class_docstr: Dataset that loads tensors via a json file containing paths to audio
  files, command class, and durations (in seconds). Each new line is a
  different sample. Example below:
  {"audio_filepath": "/path/to/audio_wav_0.wav", "duration": time_in_sec_0, "label":         target_label_0, "offset": offset_in_sec_0}
  ...
  {"audio_filepath": "/path/to/audio_wav_n.wav", "duration": time_in_sec_n, "label":         target_label_n, "offset": offset_in_sec_n}
  Args:
      manifest_filepath (Union[str, List[str]]): Path to manifest json as described above. Can
          be comma-separated paths.
      labels (Optional[list]): String containing all the possible labels to map to
          if None then automatically picks from ASRSpeechLabel collection.
      featurizer: Initialized featurizer class that converts paths of
          audio to feature tensors
      max_duration: If audio exceeds this length, do not include in dataset
      min_duration: If audio is less than this length, do not include
          in dataset
      trim: Boolean flag whether to trim the audio
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code: def _collate_fn(self, batch):
      return _speech_collate_fn(batch, pad_id=0)
  
imports: import io
  import os
  from typing import Dict, List, Optional, Union
  import torch
  import webdataset
  from nemo.collections.asr.data.audio_to_text import cache_datastore_manifests, expand_sharded_filepaths
  from nemo.collections.asr.parts.preprocessing.features import WaveformFeaturizer
  from nemo.collections.asr.parts.preprocessing.segment import available_formats
  from nemo.collections.common.parts.preprocessing import collections
  from nemo.core.classes import Dataset, IterableDataset
  from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, NeuralType, RegressionValuesType
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling the AudioToClassificationLabelDataset obj's underlying member method code with example usage: obj._collate_fn(batch) and returns: _speech_collate_fn