Comments (4)
What do you think it is... Christmas? 😄 Was finetuning not enough? haha
Multiple additional models might be complicated, the loader situation may get messy. Its not impossible, but Ill think around it. There are quire a few complexities to the loaders in the "multiple other models" scenario.
It would be relatively easy to do a "detect an extra model in this location and make an extra gradio radio box appear for that 1x model"... I think. But I am talking for 1x extra model in 1x location. Though still, Id need to think around that one.
Would 1x extra model work? or are you looking at literally 2 to ??? extra models?
Would you train the same model over and over (different voices), so just need that 1x model.
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Initially I thought it'd be like a model selector in Auto1111's stable diffusion webui I suppose.
Would you train the same model over and over (different voices), so just need that 1x model.
Is that possible? So I can train a model with one voice, then use that finetuned model again with a different voice? If that's the case then I suppose a model selector may not be needed, depending on how much other voices effect the previous finetuning.
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Ive just updated finetuning to not only compact your model, but also deal with all the model moving. Its creating a folder in the /models/ folder called trainedmodel
, now that its standardised in some way, I should be able to do something with the gradio interface that looks for a model existing in that folder and if so, gives you an extra loader option. But Ive just spent my day on a load of other bits and Im sure Ive got something else to do. So Ill get there.. but give me time :)
If you do want to compact your existing model go to this link #28
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The model has to be stored in /models/trainedmodel/
This is the default location that finetuning now moves models to after training.
If a model is detected there when AllTalk starts up, it will add the additional loader type into the Gradio interface.
At some point Ill work on the finetuning, so that you can choose to use either the base model OR your already pre-trained model.
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