  We introduce DISSC, a novel, lightweight method that converts the rhythm,
pitch contour and timbre of a recording to a target speaker in a textless
manner. Unlike DISSC, most voice conversion (VC) methods focus primarily on
timbre, and ignore people's unique speaking style (prosody). The proposed
approach uses a pretrained, self-supervised model for encoding speech to
discrete units, which makes it simple, effective, and fast to train. All
conversion modules are only trained on reconstruction like tasks, thus suitable
for any-to-many VC with no paired data. We introduce a suite of quantitative
and qualitative evaluation metrics for this setup, and empirically demonstrate
that DISSC significantly outperforms the evaluated baselines. Code and samples
are available at https://pages.cs.huji.ac.il/adiyoss-lab/dissc/.
