feat&test: [WFE] defaults, pull out calls for mocking, unit tests

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

pipeline/feature_extractors.py

@@ -407,6 +407,29 @@ class JSONFeatureExtractor(FeatureExtractor):
 
 class WordFeatureExtractor(FeatureExtractor):
     """Feature extractor for specific word detection (uses Whisper)"""
+    # set defaults for whisper settings
+    DEFAULT_MODEL_SIZE = "medium"
+    DEFAULT_DEVICE = "cpu"
+    DEFAULT_COMPUTE_TYPE = "int8"
+    DEFAULT_BEAM_SIZE = 5
+    DEFAULT_BATCH_SIZE = 16
+    DEFAULT_PIPELINE_TYPE = "batched" # or "stream"
+
+    words = []
+
+    def _transcribe(self, model, file, **kwargs):
+        """Defined here to allow for mocking in tests"""
+        return model.transcribe(file, **kwargs)
+
+    def _whispermodel(self, model_size=DEFAULT_MODEL_SIZE,
+                      device=DEFAULT_DEVICE, compute_type=DEFAULT_COMPUTE_TYPE):
+        """Defined here to allow for mocking out in tests"""
+        return WhisperModel(model_size, device=device, compute_type=compute_type)
+
+    def _batched_inference_pipeline(self, model):
+        """Defined here to allow for mocking out in tests"""
+        return BatchedInferencePipeline(model=model)
+
     def __init__(self, input_files=None, config=None):
         if not input_files:
             raise ValueError("No input files provided!")

test/test_feature_extractors.py

@@ -1,4 +1,8 @@
 """test_feature_extractors.py - test pipeline feature extractors"""
+import sys
+from unittest.mock import patch, mock_open, MagicMock #
+sys.modules["faster_whisper"] = MagicMock() # mock faster_whisper as it is a slow import
+
 import unittest
 import os
 import random
@@ -6,7 +10,6 @@ import pytest
 import pipeline.feature_extractors as extractors
 
 from pipeline.utils import Source, SourceMedia # technically makes this an integration test, but...
-from unittest.mock import patch, mock_open
 
 class TestSource():
     """Provide utils.Source for testing"""
@@ -282,3 +285,70 @@ class TestJSONFeatureExtractor(unittest.TestCase):
         m = unittest.mock.mock_open(read_data='[{"foo": "bar"}]')
         with unittest.mock.patch("builtins.open", m):
             test_extractor._read_json_from_file("foo.json")
+
+
+class TestWordFeatureExtractor(unittest.TestCase):
+    """Test WordFeatureExtractor"""
+
+    @classmethod
+    def setUpClass(cls):
+        sys.modules["faster_whisper"] = MagicMock()
+
+    _MOCK_SENTENCE = "the quick brown fox jumps over the lazy dog".split()
+    class MockSegment():
+        """Mock Segment -- has starte, end and text attributes"""
+        def __init__(self, start, end, text):
+            self.start = start
+            self.end = end
+            self.text = text
+
+    def mock_transcribe(self, *args, **kwargs):
+        """Mock for WhisperModel.model.transcribe
+
+        returns a 2-tuple:
+         - list of segments
+           + segment = start, end, text
+          - info = language, language_probability
+
+        We will mock the segments- this provides 9 segments for the sentence:
+        "the quick brown fox jumps over the lazy dog"
+        """
+        segments = []
+        for i in range(len(self._MOCK_SENTENCE)):
+            segments.append(self.MockSegment(i, i+1, self._MOCK_SENTENCE[i]))
+        return segments, {"language": "en", "language_probability": 0.9}
+
+    def test_basic_init(self):
+        video_source = TestSourceMedia().one_colour_silent_audio()
+        test_extractor = extractors.WordFeatureExtractor(input_files=video_source)
+        self.assertTrue(test_extractor)
+
+    def test_init_no_input_videos(self):
+        """test init - no input files"""
+        with self.assertRaises(ValueError):
+            test_extractor = extractors.WordFeatureExtractor()
+
+    def test_extract_no_words_supplied(self):
+        """Test extract with basic input file but no words specirfied returns zero features"""
+        video_source = TestSourceMedia().one_colour_silent_audio()
+        test_extractor = extractors.WordFeatureExtractor(input_files=video_source)
+        test_extractor.setup()
+        test_extractor.run()
+        test_extractor.teardown()
+        self.assertEqual(test_extractor.features, [])
+
+    def test_extract_mocked_transcribe(self):
+        """Mock out the actual call to transcribe"""
+        video_source = TestSourceMedia().one_colour_silent_audio()
+        test_extractor = extractors.WordFeatureExtractor(input_files=video_source)
+        # mock _transcribe and mock out model and batched pipeline for speed
+        test_extractor._transcribe = self.mock_transcribe
+        test_extractor._model = MagicMock()
+        test_extractor._batched_model = MagicMock()
+        # set up and run the extractor
+        test_extractor.setup(words=self._MOCK_SENTENCE)
+        test_extractor.run()
+        test_extractor.teardown()
+
+        self.assertEqual(len(test_extractor.features), 9)
+