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# 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 volume perturb augmentation model."""
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Support paddle 2.x (#538)
* 2.x model
* model test pass
* fix data
* fix soundfile with flac support
* one thread dataloader test pass
* export feasture size
add trainer and utils
add setup model and dataloader
update travis using Bionic dist
* add venv; test under venv
* fix unittest; train and valid
* add train and config
* add config and train script
* fix ctc cuda memcopy error
* fix imports
* fix train valid log
* fix dataset batch shuffle shift start from 1
fix rank_zero_only decreator error
close tensorboard when train over
add decoding config and code
* test process can run
* test with decoding
* test and infer with decoding
* fix infer
* fix ctc loss
lr schedule
sortagrad
logger
* aishell egs
* refactor train
add aishell egs
* fix dataset batch shuffle and add batch sampler log
print model parameter
* fix model and ctc
* sequence_mask make all inputs zeros, which cause grad be zero, this is a bug of LessThanOp
add grad clip by global norm
add model train test notebook
* ctc loss
remove run prefix
using ord value as text id
* using unk when training
compute_loss need text ids
ord id using in test mode, which compute wer/cer
* fix tester
* add lr_deacy
refactor code
* fix tools
* fix ci
add tune
fix gru model bugs
add dataset and model test
* fix decoding
* refactor repo
fix decoding
* fix musan and rir dataset
* refactor io, loss, conv, rnn, gradclip, model, utils
* fix ci and import
* refactor model
add export jit model
* add deploy bin and test it
* rm uselss egs
* add layer tools
* refactor socket server
new model from pretrain
* remve useless
* fix instability loss and grad nan or inf for librispeech training
* fix sampler
* fix libri train.sh
* fix doc
* add license on cpp
* fix doc
* fix libri script
* fix install
* clip 5 wer 7.39, clip 400 wer 7.54, 1.8 clip 400 baseline 7.49
4 years ago
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from deepspeech.frontend.augmentor.base import AugmentorBase
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class VolumePerturbAugmentor(AugmentorBase):
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"""Augmentation model for adding random volume perturbation.
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This is used for multi-loudness training of PCEN. See
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https://arxiv.org/pdf/1607.05666v1.pdf
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for more details.
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:param rng: Random generator object.
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:type rng: random.Random
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:param min_gain_dBFS: Minimal gain in dBFS.
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:type min_gain_dBFS: float
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:param max_gain_dBFS: Maximal gain in dBFS.
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:type max_gain_dBFS: float
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"""
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def __init__(self, rng, min_gain_dBFS, max_gain_dBFS):
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self._min_gain_dBFS = min_gain_dBFS
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self._max_gain_dBFS = max_gain_dBFS
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self._rng = rng
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def __call__(self, x, uttid=None, train=True):
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if not train:
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return
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self.transform_audio(x)
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def transform_audio(self, audio_segment):
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"""Change audio loadness.
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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: AudioSegmenet|SpeechSegment
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"""
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gain = self._rng.uniform(self._min_gain_dBFS, self._max_gain_dBFS)
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audio_segment.gain_db(gain)
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