title: CoMoSVC: Consistency Model-based Singing Voice Conversion
summary: The diffusion-based Singing Voice Conversion (SVC) methods have achieved
  remarkable performances, producing natural audios with high similarity to the
  target timbre. However, the iterative sampling process results in slow
  inference speed, and acceleration thus becomes crucial. In this paper, we
  propose CoMoSVC, a consistency model-based SVC method, which aims to achieve
  both high-quality generation and high-speed sampling. A diffusion-based teacher
  model is first specially designed for SVC, and a student model is further
  distilled under self-consistency properties to achieve one-step sampling.
  Experiments on a single NVIDIA GTX4090 GPU reveal that although CoMoSVC has a
  significantly faster inference speed than the state-of-the-art (SOTA)
  diffusion-based SVC system, it still achieves comparable or superior conversion
  performance based on both subjective and objective metrics. Audio samples and
  codes are available at https://comosvc.github.io/.
paper_authors._id: 65962bd20dd33f8305ea316f
paper_authors.name: Yiwen Lu
paper_authors._id: 65962bd20dd33f8305ea3170
paper_authors.name: Zhen Ye
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T15:59:04.941Z
paper_authors._id: 645f172d7c6bff8577353d1a
paper_authors.avatarUrl: /avatars/a83682e1343809257b082b78d58c582a.svg
paper_authors.fullname: ZhenYE
paper_authors.type: user
paper_authors.user: ZhenYe234
paper_authors._id: 65962bd20dd33f8305ea3171
paper_authors.name: Wei Xue
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-24T11:13:11.753Z
paper_authors._id: 6628adb14277eae0da5eee28
paper_authors.avatarUrl: /avatars/6cb41b80cc5e014e455dfc2a22682e64.svg
paper_authors.fullname: HKUST Audio
paper_authors.type: user
paper_authors.user: HKUST-Audio
paper_authors._id: 65962bd20dd33f8305ea3172
paper_authors.name: Xu Tan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-05T10:02:10.894Z
paper_authors._id: 5f1040b6e9d71719e3be71d2
paper_authors.avatarUrl: /avatars/a2f28940236ae625ed3810ad62e343ff.svg
paper_authors.fullname: Xu Tan
paper_authors.type: user
paper_authors.user: xutan
paper_authors._id: 65962bd20dd33f8305ea3173
paper_authors.name: Qifeng Liu
paper_authors._id: 65962bd20dd33f8305ea3174
paper_authors.name: Yike Guo
paper_title: CoMoSVC: Consistency Model-based Singing Voice Conversion
paper_summary: The diffusion-based Singing Voice Conversion (SVC) methods have achieved
  remarkable performances, producing natural audios with high similarity to the
  target timbre. However, the iterative sampling process results in slow
  inference speed, and acceleration thus becomes crucial. In this paper, we
  propose CoMoSVC, a consistency model-based SVC method, which aims to achieve
  both high-quality generation and high-speed sampling. A diffusion-based teacher
  model is first specially designed for SVC, and a student model is further
  distilled under self-consistency properties to achieve one-step sampling.
  Experiments on a single NVIDIA GTX4090 GPU reveal that although CoMoSVC has a
  significantly faster inference speed than the state-of-the-art (SOTA)
  diffusion-based SVC system, it still achieves comparable or superior conversion
  performance based on both subjective and objective metrics. Audio samples and
  codes are available at https://comosvc.github.io/.
paper_ai_summary: CoMoSVC is a consistency model-based singing voice conversion method that achieves fast inference speed and high-quality audio generation.
paper_ai_keywords: diffusion-based SVC consistency model diffusion-based teacher model student model self-consistency properties one-step sampling