code: def segment_wav(
      wav, segment_length, segment_hop_size, min_segment_length
  ):
      if len(wav) < segment_length:
          pad = torch.zeros(segment_length - len(wav))
          segment = torch.cat([wav, pad])
          return [segment]
      else:
          si = 0
          segments = []
          while si < len(wav) - min_segment_length:
              segment = wav[si : si + segment_length]
              if len(segment) < segment_length:
                  pad = torch.zeros(segment_length - len(segment))
                  segment = torch.cat([segment, pad])
              segments.append(segment)
              si += segment_hop_size
          return segments
  
imports: import argparse
  import json
  import os
  import time
  from multiprocessing import Pool
  from pathlib import Path
  import hydra.utils
  import librosa
  import numpy
  import torch
  from omegaconf import open_dict
  from tqdm import tqdm
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
  from nemo.collections.tts.models import ssl_tts
  from nemo.collections.tts.parts.utils.tts_dataset_utils import get_base_dir
  from nemo.core.classes import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: segment_wav(wav, segment_length, segment_hop_size, min_segment_length) and returns: list, segments