title: FastVoiceGrad: One-step Diffusion-Based Voice Conversion with
    Adversarial Conditional Diffusion Distillation
summary: Diffusion-based voice conversion (VC) techniques such as VoiceGrad have
  attracted interest because of their high VC performance in terms of speech
  quality and speaker similarity. However, a notable limitation is the slow
  inference caused by the multi-step reverse diffusion. Therefore, we propose
  FastVoiceGrad, a novel one-step diffusion-based VC that reduces the number of
  iterations from dozens to one while inheriting the high VC performance of the
  multi-step diffusion-based VC. We obtain the model using adversarial
  conditional diffusion distillation (ACDD), leveraging the ability of generative
  adversarial networks and diffusion models while reconsidering the initial
  states in sampling. Evaluations of one-shot any-to-any VC demonstrate that
  FastVoiceGrad achieves VC performance superior to or comparable to that of
  previous multi-step diffusion-based VC while enhancing the inference speed.
  Audio samples are available at
  https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/fastvoicegrad/.
paper_authors._id: 66d95844776a130de772b135
paper_authors.name: Takuhiro Kaneko
paper_authors._id: 66d95844776a130de772b136
paper_authors.name: Hirokazu Kameoka
paper_authors._id: 66d95844776a130de772b137
paper_authors.name: Kou Tanaka
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-05T09:39:38.050Z
paper_authors._id: 665b675b4dcff58f9a848b1b
paper_authors.avatarUrl: /avatars/e86c6db5f2b75aaaffac749dbaf08046.svg
paper_authors.fullname: Kou Tanaka
paper_authors.type: user
paper_authors.user: ououo
paper_authors._id: 66d95844776a130de772b138
paper_authors.name: Yuto Kondo
paper_title: FastVoiceGrad: One-step Diffusion-Based Voice Conversion with
    Adversarial Conditional Diffusion Distillation
paper_summary: Diffusion-based voice conversion (VC) techniques such as VoiceGrad have
  attracted interest because of their high VC performance in terms of speech
  quality and speaker similarity. However, a notable limitation is the slow
  inference caused by the multi-step reverse diffusion. Therefore, we propose
  FastVoiceGrad, a novel one-step diffusion-based VC that reduces the number of
  iterations from dozens to one while inheriting the high VC performance of the
  multi-step diffusion-based VC. We obtain the model using adversarial
  conditional diffusion distillation (ACDD), leveraging the ability of generative
  adversarial networks and diffusion models while reconsidering the initial
  states in sampling. Evaluations of one-shot any-to-any VC demonstrate that
  FastVoiceGrad achieves VC performance superior to or comparable to that of
  previous multi-step diffusion-based VC while enhancing the inference speed.
  Audio samples are available at
  https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/fastvoicegrad/.
paper_ai_keywords: diffusion-based voice conversion (VC) VoiceGrad reverse diffusion one-step diffusion-based VC adversarial conditional diffusion distillation (ACDD) generative adversarial networks one-shot any-to-any VC VC performance inference speed