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)