add decoding method of ctc-greedy-search in zipformer recipe (#1690)

egs/librispeech/ASR/zipformer/ctc_decode.py

@@ -21,7 +21,16 @@
 """
 Usage:
 
-(1) ctc-decoding
+(1) ctc-greedy-search
+./zipformer/ctc_decode.py \
+    --epoch 30 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --use-ctc 1 \
+    --max-duration 600 \
+    --decoding-method ctc-greedy-search
+
+(2) ctc-decoding
 ./zipformer/ctc_decode.py \
     --epoch 30 \
     --avg 15 \
@@ -30,7 +39,7 @@
     --max-duration 600 \
     --decoding-method ctc-decoding
 
-(2) 1best
+(3) 1best
 ./zipformer/ctc_decode.py \
     --epoch 30 \
     --avg 15 \
@@ -40,7 +49,7 @@
     --hlg-scale 0.6 \
     --decoding-method 1best
 
-(3) nbest
+(4) nbest
 ./zipformer/ctc_decode.py \
     --epoch 30 \
     --avg 15 \
@@ -50,7 +59,7 @@
     --hlg-scale 0.6 \
     --decoding-method nbest
 
-(4) nbest-rescoring
+(5) nbest-rescoring
 ./zipformer/ctc_decode.py \
     --epoch 30 \
     --avg 15 \
@@ -62,7 +71,7 @@
     --lm-dir data/lm \
     --decoding-method nbest-rescoring
 
-(5) whole-lattice-rescoring
+(6) whole-lattice-rescoring
 ./zipformer/ctc_decode.py \
     --epoch 30 \
     --avg 15 \
@@ -74,7 +83,7 @@
     --lm-dir data/lm \
     --decoding-method whole-lattice-rescoring
 
-(6) attention-decoder-rescoring-no-ngram
+(7) attention-decoder-rescoring-no-ngram
 ./zipformer/ctc_decode.py \
     --epoch 30 \
     --avg 15 \
@@ -84,7 +93,7 @@
     --max-duration 100 \
     --decoding-method attention-decoder-rescoring-no-ngram
 
-(7) attention-decoder-rescoring-with-ngram
+(8) attention-decoder-rescoring-with-ngram
 ./zipformer/ctc_decode.py \
     --epoch 30 \
     --avg 15 \
@@ -120,6 +129,7 @@
     load_checkpoint,
 )
 from icefall.decode import (
+    ctc_greedy_search,
     get_lattice,
     nbest_decoding,
     nbest_oracle,
@@ -220,26 +230,29 @@ def get_parser():
         default="ctc-decoding",
         help="""Decoding method.
         Supported values are:
-        - (1) ctc-decoding. Use CTC decoding. It uses a sentence piece
+        - (1) ctc-greedy-search. Use CTC greedy search. It uses a sentence piece
           model, i.e., lang_dir/bpe.model, to convert word pieces to words.
           It needs neither a lexicon nor an n-gram LM.
-        - (2) 1best. Extract the best path from the decoding lattice as the
+        - (2) ctc-decoding. Use CTC decoding. It uses a sentence piece
+          model, i.e., lang_dir/bpe.model, to convert word pieces to words.
+          It needs neither a lexicon nor an n-gram LM.
+        - (3) 1best. Extract the best path from the decoding lattice as the
           decoding result.
-        - (3) nbest. Extract n paths from the decoding lattice; the path
+        - (4) nbest. Extract n paths from the decoding lattice; the path
           with the highest score is the decoding result.
-        - (4) nbest-rescoring. Extract n paths from the decoding lattice,
+        - (5) nbest-rescoring. Extract n paths from the decoding lattice,
           rescore them with an n-gram LM (e.g., a 4-gram LM), the path with
           the highest score is the decoding result.
-        - (5) whole-lattice-rescoring. Rescore the decoding lattice with an
+        - (6) whole-lattice-rescoring. Rescore the decoding lattice with an
           n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice
           is the decoding result.
           you have trained an RNN LM using ./rnn_lm/train.py
-        - (6) nbest-oracle. Its WER is the lower bound of any n-best
+        - (7) nbest-oracle. Its WER is the lower bound of any n-best
           rescoring method can achieve. Useful for debugging n-best
           rescoring method.
-        - (7) attention-decoder-rescoring-no-ngram. Extract n paths from the decoding
+        - (8) attention-decoder-rescoring-no-ngram. Extract n paths from the decoding
           lattice, rescore them with the attention decoder.
-        - (8) attention-decoder-rescoring-with-ngram. Extract n paths from the LM
+        - (9) attention-decoder-rescoring-with-ngram. Extract n paths from the LM
           rescored lattice, rescore them with the attention decoder.
         """,
     )
@@ -381,6 +394,15 @@ def decode_one_batch(
     encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens)
     ctc_output = model.ctc_output(encoder_out)  # (N, T, C)
 
+    if params.decoding_method == "ctc-greedy-search":
+        hyps = ctc_greedy_search(ctc_output, encoder_out_lens)
+        # hyps is a list of str, e.g., ['xxx yyy zzz', ...]
+        hyps = bpe_model.decode(hyps)
+        # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
+        hyps = [s.split() for s in hyps]
+        key = "ctc-greedy-search"
+        return {key: hyps}
+
     supervision_segments = torch.stack(
         (
             supervisions["sequence_idx"],
@@ -684,6 +706,7 @@ def main():
     params.update(vars(args))
 
     assert params.decoding_method in (
+        "ctc-greedy-search",
         "ctc-decoding",
         "1best",
         "nbest",
@@ -733,7 +756,9 @@ def main():
     params.eos_id = 1
     params.sos_id = 1
 
-    if params.decoding_method in ["ctc-decoding", "attention-decoder-rescoring-no-ngram"]:
+    if params.decoding_method in [
+        "ctc-greedy-search", "ctc-decoding", "attention-decoder-rescoring-no-ngram"
+    ]:
         HLG = None
         H = k2.ctc_topo(
             max_token=max_token_id,

icefall/decode.py

@@ -1473,3 +1473,34 @@ def rescore_with_rnn_lm(
                 key = f"ngram_lm_scale_{n_scale}_attention_scale_{a_scale}_rnn_lm_scale_{r_scale}"  # noqa
                 ans[key] = best_path
     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
+) -> List[List[int]]:
+    """CTC greedy search.
+
+    Args:
+         ctc_output: (batch, seq_len, vocab_size)
+         encoder_out_lens: (batch,)
+    Returns:
+         List[List[int]]: greedy search result
+    """
+    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 = [remove_duplicates_and_blank(hyp) for hyp in hyps]
+    return hyps