First prediction - part 4

A pure data designed algorithm (part 1) generating about 10 hours of midi data using five different configurations (conditional statement alterations in the same key & pitch) to produce a dichotomy in the 10 sperate hour long performances. From these 10 hours of midi performances around 150 midi files between 4-35kb in size have been edited out, constructing a synthetic dataset almost 5mb in size. At this point, all of the original midi song files have been broken up into individual instrument files; drums, bass, chords & melody. Each instrument assigned to a specific instrument dataset. In total there are 4 datasets of 150 midi files. I convert all the midi files with Music21 into text and then back to midi after prediction. Breaking up the instruments into separate midi files allows for moving the tracks around within datasets for different LLM models (GPT&LSTM) and with the ongoing arraigning of the current score in development.

The video in this post is a unique one. This is the first prediction made from the original island midi score trained on the synthetic dataset.

TRAINING STATS

number of training files = 150 - batch size = 30 files - number of iterations (number of training files/batch) = 5 - one epoch = 5 iterations

The bass guitar heard in this video is the first instrument predicted for a single epoch. For this audio, this first predicted track has been placed it back into the original midi score. Comparing the bass guitar in the island video of my previous post (part 3) to the bass guitar in this video (part 4) demonstrates a first step using the LLM to predict a new midi score for this single instrument in the group.

Note the interesting artifacts of the bass guitar performance. While these notes are in key, they are reminiscent of stray(bad) midi notes often produced in live midi performances. Velocity and note durations can be a prediction issue. The predicted outcome will change even more so with increased epochs. Ai prediction is a very cyclic procedure. When auditioning and recording these midi outcomes, little attention is given to the audio. Please excuse the lower quality instrument sounds used to demonstrate in this video.

Also to note, are the different drum performances heard in the two videos. Drum pattern prediction differs from note prediction and will be explained in a future post.