TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis

Customizable multilingual zero-shot singing voice synthesis (SVS) has various potential applications in music composition and short video dubbing.However, existing SVS models overly depend on phoneme and note boundary annotations, limiting their robustness in zero-shot scenarios and producing poor transitions between phonemes and notes.Moreover, they also lack effective multi-level style control via diverse prompts.To overcome these challenges, we introduce TCSinger 2, a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts.TC-Singer 2 mainly includes three key modules: 1) Blurred Boundary Content (BBC) Encoder, predicts duration, extends content embedding, and applies masking to the boundaries to enable smooth transitions.2) Custom Audio Encoder, uses contrastive learning to extract aligned representations from singing, speech, and textual prompts.3) Flow-based Custom Transformer, leverages Cus-MOE, with F0 supervision, enhancing both the synthesis quality and style modeling of the generated singing voice.Experimental results show that TCSinger 2 outperforms baseline models in both subjective and objective metrics across multiple related tasks.Singing voice samples are available at

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.findings-acl.687
Citations
1
Primary Topic
Speech Recognition and Synthesis
Type
article
Field-Weighted Citation Impact
2.48

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article

TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis

Tao Jin, Zhou Zhao, Ziyue Karen Jiang, Yuhan Wang et al.
1 citations
Speech Recognition and Synthesis
2.48
article

TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis

Tao Jin, Zhou Zhao, Ziyue Karen Jiang, Yuhan Wang, Yu Zhang, Dongyu Yao, Changhao Pan, Wenxiang Guo, Zhiyuan Zhu
article en
1 citations

Abstract

Customizable multilingual zero-shot singing voice synthesis (SVS) has various potential applications in music composition and short video dubbing.However, existing SVS models overly depend on phoneme and note boundary annotations, limiting their robustness in zero-shot scenarios and producing poor transitions between phonemes and notes.Moreover, they also lack effective multi-level style control via diverse prompts.To overcome these challenges, we introduce TCSinger 2, a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts.TC-Singer 2 mainly includes three key modules: 1) Blurred Boundary Content (BBC) Encoder, predicts duration, extends content embedding, and applies masking to the boundaries to enable smooth transitions.2) Custom Audio Encoder, uses contrastive learning to extract aligned representations from singing, speech, and textual prompts.3) Flow-based Custom Transformer, leverages Cus-MOE, with F0 supervision, enhancing both the synthesis quality and style modeling of the generated singing voice.Experimental results show that TCSinger 2 outperforms baseline models in both subjective and objective metrics across multiple related tasks.Singing voice samples are available at

Zhejiang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 9%
Speech Recognition and Synthesis
2.48
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TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis — Tao Jin, Zhou Zhao, et al. · (2025) | TGRS Research Map | TGRS