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61 lines
2.1 KiB
61 lines
2.1 KiB
# Copyright 2022 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 Whisper
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"""
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from ...processing_utils import ProcessorMixin
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from ...utils import auto_docstring
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@auto_docstring
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class WhisperProcessor(ProcessorMixin):
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def __init__(self, feature_extractor, tokenizer):
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super().__init__(feature_extractor, tokenizer)
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def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True):
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return self.tokenizer.get_decoder_prompt_ids(task=task, language=language, no_timestamps=no_timestamps)
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@auto_docstring
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def __call__(self, *args, **kwargs):
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audio = kwargs.pop("audio", 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 len(args) > 0:
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audio = args[0]
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args = args[1:]
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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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if audio is not None:
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inputs = self.feature_extractor(audio, *args, 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 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 get_prompt_ids(self, text: str, return_tensors="np"):
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return self.tokenizer.get_prompt_ids(text, return_tensors=return_tensors)
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__all__ = ["WhisperProcessor"]
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