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Janne's Projects

btc-ismir19 icon btc-ismir19

"A Bi-Directional Transformer for Musical Chord Recognition" accepted on ISMIR2019

byol icon byol

Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

byol-pytorch icon byol-pytorch

Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

casper icon casper

The default personal blogging theme for Ghost

clmr icon clmr

Official PyTorch implementation of Contrastive Learning of Musical Representations

context-aware-sequential-recommendation icon context-aware-sequential-recommendation

This is the Github repository containing the code for the Context-Aware Sequential Recommendation project for the Information Retrieval 2 course at the University of Amsterdam

crepe icon crepe

CREPE: A Convolutional REpresentation for Pitch Estimation -- pre-trained model (ICASSP 2018)

crypto-data-scraper icon crypto-data-scraper

Crypto data scraper using Websockets and MongoDB to receive real-time data from cryptocurrency exchanges and save it for historic analysis (machine learning, etc).

deepbach icon deepbach

code accompanying "DeepBach: a Steerable Model for Bach Chorales Generation" paper

descript-audio-codec icon descript-audio-codec

State-of-the-art audio codec with 90x compression factor. Supports 44.1kHz, 24kHz, and 16kHz mono/stereo audio.

eurovision-dataset icon eurovision-dataset

The Eurovision Song Contest Dataset is a freely-available dataset containing audio features, metadata, contest ranking and voting data of 1735 songs that have competed in the Eurovision Song Contests between 1956 and 2023.

flashtorch icon flashtorch

Visualization toolkit for neural networks in PyTorch! Demo -->

godfather icon godfather

The Godfather resource for GTA:Network's online modification for GTA:V. The mod can be downloaded at: https://gtanet.work

gpt-2-simple icon gpt-2-simple

Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts

greedy_infomax icon greedy_infomax

Code for the paper: Putting An End to End-to-End: Gradient-Isolated Learning of Representations

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