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

TLDR

This paper introduces ESPnet‑TTS, a unified end‑to‑end text‑to‑speech toolkit that extends ESPnet and details its design and experimental evaluation. ESPnet‑TTS implements state‑of‑the‑art E2E‑TTS models (Tacotron 2, Transformer TTS, FastSpeech) with Kaldi‑style recipes, unified with ESPnet‑ASR for reproducibility, pre‑trained baselines, and integrated ASR‑based evaluation and semi‑supervised learning. Experiments show ESPnet‑TTS achieves state‑of‑the‑art performance, attaining a MOS of 4.25 on LJSpeech, and the toolkit is publicly available on GitHub.

Abstract

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.