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Previous pitch-controllable text-to-speech (TTS) models rely on directlymodeling fundamental frequency, leading to low variance in synthesized speech.To address this issue, we propose PITS, an end-to-end pitch-controllable TTSmodel that utilizes variational inference to model pitch. Based on VITS, PITSincorporates the Yingram encoder, the Yingram decoder, and adversarial trainingof pitch-shifted synthesis to achieve pitch-controllability. Experimentsdemonstrate that PITS generates high-quality speech that is indistinguishablefrom ground truth speech and has high pitch-controllability without qualitydegradation. Code, audio samples, and demo are available athttps://github.com/anonymous-pits/pits.

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