Vadym Shalts's repositories
docker-dflatten
Make flattened docker image for aufs
macrame-idea-scala-plugin
Macramé and Safe Config Intellij scala plugin
zsh-prompt-garrett
My very own personal prompt...
Awesome-Learning-with-Label-Noise
A curated list of resources for Learning with Noisy Labels
bt-ccxt-store
Fork of Ed Bartosh's CCXT Store Work
conv_arithmetic
A technical report on convolution arithmetic in the context of deep learning
coz
Coz: Causal Profiling
Deep-Reinforcement-Learning-Hands-On-Second-Edition
Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt
deepspeech.pytorch
Speech Recognition using DeepSpeech2.
deepvoice3_pytorch
PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models
fastaudio
🔊 Audio and fastai v2
fastbook
The fastai book, published as Jupyter Notebooks
Market-Making-With-Crypto
Writing a basic market making strategy on liquid and illiquid crypto/fiat pairs
pairs_trading
experiments with pair trading
PlotNeuralNet
Latex code for making neural networks diagrams
pyroomacoustics
Pyroomacoustics is a package for audio signal processing for indoor applications. It was developed as a fast prototyping platform for beamforming algorithms in indoor scenarios.
python-audio-effects
Apply audio effects such as reverb and EQ directly to audio files or NumPy ndarrays.
research_public
Quantitative research and educational materials
RL-Adventure
Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
slick-pg
Slick extensions for PostgreSQL
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.
Universal-Transformer-Pytorch
Implementation of Universal Transformer in Pytorch
voice_datasets
🔊 A comprehensive list of open-source datasets for voice and sound computing (40+ datasets).
zio-course
The official repository for the Rock the JVM ZIO course