WaveFM: A High-Fidelity and Efficient Vocoder Based on Flow Matching

• URL: http://arxiv.org/abs/2503.16689v1
• Date: Thu, 20 Mar 2025 20:17:17 GMT
• Title: WaveFM: A High-Fidelity and Efficient Vocoder Based on Flow Matching
• Authors: Tianze Luo, Xingchen Miao, Wenbo Duan,
• Abstract summary: WaveFM is a flow matching model for mel-spectrogram conditioned speech synthesis.<n>Our model achieves superior performance in both quality and efficiency compared to previous diffusion vocoders.
• Score: 1.6385815610837167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Accelerating High-Fidelity Waveform Generation via Adversarial Flow Matching Optimization [37.35829410807451]
  This paper introduces PeriodWave-Turbo, a high-fidelity and high-efficient waveform generation model via adversarial flow matching optimization. It only requires 1,000 steps of fine-tuning to achieve state-of-the-art performance across various objective metrics. By scaling up the backbone of PeriodWave from 29M to 70M parameters for improved generalization, PeriodWave-Turbo achieves unprecedented performance.
  arXiv  Detail & Related papers  (2024-08-15T08:34:00Z)
• Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching [51.70360630470263]
  Video-to-audio (V2A) generation aims to synthesize content-matching audio from silent video.<n>We propose Frieren, a V2A model based on rectified flow matching.<n>Experiments indicate that Frieren achieves state-of-the-art performance in both generation quality and temporal alignment.
  arXiv  Detail & Related papers  (2024-06-01T06:40:22Z)
• SpecDiff-GAN: A Spectrally-Shaped Noise Diffusion GAN for Speech and Music Synthesis [0.0]
  We introduce SpecDiff-GAN, a neural vocoder based on HiFi-GAN. We show the merits of our proposed model for speech and music synthesis on several datasets.
  arXiv  Detail & Related papers  (2024-01-30T09:17:57Z)
• Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction [14.037398189132468]
  We present an innovative approach named the Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for sparse-view CT reconstruction. Specifically, we establish a unified mathematical model integrating low-frequency and high-frequency generative models, achieving the solution with optimization procedure. Our method rooted in established optimization theory, comprising three distinct stages, including low-frequency generation, high-frequency refinement and domain transform.
  arXiv  Detail & Related papers  (2023-08-30T10:48:53Z)
• Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion [85.54515118077825]
  This paper proposes a linear diffusion model (LinDiff) based on an ordinary differential equation to simultaneously reach fast inference and high sample quality. To reduce computational complexity, LinDiff employs a patch-based processing approach that partitions the input signal into small patches. Our model can synthesize speech of a quality comparable to that of autoregressive models with faster synthesis speed.
  arXiv  Detail & Related papers  (2023-06-09T07:02:43Z)
• Low-Light Image Enhancement with Wavelet-based Diffusion Models [50.632343822790006]
  Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. We propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
  arXiv  Detail & Related papers  (2023-06-01T03:08:28Z)
• SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping [51.698273019061645]
  SpecGrad adapts the diffusion noise so that its time-varying spectral envelope becomes close to the conditioning log-mel spectrogram. It is processed in the time-frequency domain to keep the computational cost almost the same as the conventional DDPM-based neural vocoders.
  arXiv  Detail & Related papers  (2022-03-31T02:08:27Z)
• A Study on Speech Enhancement Based on Diffusion Probabilistic Model [63.38586161802788]
  We propose a diffusion probabilistic model-based speech enhancement model (DiffuSE) model that aims to recover clean speech signals from noisy signals. The experimental results show that DiffuSE yields performance that is comparable to related audio generative models on the standardized Voice Bank corpus task.
  arXiv  Detail & Related papers  (2021-07-25T19:23:18Z)
• PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Driven Adaptive Prior [103.00403682863427]
  We propose PriorGrad to improve the efficiency of the conditional diffusion model. We show that PriorGrad achieves a faster convergence leading to data and parameter efficiency and improved quality.
  arXiv  Detail & Related papers  (2021-06-11T14:04:03Z)
• DiffWave: A Versatile Diffusion Model for Audio Synthesis [35.406438835268816]
  DiffWave is a versatile diffusion probabilistic model for conditional and unconditional waveform generation. It produces high-fidelity audios in different waveform generation tasks, including neural vocoding conditioned on mel spectrogram. It significantly outperforms autoregressive and GAN-based waveform models in the challenging unconditional generation task.
  arXiv  Detail & Related papers  (2020-09-21T11:20:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.