parameters: (audio_data, verbose=True)
return_statement: return feature_extractor.get_deep_features(audio_data, verbose=verbose)
docstring: Calculates the deep features used by the Sound Classifier.
  
      Internally the Sound Classifier calculates deep features for both model
      creation and predictions. If the same data will be used multiple times,
      calculating the deep features just once will result in a significant speed
      up.
  
      Parameters
      ----------
      audio_data : SArray
          Audio data is represented as dicts with key 'data' and 'sample_rate',
          see `turicreate.load_audio(...)`.
  
      Examples
      --------
      >>> my_audio_data['deep_features'] = get_deep_features(my_audio_data['audio'])
      >>> train, test = my_audio_data.random_split(.8)
      >>> model = tc.sound_classifier.create(train, 'label', 'deep_features')
      >>> predictions = model.predict(test)
docstring_summary: Calculates the deep features used by the Sound Classifier.
docstring_tokens: Calculates the deep features used by the Sound Classifier .
function: def get_deep_features(audio_data, verbose=True):
      """
      Calculates the deep features used by the Sound Classifier.
  
      Internally the Sound Classifier calculates deep features for both model
      creation and predictions. If the same data will be used multiple times,
      calculating the deep features just once will result in a significant speed
      up.
  
      Parameters
      ----------
      audio_data : SArray
          Audio data is represented as dicts with key 'data' and 'sample_rate',
          see `turicreate.load_audio(...)`.
  
      Examples
      --------
      >>> my_audio_data['deep_features'] = get_deep_features(my_audio_data['audio'])
      >>> train, test = my_audio_data.random_split(.8)
      >>> model = tc.sound_classifier.create(train, 'label', 'deep_features')
      >>> predictions = model.predict(test)
      """
      from ._audio_feature_extractor import _get_feature_extractor
  
      if not _is_audio_data_sarray(audio_data):
          raise TypeError("Input must be audio data")
  
      feature_extractor_name = "VGGish"
      feature_extractor = _get_feature_extractor(feature_extractor_name)
  
      return feature_extractor.get_deep_features(audio_data, verbose=verbose)
function_tokens: def get_deep_features ( audio_data , verbose = True ) : from . _audio_feature_extractor import _get_feature_extractor if not _is_audio_data_sarray ( audio_data ) : raise TypeError ( "Input must be audio data" ) feature_extractor_name = "VGGish" feature_extractor = _get_feature_extractor ( feature_extractor_name ) return feature_extractor . get_deep_features ( audio_data , verbose = verbose )