Recent progress in large-scale zero-shot speech synthesis has been significantly advanced by language models and diffusion models. However, the generation process of both methods is slow and computationally intensive. Efficient speech synthesis using a lower computing budget to achieve quality on par with previous work remains a significant challenge. In this paper, we present FlashSpeech, a large-scale zero-shot speech synthesis system with approximately 5\% of the inference time compared with previous work. FlashSpeech is built on the latent consistency model and applies a novel adversarial consistency training approach that can train from scratch without the need for a pre-trained diffusion model as the teacher. Furthermore, a new prosody generator module enhances the diversity of prosody, making the rhythm of the speech sound more natural. The generation processes of FlashSpeech can be achieved efficiently with one or two sampling steps while maintaining high audio quality and high similarity to the audio prompt for zero-shot speech generation. Our experimental results demonstrate the superior performance of FlashSpeech. Notably, FlashSpeech can be about 20 times faster than other zero-shot speech synthesis systems while maintaining comparable performance in terms of voice quality and similarity. Furthermore, FlashSpeech demonstrates its versatility by efficiently performing tasks like voice conversion, speech editing, and diverse speech sampling. Audio samples can be found in https://flashspeech.github.io/.

翻译：近年来，大規模零样本语音合成技术通过语言模型和扩散模型取得了显著进展。然而，这两种方法的生成过程缓慢且计算密集。如何以较低的计算预算实现与先前工作质量相当的高效语音合成，仍是一项重大挑战。本文提出FlashSpeech——一种大规模零样本语音合成系统，其推理时间仅为先前工作的约5%。该系统基于隐式一致性模型，并采用了一种新颖的对抗性一致性训练方法，无需依赖预训练扩散模型作为教师即可从零开始训练。此外，新增的韵律生成器模块增强了韵律多样性，使语音节奏更加自然。FlashSpeech的生成过程可通过一至两步采样高效完成，同时保持高音频质量和对零样本语音生成中音频提示的高度相似性。实验结果表明，FlashSpeech具有卓越性能：其速度可较其他零样本语音合成系统提升约20倍，同时在语音质量和相似度方面保持可比性能。此外，FlashSpeech在语音转换、语音编辑及多样化语音采样等任务中展现出高效的多功能性。音频示例见https://flashspeech.github.io/。