class_docstr: Class that processes a manifest 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.
      parser: Str for a language specific preprocessor or a callable.
      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.
      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.
      pad_id: Id of pad symbol. Defaults to 0.
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code: def __init__(
      self,
      manifest_filepath: str,
      parser: Union[str, Callable],
      max_duration: Optional[float] = None,
      min_duration: Optional[float] = None,
      max_utts: int = 0,
      bos_id: Optional[int] = None,
      eos_id: Optional[int] = None,
      pad_id: int = 0,
      index_by_file_id: bool = False,
  ):
      self.parser = parser
  
      self.collection = collections.ASRVideoText(
          manifests_files=manifest_filepath,
          parser=parser,
          min_duration=min_duration,
          max_duration=max_duration,
          max_number=max_utts,
          index_by_file_id=index_by_file_id,
      )
  
      self.eos_id = eos_id
      self.bos_id = bos_id
      self.pad_id = pad_id
  
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 1 to create a new VSRManifestProcessor object  with example: obj = VSRManifestProcessor(manifest_filepath, parser, max_duration, min_duration, max_utts, bos_id, eos_id, pad_id, index_by_file_id)