code: def filter_text(
      transcription: str,
      dataset="train",
      acronyms=None,
      acronyms_noi=None,
  ):
      """
      This function takes a string representing a sentence in one
      of the datasets and cleans it using various regular expressions.
      The types of regular expressions applied depend on the dataset.
  
      Parameters
      ----------
      transcription : str
          A transcribed sentence
      dataset : str
          Either "train", "eval2000", or "fisher" depending on the type
          of data you want to clean.
      acronyms : dict
          Dictionary mapping acronyms to Fisher convention (only relevant for swbd1 training data)
      acronyms_noi : dict
          Dictionary mapping acronyms to Fisher convention without I (only relevant for swbd1 training data)
  
      Returns
      -------
      A string containing the cleaned sentence.
  
      """
      dataset = dataset.strip().lower()
  
      if dataset == "train":
          # This is similar to what swbd1_data_prep.sh and swbd1_map_words.pl does.
          transcription = re.sub(
              r"\[SILENCE\]", "", transcription, flags=re.IGNORECASE
          )
          transcription = re.sub(r"<.*?>", "", transcription)
          transcription = match_swbd1(transcription.strip())
  
          transcription = re.sub(r"\s\s+", " ", transcription)
  
          # Convert acronyms to Fisher convention
          if len(transcription) > 0:
              transcription = map_acronyms(
                  acronyms, acronyms_noi, transcription
              )
  
          # Split acronyms written as u._c._l._a._ into single characters (e.g. u c l a)
          transcription = remove_acronym_symbols(transcription)
          transcription = transcription.upper().strip()
  
      elif dataset in ["eval2000", "hub5", "test"]:
          # This is similar to what eval2000_data_prep.sh does.
          transcription = match_eval2000(transcription.strip())
      elif dataset == "fisher":
          # This is similar to what fisher_data_prep.sh does.
          transcription = match_fisher(transcription.strip())
      else:
          raise NameError(
              f"Invalid dataset descriptor '{dataset}' supplied."
          )
  
      # Remove redundant whitespaces
      transcription = re.sub(r"\s\s+", " ", transcription)
      return transcription.strip()
  
imports: import re
  import csv
  import logging
  import os
  from collections import defaultdict
  from speechbrain.dataio.dataio import merge_csvs
  from speechbrain.utils.data_utils import download_file, get_all_files
label_desc: Use image node_id 7 for calling a global function with example usage: filter_text(transcription, dataset, acronyms, acronyms_noi) and returns: transcription