107 lines
3.6 KiB
Python
Executable File
107 lines
3.6 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2018 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 json
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from os.path import isfile, isdir
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from prettyparse import create_parser
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from precise.network_runner import Listener
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from precise.params import inject_params
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from precise.pocketsphinx.listener import PocketsphinxListener
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from precise.pocketsphinx.scripts.test import test_pocketsphinx
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from precise.scripts.test import show_stats, calc_stats, stats_to_dict
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from precise.train_data import TrainData
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usage = '''
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Evaluate a list of models on a dataset
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:-t --use-train
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Evaluate training data instead of test data
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:-pw --pocketsphinx-wake-word str -
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Optional wake word used to
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generate a Pocketsphinx data point
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:-pd --pocketsphinx-dict str -
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Optional word dictionary used to
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generate a Pocketsphinx data point
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Format: wake-word.yy-mm-dd.dict
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:-pf --pocketsphinx-folder str -
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Optional hmm folder used to
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generate a Pocketsphinx data point.
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:-pth --pocketsphinx-threshold float 1e-90
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Optional threshold used to
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generate a Pocketsphinx data point
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:-o --output str stats.json
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Output json file
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...
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'''
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def main():
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parser = create_parser(usage)
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parser.add_argument('models', nargs='*',
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help='List of model filenames in format: wake-word.yy-mm-dd.net')
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args = TrainData.parse_args(parser)
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if not (
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bool(args.pocketsphinx_dict) ==
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bool(args.pocketsphinx_folder) ==
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bool(args.pocketsphinx_wake_word)
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):
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parser.error('Must pass all or no Pocketsphinx arguments')
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data = TrainData.from_both(args.tags_file, args.tags_folder, args.folder)
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data_files = data.train_files if args.use_train else data.test_files
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print('Data:', data)
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metrics = {}
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if args.pocketsphinx_dict and args.pocketsphinx_folder and args.pocketsphinx_wake_word:
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if not isfile(args.pocketsphinx_dict):
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parser.error('No such file: ' + args.pocketsphinx_dict)
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if not isdir(args.pocketsphinx_folder):
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parser.error('No such folder: ' + args.pocketsphinx_folder)
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listener = PocketsphinxListener(
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args.pocketsphinx_wake_word, args.pocketsphinx_dict,
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args.pocketsphinx_folder, args.pocketsphinx_threshold
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)
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stats = test_pocketsphinx(listener, data_files)
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metrics[args.pocketsphinx_dict] = stats_to_dict(stats)
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for model_name in args.models:
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print('Calculating', model_name + '...')
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inject_params(model_name)
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train, test = data.load(args.use_train, not args.use_train)
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inputs, targets = train if args.use_train else test
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predictions = Listener.find_runner(model_name)(model_name).predict(inputs)
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stats = calc_stats(sum(data_files, []), targets, predictions)
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print('----', model_name, '----')
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show_stats(stats, False)
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metrics[model_name] = stats_to_dict(stats)
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print('Writing to:', args.output)
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with open(args.output, 'w') as f:
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json.dump(metrics, f)
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if __name__ == '__main__':
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main()
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