737 lines
27 KiB
Python
737 lines
27 KiB
Python
import random
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import re
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import time
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from collections import Counter
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import jieba
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import numpy as np
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from maim_message import UserInfo
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from src.common.logger import get_module_logger
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from src.manager.mood_manager import mood_manager
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from ..message_receive.message import MessageRecv
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from src.llm_models.utils_model import LLMRequest
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from .typo_generator import ChineseTypoGenerator
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from ...config.config import global_config
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from ...common.message_repository import find_messages, count_messages
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logger = get_module_logger("chat_utils")
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def is_english_letter(char: str) -> bool:
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"""检查字符是否为英文字母（忽略大小写）"""
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return "a" <= char.lower() <= "z"
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def db_message_to_str(message_dict: dict) -> str:
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logger.debug(f"message_dict: {message_dict}")
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time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"]))
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try:
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name = "[(%s)%s]%s" % (
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message_dict["user_id"],
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message_dict.get("user_nickname", ""),
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message_dict.get("user_cardname", ""),
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)
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except Exception:
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name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}"
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content = message_dict.get("processed_plain_text", "")
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result = f"[{time_str}] {name}: {content}\n"
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logger.debug(f"result: {result}")
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return result
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def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
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"""检查消息是否提到了机器人"""
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keywords = [global_config.bot.nickname]
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nicknames = global_config.bot.alias_names
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reply_probability = 0.0
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is_at = False
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is_mentioned = False
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if (
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message.message_info.additional_config is not None
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and message.message_info.additional_config.get("is_mentioned") is not None
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):
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try:
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reply_probability = float(message.message_info.additional_config.get("is_mentioned"))
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is_mentioned = True
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return is_mentioned, reply_probability
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except Exception as e:
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logger.warning(e)
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logger.warning(
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f"消息中包含不合理的设置 is_mentioned: {message.message_info.additional_config.get('is_mentioned')}"
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)
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# 判断是否被@
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if re.search(
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rf"@(.+?)（id:{global_config.bot.qq_account}）", message.processed_plain_text
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) or re.search(
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rf"@<(.+?)(?=:{global_config.bot.qq_account}>)\:{global_config.bot.qq_account}>", message.processed_plain_text
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):
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is_at = True
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is_mentioned = True
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if is_at and global_config.normal_chat.at_bot_inevitable_reply:
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reply_probability = 1.0
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logger.info("被@，回复概率设置为100%")
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else:
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if not is_mentioned:
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# 判断是否被回复
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if re.match(
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rf"\[回复 (.+?)\({str(global_config.bot.qq_account)}\)：(.+?)\]，说：", message.processed_plain_text
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) or re.match(
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rf"\[回复<(.+?)(?=:{str(global_config.bot.qq_account)}>)\:{str(global_config.bot.qq_account)}>：(.+?)\]，说：", message.processed_plain_text
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):
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is_mentioned = True
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else:
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# 判断内容中是否被提及
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message_content = re.sub(r"@(.+?)（(\d+)）", "", message.processed_plain_text)
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message_content = re.sub(r"@<(.+?)(?=:(\d+))\:(\d+)>", "", message_content)
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message_content = re.sub(r"\[回复 (.+?)\(((\d+)|未知id)\)：(.+?)\]，说：", "", message_content)
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message_content = re.sub(r"\[回复<(.+?)(?=:(\d+))\:(\d+)>：(.+?)\]，说：", "", message_content)
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for keyword in keywords:
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if keyword in message_content:
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is_mentioned = True
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for nickname in nicknames:
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if nickname in message_content:
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is_mentioned = True
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if is_mentioned and global_config.normal_chat.mentioned_bot_inevitable_reply:
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reply_probability = 1.0
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logger.info("被提及，回复概率设置为100%")
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return is_mentioned, reply_probability
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async def get_embedding(text, request_type="embedding"):
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"""获取文本的embedding向量"""
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# TODO: API-Adapter修改标记
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llm = LLMRequest(model=global_config.model.embedding, request_type=request_type)
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# return llm.get_embedding_sync(text)
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try:
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embedding = await llm.get_embedding(text)
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except Exception as e:
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logger.error(f"获取embedding失败: {str(e)}")
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embedding = None
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return embedding
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def get_recent_group_detailed_plain_text(chat_stream_id: str, limit: int = 12, combine=False):
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filter_query = {"chat_id": chat_stream_id}
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sort_order = [("time", -1)]
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recent_messages = find_messages(message_filter=filter_query, sort=sort_order, limit=limit)
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if not recent_messages:
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return []
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message_detailed_plain_text = ""
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message_detailed_plain_text_list = []
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# 反转消息列表，使最新的消息在最后
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recent_messages.reverse()
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if combine:
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for msg_db_data in recent_messages:
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message_detailed_plain_text += str(msg_db_data["detailed_plain_text"])
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return message_detailed_plain_text
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else:
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for msg_db_data in recent_messages:
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message_detailed_plain_text_list.append(msg_db_data["detailed_plain_text"])
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return message_detailed_plain_text_list
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def get_recent_group_speaker(chat_stream_id: str, sender, limit: int = 12) -> list:
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# 获取当前群聊记录内发言的人
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filter_query = {"chat_id": chat_stream_id}
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sort_order = [("time", -1)]
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recent_messages = find_messages(message_filter=filter_query, sort=sort_order, limit=limit)
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if not recent_messages:
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return []
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who_chat_in_group = []
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for msg_db_data in recent_messages:
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user_info = UserInfo.from_dict(
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{
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"platform": msg_db_data["user_platform"],
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"user_id": msg_db_data["user_id"],
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"user_nickname": msg_db_data["user_nickname"],
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"user_cardname": msg_db_data.get("user_cardname", ""),
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}
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)
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if (
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(user_info.platform, user_info.user_id) != sender
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and user_info.user_id != global_config.bot.qq_account
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and (user_info.platform, user_info.user_id, user_info.user_nickname) not in who_chat_in_group
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and len(who_chat_in_group) < 5
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): # 排除重复，排除消息发送者，排除bot，限制加载的关系数目
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who_chat_in_group.append((user_info.platform, user_info.user_id, user_info.user_nickname))
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return who_chat_in_group
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def split_into_sentences_w_remove_punctuation(text: str) -> list[str]:
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"""将文本分割成句子，并根据概率合并
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1. 识别分割点（, ， 。 ; 空格），但如果分割点左右都是英文字母则不分割。
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2. 将文本分割成 (内容, 分隔符) 的元组。
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3. 根据原始文本长度计算合并概率，概率性地合并相邻段落。
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注意：此函数假定颜文字已在上层被保护。
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Args:
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text: 要分割的文本字符串 (假定颜文字已被保护)
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Returns:
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List[str]: 分割和合并后的句子列表
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"""
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# 预处理：处理多余的换行符
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# 1. 将连续的换行符替换为单个换行符
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text = re.sub(r"\n\s*\n+", "\n", text)
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# 2. 处理换行符和其他分隔符的组合
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text = re.sub(r"\n\s*([，,。;\s])", r"\1", text)
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text = re.sub(r"([，,。;\s])\s*\n", r"\1", text)
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# 处理两个汉字中间的换行符
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text = re.sub(r"([\u4e00-\u9fff])\n([\u4e00-\u9fff])", r"\1。\2", text)
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len_text = len(text)
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if len_text < 3:
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if random.random() < 0.01:
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return list(text) # 如果文本很短且触发随机条件,直接按字符分割
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else:
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return [text]
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# 定义分隔符
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separators = {"，", ",", " ", "。", ";"}
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segments = []
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current_segment = ""
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# 1. 分割成 (内容, 分隔符) 元组
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i = 0
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while i < len(text):
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char = text[i]
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if char in separators:
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# 检查分割条件：如果分隔符左右都是英文字母，则不分割
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can_split = True
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if 0 < i < len(text) - 1:
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prev_char = text[i - 1]
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next_char = text[i + 1]
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# if is_english_letter(prev_char) and is_english_letter(next_char) and char == ' ': # 原计划只对空格应用此规则，现应用于所有分隔符
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if is_english_letter(prev_char) and is_english_letter(next_char):
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can_split = False
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if can_split:
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# 只有当当前段不为空时才添加
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if current_segment:
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segments.append((current_segment, char))
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# 如果当前段为空，但分隔符是空格，则也添加一个空段（保留空格）
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elif char == " ":
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segments.append(("", char))
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current_segment = ""
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else:
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# 不分割，将分隔符加入当前段
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current_segment += char
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else:
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current_segment += char
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i += 1
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# 添加最后一个段（没有后续分隔符）
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if current_segment:
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segments.append((current_segment, ""))
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# 过滤掉完全空的段（内容和分隔符都为空）
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segments = [(content, sep) for content, sep in segments if content or sep]
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# 如果分割后为空（例如，输入全是分隔符且不满足保留条件），恢复颜文字并返回
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if not segments:
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# recovered_text = recover_kaomoji([text], mapping) # 恢复原文本中的颜文字 - 已移至上层处理
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# return [s for s in recovered_text if s] # 返回非空结果
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return [text] if text else [] # 如果原始文本非空，则返回原始文本（可能只包含未被分割的字符或颜文字占位符）
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# 2. 概率合并
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if len_text < 12:
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split_strength = 0.2
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elif len_text < 32:
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split_strength = 0.6
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else:
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split_strength = 0.7
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# 合并概率与分割强度相反
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merge_probability = 1.0 - split_strength
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merged_segments = []
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idx = 0
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while idx < len(segments):
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current_content, current_sep = segments[idx]
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# 检查是否可以与下一段合并
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# 条件：不是最后一段，且随机数小于合并概率，且当前段有内容（避免合并空段）
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if idx + 1 < len(segments) and random.random() < merge_probability and current_content:
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next_content, next_sep = segments[idx + 1]
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# 合并: (内容1 + 分隔符1 + 内容2, 分隔符2)
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# 只有当下一段也有内容时才合并文本，否则只传递分隔符
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if next_content:
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merged_content = current_content + current_sep + next_content
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merged_segments.append((merged_content, next_sep))
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else: # 下一段内容为空，只保留当前内容和下一段的分隔符
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merged_segments.append((current_content, next_sep))
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idx += 2 # 跳过下一段，因为它已被合并
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else:
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# 不合并，直接添加当前段
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merged_segments.append((current_content, current_sep))
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idx += 1
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# 提取最终的句子内容
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final_sentences = [content for content, sep in merged_segments if content] # 只保留有内容的段
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# 清理可能引入的空字符串和仅包含空白的字符串
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final_sentences = [
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s for s in final_sentences if s.strip()
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] # 过滤掉空字符串以及仅包含空白（如换行符、空格）的字符串
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logger.debug(f"分割并合并后的句子: {final_sentences}")
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return final_sentences
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def random_remove_punctuation(text: str) -> str:
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"""随机处理标点符号，模拟人类打字习惯
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Args:
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text: 要处理的文本
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Returns:
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str: 处理后的文本
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"""
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result = ""
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text_len = len(text)
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for i, char in enumerate(text):
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if char == "。" and i == text_len - 1: # 结尾的句号
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if random.random() > 0.1: # 90%概率删除结尾句号
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continue
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elif char == "，":
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rand = random.random()
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if rand < 0.25: # 5%概率删除逗号
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continue
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elif rand < 0.25: # 20%概率把逗号变成空格
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result += " "
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continue
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result += char
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return result
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def process_llm_response(text: str) -> list[str]:
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# 先保护颜文字
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if global_config.response_splitter.enable_kaomoji_protection:
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protected_text, kaomoji_mapping = protect_kaomoji(text)
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logger.trace(f"保护颜文字后的文本: {protected_text}")
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else:
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protected_text = text
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kaomoji_mapping = {}
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# 提取被 () 或 [] 或 （）包裹且包含中文的内容
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pattern = re.compile(r"[(\[（](?=.*[一-鿿]).*?[)\]）]")
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# _extracted_contents = pattern.findall(text)
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_extracted_contents = pattern.findall(protected_text) # 在保护后的文本上查找
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# 去除 () 和 [] 及其包裹的内容
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cleaned_text = pattern.sub("", protected_text)
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if cleaned_text == "":
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return ["呃呃"]
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logger.debug(f"{text}去除括号处理后的文本: {cleaned_text}")
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# 对清理后的文本进行进一步处理
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max_length = global_config.response_splitter.max_length * 2
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max_sentence_num = global_config.response_splitter.max_sentence_num
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# 如果基本上是中文，则进行长度过滤
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if get_western_ratio(cleaned_text) < 0.1:
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if len(cleaned_text) > max_length:
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logger.warning(f"回复过长 ({len(cleaned_text)} 字符)，返回默认回复")
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return ["懒得说"]
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typo_generator = ChineseTypoGenerator(
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error_rate=global_config.chinese_typo.error_rate,
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min_freq=global_config.chinese_typo.min_freq,
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tone_error_rate=global_config.chinese_typo.tone_error_rate,
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word_replace_rate=global_config.chinese_typo.word_replace_rate,
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)
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if global_config.response_splitter.enable:
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split_sentences = split_into_sentences_w_remove_punctuation(cleaned_text)
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else:
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split_sentences = [cleaned_text]
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sentences = []
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for sentence in split_sentences:
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if global_config.chinese_typo.enable:
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typoed_text, typo_corrections = typo_generator.create_typo_sentence(sentence)
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sentences.append(typoed_text)
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if typo_corrections:
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sentences.append(typo_corrections)
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else:
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sentences.append(sentence)
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if len(sentences) > max_sentence_num:
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logger.warning(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
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return [f"{global_config.bot.nickname}不知道哦"]
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# if extracted_contents:
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# for content in extracted_contents:
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# sentences.append(content)
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# 在所有句子处理完毕后，对包含占位符的列表进行恢复
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if global_config.response_splitter.enable_kaomoji_protection:
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sentences = recover_kaomoji(sentences, kaomoji_mapping)
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return sentences
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def calculate_typing_time(
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input_string: str,
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thinking_start_time: float,
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chinese_time: float = 0.2,
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english_time: float = 0.1,
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is_emoji: bool = False,
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) -> float:
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"""
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计算输入字符串所需的时间，中文和英文字符有不同的输入时间
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input_string (str): 输入的字符串
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chinese_time (float): 中文字符的输入时间，默认为0.2秒
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english_time (float): 英文字符的输入时间，默认为0.1秒
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is_emoji (bool): 是否为emoji，默认为False
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特殊情况：
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- 如果只有一个中文字符，将使用3倍的中文输入时间
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- 在所有输入结束后，额外加上回车时间0.3秒
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- 如果is_emoji为True，将使用固定1秒的输入时间
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"""
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# 将0-1的唤醒度映射到-1到1
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mood_arousal = mood_manager.current_mood.arousal
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# 映射到0.5到2倍的速度系数
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typing_speed_multiplier = 1.5**mood_arousal # 唤醒度为1时速度翻倍,为-1时速度减半
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chinese_time *= 1 / typing_speed_multiplier
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english_time *= 1 / typing_speed_multiplier
|
||
# 计算中文字符数
|
||
chinese_chars = sum(1 for char in input_string if "\u4e00" <= char <= "\u9fff")
|
||
|
||
# 如果只有一个中文字符，使用3倍时间
|
||
if chinese_chars == 1 and len(input_string.strip()) == 1:
|
||
return chinese_time * 3 + 0.3 # 加上回车时间
|
||
|
||
# 正常计算所有字符的输入时间
|
||
total_time = 0.0
|
||
for char in input_string:
|
||
if "\u4e00" <= char <= "\u9fff": # 判断是否为中文字符
|
||
total_time += chinese_time
|
||
else: # 其他字符（如英文）
|
||
total_time += english_time
|
||
|
||
if is_emoji:
|
||
total_time = 1
|
||
|
||
if time.time() - thinking_start_time > 10:
|
||
total_time = 1
|
||
|
||
# print(f"thinking_start_time:{thinking_start_time}")
|
||
# print(f"nowtime:{time.time()}")
|
||
# print(f"nowtime - thinking_start_time:{time.time() - thinking_start_time}")
|
||
# print(f"{total_time}")
|
||
|
||
return total_time # 加上回车时间
|
||
|
||
|
||
def cosine_similarity(v1, v2):
|
||
"""计算余弦相似度"""
|
||
dot_product = np.dot(v1, v2)
|
||
norm1 = np.linalg.norm(v1)
|
||
norm2 = np.linalg.norm(v2)
|
||
if norm1 == 0 or norm2 == 0:
|
||
return 0
|
||
return dot_product / (norm1 * norm2)
|
||
|
||
|
||
def text_to_vector(text):
|
||
"""将文本转换为词频向量"""
|
||
# 分词
|
||
words = jieba.lcut(text)
|
||
# 统计词频
|
||
word_freq = Counter(words)
|
||
return word_freq
|
||
|
||
|
||
def find_similar_topics_simple(text: str, topics: list, top_k: int = 5) -> list:
|
||
"""使用简单的余弦相似度计算文本相似度"""
|
||
# 将输入文本转换为词频向量
|
||
text_vector = text_to_vector(text)
|
||
|
||
# 计算每个主题的相似度
|
||
similarities = []
|
||
for topic in topics:
|
||
topic_vector = text_to_vector(topic)
|
||
# 获取所有唯一词
|
||
all_words = set(text_vector.keys()) | set(topic_vector.keys())
|
||
# 构建向量
|
||
v1 = [text_vector.get(word, 0) for word in all_words]
|
||
v2 = [topic_vector.get(word, 0) for word in all_words]
|
||
# 计算相似度
|
||
similarity = cosine_similarity(v1, v2)
|
||
similarities.append((topic, similarity))
|
||
|
||
# 按相似度降序排序并返回前k个
|
||
return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k]
|
||
|
||
|
||
def truncate_message(message: str, max_length=20) -> str:
|
||
"""截断消息，使其不超过指定长度"""
|
||
if len(message) > max_length:
|
||
return message[:max_length] + "..."
|
||
return message
|
||
|
||
|
||
def protect_kaomoji(sentence):
|
||
""" "
|
||
识别并保护句子中的颜文字（含括号与无括号），将其替换为占位符，
|
||
并返回替换后的句子和占位符到颜文字的映射表。
|
||
Args:
|
||
sentence (str): 输入的原始句子
|
||
Returns:
|
||
tuple: (处理后的句子, {占位符: 颜文字})
|
||
"""
|
||
kaomoji_pattern = re.compile(
|
||
r"("
|
||
r"[(\[（【]" # 左括号
|
||
r"[^()\[\]（）【】]*?" # 非括号字符（惰性匹配）
|
||
r"[^一-龥a-zA-Z0-9\s]" # 非中文、非英文、非数字、非空格字符（必须包含至少一个）
|
||
r"[^()\[\]（）【】]*?" # 非括号字符（惰性匹配）
|
||
r"[)\]）】" # 右括号
|
||
r"]"
|
||
r")"
|
||
r"|"
|
||
r"([▼▽・ᴥω･﹏^><≧≦￣｀´∀ヮДд︿﹀へ｡ﾟ╥╯╰︶︹•⁄]{2,15})"
|
||
)
|
||
|
||
kaomoji_matches = kaomoji_pattern.findall(sentence)
|
||
placeholder_to_kaomoji = {}
|
||
|
||
for idx, match in enumerate(kaomoji_matches):
|
||
kaomoji = match[0] if match[0] else match[1]
|
||
placeholder = f"__KAOMOJI_{idx}__"
|
||
sentence = sentence.replace(kaomoji, placeholder, 1)
|
||
placeholder_to_kaomoji[placeholder] = kaomoji
|
||
|
||
return sentence, placeholder_to_kaomoji
|
||
|
||
|
||
def recover_kaomoji(sentences, placeholder_to_kaomoji):
|
||
"""
|
||
根据映射表恢复句子中的颜文字。
|
||
Args:
|
||
sentences (list): 含有占位符的句子列表
|
||
placeholder_to_kaomoji (dict): 占位符到颜文字的映射表
|
||
Returns:
|
||
list: 恢复颜文字后的句子列表
|
||
"""
|
||
recovered_sentences = []
|
||
for sentence in sentences:
|
||
for placeholder, kaomoji in placeholder_to_kaomoji.items():
|
||
sentence = sentence.replace(placeholder, kaomoji)
|
||
recovered_sentences.append(sentence)
|
||
return recovered_sentences
|
||
|
||
|
||
def get_western_ratio(paragraph):
|
||
"""计算段落中字母数字字符的西文比例
|
||
原理：检查段落中字母数字字符的西文比例
|
||
通过is_english_letter函数判断每个字符是否为西文
|
||
只检查字母数字字符，忽略标点符号和空格等非字母数字字符
|
||
|
||
Args:
|
||
paragraph: 要检查的文本段落
|
||
|
||
Returns:
|
||
float: 西文字符比例(0.0-1.0)，如果没有字母数字字符则返回0.0
|
||
"""
|
||
alnum_chars = [char for char in paragraph if char.isalnum()]
|
||
if not alnum_chars:
|
||
return 0.0
|
||
|
||
western_count = sum(1 for char in alnum_chars if is_english_letter(char))
|
||
return western_count / len(alnum_chars)
|
||
|
||
|
||
def count_messages_between(start_time: float, end_time: float, stream_id: str) -> tuple[int, int]:
|
||
"""计算两个时间点之间的消息数量和文本总长度
|
||
|
||
Args:
|
||
start_time (float): 起始时间戳 (不包含)
|
||
end_time (float): 结束时间戳 (包含)
|
||
stream_id (str): 聊天流ID
|
||
|
||
Returns:
|
||
tuple[int, int]: (消息数量, 文本总长度)
|
||
"""
|
||
count = 0
|
||
total_length = 0
|
||
|
||
# 参数校验 (可选但推荐)
|
||
if start_time >= end_time:
|
||
# logger.debug(f"开始时间 {start_time} 大于或等于结束时间 {end_time}，返回 0, 0")
|
||
return 0, 0
|
||
if not stream_id:
|
||
logger.error("stream_id 不能为空")
|
||
return 0, 0
|
||
|
||
# 使用message_repository中的count_messages和find_messages函数
|
||
|
||
# 构建查询条件
|
||
filter_query = {"chat_id": stream_id, "time": {"$gt": start_time, "$lte": end_time}}
|
||
|
||
try:
|
||
# 先获取消息数量
|
||
count = count_messages(filter_query)
|
||
|
||
# 获取消息内容计算总长度
|
||
messages = find_messages(message_filter=filter_query)
|
||
total_length = sum(len(msg.get("processed_plain_text", "")) for msg in messages)
|
||
|
||
return count, total_length
|
||
|
||
except Exception as e:
|
||
logger.error(f"计算消息数量时发生意外错误: {e}")
|
||
return 0, 0
|
||
|
||
|
||
def translate_timestamp_to_human_readable(timestamp: float, mode: str = "normal") -> str:
|
||
"""将时间戳转换为人类可读的时间格式
|
||
|
||
Args:
|
||
timestamp: 时间戳
|
||
mode: 转换模式，"normal"为标准格式，"relative"为相对时间格式
|
||
|
||
Returns:
|
||
str: 格式化后的时间字符串
|
||
"""
|
||
if mode == "normal":
|
||
return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp))
|
||
elif mode == "relative":
|
||
now = time.time()
|
||
diff = now - timestamp
|
||
|
||
if diff < 20:
|
||
return "刚刚:\n"
|
||
elif diff < 60:
|
||
return f"{int(diff)}秒前:\n"
|
||
elif diff < 3600:
|
||
return f"{int(diff / 60)}分钟前:\n"
|
||
elif diff < 86400:
|
||
return f"{int(diff / 3600)}小时前:\n"
|
||
elif diff < 86400 * 2:
|
||
return f"{int(diff / 86400)}天前:\n"
|
||
else:
|
||
return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp)) + ":\n"
|
||
else: # mode = "lite" or unknown
|
||
# 只返回时分秒格式，喵~
|
||
return time.strftime("%H:%M:%S", time.localtime(timestamp))
|
||
|
||
|
||
def parse_text_timestamps(text: str, mode: str = "normal") -> str:
|
||
"""解析文本中的时间戳并转换为可读时间格式
|
||
|
||
Args:
|
||
text: 包含时间戳的文本，时间戳应以[]包裹
|
||
mode: 转换模式，传递给translate_timestamp_to_human_readable，"normal"或"relative"
|
||
|
||
Returns:
|
||
str: 替换后的文本
|
||
|
||
转换规则:
|
||
- normal模式: 将文本中所有时间戳转换为可读格式
|
||
- lite模式:
|
||
- 第一个和最后一个时间戳必须转换
|
||
- 以5秒为间隔划分时间段，每段最多转换一个时间戳
|
||
- 不转换的时间戳替换为空字符串
|
||
"""
|
||
# 匹配[数字]或[数字.数字]格式的时间戳
|
||
pattern = r"\[(\d+(?:\.\d+)?)\]"
|
||
|
||
# 找出所有匹配的时间戳
|
||
matches = list(re.finditer(pattern, text))
|
||
|
||
if not matches:
|
||
return text
|
||
|
||
# normal模式: 直接转换所有时间戳
|
||
if mode == "normal":
|
||
result_text = text
|
||
for match in matches:
|
||
timestamp = float(match.group(1))
|
||
readable_time = translate_timestamp_to_human_readable(timestamp, "normal")
|
||
# 由于替换会改变文本长度，需要使用正则替换而非直接替换
|
||
pattern_instance = re.escape(match.group(0))
|
||
result_text = re.sub(pattern_instance, readable_time, result_text, count=1)
|
||
return result_text
|
||
else:
|
||
# lite模式: 按5秒间隔划分并选择性转换
|
||
result_text = text
|
||
|
||
# 提取所有时间戳及其位置
|
||
timestamps = [(float(m.group(1)), m) for m in matches]
|
||
timestamps.sort(key=lambda x: x[0]) # 按时间戳升序排序
|
||
|
||
if not timestamps:
|
||
return text
|
||
|
||
# 获取第一个和最后一个时间戳
|
||
first_timestamp, first_match = timestamps[0]
|
||
last_timestamp, last_match = timestamps[-1]
|
||
|
||
# 将时间范围划分成5秒间隔的时间段
|
||
time_segments = {}
|
||
|
||
# 对所有时间戳按15秒间隔分组
|
||
for ts, match in timestamps:
|
||
segment_key = int(ts // 15) # 将时间戳除以15取整，作为时间段的键
|
||
if segment_key not in time_segments:
|
||
time_segments[segment_key] = []
|
||
time_segments[segment_key].append((ts, match))
|
||
|
||
# 记录需要转换的时间戳
|
||
to_convert = []
|
||
|
||
# 从每个时间段中选择一个时间戳进行转换
|
||
for _, segment_timestamps in time_segments.items():
|
||
# 选择这个时间段中的第一个时间戳
|
||
to_convert.append(segment_timestamps[0])
|
||
|
||
# 确保第一个和最后一个时间戳在转换列表中
|
||
first_in_list = False
|
||
last_in_list = False
|
||
|
||
for ts, _ in to_convert:
|
||
if ts == first_timestamp:
|
||
first_in_list = True
|
||
if ts == last_timestamp:
|
||
last_in_list = True
|
||
|
||
if not first_in_list:
|
||
to_convert.append((first_timestamp, first_match))
|
||
if not last_in_list:
|
||
to_convert.append((last_timestamp, last_match))
|
||
|
||
# 创建需要转换的时间戳集合，用于快速查找
|
||
to_convert_set = {match.group(0) for _, match in to_convert}
|
||
|
||
# 首先替换所有不需要转换的时间戳为空字符串
|
||
for _, match in timestamps:
|
||
if match.group(0) not in to_convert_set:
|
||
pattern_instance = re.escape(match.group(0))
|
||
result_text = re.sub(pattern_instance, "", result_text, count=1)
|
||
|
||
# 按照时间戳原始顺序排序，避免替换时位置错误
|
||
to_convert.sort(key=lambda x: x[1].start())
|
||
|
||
# 执行替换
|
||
# 由于替换会改变文本长度，从后向前替换
|
||
to_convert.reverse()
|
||
for ts, match in to_convert:
|
||
readable_time = translate_timestamp_to_human_readable(ts, "relative")
|
||
pattern_instance = re.escape(match.group(0))
|
||
result_text = re.sub(pattern_instance, readable_time, result_text, count=1)
|
||
|
||
return result_text
|