CoMoSVC: Consistency Model-based Singing Voice Conversion
The diffusion-based Singing Voice Conversion (SVC) methods have achieved
remarkable performances, producing natural audios with high similarity to the
target timbre. However, the iterative sampling process results in slow
inference speed, and acceleration thus becomes crucial. In this paper, we
propose CoMoSVC, a consistency model-based SVC method, which aims to achieve
both high-quality generation and high-speed sampling. A diffusion-based teacher
model is first specially designed for SVC, and a student model is further
distilled under self-consistency properties to achieve one-step sampling.
Experiments on a single NVIDIA GTX4090 GPU reveal that although CoMoSVC has a
significantly faster inference speed than the state-of-the-art (SOTA)
diffusion-based SVC system, it still achieves comparable or superior conversion
performance based on both subjective and objective metrics. Audio samples and
codes are available at https://comosvc.github.io/.
65962bd20dd33f8305ea316f
Yiwen Lu
65962bd20dd33f8305ea3170
Zhen Ye
claimed_verified
2024-12-06T15:59:04.941Z
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ZhenYE
user
ZhenYe234
65962bd20dd33f8305ea3171
Wei Xue
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2024-04-24T11:13:11.753Z
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HKUST Audio
user
HKUST-Audio
65962bd20dd33f8305ea3172
Xu Tan
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2024-04-05T10:02:10.894Z
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/avatars/a2f28940236ae625ed3810ad62e343ff.svg
Xu Tan
user
xutan
65962bd20dd33f8305ea3173
Qifeng Liu
65962bd20dd33f8305ea3174
Yike Guo
CoMoSVC: Consistency Model-based Singing Voice Conversion
The diffusion-based Singing Voice Conversion (SVC) methods have achieved
remarkable performances, producing natural audios with high similarity to the
target timbre. However, the iterative sampling process results in slow
inference speed, and acceleration thus becomes crucial. In this paper, we
propose CoMoSVC, a consistency model-based SVC method, which aims to achieve
both high-quality generation and high-speed sampling. A diffusion-based teacher
model is first specially designed for SVC, and a student model is further
distilled under self-consistency properties to achieve one-step sampling.
Experiments on a single NVIDIA GTX4090 GPU reveal that although CoMoSVC has a
significantly faster inference speed than the state-of-the-art (SOTA)
diffusion-based SVC system, it still achieves comparable or superior conversion
performance based on both subjective and objective metrics. Audio samples and
codes are available at https://comosvc.github.io/.
CoMoSVC is a consistency model-based singing voice conversion method that achieves fast inference speed and high-quality audio generation.
diffusion-based SVC consistency model diffusion-based teacher model student model self-consistency properties one-step sampling