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We propose Easy End-to-End Diffusion-based Text to Speech, a simple andefficient end-to-end text-to-speech model based on diffusion. E3 TTS directlytakes plain text as input and generates an audio waveform through an iterativerefinement process. Unlike many prior work, E3 TTS does not rely on anyintermediate representations like spectrogram features or alignmentinformation. Instead, E3 TTS models the temporal structure of the waveformthrough the diffusion process. Without relying on additional conditioninginformation, E3 TTS could support flexible latent structure within the givenaudio. This enables E3 TTS to be easily adapted for zero-shot tasks such asediting without any additional training. Experiments show that E3 TTS cangenerate high-fidelity audio, approaching the performance of a state-of-the-artneural TTS system. Audio samples are available at https://e3tts.github.io.

E3 TTS: Easy End-to-End Diffusion-based Text to Speech