class_docstr: *Fluent Speech Commands* :cite:`fluent` dataset
  
  Args:
      root (str of Path): Path to the directory where the dataset is found.
      subset (str, optional): subset of the dataset to use.
          Options: [``"train"``, ``"valid"``, ``"test"``].
          (Default: ``"train"``)
class_docstr_tok: ["*","Fluent","Speech","Commands","*",":","cite",":","`","fluent","`","dataset","Args",":","root","(","str","of","Path",")",":","Path","to","the","directory","where","the","dataset","is","found",".","subset","(","str",",","optional",")",":","subset","of","the","dataset","to","use",".","Options",":","[","``","``","train","''","``",",","``","``","valid","''","``",",","``","``","test","''","``","]",".","(","Default",":","``","``","train","''","``",")"]
code: def __init__(self, root: Union[str, Path], subset: str = "train"):
      if subset not in ["train", "valid", "test"]:
          raise ValueError(
              "`subset` must be one of ['train', 'valid', 'test']"
          )
  
      root = os.fspath(root)
      self._path = os.path.join(root, "fluent_speech_commands_dataset")
  
      if not os.path.isdir(self._path):
          raise RuntimeError("Dataset not found.")
  
      subset_path = os.path.join(
          self._path, "data", f"{subset}_data.csv"
      )
      with open(subset_path) as subset_csv:
          subset_reader = csv.reader(subset_csv)
          data = list(subset_reader)
  
      self.header = data[0]
      self.data = data[1:]
  
file_path: audio/src/torchaudio/datasets/fluentcommands.py
imports: import csv
  import os
  from pathlib import Path
  from typing import Tuple, Union
  from torch import Tensor
  from torch.utils.data import Dataset
  from torchaudio.datasets.utils import _load_waveform
label_desc: Use image node_id 1 to create a new FluentSpeechCommands object from inherited base classes: Dataset with example: obj = FluentSpeechCommands(root, subset)