class_docstr: Handles loading, partitioning, and preparing audio training data.
class_docstr_tok: ["Handles","loading",",","partitioning",",","and","preparing","audio","training","data","."]
docstr: Gather samples from the data set, applying transformations as needed.
  
  When the mode is 'training', a random selection of samples will be returned,
  otherwise the first N clips in the partition will be used. This ensures that
  validation always uses the same samples, reducing noise in the metrics.
  
  Args:
    how_many: Desired number of samples to return. -1 means the entire
      contents of this partition.
    offset: Where to start when fetching deterministically.
    model_settings: Information about the current model being trained.
    background_frequency: How many clips will have background noise, 0.0 to
      1.0.
    background_volume_range: How loud the background noise will be.
    time_shift: How much to randomly shift the clips by in time.
    mode: Which partition to use, must be 'training', 'validation', or
      'testing'.
    sess: TensorFlow session that was active when processor was created.
  
  Returns:
    List of sample data for the transformed samples, and list of label indexes
  
  Raises:
    ValueError: If background samples are too short.
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code: def get_data(
      self,
      how_many,
      offset,
      model_settings,
      background_frequency,
      background_volume_range,
      time_shift,
      mode,
      sess,
  ):
      """Gather samples from the data set, applying transformations as needed.
  
      When the mode is 'training', a random selection of samples will be returned,
      otherwise the first N clips in the partition will be used. This ensures that
      validation always uses the same samples, reducing noise in the metrics.
  
      Args:
        how_many: Desired number of samples to return. -1 means the entire
          contents of this partition.
        offset: Where to start when fetching deterministically.
        model_settings: Information about the current model being trained.
        background_frequency: How many clips will have background noise, 0.0 to
          1.0.
        background_volume_range: How loud the background noise will be.
        time_shift: How much to randomly shift the clips by in time.
        mode: Which partition to use, must be 'training', 'validation', or
          'testing'.
        sess: TensorFlow session that was active when processor was created.
  
      Returns:
        List of sample data for the transformed samples, and list of label indexes
  
      Raises:
        ValueError: If background samples are too short.
      """
      # Pick one of the partitions to choose samples from.
      candidates = self.data_index[mode]
      if how_many == -1:
          sample_count = len(candidates)
      else:
          sample_count = max(0, min(how_many, len(candidates) - offset))
      # Data and labels will be populated and returned.
      data = np.zeros(
          (sample_count, model_settings["fingerprint_size"])
      )
      labels = np.zeros(sample_count)
      desired_samples = model_settings["desired_samples"]
      use_background = self.background_data and (mode == "training")
      pick_deterministically = mode != "training"
      # Use the processing graph we created earlier to repeatedly to generate the
      # final output sample data we'll use in training.
      for i in range(offset, offset + sample_count):
          # Pick which audio sample to use.
          if how_many == -1 or pick_deterministically:
              sample_index = i
          else:
              sample_index = np.random.randint(len(candidates))
          sample = candidates[sample_index]
          # If we're time shifting, set up the offset for this sample.
          if time_shift > 0:
              time_shift_amount = np.random.randint(
                  -time_shift, time_shift
              )
          else:
              time_shift_amount = 0
          if time_shift_amount > 0:
              time_shift_padding = [[time_shift_amount, 0], [0, 0]]
              time_shift_offset = [0, 0]
          else:
              time_shift_padding = [[0, -time_shift_amount], [0, 0]]
              time_shift_offset = [-time_shift_amount, 0]
          input_dict = {
              self.wav_filename_placeholder_: sample["file"],
              self.time_shift_padding_placeholder_: time_shift_padding,
              self.time_shift_offset_placeholder_: time_shift_offset,
          }
          # Choose a section of background noise to mix in.
          if use_background or sample["label"] == SILENCE_LABEL:
              background_index = np.random.randint(
                  len(self.background_data)
              )
              background_samples = self.background_data[
                  background_index
              ]
              if (
                  len(background_samples)
                  <= model_settings["desired_samples"]
              ):
                  raise ValueError(
                      "Background sample is too short! Need more than %d"
                      " samples but only %d were found"
                      % (
                          model_settings["desired_samples"],
                          len(background_samples),
                      )
                  )
              background_offset = np.random.randint(
                  0,
                  len(background_samples)
                  - model_settings["desired_samples"],
              )
              background_clipped = background_samples[
                  background_offset : (
                      background_offset + desired_samples
                  )
              ]
              background_reshaped = background_clipped.reshape(
                  [desired_samples, 1]
              )
              if sample["label"] == SILENCE_LABEL:
                  background_volume = np.random.uniform(0, 1)
              elif np.random.uniform(0, 1) < background_frequency:
                  background_volume = np.random.uniform(
                      0, background_volume_range
                  )
              else:
                  background_volume = 0
          else:
              background_reshaped = np.zeros([desired_samples, 1])
              background_volume = 0
          input_dict[
              self.background_data_placeholder_
          ] = background_reshaped
          input_dict[
              self.background_volume_placeholder_
          ] = background_volume
          # If we want silence, mute out the main sample but leave the background.
          if sample["label"] == SILENCE_LABEL:
              input_dict[self.foreground_volume_placeholder_] = 0
          else:
              input_dict[self.foreground_volume_placeholder_] = 1
          # Run the graph to produce the output audio.
          summary, data_tensor = sess.run(
              [self.merged_summaries_, self.output_],
              feed_dict=input_dict,
          )
          self.summary_writer_.add_summary(summary)
          data[i - offset, :] = data_tensor.flatten()
          label_index = self.word_to_index[sample["label"]]
          labels[i - offset] = label_index
      return data, labels
  
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 7 for calling the AudioProcessor obj's underlying member method code with example usage: obj.get_data(how_many, offset, model_settings, background_frequency, background_volume_range, time_shift, mode, sess) and returns: data,  labels