NeuralAmpModelerCore
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sdatkinson
Core DSP library for NAM plugins
See #164. Catch2 seems reasonable. Opening this Issue to invite discussion. cc @Khalian
Migrating to using catch2 as a framework. Solves issue https://github.com/sdatkinson/NeuralAmpModelerCore/issues/165. However at a later date, I would like to move benchmodel to also use catch2 and setup valgrind memory leak...
Same as https://github.com/sdatkinson/neural-amp-modeler/issues/277, though I'm not sure if Sphinx works for C++ (never tried).
Since an impulse response can be converted losslessly to a linear NAM, it'd be neat to be able to instantiate a Linear model using a .wav file.
``` #ifndef __APPLE__ #include // and, not, or #endif ``` I add this for elimination warning clang macOS compile for every include ciso646
Sorted out my fork problems. Here's a pull request for the activation->apply overload we discussed earlier.
# TODO checklist - [ ] Factory to initialize from `.nam` file. - [ ] Document `.nam` file version 0.5.5 to include `"sequential"`. - [ ] No `"weights"` nor `"sample_rate"`...
It would be nice for models to be defined as having an arbitrary number of channels as inputs/outputs. One thing that this would allow is for "(directed, acyclic) graph" NAMs...
Just a "little" task, but it'd be swell to take some of the TODOs around e.g. ring buffer handling and make good on them to rework the Conv1D class (cf...
This assertion might be incorrect: https://github.com/sdatkinson/NeuralAmpModelerCore/blob/846968710a670d662b15e449edba852d747d748e/NAM/wavenet.cpp#L159-L166 I don't think that it should be the receptive field of the layer array, but the r.f. of the single-largest layer. Worth thinking about...