Many-to-Many Style Transfer

Made code compatible with 2+ speakers. Tested for 4 speakers.

Rearranged preprocessing to not be A and B, but be wrt ids of speakers in dict (ie 0 and 1 for two initial speakers)
Modified training to work wrt cyclic combinations of each speaker
In train.py included a main method for calling train loop - otherwise, it crashes on Windows due to num_workers>0 in data_loader
Inference has source included in saved directory name when specified (keeps track of source for many-to-many).

ebadawy / voice_conversion