version 0.3 alpha

[NickleDave]

This pull request consists of a giant "feature branch: the rough draft for version 0.3.

Since @yardencsGitHub and I are the only ones really interacting with the code base right now, I am merging this 200-something commit "feature" even though that is not really best practice for OS development. I think the structure of the codebase is actually now clean enough that future feature branches can be more targeted.

This way we can get to an alpha version that we and others can actually use and test, and move forward with other things that depend on that.

Major changes:

• switch to PyTorch
  • because Tensorflow 1.0 is deprecated
  • and because after experience with both frameworks, I think PyTorch is more suited to our needs
    • main plus is Dataset + Dataloader abstractions that seem like a better fit for
      "doing things with spectrograms / audio that don't fit neatly into what people do
      for computer vision tasks"
    • plus Python-first, 'mid-level' abstraction of framework lets code be readable but flexible
    • those are the pros, con is that not all the batteries are included, e.g. metrics computed by keras
• remove Dataset class

• choose to not maintain a giant class with many methods including those specialized
  for saving and loading
• better to have lightweight system that leverages other libraries wherever possible
• i.e. we just create a simple .csv that represents the dataset, pointing to data files in the rows
• and then use Pandas to work with this .csv "dataset abstraction"
• no need for saving, loading, etc. -- giant .json file including arrays and things that take up
  disk space with things that are already in other files
• downside is metadata that user needs is not all encapsulated in one file, things like mapping
  from integer outputs of class predictions from network to the labels that the user assigns those
  classes in the annotation
  • but it is "encapsulated" by being saved all together in the results directory
  • could take the Audacity approach and append a "file extension" to this directory, .vak and
    treat it like a "project"