Diff-TTS: A Denoising Diffusion Model for Text-to-Speech
Abstract
Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded in generating human-like speech, there is still room for improvements to its naturalness and architectural efficiency. In this work, 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. In order to learn the mel-spectrogram distribution conditioned on the text, we present a likelihood-based optimization method for TTS. Furthermore, to boost up the inference speed, we leverage the accelerated sampling method that allows Diff-TTS to generate raw waveforms much faster without significantly degrading perceptual quality. Through experiments, we verified that Diff-TTS generates 28 times faster than the real-time with a single NVIDIA 2080Ti GPU.
Cite
@article{arxiv.2104.01409,
title = {Diff-TTS: A Denoising Diffusion Model for Text-to-Speech},
author = {Myeonghun Jeong and Hyeongju Kim and Sung Jun Cheon and Byoung Jin Choi and Nam Soo Kim},
journal= {arXiv preprint arXiv:2104.01409},
year = {2021}
}
Comments
Submitted to INTERSPEECH 2021