def main():
parser = argparse.ArgumentParser(description='Parse the config path')
parser.add_argument(
"-c",
"--config",
dest="path",
help=
'The path to the config file. e.g. python run.py --config dc_config.json'
)
config = parser.parse_args()
with open(config.path) as f:
args = json.load(f)
args = AttrDict(args)
device = torch.device(args.device)
args.model = onssen.nn.chimera(args.model_options)
args.model.to(device)
args.train_loader = data.edinburgh_tts_dataloader(args.model_name,
args.feature_options,
'train',
args.cuda_option,
self.device)
args.valid_loader = data.edinburgh_tts_dataloader(args.model_name,
args.feature_options,
'validation',
args.cuda_option,
self.device)
args.optimizer = utils.build_optimizer(args.model.parameters(),
args.optimizer_options)
args.loss_fn = loss.loss_chimera_psa
trainer = onssen.utils.trainer(args)
trainer.run()
tester = onssen.utils.tester(args)
tester.eval()
def main():
parser = argparse.ArgumentParser(description='Parse the config path')
parser.add_argument(
"-c",
"--config",
dest="path",
help=
'The path to the config file. e.g. python run.py --config onfig.json')
config = parser.parse_args()
with open(config.path) as f:
args = json.load(f)
args = AttrDict(args)
device = torch.device(args.device)
args.model = nn.deep_clustering(**(args['model_options']))
args.model.to(device)
args.train_loader = data.wsj0_2mix_dataloader(args.model_name,
args.feature_options, 'tr',
device)
args.valid_loader = data.wsj0_2mix_dataloader(args.model_name,
args.feature_options, 'cv',
device)
args.test_loader = data.wsj0_2mix_dataloader(args.model_name,
args.feature_options, 'tt',
device)
args.optimizer = utils.build_optimizer(args.model.parameters(),
args.optimizer_options)
args.loss_fn = loss.loss_dc
trainer = utils.trainer(args)
trainer.run()
tester = tester_dc(args)
tester.eval()
def main():
config_path = './config.json'
with open(config_path) as f:
args = json.load(f)
args = AttrDict(args)
device = torch.device(args.device)
args.device = device
args.model = nn.ConvTasNet(**args["model_options"])
args.model.to(device)
args.train_loader = data.wsj0_2mix_dataloader(args.model_name,
args.feature_options, 'tr',
device)
args.valid_loader = data.wsj0_2mix_dataloader(args.model_name,
args.feature_options, 'cv',
device)
args.test_loader = data.wsj0_2mix_dataloader(args.model_name,
args.feature_options, 'tt',
device)
args.optimizer = utils.build_optimizer(args.model.parameters(),
args.optimizer_options)
args.loss_fn = loss.si_snr_loss
trainer = utils.trainer(args)
trainer.run()
tester = tester_tasnet(args)
tester.eval()
def main():
config_path = './config.json'
with open(config_path) as f:
args = json.load(f)
args = AttrDict(args)
device = torch.device(args.device)
args.model = nn.chimera(**(args['model_options']))
args.model.to(device)
args.train_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tr', device)
args.valid_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'cv', device)
args.test_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tt', device)
args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options)
args.loss_fn = loss.loss_chimera_msa
trainer = utils.trainer(args)
trainer.run()
tester = tester_chimera(args)
tester.eval()