lissyx:
The only solution is to reduce the model complexity, n_hidden=2048 with the current models. The temporal complexity of the model is depending quadratically on this. So roughly, if you say it takes 5 seconds to run inference on a 2 seconds audio file, it means you are mostly 2.5x slower than realtime. From there you should be able to infer the complexity of the model you require.
But re-training from scratch with smaller model dimension is going to be a complicated process: you need thousands of hours of audio, and several tentative to adjust the parameters.
Set --n_hidden to 800?
python3 DeepSpeech.py --n_hidden 800 --checkpoint_dir path / to / checkpoint / folder --epochs 3 --train_files my-train.csv --dev_files my-dev.csv --test_files my_dev.csv --learning_rate 0.0001
On which computer and how long can such training take? About. If you use Common Voice data for the English language.
Thank.