The crux of the DDPS architecture seems to be allowing a model to train the many parameters of the sound synthesizer. You would never be able to create a truly realistic piano from a software synth by merely tuning the knobs because there are too many near-imperceptible minor fluctuations in the wave form, like the way the vibrations in aggregate vibrate the wooden piano housing. Training on real life examples, in this case an impressively short clip, gives the model enough data to train these near-imperceptible variations into the synth parameters.

https://magenta.tensorflow.org/ddsp