220 lines
8.7 KiB
Python
220 lines
8.7 KiB
Python
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# coding=utf-8
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# Copyright 2023 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved.
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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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"""Tokenization class for SpeechT5."""
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import os
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from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from ...tokenization_utils import PreTrainedTokenizer
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from ...utils import logging
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from .number_normalizer import EnglishNumberNormalizer
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "spm_char.model"}
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class SpeechT5Tokenizer(PreTrainedTokenizer):
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"""
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Construct a SpeechT5 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
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contains the vocabulary necessary to instantiate a tokenizer.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The begin of sequence token.
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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normalize (`bool`, *optional*, defaults to `False`):
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Whether to convert numeric quantities in the text to their spelt-out english counterparts.
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sp_model_kwargs (`dict`, *optional*):
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Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
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SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
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to set:
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- `enable_sampling`: Enable subword regularization.
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- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
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- `nbest_size = {0,1}`: No sampling is performed.
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- `nbest_size > 1`: samples from the nbest_size results.
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- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
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using forward-filtering-and-backward-sampling algorithm.
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- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
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BPE-dropout.
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Attributes:
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sp_model (`SentencePieceProcessor`):
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The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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self,
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vocab_file,
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bos_token="<s>",
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eos_token="</s>",
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unk_token="<unk>",
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pad_token="<pad>",
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normalize=False,
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs,
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) -> None:
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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self.vocab_file = vocab_file
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self.normalize = normalize
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self._normalizer = None
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(vocab_file)
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super().__init__(
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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pad_token=pad_token,
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normalize=normalize,
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sp_model_kwargs=self.sp_model_kwargs,
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**kwargs,
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)
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def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
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normalize = kwargs.pop("normalize", self.normalize)
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if is_split_into_words:
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text = " " + text
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if normalize:
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text = self.normalizer(text)
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return (text, kwargs)
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@property
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def vocab_size(self):
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return self.sp_model.get_piece_size()
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@property
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def normalizer(self):
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if self._normalizer is None:
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self._normalizer = EnglishNumberNormalizer()
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return self._normalizer
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@normalizer.setter
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def normalizer(self, value):
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self._normalizer = value
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def get_vocab(self):
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def __getstate__(self):
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state = self.__dict__.copy()
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state["sp_model"] = None
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return state
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def __setstate__(self, d):
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self.__dict__ = d
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# for backward compatibility
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if not hasattr(self, "sp_model_kwargs"):
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self.sp_model_kwargs = {}
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(self.vocab_file)
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def _tokenize(self, text: str) -> List[str]:
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"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
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return self.sp_model.encode(text, out_type=str)
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.sp_model.piece_to_id(token)
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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token = self.sp_model.IdToPiece(index)
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return token
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# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.convert_tokens_to_string
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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current_sub_tokens = []
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out_string = ""
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prev_is_special = False
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for token in tokens:
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# make sure that special tokens are not decoded using sentencepiece model
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if token in self.all_special_tokens:
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if not prev_is_special:
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out_string += " "
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out_string += self.sp_model.decode(current_sub_tokens) + token
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prev_is_special = True
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current_sub_tokens = []
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else:
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current_sub_tokens.append(token)
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prev_is_special = False
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out_string += self.sp_model.decode(current_sub_tokens)
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return out_string.strip()
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
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"""Build model inputs from a sequence by appending eos_token_id."""
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if token_ids_1 is None:
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return token_ids_0 + [self.eos_token_id]
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# We don't expect to process pairs, but leave the pair logic for API consistency
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return token_ids_0 + token_ids_1 + [self.eos_token_id]
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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suffix_ones = [1]
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if token_ids_1 is None:
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return ([0] * len(token_ids_0)) + suffix_ones
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return ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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out_vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
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copyfile(self.vocab_file, out_vocab_file)
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elif not os.path.isfile(self.vocab_file):
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with open(out_vocab_file, "wb") as fi:
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content_spiece_model = self.sp_model.serialized_model_proto()
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fi.write(content_spiece_model)
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return (out_vocab_file,)
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