Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
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| Format: | Preprint |
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2024
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| author | Anastassiou, Philip Chen, Jiawei Chen, Jitong Chen, Yuanzhe Chen, Zhuo Chen, Ziyi Cong, Jian Deng, Lelai Ding, Chuang Gao, Lu Gong, Mingqing Huang, Peisong Huang, Qingqing Huang, Zhiying Huo, Yuanyuan Jia, Dongya Li, Chumin Li, Feiya Li, Hui Li, Jiaxin Li, Xiaoyang Li, Xingxing Liu, Lin Liu, Shouda Liu, Sichao Liu, Xudong Liu, Yuchen Liu, Zhengxi Lu, Lu Pan, Junjie Wang, Xin Wang, Yuping Wang, Yuxuan Wei, Zhen Wu, Jian Yao, Chao Yang, Yifeng Yi, Yuanhao Zhang, Junteng Zhang, Qidi Zhang, Shuo Zhang, Wenjie Zhang, Yang Zhao, Zilin Zhong, Dejian Zhuang, Xiaobin |
| author_facet | Anastassiou, Philip Chen, Jiawei Chen, Jitong Chen, Yuanzhe Chen, Zhuo Chen, Ziyi Cong, Jian Deng, Lelai Ding, Chuang Gao, Lu Gong, Mingqing Huang, Peisong Huang, Qingqing Huang, Zhiying Huo, Yuanyuan Jia, Dongya Li, Chumin Li, Feiya Li, Hui Li, Jiaxin Li, Xiaoyang Li, Xingxing Liu, Lin Liu, Shouda Liu, Sichao Liu, Xudong Liu, Yuchen Liu, Zhengxi Lu, Lu Pan, Junjie Wang, Xin Wang, Yuping Wang, Yuxuan Wei, Zhen Wu, Jian Yao, Chao Yang, Yifeng Yi, Yuanhao Zhang, Junteng Zhang, Qidi Zhang, Shuo Zhang, Wenjie Zhang, Yang Zhao, Zilin Zhong, Dejian Zhuang, Xiaobin |
| contents | 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 $\text{Seed-TTS}_\text{DiT}$, which utilizes a fully diffusion-based architecture. Unlike previous NAR-based TTS systems, $\text{Seed-TTS}_\text{DiT}$ 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 \url{https://bytedancespeech.github.io/seedtts_tech_report}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02430 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Seed-TTS: A Family of High-Quality Versatile Speech Generation Models Anastassiou, Philip Chen, Jiawei Chen, Jitong Chen, Yuanzhe Chen, Zhuo Chen, Ziyi Cong, Jian Deng, Lelai Ding, Chuang Gao, Lu Gong, Mingqing Huang, Peisong Huang, Qingqing Huang, Zhiying Huo, Yuanyuan Jia, Dongya Li, Chumin Li, Feiya Li, Hui Li, Jiaxin Li, Xiaoyang Li, Xingxing Liu, Lin Liu, Shouda Liu, Sichao Liu, Xudong Liu, Yuchen Liu, Zhengxi Lu, Lu Pan, Junjie Wang, Xin Wang, Yuping Wang, Yuxuan Wei, Zhen Wu, Jian Yao, Chao Yang, Yifeng Yi, Yuanhao Zhang, Junteng Zhang, Qidi Zhang, Shuo Zhang, Wenjie Zhang, Yang Zhao, Zilin Zhong, Dejian Zhuang, Xiaobin Audio and Speech Processing Sound 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 $\text{Seed-TTS}_\text{DiT}$, which utilizes a fully diffusion-based architecture. Unlike previous NAR-based TTS systems, $\text{Seed-TTS}_\text{DiT}$ 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 \url{https://bytedancespeech.github.io/seedtts_tech_report}. |
| title | Seed-TTS: A Family of High-Quality Versatile Speech Generation Models |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2406.02430 |