import os, sys from tqdm import tqdm now_dir = os.getcwd() sys.path.append(now_dir) import re import torch import LangSegment from typing import Dict, List, Tuple from text.cleaner import clean_text from text import cleaned_text_to_sequence from transformers import AutoModelForMaskedLM, AutoTokenizer from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method #from tools.i18n.i18n import I18nAuto #i18n = I18nAuto() 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: if (len(texts)) < 2: return texts result = [] text = "" for ele in texts: text += ele if len(text) >= threshold: result.append(text) text = "" if (len(text) > 0): if len(result) == 0: result.append(text) else: result[len(result) - 1] += text return result class TextPreprocessor: def __init__(self, bert_model:AutoModelForMaskedLM, tokenizer:AutoTokenizer, device:torch.device): self.bert_model = bert_model self.tokenizer = tokenizer self.device = device def preprocess(self, text:str, lang:str, text_split_method:str)->List[Dict]: print("############ 切分文本 ############") texts = self.pre_seg_text(text, lang, text_split_method) result = [] print("############ 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) if phones is None: continue res={ "phones": phones, "bert_features": bert_features, "norm_text": norm_text, } result.append(res) return result def pre_seg_text(self, text:str, lang:str, text_split_method:str): text = text.strip("\n") if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if lang != "en" else "." + text print("实际输入的目标文本:") print(text) seg_method = get_seg_method(text_split_method) text = seg_method(text) while "\n\n" in text: text = text.replace("\n\n", "\n") _texts = text.split("\n") _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 (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): texts.extend(split_big_text(text)) else: texts.append(text) print("实际输入的目标文本(切句后):") print(texts) return texts def segment_and_extract_feature_for_text(self, texts:list, language:str)->Tuple[list, torch.Tensor, str]: textlist, langlist = self.seg_text(texts, language) if len(textlist) == 0: return None, None, None phones, bert_features, norm_text = self.extract_bert_feature(textlist, langlist) return phones, bert_features, norm_text def seg_text(self, text:str, language:str)->Tuple[list, list]: textlist=[] langlist=[] if language in ["auto", "zh", "ja"]: LangSegment.setfilters(["zh","ja","en","ko"]) for tmp in LangSegment.getTexts(text): if tmp["text"] == "": continue if tmp["lang"] == "ko": langlist.append("zh") elif tmp["lang"] == "en": langlist.append("en") else: # 因无法区别中日文汉字,以用户输入为准 langlist.append(language if language!="auto" else tmp["lang"]) textlist.append(tmp["text"]) elif language == "en": LangSegment.setfilters(["en"]) formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) while " " in formattext: formattext = formattext.replace(" ", " ") if formattext != "": textlist.append(formattext) langlist.append("en") elif language in ["all_zh","all_ja"]: formattext = text while " " in formattext: formattext = formattext.replace(" ", " ") language = language.replace("all_","") if text == "": return [],[] textlist.append(formattext) langlist.append(language) else: raise ValueError(f"language {language} not supported") return textlist, langlist def extract_bert_feature(self, textlist:list, langlist:list): phones_list = [] bert_feature_list = [] norm_text_list = [] for i in range(len(textlist)): lang = langlist[i] phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang) _bert_feature = self.get_bert_inf(phones, word2ph, norm_text, lang) # phones_list.append(phones) phones_list.extend(phones) norm_text_list.append(norm_text) bert_feature_list.append(_bert_feature) bert_feature = torch.cat(bert_feature_list, dim=1) # phones = sum(phones_list, []) norm_text = ''.join(norm_text_list) return phones_list, bert_feature, norm_text 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: inputs[i] = inputs[i].to(self.device) res = self.bert_model(**inputs, output_hidden_states=True) res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] assert len(word2ph) == len(text) phone_level_feature = [] for i in range(len(word2ph)): repeat_feature = res[i].repeat(word2ph[i], 1) phone_level_feature.append(repeat_feature) phone_level_feature = torch.cat(phone_level_feature, dim=0) return phone_level_feature.T def clean_text_inf(self, text:str, language:str): phones, word2ph, norm_text = clean_text(text, language) phones = cleaned_text_to_sequence(phones) return phones, word2ph, norm_text 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: feature = torch.zeros( (1024, len(phones)), dtype=torch.float32, ).to(self.device) return feature