104 lines
3.0 KiB
Python
104 lines
3.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import random
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from pprint import pprint
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import argparse
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import numpy as np
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import paddle.fluid.dygraph as dg
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from paddle import fluid
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import utils
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from parakeet.utils import io
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from waveflow import WaveFlow
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def add_options_to_parser(parser):
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parser.add_argument(
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'--model',
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type=str,
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default='waveflow',
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help="general name of the model")
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parser.add_argument(
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'--name', type=str, help="specific name of the training model")
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parser.add_argument(
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'--root', type=str, help="root path of the LJSpeech dataset")
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parser.add_argument(
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'--use_gpu',
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type=utils.str2bool,
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default=True,
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help="option to use gpu training")
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parser.add_argument(
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'--use_fp16',
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type=utils.str2bool,
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default=True,
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help="option to use fp16 for inference")
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parser.add_argument(
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'--iteration',
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type=int,
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default=None,
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help=("which iteration of checkpoint to load, "
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"default to load the latest checkpoint"))
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parser.add_argument(
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'--checkpoint',
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type=str,
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default=None,
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help="path of the checkpoint to load")
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def benchmark(config):
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pprint(vars(config))
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# Get checkpoint directory path.
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run_dir = os.path.join("runs", config.model, config.name)
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checkpoint_dir = os.path.join(run_dir, "checkpoint")
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# Configurate device.
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place = fluid.CUDAPlace(0) if config.use_gpu else fluid.CPUPlace()
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with dg.guard(place):
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# Fix random seed.
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seed = config.seed
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random.seed(seed)
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np.random.seed(seed)
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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print("Random Seed: ", seed)
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# Build model.
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model = WaveFlow(config, checkpoint_dir)
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model.build(training=False)
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# Run model inference.
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model.benchmark()
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if __name__ == "__main__":
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# Create parser.
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parser = argparse.ArgumentParser(
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description="Synthesize audio using WaveNet model")
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add_options_to_parser(parser)
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utils.add_config_options_to_parser(parser)
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# Parse argument from both command line and yaml config file.
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# For conflicting updates to the same field,
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# the preceding update will be overwritten by the following one.
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config = parser.parse_args()
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config = io.add_yaml_config_to_args(config)
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benchmark(config)
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