def encode(model_name, piece, encoding_name):
model_path = './restore/'
encoding_path = './encoding/'
# Create directory for encoding
if os.path.exists(encoding_path) is False:
os.makedirs(encoding_path)
net = AutoEncoder()
net = load_model(net,model_path,model_name)
cuda_available = torch.cuda.is_available()
if cuda_available is True:
net = net.cuda()
net.eval()
# Load audio for encoding
piece = audio.load_wav(piece)
spec = audio.spectrogram(piece).astype(np.float32)
spec = torch.from_numpy(spec.T)
spec = torch.FloatTensor(spec)
spec = torch.unsqueeze(spec, 0)
spec = Variable(spec, volatile=True).contiguous()
if cuda_available is True:
spec = spec.cuda()
# Pass input audio to net forward pass
encoding = net.encoder(spec)
encoding = encoding.data.cpu().numpy()
#encoding = np.squeeze(encoding)
encoding_ndarray = encoding_path + encoding_name+ '.npy'
np.save(encoding_ndarray, encoding)
def encode(spec):
model_path = './restore/'
model_name = 'Autoencoder1.model'
net = AutoEncoder()
net = load_model(net, model_path, model_name)
cuda_available = torch.cuda.is_available()
if cuda_available is True:
net = net.cuda()
net.eval()
spec = torch.FloatTensor(torch.from_numpy(spec))
spec = torch.unsqueeze(spec, 0)
spec = Variable(spec, volatile=True).contiguous()
if cuda_available is True:
spec = spec.cuda()
# Pass input audio to net forward pass
out = net.encoder(spec)
out = out.data.cpu().numpy()
out = np.squeeze(out)
return out