# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Contains the noise perturb augmentation model.""" from deepspeech.frontend.audio import AudioSegment from deepspeech.frontend.augmentor.base import AugmentorBase from deepspeech.frontend.utility import read_manifest class NoisePerturbAugmentor(AugmentorBase): """Augmentation model for adding background noise. :param rng: Random generator object. :type rng: random.Random :param min_snr_dB: Minimal signal noise ratio, in decibels. :type min_snr_dB: float :param max_snr_dB: Maximal signal noise ratio, in decibels. :type max_snr_dB: float :param noise_manifest_path: Manifest path for noise audio data. :type noise_manifest_path: str """ def __init__(self, rng, min_snr_dB, max_snr_dB, noise_manifest_path): self._min_snr_dB = min_snr_dB self._max_snr_dB = max_snr_dB self._rng = rng self._noise_manifest = read_manifest(manifest_path=noise_manifest_path) def __call__(self, x, uttid=None, train=True): if not train: return self.transform_audio(x) return x def transform_audio(self, audio_segment): """Add background noise audio. Note that this is an in-place transformation. :param audio_segment: Audio segment to add effects to. :type audio_segment: AudioSegmenet|SpeechSegment """ noise_json = self._rng.choice(self._noise_manifest, 1, replace=False)[0] if noise_json['duration'] < audio_segment.duration: raise RuntimeError("The duration of sampled noise audio is smaller " "than the audio segment to add effects to.") diff_duration = noise_json['duration'] - audio_segment.duration start = self._rng.uniform(0, diff_duration) end = start + audio_segment.duration noise_segment = AudioSegment.slice_from_file( noise_json['audio_filepath'], start=start, end=end) snr_dB = self._rng.uniform(self._min_snr_dB, self._max_snr_dB) audio_segment.add_noise( noise_segment, snr_dB, allow_downsampling=True, rng=self._rng)