97 lines
3.0 KiB
Python
Executable File
97 lines
3.0 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
# Copyright 2018 Mycroft AI Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import numpy as np
|
|
from os.path import join
|
|
from prettyparse import create_parser
|
|
from random import randint
|
|
from shutil import get_terminal_size
|
|
from threading import Event
|
|
|
|
from precise.network_runner import Listener
|
|
from precise.util import save_audio, buffer_to_audio, activate_notify
|
|
from precise_runner import PreciseRunner
|
|
from precise_runner.runner import ListenerEngine
|
|
|
|
usage = '''
|
|
Run a model on microphone audio input
|
|
|
|
:model str
|
|
Either Keras (.net) or TensorFlow (.pb) model to run
|
|
|
|
:-c --chunk-size int 2048
|
|
Samples between inferences
|
|
|
|
:-t --threshold int 3
|
|
Number of positives to cause an activation
|
|
|
|
:-l --light-mode
|
|
Report using . or ! rather than a visual representation
|
|
|
|
:-s --save-dir str -
|
|
Folder to save false positives
|
|
|
|
:-p --save-prefix str -
|
|
Prefix for saved filenames
|
|
'''
|
|
|
|
session_id, chunk_num = '%09d' % randint(0, 999999999), 0
|
|
|
|
|
|
def main():
|
|
args = create_parser(usage).parse_args()
|
|
sensitivity = 0.5
|
|
|
|
def on_activation():
|
|
activate_notify()
|
|
|
|
if args.save_dir:
|
|
global chunk_num
|
|
nm = join(args.save_dir, args.save_prefix + session_id + '.' + str(chunk_num) + '.wav')
|
|
save_audio(nm, audio_buffer)
|
|
print()
|
|
print('Saved to ' + nm + '.')
|
|
chunk_num += 1
|
|
|
|
def on_prediction(conf):
|
|
if args.light_mode:
|
|
print('!' if conf > 0.7 else '.', end='', flush=True)
|
|
else:
|
|
max_width = 80
|
|
width = min(get_terminal_size()[0], max_width)
|
|
units = int(round(conf * width))
|
|
bar = 'X' * units + '-' * (width - units)
|
|
cutoff = round((1.0 - sensitivity) * width)
|
|
print(bar[:cutoff] + bar[cutoff:].replace('X', 'x'))
|
|
|
|
listener = Listener(args.model, args.chunk_size)
|
|
audio_buffer = np.zeros(listener.pr.buffer_samples, dtype=float)
|
|
|
|
def get_prediction(chunk):
|
|
nonlocal audio_buffer
|
|
audio = buffer_to_audio(chunk)
|
|
audio_buffer = np.concatenate((audio_buffer[len(audio):], audio))
|
|
return listener.update(chunk)
|
|
|
|
engine = ListenerEngine(listener, args.chunk_size)
|
|
engine.get_prediction = get_prediction
|
|
runner = PreciseRunner(engine, args.threshold, sensitivity=sensitivity,
|
|
on_activation=on_activation, on_prediction=on_prediction)
|
|
runner.start()
|
|
Event().wait() # Wait forever
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|