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 __len__(self) -> int:
      return len(self.data)
  
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 3 for calling the FluentSpeechCommands obj's underlying member method code with example usage: obj.__len__() and returns: len