mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2026-05-13 21:38:17 +08:00
17cb2e5acf
Add support for G2PW processing in the TTS system by introducing new methods and classes for handling G2PW segments. Update PrepareCoordinator to manage G2PW worker threads and integrate G2PW profiling into the existing framework. Enhance text preprocessing to identify segments requiring G2PW and streamline the resolution of these segments. This update improves the overall performance and maintainability of the TTS system by optimizing the handling of Chinese text processing.
431 lines
13 KiB
Python
431 lines
13 KiB
Python
import os
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import re
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import time
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import cn2an
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from pypinyin import lazy_pinyin, Style
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from pypinyin.contrib.tone_convert import to_finals_tone3, to_initials
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from text.symbols import punctuation
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from text.tone_sandhi import ToneSandhi
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from text.zh_normalization.text_normlization import TextNormalizer
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normalizer = lambda x: cn2an.transform(x, "an2cn")
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current_file_path = os.path.dirname(__file__)
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pinyin_to_symbol_map = {
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line.split("\t")[0]: line.strip().split("\t")[1]
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for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
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}
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import jieba_fast
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import logging
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jieba_fast.setLogLevel(logging.CRITICAL)
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import jieba_fast.posseg as psg
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# is_g2pw_str = os.environ.get("is_g2pw", "True")##默认开启
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# is_g2pw = False#True if is_g2pw_str.lower() == 'true' else False
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is_g2pw = True # True if is_g2pw_str.lower() == 'true' else False
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if is_g2pw:
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# print("当前使用g2pw进行拼音推理")
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from text.g2pw import G2PWPinyin, correct_pronunciation
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parent_directory = os.path.dirname(current_file_path)
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g2pw = G2PWPinyin(
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model_dir="GPT_SoVITS/text/G2PWModel",
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model_source=os.environ.get("bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"),
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v_to_u=False,
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neutral_tone_with_five=True,
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)
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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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"\n": ".",
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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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"~": "…",
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"～": "…",
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}
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tone_modifier = ToneSandhi()
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def replace_punctuation(text):
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text = text.replace("嗯", "恩").replace("呣", "母")
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pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
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replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
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replaced_text = re.sub(r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text)
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return replaced_text
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def g2p(text):
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pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
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sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
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phones, word2ph = _g2p(sentences)
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return phones, word2ph
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def _prepare_g2p_segments(segments):
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prepared_segments = []
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batch_inputs = []
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for segment in segments:
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processed_segment = re.sub("[a-zA-Z]+", "", segment)
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seg_cut = psg.lcut(processed_segment)
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seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
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prepared_segments.append(
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{
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"segment": processed_segment,
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"seg_cut": seg_cut,
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}
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)
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if processed_segment:
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batch_inputs.append(processed_segment)
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return prepared_segments, batch_inputs
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def _build_segment_from_g2pw(segment: str, seg_cut, pinyins):
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phones_list = []
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word2ph = []
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initials = []
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finals = []
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pre_word_length = 0
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for word, pos in seg_cut:
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sub_initials = []
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sub_finals = []
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now_word_length = pre_word_length + len(word)
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if pos == "eng":
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pre_word_length = now_word_length
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continue
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word_pinyins = pinyins[pre_word_length:now_word_length]
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word_pinyins = correct_pronunciation(word, word_pinyins)
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for pinyin in word_pinyins:
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if pinyin[0].isalpha():
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sub_initials.append(to_initials(pinyin))
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sub_finals.append(to_finals_tone3(pinyin, neutral_tone_with_five=True))
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else:
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sub_initials.append(pinyin)
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sub_finals.append(pinyin)
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pre_word_length = now_word_length
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sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
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sub_initials, sub_finals = _merge_erhua(sub_initials, sub_finals, word, pos)
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initials.append(sub_initials)
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finals.append(sub_finals)
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initials = sum(initials, [])
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finals = sum(finals, [])
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for c, v in zip(initials, finals):
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raw_pinyin = c + v
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if c == v:
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assert c in punctuation
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phone = [c]
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word2ph.append(1)
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else:
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v_without_tone = v[:-1]
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tone = v[-1]
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pinyin = c + v_without_tone
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assert tone in "12345"
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if c:
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v_rep_map = {
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"uei": "ui",
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"iou": "iu",
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"uen": "un",
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}
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if v_without_tone in v_rep_map.keys():
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pinyin = c + v_rep_map[v_without_tone]
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else:
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pinyin_rep_map = {
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"ing": "ying",
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"i": "yi",
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"in": "yin",
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"u": "wu",
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}
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if pinyin in pinyin_rep_map.keys():
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pinyin = pinyin_rep_map[pinyin]
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else:
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single_rep_map = {
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"v": "yu",
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"e": "e",
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"i": "y",
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"u": "w",
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}
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if pinyin[0] in single_rep_map.keys():
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pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
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assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, segment, raw_pinyin)
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new_c, new_v = pinyin_to_symbol_map[pinyin].split(" ")
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new_v = new_v + tone
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phone = [new_c, new_v]
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word2ph.append(len(phone))
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phones_list += phone
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return phones_list, word2ph
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def _build_segment_without_g2pw(segment: str, seg_cut):
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initials = []
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finals = []
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for word, pos in seg_cut:
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if pos == "eng":
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continue
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sub_initials, sub_finals = _get_initials_finals(word)
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sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
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sub_initials, sub_finals = _merge_erhua(sub_initials, sub_finals, word, pos)
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initials.append(sub_initials)
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finals.append(sub_finals)
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phones_list = []
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word2ph = []
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for c, v in zip(sum(initials, []), sum(finals, [])):
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raw_pinyin = c + v
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if c == v:
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assert c in punctuation
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phone = [c]
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word2ph.append(1)
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else:
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v_without_tone = v[:-1]
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tone = v[-1]
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pinyin = c + v_without_tone
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assert tone in "12345"
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if c:
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v_rep_map = {"uei": "ui", "iou": "iu", "uen": "un"}
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if v_without_tone in v_rep_map:
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pinyin = c + v_rep_map[v_without_tone]
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else:
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pinyin_rep_map = {"ing": "ying", "i": "yi", "in": "yin", "u": "wu"}
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if pinyin in pinyin_rep_map:
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pinyin = pinyin_rep_map[pinyin]
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else:
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single_rep_map = {"v": "yu", "e": "e", "i": "y", "u": "w"}
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if pinyin[0] in single_rep_map:
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pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
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assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, segment, raw_pinyin)
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new_c, new_v = pinyin_to_symbol_map[pinyin].split(" ")
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new_v = new_v + tone
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phone = [new_c, new_v]
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word2ph.append(len(phone))
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phones_list += phone
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return phones_list, word2ph
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def g2p_segments(segments, return_profile: bool = False):
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prepare_start = time.perf_counter()
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prepared_segments, batch_inputs = _prepare_g2p_segments(segments)
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profile = {
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"g2pw_prepare_ms": 0.0,
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"g2pw_predict_ms": 0.0,
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"g2pw_post_ms": 0.0,
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"g2pw_runtime_total_ms": 0.0,
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"g2pw_runtime_queue_wait_ms": 0.0,
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"g2pw_runtime_collect_wait_ms": 0.0,
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"g2pw_runtime_run_ms": 0.0,
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"g2pw_runtime_batch_rows": 0.0,
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"g2pw_runtime_batch_requests": 0.0,
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"g2pw_runtime_pool_workers": 0.0,
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"g2pw_runtime_shard_index": 0.0,
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}
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profile["g2pw_prepare_ms"] = float((time.perf_counter() - prepare_start) * 1000.0)
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if is_g2pw and batch_inputs:
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converter = g2pw._g2pw
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if hasattr(converter, "predict_sentences_with_profile"):
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g2pw_batch_results, predict_profile = converter.predict_sentences_with_profile(batch_inputs)
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for key, value in dict(predict_profile or {}).items():
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profile[key] = float(value)
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else:
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predict_start = time.perf_counter()
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g2pw_batch_results = converter(batch_inputs)
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profile["g2pw_predict_ms"] = float((time.perf_counter() - predict_start) * 1000.0)
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else:
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g2pw_batch_results = []
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post_start = time.perf_counter()
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results = []
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batch_cursor = 0
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for item in prepared_segments:
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segment = item["segment"]
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if not segment:
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results.append(([], [], segment))
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continue
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if not is_g2pw:
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phones, word2ph = _build_segment_without_g2pw(segment, item["seg_cut"])
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results.append((phones, word2ph, segment))
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continue
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pinyins = g2pw_batch_results[batch_cursor]
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batch_cursor += 1
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phones, word2ph = _build_segment_from_g2pw(segment, item["seg_cut"], pinyins)
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results.append((phones, word2ph, segment))
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profile["g2pw_post_ms"] = float((time.perf_counter() - post_start) * 1000.0)
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profile["g2pw_total_ms"] = float(profile["g2pw_prepare_ms"] + profile["g2pw_predict_ms"] + profile["g2pw_post_ms"])
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if return_profile:
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return results, profile
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return results
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def _get_initials_finals(word):
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initials = []
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finals = []
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orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
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orig_finals = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
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for c, v in zip(orig_initials, orig_finals):
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initials.append(c)
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finals.append(v)
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return initials, finals
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must_erhua = {"小院儿", "胡同儿", "范儿", "老汉儿", "撒欢儿", "寻老礼儿", "妥妥儿", "媳妇儿"}
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not_erhua = {
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"虐儿",
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"为儿",
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"护儿",
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"瞒儿",
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"救儿",
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"替儿",
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"有儿",
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"一儿",
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"我儿",
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"俺儿",
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"妻儿",
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"拐儿",
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"聋儿",
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"乞儿",
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"患儿",
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"幼儿",
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"孤儿",
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"婴儿",
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"婴幼儿",
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"连体儿",
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"脑瘫儿",
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"流浪儿",
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"体弱儿",
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"混血儿",
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"蜜雪儿",
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"舫儿",
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"祖儿",
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"美儿",
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"应采儿",
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"可儿",
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"侄儿",
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"孙儿",
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"侄孙儿",
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"女儿",
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"男儿",
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"红孩儿",
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"花儿",
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"虫儿",
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"马儿",
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"鸟儿",
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"猪儿",
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"猫儿",
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"狗儿",
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"少儿",
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}
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def _merge_erhua(initials: list[str], finals: list[str], word: str, pos: str) -> list[list[str]]:
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"""
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Do erhub.
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"""
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# fix er1
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for i, phn in enumerate(finals):
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if i == len(finals) - 1 and word[i] == "儿" and phn == "er1":
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finals[i] = "er2"
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# 发音
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if word not in must_erhua and (word in not_erhua or pos in {"a", "j", "nr"}):
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return initials, finals
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# "……" 等情况直接返回
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if len(finals) != len(word):
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return initials, finals
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assert len(finals) == len(word)
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# 与前一个字发同音
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new_initials = []
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new_finals = []
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for i, phn in enumerate(finals):
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if (
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i == len(finals) - 1
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and word[i] == "儿"
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and phn in {"er2", "er5"}
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and word[-2:] not in not_erhua
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and new_finals
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):
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phn = "er" + new_finals[-1][-1]
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new_initials.append(initials[i])
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new_finals.append(phn)
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return new_initials, new_finals
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def _g2p(segments):
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phones_list = []
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word2ph = []
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for phones, item_word2ph, _segment in g2p_segments(segments):
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phones_list += phones
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word2ph += item_word2ph
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return phones_list, word2ph
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def replace_punctuation_with_en(text):
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text = text.replace("嗯", "恩").replace("呣", "母")
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pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
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replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
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replaced_text = re.sub(r"[^\u4e00-\u9fa5A-Za-z" + "".join(punctuation) + r"]+", "", replaced_text)
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return replaced_text
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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 text_normalize(text):
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# https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
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tx = TextNormalizer()
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sentences = tx.normalize(text)
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dest_text = ""
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for sentence in sentences:
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dest_text += replace_punctuation(sentence)
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# 避免重复标点引起的参考泄露
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dest_text = replace_consecutive_punctuation(dest_text)
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return dest_text
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if __name__ == "__main__":
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text = "啊——但是《原神》是由,米哈\游自主，研发的一款全.新开放世界.冒险游戏"
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text = "呣呣呣～就是…大人的鼹鼠党吧？"
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text = "你好"
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text = text_normalize(text)
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print(g2p(text))
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# # 示例用法
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# text = "这是一个示例文本：,你好！这是一个测试..."
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# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试
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