class_docstr: Handles loading, partitioning, and preparing audio training data.
class_docstr_tok: ["Handles","loading",",","partitioning",",","and","preparing","audio","training","data","."]
docstr: Applies the feature transformation process to the input_wav.
  
  Runs the feature generation process (generally producing a spectrogram from
  the input samples) on the WAV file. This can be useful for testing and
  verifying implementations being run on other platforms.
  
  Args:
    wav_filename: The path to the input audio file.
    model_settings: Information about the current model being trained.
    sess: TensorFlow session that was active when processor was created.
  
  Returns:
    Numpy data array containing the generated features.
docstr_tok: ["Applies","the","feature","transformation","process","to","the","input_wav",".","Runs","the","feature","generation","process","(","generally","producing","a","spectrogram","from","the","input","samples",")","on","the","WAV","file",".","This","can","be","useful","for","testing","and","verifying","implementations","being","run","on","other","platforms",".","Args",":","wav_filename",":","The","path","to","the","input","audio","file",".","model_settings",":","Information","about","the","current","model","being","trained",".","sess",":","TensorFlow","session","that","was","active","when","processor","was","created",".","Returns",":","Numpy","data","array","containing","the","generated","features","."]
code: def get_features_for_wav(self, wav_filename, model_settings, sess):
      """Applies the feature transformation process to the input_wav.
  
      Runs the feature generation process (generally producing a spectrogram from
      the input samples) on the WAV file. This can be useful for testing and
      verifying implementations being run on other platforms.
  
      Args:
        wav_filename: The path to the input audio file.
        model_settings: Information about the current model being trained.
        sess: TensorFlow session that was active when processor was created.
  
      Returns:
        Numpy data array containing the generated features.
      """
      desired_samples = model_settings["desired_samples"]
      input_dict = {
          self.wav_filename_placeholder_: wav_filename,
          self.time_shift_padding_placeholder_: [[0, 0], [0, 0]],
          self.time_shift_offset_placeholder_: [0, 0],
          self.background_data_placeholder_: np.zeros(
              [desired_samples, 1]
          ),
          self.background_volume_placeholder_: 0,
          self.foreground_volume_placeholder_: 1,
      }
      # Run the graph to produce the output audio.
      data_tensor = sess.run([self.output_], feed_dict=input_dict)
      return data_tensor
  
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 8 for calling the AudioProcessor obj's underlying member method code with example usage: obj.get_features_for_wav(wav_filename, model_settings, sess) and returns: data_tensor