for key in testset.spk2label:
idx_to_class[str(testset.spk2label[key].squeeze().item())] = key
print(idx_to_class, '\n')
ckpt = torch.load(args.cp_path, map_location=lambda storage, loc: storage)
if args.model == 'cnn':
model = base_cnn.CNN(n_classes=args.nclasses)
elif args.model == 'vgg':
model = vgg.VGG('VGG11', n_classes=args.nclasses)
elif args.model == 'resnet':
model = resnet.ResNet12(n_classes=args.nclasses)
elif args.model == 'densenet':
model = densenet.DenseNet121(n_classes=args.nclasses)
elif args.model == 'tdnn':
model = TDNN.TDNN(n_classes=args.nclasses)
try:
print(model.load_state_dict(ckpt['model_state'], strict=True))
print('\n')
except RuntimeError as err:
print("Runtime Error: {0}".format(err))
except:
print("Unexpected error:", sys.exc_info()[0])
raise
print('\n\nNumber of parameters: {}\n'.format(
sum(p.numel() for p in model.parameters())))
if args.cuda:
device = get_freer_gpu()
def main():
# init log
log_dir = os.path.join('checkpoint', data_configs.speaker)
infolog.init(os.path.join(log_dir, 'Terminal_train_log'),
model_configs.model_name, None)
# train model
if model_configs.model_name == 'TDNN':
model = TDNN(data_configs, model_configs, training_configs)
model.initialize()
model.train()
elif model_configs.model_name == 'TDNN_LSTM':
model = TDNN_LSTM(data_configs, model_configs, training_configs)
model.initialize()
model.train()