code: import os
  import csv
  import sys
  import time
  import pandas as pd
  from sklearn.preprocessing import StandardScaler
  from typing import Tuple
  from sklearn.externals import joblib
  from sklearn.model_selection import train_test_split
  
  # 每个特征集的特征数量
  FEATURE_NUM = {
      'IS09_emotion': 384,
      'IS10_paraling': 1582,
      'IS11_speaker_state': 4368,
      'IS12_speaker_trait': 6125,
      'IS13_ComParE': 6373,
      'ComParE_2016': 6373
  }
  
  
  '''
  get_feature_opensmile(): Opensmile 提取一个音频的特征
  
  输入:
      config(Class)
      file_path: 音频路径
  
  输出：
      该音频的特征向量
  '''
  
  def get_feature_opensmile(config, filepath: str):
      # 项目路径
      BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
      # single_feature.csv 路径
      single_feat_path = os.path.join(BASE_DIR, config.feature_path, 'single_feature.csv')
      # Opensmile 配置文件路径
      opensmile_config_path = os.path.join(config.opensmile_path, 'config', config.opensmile_config + '.conf')
  
      # Opensmile 命令
      cmd = 'cd ' + config.opensmile_path + ' && ./SMILExtract -C ' + opensmile_config_path + ' -I ' + filepath + ' -O ' + single_feat_path
      print("Opensmile cmd: ", cmd)
      os.system(cmd)
      
      reader = csv.reader(open(single_feat_path,'r'))
      rows = [row for row in reader]
      last_line = rows[-1]
      return last_line[1: FEATURE_NUM[config.opensmile_config] + 1]
  
  
  '''
  load_feature(): 从 .csv 文件中加载特征数据
  
  输入:
      config(Class)
      feature_path: 特征文件路径
      train: 是否为训练数据
  
  输出:
      训练数据、测试数据和对应的标签
  '''
  
  def load_feature(config, feature_path: str, train: bool):
      # 加载特征数据
      df = pd.read_csv(feature_path)
      features = [str(i) for i in range(1, FEATURE_NUM[config.opensmile_config] + 1)]
  
      X = df.loc[:,features].values
      Y = df.loc[:,'label'].values
  
      # 标准化模型路径
      scaler_path = os.path.join(config.checkpoint_path, 'SCALER_OPENSMILE.m')
  
      if train == True:
          # 标准化数据 
          scaler = StandardScaler().fit(X)
          # 保存标准化模型
          joblib.dump(scaler, scaler_path)
          X = scaler.transform(X)
  
          # 划分训练集和测试集
          x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)
          return x_train, x_test, y_train, y_test
      else:
          # 标准化数据
          # 加载标准化模型
          scaler = joblib.load(scaler_path)
          X = scaler.transform(X)
          return X
  
  
  '''
  get_data(): 
      提取所有音频的特征: 遍历所有文件夹, 读取每个文件夹中的音频, 提取每个音频的特征，把所有特征保存在 feature_path 中
  
  输入:
      config(Class)
      data_path: 数据集文件夹/测试文件路径
      feature_path: 保存特征的路径
      train: 是否为训练数据
  
  输出:
      train = True: 训练数据、测试数据特征和对应的标签
      train = False: 预测数据特征
  '''
  
  # Opensmile 提取特征
  def get_data(config, data_path, feature_path: str, train: bool):
  
      writer = csv.writer(open(feature_path, 'w'))
      first_row = ['label']
      for i in range(1, FEATURE_NUM[config.opensmile_config] + 1):
          first_row.append(str(i))
      writer.writerow(first_row)
  
      writer = csv.writer(open(feature_path, 'a+'))
      print('Opensmile extracting...')
  
      if train == True:
          cur_dir = os.getcwd()
          sys.stderr.write('Curdir: %s\n' % cur_dir)
          os.chdir(data_path)
          # 遍历文件夹
          for i, directory in enumerate(config.class_labels):
              sys.stderr.write("Started reading folder %s\n" % directory)
              os.chdir(directory)
  
              # label_name = directory
              label = config.class_labels.index(directory)
  
              # 读取该文件夹下的音频
              for filename in os.listdir('.'):
                  if not filename.endswith('wav'):
                      continue
                  filepath = os.path.join(os.getcwd(), filename)
                  
                  # 提取该音频的特征
                  feature_vector = get_feature_opensmile(config, filepath)
                  feature_vector.insert(0, label)
                  # 把每个音频的特征整理到一个 csv 文件中
                  writer.writerow(feature_vector)
  
              sys.stderr.write("Ended reading folder %s\n" % directory)
              os.chdir('..')
          os.chdir(cur_dir)
      
      else:
          feature_vector = get_feature_opensmile(config, data_path)
          feature_vector.insert(0, '-1')
          writer.writerow(feature_vector)
  
      print('Opensmile extract done.')
  
      # 一个玄学 bug 的暂时性解决方案
      # 这里无法直接加载除了 IS10_paraling 以外的其他特征集的预测数据特征，非常玄学
      if(train == True):
          return load_feature(config, feature_path, train = train)
apis: pandas.read_csv sklearn.externals.joblib.load sklearn.preprocessing.StandardScaler sklearn.externals.joblib.dump sklearn.model_selection.train_test_split