class_docstr: Base object for computing metrics on generated audio samples.
class_docstr_tok: ["Base","object","for","computing","metrics","on","generated","audio","samples","."]
docstr: Constructor.
  
  Args:
    sample_rate: Audio sample rate.
    frame_rate: Feature frame rate.
    name: Name to be printed when logging and scope for summaries.
docstr_tok: ["Constructor",".","Args",":","sample_rate",":","Audio","sample","rate",".","frame_rate",":","Feature","frame","rate",".","name",":","Name","to","be","printed","when","logging","and","scope","for","summaries","."]
code: def __init__(self, sample_rate, frame_rate, name):
      """Constructor.
  
      Args:
        sample_rate: Audio sample rate.
        frame_rate: Feature frame rate.
        name: Name to be printed when logging and scope for summaries.
      """
      self._sample_rate = sample_rate
      self._frame_rate = frame_rate
      self._name = name
  
imports: import dataclasses
  from absl import logging
  import ddsp
  import librosa
  import mir_eval
  import note_seq
  from note_seq import sequences_lib
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new BaseMetrics object from inherited base classes: object with example: obj = BaseMetrics(sample_rate, frame_rate, name)