code: def train_hmm_from_file(
      wav_file, gt_file, hmm_model_name, mid_window, mid_step
  ):
      """
      This function trains a HMM model for segmentation-classification
      using a single annotated audio file
      ARGUMENTS:
       - wav_file:        the path of the audio filename
       - gt_file:         the path of the ground truth filename
                         (a csv file of the form <segment start in seconds>,
                         <segment end in seconds>,<segment label> in each row
       - hmm_model_name:   the name of the HMM model to be stored
       - mt_win:          mid-term window size
       - mt_step:         mid-term window step
      RETURNS:
       - hmm:            an object to the resulting HMM
       - class_names:     a list of class_names
  
      After training, hmm, class_names, along with the mt_win and mt_step
      values are stored in the hmm_model_name file
      """
  
      seg_start, seg_end, seg_labs = read_segmentation_gt(gt_file)
      flags, class_names = segments_to_labels(
          seg_start, seg_end, seg_labs, mid_step
      )
      sampling_rate, signal = audioBasicIO.read_audio_file(wav_file)
      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),
      )
      (
          class_priors,
          transumation_matrix,
          means,
          cov,
      ) = train_hmm_compute_statistics(features, flags)
      hmm = hmmlearn.hmm.GaussianHMM(class_priors.shape[0], "diag")
  
      hmm.covars_ = cov
      hmm.means_ = means
      hmm.startprob_ = class_priors
      hmm.transmat_ = transumation_matrix
  
      save_hmm(hmm_model_name, hmm, class_names, mid_window, mid_step)
  
      return hmm, class_names
  
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 10 for calling a global function with example usage: train_hmm_from_file(wav_file, gt_file, hmm_model_name, mid_window, mid_step) and returns: hmm,  class_names