  Recent advancements in speech synthesis witness significant benefits by
leveraging discrete tokens extracted from self-supervised learning (SSL)
models. Discrete tokens offer higher storage efficiency and greater operability
in intermediate representations compared to traditional continuous Mel
spectrograms. However, when it comes to singing voice synthesis(SVS), achieving
higher levels of melody expression poses a great challenge for utilizing
discrete tokens. In this paper, we introduce TokSing, a discrete-based SVS
system equipped with a token formulator that offers flexible token blendings.
We observe a melody degradation during discretization, prompting us to
integrate a melody signal with the discrete token and incorporate a
specially-designed melody enhancement strategy in the musical encoder.
Extensive experiments demonstrate that our TokSing achieves better performance
against the Mel spectrogram baselines while offering advantages in intermediate
representation space cost and convergence speed.
