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136 lines
5.2 KiB
136 lines
5.2 KiB
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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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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"""Speech processor class for SpeechT5."""
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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 SpeechT5Processor(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__(self, *args, **kwargs):
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audio = kwargs.pop("audio", None)
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text = kwargs.pop("text", None)
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text_target = kwargs.pop("text_target", None)
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audio_target = kwargs.pop("audio_target", None)
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sampling_rate = kwargs.pop("sampling_rate", None)
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if audio is not None and text is not None:
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raise ValueError(
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"Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"
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)
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if audio_target is not None and text_target is not None:
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raise ValueError(
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"Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"
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)
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if audio is None and audio_target is None and text is None and text_target is None:
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raise ValueError(
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"You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."
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)
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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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elif text is not None:
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inputs = self.tokenizer(text, **kwargs)
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else:
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inputs = None
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if audio_target is not None:
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targets = self.feature_extractor(audio_target=audio_target, *args, sampling_rate=sampling_rate, **kwargs)
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labels = targets["input_values"]
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elif text_target is not None:
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targets = self.tokenizer(text_target, **kwargs)
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labels = targets["input_ids"]
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else:
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targets = None
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if inputs is None:
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return targets
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if targets is not None:
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inputs["labels"] = labels
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decoder_attention_mask = targets.get("attention_mask")
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if decoder_attention_mask is not None:
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inputs["decoder_attention_mask"] = decoder_attention_mask
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return inputs
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def pad(self, *args, **kwargs):
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"""
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Collates the audio and text inputs, as well as their targets, into a padded batch.
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Audio inputs are padded by SpeechT5FeatureExtractor's [`~SpeechT5FeatureExtractor.pad`]. Text inputs are padded
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by SpeechT5Tokenizer's [`~SpeechT5Tokenizer.pad`].
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Valid input combinations are:
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- `input_ids` only
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- `input_values` only
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- `labels` only, either log-mel spectrograms or text tokens
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- `input_ids` and log-mel spectrogram `labels`
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- `input_values` and text `labels`
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Please refer to the docstring of the above two methods for more information.
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"""
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input_values = kwargs.pop("input_values", None)
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input_ids = kwargs.pop("input_ids", None)
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labels = kwargs.pop("labels", None)
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if input_values is not None and input_ids is not None:
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raise ValueError("Cannot process both `input_values` and `input_ids` inputs.")
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if input_values is None and input_ids is None and labels is None:
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raise ValueError(
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"You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."
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)
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if input_values is not None:
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inputs = self.feature_extractor.pad(input_values, *args, **kwargs)
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elif input_ids is not None:
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inputs = self.tokenizer.pad(input_ids, **kwargs)
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else:
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inputs = None
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if labels is not None:
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if "input_ids" in labels or (isinstance(labels, list) and "input_ids" in labels[0]):
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targets = self.tokenizer.pad(labels, **kwargs)
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labels = targets["input_ids"]
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else:
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feature_size_hack = self.feature_extractor.feature_size
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self.feature_extractor.feature_size = self.feature_extractor.num_mel_bins
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targets = self.feature_extractor.pad(labels, *args, **kwargs)
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self.feature_extractor.feature_size = feature_size_hack
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labels = targets["input_values"]
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else:
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targets = None
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if inputs is None:
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return targets
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if targets is not None:
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inputs["labels"] = labels
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decoder_attention_mask = targets.get("attention_mask")
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if decoder_attention_mask is not None:
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inputs["decoder_attention_mask"] = decoder_attention_mask
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return inputs
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__all__ = ["SpeechT5Processor"]
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