code: def __init__(
      self,
      sampling_rate: int,
      audio_norm_target_dBFS: float,
      vad_window_length,
      vad_moving_average_width,
      vad_max_silence_length,
      mel_window_length,
      mel_window_step,
      n_mels,
      partial_n_frames: int,
      min_pad_coverage: float = 0.75,
      partial_overlap_ratio: float = 0.5,
  ):
      self.sampling_rate = sampling_rate
      self.audio_norm_target_dBFS = audio_norm_target_dBFS
  
      self.vad_window_length = vad_window_length
      self.vad_moving_average_width = vad_moving_average_width
      self.vad_max_silence_length = vad_max_silence_length
  
      self.n_fft = int(mel_window_length * sampling_rate / 1000)
      self.hop_length = int(mel_window_step * sampling_rate / 1000)
      self.n_mels = n_mels
  
      self.partial_n_frames = partial_n_frames
      self.min_pad_coverage = min_pad_coverage
      self.partial_overlap_ratio = partial_overlap_ratio
  
file_path: PaddleHub/modules/audio/voice_cloning/lstm_tacotron2/audio_processor.py
imports: from pathlib import Path
  from warnings import warn
  import struct
  from scipy.ndimage.morphology import binary_dilation
  import numpy
  import librosa
label_desc: Use image node_id 1 to create a new SpeakerVerificationPreprocessor object from inherited base classes: object with example: obj = SpeakerVerificationPreprocessor(sampling_rate, audio_norm_target_dBFS, vad_window_length, vad_moving_average_width, vad_max_silence_length, mel_window_length, mel_window_step, n_mels, partial_n_frames, min_pad_coverage, partial_overlap_ratio)