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scripts/libraries: Add a non-blocking mode to MicroSpeech.
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@ -61,6 +61,7 @@ class MicroSpeech:
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self.audio_buffer = np.zeros((1, _SAMPLES_PER_STEP * 3), dtype=np.int16)
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self.spectrogram = np.zeros((1, _SLICE_COUNT * _SLICE_SIZE), dtype=np.int8)
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self.pred_history = np.zeros((_AVERAGE_WINDOW_SAMPLES, _CATEGORY_COUNT), dtype=np.float)
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self.audio_started = False
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audio.init(channels=1, frequency=_AUDIO_FREQUENCY, gain_db=24, samples=_SAMPLES_PER_STEP * 2)
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def audio_callback(self, buf):
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@ -78,11 +79,19 @@ class MicroSpeech:
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self.pred_history = np.roll(self.pred_history, -1, axis=0)
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self.pred_history[-1] = self.micro_speech.predict(self.spectrogram)[0]
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def start_audio_streaming(self):
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if self.audio_started is False:
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self.spectrogram[:] = 0
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self.pred_history[:] = 0
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audio.start_streaming(self.audio_callback)
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self.audio_started = True
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def stop_audio_streaming(self):
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audio.stop_streaming()
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self.audio_started = False
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def listen(self, timeout=0, callback=None, threshold=0.65, filter=["Yes", "No"]):
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self.spectrogram[:] = 0
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self.pred_history[:] = 0
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# Start audio streaming
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audio.start_streaming(self.audio_callback)
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self.start_audio_streaming()
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stat_ms = time.ticks_ms()
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while True:
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average_scores = np.mean(self.pred_history, axis=0)
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@ -93,10 +102,12 @@ class MicroSpeech:
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self.pred_history[:] = 0
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self.spectrogram[:] = 0
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if callback is None:
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audio.stop_streaming()
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if timeout != -1: # non-blocking mode
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self.stop_audio_streaming()
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return (label, average_scores)
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callback(label, average_scores)
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if timeout == -1: # non-blocking mode
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return (None, average_scores)
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if timeout != 0 and (time.ticks_ms() - stat_ms) > timeout:
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audio.stop_streaming()
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return (None, None)
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self.stop_audio_streaming()
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time.sleep_ms(1)
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