adobe / deep-audio-prior Goto Github PK

a = torch.mean(x, (1,2,3))
TypeError: mean() received an invalid combination of arguments - got (Tensor, tuple), but expected one of:

Hello, I am just wondering if this code supports separation of more than 2 sources. Thanks!

Hello,

Is there a requirements.txt somewhere that I can use for setup? I'm trying to understand the various requirements for the toolbox

Hi,

The current version of numba (>.0.48) has deprecated the numba.decorators function causing dap_sep.py to fail. This can be easily resolved by pinning numba==0.48 in the requirements.txt

I'm trying to run blind source separation on the example violin_basketball.wav as described in the readme.
I've created a conda environment environment with python 3.7 and 3.8 and installed the dependencies as described for each version.

However, upon running
$ cd ~/code/ $ python dap_sep.py --input_mix data/sep/violin_basketball.wav --output output/sep

I encounter the error below and it seems like there's some incompatibility between the package versions? Alternatively, is there something I'm missing? Thank you

Traceback (most recent call last): File "dap_sep.py", line 15, in <module> from net import skip, skip_mask_vec File "/home/cheng/tracking_engagement/audio_processing/Deep-Audio-Prior/code/net/__init__.py", line 13, in <module> from .skip_model import skip, skip_mask, skip_mask_vec,unet, sound_rec File "/home/cheng/tracking_engagement/audio_processing/Deep-Audio-Prior/code/net/skip_model.py", line 15, in <module> from .layers import * File "/home/cheng/tracking_engagement/audio_processing/Deep-Audio-Prior/code/net/layers.py", line 17, in <module> from .downsampler import Downsampler File "/home/cheng/tracking_engagement/audio_processing/Deep-Audio-Prior/code/net/downsampler.py", line 16, in <module> from utils.image_io import * File "/home/cheng/tracking_engagement/audio_processing/Deep-Audio-Prior/code/utils/image_io.py", line 16, in <module> import torchvision File "/home/cheng/anaconda3/envs/source_separation/lib/python3.7/site-packages/torchvision/__init__.py", line 2, in <module> from torchvision import datasets File "/home/cheng/anaconda3/envs/source_separation/lib/python3.7/site-packages/torchvision/datasets/__init__.py", line 9, in <module> from .fakedata import FakeData File "/home/cheng/anaconda3/envs/source_separation/lib/python3.7/site-packages/torchvision/datasets/fakedata.py", line 3, in <module> from .. import transforms File "/home/cheng/anaconda3/envs/source_separation/lib/python3.7/site-packages/torchvision/transforms/__init__.py", line 1, in <module> from .transforms import * File "/home/cheng/anaconda3/envs/source_separation/lib/python3.7/site-packages/torchvision/transforms/transforms.py", line 17, in <module> from . import functional as F File "/home/cheng/anaconda3/envs/source_separation/lib/python3.7/site-packages/torchvision/transforms/functional.py", line 5, in <module> from PIL import Image, ImageOps, ImageEnhance, PILLOW_VERSION ImportError: cannot import name 'PILLOW_VERSION' from 'PIL' (/home/cheng/anaconda3/envs/source_separation/lib/python3.7/site-packages/PIL/__init__.py)

Hello, this looks like a great project, but my system is having issues running it. More specifically, the Blind source separation part.

I'm not currently sure if this is due to my CUDA configuration/version or maybe some conflict with the PyTorch version and CUDA since I get an error upon hitting "Flag = s.optimize()".

What type of hardware did you run this on and what CUDA version were you running? Thanks!

Hi,

Thanks for the great project. I was hoping you could give a little more detail about creating the binary mask npy file and the selecting the proper source ID. I figured running dap_mask_1st.py would create this file but it did not.

Thank you!