class_docstr: Mask a set of frequencies in a spectrogram, à la Google AI SpecAugment. This type of data
  augmentation has proved to make speech recognition models more robust.
  
  The masked frequencies can be replaced with either the mean of the original values or a
  given constant (e.g. zero).
code: def __init__(
      self,
      min_mask_fraction: float = 0.03,
      max_mask_fraction: float = 0.25,
      fill_mode: str = "constant",
      fill_constant: float = 0.0,
      p: float = 0.5,
  ):
      super().__init__(p)
      self.min_mask_fraction = min_mask_fraction
      self.max_mask_fraction = max_mask_fraction
      assert fill_mode in ("mean", "constant")
      self.fill_mode = fill_mode
      self.fill_constant = fill_constant
  
file_path: audiomentations/audiomentations/spec_augmentations/spec_frequency_mask.py
imports: import random
  import numpy
  from audiomentations.core.transforms_interface import BaseSpectrogramTransform
label_desc: Use image node_id 1 to create a new SpecFrequencyMask object from inherited base classes: BaseSpectrogramTransform with example: obj = SpecFrequencyMask(min_mask_fraction, max_mask_fraction, fill_mode, fill_constant, p)