200 lines
8.3 KiB
Python
200 lines
8.3 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team.
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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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"""
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Speech processor class for Wav2Vec2
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"""
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import warnings
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from contextlib import contextmanager
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from typing import Optional, Union
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from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils_base import AudioInput, PreTokenizedInput, TextInput
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from .feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
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from .tokenization_wav2vec2 import Wav2Vec2CTCTokenizer
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class Wav2Vec2ProcessorKwargs(ProcessingKwargs, total=False):
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_defaults = {}
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class Wav2Vec2Processor(ProcessorMixin):
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r"""
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Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single
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processor.
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[`Wav2Vec2Processor`] offers all the functionalities of [`Wav2Vec2FeatureExtractor`] and [`PreTrainedTokenizer`].
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See the docstring of [`~Wav2Vec2Processor.__call__`] and [`~Wav2Vec2Processor.decode`] for more information.
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Args:
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feature_extractor (`Wav2Vec2FeatureExtractor`):
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An instance of [`Wav2Vec2FeatureExtractor`]. The feature extractor is a required input.
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tokenizer ([`PreTrainedTokenizer`]):
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An instance of [`PreTrainedTokenizer`]. The tokenizer is a required input.
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"""
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feature_extractor_class = "Wav2Vec2FeatureExtractor"
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tokenizer_class = "AutoTokenizer"
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def __init__(self, feature_extractor, tokenizer):
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super().__init__(feature_extractor, tokenizer)
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self.current_processor = self.feature_extractor
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self._in_target_context_manager = False
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
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try:
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return super().from_pretrained(pretrained_model_name_or_path, **kwargs)
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except (OSError, ValueError):
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warnings.warn(
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f"Loading a tokenizer inside {cls.__name__} from a config that does not"
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" include a `tokenizer_class` attribute is deprecated and will be "
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"removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
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" attribute to either your `config.json` or `tokenizer_config.json` "
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"file to suppress this warning: ",
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FutureWarning,
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)
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
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tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
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return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
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def __call__(
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self,
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audio: AudioInput = None,
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text: Optional[Union[str, list[str], TextInput, PreTokenizedInput]] = None,
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images=None,
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videos=None,
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**kwargs: Unpack[Wav2Vec2ProcessorKwargs],
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):
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"""
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This method forwards all arguments to [`Wav2Vec2FeatureExtractor.__call__`] and/or
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[`PreTrainedTokenizer.__call__`] depending on the input modality and returns their outputs. If both modalities are passed, [`Wav2Vec2FeatureExtractor.__call__`] and [`PreTrainedTokenizer.__call__`] are called.
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Args:
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audio (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`, *optional*):
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An audio input is passed to [`Wav2Vec2FeatureExtractor.__call__`].
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text (`str`, `List[str]`, *optional*):
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A text input is passed to [`PreTrainedTokenizer.__call__`].
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Returns:
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This method returns the results of each `call` method. If both are used, the output is a dictionary containing the results of both.
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"""
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if "raw_speech" in kwargs:
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warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
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audio = kwargs.pop("raw_speech")
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if audio is None and text is None:
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raise ValueError("You need to specify either an `audio` or `text` input to process.")
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output_kwargs = self._merge_kwargs(
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Wav2Vec2ProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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# For backward compatibility
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if self._in_target_context_manager:
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return self.current_processor(
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audio,
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**output_kwargs["audio_kwargs"],
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**output_kwargs["text_kwargs"],
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**output_kwargs["common_kwargs"],
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)
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if audio is not None:
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inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
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if text is not None:
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encodings = self.tokenizer(text, **output_kwargs["text_kwargs"])
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if text is None:
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return inputs
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elif audio is None:
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return encodings
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else:
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inputs["labels"] = encodings["input_ids"]
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return inputs
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def pad(self, *args, **kwargs):
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"""
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This method operates on batches of extracted features and/or tokenized text. It forwards all arguments to
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[`Wav2Vec2FeatureExtractor.pad`] and/or [`PreTrainedTokenizer.pad`] depending on the input modality and returns their outputs. If both modalities are passed, [`Wav2Vec2FeatureExtractor.pad`] and [`PreTrainedTokenizer.pad`] are called.
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Args:
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input_features:
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When the first argument is a dictionary containing a batch of tensors, or the `input_features` argument is present, it is passed to [`Wav2Vec2FeatureExtractor.pad`].
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labels:
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When the `label` argument is present, it is passed to [`PreTrainedTokenizer.pad`].
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Returns:
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This method returns the results of each `pad` method. If both are used, the output is a dictionary containing the results of both.
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"""
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# For backward compatibility
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if self._in_target_context_manager:
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return self.current_processor.pad(*args, **kwargs)
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input_features = kwargs.pop("input_features", None)
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labels = kwargs.pop("labels", None)
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if len(args) > 0:
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input_features = args[0]
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args = args[1:]
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if input_features is not None:
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input_features = self.feature_extractor.pad(input_features, *args, **kwargs)
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if labels is not None:
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labels = self.tokenizer.pad(labels, **kwargs)
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if labels is None:
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return input_features
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elif input_features is None:
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return labels
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else:
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input_features["labels"] = labels["input_ids"]
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return input_features
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
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to the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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@contextmanager
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def as_target_processor(self):
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"""
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Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning
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Wav2Vec2.
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"""
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warnings.warn(
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"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
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"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
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"your audio inputs, or in a separate call."
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)
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self._in_target_context_manager = True
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self.current_processor = self.tokenizer
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yield
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self.current_processor = self.feature_extractor
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self._in_target_context_manager = False
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__all__ = ["Wav2Vec2Processor"]
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