class_docstr: A wrapper around a pretrained CREPE model, for pitch prediction.
  
  Enables predicting pitch and confidence entirely in TF for running in batch
  on accelerators. For [full,large,small,tiny] crepe models, reads h5 models
  from installed pip package. Other saved models
class_docstr_tok: ["A","wrapper","around","a","pretrained","CREPE","model",",","for","pitch","prediction",".","Enables","predicting","pitch","and","confidence","entirely","in","TF","for","running","in","batch","on","accelerators",".","For","[","full",",","large",",","small",",","tiny","]","crepe","models",",","reads","h5","models","from","installed","pip","package",".","Other","saved","models"]
docstr: Adapted from original CREPE viterbi decdoding, but in TF.
docstr_tok: ["Adapted","from","original","CREPE","viterbi","decdoding",",","but","in","TF","."]
code: def viterbi_decode(self, acts):
      """Adapted from original CREPE viterbi decdoding, but in TF."""
      num_steps = acts.shape[1]
      hmm = self.create_hmm(num_steps)
      centers = hmm.posterior_mode(acts)
      return centers
  
imports: import crepe
  from ddsp import core
  from ddsp.core import safe_log
  from ddsp.core import tf_float32
  import gin
  import librosa
  import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
label_desc: Use image node_id 7 for calling the PretrainedCREPE obj's underlying member method code with example usage: obj.viterbi_decode(acts) and returns: centers