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tests for debounce functionality

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dscripka 2024-02-11 12:45:59 -05:00
parent 68e88c1350
commit 528f4bff2c
2 changed files with 36 additions and 10 deletions
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27
openwakeword/model.py
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@ -327,11 +327,10 @@ class Model():
)[0][-1]
predictions[cls] = verifier_prediction
# Update prediction buffer, and zero predictions for first 5 frames during model initialization
# Zero predictions for first 5 frames during model initialization
for cls in predictions.keys():
if len(self.prediction_buffer[cls]) < 5:
predictions[cls] = 0.0
self.prediction_buffer[cls].append(predictions[cls])
# Get timing information
if timing:
@ -346,14 +345,22 @@ class Model():
raise ValueError("Error! The `patience` and `debounce_time` arguments cannot be used together!")
for mdl in predictions.keys():
parent_model = self.get_parent_model_from_label(mdl)
if parent_model in patience.keys():
scores = np.array(self.prediction_buffer[mdl])[-patience[parent_model]:]
if (scores >= threshold[parent_model]).sum() < patience[parent_model]:
predictions[mdl] = 0.0
if debounce_time > 0:
n_frames = int(debounce_time*1000/80)
if (np.array(self.prediction_buffer[mdl])[-n_frames:] >= threshold[parent_model]).sum() > 0:
predictions[mdl] = 0.0
if predictions[mdl] != 0.0:
if parent_model in patience.keys():
scores = np.array(self.prediction_buffer[mdl])[-patience[parent_model]:]
if (scores >= threshold[parent_model]).sum() < patience[parent_model]:
predictions[mdl] = 0.0
elif debounce_time > 0:
if parent_model in threshold.keys():
n_frames = int(np.ceil(debounce_time/(n_prepared_samples/16000)))
recent_predictions = np.array(self.prediction_buffer[mdl])[-n_frames:]
if predictions[mdl] >= threshold[parent_model] and \
(recent_predictions >= threshold[parent_model]).sum() > 0:
predictions[mdl] = 0.0
# Update prediction buffer
for mdl in predictions.keys():
self.prediction_buffer[mdl].append(predictions[mdl])
# (optionally) get voice activity detection scores and update model scores
if self.vad_threshold > 0:

19
tests/test_models.py
View file

@ -208,6 +208,25 @@ class TestModels:
)
assert 1 == 1
def test_models_with_debounce(self):
# Load model with defaults
owwModel = openwakeword.Model()
# Get test clip
os.path.join("tests", "data", "alexa_test.wav")
# Predict with chunks of 1280 with and without debounce
predictions = owwModel.predict_clip(os.path.join("tests", "data", "alexa_test.wav"),
debounce_time=0, threshold={"alexa_v0.1": 0.5})
scores = np.array([i['alexa'] for i in predictions])
predictions = owwModel.predict_clip(os.path.join("tests", "data", "alexa_test.wav"),
debounce_time=1.25, threshold={"alexa": 0.5})
scores_with_debounce = np.array([i['alexa'] for i in predictions])
print(scores, scores_with_debounce)
assert (scores >= 0.5).sum() > 1
assert (scores_with_debounce >= 0.5).sum() == 1
def test_models_with_vad(self):
# Load model with defaults
owwModel = openwakeword.Model(vad_threshold=0.5)