Comments (4)
And I test a short sentence, negative number appear in w1, it's strange, how do you think about it ?
w1: tensor([[[ 1.8711e-01],
[ 1.8163e-02],
[ 6.9934e-02],
[-7.8350e-02],
[-4.9876e-02],
[-1.9600e-02],
[-2.8484e-02],
[ 2.5403e-01],
[ 1.2672e-01],
[-3.9872e-02],
[-1.2691e-01],
[ 3.4448e-02],
[-1.4095e-01],
[-8.9084e-02],
[-1.7390e-01],
[-3.6188e-02],
[-1.6663e-01],
[ 7.3178e-02],
[-9.7041e-02],
[-2.1955e-02],
[-1.1918e-01],
[ 3.8830e-02],
[-6.7791e-02],
[ 8.4965e-02],
[-6.8574e-02],
[-2.7004e-02],
[-9.1636e-02],
[-5.1762e-02],
[ 2.8749e-02],
[ 1.3503e-01],
[ 2.5275e-03],
[-1.7253e-01],
[-5.4970e-02],
[-1.0083e-01],
[-1.3783e-01],
[-2.8183e-01],
[-6.7833e-02],
[-1.1899e-01],
[-7.5371e-02],
[-1.7042e-01],
[ 8.6797e-03],
[ 5.7114e-02],
[ 6.5258e-04],
[ 3.3913e-02],
[-1.0919e-01],
[-5.4881e-02],
[ 7.9677e-01],
[ 7.5072e-01],
[ 8.4159e-01],
[ 7.8338e-01]]], device='cuda:0')
the other output are always bigger than 0
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hi there,
Hi, dear author, when I infer my own data, the length exceed 50 and error occurs. I find this line in gopt.py,
self.pos_embed = nn.Parameter(torch.zeros(1, 55, self.embed_dim)) So the max length is 50? If the sentence exceed 50, I must clip it into shorter fragment?
Yes, you are correct. This is because the so762 dataset's max length is 50. Unfortunately, the default positional embedding is learnable, so it is not easy for extrapolation. However, if you use the sin positional embedding and retrain the model (would be easy, we have the colab script to do so). You can extrapolate the positional embedding and inference with longer sequences.
Lines 144 to 145 in bed909d
And I test a short sentence, negative number appear in w1, it's strange, how do you think about it ?
This is a more complex question, but the first thing is to make sure your extracted GOP feature is correct. I recommend checking if the SO762 test set score has negative values, if not, then it is likely it is the GOP feature input's problem. Then you can check the statistics of your GOP feature (e.g., max, mean, std) compared with our SO762 input feature.
-Yuan
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Thanks for your kind and fast reply! I will follow your suggestions.
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Hi, I tried the speechocean762 data, and also appear negative value in w1. I have attached them, maybe you can try
train.zip
I use librispeech model
w1: tensor([[[ 0.1317],
[ 0.1545],
[ 0.0895],
[ 0.0449],
[ 0.0803],
[ 0.0930],
[ 0.2981],
[ 0.1362],
[ 0.1225],
[ 0.0795],
[ 0.1348],
[-0.0443],
[ 0.0054],
[ 0.0733],
[ 0.4038],
[ 1.1466],
[ 1.1085],
[ 1.1391],
[ 1.0634],
[ 1.2589],
[ 1.1451],
[ 1.0485],
[ 1.1034],
[ 1.1758],
[ 1.1658],
[ 1.0924],
[ 1.1657],
[ 1.1954],
[ 1.1132],
[ 1.1714],
[ 0.9821],
[ 1.1045],
[ 1.0680],
[ 0.9793],
[ 1.1042],
[ 1.1401],
[ 1.0787],
[ 1.0832],
[ 1.1980],
[ 1.1647],
[ 1.1390],
[ 1.1669],
[ 1.1677],
[ 1.1477],
[ 1.1583],
[ 1.1625],
[ 1.1313],
[ 1.0742],
[ 1.1767],
[ 1.1211]]], device='cuda:0')
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Related Issues (20)
- step 3 in inference your own data confusion HOT 2
- while running run.sh in gop_speechocean it get error in visualize_feat.py AttributeError: 'tuple' object has no attribute 'shape' HOT 4
- The result seems to be no change HOT 19
- Сomparison with other works
- Can I assess random .wav without corresponding script?
- Can't not infer my own data, the result belongs to speechocean762 dataset
- The result of u1,u2,u3,u4,u5 is always higher than 1.5 HOT 2
- About infer the data HOT 2
- 测试自己的数据时,均值和方差怎么算出来的 HOT 3
- Hi, I want to add some words to text-phone from CMU dict and librispeech-lexicon, but they are different with speechocen762 HOT 2
- the number of my own data HOT 1
- Interpreting the result HOT 7
- I could not find "load_human_scores" and "load_phone_symbol_table" from utils. HOT 1
- What is the point of cleaning lexicon in the dictionary: librispeech-lexicon.txt HOT 2
- Is it possible to apply GOPT in real life application? HOT 5
- how to infer or predict my own trained model HOT 4
- How to improve speed for GOPT HOT 1
- Can not reproduced similar result using pretrained model checkpoint and released data. HOT 1
- run the code on cpu HOT 1
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