ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech

• URL: http://arxiv.org/abs/2207.06389v1
• Date: Wed, 13 Jul 2022 17:45:43 GMT
• Title: ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech
• Authors: Rongjie Huang, Zhou Zhao, Huadai Liu, Jinglin Liu, Chenye Cui, Yi Ren
• Abstract summary: We propose ProDiff, on progressive fast diffusion model for high-quality text-to-speech. ProDiff parameterizes the denoising model by directly predicting clean data to avoid distinct quality degradation in accelerating sampling. Our evaluation demonstrates that ProDiff needs only 2 iterations to synthesize high-fidelity mel-spectrograms. ProDiff enables a sampling speed of 24x faster than real-time on a single NVIDIA 2080Ti GPU.
• Score: 63.780196620966905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hinder their applications to text-to-speech deployment. Through the preliminary study on diffusion model parameterization, we find that previous gradient-based TTS models require hundreds or thousands of iterations to guarantee high sample quality, which poses a challenge for accelerating sampling. In this work, we propose ProDiff, on progressive fast diffusion model for high-quality text-to-speech. Unlike previous work estimating the gradient for data density, ProDiff parameterizes the denoising model by directly predicting clean data to avoid distinct quality degradation in accelerating sampling. To tackle the model convergence challenge with decreased diffusion iterations, ProDiff reduces the data variance in the target site via knowledge distillation. Specifically, the denoising model uses the generated mel-spectrogram from an N-step DDIM teacher as the training target and distills the behavior into a new model with N/2 steps. As such, it allows the TTS model to make sharp predictions and further reduces the sampling time by orders of magnitude. Our evaluation demonstrates that ProDiff needs only 2 iterations to synthesize high-fidelity mel-spectrograms, while it maintains sample quality and diversity competitive with state-of-the-art models using hundreds of steps. ProDiff enables a sampling speed of 24x faster than real-time on a single NVIDIA 2080Ti GPU, making diffusion models practically applicable to text-to-speech synthesis deployment for the first time. Our extensive ablation studies demonstrate that each design in ProDiff is effective, and we further show that ProDiff can be easily extended to the multi-speaker setting. Audio samples are available at \url{https://ProDiff.github.io/.}

Related papers

• Directly Denoising Diffusion Models [6.109141407163027]
  We present Directly Denoising Diffusion Model (DDDM), a simple and generic approach for generating realistic images with few-step sampling. Our model achieves FID scores of 2.57 and 2.33 on CIFAR-10 in one-step and two-step sampling respectively, surpassing those obtained from GANs and distillation-based models. For ImageNet 64x64, our approach stands as a competitive contender against leading models.
  arXiv  Detail & Related papers  (2024-05-22T11:20:32Z)
• Towards More Accurate Diffusion Model Acceleration with A Timestep Aligner [84.97253871387028]
  A diffusion model, which is formulated to produce an image using thousands of denoising steps, usually suffers from a slow inference speed. We propose a timestep aligner that helps find a more accurate integral direction for a particular interval at the minimum cost. Experiments show that our plug-in design can be trained efficiently and boost the inference performance of various state-of-the-art acceleration methods.
  arXiv  Detail & Related papers  (2023-10-14T02:19:07Z)
• 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)
• Parallel Sampling of Diffusion Models [76.3124029406809]
  Diffusion models are powerful generative models but suffer from slow sampling. We present ParaDiGMS, a novel method to accelerate the sampling of pretrained diffusion models by denoising multiple steps in parallel.
  arXiv  Detail & Related papers  (2023-05-25T17:59:42Z)
• ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech [37.29193613404699]
  DDPMs are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. Previous works have explored speeding up inference speed by minimizing the number of inference steps but at the cost of sample quality. We propose ResGrad, a lightweight diffusion model which learns to refine the output spectrogram of an existing TTS model.
  arXiv  Detail & Related papers  (2022-12-30T02:31:35Z)
• FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis [90.3069686272524]
  This paper proposes FastDiff, a fast conditional diffusion model for high-quality speech synthesis. FastDiff employs a stack of time-aware location-variable convolutions of diverse receptive field patterns to efficiently model long-term time dependencies. Based on FastDiff, we design an end-to-end text-to-speech synthesizer, FastDiff-TTS, which generates high-fidelity speech waveforms.
  arXiv  Detail & Related papers  (2022-04-21T07:49:09Z)
• Diff-TTS: A Denoising Diffusion Model for Text-to-Speech [14.231478930274058]
  We propose a novel non-autoregressive TTS model, namely Diff-TTS, which achieves highly natural and efficient speech synthesis. Given the text, Diff-TTS exploits a denoising diffusion framework to transform the noise signal into a mel-spectrogram via diffusion time steps. We verify that Diff-TTS generates 28 times faster than the real-time with a single NVIDIA 2080Ti GPU.
  arXiv  Detail & Related papers  (2021-04-03T13:53:19Z)
• Denoising Diffusion Implicit Models [117.03720513930335]
  We present denoising diffusion implicit models (DDIMs) for iterative implicit probabilistic models with the same training procedure as DDPMs. DDIMs can produce high quality samples $10 times$ to $50 times$ faster in terms of wall-clock time compared to DDPMs.
  arXiv  Detail & Related papers  (2020-10-06T06:15:51Z)

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.