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https://github.com/k2-fsa/icefall.git
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145 lines
3.8 KiB
Python
Executable File
145 lines
3.8 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
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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 data/lm/G_3_gram.fst.txt
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The generated LG is saved in $lang_dir/LG.fst
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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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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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return parser.parse_args()
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def compile_LG(lang_dir: str) -> 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_500.
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Return:
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An FST representing LG.
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"""
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tokens = k2.SymbolTable.from_file(f"{lang_dir}/tokens.txt")
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assert "#0" in tokens
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first_token_disambig_id = tokens["#0"]
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logging.info(f"first token disambig ID: {first_token_disambig_id}")
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L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
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if Path("data/lm/G_3_gram.pt").is_file():
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logging.info("Loading pre-compiled G_3_gram")
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d = torch.load("data/lm/G_3_gram.pt")
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G = k2.Fsa.from_dict(d)
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else:
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logging.info("Loading G_3_gram.fst.txt")
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with open("data/lm/G_3_gram.fst.txt") as f:
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G = k2.Fsa.from_openfst(f.read(), acceptor=False)
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del G.aux_labels
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torch.save(G.as_dict(), "data/lm/G_3_gram.pt")
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L = k2.arc_sort(L)
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G = k2.arc_sort(G)
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logging.info("Composing L and G")
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LG = k2.compose(L, G)
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logging.info(f"LG shape: {LG.shape}, num_arcs: {LG.num_arcs}")
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del LG.aux_labels
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logging.info("Connecting LG")
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LG = k2.connect(LG)
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logging.info(
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f"LG shape after k2.connect: {LG.shape}, num_arcs: {LG.num_arcs}"
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)
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logging.info("Determinizing LG")
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LG = k2.determinize(LG)
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logging.info(
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f"LG shape after k2.determinize: {LG.shape}, num_arcs: {LG.num_arcs}"
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)
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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(
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f"LG shape after k2.connect: {LG.shape}, num_arcs: {LG.num_arcs}"
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)
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logging.info("Arc sorting LG")
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LG = k2.arc_sort(LG)
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logging.info(f"LG properties: {LG.properties_str}")
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# Possible properties is:
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# "Valid|Nonempty|ArcSorted|ArcSortedAndDeterministic|EpsilonFree|MaybeAccessible|MaybeCoaccessible" # noqa
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logging.info(
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"Caution: LG is deterministic and contains disambig symbols!!!"
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)
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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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out_filename = lang_dir / "LG.pt"
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if out_filename.is_file():
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logging.info(f"{out_filename} 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)
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logging.info(f"Saving LG to {out_filename}")
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torch.save(LG.as_dict(), out_filename)
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if __name__ == "__main__":
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formatter = (
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"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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)
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logging.basicConfig(format=formatter, level=logging.INFO)
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main()
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