class_docstr: read the kaldi ark format file
class_docstr_tok: ["read","the","kaldi","ark","format","file"]
docstr: normalization feat using mean variance
  args:
      feat_array: input feat array
      mean_variance_file: input mean variance file
  return:
      -1, array: failed
      0, array: success and return the new feat array
docstr_tok: ["normalization","feat","using","mean","variance","args",":","feat_array",":","input","feat","array","mean_variance_file",":","input","mean","variance","file","return",":","-1",",","array",":","failed","0",",","array",":","success","and","return","the","new","feat","array"]
code: def normalization_feat_by_mean_variance(
      self, feat_array, mean_variance_file
  ):
      """
      normalization feat using mean variance
      args:
          feat_array: input feat array
          mean_variance_file: input mean variance file
      return:
          -1, array: failed
          0, array: success and return the new feat array
      """
      if not self._mean_variance_statu:
          if (
              self._read_mean_variance(
                  mean_variance_file, feat_array.shape[1]
              )
              < 0
          ):
              print(
                  "[ERROR] _read_mean_variance failed with shape %s %s"
                  % (feat_array.shape[0], feat_array.shape[1])
              )
              return -1, np.array([])
  
      mean_array = np.array([self._mean_array] * feat_array.shape[0])
      variance_array = np.array(
          [self._variance_array] * feat_array.shape[0]
      )
  
      new_feat_array = (feat_array - mean_array) / variance_array
      return 0, new_feat_array
  
file_path: delta/delta/data/utils/htk_reader_lib.py
imports: import os
  import math
  import numpy
label_desc: Use image node_id 6 for calling the HtkReaderIO obj's underlying member method code with example usage: obj.normalization_feat_by_mean_variance(feat_array, mean_variance_file) and returns: int,  new_feat_array, unknown,  np