code: def mid_term_file_classification(
      input_file, model_name, model_type, plot_results=False, gt_file=""
  ):
      """
      This function performs mid-term classification of an audio stream.
      Towards this end, supervised knowledge is used,
      i.e. a pre-trained classifier.
      ARGUMENTS:
          - input_file:        path of the input WAV file
          - model_name:        name of the classification model
          - model_type:        svm or knn depending on the classifier type
          - plot_results:      True if results are to be plotted using
                               matplotlib along with a set of statistics
          - gt_file:           path to the ground truth file, if exists,
                               for calculating classification performance
      RETURNS:
      labels, class_names, accuracy, cm
            - labels:         a sequence of segment's labels: segs[i] is the label
                              of the i-th segment
            - class_names:    a string sequence of class_names used in classification:
                              class_names[i] is the name of classes[i]
            - accuracy:       the accuracy of the classification.
            - cm:             the confusion matrix of this classification
      """
      labels = []
      accuracy = 0.0
      class_names = []
      cm = np.array([])
      if not os.path.isfile(model_name):
          print(
              "mtFileClassificationError: input model_type not found!"
          )
          return labels, class_names, accuracy, cm
  
      # Load classifier:
      if model_type == "knn":
          (
              classifier,
              mean,
              std,
              class_names,
              mt_win,
              mid_step,
              st_win,
              st_step,
              compute_beat,
          ) = at.load_model_knn(model_name)
      else:
          (
              classifier,
              mean,
              std,
              class_names,
              mt_win,
              mid_step,
              st_win,
              st_step,
              compute_beat,
          ) = at.load_model(model_name)
      if compute_beat:
          print(
              "Model "
              + model_name
              + " contains long-term music features "
              "(beat etc) and cannot be used in "
              "segmentation"
          )
          return labels, class_names, accuracy, cm
      # load input file
      sampling_rate, signal = audioBasicIO.read_audio_file(input_file)
  
      # could not read file
      if sampling_rate == 0:
          return labels, class_names, accuracy, cm
  
      # convert stereo (if) to mono
      signal = audioBasicIO.stereo_to_mono(signal)
  
      # mid-term feature extraction:
      mt_feats, _, _ = mtf.mid_feature_extraction(
          signal,
          sampling_rate,
          mt_win * sampling_rate,
          mid_step * sampling_rate,
          round(sampling_rate * st_win),
          round(sampling_rate * st_step),
      )
      posterior_matrix = []
  
      # for each feature vector (i.e. for each fix-sized segment):
      for col_index in range(mt_feats.shape[1]):
          # normalize current feature v
          feature_vector = (mt_feats[:, col_index] - mean) / std
  
          # classify vector:
          label_predicted, posterior = at.classifier_wrapper(
              classifier, model_type, feature_vector
          )
          labels.append(label_predicted)
  
          # update probability matrix
          posterior_matrix.append(np.max(posterior))
      labels = np.array(labels)
  
      # convert fix-sized flags to segments and classes
      segs, classes = labels_to_segments(labels, mid_step)
      for i in range(len(segs)):
          print(segs[i], classes[i])
      segs[-1] = len(signal) / float(sampling_rate)
      # Load grount-truth:
      labels_gt, class_names_gt, accuracy, cm = load_ground_truth(
          gt_file, labels, class_names, mid_step, plot_results
      )
  
      return labels, class_names, accuracy, cm
  
imports: from __future__ import print_function
  import os
  import csv
  import glob
  import scipy
  import sklearn
  import numpy
  import hmmlearn.hmm
  import sklearn.cluster
  import pickle
  import matplotlib.pyplot
  from scipy.spatial import distance
  import sklearn.discriminant_analysis
  from sklearn.preprocessing import StandardScaler
  import sys
  import pyAudioAnalysis.audioBasicIO
  import pyAudioAnalysis.audioTrainTest
  import pyAudioAnalysis.MidTermFeatures
  import pyAudioAnalysis.ShortTermFeatures
label_desc: Use image node_id 16 for calling a global function with example usage: mid_term_file_classification(input_file, model_name, model_type, plot_results, gt_file) and returns: labels,  class_names,  accuracy,  cm, labels,  class_names,  accuracy,  cm, labels,  class_names,  accuracy,  cm, labels,  class_names,  accuracy,  cm