Vevo2: A Unified and Controllable Framework for Speech and Singing Voice Generation
Xueyao Zhang, Junan Zhang, Yuancheng Wang, Chaoren Wang, Yuanzhe Chen, Dongya Jia, Zhuo Chen, Zhizheng Wu

TL;DR
Vevo2 is a versatile framework that enables controllable speech and singing voice generation by using unified tokenizers and auto-regressive modeling, addressing data scarcity and enhancing flexibility.
Contribution
It introduces two unified audio tokenizers and a joint training approach that significantly improve controllability and quality in speech and singing voice synthesis.
Findings
Effective in both speech and singing voice generation
Enhances controllability over text, prosody, and style
Demonstrates strong generalization across tasks
Abstract
Controllable human voice generation, particularly for expressive domains like singing, remains a significant challenge. This paper introduces Vevo2, a unified framework for controllable speech and singing voice generation. To tackle issues like the scarcity of annotated singing data and to enable flexible controllability, Vevo2 introduces two audio tokenizers: (1) a unified music-notation-free prosody tokenizer that captures prosody and melody from speech, singing, and even instrumental sounds, and (2) a unified content-style tokenizer that encodes linguistic content, prosody, and style for both speech and singing, while enabling timbre disentanglement. Vevo2 consists of an auto-regressive (AR) content-style modeling stage, which aims to enable controllability over text, prosody, and style, as well as a flow-matching acoustic modeling stage that allows for timbre control. Particularly,…
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Taxonomy
TopicsSpeech Recognition and Synthesis · Voice and Speech Disorders · Phonetics and Phonology Research
