code: def _get_wav_from_filepath(self, audio_filepath):
      features = AudioSegment.segment_from_file(
          audio_filepath,
          target_sr=self.sample_rate,
          n_segments=-1,
          trim=False,
      )
      audio_samples = features.samples
      audio, audio_length = (
          torch.tensor(audio_samples),
          torch.tensor(audio_samples.shape[0]).long(),
      )
  
      # pad audio to a multiple of self.pad_multiple
      if audio.shape[0] % self.pad_multiple != 0:
          audio = torch.cat(
              [
                  audio,
                  torch.zeros(
                      self.pad_multiple
                      - audio.shape[0] % self.pad_multiple,
                      dtype=torch.float,
                  ),
              ]
          )
          audio_length = torch.tensor(audio.shape[0]).long()
  
      return audio, audio_length
  
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 3 for calling the AudioDataset obj's underlying member method code with example usage: obj._get_wav_from_filepath(audio_filepath) and returns: audio,  audio_length