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219 lines
8.1 KiB
219 lines
8.1 KiB
# Copyright (c) 2021 PaddlePaddle Authors. 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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"""Contains the data augmentation pipeline."""
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import json
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from collections.abc import Sequence
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from inspect import signature
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import numpy as np
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from deepspeech.frontend.augmentor.base import AugmentorBase
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from deepspeech.utils.dynamic_import import dynamic_import
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from deepspeech.utils.log import Log
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__all__ = ["AugmentationPipeline"]
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logger = Log(__name__).getlog()
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import_alias = dict(
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volume="deepspeech.frontend.augmentor.impulse_response:VolumePerturbAugmentor",
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shift="deepspeech.frontend.augmentor.shift_perturb:ShiftPerturbAugmentor",
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speed="deepspeech.frontend.augmentor.speed_perturb:SpeedPerturbAugmentor",
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resample="deepspeech.frontend.augmentor.resample:ResampleAugmentor",
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bayesian_normal="deepspeech.frontend.augmentor.online_bayesian_normalization:OnlineBayesianNormalizationAugmentor",
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noise="deepspeech.frontend.augmentor.noise_perturb:NoisePerturbAugmentor",
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impulse="deepspeech.frontend.augmentor.impulse_response:ImpulseResponseAugmentor",
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specaug="deepspeech.frontend.augmentor.spec_augment:SpecAugmentor", )
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class AugmentationPipeline():
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"""Build a pre-processing pipeline with various augmentation models.Such a
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data augmentation pipeline is oftern leveraged to augment the training
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samples to make the model invariant to certain types of perturbations in the
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real world, improving model's generalization ability.
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The pipeline is built according the the augmentation configuration in json
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string, e.g.
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.. code-block::
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[ {
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"type": "noise",
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"params": {"min_snr_dB": 10,
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"max_snr_dB": 20,
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"noise_manifest_path": "datasets/manifest.noise"},
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"prob": 0.0
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},
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{
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"type": "speed",
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"params": {"min_speed_rate": 0.9,
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"max_speed_rate": 1.1},
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"prob": 1.0
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},
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{
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"type": "shift",
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"params": {"min_shift_ms": -5,
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"max_shift_ms": 5},
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"prob": 1.0
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},
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{
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"type": "volume",
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"params": {"min_gain_dBFS": -10,
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"max_gain_dBFS": 10},
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"prob": 0.0
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},
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{
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"type": "bayesian_normal",
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"params": {"target_db": -20,
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"prior_db": -20,
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"prior_samples": 100},
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"prob": 0.0
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}
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]
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This augmentation configuration inserts two augmentation models
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into the pipeline, with one is VolumePerturbAugmentor and the other
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SpeedPerturbAugmentor. "prob" indicates the probability of the current
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augmentor to take effect. If "prob" is zero, the augmentor does not take
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effect.
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Params:
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augmentation_config(str): Augmentation configuration in json string.
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random_seed(int): Random seed.
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train(bool): whether is train mode.
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Raises:
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ValueError: If the augmentation json config is in incorrect format".
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"""
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SPEC_TYPES = {'specaug'}
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def __init__(self, augmentation_config: str, random_seed: int=0):
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self._rng = np.random.RandomState(random_seed)
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self.conf = {'mode': 'sequential', 'process': []}
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if augmentation_config:
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process = json.loads(augmentation_config)
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self.conf['process'] += process
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self._augmentors, self._rates = self._parse_pipeline_from('all')
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self._audio_augmentors, self._audio_rates = self._parse_pipeline_from(
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'audio')
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self._spec_augmentors, self._spec_rates = self._parse_pipeline_from(
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'feature')
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def __call__(self, xs, uttid_list=None, **kwargs):
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if not isinstance(xs, Sequence):
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is_batch = False
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xs = [xs]
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else:
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is_batch = True
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if isinstance(uttid_list, str):
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uttid_list = [uttid_list for _ in range(len(xs))]
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if self.conf.get("mode", "sequential") == "sequential":
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for idx, (func, rate) in enumerate(
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zip(self._augmentors, self._rates), 0):
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if self._rng.uniform(0., 1.) >= rate:
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continue
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# Derive only the args which the func has
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try:
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param = signature(func).parameters
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except ValueError:
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# Some function, e.g. built-in function, are failed
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param = {}
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_kwargs = {k: v for k, v in kwargs.items() if k in param}
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try:
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if uttid_list is not None and "uttid" in param:
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xs = [
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func(x, u, **_kwargs)
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for x, u in zip(xs, uttid_list)
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]
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else:
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xs = [func(x, **_kwargs) for x in xs]
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except Exception:
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logger.fatal("Catch a exception from {}th func: {}".format(
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idx, func))
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raise
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else:
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raise NotImplementedError(
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"Not supporting mode={}".format(self.conf["mode"]))
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if is_batch:
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return xs
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else:
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return xs[0]
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def transform_audio(self, audio_segment):
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"""Run the pre-processing pipeline for data augmentation.
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Note that this is an in-place transformation.
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:param audio_segment: Audio segment to process.
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:type audio_segment: AudioSegmenet|SpeechSegment
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"""
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for augmentor, rate in zip(self._audio_augmentors, self._audio_rates):
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if self._rng.uniform(0., 1.) < rate:
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augmentor.transform_audio(audio_segment)
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def transform_feature(self, spec_segment):
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"""spectrogram augmentation.
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Args:
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spec_segment (np.ndarray): audio feature, (D, T).
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"""
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for augmentor, rate in zip(self._spec_augmentors, self._spec_rates):
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if self._rng.uniform(0., 1.) < rate:
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spec_segment = augmentor.transform_feature(spec_segment)
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return spec_segment
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def _parse_pipeline_from(self, aug_type='all'):
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"""Parse the config json to build a augmentation pipelien."""
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assert aug_type in ('audio', 'feature', 'all'), aug_type
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audio_confs = []
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feature_confs = []
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all_confs = []
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for config in self.conf['process']:
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all_confs.append(config)
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if config["type"] in self.SPEC_TYPES:
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feature_confs.append(config)
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else:
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audio_confs.append(config)
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if aug_type == 'audio':
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aug_confs = audio_confs
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elif aug_type == 'feature':
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aug_confs = feature_confs
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else:
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aug_confs = all_confs
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augmentors = [
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self._get_augmentor(config["type"], config["params"])
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for config in aug_confs
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]
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rates = [config["prob"] for config in aug_confs]
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return augmentors, rates
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def _get_augmentor(self, augmentor_type, params):
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"""Return an augmentation model by the type name, and pass in params."""
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class_obj = dynamic_import(augmentor_type, import_alias)
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assert issubclass(class_obj, AugmentorBase)
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try:
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obj = class_obj(self._rng, **params)
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except Exception:
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raise ValueError("Unknown augmentor type [%s]." % augmentor_type)
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return obj
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