Re-implementation will be hard, several people (including me) have been working on related architectures, but they have a few extra tricks in WaveNet that seem

Re-implementation will be hard, several people (including me) have been working on related architectures, but they have a few extra tricks in WaveNet that seem to make all the difference, on top of what I assume is "monster scale training, tons of data". The core ideas from this can be seen in PixelRNN and PixelCNN, and there are discussions and implementations for the basic concepts of those out there [0][1]. Not to mention the fact that conditioning is very interesting / tricky in this model, at least as I read it. I am sure there are many ways to do it wrong, and getting it right is crucial to having high quality results in conditional synthesis. [0] https://github.com/tensorflow/magenta/blob/master/magenta/reviews/pixelrnn.md https://github.com/tensorflow/magenta/blob/master/magenta/re... [1] https://github.com/igul222/pixel_rnn/blob/master/pixel_rnn.py https://github.com/igul222/pixel_rnn/blob/master/pixel_rnn.p...