Steps toward visualizing chord spaces

Attempts to visualize version of a chord similarity space

from diffusers import DiffusionPipeline
from transformers import set_seed
from PIL import Image
import torch
import random
import ssl
import os
ssl._create_default_https_context = ssl._create_unverified_context

#locate library
#model_id = "./stable-diffusion-v1-5"
model_id = "dreamshaper-xl-turbo"

pipeline = DiffusionPipeline.from_pretrained(
    pretrained_model_name_or_path = "../../../../bigFiles/huggingface/dreamshaper-xl-turbo/"
)

pipeline = pipeline.to("mps")

# Recommended if your computer has < 64 GB of RAM
pipeline.enable_attention_slicing("max")

prompt = "synthesizer floating in space. 80s cartoon. retro."

for s in range(30):
  for n in [5,10]:
    seed = s+21
    num_steps = n+1
    set_seed(seed)
    
    image = pipeline(prompt,height = 1024,width = 1024,num_images_per_prompt = 1,num_inference_steps=num_steps)
    
    image_name = "images/synth_{}_{}.jpeg"
    
    image_save = image.images[0].save(image_name.format(seed,num_steps))

synthesizer floating in space. 80s cartoon. retro. - Dreamshaper v7

No time today for much explanation. Just some code that produces a visual. Lot’s of things to change up here later.

• create feature vectors for chords
• calculate similarity matrix
• find a multi-dimensional scaling solution to plot the chord names in 2-d

library(tidyverse)
# pre-processing to get the chord vectors

# load chord vectors
c_chord_excel <- rio::import("chord_vectors.xlsx")

# grab feature vectors
c_chord_matrix <- as.matrix(c_chord_excel[,4:15])

# assign row names to the third column containing chord names
row.names(c_chord_matrix) <- c_chord_excel[,3]

# define all keys
keys <- c("C","Db","D","Eb","E","F","Gb","G","Ab","A","Bb","B")


# the excel sheet only has chords in C
# loop through the keys, permute the matrix to get the chords in the next key
# add the permuted matrix to new rows in the overall chord_matrix
for (i in 1:length(keys)) {

  if (i == 1) {
    # initialize chord_matrix with C matrix
    chord_matrix <- c_chord_matrix

  } else {
    #permute the matrix as a function of iterator
    new_matrix <- cbind(c_chord_matrix[, (14-i):12],c_chord_matrix[, 1:(13-i)] )

    # rename the rows with the new key
    new_names <- gsub("C", keys[i], c_chord_excel[,3])
    row.names(new_matrix) <- new_names

    # append the new_matrix to chord_matrix
    chord_matrix <- rbind(chord_matrix,new_matrix)

  }
}

chord_properties <- tibble(
  type = rep(c_chord_excel$type,length(keys)),
  key = rep(keys, each = dim(c_chord_matrix)[1]),
  chord_names  = row.names(chord_matrix),
  synonyms = list(NA),
  database_chord = FALSE
)

first_order <- lsa::cosine(t(chord_matrix))
second_order <- lsa::cosine(first_order)

# find repeats and build synonym list
repeat_indices <- c()

first_occurrence <- c()

for(i in 1:dim(chord_matrix)[1]){
  # get the current row
  evaluate_row <- first_order[i,]

  # don't count the current item as a repeat
  evaluate_row[i] <- 0

  # repeats are the ids for any other 1s found
  repeats <- which(evaluate_row == 1 )

  if(length(repeats) == 0){
  }

  if(length(repeats) > 0){
    #add to list of repeat items
    repeat_indices <- c(repeat_indices,repeats)

    # add synonyms
    chord_properties$synonyms[i] <- list(synonyms = row.names(chord_matrix)[repeats])
  }

  if(i %in% first_occurrence == FALSE){
    if(i %in% repeat_indices == FALSE){
      first_occurrence <- c(first_occurrence,i)
      chord_properties$database_chord[i] <- TRUE
    }
  }
}

chord_properties <- chord_properties %>%
  mutate(num_notes = rowSums(chord_matrix),
         id = 1:dim(chord_matrix)[1])

# keep only unique chord, recompute similarities
chord_matrix_no_repeats <- chord_matrix[first_occurrence,]
first_order_no_repeats <- lsa::cosine(t(chord_matrix_no_repeats))
second_order_no_repeats <- lsa::cosine(first_order_no_repeats)

# remove scales and individual notes
only_chords <- chord_properties %>%
  filter(type!="scale",
         type!="key",
         database_chord == TRUE)

first_order_chords <- first_order_no_repeats[only_chords$chord_names,
                                               only_chords$chord_names]
second_order_chords <- second_order_no_repeats[only_chords$chord_names,
                                               only_chords$chord_names]

First-order similarity chord-space using 1-hot vectors.

library(ggplot2)
library(ggrepel)

mds_first_order <- cmdscale((first_order-1))
mds_first_order <- as_tibble(mds_first_order) %>%
  cbind(chord_properties)

mds_first_order <- mds_first_order %>%
  mutate(key = forcats::fct_relevel(key,"C","F","Bb","Eb","Ab","Db","Gb","B","E","A","D","G")) %>%
  filter(type != "scale")

p1 <- ggplot(mds_first_order, aes(V1, V2, 
                                  label = chord_names,
                                  color = key))+ 
  geom_point()+
  geom_text_repel(size=8, max.overlaps = 500)+
  theme_void(base_size = 50)
p1