Adding E4B audio encoder to larger models

Reddit r/LocalLLaMA 05/15/26, 04:48 PM Tools

audio-encoder model-integration linear-projection transfer-learning e4b gemma

Summary

The author proposes a method to add the E4B audio encoder to larger models by extracting the encoder, creating a linear projection layer, and fine-tuning only that layer with text-audio pairs, similar to a referenced paper but using Gemma instead of Whisper.

I am curious if anyone here has tried doing this, I did a bit of digging and it seems like it would be easier to do then I first thought and would like to ask ask for correction if my assumptions are wrong. Here is how I would go about it: 1. Extract the 300mb audio encoder from E4B or E2B 2. Create a new linear projection layer in Pytorch that translates the E4B encoder output to fit the hidden dimension size of the larger target model 3. Get a dataset of text and audio pairs 4. Freeze both the large model and audio encoder and only train the new linear projection layer Since only the new layers have to be trained it should be relatively quick to train and wouldnt negatively affect the larger models output. Basically the same as [this paper ](https://arxiv.org/html/2309.13963)but instead of using the whisper encoder using the Gemma one which has been built for low latency LLMs.

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