  Singing voice conversion is to convert the source sing voice into the target
sing voice except for the content. Currently, flow-based models can complete
the task of voice conversion, but they struggle to effectively extract latent
variables in the more rhythmically rich and emotionally expressive task of
singing voice conversion, while also facing issues with low efficiency in
speech processing. In this paper, we propose a high-fidelity flow-based model
based on multi-decoupling feature constraints, which enhances the capture of
vocal details by integrating multiple encoders. We also use iSTFT to enhance
the speed of speech processing by replacing some layers of the Vocoder. We
compare the synthesized singing voice with other models from multiple
dimensions, and our proposed model is highly consistent with the current
state-of-the-art, with the demo which is available at
\url{https://lazycat1119.github.io/RASVC-demo/}
