118 lines
5.6 KiB
Python
118 lines
5.6 KiB
Python
# 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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Audio/Text processor class for CLAP
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"""
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from ...processing_utils import ProcessorMixin
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from ...tokenization_utils_base import BatchEncoding
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class ClapProcessor(ProcessorMixin):
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r"""
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Constructs a CLAP processor which wraps a CLAP feature extractor and a RoBerta tokenizer into a single processor.
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[`ClapProcessor`] offers all the functionalities of [`ClapFeatureExtractor`] and [`RobertaTokenizerFast`]. See the
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[`~ClapProcessor.__call__`] and [`~ClapProcessor.decode`] for more information.
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Args:
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feature_extractor ([`ClapFeatureExtractor`]):
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The audio processor is a required input.
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tokenizer ([`RobertaTokenizerFast`]):
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The tokenizer is a required input.
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"""
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feature_extractor_class = "ClapFeatureExtractor"
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tokenizer_class = ("RobertaTokenizer", "RobertaTokenizerFast")
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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, text=None, audios=None, return_tensors=None, **kwargs):
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"""
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Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
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and `kwargs` arguments to RobertaTokenizerFast's [`~RobertaTokenizerFast.__call__`] if `text` is not `None` to
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encode the text. To prepare the audio(s), this method forwards the `audios` and `kwrags` arguments to
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ClapFeatureExtractor's [`~ClapFeatureExtractor.__call__`] if `audios` is not `None`. Please refer to the
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doctsring of the above two methods for more information.
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Args:
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text (`str`, `List[str]`, `List[List[str]]`):
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The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
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`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
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audios (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
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The audio or batch of audios to be prepared. Each audio can be NumPy array or PyTorch tensor. In case
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of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels,
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and T the sample length of the audio.
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return_tensors (`str` or [`~utils.TensorType`], *optional*):
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If set, will return tensors of a particular framework. Acceptable values are:
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- `'tf'`: Return TensorFlow `tf.constant` objects.
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- `'pt'`: Return PyTorch `torch.Tensor` objects.
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- `'np'`: Return NumPy `np.ndarray` objects.
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- `'jax'`: Return JAX `jnp.ndarray` objects.
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Returns:
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[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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`None`).
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- **audio_features** -- Audio features to be fed to a model. Returned when `audios` is not `None`.
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"""
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sampling_rate = kwargs.pop("sampling_rate", None)
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if text is None and audios is None:
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raise ValueError("You have to specify either text or audios. Both cannot be none.")
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if text is not None:
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encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
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if audios is not None:
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audio_features = self.feature_extractor(
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audios, sampling_rate=sampling_rate, return_tensors=return_tensors, **kwargs
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)
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if text is not None and audios is not None:
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encoding["input_features"] = audio_features.input_features
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return encoding
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elif text is not None:
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return encoding
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else:
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return BatchEncoding(data=dict(**audio_features), tensor_type=return_tensors)
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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 RobertaTokenizerFast'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 RobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer
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to 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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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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feature_extractor_input_names = self.feature_extractor.model_input_names
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return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
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