92 lines
3.5 KiB
Python
92 lines
3.5 KiB
Python
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# coding=utf-8
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# Copyright 2023 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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Processor class for CLVP
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"""
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from ...processing_utils import ProcessorMixin
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class ClvpProcessor(ProcessorMixin):
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r"""
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Constructs a CLVP processor which wraps a CLVP Feature Extractor and a CLVP Tokenizer into a single processor.
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[`ClvpProcessor`] offers all the functionalities of [`ClvpFeatureExtractor`] and [`ClvpTokenizer`]. See the
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[`~ClvpProcessor.__call__`], [`~ClvpProcessor.decode`] and [`~ClvpProcessor.batch_decode`] for more information.
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Args:
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feature_extractor (`ClvpFeatureExtractor`):
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An instance of [`ClvpFeatureExtractor`]. The feature extractor is a required input.
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tokenizer (`ClvpTokenizer`):
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An instance of [`ClvpTokenizer`]. The tokenizer is a required input.
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"""
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feature_extractor_class = "ClvpFeatureExtractor"
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tokenizer_class = "ClvpTokenizer"
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model_input_names = [
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"input_ids",
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"input_features",
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"attention_mask",
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]
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def __init__(self, feature_extractor, tokenizer):
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super().__init__(feature_extractor, tokenizer)
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def __call__(self, *args, **kwargs):
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"""
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Forwards the `audio` and `sampling_rate` arguments to [`~ClvpFeatureExtractor.__call__`] and the `text`
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argument to [`~ClvpTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more
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information.
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"""
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raw_speech = kwargs.pop("raw_speech", None)
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sampling_rate = kwargs.pop("sampling_rate", None)
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text = kwargs.pop("text", None)
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if raw_speech is None and text is None:
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raise ValueError("You need to specify either an `raw_speech` or `text` input to process.")
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if raw_speech is not None:
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inputs = self.feature_extractor(raw_speech, sampling_rate=sampling_rate, **kwargs)
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if text is not None:
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encodings = self.tokenizer(text, **kwargs)
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if text is None:
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return inputs
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elif raw_speech is None:
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return encodings
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else:
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inputs["input_ids"] = encodings["input_ids"]
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inputs["attention_mask"] = encodings["attention_mask"]
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return inputs
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# Copied from transformers.models.whisper.processing_whisper.WhisperProcessor.batch_decode with Whisper->Clvp
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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 ClvpTokenizer'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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# Copied from transformers.models.whisper.processing_whisper.WhisperProcessor.decode with Whisper->Clvp
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to ClvpTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
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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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