code: def __process_one_file(
      trans_path,
      sample_rate,
      audio_data,
      file_id,
      dst_root,
      min_slice_duration,
      file_count,
      keep_low_conf,
      rem_noises,
      emojify,
  ):
      """
      Creates one block of audio slices and their corresponding transcripts.
      Args:
          trans_path: filepath to transcript
          sample_rate: sample rate of the audio
          audio_data: numpy array of shape [samples, channels]
          file_id: identifying label, e.g. 'fe_03_01102'
          dst_root: path to destination directory
          min_slice_duration: min number of seconds for an audio slice
          file_count: total number of files processed so far
          keep_low_conf: keep utterances with low-confidence transcripts
          rem_noises: remove noise symbols
          emojify: convert noise symbols into emoji characters
      """
      count = 0
  
      with open(trans_path, encoding="utf-8") as fin:
          fin.readline()  # Comment w/ corresponding sph filename
          fin.readline()  # Comment about transcriber
  
          transcript_buffers = ["", ""]  # [A buffer, B buffer]
          audio_buffers = [[], []]
          buffer_durations = [0.0, 0.0]
  
          for line in fin:
              t_start, t_end, idx, content = __process_utterance(
                  file_id,
                  trans_path,
                  line,
                  keep_low_conf,
                  rem_noises,
                  emojify,
              )
  
              if content is None or not content:
                  continue
  
              duration = t_end - t_start
  
              # Append utterance to buffer
              transcript_buffers[idx] += content
              audio_buffers[idx].append(
                  audio_data[
                      floor(t_start * sample_rate) : ceil(
                          t_end * sample_rate
                      ),
                      idx,
                  ]
              )
              buffer_durations[idx] += duration
  
              if buffer_durations[idx] < min_slice_duration:
                  transcript_buffers[idx] += " "
              else:
                  # Write out segment and transcript
                  count += 1
                  __write_sample(
                      dst_root,
                      file_id,
                      count,
                      file_count,
                      sample_rate,
                      np.concatenate(audio_buffers[idx], axis=0),
                      buffer_durations[idx],
                      transcript_buffers[idx],
                  )
  
                  # Clear buffers
                  transcript_buffers[idx] = ""
                  audio_buffers[idx] = []
                  buffer_durations[idx] = 0.0
  
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 4 for calling a global function with example usage: __process_one_file(trans_path, sample_rate, audio_data, file_id, dst_root, min_slice_duration, file_count, keep_low_conf, rem_noises, emojify) without return types