Accelerating High-Fidelity Waveform Generation via Adversarial Flow
  Matching Optimization
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.
66becd03f1685a3adec13158
Sang-Hoon Lee
claimed_verified
2024-08-17T05:38:49.035Z
654a62c211a85feec88ef8f2
/avatars/aeff2661e7f3cbc6b3cdf43c6e31ca9b.svg
Sang-Hoon Lee
user
LeeSangHoon
66becd03f1685a3adec13159
Ha-Yeong Choi
claimed_verified
2024-08-16T08:19:11.881Z
6401a0c14a5c92eccfe333c2
https://cdn-avatars.huggingface.co/v1/production/uploads/6401a0c14a5c92eccfe333c2/-fv3jekUxMNrE058phGpA.png
Ha-Yeong Choi
user
Ha0
66becd03f1685a3adec1315a
Seong-Whan Lee
Accelerating High-Fidelity Waveform Generation via Adversarial Flow
  Matching Optimization
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.
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.
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)