File size: 7,486 Bytes
9965bf6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
import blobfile as bf
from tqdm import tqdm
import torch
import numpy as np
import os
import os.path as osp
import re
import torch as th
from collections import defaultdict
import matplotlib.pyplot as plt
import warnings
import csv
import pandas as pd
from music_rule_guidance.music_rules import note_density
warnings.filterwarnings("ignore")
plt.rcParams["figure.figsize"] = (20,3)
plt.rcParams['figure.dpi'] = 300
plt.rcParams['savefig.dpi'] = 300
# number of excerpts, LENGTH x 1.28
LENGTH = 40
# overlap between pieces
GAP = LENGTH // 2
# mamximum empty length
ALLOWABLE_ZERO = 1 # the actual zeros would be larger than allowable because smaller 128 may have empty columns
# all dataset
SRC_DIR = 'datasets/all-128-fs100'
TGT_DIR = 'datasets/all-len-40-gap-16-no-empty'
CSV_NAME = 'datasets/all_midi.csv'
CSV_TGT_NAME = TGT_DIR
DUP_MAESTRO = True
# # pixel space
# # number of excerpts, LENGTH x 1.28
# LENGTH = 2
# # overlap between pieces
# GAP = LENGTH // 2
# # mamximum empty length
# ALLOWABLE_ZERO = 1
#
# # all dataset
# SRC_DIR = 'datasets/all-128-fs12.5'
# TGT_DIR = 'datasets/all-len-2-gap-1-pixel'
# CSV_NAME = 'datasets/all_midi.csv'
# CSV_TGT_NAME = TGT_DIR
# DUP_MAESTRO = True
# contain weird sub pieces, need to do some cutoff
MODIFY_NAMES = {'MIDI-Unprocessed_05_R1_2006_01-05_ORIG_MID--AUDIO_05_R1_2006_01_Track01_wav': 349}
def extract_number_and_string(file_name):
ind = [i.start() for i in re.finditer('_', file_name)][-1]
number = int(file_name[ind+1:].split('.')[0])
name = file_name[:ind]
return number, name
def _list_image_files(data_dir):
dirs = bf.listdir(data_dir)
return [data_dir + '/' + d for d in dirs]
def extract_name_from_csv(midi_filename):
last_slash_index = midi_filename.rfind('/')
dot_mid_index = midi_filename.rfind('.mid')
save_name = midi_filename[last_slash_index + 1:dot_mid_index]
return save_name
def extract_string(file_name):
if 'loc' not in file_name:
ind = [i.start() for i in re.finditer('_', file_name)][-1]
name = file_name[:ind]
else:
ind = [i.start() for i in re.finditer('loc', file_name)][-1]
name = file_name[:ind-1]
return name
def find_class(name, df):
dataset = df.loc[df['simple_midi_name'] == name]['dataset'].item()
if dataset == 'maestro':
result = 0
elif dataset == 'muscore':
result = 1
else:
result = 2
return result
def main():
if 'maestro' not in SRC_DIR: # contains pedal and onset info
zero_pr = np.zeros((3, 128, 128))
prev_seq = np.zeros((3, 128, LENGTH*128))
else:
zero_pr = np.zeros((128, 128))
prev_seq = np.zeros((128, LENGTH*128))
for split in ['train', 'test']:
target_dir = osp.join(TGT_DIR, split)
source_dir = osp.join(SRC_DIR, split)
if not os.path.exists(target_dir):
os.makedirs(target_dir)
file_names = bf.listdir(source_dir)
name_length = defaultdict(int)
# save length for each file
for file in file_names:
number, name = extract_number_and_string(file)
if number > name_length[name]:
name_length[name] = number
for name in tqdm(name_length.keys()):
if name in MODIFY_NAMES.keys():
max_length = MODIFY_NAMES[name]
else:
max_length = name_length[name]+1
# only process those that are longer than LENGTH
if max_length >= LENGTH - ALLOWABLE_ZERO:
first_start_inds = range(0, LENGTH, GAP)
for first_start_ind in first_start_inds:
start_inds = range(first_start_ind, max_length, LENGTH)
for i in start_inds:
excerpts = []
zero_counts = 0
offset = 0 # off set because there are zeros
j = 0
last = i == start_inds[-1]
while j < LENGTH:
cur = i + j + offset
file_name = os.path.join(source_dir, name + '_' + str(cur) + '.npy')
j = j + 1
try:
excerpt = np.load(file_name)
excerpts.append(excerpt)
except:
# maximum searching time: LENGTH
if zero_counts < ALLOWABLE_ZERO or last or offset > LENGTH:
# save empty piano rolls
excerpt = zero_pr
zero_counts += 1
excerpts.append(excerpt)
# keep searching for non-empty rolls
else:
j = j - 1
offset = offset + 1
seq = np.concatenate(excerpts, axis=-1).astype(np.uint8) # concatenate along width dimension
if last and zero_counts >= ALLOWABLE_ZERO:
sub_prev_seq = prev_seq[..., - (zero_counts - ALLOWABLE_ZERO)*128:]
sub_seq = seq[..., :(LENGTH - (zero_counts - ALLOWABLE_ZERO))*128]
seq = np.concatenate((sub_prev_seq, sub_seq), axis=-1)
# check emptiness of seq
piano_roll = th.from_numpy(seq[0][None, None])
piano_roll = piano_roll / 63.5 - 1
nd = note_density(piano_roll, quantize_factor=1, interval=128)
horizontal_nd = nd[LENGTH:]
horizontal_nd[horizontal_nd < 1e-2] = 0
num_nonzeros = torch.nonzero(horizontal_nd).size(0)
if num_nonzeros > GAP: # more than half nonzero
np.save(os.path.join(target_dir, name + '_loc_' + str(first_start_ind) + '_' + str(i) + '.npy'), seq)
prev_seq = seq # save for backtrack when last=True
# write names and classes into csv
print("write names and classes into csv...")
for split in ['train', 'test']:
data_dir = osp.join(TGT_DIR, split)
if not data_dir:
raise ValueError("unspecified data directory")
all_files = _list_image_files(data_dir)
# condition on 3 classes: maestro (0), muscore (1), pop (2)
df = pd.read_csv(CSV_NAME)
df['simple_midi_name'] = [extract_name_from_csv(midi_name) for midi_name in df['midi_filename']]
all_file_names = bf.listdir(data_dir)
extracted_names = [extract_string(file_name) for file_name in all_file_names]
classes = [find_class(name, df) for name in extracted_names]
filename = CSV_TGT_NAME + '_' + split + ".csv"
with open(filename, 'w', newline='') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(["midi_filename", "classes"])
csvwriter.writerows(zip(all_files, classes))
# duplicate maestro
if DUP_MAESTRO:
df = pd.read_csv(filename)
filtered_rows = df[df['classes'] == 0]
concat_df = pd.concat([df, filtered_rows], ignore_index=True)
concat_df.to_csv(filename, index=False)
if __name__ == "__main__":
main()
|