Publications
CONFERENCE (INTERNATIONAL) ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Tomoki Hayashi (Nagoya University), Ryuichi Yamamoto, Katsuki Inoue (Okayama University), Takenori Yoshimura (Nagoya University), Shinji Watanabe (Johns Hopkins University), Tomoki Toda (Nagoya University), Kazuya Takeda (Nagoya University), Yu Zhang (Google AI), Xu Tan (Microsoft Research)
2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020)
May 04, 2020
This paper introduces a new end-to-end text-to-speech (E2E-TTS) toolkit named ESPnet-TTS, which is an extension of the open-source speech processing toolkit ESPnet. The toolkit supports state-of- the-art E2E-TTS models, including Tacotron 2, Transformer TTS, and FastSpeech, and also provides recipes inspired by the Kaldi automatic speech recognition (ASR) toolkit. The recipes are based on the design unified with the ESPnet ASR recipe, providing high reproducibility. The toolkit also provides pre-trained models and samples of all of the recipes so that users can use it as a baseline. Furthermore, the unified design enables the integration of ASR functions with TTS, e.g., ASR-based objective evaluation and semi- supervised learning with both ASR and TTS models. This paper describes the design of the toolkit and experimental evaluation in comparison with other toolkits. The experimental results show that our models can achieve state-of-the-art performance comparable to the other latest toolkits, resulting in a mean opinion score (MOS) of 4.25 on the LJSpeech dataset. The toolkit is publicly available at https://github.com/espnet/espnet.
Paper :
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
(external link)