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110 lines
4.9 KiB
Python
110 lines
4.9 KiB
Python
# Copyright (C) 2024 OpenMV, LLC.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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#
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# 1. Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in
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# the documentation and/or other materials provided with the
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# distribution.
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# 3. Any redistribution, use, or modification in source or binary form
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# is done solely for personal benefit and not for any commercial
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# purpose or for monetary gain. For commercial licensing options,
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# please contact openmv@openmv.io
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#
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# THIS SOFTWARE IS PROVIDED BY THE LICENSOR AND COPYRIGHT OWNER "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE LICENSOR OR COPYRIGHT
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# OWNER BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import time
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from ml import Model
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from micropython import const
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from ulab import numpy as np
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try:
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import audio
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except (ImportError, AttributeError):
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pass
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class MicroSpeech:
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_SLICE_SIZE = const(40)
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_SLICE_COUNT = const(49)
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_SLICE_TIME_MS = const(30)
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_AUDIO_FREQUENCY = const(16000)
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_SAMPLES_PER_STEP = const(10 * (_AUDIO_FREQUENCY // 1000)) # 10ms * 16 Samples/ms
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_CATEGORY_COUNT = const(4)
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_AVERAGE_WINDOW_SAMPLES = const(1020 // _SLICE_TIME_MS)
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def __init__(self, preprocessor=None, micro_speech=None, labels=None, **kwargs):
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self.preprocessor = preprocessor
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if preprocessor is None:
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self.preprocessor = Model("/rom/audio_preprocessor.tflite")
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self.labels, self.micro_speech = (labels, micro_speech)
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if micro_speech is None:
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self.micro_speech = Model("/rom/micro_speech.tflite")
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self.labels = self.micro_speech.labels
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# 16 samples/1ms
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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, samples=_SAMPLES_PER_STEP * 2, **kwargs)
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def audio_callback(self, buf):
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# Roll the audio buffer to the left, and add the new samples.
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self.audio_buffer = np.roll(self.audio_buffer, -(_SAMPLES_PER_STEP * 2), axis=1)
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self.audio_buffer[0, _SAMPLES_PER_STEP:] = np.frombuffer(buf, dtype=np.int16)
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# Roll the spectrogram to the left and add the new slice.
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self.spectrogram = np.roll(self.spectrogram, -_SLICE_SIZE, axis=1)
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self.spectrogram[0, -_SLICE_SIZE:] = self.preprocessor.predict([self.audio_buffer])
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# Roll the prediction history and add the new prediction.
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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.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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max_score_index = np.argmax(average_scores)
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max_score = average_scores[max_score_index]
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label = self.labels[max_score_index]
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if max_score > threshold and label in filter:
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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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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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self.stop_audio_streaming()
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return (None, average_scores)
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time.sleep_ms(1)
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