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- # Copyright 2022 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 eval_config
-
- parser = argparse.ArgumentParser(description='Export DeepSpeech model to Mindir')
- parser.add_argument('--pre_trained_model_path', type=str, default='DeepSpeech-12_508.ckpt', help=' checkpoint path')
- args = parser.parse_args()
-
- if __name__ == '__main__':
- config = eval_config
- context.set_context(device_id=6, mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
- with open(config.DataConfig.labels_path) as label_file:
- labels = json.load(label_file)
-
- deepspeech_net = DeepSpeechModel(batch_size=config.DataConfig.batch_size,
- 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=[config.DataConfig.batch_size, 1, 161, 4000]).astype(np.float32)
- length = np.random.randint(1, 10, size=[config.DataConfig.batch_size]).astype(np.int32)
- print(length)
- #length = np.array([15], dtype=np.int32)
- export(deepspeech_net, Tensor(input_np), Tensor(length),
- file_name="deepspeech2_"+str(config.DataConfig.batch_size)+".mindir", file_format='MINDIR')
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