Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
Authors: name: Philip Anastassiou, name: Jiawei Chen, name: Jitong Chen, name: Yuanzhe Chen, name: Zhuo Chen, name: Ziyi Chen, name: Jian Cong, name: Lelai Deng, name: Chuang Ding, name: Lu Gao, name: Mingqing Gong, name: Peisong Huang, name: Qingqing Huang, name: Zhiying Huang, name: Yuanyuan Huo, name: Dongya Jia, name: Chumin Li, name: Feiya Li, name: Hui Li, name: Jiaxin Li, name: Xiaoyang Li, name: Xingxing Li, name: Lin Liu, name: Shouda Liu, name: Sichao Liu, name: Xudong Liu, name: Yuchen Liu, name: Zhengxi Liu, name: Lu Lu, name: Junjie Pan, name: Xin Wang, name: Yuping Wang, name: Yuxuan Wang, name: Zhen Wei, name: Jian Wu, name: Chao Yao, name: Yifeng Yang, name: Yuanhao Yi, name: Junteng Zhang, name: Qidi Zhang, name: Shuo Zhang, name: Wenjie Zhang, name: Yang Zhang, name: Zilin Zhao, name: Dejian Zhong, name: Xiaobin Zhuang

We introduce Seed-TTS, a family of large-scale autoregressive text-to-speech
(TTS) models capable of generating speech that is virtually indistinguishable
from human speech. Seed-TTS serves as a foundation model for speech generation
and excels in speech in-context learning, achieving performance in speaker
similarity and naturalness that matches ground truth human speech in both
objective and subjective evaluations. With fine-tuning, we achieve even higher
subjective scores across these metrics. Seed-TTS offers superior
controllability over various speech attributes such as emotion and is capable
of generating highly expressive and diverse speech for speakers in the wild.
Furthermore, we propose a self-distillation method for speech factorization, as
well as a reinforcement learning approach to enhance model robustness, speaker
similarity, and controllability. We additionally present a non-autoregressive
(NAR) variant of the Seed-TTS model, named Seed-TTS_DiT, which
utilizes a fully diffusion-based architecture. Unlike previous NAR-based TTS
systems, Seed-TTS_DiT does not depend on pre-estimated phoneme
durations and performs speech generation through end-to-end processing. We
demonstrate that this variant achieves comparable performance to the language
model-based variant and showcase its effectiveness in speech editing. We
encourage readers to listen to demos at
https://bytedancespeech.github.io/seedtts_tech_report.