def __init__(self, model_dir):
sys.path.append('../../nanonet/rirnet')
from rirnet_database import RirnetDatabase
print(sys.path)
self.model_dir = model_dir
self.extractor, _ = misc.load_latest(model_dir, 'extractor')
self.autoencoder, _ = misc.load_latest(model_dir, 'autoencoder')
self.extractor_args = self.extractor.args()
use_cuda = not self.extractor_args.no_cuda and torch.cuda.is_available(
)
self.device = torch.device("cuda" if use_cuda else "cpu")
self.extractor.to(self.device)
self.autoencoder.to(self.device)
self.kwargs = {
'num_workers': 1,
'pin_memory': True
} if use_cuda else {}
data_transform = transforms.Compose([
ToNormalized('../../database/mean.npy', '../../database/std.npy')
])
target_transform = transforms.Compose(
[ToNegativeLog(), ToUnitNorm(),
ToTensor()])
self.extractor_args.val_db_path = '../../database/db-val.csv'
eval_db = RirnetDatabase(is_training=False,
args=self.extractor_args,
data_transform=data_transform,
target_transform=target_transform)
self.eval_loader = torch.utils.data.DataLoader(
eval_db,
batch_size=self.extractor_args.batch_size,
shuffle=True,
**self.kwargs)
self.audio_anechoic, self.fs = au.read_wav(
'../../audio/harvard/male.wav')
def target_transform(self):
target_transform = transforms.Compose([ToNegativeLog(), ToUnitNorm(), ToTensor()])
return target_transform
