Implementation of a U-net complete with efficient attention as well as the latest research findings

A Blender 2.80 add-on that helps you generate materials

Avalanche: an End-to-End Library for Continual Learning based on PyTorch.

Code examples in pyTorch and Tensorflow for CS230

Implementation of Siamese Networks for image one-shot learning by PyTorch, train and test model on dataset Omniglot

Simple Python version management

pyenv for Windows. pyenv is a simple python version management tool. It lets you easily switch between multiple versions of Python. It's simple, unobtrusive, and follows the UNIX tradition of singl…

Mediterranean Machine Learning school tutorials

NeuralProphet: A simple forecasting package

The hackable text editor

NVIDIA's Deep Imagination Team's PyTorch Library

A library for speech data augmentation in time-domain

PyTorch implementation of Image-to-Image Translation with Conditional Adversarial Nets (pix2pix)

Python implementation of performance metrics in Loizou's Speech Enhancement book

PESQ (Perceptual Evaluation of Speech Quality) Wrapper for Python Users (narrow band and wide band)

An LDA/PLDA estimator using KALDI in python for speaker verification tasks

In defence of metric learning for speaker recognition

Implement Wave-U-Net by PyTorch, and migrate it to the speech enhancement.

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

Multi GPU Training Code for Deep Learning with PyTorch

A PyTorch implementation of SEGAN based on INTERSPEECH 2017 paper "SEGAN: Speech Enhancement Generative Adversarial Network"

Mixing an audio file with a noise file at any Signal-to-Noise Ratio (SNR)

Deep learning for audio denoising

Audio Denoising with Deep Network Priors

Implementation of recent Deep Learning papers

A Conditional Generative Adverserial Network (cGAN) was adapted for the task of source de-noising of noisy voice auditory images. The base architecture is adapted from Pix2Pix.

A neural network for end-to-end speech denoising

Code for the book "High Performance Python" by Micha Gorelick and Ian Ozsvald with OReilly