36 lines
1.6 KiB
Python
36 lines
1.6 KiB
Python
import argparse
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import functools
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import torch
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import sys
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sys.path.append(R"../0_Res2Net")
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from macls.predict import MAClsPredictor
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from macls.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/res2net.yml', '配置文件')
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add_arg('audio_path', str, 'dataset/126153-9-0-11.wav', '测试音频路径')
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add_arg('model_path', str, '../weights/Res2Net_Fbank/best_model/', '模型权重文件路径')
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add_arg('dst_path', str, '../2_compile/fmodel/Res2Net_predictor_1x398x80.pt', '导出的pt模型文件路径')
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args = parser.parse_args()
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print_arguments(args=args)
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# 备注： 太短的音频(0.3175ms < 0.5ms)会无法预测
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# 获取识别器
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model = MAClsPredictor(configs=args.configs,
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model_path=args.model_path,
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use_gpu=False)
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# 加载音频文件，并进行预处理
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input_data = model._load_audio(audio_data=args.audio_path, sample_rate=16000)
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input_data = torch.tensor(input_data.samples, dtype=torch.float32).unsqueeze(0)
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audio_feature = model._audio_featurizer(input_data)
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print('audio_feature =',audio_feature.shape)
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# 执行预测
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output = model.predictor(audio_feature)
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print('output =',output.shape)
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# 导出模型
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pt_model = model.predictor
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traced_model = torch.jit.trace(pt_model,audio_feature,strict=False)
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torch.jit.save(traced_model,args.dst_path)
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print(f'TorchScript export success, saved in: {args.dst_path} ')
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