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')
# Setup optimizer
optimizer = optimizers.Adam()
optimizer.setup(model)