Comments (6)
That clarified for me recognized phrases which are loaded from grammars. I like your good idea how to implement the commands for training while maintaining their context.
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The training session mode has been added to develop now. You can say "start training session"
to start it or call SphinxEngine.start_training_session()
. To end a session, you can say "end training session"
or call SphinxEngine.end_training_session()
. "end training session"
doesn't start the training program GUI because it does not exist yet.
As I said in an above comment, the only difference between this mode and normal use is that no action or rule processing takes place. Keyphrases are exempt from this. Grammar contexts should still be taken into account.
A good use case for this mode is training commands that take a long time to execute their actions or are dangerous. Perhaps such commands keep getting falsely recognised and they need more training.
The phrases, threshold values and training data directory are configurable in the engine config module. If you set TRAINING_DATA_DIR
to None
in the engine config, no .wav files or transcript files will be created or written to. A warning will be displayed if a training session is started and this is the case.
I'll label this with Documentation because this feature will need to be properly documented at some point.
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an ordered list of recognised phrases - phrases to train, loaded from the folder mentioned above
Could this be read from the grammars then generated into a list?
from dragonfly.
Yes, the phrases can be generated from grammars. Did you mean as an alternative to using .wav files and recognised phrases from the engine?
It might be useful to have a training mode where recognition processing does not occur. For example, I could say 'start training session', say a bunch of commands in sequences that I want to train, then finish by saying 'end training session', after which the training program could be opened. Contexts would still work, but no action would be executed.
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The recording speech and hypothesises part is done now. The engine's default behaviour is to create a training
folder in the same folder as the module loader, write audio to trainingXXXX.wav
files and hypothesises to training.transcriptions
. The training.fileids
file is also created. The files should work with the SphinxTrainingHelper script.
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I don't think this is really needed. Even if it was, I cannot commit to working on this. I hope my ideas above and in Dragonfly's source code are helpful for those attempting the task in the future.
As it is a considerably easier task than this, I still plan on unifying the training data output by the Pocket Sphinx engine to be similar to the output from other engines (see #196).
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