PiRapGenerator - Make anyone rap the digits of pi

Overview

PiRapGenerator

Make anyone rap the digits of pi (sample files are of Ted Nivison)

Installation

This requires pydub to be installed (via pip) which requires ffmpeg to export audio
(Must be added to PATH or use pydub.AudioSegment.ffmpeg = "/path/to/ffmpeg" or AudioSegment.converter idk anymore)

Usage

Create audio clips of an exact length (in this case, 1/2 beat @ 140bpm = ~214ms) with the naming scheme shown (pi0, pi1, etc)
Create a txt containing pi up to the number of digits you want and edit the path to it in the script
Run the script using terminal/cmd as it runs roughly 2x faster than in IDLE Shell
To cancel the render early, use CTRL+C to interrupt the program. This will then save the current file's progress.
The chunk size can be changed, however a larger size may cause the program to run slower

Info & Warnings

  • No additional compression is done by default so each chunk will be the combined file size of each induvidual part
  • Currently the final chunk will be named as if it failed.
  • You may be limited by memory which is the cause of an 'Argument out of range' error.
  • Combine.py is currently very basic and will process all WAVs in the output folder/the scripts running folder. All WAVs that have been made by Combine.py must be deleted before it is run again.
Canonical Capsules: Unsupervised Capsules in Canonical Pose (NeurIPS 2021)

Canonical Capsules: Unsupervised Capsules in Canonical Pose (NeurIPS 2021) Introduction This is the official repository for the PyTorch implementation

165 Dec 07, 2022
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS 2020) Introduction AdaShare is a novel and differentiable approach fo

94 Dec 22, 2022
Research on Tabular Deep Learning (Python package & papers)

Research on Tabular Deep Learning For paper implementations, see the section "Papers and projects". rtdl is a PyTorch-based package providing a user-f

Yura Gorishniy 510 Dec 30, 2022
Loopy belief propagation for factor graphs on discrete variables, in JAX!

PGMax implements general factor graphs for discrete probabilistic graphical models (PGMs), and hardware-accelerated differentiable loopy belief propagation (LBP) in JAX.

Vicarious 62 Dec 23, 2022
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement (ICCV 2021, Oral)

Pixel-Perfect Structure-from-Motion (ICCV 2021 Oral) We introduce a framework that improves the accuracy of Structure-from-Motion by refining keypoint

Computer Vision and Geometry Lab 831 Dec 29, 2022
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition

VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition Usage First, install PyTorch 1.7.1+, torchvision 0.8.2

40 Dec 12, 2022
A PyTorch implementation of Learning to learn by gradient descent by gradient descent

Intro PyTorch implementation of Learning to learn by gradient descent by gradient descent. Run python main.py TODO Initial implementation Toy data LST

Ilya Kostrikov 300 Dec 11, 2022
Civsim is a basic civilisation simulation and modelling system built in Python 3.8.

Civsim Introduction Civsim is a basic civilisation simulation and modelling system built in Python 3.8. It requires the following packages: perlin_noi

17 Aug 08, 2022
PN-Net a neural field-based framework for depth estimation from single-view RGB images.

PN-Net We present a neural field-based framework for depth estimation from single-view RGB images. Rather than representing a 2D depth map as a single

1 Oct 02, 2021
SwinIR: Image Restoration Using Swin Transformer

SwinIR: Image Restoration Using Swin Transformer This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Win

Jingyun Liang 2.4k Jan 05, 2023
NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring

NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring Uncensored version of the following image can be found at https://i.

notAI.tech 1.1k Dec 29, 2022
An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicity.

Fast Face Classification (F²C) This is the code of our paper An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicit

33 Jun 27, 2021
Catalyst.Detection

Accelerated DL R&D PyTorch framework for Deep Learning research and development. It was developed with a focus on reproducibility, fast experimentatio

Catalyst-Team 12 Oct 25, 2021
Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving

GSAN Introduction Code for paper GSAN: Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving, wh

YE Luyao 6 Oct 27, 2022
Back to Basics: Efficient Network Compression via IMP

Back to Basics: Efficient Network Compression via IMP Authors: Max Zimmer, Christoph Spiegel, Sebastian Pokutta This repository contains the code to r

IOL Lab @ ZIB 1 Nov 19, 2021
Fastshap: A fast, approximate shap kernel

fastshap: A fast, approximate shap kernel fastshap was designed to be: Fast Calculating shap values can take an extremely long time. fastshap utilizes

Samuel Wilson 22 Sep 24, 2022
A PyTorch implementation of a Factorization Machine module in cython.

fmpytorch A library for factorization machines in pytorch. A factorization machine is like a linear model, except multiplicative interaction terms bet

Jack Hessel 167 Jul 06, 2022
A Re-implementation of the paper "A Deep Learning Framework for Character Motion Synthesis and Editing"

What is This This is a simple re-implementation of the paper "A Deep Learning Framework for Character Motion Synthesis and Editing"(1). Only Sections

102 Dec 14, 2022
Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

258 Dec 29, 2022
CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).

CCNet: Criss-Cross Attention for Semantic Segmentation Paper Links: Our most recent TPAMI version with improvements and extensions (Earlier ICCV versi

Zilong Huang 1.3k Dec 27, 2022