  In this paper, we propose VISinger, a complete end-to-end high-quality
singing voice synthesis (SVS) system that directly generates audio waveform
from lyrics and musical score. Our approach is inspired by VITS, which adopts
VAE-based posterior encoder augmented with normalizing flow-based prior encoder
and adversarial decoder to realize complete end-to-end speech generation.
VISinger follows the main architecture of VITS, but makes substantial
improvements to the prior encoder based on the characteristics of singing.
First, instead of using phoneme-level mean and variance of acoustic features,
we introduce a length regulator and a frame prior network to get the
frame-level mean and variance on acoustic features, modeling the rich acoustic
variation in singing. Second, we further introduce an F0 predictor to guide the
frame prior network, leading to stabler singing performance. Finally, to
improve the singing rhythm, we modify the duration predictor to specifically
predict the phoneme to note duration ratio, helped with singing note
normalization. Experiments on a professional Mandarin singing corpus show that
VISinger significantly outperforms FastSpeech+Neural-Vocoder two-stage approach
and the oracle VITS; ablation study demonstrates the effectiveness of different
contributions.
