# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Speech processor class for Speech2Text
"""
from contextlib import contextmanager
from .feature_extraction_speech_to_text import Speech2TextFeatureExtractor
from .tokenization_speech_to_text import Speech2TextTokenizer
[docs]class Speech2TextProcessor:
r"""
Constructs a Speech2Text processor which wraps a Speech2Text feature extractor and a Speech2Text tokenizer into a
single processor.
:class:`~transformers.Speech2TextProcessor` offers all the functionalities of
:class:`~transformers.Speech2TextFeatureExtractor` and :class:`~transformers.Speech2TextTokenizer`. See the
:meth:`~transformers.Speech2TextProcessor.__call__` and :meth:`~transformers.Speech2TextProcessor.decode` for more
information.
Args:
feature_extractor (:obj:`Speech2TextFeatureExtractor`):
An instance of :class:`~transformers.Speech2TextFeatureExtractor`. The feature extractor is a required
input.
tokenizer (:obj:`Speech2TextTokenizer`):
An instance of :class:`~transformers.Speech2TextTokenizer`. The tokenizer is a required input.
"""
def __init__(self, feature_extractor, tokenizer):
if not isinstance(feature_extractor, Speech2TextFeatureExtractor):
raise ValueError(
f"`feature_extractor` has to be of type {Speech2TextFeatureExtractor.__class__}, but is {type(feature_extractor)}"
)
if not isinstance(tokenizer, Speech2TextTokenizer):
raise ValueError(
f"`tokenizer` has to be of type {Speech2TextTokenizer.__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 Speech2Text feature extractor object and Speech2Text tokenizer object to the directory
``save_directory``, so that it can be re-loaded using the
:func:`~transformers.Speech2TextProcessor.from_pretrained` class method.
.. note::
This class method is simply calling :meth:`~transformers.PreTrainedFeatureExtractor.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.Speech2TextProcessor` from a pretrained Speech2Text processor.
.. note::
This class method is simply calling Speech2TextFeatureExtractor's
:meth:`~transformers.PreTrainedFeatureExtractor.from_pretrained` and Speech2TextTokenizer'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.PreTrainedFeatureExtractor.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.PreTrainedFeatureExtractor` and
:class:`~transformers.PreTrainedTokenizer`
"""
feature_extractor = Speech2TextFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
tokenizer = Speech2TextTokenizer.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 Speech2TextFeatureExtractor's
:meth:`~transformers.Speech2TextFeatureExtractor.__call__` and returns its output. If used in the context
:meth:`~transformers.Speech2TextProcessor.as_target_processor` this method forwards all its arguments to
Speech2TextTokenizer's :meth:`~transformers.Speech2TextTokenizer.__call__`. Please refer to the doctsring of
the above two methods for more information.
"""
return self.current_processor(*args, **kwargs)
[docs] def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Speech2TextTokenizer'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 Speech2TextTokenizer'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
Speech2Text.
"""
self.current_processor = self.tokenizer
yield
self.current_processor = self.feature_extractor