g2p.py

import pyopenjtalk, re
from packaging.version import parse as V


### Add from espnet ### 
# ESPNet:https://github.com/espnet/espnet
#######################
def pyopenjtalk_g2p_prosody(text: str, drop_unvoiced_vowels: bool = True) :
    """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

    """
    labels = _extract_fullcontext_label(text)
    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 and p3 in "aeiouAEIOUNcl":
            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

from packaging.version import parse as V
def _extract_fullcontext_label(text):
    import pyopenjtalk

    if V(pyopenjtalk.__version__) >= V("0.3.0"):
        return pyopenjtalk.make_label(pyopenjtalk.run_frontend(text))
    else:
        return pyopenjtalk.run_frontend(text)[1]


def _numeric_feature_by_regex(regex, s):
    match = re.search(regex, s)
    if match is None:
        return -50
    return int(match.group(1))

#######################

def main():
    phonome = pyopenjtalk_g2p_prosody("とてもつらい。なぜつらいか？それも分からない。")
    print(phonome)
    return 0



if __name__ =="__main__":
    main()