Install
openclaw skills install @riclinccc/video-clip-editorclip-editor
Video clip editing skill for automatically analyzing video content and generating CapCut draft templates. Uses local Whisper for speech transcription, Qwen-VL-Plus for visual scene description, and edge-tts for narration audio. Intelligently splits by dialogue/keywords/highlights, applies koubo jump-cut editing, and outputs draft_content.json ready to import into CapCut. Use this skill whenever the user mentions: video editing, clip, CapCut draft, highlight extraction, subtitle editing, keyword clipping, movie clip, narration, scene description, or uploads a video and asks to edit it.
Video Clip Editor Skill
Strict Output Contract
Every run MUST produce exactly these 4 files in video_clipper_output/:
video_clipper_output/
├── narration_XXX.mp3 ← 配音音频，每段独立文件，时间戳对齐
├── subtitles.srt ← 字幕，时间轴与 mp3 严格对应
├── clip_XXX.mp4 ← 视频切片，每段独立文件
└── draft_content.json ← 剪映草稿文件
不允许缺少任何一个。每个 mp3/mp4 的时间轴必须与 srt 中对应条目精确匹配。
Two Operating Modes
Mode A — Movie Narration (电影解说)
User provides movie name + video file → LLM generates cinematic narration.
Mode B — Interview / Dialogue (剪口播)
User provides video with real speech → Whisper transcribes → koubo jump-cut editing.
Overall Workflow
[Step 0] Collect inputs (movie name / characters / voice)
↓
[Step 1] ffprobe → original aspect ratio + video duration
↓
[Step 2] Mode A: search movie plot online → LLM generates narration script
Mode B: Whisper transcription → koubo clip analysis
↓
[Step 3] Show clip plan → wait for user approval
↓
[Step 4] Generate narration_XXX.mp3 (one per clip, edge-tts)
Measure ACTUAL mp3 duration via ffprobe
↓
[Step 5] Generate clip_XXX.mp4 (one per clip, ffmpeg)
clip duration = mp3 duration (exact match)
↓
[Step 6] Generate subtitles.srt (timestamps from mp3 durations)
↓
[Step 7] Generate draft_content.json (JianYing/CapCut importable)
↓
[Step 8] Show final output message
Step 0: Collect Inputs
Ask before starting:
Mode A:
"请提供：① 电影名称 ② 视频文件完整路径 ③ 片中主要角色（如：主角陆永瑜，反派陆金强） ④ 旁白语音（默认：zh-CN-XiaoxiaoNeural）"
Mode B:
"请提供：① 视频文件路径 ② 说话人名称（如：主持人Alex，嘉宾Sarah，可跳过） ③ 旁白语音（默认：zh-CN-XiaoxiaoNeural）"
movie_name = "误判"
video_path = "C:\\Movies\\误判.mp4"
characters = [{"name": "陆永瑜", "role": "主角"}, {"name": "陆金强", "role": "反派"}]
tts_voice = "zh-CN-XiaoxiaoNeural"
output_dir = "video_clipper_output"
Project Name Conflict Detection (草稿名冲突处理)
Before generating any files, check if a project with the same name already exists
in the JianYing draft directory. If it does, append (1), (2), (3) etc. to avoid overwriting.
import os, re
JIANYING_DRAFT_DIRS = [
# Windows
os.path.expandvars(r"%LOCALAPPDATA%\JianyingPro\User Data\Projects\com.lveditor.draft"),
# macOS
os.path.expanduser("~/Movies/JianyingPro/User Data/Projects/com.lveditor.draft"),
]
def resolve_project_name(base_name: str) -> str:
"""
Check JianYing draft directory for name conflicts.
Returns base_name if no conflict, or base_name(1) / base_name(2) / ... if taken.
"""
draft_dir = next((d for d in JIANYING_DRAFT_DIRS if os.path.isdir(d)), None)
if not draft_dir:
return base_name # can't check, proceed with original name
existing = set(os.listdir(draft_dir))
if base_name not in existing:
return base_name
# Find next available suffix
i = 1
while f"{base_name}({i})" in existing:
i += 1
resolved = f"{base_name}({i})"
# Notify user
print(f"⚠️ 草稿名称「{base_name}」已存在，自动重命名为「{resolved}」以避免覆盖。")
return resolved
# Call at the start, before generating any files
project_name = resolve_project_name(movie_name)
output_dir = f"video_clipper_output_{project_name}"
Tell the user before proceeding:
⚠️ 检测到剪映草稿「误判」已存在
本次项目将命名为「误判(1)」，输出到 video_clipper_output_误判(1)/
继续？(是 / 自定义名称)
Step 1: Probe Video + Calculate Narration Budget
# Get width, height, duration
ffprobe -v error -select_streams v:0 \
-show_entries stream=width,height,duration \
-of json <video_path>
Store: original_width, original_height, video_duration_seconds.
Narration Word Budget (字数预算)
Core rule: 1 minute of output video = 200 Chinese characters of narration
CHARS_PER_MINUTE = 200 # 1 min output = 200 chars
TARGET_DURATION_S = 60 # target output: ~60 seconds
DURATION_RANGE = (45, 75) # acceptable range: 45s – 75s
def calculate_narration_budget(video_duration_seconds: float) -> dict:
"""
Calculate how many characters and segments to generate.
Output video is always capped to ~60 seconds (±15s), regardless of source length.
"""
target_chars = CHARS_PER_MINUTE # 200 chars → ~60s output
min_chars = int(CHARS_PER_MINUTE * DURATION_RANGE[0] / 60) # 150 chars → 45s
max_chars = int(CHARS_PER_MINUTE * DURATION_RANGE[1] / 60) # 250 chars → 75s
# Typical segment: 25 chars → ~7.5s each
ideal_segment_chars = 25
ideal_segment_count = round(target_chars / ideal_segment_chars) # ~8 segments
return {
"target_chars": target_chars, # 200
"min_chars": min_chars, # 150
"max_chars": max_chars, # 250
"ideal_segment_count": ideal_segment_count, # ~8
"chars_per_segment": ideal_segment_chars, # 25
"target_output_secs": TARGET_DURATION_S, # 60
"source_duration_secs": video_duration_seconds
}
budget = calculate_narration_budget(video_duration_seconds)
Tell user the budget before searching:
📐 旁白字数预算
源视频时长 ：X 分 X 秒
目标输出时长：约 60 秒（可接受范围：45–75 秒）
旁白总字数 ：约 200 字（150–250 字区间）
预计片段数 ：约 8 段，每段约 25 字
Enforcement during narration generation:
- Each segment: aim for
chars_per_segment(25 chars), hard max 35 chars - After all segments written: count total chars
- < 150 chars → expand existing segments or add 1–2 more
- > 250 chars → trim or merge lowest-importance segments
- Final output video duration MUST land in 45–75s range
Step 2: Build Clip List
Mode A — Online Search for Precise Scene Timestamps
Search strategy — run ALL queries, then synthesize:
def build_search_queries(movie_name: str) -> list:
return [
f"{movie_name} 剧情详解 起因发展高潮结局 时间点",
f"{movie_name} 电影解说 精彩片段时间轴 几分几秒",
f"{movie_name} 剧情时间线 关键场景 时间戳",
f"{movie_name} plot breakdown scene timestamps minutes",
f"{movie_name} 完整剧情 开头结尾 高潮在第几分钟",
]
Parse search results into events with PRECISE timestamps:
Each event MUST have a specific video_timestamp (not a vague range).
If search results give ranges like "第30–40分钟", take the midpoint (35:00).
If no timestamp is found for an event, mark timestamp_confidence: "estimated".
[
{
"event_id": "evt_001",
"stage": "起因",
"video_timestamp": "00:03:20",
"video_seconds": 200,
"timestamp_confidence":"exact",
"description": "陆永瑜在父亲书房发现账本，意识到父亲长期贪腐"
},
{
"event_id": "evt_002",
"stage": "发展",
"video_timestamp": "00:18:45",
"video_seconds": 1125,
"timestamp_confidence":"exact",
"description": "陆金强约见陆永瑜，暗示他撤回举报，遭到拒绝"
},
{
"event_id": "evt_003",
"stage": "发展",
"video_timestamp": "00:35:10",
"video_seconds": 2110,
"timestamp_confidence":"estimated",
"description": "律师张远接手案件，发现监控录像有剪辑痕迹"
},
{
"event_id": "evt_004",
"stage": "高潮",
"video_timestamp": "01:12:30",
"video_seconds": 4350,
"timestamp_confidence":"exact",
"description": "庭审现场，张远当庭播放未剪辑原版监控，陆金强伪证当场崩塌"
},
{
"event_id": "evt_005",
"stage": "结局",
"video_timestamp": "01:28:00",
"video_seconds": 5280,
"timestamp_confidence":"exact",
"description": "陆永瑜无罪释放，走出法院，与家人重逢"
}
]
Segment count must match budget["ideal_segment_count"] (≈8).
If search only returns 4–5 events, expand by splitting long stages into sub-events.
If search returns 15+ events, merge adjacent same-stage events.
Each clip duration = narration TTS duration (measured AFTER TTS generation in Step 4). Source video timestamp marks WHERE to cut, not HOW LONG to hold.
Mode A — Narration Script via LLM
Generate narration for EACH event. Total char count must stay within budget (150–250 chars). Then call LLM for each event:
LLM Narration Prompt:
你是专业电影解说文案写手，风格参考"木鱼水心"。
电影：{movie_name}
角色：{characters}
事件：{event_description}
阶段：{stage}（起因/发展/高潮/结局）
上段旁白：{prev_narration}
本段目标字数：{target_chars_this_segment} 字（全片总字数预算：{total_budget} 字，已用：{used_chars} 字，剩余：{remaining_chars} 字）
要求：
- 严格控制在 {target_chars_this_segment} 字左右（±3字），不得超过 35 字
- 第一段必须用钩子开头：「眼前这个{角色}，只是{做了件小事}，才...」
- 中间段用「随后」「然而」「就在此时」衔接
- 极具感染力，制造悬念
- 禁止平铺直叙，禁止「画面中」「镜头转向」
只输出旁白文案，不要解释。
Call via build_payload.generate_cinematic_narration().
Mode B — Whisper + Koubo Analysis
Run scripts/whisper_transcribe.py → detect silent gaps > 1.5s → score segments → apply koubo cuts → enforce 90s budget.
Clip List Format (共用)
clips = [
{
"clip_id": "clip_001",
"start_time": 0.0, # seconds in source video
"end_time": 6.3, # seconds in source video
"narration_text": "眼前这个陆永瑜，只是举报了父亲的贪腐...",
"keep_original_audio": False, # True = keep source audio, no TTS
"stage": "起因",
"speaker_name": None,
},
...
]
Step 3: Approval Gate
MANDATORY — Show the FULL detailed clip plan BEFORE generating any files. Include every clip with: index, source timestamp, stage, estimated duration, full narration text.
╔══════════════════════════════════════════════════════════════════════╗
║ 📋 《误判(1)》剪辑方案预览 共 8 段 | 预估总时长：约 52 秒 ║
╚══════════════════════════════════════════════════════════════════════╝
┌─ clip_001 ────────────────────────────────────────────────────────┐
│ 阶段：起因 │
│ 源片段：00:02:15 → 00:02:22 (约 7s) │
│ 旁白文案： │
│ 「眼前这个陆永瑜，只是举报了父亲的贪腐行为，才发现自己 │
│ 已经踏入了一个无法回头的深渊...」 │
│ 音频：narration_clip_001.mp3 | 视频：clip_001.mp4 │
└───────────────────────────────────────────────────────────────────┘
┌─ clip_002 ────────────────────────────────────────────────────────┐
│ 阶段：发展 │
│ 源片段：00:18:40 → 00:18:46 (约 6s) │
│ 旁白文案： │
│ 「随后，陆金强悄悄接触了关键证人，一场精心设计的陷阱， │
│ 正在悄无声息地收紧。」 │
│ 音频：narration_clip_002.mp3 | 视频：clip_002.mp4 │
└───────────────────────────────────────────────────────────────────┘
┌─ clip_003 ────────────────────────────────────────────────────────┐
│ 阶段：发展 │
│ 源片段：00:35:10 → 00:35:16 (约 6s) │
│ 旁白文案： │
│ 「然而，就在案件似乎尘埃落定之际，律师张远发现了一处细节—— │
│ 这个细节，将让所有人的判断彻底崩塌。」 │
│ 音频：narration_clip_003.mp3 | 视频：clip_003.mp4 │
└───────────────────────────────────────────────────────────────────┘
... （以此格式列出所有 N 个片段）
══════════════════════════════════════════════════════════════════════
项目名称 ：误判(1)
输出目录 ：video_clipper_output_误判(1)/
视频比例 ：1920×1080（保持原始）
旁白语音 ：zh-CN-XiaoxiaoNeural
旁白总字数：213 字 ✅（目标 200 字，范围 150–250 字）
预估时长 ：约 64 秒 ✅（目标 60 秒，范围 45–75 秒）
══════════════════════════════════════════════════════════════════════
确认开始生成？(是 / 修改某段文案 / 删除某段 / 全部重新生成)
Rules for this display:
- Show ALL clips, no truncation — even if there are 20+ clips
- Each clip box must contain: stage, source timestamp range, estimated duration, full narration text, output filenames
- Show project name WITH conflict suffix (e.g.
误判(1)) so user sees the final name before committing - Wait for explicit user approval before proceeding to Step 4
Step 4: Generate narration_XXX.mp3
One MP3 per clip. Filename: narration_clip_001.mp3, narration_clip_002.mp3, ...
import edge_tts, asyncio, subprocess
async def tts_one(text: str, path: str, voice: str):
tts = edge_tts.Communicate(text.strip("[]"), voice=voice)
await tts.save(path)
def get_mp3_duration(path: str) -> float:
"""Measure ACTUAL mp3 duration — used to set clip_XXX.mp4 length."""
result = subprocess.run(
["ffprobe", "-v", "error", "-show_entries", "format=duration",
"-of", "default=noprint_wrappers=1:nokey=1", path],
capture_output=True, text=True, check=True
)
return float(result.stdout.strip())
# Generate + measure
for clip in clips:
mp3_path = f"{output_dir}/narration_{clip['clip_id']}.mp3"
asyncio.run(tts_one(clip["narration_text"], mp3_path, tts_voice))
clip["mp3_path"] = mp3_path
clip["mp3_duration"] = get_mp3_duration(mp3_path) # ACTUAL seconds
# Update end_time to match mp3 duration exactly
clip["end_time"] = clip["start_time"] + clip["mp3_duration"]
After this step, clip["mp3_duration"] is the authoritative duration for all downstream steps.
Step 5: Generate clip_XXX.mp4
One MP4 per clip. Duration = clip["mp3_duration"] exactly.
Filename: clip_001.mp4, clip_002.mp4, ...
import subprocess, os
def render_clip(clip: dict, video_path: str, output_dir: str,
original_width: int, original_height: int):
"""
Extract one clip from source video.
- Duration locked to mp3_duration (exact match with narration audio)
- Preserves original aspect ratio
- Mutes source audio if narration overlay (keep_original_audio=False)
- Keeps source audio if keep_original_audio=True
"""
out_path = f"{output_dir}/{clip['clip_id']}.mp4"
duration = clip["mp3_duration"] # locked to mp3
base_cmd = [
"ffmpeg", "-y",
"-ss", str(clip["start_time"]),
"-t", str(duration),
"-i", video_path,
]
if not clip.get("keep_original_audio") and clip.get("mp3_path"):
# Merge narration audio into clip
cmd = base_cmd + [
"-i", clip["mp3_path"],
"-map", "0:v:0", "-map", "1:a:0",
"-c:v", "libx264", "-preset", "fast",
"-vf", f"scale={original_width}:{original_height}"
":force_original_aspect_ratio=decrease:flags=lanczos",
"-c:a", "aac", "-b:a", "192k",
"-shortest", out_path
]
elif clip.get("keep_original_audio"):
# Keep source audio, no narration
cmd = base_cmd + [
"-c:v", "libx264", "-preset", "fast",
"-vf", f"scale={original_width}:{original_height}"
":force_original_aspect_ratio=decrease:flags=lanczos",
"-c:a", "aac", "-b:a", "192k",
out_path
]
else:
# Silent clip (no audio at all)
cmd = base_cmd + [
"-c:v", "libx264", "-preset", "fast",
"-vf", f"scale={original_width}:{original_height}"
":force_original_aspect_ratio=decrease:flags=lanczos",
"-an", out_path
]
subprocess.run(cmd, capture_output=True, check=True)
clip["mp4_path"] = out_path
print(f" {clip['clip_id']}.mp4 {duration:.2f}s")
return out_path
Step 6: Generate subtitles.srt
Timestamps derived from mp3_duration — NOT from source video timestamps.
Each clip starts where the previous ended (sequential playback timeline).
def generate_srt(clips: list, output_path: str):
"""
Build SRT where each entry's timing matches the clip's position
in the final concatenated video, not the source video.
"""
def fmt(s: float) -> str:
h, r = divmod(s, 3600)
m, s = divmod(r, 60)
return f"{int(h):02d}:{int(m):02d}:{int(s):02d},{int((s%1)*1000):03d}"
cursor = 0.0 # running position in output video timeline
entries = []
for i, clip in enumerate(clips, 1):
duration = clip["mp3_duration"]
start = cursor
end = cursor + duration
text = clip.get("narration_text") or clip.get("transcript", "")
speaker = clip.get("speaker_name")
label = f"[{speaker}] {text}" if speaker else text
entries.append(f"{i}\n{fmt(start)} --> {fmt(end)}\n{label}\n")
cursor = end # advance timeline
with open(output_path, "w", encoding="utf-8") as f:
f.write("\n".join(entries))
print(f"SRT saved: {output_path} ({len(entries)} entries)")
Step 7: Generate JianYing Draft Files
MUST output exactly 2 files — both required for a valid importable draft:
draft_meta_info.jsondraft_content.json
Call scripts/jianying_draft.py → generate_jianying_draft().
JianYing Draft Spec (剪映专业版导入规范)
| Field | Rule |
|---|---|
draft_version | Always "10.8.0" |
| Resolution | 1920 × 1080, frame rate 30 |
| Time unit | Milliseconds, 13-digit timestamp |
| Media path | Windows-style: D:/path/to/clip_001.mp4 |
| Subtitle format | JianYing XML: <font color="#FFFFFF"><size=60>文字</size></font> |
| All IDs | Unique, non-repeating (use uuid4()) |
| JSON | Strict — no trailing commas, no comments |
draft_meta_info.json
{
"cloud_package_attributes": "",
"create_time": 1700000000,
"draft_cloud_last_action_download": false,
"draft_cloud_purchase_info": "",
"draft_cloud_template_id": "",
"draft_cloud_tutorial_info": "",
"draft_cloud_videocut_purchase_info": "",
"draft_cover": "",
"draft_deeplink_url": "",
"draft_enterprise_info": {
"draft_enterprise_extra": "",
"draft_enterprise_id": "",
"draft_enterprise_name": ""
},
"draft_fold_path": "",
"draft_id": "{uuid}",
"draft_is_ai_packaging_used": false,
"draft_is_ai_shorts_used": false,
"draft_is_ai_translate_used": false,
"draft_is_article_video_used": false,
"draft_is_from_deeplink": false,
"draft_is_invisible": false,
"draft_materials": [],
"draft_name": "{project_name}",
"draft_need_rename": false,
"draft_new_version": "",
"draft_removable_storage_device": "",
"draft_root_path": "{output_dir_abs_path}",
"draft_timeline_materials_size_": 0,
"draft_type": "",
"tm_draft_create": 1700000000,
"tm_draft_modified": 1700000000,
"tm_duration": {total_duration_ms}
}
draft_content.json Structure
{
"canvas_config": {
"height": 1080,
"ratio": "original",
"width": 1920
},
"color_space": 0,
"config": {
"adjust_max_index": 1,
"attachment_info": [],
"combination_max_index": 1,
"export_range": null,
"extract_audio_last_index": 1,
"lyrics_recognition_id": "",
"lyrics_sync": true,
"lyrics_taskinfo": [],
"maintrack_adsorb": true,
"material_save_mode": 0,
"multi_language_current": "none",
"multi_language_list": [],
"multi_language_main": "none",
"multi_language_mode": "none",
"original_sound_last_index": 1,
"record_audio_last_index": 1,
"sticker_max_index": 1,
"subtitle_recognition_id": "",
"subtitle_sync": true,
"subtitle_taskinfo": [],
"system_font_list": [],
"video_mute": false,
"zoom_info_params": null
},
"cover": "",
"create_time": 0,
"duration": {total_duration_ms},
"extra_info": null,
"fps": 30.0,
"free_render_index_mode_on": false,
"group_container": null,
"id": "{draft_uuid}",
"keyframe_graph_list": [],
"keyframes": { "adjusts": [], "audios": [], "effects": [], "filters": [],
"handwrites": [], "stickers": [], "texts": [], "videos": [] },
"lyrics_effects_size_": 0,
"materials": {
"audios": [ ...narration_audio_materials... ],
"videos": [ ...video_clip_materials... ],
"texts": [ ...subtitle_text_materials... ]
},
"mutable_config": null,
"name": "{project_name}",
"new_version": "10.8.0",
"platform": { "app_id": 3704, "app_source": "lv", "app_version": "5.9.0",
"device_id": "", "hard_disk_id": "", "mac_id": "", "os": "windows",
"os_version": "" },
"relationships": [],
"render_index_track_mode_on": false,
"retouch_cover": null,
"source": "default",
"static_cover_image_path": "",
"time_marks": null,
"tracks": [
{
"attribute": 0,
"flag": 0,
"id": "{track_video_uuid}",
"is_default_name": true,
"name": "",
"segments": [ ...video_segments... ],
"type": "video"
},
{
"attribute": 0,
"flag": 0,
"id": "{track_audio_uuid}",
"is_default_name": true,
"name": "",
"segments": [ ...audio_segments... ],
"type": "audio"
},
{
"attribute": 0,
"flag": 0,
"id": "{track_text_uuid}",
"is_default_name": true,
"name": "",
"segments": [ ...text_segments... ],
"type": "text"
}
],
"update_time": 0,
"version": 360000
}
Segment Templates
Video segment:
{
"cartoon": false,
"clip": {
"alpha": 1.0,
"flip": { "horizontal": false, "vertical": false },
"rotation": 0.0,
"scale": { "x": 1.0, "y": 1.0 },
"transform": { "x": 0.0, "y": 0.0 }
},
"common_keyframes": [],
"enable_adjust": true,
"enable_color_correct_adjust": false,
"enable_color_wheels": false,
"enable_lut": false,
"enable_smart_color_adjust": false,
"extra_material_refs": [],
"group_id": "",
"hdr_settings": null,
"id": "{segment_uuid}",
"intensifies_audio": false,
"is_placeholder": false,
"is_tone_modify": false,
"keyframe_refs": [],
"last_nonzero_volume": 1.0,
"material_id": "{material_uuid}",
"render_index": 0,
"responsive_layout": { "enable": false, "horizontal_pos_layout": 0,
"size_layout": 0, "target_follow": "", "vertical_pos_layout": 0 },
"reverse": false,
"source_timerange": {
"duration": {clip_duration_ms},
"start": {clip_source_start_ms}
},
"speed": 1.0,
"target_timerange": {
"duration": {clip_duration_ms},
"start": {timeline_start_ms}
},
"template_id": "",
"template_scene": "default",
"track_attribute": 0,
"track_render_index": 0,
"uniform_scale": { "on": true, "value": 1.0 },
"visible": true,
"volume": 0.0
}
Note: volume: 0.0 for narration clips (audio replaced by TTS), volume: 1.0 for keep_original_audio clips.
Audio segment (narration TTS):
{
"app_id": 0,
"category_id": "",
"category_name": "local",
"check_flag": 63487,
"duration": {mp3_duration_ms},
"effect_id": "",
"id": "{segment_uuid}",
"intensifies_path": "",
"is_ai_clone_tone": false,
"is_text_edit_overdub": false,
"is_unified_beauty_mode": false,
"local_material_id": "{audio_material_uuid}",
"material_id": "{audio_material_uuid}",
"material_name": "narration_{clip_id}",
"render_index": 0,
"source_timerange": { "duration": {mp3_duration_ms}, "start": 0 },
"speed": 1.0,
"target_timerange": { "duration": {mp3_duration_ms}, "start": {timeline_start_ms} },
"type": "extract_music",
"volume": 1.0
}
Text segment (subtitle):
{
"content": "{subtitle_xml}",
"id": "{segment_uuid}",
"is_style_definition_segment": false,
"keyframe_refs": [],
"material_id": "{text_material_uuid}",
"render_index": 0,
"target_timerange": { "duration": {mp3_duration_ms}, "start": {timeline_start_ms} },
"track_attribute": 0,
"track_render_index": 0,
"visible": true,
"z_index": 0
}
Subtitle XML format (字幕必须用剪映 XML 格式):
<font color="#FFFFFF"><size=60>眼前这个陆永瑜，只是举报了父亲的贪腐行为</size></font>
Material Templates
Video material:
{
"audio_fade": null,
"cartoon_path": "",
"category_id": "",
"category_name": "",
"check_flag": 63487,
"crop": { "lower_left_x": 0.0, "lower_left_y": 1.0, "lower_right_x": 1.0,
"lower_right_y": 1.0, "upper_left_x": 0.0, "upper_left_y": 0.0,
"upper_right_x": 1.0, "upper_right_y": 0.0 },
"crop_ratio": "free",
"crop_scale": 1.0,
"duration": {clip_duration_ms},
"extra_type_option": 0,
"file_Path": "D:/{output_dir}/{clip_id}.mp4",
"formula_id": "",
"id": "{material_uuid}",
"import_time": 1700000000,
"import_time_ms": 1700000000000,
"item_source": 1,
"md5": "",
"metetype": "video",
"roughcut_time_range": { "duration": -1, "start": -1 },
"sub_time_range": { "duration": -1, "start": -1 },
"type": "video",
"video_algorithm": { "algorithms": [], "deflicker": null, "motion_blur_config": null,
"noise_reduction": null, "path": "", "quality_enhance": null,
"time_range": null },
"width": 1920,
"height": 1080
}
Audio material:
{
"app_id": 0,
"category_id": "",
"category_name": "local",
"check_flag": 63487,
"duration": {mp3_duration_ms},
"effect_id": "",
"file_Path": "D:/{output_dir}/narration_{clip_id}.mp3",
"formula_id": "",
"id": "{material_uuid}",
"import_time": 1700000000,
"import_time_ms": 1700000000000,
"item_source": 1,
"md5": "",
"metetype": "music",
"name": "narration_{clip_id}",
"roughcut_time_range": { "duration": -1, "start": -1 },
"sub_time_range": { "duration": -1, "start": -1 },
"type": "extract_music"
}
Text material:
{
"add_type": 0,
"alignment": 1,
"background_alpha": 0.0,
"background_color": "",
"background_height": 0.08,
"background_horizontal_offset": 0.0,
"background_round_radius": 0.0,
"background_style": 0,
"background_vertical_offset": 0.0,
"background_width": 0.14,
"bold_width": 0.0,
"border_alpha": 1.0,
"border_color": "#000000",
"border_width": 0.08,
"content": "{subtitle_xml}",
"fixed_height": -1.0,
"fixed_width": -1.0,
"font_category_id": "",
"font_category_name": "",
"font_id": "",
"font_name": "",
"font_path": "",
"font_resource_id": "",
"font_size": 60.0,
"font_source_platform": 0,
"font_time_range": { "duration": 0, "start": 0 },
"font_title": "新标准黑",
"font_url": "",
"fonts": [],
"force_apply_line_max_width": false,
"global_alpha": 1.0,
"group_id": "",
"has_shadow": true,
"id": "{material_uuid}",
"initial_scale": 1.0,
"is_rich_text": false,
"italic": false,
"italic_degree": 0,
"letter_spacing": 0.0,
"line_feed": 1,
"line_max_width": 0.82,
"line_spacing": 0.02,
"multi_language_current": "none",
"name": "",
"original_size": [],
"preset_id": "",
"recognize_task_id": "",
"recognize_type": 0,
"relevance_segment": [],
"shadow_alpha": 0.9,
"shadow_angle": -45.0,
"shadow_color": "#000000",
"shadow_distance": 8.0,
"shadow_point": { "x": 0.6364, "y": -0.6364 },
"shadow_smoothing": 0.45,
"shape_clip_x": false,
"shape_clip_y": false,
"style_name": "",
"sub_type": 0,
"text_alpha": 1.0,
"text_color": "#FFFFFF",
"text_curve": null,
"text_preset_resource_id": "",
"text_size": 60,
"text_to_audio_ids": [],
"tts_auto_update": false,
"type": "text",
"typesetting": 0,
"underline": false,
"underline_offset": 0.22,
"underline_width": 0.05,
"use_effect_default_color": true,
"words": { "end_time": [], "start_time": [], "text": [] }
}
Time Conversion Rules
def ms(seconds: float) -> int:
"""Convert seconds to milliseconds (13-digit JianYing timestamp)."""
return int(seconds * 1000)
# Timeline cursor — accumulates as clips are placed
cursor_ms = 0
for clip in clips:
clip_duration_ms = ms(clip["mp3_duration"])
clip_source_start = ms(clip["start_time"]) # position IN source video
timeline_start = cursor_ms # position IN output timeline
cursor_ms += clip_duration_ms # advance cursor
Python Generator Function
from scripts.jianying_draft import generate_jianying_draft
meta, content = generate_jianying_draft(
project_name = project_name, # already conflict-resolved
clips = clips, # with mp3_duration + mp4_path + mp3_path
output_dir = output_dir, # abs path, used in file_Path fields
original_width = 1920,
original_height = 1080
)
import json, os
os.makedirs(output_dir, exist_ok=True)
with open(f"{output_dir}/draft_meta_info.json", "w", encoding="utf-8") as f:
json.dump(meta, f, ensure_ascii=False, indent=2)
with open(f"{output_dir}/draft_content.json", "w", encoding="utf-8") as f:
json.dump(content, f, ensure_ascii=False, indent=2)
Step 8: Final Output Message
✅ 生成完成！
📁 输出目录: video_clipper_output/
配音文件 (narration_*.mp3):
narration_clip_001.mp3 6.34s
narration_clip_002.mp3 5.81s
... 共 N 个
视频切片 (clip_*.mp4):
clip_001.mp4 6.34s ← 与 narration_clip_001.mp3 精确对齐
clip_002.mp4 5.81s ← 与 narration_clip_002.mp3 精确对齐
... 共 N 个
字幕文件:
subtitles.srt (N 条，时间轴与 mp3 严格对应)
剪映草稿:
draft_content.json
首次使用时，额外显示剪映草稿导入指引：
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📌 剪映草稿导入方法（首次使用）
草稿目录：
Windows: C:\Users\{用户名}\AppData\Local\JianyingPro\User Data\Projects\com.lveditor.draft\
macOS: ~/Movies/JianyingPro/User Data/Projects/com.lveditor.draft/
步骤：
1. 在草稿目录下新建一个文件夹（随意命名）
2. 将 draft_content.json 放入该文件夹
3. 打开剪映 → 首页 → 找到刚才的草稿 → 点击「继续编辑」
4. clip_XXX.mp4 已含旁白音频，可直接在剪映时间轴使用
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
只在以下情况显示导入指引：
- 用户第一次运行（对话中无历史记录）
- 用户说"不知道怎么导入" / "草稿在哪"
Timing Alignment Rules (CRITICAL)
mp3_duration → controls everything downstream
│
├──► clip_XXX.mp4 duration = mp3_duration (ffmpeg -t flag)
│
├──► subtitles.srt timing = cumulative sum of mp3_durations
│ clip_001: 00:00:00,000 → 00:00:06,340
│ clip_002: 00:00:06,340 → 00:00:12,160
│ clip_003: 00:00:12,160 → ...
│
└──► draft_content.json srt_start / srt_end = same values
Never use source video timestamps for srt. Always use mp3_duration.
API Keys
| Service | Key | Purpose |
|---|---|---|
| Anthropic Claude | auto (OpenClaw) | LLM narration generation |
| Qwen-VL-Plus | QWEN_API_KEY env var | Silent scene description (Mode B) |
| edge-tts | no key needed | TTS narration MP3 |
| Whisper | local, no key | Speech transcription (Mode B) |
Error Handling
| Error | Resolution |
|---|---|
| ffprobe / ffmpeg not found | apt install ffmpeg / brew install ffmpeg / ffmpeg.org |
| TTS fails for a clip | Retry once; if still fails, set mp3_duration = estimated_seconds, flag in report |
| ffmpeg clip render fails | Log and skip; include in report as failed clips |
| LLM narration API error | Fall back to raw event description; rewrite style manually |
| Whisper OOM (Mode B) | Switch to base model or chunk into 10-min segments |
| Qwen-VL-Plus error | Use rule-based fallback narration |
Dependencies
pip install edge-tts openai openai-whisper --break-system-packages
# FFmpeg: https://ffmpeg.org/download.html
Scripts
scripts/build_payload.py— clip analysis, LLM narration, render_output_video, clips_to_draftscripts/narration_audio.py— edge-tts generation, SRT builder, reportscripts/whisper_transcribe.py— Mode B speech transcriptionscripts/run_clip_pipeline.py— end-to-end CLI
Related skills
- Downloads
- Security audit
- Review
- Last updated
- 2mo ago
- Current version
- v1.0.0
- License
- MIT-0