device = None
if args.model == 'mfcc':
model = model_.cnn_lstm_mfcc(n_z=args.latent_size,
proj_size=len(train_dataset.speakers_list)
if args.softmax != 'none' else 0,
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'fb':
model = model_.cnn_lstm_fb(n_z=args.latent_size,
proj_size=len(train_dataset.speakers_list)
if args.softmax != 'none' else 0,
sm_type=args.softmax)
elif args.model == 'resnet_fb':
model = model_.ResNet_fb(n_z=args.latent_size,
proj_size=len(train_dataset.speakers_list)
if args.softmax != 'none' else 0,
sm_type=args.softmax)
elif args.model == 'resnet_mfcc':
model = model_.ResNet_mfcc(n_z=args.latent_size,
proj_size=len(train_dataset.speakers_list)
if args.softmax != 'none' else 0,
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'resnet_lstm':
model = model_.ResNet_lstm(n_z=args.latent_size,
proj_size=len(train_dataset.speakers_list)
if args.softmax != 'none' else 0,
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'resnet_stats':
model = model_.ResNet_stats(n_z=args.latent_size,
'There is no checkpoint/model path. Use arg --cp-path to indicate the path!'
)
print('Cuda Mode is: {}'.format(args.cuda))
if args.cuda:
set_device()
if args.model == 'mfcc':
model = model_.cnn_lstm_mfcc(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'fb':
model = model_.cnn_lstm_fb(n_z=args.latent_size, proj_size=None)
elif args.model == 'resnet_fb':
model = model_.ResNet_fb(n_z=args.latent_size, proj_size=None)
elif args.model == 'resnet_mfcc':
model = model_.ResNet_mfcc(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'resnet_lstm':
model = model_.ResNet_lstm(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'resnet_stats':
model = model_.ResNet_stats(n_z=args.latent_size,
proj_size=None,
ncoef=args.ncoef)
elif args.model == 'lcnn9_mfcc':
model = model_.lcnn_9layers(n_z=args.latent_size,
proj_size=None,
if args.cuda:
set_device()
if args.model == 'mfcc':
model = model_.cnn_lstm_mfcc(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'fb':
model = model_.cnn_lstm_fb(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
sm_type=args.softmax)
elif args.model == 'resnet_fb':
model = model_.ResNet_fb(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
sm_type=args.softmax)
elif args.model == 'resnet_mfcc':
model = model_.ResNet_mfcc(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'resnet_lstm':
model = model_.ResNet_lstm(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
ncoef=args.ncoef,
sm_type=args.softmax)
elif args.model == 'resnet_stats':
model = model_.ResNet_stats(n_z=args.latent_size,
proj_size=len(list(labels_dict.keys())),
ncoef=args.ncoef,
type=int,
default=13,
metavar='N',
help='number of MFCCs (default: 23)')
parser.add_argument('--pairwise',
action='store_true',
default=False,
help='Enables layer-wise comparison of norms')
args = parser.parse_args()
if args.model == 'mfcc':
model = model_.cnn_lstm_mfcc(n_z=args.latent_size, ncoef=args.ncoef)
if args.model == 'fb':
model = model_.cnn_lstm_fb(n_z=args.latent_size)
elif args.model == 'resnet_fb':
model = model_.ResNet_fb(n_z=args.latent_size)
elif args.model == 'resnet_mfcc':
model = model_.ResNet_mfcc(n_z=args.latent_size, ncoef=args.ncoef)
elif args.model == 'resnet_lstm':
model = model_.ResNet_lstm(n_z=args.latent_size, ncoef=args.ncoef)
elif args.model == 'resnet_stats':
model = model_.ResNet_stats(n_z=args.latent_size, ncoef=args.ncoef)
elif args.model == 'lcnn9_mfcc':
model = model_.lcnn_9layers(n_z=args.latent_size, ncoef=args.ncoef)
elif args.model == 'lcnn29_mfcc':
model = model_.lcnn_29layers_v2(n_z=args.latent_size, ncoef=args.ncoef)
if args.pairwise:
if args.model == 'mfcc':
clone_model = model_.cnn_lstm_mfcc(n_z=args.latent_size,
