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MTG / WGANSing

Multi-voice singing voice synthesis
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assertion error #2

Open ghost opened 5 years ago

ghost commented 5 years ago

Local (My Computer) & distant (Google Colab) training stop at :

/content/WGanSing/data_pipeline.py in data_gen(mode, sec_mode)
    128         feats_targs = np.array(feats_targs)
    129 
--> 130         assert feats_targs.max()<=1.0 and feats_targs.min()>=0.0
    131 
    132         yield feats_targs, targets_f0_1, np.array(pho_targs), np.array(targets_singers)

AssertionError:

When I comment the assert line, the training start but I want to be sure that is not really a problem?

pc2752 commented 5 years ago

Hi, this might be due to an error in normalisation, could you try running the get_stats() function in data_pipeline.py and see if you still get the assertion error? Let me know if this problem persists.

ghost commented 5 years ago

I need to comment the second line in the get_stats() function that is not used and produce an error on config.backing_dir not found:

back_list = [x for x in os.listdir(config.backing_dir) if x.endswith('.hdf5') and not x.startswith('._') and not x.startswith('mir') and not x.startswith('med')]

The function run well.

I restart the training (local) and get the same assert error than previously.

ghost commented 5 years ago

Please note that when I run the prep_data_nus.py before training, I get some warning during the process:

Processing singer ADIZ
G:\AI\sing\utils.py:267: RuntimeWarning: divide by zero encountered in log2
  y=69+12*np.log2(f0/440)

I get 96 files with size:

 8.170.864 - nus_ADIZ_read_01.hdf5
15.557.744 - nus_ADIZ_read_09.hdf5
34.567.824 - nus_ADIZ_read_13.hdf5
45.938.144 - nus_ADIZ_read_18.hdf5
25.436.304 - nus_ADIZ_sing_01.hdf5
54.844.624 - nus_ADIZ_sing_09.hdf5
53.756.544 - nus_ADIZ_sing_13.hdf5
80.155.824 - nus_ADIZ_sing_18.hdf5
24.383.024 - nus_JLEE_read_05.hdf5
20.998.144 - nus_JLEE_read_08.hdf5
28.484.784 - nus_JLEE_read_11.hdf5
28.380.384 - nus_JLEE_read_15.hdf5
74.274.624 - nus_JLEE_sing_05.hdf5
55.085.904 - nus_JLEE_sing_08.hdf5
66.161.584 - nus_JLEE_sing_11.hdf5
69.924.624 - nus_JLEE_sing_15.hdf5
25.721.664 - nus_JTAN_read_07.hdf5
33.025.024 - nus_JTAN_read_15.hdf5
40.766.864 - nus_JTAN_read_16.hdf5
30.881.344 - nus_JTAN_read_20.hdf5
32.892.784 - nus_JTAN_sing_07.hdf5
68.263.504 - nus_JTAN_sing_15.hdf5
57.902.384 - nus_JTAN_sing_16.hdf5
66.187.104 - nus_JTAN_sing_20.hdf5
18.875.344 - nus_KENN_read_04.hdf5
32.224.624 - nus_KENN_read_10.hdf5
13.963.904 - nus_KENN_read_12.hdf5
48.288.304 - nus_KENN_read_17.hdf5
66.980.544 - nus_KENN_sing_04.hdf5
45.873.184 - nus_KENN_sing_10.hdf5
21.292.784 - nus_KENN_sing_12.hdf5
71.963.904 - nus_KENN_sing_17.hdf5
20.118.864 - nus_MCUR_read_04.hdf5
28.640.224 - nus_MCUR_read_10.hdf5
14.915.104 - nus_MCUR_read_12.hdf5
47.520.384 - nus_MCUR_read_17.hdf5
65.003.904 - nus_MCUR_sing_04.hdf5
46.302.384 - nus_MCUR_sing_10.hdf5
21.464.464 - nus_MCUR_sing_12.hdf5
69.822.544 - nus_MCUR_sing_17.hdf5
21.049.184 - nus_MPOL_read_05.hdf5
29.343.184 - nus_MPOL_read_11.hdf5
19.086.464 - nus_MPOL_read_19.hdf5
27.257.504 - nus_MPOL_read_20.hdf5
69.344.624 - nus_MPOL_sing_05.hdf5
64.815.984 - nus_MPOL_sing_11.hdf5
68.808.704 - nus_MPOL_sing_19.hdf5
62.663.024 - nus_MPOL_sing_20.hdf5
20.121.184 - nus_MPUR_read_02.hdf5
32.505.344 - nus_MPUR_read_03.hdf5
21.260.304 - nus_MPUR_read_06.hdf5
41.764.464 - nus_MPUR_read_14.hdf5
28.637.904 - nus_MPUR_sing_02.hdf5
34.498.224 - nus_MPUR_sing_03.hdf5
63.417.024 - nus_MPUR_sing_06.hdf5
61.846.384 - nus_MPUR_sing_14.hdf5
19.427.504 - nus_NJAT_read_07.hdf5
23.615.104 - nus_NJAT_read_15.hdf5
29.282.864 - nus_NJAT_read_16.hdf5
21.837.984 - nus_NJAT_read_20.hdf5
34.069.024 - nus_NJAT_sing_07.hdf5
68.882.944 - nus_NJAT_sing_15.hdf5
59.178.384 - nus_NJAT_sing_16.hdf5
63.108.464 - nus_NJAT_sing_20.hdf5
23.951.504 - nus_PMAR_read_05.hdf5
20.682.624 - nus_PMAR_read_08.hdf5
34.718.624 - nus_PMAR_read_11.hdf5
29.050.864 - nus_PMAR_read_15.hdf5
75.808.144 - nus_PMAR_sing_05.hdf5
53.118.544 - nus_PMAR_sing_08.hdf5
67.117.424 - nus_PMAR_sing_11.hdf5
68.398.064 - nus_PMAR_sing_15.hdf5
 6.699.984 - nus_SAMF_read_01.hdf5
15.038.064 - nus_SAMF_read_09.hdf5
29.995.104 - nus_SAMF_read_13.hdf5
45.877.824 - nus_SAMF_read_18.hdf5
25.995.424 - nus_SAMF_sing_01.hdf5
55.842.224 - nus_SAMF_sing_09.hdf5
57.561.344 - nus_SAMF_sing_13.hdf5
82.429.424 - nus_SAMF_sing_18.hdf5
21.793.904 - nus_VKOW_read_05.hdf5
31.178.304 - nus_VKOW_read_11.hdf5
25.162.544 - nus_VKOW_read_19.hdf5
29.493.984 - nus_VKOW_read_20.hdf5
69.344.624 - nus_VKOW_sing_05.hdf5
65.409.904 - nus_VKOW_sing_11.hdf5
68.750.704 - nus_VKOW_sing_19.hdf5
65.038.704 - nus_VKOW_sing_20.hdf5
18.559.824 - nus_ZHIY_read_02.hdf5
32.512.304 - nus_ZHIY_read_03.hdf5
21.434.304 - nus_ZHIY_read_06.hdf5
47.211.824 - nus_ZHIY_read_14.hdf5
26.308.624 - nus_ZHIY_sing_02.hdf5
32.630.624 - nus_ZHIY_sing_03.hdf5
64.505.104 - nus_ZHIY_sing_06.hdf5
63.082.944 - nus_ZHIY_sing_14.hdf5

Hope I help, thank you very much for your support.

pc2752 commented 4 years ago

Hi,

Thanks for the feedback. RuntimeWarning: divide by zero encountered in log2 y=69+12*np.log2(f0/440) isn't really a problem, its because of the use of log over 0, which is corrected for later.

However, could you modify the get_stats function to make sure you are not getting any nan or inf values in the dataset?

yuuSiVo commented 4 years ago

Hello I get the Dis Loss, Final Loss, Val Dis Loss is nan. I using training dataset is 60 files

pc2752 commented 4 years ago

Are you using the NUS dataset or your own dataset? Can you just check get_stats() function in data_pipeline.py to see if there are file which have nans in them? These might be what cause the assertion error.

yuuSiVo commented 4 years ago

my own dataset

bangbuken commented 3 years ago

I am using NUS dataset on fe434ff and meet the same problem. I find that "nus_KENN" is excluded when stats are calculated, https://github.com/MTG/WGANSing/blob/fe434ffdb5adcb6567f257e95124b58d18b19849/data_pipeline.py#L139 but it is still normalized during training. So, the reason, in my opinion, is that "nus_KENN" must contain some large or small numbers and make the normalized data fall outside the interval [0, 1]. I modify the code https://github.com/MTG/WGANSing/blob/fe434ffdb5adcb6567f257e95124b58d18b19849/data_pipeline.py#L31 to

and not x == 'nus_JLEE_sing_05.hdf5' and not x == 'nus_JTAN_read_07.hdf5' and not x.startswith('nus_KENN')]

and training goes well.