You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
52 lines
2.5 KiB
52 lines
2.5 KiB
# 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')
|