mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2026-05-17 15:58:15 +08:00
341 lines
14 KiB
Python
341 lines
14 KiB
Python
import os
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import sys
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import threading
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import time
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from contextlib import contextmanager
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from tqdm import tqdm
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import re
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import torch
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from text.LangSegmenter import LangSegmenter
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from text import chinese
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from typing import Dict, List, Tuple
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from text.cleaner import clean_text
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from text import cleaned_text_to_sequence
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method
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from TTS_infer_pack.prepare_bert_batch_worker import PrepareBertBatchWorker
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from tools.i18n.i18n import I18nAuto, scan_language_list
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language = os.environ.get("language", "Auto")
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language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
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i18n = I18nAuto(language=language)
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punctuation = set(["!", "?", "…", ",", ".", "-"])
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def get_first(text: str) -> str:
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pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
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text = re.split(pattern, text)[0].strip()
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return text
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def merge_short_text_in_array(texts: str, threshold: int) -> list:
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if (len(texts)) < 2:
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return texts
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result = []
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text = ""
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for ele in texts:
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text += ele
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if len(text) >= threshold:
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result.append(text)
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text = ""
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if len(text) > 0:
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if len(result) == 0:
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result.append(text)
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else:
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result[len(result) - 1] += text
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return result
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class StageLimiter:
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def __init__(self, slots: int):
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self.slots = max(1, int(slots))
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self.semaphore = threading.BoundedSemaphore(self.slots)
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self.lock = threading.Lock()
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self.inflight = 0
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self.peak_inflight = 0
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@contextmanager
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def enter(self):
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wait_start = time.perf_counter()
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self.semaphore.acquire()
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wait_ms = (time.perf_counter() - wait_start) * 1000.0
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with self.lock:
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self.inflight += 1
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current_inflight = self.inflight
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if current_inflight > self.peak_inflight:
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self.peak_inflight = current_inflight
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peak_inflight = self.peak_inflight
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try:
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yield {
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"wait_ms": wait_ms,
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"inflight": current_inflight,
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"peak_inflight": peak_inflight,
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"slots": self.slots,
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}
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finally:
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with self.lock:
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self.inflight = max(0, self.inflight - 1)
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self.semaphore.release()
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def snapshot(self) -> Dict[str, int]:
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with self.lock:
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return {
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"slots": self.slots,
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"inflight": self.inflight,
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"peak_inflight": self.peak_inflight,
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}
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class TextPreprocessor:
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def __init__(
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self,
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bert_model: AutoModelForMaskedLM,
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tokenizer: AutoTokenizer,
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device: torch.device,
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bert_stage_limiter: StageLimiter | None = None,
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bert_batch_worker: PrepareBertBatchWorker | None = None,
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):
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self.bert_model = bert_model
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self.tokenizer = tokenizer
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self.device = device
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self.bert_stage_limiter = bert_stage_limiter
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self.bert_batch_worker = bert_batch_worker
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def preprocess(self, text: str, lang: str, text_split_method: str, version: str = "v2") -> List[Dict]:
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print(f"############ {i18n('切分文本')} ############")
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text = self.replace_consecutive_punctuation(text)
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texts = self.pre_seg_text(text, lang, text_split_method)
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result = []
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print(f"############ {i18n('提取文本Bert特征')} ############")
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for text in tqdm(texts):
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phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang, version)
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if phones is None or norm_text == "":
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continue
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res = {
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"phones": phones,
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"bert_features": bert_features,
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"norm_text": norm_text,
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}
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result.append(res)
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return result
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def pre_seg_text(self, text: str, lang: str, text_split_method: str):
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text = text.strip("\n")
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if len(text) == 0:
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return []
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if text[0] not in splits and len(get_first(text)) < 4:
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text = "。" + text if lang != "en" else "." + text
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print(i18n("实际输入的目标文本:"))
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print(text)
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seg_method = get_seg_method(text_split_method)
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text = seg_method(text)
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while "\n\n" in text:
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text = text.replace("\n\n", "\n")
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_texts = text.split("\n")
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_texts = self.filter_text(_texts)
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_texts = merge_short_text_in_array(_texts, 5)
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texts = []
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for text in _texts:
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# 解决输入目标文本的空行导致报错的问题
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if len(text.strip()) == 0:
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continue
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if not re.sub("\W+", "", text):
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# 检测一下，如果是纯符号，就跳过。
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continue
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if text[-1] not in splits:
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text += "。" if lang != "en" else "."
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# 解决句子过长导致Bert报错的问题
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if len(text) > 510:
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texts.extend(split_big_text(text))
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else:
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texts.append(text)
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print(i18n("实际输入的目标文本(切句后):"))
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print(texts)
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return texts
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def segment_and_extract_feature_for_text(
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self, text: str, language: str, version: str = "v1", profile: Dict | None = None
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) -> Tuple[list, torch.Tensor, str]:
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return self.get_phones_and_bert(text, language, version, profile=profile)
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def _split_text_by_language(self, text: str, language: str) -> Tuple[List[str], List[str]]:
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textlist = []
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langlist = []
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if language == "all_zh":
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for tmp in LangSegmenter.getTexts(text, "zh"):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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elif language == "all_yue":
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for tmp in LangSegmenter.getTexts(text, "zh"):
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if tmp["lang"] == "zh":
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tmp["lang"] = "yue"
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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elif language == "all_ja":
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for tmp in LangSegmenter.getTexts(text, "ja"):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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elif language == "all_ko":
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for tmp in LangSegmenter.getTexts(text, "ko"):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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elif language == "en":
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langlist.append("en")
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textlist.append(text)
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elif language == "auto":
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for tmp in LangSegmenter.getTexts(text):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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elif language == "auto_yue":
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for tmp in LangSegmenter.getTexts(text):
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if tmp["lang"] == "zh":
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tmp["lang"] = "yue"
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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else:
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for tmp in LangSegmenter.getTexts(text):
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if langlist:
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same_group = (tmp["lang"] == "en" and langlist[-1] == "en") or (
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tmp["lang"] != "en" and langlist[-1] != "en"
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)
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if same_group:
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textlist[-1] += tmp["text"]
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continue
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if tmp["lang"] == "en":
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langlist.append(tmp["lang"])
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else:
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langlist.append(language)
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textlist.append(tmp["text"])
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return textlist, langlist
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def get_phones_and_bert(
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self, text: str, language: str, version: str, final: bool = False, profile: Dict | None = None
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):
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text = re.sub(r' {2,}', ' ', text)
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textlist, langlist = self._split_text_by_language(text, language)
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phones_list = []
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bert_list = []
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norm_text_list = []
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for segment_text, segment_lang in zip(textlist, langlist):
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phones, word2ph, norm_text = self.clean_text_inf(segment_text, segment_lang, version)
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bert = self.get_bert_inf(phones, word2ph, norm_text, segment_lang, profile=profile)
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phones_list.append(phones)
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norm_text_list.append(norm_text)
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bert_list.append(bert)
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bert = torch.cat(bert_list, dim=1)
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phones = sum(phones_list, [])
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norm_text = "".join(norm_text_list)
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if not final and len(phones) < 6:
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return self.get_phones_and_bert("." + text, language, version, final=True, profile=profile)
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return phones, bert, norm_text
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def _accumulate_profile(self, profile: Dict | None, key: str, value: float) -> None:
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if profile is None:
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return
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profile[key] = float(profile.get(key, 0.0)) + float(value)
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def _update_profile_peak(self, profile: Dict | None, key: str, value: float) -> None:
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if profile is None:
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return
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profile[key] = float(max(float(profile.get(key, 0.0)), float(value)))
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def get_bert_feature(self, text: str, word2ph: list, profile: Dict | None = None) -> torch.Tensor:
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if self.bert_batch_worker is not None:
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feature, worker_profile = self.bert_batch_worker.submit(text, word2ph)
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self._accumulate_profile(profile, "bert_wait_ms", worker_profile.get("bert_wait_ms", 0.0))
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self._accumulate_profile(profile, "bert_forward_ms", worker_profile.get("bert_forward_ms", 0.0))
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self._accumulate_profile(profile, "bert_tokenize_ms", worker_profile.get("bert_tokenize_ms", 0.0))
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self._accumulate_profile(profile, "bert_scatter_ms", worker_profile.get("bert_scatter_ms", 0.0))
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self._accumulate_profile(profile, "bert_calls", worker_profile.get("bert_calls", 1.0))
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self._update_profile_peak(
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profile, "bert_stage_inflight_peak", worker_profile.get("bert_stage_inflight_peak", 0.0)
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)
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self._update_profile_peak(profile, "bert_batch_size_peak", worker_profile.get("bert_batch_size", 0.0))
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self._update_profile_peak(profile, "bert_batch_tokens_peak", worker_profile.get("bert_batch_tokens", 0.0))
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if profile is not None:
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profile["bert_stage_slots"] = float(worker_profile.get("bert_stage_slots", 0.0))
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return feature
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limiter_stats = {"wait_ms": 0.0, "inflight": 1, "peak_inflight": 1, "slots": 0}
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if self.bert_stage_limiter is None:
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forward_start = time.perf_counter()
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with torch.no_grad():
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inputs = self.tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(self.device)
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res = self.bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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forward_ms = (time.perf_counter() - forward_start) * 1000.0
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else:
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with self.bert_stage_limiter.enter() as limiter_stats:
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forward_start = time.perf_counter()
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with torch.no_grad():
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inputs = self.tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(self.device)
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res = self.bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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forward_ms = (time.perf_counter() - forward_start) * 1000.0
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self._accumulate_profile(profile, "bert_wait_ms", limiter_stats["wait_ms"])
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self._accumulate_profile(profile, "bert_forward_ms", forward_ms)
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self._accumulate_profile(profile, "bert_calls", 1.0)
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self._update_profile_peak(profile, "bert_stage_inflight_peak", limiter_stats["peak_inflight"])
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if profile is not None:
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profile["bert_stage_slots"] = float(limiter_stats["slots"])
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assert len(word2ph) == len(text)
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phone_level_feature = []
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for i in range(len(word2ph)):
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repeat_feature = res[i].repeat(word2ph[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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return phone_level_feature.T
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def clean_text_inf(self, text: str, language: str, version: str = "v2"):
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language = language.replace("all_", "")
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phones, word2ph, norm_text = clean_text(text, language, version)
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phones = cleaned_text_to_sequence(phones, version)
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return phones, word2ph, norm_text
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def get_bert_inf(self, phones: list, word2ph: list, norm_text: str, language: str, profile: Dict | None = None):
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language = language.replace("all_", "")
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if language == "zh":
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feature = self.get_bert_feature(norm_text, word2ph, profile=profile).to(self.device)
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else:
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feature = torch.zeros(
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(1024, len(phones)),
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dtype=torch.float32,
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).to(self.device)
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return feature
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def filter_text(self, texts):
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_text = []
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if all(text in [None, " ", "\n", ""] for text in texts):
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raise ValueError(i18n("请输入有效文本"))
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for text in texts:
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if text in [None, " ", ""]:
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pass
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else:
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_text.append(text)
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return _text
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def replace_consecutive_punctuation(self, 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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