diff --git a/funasr/metrics/common.py b/funasr/metrics/common.py
index 2443e0dc5..11bd2cdfb 100644
--- a/funasr/metrics/common.py
+++ b/funasr/metrics/common.py
@@ -9,8 +9,9 @@
 import json
 import logging
 import sys
-
 from itertools import groupby
+
+from rapidfuzz.distance import Levenshtein
 import numpy as np
 import six
 
@@ -155,7 +156,6 @@ def calculate_cer_ctc(self, ys_hat, ys_pad):
         :return: average sentence-level CER score
         :rtype float
         """
-        import editdistance
 
         cers, char_ref_lens = [], []
         for i, y in enumerate(ys_hat):
@@ -175,7 +175,7 @@ def calculate_cer_ctc(self, ys_hat, ys_pad):
             hyp_chars = "".join(seq_hat)
             ref_chars = "".join(seq_true)
             if len(ref_chars) > 0:
-                cers.append(editdistance.eval(hyp_chars, ref_chars))
+                cers.append(Levenshtein.distance(hyp_chars, ref_chars))
                 char_ref_lens.append(len(ref_chars))
 
         cer_ctc = float(sum(cers)) / sum(char_ref_lens) if cers else None
@@ -214,16 +214,15 @@ def calculate_cer(self, seqs_hat, seqs_true):
         :return: average sentence-level CER score
         :rtype float
         """
-        import editdistance
 
         char_eds, char_ref_lens = [], []
         for i, seq_hat_text in enumerate(seqs_hat):
             seq_true_text = seqs_true[i]
             hyp_chars = seq_hat_text.replace(" ", "")
             ref_chars = seq_true_text.replace(" ", "")
-            char_eds.append(editdistance.eval(hyp_chars, ref_chars))
+            char_eds.append(Levenshtein.distance(hyp_chars, ref_chars))
             char_ref_lens.append(len(ref_chars))
-        return float(sum(char_eds)) / sum(char_ref_lens)
+        return float(sum(char_eds)) / sum(char_ref_lens) if char_eds else None
 
     def calculate_wer(self, seqs_hat, seqs_true):
         """Calculate sentence-level WER score.
@@ -233,13 +232,12 @@ def calculate_wer(self, seqs_hat, seqs_true):
         :return: average sentence-level WER score
         :rtype float
         """
-        import editdistance
 
         word_eds, word_ref_lens = [], []
         for i, seq_hat_text in enumerate(seqs_hat):
             seq_true_text = seqs_true[i]
             hyp_words = seq_hat_text.split()
             ref_words = seq_true_text.split()
-            word_eds.append(editdistance.eval(hyp_words, ref_words))
+            word_eds.append(Levenshtein.distance(hyp_words, ref_words))
             word_ref_lens.append(len(ref_words))
-        return float(sum(word_eds)) / sum(word_ref_lens)
+        return float(sum(word_eds)) / sum(word_ref_lens) if word_eds else None
diff --git a/setup.py b/setup.py
index fdc693e1d..de2f5d4be 100644
--- a/setup.py
+++ b/setup.py
@@ -35,7 +35,7 @@
         "jaconv",
         # Speaker & evaluation
         "umap_learn",
-        "editdistance>=0.5.2",
+        "rapidfuzz>=3.0.0",
         # Optional (training/enhancement)
         "torch_complex",
         "tensorboardX",
@@ -44,7 +44,7 @@
     ],
     # train: The modules invoked when training only.
     "train": [
-        "editdistance",
+        "rapidfuzz>=3.0.0",
     ],
     # all: The modules should be optionally installled due to some reason.
     #      Please consider moving them to "install" occasionally