Source code for transformers.models.speech_to_text.tokenization_speech_to_text

# coding=utf-8
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"""Tokenization classes for Speech2Text."""

import json
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging

logger = logging.get_logger(__name__)


    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",

    "vocab_file": {
        "facebook/s2t-small-librispeech-asr": "",
    "spm_file": {
        "facebook/s2t-small-librispeech-asr": ""

    "facebook/s2t-small-librispeech-asr": 1024,

MUSTC_LANGS = ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]


[docs]class Speech2TextTokenizer(PreTrainedTokenizer): """ Construct an Speech2Text tokenizer. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (:obj:`str`): File containing the vocabulary. spm_file (:obj:`str`): Path to the `SentencePiece <>`__ model file bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`): The beginning of sentence token. eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`): The end of sentence token. unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`): The token used for padding, for example when batching sequences of different lengths. do_upper_case (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to uppercase the output when decoding. do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to lowercase the input when tokenizing. tgt_lang (:obj:`str`, `optional`): A string representing the target language. sp_model_kwargs (:obj:`dict`, `optional`): Will be passed to the ``SentencePieceProcessor.__init__()`` method. The `Python wrapper for SentencePiece <>`__ can be used, among other things, to set: - ``enable_sampling``: Enable subword regularization. - ``nbest_size``: Sampling parameters for unigram. Invalid for BPE-Dropout. - ``nbest_size = {0,1}``: No sampling is performed. - ``nbest_size > 1``: samples from the nbest_size results. - ``nbest_size < 0``: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - ``alpha``: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. **kwargs Additional keyword arguments passed along to :class:`~transformers.PreTrainedTokenizer` """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = MAX_MODEL_INPUT_SIZES model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] def __init__( self, vocab_file, spm_file, bos_token="<s>", eos_token="</s>", pad_token="<pad>", unk_token="<unk>", do_upper_case=False, do_lower_case=False, tgt_lang=None, lang_codes=None, sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> None: self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, do_upper_case=do_upper_case, do_lower_case=do_lower_case, tgt_lang=tgt_lang, lang_codes=lang_codes, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) self.do_upper_case = do_upper_case self.do_lower_case = do_lower_case self.encoder = load_json(vocab_file) self.decoder = {v: k for k, v in self.encoder.items()} self.spm_file = spm_file self.sp_model = load_spm(spm_file, self.sp_model_kwargs) if lang_codes is not None: self.lang_codes = lang_codes self.langs = LANGUAGES[lang_codes] self.lang_tokens = [f"<lang:{lang}>" for lang in self.langs] self.lang_code_to_id = {lang: self.sp_model.PieceToId(f"<lang:{lang}>") for lang in self.langs} self._additional_special_tokens = self.lang_tokens self._tgt_lang = tgt_lang if tgt_lang is not None else self.langs[0] self.set_tgt_lang_special_tokens(self._tgt_lang) else: self.lang_code_to_id = {} @property def vocab_size(self) -> int: return len(self.encoder) @property def tgt_lang(self) -> str: return self._tgt_lang @tgt_lang.setter def tgt_lang(self, new_tgt_lang) -> None: self._tgt_lang = new_tgt_lang self.set_tgt_lang_special_tokens(new_tgt_lang) def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None: """Reset the special tokens to the target language setting. prefix=[eos, tgt_lang_code] and suffix=[eos].""" lang_code_id = self.lang_code_to_id[tgt_lang] self.prefix_tokens = [lang_code_id] def _tokenize(self, text: str) -> List[str]: return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): return self.encoder.get(token, self.encoder[self.unk_token]) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the decoder.""" return self.decoder.get(index, self.unk_token) def convert_tokens_to_string(self, tokens: List[str]) -> str: """Converts a sequence of tokens (strings for sub-words) in a single string.""" out_string = self.sp_model.decode(tokens) if self.do_upper_case: out_string = out_string.upper() return out_string
[docs] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]: """Build model inputs from a sequence by appending eos_token_id.""" if token_ids_1 is None: return self.prefix_tokens + token_ids_0 + [self.eos_token_id] # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_0 + token_ids_1 + [self.eos_token_id]
[docs] def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``prepare_for_model`` method. Args: token_ids_0 (:obj:`List[int]`): List of IDs. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not the token list is already formatted with special tokens for the model. Returns: :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) prefix_ones = [1] * len(self.prefix_tokens) suffix_ones = [1] if token_ids_1 is None: return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
def get_vocab(self) -> Dict: vocab = self.encoder.copy() vocab.update(self.added_tokens_encoder) return vocab def __getstate__(self) -> Dict: state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d: Dict) -> None: self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs)
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: save_dir = Path(save_directory) assert save_dir.is_dir(), f"{save_directory} should be a directory" vocab_save_path = save_dir / ( (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"] ) spm_save_path = save_dir / ( (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"] ) save_json(self.encoder, vocab_save_path) if not spm_save_path.exists(): copyfile(self.spm_file, spm_save_path) return (str(vocab_save_path), str(spm_save_path))
def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor: spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs) spm.Load(str(path)) return spm def load_json(path: str) -> Union[Dict, List]: with open(path, "r") as f: return json.load(f) def save_json(data, path: str) -> None: with open(path, "w") as f: json.dump(data, f, indent=2)