E3 TTS: Easy End-to-End Diffusion-based Text to Speech
Yuan Gao Nobuyuki Morioka Yu Zhang Nanxin Chen
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 https://e3tts.github.io.