26 lines
1.1 KiB
Python
26 lines
1.1 KiB
Python
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import argparse
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import functools
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import time
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from mvector.trainer import MVectorTrainer
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from mvector.utils.utils import add_arguments, print_arguments
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parser = argparse.ArgumentParser(description=__doc__)
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add_arg = functools.partial(add_arguments, argparser=parser)
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add_arg('configs', str, 'configs/ecapa_tdnn.yml', "配置文件")
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add_arg("use_gpu", bool, True, "是否使用GPU评估模型")
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add_arg('save_image_path', str, 'output/images/', "保存结果图的路径")
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add_arg('resume_model', str, 'models/ecapa_tdnn_MFCC/best_model/', "模型的路径")
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args = parser.parse_args()
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print_arguments(args=args)
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# 获取训练器
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trainer = MVectorTrainer(configs=args.configs, use_gpu=args.use_gpu)
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# 开始评估
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start = time.time()
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tpr, fpr, eer, threshold = trainer.evaluate(resume_model=args.resume_model, save_image_path=args.save_image_path)
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end = time.time()
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print('评估消耗时间：{}s，threshold：{:.2f}，tpr：{:.5f}, fpr: {:.5f}, eer: {:.5f}'
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.format(int(end - start), threshold, tpr, fpr, eer))
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