title: Takin: A Cohort of Superior Quality Zero-shot Speech Generation Models
summary: With the advent of the big data and large language model era, zero-shot
  personalized rapid customization has emerged as a significant trend. In this
  report, we introduce Takin AudioLLM, a series of techniques and models, mainly
  including Takin TTS, Takin VC, and Takin Morphing, specifically designed for
  audiobook production. These models are capable of zero-shot speech production,
  generating high-quality speech that is nearly indistinguishable from real human
  speech and facilitating individuals to customize the speech content according
  to their own needs. Specifically, we first introduce Takin TTS, a neural codec
  language model that builds upon an enhanced neural speech codec and a
  multi-task training framework, capable of generating high-fidelity natural
  speech in a zero-shot way. For Takin VC, we advocate an effective content and
  timbre joint modeling approach to improve the speaker similarity, while
  advocating for a conditional flow matching based decoder to further enhance its
  naturalness and expressiveness. Last, we propose the Takin Morphing system with
  highly decoupled and advanced timbre and prosody modeling approaches, which
  enables individuals to customize speech production with their preferred timbre
  and prosody in a precise and controllable manner. Extensive experiments
  validate the effectiveness and robustness of our Takin AudioLLM series models.
  For detailed demos, please refer to https://takinaudiollm.github.io.
paper_authors._id: 66eb8946f5b06a0e01e519d7
paper_authors.name: EverestAI
paper_authors._id: 66eb8946f5b06a0e01e519d9
paper_authors.name: Sijin Chen
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paper_authors.name: Yuan Feng
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paper_authors.name: Laipeng He
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paper_authors.name: Tianwei He
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paper_authors.name: Wendi He
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paper_authors.name: Yanni Hu
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paper_authors.fullname: YanniHu
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paper_authors.name: Bin Lin
paper_authors._id: 66eb8946f5b06a0e01e519e0
paper_authors.name: Yiting Lin
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paper_authors.fullname: Yiting Lin
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paper_authors.user: ClorisLIN
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paper_authors.name: Pengfei Tan
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paper_authors.name: Chengwei Tian
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paper_authors.name: Chen Wang
paper_authors._id: 66eb8946f5b06a0e01e519e4
paper_authors.name: Zhicheng Wang
paper_authors._id: 66eb8946f5b06a0e01e519e5
paper_authors.name: Ruoye Xie
paper_authors._id: 66eb8946f5b06a0e01e519e6
paper_authors.name: Jingjing Yin
paper_authors._id: 66eb8946f5b06a0e01e519e7
paper_authors.name: Jianhao Ye
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-19T08:56:13.703Z
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paper_authors.fullname: Jianhao Ye
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paper_authors.user: sonaxyjh
paper_authors._id: 66eb8946f5b06a0e01e519e8
paper_authors.name: Jixun Yao
paper_authors._id: 66eb8946f5b06a0e01e519e9
paper_authors.name: Quanlei Yan
paper_authors._id: 66eb8946f5b06a0e01e519ea
paper_authors.name: Yuguang Yang
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paper_title: Takin: A Cohort of Superior Quality Zero-shot Speech Generation Models
paper_summary: With the advent of the big data and large language model era, zero-shot
  personalized rapid customization has emerged as a significant trend. In this
  report, we introduce Takin AudioLLM, a series of techniques and models, mainly
  including Takin TTS, Takin VC, and Takin Morphing, specifically designed for
  audiobook production. These models are capable of zero-shot speech production,
  generating high-quality speech that is nearly indistinguishable from real human
  speech and facilitating individuals to customize the speech content according
  to their own needs. Specifically, we first introduce Takin TTS, a neural codec
  language model that builds upon an enhanced neural speech codec and a
  multi-task training framework, capable of generating high-fidelity natural
  speech in a zero-shot way. For Takin VC, we advocate an effective content and
  timbre joint modeling approach to improve the speaker similarity, while
  advocating for a conditional flow matching based decoder to further enhance its
  naturalness and expressiveness. Last, we propose the Takin Morphing system with
  highly decoupled and advanced timbre and prosody modeling approaches, which
  enables individuals to customize speech production with their preferred timbre
  and prosody in a precise and controllable manner. Extensive experiments
  validate the effectiveness and robustness of our Takin AudioLLM series models.
  For detailed demos, please refer to https://takinaudiollm.github.io.
paper_ai_keywords: neural codec language model enhanced neural speech codec multi-task training framework zero-shot speech production high-fidelity natural speech Takin TTS content and timbre joint modeling conditional flow matching based decoders high-fidelity natural speech speaker similarity Takin VC Takin Morphing highly decoupled and advanced timbre and prosody modeling speech customization precise and controllable manner