text: """Contains the noise perturb augmentation model."""
  
  from data_utils.augmentor.base import AugmentorBase
  from data_utils.utility import read_manifest
  from data_utils.audio import AudioSegment
  
  
  class NoisePerturbAugmentor(AugmentorBase):
      """用于添加背景噪声的增强模型
  
      :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 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.sample(self._noise_manifest, 1)[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)
  