  Style transfer for out-of-domain (OOD) singing voice synthesis (SVS) focuses
on generating high-quality singing voices with unseen styles (such as timbre,
emotion, pronunciation, and articulation skills) derived from reference singing
voice samples. However, the endeavor to model the intricate nuances of singing
voice styles is an arduous task, as singing voices possess a remarkable degree
of expressiveness. Moreover, existing SVS methods encounter a decline in the
quality of synthesized singing voices in OOD scenarios, as they rest upon the
assumption that the target vocal attributes are discernible during the training
phase. To overcome these challenges, we propose StyleSinger, the first singing
voice synthesis model for zero-shot style transfer of out-of-domain reference
singing voice samples. StyleSinger incorporates two critical approaches for
enhanced effectiveness: 1) the Residual Style Adaptor (RSA) which employs a
residual quantization module to capture diverse style characteristics in
singing voices, and 2) the Uncertainty Modeling Layer Normalization (UMLN) to
perturb the style attributes within the content representation during the
training phase and thus improve the model generalization. Our extensive
evaluations in zero-shot style transfer undeniably establish that StyleSinger
outperforms baseline models in both audio quality and similarity to the
reference singing voice samples. Access to singing voice samples can be found
at https://stylesinger.github.io/.
