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This paper introduces a new end-to-end text-to-speech (E2E-TTS) toolkit namedESPnet-TTS, which is an extension of the open-source speech processing toolkitESPnet. The toolkit supports state-of-the-art E2E-TTS models, includingTacotron~2, Transformer TTS, and FastSpeech, and also provides recipes inspiredby the Kaldi automatic speech recognition (ASR) toolkit. The recipes are basedon the design unified with the ESPnet ASR recipe, providing highreproducibility. The toolkit also provides pre-trained models and samples ofall of the recipes so that users can use it as a baseline. Furthermore, theunified design enables the integration of ASR functions with TTS, e.g.,ASR-based objective evaluation and semi-supervised learning with both ASR andTTS models. This paper describes the design of the toolkit and experimentalevaluation in comparison with other toolkits. The experimental results showthat our models can achieve state-of-the-art performance comparable to theother latest toolkits, resulting in a mean opinion score (MOS) of 4.25 on theLJSpeech dataset. The toolkit is publicly available athttps://github.com/espnet/espnet.

ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit