_keep_num() was incorrectly detecting overlaps

Identified when checking coverage while only testing utils

Found to be useful when writing tests

Otherwise features tend to cluster

As with VAFE, update LoudFE to expand to min duration when keeping n features

see: 72fc6b563b

The problem that I had overlooked until now (!!) due to only using small
exemplar videos for testing was that VideoActivityFeatureExtractor keeps
a fraction of small (0.5s) Features. This is not so problematic for a short
exemplar video, but ends up witha  lot of jumpy Features in an actual source
video.

Fix approach:

Like LoudnessFE, keep a specific number and expand the duration (which I think I
will do for LoudnessFE too), dropping any that would be consumed in that range

add LoudAudioFeatureExtractor._num_features (set in init, default: 5)

add _keep_num -- takes list of loudnesses and keeps top N (default: 5)

Add test cases that require reducing the number of Features

Facilitates unit tests of TTA

Pull out a one-liner for test scenarios to facilitate testing adjust()

Cases:

 - no features
 - features under target duration
 - features equal to target duration

Test _determine_margin() with margins specified as percentage

Tries to drop the lowest-scoring Features until the target time (range) is
reached. This is not optimised and a relatively naïve approach- there are many
inputs which would result in a non-ideal pruning.

TargetTimeAdjuster will adjust a list of Features until it is within an optional
margin of a target total duration.

Helper functions:

 - _determine_margin() :: figure out the max and min cutoff times, considering
   margin and margin strategy (percent / absolute)
 - _features_total_time() :: basic sum of list of Features' durations

TODO: rename to TargetDurationAdjuster ? rename 'strategy' ??

Adjusters will be used to modify a list of Features. This could either be:

 - to modify the overall set (eg to target a time)
 - to modify individual Features

The most important Adjuster will be one that targets an overall time, eg:

"modify this list of Features such that their times add up to 1 minute (either ±
a % or a hard limit)"

@see: feature_extractors.py::FeatureExtractor

Also rename positive test to indicate that

To check we do not match any words that are not present in a media file with
speech, we use the English-language Harvard sentences for audio, and compare to
the classic French pangram: "Portez ce vieux whisky au juge blond qui fume" [1]
and ensure no Features are produced

[1]: "Take this old whisky to the blond judge who is smoking"

Found out that Whisper throws a hissy fit in the form of a RuntimeError if the
there is no speech in the audio. We should consider catching this.

> RuntimeError: stack expects a non-empty TensorList
> stdout: "No active speech found in audio"

For the moment we can check that no audio throws an error and leave this as a TODO

Functional tests for WordFeatureExtractor consist of making sure it can find
words known in advance. The Harvard Sentences [1] are a useful means of doing
that. These are 'standard sentences' that are used for speech quality
measurements, and so would be decent candidates for assessing word recognition.

The Open Speech REpository [2] has samples of sentences to download.

In testing, the Whisper medium model had trouble with a few words:
 - glue
 - well
 - punch
 - truck

I'm not sure why. Even when I recorded myself speaking the Harvard sentences in
higher quality (OSR files are 8kHz range) it would still not recognise these
words. A separate functional test of only those words was added as a result.
This would perhaps be worth exploring in more detail if there was time.

[1]: See eg https://www.cs.columbia.edu/~hgs/audio/harvard.html
[2]: https://www.voiptroubleshooter.com/open_speech/index.html

Check for missing input files not needed as object cannot be initialised without them

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.

Also add note that it takes 8-10s to run a single LaughFE test

Uses a manually-crafted video with laughters between 15-20s.

Test takes LaughFE's internal Feature time adjustment into account (see related
commit).

Note: very slow test

@see: df3c559

To help functional testing, LaughFE's internal adjustment times are exposed.

Recap: when a laugh is detected by LaughFE, the time of the laugh itself it not
used directly; instead, the resulting Feature has some time prepended to try to
capture the thing that caused the laugh.

When functional testing the FEs we set up specially-crafted videos with features
at known points, so to make sure the LaughterFE is tested correctly we adjust
the tests by the amount of time the FE adjusts by so that it properly tests the
intended behaviour.

@see:
 - feature_extractors.py::LaughterFeatureExtractor

Video file with silent audio (generated with sox) -- should produce no features
since "-inf" are filtered by the FE