135 lines
4.4 KiB
Python
135 lines
4.4 KiB
Python
#!/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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import attr
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import numpy as np
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from glob import glob
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from os.path import join, basename
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from precise_runner.runner import TriggerDetector
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from prettyparse import Usage
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from precise_lite.network_runner import Listener
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from precise_lite.params import pr, inject_params
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from precise_lite.scripts.base_script import BaseScript
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from precise_lite.util import load_audio
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from precise_lite.vectorization import vectorize_raw
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@attr.s()
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class Metric:
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chunk_size = attr.ib() # type: int
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seconds = attr.ib(0.0) # type: float
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activated_chunks = attr.ib(0) # type: int
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activations = attr.ib(0) # type: int
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activation_sum = attr.ib(0.0) # type: float
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@property
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def days(self):
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return self.seconds / (60 * 60 * 24)
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def add(self, other):
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self.seconds += other.seconds
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self.activated_chunks += other.activated_chunks
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self.activations += other.activations
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self.activation_sum += other.activation_sum
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@property
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def chunks(self):
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return self.seconds * pr.sample_rate / self.chunk_size
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def info_string(self, title):
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return (
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'=== {title} ===\n'
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'Hours: {hours:.2f}\n'
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'Activations / Day: {activations_per_day:.2f}\n'
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'Activated Chunks / Day: {chunks_per_day:.2f}\n'
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'Average Activation (*100): {average_activation:.2f}'.format(
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title=title,
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hours=self.days * 24,
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activations_per_day=self.activations / self.days,
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chunks_per_day=self.activated_chunks / self.days,
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average_activation=100.0 * self.activation_sum / self.chunks
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)
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)
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class SimulateScript(BaseScript):
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usage = Usage('''
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Simulate listening to long chunks of audio to find
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unbiased false positive metrics
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:model str
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Either Keras (.net) or TensorFlow (.pb) model to test
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:folder str
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Folder with a set of long wav files to test against
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:-c --chunk_size int 4096
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Number of samples between tests
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:-t --threshold float 0.5
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Network output required to be considered an activation
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''')
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def __init__(self, args):
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super().__init__(args)
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inject_params(self.args.model)
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self.runner = Listener.find_runner(self.args.model)(self.args.model)
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self.audio_buffer = np.zeros(pr.buffer_samples, dtype=float)
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def evaluate(self, audio: np.ndarray) -> np.ndarray:
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print('MFCCs...')
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mfccs = vectorize_raw(audio)
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print('Splitting...')
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mfcc_hops = self.args.chunk_size // pr.hop_samples
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inputs = np.array([
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mfccs[i - pr.n_features:i] for i in range(pr.n_features, len(mfccs), mfcc_hops)
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])
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del mfccs
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print('Predicting...')
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predictions = self.runner.predict(inputs)
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del inputs
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return predictions
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def run(self):
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total = Metric(chunk_size=self.args.chunk_size)
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for i in glob(join(self.args.folder, '*.wav')):
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audio = load_audio(i)
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if audio.size == 0:
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continue
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predictions = self.evaluate(audio)
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detector = TriggerDetector(self.args.chunk_size, trigger_level=0, sensitivity=self.args.threshold)
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metric = Metric(
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chunk_size=self.args.chunk_size,
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seconds=len(audio) / pr.sample_rate,
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activated_chunks=(predictions > detector.sensitivity).sum(),
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activations=sum(detector.update(i) for i in predictions),
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activation_sum=predictions.sum()
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)
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total.add(metric)
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print()
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print(metric.info_string(basename(i)))
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del audio
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print()
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print()
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print(total.info_string('Total'))
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main = SimulateScript.run_main
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if __name__ == '__main__':
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main()
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