code: def process_meta_data(path):
      meta_data = {}
  
      # load meta data
      with open(path, "r") as f:
          data = csv.reader(f, delimiter="|")
          for row in data:
              frames = int(row[2])
              utt = row[3]
              audio_len = get_audio_seconds(frames)
              char_count = len(utt)
              if not meta_data.get(char_count):
                  meta_data[char_count] = {"data": []}
  
              meta_data[char_count]["data"].append(
                  {
                      "utt": utt,
                      "frames": frames,
                      "audio_len": audio_len,
                      "row": "{}|{}|{}|{}".format(
                          row[0], row[1], row[2], row[3]
                      ),
                  }
              )
  
      meta_data = append_data_statistics(meta_data)
  
      return meta_data
  
file_path: TTS/notebooks/dataset_analysis/analyze.py
imports: import matplotlib.pyplot
  from statistics import stdev, mode, mean, median
  from statistics import StatisticsError
  import argparse
  import os
  import csv
  import seaborn
  import random
  from text.cmudict import CMUDict
label_desc: Use image node_id 3 for calling a global function with example usage: process_meta_data(path) and returns: meta_data