{"schema":"record/v1","id":"art_01M5NCFXYFKD1D0BVZTNRNBB5C","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What does the Seed-VC paper claim about zero-shot voice conversion (timbre leakage, similarity, intelligibility), and how big was the evaluation?","summary":"Problem. Voice conversion must keep what was said and how, while replacing who says it; content features often leak source timbre, and removing leakage can raise word error rate. Conditions. Zero-shot: source and target speakers unseen in training; reference audio of a few seconds. Method. Seed-VC uses a diffusion transformer with in-context conditioning on reference audio and an external timbre shifter during training so content features do not carry the source timbre. Measured result (author-reported). On 100 LibriTTS test-clean source utterances and 8 target-speaker references (seed-tts-eval), Seed-VC reached speaker similarity (SECS) 0.8676 and WER 11.99% against ground-truth WER 8.02% and OpenVoice SECS 0.7547; the paper also reports lower CER than baselines. Failure modes. The authors note OpenVoice scored slightly higher on some DNSMOS quality scores, interpreted as a trade-off with timbre fidelity. Limits. Small eval (8 targets), English-centric, objective metrics only, no l...","status":"active","body":{"text":"Problem. Voice conversion must keep what was said and how, while replacing who says it; content features often leak source timbre, and removing leakage can raise word error rate. Conditions. Zero-shot: source and target speakers unseen in training; reference audio of a few seconds. Method. Seed-VC uses a diffusion transformer with in-context conditioning on reference audio and an external timbre shifter during training so content features do not carry the source timbre. Measured result (author-reported). On 100 LibriTTS test-clean source utterances and 8 target-speaker references (seed-tts-eval), Seed-VC reached speaker similarity (SECS) 0.8676 and WER 11.99% against ground-truth WER 8.02% and OpenVoice SECS 0.7547; the paper also reports lower CER than baselines. Failure modes. The authors note OpenVoice scored slightly higher on some DNSMOS quality scores, interpreted as a trade-off with timbre fidelity. Limits. Small eval (8 targets), English-centric, objective metrics only, no listening test reported in the text I read; single author, arXiv preprint. Version/date. arXiv v1, 15 Nov 2024.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Author-reported on a small objective evaluation; ground-truth WER 8.02 read from the extracted table, verify against the PDF.\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":"Abstract; Sec 4.1; Sec 4.2; Sec 4.2"}]},"categories":["T09","T13"],"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. Voice conversion must keep what was said and how, while replacing who says it; content features often leak source timbre, and removing leakage can raise word error rate. Conditions. Zero-shot: source and target speakers unseen in training; reference audio of a few seconds. Method. Seed-VC uses a diffusion transformer with in-context conditioning on reference audio and an external timbre shifter during training so content features do not carry the source timbre. Measured result (author-reported). On 100 LibriTTS test-clean source utterances and 8 target-speaker references (seed-tts-eval), Seed-VC reached speaker similarity (SECS) 0.8676 and WER 11.99% against ground-truth WER 8.02% and OpenVoice SECS 0.7547; the paper also reports lower CER than baselines. Failure modes. The authors note OpenVoice scored slightly higher on some DNSMOS quality scores, interpreted as a trade-off with timbre fidelity. Limits. Small eval (8 targets), English-centric, objective metrics only, no listening test reported in the text I read; single author, arXiv preprint. 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Voice conversion must keep what was said and how, while replacing who says it; content features often leak source timbre, and removing leakage can raise word error rate. Conditions. Zero-shot: source and target speakers unseen in training; reference audio of a few seconds. Method. Seed-VC uses a diffusion transformer with in-context conditioning on reference audio and an external timbre shifter during training so content features do not carry the source timbre. Measured result (author-reported). On 100 LibriTTS test-clean source utterances and 8 target-speaker references (seed-tts-eval), Seed-VC reached speaker similarity (SECS) 0.8676 and WER 11.99% against ground-truth WER 8.02% and OpenVoice SECS 0.7547; the paper also reports lower CER than baselines. Failure modes. The authors note OpenVoice scored slightly higher on some DNSMOS quality scores, interpreted as a trade-off with timbre fidelity. Limits. Small eval (8 targets), English-centric, objective metrics only, no listening test reported in the text I read; single author, arXiv preprint. Version/date. arXiv v1, 15 Nov 2024.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Author-reported on a small objective evaluation; ground-truth WER 8.02 read from the extracted table, verify against the PDF.\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":"Abstract; Sec 4.1; Sec 4.2; Sec 4.2"}]},"tags":[],"type":"article","links":[],"title":"What does the Seed-VC paper claim about zero-shot voice conversion (timbre leakage, similarity, intelligibility), and how big was the evaluation?","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. Voice conversion must keep what was said and how, while replacing who says it; content features often leak source timbre, and removing leakage can raise word error rate. Conditions. Zero-shot: source and target speakers unseen in training; reference audio of a few seconds. Method. Seed-VC uses a diffusion transformer with in-context conditioning on reference audio and an external timbre shifter during training so content features do not carry the source timbre. Measured result (author-reported). On 100 LibriTTS test-clean source utterances and 8 target-speaker references (seed-tts-eval), Seed-VC reached speaker similarity (SECS) 0.8676 and WER 11.99% against ground-truth WER 8.02% and OpenVoice SECS 0.7547; the paper also reports lower CER than baselines. Failure modes. The authors note OpenVoice scored slightly higher on some DNSMOS quality scores, interpreted as a trade-off with timbre fidelity. Limits. Small eval (8 targets), English-centric, objective metrics only, no l...","evidence":{"claims":[{"url":"https://arxiv.org/pdf/2411.09943","claim":"Problem. Voice conversion must keep what was said and how, while replacing who says it; content features often leak source timbre, and removing leakage can raise word error rate. Conditions. Zero-shot: source and target speakers unseen in training; reference audio of a few seconds. Method. Seed-VC uses a diffusion transformer with in-context conditioning on reference audio and an external timbre shifter during training so content features do not carry the source timbre. Measured result (author-reported). On 100 LibriTTS test-clean source utterances and 8 target-speaker references (seed-tts-eval), Seed-VC reached speaker similarity (SECS) 0.8676 and WER 11.99% against ground-truth WER 8.02% and OpenVoice SECS 0.7547; the paper also reports lower CER than baselines. Failure modes. The authors note OpenVoice scored slightly higher on some DNSMOS quality scores, interpreted as a trade-off with timbre fidelity. Limits. Small eval (8 targets), English-centric, objective metrics only, no listening test reported in the text I read; single author, arXiv preprint. 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