code: def __process_data(
      audio_root,
      transcript_root,
      dst_root,
      min_slice_duration,
      file_count,
      keep_low_conf,
      rem_noises,
      emojify,
  ):
      """
      Converts Fisher wav files to numpy arrays, segments audio and transcripts.
      Args:
          audio_root: source directory with the wav files
          transcript_root: source directory with the transcript files
              (can be the same as audio_root)
          dst_root: where the processed and segmented files will be stored
          min_slice_duration: minimum number of seconds for a slice of output
          file_count: total number of files processed so far
          keep_low_conf: whether or not to keep low confidence transcriptions
          rem_noises: whether to remove noise symbols
          emojify: whether to convert noise symbols to emoji, lower precedence
      Assumes:
          1. There is exactly one transcripts directory in data_folder
          2. Audio files are all: <audio_root>/audio-wav/fe_03_xxxxx.wav
      """
      transcript_list = glob.glob(
          os.path.join(
              transcript_root,
              "fe_03_p*_tran*",
              "data",
              "trans",
              "*",
              "*.txt",
          )
      )
      print("Found {} transcripts.".format(len(transcript_list)))
  
      count = file_count
  
      # Grab audio file associated with each transcript, and slice
      for trans_path in tqdm(
          transcript_list, desc="Matching and segmenting"
      ):
          file_id, _ = os.path.splitext(os.path.basename(trans_path))
          audio_path = os.path.join(
              audio_root, "audio_wav", file_id + ".wav"
          )
  
          sample_rate, audio_data = wavfile.read(audio_path)
  
          # Create a set of segments (a block) for each file
          __process_one_file(
              trans_path,
              sample_rate,
              audio_data,
              file_id,
              dst_root,
              min_slice_duration,
              count,
              keep_low_conf,
              rem_noises,
              emojify,
          )
          count += 1
  
      return count
  
file_path: NeMo/scripts/dataset_processing/process_fisher_data.py
imports: import argparse
  import glob
  import json
  import os
  import re
  from math import ceil, floor
  import numpy
  import scipy.io.wavfile
  from tqdm import tqdm
label_desc: Use image node_id 6 for calling a global function with example usage: __process_data(audio_root, transcript_root, dst_root, min_slice_duration, file_count, keep_low_conf, rem_noises, emojify) and returns: count