def train(args):
from train_utils import ModelTrainer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Prepare Data
train_dataset = Chime_Dataset('tr', args)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
collate_fn=lambda x: Chime_Collate(x),
num_workers=args.num_workers)
# Prepare model
if args.model_type == 'BLSTM':
model = BLSTMMaskEstimator()
model_save_dir = os.path.join(args.data_dir, 'BLSTM_model')
mkdir_p(model_save_dir)
elif args.model_type == 'FW':
model = SimpleFWMaskEstimator()
model_save_dir = os.path.join(args.data_dir, 'FW_model')
mkdir_p(model_save_dir)
else:
raise ValueError('Unknown model type. Possible are "BLSTM" and "FW"')
criterion = torch.nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
weight_decay=1e-4)
trainer = ModelTrainer(model, train_loader, criterion, optimizer, args,
device)
trainer.train(args.num_epochs)
def test(args):
from test_utils import ModelTest
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Prepare Data
test_dataset = Chime_Dataset('dt', args)
test_loader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
collate_fn=lambda x: Chime_Collate(x),
num_workers=args.num_workers)
# Prepare model
if args.model_type == 'BLSTM':
model = BLSTMMaskEstimator()
model_save_dir = os.path.join(args.data_dir, 'BLSTM_model')
mkdir_p(model_save_dir)
elif args.model_type == 'FW':
model = SimpleFWMaskEstimator()
model_save_dir = os.path.join(args.data_dir, 'FW_model')
mkdir_p(model_save_dir)
else:
raise ValueError('Unknown model type. Possible are "BLSTM" and "FW"')
criterion = torch.nn.BCELoss()
tester = ModelTest(model, test_loader, criterion, args, device)
tester.test()
parser = argparse.ArgumentParser(description='NN GEV beamforming')
parser.add_argument('model', help='Trained model file')
parser.add_argument('model_type', help='Type of model (BLSTM or FW)')
parser.add_argument('--gpu',
'-g',
default=-1,
type=int,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('data_directory', help='data experiment directory')
parser.add_argument('exNum', help='Experiment order')
args = parser.parse_args()
# Prepare model
if args.model_type == 'BLSTM':
model = BLSTMMaskEstimator()
elif args.model_type == 'FW':
model = SimpleFWMaskEstimator()
else:
raise ValueError('Unknown model type. Possible are "BLSTM" and "FW"')
serializers.load_hdf5(args.model, model)
print("data type of 'model'", type(model))
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
xp = np if args.gpu < 0 else cuda.cupy
# def single_noise():
# audio_data = get_audio_nochime('new_dataset/2m/2m_pub_new', ch_range=range(1, 9), fs=49000)
# # audio_data = get_audio_nochime('new_dataset/new_audio/AUDIO_RECORDING', ch_range=range(1, 9), fs=49000)
help='The directory where the enhanced wav files will '
'be stored.')
parser.add_argument('model',
help='Trained model file')
parser.add_argument('model_type',
help='Type of model (BLSTM or FW)')
parser.add_argument('beamformer',
help='Type of beamformer (GEV, MVDR of ALL)')
parser.add_argument('--gpu', '-g', default=-1, type=int,
help='GPU ID (negative value indicates CPU)')
args = parser.parse_args()
# Prepare model
if args.model_type == 'BLSTM':
model = BLSTMMaskEstimator()
elif args.model_type == 'FW':
model = SimpleFWMaskEstimator()
else:
raise ValueError('Unknown model type. Possible are "BLSTM" and "FW"')
if args.beamformer == 'GEV':
beamformers=['gev']
elif args.beamformer == 'MVDR':
beamformers=['mvdr']
elif args.beamformer == 'ALL':
beamformers=['gev','mvdr']
print("Selected beamformers:",' '.join(beamformers).upper())
serializers.load_hdf5(args.model, model)
if args.chime_dir != '':
log.info('Preparing training data and storing it in {}'.format(
args.data_dir))
prepare_training_data(args.chime_dir, args.data_dir)
flists = dict()
for stage in ['tr', 'dt']:
with open(os.path.join(args.data_dir,
'flist_{}.json'.format(stage))) as fid:
flists[stage] = json.load(fid)
log.debug('Loaded file lists')
# Prepare model
if args.model_type == 'BLSTM':
model = BLSTMMaskEstimator()
model_save_dir = os.path.join(args.data_dir, 'BLSTM_model')
mkdir_p(model_save_dir)
elif args.model_type == 'FW':
model = SimpleFWMaskEstimator()
model_save_dir = os.path.join(args.data_dir, 'FW_model')
mkdir_p(model_save_dir)
else:
raise ValueError('Unknown model type. Possible are "BLSTM" and "FW"')
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
xp = np if args.gpu < 0 else cuda.cupy
log.debug('Prepared model')
from nn_models import BLSTMMaskEstimator, SimpleFWMaskEstimator
parser = argparse.ArgumentParser(description='NN GEV beamforming')
parser.add_argument('model', help='Trained model file')
parser.add_argument('model_type', help='Type of model (BLSTM or FW)')
parser.add_argument('--gpu',
'-g',
default=-1,
type=int,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('experiments', help='Number of current experiment')
args = parser.parse_args()
# Prepare model
if args.model_type == 'BLSTM':
model = BLSTMMaskEstimator()
elif args.model_type == 'FW':
model = SimpleFWMaskEstimator()
else:
raise ValueError('Unknown model type. Possible are "BLSTM" and "FW"')
"""
PS: feedback loop beamforming experiment, using beamforming result +
specified channel. the beamforming as the channel
"""
# load nnet model
serializers.load_hdf5(args.model, model)
t_io = 0
t_net = 0
t_beamform = 0
parser.add_argument('chime_dir',
help='Base directory of the CHiME challenge.')
parser.add_argument('output_dir',
help='The directory where the enhanced wav files will '
'be stored.')
parser.add_argument('model',
help='Trained model file')
parser.add_argument('model_type',
help='Type of model (BLSTM or FW)')
parser.add_argument('--gpu', '-g', default=-1, type=int,
help='GPU ID (negative value indicates CPU)')
args = parser.parse_args()
# Prepare model
if args.model_type == 'BLSTM':
model = BLSTMMaskEstimator()
elif args.model_type == 'FW':
model = SimpleFWMaskEstimator()
else:
raise ValueError('Unknown model type. Possible are "BLSTM" and "FW"')
serializers.load_hdf5(args.model, model)
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
xp = np if args.gpu < 0 else cuda.cupy
stage = args.flist[:2]
scenario = args.flist.split('_')[-1]
# CHiME data handling
if args.chime_dir != '':
log.info(
'Preparing training data and storing it in {}'.format(
args.data_dir))
prepare_training_data(args.chime_dir, args.data_dir)
flists = dict()
for stage in ['tr', 'dt']:
with open(
os.path.join(args.data_dir, 'flist_{}.json'.format(stage))) as fid:
flists[stage] = json.load(fid)
log.debug('Loaded file lists')
# Prepare model
if args.model_type == 'BLSTM':
model = BLSTMMaskEstimator()
model_save_dir = os.path.join(args.data_dir, 'BLSTM_model')
mkdir_p(model_save_dir)
elif args.model_type == 'FW':
model = SimpleFWMaskEstimator()
model_save_dir = os.path.join(args.data_dir, 'FW_model')
mkdir_p(model_save_dir)
else:
raise ValueError('Unknown model type. Possible are "BLSTM" and "FW"')
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
xp = np if args.gpu < 0 else cuda.cupy
log.debug('Prepared model')
