ICLR 2025

Published as a conference paper at ICLR 2025
PERIODWAVE: MULTI-PERIOD FLOW MATCHING FOR
HIGH-FIDELITY WAVEFORM GENERATION
Sang-Hoon Lee1,2,∗
Ha-Yeong Choi3,∗
Seong-Whan Lee4†
1Department of Software and Computer Engineering, Ajou University, Suwon, Korea
2Department of Artificial Intelligence, Ajou University, Suwon, Korea
3Gen AI Lab, KT Corp., Seoul, Korea
4Department of Artificial Intelligence, Korea University, Seoul, Korea
ABSTRACT
Recently, universal waveform generation tasks have been investigated conditioned
on various out-of-distribution scenarios. Although one-step GAN-based methods
have shown their strength in fast waveform generation, they are vulnerable to
train-inference mismatch scenarios such as two-stage text-to-speech. Meanwhile,
diffusion-based models have shown their powerful generative performance in other
domains; however, they stay out of the limelight due to slow inference speed in
waveform generation tasks. Above all, there is no generator architecture that can
explicitly disentangle the natural periodic features of high-resolution waveform
signals. In this paper, we propose PeriodWave, a novel universal waveform gener-
ation model from Mel-spectrogram and neural audio codec. First, we introduce
a period-aware flow matching estimator that effectively captures the periodic fea-
tures of the waveform signal when estimating the vector fields. Additionally, we
utilize a multi-period estimator that avoids overlaps to capture different periodic
features of waveform signals. Although increasing the number of periods can
improve the performance significantly, this requires more computational costs. To
reduce this issue, we also propose a single period-conditional universal estimator
that can feed-forward parallel by period-wise batch inference. Additionally, we
first introduce FreeU to reduce the high-frequency noise for waveform generation.
Furthermore, we demonstrate the effectiveness of the proposed method in neural
audio codec decoding task, and...