For machine learning on audio data, it is often (always?) useful to modify the original dataset to make the model more general.
A way to do such manipulation that is both convenient to use from Python (a major programming language in the field and well tied in to the major frameworks) and performant is extremely welcome.
While I think this is awesome (from the perspective of audio production pipelining requirements), I can't really see the need to use a VST to apply basic audio alterations for the stated ML purpose, since there are already many native DSP libraries that can apply reverb, distortion, delay, convolution, etc. to an audio signal.
A way to do such manipulation that is both convenient to use from Python (a major programming language in the field and well tied in to the major frameworks) and performant is extremely welcome.