feat: 初始化 doco 子项目 P1 阶段(视频双路拆分预处理)

.gitignore

@@ -13,39 +13,51 @@ dist/
 build/
 .vite/
 
-# 莽聨呕暮藰聝茅聟聧莽藵沤(膰掳赂膷偶聹盲赂聧盲赂聤盲藕聽)
+# 环境配置(永远不上传)
 .env
 .env.local
 
-# 莽藕聳膷啪聭暮聶篓
+# 编辑器
 .vscode/
 .idea/
 *.swp
 
-# 膰聯聧盲藵聹莽艂钮莽钮聼
+# 操作系统
 .DS_Store
 Thumbs.db
 
-# 膰聴慕暮偶聴膷偶聬膷膭聦盲艧搂莽聣艩
+# 日志运行产物
 *.log
 cookie.txt
 cookies*.txt
 
-# 盲赂麓膰聴艣膰聳聡盲钮艣
+# 临时文件
 *.tmp
 *.bak
 homePC/
 
-# Syncthing (膰聻聛莽艩艧茅聴麓 NAS 暮聬聦膰颅慕暮藝慕暮聟藝盲艧搂莽聣艩)
+# Syncthing (本机 NAS 同步)
 .stignore
 
-# 臉媒啪脻偶芒膮赂藝脻 / SQL dump(藳钮藵艡掳膰膮啪偶芒)
+# 备份 / SQL dump
 backups/
 
-# 鏁忔劅鑴氭湰(姘镐笉涓婁紶)
+# 敏感脚本(永不提交)
 backend/scripts/reset_password.py
 
-# 前端测试/占位图片(题图上传测试产物)  
+# 前端素材
 frontend/src/assets/*.jpg
 frontend/src/assets/*.jpeg
 frontend/src/assets/*.png
+
+# Doco 素材目录(视频不进 git)
+programs/*/source/video.*
+programs/*/source/*.mp4
+programs/*/source/*.mov
+programs/*/work/
+
+# Doco .env
+doco/.env
+
+# Python build artifacts
+*.egg-info/

doco/.env.example

@@ -0,0 +1,26 @@
+# Doco 子模块凭证配置
+# 复制此文件为 .env 并填入真实凭证
+# 格式: VARIABLE_NAME=your_value
+
+# ========================================================================
+# 讯飞 ASR (录音文件转写标准版)
+# 申请地址: https://console.xfyun.cn/
+# 注意: 不要用"大模型版",用"录音文件转写标准版"
+# 凭证类型: APP_ID + SECRET_KEY
+# ========================================================================
+XFYUN_APP_ID=your_xfyun_app_id
+XFYUN_SECRET_KEY=your_xfyun_secret_key
+
+# ========================================================================
+# DeepSeek Vision (OCR 用)
+# 申请地址: https://platform.deepseek.com/
+# 凭证类型: API_KEY
+# ========================================================================
+DEEPSEEK_API_KEY=your_deepseek_api_key
+
+# ========================================================================
+# Anthropic Claude API (AI 融合层,P3 才用)
+# 申请地址: https://console.anthropic.com/
+# 凭证类型: API_KEY
+# ========================================================================
+ANTHROPIC_API_KEY=your_anthropic_api_key

doco/README.md

@@ -0,0 +1,116 @@
+# Doco - TPS 中台终版文稿生成子模块
+
+> 央视《军事科技》栏目 - 终版文稿自动生成流水线
+
+## 项目状态
+
+**当前 Phase: P1** - 视频双路拆分预处理
+
+## 功能概述
+
+Doco 将一期《军事科技》节目视频拆分为两路输入,供下游三方融合(P3)使用:
+
+| 输出 | 规格 | 存放位置 |
+|---|---|---|
+| B 稿 | 带时间戳的 txt,`[Nm Ns] 句子`格式 | `work/b_manuscript.txt` |
+| 音频 | 16kHz / 单声道 / 16bit WAV | `work/audio_16k.wav` |
+| 关键帧索引 | JSON | `work/keyframes.json` |
+
+## 系统依赖
+
+- **Python >= 3.12**
+- **ffmpeg >= 4.x** (必须安装并加入 PATH)
+  - Windows 下载: https://ffmpeg.org/download.html
+  - macOS: `brew install ffmpeg`
+  - Linux: `apt install ffmpeg`
+
+## 安装
+
+```bash
+# 1. 克隆仓库后进入 doco 目录
+cd doco
+
+# 2. 安装依赖
+pip install -e .
+
+# 3. 配置凭证
+cp .env.example .env
+# 编辑 .env,填入三组 API 凭证
+```
+
+## 凭证配置
+
+Doco 使用三组独立凭证,互不混用:
+
+| 服务 | 用途 | 申请地址 |
+|---|---|---|
+| 讯飞开放平台 - 录音文件转写(标准版) | 音频转文字 | https://console.xfyun.cn/ |
+| DeepSeek Vision | OCR 识别 | https://platform.deepseek.com/ |
+| Anthropic Claude API | AI 融合层(P3) | https://console.anthropic.com/ |
+
+> 注意: 讯飞要用"录音文件转写标准版",不要用"大模型版"
+
+## P1 使用方法
+
+### 准备素材目录
+
+```
+programs/
+└── ep001_20260612_fangkong_fandao/
+    └── source/
+        └── video.mp4   ← 放入节目视频
+```
+
+> video.mp4 由制片人放入,不放进 git
+
+### 运行拆分
+
+```bash
+doco split \
+  --episode-id ep001_20260612_fangkong_fandao \
+  --input-video programs/ep001_20260612_fangkong_fandao/source/video.mp4 \
+  --output-dir programs/ep001_20260612_fangkong_fandao/work/
+```
+
+### 输出产物
+
+```
+programs/ep001_20260612_fangkong_fandao/work/
+├── frames/                # 抽出的所有帧(临时)
+├── audio_16k.wav          # 音频(16kHz/单声道/16bit)
+├── b_manuscript.txt       # B 稿([Nm Ns] 句子格式)
+└── keyframes.json         # 关键帧索引
+```
+
+## P1 验收标准
+
+1. `work/b_manuscript.txt` 格式为 `[Nm Ns] 句子`,每行一句
+2. `work/audio_16k.wav` 规格为 16kHz/单声道/16bit,能被讯飞 ASR 接收
+3. `work/keyframes.json` 字段符合定义
+
+## 目录结构
+
+```
+doco/
+├── src/
+│   ├── __init__.py
+│   ├── cli.py              # CLI 入口
+│   ├── video_split.py      # P1 核心:视频双路拆分
+│   ├── asr_adapter.py     # 讯飞 ASR 适配层
+│   └── ocr_adapter.py      # P2:DeepSeek Vision OCR
+├── tests/
+│   ├── test_video_split.py # 单元测试
+│   └── fixtures/
+│       └── mini_test.mp4   # 迷你测试视频
+├── docs/
+├── .env.example            # 凭证模板
+├── README.md
+└── pyproject.toml
+```
+
+## 相关文档
+
+- Brief: `docs/doco/Doco子项目_Brief.md`
+- 设计文档: `docs/doco/doco_project_design.md`
+- 讯飞接入笔记: `docs/doco/doco_xfyun_integration_notes.md`
+- 主项目回复: `docs/doco/主project对Doco_PRDv2的回复.md`

doco/pyproject.toml

@@ -0,0 +1,36 @@
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "doco"
+version = "0.1.0"
+description = "TPS 中台 - 终版文稿生成子模块(视频双路拆分 + 三方融合)"
+readme = "README.md"
+requires-python = ">=3.12"
+license = { text = "Proprietary" }
+authors = [
+    { name = "刘统制片组" }
+]
+
+dependencies = [
+    "Pillow>=10.0.0",
+    "imagehash>=4.3.1",
+    "requests>=2.31.0",
+    "python-dotenv>=1.0.0",
+    "click>=8.1.0",
+    "python-docx>=1.1.0",
+    "anthropic>=0.18.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=7.4.0",
+]
+
+[project.scripts]
+doco = "doco.src.cli:main"
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["doco.src*"]

doco/src/__init__.py

@@ -0,0 +1,6 @@
+# -*- coding: utf-8 -*-
+"""
+doco - TPS 中台终版文稿生成子模块
+"""
+
+__version__ = "0.1.0"

doco/src/asr_adapter.py

@@ -0,0 +1,298 @@
+# -*- coding: utf-8 -*-
+"""
+讯飞 ASR 适配层
+=================================================
+来源: demo 跑通的 xfyun_asr_standard.py
+改动: 凭证从环境变量读取,不再硬编码
+
+接口: https://raasr.xfyun.cn/v2/api/upload  /  getResult
+签名: signa = base64(HmacSHA1(MD5(appid + ts), secretKey))
+
+特性:
+- 支持热词列表(hotWord),提升专业术语识别率
+- 支持军事领域参数(pd=mil)
+- 支持顺滑+口语规整(输出更接近书面语)
+- 默认语种 cn(中文普通话),免费包标配
+
+凭证来源: 环境变量
+- XFYUN_APP_ID
+- XFYUN_SECRET_KEY
+"""
+
+import base64
+import hashlib
+import hmac
+import json
+import os
+import re
+import time
+import wave
+from urllib.parse import quote
+from typing import List, Tuple, Optional
+
+import requests
+
+# ========================================================================
+# 凭证(从环境变量读取)
+# ========================================================================
+
+APP_ID = os.environ.get("XFYUN_APP_ID", "").strip()
+SECRET_KEY = os.environ.get("XFYUN_SECRET_KEY", "").strip()
+
+
+# ========================================================================
+# 接口配置
+# ========================================================================
+
+HOST = "https://raasr.xfyun.cn/v2/api"
+UPLOAD_URL = HOST + "/upload"
+RESULT_URL = HOST + "/getResult"
+
+# 业务参数
+LANGUAGE = "cn"           # 中文普通话
+PD = "mil"               # 军事领域优化
+ENG_SMOOTHPROC = "true"  # 顺滑(去掉"嗯/那个")
+ENG_COLLOQPROC = "true"  # 口语规整
+
+# 轮询配置
+POLL_INTERVAL_SECONDS = 30
+MAX_WAIT_MINUTES = 30
+
+
+# ========================================================================
+# 热词列表(每期节目调用前从 A 稿提取)
+# ========================================================================
+
+def get_hot_words() -> List[str]:
+    """获取热词列表,P2 实现时从 A 稿提取"""
+    return []
+
+
+# ========================================================================
+# 签名+工具
+# ========================================================================
+
+
+def make_signa(app_id: str, secret_key: str, ts: str) -> str:
+    """
+    讯飞老版签名:signa = base64(HmacSHA1(MD5(appid + ts), secretKey))
+    """
+    base_string = (app_id + ts).encode("utf-8")
+    md5_str = hashlib.md5(base_string).hexdigest()  # 32位小写hex
+    mac = hmac.new(
+        secret_key.encode("utf-8"),
+        md5_str.encode("utf-8"),
+        digestmod=hashlib.sha1,
+    )
+    signa = base64.b64encode(mac.digest()).decode("utf-8")
+    return signa
+
+
+def get_audio_duration_ms(filepath: str) -> int:
+    """获取音频时长(毫秒)。WAV用内置,MP3用mutagen。"""
+    ext = os.path.splitext(filepath)[1].lower()
+
+    if ext == ".wav":
+        with wave.open(filepath, "rb") as wf:
+            n_frames = wf.getnframes()
+            sample_rate = wf.getframerate()
+            duration_ms = int(round(n_frames / sample_rate * 1000))
+            return duration_ms
+
+    if ext == ".mp3":
+        try:
+            from mutagen.mp3 import MP3
+            return int(MP3(filepath).info.length * 1000)
+        except ImportError:
+            return 0
+
+    raise ValueError(f"不支持的音频格式: {ext}")
+
+
+# ========================================================================
+# 上传
+# ========================================================================
+
+
+def upload_audio(
+    filepath: str,
+    hot_words: Optional[List[str]] = None,
+) -> str:
+    """上传音频,返回 orderId"""
+    if not os.path.exists(filepath):
+        raise FileNotFoundError(f"音频文件不存在: {filepath}")
+
+    if not APP_ID or not SECRET_KEY:
+        raise ValueError("请先设置 XFYUN_APP_ID 和 XFYUN_SECRET_KEY 环境变量")
+
+    file_size = os.path.getsize(filepath)
+    file_name = os.path.basename(filepath)
+    duration_ms = get_audio_duration_ms(filepath)
+    ts = str(int(time.time()))
+
+    signa = make_signa(APP_ID, SECRET_KEY, ts)
+
+    # 构建URL参数
+    params = {
+        "appId": APP_ID,
+        "signa": signa,
+        "ts": ts,
+        "fileSize": str(file_size),
+        "fileName": file_name,
+        "duration": str(duration_ms),
+        "language": LANGUAGE,
+        "pd": PD,
+        "eng_smoothproc": ENG_SMOOTHPROC,
+        "eng_colloqproc": ENG_COLLOQPROC,
+    }
+
+    # 热词,用 | 分隔
+    if hot_words:
+        hot_word_str = "|".join(hot_words)
+        params["hotWord"] = hot_word_str
+
+    url_parts = [f"{quote(k, safe='')}={quote(str(v), safe='')}" for k, v in params.items()]
+    url = f"{UPLOAD_URL}?{'&'.join(url_parts)}"
+
+    headers = {
+        "Content-Type": "application/json",
+    }
+
+    with open(filepath, "rb") as f:
+        audio_bytes = f.read()
+
+    resp = requests.post(url, headers=headers, data=audio_bytes, timeout=300)
+
+    data = resp.json()
+    if data.get("code") != "000000":
+        raise RuntimeError(f"上传失败: code={data.get('code')}, desc={data.get('descInfo')}")
+
+    order_id = data["content"]["orderId"]
+    return order_id
+
+
+# ========================================================================
+# 查询结果
+# ========================================================================
+
+
+def query_result(order_id: str) -> dict:
+    """单次查询"""
+    ts = str(int(time.time()))
+    signa = make_signa(APP_ID, SECRET_KEY, ts)
+
+    params = {
+        "appId": APP_ID,
+        "signa": signa,
+        "ts": ts,
+        "orderId": order_id,
+        "resultType": "transfer",
+    }
+    url_parts = [f"{quote(k, safe='')}={quote(str(v), safe='')}" for k, v in params.items()]
+    url = f"{RESULT_URL}?{'&'.join(url_parts)}"
+
+    resp = requests.post(url, timeout=30)
+    return resp.json()
+
+
+def poll_until_done(order_id: str) -> dict:
+    """轮询直到完成"""
+    start_time = time.time()
+    while True:
+        elapsed = time.time() - start_time
+        if elapsed > MAX_WAIT_MINUTES * 60:
+            raise TimeoutError(f"超过 {MAX_WAIT_MINUTES} 分钟未完成")
+
+        data = query_result(order_id)
+        order_info = data.get("content", {}).get("orderInfo", {})
+        status = order_info.get("status")
+        fail_type = order_info.get("failType", 0)
+
+        if status == 4:
+            return data
+        if status == -1:
+            raise RuntimeError(f"转写失败: failType={fail_type}, 数据: {data}")
+
+        time.sleep(POLL_INTERVAL_SECONDS)
+
+
+# ========================================================================
+# 结果解析
+# ========================================================================
+
+
+def parse_order_result(order_result_str: str) -> List[Tuple[int, int, str]]:
+    """
+    解析嵌套JSON,返回 [(sentence_start_ms, sentence_end_ms, text), ...]
+    """
+    if not order_result_str:
+        return []
+
+    cleaned = re.sub(r"\\\\", r"\\", order_result_str)
+    outer = json.loads(cleaned)
+
+    sentences = []
+    for item in outer.get("lattice", []):
+        inner_str = item.get("json_1best", "")
+        if not inner_str:
+            continue
+        inner = json.loads(inner_str)
+        st = inner.get("st", {})
+        bg = int(st.get("bg", 0))
+        ed = int(st.get("ed", 0))
+
+        words = []
+        for rt in st.get("rt", []):
+            for ws in rt.get("ws", []):
+                for cw in ws.get("cw", []):
+                    w = cw.get("w", "").strip()
+                    wp = cw.get("wp", "n")
+                    if w and wp != "g":
+                        words.append(w)
+        sentence = "".join(words).strip()
+        if sentence:
+            sentences.append((bg, ed, sentence))
+
+    return sentences
+
+
+def format_timestamp(ms: int) -> str:
+    """毫秒转 [Nm Ns] 格式"""
+    total_sec = ms // 1000
+    return f"{total_sec // 60}m{total_sec % 60}s"
+
+
+def transcribe(
+    audio_path: str,
+    hot_words: Optional[List[str]] = None,
+) -> List[Tuple[int, int, str]]:
+    """
+    完整转写流程:上传 → 轮询 → 解析
+    返回 [(start_ms, end_ms, text), ...]
+    """
+    order_id = upload_audio(audio_path, hot_words=hot_words)
+    result_data = poll_until_done(order_id)
+    order_result_str = result_data["content"]["orderResult"]
+    return parse_order_result(order_result_str)
+
+
+def write_asr_result(
+    sentences: List[Tuple[int, int, str]],
+    output_dir: str,
+) -> Tuple[str, str]:
+    """
+    将 ASR 结果写入文件
+    返回 (timed_txt_path, raw_json_path)
+    """
+    os.makedirs(output_dir, exist_ok=True)
+
+    timed_lines = [f"[{format_timestamp(bg)}] {text}" for bg, _, text in sentences]
+    timed_path = os.path.join(output_dir, "asr_result_timed.txt")
+    with open(timed_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(timed_lines))
+
+    raw_path = os.path.join(output_dir, "asr_result_raw.json")
+    with open(raw_path, "w", encoding="utf-8") as f:
+        f.write("{}")  # 占位,P2 实现时填入原始返回
+
+    return timed_path, raw_path

doco/src/cli.py

@@ -0,0 +1,104 @@
+# -*- coding: utf-8 -*-
+"""
+doco CLI 入口
+P1: doco split 子命令
+P3: doco process 子命令(带 --input-a-draft 和 --cleanup-level)
+"""
+
+import click
+import sys
+from pathlib import Path
+
+# P1 相关
+from .video_split import split_video
+
+
+@click.group()
+@click.version_option(version="0.1.0")
+def main():
+    """TPS 中台 - 终版文稿生成工具"""
+    pass
+
+
+@main.command("split")
+@click.option(
+    "--episode-id",
+    required=True,
+    help="节目 ID,如 ep001_20260612_fangkong_fandao",
+)
+@click.option(
+    "--input-video",
+    required=True,
+    type=click.Path(exists=True),
+    help="输入视频文件路径",
+)
+@click.option(
+    "--output-dir",
+    required=True,
+    type=click.Path(),
+    help="输出目录(work/ 路径)",
+)
+@click.option(
+    "--phash-threshold",
+    default=8,
+    type=int,
+    help="pHash 海明距离阈值,用于检测字幕变化(默认 8)",
+)
+def split(episode_id: str, input_video: str, output_dir: str, phash_threshold: int):
+    """
+    P1: 视频双路拆分
+    - A 路:抽帧 + pHash 变化检测 + OCR → B 稿 txt
+    - B 路:提取音频(16kHz/单声道/16bit WAV)
+    """
+    video_path = Path(input_video)
+    out_dir = Path(output_dir)
+
+    click.echo(f"[doco split] episode_id={episode_id}")
+    click.echo(f"[doco split] input_video={video_path}")
+    click.echo(f"[doco split] output_dir={out_dir}")
+    click.echo(f"[doco split] phash_threshold={phash_threshold}")
+
+    try:
+        result = split_video(
+            video_path=video_path,
+            output_dir=out_dir,
+            episode_id=episode_id,
+            phash_threshold=phash_threshold,
+        )
+        click.echo(f"[ok] B 稿: {result['b_manuscript_path']}")
+        click.echo(f"[ok] 音频: {result['audio_path']}")
+        click.echo(f"[ok] 关键帧索引: {result['keyframes_path']}")
+        click.echo(f"[ok] 关键帧数量: {result['keyframe_count']}")
+    except Exception as e:
+        click.echo(f"[error] {e}", err=True)
+        sys.exit(1)
+
+
+@main.command("process")
+@click.option("--episode-id", required=True, help="节目 ID")
+@click.option("--input-video", required=True, type=click.Path(exists=True), help="输入视频")
+@click.option("--input-a-draft", required=True, type=click.Path(exists=True), help="A 稿 docx")
+@click.option("--output-dir", required=True, type=click.Path(), help="输出目录")
+@click.option(
+    "--cleanup-level",
+    default="medium",
+    type=click.Choice(["keep_all", "medium", "clean"]),
+    help="口语清理档位(默认 medium)",
+)
+def process(
+    episode_id: str,
+    input_video: str,
+    input_a_draft: str,
+    output_dir: str,
+    cleanup_level: str,
+):
+    """
+    P3: 三方融合全流程
+    需要 A 稿 + B 稿(本命令调用 split) + ASR 结果,融合输出终版 docx + 差异报告
+    """
+    click.echo("[doco process] P3 全流程暂未实现,请先使用 split 命令")
+    sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

doco/src/video_split.py

@@ -0,0 +1,353 @@
+# -*- coding: utf-8 -*-
+"""
+视频双路拆分 - P1 核心模块
+=================================================
+功能:
+  A 路:视频帧 → pHash 变化检测 → OCR → B 稿 txt
+  B 路:视频 → 16kHz/单声道/16bit WAV
+
+不引入 ffmpeg-python 等 wrapper,只用 subprocess 调系统 ffmpeg。
+"""
+
+import hashlib
+import json
+import os
+import shutil
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Dict, List, Tuple, Optional
+
+from PIL import Image
+import imagehash
+
+
+# ========================================================================
+# 凭证(从环境变量读取,供 OCR 调用 DeepSeek Vision)
+# ========================================================================
+
+DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY", "").strip()
+
+
+# ========================================================================
+# FFmpeg 封装
+# ========================================================================
+
+
+def check_ffmpeg():
+    """检查 ffmpeg 是否在 PATH 中"""
+    result = shutil.which("ffmpeg")
+    if result is None:
+        raise RuntimeError(
+            "ffmpeg 未找到,请先安装 ffmpeg 并加入 PATH。"
+            "下载地址: https://ffmpeg.org/download.html"
+        )
+    return result
+
+
+def extract_frames(
+    video_path: Path,
+    output_dir: Path,
+    fps: int = 1,
+) -> List[Tuple[int, int, Path]]:
+    """
+    按固定 fps 抽帧
+    返回: [(frame_index, timestamp_ms, image_path), ...]
+    """
+    check_ffmpeg()
+
+    frames_dir = output_dir / "frames"
+    frames_dir.mkdir(parents=True, exist_ok=True)
+
+    # ffmpeg 抽帧,格式 frame_%04d.png
+    frame_pattern = str(frames_dir / "frame_%04d.png")
+    cmd = [
+        "ffmpeg",
+        "-i", str(video_path),
+        "-vf", f"fps={fps}",
+        "-q:v", "2",  # JPEG 质量
+        frame_pattern,
+        "-y",  # 覆盖
+    ]
+
+    result = subprocess.run(
+        cmd,
+        capture_output=True,
+        text=True,
+    )
+    if result.returncode != 0:
+        raise RuntimeError(f"ffmpeg 抽帧失败: {result.stderr}")
+
+    # 收集抽出的帧
+    frames = []
+    for i, f in enumerate(sorted(frames_dir.glob("frame_*.png"))):
+        # 时间戳:第 i 帧就是 i 秒
+        timestamp_ms = i * 1000
+        frames.append((i, timestamp_ms, f))
+
+    return frames
+
+
+def extract_audio(
+    video_path: Path,
+    output_path: Path,
+) -> Path:
+    """
+    用 ffmpeg 提取音频,转为 16kHz/单声道/16bit WAV
+    """
+    check_ffmpeg()
+
+    cmd = [
+        "ffmpeg",
+        "-i", str(video_path),
+        "-ac", "1",           # 单声道
+        "-ar", "16000",       # 16kHz
+        "-sample_fmt", "s16", # 16bit
+        str(output_path),
+        "-y",
+    ]
+
+    result = subprocess.run(
+        cmd,
+        capture_output=True,
+        text=True,
+    )
+    if result.returncode != 0:
+        raise RuntimeError(f"ffmpeg 音频提取失败: {result.stderr}")
+
+    return output_path
+
+
+# ========================================================================
+# pHash 变化检测
+# ========================================================================
+
+
+def compute_phash(image_path: Path) -> str:
+    """计算图片的 pHash,返回 hex 字符串"""
+    img = Image.open(image_path)
+    ph = imagehash.phash(img)
+    return str(ph)
+
+
+def find_keyframes(
+    frames: List[Tuple[int, int, Path]],
+    threshold: int = 8,
+) -> List[Dict]:
+    """
+    基于 pHash 海明距离找出字幕变化的关键帧
+
+    算法:
+    - 第一帧总是关键帧
+    - 后续帧:如果与上一个关键帧的 pHash 海明距离 > threshold,则是新关键帧
+
+    threshold: 海明距离阈值,默认 8
+    """
+    if not frames:
+        return []
+
+    keyframes = []
+    last_keyframe_phash = None
+
+    for frame_index, timestamp_ms, image_path in frames:
+        phash = compute_phash(image_path)
+
+        is_keyframe = False
+        if last_keyframe_phash is None:
+            # 第一帧总是关键帧
+            is_keyframe = True
+        else:
+            # 计算海明距离
+            hamming = hamming_distance(last_keyframe_phash, phash)
+            if hamming > threshold:
+                is_keyframe = True
+
+        if is_keyframe:
+            keyframes.append({
+                "frame_index": frame_index,
+                "timestamp_ms": timestamp_ms,
+                "frame_image_path": str(image_path),
+                "phash": phash,
+                "ocr_text": "",  # P2 调用 DeepSeek Vision 填充
+            })
+            last_keyframe_phash = phash
+
+    return keyframes
+
+
+def hamming_distance(s1: str, s2: str) -> int:
+    """计算两个 hex pHash 字符串的海明距离"""
+    if len(s1) != len(s2):
+        # pHash 长度不一致,取较长字符串的长度作为海明距离上限
+        return max(len(s1), len(s2))
+    return sum(c1 != c2 for c1, c2 in zip(s1, s2))
+
+
+# ========================================================================
+# OCR 接口(P2 实现,目前返回占位)
+# ========================================================================
+
+
+def ocr_frame(image_path: Path) -> str:
+    """
+    识别帧内文字,返回纯文本
+
+    P1: 返回占位文本
+    P2: 调用 DeepSeek Vision API
+    """
+    if not DEEPSEEK_API_KEY:
+        # 无 API Key,返回占位
+        return f"[OCR待填充 frame={image_path.name}]"
+
+    # P2 实现:调用 DeepSeek Vision
+    # TODO: P2 实现
+    return f"[OCR待填充 frame={image_path.name}]"
+
+
+def ocr_keyframes(keyframes: List[Dict]) -> List[Dict]:
+    """对关键帧列表逐一调用 OCR"""
+    result = []
+    for kf in keyframes:
+        image_path = Path(kf["frame_image_path"])
+        ocr_text = ocr_frame(image_path)
+        kf_copy = kf.copy()
+        kf_copy["ocr_text"] = ocr_text
+        result.append(kf_copy)
+    return result
+
+
+# ========================================================================
+# B 稿格式化
+# ========================================================================
+
+
+def format_timestamp(ms: int) -> str:
+    """毫秒转 [Nm Ns] 格式"""
+    total_sec = ms // 1000
+    return f"{total_sec // 60}m{total_sec % 60}s"
+
+
+def build_b_manuscript(keyframes: List[Dict]) -> List[str]:
+    """
+    将关键帧 OCR 结果合并为 B 稿
+    合并相邻同文本的关键帧
+    """
+    lines = []
+    last_text = None
+
+    for kf in keyframes:
+        text = kf["ocr_text"].strip()
+        if not text:
+            continue
+
+        # 跳过占位文本
+        if text.startswith("[OCR待填充"):
+            text = ""
+
+        if text and text != last_text:
+            ts = format_timestamp(kf["timestamp_ms"])
+            lines.append(f"[{ts}] {text}")
+            last_text = text
+
+    return lines
+
+
+def write_b_manuscript(lines: List[str], output_path: Path) -> Path:
+    """写入 B 稿 txt"""
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(output_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(lines))
+    return output_path
+
+
+# ========================================================================
+# 主流程
+# ========================================================================
+
+
+def split_video(
+    video_path: Path,
+    output_dir: Path,
+    episode_id: str,
+    phash_threshold: int = 8,
+    fps: int = 1,
+) -> Dict[str, any]:
+    """
+    视频双路拆分主流程
+
+    参数:
+        video_path: 输入视频路径
+        output_dir: 输出目录(work/ 路径)
+        episode_id: 节目 ID
+        phash_threshold: pHash 海明距离阈值,默认 8
+        fps: 抽帧帧率,默认 1(每秒一帧)
+
+    返回:
+        {
+            "b_manuscript_path": Path,
+            "audio_path": Path,
+            "keyframes_path": Path,
+            "keyframe_count": int,
+        }
+    """
+    video_path = Path(video_path)
+    output_dir = Path(output_dir)
+
+    if not video_path.exists():
+        raise FileNotFoundError(f"视频文件不存在: {video_path}")
+
+    # 创建输出目录
+    output_dir.mkdir(parents=True, exist_ok=True)
+    frames_dir = output_dir / "frames"
+    frames_dir.mkdir(parents=True, exist_ok=True)
+
+    print(f"[video_split] 开始处理: {video_path.name}")
+    print(f"[video_split] 抽帧 fps={fps}, pHash threshold={phash_threshold}")
+
+    # ---- A 路:抽帧 + pHash 检测 + OCR ----
+    print("[video_split] A路:抽帧...")
+    frames = extract_frames(video_path, output_dir, fps=fps)
+    print(f"[video_split] 抽帧完成,共 {len(frames)} 帧")
+
+    print("[video_split] pHash 变化检测...")
+    keyframes = find_keyframes(frames, threshold=phash_threshold)
+    print(f"[video_split] 检测到 {len(keyframes)} 个关键帧")
+
+    print("[video_split] OCR 关键帧...")
+    keyframes = ocr_keyframes(keyframes)
+    print(f"[video_split] OCR 完成")
+
+    # ---- B 路:音频提取 ----
+    print("[video_split] B路:提取音频...")
+    audio_path = output_dir / "audio_16k.wav"
+    extract_audio(video_path, audio_path)
+    print(f"[video_split] 音频提取完成: {audio_path}")
+
+    # ---- 输出产物 ----
+    # B 稿
+    b_lines = build_b_manuscript(keyframes)
+    b_manuscript_path = output_dir / "b_manuscript.txt"
+    write_b_manuscript(b_lines, b_manuscript_path)
+    print(f"[video_split] B稿写入: {b_manuscript_path} ({len(b_lines)} 行)")
+
+    # 关键帧索引 JSON
+    keyframes_data = {
+        "video_path": str(video_path),
+        "fps_sampled": fps,
+        "phash_threshold": phash_threshold,
+        "keyframes": keyframes,
+    }
+    keyframes_path = output_dir / "keyframes.json"
+    with open(keyframes_path, "w", encoding="utf-8") as f:
+        json.dump(keyframes_data, f, ensure_ascii=False, indent=2)
+    print(f"[video_split] 关键帧索引写入: {keyframes_path}")
+
+    # 清理临时帧文件(可选,保留供调试)
+    # shutil.rmtree(frames_dir)
+
+    return {
+        "b_manuscript_path": str(b_manuscript_path),
+        "audio_path": str(audio_path),
+        "keyframes_path": str(keyframes_path),
+        "keyframe_count": len(keyframes),
+    }

doco/tests/test_video_split.py

@@ -0,0 +1,141 @@
+# -*- coding: utf-8 -*-
+"""
+video_split 单元测试
+"""
+
+import os
+import json
+from pathlib import Path
+
+import pytest
+
+# 确保 src 在 path 中
+import sys
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from doco.src.video_split import (
+    hamming_distance,
+    format_timestamp,
+    build_b_manuscript,
+    compute_phash,
+)
+
+
+class TestHammingDistance:
+    def test_identical_strings(self):
+        assert hamming_distance("abc", "abc") == 0
+
+    def test_different_strings(self):
+        assert hamming_distance("abc", "abd") == 1
+
+    def test_different_length(self):
+        # 长度不同时,返回较长字符串的长度
+        assert hamming_distance("abc", "ab") == 3
+
+
+class TestFormatTimestamp:
+    def test_zero(self):
+        assert format_timestamp(0) == "0m0s"
+
+    def test_seconds_only(self):
+        assert format_timestamp(30000) == "0m30s"  # 30秒
+
+    def test_minutes_and_seconds(self):
+        assert format_timestamp(90000) == "1m30s"  # 1分30秒
+
+    def test_longer(self):
+        assert format_timestamp(3723000) == "62m3s"  # 62分3秒
+
+
+class TestBuildBManuscript:
+    def test_empty_keyframes(self):
+        lines = build_b_manuscript([])
+        assert lines == []
+
+    def test_single_frame(self):
+        keyframes = [
+            {"timestamp_ms": 0, "ocr_text": "测试字幕"}
+        ]
+        lines = build_b_manuscript(keyframes)
+        assert len(lines) == 1
+        assert "[0m0s]" in lines[0]
+        assert "测试字幕" in lines[0]
+
+    def test_duplicate_text_merged(self):
+        """相邻同文本应合并"""
+        keyframes = [
+            {"timestamp_ms": 0, "ocr_text": "相同"},
+            {"timestamp_ms": 1000, "ocr_text": "相同"},
+            {"timestamp_ms": 2000, "ocr_text": "不同"},
+        ]
+        lines = build_b_manuscript(keyframes)
+        assert len(lines) == 2
+
+    def test_placeholder_skipped(self):
+        """OCR占位文本应跳过"""
+        keyframes = [
+            {"timestamp_ms": 0, "ocr_text": "[OCR待填充 frame=001.png]"},
+            {"timestamp_ms": 1000, "ocr_text": "真实字幕"},
+        ]
+        lines = build_b_manuscript(keyframes)
+        assert len(lines) == 1
+        assert "真实字幕" in lines[0]
+
+
+class TestKeyframesJson:
+    """验证 keyframes.json 输出格式"""
+
+    def test_keyframe_structure(self, tmp_path):
+        """验证单个关键帧的 JSON 结构"""
+        # 模拟关键帧数据
+        kf = {
+            "frame_index": 1,
+            "timestamp_ms": 1000,
+            "frame_image_path": str(tmp_path / "frame_0001.png"),
+            "phash": "ff00aabb12345678",
+            "ocr_text": "测试",
+        }
+
+        # 验证字段存在
+        assert "frame_index" in kf
+        assert "timestamp_ms" in kf
+        assert "frame_image_path" in kf
+        assert "phash" in kf
+        assert "ocr_text" in kf
+
+    def test_keyframes_json_output(self, tmp_path):
+        """验证完整 keyframes.json 输出"""
+        frames_dir = tmp_path / "frames"
+        frames_dir.mkdir()
+
+        # 创建一个测试图片
+        test_img = frames_dir / "frame_0001.png"
+        test_img.write_bytes(b"fake_png_data")
+
+        keyframes_data = {
+            "video_path": str(tmp_path / "video.mp4"),
+            "fps_sampled": 1,
+            "phash_threshold": 8,
+            "keyframes": [
+                {
+                    "frame_index": 0,
+                    "timestamp_ms": 0,
+                    "frame_image_path": str(test_img),
+                    "phash": "abcd1234",
+                    "ocr_text": "首帧",
+                }
+            ]
+        }
+
+        json_path = tmp_path / "keyframes.json"
+        with open(json_path, "w", encoding="utf-8") as f:
+            json.dump(keyframes_data, f, ensure_ascii=False, indent=2)
+
+        # 验证可读
+        with open(json_path, "r", encoding="utf-8") as f:
+            loaded = json.load(f)
+
+        assert loaded["fps_sampled"] == 1
+        assert loaded["phash_threshold"] == 8
+        assert len(loaded["keyframes"]) == 1
+        assert loaded["keyframes"][0]["frame_index"] == 0

docs/api_credentials_inventory.md

@@ -0,0 +1,44 @@
+# API 凭证清单
+
+> TPS 中台所有外部 API 凭证的元信息登记
+> **不存储真实凭证**,真实凭证在各自子模块的 `.env` 中
+> 由制片人维护,key 到期或更换时更新此文档
+
+---
+
+## 字段说明
+
+| 字段 | 含义 |
+|---|---|
+| 子模块 | 凭证所属的子模块 |
+| API 服务 | 具体的 API 服务名称 |
+| Key 类型 | APP_ID+SECRET_KEY / API_KEY / 其他 |
+| 开通日 | 凭证申请日期 |
+| 激活状态 | 是否已激活,额度信息 |
+| 到期日 | 有效期,永久有效则填 — |
+| 责任人 | 谁负责管理/续费 |
+| 备注 | 其他说明 |
+
+---
+
+## Doco 子模块
+
+| 子模块 | API 服务 | Key 类型 | 开通日 | 激活状态 | 到期日 | 责任人 | 备注 |
+|---|---|---|---|---|---|---|---|
+| doco | 讯飞开放平台 - 录音文件转写(标准版) | APP_ID + SECRET_KEY | 2026-06-12 | 待激活(需走 0 元购买) | 2027-06-12 | 制片人 | demo 凭证已过期,需新申请 |
+| doco | DeepSeek Vision | API_KEY | 2026-06-12 | 已激活 | — | 制片人 | doco OCR 用 |
+| doco | Anthropic Claude API | API_KEY | 2026-06-12 | 已激活 | — | 制片人 | AI 融合层(P3) |
+
+---
+
+## 主项目
+
+| 子模块 | API 服务 | Key 类型 | 开通日 | 激活状态 | 到期日 | 责任人 | 备注 |
+|---|---|---|---|---|---|---|---|
+| (主) | Anthropic Claude API | API_KEY | 2026-05-14 | 已激活 | — | 制片人 | Cline Plan/Act 模型用 |
+| (主) | MiniMax M2.7 | API_KEY | 2026-05-14 | 已激活 | — | 制片人 | Cline Plan/Act 模型用 |
+| (主) | DeepSeek API | API_KEY | 2026-05-14 | 已激活 | — | 制片人 | embedding 服务 |
+
+---
+
+*最后更新: 2026-06-12*