Release Version 1.0. Enjoy :)

Examples/markovify_piano.py

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+# -*- coding: utf-8 -*-
+"""Markovify_Piano.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/17SGUaA8r6nZyHHH3huOmgcsVUDH6HBWE
+
+# Markovify Piano
+
+***
+
+## Based upon absolutely amazing markovify package of @jsvine: https://github.com/jsvine/markovify
+
+## Powered by tegridy-tools TMIDI 1.4 Processors
+
+***
+
+### Project Los Angeles
+### Tegridy Code 2021
+
+***
+
+# Setup environment
+"""
+
+#@title Install dependencies
+!git clone https://github.com/asigalov61/tegridy-tools
+!pip install unidecode
+!pip install tqdm
+!apt install fluidsynth #Pip does not work for some reason. Only apt works
+!pip install midi2audio
+
+#@title Load needed modules
+print('Loading needed modules. Please wait...')
+
+import sys
+import os
+import json
+
+os.chdir('/content/tegridy-tools/tegridy-tools/')
+import TMIDI
+import markovify
+os.chdir('/content/')
+
+from pprint import pprint
+
+import tqdm.auto
+
+from midi2audio import FluidSynth
+from IPython.display import display, Javascript, HTML, Audio
+
+from google.colab import output, drive
+
+print('Creating Dataset dir...')
+if not os.path.exists('/content/Dataset'):
+    os.makedirs('/content/Dataset')
+
+os.chdir('/content/')
+print('Loading complete. Enjoy! :)')
+
+"""# Download/upload desired MIDI dataset
+
+## NOTE: Dataset must be sufficiently large and homogenous for Markov chain to train/perform properly.
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+#@title Download World Melody-ABC-Tunes MIDI dataset
+
+#@markdown Works best stand-alone/as-is for the optimal results
+# %cd /content/Dataset/
+
+!wget --no-check-certificate -O Melody-ABC-Tunes-Dataset-CC-BY-NC-SA.zip "https://onedrive.live.com/download?cid=8A0D502FC99C608F&resid=8A0D502FC99C608F%2118458&authkey=AOXe4AaDtageB-k"
+!unzip -j '/content/Dataset/Melody-ABC-Tunes-Dataset-CC-BY-NC-SA.zip'
+!rm '/content/Dataset/Melody-ABC-Tunes-Dataset-CC-BY-NC-SA.zip'
+
+# %cd /content/
+
+"""# Process the MIDI dataset
+
+## NOTE: If you are not sure what settings to select, please use original defaults
+"""
+
+#@title Process MIDIs to special MIDI dataset with Tegridy MIDI Processor
+#@markdown NOTES:
+
+#@markdown 1) Best results are achieved with the single-track, single-channel, single-instrument MIDI 0 files.
+
+#@markdown 2) MIDI Channel = -1 means all MIDI channels but drums will be processed. MIDI Channel = 16 means all channels will be processed. Otherwise, only single indicated MIDI channel will be processed.
+
+file_name_to_output_dataset_to = "/content/Markovify-Piano-Music-MIDI-Dataset" #@param {type:"string"}
+desired_MIDI_channel_to_process = 0 #@param {type:"slider", min:-1, max:15, step:1}
+
+MIDI_events_time_denominator = 10
+melody_notes_in_chords = True
+
+print('TMIDI Processor')
+print('Starting up...')
+
+###########
+
+average_note_pitch = 0
+min_note = 127
+max_note = 0
+
+files_count = 0
+
+ev = 0
+
+chords_list_f = []
+melody_list_f = []
+
+chords_list = []
+chords_count = 0
+
+melody_chords = []
+melody_count = 0
+
+###########
+
+print('Loading MIDI files...')
+print('This may take a while on a large dataset in particular.')
+
+dataset_addr = "/content/Dataset/"
+os.chdir(dataset_addr)
+filez = os.listdir(dataset_addr)
+
+print('Processing MIDI files. Please wait...')
+for f in tqdm.auto.tqdm(filez):
+  try:
+    files_count += 1
+    chords_list, melody = TMIDI.Tegridy_MIDI_Processor(f, 
+                                                      desired_MIDI_channel_to_process, 
+                                                      MIDI_events_time_denominator,
+                                                      )
+
+    fn = os.path.basename(f)
+    fno = fn.split('.')[0].replace(' ', '_')
+
+    chords_l, melody_l = TMIDI.Tegridy_Chords_Converter(chords_list, 
+                                                        melody, 
+                                                        fno, 
+                                                        melody_notes_in_chords)
+
+    chords_list_f.extend(chords_l)
+    melody_list_f.extend(melody_l)
+    chords_count += len(chords_list)
+    melody_count += len(melody_l)
+
+  except:
+    print('Problematic MIDI:', f)
+    continue
+
+average_note_pitch = int((min_note + max_note) / 2)
+
+print('Task complete :)')
+print('==================================================')
+print('Number of processed dataset MIDI files:', files_count)
+print('Average note pitch =', average_note_pitch)
+#print('Min note pitch =', min_note)
+#print('Max note pitch =', max_note)
+#print('Number of MIDI events recorded:', len(events_matrix))
+print('Number of MIDI chords recorded:', chords_count)
+print('The longest chord:', len(max(chords_list_f, key=len)), 'notes') 
+print(max(chords_list_f, key=len))
+print('Number of recorded melody events:', len(melody_list_f))
+print('First melody event:', melody_list_f[0], 'Last Melody event:', melody_list_f[-1])
+print('Total number of MIDI events recorded:', len(chords_list_f))
+
+# Dataset
+MusicDataset = [chords_list_f, melody_list_f]
+
+# Writing dataset to pickle file
+TMIDI.Tegridy_Pickle_File_Writer(MusicDataset, file_name_to_output_dataset_to)
+
+#@title Process MIDI Dataset to TXT Dataset (w/Tegridy MIDI-TXT Processor)
+full_path_to_TXT_dataset = "/content/Markovify-Piano-Music-TXT-Dataset.txt" #@param {type:"string"}
+simulate_velocity = True #@param {type:"boolean"}
+
+reduce_MIDI_channels = False
+reduce_notes_velocities = False
+line_by_line_dataset = True
+chords_durations_multiplier = 1
+
+# MIDI Dataset to txt dataset converter 
+print('TMIDI-TXT Processor')
+print('Starting up...')
+
+if simulate_velocity:
+  print('Simulated velocity mode is enabled.')
+
+TXT = ''
+number_of_chords = 0
+number_of_bad_chords = 0
+dataset_name = 'DATASET=Intelligent_VIRTUOSO_TXT_Music_Dataset'
+
+TXT, number_of_chords, number_of_bad_chords = TMIDI.Tegridy_MIDI_TXT_Processor(dataset_name, 
+                                                                               chords_list_f, 
+                                                                               melody_list_f, 
+                                                                               simulate_velocity, 
+                                                                               line_by_line_dataset,
+                                                                               0,
+                                                                               chords_durations_multiplier,
+                                                                               )
+
+print('Number of chords recorded: ', number_of_chords)
+print('Number of bad/skipped chords: ', number_of_bad_chords)
+print('Done!')
+
+TXT1, n = TMIDI.Tegridy_TXT_Reducer(TXT, 
+                                    include_MIDI_channels=reduce_MIDI_channels, 
+                                    include_notes_velocities=reduce_notes_velocities,
+                                    line_by_line_output_dataset=False)
+
+TMIDI.Tegridy_TXT_Dataset_File_Writer(full_path_to_TXT_dataset, TXT_String=TXT1)
+
+"""# Load processed TXT MIDI dataset into memory"""
+
+#@title Load/Reload processed TXT dataset
+full_path_to_TXT_dataset = "/content/Markovify-Piano-Music-TXT-Dataset.txt" #@param {type:"string"}
+
+print('Loading TXT MIDI dataset. Please wait...')
+with open(full_path_to_TXT_dataset) as f:
+    text = f.read()
+print('Dataset loaded! Enjoy :)')
+
+"""# Train TXT Markov chain/model"""
+
+#@title Train Markov-chain/model
+markov_chain_state_size = 4 #@param {type:"slider", min:1, max:10, step:1}
+
+print('Training Markov chain/model. Please wait...')
+markov_text_model = markovify.NewlineText(text, well_formed=False, state_size=markov_chain_state_size)
+
+print('Model is ready! Enjoy :)')
+
+#@title Save the model
+full_path_to_json_save_file = "/content/Markovify-Piano-Music-Model.json" #@param {type:"string"}
+
+print('Converting model to json...')
+model_json = markov_text_model.to_json()
+
+print('Saving model as json file...')
+with open(full_path_to_json_save_file, 'w') as f:
+    json.dump(model_json, f)
+
+print('Task complete! Enjoy! :)')
+
+#@title Load/Re-load saved model
+full_path_to_json_save_file = "/content/Markovify-Piano-Music-Model.json" #@param {type:"string"}
+
+print('Loading model from json file...')
+f = open(full_path_to_json_save_file)
+model_json = json.load(f)
+
+print('Restoring the model...')
+markov_text_model = markovify.Text.from_json(model_json)
+
+print('Model loaded and restored! Enjoy! :)')
+
+"""# Generate music composition"""
+
+#@title Generate Music
+
+#@markdown HINT: Each note = 3-5 characters depending on the MIDI processing settings above
+minimum_number_of_characters_to_generate = 5000 #@param {type:"slider", min:100, max:10000, step:100}
+number_of_cycles_to_try_to_generate_desired_result = 5000 #@param {type:"slider", min:10, max:10000, step:10}
+minimum_notes_to_generate = 210 #@param {type:"slider", min:10, max:1000, step:10}
+print_generated_song = False #@param {type:"boolean"}
+
+Output_TXT_String = ''
+
+attempt = 0
+
+print('Generating music composition. Please wait...')
+
+while (len(Output_TXT_String.split(' ')[1:])-2) < minimum_notes_to_generate:
+  out = markov_text_model.make_sentence(min_chars=minimum_number_of_characters_to_generate, 
+                              tries=number_of_cycles_to_try_to_generate_desired_result)
+
+  Output_TXT_String = ''.join(out)
+  print('Attempt #', attempt)
+  attempt += 1
+
+print('Generation complete!')
+print('=' * 70)
+print(Output_TXT_String.split(' ')[0], 'with', len(Output_TXT_String.split(' ')[1:])-2, 'notes.')
+print('=' * 70)
+
+if print_generated_song:
+  pprint(Output_TXT_String)
+  print('=' * 70)
+
+"""# Convert generated music composition to MIDI file and download/listen to the output :)"""
+
+#@title Convert generated song to MIDI
+download_generated_composition = False #@param {type:"boolean"}
+show_detailed_MIDI_stats = False #@param {type:"boolean"}
+
+print('Converting generated song from TXT to MIDI events...')
+SONG, SONG_Name = TMIDI.Tegridy_Reduced_TXT_to_Notes_Converter(Output_TXT_String, line_by_line_dataset=False, has_MIDI_channels=False, has_velocities=False)
+
+fname = TMIDI.Tegridy_File_Time_Stamp('/content/Markovify_Piano_Composition_Generated_on_')
+print('Composition', fname + '.mid')
+
+detailed_stats = TMIDI.Tegridy_SONG_to_MIDI_Converter(SONG=SONG, 
+                                                      output_signature='Markovify_Piano',
+                                                      track_name=SONG_Name,
+                                                      output_file_name=fname)
+
+if download_generated_composition:
+  print('Downloading your composition now...')
+  from google.colab import files
+  files.download(fname + '.mid')
+
+if show_detailed_MIDI_stats:
+  print('Detailed MIDI stats:')
+  pprint(detailed_stats)
+
+#@title Listen to the last generated composition
+#@markdown NOTE: May be very slow with the long compositions
+print('Synthesizing the last output MIDI. Please stand-by... ')
+FluidSynth("/usr/share/sounds/sf2/FluidR3_GM.sf2", 16000).midi_to_audio(str(fname + '.mid'), str(fname + '.wav'))
+Audio(str(fname + '.wav'), rate=16000)