code: def additive_noise(self, noise_type, audio):
      clean_db = 10 * np.log10(np.mean(audio**2) + 1e-4)
  
      noise_list = random.sample(
          self.noise_list[noise_type],
          random.randint(
              self.additive_noise_config[noise_type]["min_num_noises"],
              self.additive_noise_config[noise_type]["max_num_noises"],
          ),
      )
  
      audio_len = audio.shape[0]
      noises_wav = None
      for noise in noise_list:
          noiseaudio = self.ap.load_wav(noise, sr=self.ap.sample_rate)[
              :audio_len
          ]
  
          if noiseaudio.shape[0] < audio_len:
              continue
  
          noise_snr = random.uniform(
              self.additive_noise_config[noise_type]["min_snr_in_db"],
              self.additive_noise_config[noise_type]["max_num_noises"],
          )
          noise_db = 10 * np.log10(np.mean(noiseaudio**2) + 1e-4)
          noise_wav = (
              np.sqrt(10 ** ((clean_db - noise_db - noise_snr) / 10))
              * noiseaudio
          )
  
          if noises_wav is None:
              noises_wav = noise_wav
          else:
              noises_wav += noise_wav
  
      # if all possible files is less than audio, choose other files
      if noises_wav is None:
          return self.additive_noise(noise_type, audio)
  
      return audio + noises_wav
  
file_path: TTS/TTS/encoder/utils/generic_utils.py
imports: import glob
  import os
  import random
  import numpy
  from scipy import signal
  from TTS.encoder.models.lstm import LSTMSpeakerEncoder
  from TTS.encoder.models.resnet import ResNetSpeakerEncoder
label_desc: Use image node_id 3 for calling the AugmentWAV obj's underlying member method code with example usage: obj.additive_noise(noise_type, audio) and returns: unknown, self