Recently, denoising diffusion probabilistic models and generative score matching have shown high potential in modelling complex data distributions while stochastic calculus has provided a unified point of view on these techniques allowing for flexible inference schemes. In this paper we introduce Grad-TTS, a novel text-to-speech model with score-based decoder producing mel-spectrograms by gradually transforming noise predicted by encoder and aligned with text input by means of Monotonic Alignment Search. The framework of stochastic differential equations helps us to generalize conventional diffusion probabilistic models to the case of reconstructing data from noise with different parameters and allows to make this reconstruction flexible by explicitly controlling trade-off between sound quality and inference speed. Subjective human evaluation shows that Grad-TTS is competitive with state-of-the-art text-to-speech approaches in terms of Mean Opinion Score. We will make the code publicly available shortly.
翻译:最近,分解扩散概率模型和基因比分匹配在模拟复杂数据分布方面显示出很大潜力,而随机微积分则为这些技术提供了统一的观点,允许采用灵活的推论方法。在本文中,我们引入了Grad-TTS,这是一个新颖的文本到语音模型,具有基于分数的分解分解器,通过逐步改变由编码器预测的噪音,并通过单调对齐搜索与文本输入相匹配。随机差异方程式框架帮助我们将常规的传播概率模型推广到从不同参数的噪音中重建数据的案例中,并允许通过明确控制声音质量和推论速度之间的取舍,使这一重建具有灵活性。主观的人类评价表明,Grad-TTS与最先进的文本到语音方法在微调中具有竞争力。我们将很快公布该代码。