Various AI / DL audio experiments & exploration.
aimx's Introduction
aimx's People
aimx's Issues
Dataprep & train pipeline to optionally use intermediate JSON file
Try Keras mixed precision training on GPUs
Apply & explore results of the NNs on the larger datasets
Make sure scripts work cross-platform (test on Linux)
Originally they were developed under Windows. Some code (like dir.split("\\"
and other) needs to be made to work for Linux, etc.
Execution timing, duration measurement and reports
Create code metrics test to have reports on LOC, Lint and other useful code stats
Make Inferex work real-time on a mic stream
Python static code checking / lint
Fast cleanup of uninteresting contents of gen_* dirs
"Uninteresting" are usually those that are produced as a result of fast dataprep and/or fast training, for example, sanitest output and quick experimentation results. Some markers: very few epochs, very small depth, no meaningful accuracy, etc.
Possible parameter names: -notrash
Hoist hard-coded parameters into clargs
Related TODOs highlighted in comments in this commit: 5986181
Automate dir-label relationship
Create Inferex module by integrating OpenVINO for inferencing
Integrate TensorBoard for faster experimentation
CLI option to stop sanitest pipeline on error
Current default is to continue.
Make dir-label relationship work correctly with dataset views
Replace print_info() with differentiated and proper logging
INFO (white), WARNING (brown), ERROR (red), etc
Likely candidate: Python logging
Create tests for messaging verification
Error texts, warnings, etc. This is better be done later, when messaging is stabilized.
Detect all words in audio files of arbitrary length
Initial setup of GitHub Actions for the first stable CI pipeline link
Sanitest: automatically detect changed files and run only affected scripts
Add accuracy test sanitest_accuracy.py
Create a common utils module and move the relevant code there.
Add help texts for command line arguments.
Clarg for views into datasets instead of actual physical splits
Automate classification layer neuron count deduction based on labels
Mentex yardstone for audio data augmentation
To include things like mixing, trimming, etc. โ exact actions necessary will become clear and specified accordingly as we go forward.
Expected first yardstone is ability to call:
mentex.py -mapolicy random -signals ../workdir/signals -noises ../workdir/noises
Which will result in a new directory /workdir/signoises containing signals that have been paired with noises according to the random mapping policy (mapolicy). List of initial policies to include:
- random (in which case every signal will be mixed with a noise picked randomly at each runtime)
- direct (in which case every signal will be mixed with a corresponding noise picked deterministically at each runtime)
Add a minimum acceptable accuracy test
This test will ensure that a new dataset or model produce at least as good an accuracy on certain tasks as the old accepted standard.
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