GPT-SoVITS/GPT_SoVITS/text/english.py

import pickle
import os
import re
import wordsegment
from g2p_en import G2p

from string import punctuation

from text import symbols

current_file_path = os.path.dirname(__file__)
CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
CMU_DICT_FAST_PATH = os.path.join(current_file_path, "cmudict-fast.rep")
CMU_DICT_HOT_PATH = os.path.join(current_file_path, "engdict-hot.rep")
CACHE_PATH = os.path.join(current_file_path, "engdict_cache.pickle")

arpa = {
    "AH0",
    "S",
    "AH1",
    "EY2",
    "AE2",
    "EH0",
    "OW2",
    "UH0",
    "NG",
    "B",
    "G",
    "AY0",
    "M",
    "AA0",
    "F",
    "AO0",
    "ER2",
    "UH1",
    "IY1",
    "AH2",
    "DH",
    "IY0",
    "EY1",
    "IH0",
    "K",
    "N",
    "W",
    "IY2",
    "T",
    "AA1",
    "ER1",
    "EH2",
    "OY0",
    "UH2",
    "UW1",
    "Z",
    "AW2",
    "AW1",
    "V",
    "UW2",
    "AA2",
    "ER",
    "AW0",
    "UW0",
    "R",
    "OW1",
    "EH1",
    "ZH",
    "AE0",
    "IH2",
    "IH",
    "Y",
    "JH",
    "P",
    "AY1",
    "EY0",
    "OY2",
    "TH",
    "HH",
    "D",
    "ER0",
    "CH",
    "AO1",
    "AE1",
    "AO2",
    "OY1",
    "AY2",
    "IH1",
    "OW0",
    "L",
    "SH",
}


def replace_phs(phs):
    rep_map = {";": ",", ":": ",", "'": "-", '"': "-"}
    phs_new = []
    for ph in phs:
        if ph in symbols:
            phs_new.append(ph)
        elif ph in rep_map.keys():
            phs_new.append(rep_map[ph])
        else:
            print("ph not in symbols: ", ph)
    return phs_new


def read_dict():
    g2p_dict = {}
    start_line = 49
    with open(CMU_DICT_PATH) as f:
        line = f.readline()
        line_index = 1
        while line:
            if line_index >= start_line:
                line = line.strip()
                word_split = line.split("  ")
                word = word_split[0].lower()

                syllable_split = word_split[1].split(" - ")
                g2p_dict[word] = []
                for syllable in syllable_split:
                    phone_split = syllable.split(" ")
                    g2p_dict[word].append(phone_split)

            line_index = line_index + 1
            line = f.readline()

    return g2p_dict


def read_dict_new():
    g2p_dict = {}
    with open(CMU_DICT_PATH) as f:
        line = f.readline()
        line_index = 1
        while line:
            if line_index >= 57:
                line = line.strip()
                word_split = line.split("  ")
                word = word_split[0].lower()
                g2p_dict[word] = [word_split[1].split(" ")]

            line_index = line_index + 1
            line = f.readline()

    with open(CMU_DICT_FAST_PATH) as f:
        line = f.readline()
        line_index = 1
        while line:
            if line_index >= 0:
                line = line.strip()
                word_split = line.split(" ")
                word = word_split[0].lower()
                if word not in g2p_dict:
                    g2p_dict[word] = [word_split[1:]]

            line_index = line_index + 1
            line = f.readline()

    with open(CMU_DICT_HOT_PATH) as f:
        line = f.readline()
        line_index = 1
        while line:
            if line_index >= 0:
                line = line.strip()
                word_split = line.split(" ")
                word = word_split[0].lower()
                # 自定义发音词直接覆盖字典
                g2p_dict[word] = [word_split[1:]]

            line_index = line_index + 1
            line = f.readline()
    
    return g2p_dict


def cache_dict(g2p_dict, file_path):
    with open(file_path, "wb") as pickle_file:
        pickle.dump(g2p_dict, pickle_file)


def get_dict():
    if os.path.exists(CACHE_PATH):
        with open(CACHE_PATH, "rb") as pickle_file:
            g2p_dict = pickle.load(pickle_file)
    else:
        g2p_dict = read_dict_new()
        cache_dict(g2p_dict, CACHE_PATH)

    return g2p_dict


eng_dict = get_dict()


def text_normalize(text):
    # todo: eng text normalize
    # 适配 g2p_en 标点
    return text.replace(";", ",").replace(":", ",").replace('"', "'")


class en_G2p(G2p):
    def __init__(self):
        super().__init__()
        # 分词初始化
        wordsegment.load()

        # 扩展过时字典
        self.cmu = get_dict()

        # 剔除读音错误的几个缩写
        for word in ["AE", "AI", "AR", "IOS", "HUD", "OS"]:
            del self.cmu[word.lower()]

        # "A" 落单不读 "AH0" 读 "EY1"
        self.cmu['a'] = [['EY1']]


    def predict(self, word):
        # 小写 oov 长度小于等于 3 直接读字母
        if (len(word) <= 3):
            return [phone for w in word for phone in self(w)]

        # 尝试进行分词，应对复合词
        comps = wordsegment.segment(word.lower())

        # 无法分词的送回去预测
        if len(comps)==1:
            return super().predict(word)

        # 可以分词的递归处理
        return [phone for comp in comps for phone in self(comp)]


_g2p = en_G2p()


def g2p(text):
    text = text_normalize(text)

    # g2p_en 整段推理，剔除不存在的arpa返回
    phone_list = _g2p(text)
    phones = [ph if ph != "<unk>" else "UNK" for ph in phone_list if ph not in [" ", "<pad>", "UW", "</s>", "<s>"]]

    return replace_phs(phones)


if __name__ == "__main__":
    # print(get_dict())
    print(g2p("hello"))
    print(g2p("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
    # all_phones = set()
    # for k, syllables in eng_dict.items():
    #     for group in syllables:
    #         for ph in group:
    #             all_phones.add(ph)
    # print(all_phones)