Change train_incremental to not use h5
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@ -16,8 +16,7 @@ RUN mkdir /training && pip3 install precise-lite
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WORKDIR /training
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COPY scripts/precise-lite-convert-h5 /usr/local/bin/precise-lite-convert-h5
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COPY scripts/convert_h5.py /usr/local/lib/python3.6/dist-packages/precise_lite/scripts/convert_h5.py
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COPY scripts/train_incremental.py /usr/local/lib/python3.6/dist-packages/precise_lite/scripts/train_incremental.py
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COPY entrypoint.sh /entrypoint.sh
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ENTRYPOINT ["/entrypoint.sh"]
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@ -1,57 +0,0 @@
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#!/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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from prettyparse import Usage
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from precise_lite.model import create_model, ModelParams
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from precise_lite.scripts.train import BaseScript
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class ConvertH5(BaseScript):
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usage = Usage('''
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Convert a model to h5 format
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:model str
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Keras model file (.net) to load from
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:-s --sensitivity float 0.2
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Weighted loss bias. Higher values decrease increase positives
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:-nv --no-validation
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Disable accuracy and validation calculation
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to improve speed during training
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:-em --extra-metrics
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Add extra metrics during training
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...
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''') | BaseScript.usage
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def __init__(self, args):
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super().__init__(args)
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params = ModelParams(
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skip_acc=self.args.no_validation, extra_metrics=self.args.extra_metrics,
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loss_bias=1.0 - self.args.sensitivity
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)
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self.model = create_model(self.args.model, params)
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def run(self):
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print('Converting model to h5')
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self.model.save(self.args.model + '.h5') # Save with '.h5' file extension to force format
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main = ConvertH5.run_main
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if __name__ == '__main__':
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main()
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@ -1,8 +0,0 @@
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#!/usr/bin/python3
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# -*- coding: utf-8 -*-
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import re
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import sys
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from precise_lite.scripts.convert_h5 import main
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if __name__ == '__main__':
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sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
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sys.exit(main())
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@ -0,0 +1,159 @@
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#!/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 numpy as np
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from os import makedirs, rename
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from os.path import basename, splitext, isfile, join
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from prettyparse import Usage
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from random import random
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from typing import *
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from precise_lite.model import create_model, ModelParams
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from precise_lite.network_runner import Listener, KerasRunner
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from precise_lite.params import pr
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from precise_lite.scripts.train import TrainScript
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from precise_lite.train_data import TrainData
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from precise_lite.util import load_audio, save_audio, glob_all, chunk_audio
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def load_trained_fns(model_name: str) -> list:
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progress_file = model_name.replace('.net', '') + '.trained.txt'
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if isfile(progress_file):
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print('Starting from saved position in', progress_file)
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with open(progress_file, 'rb') as f:
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return f.read().decode('utf8', 'surrogatepass').split('\n')
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return []
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def save_trained_fns(trained_fns: list, model_name: str):
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with open(model_name.replace('.net', '') + '.trained.txt', 'wb') as f:
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f.write('\n'.join(trained_fns).encode('utf8', 'surrogatepass'))
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class TrainIncrementalScript(TrainScript):
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usage = Usage('''
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Train a model to inhibit activation by
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marking false activations and retraining
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:-e --epochs int 1
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Number of epochs to train before continuing evaluation
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:-ds --delay-samples int 10
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Number of false activations to save before re-training
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:-c --chunk-size int 2048
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Number of samples between testing the neural network
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:-r --random-data-folder str data/random
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Folder with properly encoded wav files of
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random audio that should not cause an activation
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:-th --threshold float 0.5
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Network output to be considered activated
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...
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''') | TrainScript.usage
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def __init__(self, args):
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super().__init__(args)
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for i in (
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join(self.args.folder, 'not-wake-word', 'generated'),
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join(self.args.folder, 'test', 'not-wake-word', 'generated')
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):
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makedirs(i, exist_ok=True)
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self.trained_fns = load_trained_fns(self.args.model)
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self.audio_buffer = np.zeros(pr.buffer_samples, dtype=float)
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params = ModelParams(
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skip_acc=self.args.no_validation, extra_metrics=self.args.extra_metrics,
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loss_bias=1.0 - self.args.sensitivity
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)
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model = create_model(self.args.model, params)
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self.listener = Listener(self.args.model, self.args.chunk_size, runner_cls=KerasRunner)
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self.listener.runner = KerasRunner(self.args.model)
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self.listener.runner.model = model
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self.samples_since_train = 0
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@staticmethod
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def load_data(args: Any):
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data = TrainData.from_tags(args.tags_file, args.tags_folder)
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return data.load(True, not args.no_validation)
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def retrain(self):
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"""Train for a session, pulling in any new data from the filesystem"""
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folder = TrainData.from_folder(self.args.folder)
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train_data, test_data = folder.load(True, not self.args.no_validation)
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train_data = TrainData.merge(train_data, self.sampled_data)
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test_data = TrainData.merge(test_data, self.test)
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train_inputs, train_outputs = train_data
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print()
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try:
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self.listener.runner.model.fit(
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train_inputs, train_outputs, self.args.batch_size, self.epoch + self.args.epochs,
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validation_data=test_data, callbacks=self.callbacks, initial_epoch=self.epoch
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)
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finally:
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self.listener.runner.model.save(self.args.model + '/')
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def train_on_audio(self, fn: str):
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"""Run through a single audio file"""
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save_test = random() > 0.8
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audio = load_audio(fn)
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num_chunks = len(audio) // self.args.chunk_size
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self.listener.clear()
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for i, chunk in enumerate(chunk_audio(audio, self.args.chunk_size)):
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print('\r' + str(i * 100. / num_chunks) + '%', end='', flush=True)
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self.audio_buffer = np.concatenate((self.audio_buffer[len(chunk):], chunk))
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conf = self.listener.update(chunk)
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if conf > self.args.threshold:
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self.samples_since_train += 1
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name = splitext(basename(fn))[0] + '-' + str(i) + '.wav'
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name = join(self.args.folder, 'test' if save_test else '', 'not-wake-word',
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'generated', name)
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save_audio(name, self.audio_buffer)
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print()
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print('Saved to:', name)
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if not save_test and self.samples_since_train >= self.args.delay_samples and \
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self.args.epochs > 0:
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self.samples_since_train = 0
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self.retrain()
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def run(self):
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"""
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Begin reading through audio files, saving false
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activations and retraining when necessary
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"""
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for fn in glob_all(self.args.random_data_folder, '*.wav'):
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if fn in self.trained_fns:
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print('Skipping ' + fn + '...')
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continue
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print('Starting file ' + fn + '...')
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self.train_on_audio(fn)
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print('\r100% ')
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self.trained_fns.append(fn)
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save_trained_fns(self.trained_fns, self.args.model)
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main = TrainIncrementalScript.run_main
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if __name__ == '__main__':
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main()
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