class_docstr: Google's Speech Commands dataset for keyword spotting (https://arxiv.org/abs/1804.03209).
  
  This contains the "v2" version for 12-way classification (10 commands,
  plus unknown and background categories).
  
  Args:
      opts: Command-line arguments
docstr: Process the dataset .json files to set up the dataset.
  
  The .json configs contain:
  [
      {
          "audio_filepath": relative path to audio from the dataset root directory,
          "duration": floating point duration in seconds,
          "command": the label of the spoken command.
      },
      ...
  ]
docstr_tok: ["Process","the","dataset",".json","files","to","set","up","the","dataset",".","The",".json","configs","contain",":","[","{","``","audio_filepath","''",":","relative","path","to","audio","from","the","dataset","root","directory",",","``","duration","''",":","floating","point","duration","in","seconds",",","``","command","''",":","the","label","of","the","spoken","command",".","}",",","...","]"]
code: def _process_dataset_config(self) -> None:
      """
      Process the dataset .json files to set up the dataset.
  
      The .json configs contain:
      [
          {
              "audio_filepath": relative path to audio from the dataset root directory,
              "duration": floating point duration in seconds,
              "command": the label of the spoken command.
          },
          ...
      ]
      """
      with open(self.dataset_config) as f:
          lines = f.readlines()
  
      self.dataset_entries = []
      for line in lines:
          self.dataset_entries.append(json.loads(line))
  
      for elem in self.dataset_entries:
          audio_path = elem["audio_filepath"]
          new_path = os.path.join(self.root, audio_path)
          elem["audio_filepath"] = new_path
  
      all_labels = sorted(
          set([elem["command"] for elem in self.dataset_entries])
      )
      self.label_to_index = {l: i for i, l in enumerate(all_labels)}
  
      if getattr(self.opts, "audio_augmentation.noise.enable"):
          # Cache this, since it loads files on initialization.
          background_dir = os.path.join(self.root, "_background_noise_")
          self.noise = Noise(self.opts, noise_files_dir=background_dir)
  
imports: import argparse
  import json
  import os
  import random
  from typing import Dict, Tuple, Union
  import torch
  import torchaudio
  from torch import Tensor
  from torch.nn import functional
  from data.datasets import DATASET_REGISTRY, dataset_base
  from data.transforms.audio import Noise, Roll, SetFixedLength
  from data.transforms.common import Compose
  from data.transforms.image_pil import BaseTransformation
label_desc: Use image node_id 2 for calling the SpeechCommandsv2Dataset obj's underlying member method code with example usage: obj._process_dataset_config() without return types