diff --git a/icefall/ctc/__init__.py b/icefall/ctc/__init__.py
index b546b31af..eb1ec47d1 100644
--- a/icefall/ctc/__init__.py
+++ b/icefall/ctc/__init__.py
@@ -4,3 +4,4 @@ from .prepare_lang import (
     make_lexicon_fst_with_silence,
 )
 from .topo import add_disambig_self_loops, add_one, build_standard_ctc_topo
+from .utils import merge_tokens
diff --git a/icefall/ctc/test_utils.py b/icefall/ctc/test_utils.py
new file mode 100755
index 000000000..6fa883dfb
--- /dev/null
+++ b/icefall/ctc/test_utils.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+# Copyright      2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+from typing import List
+
+from utils import TokenSpan, merge_tokens
+
+
+def inefficient_merge_tokens(alignment: List[int], blank: int = 0) -> List[TokenSpan]:
+    """Compute start and end frames of each token from the given alignment.
+
+    Args:
+      alignment:
+        A list of token IDs.
+      blank_id:
+        ID of the blank.
+    Returns:
+      Return a list of TokenSpan.
+    """
+    ans = []
+    last_token = None
+    last_i = None
+
+    #  import pdb
+
+    #  pdb.set_trace()
+    for i, token in enumerate(alignment):
+        if token == blank:
+            if last_token is None or last_token == token:
+                continue
+
+            # end of the last token
+            span = TokenSpan(token=last_token, start=last_i, end=i)
+            ans.append(span)
+            last_token = None
+            last_i = None
+            continue
+
+        # The current token is not a blank
+        if last_token is None or last_token == blank:
+            last_token = token
+            last_i = i
+            continue
+
+        if last_token == token:
+            continue
+
+        # end of the last token and start of the current token
+        span = TokenSpan(token=last_token, start=last_i, end=i)
+        last_token = token
+        last_i = i
+        ans.append(span)
+
+    if last_token is not None:
+        assert last_i is not None, (last_i, last_token)
+        span = TokenSpan(token=last_token, start=last_i, end=len(alignment))
+        # Note for the last token, its end is larger than len(alignment)-1
+        ans.append(span)
+
+    return ans
+
+
+def test_merge_tokens():
+    data_list = [
+        # 0  1  2  3  4  5  6  7  8  9  10 11 12 13 14
+        [0, 1, 1, 1, 2, 0, 0, 0, 2, 2, 3, 2, 3, 3, 0],
+        [0, 1, 1, 1, 2, 0, 0, 0, 2, 2, 3, 2, 3, 3],
+        [1, 1, 1, 2, 0, 0, 0, 2, 2, 3, 2, 3, 3, 0],
+        [1, 1, 1, 2, 0, 0, 0, 2, 2, 3, 2, 3, 3],
+        [0, 1, 2, 3, 0],
+        [1, 2, 3, 0],
+        [0, 1, 2, 3],
+        [1, 2, 3],
+    ]
+
+    for data in data_list:
+        span1 = merge_tokens(data)
+        span2 = inefficient_merge_tokens(data)
+        assert span1 == span2, (data, span1, span2)
+
+
+def main():
+    test_merge_tokens()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/icefall/ctc/utils.py b/icefall/ctc/utils.py
new file mode 100644
index 000000000..ad49b5ffd
--- /dev/null
+++ b/icefall/ctc/utils.py
@@ -0,0 +1,52 @@
+# Copyright      2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+from dataclasses import dataclass
+from typing import List
+
+import torch
+
+
+@dataclass
+class TokenSpan:
+    # ID of the token
+    token: int
+
+    # Start frame of this token in the output log_prob
+    start: int
+
+    # End frame of this token in the output log_prob
+    end: int
+
+
+# See also
+# https://github.com/pytorch/audio/blob/main/src/torchaudio/functional/_alignment.py#L96
+# We use torchaudio as a reference while implementing this function
+def merge_tokens(alignment: List[int], blank: int = 0) -> List[TokenSpan]:
+    """Compute start and end frames of each token from the given alignment.
+
+    Args:
+      alignment:
+        A list of token IDs.
+      blank_id:
+        ID of the blank.
+    Returns:
+      Return a list of TokenSpan.
+    """
+    alignment_tensor = torch.tensor(alignment, dtype=torch.int32)
+
+    diff = torch.diff(
+        alignment_tensor,
+        prepend=torch.tensor([-1]),
+        append=torch.tensor([-1]),
+    )
+
+    non_zero_indexes = torch.nonzero(diff != 0).squeeze().tolist()
+
+    ans = []
+    for start, end in zip(non_zero_indexes[:-1], non_zero_indexes[1:]):
+        token = alignment[start]
+        if token == blank:
+            continue
+        span = TokenSpan(token=token, start=start, end=end)
+        ans.append(span)
+    return ans