code: # -*- coding: utf-8 -*-
  """
  Created on Tue Sep 28 17:46:55 2021
  
  @author: bhupendra.singh
  """
  
  
  import numpy as np
  import pandas as pd
  from tqdm import tqdm
  import opensmile
  
  
  totalSubjects = 21
  totalUtterances = 60    #the number of utterances of words in a folder for every subject
  featureName = "mfcc"
  features_length = 6373
  
  #utterancesFolder = ".//WebBasedTest/word_level_utterances"
  utterancesFolder = ".//WebBasedTest/word_level_utterances_manual"
  featuresFolder = ".//WebBasedTest/features_files"
  
  avialableSubjectList = list(range(totalSubjects))
  avialableSubjectList = [x+1 for x in avialableSubjectList]  # adding 1 to all the itesms since the subject folder starts from 1 and not 0
  #avialableSubjectList.remove(4)
  
  totalUtterancesList = list(range(totalUtterances))
  totalUtterancesList = [x+1 for x in totalUtterancesList]  # adding 1 to all the itesms since the subject folder starts from 1 and not 0
  
  features_set = np.zeros((len(totalUtterancesList), features_length))
  fs = 48000  #i found it to be this value, change it as per your information
  
  for currentSubjectNo in tqdm(avialableSubjectList):     # iterate for all the available subjects
      #currentSubjectNo += 1
      print("Current Subject = " + str(currentSubjectNo))
      
      for currentUtteranceNo in totalUtterancesList: #iterate for for all the utterances
          #print("Current Subject = " + str(currentUtteranceNo))
          #utteranceFileName = utterancesFolder + "/utterances_subject_" + str(currentSubjectNo) + "/utterance" + str(currentUtteranceNo) + ".wav"
          utteranceFileName = utterancesFolder + "/utterances_subject_" + str(currentSubjectNo) + "/" + str(currentUtteranceNo) + ".wav"
          #print(fileName)
          #sound_file = AudioSegment.from_wav(utteranceFileName)
          #mfcc_features = mfcc(np.array(sound_file.get_array_of_samples()), fs)
          smile = opensmile.Smile(
              feature_set=opensmile.FeatureSet.ComParE_2016,
              feature_level=opensmile.FeatureLevel.Functionals,
          )
          y = smile.process_file(utteranceFileName)
          #mfcc_features.resize(128) #standardizing the size
          y = y.to_numpy()
          #y = y[0,:]
          #y = y.resize(6372) #standardizing the size
          features_set[currentUtteranceNo - 1, :] = y
          
      df = pd.DataFrame(features_set)
      featuresFileName = featuresFolder + "/Subject" + str(currentSubjectNo) + "_" + featureName + ".csv"
      df.to_csv(featuresFileName)
apis: pandas.DataFrame