We propose Easy End-to-End Diffusion-based Text to Speech, a simple and
efficient end-to-end text-to-speech model based on diffusion. E3 TTS directly
takes plain text as input and generates an audio waveform through an iterative
refinement process. Unlike many prior work, E3 TTS does not rely on any
intermediate representations like spectrogram features or alignment
information. Instead, E3 TTS models the temporal structure of the waveform
through the diffusion process. Without relying on additional conditioning
information, E3 TTS could support flexible latent structure within the given
audio. This enables E3 TTS to be easily adapted for zero-shot tasks such as
editing without any additional training. Experiments show that E3 TTS can
generate high-fidelity audio, approaching the performance of a state-of-the-art
neural TTS system. Audio samples are available at this https URL

我们提出了一种简单高效的端到端扩散式文本转语音模型，通过扩散过程直接从纯文本生成音频波形。该模型无需中间表示，能够支持给定音频的灵活潜在结构，从而实现轻松适应零样本任务。实验证明，该模型能够生成高保真音频，接近最先进的神经网络语音合成系统的性能。