhiqing Hong, Chuxin Wang, Lichao Zhang, Jinzheng He, Ziyue Jiang, Yuxin Chen, Chen Yang, Jiecheng Zhou, Xinyu Cheng, Zhou Zhao

Keywords: singing voice synthesis, singing technique, multi-lingual, realistic music score, technique control, style transfer, speech-to-singing conversion

TL;DR: This paper presents GTSinger, a large Global, multi-Technique, free-to-use, high-quality singing corpus with realistic music scores, designed for all singing tasks, along with its benchmarks.

Abstract:

The scarcity of high-quality and multi-task singing datasets significantly hinders the development of diverse controllable and personalized singing tasks, as existing singing datasets suffer from low quality, limited diversity of languages and singers, absence of multi-technique information and realistic music scores, and poor task suitability.
To tackle these problems, we present GTSinger, a large Global, multi-Technique, free-to-use, high-quality singing corpus with realistic music scores, designed for all singing tasks, along with its benchmarks.
Particularly,
(1) we collect 80.59 hours of high-quality singing voices, forming the largest recorded singing dataset;
(2) 20 professional singers across nine widely spoken languages offer diverse timbres and styles;
(3) we provide controlled comparison and phoneme-level annotations of six commonly used singing techniques, helping technique modeling and control;
(4) GTSinger offers realistic music scores, assisting real-world musical composition;
(5) singing voices are accompanied by manual phoneme-to-audio alignments, global style labels, and 16.16 hours of paired speech for various singing tasks.
Moreover, to facilitate the use of GTSinger, we conduct four benchmark experiments: technique-controllable singing voice synthesis, technique recognition, style transfer, and speech-to-singing conversion.

NeurIPS 23 Competition Analysis Paper: NeurIPS23 Competition Analysis Paper

Submission Number: 243