calculate_wer.py

"""
  WER
"""

from jiwer import wer


def word_error_rate(ref, hyp, debug=False):
  r = ref.split()
  h = hyp.split()
  # costs will holds the costs, like in the Levenshtein distance algorithm
  costs = [[0 for inner in range(len(h) + 1)] for outer in range(len(r) + 1)]
  # backtrace will hold the operations we've done.
  # so we could later backtrace, like the WER algorithm requires us to.
  backtrace = [[0 for inner in range(len(h) + 1)]
               for outer in range(len(r) + 1)]

  OP_OK = 0
  OP_SUB = 1
  OP_INS = 2
  OP_DEL = 3

  DEL_PENALTY = 1  # Tact
  INS_PENALTY = 1  # Tact
  SUB_PENALTY = 1  # Tact
  # First column represents the case where we achieve zero
  # hypothesis words by deleting all reference words.
  for i in range(1, len(r) + 1):
    costs[i][0] = DEL_PENALTY * i
    backtrace[i][0] = OP_DEL

  # First row represents the case where we achieve the hypothesis
  # by inserting all hypothesis words into a zero-length reference.
  for j in range(1, len(h) + 1):
    costs[0][j] = INS_PENALTY * j
    backtrace[0][j] = OP_INS

  # computation
  for i in range(1, len(r) + 1):
    for j in range(1, len(h) + 1):
      if r[i - 1] == h[j - 1]:
        costs[i][j] = costs[i - 1][j - 1]
        backtrace[i][j] = OP_OK
      else:
        substitutionCost = costs[i - 1][j - 1] + \
            SUB_PENALTY  # penalty is always 1
        insertionCost = costs[i][j - 1] + INS_PENALTY   # penalty is always 1
        deletionCost = costs[i - 1][j] + DEL_PENALTY   # penalty is always 1

        costs[i][j] = min(substitutionCost, insertionCost, deletionCost)
        if costs[i][j] == substitutionCost:
          backtrace[i][j] = OP_SUB
        elif costs[i][j] == insertionCost:
          backtrace[i][j] = OP_INS
        else:
          backtrace[i][j] = OP_DEL

  # back trace though the best route:
  i = len(r)
  j = len(h)
  numSub = 0
  numDel = 0
  numIns = 0
  numCor = 0
  if debug:
    print("OP\tREF\tHYP")
    lines = []
  while i > 0 or j > 0:
    if backtrace[i][j] == OP_OK:
      numCor += 1
      i -= 1
      j -= 1
      if debug:
        lines.append("OK\t" + r[i] + "\t" + h[j])
    elif backtrace[i][j] == OP_SUB:
      numSub += 1
      i -= 1
      j -= 1
      if debug:
        lines.append("SUB\t" + r[i] + "\t" + h[j])
    elif backtrace[i][j] == OP_INS:
      numIns += 1
      j -= 1
      if debug:
        lines.append("INS\t" + "****" + "\t" + h[j])
    elif backtrace[i][j] == OP_DEL:
      numDel += 1
      i -= 1
      if debug:
        lines.append("DEL\t" + r[i] + "\t" + "****")
  if debug:
    lines = reversed(lines)
    for line in lines:
      print(line)
    print("Ncor " + str(numCor))
    print("Nsub " + str(numSub))
    print("Ndel " + str(numDel))
    print("Nins " + str(numIns))
  # return (numSub + numDel + numIns) / (float)(len(r))
  wer_result = round((numSub + numDel + numIns) / (float)(len(r)), 4)
  return {
      'WER': wer_result,
      'Cor': numCor,
      'Sub': numSub,
      'Ins': numIns,
      'Del': numDel}


def main():
  """
    Main
  """
  label_file_name = 'label.txt'
  label = open(label_file_name, 'r').readline()
  transcript = open('model_output.pickle')\
      .readlines()[1].split(',')[1]
  print('\n>>>>Label from', label_file_name, 'is :', label)
  print('\n>>>>Transcript:', transcript)
  print('Word Error Rate(WER):', word_error_rate(label, transcript))


if __name__ == "__main__":
  main()