99 lines
3.8 KiB
Python
99 lines
3.8 KiB
Python
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# coding=utf-8
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# 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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class WhisperProcessor(ProcessorMixin):
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r"""
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Constructs a Whisper processor which wraps a Whisper feature extractor and a Whisper tokenizer into a single
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processor.
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[`WhisperProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`WhisperTokenizer`]. See
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the [`~WhisperProcessor.__call__`] and [`~WhisperProcessor.decode`] for more information.
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Args:
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feature_extractor (`WhisperFeatureExtractor`):
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An instance of [`WhisperFeatureExtractor`]. The feature extractor is a required input.
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tokenizer (`WhisperTokenizer`):
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An instance of [`WhisperTokenizer`]. The tokenizer is a required input.
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"""
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feature_extractor_class = "WhisperFeatureExtractor"
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tokenizer_class = "WhisperTokenizer"
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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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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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def __call__(self, *args, **kwargs):
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"""
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Forwards the `audio` argument to WhisperFeatureExtractor's [`~WhisperFeatureExtractor.__call__`] and the `text`
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argument to [`~WhisperTokenizer.__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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# For backward compatibility
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if self._in_target_context_manager:
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return self.current_processor(*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 batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to WhisperTokenizer'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 WhisperTokenizer'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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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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