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65 lines
2.7 KiB
65 lines
2.7 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 noise perturb augmentation model."""
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from deepspeech.frontend.audio import AudioSegment
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from deepspeech.frontend.augmentor.base import AugmentorBase
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from deepspeech.frontend.utility import read_manifest
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class NoisePerturbAugmentor(AugmentorBase):
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"""Augmentation model for adding background noise.
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:param rng: Random generator object.
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:type rng: random.Random
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:param min_snr_dB: Minimal signal noise ratio, in decibels.
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:type min_snr_dB: float
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:param max_snr_dB: Maximal signal noise ratio, in decibels.
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:type max_snr_dB: float
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:param noise_manifest_path: Manifest path for noise audio data.
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:type noise_manifest_path: str
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"""
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def __init__(self, rng, min_snr_dB, max_snr_dB, noise_manifest_path):
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self._min_snr_dB = min_snr_dB
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self._max_snr_dB = max_snr_dB
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self._rng = rng
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self._noise_manifest = read_manifest(manifest_path=noise_manifest_path)
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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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"""Add background noise audio.
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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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noise_json = self._rng.choice(self._noise_manifest, 1, replace=False)[0]
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if noise_json['duration'] < audio_segment.duration:
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raise RuntimeError("The duration of sampled noise audio is smaller "
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"than the audio segment to add effects to.")
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diff_duration = noise_json['duration'] - audio_segment.duration
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start = self._rng.uniform(0, diff_duration)
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end = start + audio_segment.duration
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noise_segment = AudioSegment.slice_from_file(
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noise_json['audio_filepath'], start=start, end=end)
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snr_dB = self._rng.uniform(self._min_snr_dB, self._max_snr_dB)
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audio_segment.add_noise(
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noise_segment, snr_dB, allow_downsampling=True, rng=self._rng)
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