use metric functions from espnet

main.py

@@ -119,7 +119,7 @@ def benchmark_inference(lipreader, data_dir, landmarks_dir, lines, dst_dir=""):
             if groundtruth is not None:
                 print(f"ref: {groundtruth}")
                 wer.update( get_wer(output, groundtruth), len(groundtruth.split()))
-                cer.update( get_cer(output, groundtruth), len(groundtruth))
+                cer.update( get_cer(output, groundtruth), len(groundtruth.replace(" ", "")))
                 print(
                     f"progress: {idx+1}/{len(lines)}\tcur WER: {wer.val*100:.1f}\t"
                     f"cur CER: {cer.val*100:.1f}\t"

metrics/measures.py

@@ -3,13 +3,16 @@
 
 # Copyright 2021 Imperial College London (Pingchuan Ma)
 # Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+# This code refers https://github.com/espnet/espnet/blob/24c3676a8d4c2e60d2726e9bcd9bdbed740610e0/espnet/nets/e2e_asr_common.py#L213-L249
+
 import numpy as np
 
 def get_wer(s, ref):
-    return get_er(s.split(" "), ref.split(" "))
+    return get_er(s.split(), ref.split())
 
 def get_cer(s, ref):
-    return get_er(list(s), list(ref))
+    return get_er(s.replace(" ", ""), ref.replace(" ", ""))
 
 def get_er(s, ref):
     """