if not os.path.exists(args.check_path):
os.makedirs(args.check_path)
opt_kwargs = {'lr': args.lr,
'lr_decay': args.lr_decay,
'weight_decay': args.weight_decay,
'dampening': args.dampening,
'momentum': args.momentum}
l2_dist = nn.CosineSimilarity(dim=1, eps=1e-6) if args.cos_sim else PairwiseDistance(2)
if args.acoustic_feature == 'fbank':
transform = transforms.Compose([
concateinputfromMFB(num_frames=c.NUM_FRAMES_SPECT, remove_vad=False),
# varLengthFeat(),
to2tensor()
])
transform_T = transforms.Compose([
concateinputfromMFB(num_frames=c.NUM_FRAMES_SPECT, input_per_file=args.test_input_per_file, remove_vad=False),
# varLengthFeat(),
to2tensor()
])
else:
transform = transforms.Compose([
truncatedinput(),
toMFB(),
totensor(),
# tonormal()
])
file_loader = read_audio
torch.manual_seed(args.seed)
torch.multiprocessing.set_sharing_strategy('file_system')
if args.cuda:
cudnn.benchmark = True
# Define visulaize SummaryWriter instance
kwargs = {'num_workers': args.nj, 'pin_memory': False} if args.cuda else {}
l2_dist = nn.CosineSimilarity(
dim=1, eps=1e-6) if args.cos_sim else PairwiseDistance(2)
if args.input_length == 'var':
transform = transforms.Compose([
# concateinputfromMFB(num_frames=c.NUM_FRAMES_SPECT, remove_vad=False),
varLengthFeat(remove_vad=args.remove_vad),
to2tensor()
])
transform_T = transforms.Compose([
# concateinputfromMFB(num_frames=c.NUM_FRAMES_SPECT, input_per_file=args.test_input_per_file, remove_vad=False),
varLengthFeat(remove_vad=args.remove_vad),
to2tensor()
])
elif args.input_length == 'fix':
transform = transforms.Compose(
[concateinputfromMFB(remove_vad=args.remove_vad),
to2tensor()])
transform_T = transforms.Compose([
concateinputfromMFB(input_per_file=args.test_input_per_file,
remove_vad=args.remove_vad),
to2tensor()
])
'lr': args.lr,
'lr_decay': args.lr_decay,
'weight_decay': args.weight_decay,
'dampening': args.dampening,
'momentum': args.momentum
}
l2_dist = nn.CosineSimilarity(
dim=1, eps=1e-6) if args.cos_sim else PairwiseDistance(2)
if args.mfb:
transform = transforms.Compose([
concateinputfromMFB(
remove_vad=args.remove_vad
), # num_frames=np.random.randint(low=300, high=500)),
to2tensor()
])
transform_T = transforms.Compose([
concateinputfromMFB(input_per_file=args.test_input_per_file,
remove_vad=args.remove_vad),
to2tensor()
])
file_loader = read_mat
else:
transform = transforms.Compose([
concateinputfromMFB(
remove_vad=True
), # num_frames=np.random.randint(low=300, high=500)),
# varLengthFeat(),
to2tensor(),
