101 lines
3.3 KiB
Python
Executable File
101 lines
3.3 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2019 Mycroft AI Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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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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:-l --trigger-level int 3
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Number of activated chunks to cause an activation
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:-s --sensitivity float 0.5
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Network output required to be considered activated
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:-b --basic-mode
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Report using . or ! rather than a visual representation
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:-d --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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import numpy as np
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from os.path import join
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from precise_runner import PreciseRunner
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from precise_runner.runner import ListenerEngine
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from prettyparse import Usage
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from random import randint
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from shutil import get_terminal_size
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from threading import Event
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from precise.network_runner import Listener
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from precise.scripts.base_script import BaseScript
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from precise.util import save_audio, buffer_to_audio, activate_notify
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class ListenScript(BaseScript):
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usage = Usage(__doc__)
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def __init__(self, args):
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super().__init__(args)
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self.listener = Listener(args.model, args.chunk_size)
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self.audio_buffer = np.zeros(self.listener.pr.buffer_samples, dtype=float)
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self.engine = ListenerEngine(self.listener, args.chunk_size)
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self.engine.get_prediction = self.get_prediction
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self.runner = PreciseRunner(self.engine, args.trigger_level, sensitivity=args.sensitivity,
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on_activation=self.on_activation, on_prediction=self.on_prediction)
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self.session_id, self.chunk_num = '%09d' % randint(0, 999999999), 0
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def on_activation(self):
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activate_notify()
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if self.args.save_dir:
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nm = join(self.args.save_dir, self.args.save_prefix + self.session_id + '.' + str(self.chunk_num) + '.wav')
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save_audio(nm, self.audio_buffer)
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print()
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print('Saved to ' + nm + '.')
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self.chunk_num += 1
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def on_prediction(self, conf):
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if self.args.basic_mode:
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print('!' if conf > 0.7 else '.', end='', flush=True)
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else:
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max_width = 80
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width = min(get_terminal_size()[0], max_width)
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units = int(round(conf * width))
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bar = 'X' * units + '-' * (width - units)
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cutoff = round((1.0 - self.args.sensitivity) * width)
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print(bar[:cutoff] + bar[cutoff:].replace('X', 'x'))
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def get_prediction(self, chunk):
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audio = buffer_to_audio(chunk)
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self.audio_buffer = np.concatenate((self.audio_buffer[len(audio):], audio))
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return self.listener.update(chunk)
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def run(self):
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self.runner.start()
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Event().wait() # Wait forever
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main = ListenScript.run_main
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if __name__ == '__main__':
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main()
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