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106 lines
4.0 KiB
106 lines
4.0 KiB
# 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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from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils_base import AudioInput, PreTokenizedInput, TextInput
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from ...utils import auto_docstring
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class Wav2Vec2ProcessorKwargs(ProcessingKwargs, total=False):
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_defaults = {}
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@auto_docstring
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class Wav2Vec2Processor(ProcessorMixin):
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def __init__(self, feature_extractor, tokenizer):
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super().__init__(feature_extractor, tokenizer)
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@auto_docstring
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def __call__(
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self,
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audio: AudioInput | None = None,
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text: str | list[str] | TextInput | PreTokenizedInput | None = None,
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**kwargs: Unpack[Wav2Vec2ProcessorKwargs],
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):
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r"""
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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 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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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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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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@property
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def model_input_names(self):
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# The processor doesn't return text ids and the model seems to not need them
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feature_extractor_input_names = self.feature_extractor.model_input_names
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return feature_extractor_input_names + ["labels"]
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__all__ = ["Wav2Vec2Processor"]
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