code: def __init__(self):
      super(KWS, self).__init__()
      ckpt_path = os.path.join(
          self.directory, "assets", "model.pdparams"
      )
      label_path = os.path.join(self.directory, "assets", "label.txt")
  
      self.label_list = []
      with open(label_path, "r") as f:
          for l in f:
              self.label_list.append(l.strip())
  
      self.sr = 16000
      model_conf = {
          "input_res": [40, 98],
          "patch_res": [40, 1],
          "num_classes": 35,
          "channels": 1,
          "dim": 64,
          "depth": 12,
          "pre_norm": False,
          "prob_survival": 0.9,
      }
      self.model = KW_MLP(**model_conf)
      self.model.set_state_dict(paddle.load(ckpt_path))
      self.model.eval()
  
file_path: PaddleHub/modules/audio/keyword_spotting/kwmlp_speech_commands/module.py
imports: import os
  import numpy
  import paddle
  import paddleaudio
  from .feature import compute_mfcc
  from .kwmlp import KW_MLP
  from paddlehub.module.module import moduleinfo
  from paddlehub.utils.log import logger
label_desc: Use image node_id 1 to create a new KWS object from inherited base classes: paddle.nn with example: obj = KWS()