#!/usr/bin/env python3 # Copyright (c) 2017 Mycroft AI Inc. import wave from subprocess import check_output, PIPE from prettyparse import create_parser from precise.pocketsphinx_listener import PocketsphinxListener from precise.scripts.test import show_stats from precise.train_data import TrainData usage = ''' Test a dataset using Pocketsphinx :key_phrase str Key phrase composed of words from dictionary :dict_file str Filename of dictionary with word pronunciations :hmm_folder str Folder containing hidden markov model :-th --threshold str 1e-90 Threshold for activations :-t --use-train Evaluate training data instead of test data :-nf --no-filenames Don't show the names of files that failed ... ''' def eval_file(filename) -> float: transcription = check_output(['pocketsphinx_continuous', '-kws_threshold', '1e-20', '-keyphrase', 'hey my craft', '-infile', filename], stderr=PIPE) return float(bool(transcription) and not transcription.isspace()) def main(): args = TrainData.parse_args(create_parser(usage)) data = TrainData.from_both(args.db_file, args.db_folder, args.data_dir) print('Data:', data) listener = PocketsphinxListener(args.key_phrase, args.dict_file, args.hmm_folder, args.threshold) def run_test(filenames, name): print() print('===', name, '===') negatives, positives = [], [] for filename in filenames: with wave.open(filename) as wf: frames = wf.readframes(wf.getnframes()) out = listener.found_wake_word(frames) {False: negatives, True: positives}[out].append(filename) print('!' if out else '.', end='', flush=True) print() return negatives, positives data_files = data.train_files if args.use_train else data.test_files false_neg, true_pos = run_test(data_files[0], 'Keyword') true_neg, false_pos = run_test(data_files[1], 'Not Keyword') show_stats(false_pos, false_neg, true_pos, true_neg, not args.no_filenames) if __name__ == '__main__': main()