PITS: Variational Pitch Inference without Fundamental Frequency for End-to-End Pitch-controllable TTS

Junhyeok Lee; Wonbin Jung; Hyunjae Cho; Jaeyeon Kim; Jaehwan Kim

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arXiv · 2302.12391

PITS: Variational Pitch Inference without Fundamental Frequency for End-to-End Pitch-controllable TTS

Junhyeok Lee·Wonbin Jung·Hyunjae Cho·Jaeyeon Kim·Jaehwan Kim

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Abstract

Previous pitch-controllable text-to-speech (TTS) models rely on directly modeling fundamental frequency, leading to low variance in synthesized speech. To address this issue, we propose PITS, an end-to-end pitch-controllable TTS model that utilizes variational inference to model pitch. Based on VITS, PITS incorporates the Yingram encoder, the Yingram decoder, and adversarial training of pitch-shifted synthesis to achieve pitch-controllability. Experiments demonstrate that PITS generates high-quality speech that is indistinguishable from ground truth speech and has high pitch-controllability without quality degradation. Code, audio samples, and demo are available at https://github.com/anonymous-pits/pits.

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BibTeX RIS

Junhyeok Lee, Wonbin Jung, Hyunjae Cho, Jaeyeon Kim, Jaehwan Kim. 2023-02-24. PITS: Variational Pitch Inference without Fundamental Frequency for End-to-End Pitch-controllable TTS. https://arxiv.org/abs/2302.12391

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