class_docstr: Trim leading and trailing silence from an audio signal using librosa.effects.trim
docstr: :param top_db: The threshold (in decibels) below reference to consider as silence
  :param p: The probability of applying this transform
docstr_tok: [":","param","top_db",":","The","threshold","(","in","decibels",")","below","reference","to","consider","as","silence",":","param","p",":","The","probability","of","applying","this","transform"]
code: def __init__(self, top_db: float = 30.0, p: float = 0.5):
      """
      :param top_db: The threshold (in decibels) below reference to consider as silence
      :param p: The probability of applying this transform
      """
      super().__init__(p)
      self.top_db = top_db
  
file_path: audiomentations/audiomentations/augmentations/trim.py
imports: import librosa
  import numpy
  from numpy.typing import NDArray
  from audiomentations.core.transforms_interface import BaseWaveformTransform
label_desc: Use image node_id 1 to create a new Trim object from inherited base classes: BaseWaveformTransform with example: obj = Trim(top_db, p)