diff --git a/codes/scripts/audio/word_error_rate.py b/codes/scripts/audio/word_error_rate.py
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+++ b/codes/scripts/audio/word_error_rate.py
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+# Original source: https://github.com/SeanNaren/deepspeech.pytorch/blob/master/deepspeech_pytorch/validation.py
+import os
+
+import Levenshtein as Lev
+import torch
+from tqdm import tqdm
+
+from models.tacotron2.text import cleaners
+
+
+def clean_text(text):
+  for name in ['english_cleaners']:
+    cleaner = getattr(cleaners, name)
+    if not cleaner:
+      raise Exception('Unknown cleaner: %s' % name)
+    text = cleaner(text)
+  return text
+
+
+# Converts text to all-uppercase and separates punctuation from words.
+def normalize_text(text):
+    text = text.upper()
+    for punc in ['.', ',', ':', ';']:
+        text = text.replace(punc, f' {punc}')
+    return text.strip()
+
+
+class WordErrorRate:
+    def calculate_metric(self, transcript, reference):
+        wer_inst = self.wer_calc(transcript, reference)
+        self.wer += wer_inst
+        self.n_tokens += len(reference.split())
+
+    def compute(self):
+        wer = float(self.wer) / self.n_tokens
+        return wer.item() * 100
+
+    def wer_calc(self, s1, s2):
+        """
+        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
+        """
+
+        # 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))
+
+
+if __name__ == '__main__':
+    inference_tsv = '\\\\192.168.5.3\\rtx3080_drv\\dlas\\codes\\eval_libritts_for_gpt_asr_results_WER=2.6615.tsv'
+    libri_base = 'Z:\\libritts\\test-clean'
+
+    wer = WordErrorRate()
+    wer_scores = []
+    with open(inference_tsv, 'r') as tsv_file:
+        tsv = tsv_file.read().splitlines()
+        for line in tqdm(tsv):
+            sentence_pred, wav = line.split('\t')
+            sentence_pred = normalize_text(sentence_pred)
+
+            wav_comp = wav.split('_')
+            reader = wav_comp[0]
+            book = wav_comp[1]
+            txt_file = os.path.join(libri_base, reader, book, wav.replace('.wav', '.normalized.txt'))
+            with open(txt_file, 'r') as txt_file_hndl:
+                txt_uncleaned = txt_file_hndl.read()
+                sentence_real = normalize_text(clean_text(txt_uncleaned))
+                wer_scores.append(wer.wer_calc(sentence_real, sentence_pred))
+    print(f"WER: {torch.tensor(wer_scores, dtype=torch.float).mean()}")
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