class_docstr: Handles loading, partitioning, and preparing audio training data.
class_docstr_tok: ["Handles","loading",",","partitioning",",","and","preparing","audio","training","data","."]
code: def __init__(
      self,
      data_url,
      data_dir,
      silence_percentage,
      unknown_percentage,
      wanted_words,
      validation_percentage,
      testing_percentage,
      model_settings,
      summaries_dir,
  ):
      if data_dir:
          self.data_dir = data_dir
          self.maybe_download_and_extract_dataset(data_url, data_dir)
          self.prepare_data_index(
              silence_percentage,
              unknown_percentage,
              wanted_words,
              validation_percentage,
              testing_percentage,
          )
          self.prepare_background_data()
      self.prepare_processing_graph(model_settings, summaries_dir)
  
imports: import hashlib
  import math
  import os.path
  import random
  import re
  import sys
  import tarfile
  import numpy
  import urllib
  import tensorflow
  from tensorflow.python.ops import gen_audio_ops
  from tensorflow.python.ops import io_ops
  from tensorflow.python.platform import gfile
  from tensorflow.python.util import compat
label_desc: Use image node_id 1 to create a new AudioProcessor object from inherited base classes: object with example: obj = AudioProcessor(data_url, data_dir, silence_percentage, unknown_percentage, wanted_words, validation_percentage, testing_percentage, model_settings, summaries_dir)