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,
drop_last=True)
for _, (clean_speech, noisy_speech) in enumerate(train_dataloader):
clean_speech = torch.Tensor(clean_speech).to(device)
noisy_speech = torch.Tensor(noisy_speech).to(device)
ref_batch = torch.cat([clean_speech, noisy_speech], 1)
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
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,
args.rescale_method, 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,
args.rescale_method, "None")
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')