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This paper introduces S3PRL-VC, an open-source voice conversion (VC)framework based on the S3PRL toolkit. In the context of recognition-synthesisVC, self-supervised speech representation (S3R) is valuable in its potential toreplace the expensive supervised representation adopted by state-of-the-art VCsystems. Moreover, we claim that VC is a good probing task for S3R analysis. Inthis work, we provide a series of in-depth analyses by benchmarking on the twotasks in VCC2020, namely intra-/cross-lingual any-to-one (A2O) VC, as well asan any-to-any (A2A) setting. We also provide comparisons between not onlydifferent S3Rs but also top systems in VCC2020 with supervised representations.Systematic objective and subjective evaluation were conducted, and we show thatS3R is comparable with VCC2020 top systems in the A2O setting in terms ofsimilarity, and achieves state-of-the-art in S3R-based A2A VC. We believe theextensive analysis, as well as the toolkit itself, contribute to not only theS3R community but also the VC community. The codebase is now open-sourced.

S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations