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def prep_whole_dividable(path_load, path_save, skipcount, option, firstdim, secdim):
import string
import os
import numpy as np
import scipy.io
import math
from tqdm import tqdm
# --------------------------------------------------------------
# Get files from path
path_save_one = path_save
for p in range(1,2):
if p == 0:
path_load_real = path_load + '/Train'
path_save_real = path_save_one + '/Train'
if not os.path.exists(path_save_real):
os.makedirs(path_save_real)
elif p == 1:
path_load_real = path_load + '/Validation'
path_save_real = path_save_one + '/Validation'
if not os.path.exists(path_save_real):
os.makedirs(path_save_real)
elif p == 2:
path_load_real = path_load + '/Test'
path_save_real = path_save_one + '/Test'
if not os.path.exists(path_save_real):
os.makedirs(path_save_real)
files_all = os.listdir(path_load_real)
files_all = sorted(files_all)
for files in range(0,len(files_all),skipcount):
fourierspec2 = []
labelpsd2 = []
for file in tqdm(files_all[files:files+skipcount]):
path_loadcontents = path_load_real + '/' + file
contents = np.load(path_loadcontents)
fourierspec = []
labelpsd = []
fourier = contents['dbmixed']
fourier = fourier[:firstdim,:secdim]
fourierspec2.append(fourier)
if option == 'music':
label = contents['dbmusic']
elif option == 'speech':
label = contents['dbspeech']
else:
print('option not right!')
label = label[:firstdim,:secdim]
labelpsd2.append(label)
train_data = np.array(fourierspec2)
train_data = np.reshape(train_data, (train_data.shape[0], train_data.shape[1], train_data.shape[2], 1))
train_labels = np.array(labelpsd2)
train_labels = np.reshape(train_labels, (train_labels.shape[0], train_labels.shape[1], train_labels.shape[2], 1))
path_savecontents = path_save_real + '/Processed_' + str(files) + '_' + str(train_data.shape[0]) + '_whole_dividable.npz'
np.savez(path_savecontents, x=train_data, y=train_labels)
return path_save_one
def prep_flatten_1weiter(path_load, path_save, skipcount, framelength, option):
import os
import numpy as np
import scipy.io
import math
from tqdm import tqdm
# -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
path_save_one = path_save
for p in range(0,3):
if p == 0:
path_load_real = path_load + '/Train'
path_save_real = path_save_one + '/Train'
if not os.path.exists(path_save_real):
os.makedirs(path_save_real)
elif p == 1:
path_load_real = path_load + '/Validation'
path_save_real = path_save_one + '/Validation'
if not os.path.exists(path_save_real):
os.makedirs(path_save_real)
elif p == 2:
path_load_real = path_load + '/Test'
path_save_real = path_save_one + '/Test'
if not os.path.exists(path_save_real):
os.makedirs(path_save_real)
# Get files from path
files_all = os.listdir(path_load_real)
files_all = sorted(files_all)
for files in range(0,len(files_all),skipcount):
fourierspec2 = []
labelpsd2 = []
for file in tqdm(files_all[files:files+skipcount]):
path_loadcontents = path_load_real + '/' + file
contents = np.load(path_loadcontents)
fourierspec = []
labelpsd = []
fourier = contents['dbmixed']
for n in list(range(0, (math.floor(fourier.shape[1]/framelength)-1)*framelength+1)):
fourierlil = fourier[:,n:n+framelength]
fourierlil = np.transpose(fourierlil)
fourierlil = np.reshape(fourierlil, (fourier.shape[0]*framelength))
fourierspec.append(fourierlil)
fourierspec2.extend(fourierspec)
if option == 'music':
label = contents['dbmusic']
elif option == 'speech':
label = contents['dbspeech']
else:
print('option not right!')
for n in list(range(0, (math.floor(fourier.shape[1]/framelength)-1)*framelength+1)):
labellil = label[:,n+framelength-1]
labelpsd.append(labellil)
labelpsd2.extend(labelpsd)
train_data = np.array(fourierspec2)
train_labels = np.array(labelpsd2)
path_savecontents = path_save_real + '/Processed_' + str(files) + '_' + str(train_data.shape[0]) + '_flatten_1weiter.npz'
np.savez(path_savecontents, x=train_data, y=train_labels)
diffcount = (math.floor(fourier.shape[1]/framelength)-1)*framelength+1
return diffcount
def prep_flatten_4weiter(path_load, path_save, skipcount, framelength, option):
import os
import numpy as np
import scipy.io
import math
from tqdm import tqdm
# -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
path_save_one = path_save
for p in range(0,3):
if p == 0:
path_load_real = path_load + '/Train'
path_save_real = path_save_one + '/Train'
if not os.path.exists(path_save_real):
os.makedirs(path_save_real)
elif p == 1:
path_load_real = path_load + '/Validation'
path_save_real = path_save_one + '/Validation'
if not os.path.exists(path_save_real):
os.makedirs(path_save_real)
elif p == 2:
path_load_real = path_load + '/Test'
path_save_real = path_save_one + '/Test'
if not os.path.exists(path_save_real):
os.makedirs(path_save_real)
# Get files from path
files_all = os.listdir(path_load_real)
files_all = sorted(files_all)
for files in tqdm(range(0,len(files_all),skipcount)):
fourierspec2 = []
labelpsd2 = []
for file in tqdm(files_all[files:files+skipcount]):
path_loadcontents = path_load_real + '/' + file
contents = np.load(path_loadcontents)
fourierspec = []
labelpsd = []
fourier = contents['dbmixed']
print(fourier.shape)
# if p == 0 & files == 0:
# diffcount = math.floor(fourier.shape[1]/framelength)
# return diffcount
for n in list(range(0, math.floor(fourier.shape[1]/framelength))):
fourierlil = fourier[:,framelength*n:framelength*n+framelength]
fourierlil = np.transpose(fourierlil)
fourierlil = np.reshape(fourierlil, (fourier.shape[0]*framelength))
print(np.size(fourierlil))
fourierspec.append(fourierlil)
print(np.size(fourierspec))
fourierspec2.extend(fourierspec)
if option == 'music':
label = contents['dbmusic']
elif option == 'speech':
label = contents['dbspeech']
else:
print('option not right!')
for n in list(range(0, math.floor(label.shape[1]/framelength))):
labellil = label[:,framelength*n+framelength-1]
print(np.size(labellil))
labelpsd.append(labellil)
print(np.size(labelpsd))
labelpsd2.extend(labelpsd)
train_data = np.array(fourierspec2)
train_labels = np.array(labelpsd2)
path_savecontents = path_save_real + '/Processed_' + str(files) + '_' + str(train_data.shape[0]) + '_' + str(framelength) + '_flatten.npz'
np.savez(path_savecontents, x=train_data, y=train_labels)