52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
from pathlib import Path
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from typing import Iterable
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from typing import List
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from typing import Union
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import sentencepiece as spm
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from funasr.tokenizer.abs_tokenizer import BaseTokenizer
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from funasr.register import tables
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@tables.register("tokenizer_classes", "SentencepiecesTokenizer")
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class SentencepiecesTokenizer(BaseTokenizer):
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def __init__(self, bpemodel: Union[Path, str], **kwargs):
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super().__init__(**kwargs)
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self.bpemodel = str(bpemodel)
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# NOTE(kamo):
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# Don't build SentencePieceProcessor in __init__()
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# because it's not picklable and it may cause following error,
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# "TypeError: can't pickle SwigPyObject objects",
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# when giving it as argument of "multiprocessing.Process()".
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self.sp = None
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self._build_sentence_piece_processor()
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def __repr__(self):
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return f'{self.__class__.__name__}(model="{self.bpemodel}")'
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def _build_sentence_piece_processor(self):
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# Build SentencePieceProcessor lazily.
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if self.sp is None:
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self.sp = spm.SentencePieceProcessor()
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self.sp.load(self.bpemodel)
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def text2tokens(self, line: str) -> List[str]:
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self._build_sentence_piece_processor()
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return self.sp.EncodeAsPieces(line)
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def tokens2text(self, tokens: Iterable[str]) -> str:
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self._build_sentence_piece_processor()
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return self.sp.DecodePieces(list(tokens))
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def encode(self, line: str, **kwargs) -> List[int]:
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self._build_sentence_piece_processor()
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return self.sp.EncodeAsIds(line)
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def decode(self, line: List[int], **kwargs):
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self._build_sentence_piece_processor()
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return self.sp.DecodeIds(line)
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def get_vocab_size(self):
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return self.sp.GetPieceSize()
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