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arXiv · 2409.08711

Text-To-Speech Synthesis In The Wild

Jee-weon Jung·Wangyou Zhang·Soumi Maiti·Yihan Wu·Xin Wang·Ji-Hoon Kim·Yuta Matsunaga·Seyun Um·Jinchuan Tian·Hye-jin Shim·Nicholas Evans·Joon Son Chung·Shinnosuke Takamichi·Shinji Watanabe
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Abstract

Traditional Text-to-Speech (TTS) systems rely on studio-quality speech recorded in controlled settings.a Recently, an effort known as noisy-TTS training has emerged, aiming to utilize in-the-wild data. However, the lack of dedicated datasets has been a significant limitation. We introduce the TTS In the Wild (TITW) dataset, which is publicly available, created through a fully automated pipeline applied to the VoxCeleb1 dataset. It comprises two training sets: TITW-Hard, derived from the transcription, segmentation, and selection of raw VoxCeleb1 data, and TITW-Easy, which incorporates additional enhancement and data selection based on DNSMOS. State-of-the-art TTS models achieve over 3.0 UTMOS score with TITW-Easy, while TITW-Hard remains difficult showing UTMOS below 2.8.

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Jee-weon Jung, Wangyou Zhang, Soumi Maiti, Yihan Wu, Xin Wang, Ji-Hoon Kim, Yuta Matsunaga, Seyun Um, Jinchuan Tian, Hye-jin Shim, Nicholas Evans, Joon Son Chung, Shinnosuke Takamichi, Shinji Watanabe. 2024-09-13. Text-To-Speech Synthesis In The Wild. https://arxiv.org/abs/2409.08711

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