What is the current "state of the art" in future audio sample prediction, given recent developments in "image inpainting" using deep learning? In the last few years, there have been many breakthroughs in the image processing world regarding repairing &quot;damaged&quot; images, images with corrupt pixels, or even reversing artefacts from formats such as JPEG, which have received media attention. I am trying to determine if these methodologies can similarly be used to predict future samples of audio, rather than pixels of an image.
Some examples:

https://www.engadget.com/2018-04-24-nvidia-ai-fixes-photos.html
https://www.dpreview.com/news/3422748291/this-neural-network-can-fix-damaged-photos-and-enhance-low-res-shots
https://heartbeat.fritz.ai/guide-to-image-inpainting-using-machine-learning-to-edit-and-correct-defects-in-photos-3c1b0e13bbd0

much of which derives from an NVIDIA thesis published in EECV 2018: Image Inpainting for Irregular Holes Using
Partial Convolutions, EECV 2018.
In theory, this basic method should have a direct analog to audio: where, rather than predicting missing pixels, we're trying to predict the next ms or so of future audio from the last second. So, it can really be thought of as a type of nonlinear prediction, which clearly performs very well.
QUESTION: What is the state of the art in using this method for audio processing rather than image processing? Is there any published literature on this? Does anyone have any references?
Below are some examples of the technique that I am talking about in the image processing realm. In all these situations, the missing pixel data is reconstructed from the remaining pixels, which is basically 2D sample prediction; I am hoping to get some insight into the 1D analog of this.
These Images are FAKE !!!
There is absolutely no way, no algorithm, no nothing that exist that can tell you whether the eyes of a girl are blue or black by just looking at her lips !!!
You can guess, something, good or bad, based on your prior training and the probabilities based on that, but that's just a guess.
The fact that the synthesized images look so natural is the key to deceive people into thinking that the algorithm correctly predicts the impossible to fix missing information.
No algorithms can tell whether she had a tattoo in her screwed part of the image !!!
What the algorithm is doing is that you feed it with correct information in many many other training images, from different angles, different illuminations etc. Then the algorithm will try at best (and may succeed just as painting artist would do) to replace the missing pixels from those of the available set... And if she had a tattoo in the mean time; forget about it !
Same is for audio...