{"schema":"record/v1","id":"art_01M5NCFFG198DVGFS4F5G2GQZR","type":"article","revision":1,"etag":"\"r1.s0\"","title":"For singing voice conversion, how do Seed-VC and so-vits-svc handle pitch, and what did Seed-VC measure against RVCv2?","summary":"Problem. Speech VC settings damage singing: automatic pitch prediction drifts and pitch range mismatches produce wrong notes. Conditions. Converting vocal stems (isolated, dry) to another timbre. Method. Seed-VC conditions the model on source F0 (extracted with RMVPE, quantised into 256 bins) and shifts F0 by gender or semitone setting between source and target; its README says to enable f0-condition and use 30 to 50 diffusion steps for singing. so-vits-svc warns not to use auto_predict_f0 for singing because it can cause serious pitch issues, and says silence-slicing minimum interval can be lowered to about 100 or 50 for singing. Measured result (author-reported, Seed-VC paper Table 2): on singing, Seed-VC SECS 0.7405 vs RVCv2 0.7264, both F0 correlation about 0.94, Seed-VC lower CER, RVCv2 slightly better DNSMOS. Failure modes. Wrong octave or key shift; auto pitch on; accompaniment bleed in the input (RVC README points to Ultimate Vocal Remover for stem separation; inference that...","status":"active","body":{"text":"Problem. Speech VC settings damage singing: automatic pitch prediction drifts and pitch range mismatches produce wrong notes. Conditions. Converting vocal stems (isolated, dry) to another timbre. Method. Seed-VC conditions the model on source F0 (extracted with RMVPE, quantised into 256 bins) and shifts F0 by gender or semitone setting between source and target; its README says to enable f0-condition and use 30 to 50 diffusion steps for singing. so-vits-svc warns not to use auto_predict_f0 for singing because it can cause serious pitch issues, and says silence-slicing minimum interval can be lowered to about 100 or 50 for singing. Measured result (author-reported, Seed-VC paper Table 2): on singing, Seed-VC SECS 0.7405 vs RVCv2 0.7264, both F0 correlation about 0.94, Seed-VC lower CER, RVCv2 slightly better DNSMOS. Failure modes. Wrong octave or key shift; auto pitch on; accompaniment bleed in the input (RVC README points to Ultimate Vocal Remover for stem separation; inference that separation quality bounds result quality). Limits. Small eval, per-character RVC baselines trained by the paper's author. Version/date. Seed-VC v1 paper Nov 2024; so-vits-svc 4.1-Stable.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Table 2 values are the paper author’s, with RVC models he trained; the RVC credit line is a link list not a recommendation.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/pdf/2411.09943","note":"Sec 3 / singing; Sec 4.3; Inference args; Credits"},{"url":"https://raw.githubusercontent.com/svc-develop-team/so-vits-svc/4.1-Stable/README.md","note":"Sec 3 / singing; Sec 4.3; Inference args; Credits"},{"url":"https://raw.githubusercontent.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/main/README.md","note":"Sec 3 / singing; Sec 4.3; Inference args; Credits"}]},"categories":["T13","T14"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/pdf/2411.09943","claim":"Problem. Speech VC settings damage singing: automatic pitch prediction drifts and pitch range mismatches produce wrong notes. Conditions. Converting vocal stems (isolated, dry) to another timbre. Method. Seed-VC conditions the model on source F0 (extracted with RMVPE, quantised into 256 bins) and shifts F0 by gender or semitone setting between source and target; its README says to enable f0-condition and use 30 to 50 diffusion steps for singing. so-vits-svc warns not to use auto_predict_f0 for singing because it can cause serious pitch issues, and says silence-slicing minimum interval can be lowered to about 100 or 50 for singing. Measured result (author-reported, Seed-VC paper Table 2): on singing, Seed-VC SECS 0.7405 vs RVCv2 0.7264, both F0 correlation about 0.94, Seed-VC lower CER, RVCv2 slightly better DNSMOS. Failure modes. Wrong octave or key shift; auto pitch on; accompaniment bleed in the input (RVC README points to Ultimate Vocal Remover for stem separation; inference that separation quality bounds result quality). Limits. Small eval, per-character RVC baselines trained by the paper's author. Version/date. 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Speech VC settings damage singing: automatic pitch prediction drifts and pitch range mismatches produce wrong notes. Conditions. Converting vocal stems (isolated, dry) to another timbre. Method. Seed-VC conditions the model on source F0 (extracted with RMVPE, quantised into 256 bins) and shifts F0 by gender or semitone setting between source and target; its README says to enable f0-condition and use 30 to 50 diffusion steps for singing. so-vits-svc warns not to use auto_predict_f0 for singing because it can cause serious pitch issues, and says silence-slicing minimum interval can be lowered to about 100 or 50 for singing. Measured result (author-reported, Seed-VC paper Table 2): on singing, Seed-VC SECS 0.7405 vs RVCv2 0.7264, both F0 correlation about 0.94, Seed-VC lower CER, RVCv2 slightly better DNSMOS. Failure modes. Wrong octave or key shift; auto pitch on; accompaniment bleed in the input (RVC README points to Ultimate Vocal Remover for stem separation; inference that separation quality bounds result quality). Limits. Small eval, per-character RVC baselines trained by the paper's author. Version/date. Seed-VC v1 paper Nov 2024; so-vits-svc 4.1-Stable.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Table 2 values are the paper author’s, with RVC models he trained; the RVC credit line is a link list not a recommendation.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/pdf/2411.09943","note":"Sec 3 / singing; Sec 4.3; Inference args; Credits"},{"url":"https://raw.githubusercontent.com/svc-develop-team/so-vits-svc/4.1-Stable/README.md","note":"Sec 3 / singing; Sec 4.3; Inference args; Credits"},{"url":"https://raw.githubusercontent.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/main/README.md","note":"Sec 3 / singing; Sec 4.3; Inference args; Credits"}]},"tags":[],"type":"article","links":[],"title":"For singing voice conversion, how do Seed-VC and so-vits-svc handle pitch, and what did Seed-VC measure against RVCv2?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Problem. Speech VC settings damage singing: automatic pitch prediction drifts and pitch range mismatches produce wrong notes. Conditions. Converting vocal stems (isolated, dry) to another timbre. Method. Seed-VC conditions the model on source F0 (extracted with RMVPE, quantised into 256 bins) and shifts F0 by gender or semitone setting between source and target; its README says to enable f0-condition and use 30 to 50 diffusion steps for singing. so-vits-svc warns not to use auto_predict_f0 for singing because it can cause serious pitch issues, and says silence-slicing minimum interval can be lowered to about 100 or 50 for singing. Measured result (author-reported, Seed-VC paper Table 2): on singing, Seed-VC SECS 0.7405 vs RVCv2 0.7264, both F0 correlation about 0.94, Seed-VC lower CER, RVCv2 slightly better DNSMOS. Failure modes. Wrong octave or key shift; auto pitch on; accompaniment bleed in the input (RVC README points to Ultimate Vocal Remover for stem separation; inference that...","evidence":{"claims":[{"url":"https://arxiv.org/pdf/2411.09943","claim":"Problem. Speech VC settings damage singing: automatic pitch prediction drifts and pitch range mismatches produce wrong notes. Conditions. Converting vocal stems (isolated, dry) to another timbre. Method. Seed-VC conditions the model on source F0 (extracted with RMVPE, quantised into 256 bins) and shifts F0 by gender or semitone setting between source and target; its README says to enable f0-condition and use 30 to 50 diffusion steps for singing. so-vits-svc warns not to use auto_predict_f0 for singing because it can cause serious pitch issues, and says silence-slicing minimum interval can be lowered to about 100 or 50 for singing. Measured result (author-reported, Seed-VC paper Table 2): on singing, Seed-VC SECS 0.7405 vs RVCv2 0.7264, both F0 correlation about 0.94, Seed-VC lower CER, RVCv2 slightly better DNSMOS. Failure modes. Wrong octave or key shift; auto pitch on; accompaniment bleed in the input (RVC README points to Ultimate Vocal Remover for stem separation; inference that separation quality bounds result quality). Limits. Small eval, per-character RVC baselines trained by the paper's author. Version/date. 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