[Discussion] Spleeter for real-time applications

Hi !
This issue is a follow-up of a gitter discussion.
I'm developing a C++ port of spleeter [here](https://github.com/gvne/spleeterpp) as a side project. My goal is to give people the opportunity to use the spleeter technology within plug-ins.
Lately, I realized that the architecture is built to run in batches of size ['T'](https://github.com/deezer/spleeter/wiki/3.-Models#audio-parameters) which is 512 fft frames in the pre-trained models (~12seconds). This kind of latency isn't suitable for real-time processes.
To check if the architecture is suitable for my needs, I need to evaluate the quality loss of changing that value to something lower.
My plan is to train models for 2 and 4 stems with multiple values of T and compare their quality.

I created a [repository](https://github.com/gvne/spleeter_train_parameters) to report on that, as I assume it may be of interest to others. And also to get some help in the process.

I already have a couple of questions:
_First:_
I am using MUSDB18. I noticed that the training configuration needs the description of the evaluation set. Considering that I will be using that set to evaluate the trained models, I would like to make sure that training does not use the validation set as it would mean that I am evaluating on data the network already knows. Is the `evaluation_csv` parameter used during training ?

_Second:_
I am struggling with computation power. For my first test, it took almost 4hours to run 100 000 steps on a `p2.xlarge` AWS instance (GPU Tesla k80). Is this expected ? Do you think that, considering my problem I could lower the `train_max_steps` ?

_Third:_
This one is more of a note than a discussion but I ran the evaluation using `python -m spleeter evaluate -p spleeter:2stems --mus_dir [exported db dir] -o [local path]` and got NaNs. Is the evaluation system broken atm ? I changed it in a fork [here](https://github.com/gvne/spleeter/tree/evaluation_2stems) to fix it for my case. Should I do a pull request for that ? 

Thank you for your help and for publishing your work !