Comments (4)
The weights of the different models get initialized to the same values hence producing equal outputs.
Interesting! Out of curiosity, is there a rationale for this behavior? Or is this just a side effect of how it works?
Side effect.
Thinking through how I would want it to behave in my use cases, I would prefer moving anything right at the threshold to the "Accompaniment/Other" file, and nowhere else
As a hypothetical example, if there were a very annoying organ line in an otherwise incredible KRS-One song, I would want to have it isolated away to one track, and missing from all other tracks if possible. ;)
I haven't read up yet on how the model works though, so I understand that it might be unreasonably difficult.
You can totally train your model using appropriate dataset if you have one, please check the training section of the repository wiki and if you are facing some trouble during training or want to discuss about obtained results please open a dedicated issue :).
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Hi @rcgale thanks for the feedback. After a quick look it seems something is going wrong when downloading the 4-stems model. INFO:tensorflow:Could not find trained model in model_dir: pretrained_models/4stems, running initialization to predict.
. The weights of the different models get initialized to the same values hence producing equal outputs. I'll investigate why the download fails.
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The weights of the different models get initialized to the same values hence producing equal outputs.
Interesting! Out of curiosity, is there a rationale for this behavior? Or is this just a side effect of how it works?
Thinking through how I would want it to behave in my use cases, I would prefer moving anything right at the threshold to the "Accompaniment/Other" file, and nowhere else
As a hypothetical example, if there were a very annoying organ line in an otherwise incredible KRS-One song, I would want to have it isolated away to one track, and missing from all other tracks if possible. ;)
I haven't read up yet on how the model works though, so I understand that it might be unreasonably difficult.
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Just faced a similar issue today, where the pretrained models of 2stems/4stems dont get downloaded correctly. so downloaded them manually and added them in a directory with the name of "pretrained_models" in the same directory that i issue the spleeter command from and it worked.
i guess it's a path issue somewhere in spleeter ( where spleeter expects the pretrained_models directory to be in the same location of the command execution ).
Not totally sure of the correct fix for this as i'm not a python/conda guru ( just integrating the tool in a nodejs worker )
Awesome tool btw :)
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