Why compute power_spectrum in AudioDataset.__getitem__()?

[malyasova]

I am sorry if I missed something obvious, but it seems like the power_spectrum variable isn't used in the AudioDataset.getitem() function in https://github.com/astorfi/3D-convolutional-speaker-recognition-pytorch/blob/master/code/2-enrollment/DataProviderEnrollment.py?
Also, the file is read twice.

def __getitem__(self, idx):
        # Get the sound file path
        sound_file_path = os.path.join(self.audio_dir, self.sound_files[idx].split()[1])

        ##############################
        ### Reading and processing ###
        ##############################

        # Reading .wav file
        fs, signal = wav.read(sound_file_path)

        # Reading .wav file
        import soundfile as sf
        signal, fs = sf.read(sound_file_path)

        # Label extraction
        label = int(self.sound_files[idx].split()[0])

        ###########################
        ### Feature Extraction ####
        ###########################

        # DEFAULTS:
        num_coefficient = 40

        # Staching frames
        frames = speechpy.processing.stack_frames(signal, sampling_frequency=fs, frame_length=0.025,
                                                  frame_stride=0.01,
                                                  zero_padding=True)

        # # Extracting power spectrum (choosing 3 seconds and elimination of DC)
        power_spectrum = speechpy.processing.power_spectrum(frames, fft_points=2 * num_coefficient)[:, 1:]

        logenergy = speechpy.feature.lmfe(signal, sampling_frequency=fs, frame_length=0.025, frame_stride=0.01,
                                          num_filters=num_coefficient, fft_length=1024, low_frequency=0,
                                          high_frequency=None)

        ########################
        ### Handling sample ####
        ########################

        sample = {'feature': logenergy, 'label': label}

        ########################
        ### Post Processing ####
        ########################
        if self.transform:
            sample = self.transform(sample)

        return sample