class_docstr: Functor class of common_collate_fn()
class_docstr_tok: ["Functor","class","of","common_collate_fn","(",")"]
code: def __init__(
      self,
      float_pad_value: Union[float, int] = 0.0,
      int_pad_value: int = -32768,
      label_downsampling: int = 1,
      pad: bool = False,
      rand_crop: bool = True,
      crop_audio: bool = True,
      not_sequence: Collection[str] = (),
      window_size: float = 25,
      window_shift: float = 20,
      sample_rate: float = 16,
  ):
      assert check_argument_types()
      super().__init__(
          float_pad_value=float_pad_value,
          int_pad_value=int_pad_value,
          not_sequence=not_sequence,
      )
      self.float_pad_value = float_pad_value
      self.int_pad_value = int_pad_value
      self.label_downsampling = label_downsampling
      self.pad = pad
      self.rand_crop = rand_crop
      self.crop_audio = crop_audio
      self.not_sequence = set(not_sequence)
      self.window_size = window_size
      self.window_shift = window_shift
      self.sample_rate = sample_rate
  
file_path: espnet/espnet2/train/collate_fn.py
imports: import math
  from typing import Collection, Dict, List, Tuple, Union
  import numpy
  import torch
  from typeguard import check_argument_types, check_return_type
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 1 to create a new HuBERTCollateFn object from inherited base classes: CommonCollateFn with example: obj = HuBERTCollateFn(float_pad_value, int_pad_value, label_downsampling, pad, rand_crop, crop_audio, not_sequence, window_size, window_shift, sample_rate)