If quantization-aware is giving you worse results then I suspect there’s something definitely wrong in your data input pipeline. I would investigate the data preprocessing functions really carefully and would also investigate their descriptive statistics. Is test_audio preprocessed 'cause the TFLiteConverter would expect it is. It’s a different story if your model consists the preprocessing layers inside of it already.



 Olivier_Staehli:

Is there any method or formula to figure out how large the representative dataset should be?


It is really dependent on the dataset and the problem you are using. Also, how much quality you can afford to lose. In my experience, a sample size of 100 - 1000 usually works. Therefore, I would recommend plotting a graph of Representative Dataset Size vs. Model Performance and pick one from there.
Lastly, I would come back to this suggestion:



 Sayak_Paul:

Also, when you are using the integer quantized model, it’s important to account for the scale and offset as shown here: