def __call__(self, sample):
return {
key: np.abs(
util.stft_real(value,
blockSize=self.blockSize,
hopSize=self.hopSize,
window=self.window))
for key, value in sample.items()
}
import sys
if __name__ == "__main__":
blockSize = 4096
hopSize = 2048
if len(sys.argv) != 3:
print("Usage:\n", sys.argv[0], "input_path output_path")
sys.exit(1)
#read the wav file
x, fs = util.wavread(sys.argv[1])
#downmix to single channel
x = np.mean(x, axis=-1)
#perform stft
S = util.stft_real(x, blockSize=blockSize, hopSize=hopSize)
magnitude = np.abs(S).astype(np.float32)
angle = np.angle(S).astype(np.float32)
#initialize the model
model = Model_fcn.ModelSingleStep(blockSize)
#load the pretrained model
model.load_state_dict(
torch.load("Modelfcn.pt", map_location=lambda storage, loc: storage))
#switch to eval mode
model.eval()
###################################
#Run your Model here to obtain a mask
###################################
spectro_pred = model.process(magnitude)