|
|
|
"""Los_Angeles_MIDI_Dataset_Metadata_Maker.ipynb |
|
|
|
Automatically generated by Colaboratory. |
|
|
|
Original file is located at |
|
https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/META-DATA/Los_Angeles_MIDI_Dataset_Metadata_Maker.ipynb |
|
|
|
# Los Angeles MIDI Dataset Metadata Maker (ver. 3.1) |
|
|
|
*** |
|
|
|
Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools |
|
|
|
*** |
|
|
|
#### Project Los Angeles |
|
|
|
#### Tegridy Code 2023 |
|
|
|
*** |
|
|
|
# (SETUP ENVIRONMENT) |
|
""" |
|
|
|
|
|
|
|
!git clone https://github.com/asigalov61/tegridy-tools |
|
!pip install tqdm |
|
|
|
|
|
|
|
print('Loading needed modules. Please wait...') |
|
import os |
|
|
|
import math |
|
import statistics |
|
import random |
|
from collections import Counter |
|
import pickle |
|
|
|
from tqdm import tqdm |
|
|
|
if not os.path.exists('/content/Dataset'): |
|
os.makedirs('/content/Dataset') |
|
|
|
print('Loading TMIDIX module...') |
|
os.chdir('/content/tegridy-tools/tegridy-tools') |
|
|
|
import TMIDIX |
|
|
|
print('Done!') |
|
|
|
os.chdir('/content/') |
|
print('Enjoy! :)') |
|
|
|
"""# (DOWNLOAD SOURCE MIDI DATASET)""" |
|
|
|
|
|
|
|
|
|
|
|
|
|
!wget 'http://hog.ee.columbia.edu/craffel/lmd/lmd_full.tar.gz' |
|
!tar -xvf 'lmd_full.tar.gz' |
|
!rm 'lmd_full.tar.gz' |
|
|
|
|
|
|
|
|
|
from google.colab import drive |
|
drive.mount('/content/drive') |
|
|
|
"""# (FILE LIST)""" |
|
|
|
|
|
|
|
|
|
print('Loading MIDI files...') |
|
print('This may take a while on a large dataset in particular.') |
|
|
|
dataset_addr = "/content/Dataset" |
|
|
|
filez = list() |
|
for (dirpath, dirnames, filenames) in os.walk(dataset_addr): |
|
filez += [os.path.join(dirpath, file) for file in filenames] |
|
print('=' * 70) |
|
|
|
if filez == []: |
|
print('Could not find any MIDI files. Please check Dataset dir...') |
|
print('=' * 70) |
|
|
|
print('Randomizing file list...') |
|
random.shuffle(filez) |
|
|
|
TMIDIX.Tegridy_Any_Pickle_File_Writer(filez, '/content/filez') |
|
|
|
|
|
filez = TMIDIX.Tegridy_Any_Pickle_File_Reader('/content/filez') |
|
print('Done!') |
|
|
|
"""# (PROCESS)""" |
|
|
|
|
|
|
|
print('=' * 70) |
|
print('TMIDIX MIDI Processor') |
|
print('=' * 70) |
|
print('Starting up...') |
|
print('=' * 70) |
|
|
|
|
|
|
|
START_FILE_NUMBER = 0 |
|
LAST_SAVED_BATCH_COUNT = 0 |
|
|
|
input_files_count = START_FILE_NUMBER |
|
files_count = LAST_SAVED_BATCH_COUNT |
|
|
|
melody_chords_f = [] |
|
|
|
stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] |
|
|
|
print('Processing MIDI files. Please wait...') |
|
print('=' * 70) |
|
|
|
for f in tqdm(filez[START_FILE_NUMBER:]): |
|
try: |
|
input_files_count += 1 |
|
|
|
fn = os.path.basename(f) |
|
fn1 = fn.split('.mid')[0] |
|
|
|
|
|
|
|
|
|
opus = TMIDIX.midi2opus(open(f, 'rb').read()) |
|
|
|
opus_events_matrix = [] |
|
|
|
itrack0 = 1 |
|
|
|
while itrack0 < len(opus): |
|
for event in opus[itrack0]: |
|
opus_events_matrix.append(event) |
|
itrack0 += 1 |
|
|
|
|
|
|
|
ms_score = TMIDIX.opus2score(TMIDIX.to_millisecs(opus)) |
|
|
|
ms_events_matrix = [] |
|
|
|
itrack1 = 1 |
|
|
|
while itrack1 < len(ms_score): |
|
for event in ms_score[itrack1]: |
|
if event[0] == 'note': |
|
ms_events_matrix.append(event) |
|
itrack1 += 1 |
|
|
|
ms_events_matrix.sort(key=lambda x: x[1]) |
|
|
|
|
|
|
|
|
|
score = TMIDIX.opus2score(opus) |
|
|
|
|
|
|
|
events_matrix = [] |
|
full_events_matrix = [] |
|
|
|
itrack = 1 |
|
patches = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] |
|
|
|
while itrack < len(score): |
|
for event in score[itrack]: |
|
if event[0] == 'note' or event[0] == 'patch_change': |
|
events_matrix.append(event) |
|
full_events_matrix.append(event) |
|
itrack += 1 |
|
|
|
full_events_matrix.sort(key=lambda x: x[1]) |
|
events_matrix.sort(key=lambda x: x[1]) |
|
|
|
events_matrix1 = [] |
|
|
|
for event in events_matrix: |
|
if event[0] == 'patch_change': |
|
patches[event[2]] = event[3] |
|
|
|
if event[0] == 'note': |
|
event.extend([patches[event[3]]]) |
|
events_matrix1.append(event) |
|
|
|
if len(events_matrix1) > 32: |
|
|
|
events_matrix1.sort(key=lambda x: x[1]) |
|
|
|
for e in events_matrix1: |
|
if e[0] == 'note': |
|
if e[3] == 9: |
|
e[4] = ((abs(e[4]) % 128) + 128) |
|
else: |
|
e[4] = (abs(e[4]) % 128) |
|
|
|
pitches_counts = [[y[0],y[1]] for y in Counter([y[4] for y in events_matrix1]).most_common()] |
|
pitches_counts.sort(key=lambda x: x[0], reverse=True) |
|
|
|
patches = sorted([y[6] for y in events_matrix1]) |
|
patches_counts = [[y[0], y[1]] for y in Counter(patches).most_common()] |
|
patches_counts.sort(key = lambda x: x[0]) |
|
|
|
midi_patches = sorted(list(set([y[3] for y in events_matrix if y[0] == 'patch_change']))) |
|
if len(midi_patches) == 0: |
|
midi_patches = [0] |
|
|
|
times = [] |
|
pt = ms_events_matrix[0][1] |
|
start = True |
|
for e in ms_events_matrix: |
|
if (e[1]-pt) != 0 or start == True: |
|
times.append((e[1]-pt)) |
|
start = False |
|
pt = e[1] |
|
|
|
times_sum = min(10000000, sum(times)) |
|
|
|
durs = [e[2] for e in ms_events_matrix] |
|
vels = [e[5] for e in ms_events_matrix] |
|
|
|
avg_time = int(sum(times) / len(times)) |
|
avg_dur = int(sum(durs) / len(durs)) |
|
avg_vel = int(sum(vels) / len(vels)) |
|
|
|
mode_time = statistics.mode(times) |
|
mode_dur = statistics.mode(durs) |
|
mode_vel = statistics.mode(vels) |
|
|
|
median_time = int(statistics.median(times)) |
|
median_dur = int(statistics.median(durs)) |
|
median_vel = int(statistics.median(vels)) |
|
|
|
text_events_list = ['text_event', |
|
'text_event_08', |
|
'text_event_09', |
|
'text_event_0a', |
|
'text_event_0b', |
|
'text_event_0c', |
|
'text_event_0d', |
|
'text_event_0e', |
|
'text_event_0f'] |
|
|
|
text_events_count = len([e for e in full_events_matrix if e[0] in text_events_list]) |
|
lyric_events_count = len([e for e in full_events_matrix if e[0] == 'lyric']) |
|
|
|
chords = [] |
|
pe = ms_events_matrix[0] |
|
cho = [] |
|
for e in ms_events_matrix: |
|
if (e[1] - pe[1]) == 0: |
|
if e[3] != 9: |
|
if (e[4] % 12) not in cho: |
|
cho.append(e[4] % 12) |
|
else: |
|
if len(cho) > 0: |
|
chords.append(sorted(cho)) |
|
cho = [] |
|
if e[3] != 9: |
|
if (e[4] % 12) not in cho: |
|
cho.append(e[4] % 12) |
|
|
|
pe = e |
|
|
|
if len(cho) > 0: |
|
chords.append(sorted(cho)) |
|
|
|
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]) |
|
if len(ms_chords_counts) == 0: |
|
ms_chords_counts = [[[0, 0], 0]] |
|
|
|
total_number_of_chords = len(set([y[1] for y in events_matrix1])) |
|
|
|
tempo_change_count = len([f for f in full_events_matrix if f[0] == 'set_tempo']) |
|
|
|
thirty_second_note = [e for e in events_matrix1][32] |
|
thirty_second_note_idx = full_events_matrix.index(thirty_second_note) |
|
|
|
data = [] |
|
data.append(['total_number_of_tracks', itrack]) |
|
data.append(['total_number_of_opus_midi_events', len(opus_events_matrix)]) |
|
data.append(['total_number_of_score_midi_events', len(full_events_matrix)]) |
|
data.append(['average_median_mode_time_ms', [avg_time, median_time, mode_time]]) |
|
data.append(['average_median_mode_dur_ms', [avg_dur, median_dur, mode_dur]]) |
|
data.append(['average_median_mode_vel', [avg_vel, median_vel, mode_vel]]) |
|
data.append(['total_number_of_chords', total_number_of_chords]) |
|
data.append(['total_number_of_chords_ms', len(times)]) |
|
data.append(['ms_chords_counts', ms_chords_counts]) |
|
data.append(['pitches_times_sum_ms', times_sum]) |
|
data.append(['total_pitches_counts', pitches_counts]) |
|
data.append(['midi_patches', midi_patches]) |
|
data.append(['total_patches_counts', patches_counts]) |
|
data.append(['tempo_change_count', tempo_change_count]) |
|
data.append(['text_events_count', text_events_count]) |
|
data.append(['lyric_events_count', lyric_events_count]) |
|
data.append(['midi_ticks', score[0]]) |
|
data.extend(full_events_matrix[:thirty_second_note_idx]) |
|
data.append(full_events_matrix[-1]) |
|
|
|
melody_chords_f.append([fn1, data]) |
|
|
|
|
|
|
|
|
|
files_count += 1 |
|
|
|
|
|
if files_count % 10000 == 0: |
|
print('SAVING !!!') |
|
print('=' * 70) |
|
print('Saving processed files...') |
|
print('=' * 70) |
|
print('Processed so far:', files_count, 'out of', input_files_count, '===', files_count / input_files_count, 'good files ratio') |
|
print('=' * 70) |
|
count = str(files_count) |
|
TMIDIX.Tegridy_Any_Pickle_File_Writer(melody_chords_f, '/content/drive/MyDrive/LAMD_META_DATA_'+count) |
|
melody_chords_f = [] |
|
print('=' * 70) |
|
|
|
except KeyboardInterrupt: |
|
print('Saving current progress and quitting...') |
|
break |
|
|
|
except Exception as ex: |
|
print('WARNING !!!') |
|
print('=' * 70) |
|
print('Bad MIDI:', f) |
|
print('Error detected:', ex) |
|
print('=' * 70) |
|
continue |
|
|
|
|
|
print('=' * 70) |
|
print('Saving processed files...') |
|
print('=' * 70) |
|
print('Processed so far:', files_count, 'out of', input_files_count, '===', files_count / input_files_count, 'good files ratio') |
|
print('=' * 70) |
|
count = str(files_count) |
|
TMIDIX.Tegridy_Any_Pickle_File_Writer(melody_chords_f, '/content/drive/MyDrive/LAMD_META_DATA_'+count) |
|
|
|
|
|
print('=' * 70) |
|
print('Done!') |
|
print('=' * 70) |
|
|
|
print('Resulting Stats:') |
|
print('=' * 70) |
|
print('Total good processed MIDI files:', files_count) |
|
print('=' * 70) |
|
|
|
"""# (BUILD FINAL METADATA FILE)""" |
|
|
|
|
|
full_path_to_metadata_pickle_files = "/content/drive/MyDrive" |
|
|
|
print('=' * 70) |
|
print('Los Angeles MIDI Dataset Metadata File Builder') |
|
print('=' * 70) |
|
print('Searching for files...') |
|
|
|
filez = list() |
|
for (dirpath, dirnames, filenames) in os.walk(full_path_to_metadata_pickle_files): |
|
filez += [os.path.join(dirpath, file) for file in filenames if file.split('.')[-1] == 'pickle'] |
|
print('=' * 70) |
|
|
|
filez.sort() |
|
|
|
print('Loading metadata files... Please wait...') |
|
print('=' * 70) |
|
|
|
metadata = [] |
|
|
|
for f in tqdm(filez): |
|
|
|
metadata.extend(pickle.load(open(f, 'rb'))) |
|
print('Done!') |
|
print('=' * 70) |
|
print('Loaded file:', f) |
|
print('=' * 70) |
|
|
|
print('Done!') |
|
print('=' * 70) |
|
print('Randomizing metadata entries order...') |
|
random.shuffle(metadata) |
|
print('=' * 70) |
|
print('Writing final metadata pickle file...Please wait...') |
|
|
|
with open('/content/LAMDa_META_DATA.pickle', 'wb') as handle: |
|
pickle.dump(metadata, handle, protocol=pickle.HIGHEST_PROTOCOL) |
|
|
|
print('=' * 70) |
|
print('Done!') |
|
print('=' * 70) |
|
|
|
|
|
print('=' * 70) |
|
print('Zipping... Please wait...') |
|
print('=' * 70) |
|
!zip LAMDa_META_DATA.zip LAMDa_META_DATA.pickle |
|
print('=' * 70) |
|
print('Done!') |
|
print('=' * 70) |
|
|
|
"""# Congrats! You did it! :)""" |
