The Notes section neatly showcases the characteristics of most of the publish-fast-without-reproducibility "research" hitting arxiv starting mid-'17: The paper

The Notes section neatly showcases the characteristics of most of the publish-fast-without-reproducibility "research" hitting arxiv starting mid-'17: The paper didn't mention normalization, but without normalization I couldn't get it to work. So I added layer normalization. The paper fixed the learning rate to 0.001, but it didn't work for me. So I decayed it. I tried to train Text2Mel and SSRN simultaneously, but it didn't work. I guess separating those two networks mitigates the burden of training. The authors claimed that the model can be trained within a day, but unfortunately the luck was not mine. However obviously this is much faster than Tacotron as it uses only convolution layers. The paper didn't mention dropouts. I applied them as I believe it helps for regularization.