Denoising diffusion probabilistic models (DDPMs) have shown promising performance for speech synthesis. However, a large number of iterative steps are required to achieve high sample quality, which restricts the inference speed. Maintaining sample quality while increasing sampling speed has become a challenging task. In this paper, we propose a "Co"nsistency "Mo"del-based "Speech" synthesis method, CoMoSpeech, which achieve speech synthesis through a single diffusion sampling step while achieving high audio quality. The consistency constraint is applied to distill a consistency model from a well-designed diffusion-based teacher model, which ultimately yields superior performances in the distilled CoMoSpeech. Our experiments show that by generating audio recordings by a single sampling step, the CoMoSpeech achieves an inference speed more than 150 times faster than real-time on a single NVIDIA A100 GPU, which is comparable to FastSpeech2, making diffusion-sampling based speech synthesis truly practical. Meanwhile, objective and subjective evaluations on text-to-speech and singing voice synthesis show that the proposed teacher models yield the best audio quality, and the one-step sampling based CoMoSpeech achieves the best inference speed with better or comparable audio quality to other conventional multi-step diffusion model baselines. Audio samples are available at https://comospeech.github.io/.

翻译：去噪扩散概率模型（DDPMs）在语音合成领域展现出优异性能。然而，生成高质量样本需要大量迭代步骤，这限制了推理速度。如何在提升采样速度的同时保持样本质量成为一项具有挑战性的任务。本文提出一种基于“一致性（Co）”和“模型（Mo）”的语音合成方法CoMoSpeech，该方法通过单个扩散采样步骤实现语音合成，同时保持高音频质量。通过一致性约束，将精心设计的扩散教师模型蒸馏为一致性模型，最终在蒸馏得到的CoMoSpeech中取得优异性能。实验表明，通过单步采样生成音频时，CoMoSpeech在单个NVIDIA A100 GPU上的推理速度比实时速度快150倍以上，与FastSpeech2相当，使得基于扩散采样的语音合成真正具备实用性。同时，在文本到语音和歌声合成任务上的客观与主观评估表明，所提出的教师模型能够取得最佳音频质量，而基于单步采样的CoMoSpeech在实现最快推理速度的同时，其音频质量优于或媲美其他传统多步扩散模型基线方案。音频样本可访问 https://comospeech.github.io/ 获取。