docstr: Preprocess each example, breaking it up into frames.
  
  Returns two numpy arrays: preprocessed frame and their indexes
docstr_tok: ["Preprocess","each","example",",","breaking","it","up","into","frames",".","Returns","two","numpy","arrays",":","preprocessed","frame","and","their","indexes"]
code: def _preprocess_data(audio_data, verbose=True):
      """
      Preprocess each example, breaking it up into frames.
  
      Returns two numpy arrays: preprocessed frame and their indexes
      """
      from .vggish_input import waveform_to_examples
  
      last_progress_update = _time.time()
      progress_header_printed = False
  
      # Can't run as a ".apply(...)" due to numba.jit decorator issue:
      # https://github.com/apple/turicreate/issues/1216
      preprocessed_data, audio_data_index = [], []
      for i, audio_dict in enumerate(audio_data):
          scaled_data = audio_dict["data"] / 32768.0
          data = waveform_to_examples(
              scaled_data, audio_dict["sample_rate"]
          )
  
          for j in data:
              preprocessed_data.append([j])
              audio_data_index.append(i)
  
          # If `verbose` is set, print an progress update about every 20s
          if verbose and _time.time() - last_progress_update >= 20:
              if not progress_header_printed:
                  print("Preprocessing audio data -")
                  progress_header_printed = True
              print(
                  "Preprocessed {} of {} examples".format(
                      i, len(audio_data)
                  )
              )
              last_progress_update = _time.time()
  
      if progress_header_printed:
          print(
              "Preprocessed {} of {} examples\n".format(
                  len(audio_data), len(audio_data)
              )
          )
      return _np.asarray(preprocessed_data), audio_data_index
  
file_path: turicreate/src/python/turicreate/toolkits/sound_classifier/_audio_feature_extractor.py
imports: import time
  import numpy
  import turicreate.toolkits._tf_utils
  import turicreate
  from .vggish_params import SAMPLE_RATE
  from .._internal_utils import _mac_ver
  from .._pre_trained_models import VGGish
  from turicreate._deps.minimal_package import _minimal_package_import_check
label_desc: Use image node_id 1 for calling the VGGishFeatureExtractor obj's underlying member method code with example usage: obj._preprocess_data(audio_data, verbose) and returns: _np,  audio_data_index