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 batch of waveforms.
  
  The waveforms should already be in the model's desired format.
  You can call:
  ``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
  to get a correctly converted signal in most cases.
  
  Arguments
  ---------
  wavs : torch.tensor
      Batch of waveforms [batch, time, channels].
  wav_lens : torch.tensor
      Lengths of the waveforms relative to the longest one in the
      batch, tensor of shape [batch]. The longest one should have
      relative length 1.0 and others len(waveform) / max_length.
      Used for ignoring padding.
  batch_id : torch.tensor
      id of each batch (file names etc.)
  
  Returns
  -------
  list of dictionary: List[Dict[List]]
      The emotions and their temporal boundaries.
code: def diarize_batch(self, wavs, wav_lens, batch_id):
      """Get emotion diarization of a batch of waveforms.
  
      The waveforms should already be in the model's desired format.
      You can call:
      ``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
      to get a correctly converted signal in most cases.
  
      Arguments
      ---------
      wavs : torch.tensor
          Batch of waveforms [batch, time, channels].
      wav_lens : torch.tensor
          Lengths of the waveforms relative to the longest one in the
          batch, tensor of shape [batch]. The longest one should have
          relative length 1.0 and others len(waveform) / max_length.
          Used for ignoring padding.
      batch_id : torch.tensor
          id of each batch (file names etc.)
  
      Returns
      -------
      list of dictionary: List[Dict[List]]
          The emotions and their temporal boundaries.
      """
      outputs = self.encode_batch(wavs, wav_lens)
      averaged_out = self.hparams.avg_pool(outputs)
      outputs = self.mods.output_mlp(averaged_out)
      outputs = self.hparams.log_softmax(outputs)
      score, index = torch.max(outputs, dim=-1)
      preds = self.hparams.label_encoder.decode_torch(index)
      results = self.preds_to_diarization(preds, batch_id)
      return results
  
file_path: speechbrain/speechbrain/inference/diarization.py
imports: import torch
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 4 for calling the Speech_Emotion_Diarization obj's underlying member method code with example usage: obj.diarize_batch(wavs, wav_lens, batch_id) and returns: results