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: Chop audio into overlapping frames, and push to batch dimension.
docstr_tok: ["Chop","audio","into","overlapping","frames",",","and","push","to","batch","dimension","."]
code: def batch_frames(self, audio):
      """Chop audio into overlapping frames, and push to batch dimension."""
      if audio.shape[-1] == self.frame_size:
          return audio
      else:
          frames = tf.signal.frame(
              audio, self.frame_size, self.hop_size
          )
          frames = tf.reshape(frames, [-1, self.frame_size])
          return frames
  
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 3 for calling the PretrainedCREPE obj's underlying member method code with example usage: obj.batch_frames(audio) and returns: audio, frames