code: def keyword_recognize(self, wav):
      waveform = self.load_audio(wav)
  
      # fix_length to 1s
      if len(waveform) > self.sr:
          waveform = waveform[: self.sr]
      else:
          waveform = np.pad(waveform, (0, self.sr - len(waveform)))
  
      logits = self(paddle.to_tensor(waveform)).reshape([-1])
      probs = paddle.nn.functional.softmax(logits)
      idx = paddle.argmax(probs)
      return probs[idx].numpy(), self.label_list[idx]
  
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 3 for calling the KWS obj's underlying member method code with example usage: obj.keyword_recognize(wav) and returns: probs,  self