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"""Master_MIDI_Dataset_Search_and_Filter.ipynb |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Extras/Master_MIDI_Dataset_Search_and_Filter.ipynb |
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# Master MIDI Dataset Search and Filter (ver. 4.0) |
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*** |
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Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools |
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*** |
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#### Project Los Angeles |
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#### Tegridy Code 2023 |
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*** |
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# (SETUP ENVIRONMENT) |
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""" |
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!git clone https://github.com/asigalov61/tegridy-tools |
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!pip install huggingface_hub |
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!pip install tqdm |
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print('Loading core modules... Please wait...') |
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import os |
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import copy |
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from collections import Counter |
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import random |
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import pickle |
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from tqdm import tqdm |
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import pprint |
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import statistics |
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import shutil |
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print('Creating IO dirs... Please wait...') |
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if not os.path.exists('/content/Main-MIDI-Dataset'): |
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os.makedirs('/content/Main-MIDI-Dataset') |
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if not os.path.exists('/content/Master-MIDI-Dataset'): |
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os.makedirs('/content/Master-MIDI-Dataset') |
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if not os.path.exists('/content/Output-MIDI-Dataset'): |
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os.makedirs('/content/Output-MIDI-Dataset') |
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print('Loading TMIDIX module...') |
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os.chdir('/content/tegridy-tools/tegridy-tools') |
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import TMIDIX |
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print('Done!') |
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from huggingface_hub import hf_hub_download |
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os.chdir('/content/') |
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print('Enjoy! :)') |
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"""# (PREP MAIN MIDI DATASET)""" |
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print('=' * 70) |
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print('Downloading Los Angeles MIDI Dataset...Please wait...') |
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print('=' * 70) |
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hf_hub_download(repo_id='projectlosangeles/Los-Angeles-MIDI-Dataset', |
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filename='Los-Angeles-MIDI-Dataset-Ver-4-0-CC-BY-NC-SA.zip', |
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repo_type="dataset", |
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local_dir='/content/Main-MIDI-Dataset', |
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local_dir_use_symlinks=False) |
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print('=' * 70) |
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print('Done! Enjoy! :)') |
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print('=' * 70) |
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print('=' * 70) |
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print('Unzipping Los Angeles MIDI Dataset...Please wait...') |
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!unzip 'Los-Angeles-MIDI-Dataset-Ver-4-0-CC-BY-NC-SA.zip' |
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print('=' * 70) |
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print('Done! Enjoy! :)') |
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print('=' * 70) |
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print('=' * 70) |
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print('Creating dataset files list...') |
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dataset_addr = "/content/Main-MIDI-Dataset/MIDIs" |
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filez = list() |
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for (dirpath, dirnames, filenames) in os.walk(dataset_addr): |
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filez += [os.path.join(dirpath, file) for file in filenames] |
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if filez == []: |
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print('Could not find any MIDI files. Please check Dataset dir...') |
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print('=' * 70) |
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print('=' * 70) |
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print('Randomizing file list...') |
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random.shuffle(filez) |
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print('=' * 70) |
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LAMD_files_list = [] |
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for f in tqdm(filez): |
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LAMD_files_list.append([f.split('/')[-1].split('.mid')[0], f]) |
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print('Done!') |
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print('=' * 70) |
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print('=' * 70) |
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print('Loading LAMDa data...Please wait...') |
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print('=' * 70) |
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print('Loading LAMDa META-DATA...') |
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meta_data = pickle.load(open('/content/Main-MIDI-Dataset/META_DATA/LAMDa_META_DATA.pickle', 'rb')) |
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print('Done!') |
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"""# (SEARCH AND FILTER) |
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### DO NOT FORGET TO UPLOAD YOUR MASTER DATASET TO "Master-MIDI-Dataset" FOLDER |
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""" |
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number_of_top_ratios_MIDIs_to_collect = 10 |
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maximum_match_ratio_to_search_for = 1 |
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pitches_counts_cutoff_threshold_ratio = 0 |
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search_transposed_pitches = False |
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skip_exact_matches = False |
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add_pitches_counts_ratios = False |
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add_timings_ratios = False |
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add_durations_ratios = False |
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print('=' * 70) |
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print('Master MIDI Dataset Search and Filter') |
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print('=' * 70) |
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print('Loading MIDI files...') |
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print('This may take a while on a large dataset in particular.') |
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dataset_addr = "/content/Master-MIDI-Dataset" |
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filez = list() |
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for (dirpath, dirnames, filenames) in os.walk(dataset_addr): |
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filez += [os.path.join(dirpath, file) for file in filenames] |
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print('=' * 70) |
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if filez == []: |
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print('Could not find any MIDI files. Please check Dataset dir...') |
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print('=' * 70) |
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print('Randomizing file list...') |
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random.shuffle(filez) |
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print('=' * 70) |
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if not os.path.exists('/content/Output-MIDI-Dataset'): |
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os.makedirs('/content/Output-MIDI-Dataset') |
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input_files_count = 0 |
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files_count = 0 |
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for f in filez: |
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try: |
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input_files_count += 1 |
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fn = os.path.basename(f) |
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fn1 = fn.split('.mid')[0] |
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ext = fn.split('.')[-1] |
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if ext == 'mid' or ext == 'midi' or ext == 'kar': |
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print('Processing MIDI File #', files_count+1, 'out of', len(filez)) |
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print('MIDI file name', fn) |
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print('-' * 70) |
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score = TMIDIX.midi2score(open(f, 'rb').read()) |
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events_matrix = [] |
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track_count = 0 |
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for s in score: |
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if track_count > 0: |
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track = s |
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track.sort(key=lambda x: x[1]) |
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events_matrix.extend(track) |
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else: |
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midi_ticks = s |
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track_count += 1 |
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events_matrix.sort(key=lambda x: x[1]) |
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mult_pitches_counts = [] |
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for i in range(-6, 6): |
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events_matrix1 = [] |
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for e in events_matrix: |
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ev = copy.deepcopy(e) |
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if e[0] == 'note': |
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if e[3] == 9: |
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ev[4] = ((e[4] % 128) + 128) |
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else: |
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ev[4] = ((e[4] % 128) + i) |
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events_matrix1.append(ev) |
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pitches_counts = [[y[0],y[1]] for y in Counter([y[4] for y in events_matrix1 if y[0] == 'note']).most_common()] |
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pitches_counts.sort(key=lambda x: x[0], reverse=True) |
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mult_pitches_counts.append(pitches_counts) |
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patches_list = sorted(list(set([y[3] for y in events_matrix if y[0] == 'patch_change']))) |
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ms_score = TMIDIX.midi2ms_score(open(f, 'rb').read()) |
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ms_events_matrix = [] |
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itrack1 = 1 |
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while itrack1 < len(ms_score): |
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for event in ms_score[itrack1]: |
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if event[0] == 'note': |
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ms_events_matrix.append(event) |
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itrack1 += 1 |
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ms_events_matrix.sort(key=lambda x: x[1]) |
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chords = [] |
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pe = ms_events_matrix[0] |
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cho = [] |
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for e in ms_events_matrix: |
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if (e[1] - pe[1]) == 0: |
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if e[3] != 9: |
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if (e[4] % 12) not in cho: |
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cho.append(e[4] % 12) |
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else: |
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if len(cho) > 0: |
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chords.append(sorted(cho)) |
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cho = [] |
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if e[3] != 9: |
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if (e[4] % 12) not in cho: |
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cho.append(e[4] % 12) |
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pe = e |
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if len(cho) > 0: |
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chords.append(sorted(cho)) |
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ms_chords_counts = sorted([[list(key), val] for key,val in Counter([tuple(c) for c in chords if len(c) > 1]).most_common()], reverse=True, key = lambda x: x[1]) |
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times = [] |
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pt = ms_events_matrix[0][1] |
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start = True |
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for e in ms_events_matrix: |
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if (e[1]-pt) != 0 or start == True: |
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times.append((e[1]-pt)) |
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start = False |
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pt = e[1] |
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durs = [e[2] for e in ms_events_matrix] |
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vels = [e[5] for e in ms_events_matrix] |
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avg_time = int(sum(times) / len(times)) |
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avg_dur = int(sum(durs) / len(durs)) |
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mode_time = statistics.mode(times) |
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mode_dur = statistics.mode(durs) |
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median_time = int(statistics.median(times)) |
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median_dur = int(statistics.median(durs)) |
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print('Searching for matches...Please wait...') |
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print('-' * 70) |
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final_ratios = [] |
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for d in tqdm(meta_data): |
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p_counts = d[1][10][1] |
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p_counts.sort(reverse = True, key = lambda x: x[1]) |
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max_p_count = p_counts[0][1] |
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trimmed_p_counts = [y for y in p_counts if y[1] >= (max_p_count * pitches_counts_cutoff_threshold_ratio)] |
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total_p_counts = sum([y[1] for y in trimmed_p_counts]) |
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if search_transposed_pitches: |
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search_pitches = mult_pitches_counts |
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else: |
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search_pitches = [mult_pitches_counts[6]] |
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ratios_list = [] |
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atrat = [0] |
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if add_timings_ratios: |
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source_times = [avg_time, |
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median_time, |
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mode_time] |
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match_times = meta_data[0][1][3][1] |
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times_ratios = [] |
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for i in range(len(source_times)): |
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maxtratio = max(source_times[i], match_times[i]) |
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mintratio = min(source_times[i], match_times[i]) |
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times_ratios.append(mintratio / maxtratio) |
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avg_times_ratio = sum(times_ratios) / len(times_ratios) |
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atrat[0] = avg_times_ratio |
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adrat = [0] |
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if add_durations_ratios: |
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source_durs = [avg_dur, |
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median_dur, |
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mode_dur] |
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match_durs = meta_data[0][1][4][1] |
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durs_ratios = [] |
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for i in range(len(source_durs)): |
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maxtratio = max(source_durs[i], match_durs[i]) |
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mintratio = min(source_durs[i], match_durs[i]) |
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durs_ratios.append(mintratio / maxtratio) |
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avg_durs_ratio = sum(durs_ratios) / len(durs_ratios) |
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adrat[0] = avg_durs_ratio |
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for m in search_pitches: |
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sprat = [] |
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m.sort(reverse = True, key = lambda x: x[1]) |
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max_pitches_count = m[0][1] |
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trimmed_pitches_counts = [y for y in m if y[1] >= (max_pitches_count * pitches_counts_cutoff_threshold_ratio)] |
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total_pitches_counts = sum([y[1] for y in trimmed_pitches_counts]) |
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same_pitches = set([T[0] for T in trimmed_p_counts]) & set([m[0] for m in trimmed_pitches_counts]) |
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num_same_pitches = len(same_pitches) |
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if num_same_pitches == len(trimmed_pitches_counts): |
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same_pitches_ratio = (num_same_pitches / len(trimmed_p_counts)) |
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else: |
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same_pitches_ratio = (num_same_pitches / max(len(trimmed_p_counts), len(trimmed_pitches_counts))) |
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if skip_exact_matches: |
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if same_pitches_ratio == 1: |
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same_pitches_ratio = 0 |
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sprat.append(same_pitches_ratio) |
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spcrat = [0] |
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if add_pitches_counts_ratios: |
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same_trimmed_p_counts = sorted([T for T in trimmed_p_counts if T[0] in same_pitches], reverse = True) |
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same_trimmed_pitches_counts = sorted([T for T in trimmed_pitches_counts if T[0] in same_pitches], reverse = True) |
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same_trimmed_p_counts_ratios = [[s[0], s[1] / total_p_counts] for s in same_trimmed_p_counts] |
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same_trimmed_pitches_counts_ratios = [[s[0], s[1] / total_pitches_counts] for s in same_trimmed_pitches_counts] |
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same_pitches_counts_ratios = [] |
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for i in range(len(same_trimmed_p_counts_ratios)): |
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mincratio = min(same_trimmed_p_counts_ratios[i][1], same_trimmed_pitches_counts_ratios[i][1]) |
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maxcratio = max(same_trimmed_p_counts_ratios[i][1], same_trimmed_pitches_counts_ratios[i][1]) |
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same_pitches_counts_ratios.append([same_trimmed_p_counts_ratios[i][0], mincratio / maxcratio]) |
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same_counts_ratios = [s[1] for s in same_pitches_counts_ratios] |
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if len(same_counts_ratios) > 0: |
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avg_same_pitches_counts_ratio = sum(same_counts_ratios) / len(same_counts_ratios) |
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else: |
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avg_same_pitches_counts_ratio = 0 |
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spcrat[0] = avg_same_pitches_counts_ratio |
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r_list = [sprat[0]] |
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if add_pitches_counts_ratios: |
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r_list.append(spcrat[0]) |
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if add_timings_ratios: |
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r_list.append(atrat[0]) |
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if add_durations_ratios: |
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r_list.append(adrat[0]) |
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ratios_list.append(r_list) |
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avg_ratios_list = [] |
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for r in ratios_list: |
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avg_ratios_list.append(sum(r) / len(r)) |
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final_ratio = max(avg_ratios_list) |
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if final_ratio > maximum_match_ratio_to_search_for: |
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final_ratio = 0 |
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final_ratios.append(final_ratio) |
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print('-' * 70) |
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max_ratios = sorted(set(final_ratios), reverse=True)[:number_of_top_ratios_MIDIs_to_collect] |
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print('Max match ratio', max_ratios[0]) |
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print('-' * 70) |
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print('Copying max ratios MIDIs...') |
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for m in max_ratios: |
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max_ratio_index = final_ratios.index(m) |
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ffn = meta_data[max_ratio_index][0] |
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ffn_idx = [y[0] for y in LAMD_files_list].index(ffn) |
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ff = LAMD_files_list[ffn_idx][1] |
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dir_str = str(fn1) |
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copy_path = '/content/Output-MIDI-Dataset/'+dir_str |
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if not os.path.exists(copy_path): |
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os.mkdir(copy_path) |
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fff = str(m * 100) + '_' + ffn + '.mid' |
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shutil.copy2(ff, copy_path+'/'+fff) |
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shutil.copy2(f, copy_path+'/'+fn) |
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print('Done!') |
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print('=' * 70) |
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files_count += 1 |
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except KeyboardInterrupt: |
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print('Quitting...') |
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print('Total number of processed MIDI files', files_count) |
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print('=' * 70) |
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break |
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except Exception as ex: |
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print('WARNING !!!') |
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print('=' * 70) |
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print('Bad file:', f) |
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print('Error detected:', ex) |
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print('=' * 70) |
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continue |
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print('Total number of processed MIDI files', files_count) |
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print('=' * 70) |
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"""# Congrats! You did it! :)""" |
