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"]
code: def __init__(self, model_size_or_path, hop_size=160, **kwargs):
      super().__init__(**kwargs)
      self.hop_size = hop_size
      self.frame_size = 1024
      self.sample_rate = 16000
      # Load the crepe model.
      if model_size_or_path in ["full", "large", "small", "tiny"]:
          self.core_model = crepe.core.build_and_load_model(
              model_size_or_path
          )
      else:
          self.core_model = tf.keras.models.load_model(
              model_size_or_path
          )
  
      self.model_size_or_path = model_size_or_path
  
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 1 to create a new PretrainedCREPE object from inherited base classes: tf.keras with example: obj = PretrainedCREPE(model_size_or_path, hop_size)