Python FluCoMa

Hi all,

I took to making a new major version, 2.0.0, of the Python/FluCOMa CLI Bindings. The major motivation was to learn how dataclasses can be leveraged to make very simple object representations of the returns from the algorithms. As a result, I’ve come up with something that feels far more native (to me at least).

For example:

from pathlib import Path
from flucoma import fluid

source = Path("stereo_test.wav")
mfcc = fluid.mfcc(source, numcoeffs=5, numbands=20)

stats = fluid.stats(mfcc, numderivs=0)

# Let's see the data
for i, band in enumerate(stats):
    printout = f"Stats for MFCC band number {i}: {band}"

is perhaps the most basic example. All the outputs are iterable with patterns and idioms that feel pythonic and you get a lot of flexibility in how you treat the objects as a result. Here is a more complicated example:

from pathlib import Path
from flucoma import fluid

source = Path("Nicol-LoopE-M.wav")

# Run HPSS on the source and return a custom DataClass
hpss = fluid.hpss(source)

# Retrieve the outputs by name
harmonic = hpss.harmonic
percussive = hpss.percussive
residual = hpss.residual

# This allows you to work programatically on the outputs

for x in hpss:
    # As it stands these bindings return a path for everything, even if it wasnt made
    # HPSS can return either 2 or 3 things depending on the mode its in
    # So its worth checking that in this case the residual was made
    # If it wasn't it would be a blank string so we just don't process these
    if x != "":
        sines = fluid.sines(
        )  # The path printed here can be passed to something else for processing

Anyway. I’m not sure anyone but me was using this, but its out there for anyone to play with freely.


This is fantastic. You should advertise it on python and dsp groups, as there are people there that might be interested. Maybe there is a PYO bridge in the making?

Yes, I am particularly fond of James’s genius for making this, and I am planning to use it (and advertise it:) in research!


James, this is fantastic. Thank you so much. I came here to post a question that was inspired by your work and this has helped a lot!

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Great! Thanks so much @jamesbradbury!

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