Accelerating High-Fidelity Waveform Generation via Adversarial Flow
  Matching Optimization
Authors: name: Sang-Hoon Lee, name: Ha-Yeong Choi, name: Seong-Whan Lee

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.