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.
@article{arxiv.2110.07840,
title = {ESPnet2-TTS: Extending the Edge of TTS Research},
author = {Tomoki Hayashi and Ryuichi Yamamoto and Takenori Yoshimura and Peter Wu and Jiatong Shi and Takaaki Saeki and Yooncheol Ju and Yusuke Yasuda and Shinnosuke Takamichi and Shinji Watanabe},
journal= {arXiv preprint arXiv:2110.07840},
year = {2021}
}
Comments
Submitted to ICASSP2022. Demo HP: https://espnet.github.io/icassp2022-tts/