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: Returns True if segments are overlapping.
  
  Arguments
  ---------
  end1 : float
      End time of the first segment.
  start2 : float
      Start time of the second segment.
  
  Returns
  -------
  overlapped : bool
      True of segments overlapped else False.
  
  Example
  -------
  >>> from speechbrain.processing import diarization as diar
  >>> diar.is_overlapped(5.5, 3.4)
  True
  >>> diar.is_overlapped(5.5, 6.4)
  False
code: def is_overlapped(self, end1, start2):
      """Returns True if segments are overlapping.
  
      Arguments
      ---------
      end1 : float
          End time of the first segment.
      start2 : float
          Start time of the second segment.
  
      Returns
      -------
      overlapped : bool
          True of segments overlapped else False.
  
      Example
      -------
      >>> from speechbrain.processing import diarization as diar
      >>> diar.is_overlapped(5.5, 3.4)
      True
      >>> diar.is_overlapped(5.5, 6.4)
      False
      """
  
      if start2 > end1:
          return False
      else:
          return True
  
file_path: speechbrain/speechbrain/inference/diarization.py
imports: import torch
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 7 for calling the Speech_Emotion_Diarization obj's underlying member method code with example usage: obj.is_overlapped(end1, start2) and returns: False, True