Delete infer_contrast.py

infer_contrast.py

@@ -1,51 +0,0 @@
-import argparse
-import functools
-
-import numpy as np
-import torch
-
-from utils.reader import load_audio
-from utils.utility import add_arguments, print_arguments
-
-parser = argparse.ArgumentParser(description=__doc__)
-add_arg = functools.partial(add_arguments, argparser=parser)
-add_arg('threshold',        float,   0.71,                    '判断是否为同一个人的阈值')
-add_arg('input_shape',      str,    '(1, 257, 257)',          '数据输入的形状')
-add_arg('model_path',       str,    'models_large/resnet34.pth',    '预测模型的路径')
-# args = parser.parse_args()
-args =parser.parse_known_args()[0]
-
-print_arguments(args)
-
-device = torch.device("cuda")
-
-# 加载模型
-# model = torch.jit.load(args.model_path)
-model = torch.load(args.model_path)
-# model.to(device)
-model.eval()
-
-
-# 预测音频
-def infer(audio_path):
-    input_shape = eval(args.input_shape)
-    data = load_audio(audio_path, mode='infer', spec_len=input_shape[2])
-    data = data[np.newaxis, :]
-    data = torch.tensor(data, dtype=torch.float32)
-    # 执行预测
-    feature = model(data)
-    return feature.data.cpu().numpy()
-
-
-def run(audio1,audio2):
-    # 要预测的两个人的音频文件
-    feature1 = infer(audio1)[0]
-    feature2 = infer(audio2)[0]
-    # 对角余弦值
-    dist = np.dot(feature1, feature2) / (np.linalg.norm(feature1) * np.linalg.norm(feature2))
-    if dist > args.threshold:
-        result = "Speaker1 和 Speaker2 为同一个人，相似度为：%f" % (dist)
-    else:
-        result = "Speaker1 和 Speaker2 为同一个人，相似度为：%f" % (dist)
-        
-    return result