def generate_g2p_dict(args):
if not args.temp_directory:
temp_dir = TEMP_DIR
temp_dir = os.path.join(temp_dir, 'G2P')
else:
temp_dir = os.path.expanduser(args.temp_directory)
if os.path.isdir(args.input_path):
input_dir = os.path.expanduser(args.input_path)
corpus_name = os.path.basename(args.input_path)
if corpus_name == '':
args.input_path = os.path.dirname(args.input_path)
corpus_name = os.path.basename(args.input_path)
data_directory = os.path.join(temp_dir, corpus_name)
corpus = Corpus(input_dir, data_directory)
word_set = get_word_set(corpus, args.include_bracketed)
else:
word_set = set()
with open(args.input_path, 'r', encoding='utf8') as f:
for line in f:
word_set.update(line.strip().split())
model = G2PModel(args.g2p_model_path)
gen = PhonetisaurusDictionaryGenerator(model,
word_set,
args.output_path,
temp_directory=temp_dir)
gen.generate()
def test_training(sick_dict, sick_g2p_model_path):
trainer = PhonetisaurusTrainer(sick_dict, sick_g2p_model_path, korean=False)
trainer.train()
model = G2PModel(sick_g2p_model_path)
assert (model.meta['version'] == __version__)
assert (model.meta['architecture'] == 'phonetisaurus')
assert (model.meta['phones'] == sick_dict.nonsil_phones)
def test_training(sick_dict, sick_g2p_model_path):
trainer = PhonetisaurusTrainer(sick_dict, sick_g2p_model_path, window_size=2)
trainer.validate()
trainer.train()
model = G2PModel(sick_g2p_model_path)
assert model.meta['version'] == __version__
assert model.meta['architecture'] == 'phonetisaurus'
assert model.meta['phones'] == sick_dict.nonsil_phones
def generate_dict(args):
if not args.temp_directory:
temp_dir = TEMP_DIR
else:
temp_dir = os.path.expanduser(args.temp_directory)
input_dir = os.path.expanduser(args.corpus_directory)
corpus = Corpus(input_dir, os.path.join(temp_dir, 'corpus'))
word_set = corpus.word_set
model = G2PModel(args.g2p_model_path)
gen = PhonetisaurusDictionaryGenerator(model,
word_set,
args.output_path,
temp_directory=temp_dir)
gen.generate()
def generate_dict(args):
if not args.temp_directory:
temp_dir = TEMP_DIR
else:
temp_dir = os.path.expanduser(args.temp_directory)
input_dir = os.path.expanduser(args.corpus_directory)
corpus = Corpus(input_dir, "")
model = G2PModel(args.g2p_model_path)
gen = PhonetisaurusDictionaryGenerator(model,
corpus,
args.output_path,
temp_directory=temp_dir,
korean=args.korean)
gen.generate()
def generate_dictionary(args):
print("Generating pronunciations from G2P model")
if not args.temp_directory:
temp_dir = TEMP_DIR
temp_dir = os.path.join(temp_dir, 'G2P')
else:
temp_dir = os.path.expanduser(args.temp_directory)
if os.path.isdir(args.input_path):
input_dir = os.path.expanduser(args.input_path)
corpus_name = os.path.basename(args.input_path)
if corpus_name == '':
args.input_path = os.path.dirname(args.input_path)
corpus_name = os.path.basename(args.input_path)
data_directory = os.path.join(temp_dir, corpus_name)
corpus = Corpus(input_dir, data_directory)
word_set = get_word_set(corpus, args.include_bracketed)
else:
word_set = set()
with open(args.input_path, 'r', encoding='utf8') as f:
for line in f:
word_set.update(line.strip().split())
if not args.include_bracketed:
word_set = [x for x in word_set if not check_bracketed(x)]
if args.g2p_model_path is not None:
model = G2PModel(args.g2p_model_path)
gen = PhonetisaurusDictionaryGenerator(model,
word_set,
args.output_path,
temp_directory=temp_dir)
gen.generate()
else:
with open(args.output_path, "w", encoding='utf8') as f:
for word in sorted(word_set):
pronunciation = list(word)
f.write('{} {}\n'.format(word, ' '.join(pronunciation)))
def test_generator(sick_g2p_model_path, sick_corpus, g2p_sick_output):
model = G2PModel(sick_g2p_model_path)
gen = PhonetisaurusDictionaryGenerator(model, sick_corpus, g2p_sick_output)
gen.generate()
assert (os.path.exists(g2p_sick_output))
outdir = tempfile.mkdtemp()
# Encode audio into corpse dir
subprocess.call([
'ffmpeg', '-i', AUDIO_PATH, '-ar', '16000', '-ac', '1',
os.path.join(corpse_dir_in, '1.wav')
])
# Copy text file
open(os.path.join(corpse_dir_in, '1.lab'), 'w').write(open(TEXT_PATH).read())
corpus = Corpus(corpse_dir_in, corpse_dir_out)
acoustic_model = AcousticModel('spanish.zip')
g2p_model = G2PModel('spanish_g2p.zip')
dict_dir = tempfile.mkdtemp()
with tempfile.NamedTemporaryFile() as g2pfh:
d_gen = PhonetisaurusDictionaryGenerator(g2p_model, WORDS, g2pfh.name)
d_gen.generate()
dictionary = Dictionary(g2pfh.name, dict_dir)
acoustic_model.validate(dictionary)
aligner = PretrainedAligner(corpus,
dictionary,
acoustic_model,
outdir,
outdir = tempfile.mkdtemp()
# Encode audio into corpse dir
subprocess.call([
'ffmpeg', '-i', AUDIO_PATH, '-ar', '16000', '-ac', '1',
os.path.join(corpse_dir_in, '1.wav')
])
# Copy text file
open(os.path.join(corpse_dir_in, '1.lab'), 'w').write(open(TEXT_PATH).read())
corpus = Corpus(corpse_dir_in, corpse_dir_out)
acoustic_model = AcousticModel('%s.zip' % (LANGUAGE))
g2p_model = G2PModel('%s_g2p.zip' % (LANGUAGE))
dict_dir = 'rush/dict' #tempfile.mkdtemp()
os.makedirs(dict_dir)
g2pname = 'rush/g2p'
with tempfile.NamedTemporaryFile() as g2pfh:
d_gen = PhonetisaurusDictionaryGenerator(g2p_model, WORDS,
g2pname) #g2pfh.name)
d_gen.generate()
dictionary = Dictionary(g2pname, dict_dir) #g2pfh.name, dict_dir)
acoustic_model.validate(dictionary)
aligner = PretrainedAligner(corpus,