Test coverage for model.py back to 100%

tests/test_models.py

@@ -37,6 +37,26 @@ import pytest
 
 # Tests
 class TestModels:
+    def test_load_models_by_path(self):
+        # Load model with defaults
+        owwModel = openwakeword.Model(wakeword_model_paths=[
+                                        os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
+                                      ])
+
+        # Prediction on random data
+        owwModel.predict(np.random.randint(-1000, 1000, 1280).astype(np.int16))
+
+    def test_custom_model_label_mapping_dict(self):
+        # Load model with model path
+        owwModel = openwakeword.Model(wakeword_model_paths=[
+                                        os.path.join("openwakeword", "resources", "models", "alexa_v0.1.onnx")
+                                      ],
+                                      class_mapping_dicts=[{"alexa_v0.1": {"0": "positive"}}]
+                                      )
+
+        # Prediction on random data
+        owwModel.predict(np.random.randint(-1000, 1000, 1280).astype(np.int16))
+
     def test_models(self):
         # Load model with defaults
         owwModel = openwakeword.Model()
@@ -65,6 +85,34 @@ class TestModels:
                 else:
                     assert max(predictions_flat[key]) < 0.5
 
+    def test_models_with_speex_noise_cancellation(self):
+        # Load model with defaults
+        owwModel = openwakeword.Model(enable_speex_noise_suppression=True)
+
+        # Get clips for each model (assumes that test clips will have the model name in the filename)
+        test_dict = {}
+        for mdl_name in owwModel.models.keys():
+            all_clips = [str(i) for i in Path(os.path.join("tests", "data")).glob("*.wav")]
+            test_dict[mdl_name] = [i for i in all_clips if mdl_name in i]
+
+        # Predict
+        for model, clips in test_dict.items():
+            for clip in clips:
+                # Get predictions for reach frame in the clip
+                predictions = owwModel.predict_clip(clip)
+                owwModel.reset()  # reset after each clip to ensure independent results
+
+                # Make predictions dictionary flatter
+                predictions_flat = collections.defaultdict(list)
+                [predictions_flat[key].append(i[key]) for i in predictions for key in i.keys()]
+
+            # Check scores against default threshold (0.5)
+            for key in predictions_flat.keys():
+                if key in clip:
+                    assert max(predictions_flat[key]) >= 0.5
+                else:
+                    assert max(predictions_flat[key]) < 0.5
+
     def test_predict_clip_with_array(self):
         # Load model with defaults
         owwModel = openwakeword.Model()