# from LossFunctions import weight_loss_DC_3 as Loss1, weight_loss_MSE_3 as Loss2
from GenerateModels import ChimeraNet as GenerateModel
tag = 'Feature{}Nsrc{}{}'.format(['Proposed', 'SpecSpat',
'SpecOnly'][args.featureFlag], args.srcNum,
['', 'Wei'][args.outputFlag])
print(tag)
if args.save_dir is None:
save_dir = './Models/{}'.format(tag)
else:
save_dir = args.save_dir
modelDirectory = '{}/{}'.format(save_dir, tag)
# Load the model
train_model = GenerateModel(Nsrc=args.srcNum, inputFeature=args.featureFlag)
print(train_model.summary())
# plot_model(train_model, to_file='Model.png')
initial_epoch = 0
if args.continueToTrainFlag:
savedModelDirectory = '{}/{}'.format('/Directory2TrainedModels', tag)
try:
savedModels = [
filename for path, subdirs, files in os.walk(savedModelDirectory)
for filename in files if filename.endswith(".h5")
]
if len(savedModels) > 0:
index = []
for saveModelName in savedModels:
from GenerateModels import GenerateBSSCapsNet as GenerateModel
tag = 'CapsNetModel'
saveflag = 'Caps'
elif modelMode == 4:
tag = 'OracleIBM'
saveflag = 'Oracle'
print(tag)
if modelMode < 4:
#########################################
modelDirectory = './result04April/' + tag
model = GenerateModel()
# The separation model
model.load_weights(modelDirectory + '/' + 'trained_model.h5')
print(model.summary())
# plot_model(train_model, to_file='Model.png')
import os
for genderFlag in ['MM', 'MF', 'FF']:
fullSaveDir = saveDirectoryTop + MatchedStr + '{}'.format(genderFlag)
dataDir = folderName + MatchedStr + '{}/'.format(genderFlag)
for azimuth in Azimuth_array:
fullSaveDir_azi = fullSaveDir + '/{}/'.format(azimuth)
if not os.path.exists(fullSaveDir_azi):
os.makedirs(fullSaveDir_azi)
})
from GenerateModels import ChimeraNet as GenerateModel
tag = 'Feature{}Nsrc{}{}'.format(['Proposed', 'SpecSpat',
'SpecOnly'][args.featureFlag], args.srcNum,
['', 'Wei'][args.outputFlag])
print(tag)
modelDirectory = './TrainedModels'
# from GenerateModelsOld import ChimeraNet as GenerateModel
# tag = 'ChimeraNsrc{}{}'.format(args.srcNum,['','Wei'][args.outputFlag])
# print(tag)
# modelDirectory = '/Directory2TrainedModels/ChimeraModels'
# Load the model
EncoderModel = GenerateModel(Nsrc=args.srcNum, inputFeature=args.featureFlag)
# The separation model
from keras.models import load_model
EncoderModel = load_model("{}/{}/modelsave.h5".format(modelDirectory, tag))
# EncoderModel = load_model("{}/{}/modelsave.h5".format(modelDirectory,tag), custom_objects={'loss_DC': LossA(Nsrc=args.srcNum), 'lossMSE': LossB(Nsrc=args.srcNum)})
print(EncoderModel.summary())
# plot_model(train_model, to_file='Model.png')
import itertools
# tag = 'IBM' # comment this out later, this is for IBM baseline
# tag = 'GMM' # comment this out later, this is for GMM baseline
mseResults = np.zeros((N, ), dtype='float32')
mseResults2 = np.zeros((N, ), dtype='float32')
elif args.outputFlag==1:
from GenerateModels import EncoderNetBigLinear as GenerateModel
tag = 'EncoderModel_{}_FF{}_CustomLoss'.format(['mel','linear'][args.outputFlag],args.featureFlag)
print(tag)
if args.save_dir is None:
save_dir = './EncoderResult/Models/{}'.format(tag)
else:
save_dir = args.save_dir
# Load the model
train_model = GenerateModel()
print(train_model.summary())
# plot_model(train_model, to_file='Model.png')
initial_epoch = 0
if args.continueToTrainFlag:
try:
savedModels = [filename for path, subdirs, files in os.walk(save_dir)
for filename in files if filename.endswith(".h5")]
if len(savedModels)>0:
index = []
for saveModelName in savedModels:
temp = re.findall(r'(?<=model-)(\d+).h5', saveModelName)
from keras.utils.generic_utils import get_custom_objects
from LossFuncs import my_loss as customLoss
get_custom_objects().update({"my_loss": customLoss})
if args.outputFlag == 0:
from GenerateModels import EncoderNetBigMel as GenerateModel
elif args.outputFlag == 1:
from GenerateModels import EncoderNetBigLinear as GenerateModel
tag = 'EncoderModel_{}_FF{}_CustomLoss'.format(['mel',
'linear'][args.outputFlag],
args.featureFlag)
# Load the model
EncoderModel = GenerateModel()
# print(model.summary())
# plot_model(model, to_file='FrequencyModel.png')
modelDirectory = './EncoderResultNov/Models/{}'.format(tag)
# The separation model
from keras.models import load_model
EncoderModel = load_model("{}/ConvergeModel.h5".format(modelDirectory))
print(EncoderModel.summary())
# plot_model(train_model, to_file='Model.png')
if args.synthesisFlag & args.outputFlag == 1:
import os
import librosa