Comments (10)
mycrazycracy
commented on August 15, 2024
Hi
The lines are commented out accidentally. They are needed if you do the reverberation augmentation. This augmentation is standard in the Kaldi recipe (pls see kaldi/egs/voxceleb/v2/run.sh for reference).
Generally speaking, we add reverberation, noise, music and babble in the training data.
Have you tried the pre-trained models? I just want to make sure that the misalignment occurs in the training phase.
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deciding
commented on August 15, 2024
@mycrazycracy Hi, thanks for the reply. I tested with the pretrained model on xvector_nnet_tdnn_arcsoftmax_m0.30_linear_bn_1e-2 config. And the result is roughly the same which is 2% EER. however, on my side when I trained from the very beginning, the result is much worse. (around 4%)
the misalignment of validation loss happens even at the beginning of training.
yours mine
0 4.347158 0.121704 0 5.595568 0.153933
1 4.409681 0.124732 1 6.142251 0.152045
2 4.953392 0.164300 2 5.387073 0.145626
3 4.171205 0.121704 3 5.853278 0.138200
4 6.030734 0.141988 4 4.879803 0.145833
5 3.373980 0.117647 5 3.483222 0.127753
6 3.551348 0.114610 6 2.925486 0.126240
7 3.499111 0.126222 7 2.618020 0.112649
8 2.706735 0.106290 8 2.495200 0.108464
9 2.505243 0.087221 9 2.797635 0.107489
10 2.579709 0.105946 10 3.352109 0.108477
11 2.386637 0.083784 11 2.149656 0.094273
12 2.215215 0.078906 12 2.788486 0.107827
13 2.093923 0.070809 13 2.154233 0.096413
14 2.614316 0.095335 14 2.664895 0.099308
15 2.402408 0.068661 15 2.585773 0.102958
16 2.269810 0.081136 16 2.037069 0.082545
17 2.459356 0.068966 17 2.123691 0.084204
18 1.660872 0.069354 18 1.782891 0.078462
19 1.641136 0.070407 19 1.666662 0.078414
20 1.789717 0.070994 20 1.884485 0.074890
21 1.732624 0.062880 21 1.991562 0.085085
22 1.685123 0.060852 22 1.975221 0.087411
23 1.925408 0.078476 23 1.887127 0.078248
24 1.225578 0.060852 24 1.545569 0.067716
25 1.237508 0.060852 25 1.556502 0.076639
26 1.168123 0.058824 26 1.478283 0.072876
27 1.178835 0.045722 27 1.438764 0.072354
28 1.394852 0.062880 28 1.483797 0.067086
29 1.245886 0.061878 29 1.449579 0.072498
30 1.333659 0.056795 30 1.443876 0.066027
31 0.997353 0.043031 31 1.379931 0.071366
32 0.920696 0.044625 32 1.365147 0.073002
33 1.091778 0.052738 33 1.386801 0.066677
34 1.029863 0.046250 34 1.350820 0.067212
35 0.953727 0.051637 35 1.251097 0.064168
36 0.956934 0.051722 36 1.254138 0.063059
37 0.675873 0.036846 37 1.358343 0.066842
38 0.815823 0.044149 38 1.531808 0.067086
39 0.705373 0.040568 39 1.218243 0.068974
40 0.714766 0.032454 40 1.435390 0.061162
41 0.763318 0.046653 41 1.284261 0.065576
42 0.574937 0.034483 42 1.291959 0.063184
43 0.592286 0.031686 43 1.354145 0.069352
44 0.581585 0.036511 44 1.020841 0.058024
45 0.690763 0.043893 45 1.134171 0.060919
46 0.698204 0.040568 46 0.971369 0.062687
47 0.545573 0.026369 47 1.049776 0.057784
48 0.564414 0.029049 48 1.095432 0.059408
49 0.577830 0.034483 49 1.046128 0.056462
50 0.578830 0.033454 50 1.139795 0.063059
51 0.570985 0.034483 51 0.849101 0.051990
52 0.534715 0.032454 52 0.845482 0.053041
53 0.577185 0.035037 53 0.867068 0.055129
54 0.524684 0.033184 54 0.847392 0.051038
55 0.507371 0.034354 55 0.837217 0.052612
56 0.503855 0.032454 56 0.879506 0.053876
57 0.535493 0.029426 57 0.881150 0.055632
58 0.536870 0.040568 58 0.906975 0.057269
59 0.505829 0.033793 59 0.884071 0.056639
60 0.567816 0.038540 60 0.730023 0.051479
61 0.556293 0.038540 61 0.737830 0.050850
62 0.473255 0.032454 62 0.740354 0.052108
63 0.533865 0.040224 63 0.753932 0.051596
64 0.514418 0.034483 64 0.727656 0.053619
65 0.487497 0.028398 65 0.774739 0.053870
66 0.513700 0.025216 66 0.832094 0.048081
67 0.447182 0.026369 67 0.799185 0.052486
68 0.464616 0.026369 68 0.785597 0.053367
69 0.433060 0.024231 69 0.660124 0.049570
70 0.463813 0.024233 70 0.699606 0.050598
71 0.475865 0.028398 71 0.673907 0.048836
72 0.460187 0.026679 72 0.647345 0.046193
73 0.473736 0.027065 73 0.678176 0.048212
74 0.515551 0.024341 74 0.708431 0.051314
75 0.481606 0.034483 75 0.680705 0.049200
76 0.487582 0.027921 76 0.700234 0.048962
One thing notable is that, in your log file the validation batch size is 29, however, in my case it is 32, thus I think maybe our preprocessed dataset has some difference
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mycrazycracy
commented on August 15, 2024
The valid loss can be different, since the validation set is chosen randomly. But I think the difference should not be so large.
BTW, is the worse result attained using PLDA?
… 2020年2月29日 上午11:48，deciding ***@***.***> 写道：
@mycrazycracy <https://github.com/mycrazycracy> Hi, thanks for the reply. I tested with the pretrained model on xvector_nnet_tdnn_arcsoftmax_m0.30_linear_bn_1e-2 config. And the result is roughly the same which is 2% EER. however, on my side when I trained from the very beginning, the result is much worse. (around 4%)
the misalignment of validation loss happens even at the beginning of training.
yours mine
0 4.347158 0.121704 0 5.595568 0.153933
1 4.409681 0.124732 1 6.142251 0.152045
2 4.953392 0.164300 2 5.387073 0.145626
3 4.171205 0.121704 3 5.853278 0.138200
4 6.030734 0.141988 4 4.879803 0.145833
5 3.373980 0.117647 5 3.483222 0.127753
6 3.551348 0.114610 6 2.925486 0.126240
7 3.499111 0.126222 7 2.618020 0.112649
8 2.706735 0.106290 8 2.495200 0.108464
9 2.505243 0.087221 9 2.797635 0.107489
10 2.579709 0.105946 10 3.352109 0.108477
11 2.386637 0.083784 11 2.149656 0.094273
12 2.215215 0.078906 12 2.788486 0.107827
13 2.093923 0.070809 13 2.154233 0.096413
14 2.614316 0.095335 14 2.664895 0.099308
15 2.402408 0.068661 15 2.585773 0.102958
16 2.269810 0.081136 16 2.037069 0.082545
17 2.459356 0.068966 17 2.123691 0.084204
18 1.660872 0.069354 18 1.782891 0.078462
19 1.641136 0.070407 19 1.666662 0.078414
20 1.789717 0.070994 20 1.884485 0.074890
21 1.732624 0.062880 21 1.991562 0.085085
22 1.685123 0.060852 22 1.975221 0.087411
23 1.925408 0.078476 23 1.887127 0.078248
24 1.225578 0.060852 24 1.545569 0.067716
25 1.237508 0.060852 25 1.556502 0.076639
26 1.168123 0.058824 26 1.478283 0.072876
27 1.178835 0.045722 27 1.438764 0.072354
28 1.394852 0.062880 28 1.483797 0.067086
29 1.245886 0.061878 29 1.449579 0.072498
30 1.333659 0.056795 30 1.443876 0.066027
31 0.997353 0.043031 31 1.379931 0.071366
32 0.920696 0.044625 32 1.365147 0.073002
33 1.091778 0.052738 33 1.386801 0.066677
34 1.029863 0.046250 34 1.350820 0.067212
35 0.953727 0.051637 35 1.251097 0.064168
36 0.956934 0.051722 36 1.254138 0.063059
37 0.675873 0.036846 37 1.358343 0.066842
38 0.815823 0.044149 38 1.531808 0.067086
39 0.705373 0.040568 39 1.218243 0.068974
40 0.714766 0.032454 40 1.435390 0.061162
41 0.763318 0.046653 41 1.284261 0.065576
42 0.574937 0.034483 42 1.291959 0.063184
43 0.592286 0.031686 43 1.354145 0.069352
44 0.581585 0.036511 44 1.020841 0.058024
45 0.690763 0.043893 45 1.134171 0.060919
46 0.698204 0.040568 46 0.971369 0.062687
47 0.545573 0.026369 47 1.049776 0.057784
48 0.564414 0.029049 48 1.095432 0.059408
49 0.577830 0.034483 49 1.046128 0.056462
50 0.578830 0.033454 50 1.139795 0.063059
51 0.570985 0.034483 51 0.849101 0.051990
52 0.534715 0.032454 52 0.845482 0.053041
53 0.577185 0.035037 53 0.867068 0.055129
54 0.524684 0.033184 54 0.847392 0.051038
55 0.507371 0.034354 55 0.837217 0.052612
56 0.503855 0.032454 56 0.879506 0.053876
57 0.535493 0.029426 57 0.881150 0.055632
58 0.536870 0.040568 58 0.906975 0.057269
59 0.505829 0.033793 59 0.884071 0.056639
60 0.567816 0.038540 60 0.730023 0.051479
61 0.556293 0.038540 61 0.737830 0.050850
62 0.473255 0.032454 62 0.740354 0.052108
63 0.533865 0.040224 63 0.753932 0.051596
64 0.514418 0.034483 64 0.727656 0.053619
65 0.487497 0.028398 65 0.774739 0.053870
66 0.513700 0.025216 66 0.832094 0.048081
67 0.447182 0.026369 67 0.799185 0.052486
68 0.464616 0.026369 68 0.785597 0.053367
69 0.433060 0.024231 69 0.660124 0.049570
70 0.463813 0.024233 70 0.699606 0.050598
71 0.475865 0.028398 71 0.673907 0.048836
72 0.460187 0.026679 72 0.647345 0.046193
73 0.473736 0.027065 73 0.678176 0.048212
74 0.515551 0.024341 74 0.708431 0.051314
75 0.481606 0.034483 75 0.680705 0.049200
76 0.487582 0.027921 76 0.700234 0.048962
One thing notable is that, in your log file the validation batch size is 29, however, in my case it is 32, thus I think maybe our preprocessed dataset has some difference
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deciding
commented on August 15, 2024
@mycrazycracy yes, I agree with the validation set is random, but I have ran the data preprocessing for several times, the validation loss are quite similar, and I never got the similar validation loss as your result. This may indicate some problems.
I used PLDA, if using cosine or lda-cosine the result will be even worse.
Another thing I want to state is that, even if the validation set is randomly generated, the size of it should be same I think? I shouldn't have a 32 batches validation set while yours is 29 batches. Is this correct?
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mycrazycracy
commented on August 15, 2024
The number may be *slightly* different as well, if you look at the validate set generation process. I’m not very sure how I generate the validation set… since I may use another hyperparameters. But it won’t affect the loss so much I think. Do you change the config file anyway?
… 2020年2月29日 下午12:07，deciding ***@***.***> 写道：
@mycrazycracy <https://github.com/mycrazycracy> yes, I agree with the validation set is random, but I have ran the data preprocessing for several times, the validation loss are quite similar, and I never got the similar validation loss as your result. This may indicate some problems.
I used PLDA, if using cosine or lda-cosine the result will be even worse.
Another thing I want to state is that, even if the validation set is randomly generated, the size of it should be same I think? I shouldn't have a 32 batches validation set while yours is 29 batches. Is this correct?
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shatealaboxiaowang
commented on August 15, 2024
@mycrazycracy Hi, thanks for the reply. I tested with the pretrained model on xvector_nnet_tdnn_arcsoftmax_m0.30_linear_bn_1e-2 config. And the result is roughly the same which is 2% EER. however, on my side when I trained from the very beginning, the result is much worse. (around 4%)
the misalignment of validation loss happens even at the beginning of training.
yours mine 0 4.347158 0.121704 0 5.595568 0.153933 1 4.409681 0.124732 1 6.142251 0.152045 2 4.953392 0.164300 2 5.387073 0.145626 3 4.171205 0.121704 3 5.853278 0.138200 4 6.030734 0.141988 4 4.879803 0.145833 5 3.373980 0.117647 5 3.483222 0.127753 6 3.551348 0.114610 6 2.925486 0.126240 7 3.499111 0.126222 7 2.618020 0.112649 8 2.706735 0.106290 8 2.495200 0.108464 9 2.505243 0.087221 9 2.797635 0.107489 10 2.579709 0.105946 10 3.352109 0.108477 11 2.386637 0.083784 11 2.149656 0.094273 12 2.215215 0.078906 12 2.788486 0.107827 13 2.093923 0.070809 13 2.154233 0.096413 14 2.614316 0.095335 14 2.664895 0.099308 15 2.402408 0.068661 15 2.585773 0.102958 16 2.269810 0.081136 16 2.037069 0.082545 17 2.459356 0.068966 17 2.123691 0.084204 18 1.660872 0.069354 18 1.782891 0.078462 19 1.641136 0.070407 19 1.666662 0.078414 20 1.789717 0.070994 20 1.884485 0.074890 21 1.732624 0.062880 21 1.991562 0.085085 22 1.685123 0.060852 22 1.975221 0.087411 23 1.925408 0.078476 23 1.887127 0.078248 24 1.225578 0.060852 24 1.545569 0.067716 25 1.237508 0.060852 25 1.556502 0.076639 26 1.168123 0.058824 26 1.478283 0.072876 27 1.178835 0.045722 27 1.438764 0.072354 28 1.394852 0.062880 28 1.483797 0.067086 29 1.245886 0.061878 29 1.449579 0.072498 30 1.333659 0.056795 30 1.443876 0.066027 31 0.997353 0.043031 31 1.379931 0.071366 32 0.920696 0.044625 32 1.365147 0.073002 33 1.091778 0.052738 33 1.386801 0.066677 34 1.029863 0.046250 34 1.350820 0.067212 35 0.953727 0.051637 35 1.251097 0.064168 36 0.956934 0.051722 36 1.254138 0.063059 37 0.675873 0.036846 37 1.358343 0.066842 38 0.815823 0.044149 38 1.531808 0.067086 39 0.705373 0.040568 39 1.218243 0.068974 40 0.714766 0.032454 40 1.435390 0.061162 41 0.763318 0.046653 41 1.284261 0.065576 42 0.574937 0.034483 42 1.291959 0.063184 43 0.592286 0.031686 43 1.354145 0.069352 44 0.581585 0.036511 44 1.020841 0.058024 45 0.690763 0.043893 45 1.134171 0.060919 46 0.698204 0.040568 46 0.971369 0.062687 47 0.545573 0.026369 47 1.049776 0.057784 48 0.564414 0.029049 48 1.095432 0.059408 49 0.577830 0.034483 49 1.046128 0.056462 50 0.578830 0.033454 50 1.139795 0.063059 51 0.570985 0.034483 51 0.849101 0.051990 52 0.534715 0.032454 52 0.845482 0.053041 53 0.577185 0.035037 53 0.867068 0.055129 54 0.524684 0.033184 54 0.847392 0.051038 55 0.507371 0.034354 55 0.837217 0.052612 56 0.503855 0.032454 56 0.879506 0.053876 57 0.535493 0.029426 57 0.881150 0.055632 58 0.536870 0.040568 58 0.906975 0.057269 59 0.505829 0.033793 59 0.884071 0.056639 60 0.567816 0.038540 60 0.730023 0.051479 61 0.556293 0.038540 61 0.737830 0.050850 62 0.473255 0.032454 62 0.740354 0.052108 63 0.533865 0.040224 63 0.753932 0.051596 64 0.514418 0.034483 64 0.727656 0.053619 65 0.487497 0.028398 65 0.774739 0.053870 66 0.513700 0.025216 66 0.832094 0.048081 67 0.447182 0.026369 67 0.799185 0.052486 68 0.464616 0.026369 68 0.785597 0.053367 69 0.433060 0.024231 69 0.660124 0.049570 70 0.463813 0.024233 70 0.699606 0.050598 71 0.475865 0.028398 71 0.673907 0.048836 72 0.460187 0.026679 72 0.647345 0.046193 73 0.473736 0.027065 73 0.678176 0.048212 74 0.515551 0.024341 74 0.708431 0.051314 75 0.481606 0.034483 75 0.680705 0.049200 76 0.487582 0.027921 76 0.700234 0.048962One thing notable is that, in your log file the validation batch size is 29, however, in my case it is 32, thus I think maybe our preprocessed dataset has some difference
Hi:
I have also tested with the pretrained model on xvector_nnet_tdnn_arcsoftmax_m0.30_linear_bn_1e-2 config, but the eer is 0.05, What is the distance measure you use between two embeddings? and mine is cosin. are there any points to note in the prediction process?
Thanks
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mycrazycracy
commented on August 15, 2024
@mycrazycracy Hi, thanks for the reply. I tested with the pretrained model on xvector_nnet_tdnn_arcsoftmax_m0.30_linear_bn_1e-2 config. And the result is roughly the same which is 2% EER. however, on my side when I trained from the very beginning, the result is much worse. (around 4%)
the misalignment of validation loss happens even at the beginning of training.yours mine 0 4.347158 0.121704 0 5.595568 0.153933 1 4.409681 0.124732 1 6.142251 0.152045 2 4.953392 0.164300 2 5.387073 0.145626 3 4.171205 0.121704 3 5.853278 0.138200 4 6.030734 0.141988 4 4.879803 0.145833 5 3.373980 0.117647 5 3.483222 0.127753 6 3.551348 0.114610 6 2.925486 0.126240 7 3.499111 0.126222 7 2.618020 0.112649 8 2.706735 0.106290 8 2.495200 0.108464 9 2.505243 0.087221 9 2.797635 0.107489 10 2.579709 0.105946 10 3.352109 0.108477 11 2.386637 0.083784 11 2.149656 0.094273 12 2.215215 0.078906 12 2.788486 0.107827 13 2.093923 0.070809 13 2.154233 0.096413 14 2.614316 0.095335 14 2.664895 0.099308 15 2.402408 0.068661 15 2.585773 0.102958 16 2.269810 0.081136 16 2.037069 0.082545 17 2.459356 0.068966 17 2.123691 0.084204 18 1.660872 0.069354 18 1.782891 0.078462 19 1.641136 0.070407 19 1.666662 0.078414 20 1.789717 0.070994 20 1.884485 0.074890 21 1.732624 0.062880 21 1.991562 0.085085 22 1.685123 0.060852 22 1.975221 0.087411 23 1.925408 0.078476 23 1.887127 0.078248 24 1.225578 0.060852 24 1.545569 0.067716 25 1.237508 0.060852 25 1.556502 0.076639 26 1.168123 0.058824 26 1.478283 0.072876 27 1.178835 0.045722 27 1.438764 0.072354 28 1.394852 0.062880 28 1.483797 0.067086 29 1.245886 0.061878 29 1.449579 0.072498 30 1.333659 0.056795 30 1.443876 0.066027 31 0.997353 0.043031 31 1.379931 0.071366 32 0.920696 0.044625 32 1.365147 0.073002 33 1.091778 0.052738 33 1.386801 0.066677 34 1.029863 0.046250 34 1.350820 0.067212 35 0.953727 0.051637 35 1.251097 0.064168 36 0.956934 0.051722 36 1.254138 0.063059 37 0.675873 0.036846 37 1.358343 0.066842 38 0.815823 0.044149 38 1.531808 0.067086 39 0.705373 0.040568 39 1.218243 0.068974 40 0.714766 0.032454 40 1.435390 0.061162 41 0.763318 0.046653 41 1.284261 0.065576 42 0.574937 0.034483 42 1.291959 0.063184 43 0.592286 0.031686 43 1.354145 0.069352 44 0.581585 0.036511 44 1.020841 0.058024 45 0.690763 0.043893 45 1.134171 0.060919 46 0.698204 0.040568 46 0.971369 0.062687 47 0.545573 0.026369 47 1.049776 0.057784 48 0.564414 0.029049 48 1.095432 0.059408 49 0.577830 0.034483 49 1.046128 0.056462 50 0.578830 0.033454 50 1.139795 0.063059 51 0.570985 0.034483 51 0.849101 0.051990 52 0.534715 0.032454 52 0.845482 0.053041 53 0.577185 0.035037 53 0.867068 0.055129 54 0.524684 0.033184 54 0.847392 0.051038 55 0.507371 0.034354 55 0.837217 0.052612 56 0.503855 0.032454 56 0.879506 0.053876 57 0.535493 0.029426 57 0.881150 0.055632 58 0.536870 0.040568 58 0.906975 0.057269 59 0.505829 0.033793 59 0.884071 0.056639 60 0.567816 0.038540 60 0.730023 0.051479 61 0.556293 0.038540 61 0.737830 0.050850 62 0.473255 0.032454 62 0.740354 0.052108 63 0.533865 0.040224 63 0.753932 0.051596 64 0.514418 0.034483 64 0.727656 0.053619 65 0.487497 0.028398 65 0.774739 0.053870 66 0.513700 0.025216 66 0.832094 0.048081 67 0.447182 0.026369 67 0.799185 0.052486 68 0.464616 0.026369 68 0.785597 0.053367 69 0.433060 0.024231 69 0.660124 0.049570 70 0.463813 0.024233 70 0.699606 0.050598 71 0.475865 0.028398 71 0.673907 0.048836 72 0.460187 0.026679 72 0.647345 0.046193 73 0.473736 0.027065 73 0.678176 0.048212 74 0.515551 0.024341 74 0.708431 0.051314 75 0.481606 0.034483 75 0.680705 0.049200 76 0.487582 0.027921 76 0.700234 0.048962One thing notable is that, in your log file the validation batch size is 29, however, in my case it is 32, thus I think maybe our preprocessed dataset has some difference
Hi:
I have also tested with the pretrained model on xvector_nnet_tdnn_arcsoftmax_m0.30_linear_bn_1e-2 config, but the eer is 0.05, What is the distance measure you use between two embeddings? and mine is cosin. are there any points to note in the prediction process?
Thanks
Pls use PLDA. Just follow run.sh in voxceleb egs.
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shatealaboxiaowang
commented on August 15, 2024
@mycrazycracy Hi, thanks for the reply. I tested with the pretrained model on xvector_nnet_tdnn_arcsoftmax_m0.30_linear_bn_1e-2 config. And the result is roughly the same which is 2% EER. however, on my side when I trained from the very beginning, the result is much worse. (around 4%)
the misalignment of validation loss happens even at the beginning of training.yours mine 0 4.347158 0.121704 0 5.595568 0.153933 1 4.409681 0.124732 1 6.142251 0.152045 2 4.953392 0.164300 2 5.387073 0.145626 3 4.171205 0.121704 3 5.853278 0.138200 4 6.030734 0.141988 4 4.879803 0.145833 5 3.373980 0.117647 5 3.483222 0.127753 6 3.551348 0.114610 6 2.925486 0.126240 7 3.499111 0.126222 7 2.618020 0.112649 8 2.706735 0.106290 8 2.495200 0.108464 9 2.505243 0.087221 9 2.797635 0.107489 10 2.579709 0.105946 10 3.352109 0.108477 11 2.386637 0.083784 11 2.149656 0.094273 12 2.215215 0.078906 12 2.788486 0.107827 13 2.093923 0.070809 13 2.154233 0.096413 14 2.614316 0.095335 14 2.664895 0.099308 15 2.402408 0.068661 15 2.585773 0.102958 16 2.269810 0.081136 16 2.037069 0.082545 17 2.459356 0.068966 17 2.123691 0.084204 18 1.660872 0.069354 18 1.782891 0.078462 19 1.641136 0.070407 19 1.666662 0.078414 20 1.789717 0.070994 20 1.884485 0.074890 21 1.732624 0.062880 21 1.991562 0.085085 22 1.685123 0.060852 22 1.975221 0.087411 23 1.925408 0.078476 23 1.887127 0.078248 24 1.225578 0.060852 24 1.545569 0.067716 25 1.237508 0.060852 25 1.556502 0.076639 26 1.168123 0.058824 26 1.478283 0.072876 27 1.178835 0.045722 27 1.438764 0.072354 28 1.394852 0.062880 28 1.483797 0.067086 29 1.245886 0.061878 29 1.449579 0.072498 30 1.333659 0.056795 30 1.443876 0.066027 31 0.997353 0.043031 31 1.379931 0.071366 32 0.920696 0.044625 32 1.365147 0.073002 33 1.091778 0.052738 33 1.386801 0.066677 34 1.029863 0.046250 34 1.350820 0.067212 35 0.953727 0.051637 35 1.251097 0.064168 36 0.956934 0.051722 36 1.254138 0.063059 37 0.675873 0.036846 37 1.358343 0.066842 38 0.815823 0.044149 38 1.531808 0.067086 39 0.705373 0.040568 39 1.218243 0.068974 40 0.714766 0.032454 40 1.435390 0.061162 41 0.763318 0.046653 41 1.284261 0.065576 42 0.574937 0.034483 42 1.291959 0.063184 43 0.592286 0.031686 43 1.354145 0.069352 44 0.581585 0.036511 44 1.020841 0.058024 45 0.690763 0.043893 45 1.134171 0.060919 46 0.698204 0.040568 46 0.971369 0.062687 47 0.545573 0.026369 47 1.049776 0.057784 48 0.564414 0.029049 48 1.095432 0.059408 49 0.577830 0.034483 49 1.046128 0.056462 50 0.578830 0.033454 50 1.139795 0.063059 51 0.570985 0.034483 51 0.849101 0.051990 52 0.534715 0.032454 52 0.845482 0.053041 53 0.577185 0.035037 53 0.867068 0.055129 54 0.524684 0.033184 54 0.847392 0.051038 55 0.507371 0.034354 55 0.837217 0.052612 56 0.503855 0.032454 56 0.879506 0.053876 57 0.535493 0.029426 57 0.881150 0.055632 58 0.536870 0.040568 58 0.906975 0.057269 59 0.505829 0.033793 59 0.884071 0.056639 60 0.567816 0.038540 60 0.730023 0.051479 61 0.556293 0.038540 61 0.737830 0.050850 62 0.473255 0.032454 62 0.740354 0.052108 63 0.533865 0.040224 63 0.753932 0.051596 64 0.514418 0.034483 64 0.727656 0.053619 65 0.487497 0.028398 65 0.774739 0.053870 66 0.513700 0.025216 66 0.832094 0.048081 67 0.447182 0.026369 67 0.799185 0.052486 68 0.464616 0.026369 68 0.785597 0.053367 69 0.433060 0.024231 69 0.660124 0.049570 70 0.463813 0.024233 70 0.699606 0.050598 71 0.475865 0.028398 71 0.673907 0.048836 72 0.460187 0.026679 72 0.647345 0.046193 73 0.473736 0.027065 73 0.678176 0.048212 74 0.515551 0.024341 74 0.708431 0.051314 75 0.481606 0.034483 75 0.680705 0.049200 76 0.487582 0.027921 76 0.700234 0.048962One thing notable is that, in your log file the validation batch size is 29, however, in my case it is 32, thus I think maybe our preprocessed dataset has some difference
Hi:
I have also tested with the pretrained model on xvector_nnet_tdnn_arcsoftmax_m0.30_linear_bn_1e-2 config, but the eer is 0.05, What is the distance measure you use between two embeddings? and mine is cosin. are there any points to note in the prediction process?
ThanksPls use PLDA. Just follow run.sh in voxceleb egs.
Yes it is as expected, Thank you for your sharing and replaying;
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deciding
commented on August 15, 2024
@shatealaboxiaowang hope you can have the same training result. if you can reproduce on your own training, can you update in this thread? thanks
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shatealaboxiaowang
commented on August 15, 2024
@shatealaboxiaowang hope you can have the same training result. if you can reproduce on your own training, can you update in this thread? thanks
ok, I will update in this thread if having the same training result.
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