|
|
|
"""Master_MIDI_Dataset_GPU_Search_and_Filter.ipynb |
|
|
|
Automatically generated by Colaboratory. |
|
|
|
Original file is located at |
|
https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Extras/Master_MIDI_Dataset_GPU_Search_and_Filter.ipynb |
|
|
|
# Master MIDI Dataset GPU Search and Filter (ver. 2.0) |
|
|
|
*** |
|
|
|
Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools |
|
|
|
*** |
|
|
|
#### Project Los Angeles |
|
|
|
#### Tegridy Code 2024 |
|
|
|
*** |
|
|
|
# (SETUP ENVIRONMENT) |
|
|
|
# ( GPU CHECK) |
|
""" |
|
|
|
|
|
!nvidia-smi |
|
|
|
"""# (SETUP ENVIRONMENT)""" |
|
|
|
|
|
|
|
!git clone --depth 1 https://github.com/asigalov61/Los-Angeles-MIDI-Dataset |
|
!pip install huggingface_hub |
|
|
|
|
|
|
|
print('Loading core modules... Please wait...') |
|
|
|
import os |
|
import copy |
|
from collections import Counter |
|
import random |
|
import pickle |
|
from tqdm import tqdm |
|
import pprint |
|
import statistics |
|
import shutil |
|
|
|
import cupy as cp |
|
|
|
from huggingface_hub import hf_hub_download |
|
|
|
print('Loading TMIDIX module...') |
|
os.chdir('/content/Los-Angeles-MIDI-Dataset') |
|
|
|
import TMIDIX |
|
|
|
os.chdir('/content/') |
|
|
|
print('Creating IO dirs... Please wait...') |
|
|
|
if not os.path.exists('/content/Master-MIDI-Dataset'): |
|
os.makedirs('/content/Master-MIDI-Dataset') |
|
|
|
if not os.path.exists('/content/Master-MIDI-Dataset'): |
|
os.makedirs('/content/Master-MIDI-Dataset') |
|
|
|
if not os.path.exists('/content/Output-MIDI-Dataset'): |
|
os.makedirs('/content/Output-MIDI-Dataset') |
|
|
|
print('Done!') |
|
print('Enjoy! :)') |
|
|
|
"""# (PREP MAIN MIDI DATASET)""" |
|
|
|
|
|
print('=' * 70) |
|
print('Downloading Los Angeles MIDI Dataset...Please wait...') |
|
print('=' * 70) |
|
|
|
hf_hub_download(repo_id='projectlosangeles/Los-Angeles-MIDI-Dataset', |
|
filename='Los-Angeles-MIDI-Dataset-Ver-4-0-CC-BY-NC-SA.zip', |
|
repo_type="dataset", |
|
local_dir='/content/Main-MIDI-Dataset', |
|
local_dir_use_symlinks=False) |
|
print('=' * 70) |
|
print('Done! Enjoy! :)') |
|
print('=' * 70) |
|
|
|
|
|
|
|
|
|
|
|
print('=' * 70) |
|
print('Unzipping Los Angeles MIDI Dataset...Please wait...') |
|
!unzip 'Los-Angeles-MIDI-Dataset-Ver-4-0-CC-BY-NC-SA.zip' |
|
print('=' * 70) |
|
|
|
print('Done! Enjoy! :)') |
|
print('=' * 70) |
|
|
|
|
|
|
|
print('=' * 70) |
|
print('Creating dataset files list...') |
|
dataset_addr = "/content/Main-MIDI-Dataset/MIDIs" |
|
|
|
|
|
filez = list() |
|
for (dirpath, dirnames, filenames) in os.walk(dataset_addr): |
|
filez += [os.path.join(dirpath, file) for file in filenames] |
|
|
|
if filez == []: |
|
print('Could not find any MIDI files. Please check Dataset dir...') |
|
print('=' * 70) |
|
|
|
print('=' * 70) |
|
print('Randomizing file list...') |
|
random.shuffle(filez) |
|
print('=' * 70) |
|
|
|
LAMD_files_list = [] |
|
|
|
for f in tqdm(filez): |
|
LAMD_files_list.append([f.split('/')[-1].split('.mid')[0], f]) |
|
print('Done!') |
|
print('=' * 70) |
|
|
|
|
|
|
|
print('=' * 70) |
|
print('Loading LAMDa Signatures Data...') |
|
sigs_data = pickle.load(open('/content/Main-MIDI-Dataset/SIGNATURES_DATA/LAMDa_SIGNATURES_DATA.pickle', 'rb')) |
|
print('=' * 70) |
|
|
|
print('Prepping signatures...') |
|
print('=' * 70) |
|
|
|
random.shuffle(sigs_data) |
|
|
|
signatures_file_names = [] |
|
sigs_matrixes = [ [0]*(len(TMIDIX.ALL_CHORDS)+128) for i in range(len(sigs_data))] |
|
|
|
idx = 0 |
|
for s in tqdm(sigs_data): |
|
|
|
signatures_file_names.append(s[0]) |
|
|
|
counts_sum = sum([c[1] for c in s[1]]) |
|
|
|
for ss in s[1]: |
|
sigs_matrixes[idx][ss[0]] = ss[1] / counts_sum |
|
|
|
idx += 1 |
|
|
|
print('=' * 70) |
|
print('Loading signatures...') |
|
print('=' * 70) |
|
|
|
signatures_data = cp.array(sigs_matrixes) |
|
|
|
print('Done!') |
|
print('=' * 70) |
|
|
|
"""# (SEARCH AND FILTER) |
|
|
|
### DO NOT FORGET TO UPLOAD YOUR MASTER DATASET TO "Master-MIDI-Dataset" FOLDER |
|
""" |
|
|
|
|
|
|
|
|
|
|
|
number_of_top_matches_MIDIs_to_collect = 20 |
|
search_matching_type = "ratios" |
|
distances_norm_order = 3 |
|
maximum_match_ratio_to_search_for = 0.999 |
|
|
|
print('=' * 70) |
|
print('Master MIDI Dataset GPU Search and Filter') |
|
print('=' * 70) |
|
|
|
|
|
|
|
print('Loading MIDI files...') |
|
print('This may take a while on a large dataset in particular.') |
|
|
|
dataset_addr = "/content/Master-MIDI-Dataset" |
|
|
|
filez = list() |
|
|
|
for (dirpath, dirnames, filenames) in os.walk(dataset_addr): |
|
for file in filenames: |
|
if file.endswith(('.mid', '.midi', '.kar')): |
|
filez.append(os.path.join(dirpath, file)) |
|
|
|
print('=' * 70) |
|
|
|
if filez: |
|
|
|
print('Randomizing file list...') |
|
random.shuffle(filez) |
|
print('=' * 70) |
|
|
|
|
|
|
|
if not os.path.exists('/content/Output-MIDI-Dataset'): |
|
os.makedirs('/content/Output-MIDI-Dataset') |
|
|
|
|
|
|
|
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_sigs = [] |
|
|
|
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]) |
|
|
|
sig = [] |
|
|
|
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])) |
|
checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord) |
|
|
|
sig_token = TMIDIX.ALL_CHORDS.index(checked_tones_chord) + 128 |
|
|
|
elif len(pitches) == 1: |
|
sig_token = pitches[0] |
|
|
|
sig.append(sig_token) |
|
|
|
fsig = [list(v) for v in Counter(sig).most_common()] |
|
|
|
src_sig_mat = [0] * (len(TMIDIX.ALL_CHORDS)+128) |
|
|
|
counts_sum = sum([c[1] for c in fsig]) |
|
|
|
for s in fsig: |
|
|
|
src_sig_mat[s[0]] = s[1] / counts_sum |
|
|
|
src_sigs.append(src_sig_mat) |
|
|
|
src_signatures = cp.stack(cp.array(src_sigs)) |
|
|
|
|
|
|
|
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_sig in tqdm(src_signatures): |
|
|
|
if search_matching_type == 'ratios': |
|
|
|
ratios = cp.where(target_sig != 0, cp.divide(cp.minimum(signatures_data, target_sig), cp.maximum(signatures_data, target_sig)), 0) |
|
max_comp_lengths = cp.maximum(cp.repeat(cp.sum(target_sig != 0), signatures_data.shape[0]), cp.sum(signatures_data != 0, axis=1)) |
|
|
|
results = cp.divide(cp.sum(ratios, axis=1), max_comp_lengths) |
|
|
|
elif search_matching_type == 'distances': |
|
|
|
distances = cp.power(cp.sum(cp.power(cp.abs(signatures_data - target_sig), distances_norm_order), axis=1), 1 / distances_norm_order) |
|
|
|
results = cp.max(distances) - distances |
|
|
|
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.where(cp.in1d(results, filtered_means))[0] |
|
|
|
all_filtered_means.extend(results[cp.in1d(results, filtered_means)].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 |
|
|
|
filtered_results = sorted(zip(all_filtered_means, all_filtered_idxs, all_filtered_tvs), key=lambda x: x[0], reverse=True)[: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:', signatures_file_names[filtered_results[0][1]]) |
|
print('-' * 70) |
|
print('Copying max ratios MIDIs...') |
|
|
|
for fr in filtered_results: |
|
|
|
max_ratio_index = fr[1] |
|
|
|
ffn = signatures_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/'+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) |
|
|
|
"""# Congrats! You did it! :)""" |