mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
Give an informative message when users provide an unsupported decoding method (#77)
This commit is contained in:
Fangjun Kuang
GitHub
parent
39bc8cae94
commit
5016ee3c95
@ -20,23 +20,23 @@
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import argparse
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import logging
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import math
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import sentencepiece as spm
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from typing import List
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import k2
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import kaldifeat
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import sentencepiece as spm
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import torch
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import torchaudio
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from conformer import Conformer
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from torch.nn.utils.rnn import pad_sequence
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from icefall.lexicon import Lexicon
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from icefall.decode import (
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get_lattice,
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one_best_decoding,
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rescore_with_attention_decoder,
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rescore_with_whole_lattice,
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)
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from icefall.lexicon import Lexicon
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from icefall.utils import AttributeDict, get_texts
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@ -58,7 +58,7 @@ def get_parser():
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"--lang-dir",
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type=str,
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required=True,
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help="Path to lang bpe dir.",
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help="Path to lang dir.",
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)
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parser.add_argument(
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@ -142,7 +142,7 @@ def get_parser():
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parser.add_argument(
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"--sos-id",
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type=float,
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type=int,
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default=1,
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help="""
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Used only when method is attention-decoder.
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@ -152,7 +152,7 @@ def get_parser():
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parser.add_argument(
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"--eos-id",
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type=float,
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type=int,
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default=1,
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help="""
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Used only when method is attention-decoder.
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@ -285,128 +285,121 @@ def main():
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dtype=torch.int32,
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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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if params.method == "ctc-decoding":
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logging.info("Use CTC decoding")
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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(params.lang_dir + "/bpe.model")
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bpe_model = spm.SentencePieceProcessor()
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bpe_model.load(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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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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elif 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.lang_dir}/HLG.pt")
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HLG = k2.Fsa.from_dict(
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torch.load(params.lang_dir + "/HLG.pt", map_location="cpu")
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)
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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 [
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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.lang_dir}/HLG.pt")
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HLG = k2.Fsa.from_dict(
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torch.load(params.lang_dir + "/HLG.pt", map_location="cpu")
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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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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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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 [
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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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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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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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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(
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params.lang_dir + "/words.txt"
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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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hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
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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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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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hyps = get_texts(best_path)
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word_sym_table = k2.SymbolTable.from_file(
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params.lang_dir + "/words.txt"
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)
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hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
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else:
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raise ValueError(f"Unsupported decoding method: {params.method}")
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logging.info("Decoding Done")
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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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except Exception:
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raise ValueError("Please use a supported decoding method.")
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logging.info("Decoding Done")
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if __name__ == "__main__":
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