Comments (3)
from torchvggish.
@suzhenghang I have just investigated this and the embeddings are much different. I don't know why this is, so I will be working on figuring this out over the next few weeks. Again, thanks for raising this.
from torchvggish.
Apologies for the amount of updates. I have found the problem and will be updating the code shortly to reflect this. I didn't account for the different dimensions of the data between tensorflow and pytorch when flattening the features output. As you can see, the comparison is identical:
with a cosine distance of 0. There are some variations at the 5th decimal place, but the PCA postprocessor ignores these small variations and produces the correct embeddings. I'll close this issue now as this problem has been resolved. Thank you for reporting it.
from torchvggish.
Related Issues (20)
- convert the pca state dict to torch
- VGGish should compose a VGG instead of inherit from it
- Provide setup.py so pip install still works HOT 1
- GPU version support? HOT 10
- 'Tensor' object has no attribute 'T' HOT 1
- Could you add a license file? HOT 2
- Output size HOT 4
- Original vggish vs this.. HOT 2
- How to load raw audio file HOT 2
- About the preprocess
- how to feed model batch inputs ? HOT 3
- Modify torchvggish.vggish_params.EXAMPLE_HOP_SECONDS after (or before) model load?
- Pre-activation as output of VGGish HOT 1
- URL Error HOT 1
- Missing activations. HOT 4
- PCA post-processing removes gradient from embeddings HOT 1
- Provide better documentation HOT 1
- questions about VGGISH_WEIGHTS HOT 1
- How to use this code for training on my own dataset? HOT 1
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from torchvggish.