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
code: def __init__(
      self,
      opts: argparse.Namespace,
      *args,
      **kwargs,
  ) -> None:
      super().__init__(opts=opts, *args, **kwargs)
      mode_str = self.mode
      if self.mode == "val":
          # This value is needed to calculate the correct annotation .json paths.
          mode_str = "validation"
  
      self.dataset_config = os.path.join(
          self.root, f"{mode_str}_manifest.json"
      )
  
      self._process_dataset_config()
      self.mixup = getattr(opts, "dataset.speech_commands_v2.mixup")
  
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 1 to create a new SpeechCommandsv2Dataset object from inherited base classes: dataset_base with example: obj = SpeechCommandsv2Dataset(opts)