Ensure:
 - no words are correctly not matched
 - some words are matched

Calls pulled out relate to setup and working of Whisper:
 - _whispermodel()
 - _batched_inference_pipeline()
 - _transcribe()

Defaults defined: model, device, compute type, beamsize, batchsize, pipeline type

Tests:
 - basic init
 - init with no media
 - run() with no words (early exit 0 Features)
 - run() with mocked transcribe

NOTE: these are unit tests and do not exercise Whisper

BREAKING CHANGE: no words to WFE are no longer an error, they raise a notice

WordFeatureExtractor is not fast- even the import is slow. However, it processes
files and returns Features corresponding to matched words.

WhisperFE will be slightly different to other FEs in that there is/are specific
target words to be searched for. Not specifying these could be an error (this
commit specifies this as such) but a better approach may be to downgrade that to
a (logging) notice, and simply match nothing / early exit.

Uses unittest.mock.path to patch out call to matplotlib.pyplot.savefig()

TODO: better, more-granular tests of the bits of functionality here

mocks out calls to ffmpeg for obvious reasons

Extract method _run_no_output for two calls to ffmpeg

Fix feature.path → feature.source.path after Feature API change

Includes:
 - MockInterval
 - MockFeature
 - MockSource

Gets utils coverage up to 100% (for now)

@see: 0a08af4

Problems fixed:

 - Feature repr did not include feature_extractor since that API was changed
 - Intervals that were equivalent were not equal, so Features were not properly
   sorted or equal

Covers:
 - to_json
 - move_end
 - update_duration
 - unhappy path exceptions (mostly init)

Gets test coverage for this module to 100%! (for now)

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

TestLoudAudioFeatureExtractor -- init only

TestSourceMedia is a mockinobject so

TODO: rename

Initialset of tests for LaughterFeatureExtractor and
RandomFeatureExtractor

Handles serialisation of custom classes via to_json() method if present

Outputs a representation of the Features extrated by the pipeline

Intent is to write a FE that takes the output so that a pipeline can be
're-run'

Output JSON could also be used with external tools

Annotate features, label axes

Creates a plot showing where in source video(s) the pipeline features
came from

This pvaes the way for parts of the pipeline that do not produce videos,
such as JSON, images, clips etc

TODO: rename module video_producers → producers

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

This adds functionality that permits nearby Features to be considered
overlapping, in addition to Features which actual overlap

@see utils.py::Feature

BREAKING CHANGE: source now refers to a Source object, the FE that
created the Feature is now referred to by feature_extractor; path is
dropped

This should be more consistent, plus we needed a reference to the
original Source kept around anyway -- path worked but a Source object is
more consistent and explicit about intent

This lets us serialise properly

associated TODO: make precision configurable

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]