text: # Copyright 2021 Huawei Technologies Co., Ltd
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  """
  export checkpoint file to mindir model
  """
  import json
  import argparse
  import numpy as np
  from mindspore import context, Tensor
  from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
  from src.deepspeech2 import DeepSpeechModel
  from src.config import train_config
  
  parser = argparse.ArgumentParser(description='Export DeepSpeech model to Mindir')
  parser.add_argument('--pre_trained_model_path', type=str, default='', help=' existed checkpoint path')
  args = parser.parse_args()
  
  if __name__ == '__main__':
      config = train_config
      context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=False)
      with open(config.DataConfig.labels_path) as label_file:
          labels = json.load(label_file)
  
      deepspeech_net = DeepSpeechModel(batch_size=1,
                                       rnn_hidden_size=config.ModelConfig.hidden_size,
                                       nb_layers=config.ModelConfig.hidden_layers,
                                       labels=labels,
                                       rnn_type=config.ModelConfig.rnn_type,
                                       audio_conf=config.DataConfig.SpectConfig,
                                       bidirectional=True)
  
      param_dict = load_checkpoint(args.pre_trained_model_path)
      load_param_into_net(deepspeech_net, param_dict)
      print('Successfully loading the pre-trained model')
      # 3500 is the max length in evaluation dataset(LibriSpeech). This is consistent with that in dataset.py
      # The length is fixed to this value because Mindspore does not support dynamic shape currently
      input_np = np.random.uniform(0.0, 1.0, size=[1, 1, 161, 3500]).astype(np.float32)
      length = np.array([15], dtype=np.int32)
      export(deepspeech_net, Tensor(input_np), Tensor(length), file_name="deepspeech2.mindir", file_format='MINDIR')
  