from data.data_tools import read_metadata, males_keys, females_keys
from data.dataset import Mixer
from models.adapt import Adapt
from models.dpcl import DPCL
from utils.tools import getETA
import time
import numpy as np
import tensorflow as tf
import config
import os
H5_dic = read_metadata()
chunk_size = 512*40
males = H5PY_RW('test_raw.h5py', subset = males_keys(H5_dic))
fem = H5PY_RW('test_raw.h5py', subset = females_keys(H5_dic))
print 'Data with', len(H5_dic), 'male and female speakers'
print males.length(), 'elements'
print fem.length(), 'elements'
mixed_data = Mixer([males, fem], chunk_size= chunk_size, with_mask=False, with_inputs=True)
####
#### PREVIOUS MODEL CONFIG
####
N = 256
max_pool = 256
batch_size = 32
import config
import numpy as np
import tensorflow as tf
if __name__ == "__main__":
H5_dico = read_data_header()
Males = H5PY_RW()
Males.open_h5_dataset('test.h5py', subset=males_keys(H5_dico))
Males.set_chunk(config.chunk_size)
Males.shuffle()
print 'Male voices loaded: ', Males.length(), ' items'
Females = H5PY_RW()
Females.open_h5_dataset('test.h5py', subset=females_keys(H5_dico))
Females.set_chunk(config.chunk_size)
Females.shuffle()
print 'Female voices loaded: ', Females.length(), ' items'
Mixer = Mixer([Males, Females])
das_model = DAS(S=len(Mixer.get_labels()), T=config.chunk_size)
das_model.init()
for i in range(100):
print 'Step #', i
X, Y, Ind = Mixer.get_batch(64)
x_mixture = []
import sys
import tensorflow as tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# Get your datasets
from data.dataset import H5PY_RW
from data.data_tools import read_metadata, males_keys, females_keys
file = 'test_raw_16k.h5py'
H5_dic = read_metadata()
chunk_size = 512 * 10
males = H5PY_RW(file,
subset=males_keys(H5_dic)).set_chunk(chunk_size).shuffle()
fem = H5PY_RW(file,
subset=females_keys(H5_dic)).set_chunk(chunk_size).shuffle()
print 'Data with', len(H5_dic), 'male and female speakers'
# Mixing the dataset
from data.dataset import Mixer
mixed_data = Mixer([males, fem], with_mask=False, with_inputs=True)
# Training set selection
mixed_data.select_split(0)
# Model pretrained loading
N = 256
max_pool = 128