mirror of
https://github.com/k2-fsa/icefall.git
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update ctc-decoding for pretrained.py on conformer_ctc
This commit is contained in:
Mingshuang Luo
parent
7fd9d291f3
commit
524afc02ba
@ -448,7 +448,7 @@ After downloading, you will have the following files:
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**File descriptions**:
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- ``data/lang_bpe/Linv.pt``
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It is the lexicon file.
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It is the lexicon file, with word IDs as labels and token IDs as aux_labels.
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- ``data/lang_bpe/HLG.pt``
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@ -530,7 +530,7 @@ Usage
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displays the help information.
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It supports three decoding methods:
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It supports 4 decoding methods:
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- CTC decoding
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- HLG decoding
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@ -57,16 +57,14 @@ def get_parser():
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parser.add_argument(
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"--words-file",
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type=str,
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default="./tmp/icefall_asr_librispeech_conformer_ctc/ \
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data/lang_bpe/words.txt",
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required=True,
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help="Path to words.txt",
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)
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parser.add_argument(
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"--HLG",
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type=str,
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default="./tmp/icefall_asr_librispeech_conformer_ctc/ \
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data/lang_bpe/HLG.pt",
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required=True,
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help="Path to HLG.pt.",
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)
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@ -172,8 +170,7 @@ def get_parser():
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parser.add_argument(
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"--lang-dir",
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type=str,
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default="./tmp/icefall_asr_librispeech_conformer_ctc/ \
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data/lang_bpe",
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required=True,
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help="Path to lang bpe dir.",
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)
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@ -302,111 +299,124 @@ def main():
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dtype=torch.int32,
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)
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if params.method == "ctc-decoding":
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logging.info("Building CTC topology")
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lexicon = Lexicon(params.lang_dir)
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max_token_id = max(lexicon.tokens)
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H = k2.ctc_topo(
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max_token=max_token_id,
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modified=False,
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device=device,
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)
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try:
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if params.method == "ctc-decoding":
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logging.info("Building CTC topology")
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lexicon = Lexicon(params.lang_dir)
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max_token_id = max(lexicon.tokens)
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H = k2.ctc_topo(
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max_token=max_token_id,
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modified=False,
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device=device,
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)
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logging.info("Loading BPE model")
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bpe_model = spm.SentencePieceProcessor()
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bpe_model.load(str(params.lang_dir + "/bpe.model"))
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logging.info("Loading BPE model")
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bpe_model = spm.SentencePieceProcessor()
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bpe_model.load(str(params.lang_dir + "/bpe.model"))
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lattice = get_lattice(
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nnet_output=nnet_output,
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decoding_graph=H,
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supervision_segments=supervision_segments,
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search_beam=params.search_beam,
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output_beam=params.output_beam,
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min_active_states=params.min_active_states,
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max_active_states=params.max_active_states,
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subsampling_factor=params.subsampling_factor,
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)
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lattice = get_lattice(
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nnet_output=nnet_output,
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decoding_graph=H,
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supervision_segments=supervision_segments,
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search_beam=params.search_beam,
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output_beam=params.output_beam,
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min_active_states=params.min_active_states,
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max_active_states=params.max_active_states,
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subsampling_factor=params.subsampling_factor,
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)
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logging.info("Use CTC decoding")
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best_path = one_best_decoding(
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lattice=lattice, use_double_scores=params.use_double_scores
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)
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token_ids = get_texts(best_path)
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hyps = bpe_model.decode(token_ids)
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hyps = [s.split() for s in hyps]
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else:
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logging.info(f"Loading HLG from {params.HLG}")
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HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
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HLG = HLG.to(device)
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if not hasattr(HLG, "lm_scores"):
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# For whole-lattice-rescoring and attention-decoder
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HLG.lm_scores = HLG.scores.clone()
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if params.method in ["whole-lattice-rescoring", "attention-decoder"]:
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logging.info(f"Loading G from {params.G}")
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G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
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# Add epsilon self-loops to G as we will compose
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# it with the whole lattice later
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G = G.to(device)
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G = k2.add_epsilon_self_loops(G)
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G = k2.arc_sort(G)
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G.lm_scores = G.scores.clone()
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lattice = get_lattice(
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nnet_output=nnet_output,
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decoding_graph=HLG,
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supervision_segments=supervision_segments,
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search_beam=params.search_beam,
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output_beam=params.output_beam,
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min_active_states=params.min_active_states,
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max_active_states=params.max_active_states,
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subsampling_factor=params.subsampling_factor,
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)
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if params.method == "1best":
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logging.info("Use HLG decoding")
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logging.info("Use CTC decoding")
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best_path = one_best_decoding(
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lattice=lattice, use_double_scores=params.use_double_scores
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)
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elif params.method == "whole-lattice-rescoring":
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logging.info("Use HLG decoding + LM rescoring")
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best_path_dict = rescore_with_whole_lattice(
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lattice=lattice,
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G_with_epsilon_loops=G,
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lm_scale_list=[params.ngram_lm_scale],
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)
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best_path = next(iter(best_path_dict.values()))
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elif params.method == "attention-decoder":
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logging.info("Use HLG + LM rescoring + attention decoder rescoring")
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rescored_lattice = rescore_with_whole_lattice(
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lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=None
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)
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best_path_dict = rescore_with_attention_decoder(
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lattice=rescored_lattice,
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num_paths=params.num_paths,
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model=model,
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memory=memory,
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memory_key_padding_mask=memory_key_padding_mask,
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sos_id=params.sos_id,
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eos_id=params.eos_id,
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nbest_scale=params.nbest_scale,
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ngram_lm_scale=params.ngram_lm_scale,
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attention_scale=params.attention_decoder_scale,
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)
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best_path = next(iter(best_path_dict.values()))
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token_ids = get_texts(best_path)
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hyps = bpe_model.decode(token_ids)
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hyps = [s.split() for s in hyps]
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hyps = get_texts(best_path)
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word_sym_table = k2.SymbolTable.from_file(params.words_file)
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hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
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if params.method in [
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"1best",
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"whole-lattice-rescoring",
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"attention-decoder",
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]:
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logging.info(f"Loading HLG from {params.HLG}")
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HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
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HLG = HLG.to(device)
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if not hasattr(HLG, "lm_scores"):
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# For whole-lattice-rescoring and attention-decoder
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HLG.lm_scores = HLG.scores.clone()
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s = "\n"
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for filename, hyp in zip(params.sound_files, hyps):
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words = " ".join(hyp)
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s += f"{filename}:\n{words}\n\n"
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logging.info(s)
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if params.method in [
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"whole-lattice-rescoring",
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"attention-decoder",
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]:
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logging.info(f"Loading G from {params.G}")
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G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
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# Add epsilon self-loops to G as we will compose
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# it with the whole lattice later
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G = G.to(device)
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G = k2.add_epsilon_self_loops(G)
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G = k2.arc_sort(G)
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G.lm_scores = G.scores.clone()
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logging.info("Decoding Done")
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lattice = get_lattice(
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nnet_output=nnet_output,
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decoding_graph=HLG,
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supervision_segments=supervision_segments,
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search_beam=params.search_beam,
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output_beam=params.output_beam,
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min_active_states=params.min_active_states,
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max_active_states=params.max_active_states,
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subsampling_factor=params.subsampling_factor,
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)
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if params.method == "1best":
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logging.info("Use HLG decoding")
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best_path = one_best_decoding(
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lattice=lattice, use_double_scores=params.use_double_scores
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)
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elif params.method == "whole-lattice-rescoring":
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logging.info("Use HLG decoding + LM rescoring")
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best_path_dict = rescore_with_whole_lattice(
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lattice=lattice,
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G_with_epsilon_loops=G,
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lm_scale_list=[params.ngram_lm_scale],
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)
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best_path = next(iter(best_path_dict.values()))
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elif params.method == "attention-decoder":
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logging.info(
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"Use HLG + LM rescoring + attention decoder rescoring"
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)
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rescored_lattice = rescore_with_whole_lattice(
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lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=None
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)
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best_path_dict = rescore_with_attention_decoder(
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lattice=rescored_lattice,
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num_paths=params.num_paths,
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model=model,
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memory=memory,
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memory_key_padding_mask=memory_key_padding_mask,
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sos_id=params.sos_id,
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eos_id=params.eos_id,
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nbest_scale=params.nbest_scale,
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ngram_lm_scale=params.ngram_lm_scale,
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attention_scale=params.attention_decoder_scale,
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)
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best_path = next(iter(best_path_dict.values()))
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hyps = get_texts(best_path)
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word_sym_table = k2.SymbolTable.from_file(params.words_file)
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hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
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s = "\n"
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for filename, hyp in zip(params.sound_files, hyps):
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words = " ".join(hyp)
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s += f"{filename}:\n{words}\n\n"
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logging.info(s)
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logging.info("Decoding Done")
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except Exception:
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raise ValueError("Please use a supported decoding method.")
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if __name__ == "__main__":
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