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

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

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

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

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