PeriodWave: Multi-Period Flow Matching for High-Fidelity Waveform
  Generation
Authors: name: Sang-Hoon Lee, name: Ha-Yeong Choi, name: Seong-Whan Lee

Recently, universal waveform generation tasks have been investigated
conditioned on various out-of-distribution scenarios. Although 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 generation model. First, we
introduce a period-aware flow matching estimator that can capture the periodic
features 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 utilize discrete wavelet
transform to losslessly disentangle the frequency information of waveform
signals for high-frequency modeling, and introduce FreeU to reduce the
high-frequency noise for waveform generation. The experimental results
demonstrated that our model outperforms the previous models both in
Mel-spectrogram reconstruction and text-to-speech tasks. All source code will
be available at https://github.com/sh-lee-prml/PeriodWave.