tools/utilities/pythonlibs/audio/view_audio.py
def init_data(self):
        """ initialize the spectrogram_image_data and classifier_feature_data based on the newly loaded model info """
        if self.featurizer:
            dim = (self.featurizer.output_size, self.max_spectrogram_width)
            self.spectrogram_image_data = np.zeros(dim, dtype=float)  
            if self.spectrogram_image is not None:
                self.spectrogram_image.set_data(self.spectrogram_image_data)

            if self.classifier:
                self.num_classifier_features = self.classifier.input_size // self.featurizer.output_size
                dim = (self.num_classifier_features, self.featurizer.output_size)
                self.classifier_feature_data = np.zeros(dim, dtype=float)
 initialize the spectrogram_image_data and classifier_feature_data based on the newly loaded model info 
initialize the spectrogram_image_data and classifier_feature_data based on the newly loaded model info
initialize the spectrogram_image_data and classifier_feature_data based on the newly loaded model info
def init_data(self):
        if self.featurizer:
            dim = (self.featurizer.output_size, self.max_spectrogram_width)
            self.spectrogram_image_data = np.zeros(dim, dtype=float)  
            if self.spectrogram_image is not None:
                self.spectrogram_image.set_data(self.spectrogram_image_data)
            if self.classifier:
                self.num_classifier_features = self.classifier.input_size // self.featurizer.output_size
                dim = (self.num_classifier_features, self.featurizer.output_size)
                self.classifier_feature_data = np.zeros(dim, dtype=float)
def init_data ( self ) : if self . featurizer : dim = ( self . featurizer . output_size , self . max_spectrogram_width ) self . spectrogram_image_data = np . zeros ( dim , dtype = float ) if self . spectrogram_image is not None : self . spectrogram_image . set_data ( self . spectrogram_image_data ) if self . classifier : self . num_classifier_features = self . classifier . input_size // self . featurizer . output_size dim = ( self . num_classifier_features , self . featurizer . output_size ) self . classifier_feature_data = np . zeros ( dim , dtype = float )
initialize the spectrogram_image_data and classifier_feature_data based on the newly loaded model info
initialize the spectrogram_image_data and classifier_feature_data based on the newly loaded model info
""" initialize the spectrogram_image_data and classifier_feature_data based on the newly loaded model info """
self