code: def hmm_segmentation(
      audio_file, hmm_model_name, plot_results=False, gt_file=""
  ):
      sampling_rate, signal = audioBasicIO.read_audio_file(audio_file)
  
      with open(hmm_model_name, "rb") as f_handle:
          hmm = cpickle.load(f_handle)
          class_names = cpickle.load(f_handle)
          mid_window = cpickle.load(f_handle)
          mid_step = cpickle.load(f_handle)
  
      features, _, _ = mtf.mid_feature_extraction(
          signal,
          sampling_rate,
          mid_window * sampling_rate,
          mid_step * sampling_rate,
          round(sampling_rate * 0.050),
          round(sampling_rate * 0.050),
      )
  
      # apply model
      labels = hmm.predict(features.T)
      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 13 for calling a global function with example usage: hmm_segmentation(audio_file, hmm_model_name, plot_results, gt_file) and returns: labels,  class_names,  accuracy,  cm