Refactor ctc greedy search. (#1691)

Use torch.unique_consecutive() to avoid reinventing the wheel.

icefall/decode.py

@@ -1475,21 +1475,10 @@ def rescore_with_rnn_lm(
     return ans
 
 
-def remove_duplicates_and_blank(hyp: List[int]) -> List[int]:
-    # from https://github.com/wenet-e2e/wenet/blob/main/wenet/utils/common.py
-    new_hyp: List[int] = []
-    cur = 0
-    while cur < len(hyp):
-        if hyp[cur] != 0:
-            new_hyp.append(hyp[cur])
-        prev = cur
-        while cur < len(hyp) and hyp[cur] == hyp[prev]:
-            cur += 1
-    return new_hyp
-
-
 def ctc_greedy_search(
-    ctc_output: torch.Tensor, encoder_out_lens: torch.Tensor
+    ctc_output: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    blank_id: int = 0,
 ) -> List[List[int]]:
     """CTC greedy search.
 
@@ -1501,6 +1490,10 @@ def ctc_greedy_search(
     """
     batch = ctc_output.shape[0]
     index = ctc_output.argmax(dim=-1)  # (batch, seq_len)
-    hyps = [index[i].tolist()[:encoder_out_lens[i]] for i in range(batch)]
+    hyps = [
-    hyps = [remove_duplicates_and_blank(hyp) for hyp in hyps]
+        torch.unique_consecutive(index[i, : encoder_out_lens[i]]) for i in range(batch)
+    ]
+
+    hyps = [h[h != blank_id].tolist() for h in hyps]
+
     return hyps

test/test_ctc_greedy_search.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+
+import torch
+
+from icefall.decode import ctc_greedy_search
+
+
+def test():
+    log_probs = torch.tensor(
+        [
+            [
+                [10, 1, 2, 1, 1, 3, 2, 3],
+                [10, 3, 2, 2, 1, 3, 2, 3],
+                [1, 10, 2, 2, 1, 3, 2, 3],
+                [1, 10, 2, 2, 1, 3, 2, 3],
+                [1, 1, 10, 1, 1, 3, 2, 3],
+                [10, 1, 1, 1, 1, 3, 2, 3],
+                [1, 1, 1, 10, 1, 3, 2, 3],
+            ],
+            [
+                [10, 1, 2, 1, 1, 3, 2, 3],
+                [10, 3, 2, 2, 1, 3, 2, 3],
+                [1, 10, 2, 2, 1, 3, 2, 3],
+                [1, 10, 2, 2, 1, 3, 2, 3],
+                [1, 1, 10, 1, 1, 3, 2, 3],
+                [10, 1, 1, 1, 1, 3, 2, 3],
+                [1, 1, 1, 10, 1, 3, 2, 3],
+            ],
+        ],
+        dtype=torch.float32,
+    ).log_softmax(dim=-1)
+
+    log_probs_length = torch.tensor([7, 6])
+
+    hyps = ctc_greedy_search(log_probs, log_probs_length)
+
+    assert hyps[0] == [1, 2, 3], hyps[0]
+    assert hyps[1] == [1, 2], hyps[1]
+
+
+if __name__ == "__main__":
+    test()