def pyopenjtalk_g2p_accent(text) -> List[str]:
import pyopenjtalk
import re
phones = []
for labels in pyopenjtalk.run_frontend(text)[1]:
p = re.findall(r"\-(.*?)\+.*?\/A:([0-9\-]+).*?\/F:.*?_([0-9])", labels)
if len(p) == 1:
phones += [p[0][0], p[0][2], p[0][1]]
return phones
def test_jtalk():
for text in ["今日も良い天気ですね",
"こんにちは。",
"どんまい！",
"パソコンのとりあえず知っておきたい使い方", ]:
njd_results, labels = pyopenjtalk.run_frontend(text)
_print_results(njd_results, labels)
surface = "".join(map(lambda s: s.split(",")[0], njd_results))
assert surface == text
def pyopenjtalk_g2p_accent_with_pause(text) -> List[str]:
import pyopenjtalk
import re
phones = []
for labels in pyopenjtalk.run_frontend(text)[1]:
if labels.split("-")[1].split("+")[0] == "pau":
phones += ["pau"]
continue
p = re.findall(r"\-(.*?)\+.*?\/A:([0-9\-]+).*?\/F:.*?_([0-9])", labels)
if len(p) == 1:
phones += [p[0][0], p[0][2], p[0][1]]
return phones
def pyopenjtalk_g2p_prosody(text: str,
drop_unvoiced_vowels: bool = True) -> List[str]:
"""Extract phoneme + prosoody symbol sequence from input full-context labels.
The algorithm is based on `Prosodic features control by symbols as input of
sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks.
Args:
text (str): Input text.
drop_unvoiced_vowels (bool): whether to drop unvoiced vowels.
Returns:
List[str]: List of phoneme + prosody symbols.
Examples:
>>> from espnet2.text.phoneme_tokenizer import pyopenjtalk_g2p_prosody
>>> pyopenjtalk_g2p_prosody("こんにちは。")
['^', 'k', 'o', '[', 'N', 'n', 'i', 'ch', 'i', 'w', 'a', '$']
.. _`Prosodic features control by symbols as input of sequence-to-sequence acoustic
modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104
"""
import pyopenjtalk
labels = pyopenjtalk.run_frontend(text)[1]
N = len(labels)
phones = []
for n in range(N):
lab_curr = labels[n]
# current phoneme
p3 = re.search(r"\-(.*?)\+", lab_curr).group(1)
# deal unvoiced vowels as normal vowels
if drop_unvoiced_vowels and p3 in "AEIOU":
p3 = p3.lower()
# deal with sil at the beginning and the end of text
if p3 == "sil":
assert n == 0 or n == N - 1
if n == 0:
phones.append("^")
elif n == N - 1:
# check question form or not
e3 = _numeric_feature_by_regex(r"!(\d+)_", lab_curr)
if e3 == 0:
phones.append("$")
elif e3 == 1:
phones.append("?")
continue
elif p3 == "pau":
phones.append("_")
continue
else:
phones.append(p3)
# accent type and position info (forward or backward)
a1 = _numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr)
a2 = _numeric_feature_by_regex(r"\+(\d+)\+", lab_curr)
a3 = _numeric_feature_by_regex(r"\+(\d+)/", lab_curr)
# number of mora in accent phrase
f1 = _numeric_feature_by_regex(r"/F:(\d+)_", lab_curr)
a2_next = _numeric_feature_by_regex(r"\+(\d+)\+", labels[n + 1])
# accent phrase border
if a3 == 1 and a2_next == 1:
phones.append("#")
# pitch falling
elif a1 == 0 and a2_next == a2 + 1 and a2 != f1:
phones.append("]")
# pitch rising
elif a2 == 1 and a2_next == 2:
phones.append("[")
return phones
def test_fullcontext():
_, labels = pyopenjtalk.run_frontend("こんにちは")
labels2 = pyopenjtalk.extract_fullcontext("こんにちは")
for a, b in zip(labels, labels2):
assert a == b
def test_hello():
njd_results, labels = pyopenjtalk.run_frontend("こんにちは")
_print_results(njd_results, labels)
def text_to_context(self, text: str) -> List[str]:
import pyopenjtalk
label_list = pyopenjtalk.run_frontend(text)[1]
return label_list
