title: Accelerating High-Fidelity Waveform Generation via Adversarial Flow
    Matching Optimization
summary: This paper introduces PeriodWave-Turbo, a high-fidelity and high-efficient
  waveform generation model via adversarial flow matching optimization. Recently,
  conditional flow matching (CFM) generative models have been successfully
  adopted for waveform generation tasks, leveraging a single vector field
  estimation objective for training. Although these models can generate
  high-fidelity waveform signals, they require significantly more ODE steps
  compared to GAN-based models, which only need a single generation step.
  Additionally, the generated samples often lack high-frequency information due
  to noisy vector field estimation, which fails to ensure high-frequency
  reproduction. To address this limitation, we enhance pre-trained CFM-based
  generative models by incorporating a fixed-step generator modification. We
  utilized reconstruction losses and adversarial feedback to accelerate
  high-fidelity waveform generation. Through adversarial flow matching
  optimization, it only requires 1,000 steps of fine-tuning to achieve
  state-of-the-art performance across various objective metrics. Moreover, we
  significantly reduce inference speed from 16 steps to 2 or 4 steps.
  Additionally, by scaling up the backbone of PeriodWave from 29M to 70M
  parameters for improved generalization, PeriodWave-Turbo achieves unprecedented
  performance, with a perceptual evaluation of speech quality (PESQ) score of
  4.454 on the LibriTTS dataset. Audio samples, source code and checkpoints will
  be available at https://github.com/sh-lee-prml/PeriodWave.
paper_authors._id: 66becd03f1685a3adec13158
paper_authors.name: Sang-Hoon Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-17T05:38:49.035Z
paper_authors._id: 654a62c211a85feec88ef8f2
paper_authors.avatarUrl: /avatars/aeff2661e7f3cbc6b3cdf43c6e31ca9b.svg
paper_authors.fullname: Sang-Hoon Lee
paper_authors.type: user
paper_authors.user: LeeSangHoon
paper_authors._id: 66becd03f1685a3adec13159
paper_authors.name: Ha-Yeong Choi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-16T08:19:11.881Z
paper_authors._id: 6401a0c14a5c92eccfe333c2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6401a0c14a5c92eccfe333c2/-fv3jekUxMNrE058phGpA.png
paper_authors.fullname: Ha-Yeong Choi
paper_authors.type: user
paper_authors.user: Ha0
paper_authors._id: 66becd03f1685a3adec1315a
paper_authors.name: Seong-Whan Lee
paper_title: Accelerating High-Fidelity Waveform Generation via Adversarial Flow
    Matching Optimization
paper_summary: This paper introduces PeriodWave-Turbo, a high-fidelity and high-efficient
  waveform generation model via adversarial flow matching optimization. Recently,
  conditional flow matching (CFM) generative models have been successfully
  adopted for waveform generation tasks, leveraging a single vector field
  estimation objective for training. Although these models can generate
  high-fidelity waveform signals, they require significantly more ODE steps
  compared to GAN-based models, which only need a single generation step.
  Additionally, the generated samples often lack high-frequency information due
  to noisy vector field estimation, which fails to ensure high-frequency
  reproduction. To address this limitation, we enhance pre-trained CFM-based
  generative models by incorporating a fixed-step generator modification. We
  utilized reconstruction losses and adversarial feedback to accelerate
  high-fidelity waveform generation. Through adversarial flow matching
  optimization, it only requires 1,000 steps of fine-tuning to achieve
  state-of-the-art performance across various objective metrics. Moreover, we
  significantly reduce inference speed from 16 steps to 2 or 4 steps.
  Additionally, by scaling up the backbone of PeriodWave from 29M to 70M
  parameters for improved generalization, PeriodWave-Turbo achieves unprecedented
  performance, with a perceptual evaluation of speech quality (PESQ) score of
  4.454 on the LibriTTS dataset. Audio samples, source code and checkpoints will
  be available at https://github.com/sh-lee-prml/PeriodWave.
paper_ai_summary: PeriodWave-Turbo, an enhanced adversarial flow matching model with a pre-trained CFM generator, improves waveform generation efficiency and high-frequency detail, achieving state-of-the-art performance with reduced ODE steps.
paper_ai_keywords: adversarial flow matching conditional flow matching (CFM) ODE steps GAN-based models vector field estimation reconstruction losses adversarial feedback perceptual evaluation of speech quality (PESQ)