Source code for transformers.models.wav2vec2.processing_wav2vec2

# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
"""
Speech processor class for Wav2Vec2
"""
from contextlib import contextmanager

from .feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
from .tokenization_wav2vec2 import Wav2Vec2CTCTokenizer


[docs]class Wav2Vec2Processor: r""" Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single processor. :class:`~transformers.Wav2Vec2Processor` offers all the functionalities of :class:`~transformers.Wav2Vec2FeatureExtractor` and :class:`~transformers.Wav2Vec2CTCTokenizer`. See the docstring of :meth:`~transformers.Wav2Vec2Processor.__call__` and :meth:`~transformers.Wav2Vec2Processor.decode` for more information. Args: feature_extractor (:obj:`Wav2Vec2FeatureExtractor`): An instance of :class:`~transformers.Wav2Vec2FeatureExtractor`. The feature extractor is a required input. tokenizer (:obj:`Wav2Vec2CTCTokenizer`): An instance of :class:`~transformers.Wav2Vec2CTCTokenizer`. The tokenizer is a required input. """ def __init__(self, feature_extractor, tokenizer): if not isinstance(feature_extractor, Wav2Vec2FeatureExtractor): raise ValueError( f"`feature_extractor` has to be of type {Wav2Vec2FeatureExtractor.__class__}, but is {type(feature_extractor)}" ) if not isinstance(tokenizer, Wav2Vec2CTCTokenizer): raise ValueError( f"`tokenizer` has to be of type {Wav2Vec2CTCTokenizer.__class__}, but is {type(tokenizer)}" ) self.feature_extractor = feature_extractor self.tokenizer = tokenizer self.current_processor = self.feature_extractor
[docs] def save_pretrained(self, save_directory): """ Save a Wav2Vec2 feature_extractor object and Wav2Vec2 tokenizer object to the directory ``save_directory``, so that it can be re-loaded using the :func:`~transformers.Wav2Vec2Processor.from_pretrained` class method. .. note:: This class method is simply calling :meth:`~transformers.feature_extraction_utils.FeatureExtractionMixin.save_pretrained` and :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizer.save_pretrained`. Please refer to the docstrings of the methods above for more information. Args: save_directory (:obj:`str` or :obj:`os.PathLike`): Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will be created if it does not exist). """ self.feature_extractor.save_pretrained(save_directory) self.tokenizer.save_pretrained(save_directory)
[docs] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): r""" Instantiate a :class:`~transformers.Wav2Vec2Processor` from a pretrained Wav2Vec2 processor. .. note:: This class method is simply calling Wav2Vec2FeatureExtractor's :meth:`~transformers.feature_extraction_utils.FeatureExtractionMixin.from_pretrained` and Wav2Vec2CTCTokenizer's :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizer.from_pretrained`. Please refer to the docstrings of the methods above for more information. Args: pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`): This can be either: - a string, the `model id` of a pretrained feature_extractor hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under a user or organization name, like ``dbmdz/bert-base-german-cased``. - a path to a `directory` containing a feature extractor file saved using the :meth:`~transformers.SequenceFeatureExtractor.save_pretrained` method, e.g., ``./my_model_directory/``. - a path or url to a saved feature extractor JSON `file`, e.g., ``./my_model_directory/preprocessor_config.json``. **kwargs Additional keyword arguments passed along to both :class:`~transformers.SequenceFeatureExtractor` and :class:`~transformers.PreTrainedTokenizer` """ feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs) tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs) return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
[docs] def __call__(self, *args, **kwargs): """ When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's :meth:`~transformers.Wav2Vec2FeatureExtractor.__call__` and returns its output. If used in the context :meth:`~transformers.Wav2Vec2Processor.as_target_processor` this method forwards all its arguments to Wav2Vec2CTCTokenizer's :meth:`~transformers.Wav2Vec2CTCTokenizer.__call__`. Please refer to the docstring of the above two methods for more information. """ return self.current_processor(*args, **kwargs)
[docs] def pad(self, *args, **kwargs): """ When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's :meth:`~transformers.Wav2Vec2FeatureExtractor.pad` and returns its output. If used in the context :meth:`~transformers.Wav2Vec2Processor.as_target_processor` this method forwards all its arguments to Wav2Vec2CTCTokenizer's :meth:`~transformers.Wav2Vec2CTCTokenizer.pad`. Please refer to the docstring of the above two methods for more information. """ return self.current_processor.pad(*args, **kwargs)
[docs] def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to Wav2Vec2CTCTokenizer's :meth:`~transformers.PreTrainedTokenizer.batch_decode`. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs)
[docs] def decode(self, *args, **kwargs): """ This method forwards all its arguments to Wav2Vec2CTCTokenizer's :meth:`~transformers.PreTrainedTokenizer.decode`. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs)
[docs] @contextmanager def as_target_processor(self): """ Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning Wav2Vec2. """ self.current_processor = self.tokenizer yield self.current_processor = self.feature_extractor