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adversarial-autoaugment-pytorch

Unofficial Pytorch Implementation Of AdversarialAutoAugment(ICLR2020)

alfred-prettier-clipboard

🎨 Format code in your clipboard with Prettier

ashish10alex

asteroid

The PyTorch-based audio source separation toolkit for researchers

audio-augmentations

A website that displays audio and its spectrogram when an augmentation/operation is applied to the audio.

coc.nvim

Nodejs extension host for vim & neovim, load extensions like VSCode and host language servers.

dataform-lsp-vscode

dataform-lsp-vscode

dataform_json_parser

Dataform json parser

deep-audio-prior

Audio Source Separation Without Any Training Data.

dependentree

docker-madness

Docker Image for the Madness Markdown Server

emoji-cheat-sheet

A markdown version emoji cheat sheet

flask-opencv

Display opencv video on your flask app.

football-bfs

Shows the graph between two football players indicating the shortest connection between them.

jetson-nano-setup

Helper commands to get started with using machine learning libraries on Nvidia Jetson Nano

markdown-cheatsheet

Markdown Cheatsheet for Github Readme.md

nvim-dbee

Interactive database client for neovim

openmp-jacobi

A parallel programming implementation in C using OpenMP

pnvim

Neovim configuration using lazy

python-elm

Extreme Learning Machine implementation in Python

readme-with-video

selfcrossattn

sql-metadata

Uses tokenized query returned by python-sqlparse and generates query metadata

subjective-evaluation-images

svoice

We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.

system-setup

My development environment and system setup guides

telescope.nvim

Find, Filter, Preview, Pick. All lua, all the time.

tensorflow-examples

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

test_dataform

test_git_worktree

testing git worktree cli