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https://github.com/k2-fsa/icefall.git
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153 lines
3.9 KiB
Python
Executable File
153 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 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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# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
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"""
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This script takes as input `lang_dir`, which should contain::
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- lang_dir/bpe.model,
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- lang_dir/words.txt
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and generates the following files in the directory `lang_dir`:
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- lexicon.txt
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- lexicon_disambig.txt
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- L.pt
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- L_disambig.pt
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- tokens.txt
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"""
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import argparse
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from pathlib import Path
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from typing import Dict, List, Tuple
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import k2
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import sentencepiece as spm
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import torch
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from prepare_lang import (
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Lexicon,
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add_disambig_symbols,
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lexicon_to_fst,
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write_lexicon,
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write_mapping,
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)
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def generate_lexicon(
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model_file: str, words: List[str]
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) -> Tuple[Lexicon, Dict[str, int]]:
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"""Generate a lexicon from a BPE model.
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Args:
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model_file:
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Path to a sentencepiece model.
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words:
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A list of strings representing words.
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Returns:
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Return a tuple with two elements:
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- A dict whose keys are words and values are the corresponding
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word pieces.
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- A dict representing the token symbol, mapping from tokens to IDs.
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"""
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sp = spm.SentencePieceProcessor()
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sp.load(str(model_file))
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words_pieces: List[List[str]] = sp.encode(words, out_type=str)
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lexicon = []
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for word, pieces in zip(words, words_pieces):
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lexicon.append((word, pieces))
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# The OOV word is <UNK>
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lexicon.append(("<UNK>", [sp.id_to_piece(sp.unk_id())]))
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token2id: Dict[str, int] = dict()
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for i in range(sp.vocab_size()):
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token2id[sp.id_to_piece(i)] = i
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return lexicon, token2id
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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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It should contain the bpe.model and words.txt
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""",
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)
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return parser.parse_args()
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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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model_file = lang_dir / "bpe.model"
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word_sym_table = k2.SymbolTable.from_file(lang_dir / "words.txt")
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words = word_sym_table.symbols
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excluded = ["<eps>", "!SIL", "<SPOKEN_NOISE>", "<UNK>", "#0", "<s>", "</s>"]
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for w in excluded:
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if w in words:
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words.remove(w)
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lexicon, token_sym_table = generate_lexicon(model_file, words)
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lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)
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next_token_id = max(token_sym_table.values()) + 1
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for i in range(max_disambig + 1):
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disambig = f"#{i}"
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assert disambig not in token_sym_table
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token_sym_table[disambig] = next_token_id
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next_token_id += 1
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word_sym_table.add("#0")
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word_sym_table.add("<s>")
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word_sym_table.add("</s>")
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write_mapping(lang_dir / "tokens.txt", token_sym_table)
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write_lexicon(lang_dir / "lexicon.txt", lexicon)
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write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig)
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L = lexicon_to_fst(
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lexicon,
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token2id=token_sym_table,
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word2id=word_sym_table,
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)
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L_disambig = lexicon_to_fst(
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lexicon_disambig,
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token2id=token_sym_table,
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word2id=word_sym_table,
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need_self_loops=True,
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)
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torch.save(L.as_dict(), lang_dir / "L.pt")
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torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt")
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if __name__ == "__main__":
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main()
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