pyvirtualcam VS Pytorch

There is this project. You could probably also use openCV.

The parent it seems you’re missing right now is the virtual cam. This is the repo that the OP linked. There’s another package too, pyvirtualcam. Both require you to install the actual virtual camera binary to your computer where the package simply routes your edited frames to the camera.

Just like we have pyfakecam do we have a similar library for real-time audio? Actually, I need to manipulate the audio input from the microphone and send it to the webrtc in form of microphone input. pyfakecam works seamlessly with opencv and I can easily manipulate live camera input and create a virtual camera for it. But same is not possible for audio input.

PyTorch (https://pytorch.org/)

torch is a package to manage tensors and dynamic neural networks in python (GitHub)

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():