init

.gitignore

@@ -0,0 +1,12 @@
+.DS_Store
+debug*
+Models/
+*/model/
+*/checkpoints/
+*/results/
+*/*.pyc
+*/**/*.pyc
+*/**/**/*.pyc
+*/**/**/**/*.pyc
+*/**/**/**/**/*.pyc
+*/**/__pycache__

Audio/code/atcnet_test1.py

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+
+#encoding:utf-8
+#测试一个随机的wav
+import argparse
+import scipy.misc
+import os
+import glob
+import time
+import torch
+import torch.utils
+import torch.nn as nn
+import torchvision
+from torch.autograd import Variable
+import numpy as np
+from collections import OrderedDict
+import librosa
+import python_speech_features
+
+from models import ATC_net
+
+def multi2single(model_path, id):
+    checkpoint = torch.load(model_path)
+    state_dict = checkpoint
+    if id ==1:
+        new_state_dict = OrderedDict()
+        for k, v in state_dict.items():
+            name = k[7:]
+            new_state_dict[name] = v
+        return new_state_dict
+    else:
+        return state_dict
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--cuda",
+                        default=True)
+    parser.add_argument('-i','--in_file', type=str, default='../audio/test.wav')
+    parser.add_argument("--model_name",
+                        type=str,
+                        default="../model/atcnet/atcnet_lstm_24.pth")
+    parser.add_argument("--sample_dir",
+                        type=str,
+                        default="../results/atcnet/test/")
+    parser.add_argument('--device_ids', type=str, default='0')
+    parser.add_argument('--dataset', type=str, default='lrw')
+    parser.add_argument('--lstm', type=bool, default=True)
+    # parser.add_argument('--flownet_pth', type=str, help='path of flownets model')
+    parser.add_argument('--para_dim', type=int, default=64)
+    parser.add_argument('--index', type=str, default='80,144', help='index ranges')
+    parser.add_argument('--pose', type=int, default=0, help='whether predict pose')
+    parser.add_argument('--relativeframe', type=int, default=0, help='whether use relative frame value for pose')
+
+
+    return parser.parse_args()
+config = parse_args()
+str_ids = config.index.split(',')
+config.indexes = []
+for i in range(int(len(str_ids)/2)):
+    start = int(str_ids[2*i])
+    end = int(str_ids[2*i+1])
+    if end > start:
+        config.indexes += range(start, end)
+#print('indexes', config.indexes)
+print('device', config.device_ids)
+
+
+def test():
+    os.environ["CUDA_VISIBLE_DEVICES"] = config.device_ids
+    config.is_train = 'test'
+    if config.lstm == True:
+        if config.pose == 0:
+            generator = ATC_net(config.para_dim)
+        else:
+            generator = ATC_net(config.para_dim+6)
+
+    test_file = config.in_file
+    speech, sr = librosa.load(test_file, sr=16000)
+    mfcc = python_speech_features.mfcc(speech ,16000,winstep=0.01)
+    speech = np.insert(speech, 0, np.zeros(1920))
+    speech = np.append(speech, np.zeros(1920))
+    mfcc = python_speech_features.mfcc(speech,16000,winstep=0.01)
+    #print(mfcc.shape)
+
+    state_dict = multi2single(config.model_name, 0)
+    generator.load_state_dict(state_dict)
+    print('load pretrained [{}]'.format(config.model_name))
+
+    if config.cuda:
+        generator = generator.cuda()
+    generator.eval()
+
+    ind = 3
+    with torch.no_grad():
+        input_mfcc = []
+        while ind <= int(mfcc.shape[0]/4) - 4:
+            # take 280 ms segment
+            t_mfcc =mfcc[( ind - 3)*4: (ind + 4)*4, 1:]
+            t_mfcc = torch.FloatTensor(t_mfcc).cuda()
+            input_mfcc.append(t_mfcc)
+            ind += 1
+        input_mfcc = torch.stack(input_mfcc,dim = 0)
+        input_mfcc = input_mfcc.unsqueeze(0)
+        print(input_mfcc.shape)
+        if config.cuda:
+            input_mfcc = Variable(input_mfcc.float()).cuda()
+        if config.lstm:
+            fake_coeff= generator(input_mfcc)
+            fake_coeff = fake_coeff.data.cpu().numpy()
+            if not os.path.exists(config.sample_dir):
+                os.makedirs(config.sample_dir)
+            for jj in range(len(fake_coeff[0])):
+                name = "%s/%05d.npy"%(config.sample_dir,jj)
+                np.save(name, fake_coeff[0,jj])
+
+
+test()