Cases:

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

Test _determine_margin() with margins specified as percentage

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

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

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

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

Functional tests for LoudAudioFeatureExtractor

Currently uses one manually-generated video with blank audio except between
15-20s where 1-2 sine tones are present

Only has a single property at present: SAMPLE_DIR for the path to where
sample videos are stored

TestVideoActivityFEFunctional now inherits from this instead of unittest.TestCase

Tests VAFE using a manually-created 30 seconds video with activity between
15 and 20 seconds.

Relatively quick!

Since it no longer simply produces videos this name is preferred

Also update tests to use updated module name

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

Includes:
 - MockInterval
 - MockFeature
 - MockSource

Gets utils coverage up to 100% (for now)

@see: 0a08af4

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

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

TestLoudAudioFeatureExtractor -- init only

TestSourceMedia is a mockinobject so

TODO: rename

Initialset of tests for LaughterFeatureExtractor and
RandomFeatureExtractor

This covers:
 - Source
 - SourceMedia
 - Interval