def test_detect_vad_active(_mock):
context = SpeechContext()
detector = WakewordTrigger(model_dir="wakeword_model")
for _ in range(1):
test_frame = np.random.rand(160, ).astype(np.float32)
context.is_speech = True
detector(context, test_frame)
assert not context.is_active
detector.close()
def test_detect_vad_inactive(_mock):
context = SpeechContext()
detector = WakewordTrigger(model_dir="wakeword_model")
test_frame = np.random.rand(160, ).astype(np.float32)
context.is_speech = False
detector(context, test_frame)
assert not context.is_active
def test_detect_activate(_mock):
context = SpeechContext()
detector = WakewordTrigger(model_dir="wakeword_model")
detector.detect_model.return_value[0][:] = 0.6
test_frame = np.random.rand(512, ).astype(np.float32)
context.is_speech = True
detector(context, test_frame)
context.is_speech = False
assert context.is_active
def test_detect_manual_min_delay(_mock):
context = SpeechContext()
detector = WakewordTrigger(model_dir="wakeword_model")
detector.detect_model.return_value[0][:] = 1
context.is_active = True
test_frame = np.random.rand(512, ).astype(np.float32)
detector(context, test_frame)
detector(context, test_frame)
detector(context, test_frame)
assert context.is_active
def create(
spokestack_id: str,
spokestack_secret: str,
sample_rate: int = 16000,
frame_width: int = 20,
model_dir: str = "",
**kwargs: Any,
) -> SpeechPipeline:
"""Creates a speech pipeline instance from profile
Args:
spokestack_id (str): spokestack API id.
spokestack_secret (str): Spokestack API secret.
sample_rate (int): sample rate of the audio (Hz).
frame_width (int): width of the audio frame: 10, 20, or 30 (ms).
model_dir (str): Directory containing the tflite wakeword models.
Returns:
"""
pipeline = SpeechPipeline(
input_source=PyAudioInput(frame_width=frame_width,
sample_rate=sample_rate,
**kwargs),
stages=[
AutomaticGainControl(sample_rate=sample_rate,
frame_width=frame_width),
AutomaticNoiseSuppression(sample_rate=sample_rate),
VoiceActivityDetector(
frame_width=frame_width,
sample_rate=sample_rate,
**kwargs,
),
WakewordTrigger(model_dir=model_dir, **kwargs),
ActivationTimeout(frame_width=frame_width, **kwargs),
CloudSpeechRecognizer(
spokestack_secret=spokestack_secret,
spokestack_id=spokestack_id,
**kwargs,
),
],
)
return pipeline
def main():
pipeline = SpeechPipeline(
PyAudioInput(frame_width=20,
sample_rate=16000,
exception_on_overflow=False),
[
VoiceActivityDetector(),
WakewordTrigger(pre_emphasis=0.97, model_dir="tflite"),
GoogleSpeechRecognizer(GOOGLE_CREDS),
ActivationTimeout(),
],
)
dialogue_manager = DialogueManager(
"tflite", "distilbert-base-cased-distilled-squad")
manager = TextToSpeechManager(
TextToSpeechClient(KEY_ID, KEY_SECRET),
PyAudioOutput(),
)
@pipeline.event
def on_activate(context):
print(context.is_active)
@pipeline.event
def on_recognize(context):
pipeline.pause()
answer = dialogue_manager(context.transcript)
manager.synthesize(answer, "text", "demo-male")
pipeline.resume()
@pipeline.event
def on_deactivate(context):
print(context.is_active)
manager.synthesize(dialogue_manager.greet(), "text", "demo-male")
pipeline.start()
pipeline.run()
def main():
pipeline = SpeechPipeline(
PyAudioInput(frame_width=20, sample_rate=16000, exception_on_overflow=False),
[
VoiceActivityDetector(),
WakewordTrigger(pre_emphasis=0.97, model_dir="tflite"),
CloudSpeechRecognizer(spokestack_id=KEY_ID, spokestack_secret=KEY_SECRET),
ActivationTimeout(),
],
)
nlu = TFLiteNLU("tflite")
dialogue_manager = DialogueManager()
manager = TextToSpeechManager(
TextToSpeechClient(KEY_ID, KEY_SECRET), PyAudioOutput(),
)
@pipeline.event
def on_activate(context):
print("active")
@pipeline.event
def on_recognize(context):
pipeline.pause()
results = nlu(context.transcript)
response = dialogue_manager(results)
if response:
manager.synthesize(response, "text", "demo-male")
pipeline.resume()
if results.intent == "AMAZON.StopIntent":
pipeline.stop()
manager.synthesize(Response.WELCOME.value, "text", "demo-male")
pipeline.start()
pipeline.run()
def test_invalid_args(_mock):
with pytest.raises(ValueError):
_ = WakewordTrigger(fft_window_type="hamming")
