code: def __init__(self):
      super(LanguageIdentification, 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_size": 80,
          "channels": [1024, 1024, 1024, 1024, 3072],
          "kernel_sizes": [5, 3, 3, 3, 1],
          "dilations": [1, 2, 3, 4, 1],
          "attention_channels": 128,
          "lin_neurons": 192,
      }
      self.model = Classifier(
          backbone=ECAPA_TDNN(**model_conf),
          num_class=45,
      )
      self.model.set_state_dict(paddle.load(ckpt_path))
      self.model.eval()
  
file_path: PaddleHub/modules/audio/language_identification/ecapa_tdnn_common_language/module.py
imports: import os
  import re
  from typing import List
  from typing import Union
  import numpy
  import paddle
  import paddleaudio
  from .ecapa_tdnn import Classifier
  from .ecapa_tdnn import ECAPA_TDNN
  from .feature import compute_log_fbank
  from .feature import normalize
  from paddlehub.module.module import moduleinfo
  from paddlehub.utils.log import logger
label_desc: Use image node_id 1 to create a new LanguageIdentification object from inherited base classes: paddle.nn with example: obj = LanguageIdentification()