  This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS)
toolkit. ESPnet2-TTS extends our earlier version, ESPnet-TTS, by adding many
new features, including: on-the-fly flexible pre-processing, joint training
with neural vocoders, and state-of-the-art TTS models with extensions like
full-band E2E text-to-waveform modeling, which simplify the training pipeline
and further enhance TTS performance. The unified design of our recipes enables
users to quickly reproduce state-of-the-art E2E-TTS results. We also provide
many pre-trained models in a unified Python interface for inference, offering a
quick means for users to generate baseline samples and build demos.
Experimental evaluations with English and Japanese corpora demonstrate that our
provided models synthesize utterances comparable to ground-truth ones,
achieving state-of-the-art TTS performance. The toolkit is available online at
https://github.com/espnet/espnet.
