adriansroman / deepwavetorch Goto Github PK

This repository contains a PyTorch implementation of the DeepWave model originally published at NeurIPS 2019

| paper | original code |

Get started with DeepWave (PyTorch) inference

• SphericalChebConv: Spherical Chebyshev graph convolutions
• BackProjLayer: project correlation matrix into image form (intensity map form)
• ReTanh: Rectified hyperbolic tangent action function
• DeepWave: the actual model architecture

DeepWave (PyTorch) architecture:

Deepwave: input=S (visibility matrix), trainable={mu, D, tau}
    y <- BackProjLayer(S) 
conv4 <- SphericalChebConv(I_init) + y
conv4 <- ReTanh(conv4)
conv3 <- SphericalChebConv(conv4) + y
conv3 <- ReTanh(conv3)
conv2 <- SphericalChebConv(conv3) + y
conv2 <- ReTanh(conv2)
conv1 <- SphericalChebConv(conv2) + y
conv1 <- ReTanh(conv1)
conv0 <- SphericalChebConv(conv1) + y
I_out <- ReTanh(conv0)

Many of the operations used in this implementation were borrowed from the repository deepsphere-pytorch.

Create a Python virtual environment

python3 -m venv /path/to/your_new_venv

Start your_new_venv

source /path/to/<your_new_venv>/bin/activate

• Clone DeepWaveTorch (this repo!).

git clone [email protected]:adrianSRoman/DeepWaveTorch.git 

• Initialize submodules

git submodule init

• Start ImoT_tools: plotting library used to nicely visualize DeepWave's output

cd ImoT_tools
pip install -r requirements.txt
python3 setup.py develop

• Start DeepWave: original DeepWave implementation. Used for benchmarking against the PyTorch implementation. Data loaders are also re-used from the original implementation.

cd DeepWave
python3 setup.py develop

• Start DeepWaveTorch: new DeepWave PyTorch implementation.

cd DeepWaveTorch
pip install -r requirements.txt
python3 setup.py develop

• Start your_new_venv

source /path/to/<your_new_venv>/bin/activate

• Create a Jupyter Kernel to contain your required packages (first time setup only)

pip install ipykernel
ipython kernel install --user --name=<your_new_kernel_name>

• Start Jupyter

jupyter notebook

• Select a given notebook you want to work with
• Select your_new_kernel_name under: Kernel > Change kernel > your_new_kernel_name

Extracting visibility matrices S is perhaps the main aspect you will need because they are DeepWave's input! To save you some time we created a simple notebook that for a given audio track of M microphones and N samples (we use an eigenmike32), it generates a visibility matrix (MxM) over 100 msec audio frames.

Note: the data extraction for the experiments in this repo (including extracting visibility matrices) was done using the run.sh files from the original DeepWave code see example run.sh. The run.sh files perform three tasks:

(1) Extract data from a mic array of N channels to generate visibility matrices S (this is what DeepWave takes as input). Extract a ground truth intensity field I which DeepWave learns how to generate. The script will then generate a .npz file.

(2) Merge datasets in single .npz files for the 9 different extracted frequency bands across all tracks.

(3) Train the original DeepWave model. After training is done, you will find .npz files containing the trained weights of the model for each frequency band.

Overall, you can either use the colab notebook to extract visibility matrices, or execute the run.sh scripts from the original DeepWave repo (you choose).

Shield:

This work is licensed under a Creative Commons Attribution 4.0 International License.