E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS
Authors: name: Sefik Emre Eskimez, name: Xiaofei Wang, name: Manthan Thakker, name: Canrun Li, name: Chung-Hsien Tsai, name: Zhen Xiao, name: Hemin Yang, name: Zirun Zhu, name: Min Tang, name: Xu Tan, name: Yanqing Liu, name: Sheng Zhao, name: Naoyuki Kanda

This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully
non-autoregressive zero-shot text-to-speech system that offers human-level
naturalness and state-of-the-art speaker similarity and intelligibility. In the
E2 TTS framework, the text input is converted into a character sequence with
filler tokens. The flow-matching-based mel spectrogram generator is then
trained based on the audio infilling task. Unlike many previous works, it does
not require additional components (e.g., duration model, grapheme-to-phoneme)
or complex techniques (e.g., monotonic alignment search). Despite its
simplicity, E2 TTS achieves state-of-the-art zero-shot TTS capabilities that
are comparable to or surpass previous works, including Voicebox and
NaturalSpeech 3. The simplicity of E2 TTS also allows for flexibility in the
input representation. We propose several variants of E2 TTS to improve
usability during inference. See https://aka.ms/e2tts/ for demo samples.