mona-alz
commented
5 years ago I have the same issue too, anyone got to solve it?
Open Aurora11111 opened 5 years ago
mona-alz
commented
5 years ago I have the same issue too, anyone got to solve it?
Aurora11111
commented
5 years ago @mona-alz reference: https://github.com/Aurora11111/speaker-recognition-pytorch
mona-alz
commented
5 years ago https://github.com/Aurora11111/speaker-recognition-pytorch@mona-alz reference:
what is the reference?
nidhal1231
commented
5 years ago https://github.com/Aurora11111 How did you manage to train the model on your dataset and extract the numpy files train_sequence.npy, train_cluster_ids.npy, test_sequence.npy, and test_cluster_ids.npy. Thank you in advance.
nidhal1231
commented
5 years ago @Aurora11111 How did you manage to train the model on your dataset and extract the numpy files train_sequence.npy, train_cluster_ids.npy, test_sequence.npy, and test_cluster_ids.npy. Thank you in advance.
Aurora11111
commented
5 years ago @nidhal1231 you can create it also by python dvector_create.py . I kown you want to use them in uis-rnn, but test_sequence.npy, test_cluster_ids.npy there are used to test single speaker-id.if you want to test many speaker-id you should modify the for circle by yourself
when I trained a modle with this project ,but when using it in uis rnn, it come out errors: here the train log.
/home/rice/anaconda3/envs/pytorch/bin/python3.6 /home/rice/PycharmProjects/uis-rnn-master/demo.py Iter: 0 Training Loss: -292.7123
main()
File "/home/rice/PycharmProjects/uis-rnn-master/demo.py", line 81, in main
diarization_experiment(model_args, training_args, inference_args)
File "/home/rice/PycharmProjects/uis-rnn-master/demo.py", line 61, in diarization_experiment
predicted_label = model.predict(test_sequence, inference_args)
File "/home/rice/PycharmProjects/uis-rnn-master/uisrnn/uisrnn.py", line 573, in predict
return self.predict_single(test_sequences, args)
File "/home/rice/PycharmProjects/uis-rnn-master/uisrnn/uisrnn.py", line 503, in predict_single
raise ValueError('test_sequence must be 2-dim array.')
ValueError: test_sequence must be 2-dim array.
Negative Log Likelihood: 1.1230 Sigma2 Prior: -293.8359 Regularization: 0.0006 Iter: 10 Training Loss: -292.9026
Negative Log Likelihood: 1.0294 Sigma2 Prior: -293.9326 Regularization: 0.0006 Iter: 20 Training Loss: -293.1618
Negative Log Likelihood: 0.9518 Sigma2 Prior: -294.1143 Regularization: 0.0006 Iter: 30 Training Loss: -293.3418
Negative Log Likelihood: 0.8811 Sigma2 Prior: -294.2235 Regularization: 0.0006 Iter: 40 Training Loss: -293.6046
Negative Log Likelihood: 0.7965 Sigma2 Prior: -294.4017 Regularization: 0.0006 Iter: 50 Training Loss: -293.7784
Negative Log Likelihood: 0.7400 Sigma2 Prior: -294.5190 Regularization: 0.0006 Iter: 60 Training Loss: -294.0261
Negative Log Likelihood: 0.6534 Sigma2 Prior: -294.6801 Regularization: 0.0006 Iter: 70 Training Loss: -294.1154
Negative Log Likelihood: 0.6151 Sigma2 Prior: -294.7312 Regularization: 0.0006 Iter: 80 Training Loss: -294.3496
Negative Log Likelihood: 0.5512 Sigma2 Prior: -294.9014 Regularization: 0.0006 Iter: 90 Training Loss: -294.6035
Negative Log Likelihood: 0.4841 Sigma2 Prior: -295.0882 Regularization: 0.0006 Iter: 100 Training Loss: -294.6783
Negative Log Likelihood: 0.4460 Sigma2 Prior: -295.1250 Regularization: 0.0006 Iter: 110 Training Loss: -294.9049
Negative Log Likelihood: 0.3866 Sigma2 Prior: -295.2922 Regularization: 0.0006 Iter: 120 Training Loss: -295.1215
Negative Log Likelihood: 0.3335 Sigma2 Prior: -295.4556 Regularization: 0.0006 Iter: 130 Training Loss: -295.2138
Negative Log Likelihood: 0.3084 Sigma2 Prior: -295.5229 Regularization: 0.0006 Iter: 140 Training Loss: -295.3630
Negative Log Likelihood: 0.2796 Sigma2 Prior: -295.6432 Regularization: 0.0006 Iter: 150 Training Loss: -295.5717
Negative Log Likelihood: 0.2386 Sigma2 Prior: -295.8109 Regularization: 0.0006 Iter: 160 Training Loss: -295.6212
Negative Log Likelihood: 0.2382 Sigma2 Prior: -295.8600 Regularization: 0.0006 Iter: 170 Training Loss: -295.8949
Negative Log Likelihood: 0.1961 Sigma2 Prior: -296.0916 Regularization: 0.0006 Iter: 180 Training Loss: -296.0236
Negative Log Likelihood: 0.1764 Sigma2 Prior: -296.2007 Regularization: 0.0006 Iter: 190 Training Loss: -296.1873
Negative Log Likelihood: 0.1511 Sigma2 Prior: -296.3391 Regularization: 0.0006 Iter: 200 Training Loss: -296.3225
Negative Log Likelihood: 0.1493 Sigma2 Prior: -296.4724 Regularization: 0.0006 Iter: 210 Training Loss: -296.3659
Negative Log Likelihood: 0.1548 Sigma2 Prior: -296.5213 Regularization: 0.0006 Iter: 220 Training Loss: -296.5490
Negative Log Likelihood: 0.1174 Sigma2 Prior: -296.6670 Regularization: 0.0006 Iter: 230 Training Loss: -296.6978
Negative Log Likelihood: 0.1129 Sigma2 Prior: -296.8114 Regularization: 0.0006 Iter: 240 Training Loss: -296.7265
Negative Log Likelihood: 0.1292 Sigma2 Prior: -296.8564 Regularization: 0.0006 Iter: 250 Training Loss: -296.9639
Negative Log Likelihood: 0.1191 Sigma2 Prior: -297.0836 Regularization: 0.0006 Iter: 260 Training Loss: -297.1142
Negative Log Likelihood: 0.0966 Sigma2 Prior: -297.2114 Regularization: 0.0006 Iter: 270 Training Loss: -297.3285
Negative Log Likelihood: 0.0972 Sigma2 Prior: -297.4264 Regularization: 0.0006 Iter: 280 Training Loss: -297.3492
Negative Log Likelihood: 0.1149 Sigma2 Prior: -297.4648 Regularization: 0.0006 Iter: 290 Training Loss: -297.5171
Negative Log Likelihood: 0.0907 Sigma2 Prior: -297.6085 Regularization: 0.0006 Iter: 300 Training Loss: -297.6031
Negative Log Likelihood: 0.1217 Sigma2 Prior: -297.7255 Regularization: 0.0006 Iter: 310 Training Loss: -297.7408
Negative Log Likelihood: 0.1000 Sigma2 Prior: -297.8414 Regularization: 0.0006 Iter: 320 Training Loss: -297.9149
Negative Log Likelihood: 0.0958 Sigma2 Prior: -298.0113 Regularization: 0.0006 Iter: 330 Training Loss: -298.0205
Negative Log Likelihood: 0.1114 Sigma2 Prior: -298.1325 Regularization: 0.0006 Iter: 340 Training Loss: -298.1181
Negative Log Likelihood: 0.0878 Sigma2 Prior: -298.2065 Regularization: 0.0006 Iter: 350 Training Loss: -298.3074
Negative Log Likelihood: 0.0924 Sigma2 Prior: -298.4005 Regularization: 0.0006 Iter: 360 Training Loss: -298.4767
Negative Log Likelihood: 0.0768 Sigma2 Prior: -298.5542 Regularization: 0.0006 Iter: 370 Training Loss: -298.5929
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Negative Log Likelihood: 0.1211 Sigma2 Prior: -299.1976 Regularization: 0.0006 Iter: 420 Training Loss: -299.3126
Negative Log Likelihood: 0.0831 Sigma2 Prior: -299.3963 Regularization: 0.0006 Iter: 430 Training Loss: -299.3480
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Negative Log Likelihood: 0.1005 Sigma2 Prior: -302.2620 Regularization: 0.0006 Iter: 640 Training Loss: -302.2143
Negative Log Likelihood: 0.1000 Sigma2 Prior: -302.3149 Regularization: 0.0006 Iter: 650 Training Loss: -302.4611
Negative Log Likelihood: 0.0912 Sigma2 Prior: -302.5529 Regularization: 0.0006 Iter: 660 Training Loss: -302.5591
Negative Log Likelihood: 0.0919 Sigma2 Prior: -302.6517 Regularization: 0.0006 Iter: 670 Training Loss: -302.6293
Negative Log Likelihood: 0.1121 Sigma2 Prior: -302.7421 Regularization: 0.0006 Iter: 680 Training Loss: -302.7584
Negative Log Likelihood: 0.1017 Sigma2 Prior: -302.8607 Regularization: 0.0006 Iter: 690 Training Loss: -303.0341
Negative Log Likelihood: 0.0645 Sigma2 Prior: -303.0992 Regularization: 0.0006 Iter: 700 Training Loss: -303.0613
Negative Log Likelihood: 0.0991 Sigma2 Prior: -303.1611 Regularization: 0.0006 Iter: 710 Training Loss: -303.0722
Negative Log Likelihood: 0.1232 Sigma2 Prior: -303.1961 Regularization: 0.0006 Iter: 720 Training Loss: -303.3741
Negative Log Likelihood: 0.0947 Sigma2 Prior: -303.4695 Regularization: 0.0006 Iter: 730 Training Loss: -303.5182
Negative Log Likelihood: 0.1053 Sigma2 Prior: -303.6241 Regularization: 0.0006 Iter: 740 Training Loss: -303.7680
Negative Log Likelihood: 0.0731 Sigma2 Prior: -303.8417 Regularization: 0.0006 Iter: 750 Training Loss: -303.8307
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Negative Log Likelihood: 0.0956 Sigma2 Prior: -304.3297 Regularization: 0.0006 Iter: 790 Training Loss: -304.4388
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Negative Log Likelihood: 0.1080 Sigma2 Prior: -304.7541 Regularization: 0.0006 Iter: 820 Training Loss: -304.7875
Negative Log Likelihood: 0.0952 Sigma2 Prior: -304.8833 Regularization: 0.0006 Iter: 830 Training Loss: -304.9782
Negative Log Likelihood: 0.0935 Sigma2 Prior: -305.0724 Regularization: 0.0006 Iter: 840 Training Loss: -305.1052
Negative Log Likelihood: 0.1015 Sigma2 Prior: -305.2074 Regularization: 0.0006 Iter: 850 Training Loss: -305.1952
Negative Log Likelihood: 0.1143 Sigma2 Prior: -305.3101 Regularization: 0.0006 Iter: 860 Training Loss: -305.3628
Negative Log Likelihood: 0.0894 Sigma2 Prior: -305.4528 Regularization: 0.0006 Iter: 870 Training Loss: -305.5805
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Negative Log Likelihood: 0.0992 Sigma2 Prior: -305.7954 Regularization: 0.0006 Iter: 890 Training Loss: -305.7558
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Negative Log Likelihood: 0.0935 Sigma2 Prior: -306.1192 Regularization: 0.0006 Iter: 910 Training Loss: -306.2444
Negative Log Likelihood: 0.0719 Sigma2 Prior: -306.3170 Regularization: 0.0006 Iter: 920 Training Loss: -306.2163
Negative Log Likelihood: 0.1155 Sigma2 Prior: -306.3325 Regularization: 0.0006 Iter: 930 Training Loss: -306.4367
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Negative Log Likelihood: 0.0907 Sigma2 Prior: -307.2625 Regularization: 0.0006 Iter: 990 Training Loss: -307.4298
Negative Log Likelihood: 0.0719 Sigma2 Prior: -307.5023 Regularization: 0.0006 Iter: 1000 Training Loss: -307.4031
Negative Log Likelihood: 0.1012 Sigma2 Prior: -307.5049 Regularization: 0.0006 Iter: 1010 Training Loss: -307.5197
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Negative Log Likelihood: 0.0815 Sigma2 Prior: -308.6208 Regularization: 0.0006 Iter: 1080 Training Loss: -308.6100
Negative Log Likelihood: 0.0949 Sigma2 Prior: -308.7055 Regularization: 0.0006 Iter: 1090 Training Loss: -308.7465
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Negative Log Likelihood: 0.0917 Sigma2 Prior: -309.0215 Regularization: 0.0006 Iter: 1110 Training Loss: -309.0396
Negative Log Likelihood: 0.0860 Sigma2 Prior: -309.1262 Regularization: 0.0006 Iter: 1120 Training Loss: -309.3109
Negative Log Likelihood: 0.0750 Sigma2 Prior: -309.3864 Regularization: 0.0006 Iter: 1130 Training Loss: -309.4098
Negative Log Likelihood: 0.0917 Sigma2 Prior: -309.5021 Regularization: 0.0006 Iter: 1140 Training Loss: -309.4720
Negative Log Likelihood: 0.0865 Sigma2 Prior: -309.5591 Regularization: 0.0006 Iter: 1150 Training Loss: -309.7521
Negative Log Likelihood: 0.0706 Sigma2 Prior: -309.8234 Regularization: 0.0006 Iter: 1160 Training Loss: -309.7961
Negative Log Likelihood: 0.0928 Sigma2 Prior: -309.8894 Regularization: 0.0006 Iter: 1170 Training Loss: -309.9894
Negative Log Likelihood: 0.0826 Sigma2 Prior: -310.0726 Regularization: 0.0006 Iter: 1180 Training Loss: -310.0692
Negative Log Likelihood: 0.1070 Sigma2 Prior: -310.1768 Regularization: 0.0006 Iter: 1190 Training Loss: -310.3997
Negative Log Likelihood: 0.0755 Sigma2 Prior: -310.4757 Regularization: 0.0006 Iter: 1200 Training Loss: -310.4816
Negative Log Likelihood: 0.0968 Sigma2 Prior: -310.5789 Regularization: 0.0006 Iter: 1210 Training Loss: -310.6189
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Negative Log Likelihood: 0.0893 Sigma2 Prior: -310.7822 Regularization: 0.0006 Iter: 1230 Training Loss: -310.8269
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Negative Log Likelihood: 0.0765 Sigma2 Prior: -311.4315 Regularization: 0.0006 Iter: 1270 Training Loss: -311.5592
Negative Log Likelihood: 0.0802 Sigma2 Prior: -311.6401 Regularization: 0.0006 Iter: 1280 Training Loss: -311.6955
Negative Log Likelihood: 0.0762 Sigma2 Prior: -311.7723 Regularization: 0.0006 Iter: 1290 Training Loss: -311.8909
Negative Log Likelihood: 0.0761 Sigma2 Prior: -311.9677 Regularization: 0.0006 Iter: 1300 Training Loss: -312.0223
Negative Log Likelihood: 0.0875 Sigma2 Prior: -312.1105 Regularization: 0.0006 Iter: 1310 Training Loss: -312.2397
Negative Log Likelihood: 0.0740 Sigma2 Prior: -312.3144 Regularization: 0.0006 Iter: 1320 Training Loss: -312.3552
Negative Log Likelihood: 0.0907 Sigma2 Prior: -312.4465 Regularization: 0.0006 Iter: 1330 Training Loss: -312.4939
Negative Log Likelihood: 0.0818 Sigma2 Prior: -312.5763 Regularization: 0.0006 Iter: 1340 Training Loss: -312.6237
Negative Log Likelihood: 0.0728 Sigma2 Prior: -312.6971 Regularization: 0.0006 Iter: 1350 Training Loss: -312.7400
Negative Log Likelihood: 0.0793 Sigma2 Prior: -312.8199 Regularization: 0.0006 Iter: 1360 Training Loss: -312.8621
Negative Log Likelihood: 0.0921 Sigma2 Prior: -312.9547 Regularization: 0.0006 Iter: 1370 Training Loss: -313.1195
Negative Log Likelihood: 0.0864 Sigma2 Prior: -313.2066 Regularization: 0.0006 Iter: 1380 Training Loss: -313.1552
Negative Log Likelihood: 0.0893 Sigma2 Prior: -313.2451 Regularization: 0.0006 Iter: 1390 Training Loss: -313.3605
Negative Log Likelihood: 0.0802 Sigma2 Prior: -313.4413 Regularization: 0.0006 Iter: 1400 Training Loss: -313.4704
Negative Log Likelihood: 0.0871 Sigma2 Prior: -313.5581 Regularization: 0.0006 Iter: 1410 Training Loss: -313.7135
Negative Log Likelihood: 0.0714 Sigma2 Prior: -313.7855 Regularization: 0.0006 Iter: 1420 Training Loss: -313.7669
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Negative Log Likelihood: 0.0871 Sigma2 Prior: -314.7112 Regularization: 0.0006 Iter: 1480 Training Loss: -314.6680
Negative Log Likelihood: 0.0953 Sigma2 Prior: -314.7639 Regularization: 0.0006 Iter: 1490 Training Loss: -314.9998
Negative Log Likelihood: 0.0742 Sigma2 Prior: -315.0746 Regularization: 0.0006 Iter: 1500 Training Loss: -315.0698
Negative Log Likelihood: 0.0980 Sigma2 Prior: -315.1684 Regularization: 0.0006 Iter: 1510 Training Loss: -315.2988
Negative Log Likelihood: 0.0781 Sigma2 Prior: -315.3775 Regularization: 0.0006 Iter: 1520 Training Loss: -315.3861
Negative Log Likelihood: 0.0859 Sigma2 Prior: -315.4726 Regularization: 0.0006 Iter: 1530 Training Loss: -315.6111
Negative Log Likelihood: 0.0856 Sigma2 Prior: -315.6974 Regularization: 0.0006 Iter: 1540 Training Loss: -315.6920
Negative Log Likelihood: 0.0893 Sigma2 Prior: -315.7820 Regularization: 0.0006 Iter: 1550 Training Loss: -315.8524
Negative Log Likelihood: 0.0790 Sigma2 Prior: -315.9320 Regularization: 0.0006 Iter: 1560 Training Loss: -315.9643
Negative Log Likelihood: 0.0993 Sigma2 Prior: -316.0642 Regularization: 0.0006 Iter: 1570 Training Loss: -316.1303
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Negative Log Likelihood: 0.0917 Sigma2 Prior: -316.6665 Regularization: 0.0006 Iter: 1600 Training Loss: -316.8289
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Negative Log Likelihood: 0.0877 Sigma2 Prior: -318.2771 Regularization: 0.0006 Iter: 1700 Training Loss: -318.3336
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Negative Log Likelihood: 0.0657 Sigma2 Prior: -321.3525 Regularization: 0.0006 Iter: 1880 Training Loss: -321.3665
Negative Log Likelihood: 0.0894 Sigma2 Prior: -321.4566 Regularization: 0.0006 Iter: 1890 Training Loss: -321.5111
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Negative Log Likelihood: 0.0645 Sigma2 Prior: -321.9702 Regularization: 0.0006 Iter: 1920 Training Loss: -321.9774
Negative Log Likelihood: 0.0841 Sigma2 Prior: -322.0620 Regularization: 0.0006 Iter: 1930 Training Loss: -322.1822
Negative Log Likelihood: 0.0787 Sigma2 Prior: -322.2614 Regularization: 0.0006 Iter: 1940 Training Loss: -322.4178
Negative Log Likelihood: 0.0788 Sigma2 Prior: -322.4972 Regularization: 0.0006 Iter: 1950 Training Loss: -322.5313
Negative Log Likelihood: 0.0877 Sigma2 Prior: -322.6197 Regularization: 0.0006 Iter: 1960 Training Loss: -322.7161
Negative Log Likelihood: 0.0698 Sigma2 Prior: -322.7865 Regularization: 0.0006 Iter: 1970 Training Loss: -322.8975
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Negative Log Likelihood: 2.5480 Sigma2 Prior: -734.2255 Regularization: 0.0009 Done training with 20000 iterations Traceback (most recent call last): File "/home/rice/PycharmProjects/uis-rnn-master/demo.py", line 85, in
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can you help me slove this problem?