CleanStream OBS Plugin is a powerful tool that helps clean live audio streams from unwanted words, filler words, and profanities. Created in C++, this plugin can improve the quality of live streams while saving time and effort in post-processing. In this blog post, we will take a detailed walk-through of the code for my CleanStream OBS plugin, explaining how it is built and its core functionalities.
In this blog post, we will take a closer look at the development of the OBS Background Removal Plugin, discussing its key components, functionalities, and the process behind building it. The plugin was created to address the need for virtual green screen and background removal capabilities in OBS (Open Broadcaster Software), a popular live streaming and recording software. With over 500,000 downloads and ongoing contributions from various developers, the OBS Background Removal Plugin has gained significant traction in the streaming community. Whether you’re interested in understanding how this plugin works or considering building a similar plugin yourself, this walkthrough will provide valuable insights.
Reporting on a project I worked on for the last few weeks – porting the excellent Gesture Recognition Toolkit (GRT) to Python.
Right now it’s still a pull request: https://github.com/nickgillian/grt/pull/151.
Not exactly porting, rather I’ve simply added Python bindings to GRT that allow you to access the GRT C++ APIs from Python.
Did it using the wonderful SWIG project. Such a wondrous tool, SWIG is. Magical.
Here are the deets
This is my first trial at using Jupyter notebook to write a post, hope it makes sense.
I’ve recently taught a class on generative models: http://hi.cs.stonybrook.edu/teaching/cdt450
In class we’ve manipulated face images with neural networks.
One important thing I found that helped is to align the images so the facial features overlap.
It helps the nets learn the variance in faces better, rather than waste their “representation power” on the shift between faces.
The following is some code to align face images using the excellent Dlib (python bindings) http://dlib.net. First I’m just using a standard face detector, and then using the facial fatures extractor I’m using that information for a complete alignment of the face.
After the alignment – I’m just having fun with the aligned dataset 🙂
I’ve recently made a tutorial on using Docker for machine learning purposes, and I thought also to publish it in here: http://hi.cs.stonybrook.edu/teaching/docker4ml
It includes videos, slides and code, with hands-on demonstrations in class.
A GitHub repo holds the code: https://github.com/royshil/Docker4MLTutorial
I made several scripts to make it easy to upload python code that performs an ML inference (“prediction”) operation on AWS Lambda.
Enjoy!
Roy.
Last time I’ve posted about cross compiling TF for the TK1. That however was a canned sample example from TF, based on the bazel build system.
Let’s say we want to make our own TF C++ app and just link vs. TF for inference on the TK1.
Now that’s a huge mess.
First we need to cross-compile TF with everything built in.
Then we need protobuf cross-compiled for the TK1.
Bundle everything together, cross(-compile) our fingers and pray.
The prayer doesn’t help. But let’s see what does…
Been looking around for a solid resource on how to get Tensorflow to run on the Jetson TK1. Most what I found was how to get TF 0.8 to run, which was the last TF version to allow usage of cuDNN 6 that is the latest version available for the TK1.
The TK1 is an aging platform with halted support, but it is still a cheap option for high-powered embedded compute. Unfortunately, being so outdated it’s impossible to get the latest and greatest of DNN to work on the CUDA GPU on the TK1, but we can certainly use the CPU!
So a word of disclaimer – this compiled TF version will not use the GPU, just the CPU. However, it will let you run the most recent NN architectures with the latest layer implementations.
Cross compilation for the TK1 solves the acute problem of space on the device itself, as well as speed of compilation. On the other hand it required bringing up a compilation toolchain, which took a while to find.
I am going to be assuming a Ubuntu 16.04 x86_64 machine, which is what I have, and really you can do this in a VM or a Docker container just as well on Windows.
For a class I’m teaching (on deep learning and art) I had to create a machine that auto starts a jupyter notebook with tensorflow and GPU support. Just create an instance and presto – Jupyter notebook with TF and GPU!
How awesome is that?
Well… building it wasn’t that simple.
So for your enjoyment – here’s my recipe: