Generate music from midi files using BPE and markov model

Overview

MUSIC Synth

This repo is a prototype for music synth AI. It takes in a midi file and returns a midi file.

IMAGE ALT TEXT HERE

Samples

Soundcloud

Installation

  1. Clone the repo
  2. Make sure you have python and java installed
  3. Download midi of a song you like. (mp3 to midi won't work)
  4. Put the midi in the "rawmidis" folder (you should see Beethoven's moonlight sonata there)
  5. run master.py
  6. A few new folders will be created automatically.
  7. The newly generated midi will appear in the "generatedMIDIs" folder.
  8. You can use sound fonts to make this midi sound more organic, cut out parts you don't like or simply re run master.py again to get a different result.

Working

I first convert midi to ABC notation and extract the verses I then use Byte Pair encoding to compress the repeated patterns and finally a markov model to generate the sequence.

Owner
Aditya Khadilkar
LOVE AI!
Aditya Khadilkar
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

AI Fairness 360 (AIF360) The AI Fairness 360 toolkit is an extensible open-source library containg techniques developed by the research community to h

1.9k Jan 06, 2023
JMP is a Mixed Precision library for JAX.

Mixed precision training [0] is a technique that mixes the use of full and half precision floating point numbers during training to reduce the memory bandwidth requirements and improve the computatio

DeepMind 108 Dec 31, 2022
LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRerank, Seq2Slate.

LibRerank LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRer

126 Dec 28, 2022
dirty_cat is a Python module for machine-learning on dirty categorical variables.

dirty_cat dirty_cat is a Python module for machine-learning on dirty categorical variables.

637 Dec 29, 2022
stability-selection - A scikit-learn compatible implementation of stability selection

stability-selection - A scikit-learn compatible implementation of stability selection stability-selection is a Python implementation of the stability

185 Dec 03, 2022
🌊 River is a Python library for online machine learning.

River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition is to be the go-to library for doing machine learning on strea

OnlineML 4k Jan 03, 2023
A Microsoft Azure Web App project named Covid 19 Predictor using Machine learning Model

A Microsoft Azure Web App project named Covid 19 Predictor using Machine learning Model (Random Forest Classifier Model ) that helps the user to identify whether someone is showing positive Covid sym

Priyansh Sharma 2 Oct 06, 2022
Bottleneck a collection of fast, NaN-aware NumPy array functions written in C.

Bottleneck Bottleneck is a collection of fast, NaN-aware NumPy array functions written in C. As one example, to check if a np.array has any NaNs using

Python for Data 835 Dec 27, 2022
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.

Toolkit for Building Robust ML models that generalize to unseen domains (RobustDG) Divyat Mahajan, Shruti Tople, Amit Sharma Privacy & Causal Learning

Microsoft 149 Jan 06, 2023
Software Engineer Salary Prediction

Based on 2021 stack overflow data, this machine learning web application helps one predict the salary based on years of experience, level of education and the country they work in.

Jhanvi Mimani 1 Jan 08, 2022
BudouX is the successor to Budou, the machine learning powered line break organizer tool.

BudouX Standalone. Small. Language-neutral. BudouX is the successor to Budou, the machine learning powered line break organizer tool. It is standalone

Google 868 Jan 05, 2023
Turning images into '9-pan' palettes using KMeans clustering from sklearn.

img2palette Turning images into '9-pan' palettes using KMeans clustering from sklearn. Requirements We require: Pillow, for opening and processing ima

Samuel Vidovich 2 Jan 01, 2022
Python package for concise, transparent, and accurate predictive modeling

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. 📚 docs • 📖 demo notebooks Modern

Chandan Singh 983 Jan 01, 2023
This is the code repository for Interpretable Machine Learning with Python, published by Packt.

Interpretable Machine Learning with Python, published by Packt

Packt 299 Jan 02, 2023
It is a forest of random projection trees

rpforest rpforest is a Python library for approximate nearest neighbours search: finding points in a high-dimensional space that are close to a given

Lyst 211 Dec 29, 2022
Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.

Priyansh Sharma 7 Nov 09, 2022
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Real-time water systems lab 416 Jan 06, 2023
A demo project to elaborate how Machine Learn Models are deployed on production using Flask API

This is a salary prediction website developed with the help of machine learning, this makes prediction of salary on basis of few parameters like interview score, experience test score.

1 Feb 10, 2022
MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data

MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data. We demonstrate its use

Pachter Lab 26 Nov 29, 2022
Responsible Machine Learning with Python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

ph_ 624 Jan 06, 2023