ESPnet2-TTS: Extending the Edge of TTS Research

Tomoki Hayashi; Ryuichi Yamamoto; Takenori Yoshimura; Peter Wu; Jiatong Shi; Takaaki Saeki; Yooncheol Ju; Yusuke Yasuda; Shinnosuke Takamichi; Shinji Watanabe

arXiv · 2110.07840

ESPnet2-TTS: Extending the Edge of TTS Research

Tomoki Hayashi·Ryuichi Yamamoto·Takenori Yoshimura·Peter Wu·Jiatong Shi·Takaaki Saeki·Yooncheol Ju·Yusuke Yasuda·Shinnosuke Takamichi·Shinji Watanabe

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Abstract

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.

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Tomoki Hayashi, Ryuichi Yamamoto, Takenori Yoshimura, Peter Wu, Jiatong Shi, Takaaki Saeki, Yooncheol Ju, Yusuke Yasuda, Shinnosuke Takamichi, Shinji Watanabe. 2021-10-15. ESPnet2-TTS: Extending the Edge of TTS Research. https://arxiv.org/abs/2110.07840

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