  Singing voice synthesis (SVS) systems are built to synthesize high-quality
and expressive singing voice, in which the acoustic model generates the
acoustic features (e.g., mel-spectrogram) given a music score. Previous singing
acoustic models adopt a simple loss (e.g., L1 and L2) or generative adversarial
network (GAN) to reconstruct the acoustic features, while they suffer from
over-smoothing and unstable training issues respectively, which hinder the
naturalness of synthesized singing. In this work, we propose DiffSinger, an
acoustic model for SVS based on the diffusion probabilistic model. DiffSinger
is a parameterized Markov chain that iteratively converts the noise into
mel-spectrogram conditioned on the music score. By implicitly optimizing
variational bound, DiffSinger can be stably trained and generate realistic
outputs. To further improve the voice quality and speed up inference, we
introduce a shallow diffusion mechanism to make better use of the prior
knowledge learned by the simple loss. Specifically, DiffSinger starts
generation at a shallow step smaller than the total number of diffusion steps,
according to the intersection of the diffusion trajectories of the ground-truth
mel-spectrogram and the one predicted by a simple mel-spectrogram decoder.
Besides, we propose boundary prediction methods to locate the intersection and
determine the shallow step adaptively. The evaluations conducted on a Chinese
singing dataset demonstrate that DiffSinger outperforms state-of-the-art SVS
work. Extensional experiments also prove the generalization of our methods on
text-to-speech task (DiffSpeech). Audio samples: https://diffsinger.github.io.
Codes: https://github.com/MoonInTheRiver/DiffSinger. The old title of this
work: "Diffsinger: Diffusion acoustic model for singing voice synthesis".
