mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 19:41:56 +08:00
373 lines
9.7 KiB
Python
373 lines
9.7 KiB
Python
import pickle
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import os
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import re
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import wordsegment
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from g2p_en import G2p
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from text.symbols import punctuation
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from text.symbols2 import symbols
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import unicodedata
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from builtins import str as unicode
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from g2p_en.expand import normalize_numbers
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from nltk.tokenize import TweetTokenizer
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word_tokenize = TweetTokenizer().tokenize
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from nltk import pos_tag
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current_file_path = os.path.dirname(__file__)
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CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
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CMU_DICT_FAST_PATH = os.path.join(current_file_path, "cmudict-fast.rep")
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CMU_DICT_HOT_PATH = os.path.join(current_file_path, "engdict-hot.rep")
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CACHE_PATH = os.path.join(current_file_path, "engdict_cache.pickle")
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NAMECACHE_PATH = os.path.join(current_file_path, "namedict_cache.pickle")
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arpa = {
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"AH0",
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"S",
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"AH1",
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"EY2",
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"AE2",
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"EH0",
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"OW2",
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"UH0",
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"NG",
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"B",
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"G",
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"AY0",
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"M",
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"AA0",
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"F",
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"AO0",
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"ER2",
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"UH1",
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"IY1",
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"AH2",
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"DH",
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"IY0",
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"EY1",
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"IH0",
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"K",
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"N",
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"W",
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"IY2",
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"T",
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"AA1",
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"ER1",
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"EH2",
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"OY0",
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"UH2",
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"UW1",
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"Z",
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"AW2",
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"AW1",
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"V",
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"UW2",
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"AA2",
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"ER",
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"AW0",
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"UW0",
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"R",
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"OW1",
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"EH1",
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"ZH",
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"AE0",
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"IH2",
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"IH",
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"Y",
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"JH",
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"P",
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"AY1",
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"EY0",
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"OY2",
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"TH",
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"HH",
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"D",
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"ER0",
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"CH",
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"AO1",
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"AE1",
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"AO2",
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"OY1",
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"AY2",
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"IH1",
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"OW0",
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"L",
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"SH",
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}
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def replace_phs(phs):
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rep_map = {"'": "-"}
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phs_new = []
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for ph in phs:
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if ph in symbols:
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phs_new.append(ph)
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elif ph in rep_map.keys():
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phs_new.append(rep_map[ph])
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else:
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print("ph not in symbols: ", ph)
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return phs_new
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def replace_consecutive_punctuation(text):
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punctuations = ''.join(re.escape(p) for p in punctuation)
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pattern = f'([{punctuations}])([{punctuations}])+'
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result = re.sub(pattern, r'\1', text)
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return result
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def read_dict():
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g2p_dict = {}
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start_line = 49
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with open(CMU_DICT_PATH) as f:
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line = f.readline()
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line_index = 1
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while line:
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if line_index >= start_line:
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line = line.strip()
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word_split = line.split(" ")
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word = word_split[0].lower()
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syllable_split = word_split[1].split(" - ")
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g2p_dict[word] = []
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for syllable in syllable_split:
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phone_split = syllable.split(" ")
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g2p_dict[word].append(phone_split)
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line_index = line_index + 1
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line = f.readline()
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return g2p_dict
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def read_dict_new():
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g2p_dict = {}
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with open(CMU_DICT_PATH) as f:
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line = f.readline()
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line_index = 1
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while line:
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if line_index >= 57:
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line = line.strip()
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word_split = line.split(" ")
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word = word_split[0].lower()
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g2p_dict[word] = [word_split[1].split(" ")]
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line_index = line_index + 1
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line = f.readline()
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with open(CMU_DICT_FAST_PATH) as f:
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line = f.readline()
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line_index = 1
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while line:
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if line_index >= 0:
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line = line.strip()
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word_split = line.split(" ")
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word = word_split[0].lower()
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if word not in g2p_dict:
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g2p_dict[word] = [word_split[1:]]
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line_index = line_index + 1
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line = f.readline()
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return g2p_dict
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def hot_reload_hot(g2p_dict):
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with open(CMU_DICT_HOT_PATH) as f:
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line = f.readline()
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line_index = 1
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while line:
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if line_index >= 0:
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line = line.strip()
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word_split = line.split(" ")
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word = word_split[0].lower()
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# 自定义发音词直接覆盖字典
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g2p_dict[word] = [word_split[1:]]
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line_index = line_index + 1
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line = f.readline()
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return g2p_dict
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def cache_dict(g2p_dict, file_path):
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with open(file_path, "wb") as pickle_file:
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pickle.dump(g2p_dict, pickle_file)
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def get_dict():
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if os.path.exists(CACHE_PATH):
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with open(CACHE_PATH, "rb") as pickle_file:
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g2p_dict = pickle.load(pickle_file)
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else:
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g2p_dict = read_dict_new()
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cache_dict(g2p_dict, CACHE_PATH)
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g2p_dict = hot_reload_hot(g2p_dict)
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return g2p_dict
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def get_namedict():
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if os.path.exists(NAMECACHE_PATH):
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with open(NAMECACHE_PATH, "rb") as pickle_file:
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name_dict = pickle.load(pickle_file)
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else:
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name_dict = {}
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return name_dict
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def text_normalize(text):
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# todo: eng text normalize
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# 适配中文及 g2p_en 标点
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rep_map = {
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"[;:：，；]": ",",
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'["’]': "'",
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"。": ".",
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"！": "!",
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"？": "?",
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}
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for p, r in rep_map.items():
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text = re.sub(p, r, text)
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# 来自 g2p_en 文本格式化处理
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# 增加大写兼容
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text = unicode(text)
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text = normalize_numbers(text)
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text = ''.join(char for char in unicodedata.normalize('NFD', text)
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if unicodedata.category(char) != 'Mn') # Strip accents
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text = re.sub("[^ A-Za-z'.,?!\-]", "", text)
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text = re.sub(r"(?i)i\.e\.", "that is", text)
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text = re.sub(r"(?i)e\.g\.", "for example", text)
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# 避免重复标点引起的参考泄露
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text = replace_consecutive_punctuation(text)
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return text
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class en_G2p(G2p):
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def __init__(self):
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super().__init__()
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# 分词初始化
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wordsegment.load()
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# 扩展过时字典, 添加姓名字典
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self.cmu = get_dict()
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self.namedict = get_namedict()
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# 剔除读音错误的几个缩写
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for word in ["AE", "AI", "AR", "IOS", "HUD", "OS"]:
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del self.cmu[word.lower()]
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# 修正多音字
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self.homograph2features["read"] = (['R', 'IY1', 'D'], ['R', 'EH1', 'D'], 'VBP')
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self.homograph2features["complex"] = (['K', 'AH0', 'M', 'P', 'L', 'EH1', 'K', 'S'], ['K', 'AA1', 'M', 'P', 'L', 'EH0', 'K', 'S'], 'JJ')
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def __call__(self, text):
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# tokenization
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words = word_tokenize(text)
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tokens = pos_tag(words) # tuples of (word, tag)
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# steps
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prons = []
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for o_word, pos in tokens:
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# 还原 g2p_en 小写操作逻辑
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word = o_word.lower()
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if re.search("[a-z]", word) is None:
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pron = [word]
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# 先把单字母推出去
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elif len(word) == 1:
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# 单读 A 发音修正, 这里需要原格式 o_word 判断大写
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if o_word == "A":
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pron = ['EY1']
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else:
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pron = self.cmu[word][0]
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# g2p_en 原版多音字处理
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elif word in self.homograph2features: # Check homograph
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pron1, pron2, pos1 = self.homograph2features[word]
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if pos.startswith(pos1):
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pron = pron1
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# pos1比pos长仅出现在read
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elif len(pos) < len(pos1) and pos == pos1[:len(pos)]:
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pron = pron1
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else:
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pron = pron2
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else:
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# 递归查找预测
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pron = self.qryword(o_word)
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prons.extend(pron)
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prons.extend([" "])
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return prons[:-1]
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def qryword(self, o_word):
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word = o_word.lower()
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# 查字典, 单字母除外
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if len(word) > 1 and word in self.cmu: # lookup CMU dict
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return self.cmu[word][0]
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# 单词仅首字母大写时查找姓名字典
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if o_word.istitle() and word in self.namedict:
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return self.namedict[word][0]
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# oov 长度小于等于 3 直接读字母
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if len(word) <= 3:
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phones = []
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for w in word:
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# 单读 A 发音修正, 此处不存在大写的情况
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if w == "a":
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phones.extend(['EY1'])
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else:
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phones.extend(self.cmu[w][0])
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return phones
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# 尝试分离所有格
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if re.match(r"^([a-z]+)('s)$", word):
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phones = self.qryword(word[:-2])[:]
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# P T K F TH HH 无声辅音结尾 's 发 ['S']
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if phones[-1] in ['P', 'T', 'K', 'F', 'TH', 'HH']:
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phones.extend(['S'])
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# S Z SH ZH CH JH 擦声结尾 's 发 ['IH1', 'Z'] 或 ['AH0', 'Z']
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elif phones[-1] in ['S', 'Z', 'SH', 'ZH', 'CH', 'JH']:
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phones.extend(['AH0', 'Z'])
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# B D G DH V M N NG L R W Y 有声辅音结尾 's 发 ['Z']
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# AH0 AH1 AH2 EY0 EY1 EY2 AE0 AE1 AE2 EH0 EH1 EH2 OW0 OW1 OW2 UH0 UH1 UH2 IY0 IY1 IY2 AA0 AA1 AA2 AO0 AO1 AO2
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# ER ER0 ER1 ER2 UW0 UW1 UW2 AY0 AY1 AY2 AW0 AW1 AW2 OY0 OY1 OY2 IH IH0 IH1 IH2 元音结尾 's 发 ['Z']
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else:
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phones.extend(['Z'])
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return phones
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# 尝试进行分词，应对复合词
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comps = wordsegment.segment(word.lower())
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# 无法分词的送回去预测
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if len(comps)==1:
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return self.predict(word)
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# 可以分词的递归处理
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return [phone for comp in comps for phone in self.qryword(comp)]
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_g2p = en_G2p()
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def g2p(text):
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# g2p_en 整段推理，剔除不存在的arpa返回
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phone_list = _g2p(text)
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phones = [ph if ph != "<unk>" else "UNK" for ph in phone_list if ph not in [" ", "<pad>", "UW", "</s>", "<s>"]]
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return replace_phs(phones)
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if __name__ == "__main__":
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print(g2p("hello"))
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print(g2p(text_normalize("e.g. I used openai's AI tool to draw a picture.")))
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print(g2p(text_normalize("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")))
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