code: def read_audio(waveforms_obj):
      """General audio loading, based on a custom notation.
  
      Expected use case is in conjunction with Datasets
      specified by JSON.
  
      The parameter may just be a path to a file:
      `read_audio("/path/to/wav1.wav")`
  
      Alternatively, you can specify more options in a dict, e.g.:
      ```
      # load a file from sample 8000 through 15999
      read_audio({
          "file": "/path/to/wav2.wav",
          "start": 8000,
          "stop": 16000
      })
      ```
  
      Which codecs are supported depends on your torchaudio backend.
      Refer to `torchaudio.load` documentation for further details.
  
      Arguments
      ----------
      waveforms_obj : str, dict
          Path to audio or dict with the desired configuration.
  
          Keys for the dict variant:
          - `"file"` (str): Path to the audio file.
          - `"start"` (int, optional): The first sample to load.
          If unspecified, load from the very first frame.
          - `"stop"` (int, optional): The last sample to load (exclusive).
          If unspecified or equal to start, load from `start` to the end.
          Will not fail if `stop` is past the sample count of the file and will
          return less frames.
  
      Returns
      -------
      torch.Tensor
          1-channel: audio tensor with shape: `(samples, )`.
          >=2-channels: audio tensor with shape: `(samples, channels)`.
  
      Example
      -------
      >>> dummywav = torch.rand(16000)
      >>> import os
      >>> tmpfile = str(getfixture('tmpdir') / "wave.wav")
      >>> write_audio(tmpfile, dummywav, 16000)
      >>> asr_example = { "wav": tmpfile, "spk_id": "foo", "words": "foo bar"}
      >>> loaded = read_audio(asr_example["wav"])
      >>> loaded.allclose(dummywav.squeeze(0),atol=1e-4) # replace with eq with sox_io backend
      True
      """
      if isinstance(waveforms_obj, str):
          audio, _ = torchaudio.load(waveforms_obj)
      else:
          path = waveforms_obj["file"]
          start = waveforms_obj.get("start", 0)
          # To match past SB behavior, `start == stop` or omitted `stop` means to
          # load all frames from `start` to the file end.
          stop = waveforms_obj.get("stop", start)
  
          if start < 0:
              raise ValueError(
                  f"Invalid sample range (start < 0): {start}..{stop}!"
              )
  
          if stop < start:
              # Could occur if the user tried one of two things:
              # - specify a negative value as an attempt to index from the end;
              # - specify -1 as an attempt to load up to the last sample.
              raise ValueError(
                  f"Invalid sample range (stop < start): {start}..{stop}!\n"
                  'Hint: Omit "stop" if you want to read to the end of file.'
              )
  
          # Requested to load until a specific frame?
          if start != stop:
              num_frames = stop - start
              audio, fs = torchaudio.load(
                  path, num_frames=num_frames, frame_offset=start
              )
          else:
              # Load to the end.
              audio, fs = torchaudio.load(path, frame_offset=start)
  
      audio = audio.transpose(0, 1)
      return audio.squeeze(1)
  
imports: import os
  import torch
  import logging
  import numpy
  import pickle
  import hashlib
  import csv
  import time
  import torchaudio
  import json
  import re
  from speechbrain.utils.torch_audio_backend import check_torchaudio_backend
label_desc: Use image node_id 5 for calling a global function with example usage: read_audio(waveforms_obj) and returns: audio