class_docstr: Dataset that loads tensors via a json file containing paths to video
  files, transcripts, and durations (in seconds). Each new line is a
  different sample. Example below:
  {"video_filepath": "/path/to/video.mp4", "text_filepath":
  "/path/to/video.txt", "duration": 23.147}
  ...
  {"video_filepath": "/path/to/video.mp4", "text": "the
  transcription", "offset": 301.75, "duration": 0.82, "utt":
  "utterance_id", "ctm_utt": "en_4156", "side": "A"}
  
  Args:
      manifest_filepath: Path to manifest json as described above. Can
          be comma-separated paths.
      labels: String containing all the possible characters to map to
      int_values (bool): If true, load samples as 32-bit integers. Defauts to False.
      max_duration: If video exceeds this length, do not include in dataset
      min_duration: If video is less than this length, do not include
          in dataset
      max_utts: Limit number of utterances
      blank_index: blank character index, default = -1
      unk_index: unk_character index, default = -1
      normalize: whether to normalize transcript text (default): True
      bos_id: Id of beginning of sequence symbol to append if not None
      eos_id: Id of end of sequence symbol to append if not None
      return_sample_id (bool): whether to return the sample_id as a part of each sample
      channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing.
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code: def __init__(
      self,
      manifest_filepath: str,
      labels: Union[str, List[str]],
      int_values: bool = False,
      max_duration: Optional[float] = None,
      min_duration: Optional[float] = None,
      max_utts: int = 0,
      blank_index: int = -1,
      unk_index: int = -1,
      normalize: bool = True,
      trim: bool = False,
      bos_id: Optional[int] = None,
      eos_id: Optional[int] = None,
      pad_id: int = 0,
      parser: Union[str, Callable] = "en",
      return_sample_id: bool = False,
      channel_selector: Optional[ChannelSelectorType] = None,
  ):
      self.labels = labels
  
      parser = parsers.make_parser(
          labels=labels,
          name=parser,
          unk_id=unk_index,
          blank_id=blank_index,
          do_normalize=normalize,
      )
  
      super().__init__(
          manifest_filepath=manifest_filepath,
          parser=parser,
          int_values=int_values,
          max_duration=max_duration,
          min_duration=min_duration,
          max_utts=max_utts,
          trim=trim,
          bos_id=bos_id,
          eos_id=eos_id,
          pad_id=pad_id,
          return_sample_id=return_sample_id,
          channel_selector=channel_selector,
      )
  
imports: import os
  from typing import Callable, Dict, Iterable, List, Optional, Tuple, 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.utils.audio_utils import ChannelSelectorType
  from nemo.collections.common import tokenizers
  from nemo.collections.common.parts.preprocessing import collections, parsers
  from nemo.collections.multimodal.speech_cv.parts.preprocessing.features import VideoFeaturizer
  from nemo.core.classes import Dataset, IterableDataset
  from nemo.core.neural_types import *
  from nemo.utils import logging
label_desc: Use image node_id 2 to create a new VideoToCharDataset object from inherited base classes: _VideoTextDataset with example: obj = VideoToCharDataset(manifest_filepath, labels, int_values, max_duration, min_duration, max_utts, blank_index, unk_index, normalize, trim, bos_id, eos_id, pad_id, parser, return_sample_id, channel_selector)