mirror of
https://github.com/k2-fsa/icefall.git
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150 lines
4.2 KiB
Python
Executable File
150 lines
4.2 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021-2024 Xiaomi Corp. (authors: Fangjun Kuang,
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# Kang Wei,
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# Zengrui Jin,)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This script takes as input lang_dir and generates LG from
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- L, the lexicon, built from lang_dir/L_disambig.pt
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Caution: We use a lexicon that contains disambiguation symbols
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- G, the LM, built from lang_dir/lm/G_3_gram.fst.txt
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The generated LG is saved in $lang_dir/LG.pt
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"""
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import argparse
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import logging
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from pathlib import Path
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import k2
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import torch
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from icefall.lexicon import Lexicon
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--lang-dir",
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type=str,
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help="""Input and output directory.
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""",
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)
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parser.add_argument(
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"--lm",
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type=str,
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default="G_3_gram",
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help="""Stem name for LM used in HLG compiling.
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""",
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)
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return parser.parse_args()
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def compile_LG(lang_dir: str, lm: str = "G_3_gram") -> k2.Fsa:
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"""
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Args:
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lang_dir:
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The language directory, e.g., data/lang_phone or data/lang_bpe_5000.
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Return:
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An FSA representing LG.
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"""
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lexicon = Lexicon(lang_dir)
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L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt", weights_only=False))
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if Path(f"{lang_dir}/lm/{lm}.pt").is_file():
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logging.info(f"Loading pre-compiled {lm}")
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d = torch.load(f"{lang_dir}/lm/{lm}.pt", weights_only=False)
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G = k2.Fsa.from_dict(d)
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else:
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logging.info(f"Loading {lm}.fst.txt")
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with open(f"{lang_dir}/lm/{lm}.fst.txt") as f:
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G = k2.Fsa.from_openfst(f.read(), acceptor=False)
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torch.save(G.as_dict(), f"{lang_dir}/lm/{lm}.pt")
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first_token_disambig_id = lexicon.token_table["#0"]
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first_word_disambig_id = lexicon.word_table["#0"]
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L = k2.arc_sort(L)
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G = k2.arc_sort(G)
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logging.info("Intersecting L and G")
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LG = k2.compose(L, G)
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logging.info(f"LG shape: {LG.shape}")
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logging.info("Connecting LG")
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LG = k2.connect(LG)
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logging.info(f"LG shape after k2.connect: {LG.shape}")
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logging.info(type(LG.aux_labels))
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logging.info("Determinizing LG")
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LG = k2.determinize(LG, k2.DeterminizeWeightPushingType.kLogWeightPushing)
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logging.info(type(LG.aux_labels))
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logging.info("Connecting LG after k2.determinize")
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LG = k2.connect(LG)
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logging.info("Removing disambiguation symbols on LG")
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# LG.labels[LG.labels >= first_token_disambig_id] = 0
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# see https://github.com/k2-fsa/k2/pull/1140
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labels = LG.labels
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labels[labels >= first_token_disambig_id] = 0
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LG.labels = labels
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assert isinstance(LG.aux_labels, k2.RaggedTensor)
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LG.aux_labels.values[LG.aux_labels.values >= first_word_disambig_id] = 0
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LG = k2.remove_epsilon(LG)
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logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}")
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LG = k2.connect(LG)
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LG.aux_labels = LG.aux_labels.remove_values_eq(0)
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logging.info("Arc sorting LG")
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LG = k2.arc_sort(LG)
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return LG
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def main():
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args = get_args()
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lang_dir = Path(args.lang_dir)
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if (lang_dir / "LG.pt").is_file():
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logging.info(f"{lang_dir}/LG.pt already exists - skipping")
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return
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logging.info(f"Processing {lang_dir}")
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LG = compile_LG(lang_dir, args.lm)
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logging.info(f"Saving LG.pt to {lang_dir}")
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torch.save(LG.as_dict(), f"{lang_dir}/LG.pt")
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if __name__ == "__main__":
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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logging.basicConfig(format=formatter, level=logging.INFO)
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main()
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