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Added basic debounce logic for model.predict

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dscripka 2024-02-11 12:04:41 -05:00
parent dc5a234218
commit c63384489e
1 changed files with 12 additions and 3 deletions
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15
openwakeword/model.py
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@ -227,7 +227,8 @@ class Model():
"""Reset the prediction buffer"""
self.prediction_buffer = defaultdict(partial(deque, maxlen=30))
def predict(self, x: np.ndarray, patience: dict = {}, threshold: dict = {}, timing: bool = False):
def predict(self, x: np.ndarray, patience: dict = {},
threshold: dict = {}, debounce_time: float = 0.0, timing: bool = False):
"""Predict with all of the wakeword models on the input audio frames
Args:
@ -242,9 +243,11 @@ class Model():
model names and the values are the number of frames. Can reduce false-positive
detections at the cost of a lower true-positive rate.
By default, this behavior is disabled.
threshold (dict): The threshold values to use when the `patience` behavior is enabled.
threshold (dict): The threshold values to use when the `patience` or `debounce_time` behavior is enabled.
Must be provided as an a dictionary where the keys are the
model names and the values are the thresholds.
debounce_time (float): The time (in seconds) to wait before returning another non-zero prediction
after a non-zero prediction. Can preven multiple detections of the same wake-word.
timing (bool): Whether to return timing information of the models. Can be useful to debug and
assess how efficiently models are running on the current hardware.
@ -333,16 +336,22 @@ class Model():
timing_dict["models"][mdl] = time.time() - model_start
# Update scores based on thresholds or patience arguments
if patience != {}:
if patience != {} or debounce_time > 0:
if threshold == {}:
raise ValueError("Error! When using the `patience` argument, threshold "
"values must be provided via the `threshold` argument!")
if patience != {} and debounce_time > 0:
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
# (optionally) get voice activity detection scores and update model scores
if self.vad_threshold > 0: