def main(args):
set_seed(args.seed)
test_dataset = WaveTestDataset(args.wav_root, args.test_json_path)
print("Test dataset includes {} samples.".format(len(test_dataset)))
loader = TestDataLoader(test_dataset, batch_size=1, shuffle=False)
model = ConvTasNet.build_model(args.model_path)
print(model)
print("# Parameters: {}".format(model.num_parameters))
if args.use_cuda:
if torch.cuda.is_available():
model.cuda()
model = nn.DataParallel(model)
print("Use CUDA")
else:
raise ValueError("Cannot use CUDA.")
else:
print("Does NOT use CUDA")
# Criterion
if args.criterion == 'sisdr':
criterion = NegSISDR()
else:
raise ValueError("Not support criterion {}".format(args.criterion))
pit_criterion = PIT1d(criterion, n_sources=args.n_sources)
tester = Tester(model, loader, pit_criterion, args)
tester.run()
def main(args):
set_seed(args.seed)
loader = {}
train_dataset = WaveTrainDataset(args.wav_root, args.train_json_path)
valid_dataset = WaveTrainDataset(args.wav_root, args.valid_json_path)
print("Training dataset includes {} samples.".format(len(train_dataset)))
print("Valid dataset includes {} samples.".format(len(valid_dataset)))
loader['train'] = TrainDataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
loader['valid'] = TrainDataLoader(valid_dataset, batch_size=args.batch_size, shuffle=False)
model = ConvTasNet(args.n_basis, args.kernel_size, stride=args.stride, enc_basis=args.enc_basis, dec_basis=args.dec_basis, enc_nonlinear=args.enc_nonlinear, window_fn=args.window_fn, sep_hidden_channels=args.sep_hidden_channels, sep_bottleneck_channels=args.sep_bottleneck_channels, sep_skip_channels=args.sep_skip_channels, sep_kernel_size=args.sep_kernel_size, sep_num_blocks=args.sep_num_blocks, sep_num_layers=args.sep_num_layers, dilated=args.dilated, separable=args.separable, causal=args.causal, sep_nonlinear=args.sep_nonlinear, sep_norm=args.sep_norm, mask_nonlinear=args.mask_nonlinear, n_sources=args.n_sources)
print(model)
print("# Parameters: {}".format(model.num_parameters))
if args.use_cuda:
if torch.cuda.is_available():
model.cuda()
model = nn.DataParallel(model)
print("Use CUDA")
else:
raise ValueError("Cannot use CUDA.")
else:
print("Does NOT use CUDA")
# Optimizer
if args.optimizer == 'sgd':
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
elif args.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
elif args.optimizer == 'rmsprop':
optimizer = torch.optim.RMSprop(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
else:
raise ValueError("Not support optimizer {}".format(args.optimizer))
# Criterion
if args.criterion == 'sisdr':
criterion = NegSISDR()
else:
raise ValueError("Not support criterion {}".format(args.criterion))
pit_criterion = PIT1d(criterion, n_sources=args.n_sources)
trainer = Trainer(model, loader, pit_criterion, optimizer, args)
trainer.run()
def main(args):
set_seed(args.seed)
samples = int(args.sr * args.duration)
overlap = samples // 2
max_samples = int(args.sr * args.valid_duration)
max_n_sources = max([int(number) for number in args.n_sources.split('+')])
train_dataset = MixedNumberSourcesWaveTrainDataset(
args.train_wav_root,
args.train_list_path,
samples=samples,
overlap=overlap,
max_n_sources=max_n_sources)
valid_dataset = MixedNumberSourcesWaveEvalDataset(
args.valid_wav_root,
args.valid_list_path,
max_samples=max_samples,
max_n_sources=max_n_sources)
print("Training dataset includes {} samples.".format(len(train_dataset)))
print("Valid dataset includes {} samples.".format(len(valid_dataset)))
loader = {}
loader['train'] = MixedNumberSourcesTrainDataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True)
loader['valid'] = MixedNumberSourcesEvalDataLoader(valid_dataset,
batch_size=1,
shuffle=False)
if not args.enc_nonlinear:
args.enc_nonlinear = None
model = ConvTasNet(args.n_bases,
args.kernel_size,
stride=args.stride,
enc_bases=args.enc_bases,
dec_bases=args.dec_bases,
enc_nonlinear=args.enc_nonlinear,
window_fn=args.window_fn,
sep_hidden_channels=args.sep_hidden_channels,
sep_bottleneck_channels=args.sep_bottleneck_channels,
sep_skip_channels=args.sep_skip_channels,
sep_kernel_size=args.sep_kernel_size,
sep_num_blocks=args.sep_num_blocks,
sep_num_layers=args.sep_num_layers,
dilated=args.dilated,
separable=args.separable,
causal=args.causal,
sep_nonlinear=args.sep_nonlinear,
sep_norm=args.sep_norm,
mask_nonlinear=args.mask_nonlinear,
n_sources=2)
print(model)
print("# Parameters: {}".format(model.num_parameters))
if args.use_cuda:
if torch.cuda.is_available():
model.cuda()
model = nn.DataParallel(model)
print("Use CUDA")
else:
raise ValueError("Cannot use CUDA.")
else:
print("Does NOT use CUDA")
# Optimizer
if args.optimizer == 'sgd':
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer == 'rmsprop':
optimizer = torch.optim.RMSprop(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
else:
raise ValueError("Not support optimizer {}".format(args.optimizer))
# Criterion
if args.criterion == 'sisdr':
criterion = NegSISDR()
else:
raise ValueError("Not support criterion {}".format(args.criterion))
pit_criterion = ORPIT(criterion)
trainer = AdhocTrainer(model, loader, pit_criterion, optimizer, args)
trainer.run()