141 lines
5.5 KiB
Python
141 lines
5.5 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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Text/audio processor class for MusicGen
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"""
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from typing import List, Optional
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import numpy as np
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from ...processing_utils import ProcessorMixin
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from ...utils import to_numpy
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class MusicgenProcessor(ProcessorMixin):
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r"""
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Constructs a MusicGen processor which wraps an EnCodec feature extractor and a T5 tokenizer into a single processor
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class.
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[`MusicgenProcessor`] offers all the functionalities of [`EncodecFeatureExtractor`] and [`TTokenizer`]. See
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[`~MusicgenProcessor.__call__`] and [`~MusicgenProcessor.decode`] for more information.
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Args:
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feature_extractor (`EncodecFeatureExtractor`):
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An instance of [`EncodecFeatureExtractor`]. The feature extractor is a required input.
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tokenizer (`T5Tokenizer`):
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An instance of [`T5Tokenizer`]. The tokenizer is a required input.
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"""
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feature_extractor_class = "EncodecFeatureExtractor"
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tokenizer_class = ("T5Tokenizer", "T5TokenizerFast")
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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 EncodecFeatureExtractor's [`~EncodecFeatureExtractor.__call__`] and the `text`
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argument to [`~T5Tokenizer.__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 text is not None:
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inputs = self.tokenizer(text, **kwargs)
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if audio is not None:
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audio_inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
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if audio is None:
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return inputs
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elif text is None:
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return audio_inputs
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else:
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inputs["input_values"] = audio_inputs["input_values"]
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if "padding_mask" in audio_inputs:
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inputs["padding_mask"] = audio_inputs["padding_mask"]
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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 is used to decode either batches of audio outputs from the MusicGen model, or batches of token ids
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from the tokenizer. In the case of decoding token ids, this method forwards all its arguments to T5Tokenizer's
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[`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information.
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"""
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audio_values = kwargs.pop("audio", None)
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padding_mask = kwargs.pop("padding_mask", None)
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if len(args) > 0:
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audio_values = args[0]
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args = args[1:]
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if audio_values is not None:
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return self._decode_audio(audio_values, padding_mask=padding_mask)
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else:
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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 T5Tokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the
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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 _decode_audio(self, audio_values, padding_mask: Optional = None) -> List[np.ndarray]:
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"""
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This method strips any padding from the audio values to return a list of numpy audio arrays.
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"""
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audio_values = to_numpy(audio_values)
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bsz, channels, seq_len = audio_values.shape
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if padding_mask is None:
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return list(audio_values)
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padding_mask = to_numpy(padding_mask)
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# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
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# token (so that the generated audio values are **not** treated as padded tokens)
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difference = seq_len - padding_mask.shape[-1]
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padding_value = 1 - self.feature_extractor.padding_value
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padding_mask = np.pad(padding_mask, ((0, 0), (0, difference)), "constant", constant_values=padding_value)
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audio_values = audio_values.tolist()
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for i in range(bsz):
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sliced_audio = np.asarray(audio_values[i])[
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padding_mask[i][None, :] != self.feature_extractor.padding_value
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]
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audio_values[i] = sliced_audio.reshape(channels, -1)
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return audio_values
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