Source code for espnet2.text.token_id_converter

from pathlib import Path
from typing import Dict, Iterable, List, Union

import numpy as np
from typeguard import typechecked

[docs]class TokenIDConverter: @typechecked def __init__( self, token_list: Union[Path, str, Iterable[str]], unk_symbol: str = "<unk>", ): if isinstance(token_list, (Path, str)): token_list = Path(token_list) self.token_list_repr = str(token_list) self.token_list: List[str] = [] with"r", encoding="utf-8") as f: for idx, line in enumerate(f): line = line[0] + line[1:].rstrip() self.token_list.append(line) else: self.token_list: List[str] = list(token_list) self.token_list_repr = "" for i, t in enumerate(self.token_list): if i == 3: break self.token_list_repr += f"{t}, " self.token_list_repr += f"... (NVocab={(len(self.token_list))})" self.token2id: Dict[str, int] = {} for i, t in enumerate(self.token_list): if t in self.token2id: raise RuntimeError(f'Symbol "{t}" is duplicated') self.token2id[t] = i self.unk_symbol = unk_symbol if self.unk_symbol not in self.token2id: raise RuntimeError( f"Unknown symbol '{unk_symbol}' doesn't exist in the token_list" ) self.unk_id = self.token2id[self.unk_symbol]
[docs] def get_num_vocabulary_size(self) -> int: return len(self.token_list)
[docs] def ids2tokens(self, integers: Union[np.ndarray, Iterable[int]]) -> List[str]: if isinstance(integers, np.ndarray) and integers.ndim != 1: raise ValueError(f"Must be 1 dim ndarray, but got {integers.ndim}") return [self.token_list[i] for i in integers]
[docs] def tokens2ids(self, tokens: Iterable[str]) -> List[int]: return [self.token2id.get(i, self.unk_id) for i in tokens]