code: def read_audio_multichannel(waveforms_obj):
      """General audio loading, based on a custom notation.
  
      Expected use case is in conjunction with Datasets
      specified by JSON.
  
      The custom notation:
  
      The annotation can be just a path to a file:
      "/path/to/wav1.wav"
  
      Multiple (possibly multi-channel) files can be specified, as long as they
      have the same length:
      {"files": [
          "/path/to/wav1.wav",
          "/path/to/wav2.wav"
          ]
      }
  
      Or you can specify a single file more succinctly:
      {"files": "/path/to/wav2.wav"}
  
      Offset number samples and stop number samples also can be specified to read
      only a segment within the files.
      {"files": [
          "/path/to/wav1.wav",
          "/path/to/wav2.wav"
          ]
      "start": 8000
      "stop": 16000
      }
  
      Arguments
      ----------
      waveforms_obj : str, dict
          Audio reading annotation, see above for format.
  
      Returns
      -------
      torch.Tensor
          Audio tensor with shape: (samples, ).
  
      Example
      -------
      >>> dummywav = torch.rand(16000, 2)
      >>> 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)
          return audio.transpose(0, 1)
  
      files = waveforms_obj["files"]
      if not isinstance(files, list):
          files = [files]
  
      waveforms = []
      start = waveforms_obj.get("start", 0)
      # Default stop to start -> if not specified, num_frames becomes 0,
      # which is the torchaudio default
      stop = waveforms_obj.get("stop", start - 1)
      num_frames = stop - start
      for f in files:
          audio, fs = torchaudio.load(
              f, num_frames=num_frames, frame_offset=start
          )
          waveforms.append(audio)
  
      out = torch.cat(waveforms, 0)
      return out.transpose(0, 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 6 for calling a global function with example usage: read_audio_multichannel(waveforms_obj) and returns: out, audio