69 lines
1.9 KiB
Python
Executable File
69 lines
1.9 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright (c) 2017 Mycroft AI Inc.
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from os.path import join
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from random import randint
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from subprocess import Popen
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from threading import Event
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import numpy as np
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from prettyparse import create_parser
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from precise.network_runner import Listener
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from precise.util import save_audio, buffer_to_audio
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from precise_runner import PreciseRunner
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from precise_runner.runner import ListenerEngine
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usage = '''
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Run a model on microphone audio input
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:model str
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Either Keras (.net) or Tensorflow (.pb) model to run
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:-c --chunk-size int 2048
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Samples between inferences
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:-s --save-dir str -
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Folder to save false positives
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:-p --save-prefix str -
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Prefix for saved filenames
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'''
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session_id, chunk_num = '%03d' % randint(0, 999), 0
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def main():
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args = create_parser(usage).parse_args()
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def on_activation():
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Popen(['aplay', '-q', 'data/activate.wav'])
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if args.save_dir:
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global chunk_num
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nm = join(args.save_dir, args.save_prefix + session_id + '.' + str(chunk_num) + '.wav')
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save_audio(nm, audio_buffer)
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print()
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print('Saved to ' + nm + '.')
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chunk_num += 1
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def on_prediction(conf):
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print('!' if conf > 0.5 else '.', end='', flush=True)
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listener = Listener(args.model, args.chunk_size)
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audio_buffer = np.zeros(listener.pr.buffer_samples, dtype=float)
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def get_prediction(chunk):
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nonlocal audio_buffer
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audio = buffer_to_audio(chunk)
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audio_buffer = np.concatenate((audio_buffer[len(audio):], audio))
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return listener.update(chunk)
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engine = ListenerEngine(listener)
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engine.get_prediction = get_prediction
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runner = PreciseRunner(engine, 3, on_activation=on_activation, on_prediction=on_prediction)
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runner.start()
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Event().wait() # Wait forever
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if __name__ == '__main__':
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main()
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