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64 lines
2.4 KiB
64 lines
2.4 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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"""Contain the online bayesian normalization augmentation model."""
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from paddlespeech.s2t.frontend.augmentor.base import AugmentorBase
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class OnlineBayesianNormalizationAugmentor(AugmentorBase):
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"""Augmentation model for adding online bayesian normalization.
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:param rng: Random generator object.
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:type rng: random.Random
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:param target_db: Target RMS value in decibels.
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:type target_db: float
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:param prior_db: Prior RMS estimate in decibels.
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:type prior_db: float
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:param prior_samples: Prior strength in number of samples.
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:type prior_samples: int
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:param startup_delay: Default 0.0s. If provided, this function will
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accrue statistics for the first startup_delay
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seconds before applying online normalization.
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:type starup_delay: float.
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"""
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def __init__(self,
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rng,
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target_db,
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prior_db,
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prior_samples,
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startup_delay=0.0):
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self._target_db = target_db
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self._prior_db = prior_db
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self._prior_samples = prior_samples
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self._rng = rng
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self._startup_delay = startup_delay
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def __call__(self, x, uttid=None, train=True):
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if not train:
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return x
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self.transform_audio(x)
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return x
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def transform_audio(self, audio_segment):
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"""Normalizes the input audio using the online Bayesian approach.
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Note that this is an in-place transformation.
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:param audio_segment: Audio segment to add effects to.
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:type audio_segment: AudioSegment|SpeechSegment
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"""
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audio_segment.normalize_online_bayesian(self._target_db, self._prior_db,
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self._prior_samples,
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self._startup_delay)
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