OpenVoiceOS precise trainer
WIP - open during construction, sharing for early feedback
you can now easily train precise models, all updated to latest tensorflow, this repo is recommended to train and convert tflite models
Several training strategies are available, each may provide better results for different datasets and wake words, some sounds might be easier to learn than others and the kinds of data available for each word will be different
- train - select epochs, batch size and go!
- train with replacement - use a different subset of the training data in every epoch, helps avoid overfitting
- train incremental - every epoch test the model and move false positives to training set, helps if you have an unbalanced dataset (a lot not-ww samples)
- train incremental with replacement - unbalanced dataset + overfitting to specific voices
- train optimized - using bbopt search the optimal hyperparams (dropout and recurrent units), train several models and keep best one
- train optimized incremental
- train optimized with replacement
from precise_trainer import PreciseTrainer
model_name = "hey_computer"
folder = f"/home/user/ww_datasets/{model_name}" # dataset here
model_path = f"/home/user/trained_models/{model_name}" # save here
log_dir = f"logs/fit/{model_name}" # for tensorboard
# pick one training method
model_file = trainer.train()
model_file = trainer.train_with_replacement(mini_epochs=10)
model_file = trainer.train_incremental(mini_epochs=20)
model_file = trainer.train_incremental_with_replacement(balanced=True, porportion=0.6)
model_file = trainer.train_optimized(cycles=20)
model_file = trainer.train_optimized_with_replacement(porportion=0.8)
model_file = trainer.train_optimized_incremental(cycles=50)
# convert a previous model
model_file = ".../my_model"
PreciseTrainer.convert(model_file, model_file + ".tflite")
# test a previous model
model_file = ".../my_model.tflite"
PreciseTrainer.test(model_file, folder)
