This was done so the collection of loudnesses from an audio file could be mocked
in testing, but improves readability

TODO: review number of params and consider further refactoring

Runs a command returns stout as string, defined to aid testing

facilitates test mocking

Parses JSON, eg that output by the pipeline or defined manually and
produces Features that can be (re-)used with the pipeline

Take the mean of non-overlapping windows of scores

    Input:  list of tuples in the format (time, score)
    Output: list of tuples in the format (time, mean_score)
            (reduced set)

Drops lowest n% (default:33%) of scdet scores, since it scores every
frame

Python being what it is, this could be a single line in another method
but pulling it out into another function:

 - makes explicit what we are doing and lets us document why
 - makes for easier testing

Uses ffmpeg's scdet under the hood apropos of recommendation on
ffmpeg-user ML [1]

[1]: https://ffmpeg.org/pipermail/ffmpeg-user/2024-August/058535.html

Uses pyloudnorm under the hood to determine the loudness of the supplied
media file (handles videos transparently)

TBC: some sort of limiter on the number produced

TBC

TBC

Simple feature extractor that picks random times, used to exercise other
parts of the pipeline

This adds functionality for getting laughter by using jrgillick's
laughter detection library

NB python expects all of the feature extractor's dependencies to be
available; perhaps in future we can do something even fancier like
activating another python env

[retroactive commit]

for jrgillick/laughter-detection to slot into

or at least what passes for an interface in python