84 lines
2.3 KiB
Python
84 lines
2.3 KiB
Python
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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 jsonargparse
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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 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=bool,
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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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'--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(jsonargparse.namespace_to_dict(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 = jsonargparse.ArgumentParser(
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description="Synthesize audio using WaveNet model",
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formatter_class='default_argparse')
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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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benchmark(config)
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