Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)
* ruff check --fix * ruff format --line-length 120 --target-version py39 * Change the link for G2PW Model * update pytorch version and colab
This commit is contained in:
XXXXRT666
File diff suppressed because it is too large
Load Diff
@@ -1,8 +1,9 @@
|
||||
|
||||
import os, sys
|
||||
import os
|
||||
import sys
|
||||
import threading
|
||||
|
||||
from tqdm import tqdm
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
|
||||
@@ -18,17 +19,19 @@ from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_
|
||||
|
||||
from tools.i18n.i18n import I18nAuto, scan_language_list
|
||||
|
||||
language=os.environ.get("language","Auto")
|
||||
language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
|
||||
language = os.environ.get("language", "Auto")
|
||||
language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
|
||||
i18n = I18nAuto(language=language)
|
||||
punctuation = set(['!', '?', '…', ',', '.', '-'])
|
||||
punctuation = set(["!", "?", "…", ",", ".", "-"])
|
||||
|
||||
def get_first(text:str) -> str:
|
||||
|
||||
def get_first(text: str) -> str:
|
||||
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
||||
text = re.split(pattern, text)[0].strip()
|
||||
return text
|
||||
|
||||
def merge_short_text_in_array(texts:str, threshold:int) -> list:
|
||||
|
||||
def merge_short_text_in_array(texts: str, threshold: int) -> list:
|
||||
if (len(texts)) < 2:
|
||||
return texts
|
||||
result = []
|
||||
@@ -38,7 +41,7 @@ def merge_short_text_in_array(texts:str, threshold:int) -> list:
|
||||
if len(text) >= threshold:
|
||||
result.append(text)
|
||||
text = ""
|
||||
if (len(text) > 0):
|
||||
if len(text) > 0:
|
||||
if len(result) == 0:
|
||||
result.append(text)
|
||||
else:
|
||||
@@ -46,28 +49,24 @@ def merge_short_text_in_array(texts:str, threshold:int) -> list:
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class TextPreprocessor:
|
||||
def __init__(self, bert_model:AutoModelForMaskedLM,
|
||||
tokenizer:AutoTokenizer, device:torch.device):
|
||||
def __init__(self, bert_model: AutoModelForMaskedLM, tokenizer: AutoTokenizer, device: torch.device):
|
||||
self.bert_model = bert_model
|
||||
self.tokenizer = tokenizer
|
||||
self.device = device
|
||||
self.bert_lock = threading.RLock()
|
||||
|
||||
def preprocess(self, text:str, lang:str, text_split_method:str, version:str="v2")->List[Dict]:
|
||||
print(f'############ {i18n("切分文本")} ############')
|
||||
def preprocess(self, text: str, lang: str, text_split_method: str, version: str = "v2") -> List[Dict]:
|
||||
print(f"############ {i18n('切分文本')} ############")
|
||||
text = self.replace_consecutive_punctuation(text)
|
||||
texts = self.pre_seg_text(text, lang, text_split_method)
|
||||
result = []
|
||||
print(f'############ {i18n("提取文本Bert特征")} ############')
|
||||
print(f"############ {i18n('提取文本Bert特征')} ############")
|
||||
for text in tqdm(texts):
|
||||
phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang, version)
|
||||
if phones is None or norm_text=="":
|
||||
if phones is None or norm_text == "":
|
||||
continue
|
||||
res={
|
||||
res = {
|
||||
"phones": phones,
|
||||
"bert_features": bert_features,
|
||||
"norm_text": norm_text,
|
||||
@@ -75,11 +74,11 @@ class TextPreprocessor:
|
||||
result.append(res)
|
||||
return result
|
||||
|
||||
def pre_seg_text(self, text:str, lang:str, text_split_method:str):
|
||||
def pre_seg_text(self, text: str, lang: str, text_split_method: str):
|
||||
text = text.strip("\n")
|
||||
if len(text) == 0:
|
||||
return []
|
||||
if (text[0] not in splits and len(get_first(text)) < 4):
|
||||
if text[0] not in splits and len(get_first(text)) < 4:
|
||||
text = "。" + text if lang != "en" else "." + text
|
||||
print(i18n("实际输入的目标文本:"))
|
||||
print(text)
|
||||
@@ -95,18 +94,18 @@ class TextPreprocessor:
|
||||
_texts = merge_short_text_in_array(_texts, 5)
|
||||
texts = []
|
||||
|
||||
|
||||
for text in _texts:
|
||||
# 解决输入目标文本的空行导致报错的问题
|
||||
if (len(text.strip()) == 0):
|
||||
continue
|
||||
if len(text.strip()) == 0:
|
||||
continue
|
||||
if not re.sub("\W+", "", text):
|
||||
# 检测一下，如果是纯符号，就跳过。
|
||||
continue
|
||||
if (text[-1] not in splits): text += "。" if lang != "en" else "."
|
||||
if text[-1] not in splits:
|
||||
text += "。" if lang != "en" else "."
|
||||
|
||||
# 解决句子过长导致Bert报错的问题
|
||||
if (len(text) > 510):
|
||||
if len(text) > 510:
|
||||
texts.extend(split_big_text(text))
|
||||
else:
|
||||
texts.append(text)
|
||||
@@ -115,78 +114,79 @@ class TextPreprocessor:
|
||||
print(texts)
|
||||
return texts
|
||||
|
||||
def segment_and_extract_feature_for_text(self, text:str, language:str, version:str="v1")->Tuple[list, torch.Tensor, str]:
|
||||
def segment_and_extract_feature_for_text(
|
||||
self, text: str, language: str, version: str = "v1"
|
||||
) -> Tuple[list, torch.Tensor, str]:
|
||||
return self.get_phones_and_bert(text, language, version)
|
||||
|
||||
def get_phones_and_bert(self, text:str, language:str, version:str, final:bool=False):
|
||||
def get_phones_and_bert(self, text: str, language: str, version: str, final: bool = False):
|
||||
with self.bert_lock:
|
||||
if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
|
||||
# language = language.replace("all_","")
|
||||
formattext = text
|
||||
while " " in formattext:
|
||||
formattext = formattext.replace(" ", " ")
|
||||
if language == "all_zh":
|
||||
if re.search(r'[A-Za-z]', formattext):
|
||||
formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
|
||||
formattext = chinese.mix_text_normalize(formattext)
|
||||
return self.get_phones_and_bert(formattext,"zh",version)
|
||||
else:
|
||||
phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
|
||||
bert = self.get_bert_feature(norm_text, word2ph).to(self.device)
|
||||
elif language == "all_yue" and re.search(r'[A-Za-z]', formattext):
|
||||
formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
|
||||
formattext = chinese.mix_text_normalize(formattext)
|
||||
return self.get_phones_and_bert(formattext,"yue",version)
|
||||
else:
|
||||
phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float32,
|
||||
).to(self.device)
|
||||
elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
|
||||
textlist=[]
|
||||
langlist=[]
|
||||
if language == "auto":
|
||||
for tmp in LangSegmenter.getTexts(text):
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
elif language == "auto_yue":
|
||||
for tmp in LangSegmenter.getTexts(text):
|
||||
if tmp["lang"] == "zh":
|
||||
tmp["lang"] = "yue"
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
for tmp in LangSegmenter.getTexts(text):
|
||||
if tmp["lang"] == "en":
|
||||
langlist.append(tmp["lang"])
|
||||
else:
|
||||
# 因无法区别中日韩文汉字,以用户输入为准
|
||||
langlist.append(language)
|
||||
textlist.append(tmp["text"])
|
||||
# print(textlist)
|
||||
# print(langlist)
|
||||
phones_list = []
|
||||
bert_list = []
|
||||
norm_text_list = []
|
||||
for i in range(len(textlist)):
|
||||
lang = langlist[i]
|
||||
phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang, version)
|
||||
bert = self.get_bert_inf(phones, word2ph, norm_text, lang)
|
||||
phones_list.append(phones)
|
||||
norm_text_list.append(norm_text)
|
||||
bert_list.append(bert)
|
||||
bert = torch.cat(bert_list, dim=1)
|
||||
phones = sum(phones_list, [])
|
||||
norm_text = ''.join(norm_text_list)
|
||||
if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
|
||||
# language = language.replace("all_","")
|
||||
formattext = text
|
||||
while " " in formattext:
|
||||
formattext = formattext.replace(" ", " ")
|
||||
if language == "all_zh":
|
||||
if re.search(r"[A-Za-z]", formattext):
|
||||
formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
|
||||
formattext = chinese.mix_text_normalize(formattext)
|
||||
return self.get_phones_and_bert(formattext, "zh", version)
|
||||
else:
|
||||
phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
|
||||
bert = self.get_bert_feature(norm_text, word2ph).to(self.device)
|
||||
elif language == "all_yue" and re.search(r"[A-Za-z]", formattext):
|
||||
formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
|
||||
formattext = chinese.mix_text_normalize(formattext)
|
||||
return self.get_phones_and_bert(formattext, "yue", version)
|
||||
else:
|
||||
phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version)
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float32,
|
||||
).to(self.device)
|
||||
elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
|
||||
textlist = []
|
||||
langlist = []
|
||||
if language == "auto":
|
||||
for tmp in LangSegmenter.getTexts(text):
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
elif language == "auto_yue":
|
||||
for tmp in LangSegmenter.getTexts(text):
|
||||
if tmp["lang"] == "zh":
|
||||
tmp["lang"] = "yue"
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
for tmp in LangSegmenter.getTexts(text):
|
||||
if tmp["lang"] == "en":
|
||||
langlist.append(tmp["lang"])
|
||||
else:
|
||||
# 因无法区别中日韩文汉字,以用户输入为准
|
||||
langlist.append(language)
|
||||
textlist.append(tmp["text"])
|
||||
# print(textlist)
|
||||
# print(langlist)
|
||||
phones_list = []
|
||||
bert_list = []
|
||||
norm_text_list = []
|
||||
for i in range(len(textlist)):
|
||||
lang = langlist[i]
|
||||
phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang, version)
|
||||
bert = self.get_bert_inf(phones, word2ph, norm_text, lang)
|
||||
phones_list.append(phones)
|
||||
norm_text_list.append(norm_text)
|
||||
bert_list.append(bert)
|
||||
bert = torch.cat(bert_list, dim=1)
|
||||
phones = sum(phones_list, [])
|
||||
norm_text = "".join(norm_text_list)
|
||||
|
||||
if not final and len(phones) < 6:
|
||||
return self.get_phones_and_bert("." + text,language,version,final=True)
|
||||
if not final and len(phones) < 6:
|
||||
return self.get_phones_and_bert("." + text, language, version, final=True)
|
||||
|
||||
return phones, bert, norm_text
|
||||
return phones, bert, norm_text
|
||||
|
||||
|
||||
def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor:
|
||||
def get_bert_feature(self, text: str, word2ph: list) -> torch.Tensor:
|
||||
with torch.no_grad():
|
||||
inputs = self.tokenizer(text, return_tensors="pt")
|
||||
for i in inputs:
|
||||
@@ -201,14 +201,14 @@ class TextPreprocessor:
|
||||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||||
return phone_level_feature.T
|
||||
|
||||
def clean_text_inf(self, text:str, language:str, version:str="v2"):
|
||||
language = language.replace("all_","")
|
||||
def clean_text_inf(self, text: str, language: str, version: str = "v2"):
|
||||
language = language.replace("all_", "")
|
||||
phones, word2ph, norm_text = clean_text(text, language, version)
|
||||
phones = cleaned_text_to_sequence(phones, version)
|
||||
return phones, word2ph, norm_text
|
||||
|
||||
def get_bert_inf(self, phones:list, word2ph:list, norm_text:str, language:str):
|
||||
language=language.replace("all_","")
|
||||
def get_bert_inf(self, phones: list, word2ph: list, norm_text: str, language: str):
|
||||
language = language.replace("all_", "")
|
||||
if language == "zh":
|
||||
feature = self.get_bert_feature(norm_text, word2ph).to(self.device)
|
||||
else:
|
||||
@@ -219,21 +219,19 @@ class TextPreprocessor:
|
||||
|
||||
return feature
|
||||
|
||||
|
||||
def filter_text(self,texts):
|
||||
_text=[]
|
||||
if all(text in [None, " ", "\n",""] for text in texts):
|
||||
def filter_text(self, texts):
|
||||
_text = []
|
||||
if all(text in [None, " ", "\n", ""] for text in texts):
|
||||
raise ValueError(i18n("请输入有效文本"))
|
||||
for text in texts:
|
||||
if text in [None, " ", ""]:
|
||||
if text in [None, " ", ""]:
|
||||
pass
|
||||
else:
|
||||
_text.append(text)
|
||||
return _text
|
||||
|
||||
|
||||
def replace_consecutive_punctuation(self,text):
|
||||
punctuations = ''.join(re.escape(p) for p in punctuation)
|
||||
pattern = f'([{punctuations}])([{punctuations}])+'
|
||||
result = re.sub(pattern, r'\1', text)
|
||||
def replace_consecutive_punctuation(self, text):
|
||||
punctuations = "".join(re.escape(p) for p in punctuation)
|
||||
pattern = f"([{punctuations}])([{punctuations}])+"
|
||||
result = re.sub(pattern, r"\1", text)
|
||||
return result
|
||||
|
||||
@@ -1 +1 @@
|
||||
from . import TTS, text_segmentation_method
|
||||
from . import TTS, text_segmentation_method
|
||||
|
||||
@@ -1,41 +1,57 @@
|
||||
|
||||
|
||||
|
||||
|
||||
import re
|
||||
from typing import Callable
|
||||
|
||||
punctuation = set(['!', '?', '…', ',', '.', '-'," "])
|
||||
punctuation = set(["!", "?", "…", ",", ".", "-", " "])
|
||||
METHODS = dict()
|
||||
|
||||
def get_method(name:str)->Callable:
|
||||
|
||||
def get_method(name: str) -> Callable:
|
||||
method = METHODS.get(name, None)
|
||||
if method is None:
|
||||
raise ValueError(f"Method {name} not found")
|
||||
return method
|
||||
|
||||
def get_method_names()->list:
|
||||
|
||||
def get_method_names() -> list:
|
||||
return list(METHODS.keys())
|
||||
|
||||
|
||||
def register_method(name):
|
||||
def decorator(func):
|
||||
METHODS[name] = func
|
||||
return func
|
||||
|
||||
return decorator
|
||||
|
||||
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
|
||||
|
||||
splits = {
|
||||
"，",
|
||||
"。",
|
||||
"？",
|
||||
"！",
|
||||
",",
|
||||
".",
|
||||
"?",
|
||||
"!",
|
||||
"~",
|
||||
":",
|
||||
"：",
|
||||
"—",
|
||||
"…",
|
||||
}
|
||||
|
||||
|
||||
def split_big_text(text, max_len=510):
|
||||
# 定义全角和半角标点符号
|
||||
punctuation = "".join(splits)
|
||||
|
||||
# 切割文本
|
||||
segments = re.split('([' + punctuation + '])', text)
|
||||
|
||||
segments = re.split("([" + punctuation + "])", text)
|
||||
|
||||
# 初始化结果列表和当前片段
|
||||
result = []
|
||||
current_segment = ''
|
||||
|
||||
current_segment = ""
|
||||
|
||||
for segment in segments:
|
||||
# 如果当前片段加上新的片段长度超过max_len，就将当前片段加入结果列表，并重置当前片段
|
||||
if len(current_segment + segment) > max_len:
|
||||
@@ -43,13 +59,12 @@ def split_big_text(text, max_len=510):
|
||||
current_segment = segment
|
||||
else:
|
||||
current_segment += segment
|
||||
|
||||
|
||||
# 将最后一个片段加入结果列表
|
||||
if current_segment:
|
||||
result.append(current_segment)
|
||||
|
||||
return result
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def split(todo_text):
|
||||
@@ -90,7 +105,7 @@ def cut1(inp):
|
||||
if len(split_idx) > 1:
|
||||
opts = []
|
||||
for idx in range(len(split_idx) - 1):
|
||||
opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
|
||||
opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
|
||||
else:
|
||||
opts = [inp]
|
||||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||||
@@ -123,6 +138,7 @@ def cut2(inp):
|
||||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||||
return "\n".join(opts)
|
||||
|
||||
|
||||
# 按中文句号。切
|
||||
@register_method("cut3")
|
||||
def cut3(inp):
|
||||
@@ -131,26 +147,28 @@ def cut3(inp):
|
||||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||||
return "\n".join(opts)
|
||||
|
||||
#按英文句号.切
|
||||
|
||||
# 按英文句号.切
|
||||
@register_method("cut4")
|
||||
def cut4(inp):
|
||||
inp = inp.strip("\n")
|
||||
opts = re.split(r'(?<!\d)\.(?!\d)', inp.strip("."))
|
||||
opts = re.split(r"(?<!\d)\.(?!\d)", inp.strip("."))
|
||||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||||
return "\n".join(opts)
|
||||
|
||||
|
||||
# 按标点符号切
|
||||
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
||||
@register_method("cut5")
|
||||
def cut5(inp):
|
||||
inp = inp.strip("\n")
|
||||
punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
|
||||
punds = {",", ".", ";", "?", "!", "、", "，", "。", "？", "！", ";", "：", "…"}
|
||||
mergeitems = []
|
||||
items = []
|
||||
|
||||
for i, char in enumerate(inp):
|
||||
if char in punds:
|
||||
if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
|
||||
if char == "." and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
|
||||
items.append(char)
|
||||
else:
|
||||
items.append(char)
|
||||
@@ -166,8 +184,6 @@ def cut5(inp):
|
||||
return "\n".join(opt)
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
method = get_method("cut5")
|
||||
print(method("你好，我是小明。你好，我是小红。你好，我是小刚。你好，我是小张。"))
|
||||
|
||||
|
||||
Reference in New Issue
Block a user