class_docstr: A ready-to-use SED interface (audio -> emotions and their durations)
  
  Arguments
  ---------
  hparams
      Hyperparameters (from HyperPyYAML)
  
  Example
  -------
  >>> from speechbrain.inference.diarization import Speech_Emotion_Diarization
  >>> tmpdir = getfixture("tmpdir")
  >>> sed_model = Speech_Emotion_Diarization.from_hparams(source="speechbrain/emotion-diarization-wavlm-large", savedir=tmpdir,) # doctest: +SKIP
  >>> sed_model.diarize_file("speechbrain/emotion-diarization-wavlm-large/example.wav") # doctest: +SKIP
docstr: Get emotion diarization of a spoken utterance.
  
  Arguments
  ---------
  path : str
      Path to audio file which to diarize.
  
  Returns
  -------
  list of dictionary: List[Dict[List]]
      The emotions and their temporal boundaries.
code: def diarize_file(self, path):
      """Get emotion diarization of a spoken utterance.
  
      Arguments
      ---------
      path : str
          Path to audio file which to diarize.
  
      Returns
      -------
      list of dictionary: List[Dict[List]]
          The emotions and their temporal boundaries.
      """
      waveform = self.load_audio(path)
      # Fake a batch:
      batch = waveform.unsqueeze(0)
      rel_length = torch.tensor([1.0])
      frame_class = self.diarize_batch(batch, rel_length, [path])
      return frame_class
  
file_path: speechbrain/speechbrain/inference/diarization.py
imports: import torch
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 2 for calling the Speech_Emotion_Diarization obj's underlying member method code with example usage: obj.diarize_file(path) and returns: frame_class