Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias

Scaling text-to-speech to a large and wild dataset has been proven to be highly effective in achieving timbre and speech style generalization, particularly in zero-shot TTS. However, previous works usually encode speech into latent using audio codec and use autoregressive language models or diffusion models to generate it, which ignores the intrinsic nature of speech and may lead to inferior or uncontrollable results.

Data and Resources

Cite this as

Ziyue Jiang, Yi Ren, Zhenhui Ye, Jinglin Liu, Chen Zhang, Qian Yang, Shengpeng Ji, Rongjie Huang, Chunfeng Wang, Xiang Yin, Zejun Ma, Zhou Zhao (2024). Dataset: Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias. https://doi.org/10.57702/0yzgeweh

DOI retrieved: December 16, 2024

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2306.03509
Author Ziyue Jiang
More Authors
Yi Ren
Zhenhui Ye
Jinglin Liu
Chen Zhang
Qian Yang
Shengpeng Ji
Rongjie Huang
Chunfeng Wang
Xiang Yin
Zejun Ma
Zhou Zhao
Homepage https://mega-tts.github.io/demo-page