mirror of
https://github.com/linyqh/NarratoAI.git
synced 2026-07-05 05:45:06 +00:00
ed4a5d07e5
- 新增短剧混剪脚本生成专用提示词类并完成注册 - 优化merge_script工具函数，支持多视频路径输入、自动填充视频信息 - 扩展SDP处理流水线，新增直接基于剧情分析和字幕生成剪辑脚本的逻辑 - 更新WebUI相关组件与工具函数，适配新的短剧混剪脚本生成流程 - 添加字幕时间戳校验与路径规范化工具，确保生成脚本合法性
399 lines
15 KiB
Python
399 lines
15 KiB
Python
"""
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使用统一LLM服务，分析字幕文件，返回剧情梗概和爆点
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"""
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import os
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import traceback
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import json
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from loguru import logger
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from app.services.subtitle_text import has_timecodes, normalize_subtitle_text, read_subtitle_text
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from app.services.short_drama_narration_validation import (
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build_subtitle_index,
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parse_script_timestamp_range,
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)
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# 导入新的提示词管理系统
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from app.services.prompts import PromptManager
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# 导入统一LLM服务
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from app.services.llm.unified_service import UnifiedLLMService
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# 导入安全的异步执行函数
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from app.services.llm.migration_adapter import _run_async_safely
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def _normalize_paths(paths):
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if isinstance(paths, str):
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paths = [paths]
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if not paths:
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return []
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normalized_paths = []
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seen = set()
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for path in paths:
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if not isinstance(path, str):
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continue
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path = path.strip()
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if not path or path in seen:
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continue
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normalized_paths.append(path)
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seen.add(path)
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return normalized_paths
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def _coerce_positive_int(value):
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try:
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number = int(value)
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except (TypeError, ValueError):
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return None
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return number if number > 0 else None
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def _match_video_id_by_name(video_name, video_paths):
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video_name = os.path.basename(str(video_name or "").strip())
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if not video_name:
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return None
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for index, video_path in enumerate(video_paths, start=1):
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if os.path.basename(video_path) == video_name:
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return index
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return None
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def _default_video_name(video_id, video_paths):
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if 1 <= video_id <= len(video_paths):
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return os.path.basename(video_paths[video_id - 1])
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return ""
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def _normalize_short_mix_items(items, video_paths, subtitle_content):
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if not isinstance(items, list) or not items:
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raise ValueError("短剧混剪脚本 items 必须是非空数组")
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normalized_video_paths = _normalize_paths(video_paths)
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subtitle_index = build_subtitle_index(subtitle_content, normalized_video_paths)
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available_video_ids = {cue.video_id for cue in subtitle_index}
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if normalized_video_paths:
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available_video_ids.update(range(1, len(normalized_video_paths) + 1))
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normalized_items = []
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ranges_by_video = {}
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for index, raw_item in enumerate(items, start=1):
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if not isinstance(raw_item, dict):
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raise ValueError(f"第 {index} 个混剪片段必须是对象")
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item_id = index
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video_id = (
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_match_video_id_by_name(raw_item.get("video_name") or raw_item.get("source_video"), normalized_video_paths)
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or _coerce_positive_int(raw_item.get("video_id") or raw_item.get("video_index"))
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or 1
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)
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if available_video_ids and video_id not in available_video_ids:
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raise ValueError(f"片段 {item_id} 的 video_id={video_id} 不在已选视频范围内")
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try:
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start_ms, end_ms, timestamp = parse_script_timestamp_range(raw_item.get("timestamp", ""))
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except ValueError as exc:
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raise ValueError(f"片段 {item_id}: {exc}") from exc
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if start_ms >= end_ms:
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raise ValueError(f"片段 {item_id} 的开始时间必须早于结束时间")
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video_cues = [cue for cue in subtitle_index if cue.video_id == video_id]
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if video_cues:
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min_start = min(cue.start_ms for cue in video_cues)
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max_end = max(cue.end_ms for cue in video_cues)
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if start_ms < min_start or end_ms > max_end:
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raise ValueError(f"片段 {item_id} 的时间戳不在视频 {video_id} 的字幕范围内")
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if not any(start_ms < cue.end_ms and end_ms > cue.start_ms for cue in video_cues):
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raise ValueError(f"片段 {item_id} 的时间戳没有命中视频 {video_id} 的字幕内容")
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picture = str(
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raw_item.get("picture")
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or raw_item.get("title")
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or raw_item.get("narrative_function")
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or raw_item.get("intent")
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or raw_item.get("story_role")
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or ""
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).strip()
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if not picture:
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raise ValueError(f"片段 {item_id} 的 picture 不能为空")
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video_name = str(raw_item.get("video_name") or "").strip()
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if normalized_video_paths:
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video_name = _default_video_name(video_id, normalized_video_paths)
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normalized_items.append(
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{
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"_id": item_id,
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"video_id": video_id,
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"video_name": video_name,
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"timestamp": timestamp,
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"picture": picture,
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"narration": f"播放原片{item_id}",
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"OST": 1,
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}
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)
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ranges_by_video.setdefault(video_id, []).append((start_ms, end_ms, item_id))
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for video_id, ranges in ranges_by_video.items():
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ranges = sorted(ranges, key=lambda item: (item[0], item[1], item[2]))
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previous_start, previous_end, previous_id = ranges[0]
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for start_ms, end_ms, item_id in ranges[1:]:
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if start_ms < previous_end:
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raise ValueError(f"视频 {video_id} 的片段 {item_id} 与片段 {previous_id} 时间戳重叠")
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if end_ms > previous_end:
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previous_start, previous_end, previous_id = start_ms, end_ms, item_id
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return normalized_items
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def _generate_short_mix_script(
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*,
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subtitle_content,
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plot_analysis,
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custom_clips,
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provider,
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model_name,
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api_key,
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base_url,
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video_paths=None,
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short_name="",
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drama_genre="",
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):
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script_generation_prompt = PromptManager.get_prompt(
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category="short_drama_editing",
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name="script_generation",
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parameters={
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"drama_name": short_name or "短剧",
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"drama_genre": drama_genre or "短剧",
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"plot_analysis": plot_analysis,
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"subtitle_content": subtitle_content,
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"custom_clips": int(custom_clips or 5),
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},
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)
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response = _run_async_safely(
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UnifiedLLMService.generate_text,
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prompt=script_generation_prompt,
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provider=provider,
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model=model_name,
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api_key=api_key,
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base_url=base_url,
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temperature=0.1,
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max_tokens=4000,
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)
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from webui.tools.generate_short_summary import parse_and_fix_json
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script_data = parse_and_fix_json(response)
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if not script_data:
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raise ValueError("无法解析短剧混剪脚本JSON")
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script_items = script_data.get("items") or script_data.get("segments") or script_data.get("plot_points")
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return _normalize_short_mix_items(script_items, video_paths, subtitle_content)
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def analyze_subtitle(
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model_name: str,
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api_key: str = None,
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base_url: str = None,
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custom_clips: int = 5,
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provider: str = None,
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srt_path: str = None,
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subtitle_content: str = None,
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plot_analysis: str = None,
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video_paths=None,
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short_name: str = "",
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drama_genre: str = "",
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) -> dict:
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"""分析字幕内容，返回完整的分析结果
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Args:
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model_name (str): 大模型名称
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api_key (str, optional): 大模型API密钥. Defaults to None.
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base_url (str, optional): 大模型API基础URL. Defaults to None.
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custom_clips (int): 需要提取的片段数量. Defaults to 5.
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provider (str, optional): LLM服务提供商. Defaults to None.
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srt_path (str, optional): SRT字幕文件路径（与subtitle_content二选一）
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subtitle_content (str, optional): SRT字幕文本内容（与srt_path二选一）
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plot_analysis (str, optional): 已审核/缓存的剧情理解文本，提供时直接进入混剪脚本生成
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video_paths (list, optional): 原始视频路径列表，用于补齐 video_id/video_name
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short_name (str, optional): 短剧名称
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drama_genre (str, optional): 短剧类型
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Returns:
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dict: 包含剧情梗概和结构化的时间段分析的字典
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"""
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try:
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# 读取并规范化字幕文本（不依赖结构化 SRT 解析，提升兼容性）
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if subtitle_content and str(subtitle_content).strip():
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normalized_subtitle_text = normalize_subtitle_text(subtitle_content)
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source_label = "字幕内容（直接传入）"
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elif srt_path:
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decoded = read_subtitle_text(srt_path)
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normalized_subtitle_text = decoded.text
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source_label = f"字幕文件: {srt_path} (encoding: {decoded.encoding})"
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else:
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raise ValueError("必须提供 srt_path 或 subtitle_content 参数")
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# 基础校验：必须有内容且包含可用于定位的时间码
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if not normalized_subtitle_text or len(normalized_subtitle_text.strip()) < 10:
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error_msg = (
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f"字幕来源 [{source_label}] 内容为空或过短。\n"
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f"请检查：\n"
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f"1. 文件格式是否为标准 SRT\n"
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f"2. 文件编码是否为 UTF-8、UTF-16、GBK 或 GB2312\n"
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f"3. 文件内容是否为空"
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)
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logger.error(error_msg)
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raise ValueError(error_msg)
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if not has_timecodes(normalized_subtitle_text):
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error_msg = (
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f"字幕来源 [{source_label}] 未检测到有效时间码，无法进行时间段定位。\n"
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f"请确保字幕包含类似以下格式的时间轴：\n"
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f"00:00:01,000 --> 00:00:02,000\n"
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f"（若毫秒分隔符为'.'，系统会自动规范化为','）"
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)
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logger.error(error_msg)
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raise ValueError(error_msg)
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logger.info(f"成功加载字幕来源 [{source_label}]，字符数: {len(normalized_subtitle_text)}")
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subtitle_content = normalized_subtitle_text
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# 如果没有指定provider，根据model_name推断
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if not provider:
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if "deepseek" in model_name.lower():
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provider = "deepseek"
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elif "gpt" in model_name.lower():
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provider = "openai"
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elif "gemini" in model_name.lower():
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provider = "gemini"
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else:
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provider = "openai" # 默认使用openai
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logger.info(f"使用LLM服务分析字幕，提供商: {provider}, 模型: {model_name}")
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if plot_analysis and str(plot_analysis).strip():
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logger.info("使用已有剧情理解直接生成短剧混剪脚本")
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script_items = _generate_short_mix_script(
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subtitle_content=subtitle_content,
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plot_analysis=str(plot_analysis).strip(),
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custom_clips=custom_clips,
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provider=provider,
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model_name=model_name,
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api_key=api_key,
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base_url=base_url,
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video_paths=video_paths,
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short_name=short_name,
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drama_genre=drama_genre,
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)
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return {
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"summary": str(plot_analysis).strip(),
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"plot_titles": [],
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"plot_points": [],
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"script_items": script_items,
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}
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# 使用新的提示词管理系统
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subtitle_analysis_prompt = PromptManager.get_prompt(
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category="short_drama_editing",
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name="subtitle_analysis",
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parameters={
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"subtitle_content": subtitle_content,
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"custom_clips": custom_clips
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}
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)
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# 使用统一LLM服务生成文本
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logger.info("开始分析字幕内容...")
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response = _run_async_safely(
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UnifiedLLMService.generate_text,
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prompt=subtitle_analysis_prompt,
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provider=provider,
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model=model_name,
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api_key=api_key,
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base_url=base_url,
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temperature=0.1, # 使用较低的温度以获得更稳定的结果
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max_tokens=4000
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)
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# 解析JSON响应
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from webui.tools.generate_short_summary import parse_and_fix_json
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summary_data = parse_and_fix_json(response)
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if not summary_data:
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raise Exception("无法解析LLM返回的JSON数据")
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logger.info(f"字幕分析完成，找到 {len(summary_data.get('plot_titles', []))} 个关键情节")
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logger.debug(json.dumps(summary_data, indent=4, ensure_ascii=False))
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try:
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script_items = _generate_short_mix_script(
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subtitle_content=subtitle_content,
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plot_analysis=json.dumps(summary_data, ensure_ascii=False, indent=2),
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custom_clips=custom_clips,
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provider=provider,
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model_name=model_name,
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api_key=api_key,
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base_url=base_url,
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video_paths=video_paths,
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short_name=short_name,
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drama_genre=drama_genre,
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)
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return {
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"summary": summary_data.get("summary", ""),
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"plot_titles": summary_data.get("plot_titles", []),
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"plot_points": [],
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"script_items": script_items,
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}
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except Exception as direct_script_error:
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logger.warning(f"直接生成短剧混剪脚本失败，回退到时间段定位: {direct_script_error}")
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# 构建爆点标题列表
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plot_titles_text = ""
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logger.info(f"找到 {len(summary_data.get('plot_titles', []))} 个片段")
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for i, point in enumerate(summary_data['plot_titles'], 1):
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plot_titles_text += f"{i}. {point}\n"
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# 使用新的提示词管理系统
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plot_extraction_prompt = PromptManager.get_prompt(
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category="short_drama_editing",
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name="plot_extraction",
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parameters={
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"subtitle_content": subtitle_content,
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"plot_summary": summary_data['summary'],
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"plot_titles": plot_titles_text
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}
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)
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# 使用统一LLM服务进行爆点时间段分析
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logger.info("开始分析爆点时间段...")
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response = _run_async_safely(
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UnifiedLLMService.generate_text,
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prompt=plot_extraction_prompt,
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provider=provider,
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model=model_name,
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api_key=api_key,
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base_url=base_url,
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temperature=0.1,
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max_tokens=4000
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)
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# 解析JSON响应
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plot_data = parse_and_fix_json(response)
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if not plot_data:
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raise Exception("无法解析爆点分析的JSON数据")
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logger.info(f"爆点分析完成，找到 {len(plot_data.get('plot_points', []))} 个时间段")
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# 合并结果
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result = {
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"summary": summary_data.get("summary", ""),
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"plot_titles": summary_data.get("plot_titles", []),
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"plot_points": plot_data.get("plot_points", [])
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}
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return result
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except Exception as e:
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logger.error(f"分析字幕时发生错误: {str(e)}")
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raise Exception(f"分析字幕时发生错误：{str(e)}\n{traceback.format_exc()}")
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