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**深度切片（默认）** | **30 秒～300 秒**，完整语义单元 | 6～10 | 提问→回答、整场观点 | +| **运营短切片** | **15～30 秒**（可 `--min-duration` / `--max-duration` 微调） | **20～30**（默认脚本 **24**） | **京剧/戏曲比喻梗**、**当场热点词**、强反差金句，适合抖音高密度测试 | + +运营短切片流程与深度切片相同（转录 → `identify_highlights` → `batch_clip` → `soul_enhance`），区别在 **高光 preset** 与 **prompt**：`identify_highlights.py --preset ops-short` 会在提示词中要求模型**整场均匀取点**，并优先京剧相关比喻/唱腔梗与热点表达；过滤逻辑会**丢弃**短于 15 秒或长于 30 秒的区间（深度模式只卡最短 60 秒、不卡上限）。 + +**开场 ASR 噪声**：派对录播常在开场出现同一短句循环（如「我看你不太好」），会把模型注意力锁死在前几分钟。**ops-short 默认**将送模型的文字稿与成片时间轴**从约 7:30（450 秒）之后**才开始（`--prompt-min-sec`，可改）。若你的场次正片明显更早开始，可改小该值或临时改 `long` 再人工筛 `highlights.json`。 + +**批量节奏（人工剪辑对齐）**：一场录播可先按 **15～30 条**为一轮做高光与切片，再进成片；多轮叠加时注意 `highlights.json` 备份，避免覆盖。 + +**标题**：深度模式用一句**刺激性观点**；短切片标题 **4～10 个汉字** 为宜（见 `Soul竖屏切片_SKILL.md`）。 **提问→回答 结构**：若片段内有人提问，前3秒优先展示**提问问题**，再播回答；高光识别填 `question` 且 `hook_3sec` 与之一致，成片整条去语助词。详见 `参考资料/视频结构_提问回答与高光.md`、`参考资料/高光识别提示词.md`。 @@ -54,6 +67,15 @@ cd 03_卡木（木）/木叶_视频内容/视频切片/脚本 conda activate mlx-whisper python3 soul_slice_pipeline.py --video "/path/to/soul派对会议第57场.mp4" --clips 6 +# 运营短切片（15～30 秒 × 约 24 条，京剧梗+热点优先，两目录+竖屏成片） +python3 soul_slice_pipeline.py -v "视频.mp4" -o "/path/to/场次_output" --two-folders --ops-short --prefix soul127 + +# 已转录场次仅重跑高光+切片+成片（省 MLX） +python3 soul_slice_pipeline.py -v "视频.mp4" -o "/path/to/场次_output" --two-folders --ops-short --skip-transcribe --prefix soul127 + +# 自定义条数与时长区间 +python3 soul_slice_pipeline.py -v "视频.mp4" -o "/path/to/out" --two-folders --highlight-preset ops-short -n 28 --min-clip-sec 10 --max-clip-sec 30 + # 仅重新烧录（字幕转简体后重跑增强） python3 soul_slice_pipeline.py -v "视频.mp4" -n 6 --skip-transcribe --skip-highlights --skip-clips @@ -61,6 +83,14 @@ python3 soul_slice_pipeline.py -v "视频.mp4" -n 6 --skip-transcribe --skip-hig python3 soul_slice_pipeline.py -v "视频.mp4" -n 8 --two-folders --quick-montage ``` +**分步：仅高光（运营短切片）** + +```bash +python3 identify_highlights.py -t transcript.srt -o highlights.json --preset ops-short -n 24 +# 或显式时长 + 长视频也强调京剧/热点： +python3 identify_highlights.py -t transcript.srt -o highlights.json --preset ops-short -n 26 --min-duration 15 --max-duration 30 --ops-jingju-hotspot +``` + 流程：**转录 → 字幕转简体 → 高光识别 → 批量切片 → 增强** #### 分步命令 @@ -155,6 +185,8 @@ python3 analyze_feishu_ui_crop.py "/path/to/原片.mp4" --at 0.2 将输出的 `CROP_VF` 传给：`python3 soul_enhance.py ... --vertical --crop-vf '...'`（`OVERLAY_X` 脚本会一并打印；也可用 `--overlay-x` 覆盖）。 +**全画面入画（不裁竖条）**：加 `--vertical-fit-full`，整幅 16:9 缩放入 498×1080 + 上下黑边，左右内容都可见。详见 `Soul竖屏切片_SKILL.md` 第六节 B。 + 详见：`参考资料/竖屏中段裁剪参数说明.md`、`脚本/analyze_feishu_ui_crop.py`。 **FFmpeg 一条命令（固定参数）：** @@ -350,7 +382,7 @@ python3 scripts/burn_subtitles_clean.py -i enhanced.mp4 -s clean.srt -o 成片.m | **kill_ffmpeg_when_clip_done.py** | 剪辑结束后自动关掉 ffmpeg（监视剪映/PID 或立即杀） | ⭐ 按需 | | **scene_detect_to_highlights.py** | 镜头/场景检测 → highlights.json（PySceneDetect，可接 batch_clip） | ⭐⭐ | | chapter_themes_to_highlights.py | 按章节 .md 主题提取片段（本地模型→highlights.json） | ⭐⭐⭐ | -| identify_highlights.py | 高光识别（API 优先→Ollama→规则，默认 gpt-4o） | ⭐⭐ | +| identify_highlights.py | 高光识别（API→Ollama→规则；`--preset ops-short` 为 15～30 秒运营密度） | ⭐⭐ | | batch_clip.py | 批量切片 | ⭐⭐ | | one_video.py | 单视频一键成片 | ⭐⭐ | | burn_subtitles_clean.py | 字幕烧录（无阴影） | ⭐ | diff --git a/03_卡木（木）/木叶_视频内容/视频切片/Soul竖屏切片_SKILL.md b/03_卡木（木）/木叶_视频内容/视频切片/Soul竖屏切片_SKILL.md index 95de6031..cc38fd0c 100644 --- a/03_卡木（木）/木叶_视频内容/视频切片/Soul竖屏切片_SKILL.md +++ b/03_卡木（木）/木叶_视频内容/视频切片/Soul竖屏切片_SKILL.md @@ -1,7 +1,7 @@ --- name: Soul竖屏切片 description: Soul 派对视频→竖屏成片（498×1080），剪辑→成片两文件夹；竖屏裁剪以全画面 1920×1080 标定（analyze_feishu_ui_crop.py），默认深色带 crop=568@508+居中498、无右侧白边。MLX 转录→高光→batch_clip→soul_enhance（封面+字幕同步+逐字可选+去语助词+纠错+违禁词）→visual_enhance v8 可选。LTX/基因胶囊可选。 -triggers: Soul竖屏切片、视频切片、热点切片、竖屏成片、派对切片、全画面标定、竖屏裁剪、白边、飞书录屏、LTX、AI生成视频、Retake重剪、字幕优化、字幕同步、逐字字幕 +triggers: Soul竖屏切片、视频切片、热点切片、竖屏成片、派对切片、全画面标定、竖屏裁剪、全画面成片、letterbox、画面显示全、白边、飞书录屏、LTX、AI生成视频、Retake重剪、字幕优化、字幕同步、逐字字幕 owner: 木叶 group: 木 version: "1.4" @@ -72,7 +72,7 @@ python3 analyze_feishu_ui_crop.py "/path/to/全画面.jpg" |----|------| | **单段时长** | **30～300 秒**，由完整片段起止决定 | | **完整性** | 每段是一个完整话题/情节，有头有尾 | -| **标题** | **一句刺激性观点**（金句、反常识、结论句） | +| **标题** | **一句刺激性观点**，**4～6 个汉字**为宜（单行封面好读、主题一眼懂）；忌长句当文件名 | | **数量** | 建议 ≤10 段/场 | | **语助词** | 识别与剪辑须符合 `参考资料/高光识别提示词.md`，成片由 soul_enhance 统一去语助词 | @@ -81,7 +81,8 @@ python3 analyze_feishu_ui_crop.py "/path/to/全画面.jpg" ## 五、成片：封面 + 字幕 + 竖屏 - **封面**：竖屏 498×1080 内**不超出界面**；**半透明质感**（背景 alpha=165）；深色渐变、左上角 Soul logo；**封面显示标题 = 成片文件名 = highlights.title**（去杠、去下划线后一致，无 `：｜—/_`、无序号）；标题文字严格居中、多行自动换行。透明度由 `VERTICAL_COVER_ALPHA` 调节。 -- **字幕**：封面结束后才显示，**居中**在竖屏内；先尝试**单次 FFmpeg 通道**（一次 pass 完成所有字幕叠加，最快）；若失败自动回退到分批模式（batch_size=40）；语助词在解析阶段已由 `clean_filler_words` 去除。重新加字幕时加 `--force-burn-subs`。⚠️ 注意：当前 FFmpeg 不支持 drawtext/subtitles 滤镜，只能用 PIL 图像 overlay 方案。 +- **字幕**：封面结束后先留**约 3 秒纯画面**（无字幕），再开始叠字幕；字幕**居中**在竖屏内。先尝试**单次 FFmpeg 通道**（一次 pass 完成所有字幕叠加，最快）；若失败自动回退到分批模式（batch_size=40）；语助词在解析阶段已由 `clean_filler_words` 去除。重新加字幕时加 `--force-burn-subs`。⚠️ 注意：当前 FFmpeg 不支持 drawtext/subtitles 滤镜，只能用 PIL 图像 overlay 方案。（脚本常量：`SUBS_START_AFTER_COVER_SEC`，默认 3.0） +- **封面标题**：高光 `title` 建议 **4～6 个汉字**；成片内封面主标题最多显示 **6 个汉字**（超长由 `soul_enhance` 自动截断，与文件名 `--title-only` 一致）。 - **竖屏**：498×1080，crop 参数与 `参考资料/竖屏中段裁剪参数说明.md` 一致 ### ⚠️ 字幕烧录常见坑（已修复） @@ -98,14 +99,26 @@ python3 analyze_feishu_ui_crop.py "/path/to/全画面.jpg" --- -## 六、竖屏裁剪参数（成片内嵌） +## 六、竖屏输出两种模式（成片内嵌） + +### A. 竖条模式（默认，小程序无白边） + +只取横向**中间深色带**，再裁成 498 宽，适合抖音全屏铺满、不要桌面白边。 | 步骤 | 滤镜 | |------|------| | 1 | crop=568:1080:508:0（整段深色小程序主体，不含右侧桌面白边） | | 2 | crop=498:1080:35:0（568 内水平居中取 498） | -**输出**：498×1080 竖屏。 +### B. 全画面模式（`--vertical-fit-full`） + +**不裁中间竖条**：整幅 16:9 **完整入画**，等比缩放到宽度 498，**上下加黑边** 到 1080 高。左侧小程序 + 右侧人像/桌面都会在画面里，适合「画面要显示全」的成片。 + +- 封面、字幕先在 **完整横版分辨率** 上叠加（`overlay=0:0`），再整体走： + `scale=w=498:h=1080:force_original_aspect_ratio=decrease,pad=498:1080:(ow-iw)/2:(oh-ih)/2:color=black` +- 命令：在原有 `soul_enhance.py ... --vertical --title-only` 上增加 **`--vertical-fit-full`** + +**输出**：两种模式均为 **498×1080** 竖屏文件。 --- diff --git a/03_卡木（木）/木叶_视频内容/视频切片/参考资料/高光识别提示词.md b/03_卡木（木）/木叶_视频内容/视频切片/参考资料/高光识别提示词.md index 36352f05..47d3afae 100644 --- a/03_卡木（木）/木叶_视频内容/视频切片/参考资料/高光识别提示词.md +++ b/03_卡木（木）/木叶_视频内容/视频切片/参考资料/高光识别提示词.md @@ -121,7 +121,7 @@ CTA的目的是引导用户完成下一步动作。 [ { "rank": 1, - "title": "简短有力的标题（用于短视频）", + "title": "4～6 个汉字的刺激性观点（用于短视频封面/文件名，单一主题）", "start_time": "00:12:34", "end_time": "00:13:56", "duration_sec": 82, @@ -192,7 +192,7 @@ CTA的目的是引导用户完成下一步动作。 - 优先：有步骤、有起伏、起承转合清晰的片段 - 避免：大段碎碎念、断句混乱、同一句话重复多遍、长时间无信息量停顿 -- 与主题片段提取规则一致：每段为完整语义单元，时长 30～300 秒，标题为一句刺激性观点 +- 与主题片段提取规则一致：每段为完整语义单元，时长 30～300 秒；**title 固定 4～6 个汉字**，一句刺激性观点、单一主题（用于竖屏封面字） ## 文字稿格式要求 diff --git a/03_卡木（木）/木叶_视频内容/视频切片/场次稿/第127场_20260318_highlights.json b/03_卡木（木）/木叶_视频内容/视频切片/场次稿/第127场_20260318_highlights.json new file mode 100644 index 00000000..5c4d0d0b --- /dev/null +++ b/03_卡木（木）/木叶_视频内容/视频切片/场次稿/第127场_20260318_highlights.json @@ -0,0 +1,82 @@ +[ + { + "title": "高薪先看消耗", + "start_time": "00:07:30", + "end_time": "00:12:00", + "hook_3sec": "想两万月薪？先看AI月烧多少", + "question": "高薪硬指标是什么？", + "cta_ending": "今天就到这里，点个关注下次不迷路", + "transcript_excerpt": "想拿2万工资，AI月消耗至少1000块以上。消耗多，说明你是实实在在用AI在解决职业里的问题", + "reason": "TOKEN消耗硬指标" + }, + { + "title": "辅助暴利快收", + "start_time": "00:12:00", + "end_time": "00:18:00", + "hook_3sec": "游戏辅助来钱快，定性也狠", + "question": "做游戏AI能挣多少？", + "cta_ending": "今天就到这里，点个关注下次不迷路", + "transcript_excerpt": "三角洲辅助瞄准，AI训练人物识别模型。闲鱼抖音直播分销，单价500块一个人一个月", + "reason": "高收益高风险" + }, + { + "title": "保镖钱在后端", + "start_time": "00:18:00", + "end_time": "00:23:00", + "hook_3sec": "真赚的不是保镖费，是后端", + "question": "保镖怎么赚大钱？", + "cta_ending": "今天就到这里，点个关注下次不迷路", + "transcript_excerpt": "初级2万一个月，中级3万，高级4万。真正赚钱的是商务中介、介绍投资、拉业务合作", + "reason": "信任关系变现" + }, + { + "title": "别两头扛流量", + "start_time": "00:23:00", + "end_time": "00:28:00", + "hook_3sec": "流量交付同时扛，必崩", + "question": "做流量还是交付？", + "cta_ending": "今天就到这里，点个关注下次不迷路", + "transcript_excerpt": "现在这条赛道缺的是流量，不是交付。和群主合作要分钱，不分必被排挤", + "reason": "一端打穿" + }, + { + "title": "分钱分缺口", + "start_time": "00:28:00", + "end_time": "00:33:00", + "hook_3sec": "分他靠自己赚不到的那块", + "question": "招人怎么分钱？", + "cta_ending": "今天就到这里，点个关注下次不迷路", + "transcript_excerpt": "员工自己只能挣5000到8000，你给他1万，他多拿2000，他才愿意跟你干", + "reason": "分钱逻辑" + }, + { + "title": "推流就三板斧", + "start_time": "00:33:00", + "end_time": "00:38:00", + "hook_3sec": "二百七十万推流，密码就几条", + "question": "派对流量怎么来？", + "cta_ending": "今天就到这里，点个关注下次不迷路", + "transcript_excerpt": "流量密码就那几个：职场、搞钱、MBTI性格匹配，最容易共鸣", + "reason": "Soul数据复盘" + }, + { + "title": "面试三面定人", + "start_time": "00:38:00", + "end_time": "00:43:00", + "hook_3sec": "三百简历，最后只要两三个", + "question": "怎么筛人？", + "cta_ending": "今天就到这里，点个关注下次不迷路", + "transcript_excerpt": "二面线上做题、跟团队开25分钟会，看配合。三面定薪资岗位，7天试岗", + "reason": "面试流程" + }, + { + "title": "实操碾压学历", + "start_time": "00:43:00", + "end_time": "00:48:00", + "hook_3sec": "不学AI，连班都难上", + "question": null, + "cta_ending": "今天就到这里，点个关注下次不迷路", + "transcript_excerpt": "想拿2万工资，AI月消耗至少1000。这不是卡学历，是卡你有没有真在用AI干活", + "reason": "实操门槛" + } +] diff --git a/03_卡木（木）/木叶_视频内容/视频切片/脚本/identify_highlights.py b/03_卡木（木）/木叶_视频内容/视频切片/脚本/identify_highlights.py index 27c949d7..d48bedae 100644 --- a/03_卡木（木）/木叶_视频内容/视频切片/脚本/identify_highlights.py +++ b/03_卡木（木）/木叶_视频内容/视频切片/脚本/identify_highlights.py @@ -11,12 +11,19 @@ import os import re import sys from pathlib import Path +from typing import Optional OLLAMA_URL = "http://localhost:11434" DEFAULT_CTA = "关注我，每天学一招私域干货" CLIP_COUNT = 15 -MIN_DURATION = 60 # 最少 1 分钟 +MIN_DURATION = 60 # 最少 1 分钟（长切片默认） MAX_DURATION = 300 # 最多 5 分钟 +# 运营短切片默认：单场产出高密度短视频，便于抖音/热点测试 +OPS_SHORT_MIN = 15 +OPS_SHORT_MAX = 30 +OPS_SHORT_CLIPS = 24 +# 飞书/录屏开场常见 ASR 鬼畜循环，运营短切片喂给模型的文字稿从该秒之后开始（约 7:30） +OPS_SHORT_PROMPT_MIN_SEC_DEFAULT = 450.0 # API 默认模型：优先用当前可用最佳（可被 OPENAI_MODEL / OPENAI_MODELS 覆盖） DEFAULT_API_MODEL = "gpt-4o" @@ -43,18 +50,27 @@ def parse_srt_segments(srt_path: str) -> list: return segments -def fallback_highlights(transcript_path: str, clip_count: int) -> list: - """规则备用：每段 60-300 秒（1-5 分钟）""" +def fallback_highlights( + transcript_path: str, + clip_count: int, + min_dur: float = 60, + max_dur: float = 300, + start_from_sec: float = 0, +) -> list: + """规则备用：按 min_dur～max_dur 均匀切分；可从 start_from_sec 起切（运营短切片对齐正片起点）。""" segments = parse_srt_segments(transcript_path) if not segments: return [] total = segments[-1]["end_sec"] if segments else 0 - seg_dur = min(300, max(60, total / clip_count)) # 每段 1-5 分钟 + start_from_sec = max(0, min(float(start_from_sec), max(0, total - min_dur - 2))) + usable = max(0, total - start_from_sec - 2) + target = usable / max(1, clip_count) + seg_dur = min(max_dur, max(min_dur, target)) result = [] for i in range(clip_count): - start_sec = int(i * seg_dur) - end_sec = min(int(start_sec + seg_dur), int(total - 5)) - if end_sec <= start_sec + 59: # 不足 1 分钟跳过 + start_sec = int(start_from_sec + i * seg_dur) + end_sec = min(int(start_sec + seg_dur), int(total - 2)) + if end_sec <= start_sec + max(5, min_dur - 1): continue # 找该时间段内的字幕 texts = [s["text"] for s in segments if s["end_sec"] >= start_sec and s["start_sec"] <= end_sec] @@ -116,6 +132,40 @@ def srt_to_timestamped_text(srt_path: str, skip_repetitive_head: int = 150) -> s return "\n".join(f"[{s}] {t}" for s, t in out) +def srt_text_from_min_sec(srt_path: str, min_start_sec: float) -> str: + """只保留字幕开始时间 >= min_start_sec 的行，拼成带时间戳文本（削掉开场噪声再送模型）。""" + segments = parse_srt_segments(srt_path) + if not segments: + return "" + lines = [ + f"[{s['start_time']}] {s['text']}" + for s in segments + if s["start_sec"] >= min_start_sec + ] + return "\n".join(lines) + + +def _filter_start_not_before(data: list, min_start_sec: float) -> list: + """丢弃开始时间早于 min_start_sec 的片段（运营短切片防开场鬼畜）。""" + out = [] + for item in data: + if not isinstance(item, dict): + continue + st = item.get("start_time") or item.get("start") or "00:00:00" + if isinstance(st, (int, float)): + sec = float(st) + else: + sec = _parse_time_to_sec(str(st)) + if sec >= min_start_sec: + out.append(item) + else: + print( + f" 过滤过早片段: {item.get('title', '?')} (起 {sec:.0f}s < {min_start_sec:.0f}s)", + file=sys.stderr, + ) + return out + + def _sec_to_hhmmss(sec: float) -> str: """秒数转为 HH:MM:SS""" s = int(sec) @@ -140,8 +190,8 @@ def _parse_time_to_sec(t: str) -> float: return 0 -def _filter_short_clips(data: list) -> list: - """过滤掉时长 < 60 秒的切片""" +def _filter_clips_by_duration(data: list, min_sec: float, max_sec: Optional[float]) -> list: + """按时长过滤；max_sec 为 None 时不限制上限""" result = [] for item in data: if not isinstance(item, dict): @@ -149,26 +199,95 @@ def _filter_short_clips(data: list) -> list: st = item.get("start_time") or item.get("start") or "00:00:00" et = item.get("end_time") or item.get("end") or "00:01:00" dur = _parse_time_to_sec(et) - _parse_time_to_sec(st) - if dur >= 60: + ok_min = dur >= min_sec + ok_max = max_sec is None or dur <= max_sec + if ok_min and ok_max: result.append(item) else: - print(f" 过滤短片段: {item.get('title','?')} (仅{dur:.0f}秒)", file=sys.stderr) + why = [] + if not ok_min: + why.append(f"短于{min_sec:.0f}秒") + if not ok_max: + why.append(f"长于{max_sec:.0f}秒") + print( + f" 过滤片段: {item.get('title','?')} ({dur:.0f}秒, {','.join(why)})", + file=sys.stderr, + ) return result -def _build_prompt(transcript: str, clip_count: int) -> str: +def _ops_short_ai_plausible( + data: list, + min_dur: float, + max_dur: float, + min_start_sec: float, + min_count: int = 5, +) -> bool: + """运营短切片：AI 必须给出足够条数，且每条时长与起点符合窗口，否则走规则均匀切。""" + if not data or not isinstance(data, list) or len(data) < min_count: + return False + tol = 1.5 + for item in data: + if not isinstance(item, dict): + return False + st = item.get("start_time") or item.get("start") or "00:00:00" + et = item.get("end_time") or item.get("end") or "00:01:00" + if isinstance(st, (int, float)): + st = _sec_to_hhmmss(float(st)) + if isinstance(et, (int, float)): + et = _sec_to_hhmmss(float(et)) + try: + ssec = _parse_time_to_sec(str(st)) + esec = _parse_time_to_sec(str(et)) + except Exception: + return False + dur = esec - ssec + if dur < min_dur - tol or dur > max_dur + tol: + return False + if min_start_sec > 0 and ssec < min_start_sec - tol: + return False + return True + + +def _transcript_for_prompt(transcript: str, clip_count: int, min_dur: float) -> str: + """长视频多短切片时需要更大上下文，避免高光只落在开头""" + if min_dur < 45 or clip_count > 12: + cap = 120000 + else: + cap = 5000 + return transcript[:cap] if len(transcript) > cap else transcript + + +def _build_prompt( + transcript: str, + clip_count: int, + min_dur: float = 60, + max_dur: float = 300, + ops_jingju_hotspot: bool = False, +) -> str: """构建高光识别 prompt（提问→回答：有提问时 question/hook_3sec 用提问问题）""" - txt = transcript[:5000] if len(transcript) > 5000 else transcript + txt = _transcript_for_prompt(transcript, clip_count, min_dur) + dur_rule = f"每段时长必须严格在 {int(min_dur)}～{int(max_dur)} 秒之间（看时间戳相减），不要输出低于 {int(min_dur)} 秒或超过 {int(max_dur)} 秒的区间。" + extra = "" + if ops_jingju_hotspot: + extra = """ +## 运营短切片选题（优先） +- 优先剪：说话人用**京剧、戏曲、唱腔、行当、锣鼓**等做的比喻或梗（有趣、反差、易传播）。 +- 其次：当场**热点词**（平台规则、搞钱案例、AI/职场/流量等强刺激观点），一句话能当标题。 +- 仍遵守提问→回答：有提问时 question + hook_3sec 一致。 +- 标题 **4～10 个汉字**，要像抖音封面，忌长句。 +""" return f"""识别视频文字稿中的 {clip_count} 个高光片段，直接输出 JSON 数组，第一个字符必须是 [。 重要：每个话题均优先提问→回答。若某片段里有人提问（观众/连麦者问的问题），必须提取提问内容填 question，且 hook_3sec 用该提问；成片前3秒先展示提问，再播回答。 - +{dur_rule} +{extra} 示例（有提问）： [{{"title":"普通人怎么敢跟ZF搞","start_time":"01:12:30","end_time":"01:15:30","question":"普通人怎么敢跟ZF搞？","hook_3sec":"普通人怎么敢跟ZF搞？","cta_ending":"{DEFAULT_CTA}","transcript_excerpt":"维权起头跑通就成生意","reason":"提问+回答完整"}}] 示例（无提问）： [{{"title":"起头难","start_time":"00:05:55","end_time":"00:08:00","hook_3sec":"没人起头就起头","cta_ending":"{DEFAULT_CTA}","transcript_excerpt":"起头难跑通就能变成付费服务","reason":"核心观点"}}] -文字稿（从时间戳提取 start_time、end_time，每段 60-300 秒）： +文字稿（从时间戳提取 start_time、end_time；整场均匀覆盖，不要扎堆在同一分钟）： {txt} 直接输出 JSON 数组，以 [ 开头。有提问的片段必须带 question 且 hook_3sec 与 question 一致。""" @@ -279,18 +398,27 @@ def _build_api_provider_queue() -> list: return queue -def call_openai_api(transcript: str, clip_count: int, provider: dict) -> str: +def call_openai_api( + transcript: str, + clip_count: int, + provider: dict, + min_dur: float = 60, + max_dur: float = 300, + ops_jingju_hotspot: bool = False, +) -> str: """调用 OpenAI 兼容 API（Chat Completion），使用指定 base_url / api_key / model。""" try: from openai import OpenAI except ImportError: raise RuntimeError("未安装 openai 库，请执行: pip install openai") - prompt = _build_prompt(transcript, clip_count) + prompt = _build_prompt( + transcript, clip_count, min_dur, max_dur, ops_jingju_hotspot=ops_jingju_hotspot + ) system = ( "你是短视频策划师。用户会提供视频文字稿，你只输出一个 JSON 数组。" "若某片段内有人提问（观众/连麦者问的问题），必须提取提问原文填 question，且 hook_3sec 用该提问（前3秒先展示提问再回答）；无提问则 hook_3sec 用金句/悬念。" "格式含 title, start_time, end_time, hook_3sec, cta_ending, transcript_excerpt, reason；有提问时加 question。" - "禁止输出任何非 JSON 内容。" + "必须严格遵守用户给出的单段时长区间（秒）。禁止输出任何非 JSON 内容。" ) client = OpenAI(api_key=provider["api_key"], base_url=provider["base_url"]) resp = client.chat.completions.create( @@ -308,15 +436,24 @@ def call_openai_api(transcript: str, clip_count: int, provider: dict) -> str: return content -def call_ollama(transcript: str, clip_count: int = CLIP_COUNT, model: str = "qwen2.5:3b") -> str: +def call_ollama( + transcript: str, + clip_count: int = CLIP_COUNT, + model: str = "qwen2.5:3b", + min_dur: float = 60, + max_dur: float = 300, + ops_jingju_hotspot: bool = False, +) -> str: """调用卡若AI本地模型（Ollama），使用 chat 接口避免对话式误判""" import requests - prompt = _build_prompt(transcript, clip_count) + prompt = _build_prompt( + transcript, clip_count, min_dur, max_dur, ops_jingju_hotspot=ops_jingju_hotspot + ) system = ( "你是短视频策划师。用户会提供视频文字稿，你只输出一个 JSON 数组。" "若某片段内有人提问（观众/连麦者问的问题），必须提取提问原文填 question，且 hook_3sec 用该提问（前3秒先展示提问再回答）；无提问则 hook_3sec 用金句/悬念。" "格式含 title, start_time, end_time, hook_3sec, cta_ending, transcript_excerpt, reason；有提问时加 question。" - "禁止输出任何非 JSON 内容。" + "必须严格遵守用户给出的单段时长区间（秒）。禁止输出任何非 JSON 内容。" ) try: r = requests.post( @@ -348,14 +485,80 @@ def main(): parser = argparse.ArgumentParser(description="高光识别 - AI 分析文字稿输出 highlights.json") parser.add_argument("--transcript", "-t", required=True, help="transcript.srt 路径") parser.add_argument("--output", "-o", required=True, help="highlights.json 输出路径") - parser.add_argument("--clips", "-n", type=int, default=CLIP_COUNT, help="切片数量") + parser.add_argument("--clips", "-n", type=int, default=None, help="切片数量（默认随 preset）") + parser.add_argument( + "--preset", + choices=["long", "ops-short"], + default="long", + help="long=单场深度切片 60～300 秒；ops-short=运营短切片 15～30 秒×约24条，京剧梗+热点优先", + ) + parser.add_argument( + "--min-duration", + type=float, + default=None, + help="单段最小时长（秒），默认随 preset", + ) + parser.add_argument( + "--max-duration", + type=float, + default=None, + help="单段最大时长（秒），默认随 preset；长切片模式可不限制上限则传极大值", + ) + parser.add_argument( + "--ops-jingju-hotspot", + action="store_true", + help="在 prompt 中强调京剧比喻/唱腔梗 + 热点选题（可与 ops-short 同用）", + ) + parser.add_argument( + "--prompt-min-sec", + type=float, + default=None, + help="送模型的 SRT 从该秒之后截取；ops-short 默认 450（约7:30），long 默认 0", + ) parser.add_argument("--require-ai", action="store_true", help="必须用 AI 识别，失败则退出不兜底") args = parser.parse_args() + + if args.preset == "ops-short": + min_dur = float(args.min_duration if args.min_duration is not None else OPS_SHORT_MIN) + max_dur = float(args.max_duration if args.max_duration is not None else OPS_SHORT_MAX) + clip_n = int(args.clips if args.clips is not None else OPS_SHORT_CLIPS) + ops_focus = True # 运营短切片默认强调京剧梗+热点 + filter_max_sec = max_dur # 严格卡上限 + else: + min_dur = float(args.min_duration if args.min_duration is not None else MIN_DURATION) + max_dur = float(args.max_duration if args.max_duration is not None else MAX_DURATION) + clip_n = int(args.clips if args.clips is not None else CLIP_COUNT) + ops_focus = bool(args.ops_jingju_hotspot) + filter_max_sec = None # 与历史一致：只滤掉过短，不因略超 300 秒丢片 + prompt_min_sec = ( + float(args.prompt_min_sec) + if args.prompt_min_sec is not None + else ( + OPS_SHORT_PROMPT_MIN_SEC_DEFAULT + if args.preset == "ops-short" + else 0.0 + ) + ) transcript_path = Path(args.transcript) if not transcript_path.exists(): print(f"❌ 文字稿不存在: {transcript_path}", file=sys.stderr) sys.exit(1) - text = srt_to_timestamped_text(str(transcript_path)) + if prompt_min_sec > 0: + text = srt_text_from_min_sec(str(transcript_path), prompt_min_sec) + if len(text) < 400: + print( + "⚠️ 截断后文字稿过短，回退 srt_to_timestamped_text 全长", + file=sys.stderr, + ) + text = srt_to_timestamped_text(str(transcript_path)) + else: + print( + f" 运营短切片：送模型文字稿已从 {prompt_min_sec:.0f}s 之后截取（约 {len(text)} 字）", + flush=True, + ) + else: + text = srt_to_timestamped_text(str(transcript_path)) + fb_start = float(prompt_min_sec) if args.preset == "ops-short" else 0.0 if len(text) < 100: print("❌ 文字稿过短，请检查 SRT 格式", file=sys.stderr) sys.exit(1) @@ -366,13 +569,29 @@ def main(): for provider in api_queue: try: print(f"正在调用 API {provider.get('model', '?')} 分析高光片段...") - raw = call_openai_api(text, args.clips, provider) + raw = call_openai_api( + text, + clip_n, + provider, + min_dur=min_dur, + max_dur=max_dur, + ops_jingju_hotspot=ops_focus, + ) if not raw: raise ValueError("API 返回空") data = _parse_ai_json(raw) if data and isinstance(data, list) and len(data) > 0: - print(f" ✓ API ({provider.get('model', '?')}) 成功，识别 {len(data)} 段") - break + if args.preset == "ops-short" and not _ops_short_ai_plausible( + data, min_dur, max_dur, prompt_min_sec + ): + print( + " API 结果不符合运营短切片规则，丢弃并尝试下一通道", + file=sys.stderr, + ) + data = None + else: + print(f" ✓ API ({provider.get('model', '?')}) 成功，识别 {len(data)} 段") + break except Exception as e: print(f" API ({provider.get('model', '?')}) 失败: {e}", file=sys.stderr) if raw: @@ -388,13 +607,29 @@ def main(): for model in OLLAMA_MODELS: try: print(f"正在调用 Ollama {model} 分析高光片段...") - raw = call_ollama(text, args.clips, model) + raw = call_ollama( + text, + clip_n, + model, + min_dur=min_dur, + max_dur=max_dur, + ops_jingju_hotspot=ops_focus, + ) if not raw: raise ValueError("模型返回空") data = _parse_ai_json(raw) if data and isinstance(data, list) and len(data) > 0: - print(f" ✓ {model} 成功，识别 {len(data)} 段") - break + if args.preset == "ops-short" and not _ops_short_ai_plausible( + data, min_dur, max_dur, prompt_min_sec + ): + print( + f" {model} 结果不符合运营短切片规则，尝试下一模型", + file=sys.stderr, + ) + data = None + else: + print(f" ✓ {model} 成功，识别 {len(data)} 段") + break except Exception as e: print(f" {model} 失败: {e}", file=sys.stderr) if raw: @@ -405,14 +640,16 @@ def main(): print("❌ 必须用 AI 识别，当前无可用模型或解析失败", file=sys.stderr) sys.exit(1) print("使用规则备用切分", file=sys.stderr) - data = fallback_highlights(str(transcript_path), args.clips) + data = fallback_highlights( + str(transcript_path), clip_n, min_dur, max_dur, start_from_sec=fb_start + ) if not data: - data = fallback_highlights(str(transcript_path), args.clips) + data = fallback_highlights( + str(transcript_path), clip_n, min_dur, max_dur, start_from_sec=fb_start + ) if not isinstance(data, list): data = [data] - # 过滤短于 1 分钟的切片 - data = _filter_short_clips(data) - # 统一 start_time/end_time 为 HH:MM:SS（兼容 Ollama 返回秒数） + # 先统一时间为 HH:MM:SS，再按时长过滤（兼容模型返回数值秒） for item in data: if not isinstance(item, dict): continue @@ -422,10 +659,15 @@ def main(): et = item.get("end_time") or item.get("end") if isinstance(et, (int, float)): item["end_time"] = _sec_to_hhmmss(et) + if args.preset == "ops-short" and prompt_min_sec > 0: + data = _filter_start_not_before(data, prompt_min_sec) + data = _filter_clips_by_duration(data, min_dur, filter_max_sec) # 若 AI 返回的片段全被过滤，用规则备用 if not data and transcript_path.exists(): - print(" AI 片段时长无效，改用规则切分（1-5 分钟）", file=sys.stderr) - data = fallback_highlights(str(transcript_path), args.clips) + print(" AI 片段时长无效，改用规则切分", file=sys.stderr) + data = fallback_highlights( + str(transcript_path), clip_n, min_dur, max_dur, start_from_sec=fb_start + ) # 强制中文 print(" 确保导出名与封面为简体中文...") data = _ensure_chinese_highlights(data) diff --git a/03_卡木（木）/木叶_视频内容/视频切片/脚本/soul_enhance.py b/03_卡木（木）/木叶_视频内容/视频切片/脚本/soul_enhance.py index 8b2331d2..62e09c17 100644 --- a/03_卡木（木）/木叶_视频内容/视频切片/脚本/soul_enhance.py +++ b/03_卡木（木）/木叶_视频内容/视频切片/脚本/soul_enhance.py @@ -64,6 +64,12 @@ CROP_VF = "crop=568:1080:508:0,crop=498:1080:35:0" VERTICAL_W, VERTICAL_H = 498, 1080 OVERLAY_X = 543 # 508+35，与历史 483+60 对齐，避免封面/字幕错位 +# 竖屏「全画面入画」：不裁中间竖条；整幅横版等比缩放入 498×1080，上下黑边（letterbox） +VERTICAL_FIT_FULL_VF = ( + "scale=w=498:h=1080:force_original_aspect_ratio=decrease:flags=lanczos," + "pad=498:1080:(ow-iw)/2:(oh-ih)/2:color=black" +) + def _overlay_x_from_crop_vf(crop_vf: str): """从两段 crop 解析字幕/封面叠在横版上的 x：crop=W:1080:X:0,crop=498:1080:Y:0 → X+Y""" @@ -81,13 +87,14 @@ def build_typewriter_subtitle_images( temp_dir, out_w, out_h, - cover_duration, + subtitle_overlay_start, min_step_sec=0.05, max_steps_per_line=28, ): """ 将每条字幕拆成多帧：同一时间段内前缀逐字（逐段）变长，读起来更顺、更像跟读语音。 长句按步数上限均分字符，避免单条 concat 段过多。 + subtitle_overlay_start：最早显示字幕的时间轴（秒），须 ≥ 封面结束 + 留白。 """ sub_images = [] img_idx = 0 @@ -96,7 +103,7 @@ def build_typewriter_subtitle_images( if not safe_text or not safe_text.strip(): continue s, e = float(sub["start"]), float(sub["end"]) - s = max(s, cover_duration) + s = max(s, subtitle_overlay_start) if s >= e - 0.02: continue dur = e - s @@ -299,6 +306,11 @@ STYLE = { } } +# 字幕与语音同步的全局延迟补偿（秒）；封面后留白再叠字幕；封面标题汉字上限（须在本文件先于 _limit_cover_title_cjk 定义） +SUBTITLE_DELAY_SEC = 2.0 +SUBS_START_AFTER_COVER_SEC = 3.0 +COVER_TITLE_MAX_CJK = 6 + # ============ 工具函数 ============ def get_font(font_path, size): @@ -339,6 +351,21 @@ def _normalize_title_for_display(title: str) -> str: return s +def _limit_cover_title_cjk(text: str, max_cjk: int = COVER_TITLE_MAX_CJK) -> str: + """封面标题最多保留 max_cjk 个汉字（含汉字即计数）；超长截断，避免封面字过小或换行过多。""" + if not text or max_cjk <= 0: + return text or "" + out = [] + n_cjk = 0 + for ch in text: + if "\u4e00" <= ch <= "\u9fff": + n_cjk += 1 + if n_cjk > max_cjk: + break + out.append(ch) + return "".join(out).strip() + + # macOS/APFS 文件名允许的中文标点（保留刺激性标题所需的标点） _SAFE_CJK_PUNCT = set("，。？！；：·、…（）【】「」《》～—·+") @@ -449,14 +476,6 @@ def _detect_clip_pts_offset(clip_path: str) -> float: return 0.0 -# 字幕与语音同步的全局延迟补偿（秒） -# batch_clip -ss input seeking 导致实际切割比请求早 0~3 秒（关键帧对齐） -# 字幕按 highlights.start_time 算相对时间，会比实际音频提前 -# 加正值延迟 = 字幕往后推 = 与声音更同步 -# 2025-03 实测：Soul派对直播视频关键帧间距 2-4 秒，补偿需约 2.0s -SUBTITLE_DELAY_SEC = 2.0 # 增大到 2.0，避免字幕超前于说话 - - def _is_noise_line(text: str) -> bool: """检测是否为噪声行（单字母、重复符号、ASR幻觉等）""" if not text: @@ -625,9 +644,9 @@ def _sec_to_srt_time(sec): return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}" -def write_clip_srt(srt_path, subtitles, cover_duration): - """写出用于烧录的 SRT（仅保留封面结束后的字幕，时间已相对片段）""" - safe_start = cover_duration + 0.3 +def write_clip_srt(srt_path, subtitles, cover_duration, subs_after_cover_sec=SUBS_START_AFTER_COVER_SEC): + """写出用于烧录的 SRT（仅保留封面结束+留白后的字幕，时间已相对片段）""" + safe_start = cover_duration + subs_after_cover_sec + 0.3 lines = [] idx = 1 for sub in subtitles: @@ -726,8 +745,11 @@ def get_video_info(video_path): '-of', 'json', video_path ] result = subprocess.run(cmd, capture_output=True, text=True) - info = json.loads(result.stdout) - stream = info['streams'][0] + info = json.loads(result.stdout or "{}") + streams = info.get("streams") or [] + if not streams: + raise ValueError(f"ffprobe 无视频流: {video_path}") + stream = streams[0] # 获取时长 cmd2 = [ @@ -909,31 +931,39 @@ def create_cover_image(hook_text, width, height, output_path, video_path=None): # ============ 字幕图片生成 ============ def create_subtitle_image(text, width, height, output_path): - """创建字幕图片（关键词加粗加大突出）。竖屏 498 宽时字号略小、保证居中且不溢出。""" + """创建字幕图片（关键词加粗加大突出）。498 竖条时居中；全幅横版时偏下居中（为 --vertical-fit-full）。""" style = STYLE['subtitle'] img = Image.new('RGBA', (width, height), (0, 0, 0, 0)) draw = ImageDraw.Draw(img) - # 竖屏窄幅时缩小字号，保证整行在画面内且居中 base_size = style['font_size'] if (width, height) == (VERTICAL_W, VERTICAL_H): base_size = min(base_size, 38) + elif height == 1080 and width >= 1280: + # 1920×1080 全画面叠字：字号略大，条带靠下，避免挡脸 + base_size = min(max(base_size, 46), 56) font = get_font(FONT_BOLD, base_size) text_w, text_h = get_text_size(draw, text, font) - while text_w > width - 80 and base_size > 24: + margin_x = 120 if width >= 1280 else 80 + while text_w > width - margin_x and base_size > 24: base_size -= 2 font = get_font(FONT_BOLD, base_size) text_w, text_h = get_text_size(draw, text, font) kw_size = base_size + style.get('keyword_size_add', 4) kw_font = get_font(FONT_HEAVY, kw_size) - # 字幕完全居中（水平+垂直正中间）；竖屏时限制在界面内不超出 base_x = (width - text_w) // 2 if (width, height) == (VERTICAL_W, VERTICAL_H): pad = 24 base_x = max(pad, min(width - pad - text_w, base_x)) - base_y = (height - text_h) // 2 + base_y = (height - text_h) // 2 + elif height == 1080 and width >= 1280: + pad = 40 + base_x = max(pad, min(width - pad - text_w, base_x)) + base_y = height - text_h - 100 + else: + base_y = (height - text_h) // 2 # 背景条（不超出画布） padding = 15 @@ -1085,10 +1115,11 @@ def _parse_clip_index(filename: str) -> int: def enhance_clip(clip_path, output_path, highlight_info, temp_dir, transcript_path, force_burn_subs=False, skip_subs=False, vertical=False, - crop_vf=None, overlay_x=None, typewriter_subs=False): - """增强单个切片。vertical=True 时最后裁成竖屏 498x1080 直出成片。 - crop_vf / overlay_x：场次取景微调（先截 20% 帧对一下小程序黑框再填）。 - typewriter_subs：同一条字幕时间内前缀逐字渐显（更跟口型）。 + crop_vf=None, overlay_x=None, typewriter_subs=False, + vertical_fit_full=False): + """增强单个切片。vertical=True 时最后输出 498×1080。 + vertical_fit_full：不裁中间竖条；整幅画面等比缩放入 498×1080 + 上下黑边，前后内容都可见。 + 否则沿用 crop 竖条（全画面标定深色带）。 """ print(f" 输入: {os.path.basename(clip_path)}", flush=True) @@ -1108,17 +1139,28 @@ def enhance_clip(clip_path, output_path, highlight_info, temp_dir, transcript_pa hook_text = _normalize_title_for_display(raw_title) or raw_title or '精彩切片' # 封面文字同样做安全处理 hook_text = apply_platform_safety(hook_text) + hook_text = _limit_cover_title_cjk(hook_text, COVER_TITLE_MAX_CJK) or hook_text cover_duration = STYLE['cover']['duration'] + subtitle_overlay_start = cover_duration + SUBS_START_AFTER_COVER_SEC - # 竖屏成片：封面/字幕按 498x1080 做，叠在裁切区域，文字与字幕在竖屏上完整且居中 - out_w, out_h = (VERTICAL_W, VERTICAL_H) if vertical else (width, height) - vf_use = (crop_vf or CROP_VF).strip() if vertical else CROP_VF - ox = overlay_x - if vertical and ox is None and crop_vf: - ox = _overlay_x_from_crop_vf(crop_vf) - if vertical and ox is None: - ox = OVERLAY_X - overlay_pos = f"{int(ox)}:0" if vertical else "0:0" + # 竖屏：默认封面/字幕按 498×1080 叠在竖条上；全画面模式按原分辨率全屏叠加再整体缩放 + if vertical and vertical_fit_full: + out_w, out_h = width, height + vf_use = "" + overlay_pos = "0:0" + elif vertical: + out_w, out_h = VERTICAL_W, VERTICAL_H + vf_use = (crop_vf or CROP_VF).strip() + ox = overlay_x + if ox is None and crop_vf: + ox = _overlay_x_from_crop_vf(crop_vf) + if ox is None: + ox = OVERLAY_X + overlay_pos = f"{int(ox)}:0" + else: + out_w, out_h = width, height + vf_use = CROP_VF + overlay_pos = "0:0" # 1. 生成封面 print(f" [1/5] 封面生成中…", flush=True) @@ -1198,7 +1240,7 @@ def enhance_clip(clip_path, output_path, highlight_info, temp_dir, transcript_pa print(f" ✓ 字幕解析 ({len(subtitles)}条)，将烧录为{mode}字幕", flush=True) if typewriter_subs: sub_images = build_typewriter_subtitle_images( - subtitles, temp_dir, out_w, out_h, cover_duration + subtitles, temp_dir, out_w, out_h, subtitle_overlay_start ) else: for i, sub in enumerate(subtitles): @@ -1254,10 +1296,10 @@ def enhance_clip(clip_path, output_path, highlight_info, temp_dir, transcript_pa # duration X.XXX # 最后一行不写 duration（用于循环/截断防报错） concat_lines = [] - prev_end = cover_duration # 字幕从封面结束后开始 + prev_end = subtitle_overlay_start # 字幕从「封面结束 + SUBS_START_AFTER_COVER_SEC」起 for img in sub_images: - sub_start = max(img['start'], cover_duration) + sub_start = max(img['start'], subtitle_overlay_start) sub_end = img['end'] if sub_start >= sub_end: continue @@ -1327,15 +1369,26 @@ def enhance_clip(clip_path, output_path, highlight_info, temp_dir, transcript_pa if result.stderr: print(f" {str(result.stderr)[:300]}", file=sys.stderr) - # 5.4 输出：竖屏则裁成 498x1080 直出（高光区域裁剪，成片必做） - print(f" [5/5] 竖屏裁剪中（498×1080）…", flush=True) - if vertical: + # 5.4 输出：竖屏 498×1080（竖条裁剪 或 全画面 letterbox） + print(f" [5/5] 竖屏输出（498×1080）…", flush=True) + if vertical and vertical_fit_full: + r = subprocess.run([ + 'ffmpeg', '-y', '-i', current_video, + '-vf', VERTICAL_FIT_FULL_VF, '-c:a', 'copy', output_path + ], capture_output=True, text=True) + if r.returncode == 0 and os.path.exists(output_path): + print(f" ✓ 全画面缩放+上下黑边完成（未裁中间竖条）", flush=True) + else: + print(f" ❌ 全画面缩放失败: {(r.stderr or '')[:400]}", file=sys.stderr) + shutil.copy(current_video, output_path) + print(f" ⚠ 已回退为未缩放版本", flush=True) + elif vertical: r = subprocess.run([ 'ffmpeg', '-y', '-i', current_video, '-vf', vf_use, '-c:a', 'copy', output_path ], capture_output=True, text=True) if r.returncode == 0 and os.path.exists(output_path): - print(f" ✓ 竖屏裁剪完成", flush=True) + print(f" ✓ 竖屏竖条裁剪完成", flush=True) else: print(f" ❌ 竖屏裁剪失败: {(r.stderr or '')[:300]}", file=sys.stderr) shutil.copy(current_video, output_path) @@ -1378,6 +1431,11 @@ def main(): action="store_true", help="字幕在同一条时间内前缀逐字渐显（更通顺、更跟读）", ) + parser.add_argument( + "--vertical-fit-full", + action="store_true", + help="竖屏成片不裁中间竖条：整幅 16:9 等比缩放入 498×1080，上下黑边，画面显示全；封面/字幕先叠满横版再缩放", + ) args = parser.parse_args() clips_dir = Path(args.clips) if args.clips else CLIPS_DIR @@ -1404,12 +1462,14 @@ def main(): overlay_x_arg = getattr(args, "overlay_x", -1) overlay_x_arg = None if overlay_x_arg < 0 else overlay_x_arg typewriter = getattr(args, "typewriter_subs", False) + vfit = getattr(args, "vertical_fit_full", False) print( f"功能: 封面+字幕+加速10%+去语气词" + ("+竖屏498x1080" if vertical else "") + + ("+全画面letterbox(不裁竖条)" if vertical and vfit else "") + ("+逐字字幕" if typewriter else "") ) - if vertical and crop_vf_arg: + if vertical and crop_vf_arg and not vfit: print(f"取景: --crop-vf {crop_vf_arg}") print(f"输入: {clips_dir}") print(f"输出: {output_dir}" + ("（成片，文件名=标题）" if title_only else "")) @@ -1439,6 +1499,9 @@ def main(): if getattr(args, 'title_only', False): title = (highlight_info.get('title') or highlight_info.get('hook_3sec') or clip_path.stem) + title = _limit_cover_title_cjk( + _normalize_title_for_display(str(title)) or str(title), COVER_TITLE_MAX_CJK + ) or str(title) name = sanitize_filename(title) + '.mp4' output_path = output_dir / name else: @@ -1458,6 +1521,7 @@ def main(): crop_vf=crop_vf_arg or None, overlay_x=overlay_x_arg, typewriter_subs=typewriter, + vertical_fit_full=vfit, ): success_count += 1 finally: diff --git a/03_卡木（木）/木叶_视频内容/视频切片/脚本/soul_slice_pipeline.py b/03_卡木（木）/木叶_视频内容/视频切片/脚本/soul_slice_pipeline.py index 8060f7b6..8ba556e0 100644 --- a/03_卡木（木）/木叶_视频内容/视频切片/脚本/soul_slice_pipeline.py +++ b/03_卡木（木）/木叶_视频内容/视频切片/脚本/soul_slice_pipeline.py @@ -105,7 +105,31 @@ def main(): parser = argparse.ArgumentParser(description="Soul 切片一体化流水线") parser.add_argument("--video", "-v", required=True, help="输入视频路径") parser.add_argument("--output", "-o", help="输出目录（默认：视频同目录下 视频名_output）") - parser.add_argument("--clips", "-n", type=int, default=8, help="切片数量") + parser.add_argument("--clips", "-n", type=int, default=8, help="切片数量（--ops-short 且未改 -n 时默认 24）") + parser.add_argument( + "--highlight-preset", + choices=["long", "ops-short"], + default="long", + help="高光识别 preset：long=60～300 秒深度切片；ops-short=15～30 秒运营密度切片", + ) + parser.add_argument( + "--ops-short", + action="store_true", + help="运营短切片一键：preset=ops-short，未指定 -n 时用 24 条", + ) + parser.add_argument("--min-clip-sec", type=float, default=None, help="传给 identify_highlights --min-duration") + parser.add_argument("--max-clip-sec", type=float, default=None, help="传给 identify_highlights --max-duration") + parser.add_argument( + "--ops-jingju-hotspot", + action="store_true", + help="高光 prompt 强调京剧梗+热点（可与 ops-short 同用）", + ) + parser.add_argument( + "--prompt-min-sec", + type=float, + default=None, + help="传给 identify_highlights：送模型的 SRT 从该秒之后截取（ops-short 默认 450）", + ) parser.add_argument("--skip-transcribe", action="store_true", help="跳过转录（已有 transcript.srt）") parser.add_argument("--skip-highlights", action="store_true", help="跳过高光识别（已有 highlights.json）") parser.add_argument("--skip-clips", action="store_true", help="跳过切片（已有 clips/，仅重新增强）") @@ -122,6 +146,11 @@ def main(): parser.add_argument("--montage-seconds", type=float, default=4.0, help="快速混剪每条截取秒数") args = parser.parse_args() + if getattr(args, "ops_short", False): + args.highlight_preset = "ops-short" + if args.clips == 8: + args.clips = 24 + video_path = Path(args.video).resolve() if not video_path.exists(): print(f"❌ 视频不存在: {video_path}") @@ -148,7 +177,7 @@ def main(): print("=" * 60) print(f"输入视频: {video_path}") print(f"输出目录: {base_dir}") - print(f"切片数量: {args.clips}") + print(f"切片数量: {args.clips} ｜ 高光 preset: {args.highlight_preset}") print("=" * 60) # 0. 强制重转录时删除旧产物（含 audio 以重提完整音频） @@ -201,16 +230,26 @@ def main(): # 2. 高光识别 if not args.skip_highlights: + hl_cmd = [ + sys.executable, + str(SCRIPT_DIR / "identify_highlights.py"), + "--transcript", str(transcript_path), + "--output", str(highlights_path), + "--clips", str(args.clips), + "--preset", str(args.highlight_preset), + ] + if args.min_clip_sec is not None: + hl_cmd.extend(["--min-duration", str(args.min_clip_sec)]) + if args.max_clip_sec is not None: + hl_cmd.extend(["--max-duration", str(args.max_clip_sec)]) + if getattr(args, "ops_jingju_hotspot", False): + hl_cmd.append("--ops-jingju-hotspot") + if getattr(args, "prompt_min_sec", None) is not None: + hl_cmd.extend(["--prompt-min-sec", str(args.prompt_min_sec)]) run( - [ - sys.executable, - str(SCRIPT_DIR / "identify_highlights.py"), - "--transcript", str(transcript_path), - "--output", str(highlights_path), - "--clips", str(args.clips), - ], - "高光识别（Ollama→规则）", - timeout=180, + hl_cmd, + "高光识别（API→Ollama→规则）", + timeout=600, ) if not highlights_path.exists(): print(f"❌ 需要 highlights.json: {highlights_path}") @@ -283,7 +322,11 @@ def main(): ] if getattr(args, "skip_subs", False): enhance_cmd.append("--skip-subs") - if getattr(args, "force_burn_subs", False): + if use_two_folders: + enhance_cmd.extend(["--vertical", "--title-only"]) + if (getattr(args, "force_burn_subs", False) or use_two_folders) and not getattr( + args, "skip_subs", False + ): enhance_cmd.append("--force-burn-subs") enhance_timeout = max(900, 600 + len(clips_list) * 90) # 约 90 秒/片 ok = run(enhance_cmd, "增强处理（封面+字幕+加速）", timeout=enhance_timeout, check=False) diff --git a/SKILL_REGISTRY.md b/SKILL_REGISTRY.md index c6b9b34f..fa67eda0 100644 --- a/SKILL_REGISTRY.md +++ b/SKILL_REGISTRY.md @@ -104,7 +104,7 @@ | # | 技能 | 成员 | 触发词 | SKILL 路径 | 一句话 | |:--|:---|:---|:---|:---|:---| -| M01 | 视频切片 | 木叶 | **视频剪辑、切片发布、切片动效包装、程序化包装、片头片尾、批量封面、视频包装** | `03_卡木（木）/木叶_视频内容/视频切片/SKILL.md` | 长视频切片+字幕+发布；联动切片动效包装（片头/片尾/程序化） | +| M01 | 视频切片 | 木叶 | **视频剪辑、切片发布、切片动效包装、程序化包装、片头片尾、批量封面、视频包装、运营短切片、15秒切片、热点密度、京剧梗** | `03_卡木（木）/木叶_视频内容/视频切片/SKILL.md` | 长视频切片+字幕+发布；**ops-short 15～30 秒×20～30 条**；联动切片动效包装（片头/片尾/程序化） | | M01b | 抖音视频解析 | 木叶 | **抖音视频、抖音链接、抖音解析、抖音下载、提取抖音文案、抖音无水印** | `03_卡木（木）/木叶_视频内容/抖音视频解析/SKILL.md` | 链接→解析ID→提取文案→下载无水印视频 | | M01c | 抖音发布 | 木叶 | **抖音发布、发布到抖音、抖音登录、抖音上传、腕推抖音** | `03_卡木（木）/木叶_视频内容/抖音发布/SKILL.md` | 纯 API 视频上传+发布（VOD + bd-ticket-guard），无需浏览器 | | M01d | B站发布 | 木叶 | **B站发布、发布到B站、B站登录、B站上传、bilibili发布** | `03_卡木（木）/木叶_视频内容/B站发布/SKILL.md` | 纯 API（preupload 分片），Cookie 有效期约6个月 | diff --git a/运营中枢/工作台/gitea_push_log.md b/运营中枢/工作台/gitea_push_log.md index 219b5002..52f8e9a6 100644 --- a/运营中枢/工作台/gitea_push_log.md +++ b/运营中枢/工作台/gitea_push_log.md @@ -403,3 +403,4 @@ | 2026-03-20 11:27:30 | 🔄 卡若AI 同步 2026-03-20 11:27 | 更新：水溪整理归档、运营中枢工作台 | 排除 >20MB: 11 个 | | 2026-03-20 12:22:19 | 🔄 卡若AI 同步 2026-03-20 12:22 | 更新：水桥平台对接、运营中枢工作台 | 排除 >20MB: 11 个 | | 2026-03-20 13:39:57 | 🔄 卡若AI 同步 2026-03-20 12:40 | 更新：水桥平台对接 | 排除 >20MB: 11 个 | +| 2026-03-20 16:08:50 | 🔄 卡若AI 同步 2026-03-20 16:08 | 更新：水桥平台对接、卡木、运营中枢工作台 | 排除 >20MB: 11 个 | diff --git a/运营中枢/工作台/scripts/s2_admin_fullpage_capture.py b/运营中枢/工作台/scripts/s2_admin_fullpage_capture.py new file mode 100644 index 00000000..8cb26411 --- /dev/null +++ b/运营中枢/工作台/scripts/s2_admin_fullpage_capture.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +""" +S2 私域管理后台：按路由批量全页截图（Playwright） + +用法（需已登录态，任选其一）： + 1) 先在本机 Chrome 登录后台，再用「用户数据目录」启动浏览器： + python3 s2_admin_fullpage_capture.py --user-data-dir "$HOME/Library/Application Support/Google/Chrome" --channel chromium --headless + （无 Google Chrome.app 时用 channel chromium；有则用 --channel chrome。建议先关 Chrome 避免配置锁。） + + 2) 或使用已导出的 storage_state.json： + playwright install chromium + python3 s2_admin_fullpage_capture.py --storage-state /path/to/state.json + +默认输出：卡若Ai 报告目录下 screenshots/fullpage_playwright/ +""" +from __future__ import annotations + +import argparse +import asyncio +import re +from pathlib import Path + +import httpx + +BASE = "https://s2.siyuguanli.com/admin/static/js/app.6dd8bb884b28919ef0f9.js" +ADMIN = "https://s2.siyuguanli.com/admin/#" + +DEST_DEFAULT = Path( + "/Users/karuo/Documents/卡若Ai的文件夹/报告/S2私域管理后台_功能与接口调研/screenshots/fullpage_playwright" +) + + +def parse_routes_from_app_js(text: str) -> list[str]: + paths = re.findall(r'path:"([^"]+)"', text) + stack: list[str] = [] + out: list[str] = [] + for p in paths: + if p in ("*", "/401", "/404", "/login", "/authredirect", "/"): + continue + if p.startswith("/"): + stack = [p.rstrip("/")] + continue + if not stack: + stack = [""] + segs = [s for s in stack if s] + full = "/" + "/".join(segs + [p]) + full = re.sub(r"/+", "/", full) + out.append(full) + seen: set[str] = set() + uniq: list[str] = [] + for x in out: + if x not in seen: + seen.add(x) + uniq.append(x) + uniq = ["/home" if x == "/home/home" else x for x in uniq] + if "/home" not in uniq: + uniq.insert(0, "/home") + # 线上大屏常见 hash 为 #/datav/overview，与 bundle 内 /dataReport/datav/overview 并存 + if "/datav/overview" not in uniq: + uniq.append("/datav/overview") + return uniq + + +async def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--dest", type=Path, default=DEST_DEFAULT) + ap.add_argument("--storage-state", type=Path, default=None) + ap.add_argument("--user-data-dir", type=Path, default=None) + ap.add_argument("--channel", default="chrome", help="与 user-data-dir 联用：chrome | chromium") + ap.add_argument("--wait-ms", type=int, default=2500) + ap.add_argument("--max", type=int, default=0, help="仅截取前 N 条路由，0 表示全量") + ap.add_argument( + "--headless", + action="store_true", + help="与 --user-data-dir 联用：无界面跑持久化上下文（需本机已登录该 Profile）", + ) + ap.add_argument( + "--no-escape", + action="store_true", + help="不在每页加载后按 Escape（默认按一次以尝试关闭 v-modal 遮罩）", + ) + args = ap.parse_args() + + from playwright.async_api import async_playwright + + async with httpx.AsyncClient(verify=False, timeout=60) as client: + js = (await client.get(BASE)).text + routes = parse_routes_from_app_js(js) + if args.max and args.max > 0: + routes = routes[: args.max] + + args.dest.mkdir(parents=True, exist_ok=True) + + async with async_playwright() as p: + if args.user_data_dir: + browser = await p.chromium.launch_persistent_context( + user_data_dir=str(args.user_data_dir), + channel=args.channel if args.channel in ("chrome", "msedge") else None, + headless=args.headless, + viewport={"width": 1440, "height": 900}, + ) + page = browser.pages[0] if browser.pages else await browser.new_page() + else: + browser = await p.chromium.launch(headless=True) + context = await browser.new_context( + viewport={"width": 1440, "height": 900}, + storage_state=str(args.storage_state) if args.storage_state else None, + ) + page = await context.new_page() + + for i, route in enumerate(routes, 1): + url = f"{ADMIN}{route}" + name = route.strip("/").replace("/", "__") or "home" + fp = args.dest / f"{i:03d}_{name}.png" + try: + await page.goto(url, wait_until="networkidle", timeout=120000) + except Exception: + await page.goto(url, wait_until="domcontentloaded", timeout=120000) + await page.wait_for_timeout(args.wait_ms) + if not args.no_escape: + await page.keyboard.press("Escape") + await page.wait_for_timeout(300) + await page.screenshot(path=str(fp), full_page=True) + print(f"OK {i}/{len(routes)} {fp.name}") + + if args.user_data_dir: + await browser.close() + else: + await browser.close() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/运营中枢/工作台/代码管理.md b/运营中枢/工作台/代码管理.md index d88f6a2e..1426a68a 100644 --- a/运营中枢/工作台/代码管理.md +++ b/运营中枢/工作台/代码管理.md @@ -406,3 +406,4 @@ | 2026-03-20 11:27:30 | 成功 | 成功 | 🔄 卡若AI 同步 2026-03-20 11:27 | 更新：水溪整理归档、运营中枢工作台 | 排除 >20MB: 11 个 | [仓库](http://open.quwanzhi.com:3000/fnvtk/karuo-ai) [百科](http://open.quwanzhi.com:3000/fnvtk/karuo-ai/wiki) | | 2026-03-20 12:22:19 | 成功 | 成功 | 🔄 卡若AI 同步 2026-03-20 12:22 | 更新：水桥平台对接、运营中枢工作台 | 排除 >20MB: 11 个 | [仓库](http://open.quwanzhi.com:3000/fnvtk/karuo-ai) [百科](http://open.quwanzhi.com:3000/fnvtk/karuo-ai/wiki) | | 2026-03-20 13:39:57 | 成功 | 成功 | 🔄 卡若AI 同步 2026-03-20 12:40 | 更新：水桥平台对接 | 排除 >20MB: 11 个 | [仓库](http://open.quwanzhi.com:3000/fnvtk/karuo-ai) [百科](http://open.quwanzhi.com:3000/fnvtk/karuo-ai/wiki) | +| 2026-03-20 16:08:50 | 成功 | 成功 | 🔄 卡若AI 同步 2026-03-20 16:08 | 更新：水桥平台对接、卡木、运营中枢工作台 | 排除 >20MB: 11 个 | [仓库](http://open.quwanzhi.com:3000/fnvtk/karuo-ai) [百科](http://open.quwanzhi.com:3000/fnvtk/karuo-ai/wiki) |