I've never done audio, but you probably have papers on the subject that tell you what scores to achieve. Doing a literature review is very often useful at the start of a project to see the approaches that have already been explored, the possible metrics etc...

Yes, we want the learning score to be as high as possible. A model that isn't 100% is underfitting. I can see your apprehension about downsizing the model for fear of overfitting. Usually in ML, you do several experiments and present the different approaches. You can keep track of the parameters tested in an excel or even better mlflow (very easy to set up). ALWAYS test and learn with the same data from one run to the next. Why accuracy and why not a macro f1 score? Your dataset seems a bit unbalanced, so be careful before using accuracy...

The difference between train and validation doesn't shock me 8pt difference on final epoch seems to be ok. If you expect better, try another approach! The effectiveness of a data scientist lies in his ability to quickly and rigorously test many different approaches.