I trained it for about 24 hours, although there didn't seem to be that much improvement after the first 12 or so. Generating a song with the trained network actually happens almost in real time. I'm tempted to try to make it continuously generate new music and stream it, but even the small cost for the instance would start to add up, so I haven't actually tried setting that up yet.
Another interesting data point: the learned set of weights ends up being about 15MB.
Nicely done but wait, 15MB?? Clearly the model isn't big enough :) Are you in under or overfitting regime?
As I was listening through the samples it seemed to me that it would start out quite energetic and then converge on repetitive, slow chords. Any ideas on why this could be happening? Or perhaps it's not true.
Also, you should label the samples with numbers so that it's possible to refer to them easily. I liked 4th from bottom quite a bit in the beginning.
Actually, I was looking for a nice sound source for a pet project, gridmix.[1] If you'd be alright with it, I could continuously stream a live feed to one of the cells.
It would help demo the point of the service, and it would be a very fitting sound generator :)
What do you think?
Astonishingly good write-up, by the way. Very impressive!
If you are ok with someone else setting up a service like this, could you please share the model data? I have some free computing power, and I wanted to play with deep learning for quite some time. (Also, If I set up a service like this, I'll link back to your blog post)
Another interesting data point: the learned set of weights ends up being about 15MB.