class_docstr: A ready-to-use model for speaker recognition. It can be used to
  perform speaker verification with verify_batch().
  
  ```
  Example
  -------
  >>> import torchaudio
  >>> from speechbrain.inference.speaker import SpeakerRecognition
  >>> # Model is downloaded from the speechbrain HuggingFace repo
  >>> tmpdir = getfixture("tmpdir")
  >>> verification = SpeakerRecognition.from_hparams(
  ...     source="speechbrain/spkrec-ecapa-voxceleb",
  ...     savedir=tmpdir,
  ... )
  
  >>> # Perform verification
  >>> signal, fs = torchaudio.load("tests/samples/single-mic/example1.wav")
  >>> signal2, fs = torchaudio.load("tests/samples/single-mic/example2.flac")
  >>> score, prediction = verification.verify_batch(signal, signal2)
docstr: Speaker verification with cosine distance
  
  Returns the score and the decision (0 different speakers,
  1 same speakers).
  
  Returns
  -------
  score
      The score associated to the binary verification output
      (cosine distance).
  prediction
      The prediction is 1 if the two signals in input are from the same
      speaker and 0 otherwise.
docstr_tok: ["Speaker","verification","with","cosine","distance","Returns","the","score","and","the","decision","(","0","different","speakers",",","1","same","speakers",")",".","Returns","--","--","--","-","score","The","score","associated","to","the","binary","verification","output","(","cosine","distance",")",".","prediction","The","prediction","is","1","if","the","two","signals","in","input","are","from","the","same","speaker","and","0","otherwise","."]
code: def verify_files(self, path_x, path_y, **kwargs):
      """Speaker verification with cosine distance
  
      Returns the score and the decision (0 different speakers,
      1 same speakers).
  
      Returns
      -------
      score
          The score associated to the binary verification output
          (cosine distance).
      prediction
          The prediction is 1 if the two signals in input are from the same
          speaker and 0 otherwise.
      """
      waveform_x = self.load_audio(path_x, **kwargs)
      waveform_y = self.load_audio(path_y, **kwargs)
      # Fake batches:
      batch_x = waveform_x.unsqueeze(0)
      batch_y = waveform_y.unsqueeze(0)
      # Verify:
      score, decision = self.verify_batch(batch_x, batch_y)
      # Squeeze:
      return score[0], decision[0]
  
imports: import torch
  from speechbrain.inference.classifiers import EncoderClassifier
label_desc: Use image node_id 3 for calling the SpeakerRecognition obj's underlying member method code with example usage: obj.verify_files(path_x, path_y) and returns: score,  decision