Merge pull request #139 from MycroftAI/bugfix/unittests

Make Unittests runnable

MANIFEST.in

@@ -0,0 +1 @@
+include precise/data/activate.wav

precise/util.py

@@ -80,8 +80,7 @@ def play_audio(filename: str):
 
 def activate_notify():
     audio = 'data/activate.wav'
-    audio = abspath(dirname(abspath(__file__)) + '/../' + audio)
+    audio = join(dirname(abspath(__file__)), audio)
-
     play_audio(audio)
 
 

setup.py

@@ -69,8 +69,9 @@ setup(
             'precise-calc-threshold=precise.scripts.calc_threshold:main',
         ]
     },
+    include_package_data=True,
     install_requires=[
-        'numpy',
+        'numpy==1.16',
         'tensorflow>=1.13,<1.14',  # Must be on piwheels
         'sonopy',
         'pyaudio',

test/Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.7-slim
+ENV TERM linux
+ENV DEBIAN_FRONTEND noninteractive
+RUN apt-get update && apt-get -y install git python3-scipy cython libhdf5-dev python3-h5py portaudio19-dev swig libpulse-dev libatlas-base-dev
+ADD . mycroft-precise
+WORKDIR mycroft-precise
+RUN pip install .
+RUN pip install pytest
+ENV PYTHONPATH /mycroft-precise
+ENTRYPOINT ["pytest"]

test/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2019 Mycroft AI Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

test/scripts/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2019 Mycroft AI Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

test/scripts/dummy_audio_folder.py

@@ -35,8 +35,14 @@ class DummyAudioFolder:
         return min + (max - min) * np.random.random() * pr.buffer_t
 
     def generate_samples(self, folder, name, value, duration):
+        """Generate sample file.
+
+        The file is generated in the specified folder, with the specified name,
+        dummy value and duration.
+        """
         for i in range(self.count):
-            save_audio(join(folder, name.format(i)), np.array([value] * int(duration * pr.sample_rate)))
+            save_audio(join(folder, name.format(i)),
+                       np.array([value] * int(duration * pr.sample_rate)))
 
     def subdir(self, *parts):
         folder = self.path(*parts)

test/scripts/test_combined.py

@@ -26,18 +26,27 @@ def read_content(filename):
 
 
 def test_combined(train_folder, train_script):
+    """Test a "normal" development cycle, train, evaluate and calc threshold.
+    """
     train_script.run()
     params_file = train_folder.model + '.params'
     assert isfile(train_folder.model)
     assert isfile(params_file)
 
-    EvalScript.create(folder=train_folder.root, models=[train_folder.model]).run()
+    EvalScript.create(folder=train_folder.root,
+                      models=[train_folder.model]).run()
 
+    # Ensure that the graph script generates a numpy savez file
     out_file = train_folder.path('outputs.npz')
-    graph_script = GraphScript.create(folder=train_folder.root, models=[train_folder.model], output_file=out_file)
+    graph_script = GraphScript.create(folder=train_folder.root,
+                                      models=[train_folder.model],
+                                      output_file=out_file)
     graph_script.run()
     assert isfile(out_file)
 
+    # Esure the params are updated after threshold is calculated
     params_before = read_content(params_file)
-    CalcThresholdScript.create(folder=train_folder.root, model=train_folder.model, input_file=out_file).run()
+    CalcThresholdScript.create(folder=train_folder.root,
+                               model=train_folder.model,
+                               input_file=out_file).run()
     assert params_before != read_content(params_file)

test/scripts/test_engine.py

@@ -36,6 +36,10 @@ class FakeStdout:
 
 
 def test_engine(train_folder, train_script):
+    """
+    Test t hat the output format of the engina matches a decimal form in the
+    range 0.0 - 1.0.
+    """
     train_script.run()
     with open(glob.glob(join(train_folder.root, 'wake-word', '*.wav'))[0], 'rb') as f:
         data = f.read()

test/scripts/test_train.py

@@ -22,15 +22,20 @@ from test.scripts.dummy_audio_folder import DummyAudioFolder
 class DummyTrainFolder(DummyAudioFolder):
     def __init__(self, count=10):
         super().__init__(count)
-        self.generate_samples(self.subdir('wake-word'), 'ww-{}.wav', 1.0, self.rand(0, 2 * pr.buffer_t))
+        self.generate_samples(self.subdir('wake-word'), 'ww-{}.wav', 1.0,
-        self.generate_samples(self.subdir('not-wake-word'), 'nww-{}.wav', 0.0, self.rand(0, 2 * pr.buffer_t))
+                              self.rand(0, 2 * pr.buffer_t))
-        self.generate_samples(self.subdir('test', 'wake-word'), 'ww-{}.wav', 1.0, self.rand(0, 2 * pr.buffer_t))
+        self.generate_samples(self.subdir('not-wake-word'), 'nww-{}.wav', 0.0,
-        self.generate_samples(self.subdir('test', 'not-wake-word'), 'nww-{}.wav', 0.0, self.rand(0, 2 * pr.buffer_t))
+                              self.rand(0, 2 * pr.buffer_t))
+        self.generate_samples(self.subdir('test', 'wake-word'), 'ww-{}.wav',
+                              1.0, self.rand(0, 2 * pr.buffer_t))
+        self.generate_samples(self.subdir('test', 'not-wake-word'),
+                              'nww-{}.wav', 0.0, self.rand(0, 2 * pr.buffer_t))
         self.model = self.path('model.net')
 
 
 class TestTrain:
     def test_run_basic(self):
+        """Run a training and check that a model is generated."""
         folders = DummyTrainFolder(10)
         script = TrainScript.create(model=folders.model, folder=folders.root)
         script.run()