# 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
"""
import warnings
from contextlib import contextmanager
from ...tokenization_utils import PreTrainedTokenizer
from ..auto.tokenization_auto import AutoTokenizer
from .feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
from .tokenization_wav2vec2 import Wav2Vec2CTCTokenizer
class Wav2Vec2Processor:
r"""
Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single
processor.
[`Wav2Vec2Processor`] offers all the functionalities of
[`Wav2Vec2FeatureExtractor`] and [`PreTrainedTokenizer`]. See the docstring
of [`~Wav2Vec2Processor.__call__`] and [`~Wav2Vec2Processor.decode`] for more
information.
Args:
feature_extractor (`Wav2Vec2FeatureExtractor`):
An instance of [`Wav2Vec2FeatureExtractor`]. The feature extractor is a required input.
tokenizer ([`PreTrainedTokenizer`]):
An instance of [`PreTrainedTokenizer`]. 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, PreTrainedTokenizer):
raise ValueError(
f"`tokenizer` has to be of type {PreTrainedTokenizer.__class__}, but is {type(tokenizer)}"
)
self.feature_extractor = feature_extractor
self.tokenizer = tokenizer
self.current_processor = self.feature_extractor
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 [`~Wav2Vec2Processor.from_pretrained`] class method.
<Tip>
This class method is simply calling
[`~feature_extraction_utils.FeatureExtractionMixin.save_pretrained`] and
[`~tokenization_utils_base.PreTrainedTokenizer.save_pretrained`]. Please refer to the
docstrings of the methods above for more information.
</Tip>
Args:
save_directory (`str` or `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)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r"""
Instantiate a [`Wav2Vec2Processor`] from a pretrained Wav2Vec2 processor.
<Tip>
This class method is simply calling Wav2Vec2FeatureExtractor's
[`~feature_extraction_utils.FeatureExtractionMixin.from_pretrained`] and
PreTrainedTokenizer's [`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`].
Please refer to the docstrings of the methods above for more information.
</Tip>
Args:
pretrained_model_name_or_path (`str` or `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
[`~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 [`SequenceFeatureExtractor`] and
[`PreTrainedTokenizer`]
"""
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
# load generic `AutoTokenizer`
# need fallback here for backward compatibility in case processor is
# loaded from just a tokenizer file that does not have a `tokenizer_class` attribute
# behavior should be deprecated in major future release
try:
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
except OSError:
warnings.warn(
f"Loading a tokenizer inside {cls.__name__} from a config that does not"
" include a `tokenizer_class` attribute is deprecated and will be "
"removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
" attribute to either your `config.json` or `tokenizer_config.json` "
"file to suppress this warning: ",
FutureWarning,
)
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
def __call__(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
[`~Wav2Vec2FeatureExtractor.__call__`] and returns its output. If used in the context
[`~Wav2Vec2Processor.as_target_processor`] this method forwards all its arguments to
PreTrainedTokenizer's [`~PreTrainedTokenizer.__call__`]. Please refer to the docstring of the
above two methods for more information.
"""
return self.current_processor(*args, **kwargs)
def pad(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
[`~Wav2Vec2FeatureExtractor.pad`] and returns its output. If used in the context
[`~Wav2Vec2Processor.as_target_processor`] this method forwards all its arguments to
PreTrainedTokenizer's [`~PreTrainedTokenizer.pad`]. Please refer to the docstring of the above
two methods for more information.
"""
return self.current_processor.pad(*args, **kwargs)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's
[`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more
information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's
[`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more
information.
"""
return self.tokenizer.decode(*args, **kwargs)
@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