If what you are asking is if combining them at all is possible or not it would be possible, but would need retraining.

First you would have to extract the the 300mb audio encoder from E4B (These parameters will have to be frozen during training)

Then create a new linear projection layer in Pytorch that translates the E4B encoder output to fit the hidden dimension size of 31B or what ever you are using.

Now you need to retrain 31B on audio and text pairs, maybe even full on audio to thinking to response pairs.

This has been done before with vision in the old llama days see: https://github.com/haotian-liu/LLaVA

Since a lot of weights are frozen it shouldnt be too expensive to train, but the biggest problem will be the dataset.

As far as I know the audio gets turned into latent vector embeddings by E4B without ever turning into normal text, since the 31B version isnt trained on this type of data it wont be able to do anything with it.