opts['log_label_min'] = 0
real_data_min = 13.76
real_data_max = -19.35
dis_opts = {}
if adversarial_loss == "wgangp_loss":
dis_opts["alpha"] = 10.
elif adversarial_loss == "hinge_loss":
dis_opts["margin"] = 1.
if args.feature_domain.lower() == "mel":
from kaldi_fbank_dataset import FbankDataloader, FrameDataset
else:
from kaldi_fft_dataset import FbankDataloader, FrameDataset
train_dataset = FrameDataset([-6, -3, 0, 3, 6, 9], NOISE_LIST, TR05_SIMU_LIST, args.clean_type, True, None, args.data_augment)
train_dataloader = FbankDataloader(train_dataset, opts, BATCH_SIZE, True, num_workers=4, drop_last=True)
valid_dataset = FrameDataset([-6, -3, 0, 3, 6, 9], NOISE_LIST, DT05_SIMU_LIST, args.clean_type, False, None, args.data_augment)
valid_dataloader = FbankDataloader(valid_dataset, opts, BATCH_SIZE, True, num_workers=4, drop_last=True)
logger.info("Done.")
logger.info("Start to construct model...")
device = torch.device('cuda')
Generator, Discriminator = get_model(gen_model, dis_model)
if adversarial_loss:
disc_d_loss, disc_g_loss = get_disc_loss(adversarial_loss)
reconstruction_loss = get_recon_loss(reconstruction_loss)
calc_target = get_target(args.target_type)
if "gp" in args.adversarial_loss:
dis_opts["gp_alpha"] = 0.
if args.adversarial_loss == "hinge":
dis_opts["hinge_margin"] = 1.
dis_opts["l1_alpha"] = args.l1_alpha
dis_opts["l2_alpha"] = args.l2_alpha
if args.feature_domain.lower() == "mel":
from kaldi_fbank_dataset import FbankDataloader, FrameDataset
elif args.feature_domain.lower() == "waveform":
from segan_dataset import FbankDataloader, FrameDataset
else:
from kaldi_fft_dataset import FbankDataloader, FrameDataset
train_dataset = FrameDataset([-6, -3, 0, 3, 6, 9], NOISE_LIST,
TR05_SIMU_LIST, args.clean_type, True,
"value_norm", args.data_augment)
train_dataloader = FbankDataloader(train_dataset,
opts,
BATCH_SIZE,
True,
num_workers=4,
drop_last=True)
valid_dataset = FrameDataset([-6, -3, 0, 3, 6, 9], NOISE_LIST,
DT05_SIMU_LIST, args.clean_type, False,
"value_norm", "None")
valid_dataloader = FbankDataloader(valid_dataset,
opts,
BATCH_SIZE,
True,
num_workers=4,
opts['clip_low'] = 0.
opts['clip_high'] = 1.
opts['log_power_offset'] = 10.
dis_opts = {}
if adversarial_loss == "wgangp_loss":
dis_opts["alpha"] = 10.
elif adversarial_loss == "hinge_loss":
dis_opts["margin"] = 1.
if args.feature_domain.lower() == "mel":
from kaldi_fbank_dataset import FbankDataloader, FrameDataset
else:
from kaldi_fft_dataset import FbankDataloader, FrameDataset
train_dataset = FrameDataset([0, 3, 6], NOISE_LIST, TR05_SIMU_LIST,
args.clean_type, True, None)
train_dataloader = FbankDataloader(train_dataset,
opts,
BATCH_SIZE,
True,
num_workers=4,
drop_last=True)
valid_dataset = FrameDataset([0, 3, 6], NOISE_LIST, DT05_SIMU_LIST,
args.clean_type, False, None)
valid_dataloader = FbankDataloader(valid_dataset,
opts,
BATCH_SIZE,
True,
num_workers=4,
drop_last=True)
logger.info("Done.")