code: def vb_hmm(
      segments,
      in_labels,
      xvectors,
      plda_psi,
      init_smoothing,
      loop_prob,
      fa,
      fb,
  ):
      x = np.array(xvectors)
      dim = x.shape[1]
  
      # Smooth the hard labels obtained from AHC to soft assignments of
      # x-vectors to speakers
      q_init = np.zeros((len(in_labels), np.max(in_labels) + 1))
      q_init[range(len(in_labels)), in_labels] = 1.0
      q_init = softmax(q_init * init_smoothing, axis=1)
  
      # Prepare model for VB-HMM clustering
      ubmWeights = np.array([1.0])
      ubmMeans = np.zeros((1, dim))
      invSigma = np.ones((1, dim))
      V = np.diag(np.sqrt(plda_psi[:dim]))[:, np.newaxis, :]
  
      # Use VB-HMM for x-vector clustering. Instead of i-vector extractor model,
      # we use PLDA
      # => GMM with only 1 component, V derived across-class covariance,
      # and invSigma is inverse
      # within-class covariance (i.e. identity)
      q, _, _ = VB_diarization.VB_diarization(
          x,
          ubmMeans,
          invSigma,
          ubmWeights,
          V,
          pi=None,
          gamma=q_init,
          maxSpeakers=q_init.shape[1],
          maxIters=40,
          epsilon=1e-6,
          loopProb=loop_prob,
          Fa=fa,
          Fb=fb,
      )
  
      labels = np.unique(q.argmax(1), return_inverse=True)[1]
  
      return {seg: label for seg, label in zip(segments, labels)}
  
file_path: espnet/egs/libri_css/asr1/diarization/vb_hmm_xvector.py
imports: import argparse
  import kaldi_io
  import numpy
  import VB_diarization
  from scipy.special import softmax
label_desc: Use image node_id 5 for calling a global function with example usage: vb_hmm(segments, in_labels, xvectors, plda_psi, init_smoothing, loop_prob, fa, fb) and returns: unknown