def test_wfst_decoder_normal_transition():
phoneme_table = PhonemeTable()
phoneme_table.add_labels(phonemes)
fst_compiler = _FstCompiler()
eps = phoneme_table.get_epsilon_id()
blank = phoneme_table.get_blank_id()
a = phoneme_table.get_label_id('a')
i = phoneme_table.get_label_id('i')
fst_compiler.add_arc(0, 1, blank, eps, 0.2)
fst_compiler.add_arc(1, 2, a, eps, 0.1)
fst_compiler.add_arc(1, 3, i, eps, 0.2)
fst = fst_compiler.compile()
wfst_decoder = WFSTDecoder(fst)
prev_paths = {
0: wfst_decoder.Path(score=0,
prev_path=None,
frame_index=0,
olabel=None)
}
curr_paths = {}
wfst_decoder.normal_transition(prev_paths, curr_paths, 0, blank)
assert 1 in curr_paths
assert round(curr_paths[1].score, 6) == 0.2
assert round(curr_paths[1].prev_path.score, 6) == 0
prev_paths = curr_paths
curr_paths = {}
wfst_decoder.normal_transition(prev_paths, curr_paths, 1, a)
assert 2 in curr_paths
assert round(curr_paths[2].score, 6) == 0.3
assert curr_paths[2].frame_index == 1
assert round(curr_paths[2].prev_path.score, 6) == 0.2
def test_lexicon_create_fst_without_homophones(workdir,
words_without_homophones):
vocab = get_vocabulary_table(workdir, words_without_homophones)
lexicon = get_lexicon(words_without_homophones)
phoneme_table = PhonemeTable()
phoneme_table.add_labels(phonemes)
epsilon_id = phoneme_table.get_epsilon_id()
a = phoneme_table.get_label_id('a')
i = phoneme_table.get_label_id('i')
o = phoneme_table.get_label_id('o')
fst = lexicon.create_fst(phoneme_table, vocab, min_freq=0)
assert (fst.num_states() == 7)
aux0 = phoneme_table.get_auxiliary_label_id('#0')
state = 0
assert (fst.num_arcs(0) == 2)
gen = fst.arcs(state)
arc = next(gen)
is_expected_arc(arc, a, vocab.get_label_id('愛'), 1)
arc = gen.__next__()
is_expected_arc(arc, a, vocab.get_label_id('青'), 4)
state = 1
assert (fst.num_arcs(state) == 1)
arc = next(fst.arcs(state))
is_expected_arc(arc, i, epsilon_id, 2)
state = 2
assert (fst.num_arcs(state) == 1)
arc = next(fst.arcs(state))
is_expected_arc(arc, aux0, epsilon_id, 3)
state = 3
assert (fst.num_arcs(state) == 1)
arc = next(fst.arcs(state))
is_expected_arc(arc, epsilon_id, epsilon_id, 0)
state = 4
assert (fst.num_arcs(state) == 1)
arc = next(fst.arcs(state))
is_expected_arc(arc, o, epsilon_id, 5)
state = 5
assert (fst.num_arcs(state) == 1)
arc = next(fst.arcs(state))
is_expected_arc(arc, aux0, epsilon_id, 6)
state = 6
assert (fst.num_arcs(state) == 1)
arc = next(fst.arcs(state))
is_expected_arc(arc, epsilon_id, epsilon_id, 0)
def test_token_create_fst_with_auxiliary_labels():
phoneme_table = PhonemeTable()
phoneme_table.add_labels(['a', 'i'])
epsilon_id = phoneme_table.get_epsilon_id()
blank_id = phoneme_table.get_blank_id()
a = phoneme_table.get_label_id('a')
i = phoneme_table.get_label_id('i')
phoneme_table.set_auxiliary_label('#0')
phoneme_table.set_auxiliary_label('#1')
aux0 = phoneme_table.get_auxiliary_label_id('#0')
aux1 = phoneme_table.get_auxiliary_label_id('#1')
fst = Token().create_fst(phoneme_table)
assert (fst.num_states() == 5)
# start state
state = 0
assert (fst.num_arcs(state) == 3)
gen_arc = fst.arcs(state)
is_expected_arc(next(gen_arc), blank_id, epsilon_id, state)
is_expected_arc(next(gen_arc), a, a, 3)
is_expected_arc(next(gen_arc), i, i, 4)
# second state
state = 1
assert (fst.num_arcs(state) == 2)
gen_arc = fst.arcs(state)
is_expected_arc(next(gen_arc), blank_id, epsilon_id, state)
is_expected_arc(next(gen_arc), epsilon_id, epsilon_id, 2)
# final(auxiliary) state
state = 2
assert (fst.num_arcs(state) == 3)
gen_arc = fst.arcs(state)
is_expected_arc(next(gen_arc), epsilon_id, epsilon_id, 0)
is_expected_arc(next(gen_arc), epsilon_id, aux0, state)
is_expected_arc(next(gen_arc), epsilon_id, aux1, state)
# a
state = 3
assert (fst.num_arcs(state) == 2)
gen_arc = fst.arcs(state)
is_expected_arc(next(gen_arc), a, epsilon_id, state)
is_expected_arc(next(gen_arc), epsilon_id, epsilon_id, 1)
# b
state = 4
assert (fst.num_arcs(state) == 2)
gen_arc = fst.arcs(state)
is_expected_arc(next(gen_arc), i, epsilon_id, state)
is_expected_arc(next(gen_arc), epsilon_id, epsilon_id, 1)
def test_wfst_decoder_epsilon_transition():
phoneme_table = PhonemeTable()
phoneme_table.add_labels(phonemes)
fst_compiler = _FstCompiler()
eps = phoneme_table.get_epsilon_id()
a = phoneme_table.get_label_id('a')
fst_compiler.add_arc(0, 1, eps, eps, 0.1)
fst_compiler.add_arc(1, 2, eps, eps, 0.2)
fst_compiler.add_arc(1, 3, eps, eps, 0.3)
fst_compiler.add_arc(0, 2, eps, eps, 0.15)
fst_compiler.add_arc(0, 3, eps, eps, 0.5)
fst_compiler.add_arc(0, 4, a, eps, 0.15)
fst_compiler.set_final(4)
fst = fst_compiler.compile()
wfst_decoder = WFSTDecoder(fst)
paths = {
0: wfst_decoder.Path(score=0,
prev_path=None,
frame_index=0,
olabel=None)
}
frame_index = 0
wfst_decoder.epsilon_transition(paths, phoneme_table, frame_index)
# check new state 1 is added to paths
# TODO: state:1が残るのは果たしてよいのか？無限ループしそう
assert 1 in paths
assert round(paths[1].score, 6) == 0.1
assert paths[1].prev_path.score == 0
# check existing state 2 is updated via better path
assert 2 in paths
assert round(paths[2].score, 6) == 0.15
assert paths[2].prev_path.score == 0
# check existing state 3 is not updated
assert round(paths[3].score, 6) == 0.4
assert round(paths[3].prev_path.score, 6) == 0.1
assert paths[3].prev_path.prev_path.score == 0
# check new state 4 is not added to paths
# because it's not epsilon transition
assert 4 not in paths
def test_phoneme_table_get_epsilon_id():
phoneme_table = PhonemeTable()
assert phoneme_table.get_epsilon_id() == 0
assert phoneme_table.get_label_id('<epsilon>') == 0
assert phoneme_table.get_label(0) == '<epsilon>'
parser.add_argument('workdir', help='Directory path where files are saved')
parser.add_argument('wav_files', nargs='*', help='wave files to recognize')
parser.add_argument('--vocabulary-symbol-file', type=str, default='vocab.syms',
help='Vocabulary symbol file name')
parser.add_argument('--feature-params-file', type=str,
default='feature_params.json',
help='Feature params file name')
parser.add_argument('--model-file', type=str, default='model.bin',
help='model file name')
parser.add_argument('--decoder-fst-file', type=str, default='decoder.fst',
help='Decoder FST file name')
args = parser.parse_args()
phoneme_table = PhonemeTable()
phoneme_table.add_labels(phonemes)
epsilon_id = phoneme_table.get_epsilon_id()
print('Loading model ...')
model_path = os.path.join(args.workdir, args.model_file)
model = EESENAcousticModel.load(model_path)
feature_params_path = os.path.join(args.workdir, args.feature_params_file)
feature_params = FeatureParams.load(feature_params_path)
batch = []
for wav_file in args.wav_files:
data = extract_feature_from_wavfile(wav_file, feature_params)
batch.append(torch.from_numpy(data))
output = model.predict(pad_sequence(batch))
for idx, wav_file in enumerate(args.wav_files):
print('Decoding {} ... '.format(wav_file))
frame_labels = [int(frame_label) for frame_label in output[:, idx]]
print(' acoustic labels = {}'.format(' '.join(
[phoneme_table.get_label(frame_label) for frame_label in frame_labels