My Cpp code is based on example_standalone_inferencing from github and the CPP11 library export.
1 - read in wav file
2 - strip header and convert to 16b PCM
3 - extract remaining wav data needed for window size, convert to 16b PCM, pass (*with header) into raw features vector
4 - run inferencing (same exact code as emample) sprintf results and write output.
5 - move file pointer and repeat N times.
Each time a new model is trained and deployed I delete all 3 library files and recompile with new files. The results have been perfect for the majority of deployments with the exception of two models.
One did not work whatsoever. Everything was classified as the first label at >99% confidence. Retraining the model didnt work. Had to scratch and redo the whole project for it to work. The newer deployment works exactly as expected and tested in the “model testing” tab, with the exception of one class, ting. Not sure if this is a bug. I thought it could possibly be overfitting so I added 0.1 dropout layer and the model testing worked better, but still not deploying properly with the “ting” label.