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: Performs speaker verification with cosine distance.
  
  It returns the score and the decision (0 different speakers,
  1 same speakers).
  
  Arguments
  ---------
  wavs1 : Torch.Tensor
          Tensor containing the speech waveform1 (batch, time).
          Make sure the sample rate is fs=16000 Hz.
  wavs2 : Torch.Tensor
          Tensor containing the speech waveform2 (batch, time).
          Make sure the sample rate is fs=16000 Hz.
  wav1_lens: Torch.Tensor
          Tensor containing the relative length for each sentence
          in the length (e.g., [0.8 0.6 1.0])
  wav2_lens: Torch.Tensor
          Tensor containing the relative length for each sentence
          in the length (e.g., [0.8 0.6 1.0])
  threshold: Float
          Threshold applied to the cosine distance to decide if the
          speaker is different (0) or the same (1).
  
  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: ["Performs","speaker","verification","with","cosine","distance",".","It","returns","the","score","and","the","decision","(","0","different","speakers",",","1","same","speakers",")",".","Arguments","--","--","--","--","-","wavs1",":","Torch.Tensor","Tensor","containing","the","speech","waveform1","(","batch",",","time",")",".","Make","sure","the","sample","rate","is","fs=16000","Hz",".","wavs2",":","Torch.Tensor","Tensor","containing","the","speech","waveform2","(","batch",",","time",")",".","Make","sure","the","sample","rate","is","fs=16000","Hz",".","wav1_lens",":","Torch.Tensor","Tensor","containing","the","relative","length","for","each","sentence","in","the","length","(","e.g.",",","[","0.8","0.6","1.0","]",")","wav2_lens",":","Torch.Tensor","Tensor","containing","the","relative","length","for","each","sentence","in","the","length","(","e.g.",",","[","0.8","0.6","1.0","]",")","threshold",":","Float","Threshold","applied","to","the","cosine","distance","to","decide","if","the","speaker","is","different","(","0",")","or","the","same","(","1",")",".","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_batch(
      self, wavs1, wavs2, wav1_lens=None, wav2_lens=None, threshold=0.25
  ):
      """Performs speaker verification with cosine distance.
  
      It returns the score and the decision (0 different speakers,
      1 same speakers).
  
      Arguments
      ---------
      wavs1 : Torch.Tensor
              Tensor containing the speech waveform1 (batch, time).
              Make sure the sample rate is fs=16000 Hz.
      wavs2 : Torch.Tensor
              Tensor containing the speech waveform2 (batch, time).
              Make sure the sample rate is fs=16000 Hz.
      wav1_lens: Torch.Tensor
              Tensor containing the relative length for each sentence
              in the length (e.g., [0.8 0.6 1.0])
      wav2_lens: Torch.Tensor
              Tensor containing the relative length for each sentence
              in the length (e.g., [0.8 0.6 1.0])
      threshold: Float
              Threshold applied to the cosine distance to decide if the
              speaker is different (0) or the same (1).
  
      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.
      """
      emb1 = self.encode_batch(wavs1, wav1_lens, normalize=False)
      emb2 = self.encode_batch(wavs2, wav2_lens, normalize=False)
      score = self.similarity(emb1, emb2)
      return score, score > threshold
  
imports: import torch
  from speechbrain.inference.classifiers import EncoderClassifier
label_desc: Use image node_id 2 for calling the SpeakerRecognition obj's underlying member method code with example usage: obj.verify_batch(wavs1, wavs2, wav1_lens, wav2_lens, threshold) and returns: score,  unknown