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tokenization_wav2vec2.py
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tokenization_wav2vec2.py
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# coding=utf-8
# Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization class for Wav2Vec2."""
import json
import os
import warnings
from dataclasses import dataclass
from itertools import groupby
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...utils import (
ModelOutput,
PaddingStrategy,
TensorType,
add_end_docstrings,
is_flax_available,
is_tf_available,
is_torch_available,
logging,
to_py_obj,
)
logger = logging.get_logger(__name__)
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
if is_flax_available():
import jax.numpy as jnp # noqa: F401
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "tokenizer_config.json",
}
# Wav2Vec2 has no max input length
WAV2VEC2_KWARGS_DOCSTRING = r"""
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
pad_to_multiple_of (`int`, *optional*):
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
verbose (`bool`, *optional*, defaults to `True`):
Whether or not to print more information and warnings.
"""
ListOfDict = List[Dict[str, Union[int, str]]]
@dataclass
class Wav2Vec2CTCTokenizerOutput(ModelOutput):
"""
Output type of [` Wav2Vec2CTCTokenizer`], with transcription.
Args:
text (list of `str` or `str`):
Decoded logits in text from. Usually the speech transcription.
char_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`):
Offsets of the decoded characters. In combination with sampling rate and model downsampling rate char
offsets can be used to compute time stamps for each charater. Total logit score of the beam associated with
produced text.
word_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`):
Offsets of the decoded words. In combination with sampling rate and model downsampling rate word offsets
can be used to compute time stamps for each word.
"""
text: Union[List[str], str]
char_offsets: Union[List[ListOfDict], ListOfDict] = None
word_offsets: Union[List[ListOfDict], ListOfDict] = None
class Wav2Vec2CTCTokenizer(PreTrainedTokenizer):
"""
Constructs a Wav2Vec2CTC tokenizer.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
the superclass for more information regarding such methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sentence token.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sentence token.
unk_token (`str`, *optional*, defaults to `"<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 (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
word_delimiter_token (`str`, *optional*, defaults to `"|"`):
The token used for defining the end of a word.
do_lower_case (`bool`, *optional*, defaults to `False`):
Whether or not to accept lowercase input and lowercase the output when decoding.
target_lang (`str`, *optional*):
A target language the tokenizer should set by default. `target_lang` has to be defined for multi-lingual,
nested vocabulary such as [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all).
**kwargs
Additional keyword arguments passed along to [`PreTrainedTokenizer`]
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
word_delimiter_token="|",
replace_word_delimiter_char=" ",
do_lower_case=False,
target_lang=None,
**kwargs,
):
self._word_delimiter_token = word_delimiter_token
self.do_lower_case = do_lower_case
self.replace_word_delimiter_char = replace_word_delimiter_char
self.target_lang = target_lang
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.vocab = json.load(vocab_handle)
# if target lang is defined vocab must be a nested dict
# with each target lang being one vocabulary
if target_lang is not None:
self.encoder = self.vocab[target_lang]
else:
self.encoder = self.vocab
self.decoder = {v: k for k, v in self.encoder.items()}
super().__init__(
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
do_lower_case=do_lower_case,
word_delimiter_token=word_delimiter_token,
replace_word_delimiter_char=replace_word_delimiter_char,
target_lang=target_lang,
**kwargs,
)
# make sure that tokens made of several
# characters are not split at tokenization
for token in self.encoder.keys():
if len(token) > 1:
self.add_tokens(AddedToken(token, rstrip=True, lstrip=True, normalized=False))
def set_target_lang(self, target_lang: str):
"""
Set the target language of a nested multi-lingual dictionary
"""
if self.vocab == self.encoder:
raise ValueError(f"{self.vocab} is not a multi-lingual, nested tokenizer. Cannot set target language.")
if target_lang not in self.vocab:
raise ValueError(f"{target_lang} does not exist. Choose one of {', '.join(self.vocab.keys())}.")
self.target_lang = target_lang
self.init_kwargs["target_lang"] = target_lang
self.encoder = self.vocab[target_lang]
self.decoder = {v: k for k, v in self.encoder.items()}
# make sure that tokens made of several
# characters are not split at tokenization
for token in self.encoder.keys():
if len(token) > 1:
self.add_tokens(AddedToken(token, rstrip=True, lstrip=True, normalized=False))
@property
def word_delimiter_token(self) -> str:
"""
`str`: Word delimiter token. Log an error if used while not having been set.
"""
if self._word_delimiter_token is None and self.verbose:
logger.error("Using word_delimiter_token, but it is not set yet.")
return None
return str(self._word_delimiter_token)
@property
def word_delimiter_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been
set.
"""
if self._word_delimiter_token is None:
return None
return self.convert_tokens_to_ids(self.word_delimiter_token)
@word_delimiter_token.setter
def word_delimiter_token(self, value):
self._word_delimiter_token = value
@word_delimiter_token_id.setter
def word_delimiter_token_id(self, value):
self._word_delimiter_token = self.convert_tokens_to_ids(value)
@property
def vocab_size(self) -> int:
return len(self.decoder)
def get_vocab(self) -> Dict:
vocab = dict(self.encoder)
vocab.update(self.added_tokens_encoder)
return vocab
def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
# Overwritten to never strip!
to_add = []
for token in new_tokens:
if isinstance(token, str):
to_add.append(AddedToken(token, rstrip=False, lstrip=False, normalized=False))
else:
to_add.append(token)
return super()._add_tokens(to_add, special_tokens)
def _tokenize(self, text, **kwargs):
"""
Converts a string into a sequence of tokens (string), using the tokenizer.
"""
if self.do_lower_case:
text = text.upper()
return list(text.replace(" ", self.word_delimiter_token))
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (str) in an index (integer) using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the vocab."""
result = self.decoder.get(index, self.unk_token)
return result
def convert_tokens_to_string(
self,
tokens: List[str],
group_tokens: bool = True,
spaces_between_special_tokens: bool = False,
output_char_offsets: bool = False,
output_word_offsets: bool = False,
) -> Dict[str, Union[str, float]]:
"""
Converts a connectionist-temporal-classification (CTC) output tokens into a single string.
"""
if len(tokens) == 0:
return {"text": "", "char_offsets": [], "word_offsets": []}
# group same tokens into non-repeating tokens in CTC style decoding
if group_tokens:
chars, char_repetitions = zip(*((token, len(list(group_iter))) for token, group_iter in groupby(tokens)))
else:
chars = tokens
char_repetitions = len(tokens) * [1]
# filter self.pad_token which is used as CTC-blank token
processed_chars = list(filter(lambda char: char != self.pad_token, chars))
# replace delimiter token
processed_chars = [
self.replace_word_delimiter_char if char == self.word_delimiter_token else char for char in processed_chars
]
# retrieve offsets
char_offsets = word_offsets = None
if output_char_offsets or output_word_offsets:
char_offsets = self._compute_offsets(char_repetitions, chars, self.pad_token)
if len(char_offsets) != len(processed_chars):
raise ValueError(
f"`char_offsets`: {char_offsets} and `processed_tokens`: {processed_chars}"
" have to be of the same length, but are: "
f"`len(offsets)`: {len(char_offsets)} and `len(processed_tokens)`:"
f" {len(processed_chars)}"
)
# set tokens to correct processed token
for i, char in enumerate(processed_chars):
char_offsets[i]["char"] = char
# retrieve word offsets from character offsets
word_offsets = None
if output_word_offsets:
word_offsets = self._get_word_offsets(char_offsets, self.replace_word_delimiter_char)
# don't output chars if not set to True
if not output_char_offsets:
char_offsets = None
# join to string
join_char = " " if spaces_between_special_tokens else ""
string = join_char.join(processed_chars).strip()
if self.do_lower_case:
string = string.lower()
return {"text": string, "char_offsets": char_offsets, "word_offsets": word_offsets}
@staticmethod
def _compute_offsets(
char_repetitions: List[int], chars: List[str], ctc_token: int
) -> List[Dict[str, Union[str, int]]]:
end_indices = np.asarray(char_repetitions).cumsum()
start_indices = np.concatenate(([0], end_indices[:-1]))
offsets = [
{"char": t, "start_offset": s, "end_offset": e} for t, s, e in zip(chars, start_indices, end_indices)
]
# filter out CTC token
offsets = list(filter(lambda offsets: offsets["char"] != ctc_token, offsets))
return offsets
@staticmethod
def _get_word_offsets(
offsets: Dict[str, Union[str, float]], word_delimiter_char: str = " "
) -> Dict[str, Union[str, float]]:
word_offsets = []
last_state = "SPACE"
word = ""
start_offset = 0
end_offset = 0
for i, offset in enumerate(offsets):
char = offset["char"]
state = "SPACE" if char == word_delimiter_char else "WORD"
if state == last_state:
# If we are in the same state as before, we simply repeat what we've done before
end_offset = offset["end_offset"]
word += char
else:
# Switching state
if state == "SPACE":
# Finishing a word
word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset})
else:
# Starting a new word
start_offset = offset["start_offset"]
end_offset = offset["end_offset"]
word = char
last_state = state
if last_state == "WORD":
word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset})
return word_offsets
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
if is_split_into_words:
text = " " + text
return (text, kwargs)
def _decode(
self,
token_ids: List[int],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
group_tokens: bool = True,
spaces_between_special_tokens: bool = False,
output_word_offsets: Optional[bool] = False,
output_char_offsets: Optional[bool] = False,
) -> str:
"""
special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the
same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on
the whole token list and not individually on added tokens
"""
filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
result = []
for token in filtered_tokens:
if skip_special_tokens and (
token in self.all_special_ids or (token != self.pad_token and token in self.all_special_tokens)
):
continue
result.append(token)
string_output = self.convert_tokens_to_string(
result,
group_tokens=group_tokens,
spaces_between_special_tokens=spaces_between_special_tokens,
output_word_offsets=output_word_offsets,
output_char_offsets=output_char_offsets,
)
text = string_output["text"]
clean_up_tokenization_spaces = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
text = self.clean_up_tokenization(text)
if output_word_offsets or output_char_offsets:
return Wav2Vec2CTCTokenizerOutput(
text=text,
char_offsets=string_output["char_offsets"],
word_offsets=string_output["word_offsets"],
)
else:
return text
# overwritten from `tokenization_utils_base.py` because tokenizer can output
# `ModelOutput` which should not be a list for batched output and
# because we need docs for `output_char_offsets` here
def batch_decode(
self,
sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
output_char_offsets: bool = False,
output_word_offsets: bool = False,
**kwargs,
) -> List[str]:
"""
Convert a list of lists of token ids into a list of strings by calling decode.
Args:
sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*):
Whether or not to clean up the tokenization spaces.
output_char_offsets (`bool`, *optional*, defaults to `False`):
Whether or not to output character offsets. Character offsets can be used in combination with the
sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.
<Tip>
Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make
use of `output_char_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched
output.
</Tip>
output_word_offsets (`bool`, *optional*, defaults to `False`):
Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
and model downsampling rate to compute the time-stamps of transcribed words.
<Tip>
Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make
use of `output_word_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched
output.
</Tip>
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`List[str]` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded
sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when
`output_char_offsets == True` or `output_word_offsets == True`.
"""
batch_decoded = [
self.decode(
seq,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
output_char_offsets=output_char_offsets,
output_word_offsets=output_word_offsets,
**kwargs,
)
for seq in sequences
]
if output_char_offsets or output_word_offsets:
# transform list of dicts to dict of lists
return Wav2Vec2CTCTokenizerOutput({k: [d[k] for d in batch_decoded] for k in batch_decoded[0]})
return batch_decoded
# overwritten from `tokenization_utils_base.py` because we need docs for `output_char_offsets`
# and `output_word_offsets` here
def decode(
self,
token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
output_char_offsets: bool = False,
output_word_offsets: bool = False,
**kwargs,
) -> str:
"""
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*):
Whether or not to clean up the tokenization spaces.
output_char_offsets (`bool`, *optional*, defaults to `False`):
Whether or not to output character offsets. Character offsets can be used in combination with the
sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.
<Tip>
Please take a look at the example below to better understand how to make use of `output_char_offsets`.
</Tip>
output_word_offsets (`bool`, *optional*, defaults to `False`):
Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
and model downsampling rate to compute the time-stamps of transcribed words.
<Tip>
Please take a look at the example below to better understand how to make use of `output_word_offsets`.
</Tip>
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`str` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded
sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when
`output_char_offsets == True` or `output_word_offsets == True`.
Example:
```python
>>> # Let's see how to retrieve time steps for a model
>>> from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC
>>> from datasets import load_dataset
>>> import datasets
>>> import torch
>>> # import model, feature extractor, tokenizer
>>> model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
>>> # load first sample of English common_voice
>>> dataset = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="train", streaming=True)
>>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000))
>>> dataset_iter = iter(dataset)
>>> sample = next(dataset_iter)
>>> # forward sample through model to get greedily predicted transcription ids
>>> input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values
>>> logits = model(input_values).logits[0]
>>> pred_ids = torch.argmax(logits, axis=-1)
>>> # retrieve word stamps (analogous commands for `output_char_offsets`)
>>> outputs = tokenizer.decode(pred_ids, output_word_offsets=True)
>>> # compute `time_offset` in seconds as product of downsampling ratio and sampling_rate
>>> time_offset = model.config.inputs_to_logits_ratio / feature_extractor.sampling_rate
>>> word_offsets = [
... {
... "word": d["word"],
... "start_time": round(d["start_offset"] * time_offset, 2),
... "end_time": round(d["end_offset"] * time_offset, 2),
... }
... for d in outputs.word_offsets
... ]
>>> # compare word offsets with audio `en_train_0/common_voice_en_19121553.mp3` online on the dataset viewer:
>>> # https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/en
>>> word_offsets[:3]
[{'word': 'THE', 'start_time': 0.7, 'end_time': 0.78}, {'word': 'TRICK', 'start_time': 0.88, 'end_time': 1.08}, {'word': 'APPEARS', 'start_time': 1.2, 'end_time': 1.64}]
```"""
# Convert inputs to python lists
token_ids = to_py_obj(token_ids)
return self._decode(
token_ids=token_ids,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
output_char_offsets=output_char_offsets,
output_word_offsets=output_word_offsets,
**kwargs,
)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
return (vocab_file,)
class Wav2Vec2Tokenizer(PreTrainedTokenizer):
"""
Constructs a Wav2Vec2 tokenizer.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
the superclass for more information regarding such methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sentence token.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sentence token.
unk_token (`str`, *optional*, defaults to `"<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 (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
word_delimiter_token (`str`, *optional*, defaults to `"|"`):
The token used for defining the end of a word.
do_lower_case (`bool`, *optional*, defaults to `False`):
Whether or not to lowercase the output when decoding.
do_normalize (`bool`, *optional*, defaults to `False`):
Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly
improve the performance for some models, *e.g.*,
[wav2vec2-lv60](https://huggingface.co/models?search=lv60).
return_attention_mask (`bool`, *optional*, defaults to `False`):
Whether or not [`~Wav2Vec2Tokenizer.__call__`] should return `attention_mask`.
<Tip>
Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as
[wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using
`attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask`
should be passed.
For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as
[wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be
passed for batched inference.
</Tip>
**kwargs
Additional keyword arguments passed along to [`PreTrainedTokenizer`]
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = {
"vocab_file": {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json"
},
"tokenizer_config_file": {
"facebook/wav2vec2-base-960h": (
"https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer.json"
),
},
}
model_input_names = ["input_values", "attention_mask"]
def __init__(
self,
vocab_file,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
word_delimiter_token="|",
do_lower_case=False,
do_normalize=False,
return_attention_mask=False,
**kwargs,
):
warnings.warn(
"The class `Wav2Vec2Tokenizer` is deprecated and will be removed in version 5 of Transformers. Please use"
" `Wav2Vec2Processor` or `Wav2Vec2CTCTokenizer` instead.",
FutureWarning,
)
self._word_delimiter_token = word_delimiter_token
self.do_lower_case = do_lower_case
self.return_attention_mask = return_attention_mask
self.do_normalize = do_normalize
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
super().__init__(
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
do_lower_case=do_lower_case,
do_normalize=do_normalize,
return_attention_mask=return_attention_mask,
word_delimiter_token=word_delimiter_token,
**kwargs,
)
@property
def word_delimiter_token(self) -> str:
"""
`str`: Padding token. Log an error if used while not having been set.
"""
if self._word_delimiter_token is None and self.verbose:
logger.error("Using word_delimiter_token, but it is not set yet.")
return None
return str(self._word_delimiter_token)
@property
def word_delimiter_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been
set.
"""
if self._word_delimiter_token is None:
return None
return self.convert_tokens_to_ids(self.word_delimiter_token)
@word_delimiter_token.setter
def word_delimiter_token(self, value):
self._word_delimiter_token = value
@word_delimiter_token_id.setter
def word_delimiter_token_id(self, value):
self._word_delimiter_token = self.convert_tokens_to_ids(value)
@add_end_docstrings(WAV2VEC2_KWARGS_DOCSTRING)
def __call__(
self,
raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
padding: Union[bool, str, PaddingStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences.
Args:
raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
values, a list of numpy array or a list of list of float values. Must be mono channel audio, not
stereo, i.e. single float per timestep.
"""
is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}")
is_batched = is_batched_numpy or (
isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
)
# make sure input is in list format
if is_batched and not isinstance(raw_speech[0], np.ndarray):
raw_speech = [np.asarray(speech) for speech in raw_speech]
elif not is_batched and not isinstance(raw_speech, np.ndarray):
raw_speech = np.asarray(raw_speech)
# always return batch
if not is_batched:
raw_speech = [raw_speech]
# zero-mean and unit-variance normalization
if self.do_normalize:
raw_speech = [(x - np.mean(x)) / np.sqrt(np.var(x) + 1e-5) for x in raw_speech]
# convert into correct format for padding
encoded_inputs = BatchEncoding({"input_values": raw_speech})
padded_inputs = self.pad(
encoded_inputs,
padding=padding,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=self.return_attention_mask,
return_tensors=return_tensors,
verbose=verbose,
)
return padded_inputs
@property
def vocab_size(self) -> int:
return len(self.decoder)
def get_vocab(self) -> Dict:
return dict(self.encoder, **self.added_tokens_encoder)
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (str) in an index (integer) using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the vocab."""
result = self.decoder.get(index, self.unk_token)
return result
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""
Converts a connectionist-temporal-classification (CTC) output tokens into a single string.
"""
# group same tokens into non-repeating tokens in CTC style decoding
grouped_tokens = [token_group[0] for token_group in groupby(tokens)]
# filter self.pad_token which is used as CTC-blank token
filtered_tokens = list(filter(lambda token: token != self.pad_token, grouped_tokens))
# replace delimiter token
string = "".join([" " if token == self.word_delimiter_token else token for token in filtered_tokens]).strip()
if self.do_lower_case:
string = string.lower()
return string
def _decode(
self,
token_ids: List[int],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
**kwargs,
) -> str:
"""
special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the
same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on
the whole token list and not individually on added tokens
"""
filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
result = []
for token in filtered_tokens:
if skip_special_tokens and (
token in self.all_special_ids or (token != self.pad_token and token in self.all_special_tokens)
):
continue
result.append(token)
text = self.convert_tokens_to_string(result)
clean_up_tokenization_spaces = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
clean_text = self.clean_up_tokenization(text)
return clean_text
else:
return text
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
return (vocab_file,)