Hardware requirements for test

🐛 Bug

It seems to be relatively easy to get out of memory for the first example provided in the README on the GPU. Maybe it would be nice to add some hardware requirements or estimation how much memory you need per second of input signal.

To Reproduce

Steps to reproduce the behavior:

>>> python test.py ~/data/musdb18-wav/test/Al\ James\ -\ Schoolboy\ Facination/mixture.wav --model umxhq
Traceback (most recent call last):
  File "test.py", line 301, in <module>
    device=device
  File "test.py", line 166, in separate
    use_softmask=softmask)
  File "/home/audeering.local/hwierstorf/.anaconda3/envs/open-unmix-pytorch-gpu/lib/python3.7/site-packages/norbert/__init__.py", line 260, in wiener
    y = expectation_maximization(y/max_abs, x_scaled, iterations, eps=eps)[0]
  File "/home/audeering.local/hwierstorf/.anaconda3/envs/open-unmix-pytorch-gpu/lib/python3.7/site-packages/norbert/__init__.py", line 141, in expectation_maximization
    eps)
  File "/home/audeering.local/hwierstorf/.anaconda3/envs/open-unmix-pytorch-gpu/lib/python3.7/site-packages/norbert/__init__.py", line 511, in get_local_gaussian_model
    C_j = _covariance(y_j)
  File "/home/audeering.local/hwierstorf/.anaconda3/envs/open-unmix-pytorch-gpu/lib/python3.7/site-packages/norbert/__init__.py", line 468, in _covariance
    y_j.dtype)
MemoryError

Environment

Please add some information about your environment

• Any other relevant information: NVIDIA GP107M [GeForce GTX 1050 Mobile]

If unsure you can paste the output from the pytorch environment collection script (or fill out the checklist below manually).

You can get that script and run it with:

wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py

PyTorch version: 1.2.0
Is debug build: No
CUDA used to build PyTorch: 10.0.130

OS: Ubuntu 18.04.3 LTS
GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
CMake version: version 3.10.2

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: GeForce GTX 1050
Nvidia driver version: 430.40
cuDNN version: Could not collect

Versions of relevant libraries:
[pip3] numpy==1.13.3
[conda] mkl                       2019.4                      243  
[conda] pytorch                   1.2.0           py3.7_cuda10.0.130_cudnn7.6.2_0    pytorch

[aadibajpai]

I modified the norbert package through this pull request

this should make the memory usage smaller @hagenw @aadibajpai could you try it again with the new norbert, through:

pip install norbert==0.2.1

worked!

[faroit]

@hagenw feel free to reopen if you have further comments