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add decoding method of ctc-greedy-search in zipformer recipe (#1690)
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Zengwei Yao
GitHub
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d47c078286
@ -21,7 +21,16 @@
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"""
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Usage:
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(1) ctc-decoding
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(1) ctc-greedy-search
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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--exp-dir ./zipformer/exp \
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--use-ctc 1 \
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--max-duration 600 \
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--decoding-method ctc-greedy-search
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(2) ctc-decoding
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -30,7 +39,7 @@ Usage:
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--max-duration 600 \
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--decoding-method ctc-decoding
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(2) 1best
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(3) 1best
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -40,7 +49,7 @@ Usage:
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--hlg-scale 0.6 \
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--decoding-method 1best
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(3) nbest
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(4) nbest
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -50,7 +59,7 @@ Usage:
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--hlg-scale 0.6 \
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--decoding-method nbest
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(4) nbest-rescoring
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(5) nbest-rescoring
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -62,7 +71,7 @@ Usage:
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--lm-dir data/lm \
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--decoding-method nbest-rescoring
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(5) whole-lattice-rescoring
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(6) whole-lattice-rescoring
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -74,7 +83,7 @@ Usage:
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--lm-dir data/lm \
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--decoding-method whole-lattice-rescoring
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(6) attention-decoder-rescoring-no-ngram
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(7) attention-decoder-rescoring-no-ngram
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -84,7 +93,7 @@ Usage:
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--max-duration 100 \
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--decoding-method attention-decoder-rescoring-no-ngram
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(7) attention-decoder-rescoring-with-ngram
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(8) attention-decoder-rescoring-with-ngram
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -120,6 +129,7 @@ from icefall.checkpoint import (
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load_checkpoint,
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)
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from icefall.decode import (
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ctc_greedy_search,
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get_lattice,
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nbest_decoding,
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nbest_oracle,
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@ -220,26 +230,29 @@ def get_parser():
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default="ctc-decoding",
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help="""Decoding method.
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Supported values are:
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- (1) ctc-decoding. Use CTC decoding. It uses a sentence piece
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- (1) ctc-greedy-search. Use CTC greedy search. It uses a sentence piece
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model, i.e., lang_dir/bpe.model, to convert word pieces to words.
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It needs neither a lexicon nor an n-gram LM.
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- (2) 1best. Extract the best path from the decoding lattice as the
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- (2) ctc-decoding. Use CTC decoding. It uses a sentence piece
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model, i.e., lang_dir/bpe.model, to convert word pieces to words.
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It needs neither a lexicon nor an n-gram LM.
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- (3) 1best. Extract the best path from the decoding lattice as the
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decoding result.
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- (3) nbest. Extract n paths from the decoding lattice; the path
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- (4) nbest. Extract n paths from the decoding lattice; the path
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with the highest score is the decoding result.
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- (4) nbest-rescoring. Extract n paths from the decoding lattice,
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- (5) nbest-rescoring. Extract n paths from the decoding lattice,
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rescore them with an n-gram LM (e.g., a 4-gram LM), the path with
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the highest score is the decoding result.
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- (5) whole-lattice-rescoring. Rescore the decoding lattice with an
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- (6) whole-lattice-rescoring. Rescore the decoding lattice with an
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n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice
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is the decoding result.
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you have trained an RNN LM using ./rnn_lm/train.py
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- (6) nbest-oracle. Its WER is the lower bound of any n-best
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- (7) nbest-oracle. Its WER is the lower bound of any n-best
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rescoring method can achieve. Useful for debugging n-best
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rescoring method.
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- (7) attention-decoder-rescoring-no-ngram. Extract n paths from the decoding
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- (8) attention-decoder-rescoring-no-ngram. Extract n paths from the decoding
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lattice, rescore them with the attention decoder.
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- (8) attention-decoder-rescoring-with-ngram. Extract n paths from the LM
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- (9) attention-decoder-rescoring-with-ngram. Extract n paths from the LM
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rescored lattice, rescore them with the attention decoder.
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""",
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)
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@ -381,6 +394,15 @@ def decode_one_batch(
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encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens)
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ctc_output = model.ctc_output(encoder_out) # (N, T, C)
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if params.decoding_method == "ctc-greedy-search":
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hyps = ctc_greedy_search(ctc_output, encoder_out_lens)
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# hyps is a list of str, e.g., ['xxx yyy zzz', ...]
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hyps = bpe_model.decode(hyps)
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# hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
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hyps = [s.split() for s in hyps]
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key = "ctc-greedy-search"
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return {key: hyps}
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supervision_segments = torch.stack(
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(
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supervisions["sequence_idx"],
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@ -684,6 +706,7 @@ def main():
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params.update(vars(args))
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assert params.decoding_method in (
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"ctc-greedy-search",
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"ctc-decoding",
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"1best",
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"nbest",
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@ -733,7 +756,9 @@ def main():
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params.eos_id = 1
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params.sos_id = 1
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if params.decoding_method in ["ctc-decoding", "attention-decoder-rescoring-no-ngram"]:
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if params.decoding_method in [
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"ctc-greedy-search", "ctc-decoding", "attention-decoder-rescoring-no-ngram"
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]:
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HLG = None
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H = k2.ctc_topo(
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max_token=max_token_id,
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@ -1473,3 +1473,34 @@ def rescore_with_rnn_lm(
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key = f"ngram_lm_scale_{n_scale}_attention_scale_{a_scale}_rnn_lm_scale_{r_scale}" # noqa
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ans[key] = best_path
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return ans
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def remove_duplicates_and_blank(hyp: List[int]) -> List[int]:
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# from https://github.com/wenet-e2e/wenet/blob/main/wenet/utils/common.py
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new_hyp: List[int] = []
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cur = 0
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while cur < len(hyp):
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if hyp[cur] != 0:
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new_hyp.append(hyp[cur])
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prev = cur
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while cur < len(hyp) and hyp[cur] == hyp[prev]:
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cur += 1
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return new_hyp
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def ctc_greedy_search(
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ctc_output: torch.Tensor, encoder_out_lens: torch.Tensor
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) -> List[List[int]]:
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"""CTC greedy search.
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Args:
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ctc_output: (batch, seq_len, vocab_size)
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encoder_out_lens: (batch,)
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Returns:
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List[List[int]]: greedy search result
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"""
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batch = ctc_output.shape[0]
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index = ctc_output.argmax(dim=-1) # (batch, seq_len)
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hyps = [index[i].tolist()[:encoder_out_lens[i]] for i in range(batch)]
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hyps = [remove_duplicates_and_blank(hyp) for hyp in hyps]
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return hyps
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