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"""Monster_MIDI_Dataset_GPU_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/Monster-MIDI-Dataset/blob/main/Monster_MIDI_Dataset_GPU_Search_and_Filter.ipynb |
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# Monster MIDI Dataset GPU Search and Filter (ver. 3.0) |
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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 2024 |
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*** |
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# (GPU CHECK) |
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""" |
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!nvidia-smi |
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"""# (SETUP ENVIRONMENT)""" |
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!git clone --depth 1 https://github.com/asigalov61/Monster-MIDI-Dataset |
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!pip install huggingface_hub |
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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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import locale |
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locale.getpreferredencoding = lambda: "UTF-8" |
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import cupy as cp |
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from huggingface_hub import hf_hub_download |
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from google.colab import files |
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print('Loading TMIDIX module...') |
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os.chdir('/content/Monster-MIDI-Dataset') |
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import TMIDIX |
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os.chdir('/content/') |
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print('Creating IO dirs... Please wait...') |
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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('Done!') |
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print('Enjoy! :)') |
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"""# (DOWNLOAD AND UNZIP MAIN MIDI DATASET)""" |
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print('=' * 70) |
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print('Downloading Monster MIDI Dataset...Please wait...') |
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print('=' * 70) |
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hf_hub_download(repo_id='projectlosangeles/Monster-MIDI-Dataset', |
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filename='Monster-MIDI-Dataset-Ver-1-0-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 Monster MIDI Dataset...Please wait...') |
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!unzip 'Monster-MIDI-Dataset-Ver-1-0-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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"""# (CREATE MAIN MIDI DATASET FILES LIST)""" |
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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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"""# (SIGNATURES SEARCH)""" |
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print('=' * 70) |
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print('Loading Monster MIDI Dataset Signatures Data...') |
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sigs_data = pickle.load(open('/content/Main-MIDI-Dataset/SIGNATURES_DATA/MONSTER_SIGNATURES_DATA.pickle', 'rb')) |
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print('=' * 70) |
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print('Prepping signatures...') |
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print('=' * 70) |
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random.shuffle(sigs_data) |
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signatures_file_names = [] |
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sigs_matrixes = [ [0]*(len(TMIDIX.ALL_CHORDS)+256) for i in range(len(sigs_data))] |
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idx = 0 |
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for s in tqdm(sigs_data): |
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signatures_file_names.append(s[0]) |
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for ss in s[1]: |
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sigs_matrixes[idx][ss[0]] = ss[1] |
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idx += 1 |
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print('=' * 70) |
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print('Loading signatures...') |
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print('=' * 70) |
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signatures_data_full = cp.array(sigs_matrixes) |
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print('Done!') |
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print('=' * 70) |
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number_of_top_matches_MIDIs_to_collect = 30 |
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search_matching_type = "Ratios" |
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maximum_match_ratio_to_search_for = 1 |
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match_results_weight = 2 |
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match_lengths_weight = 1 |
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match_counts_weight = 1 |
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distances_norm_order = 3 |
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epsilon = 0.5 |
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match_drums = False |
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print('=' * 70) |
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print('Monster MIDI Dataset GPU Search and Filter') |
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print('=' * 70) |
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search_settings_string = '' |
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if match_drums: |
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search_settings_string += 'Chords_Drums' |
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else: |
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search_settings_string += 'Chords' |
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if search_matching_type == 'Distances': |
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search_settings_string += '_O_' + str(distances_norm_order) |
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search_settings_string += '_W_' |
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search_settings_string += str(match_results_weight) + '_' |
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search_settings_string += str(match_lengths_weight) + '_' |
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search_settings_string += str(match_counts_weight) |
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search_settings_string += '_E_' + str(epsilon) |
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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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for file in filenames: |
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if file.endswith(('.mid', '.midi', '.kar')): |
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filez.append(os.path.join(dirpath, file)) |
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print('=' * 70) |
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if filez: |
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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/'+search_matching_type+'_'+search_settings_string): |
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os.makedirs('/content/Output-MIDI-Dataset/'+search_matching_type+'_'+search_settings_string) |
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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 = os.path.splitext(fn)[0] |
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ext = os.path.splitext(f)[1] |
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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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raw_score = TMIDIX.midi2single_track_ms_score(open(f, 'rb').read()) |
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escore = TMIDIX.advanced_score_processor(raw_score, return_score_analysis=False, return_enhanced_score_notes=True)[0] |
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for e in escore: |
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e[1] = int(e[1] / 16) |
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e[2] = int(e[2] / 16) |
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drums_offset = len(TMIDIX.ALL_CHORDS_SORTED) + 128 |
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src_sigs = [] |
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for i in range(-6, 6): |
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escore_copy = copy.deepcopy(escore) |
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for e in escore_copy: |
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e[4] += i |
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cscore = TMIDIX.chordify_score([1000, escore_copy]) |
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sig = [] |
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dsig = [] |
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for c in cscore: |
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pitches = sorted(set([p[4] for p in c if p[3] != 9])) |
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drums = sorted(set([p[4]+drums_offset for p in c if p[3] == 9])) |
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if pitches: |
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if len(pitches) > 1: |
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tones_chord = sorted(set([p % 12 for p in pitches])) |
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try: |
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sig_token = TMIDIX.ALL_CHORDS_SORTED.index(tones_chord) + 128 |
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except: |
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checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord) |
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sig_token = TMIDIX.ALL_CHORDS_SORTED.index(checked_tones_chord) + 128 |
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elif len(pitches) == 1: |
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sig_token = pitches[0] |
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sig.append(sig_token) |
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if drums: |
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dsig.extend(drums) |
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sig_p = dict.fromkeys(sig+dsig, 0) |
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for item in sig+dsig: |
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sig_p[item] += 1 |
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fsig = [list(v) for v in sig_p.items()] |
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src_sig_mat = [0] * (len(TMIDIX.ALL_CHORDS)+256) |
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for s in fsig: |
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src_sig_mat[s[0]] = s[1] |
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src_sigs.append(src_sig_mat) |
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src_signatures = cp.stack(cp.array(src_sigs)) |
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if not match_drums: |
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src_signatures = src_signatures[:,:drums_offset] |
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signatures_data = signatures_data_full[:,:drums_offset] |
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else: |
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signatures_data = signatures_data_full |
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print('Searching for matches...Please wait...') |
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print('-' * 70) |
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lower_threshold = 0.0 |
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upper_threshold = maximum_match_ratio_to_search_for |
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filter_size = number_of_top_matches_MIDIs_to_collect |
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final_ratios = [] |
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avg_idxs = [] |
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all_filtered_means = [] |
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all_filtered_idxs = [] |
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all_filtered_tvs = [] |
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tv_idx = -6 |
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for target_sig in tqdm(src_signatures): |
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comps_lengths = cp.vstack((cp.repeat(cp.sum(target_sig != 0), signatures_data.shape[0]), cp.sum(signatures_data != 0, axis=1))) |
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comps_lengths_ratios = cp.divide(cp.min(comps_lengths, axis=0), cp.max(comps_lengths, axis=0)) |
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comps_counts_sums = cp.vstack((cp.repeat(cp.sum(target_sig), signatures_data.shape[0]), cp.sum(signatures_data, axis=1))) |
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comps_counts_sums_ratios = cp.divide(cp.min(comps_counts_sums, axis=0), cp.max(comps_counts_sums, axis=0)) |
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if search_matching_type == 'Ratios': |
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ratios = cp.where(target_sig != 0, cp.divide(cp.minimum(signatures_data, target_sig), cp.maximum(signatures_data, target_sig)), epsilon) |
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results = cp.mean(ratios, axis=1) |
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elif search_matching_type == 'Distances': |
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distances = cp.power(cp.sum(cp.power(cp.abs(signatures_data - target_sig), distances_norm_order), axis=1), 1 / distances_norm_order) |
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distances_mean = cp.mean(distances) |
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distances_std = cp.std(distances) |
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results = 1 - cp.divide((distances - distances_mean), distances_std) |
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elif search_matching_type == 'Correlations': |
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main_array_mean = cp.mean(signatures_data, axis=1, keepdims=True) |
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main_array_std = cp.std(signatures_data, axis=1, keepdims=True) |
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target_array_mean = cp.mean(target_sig) |
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target_array_std = cp.std(target_sig) |
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signatures_data_normalized = cp.where(main_array_std != 0, (signatures_data - main_array_mean) / main_array_std, epsilon) |
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target_sig_normalized = cp.where(target_array_std != 0, (target_sig - target_array_mean) / target_array_std, epsilon) |
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correlations = cp.divide(cp.einsum('ij,j->i', signatures_data_normalized, target_sig_normalized), (signatures_data.shape[1] - 1)) |
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scaled_correlations = cp.divide(correlations, cp.sqrt(cp.sum(correlations**2))) |
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exp = cp.exp(scaled_correlations - cp.max(scaled_correlations)) |
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results = cp.multiply(cp.divide(exp, cp.sum(exp)), 1e5) |
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results_weight = match_results_weight |
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comp_lengths_weight = match_lengths_weight |
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comp_counts_sums_weight = match_counts_weight |
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results = cp.divide(cp.add(cp.add(results_weight, comp_lengths_weight), comp_counts_sums_weight), cp.add(cp.add(cp.divide(results_weight, cp.where(results !=0, results, epsilon)), cp.divide(comp_lengths_weight, cp.where(comps_lengths_ratios !=0, comps_lengths_ratios, epsilon))), cp.divide(comp_counts_sums_weight, cp.where(comps_counts_sums_ratios !=0, comps_counts_sums_ratios, epsilon)))) |
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unique_means = cp.unique(results) |
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sorted_means = cp.sort(unique_means)[::-1] |
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filtered_means = sorted_means[(sorted_means >= lower_threshold) & (sorted_means <= upper_threshold)][:filter_size] |
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filtered_idxs = cp.nonzero(cp.in1d(results, filtered_means))[0] |
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all_filtered_means.extend(results[filtered_idxs].tolist()) |
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all_filtered_idxs.extend(filtered_idxs.tolist()) |
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filtered_tvs = [tv_idx] * filtered_idxs.shape[0] |
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all_filtered_tvs.extend(filtered_tvs) |
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tv_idx += 1 |
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f_results = sorted(zip(all_filtered_means, all_filtered_idxs, all_filtered_tvs), key=lambda x: x[0], reverse=True) |
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triplet_dict = {} |
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for triplet in f_results: |
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if triplet[0] not in triplet_dict: |
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triplet_dict[triplet[0]] = triplet |
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else: |
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if triplet[2] == 0: |
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triplet_dict[triplet[0]] = triplet |
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filtered_results = list(triplet_dict.values())[:filter_size] |
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print('Done!') |
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print('-' * 70) |
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print('Max match ratio:', filtered_results[0][0]) |
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print('Max match transpose value:', filtered_results[0][2]) |
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print('Max match signature index:', filtered_results[0][1]) |
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print('Max match file name:', signatures_file_names[filtered_results[0][1]]) |
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print('-' * 70) |
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print('Copying max ratios MIDIs...') |
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for fr in filtered_results: |
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max_ratio_index = fr[1] |
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ffn = signatures_file_names[fr[1]] |
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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/'+search_matching_type+'_'+search_settings_string+'/'+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(fr[0] * 100) + '_' + str(fr[2]) + '_' + 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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else: |
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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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"""# (KILO-CHORDS SEARCH)""" |
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search_matching_type = "Full-Kilo-Chords" |
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print('=' * 70) |
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print('Loading Monster MIDI Dataset Kilo-Chords Data...') |
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kilo_chords = pickle.load(open('/content/Main-MIDI-Dataset/KILO_CHORDS_DATA/MONSTER_KILO_CHORDS_DATA.pickle', 'rb')) |
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print('=' * 70) |
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print('Prepping Kilo-Chords...') |
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print('=' * 70) |
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random.shuffle(kilo_chords) |
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if search_matching_type == 'Full-Kilo-Chords': |
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kilo_chords_file_names = [] |
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for kc in tqdm(kilo_chords): |
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kilo_chords_file_names.append(kc[0]) |
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kcho = kc[1] |
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kcho += [0] * (1000 - len(kcho)) |
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print('=' * 70) |
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print('Loading Kilo-Chords...') |
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print('=' * 70) |
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kilo_chords_data = cp.array([kc[1] for kc in kilo_chords]) |
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else: |
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kilo_chords_file_names = [] |
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kilo_chords_matrixes = [ [0]*(len(TMIDIX.ALL_CHORDS)+128) for i in range(len(kilo_chords))] |
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idx = 0 |
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for kc in tqdm(kilo_chords): |
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kilo_chords_file_names.append(kc[0]) |
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for c in kc[1]: |
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kilo_chords_matrixes[idx][c] += 1 |
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idx += 1 |
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print('=' * 70) |
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print('Loading Kilo-Chords...') |
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print('=' * 70) |
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kilo_chords_data = cp.array(kilo_chords_matrixes) |
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print('Done!') |
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print('=' * 70) |
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number_of_top_matches_MIDIs_to_collect = 30 |
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maximum_match_ratio_to_search_for = 1 |
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match_results_weight = 2 |
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match_lengths_weight = 1 |
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match_counts_weight = 1 |
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epsilon = 0.5 |
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print('=' * 70) |
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print('Monster MIDI Dataset GPU Search and Filter') |
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print('=' * 70) |
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search_settings_string = '' |
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search_settings_string += str(search_matching_type).replace('-', '_') |
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search_settings_string += '_W_' |
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search_settings_string += str(match_results_weight) + '_' |
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search_settings_string += str(match_lengths_weight) + '_' |
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search_settings_string += str(match_counts_weight) |
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search_settings_string += '_E_' + str(epsilon) |
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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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for file in filenames: |
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if file.endswith(('.mid', '.midi', '.kar')): |
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filez.append(os.path.join(dirpath, file)) |
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print('=' * 70) |
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if filez: |
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print('Randomizing file list...') |
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random.shuffle(filez) |
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print('=' * 70) |
|
|
|
|
|
|
|
if not os.path.exists('/content/Output-MIDI-Dataset/'+search_settings_string): |
|
os.makedirs('/content/Output-MIDI-Dataset/'+search_settings_string) |
|
|
|
|
|
|
|
input_files_count = 0 |
|
files_count = 0 |
|
|
|
for f in filez: |
|
|
|
try: |
|
|
|
input_files_count += 1 |
|
|
|
fn = os.path.basename(f) |
|
fn1 = os.path.splitext(fn)[0] |
|
ext = os.path.splitext(f)[1] |
|
|
|
print('Processing MIDI File #', files_count+1, 'out of', len(filez)) |
|
print('MIDI file name', fn) |
|
print('-' * 70) |
|
|
|
|
|
|
|
raw_score = TMIDIX.midi2single_track_ms_score(open(f, 'rb').read()) |
|
escore = TMIDIX.advanced_score_processor(raw_score, return_score_analysis=False, return_enhanced_score_notes=True)[0] |
|
|
|
for e in escore: |
|
e[1] = int(e[1] / 16) |
|
e[2] = int(e[2] / 16) |
|
|
|
src_kilo_chords = [] |
|
|
|
for i in range(-6, 6): |
|
|
|
escore_copy = copy.deepcopy(escore) |
|
|
|
for e in escore_copy: |
|
e[4] += i |
|
|
|
cscore = TMIDIX.chordify_score([1000, escore_copy]) |
|
|
|
kilo_chord = [] |
|
|
|
for c in cscore: |
|
|
|
pitches = sorted(set([p[4] for p in c if p[3] != 9])) |
|
|
|
if pitches: |
|
if len(pitches) > 1: |
|
tones_chord = sorted(set([p % 12 for p in pitches])) |
|
|
|
try: |
|
chord_token = TMIDIX.ALL_CHORDS_SORTED.index(tones_chord) + 128 |
|
except: |
|
checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord) |
|
chord_token = TMIDIX.ALL_CHORDS_SORTED.index(checked_tones_chord) + 128 |
|
|
|
elif len(pitches) == 1: |
|
chord_token = pitches[0] |
|
|
|
kilo_chord.append(chord_token) |
|
|
|
if search_matching_type == 'Full-Kilo-Chords': |
|
|
|
kilo_chord = kilo_chord[:1000] |
|
kilo_chord_matrix = kilo_chord + [0] * (1000 - len(kilo_chord)) |
|
|
|
else: |
|
|
|
kilo_chord_matrix = [0] * (len(TMIDIX.ALL_CHORDS)+128) |
|
|
|
for c in kilo_chord: |
|
kilo_chord_matrix[c] += 1 |
|
|
|
src_kilo_chords.append(kilo_chord_matrix) |
|
|
|
src_kilo_chords = cp.stack(cp.array(src_kilo_chords)) |
|
|
|
|
|
|
|
print('Searching for matches...Please wait...') |
|
print('-' * 70) |
|
|
|
lower_threshold = 0.0 |
|
upper_threshold = maximum_match_ratio_to_search_for |
|
filter_size = number_of_top_matches_MIDIs_to_collect |
|
|
|
final_ratios = [] |
|
|
|
avg_idxs = [] |
|
|
|
all_filtered_means = [] |
|
all_filtered_idxs = [] |
|
all_filtered_tvs = [] |
|
|
|
tv_idx = -6 |
|
|
|
for target_kc in tqdm(src_kilo_chords): |
|
|
|
comps_lengths = cp.vstack((cp.repeat(cp.sum(target_kc != 0), kilo_chords_data.shape[0]), cp.sum(kilo_chords_data != 0, axis=1))) |
|
comps_lengths_ratios = cp.divide(cp.min(comps_lengths, axis=0), cp.max(comps_lengths, axis=0)) |
|
|
|
comps_counts_sums = cp.vstack((cp.repeat(cp.sum(target_kc), kilo_chords_data.shape[0]), cp.sum(kilo_chords_data, axis=1))) |
|
comps_counts_sums_ratios = cp.divide(cp.min(comps_counts_sums, axis=0), cp.max(comps_counts_sums, axis=0)) |
|
|
|
intersections = cp.where((kilo_chords_data == target_kc), kilo_chords_data, 0) |
|
results = cp.mean(intersections != 0, axis=1) |
|
|
|
results_weight = match_results_weight |
|
comp_lengths_weight = match_lengths_weight |
|
comp_counts_sums_weight = match_counts_weight |
|
|
|
results = cp.divide(cp.add(cp.add(results_weight, comp_lengths_weight), comp_counts_sums_weight), cp.add(cp.add(cp.divide(results_weight, cp.where(results !=0, results, epsilon)), cp.divide(comp_lengths_weight, cp.where(comps_lengths_ratios !=0, comps_lengths_ratios, epsilon))), cp.divide(comp_counts_sums_weight, cp.where(comps_counts_sums_ratios !=0, comps_counts_sums_ratios, epsilon)))) |
|
|
|
unique_means = cp.unique(results) |
|
sorted_means = cp.sort(unique_means)[::-1] |
|
|
|
filtered_means = sorted_means[(sorted_means >= lower_threshold) & (sorted_means <= upper_threshold)][:filter_size] |
|
|
|
filtered_idxs = cp.nonzero(cp.in1d(results, filtered_means))[0] |
|
|
|
all_filtered_means.extend(results[filtered_idxs].tolist()) |
|
|
|
all_filtered_idxs.extend(filtered_idxs.tolist()) |
|
|
|
filtered_tvs = [tv_idx] * filtered_idxs.shape[0] |
|
|
|
all_filtered_tvs.extend(filtered_tvs) |
|
|
|
tv_idx += 1 |
|
|
|
f_results = sorted(zip(all_filtered_means, all_filtered_idxs, all_filtered_tvs), key=lambda x: x[0], reverse=True) |
|
|
|
triplet_dict = {} |
|
|
|
for triplet in f_results: |
|
|
|
if triplet[0] not in triplet_dict: |
|
triplet_dict[triplet[0]] = triplet |
|
else: |
|
if triplet[2] == 0: |
|
triplet_dict[triplet[0]] = triplet |
|
|
|
filtered_results = list(triplet_dict.values())[:filter_size] |
|
|
|
|
|
|
|
print('Done!') |
|
print('-' * 70) |
|
print('Max match ratio:', filtered_results[0][0]) |
|
print('Max match transpose value:', filtered_results[0][2]) |
|
print('Max match signature index:', filtered_results[0][1]) |
|
print('Max match file name:', kilo_chords_file_names[filtered_results[0][1]]) |
|
print('-' * 70) |
|
print('Copying max ratios MIDIs...') |
|
|
|
for fr in filtered_results: |
|
|
|
max_ratio_index = fr[1] |
|
|
|
ffn = kilo_chords_file_names[fr[1]] |
|
ffn_idx = [y[0] for y in LAMD_files_list].index(ffn) |
|
|
|
ff = LAMD_files_list[ffn_idx][1] |
|
|
|
|
|
|
|
dir_str = str(fn1) |
|
copy_path = '/content/Output-MIDI-Dataset/'+search_settings_string+'/'+dir_str |
|
if not os.path.exists(copy_path): |
|
os.mkdir(copy_path) |
|
|
|
fff = str(fr[0] * 100) + '_' + str(fr[2]) + '_' + ffn + '.mid' |
|
|
|
shutil.copy2(ff, copy_path+'/'+fff) |
|
|
|
shutil.copy2(f, copy_path+'/'+fn) |
|
|
|
|
|
print('Done!') |
|
print('=' * 70) |
|
|
|
|
|
|
|
|
|
files_count += 1 |
|
|
|
except KeyboardInterrupt: |
|
print('Quitting...') |
|
print('Total number of processed MIDI files', files_count) |
|
print('=' * 70) |
|
break |
|
|
|
except Exception as ex: |
|
print('WARNING !!!') |
|
print('=' * 70) |
|
print('Bad file:', f) |
|
print('Error detected:', ex) |
|
print('=' * 70) |
|
continue |
|
|
|
print('Total number of processed MIDI files', files_count) |
|
print('=' * 70) |
|
|
|
else: |
|
print('Could not find any MIDI files. Please check Dataset dir...') |
|
print('=' * 70) |
|
|
|
"""# (DOWNLOAD SEARCH RESULTS)""" |
|
|
|
|
|
|
|
|
|
print('=' * 70) |
|
|
|
try: |
|
os.remove('Monster_MIDI_Dataset_Search_Results.zip') |
|
except OSError: |
|
pass |
|
|
|
print('Zipping... Please wait...') |
|
print('=' * 70) |
|
|
|
|
|
!zip -r Monster_MIDI_Dataset_Search_Results.zip * |
|
|
|
|
|
print('=' * 70) |
|
print('Done!') |
|
print('=' * 70) |
|
|
|
print('Downloading final zip file...') |
|
print('=' * 70) |
|
|
|
files.download('/content/Output-MIDI-Dataset/Monster_MIDI_Dataset_Search_Results.zip') |
|
|
|
print('Done!') |
|
print('=' * 70) |
|
|
|
|
|
|
|
|
|
|
|
print('=' * 70) |
|
print('Deleting... Please wait...') |
|
print('=' * 70) |
|
|
|
!rm -rf /content/Output-MIDI-Dataset |
|
print('Done!') |
|
print('=' * 70) |
|
|
|
"""# (META DATA SEARCH)""" |
|
|
|
|
|
print('=' * 70) |
|
print('Loading Monster MIDI Dataset Metadata...') |
|
meta_data = pickle.load(open('/content/Main-MIDI-Dataset/META_DATA/MONSTER_META_DATA.pickle', 'rb')) |
|
print('Done!') |
|
print('=' * 70) |
|
print('Enjoy!') |
|
print('=' * 70) |
|
|
|
|
|
|
|
|
|
|
|
search_query = "Come To My Window" |
|
md5_hash_MIDI_file_name = "68dfb00f24f5ebd9bb52823fa9a04c3e" |
|
case_sensitive_search = False |
|
|
|
fields_to_search = ['track_name', |
|
'text_event', |
|
'lyric', |
|
'copyright_text_event', |
|
'marker', |
|
'text_event_08', |
|
'text_event_09', |
|
'text_event_0a', |
|
'text_event_0b', |
|
'text_event_0c', |
|
'text_event_0d', |
|
'text_event_0e', |
|
'text_event_0f', |
|
] |
|
|
|
print('=' * 70) |
|
print('Los Angeles MIDI Dataset Metadata Search') |
|
print('=' * 70) |
|
print('Searching...') |
|
print('=' * 70) |
|
|
|
if md5_hash_MIDI_file_name != '': |
|
for d in tqdm(meta_data): |
|
try: |
|
if d[0] == md5_hash_MIDI_file_name: |
|
print('Found!') |
|
print('=' * 70) |
|
print('Metadata index:', meta_data.index(d)) |
|
print('MIDI file name:', meta_data[meta_data.index(d)][0]) |
|
print('-' * 70) |
|
pprint.pprint(['Result:', d[1][:16]], compact = True) |
|
print('=' * 70) |
|
break |
|
|
|
except KeyboardInterrupt: |
|
print('Ending search...') |
|
print('=' * 70) |
|
break |
|
|
|
except Exception as e: |
|
print('WARNING !!!') |
|
print('=' * 70) |
|
print('Error detected:', e) |
|
print('=' * 70) |
|
continue |
|
|
|
if d[0] != md5_hash_MIDI_file_name: |
|
print('Not found!') |
|
print('=' * 70) |
|
print('md5 hash was not found!') |
|
print('Ending search...') |
|
print('=' * 70) |
|
|
|
else: |
|
for d in tqdm(meta_data): |
|
try: |
|
for dd in d[1]: |
|
if dd[0] in fields_to_search: |
|
if case_sensitive_search: |
|
if str(search_query) in str(dd[2]): |
|
print('Found!') |
|
print('=' * 70) |
|
print('Metadata index:', meta_data.index(d)) |
|
print('MIDI file name:', meta_data[meta_data.index(d)][0]) |
|
print('-' * 70) |
|
pprint.pprint(['Result:', dd[2][:16]], compact = True) |
|
print('=' * 70) |
|
|
|
else: |
|
if str(search_query).lower() in str(dd[2]).lower(): |
|
print('Found!') |
|
print('=' * 70) |
|
print('Metadata index:', meta_data.index(d)) |
|
print('MIDI file name:', meta_data[meta_data.index(d)][0]) |
|
print('-' * 70) |
|
pprint.pprint(['Result:', dd[2][:16]], compact = True) |
|
print('=' * 70) |
|
|
|
except KeyboardInterrupt: |
|
print('Ending search...') |
|
print('=' * 70) |
|
break |
|
|
|
except: |
|
print('Ending search...') |
|
print('=' * 70) |
|
break |
|
|
|
"""# Congrats! You did it! :)""" |
