los_angeles_music_composer_ttm_edition.py

# -*- coding: utf-8 -*-
"""Los_Angeles_Music_Composer_TTM_Edition.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/github/asigalov61/Los-Angeles-Music-Composer/blob/main/Los_Angeles_Music_Composer_TTM_Edition.ipynb

# Los Angeles Music Composer TTM Edition (ver. 1.0)

***

Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools

***

WARNING: This complete implementation is a functioning model of the Artificial Intelligence. Please excercise great humility, care, and respect. https://www.nscai.gov/

***

#### Project Los Angeles

#### Tegridy Code 2023

***

# (GPU CHECK)
"""

#@title NVIDIA GPU check
!nvidia-smi

"""# (SETUP ENVIRONMENT)"""

#@title Install dependencies
!git clone https://github.com/asigalov61/Los-Angeles-Music-Composer
!pip install torch
!pip install einops
!pip install fuzzywuzzy[speedup]
!pip install torch-summary
!pip install sklearn
!pip install tqdm
!pip install matplotlib
!apt install fluidsynth #Pip does not work for some reason. Only apt works
!pip install midi2audio

# Commented out IPython magic to ensure Python compatibility.
#@title Import modules

print('=' * 70)
print('Loading core Los Angeles Music Composer modules...')

import os
import pickle
import random
import secrets
import statistics
from time import time
import tqdm

print('=' * 70)
print('Loading main Los Angeles Music Composer modules...')
import torch

# %cd /content/Los-Angeles-Music-Composer

import TMIDIX
from lwa_transformer import *

# %cd /content/

from fuzzywuzzy import process

print('=' * 70)
print('Loading aux Los Angeles Music Composer modeules...')

import matplotlib.pyplot as plt

from torchsummary import summary
from sklearn import metrics

from midi2audio import FluidSynth
from IPython.display import Audio, display

print('=' * 70)
print('Done!')
print('Enjoy! :)')
print('=' * 70)

"""# (LOAD MODEL)"""

# Commented out IPython magic to ensure Python compatibility.
#@title Unzip Pre-Trained Los Angeles Music Composer Model
print('=' * 70)
# %cd /content/Los-Angeles-Music-Composer/Model

print('=' * 70)
print('Unzipping pre-trained Los Angeles Music Composer model...Please wait...')

!cat /content/Los-Angeles-Music-Composer/Model/Los_Angeles_Music_Composer_Trained_Model.zip* > /content/Los-Angeles-Music-Composer/Model/Los_Angeles_Music_Composer_Trained_Model.zip
print('=' * 70)

!unzip -j /content/Los-Angeles-Music-Composer/Model/Los_Angeles_Music_Composer_Trained_Model.zip
print('=' * 70)

print('Done! Enjoy! :)')
print('=' * 70)
# %cd /content/
print('=' * 70)

#@title Load Los Angeles Music Composer Model
full_path_to_model_checkpoint = "/content/Los-Angeles-Music-Composer/Model/Los_Angeles_Music_Composer_Model_88835_steps_0.643_loss.pth" #@param {type:"string"}

print('=' * 70)
print('Loading Los Angeles Music Composer Pre-Trained Model...')
print('Please wait...')
print('=' * 70)
print('Instantiating model...')

SEQ_LEN = 4096

# instantiate the model

model = LocalTransformer(
    num_tokens = 2831,
    dim = 1024,
    depth = 36,
    causal = True,
    local_attn_window_size = 512,
    max_seq_len = SEQ_LEN
)

model = torch.nn.DataParallel(model)

model.cuda()
print('=' * 70)

print('Loading model checkpoint...')

model.load_state_dict(torch.load(full_path_to_model_checkpoint))
print('=' * 70)

model.eval()

print('Done!')
print('=' * 70)

# Model stats
print('Model summary...')
summary(model)

# Plot Token Embeddings
tok_emb = model.module.token_emb.weight.detach().cpu().tolist()

tok_emb1 = []

for t in tok_emb:
    tok_emb1.append([abs(statistics.median(t))])

cos_sim = metrics.pairwise_distances(
   tok_emb1, metric='euclidean'
)
plt.figure(figsize=(7, 7))
plt.imshow(cos_sim, cmap="inferno", interpolation="nearest")
im_ratio = cos_sim.shape[0] / cos_sim.shape[1]
plt.colorbar(fraction=0.046 * im_ratio, pad=0.04)
plt.xlabel("Position")
plt.ylabel("Position")
plt.tight_layout()
plt.plot()
plt.savefig("/content/Los-Angeles-Music-Composer-Tokens-Embeddings-Plot.png", bbox_inches="tight")

"""# (LOAD AUX DATA)"""

# Commented out IPython magic to ensure Python compatibility.
#@title Unzip Los Angeles Music Composer Aux Data
print('=' * 70)
# %cd /content/Los-Angeles-Music-Composer/Aux-Data

print('=' * 70)
print('Unzipping Los Angeles Music Composer Aux Data...Please wait...')

!cat /content/Los-Angeles-Music-Composer/Aux-Data/Los_Angeles_Music_Composer_Aux_Data.zip* > /content/Los-Angeles-Music-Composer/Aux-Data/Los_Angeles_Music_Composer_Aux_Data.zip
print('=' * 70)

!unzip -j /content/Los-Angeles-Music-Composer/Aux-Data/Los_Angeles_Music_Composer_Aux_Data.zip
print('=' * 70)

print('Done! Enjoy! :)')
print('=' * 70)
# %cd /content/
print('=' * 70)

#@title Load Los Angeles Music Composer Aux Data

AUX_DATA = TMIDIX.Tegridy_Any_Pickle_File_Reader('/content/Los-Angeles-Music-Composer/Aux-Data/Los_Angeles_Music_Composer_Aux_Data')

print('Done!')

"""# (GENERATE)"""

#@title Standard/Simple Continuation

enter_desired_song_title = "Can You Feel The Love Tonight" #@param {type:"string"}
number_of_tokens_to_generate = 512 #@param {type:"slider", min:32, max:4096, step:32}
number_of_batches_to_generate = 4 #@param {type:"slider", min:1, max:16, step:1}
allow_model_to_stop_generation_if_needed = False #@param {type:"boolean"}
temperature = 0.9 #@param {type:"slider", min:0.1, max:1, step:0.1}

print('=' * 70)
print('Los Angeles Music Composer TTM Model Generator')
print('=' * 70)

print('Searching titles...Please wait...')
random.shuffle(AUX_DATA)

titles_index = []

for A in AUX_DATA:
  titles_index.append(A[0])

search_match = process.extract(query=enter_desired_song_title, choices=titles_index, limit=1)
search_index = titles_index.index(search_match[0][0])

print('Done!')
print('=' * 70)
print('Selected title:', AUX_DATA[search_index][0])
print('=' * 70)

if allow_model_to_stop_generation_if_needed:
  min_stop_token = 2816
else:
  min_stop_token = 0

outy = AUX_DATA[search_index][1]

inp = [outy] * number_of_batches_to_generate

inp = torch.LongTensor(inp).cuda()

#start_time = time()

out = model.module.generate(inp, 
                      number_of_tokens_to_generate, 
                      temperature=temperature, 
                      return_prime=True, 
                      min_stop_token=min_stop_token, 
                      verbose=True)

out0 = out.tolist()
print('=' * 70)
print('Done!')
print('=' * 70)
#print('Generation took', time() - start_time, "seconds")
#print('=' * 70)
#======================================================================
print('Rendering results...')
print('=' * 70)

for i in range(number_of_batches_to_generate):

  print('=' * 70)
  print('Batch #', i)
  print('=' * 70)

  out1 = out0[i]

  print('Sample INTs', out1[:12])
  print('=' * 70)

  if len(out) != 0:
      
      song = out1
      song_f = []
      tim = 0
      dur = 0
      vel = 0
      pitch = 0
      channel = 0

      son = []
      song1 = []

      for s in song:
        if s >= 128 and s < (12*128)+1152:
          son.append(s)
        else:
          if len(son) == 3:
            song1.append(son)
          son = []
          son.append(s)
                      
      for ss in song1:

        tim += ss[0] * 10

        dur = ((ss[1]-128) // 8) * 20
        vel = (((ss[1]-128) % 8)+1) * 15
    
        channel = (ss[2]-1152) // 128
        pitch = (ss[2]-1152) % 128
                        
        song_f.append(['note', tim, dur, channel, pitch, vel ])

      detailed_stats = TMIDIX.Tegridy_SONG_to_MIDI_Converter(song_f,
                                                          output_signature = 'Los Angeles Music Composer',  
                                                          output_file_name = '/content/Los-Angeles-Music-Composer-Music-Composition_'+str(i), 
                                                          track_name='Project Los Angeles',
                                                          list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0],
                                                          number_of_ticks_per_quarter=500)
      print('=' * 70)
      print('Displaying resulting composition...')
      print('=' * 70)

      fname = '/content/Los-Angeles-Music-Composer-Music-Composition_'+str(i)

      x = []
      y =[]
      c = []

      colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver']

      for s in song_f:
        x.append(s[1] / 1000)
        y.append(s[4])
        c.append(colors[s[3]])

      FluidSynth("/usr/share/sounds/sf2/FluidR3_GM.sf2", 16000).midi_to_audio(str(fname + '.mid'), str(fname + '.wav'))
      display(Audio(str(fname + '.wav'), rate=16000))

      plt.figure(figsize=(14,5))
      ax=plt.axes(title=fname)
      ax.set_facecolor('black')

      plt.scatter(x,y, c=c)
      plt.xlabel("Time")
      plt.ylabel("Pitch")
      plt.show()

"""# Congrats! You did it! :)"""