title: FreGrad: Lightweight and Fast Frequency-aware Diffusion Vocoder
summary: The goal of this paper is to generate realistic audio with a lightweight and
  fast diffusion-based vocoder, named FreGrad. Our framework consists of the
  following three key components: (1) We employ discrete wavelet transform that
  decomposes a complicated waveform into sub-band wavelets, which helps FreGrad
  to operate on a simple and concise feature space, (2) We design a
  frequency-aware dilated convolution that elevates frequency awareness,
  resulting in generating speech with accurate frequency information, and (3) We
  introduce a bag of tricks that boosts the generation quality of the proposed
  model. In our experiments, FreGrad achieves 3.7 times faster training time and
  2.2 times faster inference speed compared to our baseline while reducing the
  model size by 0.6 times (only 1.78M parameters) without sacrificing the output
  quality. Audio samples are available at:
  https://mm.kaist.ac.kr/projects/FreGrad.
paper_authors._id: 65a9e92668139e3c423d0fc2
paper_authors.name: Tan Dat Nguyen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-19T13:35:56.495Z
paper_authors._id: 64b0edb86c5beff5c5a9ed31
paper_authors.avatarUrl: /avatars/9e96c56be223e42cc30a6103661fdfe9.svg
paper_authors.fullname: Tan Dat Nguyen
paper_authors.type: user
paper_authors.user: ntdatvn
paper_authors._id: 65a9e92668139e3c423d0fc3
paper_authors.name: Ji-Hoon Kim
paper_authors._id: 65a9e92668139e3c423d0fc4
paper_authors.name: Youngjoon Jang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-19T13:36:20.927Z
paper_authors._id: 65a4c4ed2548c41ad9b1421c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65a4c4ed2548c41ad9b1421c/bMQbowjHKvq-bKpzalvWm.jpeg
paper_authors.fullname: YoungJoon Jang
paper_authors.type: user
paper_authors.user: yjoonjang
paper_authors._id: 65a9e92668139e3c423d0fc5
paper_authors.name: Jaehun Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-19T13:36:27.948Z
paper_authors._id: 6213510ea82164d119812be3
paper_authors.avatarUrl: /avatars/e4802350e3bcc3688e7b2ecb667bf430.svg
paper_authors.fullname: Jaehun Kim
paper_authors.type: user
paper_authors.user: smoked22
paper_authors._id: 65a9e92668139e3c423d0fc6
paper_authors.name: Joon Son Chung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-19T13:36:35.286Z
paper_authors._id: 647dcc87e4d52fe0e01c8ffc
paper_authors.avatarUrl: /avatars/56fbf5604dc4082b0c494bd49f345c71.svg
paper_authors.fullname: Joon Son Chung
paper_authors.type: user
paper_authors.user: joonson
paper_title: FreGrad: Lightweight and Fast Frequency-aware Diffusion Vocoder
paper_summary: The goal of this paper is to generate realistic audio with a lightweight and
  fast diffusion-based vocoder, named FreGrad. Our framework consists of the
  following three key components: (1) We employ discrete wavelet transform that
  decomposes a complicated waveform into sub-band wavelets, which helps FreGrad
  to operate on a simple and concise feature space, (2) We design a
  frequency-aware dilated convolution that elevates frequency awareness,
  resulting in generating speech with accurate frequency information, and (3) We
  introduce a bag of tricks that boosts the generation quality of the proposed
  model. In our experiments, FreGrad achieves 3.7 times faster training time and
  2.2 times faster inference speed compared to our baseline while reducing the
  model size by 0.6 times (only 1.78M parameters) without sacrificing the output
  quality. Audio samples are available at:
  https://mm.kaist.ac.kr/projects/FreGrad.
paper_ai_keywords: discrete wavelet transform sub-band wavelets feature space frequency-aware dilated convolution bag of tricks