code: def wer(self, s1, s2):
          
  
          # build mapping of words to integers
          b = set(s1.split() + s2.split())
          word2char = dict(zip(b, range(len(b))))
  
          # map the words to a char array (Levenshtein packages only accepts
          # strings)
          w1 = [chr(word2char[w]) for w in s1.split()]
          w2 = [chr(word2char[w]) for w in s2.split()]
  
          return Lev.distance(''.join(w1), ''.join(w2))
docs: Computes the Word Error Rate, defined as the edit distance between the
          two provided sentences after tokenizing to words.
          Arguments:
              s1 (string): space-separated sentence
              s2 (string): space-separated sentence