import os


length_file = 2.3
SNR_matrix = [-10,0,10]
option = 'most'
path_s = '/media/aneumann/Harddisk/Bachelorarbeit/TIMIT/timit_16kHz_wav'
path_m = '/media/aneumann/Harddisk/Bachelorarbeit/MedleyDB/Audio'
path_save = '/media/aneumann/Harddisk/Bachelorarbeit/BATest'
# path_s = '/ba/TIMIT/timit_16kHz_wav_concatenated/train'
# path_m = '/ba/MedleyDB/Audio'
# path_save = '/ba/BATest'
testname = '/test_whole'
pattern = "*.wav"
split = 0.8

path_save_augmentation = path_save + '/Gemischte_Signale'


option = 'music'

path_train = path_save_augmentation + '/Train'
path_valid = path_save_augmentation + '/Validation'
batch_size = 20
epochs = 1
reduction_divisor = 10

from Generators.DataGenerator_whole import DataGenerator

# train = os.listdir(path_train)
# train = sorted(train,key=lambda x: ((int(x.split("_")[2])),(int(x.split("_")[3])),(int(x.split("_")[4])),(x.split("_")[5])))
# valid = os.listdir(path_valid)
# valid = sorted(valid,key=lambda x: ((int(x.split("_")[2])),(int(x.split("_")[3])),(int(x.split("_")[4])),(x.split("_")[5])))

train_generator = DataGenerator(path_train, option, reduction_divisor, batch_size, True)
# valid_generator = DataGenerator(path_valid, option, reduction_divisor, batch_size, True)

for X, Y in train_generator:
    print(X.shape, Y.shape)