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WaveFM: A High-Fidelity and Efficient Vocoder Based on Flow Matching by Luo, Tianze; Miao, Xingchen; Duan, Wenbo is a scholarly article available to read on EtoBox.

What is WaveFM: A High-Fidelity and Efficient Vocoder Based on Flow Matching about?

Flow matching offers a robust and stable approach to training diffusion models. However, directly applying flow matching to neural vocoders can result in subpar audio quality. In this work, we present WaveFM, a reparameterized flow matching model for mel-spectrogram conditioned speech synthesis, designed to enhance both sample quality and generation speed for diffusion vocoders. Since mel-spectrograms represent the energy distribution of waveforms, WaveFM adopts a mel-conditioned prior distribution instead of a standard Gaussian prior to minimize unnecessary transportation costs during synthesis. Moreover, while most diffusion vocoders rely on a single loss function, we argue that incorporating auxiliary losses, including a refined multi-resolution STFT loss, can further improve audio quality. To speed up inference without degrading sample quality significantly, we introduce a tailored consistency distillation method for WaveFM. Experiment results demonstrate that our model achieves superior performance in both quality and efficiency compared to previous diffusion vocoders, while enabling waveform generation in a single inference step.

Author

Luo, Tianze; Miao, Xingchen; Duan, Wenbo

Published

2025

Language

EN