Update standard image to 2.4.4, remove unnecessary script with fork

Dockerfile

@@ -12,11 +12,10 @@ FROM python-ubuntu
 
 RUN apt-get update &&  apt-get install liblapack-dev libpng-dev libfreetype6-dev libqhull-dev git pkg-config portaudio19-dev swig libpulse-ocaml-dev gfortran libopenblas-dev libatlas-base-dev -y
 
-RUN mkdir /training && pip3 install precise-lite
+RUN mkdir /training && pip3 install git+https://github.com/tystuyfzand/precise-lite.git@feature/tf24
 
 WORKDIR /training
 
-COPY scripts/train_incremental.py /usr/local/lib/python3.6/dist-packages/precise_lite/scripts/train_incremental.py
 COPY entrypoint.sh /entrypoint.sh
 
 ENTRYPOINT ["/entrypoint.sh"]

Dockerfile.gpu

@@ -18,7 +18,6 @@ RUN mkdir /training && pip3 install git+https://github.com/tystuyfzand/precise-l
 
 WORKDIR /training
 
-COPY scripts/train_incremental.py /usr/local/lib/python3.6/dist-packages/precise_lite/scripts/train_incremental.py
 COPY entrypoint.sh /entrypoint.sh
 
 ENTRYPOINT ["/entrypoint.sh"]

scripts/train_incremental.py

@@ -1,159 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2019 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 import makedirs, rename
-from os.path import basename, splitext, isfile, join
-from prettyparse import Usage
-from random import random
-from typing import *
-
-from precise_lite.model import create_model, ModelParams
-from precise_lite.network_runner import Listener, KerasRunner
-from precise_lite.params import pr
-from precise_lite.scripts.train import TrainScript
-from precise_lite.train_data import TrainData
-from precise_lite.util import load_audio, save_audio, glob_all, chunk_audio
-
-
-def load_trained_fns(model_name: str) -> list:
-    progress_file = model_name.replace('.net', '') + '.trained.txt'
-    if isfile(progress_file):
-        print('Starting from saved position in', progress_file)
-        with open(progress_file, 'rb') as f:
-            return f.read().decode('utf8', 'surrogatepass').split('\n')
-    return []
-
-
-def save_trained_fns(trained_fns: list, model_name: str):
-    with open(model_name.replace('.net', '') + '.trained.txt', 'wb') as f:
-        f.write('\n'.join(trained_fns).encode('utf8', 'surrogatepass'))
-
-
-class TrainIncrementalScript(TrainScript):
-    usage = Usage('''
-        Train a model to inhibit activation by
-        marking false activations and retraining
-
-        :-e --epochs int 1
-            Number of epochs to train before continuing evaluation
-
-        :-ds --delay-samples int 10
-            Number of false activations to save before re-training
-
-        :-c --chunk-size int 2048
-            Number of samples between testing the neural network
-
-        :-r --random-data-folder str data/random
-            Folder with properly encoded wav files of
-            random audio that should not cause an activation
-
-        :-th --threshold float 0.5
-            Network output to be considered activated
-
-        ...
-    ''') | TrainScript.usage
-
-    def __init__(self, args):
-        super().__init__(args)
-
-        for i in (
-                join(self.args.folder, 'not-wake-word', 'generated'),
-                join(self.args.folder, 'test', 'not-wake-word', 'generated')
-        ):
-            makedirs(i, exist_ok=True)
-
-        self.trained_fns = load_trained_fns(self.args.model)
-        self.audio_buffer = np.zeros(pr.buffer_samples, dtype=float)
-
-        params = ModelParams(
-            skip_acc=self.args.no_validation, extra_metrics=self.args.extra_metrics,
-            loss_bias=1.0 - self.args.sensitivity
-        )
-        model = create_model(self.args.model, params)
-        self.listener = Listener(self.args.model, self.args.chunk_size, runner_cls=KerasRunner)
-        self.listener.runner = KerasRunner(self.args.model)
-        self.listener.runner.model = model
-        self.samples_since_train = 0
-
-    @staticmethod
-    def load_data(args: Any):
-        data = TrainData.from_tags(args.tags_file, args.tags_folder)
-        return data.load(True, not args.no_validation)
-
-    def retrain(self):
-        """Train for a session, pulling in any new data from the filesystem"""
-        folder = TrainData.from_folder(self.args.folder)
-        train_data, test_data = folder.load(True, not self.args.no_validation)
-
-        train_data = TrainData.merge(train_data, self.sampled_data)
-        test_data = TrainData.merge(test_data, self.test)
-        train_inputs, train_outputs = train_data
-        print()
-        try:
-            self.listener.runner.model.fit(
-                train_inputs, train_outputs, self.args.batch_size, self.epoch + self.args.epochs,
-                validation_data=test_data, callbacks=self.callbacks, initial_epoch=self.epoch
-            )
-        finally:
-            self.listener.runner.model.save(self.args.model + '/')
-
-    def train_on_audio(self, fn: str):
-        """Run through a single audio file"""
-        save_test = random() > 0.8
-        audio = load_audio(fn)
-        num_chunks = len(audio) // self.args.chunk_size
-
-        self.listener.clear()
-
-        for i, chunk in enumerate(chunk_audio(audio, self.args.chunk_size)):
-            print('\r' + str(i * 100. / num_chunks) + '%', end='', flush=True)
-            self.audio_buffer = np.concatenate((self.audio_buffer[len(chunk):], chunk))
-            conf = self.listener.update(chunk)
-            if conf > self.args.threshold:
-                self.samples_since_train += 1
-                name = splitext(basename(fn))[0] + '-' + str(i) + '.wav'
-                name = join(self.args.folder, 'test' if save_test else '', 'not-wake-word',
-                            'generated', name)
-                save_audio(name, self.audio_buffer)
-                print()
-                print('Saved to:', name)
-
-            if not save_test and self.samples_since_train >= self.args.delay_samples and \
-                    self.args.epochs > 0:
-                self.samples_since_train = 0
-                self.retrain()
-
-    def run(self):
-        """
-        Begin reading through audio files, saving false
-        activations and retraining when necessary
-        """
-        for fn in glob_all(self.args.random_data_folder, '*.wav'):
-            if fn in self.trained_fns:
-                print('Skipping ' + fn + '...')
-                continue
-
-            print('Starting file ' + fn + '...')
-            self.train_on_audio(fn)
-            print('\r100%                 ')
-
-            self.trained_fns.append(fn)
-            save_trained_fns(self.trained_fns, self.args.model)
-
-
-main = TrainIncrementalScript.run_main
-
-if __name__ == '__main__':
-    main()