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Currently, the process is manual. Typically, you would:
Look up a relevant interface in speechbrain/inference (as of 1.0), e.g. WaveformEnhancement
Check what hparams/mods it attempts to use
Copy over the relevant files in a directory (e.g. here likely just model.ckpt)
Build an inference hyperparams.yaml in the same directory from your training hyperparams (optionally taking inspiration from a similar published model on HF)
Finally, write and use a test script that uses the inference interface e.g. enhancement = WaveformEnhancement.from_hparams("./mymodeldirectory/", run_opts={"device": device})
Describe the bug
I trained with my custom dataset with minimal modifications to https://github.com/speechbrain/speechbrain/tree/develop/templates/enhancement (0.5.16 version)
After, I end up with a result folder structure such as
How could I load/use the model I trained for enhancement in my python script, like
Expected behaviour
Being able to use it like
To Reproduce
No response
Environment Details
No response
Relevant Log Output
No response
Additional Context
No response
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