50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
import torch
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import numpy as np
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import sys
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sys.path.append(R"../0_Res2Net")
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import argparse
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import functools
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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', '音频路径')
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add_arg('model_path', str, '../weights/Res2Net_Fbank/best_model/', '导出的预测模型文件路径')
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add_arg('pt_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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# 获取识别器
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predictor = 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 = predictor._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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print('input_data =',input_data.shape)
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# 提取音频特征
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audio_feature = predictor._audio_featurizer(input_data).to(torch.device("cpu"))
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print('audio_feature =',audio_feature.shape)
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# load pt model
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model = torch.jit.load(args.pt_path)
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# 执行预测
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output = model(audio_feature)
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print('output =',output,output.shape)
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result = torch.nn.functional.softmax(output, dim=-1)[0]
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result = result.data.cpu().numpy()
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# 最大概率的label
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lab = np.argsort(result)[-1]
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score = result[lab]
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CLASS_LABEL = ['air_conditioner', 'car_horn', 'children_playing', 'dog_bark', 'drilling', 'engine_idling', 'gun_shot', 'jackhammer', 'siren', 'street_music']
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label = CLASS_LABEL[lab]
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score = round(float(score), 5)
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print(f'音频：{args.audio_path} 的预测结果标签为：{label}，得分：{score}')
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