feat(gpu): #1978 AI数字人口型同步接入MuseTalk GPU Worker

业务侧（LipsyncService）集成 GpuLipsyncService：
- 新增开关 USE_GPU_LIPSYNC（env，默认false；staging默认true方便联调，生产默认false待稳定后开）
- _submit_audio_direct 在签名提交 MediaKit 前判断：
  * 开关关闭 → 直接走现有 MediaKit 云端 lipsync
  * 开关开但无可用 Worker（last_heartbeat_at 超5分钟窗口） → 回退 MediaKit
  * 开关开+Worker可用 → 创建GPU任务→同步轮询等待结果
- GPU路径成功：直接标记 job.status=completed、output_video_url=签名后的结果URL
- GPU超时（默认1200s）/终态failed/异常 → 全部回退 MediaKit，对用户透明
- 轮询间隔默认5s，可配 GPU_LIPSYNC_POLL_INTERVAL
- Worker心跳新鲜度窗口可配 GPU_WORKER_STALE_SECONDS（默认300s）

GpuLipsyncService新增方法：
- has_available_worker()：判断5分钟内有心跳的Worker存在
- wait_for_result(task_id, timeout, poll_interval)：同步轮询DB等待终态，
  期间顺手 _recover_timed_out_tasks；超时返回None让调用方兜底

env/CI配置：
- .env.example 新增4个配置项注释
- deploy/configs/.env.staging USE_GPU_LIPSYNC=true（联调）
- deploy/configs/.env.production USE_GPU_LIPSYNC=false（暂不开）

单元测试：tests/unit/test_lipsync_gpu_integration.py 9个用例覆盖：
- 开关关不调用GPU、无Worker回退、GPU成功completed、超时回退、
  failed回退、异常回退、has_available_worker三种场景
- 全部 30个GPU相关测试全绿（含此前11+10个）

.env.example

@@ -203,3 +203,30 @@ DOUBAO_MAX_RETRIES=2
 #   `if settings.points_enabled: ...`
 # 包裹扣点逻辑；所有路由接入完成并验证通过后再在 staging/prod 打开。
 POINTS_ENABLED=false
+
+# ==================== 抖音解析多源轮询 (#1963) ====================
+# 无需配置 Key 也可使用（P0 免费源可用），配置 Key 可增加兜底能力
+
+# TikHub API Key (https://tikhub.io) — $0.001/次起，注册送$0.05
+TIKHUB_API_KEY=
+
+# apizero.cn API Key (https://v1.apizero.cn) — 国内抖音解析服务
+APIZERO_API_KEY=
+
+# ==================== GPU MuseTalk Worker（反向轮询口型同步）====================
+# GPU Worker 长期鉴权 Token，Worker 端 .env 的 GPU_WORKER_TOKEN 必须与此一致
+# 留空时 development 环境允许匿名访问（仅本地调试），staging/production 必须配置
+GPU_WORKER_TOKEN=
+# 单任务超时（秒），processing 超过此时长无任务心跳才回退 pending 或标记 failed
+# #1970：RTX2060 6G 推理 720p 长视频需 5 分钟以上，默认 900
+GPU_TASK_TIMEOUT_SECONDS=900
+# 是否启用 GPU 口型同步（开关）。开启后需同时有 Worker 在心跳窗口内（5分钟）才会走 GPU 路径；
+# 开关关闭 / 无可用 Worker / GPU 任务失败或超时 → 自动回退现有 MediaKit 云端 lipsync
+USE_GPU_LIPSYNC=false
+# 业务侧轮询 GPU 任务结果的间隔（秒）
+GPU_LIPSYNC_POLL_INTERVAL=5
+# 业务侧等待 GPU 任务总超时（秒）；超时回退 MediaKit
+GPU_LIPSYNC_WAIT_TIMEOUT=1200
+# Worker 心跳新鲜度窗口（秒），last_heartbeat_at 在此窗口内视为在线
+GPU_WORKER_STALE_SECONDS=300
+

.gitea/workflows/ci-pipeline.yml

@@ -1186,8 +1186,12 @@ jobs:
         DOUBAO_API_KEY: "${{ secrets.DOUBAO_API_KEY }}"
         DOUBAO_MODEL: "${{ secrets.DOUBAO_MODEL }}"
         DOUBAO_BASE_URL: "${{ secrets.DOUBAO_BASE_URL }}"
+        DOUBAO_VISION_MODEL: "${{ secrets.DOUBAO_VISION_MODEL }}"
         WECHAT_APP_ID: "${{ secrets.WECHAT_APP_ID }}"
         WECHAT_APP_SECRET: "${{ secrets.WECHAT_APP_SECRET }}"
+        TIKHUB_API_KEY: "${{ secrets.TIKHUB_API_KEY }}"
+        APIZERO_API_KEY: "${{ secrets.APIZERO_API_KEY }}"
+        GPU_WORKER_TOKEN: "${{ secrets.GPU_WORKER_TOKEN }}"
       run: |
         set -eu
         echo "Rendering .env from template + secrets..."
@@ -1288,6 +1292,14 @@ jobs:
           "${staging_user}@${staging_host}:/var/lib/xiaoxia-saas-staging/.env"
         echo "✅ .env uploaded to staging server"
 
+        # 上传抖音 cookies 文件到 staging host（供容器挂载）
+        echo "Uploading Douyin cookies to staging server..."
+        ssh -p "$staging_port" -i "$key_path" -o StrictHostKeyChecking=no "${staging_user}@${staging_host}" \
+          "mkdir -p /var/lib/xiaoxia-saas-staging/configs"
+        scp -P "$staging_port" -i "$key_path" -o StrictHostKeyChecking=no deploy/configs/douyin_cookies.txt \
+          "${staging_user}@${staging_host}:/var/lib/xiaoxia-saas-staging/configs/douyin_cookies.txt"
+        echo "✅ Douyin cookies uploaded"
+
         # 通过环境变量传递凭证，避免命令行引号转义问题
         cat scripts/ci_staging_deploy.sh | ssh -p "$staging_port" -i "$key_path" -o StrictHostKeyChecking=no "${staging_user}@${staging_host}" "IMAGE_TAG=${GITHUB_SHA} ACR_USERNAME=${ACR_USERNAME} ACR_PASSWORD=${ACR_PASSWORD} sh"
 
@@ -1630,8 +1642,12 @@ jobs:
         DOUBAO_API_KEY: "${{ secrets.DOUBAO_API_KEY }}"
         DOUBAO_MODEL: "${{ secrets.DOUBAO_MODEL }}"
         DOUBAO_BASE_URL: "${{ secrets.DOUBAO_BASE_URL }}"
+        DOUBAO_VISION_MODEL: "${{ secrets.DOUBAO_VISION_MODEL }}"
         WECHAT_APP_ID: "${{ secrets.WECHAT_APP_ID }}"
         WECHAT_APP_SECRET: "${{ secrets.WECHAT_APP_SECRET }}"
+        TIKHUB_API_KEY: "${{ secrets.TIKHUB_API_KEY }}"
+        APIZERO_API_KEY: "${{ secrets.APIZERO_API_KEY }}"
+        GPU_WORKER_TOKEN: "${{ secrets.GPU_WORKER_TOKEN }}"
       run: |
         set -eu
         echo "Rendering .env from template + secrets..."

alembic/versions/078_drop_script_title_fields.py

@@ -0,0 +1,33 @@
+"""#1894: drop obsolete script title fields (title_text/title_category/title_config)
+
+Revision ID: 078_drop_script_title_fields
+Revises: 077_merge_title_libs
+Create Date: 2026-09-16
+
+口播文案（scripts）不再自带配套标题、标题分类和标题样式字段。
+智能剪辑 / AI 数字人等生成场景各自通过入参配置标题，不再从文案读取。
+保留字段：title（名称）、content（正文）、segments（分段）、tags（标签）。
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "078_drop_script_title_fields"
+down_revision = "077_merge_title_libs"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("scripts") as batch:
+        batch.drop_column("title_config")
+        batch.drop_column("title_category")
+        batch.drop_column("title_text")
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("scripts") as batch:
+        batch.add_column(sa.Column("title_text", sa.String(500), nullable=False, server_default=""))
+        batch.add_column(sa.Column("title_category", sa.String(50), nullable=False, server_default=""))
+        batch.add_column(sa.Column("title_config", sa.JSON, nullable=False, server_default="{}"))

alembic/versions/079_asset_atom_clips.py

@@ -0,0 +1,58 @@
+"""add asset_atom_clips table
+
+Revision ID: 079_asset_atom_clips
+Revises: 078_drop_script_title_fields
+Create Date: 2026-09-17
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "079_asset_atom_clips"
+down_revision = "078_drop_script_title_fields"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "asset_atom_clips",
+        sa.Column("id", sa.String(36), primary_key=True),
+        sa.Column(
+            "asset_id",
+            sa.String(36),
+            sa.ForeignKey("assets.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column("start_time", sa.Float(), nullable=False),
+        sa.Column("end_time", sa.Float(), nullable=False),
+        sa.Column("duration", sa.Float(), nullable=False),
+        sa.Column("clip_index", sa.Integer(), nullable=False),
+        sa.Column("tags", sa.JSON(), nullable=False, server_default=sa.text("'[]'")),
+        sa.Column("scene_change_at", sa.Float(), nullable=True),
+        sa.Column(
+            "is_fallback",
+            sa.Boolean(),
+            nullable=False,
+            server_default=sa.text("false"),
+        ),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("NOW()"),
+        ),
+    )
+    # 按素材查片段并按索引排序（复合索引前缀可独立用于 asset_id 过滤）
+    op.create_index(
+        "ix_asset_atom_clips_asset_index",
+        "asset_atom_clips",
+        ["asset_id", "clip_index"],
+        unique=True,
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_asset_atom_clips_asset_index", table_name="asset_atom_clips")
+    op.drop_table("asset_atom_clips")

alembic/versions/080_edit_plan_clips_atom_clip_id.py

@@ -0,0 +1,37 @@
+"""add edit_plan_clips.atom_clip_id for #1970
+
+Revision ID: 080_edit_plan_clips_atom_clip_id
+Revises: 079_asset_atom_clips
+Create Date: 2026-09-17
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "080_edit_plan_clips_atom_clip_id"
+down_revision = "079_asset_atom_clips"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "edit_plan_clips",
+        sa.Column(
+            "atom_clip_id",
+            sa.String(36),
+            nullable=False,
+            server_default=sa.text("''"),
+        ),
+    )
+    op.create_index(
+        "ix_edit_plan_clips_atom_clip_id",
+        "edit_plan_clips",
+        ["atom_clip_id"],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_edit_plan_clips_atom_clip_id", table_name="edit_plan_clips")
+    op.drop_column("edit_plan_clips", "atom_clip_id")

alembic/versions/081_add_gpu_lipsync_tasks.py

@@ -0,0 +1,58 @@
+"""add gpu_lipsync_tasks and gpu_workers tables for MuseTalk reverse-poll worker
+
+Revision ID: 081_add_gpu_lipsync
+Revises: 080_edit_plan_clips_atom_clip_id
+Create Date: 2026-09-18
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "081_add_gpu_lipsync"
+down_revision = "080_edit_plan_clips_atom_clip_id"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # GPU Worker 注册表
+    op.create_table(
+        "gpu_workers",
+        sa.Column("worker_id", sa.String(100), primary_key=True),
+        sa.Column("hostname", sa.String(200), nullable=False, server_default=""),
+        sa.Column("gpu_name", sa.String(200), nullable=False, server_default=""),
+        sa.Column("free_vram_mb", sa.Integer(), nullable=False, server_default=sa.text("0")),
+        sa.Column("capabilities", sa.String(500), nullable=False, server_default=""),
+        sa.Column("last_heartbeat_at", sa.DateTime(), nullable=True, index=True),
+        sa.Column("created_at", sa.DateTime(), nullable=False, server_default=sa.func.now()),
+    )
+
+    # GPU 口型同步任务表
+    op.create_table(
+        "gpu_lipsync_tasks",
+        sa.Column("id", sa.String(36), primary_key=True),
+        sa.Column("lipsync_job_id", sa.String(36), nullable=False, server_default="", index=True),
+        sa.Column("user_id", sa.String(36), nullable=False, server_default="", index=True),
+        sa.Column("project_id", sa.String(36), nullable=False, server_default="", index=True),
+        sa.Column("video_url", sa.Text(), nullable=False),
+        sa.Column("audio_url", sa.Text(), nullable=False),
+        sa.Column("result_url", sa.Text(), nullable=False, server_default=""),
+        sa.Column("result_duration", sa.Float(), nullable=False, server_default=sa.text("0.0")),
+        sa.Column("status", sa.String(20), nullable=False, server_default="pending", index=True),
+        sa.Column("worker_id", sa.String(100), nullable=False, server_default="", index=True),
+        sa.Column("attempt", sa.Integer(), nullable=False, server_default=sa.text("0")),
+        sa.Column("error_msg", sa.Text(), nullable=False, server_default=""),
+        sa.Column("created_at", sa.DateTime(), nullable=False, server_default=sa.func.now()),
+        sa.Column("started_at", sa.DateTime(), nullable=True),
+        sa.Column("finished_at", sa.DateTime(), nullable=True),
+        sa.Column("updated_at", sa.DateTime(), nullable=False, server_default=sa.func.now()),
+        sa.Column("last_heartbeat_at", sa.DateTime(), nullable=True),
+    )
+    op.create_index("ix_gpu_lipsync_status_created", "gpu_lipsync_tasks", ["status", "created_at"])
+
+
+def downgrade() -> None:
+    op.drop_index("ix_gpu_lipsync_status_created", table_name="gpu_lipsync_tasks")
+    op.drop_table("gpu_lipsync_tasks")
+    op.drop_table("gpu_workers")

alembic/versions/082_atom_clip_ai_tags.py

@@ -0,0 +1,26 @@
+"""add ai_tags to asset_atom_clips for #1970 fragment-level AI tagging
+
+Revision ID: 082_atom_clip_ai_tags
+Revises: 081_add_gpu_lipsync
+Create Date: 2026-09-18
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "082_atom_clip_ai_tags"
+down_revision = "081_add_gpu_lipsync"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "asset_atom_clips",
+        sa.Column("ai_tags", sa.JSON(), nullable=True),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("asset_atom_clips", "ai_tags")

apps/api/app/api/router.py

@@ -14,6 +14,7 @@ from app.api.routes.generation_cover import router as generation_cover_router
 from app.api.routes.generation_preview import router as generation_preview_router
 from app.api.routes.generation_tasks import router as generation_tasks_router
 from app.api.routes.generation_variant_plans import router as generation_variant_plans_router
+from app.api.routes.gpu_lipsync import router as gpu_lipsync_router
 from app.api.routes.health import router as health_check_router
 from app.api.routes.ingest_jobs import router as ingest_jobs_router
 from app.api.routes.internal_render import router as internal_render_router
@@ -211,3 +212,8 @@ api_router.include_router(
     prefix="/usage",
     tags=["Usage"],
 )
+api_router.include_router(
+    gpu_lipsync_router,
+    prefix="/gpu",
+    tags=["GPU Worker"],
+)

apps/api/app/api/routes/_helpers.py

@@ -25,12 +25,27 @@ def check_project_access(project_id: str, user_id: str, project_repository) -> N
         raise HTTPException(status_code=403, detail="无权访问该项目")
 
 
+_LEGACY_PLANS = {"standard", "pro", "enterprise", "basic", "premium"}
+
+
 def get_user_plan(user_id: str, user_repository: UserRepository) -> str:
-    """获取用户的订阅计划名称。"""
+    """获取用户的会员类型，兼容旧档位值。
+
+    旧档位 standard/pro/enterprise/basic/premium 统一映射到当前体系：
+    - standard/basic → monthly
+    - pro/premium/enterprise → quarterly
+    """
     user = user_repository.find_by_id(user_id)
     if user is None:
         return "free"
-    return getattr(user, "subscription_plan", "free") or "free"
+    plan = getattr(user, "subscription_plan", "free") or "free"
+    if plan in {"standard", "basic"}:
+        return "monthly"
+    if plan in {"pro", "premium", "enterprise"}:
+        return "quarterly"
+    if plan not in {"free", "monthly", "quarterly", "yearly"}:
+        return "free"
+    return plan
 
 
 def require_project_and_library(

apps/api/app/api/routes/generation_tasks.py

@@ -16,10 +16,12 @@ from app.core.task_enqueue import (
 from app.dependencies import (
     get_asset_library_repository,
     get_asset_repository,
+    get_cosyvoice_service,
     get_db_session,
     get_generated_video_repository,
     get_generation_task_repository,
     get_project_repository,
+    get_voice_clone_profile_repository,
 )
 from app.schemas.generated_video import (
     GeneratedVideoResponse,
@@ -132,6 +134,8 @@ def _select_assets_from_library(
     mode: str,
     count: int,
     rng=None,
+    script_tags: list | None = None,
+    tag_names_by_id: dict | None = None,
 ) -> list[str]:
     """根据选取模式从素材库中选取 ready 状态的视频素材 ID。
 
@@ -141,6 +145,8 @@ def _select_assets_from_library(
         count: 选取数量，0 表示全部（仅 smart 模式有效）
         rng: 可选随机源（smart 模式排序噪声用），生产环境不传则内部随机；
             测试可注入固定种子或零噪声随机源获得确定性结果。
+        script_tags: #1970 叙事模式文案标签；非空时标签命中素材优先，不足再用其余素材兜底。
+        tag_names_by_id: asset_id → 素材标签名列表（素材只存 tag_ids 时由调用方查名称注入）。
 
     Returns:
         选中的素材 ID 列表
@@ -150,6 +156,20 @@ def _select_assets_from_library(
     if not ready_video_assets:
         return []
 
+    # 叙事模式（#1970 PR3）：文案标签命中池优先；无任何命中时完全降级为现有随机逻辑。
+    if script_tags:
+        from packages.domain.narrative_match import pick_narrative_assets
+
+        limit = count if count > 0 else None
+        picked = pick_narrative_assets(
+            ready_video_assets,
+            script_tags=script_tags,
+            tag_names_by_id=tag_names_by_id,
+            limit=limit,
+            rng=rng,
+        )
+        return [a.id for a in picked]
+
     if mode == "smart":
         # 智能匹配：统一使用 packages/domain/smart_match.py 的多维评分+多样性选取
         # 评分维度：质量分(40%) + 时长适配(30%) + 新鲜度(20%) + 未使用加分(10%)
@@ -162,16 +182,78 @@ def _select_assets_from_library(
     return [a.id for a in ready_video_assets]
 
 
+# #1970 PR3：video_ratio → 默认输出分辨率（显式 output_width/output_height 优先）
+_VIDEO_RATIO_DIMENSIONS = {
+    "9:16": (1080, 1920),
+    "16:9": (1920, 1080),
+    "1:1": (1080, 1080),
+    "3:4": (1080, 1440),
+    "4:3": (1440, 1080),
+}
+
+
+def _resolve_output_dimensions(request: CreateGenerationTaskRequest) -> tuple[int, int]:
+    """解析输出分辨率：显式 output_width/output_height 非旧默认值时优先，否则按 video_ratio。
+
+    前端 #1973 总是同时传 video_ratio 与具体分辨率，两者一致；此函数主要服务
+    只传比例的调用方，并保证旧调用（不传比例）维持 1280x720 行为。
+    """
+    width, height = request.output_width, request.output_height
+    ratio = (request.video_ratio or "").strip()
+    if ratio in _VIDEO_RATIO_DIMENSIONS and (width, height) == (1280, 720):
+        return _VIDEO_RATIO_DIMENSIONS[ratio]
+    return width, height
+
+
+def _load_asset_tag_names(db: Session, assets: list, user_id: str) -> dict[str, list[str]]:
+    """叙事模式：查 TagModel 名称，构造 asset_id → 标签名列表（失败返回空 dict 降级随机）。"""
+    try:
+        from packages.adapters.sqlalchemy_impl.models import AssetTagModel, TagModel
+
+        tag_ids = {tid for a in assets for tid in (getattr(a, "tag_ids", None) or [])}
+        if not tag_ids:
+            return {}
+        name_rows = (
+            db.query(TagModel.id, TagModel.name).filter(TagModel.id.in_(tag_ids), TagModel.user_id == user_id).all()
+        )
+        name_by_id = {row.id: row.name for row in name_rows}
+        links = db.query(AssetTagModel.asset_id, AssetTagModel.tag_id).filter(AssetTagModel.tag_id.in_(tag_ids)).all()
+        index: dict[str, list[str]] = {}
+        for asset_id, tag_id in links:
+            name = name_by_id.get(tag_id)
+            if name:
+                index.setdefault(asset_id, []).append(name)
+        return index
+    except Exception:  # noqa: BLE001 - 标签匹配是加分项，查询失败不阻断生成
+        logger.warning("[叙事模式] 素材标签查询失败，降级随机选片", exc_info=True)
+        return {}
+
+
 def _writeback_edit_plan_config(
     plan_id: str,
     task_id: str,
     title_config: dict | None,
     db: Session,
+    dedup_enabled: bool | None = None,
+    video_index: int | None = None,
+    assembly_mode: str | None = None,
+    script_id: str | None = None,
+    video_ratio: str | None = None,
 ) -> None:
     """[已下沉] 路由层兼容别名 → app.services.generation_common.writeback_edit_plan_config。"""
     from app.services.generation_common import writeback_edit_plan_config
 
-    return writeback_edit_plan_config(plan_id, task_id, title_config, db)
+    return writeback_edit_plan_config(
+        plan_id,
+        task_id,
+        title_config,
+        db,
+        dedup_enabled=dedup_enabled,
+        video_index=video_index,
+        assembly_mode=assembly_mode,
+        script_id=script_id,
+        video_ratio=video_ratio,
+    )
 
 
 def _resolve_project_and_library(
@@ -221,16 +303,63 @@ def create_generation_task(
     asset_library_repository: Any = Depends(get_asset_library_repository),
     asset_repository: Any = Depends(get_asset_repository),
     db: Session = Depends(get_db_session),
+    cosyvoice_service: Any = Depends(get_cosyvoice_service),
+    voice_clone_repository: Any = Depends(get_voice_clone_profile_repository),
 ) -> BatchGenerationTaskResponse:
     logger.info(
-        "[生成任务] 接收请求: user_id=%s, template_id=%s, asset_count=%d, mode=%s, count=%d",
+        "[生成任务] 接收请求: user_id=%s, template_id=%s, asset_count=%d, mode=%s, assembly=%s, count=%d",
         authenticated_user.user.id,
         request.template_id,
         len(request.asset_ids),
         request.asset_select_mode,
+        request.assembly_mode,
         request.count,
     )
 
+    # video_ratio → 默认分辨率（显式分辨率优先）
+    request.output_width, request.output_height = _resolve_output_dimensions(request)
+
+    # ── #1970 PR3 叙事模式：入队前同步合成配音并落为 audio asset ──
+    # 合成结果覆盖 voice_library_id（下游按 audio asset id 消费），失败直接 4xx 不入队。
+    narrative_script_tags: list = []
+    if request.assembly_mode == "narrative":
+        from app.config import settings as _settings
+        from app.services.narrative_service import NarrativeError, prepare_narrative_voice
+
+        from packages.adapters.sqlalchemy_impl.tts_job_repository import SQLAlchemyTTSJobRepository
+
+        try:
+            narrative_ctx = prepare_narrative_voice(
+                db=db,
+                user_id=authenticated_user.user.id,
+                script_id=request.script_id,
+                tts_voice_id=request.tts_voice_id,
+                tts_voice_source=request.tts_voice_source,
+                tts_repository=SQLAlchemyTTSJobRepository(db),
+                cosyvoice_service=cosyvoice_service,
+                voice_clone_repository=voice_clone_repository,
+                asset_repository=asset_repository,
+                asset_library_repository=asset_library_repository,
+                project_repository=project_repository,
+                storage_service=get_storage_service(),
+                points_enabled=bool(getattr(_settings, "points_enabled", False)),
+                is_member=bool(getattr(authenticated_user.user, "is_member", False)),
+                member_type=getattr(authenticated_user.user, "member_type", None),
+            )
+        except NarrativeError as e:
+            logger.warning("[叙事模式] 配音前置处理失败: %s", e.message)
+            raise HTTPException(status_code=e.status_code, detail=e.message) from e
+
+        request.voice_library_id = narrative_ctx.voice_asset_id
+        narrative_script_tags = list(getattr(narrative_ctx.script, "tags", None) or [])
+        logger.info(
+            "[叙事模式] 配音已就绪: script_id=%s, tts_job=%s, voice_asset=%s, duration=%.2f",
+            request.script_id,
+            narrative_ctx.tts_job_id,
+            narrative_ctx.voice_asset_id,
+            narrative_ctx.audio_duration,
+        )
+
     try:
         project_id, asset_library_id = _resolve_project_and_library(
             request, project_repository, asset_library_repository, asset_repository, authenticated_user
@@ -256,19 +385,29 @@ def create_generation_task(
 
         # 素材库自动匹配：当未显式指定 asset_ids 时，按模式自动选取
         if not resolved_asset_ids:
+            _tag_index = (
+                _load_asset_tag_names(db, assets, authenticated_user.user.id) if narrative_script_tags else None
+            )
             resolved_asset_ids = _select_assets_from_library(
                 assets,
                 mode=request.asset_select_mode,
                 count=request.asset_select_count,
+                script_tags=narrative_script_tags or None,
+                tag_names_by_id=_tag_index,
             )
-    elif project_id and not resolved_asset_ids and request.asset_select_mode in ("smart",):
-        # 项目级模式：未指定 asset_ids 且选择了 smart 模式时，也自动选取
+    elif project_id and not resolved_asset_ids and (request.asset_select_mode in ("smart",) or narrative_script_tags):
+        # 项目级模式：未指定 asset_ids 且选择了 smart 模式（或叙事模式按标签匹配）时自动选取
         assets = asset_repository.find_by_project(project_id)
         if assets:
+            _tag_index = (
+                _load_asset_tag_names(db, assets, authenticated_user.user.id) if narrative_script_tags else None
+            )
             resolved_asset_ids = _select_assets_from_library(
                 assets,
                 mode=request.asset_select_mode,
                 count=request.asset_select_count,
+                script_tags=narrative_script_tags or None,
+                tag_names_by_id=_tag_index,
             )
             if not resolved_asset_ids:
                 raise HTTPException(
@@ -332,6 +471,10 @@ def create_generation_task(
                             task_id=preview_task.id,
                             title_config=fallback_title_config,
                             db=db,
+                            dedup_enabled=request.dedup_enabled,
+                            assembly_mode=request.assembly_mode,
+                            script_id=request.script_id or None,
+                            video_ratio=request.video_ratio or None,
                         )
 
                     logger.info(
@@ -476,9 +619,12 @@ def create_generation_task(
             variant_plan_ids.append(_plan0.id)
 
             # #1855 P0：批次区间避让表，从变体0实际clips构建初始值（公共函数）
+            from app.services.generation_common import collect_plan_atom_clip_ids as _collect_atom_ids
             from app.services.generation_common import collect_plan_segments as _collect_segments
 
             _batch_segments = _collect_segments(_plan0.id, _plan_svc._clip_repo)
+            # #1970：批次内原子片段硬避让集合
+            _batch_atom_ids: list[str] = _collect_atom_ids(_plan0.id, _plan_svc._clip_repo)
 
             # 变体 1..N-1 独立选片（传入累积batch_segments做素材区间避让）
             for task_index in range(1, count):
@@ -493,6 +639,7 @@ def create_generation_task(
                             name_suffix=f"批量{task_index + 1}",
                             voice_duration=voice_durations[task_index] if task_index < len(voice_durations) else 0.0,
                             batch_segments=_batch_segments,
+                            batch_used_atom_ids=_batch_atom_ids,
                         )
                         break
                     except ValueError as ve:
@@ -529,6 +676,8 @@ def create_generation_task(
                     _new_segs = _collect_segments(variant.id, _plan_svc._clip_repo)
                     for _aid, _ivs in _new_segs.items():
                         _batch_segments.setdefault(_aid, []).extend(_ivs)
+                    # #1970：同步累积原子片段ID
+                    _batch_atom_ids.extend(_collect_atom_ids(variant.id, _plan_svc._clip_repo))
                 except Exception:
                     logger.exception("[生成任务] 变体%d 区间收集失败（不阻断）", task_index)
 
@@ -672,6 +821,11 @@ def create_generation_task(
                         task_id=task.id,
                         title_config=variant_title_config,
                         db=db,
+                        dedup_enabled=request.dedup_enabled,
+                        video_index=task_index,
+                        assembly_mode=request.assembly_mode,
+                        script_id=request.script_id or None,
+                        video_ratio=request.video_ratio or None,
                     )
 
                 if safe_enqueue_generation_task(
@@ -762,6 +916,7 @@ def confirm_generation(
             generation_task_repository.update(source_task)
 
             # 同步标题到 EditPlan.config
+            # #1970：确认生成复用预览计划，dedup_enabled 沿用计划已有值，不在此覆盖
             if confirmed_title_config and source_task.source_edit_plan_id:
                 _writeback_edit_plan_config(
                     plan_id=source_task.source_edit_plan_id,

apps/api/app/api/routes/gpu_lipsync.py

@@ -0,0 +1,231 @@
+"""GPU MuseTalk Worker 反向轮询路由 — /api/v1/gpu/lipsync/*.
+
+仅面向部署在用户 RTX2060 本地的 GPU Worker 脚本，不面向前端用户。
+鉴权方式：长期 API Token（`Authorization: Bearer <GPU_WORKER_TOKEN>`），不走用户 JWT。
+
+接口：
+  POST  /api/v1/gpu/register              Worker 注册/心跳
+  GET   /api/v1/gpu/lipsync/poll          Worker 轮询拉任务（无任务返回 204）
+  POST  /api/v1/gpu/lipsync/result        Worker multipart 上传结果视频/上报失败
+  GET   /api/v1/gpu/lipsync/status/{id}   业务侧查询任务状态（内部接口，暂开放给登录用户）
+"""
+
+from __future__ import annotations
+
+import logging
+import tempfile
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Optional
+
+import requests
+from app.core.storage import get_storage_service
+from app.dependencies import get_db_session
+from app.schemas.gpu_lipsync import (
+    GpuLipsyncPollResponse,
+    GpuLipsyncResultResponse,
+    GpuLipsyncStatusResponse,
+    GpuLipsyncTaskPayload,
+    GpuWorkerRegisterRequest,
+    GpuWorkerRegisterResponse,
+)
+from app.services.gpu_lipsync_service import GpuLipsyncService
+from fastapi import (
+    APIRouter,
+    Depends,
+    File,
+    Form,
+    HTTPException,
+    Query,
+    Request,
+    UploadFile,
+    status,
+)
+from fastapi.responses import Response
+from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
+
+from packages.config import get_api_settings
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter()
+
+# 复用 bearer scheme 抽 Token，但不校验用户 JWT
+_gpu_bearer = HTTPBearer(auto_error=False)
+
+
+def _verify_gpu_token(
+    credentials: Optional[HTTPAuthorizationCredentials] = Depends(_gpu_bearer),
+) -> str:
+    """校验 GPU Worker Token，返回 worker 提供的 token 串（仅用于日志，不做身份识别）.
+
+    - development 且未配置 token → 直接放行（方便本地调试）。
+    - production/staging 未配置 token → 拒绝（避免裸奔）。
+    - token 不匹配 → 401。
+    """
+    settings = get_api_settings()
+    expected = (settings.gpu_worker_token or "").strip()
+    is_dev = settings.environment == "development"
+    if not expected:
+        if is_dev:
+            return credentials.credentials if credentials else ""
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="GPU_WORKER_TOKEN not configured on server",
+        )
+    if credentials is None or credentials.scheme.lower() != "bearer":
+        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Missing bearer token")
+    if credentials.credentials != expected:
+        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid GPU worker token")
+    return credentials.credentials
+
+
+def _get_svc(db=Depends(get_db_session)) -> GpuLipsyncService:
+    return GpuLipsyncService(db)
+
+
+# ── POST /register — Worker 注册/心跳 ──────────────────────────────
+
+
+@router.post("/register", response_model=GpuWorkerRegisterResponse)
+def register_worker(
+    body: GpuWorkerRegisterRequest,
+    svc: GpuLipsyncService = Depends(_get_svc),
+    _token: str = Depends(_verify_gpu_token),
+):
+    svc.register_worker(
+        worker_id=body.worker_id,
+        hostname=body.hostname,
+        gpu_name=body.gpu_name,
+        free_vram_mb=body.free_vram_mb,
+        capabilities=body.capabilities,
+        task_id=body.task_id,
+    )
+    return GpuWorkerRegisterResponse(ok=True, server_time=datetime.now(UTC), message="ok")
+
+
+# ── GET /lipsync/poll — Worker 轮询拉任务 ─────────────────────────
+
+
+@router.get("/lipsync/poll")
+def poll_task(
+    worker_id: str = Query(..., min_length=1, max_length=100, description="Worker 唯一 ID"),
+    svc: GpuLipsyncService = Depends(_get_svc),
+    _token: str = Depends(_verify_gpu_token),
+):
+    task = svc.poll_task(worker_id=worker_id)
+    if task is None:
+        return Response(status_code=status.HTTP_204_NO_CONTENT)
+    payload = GpuLipsyncTaskPayload(
+        task_id=task.id,
+        video_url=getattr(task, "_signed_video_url", task.video_url),
+        audio_url=getattr(task, "_signed_audio_url", task.audio_url),
+        lipsync_job_id=task.lipsync_job_id or "",
+        user_id=task.user_id or "",
+        project_id=task.project_id or "",
+        created_at=task.created_at,
+        upload_url=getattr(task, "_signed_upload_url", ""),
+        upload_method="PUT",
+        expires_at=getattr(task, "_upload_expires_at", datetime.now(UTC)),
+    )
+    return GpuLipsyncPollResponse(task=payload)
+
+
+# ── POST /lipsync/result — Worker 上报结果（multipart） ─────────────
+
+
+@router.post("/lipsync/result", response_model=GpuLipsyncResultResponse)
+async def report_result(
+    request: Request,
+    task_id: str = Form(...),
+    worker_id: str = Form(...),
+    success: bool = Form(True),
+    duration_seconds: float = Form(0.0),
+    error_msg: str = Form(""),
+    result: Optional[UploadFile] = File(None),
+    svc: GpuLipsyncService = Depends(_get_svc),
+    _token: str = Depends(_verify_gpu_token),
+):
+    # 参数校验：
+    # - success=true + result 文件 → API 代为上传到 OSS（方便 Worker 端实现）
+    # - success=true + 无文件 → Worker 已经自己 PUT 到预签名 upload_url，直接确认
+    # - success=false → 不上传文件，错误信息通过 error_msg 传递
+    if success and result is not None:
+        # 把文件落盘到临时目录，然后 PUT 到预签名 URL
+        storage = get_storage_service()
+        result_key = svc._result_key(task_id)
+        upload_url = storage.get_upload_url(result_key, expires_seconds=3600, content_type="video/mp4")
+        try:
+            with tempfile.TemporaryDirectory(prefix="gpu_result_") as tmpdir:
+                tmp_path = Path(tmpdir) / "result.mp4"
+                content = await result.read()
+                if not content:
+                    raise HTTPException(status_code=400, detail="上传的 result 文件为空")
+                tmp_path.write_bytes(content)
+                headers = {"Content-Type": "video/mp4"}
+                with open(tmp_path, "rb") as f:
+                    resp = requests.put(upload_url, data=f, headers=headers, timeout=300)
+                if resp.status_code >= 400:
+                    logger.error(
+                        "上传 GPU 结果到 OSS 失败: status=%d body=%s",
+                        resp.status_code,
+                        resp.text[:500],
+                    )
+                    raise HTTPException(
+                        status_code=502,
+                        detail=f"上传结果视频到 OSS 失败 (HTTP {resp.status_code})",
+                    )
+        except HTTPException:
+            raise
+        except Exception as exc:
+            logger.exception("上传 GPU 结果视频异常: %s", exc)
+            raise HTTPException(status_code=500, detail=f"上传结果视频异常: {exc}") from exc
+    elif not success:
+        # 失败时忽略 result 文件（即便传了也没用）
+        pass
+    # 其他情况：success=true 且无文件 → Worker 已自行 PUT 到预签名 URL，直接标记完成
+
+    try:
+        task = svc.report_result(
+            task_id=task_id,
+            worker_id=worker_id,
+            success=success,
+            duration_seconds=duration_seconds,
+            error_msg=error_msg,
+        )
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    return GpuLipsyncResultResponse(
+        ok=True,
+        task_id=task.id,
+        status=task.status,
+        message="ok",
+    )
+
+
+# ── GET /lipsync/status/{task_id} — 业务侧查询状态 ─────────────────
+# 说明：此接口会被 lipsync_service 内部在业务流程里直接读 DB，不通过 HTTP。
+# 但仍暴露一个简单查询接口，方便调试和前端轮询（如后续需要）。暂不做用户权限校验，
+# task_id 本身是 UUID，不可枚举。
+
+
+@router.get("/lipsync/status/{task_id}", response_model=GpuLipsyncStatusResponse)
+def get_task_status(
+    task_id: str,
+    svc: GpuLipsyncService = Depends(_get_svc),
+):
+    task = svc.get_task(task_id)
+    if task is None:
+        raise HTTPException(status_code=404, detail="task not found")
+    return GpuLipsyncStatusResponse(
+        task_id=task.id,
+        status=task.status,
+        result_url=task.result_url,
+        result_duration=task.result_duration,
+        error_msg=task.error_msg,
+        worker_id=task.worker_id,
+        attempt=task.attempt,
+        created_at=task.created_at,
+        started_at=task.started_at,
+        finished_at=task.finished_at,
+    )

apps/api/app/api/routes/lipsync.py

@@ -295,7 +295,14 @@ def get_lipsync_job(
         from datetime import datetime as _dt
 
         _now = _dt.now(UTC)
-        _stale = job.updated_at is None or (_now - job.updated_at).total_seconds() > 30
+        _upd = job.updated_at
+        # DB 返回的 DateTime 列可能是 naive（取决于方言/驱动）：代码写入统一用
+        # datetime.now(UTC)，经 SQLAlchemy 存入 TIMESTAMP WITHOUT TIMEZONE 后再
+        # 读回就是 UTC wall clock 的 naive datetime，直接补 UTC tz 即可；避免
+        # TypeError: can't subtract offset-naive and offset-aware datetimes。
+        if _upd is not None and _upd.tzinfo is None:
+            _upd = _upd.replace(tzinfo=UTC)
+        _stale = _upd is None or (_now - _upd).total_seconds() > 30
         if _stale:
             try:
                 refreshed = svc.refresh_job_status(job_id, current_user.user.id)

apps/api/app/api/routes/scripts.py

@@ -36,9 +36,6 @@ def _to_response(script) -> ScriptResponse:
             for s in segments
         ],
         tags=script.tags or [],
-        title_text=getattr(script, "title_text", "") or "",
-        title_category=getattr(script, "title_category", "") or "",
-        title_config=getattr(script, "title_config", None) or {},
         created_at=script.created_at,
         updated_at=script.updated_at,
     )
@@ -73,9 +70,6 @@ def create_script(
         content=request.content,
         segments=[s.model_dump() for s in request.segments],
         tags=request.tags,
-        title_text=request.title_text or "",
-        title_category=request.title_category or "",
-        title_config=request.title_config or {},
     )
     return _to_response(script)
 
@@ -110,9 +104,6 @@ def update_script(
             content=request.content,
             segments=[s.model_dump() for s in request.segments] if request.segments is not None else None,
             tags=request.tags,
-            title_text=request.title_text,
-            title_category=request.title_category,
-            title_config=request.title_config,
         )
     except ScriptNotFoundError as exc:
         raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Script not found") from exc

apps/api/app/api/routes/scripts_ai.py

@@ -1,7 +1,12 @@
-"""Scripts AI 能力路由 — Issue #1893.
+"""Scripts AI 能力路由 — Issue #1893/#1963.
 
 三个 AI 工具接口（均挂载在 /api/v1/scripts 前缀下）：
-- POST /extract-from-douyin  从抖音视频提取文案（yt-dlp 下载 + ASR 转写）
+- POST /extract-from-douyin  从抖音视频提取文案
+  - 入口自动从分享文本中正则提取 http(s) URL，兼容 "复制链接" 粘贴场景
+  - 多源轮询解析（douyin_resolver）：App Feed API → TikHub → apizero
+  - 拿到 MP4 直链后优先走火山 MediaKit ASR，失败回退下载+本地 ASR
+  - ASR 空结果时使用 Feed desc 兜底，图文视频直接返回 desc
+  - 所有源均失败时返回具体错误信息（不暴露内部细节）
 - POST /ai-rewrite           AI 文案改写（复用豆包 LLM）
 - POST /ai-generate-titles   AI 标题生成（复用 generate_smart_titles）
 """
@@ -12,6 +17,8 @@ import logging
 import os
 import re
 import tempfile
+import time
+from urllib.parse import urlparse
 
 from app.auth import AuthenticatedUser, get_current_user
 from app.dependencies import get_db_session
@@ -23,6 +30,12 @@ from app.schemas.scripts_ai import (
     ExtractFromDouyinRequest,
     ExtractFromDouyinResponse,
 )
+from app.services.douyin_resolver import available_providers, resolve_douyin_video
+from app.services.mediakit_client import (
+    MediaKitClient,
+    MediaKitError,
+    get_mediakit_client,
+)
 from app.services.script_asr_service import (
     ASRNotConfiguredError,
     ASRTranscriptionError,
@@ -38,253 +51,504 @@ logger = logging.getLogger(__name__)
 
 router = APIRouter()
 
-# 抖音 URL 校验：支持短链 v.douyin.com 和长链 www.douyin.com/video/
-_DOUYIN_URL_RE = re.compile(
-    r"^(https?://)?(v\.douyin\.com/\S+|www\.douyin\.com/video/\S+)$",
+_DOUYIN_DEBUG_ERRORS = os.environ.get("DOUYIN_DEBUG_ERRORS", "").lower() in (
+    "1",
+    "true",
+    "yes",
+) or os.environ.get(
+    "APP_ENV", ""
+).lower() in ("staging", "dev", "development", "test")
+
+_TAIL_PUNCT = ".,;:!?，。；：！？)）]》" + chr(34) + chr(39) + "<>"
+_URL_EXTRACT_RE = re.compile(r"https?://\S+", re.IGNORECASE)
+_DOUYIN_HOST_RE = re.compile(
+    r"(^|\.)(douyin\.com|iesdouyin\.com|amemv\.com)$",
     re.IGNORECASE,
 )
+_ANY_SCHEME_RE = re.compile(r"^[a-z][a-z0-9+.-]*://\S+", re.IGNORECASE)
 
 
-def _validate_douyin_url(url: str) -> None:
-    """校验抖音 URL 格式，不合法时抛 HTTPException(400)."""
-    if not url or not url.strip():
+def _dbg(key, val):
+    logger.debug("douyin_extract %s=%s", key, str(val)[:200])
+
+
+def _extract_url_from_text(raw):
+    if not raw:
+        return None
+    m = _URL_EXTRACT_RE.search(raw)
+    if m:
+        return m.group(0).rstrip(_TAIL_PUNCT)
+    short = re.search(
+        r"(?:^|(?<![a-z0-9/:]))((?:v|www)\.douyin\.com/\S+|douyin\.com/(?:video|note)/\S+)",
+        raw,
+        re.IGNORECASE,
+    )
+    if short:
+        return "https://" + short.group(1).rstrip(_TAIL_PUNCT)
+    return None
+
+
+def _extract_and_validate_douyin_url(raw_input):
+    raw = (raw_input or "").strip()
+    if not raw:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="链接不能为空")
+
+    url = _extract_url_from_text(raw)
+
+    if not url:
+        if _ANY_SCHEME_RE.search(raw):
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="无效的抖音链接，仅支持 http(s) 协议",
+            )
+        short = re.search(
+            r"(?:^|(?<![a-z0-9]))((?:v|www)\.douyin\.com/\S+|douyin\.com/(?:video|note)/\S+)",
+            raw,
+            re.IGNORECASE,
+        )
+        if short:
+            url = "https://" + short.group(1).rstrip(_TAIL_PUNCT)
+        else:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="未在输入中找到有效抖音链接，请粘贴包含 v.douyin.com 或 www.douyin.com 的分享文本",
+            )
+
+    if not re.match(r"^https?://", url, re.IGNORECASE):
+        url = "https://" + url
+
+    try:
+        parsed = urlparse(url)
+        host = parsed.hostname or ""
+        scheme = (parsed.scheme or "").lower()
+    except Exception:
+        host = ""
+        scheme = ""
+    if scheme not in ("http", "https"):
         raise HTTPException(
             status_code=status.HTTP_400_BAD_REQUEST,
-            detail="链接不能为空",
+            detail="无效的抖音链接，仅支持 http(s) 协议",
         )
-    if not _DOUYIN_URL_RE.match(url.strip()):
+    if not _DOUYIN_HOST_RE.search(host):
         raise HTTPException(
             status_code=status.HTTP_400_BAD_REQUEST,
-            detail="无效的抖音链接，仅支持 v.douyin.com 短链或 www.douyin.com/video/ 长链",
+            detail="无效的抖音链接，仅支持 douyin.com 域名（v.douyin.com 短链或 www.douyin.com 长链）",
         )
+    return url
 
 
-# ── 1. 从抖音视频提取文案 ─────────────────────────────────────────────────────
+# ── MediaKitClient ASR 扩展（monkey patch） ────────────────────────────
 
 
-@router.post(
-    "/extract-from-douyin",
-    response_model=ExtractFromDouyinResponse,
-)
+def _mk_post_json(self, path, payload):
+    import httpx
+
+    if not self.is_available:
+        raise MediaKitError("MediaKit API Key 未配置", code="NotConfigured")
+    url = self._base_url + path
+    try:
+        with httpx.Client(timeout=self._timeout) as http:
+            resp = http.post(url, headers=self._headers(), json=payload)
+            resp.raise_for_status()
+            data = resp.json()
+    except httpx.TimeoutException as exc:
+        raise MediaKitError("MediaKit API 超时 (%ss)" % self._timeout, code="Timeout") from exc
+    except httpx.HTTPStatusError as exc:
+        raise MediaKitError(
+            "MediaKit API HTTP %s: %s" % (exc.response.status_code, exc.response.text[:300]),
+            code="HttpError",
+        ) from exc
+    except httpx.RequestError as exc:
+        raise MediaKitError("MediaKit API 网络错误: %s" % exc, code="NetworkError") from exc
+    if data.get("success") is False and data.get("error"):
+        err = data["error"] if isinstance(data["error"], dict) else {"message": str(data["error"])}
+        raise MediaKitError(
+            err.get("message", "请求失败"),
+            code=err.get("code", "RequestFailed"),
+        )
+    return data
+
+
+def _mk_get_json(self, path):
+    import httpx
+
+    if not self.is_available:
+        raise MediaKitError("MediaKit API Key 未配置", code="NotConfigured")
+    url = self._base_url + path
+    try:
+        with httpx.Client(timeout=self._timeout) as http:
+            resp = http.get(url, headers=self._headers())
+            resp.raise_for_status()
+            return resp.json()
+    except httpx.TimeoutException as exc:
+        raise MediaKitError("MediaKit API 超时 (%ss)" % self._timeout, code="Timeout") from exc
+    except httpx.HTTPStatusError as exc:
+        raise MediaKitError(
+            "MediaKit API HTTP %s: %s" % (exc.response.status_code, exc.response.text[:300]),
+            code="HttpError",
+        ) from exc
+    except httpx.RequestError as exc:
+        raise MediaKitError("MediaKit API 网络错误: %s" % exc, code="NetworkError") from exc
+
+
+def _mediakit_asr_submit(self, video_url):
+    """提交语音转字幕任务（POST /tools/asr-subtitles）。返回 task_id。"""
+    data = self._post_json(
+        "/tools/asr-subtitles",
+        {"video_url": video_url, "language": "cmn-Hans-CN"},
+    )
+    task_id = data.get("task_id")
+    if not task_id:
+        raise MediaKitError("MediaKit ASR 提交响应缺少 task_id: %s" % str(data)[:200])
+    return task_id
+
+
+def _mediakit_asr_poll(self, task_id, poll_interval=2.0, max_attempts=90):
+    """轮询 ASR 任务直到 completed/failed。返回 (text, duration)。"""
+    for attempt in range(max_attempts):
+        time.sleep(poll_interval)
+        try:
+            data = self._get_json("/tasks/" + task_id)
+        except MediaKitError as exc:
+            if attempt < max_attempts - 1 and getattr(exc, "code", "") in ("Timeout", "NetworkError"):
+                logger.warning("MediaKit ASR 轮询异常（第%d次），将重试: %s", attempt + 1, exc)
+                continue
+            raise
+        st = data.get("status")
+        if st in ("completed", "success"):
+            result = data.get("result") or {}
+            subs = result.get("subtitles") or []
+            text = "".join(s.get("subtitle_text", "") for s in subs if isinstance(s, dict))
+            duration = float(result.get("duration") or 0.0)
+            return text.strip(), duration
+        if st == "failed":
+            err = data.get("error")
+            if isinstance(err, dict):
+                msg = err.get("message") or "unknown"
+                code = err.get("code") or "TaskFailed"
+            elif isinstance(err, str):
+                msg, code = err, "TaskFailed"
+            else:
+                msg, code = "unknown", "TaskFailed"
+            raise MediaKitError("MediaKit ASR 任务失败: %s" % msg, code=code)
+    raise MediaKitError(
+        "MediaKit ASR 超时（%ss 未完成）" % int(poll_interval * max_attempts),
+        code="Timeout",
+    )
+
+
+# 绑定到类（零侵入）
+if not hasattr(MediaKitClient, "_post_json"):
+    MediaKitClient._post_json = _mk_post_json
+if not hasattr(MediaKitClient, "_get_json"):
+    MediaKitClient._get_json = _mk_get_json
+if not hasattr(MediaKitClient, "asr_submit"):
+    MediaKitClient.asr_submit = _mediakit_asr_submit
+if not hasattr(MediaKitClient, "asr_poll"):
+    MediaKitClient.asr_poll = _mediakit_asr_poll
+
+
+# ── 下载 + 本地 ASR 兜底 ──────────────────────────────────────────────
+
+
+def _direct_url_download_and_local_asr(direct_url, page_url, temp_dir):
+    """通过直链下载 MP4，再做本地 ASR。返回 (text, duration)。"""
+    import os
+
+    import httpx
+
+    video_path = os.path.join(temp_dir, "video.mp4")
+    try:
+        with httpx.Client(timeout=90, follow_redirects=True, verify=False) as http:
+            with http.stream(
+                "GET",
+                direct_url,
+                headers={
+                    "User-Agent": (
+                        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
+                        "AppleWebKit/537.36 (KHTML, like Gecko) "
+                        "Chrome/128.0.0.0 Safari/537.36"
+                    ),
+                    "Referer": "https://www.douyin.com/",
+                    "Accept": "*/*",
+                    "Accept-Language": "zh-CN,zh;q=0.9",
+                },
+            ) as resp:
+                resp.raise_for_status()
+                downloaded = 0
+                with open(video_path, "wb") as f:
+                    for chunk in resp.iter_bytes(chunk_size=65536):
+                        if chunk:
+                            f.write(chunk)
+                            downloaded += len(chunk)
+                if downloaded == 0:
+                    raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail="直链下载为空")
+    except HTTPException:
+        raise
+    except httpx.TimeoutException:
+        logger.warning("直链下载超时: %s", page_url)
+        raise HTTPException(status_code=status.HTTP_504_GATEWAY_TIMEOUT, detail="视频下载超时，请稍后重试") from None
+    except Exception as exc:  # noqa: BLE001
+        logger.exception("直链下载失败: url=%s err=%s", page_url, exc)
+        raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail="视频下载失败: " + str(exc)[:200]) from exc
+
+    try:
+        text = transcribe_to_text(video_path)
+        return text.strip(), 0.0
+    except ASRNotConfiguredError as exc:
+        raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail=str(exc)) from exc
+    except ASRTranscriptionError as exc:
+        raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail=str(exc)) from exc
+    except Exception as exc:
+        logger.exception("直链下载后 ASR 转写异常: path=%s", video_path)
+        raise HTTPException(
+            status_code=status.HTTP_502_BAD_GATEWAY,
+            detail="语音识别失败: " + str(exc)[:200],
+        ) from exc
+
+
+# ── 1. 从抖音视频提取文案 ─────────────────────────────────────────────
+
+
+@router.get("/douyin/__debug_diag")
+def douyin_diag():
+    """[Staging/Dev only] 抖音解析源诊断。"""
+    import time as _t
+
+    import httpx as _httpx
+    from app.services.douyin_resolver import APIZERO_API_KEY as _api_key_apizero
+    from app.services.douyin_resolver import TIKHUB_API_KEY as _api_key_tikhub
+
+    results = {
+        "providers": available_providers(),
+        "env": {
+            "APP_ENV": os.environ.get("APP_ENV", ""),
+            "MEDIAKIT_CONFIGURED": bool(os.environ.get("MEDIAKIT_API_KEY", "")),
+        },
+    }
+
+    test_url = "https://v.douyin.com/hb-giW8cC1Q/"
+
+    t0 = _t.time()
+    try:
+        r = resolve_douyin_video(test_url)
+        results["resolver"] = {
+            "ok": bool(r),
+            "source": r.source if r else None,
+            "desc_len": len(r.desc) if r else 0,
+            "has_video_url": bool(r.video_url) if r else False,
+            "url_domain": r.video_url.split("/")[2] if r and r.video_url and "/" in r.video_url else None,
+            "time": round(_t.time() - t0, 2),
+        }
+    except Exception as e:
+        results["resolver"] = {"ok": False, "error": str(e)[:200], "time": round(_t.time() - t0, 2)}
+
+    if _api_key_apizero:
+        t0 = _t.time()
+        try:
+            with _httpx.Client(timeout=8, verify=False) as c:
+                r = c.get(
+                    "https://v1.apizero.cn/api/video-parse",
+                    params={"url": test_url, "flat": 2},
+                    headers={"Authorization": f"Bearer {_api_key_apizero}"},
+                )
+                results["apizero"] = {"status": r.status_code, "prefix": r.text[:200], "time": round(_t.time() - t0, 2)}
+        except Exception as e:
+            results["apizero"] = {"error": str(e)[:200], "time": round(_t.time() - t0, 2)}
+
+    if _api_key_tikhub:
+        t0 = _t.time()
+        try:
+            with _httpx.Client(timeout=8, verify=False) as c:
+                r = c.get(
+                    "https://api.tikhub.io/api/v1/douyin/web/get_aweme_id",
+                    params={"url": test_url},
+                    headers={"Authorization": f"Bearer {_api_key_tikhub}"},
+                )
+                results["tikhub"] = {"status": r.status_code, "prefix": r.text[:200], "time": round(_t.time() - t0, 2)}
+        except Exception as e:
+            results["tikhub"] = {"error": str(e)[:200], "time": round(_t.time() - t0, 2)}
+
+    return results
+
+
+@router.post("/extract-from-douyin", response_model=ExtractFromDouyinResponse)
 @points_gate("douyin_extract")
 def extract_from_douyin(
     request: ExtractFromDouyinRequest,
     current_user: AuthenticatedUser = Depends(get_current_user),
     db: Session = Depends(get_db_session),
-) -> ExtractFromDouyinResponse:
-    """从抖音视频下载无水印视频并通过 ASR 提取文案."""
-    source_url = request.url.strip()
-    _validate_douyin_url(source_url)
+):
+    page_url = _extract_and_validate_douyin_url(request.url)
+    _dbg("page_url", page_url)
 
-    # 确保 URL 有 scheme（yt-dlp 需要完整 URL）
-    url_for_download = source_url
-    if not re.match(r"^https?://", url_for_download, re.IGNORECASE):
-        url_for_download = "https://" + url_for_download
+    # ── Phase A：多源轮询解析 MP4 直链 ──
+    last_err_stage = "parse"
+    t0 = time.time()
+    result = resolve_douyin_video(page_url)
+    resolve_elapsed = time.time() - t0
+    logger.info("抖音解析耗时: %.2fs providers=%s", resolve_elapsed, available_providers())
 
-    text: str = ""
-    duration: float = 0.0
+    direct_url = result.video_url if result else None
+    feed_desc = (result.desc or "").strip() if result else ""
 
-    try:
-        with tempfile.TemporaryDirectory(prefix="douyin_extract_") as temp_dir:
-            # 延迟导入 yt-dlp，避免模块缺失时影响其他路由启动
-            try:
-                import yt_dlp
-            except ImportError as exc:
-                logger.error("yt-dlp 未安装，抖音提取功能不可用: %s", exc)
-                raise HTTPException(
-                    status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
-                    detail="抖音提取功能暂不可用（缺少依赖 yt-dlp）",
-                ) from exc
+    # 图文视频（无 video_url 但有 desc）直接返回文案，跳过 ASR
+    if result and not direct_url and feed_desc:
+        logger.info("图文视频直接返回文案: source=%s desc_len=%d", result.source, len(feed_desc))
+        return ExtractFromDouyinResponse(
+            text=feed_desc,
+            duration_seconds=0.0,
+            source_url=page_url,
+        )
 
-            ydl_opts = {
-                "format": "best[ext=mp4]/best",
-                "outtmpl": f"{temp_dir}/%(id)s.%(ext)s",
-                "quiet": True,
-                "no_warnings": True,
-                "noplaylist": True,
-            }
+    if not direct_url:
+        if _DOUYIN_DEBUG_ERRORS:
+            detail = f"抖音视频链接解析失败，请检查链接是否正确或稍后重试 [debug: providers={available_providers()}]"
+        else:
+            detail = "抖音视频链接解析失败，请检查链接是否正确或稍后重试"
+        logger.warning("抖音解析全部失败: url=%s providers=%s", page_url, available_providers())
+        raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail=detail)
 
-            try:
-                ydl = yt_dlp.YoutubeDL(ydl_opts)
-                info = ydl.extract_info(url_for_download, download=True)
-            except yt_dlp.utils.DownloadError as exc:
-                # yt-dlp 官方异常类型：HTTP 错误、短链失效、视频下架等
-                msg = str(exc)
-                logger.warning("抖音下载失败: url=%s error=%s", source_url, msg)
-                # 404/视频不存在/不可下载 → 400；网络问题/上游异常 → 502
-                is_bad_url = any(
-                    kw in msg.lower() for kw in ("404", "not found", "unable to download webpage", "unsupported url", "no video formats")
+    # ── Phase B：ASR 转文字 ──
+    mk_client = get_mediakit_client()
+    text = ""
+    duration = 0.0
+
+    # B1：MediaKit 云端 ASR（不下载视频，最快）
+    if mk_client.is_available:
+        last_err_stage = "asr"
+        try:
+            task_id = mk_client.asr_submit(direct_url)
+            text, duration = mk_client.asr_poll(task_id)
+            text = text.strip()
+            if text:
+                logger.info(
+                    "抖音 MediaKit ASR 成功: source=%s text_len=%d duration=%.1f total_time=%.1fs",
+                    result.source,
+                    len(text),
+                    duration,
+                    time.time() - t0,
                 )
-                raise HTTPException(
-                    status_code=status.HTTP_400_BAD_REQUEST if is_bad_url else status.HTTP_502_BAD_GATEWAY,
-                    detail=("无法解析该抖音链接，请确认链接有效且视频未被下架" if is_bad_url else f"视频下载失败: {msg[:200]}"),
-                ) from exc
-            except Exception as exc:
-                logger.exception("抖音视频下载异常: url=%s", source_url)
-                raise HTTPException(
-                    status_code=status.HTTP_502_BAD_GATEWAY,
-                    detail=f"视频下载失败: {str(exc)[:200]}",
-                ) from exc
+            else:
+                logger.info("抖音 MediaKit ASR 返回空文本（无旁白/BGM视频）")
+        except MediaKitError as exc:
+            logger.warning("MediaKit ASR 失败，回退本地 ASR: %s", exc)
+            text = ""
 
-            if info is None:
-                raise HTTPException(
-                    status_code=status.HTTP_400_BAD_REQUEST,
-                    detail="无法解析该抖音链接",
-                )
+    # B2：回退下载 + 本地 ASR
+    if not text:
+        last_err_stage = "download"
+        try:
+            with tempfile.TemporaryDirectory(prefix="douyin_extract_") as temp_dir:
+                text, dl_duration = _direct_url_download_and_local_asr(direct_url, page_url, temp_dir)
+                text = (text or "").strip()
+                if dl_duration and not duration:
+                    duration = dl_duration
+                if text:
+                    logger.info(
+                        "抖音本地 ASR 成功: source=%s text_len=%d total_time=%.1fs",
+                        result.source,
+                        len(text),
+                        time.time() - t0,
+                    )
+                    last_err_stage = "asr"
+        except HTTPException as exc:
+            # 下载超时（504）是明确的网络错误，直接抛出
+            if exc.status_code == status.HTTP_504_GATEWAY_TIMEOUT:
+                raise
+            # 本地 ASR 不可用/失败（502/503）时记录后继续走 desc 兜底，
+            # 不直接抛 502，避免 API 镜像缺 worker 模块时整条链路挂掉
+            logger.warning("本地 ASR 链路失败(status=%d): %s", exc.status_code, exc.detail)
+            text = ""
+            # 如果是下载失败（非ASR错误），保持stage为download
+            if "语音识别" in str(exc.detail) or "ASR" in str(exc.detail):
+                last_err_stage = "asr"
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("本地 ASR 链路异常: %s", exc)
+            text = ""
 
-            video_path = ydl.prepare_filename(info)
-            try:
-                duration = float(info.get("duration") or 0)
-            except (TypeError, ValueError):
-                duration = 0.0
+    # ── Phase C：结果判定 & 兜底 ──
 
-            # 校验下载的文件是否真的存在（某些 yt-dlp 版本可能 info 成功但未下载到文件）
-            if not os.path.isfile(video_path) or os.path.getsize(video_path) == 0:
-                logger.error("yt-dlp 未产生有效视频文件: path=%s", video_path)
-                raise HTTPException(
-                    status_code=status.HTTP_502_BAD_GATEWAY,
-                    detail="视频下载异常：未获取到有效文件",
-                )
+    # ASR 空结果（无旁白视频）→ 使用解析源 desc 兜底
+    if not text and feed_desc:
+        text = feed_desc
+        logger.info("抖音 ASR 空结果，使用解析源 desc 兜底: desc_len=%d", len(text))
 
-            # ASR 转写（兜底捕获所有异常，避免 500）
-            try:
-                text = transcribe_to_text(video_path)
-            except ASRNotConfiguredError as exc:
-                raise HTTPException(
-                    status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
-                    detail=str(exc),
-                ) from exc
-            except ASRTranscriptionError as exc:
-                raise HTTPException(
-                    status_code=status.HTTP_502_BAD_GATEWAY,
-                    detail=str(exc),
-                ) from exc
-            except Exception as exc:
-                logger.exception("ASR 转写异常: path=%s", video_path)
-                raise HTTPException(
-                    status_code=status.HTTP_502_BAD_GATEWAY,
-                    detail=f"语音识别失败: {str(exc)[:200]}",
-                ) from exc
-
-    except HTTPException:
-        raise
-    except Exception as exc:
-        # 最后兜底：任何未捕获异常都转成 502/400，不允许冒泡成 500
-        logger.exception("抖音文案提取未预期异常: url=%s", source_url)
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"抖音文案提取失败: {str(exc)[:200]}",
-        ) from exc
+    if not text:
+        stage_msg = {
+            "parse": "抖音视频链接解析失败，请检查链接是否正确或稍后重试",
+            "download": "抖音视频下载失败，请检查网络或稍后重试",
+            "asr": "抖音语音识别失败，请稍后重试或手动输入文案",
+        }
+        user_msg = stage_msg.get(last_err_stage, "抖音链接解析暂时不可用，请稍后重试或手动输入文案")
+        if _DOUYIN_DEBUG_ERRORS:
+            user_msg = user_msg + f" [debug: stage={last_err_stage} source={result.source}]"
+        logger.warning("抖音文案提取失败: url=%s stage=%s source=%s", page_url, last_err_stage, result.source)
+        raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail=user_msg)
 
     return ExtractFromDouyinResponse(
         text=text,
         duration_seconds=duration,
-        source_url=source_url,
+        source_url=page_url,
     )
 
 
-# ── 2. AI 文案改写 ───────────────────────────────────────────────────────────
+# ── 2. AI 文案改写 ────────────────────────────────────────────────────
 
 
-@router.post(
-    "/ai-rewrite",
-    response_model=AiRewriteResponse,
-)
+@router.post("/ai-rewrite", response_model=AiRewriteResponse)
 @points_gate("ai_rewrite")
 def ai_rewrite(
     request: AiRewriteRequest,
     current_user: AuthenticatedUser = Depends(get_current_user),
     db: Session = Depends(get_db_session),
-) -> AiRewriteResponse:
-    """使用豆包大模型改写文案."""
+):
     content = (request.content or "").strip()
     if not content:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="文案内容不能为空",
-        )
-
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="文案内容不能为空")
     style = request.style or "口语化"
-
     client = get_doubao_client()
     if not client.is_available:
         raise HTTPException(
             status_code=status.HTTP_502_BAD_GATEWAY,
             detail="AI 服务不可用，请联系管理员配置豆包大模型 API Key",
         )
-
     system_prompt = (
         "你是一个专业的短视频文案改写专家。请对以下文案进行改写，"
         "要求：保留原意、口语化、适合短视频口播、调整语序避免查重。"
     )
     if style:
-        system_prompt += f"\n风格要求：{style}"
-
-    user_prompt = f"请改写以下文案：\n\n{content}"
-
+        system_prompt = system_prompt + "\n风格要求：" + style
     messages = [
         {"role": "system", "content": system_prompt},
-        {"role": "user", "content": user_prompt},
+        {"role": "user", "content": "请改写以下文案：\n\n" + content},
     ]
-
     try:
-        rewritten = client.chat_completion(
-            messages=messages,
-            temperature=0.8,
-            max_tokens=2048,
-        )
+        rewritten = client.chat_completion(messages=messages, temperature=0.8, max_tokens=2048)
     except Exception as exc:
         logger.error("AI 改写调用失败: %s", exc)
-        raise HTTPException(
-            status_code=status.HTTP_502_BAD_GATEWAY,
-            detail=f"AI 改写失败: {exc}",
-        ) from exc
-
+        raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail="AI 改写失败: " + str(exc)) from exc
     if not rewritten:
-        raise HTTPException(
-            status_code=status.HTTP_502_BAD_GATEWAY,
-            detail="AI 改写未返回有效结果",
-        )
-
-    return AiRewriteResponse(
-        original=content,
-        rewritten=rewritten.strip(),
-        style=style,
-    )
+        raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail="AI 改写未返回有效结果")
+    return AiRewriteResponse(original=content, rewritten=rewritten.strip(), style=style)
 
 
-# ── 3. AI 标题生成 ───────────────────────────────────────────────────────────
+# ── 3. AI 标题生成 ────────────────────────────────────────────────────
 
 
-@router.post(
-    "/ai-generate-titles",
-    response_model=AiGenerateTitlesResponse,
-)
+@router.post("/ai-generate-titles", response_model=AiGenerateTitlesResponse)
 @points_gate("ai_title")
 def ai_generate_titles(
     request: AiGenerateTitlesRequest,
     current_user: AuthenticatedUser = Depends(get_current_user),
     db: Session = Depends(get_db_session),
-) -> AiGenerateTitlesResponse:
-    """使用现有 generate_smart_titles 生成标题."""
+):
     content = (request.content or "").strip()
     if not content:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="文案内容不能为空",
-        )
-
-    # count 限制在 1-5（Pydantic ge=1 le=5 已校验），但为兼容直接调用场景截断
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="文案内容不能为空")
     count = max(1, min(5, request.count))
-
     from app.services.ai_service import generate_smart_titles
 
-    result = generate_smart_titles(
-        description=content,
-        style="viral",
-        count=count,
-    )
-
+    result = generate_smart_titles(description=content, style="viral", count=count)
     titles = result.get("titles", [])[:count]
-
     return AiGenerateTitlesResponse(titles=titles)

apps/api/app/api/routes/subscription.py

@@ -10,9 +10,11 @@ from typing import Any
 from app.auth import AuthenticatedUser, get_current_user
 from app.dependencies import get_user_repository
 from app.schemas.subscription import (
+    BillingCycle,
     BillingRecord,
     ChangePlanRequest,
     ChangePlanResponse,
+    MembershipType,
     SimpleResponse,
     SubscriptionInfo,
     ToggleAutoRenewRequest,
@@ -26,43 +28,18 @@ logger = logging.getLogger(__name__)
 router = APIRouter()
 
 
-# ============ 配额定义（硬编码，后续可迁移到配置中心） ============
+# ============ 会员展示名称（与 packages.domain.points_rules.MEMBERSHIP_PRICES 对应）============
 
-PLAN_QUOTAS = {
-    "free": {"max_projects": 3, "max_storage_gb": 10},
-    "standard": {"max_projects": 10, "max_storage_gb": 50},
-    "pro": {"max_projects": -1, "max_storage_gb": 100},
-    "enterprise": {"max_projects": -1, "max_storage_gb": 1000},
+_PLAN_NAMES: dict[str, str] = {
+    MembershipType.FREE: "免费用户",
+    MembershipType.MONTHLY: "月卡会员",
+    MembershipType.QUARTERLY: "季卡会员",
+    MembershipType.YEARLY: "年卡会员",
 }
 
 
-# ============ Helper Functions ============
-
-
 def _get_plan_name(plan_id: str) -> str:
-    """获取套餐显示名称"""
-    plan_names = {
-        "free": "体验版",
-        "standard": "标准版",
-        "pro": "专业版",
-        "enterprise": "企业版",
-    }
-    return plan_names.get(plan_id, "未知套餐")
-
-
-def _get_plan_price(plan_id: str, billing_cycle: str) -> float:
-    """获取套餐价格"""
-    prices = {
-        ("free", "monthly"): 0,
-        ("free", "yearly"): 0,
-        ("standard", "monthly"): 99,
-        ("standard", "yearly"): 999,
-        ("pro", "monthly"): 299,
-        ("pro", "yearly"): 2999,
-        ("enterprise", "monthly"): 999,
-        ("enterprise", "yearly"): 9999,
-    }
-    return prices.get((plan_id, billing_cycle), 0)
+    return _PLAN_NAMES.get(plan_id, "免费用户")
 
 
 def _build_subscription_info(user: AuthenticatedUser) -> SubscriptionInfo:
@@ -75,15 +52,20 @@ def _build_subscription_info(user: AuthenticatedUser) -> SubscriptionInfo:
         period_start = now.isoformat()
         period_end = now.isoformat()
 
+    plan_id = user.user.subscription_plan or MembershipType.FREE
+    # 旧档位（standard/pro/enterprise）统一降级为 monthly，避免前端炸掉
+    if plan_id in {"standard", "pro", "enterprise"}:
+        plan_id = MembershipType.MONTHLY
+
     return SubscriptionInfo(
         id=f"sub-{user.user.id[:8]}",
-        plan_id=user.user.subscription_plan or "free",
-        plan_name=_get_plan_name(user.user.subscription_plan or "free"),
+        plan_id=plan_id,
+        plan_name=_get_plan_name(plan_id),
         status=user.user.subscription_status or "active",
-        billing_cycle="monthly",
+        billing_cycle=plan_id if plan_id != MembershipType.FREE else BillingCycle.MONTHLY,
         current_period_start=period_start,
         current_period_end=period_end,
-        amount=_get_plan_price(user.user.subscription_plan or "free", "monthly"),
+        amount=0 if plan_id == MembershipType.FREE else 0,  # 金额由前端 /plans 接口展示
         auto_renew=True,
         created_at=user.user.created_at.isoformat() if user.user.created_at else now.isoformat(),
     )
@@ -115,11 +97,11 @@ def list_membership_plans(
         days = info["duration_days"]
         monthly_cents = round(info["price_cents"] * 30 / days)
         features: dict[str, Any] = {"max_resolution": "1080p"}
-        if plan_id == "monthly":
+        if plan_id == MembershipType.MONTHLY:
             features.update({"free_clips_daily": 2})
-        elif plan_id == "quarterly":
+        elif plan_id == MembershipType.QUARTERLY:
             features.update({"free_clips_daily": 5})
-        elif plan_id == "yearly":
+        elif plan_id == MembershipType.YEARLY:
             features.update({"free_clips_daily": "unlimited"})
         plans.append({
             "plan_id": plan_id,
@@ -151,7 +133,7 @@ async def get_billing_records(
         return [
             BillingRecord(
                 id=r.id,
-                plan_name=r.plan_name,
+                plan_name=_get_plan_name(r.plan_name),
                 amount=r.amount,
                 billing_cycle=r.billing_cycle,
                 status=r.status,
@@ -165,6 +147,10 @@ async def get_billing_records(
         session.close()
 
 
+_VALID_PLANS = {MembershipType.MONTHLY, MembershipType.QUARTERLY, MembershipType.YEARLY}
+_VALID_CYCLES = {BillingCycle.MONTHLY, BillingCycle.QUARTERLY, BillingCycle.YEARLY}
+
+
 @router.post("/change-plan", response_model=ChangePlanResponse)
 async def change_plan(
     request: ChangePlanRequest,
@@ -173,47 +159,45 @@ async def change_plan(
 ) -> ChangePlanResponse:
     """变更订阅套餐（升级/降级）"""
     # TODO: 接入支付验证（支付宝/微信支付）
-    valid_plans = {"free", "standard", "pro", "enterprise"}
-    if request.target_plan_id not in valid_plans:
+    target_plan = request.target_plan_id
+    if target_plan not in _VALID_PLANS:
         raise HTTPException(
             status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"无效的套餐ID。支持的套餐: {', '.join(valid_plans)}",
+            detail=f"无效的会员类型。支持: {', '.join(sorted(_VALID_PLANS))}",
         )
 
-    valid_cycles = {"monthly", "yearly"}
-    if request.billing_cycle not in valid_cycles:
+    if request.billing_cycle not in _VALID_CYCLES:
         raise HTTPException(
             status_code=status.HTTP_400_BAD_REQUEST,
-            detail="无效的计费周期。支持: monthly, yearly",
+            detail=f"无效的计费周期。支持: {', '.join(sorted(_VALID_CYCLES))}",
         )
 
     user = current_user.user
-    current_plan = user.subscription_plan or "free"
-    target_plan = request.target_plan_id
+    current_plan = user.subscription_plan or MembershipType.FREE
+    # 旧档位归一化，避免永远显示"您已经是xxx"
+    if current_plan in {"standard", "pro", "enterprise"}:
+        current_plan = MembershipType.MONTHLY
 
     if current_plan == target_plan:
         return ChangePlanResponse(
             success=False,
-            message=f"您已经是 {_get_plan_name(target_plan)}",
+            message=f"您已经是{_get_plan_name(target_plan)}",
         )
 
-    # 通过 dataclasses.replace 创建新实例（不直接修改 dataclass）
-    quotas = PLAN_QUOTAS.get(target_plan, PLAN_QUOTAS["free"])
     updated_user = replace(
         user,
         subscription_plan=target_plan,
         subscription_status="active",
-        max_projects=quotas["max_projects"],
-        max_storage_gb=quotas["max_storage_gb"],
+        max_projects=-1,  # 付费会员不限项目数
+        max_storage_gb=100,
     )
     user_repository.save(updated_user)
 
-    # 用更新后的用户构造响应
     refreshed_auth_user = AuthenticatedUser(user=updated_user)
 
     return ChangePlanResponse(
         success=True,
-        message=f"套餐已成功变更为 {_get_plan_name(target_plan)}",
+        message=f"套餐已成功变更为{_get_plan_name(target_plan)}",
         new_subscription=_build_subscription_info(refreshed_auth_user),
     )
 
@@ -225,10 +209,11 @@ async def cancel_subscription(
 ) -> SimpleResponse:
     """取消订阅"""
     user = current_user.user
-    if user.subscription_plan == "free":
+    plan_id = user.subscription_plan or MembershipType.FREE
+    if plan_id == MembershipType.FREE:
         raise HTTPException(
             status_code=status.HTTP_400_BAD_REQUEST,
-            detail="体验版无需取消",
+            detail="免费用户无需取消订阅",
         )
 
     updated_user = replace(user, subscription_status="cancelled")
@@ -236,7 +221,7 @@ async def cancel_subscription(
 
     return SimpleResponse(
         success=True,
-        message="订阅已取消，当前周期结束后停止服务",
+        message="订阅已取消，当前周期结束后将降级为免费用户",
     )
 
 
@@ -262,11 +247,14 @@ async def payment_callback(
     if SessionLocal is None:
         raise HTTPException(status_code=500, detail="Database not available")
 
+    # 仅接受当前会员体系的 plan 值
+    if plan not in _VALID_PLANS:
+        raise HTTPException(status_code=400, detail=f"未知的会员类型: {plan}")
+
     session = SessionLocal()
     try:
         repo = SQLAlchemyBillingRepository(session)
 
-        # 创建账单记录
         record_id = uuid.uuid4().hex
         repo.create(
             {
@@ -279,19 +267,20 @@ async def payment_callback(
             }
         )
 
-        # 在事务中标记支付成功并更新订阅
         repo.mark_paid(record_id, payment_method, payment_id)
 
-        # 计算到期时间
-        days = 365 if billing_cycle == "yearly" else 30
+        days_map = {BillingCycle.MONTHLY: 30, BillingCycle.QUARTERLY: 90, BillingCycle.YEARLY: 365}
+        days = days_map.get(billing_cycle, 30)
         expires_at = datetime.now(UTC) + timedelta(days=days)
         repo.update_subscription_on_payment(user_id, plan, expires_at)
 
         return {"success": True, "message": "支付成功", "record_id": record_id}
+    except HTTPException:
+        session.rollback()
+        raise
     except Exception as e:
         session.rollback()
-        logger.error(f"支付回调处理失败: user_id={user_id}, plan={plan}, error={e}")
-        # 不返回原始异常信息，避免泄漏内部实现细节
+        logger.error("支付回调处理失败: user_id=%s, plan=%s, error=%s", user_id, plan, e)
         raise HTTPException(status_code=500, detail="支付处理失败，请稍后重试") from e
     finally:
         session.close()
@@ -303,10 +292,5 @@ async def toggle_auto_renew(
     current_user: AuthenticatedUser = Depends(get_current_user),
 ) -> SimpleResponse:
     """切换自动续费"""
-    # TODO: 实际需要在数据库中存储 auto_renew 字段
     status_text = "已开启自动续费" if request.enabled else "已关闭自动续费"
-
-    return SimpleResponse(
-        success=True,
-        message=status_text,
-    )
+    return SimpleResponse(success=True, message=status_text)

apps/api/app/api/routes/titles.py

@@ -1,243 +1,35 @@
-"""Title library CRUD routes.
+"""Title library routes — DEPRECATED (#1894).
 
-.. deprecated::
-    标题库 API 已废弃（#1894），标题配置已整合到 scripts 模型。
-    所有接口保留向后兼容，但返回 Warning header 并记录日志。
+独立标题库已废弃。前端应直接调用 GET /api/v1/scripts 获取文案列表，
+取每条文案的 `title` 字段作为标题候选。
+
+所有 /api/v1/titles 端点统一返回 HTTP 410 Gone。
 """
 
 from __future__ import annotations
 
-import logging
-from typing import Optional
-
-from app.api.routes._helpers import get_user_plan
-from app.auth import AuthenticatedUser, get_current_user
-from app.dependencies import get_db_session, get_user_repository
-from app.schemas.title_library import (
-    CreateTitleLibraryRequest,
-    ListTitleLibraryResponse,
-    TitleLibraryItemResponse,
-    UpdateTitleLibraryRequest,
-)
-from fastapi import APIRouter, Depends, HTTPException, Query, Response, status
-from sqlalchemy.orm import Session
-
-from packages.adapters.sqlalchemy_impl.title_library_repository import SQLAlchemyTitleLibraryRepository
-from packages.application.title_library.commands import (
-    CreateTitleLibraryCommand,
-    PickTitleCommand,
-    UpdateTitleLibraryCommand,
-)
-from packages.application.title_library.use_cases import (
-    CreateTitleLibraryUseCase,
-    DeleteTitleLibraryUseCase,
-    GetTitleLibraryUseCase,
-    ListTitleLibraryUseCase,
-    NotFoundError,
-    PickTitleUseCase,
-    QuotaExceededError,
-    UpdateTitleLibraryUseCase,
-)
-from packages.ports.user_repository import UserRepository
+from fastapi import APIRouter, Response, status
 
 router = APIRouter()
-logger = logging.getLogger(__name__)
 
-_DEPRECATION_WARNING = (
-    '299 - "Title library API is deprecated; migrate to scripts.title_text/'
-    'title_category/title_config (issue #1894)"'
+_GONE_MESSAGE = (
+    "标题库 API 已废弃（#1894）：独立标题库已合并进文案库，"
+    "请使用 GET /api/v1/scripts 获取文案列表并取 title 字段作为标题。"
 )
 
 
-def _deprecation_headers() -> dict:
-    """返回 deprecation Warning header (ASCII-only, RFC 7234 §5.5)."""
-    return {"Warning": _DEPRECATION_WARNING, "Deprecation": "true"}
+def _gone(response: Response) -> dict:
+    response.status_code = status.HTTP_410_GONE
+    response.headers["Deprecation"] = "true"
+    response.headers["Sunset"] = "Tue, 16 Sep 2026 00:00:00 GMT"
+    return {"error": {"code": "GONE", "message": _GONE_MESSAGE}}
 
 
-def _log_deprecation(endpoint: str) -> None:
-    logger.warning("[Deprecated] title_library API 调用: %s — %s", endpoint, _DEPRECATION_WARNING)
+@router.api_route("", methods=["GET", "POST", "PUT", "DELETE", "PATCH"])
+def titles_root_gone(response: Response) -> dict:
+    return _gone(response)
 
 
-def _get_title_repository(session: Session = Depends(get_db_session)) -> SQLAlchemyTitleLibraryRepository:
-    return SQLAlchemyTitleLibraryRepository(session)
-
-
-def _to_response(item) -> TitleLibraryItemResponse:
-    return TitleLibraryItemResponse(
-        id=item.id,
-        user_id=item.user_id,
-        name=item.name,
-        text=item.text,
-        category=item.category,
-        description=item.description,
-        tags=item.tags,
-        usage_count=item.usage_count,
-        is_active=item.is_active,
-        created_at=item.created_at,
-        updated_at=item.updated_at,
-    )
-
-
-@router.get("", response_model=ListTitleLibraryResponse)
-def list_titles(
-    response: Response,
-    category: Optional[str] = Query(None),
-    skip: int = Query(0, ge=0),
-    limit: int = Query(50, ge=1, le=200),
-    authenticated_user: AuthenticatedUser = Depends(get_current_user),
-    title_repository: SQLAlchemyTitleLibraryRepository = Depends(_get_title_repository),
-) -> ListTitleLibraryResponse:
-    """[Deprecated] 请使用 scripts API 的 title_text/title_category 字段替代."""
-    _log_deprecation("list_titles")
-    for k, v in _deprecation_headers().items():
-        response.headers[k] = v
-    user_id = authenticated_user.user.id
-    use_case = ListTitleLibraryUseCase(title_repository)
-    items = use_case.execute(user_id, category=category, skip=skip, limit=limit)
-    total = title_repository.count_by_user(user_id)
-    return ListTitleLibraryResponse(
-        items=[_to_response(i) for i in items],
-        total=total,
-    )
-
-
-@router.post("/pick", response_model=TitleLibraryItemResponse)
-def pick_title(
-    response: Response,
-    category: Optional[str] = Query(None, description="按分类筛选，不填则从全部标题中选"),
-    exclude_ids: Optional[str] = Query(
-        None,
-        description="排除的标题ID（逗号分隔），用于批量生成时避免重复",
-    ),
-    authenticated_user: AuthenticatedUser = Depends(get_current_user),
-    title_repository: SQLAlchemyTitleLibraryRepository = Depends(_get_title_repository),
-) -> TitleLibraryItemResponse:
-    """[Deprecated] 请使用 scripts API 的 title_text/title_category 字段替代.
-
-    智能选择一个标题。
-
-    策略：优先使用次数少的，从最少的前5个中随机选一个，兼顾公平和多样性。
-    """
-    _log_deprecation("pick_title")
-    for k, v in _deprecation_headers().items():
-        response.headers[k] = v
-    user_id = authenticated_user.user.id
-    exclude_list: list[str] = []
-    if exclude_ids:
-        exclude_list = [t.strip() for t in exclude_ids.split(",") if t.strip()]
-
-    use_case = PickTitleUseCase(title_repository)
-    item = use_case.execute(
-        PickTitleCommand(
-            user_id=user_id,
-            category=category,
-            exclude_ids=exclude_list,
-        )
-    )
-    if item is None:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="标题库为空，请先添加标题",
-        )
-    return _to_response(item)
-
-
-@router.get("/{title_id}", response_model=TitleLibraryItemResponse)
-def get_title(
-    title_id: str,
-    response: Response,
-    authenticated_user: AuthenticatedUser = Depends(get_current_user),
-    title_repository: SQLAlchemyTitleLibraryRepository = Depends(_get_title_repository),
-) -> TitleLibraryItemResponse:
-    """[Deprecated] 请使用 scripts API 的 title_text/title_category 字段替代."""
-    _log_deprecation("get_title")
-    for k, v in _deprecation_headers().items():
-        response.headers[k] = v
-    user_id = authenticated_user.user.id
-    use_case = GetTitleLibraryUseCase(title_repository)
-    item = use_case.execute(title_id, user_id)
-    if item is None:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Title not found")
-    return _to_response(item)
-
-
-@router.post("", response_model=TitleLibraryItemResponse, status_code=status.HTTP_201_CREATED)
-def create_title(
-    response: Response,
-    request: CreateTitleLibraryRequest,
-    authenticated_user: AuthenticatedUser = Depends(get_current_user),
-    title_repository: SQLAlchemyTitleLibraryRepository = Depends(_get_title_repository),
-    user_repository: UserRepository = Depends(get_user_repository),
-) -> TitleLibraryItemResponse:
-    """[Deprecated] 请使用 scripts API 的 title_text/title_category 字段替代."""
-    _log_deprecation("create_title")
-    for k, v in _deprecation_headers().items():
-        response.headers[k] = v
-    user_id = authenticated_user.user.id
-    plan_name = get_user_plan(user_id, user_repository)
-    command = CreateTitleLibraryCommand(
-        user_id=user_id,
-        name=request.name,
-        text=request.text,
-        category=request.category,
-        description=request.description,
-        tags=request.tags,
-    )
-    use_case = CreateTitleLibraryUseCase(title_repository)
-    try:
-        item = use_case.execute(command, plan_name=plan_name)
-    except QuotaExceededError as exc:
-        raise HTTPException(
-            status_code=status.HTTP_429_TOO_MANY_REQUESTS,
-            detail=f"标题库配额已满（{exc.used}/{exc.limit}），请升级套餐",
-        ) from exc
-    return _to_response(item)
-
-
-@router.put("/{title_id}", response_model=TitleLibraryItemResponse)
-def update_title(
-    title_id: str,
-    response: Response,
-    request: UpdateTitleLibraryRequest,
-    authenticated_user: AuthenticatedUser = Depends(get_current_user),
-    title_repository: SQLAlchemyTitleLibraryRepository = Depends(_get_title_repository),
-) -> TitleLibraryItemResponse:
-    """[Deprecated] 请使用 scripts API 的 title_text/title_category 字段替代."""
-    _log_deprecation("update_title")
-    for k, v in _deprecation_headers().items():
-        response.headers[k] = v
-    user_id = authenticated_user.user.id
-    command = UpdateTitleLibraryCommand(
-        title_id=title_id,
-        user_id=user_id,
-        name=request.name,
-        text=request.text,
-        category=request.category,
-        description=request.description,
-        tags=request.tags,
-    )
-    use_case = UpdateTitleLibraryUseCase(title_repository)
-    try:
-        item = use_case.execute(command)
-    except NotFoundError as _e:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Title not found") from _e
-    return _to_response(item)
-
-
-@router.delete("/{title_id}", status_code=status.HTTP_204_NO_CONTENT, response_model=None, response_class=Response)
-def delete_title(
-    title_id: str,
-    response: Response,
-    authenticated_user: AuthenticatedUser = Depends(get_current_user),
-    title_repository: SQLAlchemyTitleLibraryRepository = Depends(_get_title_repository),
-) -> Response:
-    """[Deprecated] 请使用 scripts API 的 title_text/title_category 字段替代."""
-    _log_deprecation("delete_title")
-    for k, v in _deprecation_headers().items():
-        response.headers[k] = v
-    user_id = authenticated_user.user.id
-    use_case = DeleteTitleLibraryUseCase(title_repository)
-    deleted = use_case.execute(title_id, user_id)
-    if not deleted:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Title not found")
-    return
+@router.api_route("/{path:path}", methods=["GET", "POST", "PUT", "DELETE", "PATCH"])
+def titles_subpath_gone(response: Response, path: str) -> dict:
+    return _gone(response)

apps/api/app/schemas/generation_task.py

@@ -98,6 +98,24 @@ class CreateGenerationTaskRequest(BaseModel):
         description="各变体独立标题文字数组：长度1=共用，长度=count=独立。为空时使用 title_config.text",
     )
 
+    # ── 智能降重开关（#1970）──
+    # True（默认）：edge_crop + 片段级微变换（hflip/变速/亮度/对比度/饱和度/BGM偏移）全部生效；
+    # False：跳过 edge_crop、不注入微变换，渲染确定性（固定种子）。
+    dedup_enabled: bool = Field(default=True, description="智能降重开关，默认开启；关闭后跳过边缘裁切与微变换")
+
+    # ── 剪辑组装模式（#1970 PR3）──
+    # random（默认，完全兼容现有随机混剪）/ narrative（叙事剪辑：文案→TTS 配音→标签匹配画面）
+    assembly_mode: str = Field(default="random", description="组装模式：random=随机混剪（默认），narrative=叙事剪辑")
+    # 叙事模式必填：文案库 scripts.id（后端据此读取 content 合成 TTS）
+    script_id: str = Field(default="", description="叙事模式必填：文案库 ID")
+    # 叙事模式必填：TTS 音色 ID（preset 为 CosyVoice 音色 id；clone 为克隆档案 id）
+    tts_voice_id: str = Field(default="", description="叙事模式必填：TTS 音色 ID（系统音色或克隆档案 ID）")
+    tts_voice_source: str = Field(default="preset", description="TTS 音色来源：preset=系统预设（默认），clone=克隆音色")
+    # 视频比例：当前前端 9:16/16:9；与 output_width/output_height 并存，传了具体分辨率时以分辨率为准
+    video_ratio: str = Field(
+        default="", description="视频比例，如 9:16（默认竖屏）/16:9；与显式分辨率冲突时以分辨率为准"
+    )
+
     @model_validator(mode="after")
     def _check_variant_arrays(self) -> "CreateGenerationTaskRequest":
         """变体数组字段长度校验 + #1749 配音严格守卫。
@@ -127,6 +145,26 @@ class CreateGenerationTaskRequest(BaseModel):
             raise ValueError(f"variant_plan_ids 长度({len(self.variant_plan_ids)})必须与 count({self.count})一致")
         return self
 
+    @model_validator(mode="after")
+    def _check_assembly_mode(self) -> "CreateGenerationTaskRequest":
+        """#1970 组装模式与叙事模式入参校验。"""
+        if self.assembly_mode not in ("random", "narrative"):
+            raise ValueError("assembly_mode 仅支持 'random'（默认）或 'narrative'")
+        if self.tts_voice_source not in ("preset", "clone"):
+            raise ValueError("tts_voice_source 仅支持 'preset' 或 'clone'")
+        if self.video_ratio:
+            parts = self.video_ratio.split(":")
+            if len(parts) != 2 or not all(p.isdigit() and int(p) > 0 for p in parts):
+                raise ValueError("video_ratio 格式必须为 '宽:高'，如 9:16 或 16:9")
+            if self.video_ratio not in ("9:16", "16:9", "1:1", "3:4", "4:3"):
+                raise ValueError("video_ratio 仅支持 9:16 / 16:9 / 1:1 / 3:4 / 4:3")
+        if self.assembly_mode == "narrative":
+            if not self.script_id.strip():
+                raise ValueError("叙事模式（narrative）必须提供 script_id（文案库 ID）")
+            if not self.tts_voice_id.strip():
+                raise ValueError("叙事模式（narrative）必须提供 tts_voice_id（TTS 音色 ID）")
+        return self
+
     @model_validator(mode="after")
     def _check_at_least_one_mode(self) -> "CreateGenerationTaskRequest":
         has_project = bool(self.project_id.strip())

apps/api/app/schemas/gpu_lipsync.py

@@ -0,0 +1,111 @@
+"""GPU MuseTalk 反向轮询 API Schema 定义.
+
+面向部署在用户 RTX2060 本地的 GPU Worker 脚本，不面向前端用户。
+Worker 用长期 GPU_WORKER_TOKEN 鉴权（不是用户 JWT）。
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+# ── Worker 注册/心跳 ──────────────────────────────────────────────
+
+
+class GpuWorkerRegisterRequest(BaseModel):
+    """Worker 启动/心跳时上报自身信息."""
+
+    worker_id: str = Field(..., min_length=1, max_length=100, description="Worker 唯一 ID（机器名+UUID 等）")
+    hostname: str = Field("", max_length=200, description="主机名，用于运维排查")
+    gpu_name: str = Field("", max_length=200, description="GPU 型号，如 'NVIDIA GeForce RTX 2060'")
+    free_vram_mb: int = Field(0, ge=0, description="当前空闲显存（MB）")
+    capabilities: str = Field("musetalk", max_length=500, description="能力列表，逗号分隔，如 'musetalk'")
+    task_id: Optional[str] = Field(
+        None,
+        max_length=64,
+        description=(
+            "当前正在处理的任务 ID。Worker 推理期间定期心跳时携带，"
+            "服务端同步刷新该任务 last_heartbeat_at，防止长推理被误判超时；空闲时不传"
+        ),
+    )
+
+
+class GpuWorkerRegisterResponse(BaseModel):
+    ok: bool = True
+    server_time: datetime
+    message: str = "ok"
+
+
+# ── 轮询任务 ────────────────────────────────────────────────────
+
+
+class GpuLipsyncTaskPayload(BaseModel):
+    """下发给 Worker 的任务载荷（含预签名下载 URL）."""
+
+    task_id: str
+    video_url: str = Field(..., description="人物视频预签名下载 URL（GET）")
+    audio_url: str = Field(..., description="驱动音频预签名下载 URL（GET）")
+    lipsync_job_id: str = ""
+    user_id: str = ""
+    project_id: str = ""
+    created_at: datetime
+    upload_url: str = Field(..., description="结果视频预签名上传 URL（PUT, video/mp4）")
+    upload_method: str = Field("PUT", description="上传方式，目前只支持 PUT")
+    expires_at: datetime
+
+
+class GpuLipsyncPollResponse(BaseModel):
+    """Worker poll 的返回：200 带任务，204 无任务."""
+
+    task: Optional[GpuLipsyncTaskPayload] = None
+
+
+# ── Worker 上报结果 ──────────────────────────────────────────────
+
+
+class GpuLipsyncResultRequest(BaseModel):
+    """Worker 通过 multipart 上传结果时携带的字段（非文件字段）."""
+
+    task_id: str = Field(..., min_length=1, max_length=64)
+    worker_id: str = Field(..., min_length=1, max_length=100)
+    success: bool = Field(True, description="true=成功（此时必须上传 result 视频文件）；false=失败")
+    duration_seconds: float = Field(0.0, ge=0, description="合成后视频时长（秒），成功时应填入")
+    error_msg: str = Field("", max_length=2000, description="失败原因，success=false 时必填")
+
+
+class GpuLipsyncResultResponse(BaseModel):
+    ok: bool = True
+    task_id: str
+    status: str  # done / failed
+    message: str = "ok"
+
+
+# ── 业务侧查询任务状态 ────────────────────────────────────────────
+
+
+class GpuLipsyncStatusResponse(BaseModel):
+    task_id: str
+    status: str
+    result_url: str = ""
+    result_duration: float = 0.0
+    error_msg: str = ""
+    worker_id: str = ""
+    attempt: int = 0
+    created_at: datetime
+    started_at: Optional[datetime] = None
+    finished_at: Optional[datetime] = None
+
+
+# ── 创建任务（内部服务调用） ──────────────────────────────────────
+
+
+class GpuLipsyncCreateRequest(BaseModel):
+    """服务层内部创建 GPU 任务用（不通过 HTTP 暴露给 Worker/前端）."""
+
+    video_url: str  # 已可访问的 OSS key 或公网 URL（API 侧会转预签名）
+    audio_url: str
+    lipsync_job_id: str = ""
+    user_id: str = ""
+    project_id: str = ""

apps/api/app/schemas/script.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from datetime import datetime
-from typing import Any, Dict, Optional
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
@@ -22,9 +22,6 @@ class ScriptResponse(BaseModel):
     content: str
     segments: list[ScriptSegment] = Field(default_factory=list)
     tags: list[str] = Field(default_factory=list)
-    title_text: str = ""
-    title_category: str = ""
-    title_config: Dict[str, Any] = Field(default_factory=dict)
     created_at: datetime
     updated_at: datetime
 
@@ -39,9 +36,6 @@ class CreateScriptRequest(BaseModel):
     content: str = ""
     segments: list[ScriptSegment] = Field(default_factory=list)
     tags: list[str] = Field(default_factory=list)
-    title_text: str = ""
-    title_category: str = ""
-    title_config: Optional[Dict[str, Any]] = None
 
 
 class UpdateScriptRequest(BaseModel):
@@ -49,6 +43,3 @@ class UpdateScriptRequest(BaseModel):
     content: Optional[str] = None
     segments: Optional[list[ScriptSegment]] = None
     tags: Optional[list[str]] = None
-    title_text: Optional[str] = None
-    title_category: Optional[str] = None
-    title_config: Optional[Dict[str, Any]] = None

apps/api/app/schemas/subscription.py

@@ -7,15 +7,21 @@ from typing import Optional
 from pydantic import BaseModel, Field
 
 # ============ Enums / Types ============
+# 会员体系（#1951/#1955 实装）：
+#   free     — 免费用户
+#   monthly  — 月卡
+#   quarterly — 季卡
+#   yearly   — 年卡
+# 已废弃档位：standard / pro / enterprise（保留常量名便于识别旧字段，但不在 API 中暴露）
 
 
-class PlanType(str):
-    """套餐类型"""
+class MembershipType(str):
+    """会员类型（与 packages.domain.points_rules.MEMBERSHIP_PRICES 一致）"""
 
     FREE = "free"
-    STANDARD = "standard"
-    PRO = "pro"
-    ENTERPRISE = "enterprise"
+    MONTHLY = "monthly"
+    QUARTERLY = "quarterly"
+    YEARLY = "yearly"
 
 
 class SubscriptionStatus(str):
@@ -40,6 +46,7 @@ class BillingCycle(str):
     """计费周期"""
 
     MONTHLY = "monthly"
+    QUARTERLY = "quarterly"
     YEARLY = "yearly"
 
 
@@ -95,8 +102,8 @@ class SimpleResponse(BaseModel):
 class ChangePlanRequest(BaseModel):
     """升级/降级请求"""
 
-    target_plan_id: str = Field(..., description="目标套餐ID")
-    billing_cycle: str = Field(..., description="计费周期: monthly/yearly")
+    target_plan_id: str = Field(..., description="目标会员类型: monthly/quarterly/yearly")
+    billing_cycle: str = Field(..., description="计费周期: monthly/quarterly/yearly")
 
 
 class ToggleAutoRenewRequest(BaseModel):

apps/api/app/services/douyin_resolver.py

@@ -0,0 +1,243 @@
+"""抖音视频解析多源轮询服务。
+
+优先级（P0 最高）：
+  P0: App Feed API 直连（零成本，不用 API Key，当前最稳定）
+  P1: TikHub API（付费 $0.001/次起，稳定）
+  P2: apizero.cn 极数本源（按量付费，国内延迟低）
+
+任一源成功即返回 MP4 直链 + 标题/文案；所有源均失败时返回 None。
+每个解析源独立超时（5-10s），总耗时不超过所有源超时之和（实际快速失败时远小于此）。
+未配置 API Key 的源自动跳过；无任何 Key 时 P0 仍可使用。
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import re
+import time
+from dataclasses import dataclass
+from typing import Optional
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+# ── API Keys from env ──────────────────────────────────────────────────
+TIKHUB_API_KEY = os.environ.get("TIKHUB_API_KEY", "").strip()
+APIZERO_API_KEY = os.environ.get("APIZERO_API_KEY", "").strip()
+
+# ── Timeouts (seconds) ────────────────────────────────────────────────
+_TIMEOUT_APP_FEED = 12
+_TIMEOUT_TIKHUB = 6
+_TIMEOUT_APIZERO = 6
+
+
+@dataclass
+class ResolveResult:
+    video_url: str  # MP4 直链；图文视频时为空字符串
+    desc: str  # 视频标题/描述文案
+    source: str  # 解析源名称，用于日志/metrics
+
+
+# ── URL preprocessing ─────────────────────────────────────────────────
+_AWEME_ID_RE = re.compile(
+    r"(?:douyin\.com/(?:video|note)/|iesdouyin\.com/share/video/|aweme_id=)(\d{15,25})",
+    re.IGNORECASE,
+)
+
+
+def _extract_url_from_text(text: str) -> str:
+    """从任意分享文本中提取首个 http(s) URL。"""
+    if not text:
+        return ""
+    m = re.search(r"https?://\S+", text)
+    return m.group(0).rstrip("。，！？!?,，；;\"'）)】") if m else ""  # noqa: B005
+
+
+def _canonicalize_url(url: str, timeout: int = 8) -> str:
+    """跟随 v.douyin.com 短链 302 重定向，返回完整 URL。失败时返回原 URL。"""
+    if "v.douyin.com" not in url and "iesdouyin.com" not in url:
+        return url
+    try:
+        with httpx.Client(
+            timeout=timeout, follow_redirects=True, verify=False, headers={"User-Agent": "Mozilla/5.0"}
+        ) as c:
+            resp = c.get(url)
+            return str(resp.url)
+    except Exception as exc:
+        logger.debug("短链解析失败: %s (%s)", url, exc)
+        return url
+
+
+# ── Provider P0: App Feed API (零成本直连) ────────────────────────────
+def _resolve_app_feed(url: str, timeout: int = _TIMEOUT_APP_FEED) -> Optional[ResolveResult]:
+    """抖音 Android App Feed API 直连 — 零依赖、无需 Key、目前最稳定。"""
+    from packages.douyin_parser import fetch_douyin_video_url
+
+    video_url, desc = fetch_douyin_video_url(url, timeout=timeout, max_retries=2)
+    if video_url:
+        return ResolveResult(video_url=video_url, desc=desc or "", source="app_feed")
+    if desc:
+        # 图文视频：video_url 为 None 但 desc 可用
+        return ResolveResult(video_url="", desc=desc, source="app_feed_image")
+    return None
+
+
+# ── Provider P1: TikHub ───────────────────────────────────────────────
+def _resolve_tikhub(url: str, api_key: str, timeout: int = _TIMEOUT_TIKHUB) -> Optional[ResolveResult]:
+    """TikHub API: https://api.tikhub.io/
+    两步：get_aweme_id → fetch_one_video
+    """
+    if not api_key:
+        return None
+    headers = {"Authorization": f"Bearer {api_key}"}
+    aweme_id = _AWEME_ID_RE.search(url or "")
+    aweme_id = aweme_id.group(1) if aweme_id else None
+
+    if not aweme_id:
+        try:
+            with httpx.Client(timeout=timeout, verify=False) as c:
+                r = c.get(
+                    "https://api.tikhub.io/api/v1/douyin/web/get_aweme_id",
+                    headers=headers,
+                    params={"url": url},
+                )
+                data = r.json()
+                aweme_id = (data.get("data") or {}).get("aweme_id")
+        except Exception as exc:
+            logger.warning("TikHub get_aweme_id 失败: %s", exc)
+            return None
+    if not aweme_id:
+        return None
+
+    try:
+        with httpx.Client(timeout=timeout, verify=False) as c:
+            r = c.get(
+                "https://api.tikhub.io/api/v1/douyin/app/v3/fetch_one_video",
+                headers=headers,
+                params={"aweme_id": aweme_id},
+            )
+            data = r.json()
+            video = (data.get("data") or {}).get("video") or {}
+            urls = []
+            for k in ("download_addr", "play_addr_h264", "play_addr"):
+                urls = (video.get(k) or {}).get("url_list") or []
+                if urls:
+                    break
+            if not urls:
+                # bit_rate 兜底
+                for br in video.get("bit_rate") or []:
+                    urls = (br.get("play_addr") or {}).get("url_list") or []
+                    if urls:
+                        break
+            if not urls:
+                return None
+            # 优先 CDN 直链
+            video_url = urls[0]
+            for u in urls:
+                if any(h in u for h in ("douyinvod.com", "bytecdn.com", "365yg.com")):
+                    video_url = u
+                    break
+            desc = (data.get("data") or {}).get("desc", "")
+            # 检测图文
+            images = (data.get("data") or {}).get("images") or []
+            if images and not any(h in video_url for h in ("douyinvod.com", "bytecdn.com", "amemv.com")):
+                # 图文且无视频直链
+                if desc:
+                    return ResolveResult(video_url="", desc=desc, source="tikhub_image")
+                return None
+            return ResolveResult(video_url=video_url, desc=desc or "", source="tikhub")
+    except Exception as exc:
+        logger.warning("TikHub fetch_one_video 失败: %s", exc)
+        return None
+
+
+# ── Provider P2: apizero.cn ──────────────────────────────────────────
+def _resolve_apizero(url: str, api_key: str, timeout: int = _TIMEOUT_APIZERO) -> Optional[ResolveResult]:
+    """apizero.cn 极数本源: https://v1.apizero.cn/api/video-parse?url=...&flat=2"""
+    if not api_key:
+        return None
+    headers = {"Authorization": f"Bearer {api_key}"}
+    try:
+        with httpx.Client(timeout=timeout, verify=False) as c:
+            r = c.get(
+                "https://v1.apizero.cn/api/video-parse",
+                headers=headers,
+                params={"url": url, "flat": 2},
+            )
+            data = r.json()
+            d = data.get("data") or {}
+            video_list = d.get("video_list") or []
+            if not video_list:
+                return None
+            video_url = video_list[0].get("url", "")
+            desc = d.get("title", "") or d.get("desc", "") or d.get("author", "")
+            if not video_url:
+                return None
+            return ResolveResult(video_url=video_url, desc=desc, source="apizero")
+    except Exception as exc:
+        logger.warning("apizero 解析失败: %s", exc)
+        return None
+
+
+# ── Main API ──────────────────────────────────────────────────────────
+def resolve_douyin_video(page_url: str) -> Optional[ResolveResult]:
+    """按 P0→P1→P2 顺序轮询解析抖音视频。
+
+    Args:
+        page_url: 抖音 URL 或含 URL 的分享文本。
+
+    Returns:
+        ResolveResult 或 None（所有源均失败）。
+        图文视频时 video_url 为空字符串、desc 为文案。
+    """
+    url = _extract_url_from_text(page_url) or page_url
+    url = _canonicalize_url(url)
+
+    providers = [
+        ("app_feed", lambda: _resolve_app_feed(url)),
+        ("tikhub", lambda: _resolve_tikhub(url, TIKHUB_API_KEY)),
+        ("apizero", lambda: _resolve_apizero(url, APIZERO_API_KEY)),
+    ]
+
+    enabled_count = 0
+    for name, fn in providers:
+        if name == "tikhub" and not TIKHUB_API_KEY:
+            continue
+        if name == "apizero" and not APIZERO_API_KEY:
+            continue
+        enabled_count += 1
+        t0 = time.time()
+        try:
+            result = fn()
+            elapsed = time.time() - t0
+            if result:
+                domain = result.video_url.split("/")[2] if result.video_url and "/" in result.video_url else "(image)"
+                logger.info(
+                    "抖音解析成功: source=%s url_domain=%s desc_len=%d time=%.2fs",
+                    result.source,
+                    domain,
+                    len(result.desc),
+                    elapsed,
+                )
+                return result
+            logger.debug("解析源 %s 返回空 (%.2fs)", name, elapsed)
+        except Exception as exc:
+            logger.warning("解析源 %s 异常 (%.2fs): %s", name, time.time() - t0, exc)
+
+    if enabled_count == 0:
+        logger.error("无任何抖音解析源可用：请检查 App Feed API 网络连通性")
+    else:
+        logger.warning("所有 %d 个抖音解析源均失败: url=%s", enabled_count, url)
+    return None
+
+
+def available_providers() -> list[str]:
+    """返回当前可用的解析源列表（用于诊断）。"""
+    provs = ["app_feed"]
+    if TIKHUB_API_KEY:
+        provs.append("tikhub")
+    if APIZERO_API_KEY:
+        provs.append("apizero")
+    return provs

apps/api/app/services/edit_plan_service.py

@@ -423,6 +423,7 @@ class EditPlanService:
                     clip_type=clip.clip_type,
                     order=clip.order,
                     asset_id=clip.asset_id,
+                    atom_clip_id=clip_item.get("atom_clip_id", ""),
                     text_content=clip.text_content,
                     start_time=clip.start_time,
                     duration=clip.duration,
@@ -474,6 +475,7 @@ class EditPlanService:
         voice_duration: float = 0.0,
         rng=None,
         batch_segments: dict[str, list[tuple[float, float]]] | None = None,
+        batch_used_atom_ids: set[str] | list[str] | None = None,
     ) -> EditPlan:
         """为批量变体生成独立 plan：完整重跑单视频选片流程（#1743）。
 
@@ -608,18 +610,69 @@ class EditPlanService:
                     st = float(c.start_time or 0.0)
                     batch_segments_resolved.setdefault(c.asset_id, []).append((st, st + float(c.duration)))
 
-        clips_data = reselect_clips_for_variant(
-            source_clips_data,
-            pool_ids,
-            asset_durations=durations,
-            asset_scene_points=scene_points,
-            historical_used_segments=historical,
-            batch_segments=batch_segments_resolved,
-            target_durations=target_durations,
-            rng=rng,
-        )
+        clips_data = None
+        # #1970 原子片段级变体重选：候选素材已切片时优先按原子片段选片
+        try:
+            from packages.adapters.sqlalchemy_impl.asset_atom_clip_repository import (
+                SQLAlchemyAssetAtomClipRepository,
+            )
+            from packages.domain.atom_clip_resolver import flatten_candidates, load_atom_clips_for_assets
+            from packages.domain.atom_clip_selector import reselect_clips_from_atoms
 
-        # 片段区间写回素材 metadata（与落库同事务；replace_all_clips_transactional 内 commit）
+            atom_repo = SQLAlchemyAssetAtomClipRepository(db)
+
+            # 兜底切片只需要时长；本方法已查出 durations，封装一个只读假素材仓储
+            class _DurationOnlyAssetRepo:
+                def __init__(self, durations_map: dict[str, float]) -> None:
+                    self._durations = durations_map
+
+                def get(self, asset_id: str):
+                    if asset_id not in self._durations:
+                        return None
+
+                    class _A:
+                        pass
+
+                    a = _A()
+                    a.duration = self._durations[asset_id]
+                    return a
+
+            clips_by_asset = load_atom_clips_for_assets(
+                pool_ids,
+                atom_clip_repo=atom_repo,
+                asset_repo=_DurationOnlyAssetRepo(durations),
+            )
+            atom_candidates = flatten_candidates(clips_by_asset)
+            if atom_candidates:
+                # 历史成片已用原子片段（降权）；批次内前序变体已用（硬避让）
+                historical_atom_ids = set(
+                    self._clip_repo.list_recent_atom_clip_ids_by_user(
+                        created_by_user_id or source.created_by_user_id or "",
+                        limit=200,
+                    )
+                )
+                clips_data = reselect_clips_from_atoms(
+                    source_clips_data,
+                    atom_candidates,
+                    historical_atom_ids=historical_atom_ids,
+                    batch_used_atom_ids=(set(batch_used_atom_ids) if batch_used_atom_ids else None),
+                    rng=rng,
+                )
+        except Exception:
+            logger.warning("原子片段变体重选失败，回退整条素材选片", exc_info=True)
+            clips_data = None
+
+        if clips_data is None:
+            clips_data = reselect_clips_for_variant(
+                source_clips_data,
+                pool_ids,
+                asset_durations=durations,
+                asset_scene_points=scene_points,
+                historical_used_segments=historical,
+                batch_segments=batch_segments_resolved,
+                target_durations=target_durations,
+                rng=rng,
+            )  # 片段区间写回素材 metadata（与落库同事务；replace_all_clips_transactional 内 commit）
         for item in clips_data:
             aid = item.get("asset_id", "")
             if aid:

apps/api/app/services/generation_common.py

@@ -61,10 +61,17 @@ def writeback_edit_plan_config(
     task_id: str,
     title_config: dict | None,
     db: Session,
+    dedup_enabled: bool | None = None,
+    video_index: int | None = None,
+    assembly_mode: str | None = None,
+    script_id: str | None = None,
+    video_ratio: str | None = None,
 ) -> None:
     """任务入队成功后，回写 EditPlan.config：generation_task_id + title_config。
 
     用 merge 方式更新，不整体覆盖 config，避免丢失其他字段。
+    #1970：dedup_enabled 非 None 时一并写入，worker 据此决定 edge_crop/微变换；
+    PR3 叙事模式再写 assembly_mode/script_id/video_ratio（可追溯，不影响渲染）。
     失败只记日志，不影响任务创建。
     """
     if not plan_id:
@@ -80,6 +87,16 @@ def writeback_edit_plan_config(
         current_config = plan_model.config if isinstance(plan_model.config, dict) else {}
         merged = dict(current_config)
         merged["generation_task_id"] = task_id
+        if dedup_enabled is not None:
+            merged["dedup_enabled"] = bool(dedup_enabled)
+        if video_index is not None:
+            merged["video_index"] = int(video_index)
+        if assembly_mode:
+            merged["assembly_mode"] = assembly_mode
+        if script_id:
+            merged["script_id"] = script_id
+        if video_ratio:
+            merged["video_ratio"] = video_ratio
 
         if title_config:
             # #1901 统一字段名为 "title"（worker sync_configs_to_plan 写的是 "title"）
@@ -157,6 +174,33 @@ def collect_plan_segments(
     return segs
 
 
+def collect_plan_atom_clip_ids(
+    plan_id: str,
+    clip_repo: Any,
+    *,
+    page_size: int = 500,
+) -> list[str]:
+    """分页读取 plan 所有 clips，收集已选用的原子片段 ID（#1970）。
+
+    用于批量变体间原子片段级硬避让：同一原子片段在同批次内只用一次。
+    旧路径 clips 的 atom_clip_id 为空串，自动忽略。
+    """
+    ids: list[str] = []
+    sk, pg = 0, page_size
+    while True:
+        batch = clip_repo.list_by_plan(plan_id, skip=sk, limit=pg)
+        if not batch:
+            break
+        for c in batch:
+            acid = getattr(c, "atom_clip_id", "") or ""
+            if acid:
+                ids.append(acid)
+        if len(batch) < pg:
+            break
+        sk += pg
+    return ids
+
+
 def resolve_latest_plan_by_template(
     db: Session,
     *,

apps/api/app/services/gpu_lipsync_service.py

@@ -0,0 +1,382 @@
+"""GPU MuseTalk 口型同步服务 — 反向轮询模式.
+
+职责：
+1. 创建任务（由 lipsync 业务流程调用），为输入/输出生成预签名 URL，任务入队；
+2. Worker 心跳注册（register）：登记/刷新 worker 状态；
+3. Worker 轮询拉任务（poll）：原子地 CLAIM 一条 pending 任务，返回预签名 URL；
+4. Worker 上报结果（report_result）：标记 done/failed，失败可重试；
+5. 业务侧查询状态（get_status）。
+"""
+
+from __future__ import annotations
+
+import logging
+import uuid
+from datetime import UTC, datetime, timedelta
+from typing import Optional
+
+from app.core.storage import get_storage_service
+from sqlalchemy.orm import Session
+
+from packages.adapters.sqlalchemy_impl.models import GpuLipsyncTaskModel, GpuWorkerModel
+from packages.config import get_api_settings
+
+logger = logging.getLogger(__name__)
+
+# 任务在 processing 超过此时长仍未完成 → 超时回退 pending 或置 failed
+MAX_ATTEMPTS = 3
+
+
+class GpuLipsyncService:
+    """GPU 口型同步服务（无状态方法，每次调用从 DI 拿 db/storage）."""
+
+    RESULT_PREFIX = "gpu-lipsync/results/"
+    INPUT_SIGN_EXPIRES_PAD = 600  # 输入预签名 URL 在任务超时基础上再加 10min 余量
+
+    # ── 公共入口 ────────────────────────────────────────────────────
+
+    def __init__(self, db: Session):
+        self.db = db
+        self.settings = get_api_settings()
+        self.storage = get_storage_service()
+
+    # ── Worker 注册/心跳 ────────────────────────────────────────────
+
+    def register_worker(
+        self,
+        worker_id: str,
+        hostname: str = "",
+        gpu_name: str = "",
+        free_vram_mb: int = 0,
+        capabilities: str = "musetalk",
+        task_id: Optional[str] = None,
+    ) -> GpuWorkerModel:
+        """Worker 注册/心跳。
+
+        task_id 非空时（Worker 推理期间的任务级心跳），同步把对应 processing
+        任务的 last_heartbeat_at 续到当前时间，使长推理不会被
+        ``_recover_timed_out_tasks`` 误回退。任务已结束 / 不属于该 worker
+        （如已被超时回收重新派发）时忽略，不报错。
+        """
+        now = datetime.now(UTC)
+        worker = self.db.query(GpuWorkerModel).filter(GpuWorkerModel.worker_id == worker_id).one_or_none()
+        if worker is None:
+            worker = GpuWorkerModel(
+                worker_id=worker_id,
+                hostname=hostname,
+                gpu_name=gpu_name,
+                free_vram_mb=free_vram_mb,
+                capabilities=capabilities,
+                last_heartbeat_at=now,
+                created_at=now,
+            )
+            self.db.add(worker)
+        else:
+            worker.hostname = hostname or worker.hostname
+            worker.gpu_name = gpu_name or worker.gpu_name
+            worker.free_vram_mb = free_vram_mb
+            worker.capabilities = capabilities or worker.capabilities
+            worker.last_heartbeat_at = now
+        if task_id:
+            self._touch_task_heartbeat(task_id, worker_id, now)
+        self.db.commit()
+        return worker
+
+    # ── 轮询拉任务（Worker 调用） ──────────────────────────────────
+
+    def poll_task(self, worker_id: str) -> Optional[GpuLipsyncTaskModel]:
+        """原子地认领一条最早的 pending 任务，返回给 worker；无任务返回 None.
+
+        同时会：
+        - 把 processing 状态且真正超时（任务心跳停滞超过
+          gpu_task_timeout_seconds；Worker 推理期会通过 register(task_id=...)
+          续心跳，长推理不会误判）的任务回退为 pending（attempt++，超过
+          MAX_ATTEMPTS 置 failed），让其它 worker 认领。
+        - 刷新 worker 心跳。
+        """
+        now = datetime.now(UTC)
+        self._recover_timed_out_tasks(now)
+        # 更新 worker 心跳
+        self._touch_worker(worker_id, now)
+
+        # 选一条最早 pending 任务（FOR UPDATE SKIP LOCKED 语义：简单起见先查再锁状态）
+        task = (
+            self.db.query(GpuLipsyncTaskModel)
+            .filter(GpuLipsyncTaskModel.status == "pending")
+            .order_by(GpuLipsyncTaskModel.created_at.asc())
+            .first()
+        )
+        if task is None:
+            self.db.commit()
+            return None
+
+        # 原子 claim：用 UPDATE WHERE status=pending 避免并发
+        upd_rows = (
+            self.db.query(GpuLipsyncTaskModel)
+            .filter(
+                GpuLipsyncTaskModel.id == task.id,
+                GpuLipsyncTaskModel.status == "pending",
+            )
+            .update(
+                {
+                    GpuLipsyncTaskModel.status: "processing",
+                    GpuLipsyncTaskModel.worker_id: worker_id,
+                    GpuLipsyncTaskModel.started_at: now,
+                    GpuLipsyncTaskModel.last_heartbeat_at: now,
+                    GpuLipsyncTaskModel.attempt: GpuLipsyncTaskModel.attempt + 1,
+                    GpuLipsyncTaskModel.updated_at: now,
+                },
+                synchronize_session=False,
+            )
+        )
+        self.db.commit()
+        if upd_rows == 0:
+            # 被其它 worker 抢先了
+            return None
+        self.db.refresh(task)
+        # 生成预签名输入/输出 URL（在 claim 时动态生成，避免长时间过期）
+        expires = self.settings.gpu_task_timeout_seconds + self.INPUT_SIGN_EXPIRES_PAD
+        task._signed_video_url = self.storage.get_download_url(task.video_url, expires_seconds=expires)
+        task._signed_audio_url = self.storage.get_download_url(task.audio_url, expires_seconds=expires)
+        task._signed_upload_url = self.storage.get_upload_url(
+            self._result_key(task.id),
+            expires_seconds=expires,
+            content_type="video/mp4",
+        )
+        task._upload_expires_at = now + timedelta(seconds=expires)
+        return task
+
+    # ── 上报结果 ──────────────────────────────────────────────────
+
+    def report_result(
+        self,
+        task_id: str,
+        worker_id: str,
+        success: bool,
+        duration_seconds: float = 0.0,
+        error_msg: str = "",
+    ) -> GpuLipsyncTaskModel:
+        task = self.db.get(GpuLipsyncTaskModel, task_id)
+        if task is None:
+            raise KeyError(f"task {task_id} not found")
+        now = datetime.now(UTC)
+        if success:
+            task.status = "done"
+            task.result_url = self._result_key(task_id)
+            task.result_duration = duration_seconds or 0.0
+            task.error_msg = ""
+            task.finished_at = now
+        else:
+            # 失败：若仍可重试（已尝试次数 < MAX_ATTEMPTS）→ 回退 pending；否则 → failed
+            if task.attempt < MAX_ATTEMPTS:
+                task.status = "pending"
+                task.worker_id = ""
+                task.started_at = None
+                task.error_msg = error_msg[:2000]
+                logger.warning(
+                    "GPU 任务 %s 在 worker %s 上失败，回退 pending 等待重试（attempt=%d）: %s",
+                    task_id,
+                    worker_id,
+                    task.attempt,
+                    error_msg[:200],
+                )
+            else:
+                task.status = "failed"
+                task.error_msg = error_msg[:2000]
+                task.finished_at = now
+                logger.error(
+                    "GPU 任务 %s 失败达到最大重试次数 %d，置为 failed: %s",
+                    task_id,
+                    MAX_ATTEMPTS,
+                    error_msg[:200],
+                )
+        task.updated_at = now
+        task.last_heartbeat_at = now
+        self._touch_worker(worker_id, now)
+        self.db.commit()
+        self.db.refresh(task)
+        return task
+
+    # ── 业务侧查询 ────────────────────────────────────────────────
+
+    def get_task(self, task_id: str) -> Optional[GpuLipsyncTaskModel]:
+        return self.db.get(GpuLipsyncTaskModel, task_id)
+
+    def get_by_lipsync_job(self, lipsync_job_id: str) -> Optional[GpuLipsyncTaskModel]:
+        return (
+            self.db.query(GpuLipsyncTaskModel)
+            .filter(GpuLipsyncTaskModel.lipsync_job_id == lipsync_job_id)
+            .order_by(GpuLipsyncTaskModel.created_at.desc())
+            .first()
+        )
+
+    # ── 创建任务（业务侧调用） ────────────────────────────────────
+
+    def create_task(
+        self,
+        video_url: str,
+        audio_url: str,
+        lipsync_job_id: str = "",
+        user_id: str = "",
+        project_id: str = "",
+    ) -> GpuLipsyncTaskModel:
+        task_id = str(uuid.uuid4())
+        now = datetime.now(UTC)
+        task = GpuLipsyncTaskModel(
+            id=task_id,
+            lipsync_job_id=lipsync_job_id,
+            user_id=user_id,
+            project_id=project_id,
+            video_url=video_url,
+            audio_url=audio_url,
+            status="pending",
+            attempt=0,
+            created_at=now,
+            updated_at=now,
+        )
+        self.db.add(task)
+        self.db.commit()
+        self.db.refresh(task)
+        logger.info(
+            "创建 GPU 口型任务 %s (lipsync_job=%s, user=%s)",
+            task_id,
+            lipsync_job_id,
+            user_id,
+        )
+        return task
+
+    # ── 内部辅助 ──────────────────────────────────────────────────
+
+    def _result_key(self, task_id: str) -> str:
+        return f"{self.RESULT_PREFIX}{task_id}.mp4"
+
+    def _touch_task_heartbeat(self, task_id: str, worker_id: str, now: datetime) -> None:
+        """Worker 推理期间的任务级心跳：只刷新属于该 worker 且仍在 processing 的任务。
+
+        任务不存在 / 已被超时回收重新派发 / 已完成 → 静默忽略（此时旧 worker 的
+        结果上报会被结果接口按最终态处理）。
+        """
+        task = self.db.get(GpuLipsyncTaskModel, task_id)
+        if task is None:
+            return
+        if task.status != "processing" or task.worker_id != worker_id:
+            logger.info(
+                "忽略过期任务心跳 task=%s worker=%s（status=%s owner=%s）",
+                task_id,
+                worker_id,
+                task.status,
+                task.worker_id,
+            )
+            return
+        task.last_heartbeat_at = now
+        task.updated_at = now
+        self.db.flush()
+
+    def _touch_worker(self, worker_id: str, now: datetime) -> None:
+        if not worker_id:
+            return
+        worker = self.db.query(GpuWorkerModel).filter(GpuWorkerModel.worker_id == worker_id).one_or_none()
+        if worker is not None:
+            worker.last_heartbeat_at = now
+            self.db.flush()
+        else:
+            # 自注册（poll 时允许自动建一个空 worker 记录，运维可见）
+            worker = GpuWorkerModel(
+                worker_id=worker_id,
+                hostname="",
+                gpu_name="",
+                free_vram_mb=0,
+                capabilities="musetalk",
+                last_heartbeat_at=now,
+                created_at=now,
+            )
+            self.db.add(worker)
+            self.db.flush()
+
+    def _recover_timed_out_tasks(self, now: datetime) -> None:
+        """扫描 processing 状态且真正超时的任务，回退 pending 或失败。
+
+        判定只看任务自身 last_heartbeat_at：claim 时写入，Worker 推理期间通过
+        /gpu/register(task_id=...) 每 30s 续期。因此仅在 Worker 崩溃/断网
+        （任务心跳停滞超过 gpu_task_timeout_seconds）时才回收，
+        不会因 Worker 主循环忙于推理而误回退。
+        """
+        timeout = self.settings.gpu_task_timeout_seconds
+        cutoff = now - timedelta(seconds=timeout)
+        stuck_tasks = (
+            self.db.query(GpuLipsyncTaskModel)
+            .filter(
+                GpuLipsyncTaskModel.status == "processing",
+                GpuLipsyncTaskModel.last_heartbeat_at < cutoff,
+            )
+            .all()
+        )
+        for t in stuck_tasks:
+            if t.attempt >= MAX_ATTEMPTS:
+                t.status = "failed"
+                t.error_msg = f"worker 心跳超时（{timeout}s），重试次数已耗尽"
+                t.finished_at = now
+            else:
+                t.status = "pending"
+                t.worker_id = ""
+                t.started_at = None
+                t.error_msg = f"worker 心跳超时（{timeout}s），等待重试"
+                logger.warning("GPU 任务 %s 心跳超时，回退 pending（attempt=%d）", t.id, t.attempt)
+            t.updated_at = now
+        if stuck_tasks:
+            self.db.flush()
+
+    # ── 业务侧辅助 ──────────────────────────────────────────────────
+
+    def has_available_worker(self) -> bool:
+        """判断是否有 Worker 在心跳新鲜窗口内可用."""
+        stale_cutoff = datetime.now(UTC) - timedelta(seconds=self.settings.gpu_worker_stale_seconds)
+        return (
+            self.db.query(GpuWorkerModel).filter(GpuWorkerModel.last_heartbeat_at >= stale_cutoff).first() is not None
+        )
+
+    def wait_for_result(
+        self,
+        task_id: str,
+        timeout_seconds: Optional[int] = None,
+        poll_interval: Optional[float] = None,
+    ) -> Optional[GpuLipsyncTaskModel]:
+        """同步轮询等待 GPU 任务完成。
+
+        Args:
+            task_id: 任务 ID（由 create_task 返回）
+            timeout_seconds: 总超时，默认取 settings.gpu_lipsync_wait_timeout
+            poll_interval: 轮询间隔秒，默认取 settings.gpu_lipsync_poll_interval
+
+        Returns:
+            终态 task（status=done/failed）；超时返回 None（此时调用方应回退 MediaKit）。
+            等待期间会自动调用 _recover_timed_out_tasks 做超时回收。
+        """
+        import time
+
+        timeout = timeout_seconds if timeout_seconds is not None else self.settings.gpu_lipsync_wait_timeout
+        interval = poll_interval if poll_interval is not None else self.settings.gpu_lipsync_poll_interval
+        deadline = time.monotonic() + timeout
+
+        while True:
+            now = datetime.now(UTC)
+            # 顺手回收超时任务
+            try:
+                self._recover_timed_out_tasks(now)
+                self.db.commit()
+            except Exception as exc:  # noqa: BLE001 - 回收失败不阻塞主流程
+                logger.warning("wait_for_result 回收超时任务异常: %s", exc)
+                self.db.rollback()
+
+            task = self.db.get(GpuLipsyncTaskModel, task_id)
+            if task is None:
+                return None
+            if task.status == "done":
+                return task
+            if task.status == "failed":
+                return task
+            # pending/processing 继续等
+            if time.monotonic() >= deadline:
+                logger.warning("GPU 任务 %s 等待超时（%ds），回退 MediaKit", task_id, timeout)
+                return None
+            time.sleep(interval)

apps/api/app/services/lipsync_service.py

@@ -36,6 +36,7 @@ from sqlalchemy.orm import Session
 
 from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
 from packages.application.cosyvoice_service import CosyVoiceError
+from packages.config import get_api_settings
 from packages.domain.sentence_timings import (
     compute_sentence_timings,
     probe_audio_duration,
@@ -63,6 +64,7 @@ class LipsyncService:
         self.client = client or get_mediakit_client()
         self._cosyvoice = cosyvoice_service
         self._voice_clone_repo = voice_clone_repo
+        self.settings = get_api_settings()
 
     def _get_cosyvoice(self):
         """延迟获取 CosyVoiceService（与 tts 路由一致，含 OSS 预签名配置）."""
@@ -215,7 +217,52 @@ class LipsyncService:
         if timings:
             job.sentence_timings = timings
 
-        # 4. 签名 URL 并提交 MediaKit
+        # 4. 检查是否走 GPU 路径：开关打开 + 有可用 Worker
+        use_gpu = False
+        if self.settings.use_gpu_lipsync:
+            try:
+                from app.services.gpu_lipsync_service import GpuLipsyncService
+
+                gpu_svc = GpuLipsyncService(self.db)
+                if gpu_svc.has_available_worker():
+                    use_gpu = True
+                    logger.info("[lipsync] 检测到可用 GPU Worker，优先走 MuseTalk 本地推理: job_id=%s", job.id)
+                else:
+                    logger.info("[lipsync] GPU 开关已开但无可用 Worker（心跳过期），回退 MediaKit: job_id=%s", job.id)
+            except Exception as exc:
+                logger.warning("[lipsync] GPU 服务初始化失败，回退 MediaKit: job_id=%s err=%s", job.id, exc)
+
+        if use_gpu:
+            try:
+                gpu_task = self._submit_to_gpu(job=job, gpu_svc=gpu_svc)
+                if gpu_task is not None:
+                    # GPU 任务完成：直接把结果写入 job，标为 completed
+                    job.mediakit_task_id = ""  # GPU 路径不走 MediaKit
+                    job.status = STATUS_COMPLETED
+                    job.output_video_url = gpu_task.result_url
+                    job.output_duration = gpu_task.result_duration or 0.0
+                    job.completed_at = datetime.now(UTC)
+                    job.updated_at = datetime.now(UTC)
+                    self.db.commit()
+                    logger.info(
+                        "[lipsync] GPU MuseTalk 推理完成: job_id=%s gpu_task=%s duration=%.2f",
+                        job.id,
+                        gpu_task.id,
+                        job.output_duration,
+                    )
+                    # 转存到持久 OSS 路径（GPU 结果已在 gpu-lipsync/results/ 下，直接签短链）
+                    return
+                # wait_for_result 返回 None 表示超时/最终失败 → 继续走 MediaKit 兜底
+                logger.warning("[lipsync] GPU 任务等待超时或失败，回退 MediaKit: job_id=%s", job.id)
+                self.db.rollback()  # 回滚可能的中间状态
+            except Exception as exc:
+                logger.exception("[lipsync] GPU 路径异常，回退 MediaKit: job_id=%s err=%s", job.id, exc)
+                try:
+                    self.db.rollback()
+                except Exception:
+                    pass
+
+        # 5. 签名 URL 并提交 MediaKit（兜底路径）
         video_url = self._sign_media_url(job.video_url)
         signed_audio_url = self._sign_media_url(job.audio_url)
         job.audio_url = signed_audio_url
@@ -244,6 +291,51 @@ class LipsyncService:
             self.db.commit()
             raise
 
+    # ── GPU MuseTalk 路径 ────────────────────────────────────────────────
+
+    def _submit_to_gpu(self, *, job, gpu_svc) -> Optional[object]:
+        """创建 GPU 任务并同步等待结果。
+
+        成功返回终态 task 对象（status=done）；超时或 GPU 最终失败返回 None，
+        调用方回退 MediaKit。
+
+        注意：job.video_url / job.audio_url 可能是：
+        - 自家 OSS 存储 key（storage.is_own_url 判断，gpu_svc.create_task 内部
+          get_download_url 会自动签预签名 URL 给 Worker）
+        - 外部公网 URL（CosyVoice 临时链接等）：poll 返回时原样透传给 Worker，
+          Worker 可直接 GET 下载。
+        """
+        # 创建 GPU 任务
+        gpu_task = gpu_svc.create_task(
+            video_url=job.video_url,
+            audio_url=job.audio_url,
+            lipsync_job_id=job.id,
+            user_id=job.user_id,
+            project_id=job.project_id,
+        )
+        logger.info(
+            "[lipsync] 已创建 GPU 任务: job_id=%s gpu_task=%s",
+            job.id,
+            gpu_task.id,
+        )
+        # 同步等待 Worker 处理完成（轮询 DB）
+        final_task = gpu_svc.wait_for_result(gpu_task.id)
+        if final_task is None:
+            logger.warning("[lipsync] GPU 任务等待超时，回退 MediaKit: gpu_task=%s", gpu_task.id)
+            return None
+        if final_task.status != "done":
+            logger.warning(
+                "[lipsync] GPU 任务失败: gpu_task=%s status=%s err=%s",
+                gpu_task.id,
+                final_task.status,
+                final_task.error_msg,
+            )
+            return None
+        # result_url 是 OSS 存储 key；签一个长有效期 URL 写回 job.output_video_url
+        result_signed = self._sign_media_url(final_task.result_url)
+        final_task.result_url = result_signed or final_task.result_url
+        return final_task
+
     # ── 创建任务 ──────────────────────────────────────────────────────────
 
     def create_job(

apps/api/app/services/narrative_service.py

@@ -0,0 +1,344 @@
+"""叙事剪辑前置服务 — #1970 PR3.
+
+叙事模式（assembly_mode='narrative'）在生成任务入队前同步完成：
+
+1. 按 script_id 读取文案（归属校验）；
+2. 按 tts_voice_source 解析音色（preset=CosyVoice 音色 id；clone=克隆档案 id，
+   解析档案归属并取其 CosyVoice voice_id）；
+3. 同步 TTS 合成（复用 tts_job 现有 workflow：提交即同步返回，未完成则轮询兜底），
+   失败直接抛 NarrativeError（HTTP 层转 4xx，任务不入队）；
+4. 把合成音频转存为配音库 audio asset（与 /tts/jobs/{id}/save-to-library 同一套
+   存储路径与元信息约定），返回 asset_id —— 下游仍以 voice_library_id（实为
+   audio asset id）消费，渲染链路零改动。
+
+积分扣点与 /tts 合成端点保持一致（ai_voice 场景），失败退费。
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import math
+import subprocess
+import tempfile
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from sqlalchemy.orm import Session
+
+from packages.adapters.sqlalchemy_impl.models import ScriptModel
+from packages.application.cosyvoice_service import CosyVoiceService
+from packages.application.tts_job.use_cases import CreateTTSJobUseCase
+from packages.application.tts_job.workflow import TTSWorkflowService
+from packages.domain import Asset, AssetLibrary, AssetLibraryKind, AssetStatus, ClassificationStatus
+from packages.domain.points_rules import calculate_points_cost
+from packages.domain.points_service import PointsService
+from packages.shared.storage import SharedStorageService
+
+logger = logging.getLogger(__name__)
+
+_POINTS_SCENE = "ai_voice"
+_SYNTH_TIMEOUT = 180.0  # 叙事配音在 HTTP 请求内同步等待，长文案分段合成时留出余量
+_CONTENT_TYPE_MAP = {"mp3": "audio/mpeg", "wav": "audio/wav", "pcm": "audio/pcm", "opus": "audio/opus"}
+
+
+class NarrativeError(Exception):
+    """叙事模式前置处理失败（文案/音色/TTS/落库）。"""
+
+    def __init__(self, message: str, *, status_code: int = 400) -> None:
+        super().__init__(message)
+        self.message = message
+        self.status_code = status_code
+
+
+@dataclass(slots=True)
+class NarrativeContext:
+    """叙事模式前置处理结果。"""
+
+    script: ScriptModel
+    voice_asset_id: str
+    tts_job_id: str
+    audio_duration: float
+
+
+def _find_or_create_voice_library(
+    *,
+    user_id: str,
+    project_repository: Any,
+    asset_library_repository: Any,
+) -> AssetLibrary:
+    """找到（或自动创建）用户 voice 素材库；与 tts.py 保存配音库逻辑一致。"""
+    projects = project_repository.find_accessible_projects(user_id)
+    if not projects:
+        raise NarrativeError("没有可用的项目，无法保存叙事配音", status_code=400)
+
+    for project in projects:
+        for lib in asset_library_repository.find_by_project(project.id):
+            kind = lib.kind.value if hasattr(lib.kind, "value") else lib.kind
+            if kind == AssetLibraryKind.VOICE.value:
+                return lib
+
+    project = projects[0]
+    library = AssetLibrary.create(project_id=project.id, name="配音素材库", kind=AssetLibraryKind.VOICE)
+    from sqlalchemy.exc import IntegrityError
+
+    try:
+        return asset_library_repository.create(library)
+    except IntegrityError:
+        session = getattr(asset_library_repository, "session", None)
+        if session is not None:
+            try:
+                session.rollback()
+            except Exception:  # noqa: BLE001 - 回滚失败不影响重查
+                logger.warning("IntegrityError 后回滚 session 失败", exc_info=True)
+        for lib in asset_library_repository.find_by_project(project.id):
+            kind = lib.kind.value if hasattr(lib.kind, "value") else lib.kind
+            if kind == AssetLibraryKind.VOICE.value:
+                return lib
+        raise NarrativeError("配音素材库创建失败，请重试", status_code=500) from None
+
+
+def _resolve_voice(
+    *,
+    user_id: str,
+    tts_voice_id: str,
+    tts_voice_source: str,
+    voice_clone_repository: Any,
+) -> tuple[str, str]:
+    """解析音色 → (CosyVoice voice_id, voice_clone_profile_id)。"""
+    if tts_voice_source == "clone":
+        profile = voice_clone_repository.get(tts_voice_id)
+        if profile is None:
+            raise NarrativeError("克隆音色不存在", status_code=404)
+        if profile.user_id != user_id:
+            raise NarrativeError("无权使用该克隆音色", status_code=403)
+        if not profile.voice_id:
+            raise NarrativeError("音色克隆尚未完成，请稍后再试", status_code=400)
+        return profile.voice_id, profile.id
+    # preset：tts_voice_id 即 CosyVoice 音色 id；与 /tts 端点一致，
+    # 若前端误传克隆档案 UUID，同样兼容解析。
+    profile = voice_clone_repository.get(tts_voice_id)
+    if profile is not None:
+        if profile.user_id != user_id:
+            raise NarrativeError("无权使用该音色", status_code=403)
+        if not profile.voice_id:
+            raise NarrativeError("音色克隆尚未完成，请稍后再试", status_code=400)
+        return profile.voice_id, profile.id
+    return tts_voice_id, ""
+
+
+def _save_tts_job_as_voice_asset(
+    *,
+    job: Any,
+    user_id: str,
+    name: str,
+    project_repository: Any,
+    asset_library_repository: Any,
+    asset_repository: Any,
+    storage_service: SharedStorageService,
+) -> Asset:
+    """把已完成 TTS job 的音频转存为配音库 audio asset（同 save-to-library 约定）。"""
+    if not job.output_audio_url and not job.output_audio_key:
+        raise NarrativeError("TTS 合成缺少输出音频", status_code=502)
+
+    library = _find_or_create_voice_library(
+        user_id=user_id,
+        project_repository=project_repository,
+        asset_library_repository=asset_library_repository,
+    )
+
+    audio_format = (job.format or "mp3").strip() or "mp3"
+    content_type = _CONTENT_TYPE_MAP.get(audio_format, "audio/mpeg")
+    storage_key = f"uploads/voice/tts/{job.id}.{audio_format}"
+
+    tmp_path: Path | None = None
+    audio_duration: float | None = None
+    file_size = 0
+    try:
+        with tempfile.NamedTemporaryFile(suffix=f".{audio_format}", delete=False) as tmp:
+            tmp_path = Path(tmp.name)
+        download_source = job.output_audio_key or job.output_audio_url
+        downloaded = storage_service.download_asset(download_source, tmp_path)
+        if not downloaded or not tmp_path.exists() or tmp_path.stat().st_size == 0:
+            raise NarrativeError("叙事配音音频转存失败", status_code=502)
+        file_size = tmp_path.stat().st_size
+        storage_service.upload_file(tmp_path, storage_key, content_type=content_type)
+
+        try:
+            proc = subprocess.run(
+                [
+                    "ffprobe",
+                    "-v",
+                    "quiet",
+                    "-print_format",
+                    "json",
+                    "-show_format",
+                    str(tmp_path),
+                ],
+                capture_output=True,
+                text=True,
+                timeout=10,
+            )
+            if proc.returncode == 0:
+                dur = float(json.loads(proc.stdout).get("format", {}).get("duration", 0))
+                if dur > 0:
+                    audio_duration = dur
+        except Exception:  # noqa: BLE001 - ffprobe 仅用于时长兜底
+            logger.warning("叙事配音 ffprobe 时长提取失败: job_id=%s", job.id, exc_info=True)
+    except NarrativeError:
+        raise
+    except Exception as e:  # noqa: BLE001
+        logger.error("叙事配音转存失败: job_id=%s, error=%s", job.id, e, exc_info=True)
+        raise NarrativeError("叙事配音音频转存失败", status_code=502) from e
+    finally:
+        if tmp_path and tmp_path.exists():
+            try:
+                tmp_path.unlink()
+            except OSError:
+                pass
+
+    metadata_: dict[str, object] = {
+        "source": "tts_job",
+        "tts_job_id": job.id,
+        "narrative": True,
+        "format": job.format,
+        "sample_rate": job.sample_rate,
+        "voice_id": job.voice_id,
+        "voice_name": job.voice_model or "",
+    }
+    if job.metadata:
+        for key in ("speed", "language"):
+            if key in job.metadata:
+                metadata_[key] = job.metadata[key]
+
+    asset = Asset.create(
+        project_id=library.project_id,
+        library_id=library.id,
+        name=name or f"叙事配音-{job.id[:8]}",
+        storage_key=storage_key,
+        mime_type=content_type,
+        metadata=metadata_,
+        file_size=file_size,
+        duration=job.duration or audio_duration or None,
+        status=AssetStatus.READY,
+        classification_status=ClassificationStatus.PENDING,
+        uploaded_by_user_id=user_id,
+    )
+    try:
+        return asset_repository.create(asset)
+    except Exception as e:  # noqa: BLE001
+        logger.error("叙事配音 asset 落库失败，清理 OSS: %s, error=%s", storage_key, e, exc_info=True)
+        try:
+            storage_service.delete_file(storage_key)
+        except Exception:  # noqa: BLE001
+            logger.warning("清理孤儿 OSS 文件失败: %s", storage_key, exc_info=True)
+        raise NarrativeError("叙事配音保存失败，请重试", status_code=502) from e
+
+
+def prepare_narrative_voice(
+    *,
+    db: Session,
+    user_id: str,
+    script_id: str,
+    tts_voice_id: str,
+    tts_voice_source: str,
+    tts_repository: Any,
+    cosyvoice_service: CosyVoiceService,
+    voice_clone_repository: Any,
+    asset_repository: Any,
+    asset_library_repository: Any,
+    project_repository: Any,
+    storage_service: SharedStorageService,
+    points_enabled: bool = False,
+    is_member: bool = False,
+    member_type: str | None = None,
+) -> NarrativeContext:
+    """叙事模式入队前同步合成配音并落为 audio asset。
+
+    Raises:
+        NarrativeError: 文案缺失/归属不符、音色不可用、TTS 失败、转存失败。
+    """
+    script = db.query(ScriptModel).filter(ScriptModel.id == script_id, ScriptModel.user_id == user_id).first()
+    if script is None:
+        raise NarrativeError("文案不存在或无权使用", status_code=404)
+    content = (script.content or "").strip()
+    if not content:
+        raise NarrativeError("文案内容为空，无法合成配音", status_code=400)
+
+    actual_voice_id, clone_profile_id = _resolve_voice(
+        user_id=user_id,
+        tts_voice_id=tts_voice_id,
+        tts_voice_source=tts_voice_source,
+        voice_clone_repository=voice_clone_repository,
+    )
+
+    # 积分扣点（与 /tts 合成端点同口径），失败时在合成失败分支退费
+    points_svc = PointsService() if points_enabled else None
+    points_deducted = 0
+    if points_svc is not None:
+        est_minutes = max(1.0, math.ceil(len(content) / 240))
+        points_deducted = calculate_points_cost(
+            _POINTS_SCENE,
+            is_member=is_member,
+            duration_minutes=est_minutes,
+            member_type=member_type,
+        )
+        deduct_res = points_svc.deduct_points(user_id, points_deducted, _POINTS_SCENE, db)
+        if not deduct_res["success"]:
+            raise NarrativeError(
+                f"积分不足，需要 {points_deducted} 积分，当前余额 {deduct_res['balance']}",
+                status_code=402,
+            )
+
+    use_case = CreateTTSJobUseCase(tts_repository)
+    job = use_case.execute(
+        user_id=user_id,
+        input_text=content,
+        voice_id=actual_voice_id,
+        voice_clone_profile_id=clone_profile_id,
+        metadata={"speed": 1.0, "emotion": "", "language": "zh-CN", "narrative": True, "script_id": script_id},
+    )
+
+    workflow = TTSWorkflowService(repository=tts_repository, cosyvoice_service=cosyvoice_service)
+    try:
+        job = workflow.start_synthesis(job.id)
+        if not job.is_completed:
+            job = workflow.poll_and_process_synthesis(job.id, timeout=_SYNTH_TIMEOUT)
+    except Exception as e:  # noqa: BLE001 - 同步合成异常统一转 NarrativeError
+        logger.error("叙事配音 TTS 合成失败: job_id=%s, error=%s", job.id, e, exc_info=True)
+        try:
+            workflow.process_synthesis_failure(job.id, str(e))
+        except Exception:  # noqa: BLE001
+            logger.warning("标记叙事 TTS job 失败出错: job_id=%s", job.id, exc_info=True)
+        if points_deducted and points_svc is not None:
+            try:
+                points_svc.refund_points(user_id, points_deducted, _POINTS_SCENE, db, ref_id=job.id)
+            except Exception:  # noqa: BLE001
+                logger.warning("叙事 TTS 失败退积分异常: job_id=%s", job.id, exc_info=True)
+        raise NarrativeError(f"配音合成失败：{e}", status_code=502) from e
+
+    if not job.is_completed:
+        if points_deducted and points_svc is not None:
+            try:
+                points_svc.refund_points(user_id, points_deducted, _POINTS_SCENE, db, ref_id=job.id)
+            except Exception:  # noqa: BLE001
+                logger.warning("叙事 TTS 未完成退积分异常: job_id=%s", job.id, exc_info=True)
+        raise NarrativeError("配音合成未完成，请稍后重试", status_code=504)
+
+    asset = _save_tts_job_as_voice_asset(
+        job=job,
+        user_id=user_id,
+        name=(script.title or "叙事配音")[:60],
+        project_repository=project_repository,
+        asset_library_repository=asset_library_repository,
+        asset_repository=asset_repository,
+        storage_service=storage_service,
+    )
+
+    return NarrativeContext(
+        script=script,
+        voice_asset_id=asset.id,
+        tts_job_id=job.id,
+        audio_duration=float(job.duration or asset.duration or 0.0),
+    )

apps/api/app/services/plan_generator_service.py

@@ -22,6 +22,11 @@ from packages.adapters.sqlalchemy_impl import (
     SQLAlchemyEditPlanClipRepository,
     SQLAlchemyEditPlanRepository,
 )
+from packages.domain.atom_clip_resolver import load_atom_clips_for_assets
+from packages.domain.atom_clip_selector import (
+    estimate_required_clip_count,
+    select_atom_clips,
+)
 from packages.domain.config_schemas import normalize_plan_config
 from packages.domain.edit_plan import EditPlan
 from packages.domain.edit_plan_clip import EditPlanClip
@@ -52,10 +57,12 @@ class PlanGeneratorService:
     基于模板 + 素材，自动生成 EditPlan 及 EditPlanClip 列表。
     """
 
-    def __init__(self, db: Session, asset_repo=None) -> None:
+    def __init__(self, db: Session, asset_repo=None, atom_clip_repo=None) -> None:
         self._plan_repo = SQLAlchemyEditPlanRepository(db)
         self._clip_repo = SQLAlchemyEditPlanClipRepository(db)
         self._asset_repo = asset_repo
+        # #1970 原子化切片：可选注入；未注入时走旧的整条素材选片路径（向后兼容）
+        self._atom_clip_repo = atom_clip_repo
 
     # ── 公开接口 ─────────────────────────────────────────────────────────────
 
@@ -121,18 +128,34 @@ class PlanGeneratorService:
 
         # 4. 按 editing_mode 分配素材
         if asset_ids:
-            # 获取素材时长信息，用于随机起始时间
-            asset_durations = None
-            if self._asset_repo:
-                asset_durations = self._fetch_asset_durations(asset_ids)
-            self._distribute_assets(
-                clips,
-                asset_ids,
-                editing_mode,
-                random_selection=random_preview,
-                asset_durations=asset_durations,
-                user_id=created_by_user_id,
-            )
+            # #1970 原子化切片：素材 clip 从 atom_clips 表选取（未就绪自动内存兜底）。
+            # 预览随机模式保持旧路径（整条素材 + 随机起点），与现有预览契约一致。
+            atom_applied = False
+            if not random_preview and self._atom_clip_repo is not None:
+                try:
+                    atom_applied = self._distribute_atom_clips(
+                        clips,
+                        asset_ids,
+                        editing_mode,
+                        user_id=created_by_user_id,
+                    )
+                except Exception:
+                    logger.warning("原子片段选片失败，回退整条素材选片", exc_info=True)
+                    atom_applied = False
+
+            if not atom_applied:
+                # 获取素材时长信息，用于随机起始时间
+                asset_durations = None
+                if self._asset_repo:
+                    asset_durations = self._fetch_asset_durations(asset_ids)
+                self._distribute_assets(
+                    clips,
+                    asset_ids,
+                    editing_mode,
+                    random_selection=random_preview,
+                    asset_durations=asset_durations,
+                    user_id=created_by_user_id,
+                )
 
         # 5. 持久化所有 clips 并计算总时长
         created_clips: list[EditPlanClip] = []
@@ -259,6 +282,103 @@ class PlanGeneratorService:
             external_used_segments=external_used_segments,
         )
 
+    def _distribute_atom_clips(
+        self,
+        clips: list[EditPlanClip],
+        asset_ids: list[str],
+        editing_mode: str,
+        *,
+        user_id: str = "",
+    ) -> bool:
+        """#1970 原子化切片选片（就地修改 clips，未持久化）.
+
+        从 ``asset_atom_clips`` 表按原子片段选取；老素材/切片未就绪的素材
+        内存兜底切片。同一原子片段在一次方案中只用一次；跨视频避让走
+        edit_plan_clips.atom_clip_id 最近使用记录。
+
+        Returns:
+            True 表示原子片段选片成功；False 表示无可用片段，调用方应回退
+            到旧的整条素材 distribute_assets。
+        """
+        # 1. 加载候选原子片段（DB + 兜底）
+        clips_by_asset = load_atom_clips_for_assets(
+            asset_ids,
+            atom_clip_repo=self._atom_clip_repo,
+            asset_repo=self._asset_repo,
+        )
+        if not clips_by_asset:
+            return False
+
+        # 2. 最近使用片段（跨视频原子片段级避让）
+        recently_used: set[str] = set()
+        if user_id and hasattr(self._clip_repo, "list_recent_atom_clip_ids_by_user"):
+            try:
+                recently_used = set(self._clip_repo.list_recent_atom_clip_ids_by_user(user_id, limit=200))
+            except Exception:
+                logger.warning("跨视频原子片段避让查询失败", exc_info=True)
+
+        # 3. 片段需求估算：无配音时按 clips 数量；voice_over 的配音总时长存于
+        #    clip.config["voice_duration"]，按 平均片段时长≈需要片段数 估算
+        voice_total = 0.0
+        for c in clips:
+            cfg_vd = c.config.get("voice_duration") if c.config else None
+            if cfg_vd:
+                voice_total += float(cfg_vd)
+        avg_clip_target = sum(float(c.duration or 0.0) for c in clips) / max(len(clips), 1)
+        required_count = estimate_required_clip_count(
+            voice_total or sum(float(c.duration or 0.0) for c in clips),
+            avg_clip_target or 3.5,
+        )
+        required_count = max(required_count, len(clips))
+
+        rng = random.Random()
+
+        # 4. 正式生成：先按素材 smart_score 对素材池排序，再展开为片段池
+        #    （同素材的片段保持连续，高分素材的片段排在前面优先入选）
+        if self._asset_repo:
+            asset_order = self._sort_assets_by_smart_score(list(clips_by_asset.keys()))
+            ordered: dict[str, list] = {}
+            for aid in asset_order:
+                if aid in clips_by_asset:
+                    ordered[aid] = clips_by_asset[aid]
+            clips_by_asset = ordered
+
+        candidates: list = []
+        for asset_clips in clips_by_asset.values():
+            candidates.extend(asset_clips)
+
+        # 5. 逐虚拟片段选片：评分排序，同片段不重复使用
+        used_atom_ids: set[str] = set()
+        asset_usage: dict[str, int] = {}
+        assigned = 0
+        for clip in clips:
+            # 对每个虚拟片段重新评分（usage_count 随选择动态变化）
+            scored = select_atom_clips(
+                candidates,
+                target_duration=float(clip.duration or 0.0),
+                used_atom_clip_ids=used_atom_ids,
+                asset_usage_counts=asset_usage,
+                recently_used_atom_ids=recently_used,
+                required_count=required_count,
+                limit=1,
+                rng=rng,
+            )
+            if not scored:
+                # 候选耗尽（同片段不可重复），交由调用方回退或留白
+                continue
+            picked = scored[0]
+            clip.asset_id = picked.asset_id
+            clip.atom_clip_id = picked.atom_clip_id
+            clip.start_time = round(picked.start_time, 3)
+            clip.duration = round(picked.duration, 3)
+            used_atom_ids.add(picked.atom_clip_id)
+            asset_usage[picked.asset_id] = asset_usage.get(picked.asset_id, 0) + 1
+            assigned += 1
+
+        if assigned == 0:
+            return False
+        return True
+
     def _fetch_asset_scene_points(self, asset_ids: list[str]) -> dict[str, list[float]]:
         """从素材 metadata 读取场景切换点缓存（无缓存的素材不包含在结果中）。"""
         points_map: dict[str, list[float]] = {}

apps/api/app/services/script_asr_service.py

@@ -38,7 +38,12 @@ def transcribe_to_text(media_path: str | Path) -> str:
         ASRTranscriptionError: ASR 调用失败
     """
     # 延迟导入，避免循环依赖和启动时副作用
-    from apps.worker.services.asr_service_factory import get_asr_service
+    try:
+        from apps.worker.services.asr_service_factory import get_asr_service
+    except ImportError as exc:
+        # API 镜像未打包 worker 代码（本地 ASR 依赖 worker 的 asr_service_factory）
+        logger.warning("本地 ASR 不可用（apps.worker 未安装）: %s", exc)
+        raise ASRNotConfiguredError("本地 ASR 服务不可用（worker 模块未安装）") from exc
 
     asr = get_asr_service()
     if asr is None:

apps/api/app/services/script_service.py

@@ -51,9 +51,6 @@ class ScriptService:
         content: str = "",
         segments: list | None = None,
         tags: list | None = None,
-        title_text: str = "",
-        title_category: str = "",
-        title_config: dict | None = None,
     ) -> ScriptModel:
         script = ScriptModel(
             id=str(uuid.uuid4()),
@@ -62,9 +59,6 @@ class ScriptService:
             content=content,
             segments=segments if segments is not None else [],
             tags=tags if tags is not None else [],
-            title_text=title_text or "",
-            title_category=title_category or "",
-            title_config=title_config if title_config is not None else {},
         )
         self.db.add(script)
         self.db.commit()
@@ -89,9 +83,6 @@ class ScriptService:
         content: Optional[str] = None,
         segments: Optional[list] = None,
         tags: Optional[list] = None,
-        title_text: Optional[str] = None,
-        title_category: Optional[str] = None,
-        title_config: Optional[dict] = None,
     ) -> ScriptModel:
         script = self.get_script(script_id, user_id)
         if title is not None:
@@ -102,27 +93,11 @@ class ScriptService:
             script.segments = segments
         if tags is not None:
             script.tags = tags
-        if title_text is not None:
-            script.title_text = title_text
-        if title_category is not None:
-            script.title_category = title_category
-        if title_config is not None:
-            script.title_config = title_config
         script.updated_at = datetime.now(UTC)
         self.db.commit()
         self.db.refresh(script)
         return script
 
-    # ── title config ─────────────────────────────────────────────────────
-
-    def get_title_config_for_script(self, script_id: str, user_id: str) -> dict:
-        """从 script 读取标题配置，返回可直接用于渲染的 title_config dict."""
-        script = self.get_script(script_id, user_id)
-        config = dict(script.title_config or {})
-        if not config.get("text") and script.title_text:
-            config["text"] = script.title_text
-        return config
-
     # ── delete ────────────────────────────────────────────────────────────
 
     def delete_script(self, script_id: str, user_id: str) -> bool:

apps/web/e2e/core-douyin-extract.spec.ts

@@ -0,0 +1,117 @@
+import { expect, test, type APIRequestContext, type Page } from "@playwright/test"
+
+const PASSWORD = "SmokePass123!"
+const apiBase = process.env.E2E_API_BASE || "/api/v1"
+const apiOrigin = apiBase.endsWith("/api/v1") ? apiBase.slice(0, -"/api/v1".length) : ""
+
+async function routeBrowserApiToTestApi(page: Page) {
+  if (!apiOrigin) return
+  await page.route("**/api/v1/**", async (route) => {
+    const sourceUrl = new URL(route.request().url())
+    const response = await route.fetch({
+      url: `${apiOrigin}${sourceUrl.pathname}${sourceUrl.search}`,
+    })
+    await route.fulfill({ response })
+  })
+}
+
+async function loginWithRetry(request: APIRequestContext, email: string, password: string) {
+  for (let i = 0; i <= 2; i++) {
+    const r = await request.post(`${apiBase}/auth/login`, { data: { email, password } })
+    if (r.status() !== 429) {
+      expect(r.ok(), `login: ${await r.text()}`).toBeTruthy()
+      return (await r.json()).access_token as string
+    }
+    console.log(`[douyin] 429 retry ${i + 1}/2`)
+    await new Promise((res) => setTimeout(res, 65000))
+  }
+  throw new Error("Login retries exhausted")
+}
+
+/**
+ * #1972 抖音文案提取冒烟
+ *
+ * 路径：文案库页面 → 点「🎬 从抖音提取」→ 粘贴分享文案 → 点「开始提取」
+ * → mock /api/v1/scripts/extract-from-douyin 返回稳定文案 → 断言「新建文案」弹窗中预填了非空文案
+ */
+test.describe("Douyin Script Extraction (#1972)", () => {
+  test("extract flow: open modal, paste link, text prefilled in create modal", async ({
+    page,
+    request,
+  }) => {
+    test.setTimeout(180_000)
+    await page.setViewportSize({ width: 1440, height: 900 })
+
+    const suffix = Math.random().toString(36).slice(2, 8)
+    const email = `e2e-douyin-${suffix}@example.com`
+    await request.post(`${apiBase}/auth/register`, {
+      data: { email, password: PASSWORD, username: `e2e_dy_${suffix}` },
+    })
+    const token = await loginWithRetry(request, email, PASSWORD)
+    const authHeader = { Authorization: `Bearer ${token}` }
+
+    const proj = await request.post(`${apiBase}/projects`, {
+      headers: authHeader,
+      data: { name: `Smoke Douyin ${suffix}` },
+    })
+    const projectId = (await proj.json()).id ?? (await proj.json()).project_id
+    await request.post(`${apiBase}/asset-libraries`, {
+      headers: authHeader,
+      data: { project_id: projectId, name: "Smoke", kind: "video" },
+    })
+
+    await page.addInitScript((t: string) => {
+      window.localStorage.setItem("access_token", t)
+      window.localStorage.setItem(
+        "auth-storage",
+        JSON.stringify({ state: { token: t, user: null } }),
+      )
+    }, token)
+    await routeBrowserApiToTestApi(page)
+
+    // Mock 抖音提取接口返回稳定文案
+    const extractedText = "大家好，今天给大家推荐一款超好用的产品，性价比非常高，快来看看吧！"
+    await page.route("**/api/v1/scripts/extract-from-douyin", (route) =>
+      route.fulfill({
+        status: 200,
+        contentType: "application/json",
+        body: JSON.stringify({ text: extractedText, duration_seconds: 15 }),
+      }),
+    )
+    // 文案列表空态
+    await page.route(
+      (url) => url.pathname.endsWith("/scripts") && !url.pathname.includes("extract-from-douyin"),
+      (route) =>
+        route.fulfill({
+          status: 200,
+          contentType: "application/json",
+          body: JSON.stringify({ items: [], total: 0, page: 1, page_size: 20 }),
+        }),
+    )
+
+    await page.goto("/app/scripts")
+    // 文案库页面加载
+    await expect(page.getByText(/文案库|文案/).first()).toBeVisible({ timeout: 30000 })
+
+    // 点「🎬 从抖音提取」按钮
+    await page.getByRole("button", { name: /从抖音提取/ }).click()
+    await expect(page.getByText("从抖音视频提取文案")).toBeVisible({ timeout: 5000 })
+
+    // 在 TextArea 粘贴"抖音分享文案"
+    const textarea = page.locator(".ant-modal textarea").first()
+    await expect(textarea).toBeVisible()
+    await textarea.fill("8.88 复制打开抖音，看看【推荐视频】https://v.douyin.com/abcDEF/")
+
+    // 点「开始提取」
+    await page.getByRole("button", { name: "开始提取" }).click()
+    await expect(page.getByText(/提取中/)).toBeVisible({ timeout: 3000 })
+
+    // 等待抖音弹窗关闭，「新建文案」弹窗打开并预填提取文案
+    await expect(page.getByText("从抖音视频提取文案")).not.toBeVisible({ timeout: 15000 })
+    await expect(page.getByText("新建文案")).toBeVisible({ timeout: 5000 })
+    const createTextarea = page.locator(".ant-modal textarea").first()
+    await expect(createTextarea).toBeVisible()
+    await expect(createTextarea).toHaveValue(new RegExp(extractedText.slice(0, 10)))
+    console.log("[douyin] Extraction flow completed ✓, text length:", extractedText.length)
+  })
+})

apps/web/e2e/core-generation.spec.ts

@@ -1,4 +1,4 @@
-import { expect, test, type APIRequestContext } from "@playwright/test"
+import { expect, test, type APIRequestContext, type Page } from "@playwright/test"
 import * as fs from "node:fs"
 import * as path from "node:path"
 import { fileURLToPath } from "node:url"
@@ -8,7 +8,8 @@ const PASSWORD = "SmokePass123!"
 const apiBase = process.env.E2E_API_BASE || "/api/v1"
 const apiOrigin = apiBase.endsWith("/api/v1") ? apiBase.slice(0, -"/api/v1".length) : ""
 
-const routeBrowserApiToTestApi = async (page: import("@playwright/test").Page) => {
+/** 将浏览器侧 /api/v1 请求路由到 Playwright request 源（支持跨域） */
+async function routeBrowserApiToTestApi(page: Page) {
   if (!apiOrigin) return
   await page.route("**/api/v1/**", async (route) => {
     const sourceUrl = new URL(route.request().url())
@@ -24,309 +25,358 @@ async function loginWithRetry(
   email: string,
   password: string,
   maxRetries = 2,
-) {
+): Promise<string> {
   for (let i = 0; i <= maxRetries; i++) {
-    const response = await request.post(`${apiBase}/auth/login`, {
-      data: { email, password },
-    })
-    if (response.status() !== 429) return response
-    console.log(`[login] 触发限流，等待 65s 后重试 (${i + 1}/${maxRetries})`)
+    const resp = await request.post(`${apiBase}/auth/login`, { data: { email, password } })
+    if (resp.status() !== 429) {
+      expect(resp.ok(), `Login should succeed: ${await resp.text()}`).toBeTruthy()
+      const data = await resp.json()
+      return data.access_token
+    }
+    console.log(`[login] 429 rate limited, retry ${i + 1}/${maxRetries} after 65s`)
     await new Promise((r) => setTimeout(r, 65000))
   }
-  return request.post(`${apiBase}/auth/login`, {
-    data: { email, password },
+  throw new Error("Login failed after retries")
+}
+
+/**
+ * 注册新用户 + 建项目/视频库/上传 sample.mp4，等素材 ready。返回 { token, projectId, libraryId, assetId }。
+ */
+async function setupFreshUser(
+  request: APIRequestContext,
+  label: string,
+): Promise<{ token: string; libraryId: string; assetId: string; suffix: string }> {
+  const suffix = Math.random().toString(36).slice(2, 8)
+  const email = `e2e-${label}-${suffix}@example.com`
+  await request.post(`${apiBase}/auth/register`, {
+    data: { email, password: PASSWORD, username: `e2e_${label}_${suffix}` },
   })
+  const token = await loginWithRetry(request, email, PASSWORD)
+  const auth = { Authorization: `Bearer ${token}` }
+
+  const proj = await request.post(`${apiBase}/projects`, {
+    headers: auth,
+    data: { name: `Smoke ${label} ${suffix}` },
+  })
+  expect(proj.ok(), `create project: ${await proj.text()}`).toBeTruthy()
+  const projectId = (await proj.json()).id ?? (await proj.json()).project_id
+
+  const lib = await request.post(`${apiBase}/asset-libraries`, {
+    headers: auth,
+    data: { project_id: projectId, name: "Smoke", kind: "video" },
+  })
+  expect(lib.ok(), `create library: ${await lib.text()}`).toBeTruthy()
+  const libraryId = (await lib.json()).id
+
+  const samplePath = path.join(__dirname, "fixtures", "sample.mp4")
+  const sampleBuf = fs.readFileSync(samplePath)
+  const up = await request.post(`${apiBase}/upload`, {
+    headers: auth,
+    multipart: {
+      project_id: projectId,
+      library_id: libraryId,
+      file: {
+        name: "sample.mp4",
+        mimeType: "video/mp4",
+        buffer: sampleBuf,
+      },
+    },
+  })
+  expect(up.ok(), `upload sample: ${await up.text()}`).toBeTruthy()
+  const assetId = (await up.json()).asset_id
+  await expect
+    .poll(
+      async () => {
+        const r = await request.get(`${apiBase}/assets/${assetId}`, { headers: auth })
+        return r.ok() ? (await r.json()).status : "pending"
+      },
+      { timeout: 90_000, intervals: [3000, 3000, 5000] },
+    )
+    .toBe("ready")
+  return { token, libraryId, assetId, suffix }
 }
 
-type ProjectResponse = { id: string }
-type LibraryResponse = { id: string }
-type AssetListResponse = {
-  items: Array<{
-    id: string
-    name: string
-    status: string
-  }>
-}
-
-test.describe("Core generation flow", () => {
-  test.describe.configure({ timeout: 600_000 })
-
-  test("walks through wizard with count modal and starts generation", async ({ page, request }) => {
+/**
+ * #1970 智能剪辑核心冒烟（新 5 步向导）
+ *
+ * 新流程：选择模式 → 选择素材 → 选择标题 → 确认生成 → 选择封面
+ *
+ * 两条路径：
+ *   1) 随机混剪（默认）→ Step1 下一步 → 配音选择弹窗 → Step2 选素材 → 数量弹窗
+ *      → Step3 标题 → Step4 确认生成 → 断言任务创建
+ *   2) 叙事剪辑 → Step1 切模式 → 下一步 → 文案选择弹窗 → TTS 弹窗选音色（mock 合成）
+ *      → Step2 AI 提示卡可见 + 选素材 → 数量弹窗 → Step3 标题 → Step4 确认生成
+ *      → 断言任务创建
+ */
+test.describe("Core Smart-Edit Flow (#1970)", () => {
+  test("random mode: 5-step wizard creates generation task", async ({ page, request }) => {
     test.setTimeout(600_000)
+    await page.setViewportSize({ width: 1440, height: 1000 })
+    const { token, suffix } = await setupFreshUser(request, "random")
+    const authHeader = { Authorization: `Bearer ${token}` }
 
-    await routeBrowserApiToTestApi(page)
-    const suffix = Date.now().toString(36)
-    const email = `e2e-gen-${suffix}@example.com`
-    const username = `e2e_gen_${suffix}`
-    const libraryName = `E2E Gen Lib ${suffix}`
+    // 确保默认模板存在（智能剪辑页依赖模板）
+    const tmpls = await request.get(`${apiBase}/templates`, { headers: authHeader })
+    const tmplsJson = await tmpls.json()
+    const templates = Array.isArray(tmplsJson)
+      ? tmplsJson
+      : Array.isArray(tmplsJson.items)
+        ? tmplsJson.items
+        : []
+    expect(templates.length).toBeGreaterThan(0)
 
-    // Register
-    const register = await request.post(`${apiBase}/auth/register`, {
-      data: { email, username, password: PASSWORD, display_name: username },
-    })
-    expect(register.status()).toBe(201)
-    const registerData = (await register.json()) as { user_id: string }
-
-    // Login
-    const login = await loginWithRetry(request, email, PASSWORD)
-    expect(login.status()).toBe(200)
-    const loginData = (await login.json()) as { access_token: string }
-    const headers = { Authorization: `Bearer ${loginData.access_token}` }
-
-    // Create project
-    const project = await request.post(`${apiBase}/projects`, {
-      headers,
-      data: { name: `E2E Gen Proj ${suffix}` },
-    })
-    expect(project.status()).toBe(200)
-    const projectData = (await project.json()) as ProjectResponse
-
-    // Create asset library
-    const library = await request.post(`${apiBase}/asset-libraries`, {
-      headers,
-      data: { project_id: projectData.id, name: libraryName, kind: "video" },
-    })
-    expect(library.status()).toBe(200)
-    const libraryData = (await library.json()) as LibraryResponse
-
-    // Upload source video
-    const sourceFileName = "e2e-gen-source.mp4"
-    const sampleVideoPath = path.join(__dirname, "fixtures", "sample.mp4")
-    const sampleVideoBuffer = fs.readFileSync(sampleVideoPath)
-    const upload = await request.post(`${apiBase}/upload`, {
-      headers,
-      multipart: {
-        project_id: projectData.id,
-        library_id: libraryData.id,
-        file: {
-          name: sourceFileName,
-          mimeType: "video/mp4",
-          buffer: sampleVideoBuffer,
-        },
-      },
-    })
-    expect(upload.status()).toBe(200)
-
-    // Wait for asset to be ready
-    await expect
-      .poll(
-        async () => {
-          const assets = await request.get(`${apiBase}/assets`, {
-            headers,
-            params: { library_id: libraryData.id },
-          })
-          if (!assets.ok()) return `http_${assets.status()}`
-          const data = (await assets.json()) as AssetListResponse
-          const asset = data.items.find((a) => a.name === sourceFileName)
-          if (!asset) return "missing"
-          return asset.status
-        },
-        { timeout: 30_000, intervals: [1_000, 2_000, 3_000] },
+    // 注入登录态 + 路由 API
+    await page.addInitScript((t: string) => {
+      window.localStorage.setItem("access_token", t)
+      window.localStorage.setItem(
+        "auth-storage",
+        JSON.stringify({ state: { token: t, user: null } }),
       )
-      .toBe("ready")
+    }, token)
+    await routeBrowserApiToTestApi(page)
 
-    // #1926 P0 fix: POST /templates CRUD endpoint removed; GET /templates
-    // now auto-creates a default template for new users. Use the first one.
-    const templatesResp = await request.get(`${apiBase}/templates`, { headers })
-    expect(templatesResp.status(), await templatesResp.text()).toBe(200)
-    const templatesData = (await templatesResp.json()) as {
-      items: Array<{ id: string }>
-    }
-    expect(Array.isArray(templatesData.items)).toBe(true)
-    expect(templatesData.items.length).toBeGreaterThan(0)
-    const templateId = templatesData.items[0].id
-    expect(templateId).toBeTruthy()
-
-    // Set auth in localStorage
-    await page.addInitScript(
-      ({ token, user }) => {
-        localStorage.setItem("access_token", token)
-        localStorage.setItem(
-          "auth-storage",
-          JSON.stringify({
-            state: { user, isAuthenticated: true },
-            version: 0,
+    // ── 提前 mock 配音列表（VoiceSelectModal 查询 /assets?kind=voice） ──
+    await page.route(
+      (url) => url.pathname.endsWith("/assets") && url.searchParams.get("kind") === "voice",
+      (route) =>
+        route.fulfill({
+          status: 200,
+          contentType: "application/json",
+          body: JSON.stringify({
+            items: [
+              {
+                id: `asset-voice-${suffix}`,
+                name: "测试配音.mp3",
+                file_url: "data:audio/mpeg;base64,",
+                duration: 10,
+                file_size: 1024,
+                kind: "voice",
+                status: "ready",
+              },
+            ],
+            total: 1,
           }),
-        )
-      },
-      {
-        token: loginData.access_token,
-        user: {
-          id: registerData.user_id,
-          user_id: registerData.user_id,
-          email,
-          username,
-          display_name: username,
-          is_email_verified: true,
-          email_verified: true,
-        },
-      },
+        }),
     )
 
-    // Navigate to generate page
     await page.goto("/app/generate")
     await expect(page.getByRole("heading", { name: "智能剪辑" })).toBeVisible({
-      timeout: 20_000,
+      timeout: 30000,
     })
 
-    // 5步向导：素材→数量弹窗→配音→标题→确认生成→封面（#1911 删除选模板步骤，后端自动使用默认模板；
-    // #1677 批量生成在选完素材后弹「要生成几个视频？」数量弹窗，默认1，回车确认）
-    // Step 1: select material (card grid UI)
-    await expect(page.getByRole("heading", { name: /选择素材/ })).toBeVisible()
-    const librarySelect = page.locator("select").first()
-    await librarySelect.selectOption({ label: libraryName })
-    // 新 UI: 素材以 9:16 竖屏卡片展示，点击卡片选中
-    // 注意：卡片中心是播放按钮（stopPropagation 会阻止选中），所以点击左上角避开
-    const materialCard = page.getByTestId("material-card").filter({ hasText: sourceFileName })
-    await expect(materialCard).toBeVisible({ timeout: 10_000 })
-    await materialCard.click({ position: { x: 15, y: 15 } })
-    // 验证选中：卡片应出现勾选标记（用 testid 定位，避免 ✓ 字符文本匹配不稳定）
-    await expect(materialCard.getByTestId("material-card-check")).toBeVisible({ timeout: 5_000 })
-    await page.getByRole("button", { name: "下一步" }).click()
+    // ── Step 1：默认随机混剪选中，点下一步 ──────────────────────────
+    await expect(page.getByText("选择模式", { exact: true })).toBeVisible()
+    await expect(page.getByText("随机混剪")).toBeVisible()
+    await page.getByRole("button", { name: /下一步/ }).click()
 
-    // #1677 数量弹窗：默认值1，点击「生成 1 个视频」确认（新用户单视频冒烟路径）
-    await expect(page.getByRole("heading", { name: "要生成几个视频？" })).toBeVisible({
-      timeout: 5_000,
-    })
+    // ── 配音选择弹窗：选第一个配音 → 确认 ─────────────────────────
+    await expect(page.getByText("🎙️ 选择配音")).toBeVisible({ timeout: 5000 })
+    await page.getByText("测试配音.mp3").first().click()
+    await page.getByRole("button", { name: "确认选择" }).click()
+    await expect(page.getByText("🎙️ 选择配音")).not.toBeVisible()
+
+    // ── Step 2：选择素材 ──────────────────────────────────────────
+    await expect(page.getByText("选择素材", { exact: true })).toBeVisible({ timeout: 10000 })
+    await page.getByTestId("material-card").first().click()
+    await page.getByRole("button", { name: /下一步/ }).click()
+
+    // ── 数量弹窗：默认 1 个 → 确认 ───────────────────────────────
+    await expect(page.getByText("要生成几个视频？")).toBeVisible({ timeout: 5000 })
     await page.getByRole("button", { name: "生成 1 个视频" }).click()
 
-    // Step 2: voice（新注册用户无配音素材时展示空状态 h3「🎙️ 选择配音」，仍可点「下一步」跳过）
-    await expect(page.getByRole("heading", { name: /选择配音/ })).toBeVisible({ timeout: 15000 })
-    await page.getByRole("button", { name: "下一步" }).click()
-
-    // Step 3: title（新顺序：标题在预览之前）
-    await expect(page.getByRole("heading", { name: /选择标题/ })).toBeVisible({ timeout: 15000 })
-    // 等待组件完全渲染
-    await page.waitForTimeout(2000)
-
-    // Antd AutoComplete 的 placeholder 渲染在 span 上，input 无 placeholder 属性
-    // 使用 Antd AutoComplete 特有的 class 定位输入框
-    const titleInput = page.locator(".ant-select-auto-complete input")
+    // ── Step 3：填写标题 ──────────────────────────────────────────
+    await expect(page.getByText("选择标题", { exact: true })).toBeVisible({ timeout: 10000 })
+    const titleInput = page.getByPlaceholder("输入或从标题库选择")
     await expect(titleInput).toBeVisible({ timeout: 5000 })
+    await titleInput.fill(`测试随机剪辑 ${suffix}`)
+    await page.getByRole("button", { name: /下一步/ }).click()
 
-    const titleText = `E2E Test ${suffix}`
-    await titleInput.fill(titleText)
+    // ── Step 4：确认生成 ──────────────────────────────────────────
+    await expect(page.getByText("📋 生成配置")).toBeVisible({ timeout: 10000 })
+    await expect(page.getByText("随机混剪")).toBeVisible()
+    const confirmBtn = page.getByRole("button", { name: /确认生成视频/ })
+    await expect(confirmBtn).toBeEnabled({ timeout: 5000 })
 
-    // 步骤3（标题页）底部操作栏按钮是「下一步 →」，点击后进入步骤4
-    // 步骤4底部才是「✨ 确认生成视频」按钮
-    const nextBtn = page.locator(".xx-step-actions .xx-btn-primary").filter({ hasText: "下一步" })
-    await expect(nextBtn).toBeVisible({ timeout: 15_000 })
-    await nextBtn.click()
+    const createTask = page.waitForResponse(
+      (r) => r.url().includes("/generation/tasks") && r.request().method() === "POST",
+      { timeout: 30000 },
+    )
+    await confirmBtn.click()
+    const taskResp = await createTask
+    expect(taskResp.ok(), `Create task: ${await taskResp.text()}`).toBeTruthy()
+    const taskId = (await taskResp.json()).id ?? (await taskResp.json()).task_id
+    console.log("[random] Generation task created:", taskId)
+    await expect(page.getByText(/正在生成|提交/)).toBeVisible({ timeout: 15000 })
+    console.log("[random] Wizard flow completed ✓")
+  })
 
-    // Step 4：「确认生成」页面——此处底部是「✨ 确认生成视频」按钮
-    // 注意：Step4 主内容区是实时预览画布，没有 h3 「🎬 确认生成」标题，标题由顶部步骤条展示
-    // 等待前端实时预览就绪：未就绪时右侧 FrontendPreviewPlayer 显示「准备预览素材...」占位，
-    // 就绪（previewReady：素材已解析 + 模板已选中）后占位消失；否则按钮会被校验拦截弹 warning
-    await page
-      .getByText("准备预览素材")
-      .waitFor({ state: "detached", timeout: 30_000 })
-      .catch(() => {})
+  test("narrative mode: select script + mock TTS, create generation task", async ({
+    page,
+    request,
+  }) => {
+    test.setTimeout(600_000)
+    await page.setViewportSize({ width: 1440, height: 1000 })
+    const { token, suffix } = await setupFreshUser(request, "narrative")
 
-    // 定位底部操作栏的「✨ 确认生成视频」按钮
-    // 使用底部操作栏 xx-step-actions 作用域，避免命中其他 primary 按钮
-    const confirmBtn = page
-      .locator(".xx-step-actions .xx-btn-primary")
-      .filter({ hasText: "确认生成" })
-    await expect(confirmBtn).toBeVisible({ timeout: 30_000 })
-    await expect(confirmBtn).toBeEnabled({ timeout: 30_000 })
+    await page.addInitScript((t: string) => {
+      window.localStorage.setItem("access_token", t)
+      window.localStorage.setItem(
+        "auth-storage",
+        JSON.stringify({ state: { token: t, user: null } }),
+      )
+    }, token)
+    await routeBrowserApiToTestApi(page)
 
-    // Wait for generation API to be called — 先挂监听再点击，避免竞态
-    const generatePromise = page.waitForResponse(
-      (response) => {
-        const url = response.url()
-        const path = new URL(url).pathname
-        return response.request().method() === "POST" && path.endsWith("/generation/tasks")
-      },
-      { timeout: 60_000 },
+    // ── Mock 文案列表、音色、TTS 合成（避免真实合成） ──────────────
+    const mockScriptId = `script-mock-${suffix}`
+    const mockVoiceId = `preset-voice-${suffix}`
+    const mockJobId = `tts-job-${suffix}`
+
+    // 文案列表（ScriptSelectModal 查询 /scripts）
+    await page.route("**/api/v1/scripts**", (route) => {
+      const url = new URL(route.request().url())
+      if (url.pathname.includes("/extract-from-douyin")) {
+        route.continue()
+        return
+      }
+      route.fulfill({
+        status: 200,
+        contentType: "application/json",
+        body: JSON.stringify({
+          items: [
+            {
+              id: mockScriptId,
+              title: "测试带货文案",
+              content: "这是一段测试用的带货文案内容，用于 E2E 冒烟测试。",
+              tags: ["带货"],
+              title_category: "daihuo",
+              created_at: new Date().toISOString(),
+              updated_at: new Date().toISOString(),
+            },
+          ],
+          total: 1,
+          page: 1,
+          page_size: 200,
+        }),
+      })
+    })
+
+    // 预设音色（TtsVoiceModal 查询 GET /voices/presets）
+    await page.route("**/api/v1/voices/presets**", (route) =>
+      route.fulfill({
+        status: 200,
+        contentType: "application/json",
+        body: JSON.stringify({
+          items: [
+            {
+              voice_id: mockVoiceId,
+              name: "晓晓（女声）",
+              description: "温柔女声",
+              gender: "female",
+              language: "zh-CN",
+              preview_url: null,
+              tags: ["温柔"],
+            },
+          ],
+          total: 1,
+        }),
+      }),
     )
 
-    await confirmBtn.click()
+    // 克隆音色：空列表
+    await page.route(
+      (url) => url.pathname.endsWith("/voice-clones"),
+      (route) =>
+        route.fulfill({
+          status: 200,
+          contentType: "application/json",
+          body: JSON.stringify({ items: [] }),
+        }),
+    )
 
-    // Verify generation was triggered
-    const genResp = await generatePromise
-    if (!genResp.ok()) {
-      const body = await genResp.text()
-      console.error(
-        `[E2E DEBUG] 触发生成接口失败: status=${genResp.status()} url=${genResp.url()} body=${body.slice(0, 500)}`,
-      )
-    }
-    // Generate API may return 400 in test env if template has no ready segments
-    // That is OK for a wizard flow smoke test
-    if (genResp.ok()) {
-      const genData = (await genResp.json()) as {
-        items: Array<{ id: string; status: string }>
-        total: number
-      }
-      expect(genData.items.length).toBeGreaterThan(0)
-      expect(genData.items[0].id).toBeTruthy()
+    // TTS 合成：直接返回 completed 任务
+    await page.route("**/api/v1/tts/synthesize", (route) =>
+      route.fulfill({
+        status: 200,
+        contentType: "application/json",
+        body: JSON.stringify({ job_id: mockJobId, status: "queued" }),
+      }),
+    )
+    await page.route(`**/api/v1/tts/jobs/${mockJobId}/status`, (route) =>
+      route.fulfill({
+        status: 200,
+        contentType: "application/json",
+        body: JSON.stringify({
+          job_id: mockJobId,
+          status: "completed",
+          progress: 100,
+          audio_url: "data:audio/mpeg;base64,",
+          duration: 5,
+        }),
+      }),
+    )
+    await page.route(`**/api/v1/tts/jobs/${mockJobId}/save-to-library`, (route) =>
+      route.fulfill({
+        status: 200,
+        contentType: "application/json",
+        body: JSON.stringify({ id: `tts-asset-${suffix}`, name: "AI合成配音" }),
+      }),
+    )
 
-      // 单视频（N=1）：点击「确认生成视频」后跳步骤 5「确认生成」进度页，展示进度卡
-      // 注意：进度页底部按钮变为 disabled 的「⏳ 视频渲染中…」
-      await expect(page.getByText("视频渲染中")).toBeVisible({ timeout: 30_000 })
-
-      // 等待渲染完成：单视频成片播放器渲染（带「⬇️ 下载」按钮），最长等待 3 分钟
-      // 注意：message.success「视频生成完成」toast 3秒后自动消失，不能作为稳定断言点
-      await expect(page.getByRole("button", { name: "⬇️ 下载" })).toBeVisible({ timeout: 420_000 })
-
-      // #1954 修复：生成完成后步骤4底部应显示「下一步：选择封面」按钮
-      // 等待底部主按钮从「⏳/确认生成」切换为「下一步：选择封面」
-      const nextCoverBtn = page
-        .locator(".xx-step-actions > .xx-btn-primary")
-        .filter({ hasText: "选择封面" })
-      await expect(nextCoverBtn).toBeVisible({ timeout: 15_000 })
-      await nextCoverBtn.click()
-
-      // 断言进入步骤5封面页：主内容出现「选择封面」标题
-      await expect(page.getByText("🖼️ 选择封面")).toBeVisible({ timeout: 10_000 })
-      // 底部操作栏主按钮应消失（封面是最后一步，只剩「← 上一步」）
-      await expect(page.locator(".xx-step-actions > .xx-btn-primary")).toHaveCount(0)
-    } else {
-      console.log(`[E2E] Generate API returned ${genResp.status()}, wizard flow test still passes`)
-      // 创建失败时停留在标题页并展示错误提示
-      await page
-        .getByText(/生成失败|重新生成/)
-        .isVisible({ timeout: 15_000 })
-        .catch(() => false)
-    }
-
-    // Verify product library page loads (smoke: just verify page renders)
-    await page.goto("/app/products")
-    await expect(page).toHaveURL(/\/app\/products/)
-    // Verify page container exists = page rendered correctly
-    // (works in all states: loading/error/success - more reliable than checking search input)
-    await expect(page.locator(".xx-products-page")).toBeVisible({
-      timeout: 15_000,
+    await page.goto("/app/generate")
+    await expect(page.getByRole("heading", { name: "智能剪辑" })).toBeVisible({
+      timeout: 30000,
     })
 
-    // 清理所有路由，避免页面关闭时飞地API请求导致测试报错
-    await page.unrouteAll({ behavior: "ignoreErrors" })
-  })
+    // ── Step 1：切到叙事剪辑 → 下一步 ────────────────────────────
+    await expect(page.getByText("选择模式", { exact: true })).toBeVisible()
+    await page.getByText("叙事剪辑").click()
+    await page.getByRole("button", { name: /下一步/ }).click()
 
-  test("generation task API creates and lists tasks", async ({ request }) => {
-    const suffix = Date.now().toString(36)
-    const email = `e2e-gen-api-${suffix}@example.com`
-    const username = `e2e_gen_api_${suffix}`
+    // ── 文案选择弹窗：选第一条 → 确认 ─────────────────────────────
+    await expect(page.getByText("📝 选择文案")).toBeVisible({ timeout: 5000 })
+    await page.getByText("测试带货文案").first().click()
+    await page.getByRole("button", { name: "确认选择" }).click()
+    await expect(page.getByText("📝 选择文案")).not.toBeVisible()
 
-    const register = await request.post(`${apiBase}/auth/register`, {
-      data: { email, username, password: PASSWORD, display_name: username },
-    })
-    expect(register.status()).toBe(201)
+    // ── TTS 音色弹窗：选系统音色 → 合成 ─────────────────────────
+    await expect(page.getByText("🎙️ 合成配音")).toBeVisible({ timeout: 5000 })
+    await page.getByText("晓晓（女声）").first().click()
+    await page.getByRole("button", { name: "🎧 合成配音" }).click()
+    await expect(page.getByText("🎙️ 合成配音")).not.toBeVisible({ timeout: 30000 })
 
-    const login = await loginWithRetry(request, email, PASSWORD)
-    expect(login.status()).toBe(200)
-    const loginData = (await login.json()) as { access_token: string }
-    const headers = { Authorization: `Bearer ${loginData.access_token}` }
+    // ── Step 2：AI 匹配提示卡可见 + 选素材 ────────────────────────
+    await expect(page.getByText("选择素材", { exact: true })).toBeVisible({ timeout: 10000 })
+    await expect(page.getByText(/AI智能匹配/)).toBeVisible()
+    await page.getByTestId("material-card").first().click()
+    await page.getByRole("button", { name: /下一步/ }).click()
 
-    const project = await request.post(`${apiBase}/projects`, {
-      headers,
-      data: { name: `E2E API Proj ${suffix}` },
-    })
-    expect(project.status()).toBe(200)
+    // ── 数量弹窗 ─────────────────────────────────────────────────
+    await expect(page.getByText("要生成几个视频？")).toBeVisible({ timeout: 5000 })
+    await page.getByRole("button", { name: "生成 1 个视频" }).click()
 
-    // List generation tasks via task center API
-    const tasks = await request.get(`${apiBase}/tasks`, { headers })
-    expect(tasks.status()).toBe(200)
-    const tasksData = await tasks.json()
-    expect(Array.isArray(tasksData.items)).toBe(true)
+    // ── Step 3：填写标题（handleScriptModalConfirm 已预填 script.title，但我们再覆盖一次） ─
+    await expect(page.getByText("选择标题", { exact: true })).toBeVisible({ timeout: 10000 })
+    const titleInput2 = page.getByPlaceholder("输入或从标题库选择")
+    await expect(titleInput2).toBeVisible({ timeout: 5000 })
+    await titleInput2.fill(`测试叙事剪辑 ${suffix}`)
+    await page.getByRole("button", { name: /下一步/ }).click()
+
+    // ── Step 4：确认生成 ──────────────────────────────────────────
+    await expect(page.getByText("📋 生成配置")).toBeVisible({ timeout: 10000 })
+    await expect(page.getByText("叙事剪辑")).toBeVisible()
+    const confirmBtn2 = page.getByRole("button", { name: /确认生成视频/ })
+    await expect(confirmBtn2).toBeEnabled({ timeout: 5000 })
+
+    const createTask2 = page.waitForResponse(
+      (r) => r.url().includes("/generation/tasks") && r.request().method() === "POST",
+      { timeout: 30000 },
+    )
+    await confirmBtn2.click()
+    const taskResp2 = await createTask2
+    expect(taskResp2.ok(), `Create task: ${await taskResp2.text()}`).toBeTruthy()
+    console.log("[narrative] Generation task created:", (await taskResp2.json()).id)
+    await expect(page.getByText(/正在生成|提交/)).toBeVisible({ timeout: 15000 })
+    console.log("[narrative] Wizard flow completed ✓")
   })
 })

apps/web/e2e/core-navigation.spec.ts

@@ -0,0 +1,105 @@
+import { expect, test, type APIRequestContext, type Page } from "@playwright/test"
+
+const PASSWORD = "SmokePass123!"
+const apiBase = process.env.E2E_API_BASE || "/api/v1"
+const apiOrigin = apiBase.endsWith("/api/v1") ? apiBase.slice(0, -"/api/v1".length) : ""
+
+async function routeBrowserApiToTestApi(page: Page) {
+  if (!apiOrigin) return
+  await page.route("**/api/v1/**", async (route) => {
+    const sourceUrl = new URL(route.request().url())
+    const response = await route.fetch({
+      url: `${apiOrigin}${sourceUrl.pathname}${sourceUrl.search}`,
+    })
+    await route.fulfill({ response })
+  })
+}
+
+async function loginWithRetry(request: APIRequestContext, email: string, password: string) {
+  for (let i = 0; i <= 2; i++) {
+    const r = await request.post(`${apiBase}/auth/login`, { data: { email, password } })
+    if (r.status() !== 429) {
+      expect(r.ok(), `login: ${await r.text()}`).toBeTruthy()
+      return (await r.json()).access_token as string
+    }
+    console.log(`[nav] 429 retry ${i + 1}/2`)
+    await new Promise((res) => setTimeout(res, 65000))
+  }
+  throw new Error("Login retries exhausted")
+}
+
+/**
+ * 核心页面导航冒烟：侧边栏主要入口能访问、文案库/配音库页面能正常加载（不出白屏/无致命 js error）
+ */
+test.describe("Core Navigation", () => {
+  let authToken: string
+
+  test.beforeAll(async ({ request }) => {
+    const suffix = Math.random().toString(36).slice(2, 8)
+    const email = `e2e-nav-${suffix}@example.com`
+    await request.post(`${apiBase}/auth/register`, {
+      data: { email, password: PASSWORD, username: `e2e_nav_${suffix}` },
+    })
+    authToken = await loginWithRetry(request, email, PASSWORD)
+    const authHeader = { Authorization: `Bearer ${authToken}` }
+    const proj = await request.post(`${apiBase}/projects`, {
+      headers: authHeader,
+      data: { name: `Smoke Nav ${suffix}` },
+    })
+    if (proj.ok()) {
+      const projectId = (await proj.json()).id ?? (await proj.json()).project_id
+      await request.post(`${apiBase}/asset-libraries`, {
+        headers: authHeader,
+        data: { project_id: projectId, name: "Nav Lib", kind: "video" },
+      })
+    }
+  })
+
+  test.beforeEach(async ({ page }) => {
+    await page.setViewportSize({ width: 1440, height: 900 })
+    await page.addInitScript((t: string) => {
+      window.localStorage.setItem("access_token", t)
+      window.localStorage.setItem(
+        "auth-storage",
+        JSON.stringify({ state: { token: t, user: null } }),
+      )
+    }, authToken)
+    await routeBrowserApiToTestApi(page)
+  })
+
+  const navCases = [
+    { path: "/app/dashboard", marker: /概览|工作台|最近/i, name: "概览" },
+    { path: "/app/generate", marker: /智能剪辑|剪辑/, name: "智能剪辑" },
+    { path: "/app/assets", marker: /视频库|素材/, name: "视频库" },
+    { path: "/app/scripts", marker: /文案/, name: "文案库" },
+    { path: "/app/voices", marker: /配音|我的音色|配音库/, name: "配音库" },
+    { path: "/app/products", marker: /成品|作品/, name: "成品库" },
+    { path: "/app/history", marker: /历史|任务/, name: "任务历史" },
+    { path: "/app/tasks", marker: /任务中心|任务列表/, name: "任务中心" },
+    { path: "/app/points", marker: /积分|我的积分/, name: "积分中心" },
+  ]
+
+  for (const c of navCases) {
+    test(`visit ${c.name} (${c.path}) loads without fatal pageerror`, async ({ page }) => {
+      const errors: Error[] = []
+      page.on("pageerror", (e) => errors.push(e))
+      await page.goto(c.path)
+      await expect(page.locator("body")).not.toBeEmpty({ timeout: 20000 })
+      // 过滤掉常见第三方/非致命错误
+      const fatal = errors.filter(
+        (e) =>
+          !/ResizeObserver|Loading chunk|network error|Failed to fetch|chunkLoadError/i.test(
+            e.message,
+          ),
+      )
+      expect(fatal, `${c.name} pageerrors: ${fatal.map((e) => e.message).join("; ")}`).toHaveLength(
+        0,
+      )
+      await expect(
+        page.getByText(c.marker).first(),
+        `${c.name} should show relevant text`,
+      ).toBeVisible({ timeout: 15000 })
+      console.log(`[nav] ${c.name} loaded ✓`)
+    })
+  }
+})

apps/web/src/api/scripts/scripts.ts

@@ -11,8 +11,12 @@ import type {
   ScriptCategory,
 } from "./types"
 
-/** 是否启用 mock（后端合入后改为 false） */
-export const SCRIPTS_API_MOCK = true
+/**
+ * 是否启用 mock。
+ * #1894：文案库接口已上线，默认 false 走真实 API；
+ * 通过 SCRIPTS_API_MOCK=true 环境变量可本地开启 mock 调试（行为同 POINTS_API_MOCK）。
+ */
+export const SCRIPTS_API_MOCK = (process.env.SCRIPTS_API_MOCK as string | undefined) === "true"
 
 // ==================== Mock 数据 ====================
 

apps/web/src/api/tasks/types.ts

@@ -71,6 +71,16 @@ export interface CreateGenerationTaskRequest {
   duration?: number
   /** 视频宽高比，如 "9:16" */
   video_ratio?: string
+  /** #1970：剪辑模式 random/narrative */
+  assembly_mode?: "random" | "narrative"
+  /** #1970：叙事模式下的文案 ID */
+  script_id?: string
+  /** #1970：TTS 音色 ID */
+  tts_voice_id?: string
+  /** #1970：TTS 音色来源 preset/clone */
+  tts_voice_source?: "preset" | "clone"
+  /** #1970：智能降重开关（默认 true） */
+  dedup_enabled?: boolean
   /** 标题烧录配置 */
   title_config?: {
     text?: string

apps/web/src/api/titles/index.ts

@@ -1,19 +0,0 @@
-/**
- * 标题相关 API — 目录化入口
- * 保持与原 titles.ts 相同导出，向后兼容
- */
-
-// 类型
-export type {
-  TitleItem,
-  BackendTitleResponse,
-  BackendCreateTitleRequest,
-  BackendUpdateTitleRequest,
-  CreateTitleRequest,
-} from "./types"
-
-// 工具函数
-export { toTitleItem } from "./utils"
-
-// API 函数
-export { getTitles, createTitle, updateTitle, deleteTitle, batchImportTitles } from "./titles"

apps/web/src/api/titles/titles.ts

@@ -1,65 +0,0 @@
-/**
- * 标题相关 API 函数
- * Phase 1 新增：全局标题库
- * 注意：后端 schema 使用 name + text 字段，前端 UI 用 content 展示
- */
-import apiClient from "../client"
-import type {
-  BackendCreateTitleRequest,
-  BackendTitleResponse,
-  BackendUpdateTitleRequest,
-  CreateTitleRequest,
-  TitleItem,
-} from "./types"
-import { toTitleItem } from "./utils"
-
-/** 获取当前用户的所有标题 */
-export const getTitles = async (): Promise<TitleItem[]> => {
-  const response = await apiClient.get<{ items: BackendTitleResponse[] } | BackendTitleResponse[]>(
-    "/titles",
-  )
-  // 兼容两种后端返回格式：{ items: [...] } 或直接 [...]
-  const items = Array.isArray(response.data) ? response.data : response.data.items || []
-  return items.map(toTitleItem)
-}
-
-/** 创建标题 */
-export const createTitle = async (data: CreateTitleRequest): Promise<TitleItem> => {
-  // 后端要求 name（≤255）和 text（≤500），name 从 content 截取
-  const payload: BackendCreateTitleRequest = {
-    name: data.content.slice(0, 255),
-    text: data.content.slice(0, 500),
-    category: data.category || "default",
-  }
-  const response = await apiClient.post<BackendTitleResponse>("/titles", payload)
-  return toTitleItem(response.data)
-}
-
-/** 更新标题 */
-export const updateTitle = async (
-  titleId: string,
-  data: Partial<CreateTitleRequest>,
-): Promise<TitleItem> => {
-  const payload: BackendUpdateTitleRequest = {}
-  if (data.content !== undefined) {
-    payload.name = data.content.slice(0, 255)
-    payload.text = data.content.slice(0, 500)
-  }
-  if (data.category !== undefined) {
-    payload.category = data.category
-  }
-  // 后端用 PUT，非 PATCH
-  const response = await apiClient.put<BackendTitleResponse>(`/titles/${titleId}`, payload)
-  return toTitleItem(response.data)
-}
-
-/** 删除标题 */
-export const deleteTitle = async (titleId: string): Promise<void> => {
-  await apiClient.delete(`/titles/${titleId}`)
-}
-
-/** 批量导入标题 */
-export const batchImportTitles = async (titles: string[]): Promise<{ imported_count: number }> => {
-  const response = await apiClient.post("/titles/batch-import", { titles })
-  return response.data
-}

apps/web/src/api/titles/types.ts

@@ -1,54 +0,0 @@
-/**
- * 标题相关类型定义
- */
-
-/** 标题条目（前端展示用） */
-export interface TitleItem {
-  id: string
-  content: string
-  category?: string
-  source?: string
-  word_count?: number
-  is_favorite?: boolean
-  created_at?: string
-  updated_at?: string
-}
-
-/** 后端标题响应格式 */
-export interface BackendTitleResponse {
-  id: string
-  user_id: string
-  name: string
-  text: string
-  category: string
-  description: string
-  tags: string[]
-  usage_count: number
-  is_active: boolean
-  created_at: string
-  updated_at: string
-}
-
-/** 后端创建标题请求格式 */
-export interface BackendCreateTitleRequest {
-  name: string
-  text: string
-  category: string
-  description?: string
-  tags?: string[]
-}
-
-/** 后端更新标题请求格式 */
-export interface BackendUpdateTitleRequest {
-  name?: string
-  text?: string
-  category?: string
-  description?: string
-  tags?: string[]
-}
-
-/** 创建标题请求（前端接口，保持向后兼容） */
-export interface CreateTitleRequest {
-  content: string
-  category?: string
-}

apps/web/src/api/titles/utils.ts

@@ -1,14 +0,0 @@
-/**
- * 标题数据转换工具函数
- */
-import type { BackendTitleResponse, TitleItem } from "./types"
-
-/** 将后端响应映射为前端 TitleItem */
-export const toTitleItem = (item: BackendTitleResponse): TitleItem => ({
-  id: item.id,
-  content: item.text,
-  category: item.category,
-  word_count: item.text?.length || 0,
-  created_at: item.created_at,
-  updated_at: item.updated_at,
-})

apps/web/src/pages/ai-avatar/AiAvatarPage.tsx

@@ -619,6 +619,7 @@ const AiAvatarPage: React.FC = () => {
                   scriptText={state.scriptText}
                   onScriptTextChange={state.setScriptText}
                   onOpenScriptModal={() => state.setShowScriptModal(true)}
+                  onScriptCreated={(s) => state.selectScript(s as import("./types").Script)}
                 />
                 <div className="aa-step-btn-row">
                   <button
@@ -1146,8 +1147,9 @@ const ScriptSelectModalLazy: React.FC<{
   useEffect(() => {
     if (!open) return
     setLoading(true)
-    getScripts()
-      .then((items) => setScripts(Array.isArray(items) ? items : []))
+    // #1894: getScripts 返回 { items, total } 分页结构，取 items 即可
+    getScripts({ page_size: 200 })
+      .then((res) => setScripts(Array.isArray(res) ? res : (res.items ?? [])))
       .catch(() => setScripts([]))
       .finally(() => setLoading(false))
   }, [open])

apps/web/src/pages/ai-avatar/api/aiAvatar.ts

@@ -2,31 +2,15 @@
  * AI数字人 — API 封装（#1822 契约对齐）
  */
 import apiClient from "@/api/client"
-import type { Script, LipsyncJob, RenderJob, BRollSegment, SentenceTiming } from "../types"
+// #1894: Script 类型统一从 @/api/scripts 取（ai-avatar 本地 Script 仅保留渲染/对口型等自有类型）
+import type { LipsyncJob, RenderJob, BRollSegment, SentenceTiming } from "../types"
 
-/* ── 文案库 ── */
-export const getScripts = async (): Promise<Script[]> => {
-  const response = await apiClient.get<{ items?: Script[] } | Script[]>("/scripts")
-  // 后端列表返回 { items, total } 分页对象，做兼容解包 + 数组防御（#1809 白屏修复）
-  const data = response.data as unknown
-  if (Array.isArray(data)) return data
-  const items = (data as { items?: Script[] })?.items
-  return Array.isArray(items) ? items : []
-}
-
-export const getScriptById = async (id: string): Promise<Script> => {
-  const response = await apiClient.get<Script>(`/scripts/${id}`)
-  return response.data
-}
-
-export const createScript = async (data: { title: string; content: string }): Promise<Script> => {
-  const response = await apiClient.post<Script>("/scripts", data)
-  return response.data
-}
-
-export const deleteScript = async (id: string): Promise<void> => {
-  await apiClient.delete(`/scripts/${id}`)
-}
+/* ── 文案库 ──
+ * #1894: 统一走 @/api/scripts 的 getScripts，不再各自封装；
+ * 这样 mock 开关、分页/搜索参数、字段对齐都和文案库页面保持一致。
+ */
+// #1894: 统一复用文案库 API，不再在 ai-avatar 里重复实现
+export { getScripts, getScript as getScriptById, createScript, deleteScript } from "@/api/scripts"
 
 /* ── 素材单查（拿到 file_url 作为对口型的 video_url） ── */
 export const getAssetById = async (id: string): Promise<{ file_url?: string; id: string }> => {

apps/web/src/pages/ai-avatar/components/PanelScript.tsx

@@ -1,13 +1,17 @@
 /**
  * AI数字人 — 文案面板（步骤1用）
- * 文案库选择 / 手动输入 + 字数统计
+ * #1894: 文案库选择走 @/api/scripts；手动输入支持一键「保存到文案库」
  */
 import { useState } from "react"
+import { message } from "antd"
+import { createScript } from "../api/aiAvatar"
 
 interface PanelScriptProps {
   scriptText: string
   onScriptTextChange: (text: string) => void
   onOpenScriptModal: () => void
+  /** 手动保存到文案库后回调（把新脚本传入，父组件可更新 selectedScript） */
+  onScriptCreated?: (script: { id: string; title: string; content: string }) => void
 }
 
 type ScriptTab = "library" | "manual"
@@ -16,8 +20,30 @@ export function PanelScript({
   scriptText,
   onScriptTextChange,
   onOpenScriptModal,
+  onScriptCreated,
 }: PanelScriptProps) {
   const [scriptTab, setScriptTab] = useState<ScriptTab>("library")
+  const [saving, setSaving] = useState(false)
+
+  const handleSaveToLibrary = async () => {
+    const text = scriptText.trim()
+    if (!text) {
+      message.warning("请先输入文案内容")
+      return
+    }
+    // 用正文前 20 字作为默认标题
+    const autoTitle = text.slice(0, 20).replace(/\n+/g, " ").trim() || "手动输入文案"
+    setSaving(true)
+    try {
+      const created = await createScript({ title: autoTitle, content: text, tags: [] })
+      message.success({ content: "已保存到文案库", duration: 1 })
+      onScriptCreated?.(created)
+    } catch {
+      message.error("保存到文案库失败，请稍后重试")
+    } finally {
+      setSaving(false)
+    }
+  }
 
   return (
     <div className="aa-script-lipsync">
@@ -59,7 +85,20 @@ export function PanelScript({
         }
         onChange={(e) => onScriptTextChange(e.target.value)}
       />
-      <div className="aa-char-count">{scriptText.length} 字</div>
+      <div style={{ display: "flex", justifyContent: "space-between", alignItems: "center" }}>
+        <div className="aa-char-count">{scriptText.length} 字</div>
+        {scriptTab === "manual" && scriptText.trim().length > 0 && (
+          <button
+            type="button"
+            className="aa-btn aa-btn--text"
+            disabled={saving}
+            onClick={handleSaveToLibrary}
+            style={{ fontSize: 12, padding: "2px 8px" }}
+          >
+            {saving ? "保存中..." : "💾 保存到文案库"}
+          </button>
+        )}
+      </div>
     </div>
   )
 }

apps/web/src/pages/ai-avatar/components/PanelTitleConfig.tsx

@@ -15,7 +15,8 @@ import type { TitleOption } from "@/pages/generate/components/title/TitleLibrary
 import type { TitleSettings } from "@/pages/generate/types"
 import { POSITION_OPTIONS, FONT_OPTIONS, TITLE_PRESETS } from "@/pages/generate/constants"
 import type { AiAvatarTitleConfig } from "../types"
-import { getTitles } from "@/api/titles"
+// #1894: 标题数据源切换到文案库，取 script.title 作为候选
+import { getScripts } from "@/api/scripts"
 
 const { TextArea } = Input
 
@@ -28,11 +29,23 @@ const PanelTitleConfig: React.FC<PanelTitleConfigProps> = ({ titleConfig, onUpda
   /** TitleStylePanel 内部高亮的预设 key（面板本地状态） */
   const [activePreset, setActivePreset] = useState<string | null>(null)
 
-  /** 标题库选项（复用智能剪辑的标题库） */
+  /** 标题库选项（#1894：从文案库 scripts[].title 取候选） */
   const [titleOptions, setTitleOptions] = useState<TitleOption[]>([])
   useEffect(() => {
-    getTitles()
-      .then((items) => setTitleOptions(items.map((t) => ({ label: t.content, value: t.content }))))
+    getScripts({ page_size: 200 })
+      .then((res) => {
+        const items = Array.isArray(res) ? res : (res.items ?? [])
+        // 去重 + 过滤空标题
+        const seen = new Set<string>()
+        const opts: TitleOption[] = []
+        for (const s of items) {
+          const t = (s.title || "").trim()
+          if (!t || seen.has(t)) continue
+          seen.add(t)
+          opts.push({ label: t, value: t })
+        }
+        setTitleOptions(opts)
+      })
       .catch(() => setTitleOptions([]))
   }, [])
 
@@ -84,10 +97,12 @@ const PanelTitleConfig: React.FC<PanelTitleConfigProps> = ({ titleConfig, onUpda
           style={{ fontSize: 15 }}
         />
         <div style={{ marginTop: 8, display: "flex", alignItems: "center", gap: 8 }}>
-          <span style={{ fontSize: 12, color: "#8c8ca1", whiteSpace: "nowrap" }}>📚 标题库</span>
+          <span style={{ fontSize: 12, color: "#8c8ca1", whiteSpace: "nowrap" }}>
+            📚 文案库标题
+          </span>
           <TitleLibraryAutoComplete
             key={titleConfig.title}
-            placeholder="选择标题填入上方"
+            placeholder="从文案库选择标题"
             value=""
             onChange={(val) => {
               if (val) onUpdate({ title: val })

apps/web/src/pages/ai-avatar/components/TitleLibraryModal.tsx

@@ -1,97 +0,0 @@
-/**
- * AI数字人 — 标题库选择弹窗
- * 复用智能剪辑的标题库 API，选择标题后填入输入框
- */
-import React, { useEffect, useState } from "react"
-import { getTitles } from "@/api/titles"
-import type { TitleItem } from "@/api/titles/types"
-
-interface TitleLibraryModalProps {
-  open: boolean
-  onClose: () => void
-  onSelect: (title: string) => void
-}
-
-const TitleLibraryModal: React.FC<TitleLibraryModalProps> = ({ open, onClose, onSelect }) => {
-  const [titles, setTitles] = useState<TitleItem[]>([])
-  const [loading, setLoading] = useState(false)
-  const [search, setSearch] = useState("")
-
-  useEffect(() => {
-    if (!open) return
-    setLoading(true)
-    getTitles()
-      .then((items) => setTitles(items))
-      .catch(() => setTitles([]))
-      .finally(() => setLoading(false))
-  }, [open])
-
-  const filtered = titles.filter(
-    (t) => !search || t.content.toLowerCase().includes(search.toLowerCase()),
-  )
-
-  if (!open) return null
-
-  return (
-    <div className="aa-modal-overlay" onClick={onClose}>
-      <div className="aa-modal" onClick={(e) => e.stopPropagation()} style={{ maxWidth: 600 }}>
-        <div className="aa-modal__header">
-          <span className="aa-modal__title">从标题库选择</span>
-          <button className="aa-modal__close" onClick={onClose}></button>
-        </div>
-        <div className="aa-modal__body">
-          <div style={{ marginBottom: 12 }}>
-            <input
-              className="aa-input"
-              placeholder="搜索标题..."
-              value={search}
-              onChange={(e) => setSearch(e.target.value)}
-            />
-          </div>
-          {loading ? (
-            <div style={{ textAlign: "center", padding: 40, color: "#8c8ca1" }}>加载中...</div>
-          ) : filtered.length === 0 ? (
-            <div style={{ textAlign: "center", padding: 40, color: "#8c8ca1" }}>
-              暂无标题，请先在标题库创建
-            </div>
-          ) : (
-            <div style={{ maxHeight: 400, overflowY: "auto" }}>
-              {filtered.map((t) => (
-                <div
-                  key={t.id}
-                  style={{
-                    padding: "12px 16px",
-                    marginBottom: 8,
-                    background: "#f8f8fc",
-                    borderRadius: 8,
-                    cursor: "pointer",
-                    transition: "background 0.2s",
-                  }}
-                  onMouseEnter={(e) => (e.currentTarget.style.background = "#eef0ff")}
-                  onMouseLeave={(e) => (e.currentTarget.style.background = "#f8f8fc")}
-                  onClick={() => {
-                    onSelect(t.content)
-                    onClose()
-                  }}
-                >
-                  <div style={{ fontSize: 14, color: "#1a1a2e", marginBottom: 4 }}>{t.content}</div>
-                  <div style={{ fontSize: 12, color: "#8c8ca1" }}>
-                    {t.word_count ?? t.content.length}字 ·{" "}
-                    {t.created_at ? new Date(t.created_at).toLocaleDateString() : ""}
-                  </div>
-                </div>
-              ))}
-            </div>
-          )}
-        </div>
-        <div className="aa-modal__footer">
-          <button className="aa-btn" onClick={onClose}>
-            取消
-          </button>
-        </div>
-      </div>
-    </div>
-  )
-}
-
-export default TitleLibraryModal

apps/web/src/pages/ai-avatar/types.ts

@@ -56,15 +56,11 @@ export interface TtsPreviewResult {
   error: string | null
 }
 
-/* ── 文案 ── */
-export interface Script {
-  id: string
-  title: string
-  content: string
-  char_count: number
-  created_at: string
-  updated_at?: string
-}
+/* ── 文案 ──
+ * #1894: 直接复用文案库的 ScriptItem 类型，保证字段（title/content/tags/...）一致；
+ * 个别 ai-avatar 专属属性如有需要再在此处扩展。
+ */
+export type Script = import("@/api/scripts").ScriptItem
 
 /* ── 对口型任务 ── */
 export interface LipsyncJob {

apps/web/src/pages/generate/GeneratePage.tsx

@@ -11,6 +11,9 @@ import type { VoiceClone } from "@/api/voice-clone"
 import { useQuery } from "@tanstack/react-query"
 import { useCloneProgress } from "@/hooks/useCloneProgress"
 import CloneModal from "@/components/voice/CloneModal"
+import VoiceSelectModal from "./components/VoiceSelectModal"
+import ScriptSelectModal from "./components/ScriptSelectModal"
+import TtsVoiceModal from "./components/TtsVoiceModal"
 import GenerateHeader from "./components/GenerateHeader"
 import FrontendPreviewPlayer from "./components/FrontendPreviewPlayer"
 import CanvasPreviewGrid from "./components/CanvasPreviewGrid"
@@ -62,11 +65,26 @@ const GeneratePage: React.FC = () => {
     selectedVoice,
     setSelectedVoice,
     voiceMode,
+    setVoiceMode,
     selectedClonedVoice,
+    setSelectedClonedVoice,
+    editMode,
+    setEditMode,
+    selectedScript,
+    setSelectedScript,
+    ttsVoiceId,
+    setTtsVoiceId,
+    ttsVoiceSource,
+    setTtsVoiceSource,
+    ttsVoiceAssetId,
+    setTtsVoiceAssetId,
+    dedupEnabled,
+    setDedupEnabled,
 
     cloneModalOpen,
     setCloneModalOpen,
     videoRatio,
+    setVideoRatio,
     duration,
     style,
     autoSubtitles,
@@ -124,6 +142,11 @@ const GeneratePage: React.FC = () => {
   /* ── 数量选择弹窗 ── */
   const [countModalOpen, setCountModalOpen] = useState(false)
 
+  /* ── #1970 流程重构：分支弹窗 ── */
+  const [voiceModalOpen, setVoiceModalOpen] = useState(false)
+  const [scriptModalOpen, setScriptModalOpen] = useState(false)
+  const [ttsModalOpen, setTtsModalOpen] = useState(false)
+
   /* ── 标题样式回调 ── */
   const styleUpdaters = useTitleStyleUpdaters({
     titleSettings,
@@ -301,6 +324,12 @@ const GeneratePage: React.FC = () => {
     selectedClonedVoice,
     coverSettings,
     videoRatio,
+    editMode,
+    selectedScript,
+    ttsVoiceId,
+    ttsVoiceSource,
+    ttsVoiceAssetId,
+    dedupEnabled,
     style,
     duration,
     autoSubtitles,
@@ -340,7 +369,7 @@ const GeneratePage: React.FC = () => {
         return Array.from({ length: count }, (_, i) => list[i] ?? "")
       })
       setSelectedVariantIds(Array.from({ length: count }, (_, i) => i))
-      setCurrentStep(2)
+      setCurrentStep(3)
     },
     [
       setPreviewCount,
@@ -354,6 +383,58 @@ const GeneratePage: React.FC = () => {
     ],
   )
 
+  /* ── #1970：Step1 弹窗回调 ── */
+  const handleVoiceModalConfirm = useCallback(
+    (voiceAssetId: string) => {
+      setSelectedVoice(voiceAssetId)
+      setVoiceMode("custom")
+      setVoiceModalOpen(false)
+      setCurrentStep(2)
+    },
+    [setSelectedVoice, setVoiceMode, setCurrentStep],
+  )
+
+  const handleScriptModalConfirm = useCallback(
+    (script: import("@/api/scripts").ScriptItem) => {
+      setSelectedScript(script)
+      // 自动带入标题（若标题为空则预填）
+      if (!titleSettings.title?.trim() && script.title) {
+        setTitleSettings((prev) => ({ ...prev, title: script.title, aiAutoSelect: false }))
+      }
+      setScriptModalOpen(false)
+      // 自动打开 TTS 弹窗
+      setTtsModalOpen(true)
+    },
+    [setSelectedScript, setTitleSettings, titleSettings.title],
+  )
+
+  const handleTtsSynthesized = useCallback(
+    (payload: { voiceAssetId: string; ttsVoiceId: string; ttsVoiceSource: "preset" | "clone" }) => {
+      setTtsVoiceId(payload.ttsVoiceId)
+      setTtsVoiceSource(payload.ttsVoiceSource)
+      setTtsVoiceAssetId(payload.voiceAssetId)
+      if (payload.ttsVoiceSource === "clone") {
+        setSelectedClonedVoice(payload.ttsVoiceId)
+        setVoiceMode("clone")
+      } else {
+        setSelectedVoice(payload.ttsVoiceId)
+        setVoiceMode("preset")
+      }
+      setTtsModalOpen(false)
+      message.success("配音合成成功")
+      setCurrentStep(2)
+    },
+    [
+      setTtsVoiceId,
+      setTtsVoiceSource,
+      setTtsVoiceAssetId,
+      setSelectedVoice,
+      setSelectedClonedVoice,
+      setVoiceMode,
+      setCurrentStep,
+    ],
+  )
+
   /* ── 步骤3「确认生成视频」：校验通过 → 创建正式生成任务 → 跳步骤4看实时进展 ── */
   const handleConfirmGenerate = useCallback(async () => {
     // 积分预检查
@@ -412,12 +493,20 @@ const GeneratePage: React.FC = () => {
   const { goNext, goPrev } = useStepNavigation({
     currentStep,
     setCurrentStep,
+    editMode,
     materialMode,
     selectedMaterials,
     smartSelectedIds,
     titleSettings,
     generated,
     onOpenCountModal: () => setCountModalOpen(true),
+    onOpenStep1Modal: () => {
+      if (editMode === "random") {
+        setVoiceModalOpen(true)
+      } else {
+        setScriptModalOpen(true)
+      }
+    },
   })
 
   /* ── 最终成片 ── */
@@ -462,7 +551,7 @@ const GeneratePage: React.FC = () => {
             {!isBatch ? (
               <FrontendPreviewPlayer
                 assets={previewAssets}
-                videoRatio={videoRatio}
+                videoRatio={videoRatio as "9:16" | "16:9"}
                 ready={previewAssets.length > 0}
                 serverClips={serverClips}
                 voiceAudioUrl={previewVoiceAudioUrl || undefined}
@@ -497,7 +586,7 @@ const GeneratePage: React.FC = () => {
                 <CanvasPreviewGrid
                   count={previewCount}
                   assets={previewAssets}
-                  videoRatio={videoRatio}
+                  videoRatio={videoRatio as "9:16" | "16:9"}
                   titles={previewTitles}
                   titleSettings={titleSettings}
                   voiceAudioUrls={variantVoiceAudioUrls}
@@ -545,6 +634,16 @@ const GeneratePage: React.FC = () => {
             coverSettings={coverSettings}
             onCoverSettingsChange={setCoverSettings}
             selectedVoice={selectedVoice}
+            editMode={editMode}
+            onEditModeChange={setEditMode}
+            dedupEnabled={dedupEnabled}
+            onDedupEnabledChange={setDedupEnabled}
+            onPreviewCountChange={setPreviewCount}
+            videoRatio={videoRatio as "9:16" | "16:9"}
+            onVideoRatioChange={(r) => setVideoRatio(r)}
+            selectedScript={selectedScript}
+            ttsVoiceId={ttsVoiceId}
+            ttsVoiceSource={ttsVoiceSource}
             onSelectedVoiceChange={setSelectedVoice}
             onServerClipsChange={setServerClips}
             generating={generating}
@@ -671,6 +770,27 @@ const GeneratePage: React.FC = () => {
         onClose={() => setCloneModalOpen(false)}
         onSuccess={handleCloneSuccess}
       />
+
+      {/* #1970 流程弹窗 */}
+      <VoiceSelectModal
+        open={voiceModalOpen}
+        selectedVoice={selectedVoice}
+        onCancel={() => setVoiceModalOpen(false)}
+        onConfirm={handleVoiceModalConfirm}
+      />
+      <ScriptSelectModal
+        open={scriptModalOpen}
+        selectedScriptId={selectedScript?.id ?? null}
+        onCancel={() => setScriptModalOpen(false)}
+        onConfirm={handleScriptModalConfirm}
+      />
+      <TtsVoiceModal
+        open={ttsModalOpen}
+        scriptText={selectedScript?.content ?? ""}
+        scriptTitle={selectedScript?.title ?? ""}
+        onCancel={() => setTtsModalOpen(false)}
+        onSynthesized={handleTtsSynthesized}
+      />
     </div>
   )
 }

apps/web/src/pages/generate/components/GenerateStepContent.tsx

@@ -1,14 +1,16 @@
 /**
- * GeneratePage 步骤内容渲染（#1899 简化为 5 步，#1913 传递 selectedTemplate）
- * 步骤顺序：素材(1) → 配音(2) → 标题(3) → 确认生成(4) → 封面(5)
- * 步骤3预览（Canvas 网格）与步骤4进度（批量渲染网格）由 GeneratePage 直接渲染在左侧大区域。
+ * GeneratePage 步骤内容渲染（#1970 流程重构）
+ * 步骤顺序：选择模式(1) → 选择素材(2) → 选择标题(3) → 确认生成(4) → 选择封面(5)
+ * 原步骤"选择配音"已从主流程移除，改为 Step1 下一步分支弹窗（VoiceSelectModal / ScriptSelectModal → TtsVoiceModal）。
  */
 import React from "react"
 import type { EditPlanClip } from "@/api/template-editor"
 import type { CoverConfig } from "../types/cover"
 import type { TitleSettings } from "../types"
+import type { ScriptItem } from "@/api/scripts"
+import Step1EditMode from "./Step1EditMode"
+import type { EditMode } from "./Step1EditMode"
 import Step2MaterialSelect from "../components/Step2MaterialSelect"
-import Step3VoiceWithMode from "./Step3VoiceWithMode"
 import Step4TitleSettings from "../components/Step4TitleSettings"
 import Step6CoverSettings from "../components/Step6CoverSettings"
 import BatchGenerationGrid from "./BatchGenerationGrid"
@@ -17,19 +19,28 @@ import type { GeneratedVideo } from "@/api/template-editor"
 
 export interface GenerateStepContentProps {
   currentStep: number
-  /* 片段数量（#1899） */
+  /* Step1：剪辑模式 + 生成设置 */
+  editMode: EditMode
+  onEditModeChange: (m: EditMode) => void
+  dedupEnabled: boolean
+  onDedupEnabledChange: (v: boolean) => void
+  /* ── 片段数量（#1899） ── */
   clipCount: number
   onClipCountChange: (n: number) => void
-  /* 素材 */
+  /* ── 生成数量/比例（Step1 设置） ── */
+  previewCount: number
+  onPreviewCountChange: (n: number) => void
+  videoRatio: "9:16" | "16:9"
+  onVideoRatioChange: (r: "9:16" | "16:9") => void
+  /* ── 素材 ── */
   materialMode: "manual" | "auto"
   onMaterialModeChange: (mode: "manual" | "auto") => void
   selectedMaterials: string[]
   onSelectedMaterialsChange: (ids: string[]) => void
   smartSelectedIds: string[]
   onSmartSelectedIdsChange: (ids: string[]) => void
-  /* 当前选中的模板/草稿 ID；空串时由后端自动兜底（#1913） */
   selectedTemplate?: string
-  /* 标题 */
+  /* ── 标题 ── */
   titleSettings: TitleSettings
   onTitleSettingsChange: (settings: TitleSettings) => void
   onUpdatePosition: (position: string) => void
@@ -42,14 +53,14 @@ export interface GenerateStepContentProps {
   onApplyPreset: (presetKey: string) => void
   activePreset: string | null
   titlePresets: { key: string; label: string; previewStyle: React.CSSProperties }[]
-  /* 封面 */
+  /* ── 封面 ── */
   coverSettings: CoverConfig
   onCoverSettingsChange: (settings: CoverConfig) => void
-  /* 配音 */
+  /* ── 配音 ── */
   selectedVoice: string
   onSelectedVoiceChange: (id: string) => void
   onServerClipsChange: (clips: EditPlanClip[]) => void
-  /* 生成 */
+  /* ── 生成 ── */
   generating: boolean
   generated: boolean
   generateError: string | null
@@ -58,14 +69,10 @@ export interface GenerateStepContentProps {
   onRetry: () => void
   onRetryBatchTask: (taskId: string) => void
   onDismissError: () => void
-  /** 批量：每个正式生成任务的独立状态（步骤4进度网格） */
   batchTasks: BatchTaskState[]
-  /** BGM 开关 */
   bgm: boolean
-  /** BGM 配置 */
   bgmConfig?: { enabled: boolean; music_id?: string }
-  /* ── 批量生成（#1677）── */
-  previewCount: number
+  /* ── 批量生成 ── */
   previewTitles: string[]
   onPreviewTitlesChange: (titles: string[]) => void
   voiceModePerVideo: boolean
@@ -74,15 +81,27 @@ export interface GenerateStepContentProps {
   onVoiceLibraryIdsChange: (ids: string[]) => void
   previewCovers: string[]
   onPreviewCoversChange: (urls: string[]) => void
-  /** 批量模式勾选的变体索引 */
   selectedVariantIds?: number[]
+  /* ── 摘要信息（#1970 Step4 展示用） ── */
+  selectedScript: ScriptItem | null
+  ttsVoiceId: string
+  ttsVoiceSource: "preset" | "clone"
 }
 
 export const GenerateStepContent: React.FC<GenerateStepContentProps> = (props) => {
+  // Only destructure props actually referenced in JSX below
   const {
     currentStep,
+    editMode,
+    onEditModeChange,
+    dedupEnabled,
+    onDedupEnabledChange,
     clipCount,
     onClipCountChange,
+    previewCount,
+    onPreviewCountChange,
+    videoRatio,
+    onVideoRatioChange,
     materialMode,
     onMaterialModeChange,
     selectedMaterials,
@@ -104,31 +123,24 @@ export const GenerateStepContent: React.FC<GenerateStepContentProps> = (props) =
     titlePresets,
     coverSettings,
     onCoverSettingsChange,
-    selectedVoice,
-    onSelectedVoiceChange,
     onServerClipsChange,
     generating,
-    generated,
     generateError,
     progress,
     onRetry,
     generatedVideos,
     batchTasks,
     onRetryBatchTask,
-    previewCount,
     previewTitles,
     onPreviewTitlesChange,
-    voiceModePerVideo,
-    onVoiceModePerVideoChange,
-    voiceLibraryIds,
-    onVoiceLibraryIdsChange,
     previewCovers,
     onPreviewCoversChange,
     selectedVariantIds,
+    selectedScript,
+    ttsVoiceId,
+    ttsVoiceSource,
   } = props
 
-  // #1913：包装 onServerClipsChange，适配 hook 的 (clips, templateId?) 签名
-  // 如果 hook 传回了后端兜底创建的 templateId，同时通知外层更新 selectedTemplate
   const handleClipsChange = React.useCallback(
     (clips: EditPlanClip[], _templateId?: string) => {
       onServerClipsChange(clips)
@@ -138,8 +150,22 @@ export const GenerateStepContent: React.FC<GenerateStepContentProps> = (props) =
 
   switch (currentStep) {
     case 1:
+      return (
+        <Step1EditMode
+          editMode={editMode}
+          onEditModeChange={onEditModeChange}
+          previewCount={previewCount}
+          onPreviewCountChange={onPreviewCountChange}
+          videoRatio={videoRatio}
+          onVideoRatioChange={onVideoRatioChange}
+          dedupEnabled={dedupEnabled}
+          onDedupEnabledChange={onDedupEnabledChange}
+        />
+      )
+    case 2:
       return (
         <Step2MaterialSelect
+          editMode={editMode}
           materialMode={materialMode}
           onMaterialModeChange={onMaterialModeChange}
           selectedMaterials={selectedMaterials}
@@ -152,18 +178,6 @@ export const GenerateStepContent: React.FC<GenerateStepContentProps> = (props) =
           onServerClipsChange={handleClipsChange}
         />
       )
-    case 2:
-      return (
-        <Step3VoiceWithMode
-          previewCount={previewCount}
-          selectedVoice={selectedVoice}
-          onSelectedVoiceChange={onSelectedVoiceChange}
-          voiceModePerVideo={voiceModePerVideo}
-          onVoiceModePerVideoChange={onVoiceModePerVideoChange}
-          voiceLibraryIds={voiceLibraryIds}
-          onVoiceLibraryIdsChange={onVoiceLibraryIdsChange}
-        />
-      )
     case 3:
       return (
         <Step4TitleSettings
@@ -185,20 +199,49 @@ export const GenerateStepContent: React.FC<GenerateStepContentProps> = (props) =
         />
       )
     case 4:
-      /* 确认生成页：批量=逐任务进度网格；单视频=仅渲染进度/失败状态 */
-      if (previewCount > 1) {
-        return (
-          <BatchGenerationGrid
-            tasks={batchTasks}
-            titles={previewTitles}
-            onRetryTask={onRetryBatchTask}
-          />
-        )
-      }
-      if (generated && !generating && !generateError) return null
       return (
         <div className="xx-form-section">
-          {generating && (
+          {/* 配置摘要（#1970） */}
+          <div
+            style={{
+              padding: 14,
+              background: "#f9fafb",
+              borderRadius: 8,
+              marginBottom: 16,
+              fontSize: 13,
+              lineHeight: 1.8,
+              color: "#374151",
+            }}
+          >
+            <div style={{ fontWeight: 600, fontSize: 14, marginBottom: 6, color: "#111" }}>
+              📋 生成配置
+            </div>
+            <div>🎬 剪辑模式：{editMode === "random" ? "🎲 随机混剪" : "📖 叙事剪辑"}</div>
+            {editMode === "random" ? (
+              <div>🎙️ 配音来源：配音库音频</div>
+            ) : (
+              <>
+                <div>📝 文案：{selectedScript?.title ?? "未选择"}</div>
+                <div>
+                  🎙️ 合成配音音色：
+                  {ttsVoiceId
+                    ? `${ttsVoiceSource === "clone" ? "克隆音色" : "系统音色"}（${ttsVoiceId.slice(0, 8)}...）`
+                    : "未选择"}
+                </div>
+              </>
+            )}
+            <div>📱 视频比例：{videoRatio}</div>
+            <div>🎯 智能降重：{dedupEnabled ? "已开启" : "已关闭"}</div>
+            {previewCount > 1 && <div>📦 生成数量：{previewCount} 个</div>}
+          </div>
+
+          {previewCount > 1 ? (
+            <BatchGenerationGrid
+              tasks={batchTasks}
+              titles={previewTitles}
+              onRetryTask={onRetryBatchTask}
+            />
+          ) : generating ? (
             <div className="xx-gen-progress-card">
               <div className="xx-gen-progress-header">
                 <div className="xx-gen-progress-info">
@@ -217,8 +260,7 @@ export const GenerateStepContent: React.FC<GenerateStepContentProps> = (props) =
                 />
               </div>
             </div>
-          )}
-          {generateError && !generating && (
+          ) : generateError ? (
             <div className="xx-gen-error-card">
               <div className="xx-gen-error-info">
                 <div className="xx-gen-error-title">生成失败</div>
@@ -228,7 +270,7 @@ export const GenerateStepContent: React.FC<GenerateStepContentProps> = (props) =
                 🔄 重试
               </button>
             </div>
-          )}
+          ) : null}
         </div>
       )
     case 5:

apps/web/src/pages/generate/components/ScriptSelectModal.tsx

@@ -0,0 +1,243 @@
+/**
+ * 叙事剪辑 — 文案选择弹窗（#1970）
+ * - 搜索框：防抖 300ms，命中文字黄色高亮
+ * - 标签筛选行：全部/带货/工厂/测评/教程/口播/种草
+ * - 数量统计 + 卡片列表（可滚动，max-height 420px）
+ * - 调用 GET /api/v1/scripts?keyword=&tag=&page_size=200
+ */
+import React, { useState, useEffect, useMemo, useRef, useCallback } from "react"
+import { Modal, Input, Tag, Spin } from "antd"
+import { SearchOutlined, CheckCircleFilled } from "@ant-design/icons"
+import { useQuery } from "@tanstack/react-query"
+import { getScripts } from "@/api/scripts"
+import type { ScriptItem } from "@/api/scripts"
+
+interface ScriptSelectModalProps {
+  open: boolean
+  selectedScriptId: string | null
+  onCancel: () => void
+  onConfirm: (script: ScriptItem) => void
+}
+
+const SCRIPT_TABS = [
+  { key: "all", label: "全部" },
+  { key: "带货", label: "带货" },
+  { key: "工厂", label: "工厂" },
+  { key: "测评", label: "测评" },
+  { key: "教程", label: "教程" },
+  { key: "口播", label: "口播" },
+  { key: "种草", label: "种草" },
+]
+
+/** 在文本中用 <mark> 高亮关键词（黄色背景） */
+function highlight(text: string, keyword: string): React.ReactNode {
+  if (!keyword) return text
+  const idx = text.toLowerCase().indexOf(keyword.toLowerCase())
+  if (idx < 0) return text
+  return (
+    <>
+      {text.slice(0, idx)}
+      <mark style={{ background: "#fef08a", color: "#713f12", padding: "0 2px", borderRadius: 2 }}>
+        {text.slice(idx, idx + keyword.length)}
+      </mark>
+      {text.slice(idx + keyword.length)}
+    </>
+  )
+}
+
+const ScriptSelectModal: React.FC<ScriptSelectModalProps> = ({
+  open,
+  selectedScriptId,
+  onCancel,
+  onConfirm,
+}) => {
+  const [innerSelected, setInnerSelected] = useState<string | null>(selectedScriptId)
+  const [activeTag, setActiveTag] = useState<string>("all")
+  const [searchInput, setSearchInput] = useState("")
+  const [debouncedKw, setDebouncedKw] = useState("")
+  const debounceRef = useRef<ReturnType<typeof setTimeout> | null>(null)
+
+  useEffect(() => {
+    if (open) {
+      setInnerSelected(selectedScriptId)
+      setActiveTag("all")
+      setSearchInput("")
+      setDebouncedKw("")
+    }
+  }, [open, selectedScriptId])
+
+  // 300ms 防抖
+  useEffect(() => {
+    if (debounceRef.current) clearTimeout(debounceRef.current)
+    debounceRef.current = setTimeout(() => setDebouncedKw(searchInput.trim()), 300)
+    return () => {
+      if (debounceRef.current) clearTimeout(debounceRef.current)
+    }
+  }, [searchInput])
+
+  const { data, isLoading } = useQuery({
+    queryKey: ["scripts", "select-modal", debouncedKw, activeTag],
+    queryFn: () =>
+      getScripts({
+        page: 1,
+        page_size: 200,
+        keyword: debouncedKw || undefined,
+        tag: activeTag === "all" ? undefined : activeTag,
+      }),
+    enabled: open,
+  })
+
+  const scripts: ScriptItem[] = useMemo(() => data?.items ?? [], [data])
+  const selected = useMemo(
+    () => scripts.find((s) => s.id === innerSelected) ?? null,
+    [scripts, innerSelected],
+  )
+
+  const handleConfirm = useCallback(() => {
+    if (selected) onConfirm(selected)
+  }, [selected, onConfirm])
+
+  return (
+    <Modal
+      title="📝 选择文案"
+      open={open}
+      onCancel={onCancel}
+      onOk={handleConfirm}
+      okText="确认选择"
+      cancelText="取消"
+      okButtonProps={{ disabled: !selected, style: { background: "#7c3aed" } }}
+      width={680}
+      destroyOnClose
+    >
+      {/* 搜索 */}
+      <Input
+        allowClear
+        prefix={<SearchOutlined style={{ color: "#9ca3af" }} />}
+        placeholder="搜索标题、内容或标签"
+        value={searchInput}
+        onChange={(e) => setSearchInput(e.target.value)}
+        style={{ marginBottom: 12 }}
+      />
+
+      {/* 标签筛选 */}
+      <div style={{ display: "flex", flexWrap: "wrap", gap: 8, marginBottom: 12 }}>
+        {SCRIPT_TABS.map((t) => {
+          const active = activeTag === t.key
+          return (
+            <Tag
+              key={t.key}
+              onClick={() => setActiveTag(t.key)}
+              style={{
+                cursor: "pointer",
+                padding: "4px 14px",
+                borderRadius: 16,
+                border: active ? "1px solid #7c3aed" : "1px solid #e5e7eb",
+                background: active ? "#ede9fe" : "#fff",
+                color: active ? "#7c3aed" : "#4b5563",
+                margin: 0,
+                fontSize: 13,
+              }}
+            >
+              {t.label}
+            </Tag>
+          )
+        })}
+      </div>
+
+      {/* 数量统计 */}
+      <div style={{ fontSize: 12, color: "#6b7280", marginBottom: 8 }}>
+        共 {data?.total ?? scripts.length} 条文案
+      </div>
+
+      {/* 卡片列表 */}
+      <div style={{ maxHeight: 420, overflowY: "auto", paddingRight: 4 }}>
+        {isLoading ? (
+          <div style={{ textAlign: "center", padding: "40px 0" }}>
+            <Spin />
+          </div>
+        ) : scripts.length === 0 ? (
+          <div style={{ textAlign: "center", padding: "40px 0", color: "#9ca3af" }}>
+            暂无匹配文案
+          </div>
+        ) : (
+          <div style={{ display: "flex", flexDirection: "column", gap: 10 }}>
+            {scripts.map((s) => {
+              const isSel = innerSelected === s.id
+              const preview = (s.content || "").replace(/\s+/g, " ").slice(0, 80)
+              return (
+                <div
+                  key={s.id}
+                  onClick={() => setInnerSelected(s.id)}
+                  style={{
+                    padding: 14,
+                    borderRadius: 8,
+                    border: isSel ? "2px solid #7c3aed" : "1px solid #e5e7eb",
+                    background: isSel ? "#faf5ff" : "#fff",
+                    cursor: "pointer",
+                    transition: "all 0.2s",
+                    position: "relative",
+                  }}
+                >
+                  {isSel && (
+                    <CheckCircleFilled
+                      style={{
+                        position: "absolute",
+                        top: 12,
+                        right: 12,
+                        color: "#7c3aed",
+                        fontSize: 18,
+                      }}
+                    />
+                  )}
+                  <div
+                    style={{
+                      fontSize: 14,
+                      fontWeight: 600,
+                      color: isSel ? "#6d28d9" : "#111",
+                      marginBottom: 4,
+                      paddingRight: 24,
+                    }}
+                  >
+                    {highlight(s.title || "未命名", debouncedKw)}
+                  </div>
+                  <div
+                    style={{
+                      fontSize: 12,
+                      color: "#6b7280",
+                      lineHeight: 1.6,
+                      marginBottom: 8,
+                    }}
+                  >
+                    {highlight(preview + ((s.content || "").length > 80 ? "..." : ""), debouncedKw)}
+                  </div>
+                  {s.tags && s.tags.length > 0 && (
+                    <div style={{ display: "flex", gap: 4, flexWrap: "wrap" }}>
+                      {s.tags.slice(0, 5).map((tg) => (
+                        <Tag
+                          key={tg}
+                          style={{
+                            margin: 0,
+                            fontSize: 11,
+                            padding: "1px 8px",
+                            borderRadius: 10,
+                            background: "#f3f4f6",
+                            border: "none",
+                            color: "#6b7280",
+                          }}
+                        >
+                          {tg}
+                        </Tag>
+                      ))}
+                    </div>
+                  )}
+                </div>
+              )
+            })}
+          </div>
+        )}
+      </div>
+    </Modal>
+  )
+}
+
+export default ScriptSelectModal

apps/web/src/pages/generate/components/Step1EditMode.tsx

@@ -0,0 +1,264 @@
+/**
+ * Step 1 选择剪辑模式 + 生成设置（#1970 新流程第一步）
+ * - 剪辑模式：🎲随机混剪 / 📖叙事剪辑，二选一，选中紫底紫框
+ * - 生成设置：生成数量（-/+ 1-10 默认1）、视频比例（9:16/16:9 默认9:16）、智能降重开关（默认开）
+ */
+import React from "react"
+import { MinusOutlined, PlusOutlined } from "@ant-design/icons"
+
+export type EditMode = "random" | "narrative"
+
+interface Step1EditModeProps {
+  editMode: EditMode
+  onEditModeChange: (mode: EditMode) => void
+  /** 生成数量（1-10，默认1） */
+  previewCount: number
+  onPreviewCountChange: (n: number) => void
+  /** 视频比例 */
+  videoRatio: "9:16" | "16:9"
+  onVideoRatioChange: (ratio: "9:16" | "16:9") => void
+  /** 智能降重开关（默认 true） */
+  dedupEnabled: boolean
+  onDedupEnabledChange: (v: boolean) => void
+}
+
+const PURPLE = "#7c3aed"
+const PURPLE_BG = "linear-gradient(135deg, #ede9fe, #ddd6fe)"
+const PURPLE_BORDER = "2px solid #7c3aed"
+
+const MODE_CARDS: Array<{
+  key: EditMode
+  emoji: string
+  title: string
+  desc: string
+  features: string[]
+}> = [
+  {
+    key: "random",
+    emoji: "🎲",
+    title: "随机混剪",
+    desc: "根据配音时长随机抽取素材片段，灵活组合",
+    features: ["随机抽帧组合", "每次画面不同", "适合批量生成"],
+  },
+  {
+    key: "narrative",
+    emoji: "📖",
+    title: "叙事剪辑",
+    desc: "按文案内容匹配相关画面，有逻辑组织镜头",
+    features: ["画面匹配文案", "叙事感更强", "需要素材标签"],
+  },
+]
+
+const Step1EditMode: React.FC<Step1EditModeProps> = ({
+  editMode,
+  onEditModeChange,
+  previewCount,
+  onPreviewCountChange,
+  videoRatio,
+  onVideoRatioChange,
+  dedupEnabled,
+  onDedupEnabledChange,
+}) => {
+  return (
+    <div className="xx-form-section">
+      <h3>🎬 选择剪辑模式</h3>
+      <p style={{ color: "#666", fontSize: 14, marginBottom: 16 }}>
+        选择适合您的剪辑方式，后续流程会根据模式自动调整
+      </p>
+
+      <div
+        style={{
+          display: "grid",
+          gridTemplateColumns: "repeat(auto-fit, minmax(240px, 1fr))",
+          gap: 16,
+          marginBottom: 24,
+        }}
+      >
+        {MODE_CARDS.map((card) => {
+          const selected = editMode === card.key
+          return (
+            <div
+              key={card.key}
+              onClick={() => onEditModeChange(card.key)}
+              style={{
+                padding: 20,
+                borderRadius: 12,
+                border: selected ? PURPLE_BORDER : "1px solid #e5e7eb",
+                background: selected ? PURPLE_BG : "#fff",
+                cursor: "pointer",
+                transition: "all 0.2s",
+              }}
+            >
+              <div style={{ fontSize: 36, marginBottom: 8 }}>{card.emoji}</div>
+              <div
+                style={{
+                  fontSize: 18,
+                  fontWeight: 600,
+                  color: selected ? PURPLE : "#111",
+                  marginBottom: 6,
+                }}
+              >
+                {card.title}
+              </div>
+              <div style={{ fontSize: 13, color: "#666", marginBottom: 12 }}>{card.desc}</div>
+              <div style={{ display: "flex", flexDirection: "column", gap: 4 }}>
+                {card.features.map((f) => (
+                  <div key={f} style={{ fontSize: 12, color: selected ? "#6d28d9" : "#6b7280" }}>
+                    ✅ {f}
+                  </div>
+                ))}
+              </div>
+            </div>
+          )
+        })}
+      </div>
+
+      <h3 style={{ marginTop: 8 }}>⚙️ 生成设置</h3>
+
+      <div className="xx-form-field" style={{ marginTop: 12 }}>
+        <label>生成数量</label>
+        <div style={{ display: "flex", alignItems: "center", gap: 12 }}>
+          <div
+            style={{
+              display: "inline-flex",
+              alignItems: "center",
+              border: "1px solid #e5e7eb",
+              borderRadius: 8,
+              overflow: "hidden",
+              background: "#fff",
+            }}
+          >
+            <button
+              type="button"
+              onClick={() => onPreviewCountChange(Math.max(1, previewCount - 1))}
+              disabled={previewCount <= 1}
+              style={{
+                width: 36,
+                height: 36,
+                border: "none",
+                background: "transparent",
+                cursor: previewCount <= 1 ? "not-allowed" : "pointer",
+                color: previewCount <= 1 ? "#d1d5db" : "#374151",
+                fontSize: 16,
+              }}
+            >
+              <MinusOutlined />
+            </button>
+            <span
+              style={{
+                minWidth: 40,
+                textAlign: "center",
+                fontSize: 16,
+                fontWeight: 600,
+                color: "#111",
+              }}
+            >
+              {previewCount}
+            </span>
+            <button
+              type="button"
+              onClick={() => onPreviewCountChange(Math.min(10, previewCount + 1))}
+              disabled={previewCount >= 10}
+              style={{
+                width: 36,
+                height: 36,
+                border: "none",
+                background: "transparent",
+                cursor: previewCount >= 10 ? "not-allowed" : "pointer",
+                color: previewCount >= 10 ? "#d1d5db" : "#374151",
+                fontSize: 16,
+              }}
+            >
+              <PlusOutlined />
+            </button>
+          </div>
+          <span style={{ fontSize: 12, color: "#6b7280" }}>最多一次生成 10 个</span>
+        </div>
+      </div>
+
+      <div className="xx-form-field" style={{ marginTop: 16 }}>
+        <label>视频比例</label>
+        <div style={{ display: "flex", gap: 12, marginTop: 4 }}>
+          {[
+            { key: "9:16" as const, emoji: "📱", label: "竖屏 9:16" },
+            { key: "16:9" as const, emoji: "🖥️", label: "横屏 16:9" },
+          ].map((opt) => {
+            const selected = videoRatio === opt.key
+            return (
+              <button
+                key={opt.key}
+                type="button"
+                onClick={() => onVideoRatioChange(opt.key)}
+                style={{
+                  padding: "10px 20px",
+                  borderRadius: 8,
+                  border: selected ? PURPLE_BORDER : "1px solid #e5e7eb",
+                  background: selected ? PURPLE_BG : "#fff",
+                  color: selected ? PURPLE : "#374151",
+                  cursor: "pointer",
+                  fontSize: 14,
+                  fontWeight: selected ? 600 : 400,
+                  transition: "all 0.2s",
+                }}
+              >
+                {opt.emoji} {opt.label}
+              </button>
+            )
+          })}
+        </div>
+      </div>
+
+      <div
+        className="xx-form-field"
+        style={{
+          marginTop: 16,
+          padding: "12px 16px",
+          background: "#f9fafb",
+          borderRadius: 8,
+        }}
+      >
+        <div style={{ display: "flex", alignItems: "center", gap: 8 }}>
+          <span style={{ fontSize: 14, fontWeight: 500, color: "#111" }}>
+            🎯 智能降重 {dedupEnabled ? "已开启" : "已关闭"}
+          </span>
+          <button
+            type="button"
+            onClick={() => onDedupEnabledChange(!dedupEnabled)}
+            style={{
+              width: 44,
+              height: 24,
+              borderRadius: 12,
+              border: "none",
+              background: dedupEnabled ? PURPLE : "#d1d5db",
+              position: "relative",
+              cursor: "pointer",
+              transition: "background 0.2s",
+              padding: 0,
+              flexShrink: 0,
+            }}
+            aria-label="toggle dedup"
+          >
+            <span
+              style={{
+                position: "absolute",
+                top: 2,
+                left: dedupEnabled ? 22 : 2,
+                width: 20,
+                height: 20,
+                borderRadius: "50%",
+                background: "#fff",
+                transition: "left 0.2s",
+                boxShadow: "0 1px 3px rgba(0,0,0,0.2)",
+              }}
+            />
+          </button>
+        </div>
+        <div style={{ fontSize: 12, color: "#6b7280", marginTop: 4 }}>
+          自动对画面做微调，避免查重不过
+        </div>
+      </div>
+    </div>
+  )
+}
+
+export default Step1EditMode

apps/web/src/pages/generate/components/Step2MaterialSelect.tsx

@@ -11,6 +11,8 @@ import SmartMatchInput from "./material/SmartMatchInput"
 import SmartMatchResults from "./material/SmartMatchResults"
 
 interface Step2MaterialSelectProps {
+  /** 剪辑模式：random 随机混剪 / narrative 叙事剪辑（#1970） */
+  editMode?: "random" | "narrative"
   materialMode: "manual" | "auto"
   onMaterialModeChange: (mode: "manual" | "auto") => void
   selectedMaterials: string[]
@@ -43,6 +45,27 @@ const Step2MaterialSelect: React.FC<Step2MaterialSelectProps> = (props) => {
     <div className="xx-form-section">
       <h3>📦 选择素材</h3>
 
+      {/* 叙事剪辑：AI 智能匹配提示卡（#1970） */}
+      {props.editMode === "narrative" && (
+        <div
+          style={{
+            marginTop: 12,
+            padding: "12px 16px",
+            background: "linear-gradient(135deg,#ede9fe,#f5f3ff)",
+            border: "1px solid #c4b5fd",
+            borderRadius: 8,
+            fontSize: 13,
+            color: "#5b21b6",
+            display: "flex",
+            alignItems: "center",
+            gap: 8,
+          }}
+        >
+          <span style={{ fontSize: 18 }}>🤖</span>
+          <span>AI智能匹配：系统将根据您的文案内容，从素材库自动匹配合适的视频片段</span>
+        </div>
+      )}
+
       {/* 片段数量（#1899） */}
       <div className="xx-form-field" style={{ marginTop: 12 }}>
         <label>片段数量</label>

apps/web/src/pages/generate/components/TtsVoiceModal.tsx

@@ -0,0 +1,491 @@
+/**
+ * 叙事剪辑 — TTS 音色选择 + 合成配音弹窗（#1970）
+ * - Tabs：✨系统音色 / 🎙️我的克隆音色
+ * - 2列音色卡片（头像emoji+名称+描述+标签+▶试听+选中✓）
+ * - 底部：取消 / 🎧 合成配音（主按钮，必须选音色才能点）
+ * - 合成中：紫色 spinner + "正在合成配音..." + "请稍候，通常需要10-30秒"
+ * - 合成成功：保存到配音库并回调（voiceAssetId + ttsVoiceId + ttsVoiceSource）
+ *
+ * 复用现有 /api/tts 的 synthesizeSpeech + 轮询 getTTSJobStatus 逻辑；
+ * 不直接复用 TtsModal（它是页面配音弹窗，含文本输入/语速/情感等字段，叙事模式文本来自文案）。
+ */
+import React, { useState, useEffect, useMemo, useRef, useCallback } from "react"
+import { Modal, Tabs, Spin, message } from "antd"
+import { CheckCircleFilled, SoundOutlined } from "@ant-design/icons"
+import { useQuery } from "@tanstack/react-query"
+import { fetchPresetVoices } from "@/api/voices"
+import { getVoiceClones } from "@/api/voice-clone"
+import { synthesizeSpeech, getTTSJobStatus, saveTtsToLibrary } from "@/api/tts"
+import type { PresetVoiceItem } from "@/api/voices"
+import type { VoiceClone } from "@/api/voice-clone"
+import { VOICE_GENDER_ICON } from "../constants"
+
+interface TtsVoiceModalProps {
+  open: boolean
+  /** 需要合成的文本（来自选中的文案 content） */
+  scriptText: string
+  scriptTitle: string
+  onCancel: () => void
+  /** 合成成功回调：asset_id 为保存到配音库后的素材ID */
+  onSynthesized: (payload: {
+    voiceAssetId: string
+    ttsVoiceId: string
+    ttsVoiceSource: "preset" | "clone"
+  }) => void
+}
+
+type TtsSynthStatus = "idle" | "synthesizing" | "saving" | "done" | "error"
+
+const TtsVoiceModal: React.FC<TtsVoiceModalProps> = ({
+  open,
+  scriptText,
+  scriptTitle,
+  onCancel,
+  onSynthesized,
+}) => {
+  const [activeTab, setActiveTab] = useState<"preset" | "clone">("preset")
+  const [selectedVoiceId, setSelectedVoiceId] = useState<string>("")
+  const [status, setStatus] = useState<TtsSynthStatus>("idle")
+  const [error, setError] = useState<string | null>(null)
+  const [previewingId, setPreviewingId] = useState<string | null>(null)
+  const audioRef = useRef<HTMLAudioElement | null>(null)
+  const timerRef = useRef<ReturnType<typeof setInterval> | null>(null)
+
+  /* 系统音色 */
+  const { data: presetData } = useQuery({
+    queryKey: ["preset-voices", "modal"],
+    queryFn: fetchPresetVoices,
+    enabled: open,
+  })
+  const presetVoices: PresetVoiceItem[] = useMemo(() => presetData?.items ?? [], [presetData])
+
+  /* 克隆音色（仅 ready 状态可用） */
+  const { data: cloneListRaw = [] } = useQuery({
+    queryKey: ["voice-clones", "ready"],
+    queryFn: () => getVoiceClones({ status: "ready" }),
+    enabled: open,
+  })
+  const cloneVoices: VoiceClone[] = useMemo(
+    () => cloneListRaw.filter((v: VoiceClone) => v.status === "ready"),
+    [cloneListRaw],
+  )
+
+  /* 打开时重置状态 */
+  useEffect(() => {
+    if (open) {
+      setSelectedVoiceId("")
+      setStatus("idle")
+      setError(null)
+      setActiveTab("preset")
+    } else {
+      if (timerRef.current) {
+        clearInterval(timerRef.current)
+        timerRef.current = null
+      }
+      if (audioRef.current) {
+        audioRef.current.pause()
+        audioRef.current = null
+      }
+      setPreviewingId(null)
+    }
+    return () => {
+      if (timerRef.current) clearInterval(timerRef.current)
+    }
+  }, [open])
+
+  const handlePreview = useCallback(
+    (voiceId: string, previewUrl: string | null | undefined) => {
+      if (!previewUrl) {
+        message.info("该音色暂无试听音频")
+        return
+      }
+      if (previewingId === voiceId && audioRef.current) {
+        audioRef.current.pause()
+        setPreviewingId(null)
+        return
+      }
+      if (audioRef.current) audioRef.current.pause()
+      const a = new Audio(previewUrl)
+      audioRef.current = a
+      setPreviewingId(voiceId)
+      a.onended = () => {
+        setPreviewingId(null)
+        audioRef.current = null
+      }
+      a.play().catch(() => {
+        setPreviewingId(null)
+        audioRef.current = null
+      })
+    },
+    [previewingId],
+  )
+
+  const textToSynth = useMemo(() => {
+    // 文案内容取首段（过长会被 TTS 截断，保持和用户感知一致）
+    const t = (scriptText || "").trim()
+    return t.length > 500 ? t.slice(0, 500) : t
+  }, [scriptText])
+
+  const handleSynthesize = useCallback(async () => {
+    if (!selectedVoiceId) {
+      message.warning("请先选择一个音色")
+      return
+    }
+    if (!textToSynth) {
+      message.warning("文案内容为空，无法合成")
+      return
+    }
+    setStatus("synthesizing")
+    setError(null)
+    try {
+      const isClone = activeTab === "clone"
+      const payload: Record<string, unknown> = {
+        text: textToSynth,
+        speed: 1.0,
+        language: "zh-CN",
+      }
+      if (isClone) {
+        payload.voice_clone_profile_id = selectedVoiceId
+      } else {
+        payload.voice_id = selectedVoiceId
+      }
+      const resp = await synthesizeSpeech(
+        payload as unknown as Parameters<typeof synthesizeSpeech>[0],
+      )
+      const jobId = resp.job_id
+
+      await new Promise<void>((resolve, reject) => {
+        timerRef.current = setInterval(async () => {
+          try {
+            const job = await getTTSJobStatus(jobId)
+            if (job.status === "completed") {
+              if (timerRef.current) clearInterval(timerRef.current)
+              timerRef.current = null
+              resolve()
+            } else if (job.status === "failed") {
+              if (timerRef.current) clearInterval(timerRef.current)
+              timerRef.current = null
+              reject(new Error(job.error_message || "合成失败"))
+            }
+          } catch (e) {
+            if (timerRef.current) clearInterval(timerRef.current)
+            timerRef.current = null
+            reject(e)
+          }
+        }, 2000)
+      })
+
+      // 保存到配音库
+      setStatus("saving")
+      await saveTtsToLibrary(jobId, { name: scriptTitle?.slice(0, 30) || "AI合成配音" })
+      setStatus("done")
+
+      // 合成成功后回调；voiceAssetId 由后端在保存时产出，这里用 ttsVoiceId 占位，
+      // 父流程会在下一次 asset 列表刷新后重新选取；前端直接以 ttsVoiceId 为 key 传给后端
+      // （叙事模式后端通过 script_id + tts_voice_id 自行再合成，不依赖 asset_id）。
+      onSynthesized({
+        voiceAssetId: jobId,
+        ttsVoiceId: selectedVoiceId,
+        ttsVoiceSource: isClone ? "clone" : "preset",
+      })
+    } catch (err: unknown) {
+      setStatus("error")
+      const msg = err instanceof Error ? err.message : "合成失败，请稍后重试"
+      setError(msg)
+    }
+  }, [selectedVoiceId, textToSynth, activeTab, scriptTitle, onSynthesized])
+
+  const renderVoiceCard = (v: {
+    id: string
+    name: string
+    description?: string
+    gender?: string
+    tags?: string[]
+    preview_url?: string | null
+  }) => {
+    const isSel = selectedVoiceId === v.id
+    const isPlaying = previewingId === v.id
+    const emoji = v.gender ? (VOICE_GENDER_ICON[v.gender] ?? "🎤") : "🎤"
+    return (
+      <div
+        key={v.id}
+        onClick={() => setSelectedVoiceId(v.id)}
+        style={{
+          padding: 12,
+          borderRadius: 8,
+          border: isSel ? "2px solid #7c3aed" : "1px solid #e5e7eb",
+          background: isSel ? "#faf5ff" : "#fff",
+          cursor: "pointer",
+          transition: "all 0.2s",
+          position: "relative",
+        }}
+      >
+        {isSel && (
+          <CheckCircleFilled
+            style={{
+              position: "absolute",
+              top: 10,
+              right: 10,
+              color: "#7c3aed",
+            }}
+          />
+        )}
+        <div style={{ display: "flex", alignItems: "center", gap: 10, marginBottom: 8 }}>
+          <div
+            style={{
+              width: 36,
+              height: 36,
+              borderRadius: "50%",
+              background: isSel ? "linear-gradient(135deg,#7c3aed,#a78bfa)" : "#f3f4f6",
+              display: "flex",
+              alignItems: "center",
+              justifyContent: "center",
+              fontSize: 18,
+            }}
+          >
+            {emoji}
+          </div>
+          <div style={{ flex: 1, minWidth: 0 }}>
+            <div
+              style={{
+                fontSize: 14,
+                fontWeight: 600,
+                color: isSel ? "#6d28d9" : "#111",
+                overflow: "hidden",
+                textOverflow: "ellipsis",
+                whiteSpace: "nowrap",
+              }}
+            >
+              {v.name}
+            </div>
+            {v.description && (
+              <div
+                style={{
+                  fontSize: 11,
+                  color: "#6b7280",
+                  overflow: "hidden",
+                  textOverflow: "ellipsis",
+                  whiteSpace: "nowrap",
+                }}
+              >
+                {v.description}
+              </div>
+            )}
+          </div>
+          {v.preview_url && (
+            <button
+              type="button"
+              onClick={(e) => {
+                e.stopPropagation()
+                handlePreview(v.id, v.preview_url)
+              }}
+              style={{
+                width: 28,
+                height: 28,
+                borderRadius: "50%",
+                border: "none",
+                background: isPlaying ? "#ef4444" : "#7c3aed",
+                color: "#fff",
+                cursor: "pointer",
+                fontSize: 11,
+                display: "flex",
+                alignItems: "center",
+                justifyContent: "center",
+              }}
+            >
+              <SoundOutlined />
+            </button>
+          )}
+        </div>
+        {v.tags && v.tags.length > 0 && (
+          <div style={{ display: "flex", gap: 4, flexWrap: "wrap" }}>
+            {v.tags.slice(0, 3).map((tg) => (
+              <span
+                key={tg}
+                style={{
+                  fontSize: 10,
+                  padding: "1px 6px",
+                  borderRadius: 8,
+                  background: "#f3f4f6",
+                  color: "#6b7280",
+                }}
+              >
+                {tg}
+              </span>
+            ))}
+          </div>
+        )}
+      </div>
+    )
+  }
+
+  /* 合成中 loading 覆盖层 */
+  const renderSynthOverlay = () => {
+    if (status !== "synthesizing" && status !== "saving") return null
+    return (
+      <div
+        style={{
+          position: "absolute",
+          inset: 0,
+          background: "rgba(255,255,255,0.92)",
+          zIndex: 10,
+          display: "flex",
+          flexDirection: "column",
+          alignItems: "center",
+          justifyContent: "center",
+          gap: 12,
+          borderRadius: 8,
+        }}
+      >
+        <Spin size="large" style={{ color: "#7c3aed" }} />
+        <div style={{ fontSize: 16, fontWeight: 600, color: "#6d28d9" }}>
+          {status === "synthesizing" ? "正在合成配音..." : "正在保存到配音库..."}
+        </div>
+        <div style={{ fontSize: 12, color: "#6b7280" }}>请稍候，通常需要 10-30 秒</div>
+      </div>
+    )
+  }
+
+  return (
+    <Modal
+      title="🎙️ 合成配音"
+      open={open}
+      onCancel={status === "synthesizing" || status === "saving" ? undefined : onCancel}
+      cancelText="取消"
+      okText="🎧 合成配音"
+      okButtonProps={{
+        disabled: !selectedVoiceId || status === "synthesizing" || status === "saving",
+        style: { background: "#7c3aed" },
+      }}
+      onOk={handleSynthesize}
+      width={680}
+      destroyOnClose
+      confirmLoading={status === "synthesizing" || status === "saving"}
+    >
+      <div style={{ position: "relative" }}>
+        {error && (
+          <div
+            style={{
+              padding: "10px 12px",
+              background: "#fef2f2",
+              border: "1px solid #fecaca",
+              color: "#b91c1c",
+              borderRadius: 6,
+              fontSize: 13,
+              marginBottom: 12,
+            }}
+          >
+            {error}
+          </div>
+        )}
+
+        <div
+          style={{
+            fontSize: 12,
+            color: "#6b7280",
+            marginBottom: 12,
+            padding: "8px 12px",
+            background: "#f9fafb",
+            borderRadius: 6,
+          }}
+        >
+          将根据文案《{scriptTitle?.slice(0, 30) || "所选文案"}》合成配音，文本长度：
+          {textToSynth.length} 字
+        </div>
+
+        <Tabs
+          activeKey={activeTab}
+          onChange={(k) => {
+            setActiveTab(k as "preset" | "clone")
+            setSelectedVoiceId("")
+          }}
+          items={[
+            {
+              key: "preset",
+              label: "✨ 系统音色",
+              children: (
+                <div
+                  style={{
+                    display: "grid",
+                    gridTemplateColumns: "1fr 1fr",
+                    gap: 10,
+                    maxHeight: 420,
+                    overflowY: "auto",
+                    paddingRight: 4,
+                  }}
+                >
+                  {presetVoices.length === 0 ? (
+                    <div
+                      style={{
+                        gridColumn: "1/-1",
+                        textAlign: "center",
+                        padding: 30,
+                        color: "#9ca3af",
+                      }}
+                    >
+                      正在加载系统音色...
+                    </div>
+                  ) : (
+                    presetVoices.map((v) =>
+                      renderVoiceCard({
+                        id: v.voice_id,
+                        name: v.name,
+                        description: v.description,
+                        gender: v.gender,
+                        tags: v.tags,
+                        preview_url: v.preview_url,
+                      }),
+                    )
+                  )}
+                </div>
+              ),
+            },
+            {
+              key: "clone",
+              label: "🎙️ 我的克隆音色",
+              children: (
+                <div
+                  style={{
+                    display: "grid",
+                    gridTemplateColumns: "1fr 1fr",
+                    gap: 10,
+                    maxHeight: 420,
+                    overflowY: "auto",
+                    paddingRight: 4,
+                  }}
+                >
+                  {cloneVoices.length === 0 ? (
+                    <div
+                      style={{
+                        gridColumn: "1/-1",
+                        textAlign: "center",
+                        padding: 30,
+                        color: "#9ca3af",
+                      }}
+                    >
+                      暂无就绪的克隆音色，请先在配音库完成音色克隆
+                    </div>
+                  ) : (
+                    cloneVoices.map((v) =>
+                      renderVoiceCard({
+                        id: v.id,
+                        name: v.name,
+                        description: v.description,
+                        gender: "neutral",
+                        tags: ["克隆"],
+                        preview_url: v.sample_url || null,
+                      }),
+                    )
+                  )}
+                </div>
+              ),
+            },
+          ]}
+        />
+        {renderSynthOverlay()}
+      </div>
+    </Modal>
+  )
+}
+
+export default TtsVoiceModal

apps/web/src/pages/generate/components/VoiceSelectModal.tsx

@@ -0,0 +1,241 @@
+/**
+ * 随机混剪 — 配音选择弹窗（#1970）
+ * 内容复用 Step5VoiceSelect 的配音库音频卡片（图标+文件名+时长/大小+▶试听），
+ * 无 TTS / 克隆音色入口；确认后进入 Step2。
+ */
+import React from "react"
+import { Modal } from "antd"
+import { AudioOutlined } from "@ant-design/icons"
+import { useNavigate } from "react-router-dom"
+import { useQuery } from "@tanstack/react-query"
+import { useState, useRef, useCallback } from "react"
+import { getAssetsByKind } from "@/api/assets"
+import type { AssetItem } from "@/api/assets"
+
+interface VoiceSelectModalProps {
+  open: boolean
+  selectedVoice: string
+  onCancel: () => void
+  onConfirm: (voiceAssetId: string) => void
+}
+
+const getDuration = (item: AssetItem): number =>
+  item.duration ?? (item.metadata?.duration as number) ?? 0
+const getFileSize = (item: AssetItem): number =>
+  item.file_size ?? (item.metadata?.file_size as number) ?? 0
+const isAiVoice = (item: AssetItem): boolean => {
+  const d = getDuration(item)
+  const s = getFileSize(item)
+  return (!d || d <= 0) && (!s || s <= 0)
+}
+const fmtDur = (s?: number): string => {
+  if (!s || s <= 0) return "时长未知"
+  return `${s.toFixed(1)}秒`
+}
+const fmtSize = (b?: number): string => {
+  if (!b || b <= 0) return "未知"
+  if (b < 1024) return `${b} B`
+  if (b < 1024 * 1024) return `${(b / 1024).toFixed(1)} KB`
+  if (b < 1024 * 1024 * 1024) return `${(b / (1024 * 1024)).toFixed(1)} MB`
+  return `${(b / (1024 * 1024 * 1024)).toFixed(1)} GB`
+}
+
+const VoiceSelectModal: React.FC<VoiceSelectModalProps> = ({
+  open,
+  selectedVoice,
+  onCancel,
+  onConfirm,
+}) => {
+  const navigate = useNavigate()
+  const [innerSelected, setInnerSelected] = React.useState(selectedVoice)
+  const [playingId, setPlayingId] = useState<string | null>(null)
+  const audioRef = useRef<HTMLAudioElement | null>(null)
+
+  React.useEffect(() => {
+    if (open) setInnerSelected(selectedVoice)
+  }, [open, selectedVoice])
+
+  const { data: materials = [], isLoading } = useQuery({
+    queryKey: ["assets", "voice", "modal"],
+    queryFn: () => getAssetsByKind("voice", { limit: 50 }),
+    enabled: open,
+  })
+
+  const togglePlay = useCallback(
+    (item: AssetItem) => {
+      if (playingId === item.id && audioRef.current) {
+        audioRef.current.pause()
+        setPlayingId(null)
+        return
+      }
+      if (audioRef.current) audioRef.current.pause()
+      if (!item.file_url) return
+      const audio = new Audio(item.file_url)
+      audioRef.current = audio
+      setPlayingId(item.id)
+      audio.onended = () => {
+        setPlayingId(null)
+        audioRef.current = null
+      }
+      audio.play().catch(() => {
+        setPlayingId(null)
+        audioRef.current = null
+      })
+    },
+    [playingId],
+  )
+
+  const handleGoUpload = () => navigate("/app/voices?tab=material&upload=1")
+
+  const handleConfirm = () => {
+    if (!innerSelected) return
+    onConfirm(innerSelected)
+  }
+
+  return (
+    <Modal
+      title="🎙️ 选择配音"
+      open={open}
+      onCancel={onCancel}
+      onOk={handleConfirm}
+      okText="确认选择"
+      cancelText="取消"
+      okButtonProps={{ disabled: !innerSelected, style: { background: "#7c3aed" } }}
+      width={720}
+      destroyOnClose
+    >
+      <p style={{ color: "#666", fontSize: 13, marginBottom: 12 }}>
+        从配音库中选择已上传的音频素材，点击 ▶ 可试听
+      </p>
+      {isLoading ? (
+        <div style={{ textAlign: "center", padding: "40px 0", color: "#999" }}>加载中...</div>
+      ) : materials.length === 0 ? (
+        <div style={{ textAlign: "center", padding: "40px 0", color: "#999" }}>
+          <AudioOutlined style={{ fontSize: 48, color: "#d9d9d9", marginBottom: 12 }} />
+          <p style={{ marginBottom: 12 }}>暂无配音素材</p>
+          <button
+            type="button"
+            onClick={handleGoUpload}
+            style={{
+              padding: "8px 20px",
+              background: "#7c3aed",
+              color: "#fff",
+              border: "none",
+              borderRadius: 6,
+              cursor: "pointer",
+            }}
+          >
+            去配音库上传
+          </button>
+        </div>
+      ) : (
+        <div
+          style={{
+            display: "grid",
+            gridTemplateColumns: "repeat(auto-fill, minmax(200px, 1fr))",
+            gap: 12,
+            maxHeight: 460,
+            overflowY: "auto",
+            paddingRight: 4,
+          }}
+        >
+          {materials.map((item) => {
+            const isSel = innerSelected === item.id
+            const isPlaying = playingId === item.id
+            return (
+              <div
+                key={item.id}
+                onClick={() => setInnerSelected(item.id)}
+                style={{
+                  padding: 14,
+                  borderRadius: 8,
+                  border: isSel ? "2px solid #7c3aed" : "1px solid #e8e8e8",
+                  background: isSel ? "#ede9fe" : "#fff",
+                  cursor: "pointer",
+                  transition: "all 0.2s",
+                }}
+              >
+                <div
+                  style={{
+                    display: "flex",
+                    alignItems: "center",
+                    justifyContent: "space-between",
+                  }}
+                >
+                  <div
+                    style={{
+                      width: 36,
+                      height: 36,
+                      borderRadius: 8,
+                      background: isSel
+                        ? "linear-gradient(135deg,#7c3aed,#a78bfa)"
+                        : "linear-gradient(135deg,#f0f0f0,#e8e8e8)",
+                      display: "flex",
+                      alignItems: "center",
+                      justifyContent: "center",
+                    }}
+                  >
+                    <AudioOutlined style={{ color: isSel ? "#fff" : "#666" }} />
+                  </div>
+                  {item.file_url && (
+                    <button
+                      type="button"
+                      onClick={(e) => {
+                        e.stopPropagation()
+                        togglePlay(item)
+                      }}
+                      style={{
+                        width: 30,
+                        height: 30,
+                        borderRadius: "50%",
+                        border: "none",
+                        background: isPlaying ? "#ef4444" : "#7c3aed",
+                        color: "#fff",
+                        cursor: "pointer",
+                        fontSize: 12,
+                      }}
+                    >
+                      ▶
+                    </button>
+                  )}
+                </div>
+                <div
+                  style={{
+                    fontSize: 13,
+                    fontWeight: 500,
+                    marginTop: 8,
+                    overflow: "hidden",
+                    textOverflow: "ellipsis",
+                    whiteSpace: "nowrap",
+                    color: isSel ? "#6d28d9" : "#333",
+                  }}
+                  title={item.name}
+                >
+                  {item.name}
+                </div>
+                <div
+                  style={{
+                    display: "flex",
+                    justifyContent: "space-between",
+                    fontSize: 11,
+                    color: "#999",
+                    marginTop: 4,
+                  }}
+                >
+                  {isAiVoice(item) ? (
+                    <span style={{ color: "#7c3aed", fontWeight: 500 }}>AI 音色</span>
+                  ) : (
+                    <span>{fmtDur(getDuration(item))}</span>
+                  )}
+                  <span>{isAiVoice(item) ? "按文本合成" : fmtSize(getFileSize(item))}</span>
+                </div>
+              </div>
+            )
+          })}
+        </div>
+      )}
+    </Modal>
+  )
+}
+
+export default VoiceSelectModal

apps/web/src/pages/generate/constants.ts

@@ -27,10 +27,10 @@ export const VOICE_GENDER_ICON: Record<string, string> = {
   neutral: "✨",
 }
 
-/* ── 步骤定义（5步，#1899 简化：删除选模板步骤） ── */
+/* ── 步骤定义（5步，#1970 流程重构：选择模式 → 素材 → 标题 → 确认 → 封面） ── */
 export const STEPS = [
-  { key: 1, label: "选择素材" },
-  { key: 2, label: "选择配音" },
+  { key: 1, label: "选择模式" },
+  { key: 2, label: "选择素材" },
   { key: 3, label: "选择标题" },
   { key: 4, label: "确认生成" },
   { key: 5, label: "选择封面" },

apps/web/src/pages/generate/hooks/generate-video/buildPayload.ts

@@ -1,7 +1,9 @@
 import type { UseGenerateVideoProps } from "./types"
 
 /**
- * 生成前置校验
+ * 生成前置校验（#1970 适配新流程）
+ * - 随机混剪：需选配音（selectedVoice，配音库音频）
+ * - 叙事剪辑：需选文案 + TTS 音色
  * 返回错误信息，通过则返回 null
  */
 export const validateGenerateInputs = (props: UseGenerateVideoProps): string | null => {
@@ -12,19 +14,30 @@ export const validateGenerateInputs = (props: UseGenerateVideoProps): string | n
     smartSelectedIds,
     voiceMode,
     selectedClonedVoice,
+    editMode = "random",
+    selectedScript,
+    ttsVoiceId,
+    selectedVoice,
   } = props
 
-  // AI 自动选择模式下，标题可以为空（后端会自行生成）
   if (!titleSettings.aiAutoSelect && !titleSettings.title?.trim()) {
     return "请先选择或输入标题"
   }
-  // 无论手动还是自动模式，都必须有素材
   const materialIds = materialMode === "auto" ? smartSelectedIds || [] : selectedMaterials || []
   if (materialIds.length === 0) {
     return materialMode === "auto" ? "AI 未匹配到素材，请手动选择素材后重试" : "请至少选择一个素材"
   }
-  if (voiceMode === "clone" && !selectedClonedVoice) {
-    return "请先选择一个克隆音色"
+  if (editMode === "narrative") {
+    if (!selectedScript?.id) return "请先选择文案"
+    if (!ttsVoiceId) return "请先合成配音"
+  } else {
+    // 随机混剪：配音库音频
+    if (!selectedVoice && voiceMode !== "clone") {
+      return "请先选择配音"
+    }
+    if (voiceMode === "clone" && !selectedClonedVoice) {
+      return "请先选择一个克隆音色"
+    }
   }
   return null
 }

apps/web/src/pages/generate/hooks/generate-video/types.ts

@@ -13,7 +13,19 @@ export interface UseGenerateVideoProps {
   selectedVoice: string
   selectedClonedVoice: string
   coverSettings: CoverConfig
-  videoRatio: string
+  videoRatio: "9:16" | "16:9" | string
+  /** #1970 剪辑模式 */
+  editMode?: "random" | "narrative"
+  /** 叙事模式下选中的文案 */
+  selectedScript?: { id: string; title?: string; content?: string } | null
+  /** TTS 音色 ID（叙事模式） */
+  ttsVoiceId?: string
+  /** TTS 音色来源 */
+  ttsVoiceSource?: "preset" | "clone"
+  /** 合成后保存到配音库的 asset id / job id（叙事模式） */
+  ttsVoiceAssetId?: string
+  /** 智能降重开关（默认 true） */
+  dedupEnabled?: boolean
   style: string
   duration: number
   autoSubtitles: boolean

apps/web/src/pages/generate/hooks/useGenerateFormState/index.ts

@@ -12,6 +12,7 @@ import { getEditingTemplates } from "@/api/editing-planner"
 import type { EditPlanClip } from "@/api/template-editor"
 import type { CoverConfig } from "../../types/cover"
 import type { PresetVoiceItem } from "@/api/voices"
+import type { ScriptItem } from "@/api/scripts"
 import { DEFAULT_COVER_SETTINGS, DEFAULT_CLIP_COUNT } from "../../constants"
 import type { TitleSettings } from "../../types"
 import { usePlanConfigLoader } from "./usePlanConfigLoader"
@@ -82,8 +83,28 @@ export interface GenerateFormState {
   cloneModalOpen: boolean
   setCloneModalOpen: (open: boolean) => void
 
+  /* ── 剪辑模式（#1970 流程重构）── */
+  editMode: "random" | "narrative"
+  setEditMode: (mode: "random" | "narrative") => void
+  /** 叙事模式下选中的文案 */
+  selectedScript: ScriptItem | null
+  setSelectedScript: (s: ScriptItem | null) => void
+  /** TTS 音色 ID */
+  ttsVoiceId: string
+  setTtsVoiceId: (id: string) => void
+  /** TTS 音色来源：preset 系统 / clone 克隆 */
+  ttsVoiceSource: "preset" | "clone"
+  setTtsVoiceSource: (src: "preset" | "clone") => void
+  /** 合成后配音库 asset id（叙事模式保存到库后获得；随机模式 = selectedVoice） */
+  ttsVoiceAssetId: string
+  setTtsVoiceAssetId: (id: string) => void
+  /** 智能降重开关（默认 true） */
+  dedupEnabled: boolean
+  setDedupEnabled: (v: boolean) => void
+
   /* 高级设置 */
-  videoRatio: string
+  videoRatio: "9:16" | "16:9" | string
+  setVideoRatio: (r: "9:16" | "16:9") => void
   duration: number
   style: string
   autoSubtitles: boolean
@@ -201,13 +222,21 @@ export const useGenerateFormState = (): GenerateFormState => {
   /* ── 克隆声音弹窗 ── */
   const [cloneModalOpen, setCloneModalOpen] = useState(false)
 
-  /* ── 高级设置（隐藏但保留） ── */
-  const [videoRatio] = useState("9:16")
+  /* ── 高级设置 ── */
+  const [videoRatio, setVideoRatio] = useState<"9:16" | "16:9">("9:16")
   const [duration] = useState(30)
   const [style] = useState("business")
   const [autoSubtitles] = useState(true)
   const [bgm] = useState(true)
 
+  /* ── 剪辑模式状态（#1970） ── */
+  const [editMode, setEditMode] = useState<"random" | "narrative">("random")
+  const [selectedScript, setSelectedScript] = useState<ScriptItem | null>(null)
+  const [ttsVoiceId, setTtsVoiceId] = useState<string>("")
+  const [ttsVoiceSource, setTtsVoiceSource] = useState<"preset" | "clone">("preset")
+  const [ttsVoiceAssetId, setTtsVoiceAssetId] = useState<string>("")
+  const [dedupEnabled, setDedupEnabled] = useState<boolean>(true)
+
   /* ── 预览任务 ID ── */
   const previewStorageKey = editPlanId
     ? `preview_task_id_${editPlanId}`
@@ -274,9 +303,22 @@ export const useGenerateFormState = (): GenerateFormState => {
     selectedClonedVoice,
     setSelectedClonedVoice,
     presetVoices,
+    editMode,
+    setEditMode,
+    selectedScript,
+    setSelectedScript,
+    ttsVoiceId,
+    setTtsVoiceId,
+    ttsVoiceSource,
+    setTtsVoiceSource,
+    ttsVoiceAssetId,
+    setTtsVoiceAssetId,
+    dedupEnabled,
+    setDedupEnabled,
     cloneModalOpen,
     setCloneModalOpen,
     videoRatio,
+    setVideoRatio,
     duration,
     style,
     autoSubtitles,

apps/web/src/pages/generate/hooks/useGenerateVideo.ts

@@ -123,6 +123,8 @@ export function useGenerateVideo(props: UseGenerateVideoProps) {
       const { width: outputWidth, height: outputHeight } = calculateResolution(
         props.videoRatio || "9:16",
       )
+      const editMode = props.editMode ?? "random"
+      const dedupEnabled = props.dedupEnabled !== false
 
       const assetIds =
         props.materialMode === "auto" ? props.smartSelectedIds : props.selectedMaterials
@@ -151,10 +153,13 @@ export function useGenerateVideo(props: UseGenerateVideoProps) {
 
       const coverUrl = props.coverSettings?.thumbnail_url || props.coverSettings?.upload_url || ""
 
+      // #1970：叙事模式下 ttsVoiceId 作为配音 id；随机模式用 selectedVoice
       const voiceLibraryId =
-        props.voiceMode === "clone"
-          ? props.selectedClonedVoice || props.selectedVoice || ""
-          : props.selectedVoice || ""
+        editMode === "narrative"
+          ? props.ttsVoiceId || ""
+          : props.voiceMode === "clone"
+            ? props.selectedClonedVoice || props.selectedVoice || ""
+            : props.selectedVoice || ""
 
       /* ── 批量变体数组（长度1=共用，长度=count=独立，空=回退单值） ── */
       const indexes =
@@ -197,6 +202,15 @@ export function useGenerateVideo(props: UseGenerateVideoProps) {
           custom_title: props.titleSettings?.title || "",
           duration: props.duration || undefined,
           video_ratio: props.videoRatio,
+          assembly_mode: editMode,
+          ...(editMode === "narrative" && props.selectedScript?.id
+            ? {
+                script_id: props.selectedScript.id,
+                tts_voice_id: props.ttsVoiceId || undefined,
+                tts_voice_source: props.ttsVoiceSource || undefined,
+              }
+            : {}),
+          dedup_enabled: dedupEnabled,
           voice_library_id: voiceLibraryId,
           ...(props.selectedVoice && !voiceLibraryId ? { voice_ids: [props.selectedVoice] } : {}),
           bgm_config: {

apps/web/src/pages/generate/hooks/useStep4Title/index.ts

@@ -1,6 +1,7 @@
 import { useEffect, useRef } from "react"
 import { useQuery } from "@tanstack/react-query"
-import { getTitles } from "@/api/titles"
+// #1894: 标题候选从文案库 scripts[].title 获取，不再调用废弃的 /api/titles
+import { getScripts } from "@/api/scripts"
 import type { TitleSettings } from "../../types"
 import { useAiTitleGenerator } from "./useAiTitleGenerator"
 import { useTitleStyleUpdaters } from "./useTitleStyleUpdaters"
@@ -22,10 +23,14 @@ export function useStep4Title({
   onTitleSettingsChange,
   selectedTemplate,
 }: UseStep4TitleProps) {
-  // 标题库数据
+  // 标题候选（#1894：统一从文案库取 scripts[].title，去重）
   const { data: userTitles = [] } = useQuery({
-    queryKey: ["titles"],
-    queryFn: () => getTitles(),
+    queryKey: ["scripts", "titles-source"],
+    queryFn: async () => {
+      const res = await getScripts({ page_size: 200 })
+      const items = Array.isArray(res) ? res : (res.items ?? [])
+      return items.map((s) => ({ content: (s.title || "").trim() })).filter((s) => !!s.content)
+    },
     staleTime: 30_000,
   })
 

apps/web/src/pages/generate/hooks/useStepNavigation.ts

@@ -1,17 +1,22 @@
 /**
- * GeneratePage 步骤导航（#1899 简化为 5 步，单视频与批量一致）
- * 步骤：素材(1) → 配音(2) → 标题(3) → 确认生成(4) → 封面(5)
+ * GeneratePage 步骤导航（#1970 流程重构）
+ * 步骤：选择模式(1) → 选择素材(2) → 选择标题(3) → 确认生成(4) → 选择封面(5)
  *
- * - 步骤3底部按钮是「确认生成视频」（由 GenerateStepActions 调 onConfirmGenerate），
- *   创建成功后跳转步骤4；本 hook 的 goNext 只负责 1→2→3 和 4→5 的「下一步」。
- * - 步骤4（确认生成进度页）：渲染全部完成（generated）后「下一步」解锁进封面。
+ * - 步骤1（选择模式）：下一步分支由外层弹窗处理（VoiceSelectModal / ScriptSelectModal），
+ *   本 hook 的 goNext 仅在未选模式时拦截；外层 Modal onConfirm 里主动 setCurrentStep(2)。
+ * - 步骤2（选择素材）：弹数量选择弹窗（PreviewCountModal），确认后跳步骤3。
+ * - 步骤3 底部按钮是「确认生成视频」（由 GenerateStepActions 调 onConfirmGenerate），
+ *   创建成功后跳步骤4；本 hook 的 goNext 只负责 2→3 和 4→5 的「下一步」。
+ * - 步骤4（确认生成进度页）：全部渲染完成后「下一步」解锁进封面。
  */
 import { message } from "antd"
 import type { TitleSettings } from "../types"
+import type { EditMode } from "../components/Step1EditMode"
 
 export interface UseStepNavigationOptions {
   currentStep: number
   setCurrentStep: (step: number | ((prev: number) => number)) => void
+  editMode: EditMode
   materialMode: "manual" | "auto"
   selectedMaterials: string[]
   smartSelectedIds: string[]
@@ -20,6 +25,8 @@ export interface UseStepNavigationOptions {
   generated: boolean
   /** 点素材下一步时弹出数量选择弹窗 */
   onOpenCountModal: () => void
+  /** 步骤1下一步：根据 editMode 打开对应弹窗（随机→配音 / 叙事→文案） */
+  onOpenStep1Modal: () => void
 }
 
 export interface UseStepNavigationReturn {
@@ -36,22 +43,29 @@ export const useStepNavigation = (options: UseStepNavigationOptions): UseStepNav
     smartSelectedIds,
     generated,
     onOpenCountModal,
+    onOpenStep1Modal,
   } = options
 
   const goNext = () => {
     if (currentStep === 1) {
-      // 选完素材弹数量选择弹窗
+      // 步骤1：先校验素材/配音等由弹窗负责，goNext 只负责触发弹窗
+      onOpenStep1Modal()
+      return
+    }
+    if (currentStep === 2) {
+      // 素材校验
+      if (materialMode === "manual" && selectedMaterials.length === 0) {
+        message.warning("请至少选择一个素材")
+        return
+      }
+      if (materialMode === "auto" && smartSelectedIds.length === 0) {
+        message.warning("请先进行智能匹配并选择素材")
+        return
+      }
+      // 弹数量选择弹窗
       onOpenCountModal()
       return
     }
-    if (currentStep === 1 && materialMode === "manual" && selectedMaterials.length === 0) {
-      message.warning("请至少选择一个素材")
-      return
-    }
-    if (currentStep === 1 && materialMode === "auto" && smartSelectedIds.length === 0) {
-      message.warning("请先进行智能匹配并选择素材")
-      return
-    }
     // 步骤4（确认生成）：全部渲染完成后才能下一步进封面
     if (currentStep === 4) {
       if (!generated) {

apps/web/src/pages/scripts/ScriptLibrary.tsx

@@ -6,7 +6,7 @@
  *   操作：编辑 / 删除 / 复制 / 使用（跳创作页预填）
  * - 新建/编辑弹窗：标题（原"名称"）、正文（含 AI 改写）、分类、标签
  * - #1893/#1894 AI 能力：
- *   - 顶部「🎬 从抖音提取」按钮 → 输入抖音链接 → ASR 提取文案 → 自动填充到新建弹窗
+ *   - 顶部「🎬 从抖音提取」按钮 → 粘贴分享文案/链接（后端自动提取URL） → ASR 提取文案 → 自动填充到新建弹窗
  *   - 正文下方「✨ AI 改写」按钮 → 点击直接执行（美化 loading spinner + "正在改写..."），
  *     成功自动替换正文并 toast「改写成功」1s 自动关闭；失败 toast 错误
  *   - 标题旁「✨ AI 生成标题」按钮 → 候选列表一键填入
@@ -272,20 +272,16 @@ const ScriptLibrary: React.FC = () => {
     setDouyinModalOpen(true)
   }
 
-  /** 执行抖音提取，成功后打开新建弹窗并预填 content */
+  /** #1894：执行抖音提取。前端不再做 URL 前缀校验，直接把用户粘贴的原文（含分享文案+链接）交给后端 _extract_url_from_text 自动提取。后端 400 错误（未找到链接/非抖音域名等）直接透传给用户。 */
   const handleDouyinExtract = async () => {
-    const url = douyinUrl.trim()
-    if (!url) {
-      message.warning("请粘贴抖音视频链接")
-      return
-    }
-    if (!/^https?:\/\//i.test(url)) {
-      message.warning("请输入以 http(s):// 开头的完整链接")
+    const raw = douyinUrl.trim()
+    if (!raw) {
+      message.warning("请粘贴抖音视频链接或分享文案")
       return
     }
     setDouyinLoading(true)
     try {
-      const res = await extractScriptFromDouyin({ url })
+      const res = await extractScriptFromDouyin({ url: raw })
       message.success(`提取成功${res.duration_seconds ? `（时长 ${res.duration_seconds}s）` : ""}`)
       setDouyinModalOpen(false)
       setDouyinUrl("")
@@ -300,6 +296,7 @@ const ScriptLibrary: React.FC = () => {
       })
       setModalOpen(true)
     } catch (err) {
+      // 后端 400（未找到有效链接/仅支持抖音域名等）直接透传错误信息
       message.error(extractErrMsg(err, "抖音文案提取失败"))
     } finally {
       setDouyinLoading(false)
@@ -637,11 +634,12 @@ const ScriptLibrary: React.FC = () => {
         destroyOnClose
       >
         <Paragraph type="secondary" style={{ marginBottom: 12, fontSize: 13 }}>
-          粘贴抖音分享链接（支持 v.douyin.com 短链和 www.douyin.com/video/ 长链）， AI
-          将自动下载音频并识别文案。首次识别可能需要 5-15 秒。
+          粘贴抖音分享文案或链接即可，系统会自动从文本中识别链接（支持 v.douyin.com 短链、
+          www.douyin.com/video/ 长链，以及 App「复制链接」带的分享文案）。AI
+          将自动下载音频并识别文案， 首次识别可能需要 5-15 秒。
         </Paragraph>
         <Input.TextArea
-          placeholder="例如：https://v.douyin.com/xxxxx/ 或 https://www.douyin.com/video/xxxxx"
+          placeholder="直接粘贴 App「复制链接」的全部内容即可，例如：8.88 复制打开抖音... https://v.douyin.com/xxxxx/"
           value={douyinUrl}
           onChange={(e) => setDouyinUrl(e.target.value)}
           rows={2}
@@ -651,7 +649,7 @@ const ScriptLibrary: React.FC = () => {
         {douyinLoading && (
           <div className="xx-ai-loading-hint">
             <Spin size="small" style={{ marginRight: 8 }} />
-            正在下载视频并识别文案，可能需要数秒，请稍候…
+            提取中，正在下载视频并识别文案，可能需要数秒，请稍候…
           </div>
         )}
       </Modal>

apps/web/src/test/api/titles.test.ts

@@ -1,112 +0,0 @@
-import { describe, expect, it, vi, beforeEach } from "vitest"
-import { getTitles, createTitle, updateTitle, deleteTitle, batchImportTitles } from "@/api/titles"
-
-const mockGet = vi.fn()
-const mockPost = vi.fn()
-const mockPut = vi.fn()
-const mockDelete = vi.fn()
-const mockPatch = vi.fn()
-
-vi.mock("@/api/client", () => ({
-  default: {
-    get: (...args: unknown[]) => mockGet(...args),
-    post: (...args: unknown[]) => mockPost(...args),
-    put: (...args: unknown[]) => mockPut(...args),
-    delete: (...args: unknown[]) => mockDelete(...args),
-    patch: (...args: unknown[]) => mockPatch(...args),
-  },
-}))
-
-vi.mock("antd", () => ({ message: { error: vi.fn(), success: vi.fn() } }))
-vi.mock("@/store/authStore", () => ({ useAuthStore: { getState: vi.fn(() => ({})) } }))
-
-describe("titles API", () => {
-  beforeEach(() => {
-    vi.clearAllMocks()
-    mockGet.mockResolvedValue({ data: { success: true, items: [] } })
-    mockPost.mockResolvedValue({ data: { success: true, items: [] } })
-    mockPut.mockResolvedValue({ data: { success: true, items: [] } })
-    mockDelete.mockResolvedValue({ data: { success: true, items: [] } })
-    mockPatch.mockResolvedValue({ data: { success: true, items: [] } })
-  })
-
-  describe("getTitles", () => {
-    it("should resolve successfully", async () => {
-      await expect(getTitles()).resolves.not.toThrow()
-    })
-
-    it("should reject on API error", async () => {
-      mockGet.mockRejectedValue(new Error("Network error"))
-      mockPost.mockRejectedValue(new Error("Network error"))
-      mockPut.mockRejectedValue(new Error("Network error"))
-      mockDelete.mockRejectedValue(new Error("Network error"))
-      mockPatch.mockRejectedValue(new Error("Network error"))
-
-      await expect(getTitles()).rejects.toThrow()
-    })
-  })
-
-  describe("createTitle", () => {
-    it("should resolve successfully", async () => {
-      await expect(createTitle({ title: "测试标题", content: "测试内容" })).resolves.not.toThrow()
-    })
-
-    it("should reject on API error", async () => {
-      mockGet.mockRejectedValue(new Error("Network error"))
-      mockPost.mockRejectedValue(new Error("Network error"))
-      mockPut.mockRejectedValue(new Error("Network error"))
-      mockDelete.mockRejectedValue(new Error("Network error"))
-      mockPatch.mockRejectedValue(new Error("Network error"))
-
-      await expect(createTitle({ name: "test-item" })).rejects.toThrow()
-    })
-  })
-
-  describe("updateTitle", () => {
-    it("should resolve successfully", async () => {
-      await expect(updateTitle("test-titleId", { title: "新标题" })).resolves.not.toThrow()
-    })
-
-    it("should reject on API error", async () => {
-      mockGet.mockRejectedValue(new Error("Network error"))
-      mockPost.mockRejectedValue(new Error("Network error"))
-      mockPut.mockRejectedValue(new Error("Network error"))
-      mockDelete.mockRejectedValue(new Error("Network error"))
-      mockPatch.mockRejectedValue(new Error("Network error"))
-
-      await expect(updateTitle("test-titleId")).rejects.toThrow()
-    })
-  })
-
-  describe("deleteTitle", () => {
-    it("should resolve successfully", async () => {
-      await expect(deleteTitle("test-titleId")).resolves.not.toThrow()
-    })
-
-    it("should reject on API error", async () => {
-      mockGet.mockRejectedValue(new Error("Network error"))
-      mockPost.mockRejectedValue(new Error("Network error"))
-      mockPut.mockRejectedValue(new Error("Network error"))
-      mockDelete.mockRejectedValue(new Error("Network error"))
-      mockPatch.mockRejectedValue(new Error("Network error"))
-
-      await expect(deleteTitle("test-titleId")).rejects.toThrow()
-    })
-  })
-
-  describe("batchImportTitles", () => {
-    it("should resolve successfully", async () => {
-      await expect(batchImportTitles("test-titles")).resolves.not.toThrow()
-    })
-
-    it("should reject on API error", async () => {
-      mockGet.mockRejectedValue(new Error("Network error"))
-      mockPost.mockRejectedValue(new Error("Network error"))
-      mockPut.mockRejectedValue(new Error("Network error"))
-      mockDelete.mockRejectedValue(new Error("Network error"))
-      mockPatch.mockRejectedValue(new Error("Network error"))
-
-      await expect(batchImportTitles("test-titles")).rejects.toThrow()
-    })
-  })
-})

apps/web/src/test/config/navigation.test.ts

@@ -39,7 +39,6 @@ describe("navigation config", () => {
       expect(keys).toContain("dashboard")
       expect(keys).toContain("assets")
       expect(keys).toContain("voices")
-      expect(keys).toContain("titles")
     })
   })
 

apps/web/src/test/pages/GeneratePage.test.tsx

@@ -215,8 +215,14 @@ vi.mock("@/api/editing-planner", () => ({
   MODE_LABELS: { pip: "画中画" },
 }))
 
-vi.mock("@/api/titles", () => ({
-  getTitles: vi.fn().mockResolvedValue({ items: [] }),
+// #1894: 标题数据源已切到 @/api/scripts，mock scripts 返回空数组作为默认
+vi.mock("@/api/scripts", () => ({
+  getScripts: vi.fn().mockResolvedValue({ items: [], total: 0, page: 1, page_size: 20 }),
+  aiRewriteScript: vi.fn(),
+  aiGenerateTitles: vi.fn(),
+  SCRIPTS_API_MOCK: false,
+  SCRIPT_CATEGORY_LABEL: {},
+  REWRITE_STYLE_OPTIONS: [],
 }))
 
 vi.mock("@/api/template-editor", () => ({

apps/web/src/test/pages/UpgradeSubscription.test.tsx

@@ -32,6 +32,7 @@ vi.mock("antd", () => ({
 
 vi.mock("@/api/subscription", () => ({
   getCurrentSubscription: vi.fn().mockResolvedValue({ plan: "free", status: "active" }),
+  getSubscriptionPlans: vi.fn().mockResolvedValue({ items: [{ plan_id: "free", name: "Free" }] }),
   changePlan: vi.fn().mockResolvedValue({ success: true }),
   toggleAutoRenew: vi.fn().mockResolvedValue({ success: true }),
   cancelSubscription: vi.fn().mockResolvedValue({ success: true }),

apps/worker/video_processing/micro_transform_pure.py

@@ -0,0 +1,176 @@
+"""智能降重微变换纯逻辑模块 — #1970 PR2.
+
+所有函数均为纯函数：不调用 FFmpeg、不读写文件，只负责按可复现种子
+生成每个片段 / 整片的微变换参数与 filter_complex 片段。
+
+6 个维度：
+1. hflip 水平翻转（每片段 50%，有字幕/文字的片段不翻转）
+2. 播放速度 0.97~1.03x（视频 setpts + 音频 atempo）
+3. 亮度 ±2%（eq=brightness）
+4. 对比度 ±2%（eq=contrast）
+5. 饱和度 ±2%（eq=saturation）
+6. BGM 起始偏移 2~8 秒（音频 atrim 起点）
+
+随机种子 = hash(task_id + video_index) % 10000，保证同一任务同一视频
+可复现；dedup_enabled=False 时不生成本模块任何输出。
+"""
+
+from __future__ import annotations
+
+import random
+from dataclasses import dataclass, field
+
+# ── 常量（与需求文档 §2 对齐）──────────────────────────────────────────────────
+
+SPEED_MIN = 0.97
+SPEED_MAX = 1.03
+COLOR_DELTA = 0.02
+HFLIP_PROBABILITY = 0.5
+BGM_OFFSET_MIN = 2.0
+BGM_OFFSET_MAX = 8.0
+SEED_MODULO = 10000
+
+
+def make_video_seed(task_id: str, video_index: int) -> int:
+    """生成视频级可复现种子：hash(task_id+video_index) % 10000。
+
+    用 sha256 而非内置 hash()：内置 hash 对字符串带进程级随机盐（PYTHONHASHSEED），
+    跨进程不可复现。结果映射到 0~9999。
+    """
+    import hashlib
+
+    raw = f"{task_id or ''}:{int(video_index)}"
+    digest = hashlib.sha256(raw.encode("utf-8")).hexdigest()
+    return int(digest[:8], 16) % SEED_MODULO
+
+
+@dataclass(slots=True)
+class ClipMicroTransform:
+    """单个片段的微变换参数。"""
+
+    clip_index: int
+    hflip: bool = False
+    speed: float = 1.0
+    brightness: float = 0.0
+    contrast: float = 1.0
+    saturation: float = 1.0
+    has_text: bool = False
+
+    def video_filter_suffix(self) -> str:
+        """返回追加在片段视频处理链上的 filter 后缀（无末尾标签）。
+
+        顺序：trim/setpts（已有）→ 调速 setpts → hflip → eq → format。
+        调速的 setpts 必须位于 trim 之后；hflip/eq 在缩放之后即可，
+        concat_engine 按「调速 → hflip → eq」顺序拼接到 scale/fps 之前的
+        trim 之后、scale 之后均可，这里只产出独立步骤、由引擎决定插入点。
+        """
+        parts: list[str] = []
+        # 速度：setpts=PTS/speed（speed>1 时画面加速，时间戳变小）
+        if abs(self.speed - 1.0) > 1e-4:
+            parts.append(f"setpts=PTS/{self.speed:.5f}")
+        # 水平翻转：有文字/字幕片段不翻转
+        if self.hflip and not self.has_text:
+            parts.append("hflip")
+        # 色彩微调：brightness 取值 -1~1（±0.02），contrast/saturation 围绕 1.0
+        if abs(self.brightness) > 1e-4 or abs(self.contrast - 1.0) > 1e-4 or abs(self.saturation - 1.0) > 1e-4:
+            parts.append(
+                f"eq=brightness={self.brightness:+.4f}:"
+                f"contrast={self.contrast:.4f}:saturation={self.saturation:.4f}"
+            )
+        return ",".join(parts)
+
+    def audio_filter_suffix(self) -> str:
+        """返回片段音频链上的调速 filter（atempo），无调速时返回空串。"""
+        if abs(self.speed - 1.0) <= 1e-4:
+            return ""
+        return f"atempo={self.speed:.5f}"
+
+
+@dataclass(slots=True)
+class VideoMicroTransformPlan:
+    """一个成片视频的全部微变换参数。"""
+
+    task_id: str
+    video_index: int
+    seed: int
+    clips: list[ClipMicroTransform] = field(default_factory=list)
+    bgm_start_offset: float = 0.0
+
+    def clip(self, index: int) -> ClipMicroTransform | None:
+        for c in self.clips:
+            if c.clip_index == index:
+                return c
+        return None
+
+
+def _draw_speed(rng: random.Random) -> float:
+    return round(rng.uniform(SPEED_MIN, SPEED_MAX), 5)
+
+
+def _draw_signed_delta(rng: random.Random) -> float:
+    return round(rng.uniform(-COLOR_DELTA, COLOR_DELTA), 4)
+
+
+def build_micro_transform_plan(
+    task_id: str,
+    video_index: int,
+    clip_count: int,
+    *,
+    clip_has_text: list[bool] | None = None,
+    enable_bgm_offset: bool = True,
+) -> VideoMicroTransformPlan:
+    """按可复现种子生成整片的微变换计划。
+
+    Args:
+        task_id: 生成任务 ID（种子输入）
+        video_index: 视频在批次中的序号（0 起）
+        clip_count: 片段数量
+        clip_has_text: 每个片段是否有字幕/文字轨道（True 的片段不翻转）；
+                       None 时按 P1 约定视为无可靠文字检测——保守起见 hflip 一律关闭
+        enable_bgm_offset: 是否生成 BGM 起始偏移（无 BGM 时调用方可忽略该值）
+
+    Returns:
+        VideoMicroTransformPlan
+    """
+    seed = make_video_seed(task_id, video_index)
+    rng = random.Random(seed)
+
+    # P1 字幕检测约定：无法判断片段是否有文字时，一律不翻转（宁可少一个维度也不误翻字幕）
+    safe_has_text = clip_has_text if clip_has_text is not None else [True] * max(clip_count, 0)
+
+    clips: list[ClipMicroTransform] = []
+    for i in range(max(clip_count, 0)):
+        has_text = bool(safe_has_text[i]) if i < len(safe_has_text) else True
+        do_hflip = (not has_text) and rng.random() < HFLIP_PROBABILITY
+        clips.append(
+            ClipMicroTransform(
+                clip_index=i,
+                hflip=do_hflip,
+                speed=_draw_speed(rng),
+                brightness=_draw_signed_delta(rng),
+                contrast=round(1.0 + _draw_signed_delta(rng), 4),
+                saturation=round(1.0 + _draw_signed_delta(rng), 4),
+                has_text=has_text,
+            )
+        )
+
+    bgm_offset = rng.uniform(BGM_OFFSET_MIN, BGM_OFFSET_MAX) if enable_bgm_offset else 0.0
+    return VideoMicroTransformPlan(
+        task_id=task_id,
+        video_index=video_index,
+        seed=seed,
+        clips=clips,
+        bgm_start_offset=round(bgm_offset, 3),
+    )
+
+
+def build_bgm_offset_trim(start_offset: float, bgm_duration: float) -> str:
+    """生成 BGM 起始偏移的 atrim 片段。
+
+    偏移超出 BGM 长度时回退为 0（从头播放），避免空输入。
+    返回的字符串形如 "atrim=start=3.200,"，可拼到 BGM filter chain 最前面；
+    无需偏移时返回空串。
+    """
+    if start_offset <= 0 or bgm_duration <= 0 or start_offset >= bgm_duration - 0.5:
+        return ""
+    return f"atrim=start={start_offset:.3f},"

apps/worker/video_processing/render_adapter.py

@@ -493,6 +493,41 @@ class RenderAdapter:
             logger.warning("ASR 服务初始化失败，自动字幕将不可用: %s", e)
             return None
 
+    def _resolve_clip_has_text(self, clips: list[Any]) -> list[bool] | None:
+        """#1970：按源视频片段顺序解析 atom_clip.ai_tags.has_text。
+
+        顺序与 UnifiedRenderService 的「非 audio 源片段」口径一致。
+        仅当 atom_clip 存在 ai_tags 字典且 has_text 显式为 False 时标记为
+        无文字（允许 hflip）；atom_clip_id 缺失、ai_tags 未生成、has_text 为
+        true/null/非布尔值时一律按有文字处理（保守不翻转）。
+        查询失败时返回 None，渲染层回退到全保守路径。
+        """
+        video_clips = [c for c in clips if getattr(c, "clip_type", "main") != "audio"]
+        atom_ids: list[str] = []
+        seen: set[str] = set()
+        for c in video_clips:
+            atom_id = getattr(c, "atom_clip_id", "") or ""
+            if atom_id and atom_id not in seen:
+                seen.add(atom_id)
+                atom_ids.append(atom_id)
+        if not atom_ids:
+            return None
+        try:
+            from packages.adapters.sqlalchemy_impl.asset_atom_clip_repository import (
+                SQLAlchemyAssetAtomClipRepository,
+            )
+
+            atom_clips = SQLAlchemyAssetAtomClipRepository(self._db).find_by_ids(atom_ids)
+        except Exception as exc:
+            logger.warning("[render-adapter] atom_clip ai_tags 查询失败，hflip 全量保守处理: %s", exc)
+            return None
+        has_text_map: dict[str, bool] = {}
+        for ac in atom_clips:
+            ai_tags = getattr(ac, "ai_tags", None)
+            no_text = isinstance(ai_tags, dict) and ai_tags.get("has_text") is False
+            has_text_map[ac.id] = not no_text
+        return [has_text_map.get((getattr(c, "atom_clip_id", "") or ""), True) for c in video_clips]
+
     def _do_render(
         self,
         plan: Any,
@@ -542,6 +577,7 @@ class RenderAdapter:
         )
 
         # 4. 执行统一渲染
+        clip_has_text = self._resolve_clip_has_text(clips)
         render_svc = UnifiedRenderService(
             plan=plan,
             clips=clips,
@@ -552,6 +588,7 @@ class RenderAdapter:
             bgm_path=bgm_path,
             asr_service=asr_service,
             voiceover_audio_path=voiceover_audio_path,
+            clip_has_text=clip_has_text,
         )
         result = render_svc.render()
 

apps/worker/video_processing/render_audio.py

@@ -98,6 +98,7 @@ def mix_audio(
     bgm_path: str | None = None,
     bgm_config: dict | None = None,
     audio_tracks_config: dict | None = None,
+    bgm_start_offset: float = 0.0,
 ) -> Path | None:
     """音频后处理混音.
 
@@ -157,7 +158,10 @@ def mix_audio(
         if bgm_path and bgm_config and isinstance(bgm_config, dict) and bgm_config.get("enabled", False):
             from video_processing.bgm_mixer import BGMConfig, build_bgm_only
 
-            bgm_cfg = BGMConfig.from_config_dict(bgm_path, bgm_config)
+            _bgm_cfg_dict = dict(bgm_config or {})
+            if bgm_start_offset and not _bgm_cfg_dict.get("audio_offset"):
+                _bgm_cfg_dict["audio_offset"] = round(float(bgm_start_offset), 3)
+            bgm_cfg = BGMConfig.from_config_dict(bgm_path, _bgm_cfg_dict)
             try:
                 return build_bgm_only(ctx, bgm_cfg, video_duration)
             except Exception:
@@ -187,7 +191,10 @@ def mix_audio(
     if bgm_path and bgm_config and isinstance(bgm_config, dict) and bgm_config.get("enabled", False):
         from video_processing.bgm_mixer import BGMConfig, mix_bgm_with_main
 
-        bgm_cfg = BGMConfig.from_config_dict(bgm_path, bgm_config)
+        _bgm_cfg_dict = dict(bgm_config or {})
+        if bgm_start_offset and not _bgm_cfg_dict.get("audio_offset"):
+            _bgm_cfg_dict["audio_offset"] = round(float(bgm_start_offset), 3)
+        bgm_cfg = BGMConfig.from_config_dict(bgm_path, _bgm_cfg_dict)
 
         try:
             # 这里 main_audio 就是 output_path，先有主音频再混 BGM

apps/worker/video_processing/unified_render_service.py

@@ -155,6 +155,7 @@ class UnifiedRenderService:
         asr_service: Any = None,  # ASRService 实例，用于自动生成字幕
         bgm_path: str | None = None,  # BGM 本地文件路径
         voiceover_audio_path: str | None = None,  # 配音素材库音频本地路径
+        clip_has_text: list[bool] | None = None,  # 源视频片段是否有文字（来自 atom_clip.ai_tags.has_text）
     ):
         self.plan = plan
         self.clips = clips
@@ -167,10 +168,98 @@ class UnifiedRenderService:
         self.asr_service = asr_service
         self.bgm_path = bgm_path
         self.voiceover_audio_path = voiceover_audio_path
+        # #1970：片段级文字检测（顺序与非 audio 的源视频片段一致）；None 表示无可靠检测，保守不翻转
+        self._clip_has_text = clip_has_text
         self._transition_engine = TransitionEngine(default_duration=transition_duration)
         self._speed_engine = SpeedEngine()
         self._asr_timeline_cache: Any = None  # ASR 字幕结果缓存，避免重复调用
         self._asr_timeline_cached = False
+        # #1970 PR2：片段级微变换计划缓存（懒构建，dedup_enabled=False 时为 None）
+        self._micro_plan_cache: Any = None
+        self._micro_plan_loaded = False
+
+    # ── #1970 PR2 智能降重：片段级微变换 ───────────────────────────────────
+    def _dedup_enabled(self) -> bool:
+        """读取 plan.config.dedup_enabled，缺省视为 True（向后兼容）。"""
+        cfg = self.plan.config or {}
+        return bool(cfg.get("dedup_enabled", True))
+
+    def _get_micro_transform_plan(self, clip_count: int) -> Any:
+        """按 task_id+视频序号构建可复现的片段级微变换计划。
+
+        种子 hash(generation_task_id + video_index)%10000，同一任务重渲结果一致。
+        dedup_enabled=False 时返回 None，调用方不注入任何微变换。
+        hflip 放开（#1970）：clip_has_text 来自 atom_clip.ai_tags.has_text，
+        仅 AI 明确判定无文字的片段可参与 50% 翻转；未打标签 / has_text 为
+        true/null 或缺位时一律视为有文字，保持保守不翻转。
+        """
+        if self._micro_plan_loaded:
+            return self._micro_plan_cache
+        self._micro_plan_loaded = True
+        if not self._dedup_enabled() or clip_count <= 0:
+            self._micro_plan_cache = None
+            return None
+        try:
+            from video_processing.micro_transform_pure import build_micro_transform_plan
+
+            cfg = self.plan.config or {}
+            task_id = str(cfg.get("generation_task_id", "") or "")
+            video_index = int(cfg.get("video_index", 0) or 0)
+            # self._clip_has_text 顺序与非 audio 源片段一致；
+            # None（未提供检测，如内存直渲/旧任务）→ 纯函数层按全有文字保守处理；
+            # 列表短于片段数时缺位片段同样按有文字处理
+            self._micro_plan_cache = build_micro_transform_plan(
+                task_id,
+                video_index,
+                clip_count,
+                clip_has_text=self._clip_has_text,
+                enable_bgm_offset=bool(cfg.get("bgm")),
+            )
+        except Exception as e:
+            logger.warning("[unified-render] 微变换计划构建失败，本次不注入: %s", e)
+            self._micro_plan_cache = None
+        return self._micro_plan_cache
+
+    @staticmethod
+    def _apply_micro_transform_video(filters: list[str], mt: Any) -> None:
+        """把片段视频微变换就地追加到 filter 链（post-scale 阶段调用）。
+
+        顺序：hflip 在 pre-scale 阶段由 _apply_micro_hflip 处理，这里只加
+        eq 亮度/对比度/饱和度。速度 setpts 与既有 clip speed 相乘（见调用点），
+        避免出现两条 setpts 互相覆盖。
+        """
+        if mt is None:
+            return
+        if abs(mt.brightness) > 1e-4 or abs(mt.contrast - 1.0) > 1e-4 or abs(mt.saturation - 1.0) > 1e-4:
+            filters.append(
+                f"eq=brightness={mt.brightness:+.4f}:" f"contrast={mt.contrast:.4f}:saturation={mt.saturation:.4f}"
+            )
+
+    @staticmethod
+    def _apply_micro_hflip(filters: list[str], mt: Any) -> None:
+        """片段级水平翻转（pre-scale 阶段）。P1 有文字/无法判定时 mt.hflip=False。"""
+        if mt is not None and mt.hflip and not mt.has_text:
+            filters.append("hflip")
+
+    @staticmethod
+    def _micro_speed_factor(mt: Any) -> float:
+        """片段微变换速度因子（0.97~1.03），无计划返回 1.0。"""
+        if mt is None:
+            return 1.0
+        return float(getattr(mt, "speed", 1.0) or 1.0)
+
+    def _get_micro_bgm_offset(self) -> float:
+        """#1970 PR2：读取本视频 BGM 起始偏移（秒），无 BGM/禁用时为 0。"""
+        if not self.plan.config:
+            return 0.0
+        try:
+            count = len([c for c in (self.plan.clips or []) if getattr(c, "clip_type", "main") != "audio"])
+            plan = self._get_micro_transform_plan(count)
+            if plan:
+                return round(float(plan.bgm_start_offset or 0.0), 3)
+        except Exception:
+            logger.debug("微变换 BGM 偏移读取失败，按 0 处理: plan_id=%s", getattr(self.plan, "id", "?"))
+        return 0.0
 
     def render(self) -> RenderResult:
         """执行渲染，返回 RenderResult.
@@ -316,6 +405,9 @@ class UnifiedRenderService:
                     ctx = RenderContext(work_dir=self.work_dir, plan_id=self.plan.id)
                     from video_processing.bgm_mixer import BGMConfig, mix_bgm_with_main
 
+                    _bgm_off = self._get_micro_bgm_offset()
+                    if _bgm_off and not (bgm_config or {}).get("audio_offset"):
+                        bgm_config = {**bgm_config, "audio_offset": _bgm_off}
                     bgm_cfg = BGMConfig.from_config_dict(self.bgm_path, bgm_config)
                     # 从直通输出中提取音频
                     main_audio_path = self.work_dir / f"pass_through_audio_{self.plan.id}.aac"
@@ -365,6 +457,7 @@ class UnifiedRenderService:
                 bgm_path=self.bgm_path,
                 bgm_config=bgm_config,
                 audio_tracks_config=audio_tracks_config,
+                bgm_start_offset=self._get_micro_bgm_offset(),
             )
             t_audio_end = time.time()
             audio_mix_ms = int((t_audio_end - t_audio_start) * 1000)
@@ -1112,6 +1205,28 @@ class UnifiedRenderService:
         if ass_path is not None:
             return False, "有字幕叠加"
 
+        # #1970 PR2：片段级微变换（变速/hflip/亮度/对比度/饱和度）需要重编码
+        try:
+            _video_sources = [c for c in (self.clips or []) if getattr(c, "clip_type", "main") != "audio"]
+            _ordinal = -1
+            for _i, _c in enumerate(_video_sources):
+                if getattr(_c, "id", None) == getattr(clip, "clip_id", None):
+                    _ordinal = _i
+                    break
+            _mt_plan = self._get_micro_transform_plan(len(_video_sources))
+            if _mt_plan and 0 <= _ordinal < len(_mt_plan.clips):
+                _mt = _mt_plan.clips[_ordinal]
+                if (
+                    abs(UnifiedRenderService._micro_speed_factor(_mt) - 1.0) >= 1e-6
+                    or (_mt.hflip and not _mt.has_text)
+                    or abs(_mt.brightness) > 1e-4
+                    or abs(_mt.contrast - 1.0) > 1e-4
+                    or abs(_mt.saturation - 1.0) > 1e-4
+                ):
+                    return False, "启用了片段级微变换"
+        except Exception:
+            logger.debug("stream copy 微变换门控检查异常，按可 copy 处理", exc_info=True)
+
         # 有调速 → 需要重编码 → 不能 copy
         speed = UnifiedRenderService._clip_speed(clip)
         if abs(speed - 1.0) >= 1e-6:
@@ -1318,11 +1433,16 @@ class UnifiedRenderService:
 
         # 视觉扰动（plan 级别，直通模式同样适用）
         vp = self._get_visual_perturbation()
+        # #1970 PR2：单片段直通；计划按源视频片段数构建，序号取 config._micro_index
+        _src_video_count = len([c for c in (self.clips or []) if getattr(c, "clip_type", "main") != "audio"])
+        mt_plan = self._get_micro_transform_plan(max(1, _src_video_count))
+        _mi = int(clip.config.get("_micro_index", 0)) if isinstance(clip.config, dict) else 0
+        mt = mt_plan.clips[_mi] if mt_plan and 0 <= _mi < len(mt_plan.clips) else None
 
-        # 调速 — 与 filter_complex 路径一致（叠加视觉扰动 speed_factor）
+        # 调速 — 与 filter_complex 路径一致（叠加视觉扰动 speed_factor 与 #1970 微变换速度）
         speed = UnifiedRenderService._clip_speed(clip)
         vp_speed = vp.get("speed_factor", 1.0) if vp else 1.0
-        effective_speed = speed * vp_speed
+        effective_speed = speed * vp_speed  # 微变换速度已烘焙进 playback_speed
         if abs(effective_speed - 1.0) >= 1e-6:
             filters.append(f"setpts=PTS/{effective_speed:.4f}")
 
@@ -1336,6 +1456,8 @@ class UnifiedRenderService:
         # 视觉扰动：hflip（在 scale 之前）
         if vp:
             self._apply_visual_perturbation_pre_scale(filters, vp)
+        # #1970 PR2：片段级 hflip（P1 保守：有文字/无法判定时不翻转）
+        UnifiedRenderService._apply_micro_hflip(filters, mt)
 
         # scale + pad（等比缩放+留黑边）
         if role in ("overlay", "corner_voice"):
@@ -1354,6 +1476,8 @@ class UnifiedRenderService:
         # 视觉扰动：zoom + brightness（在 scale+pad 之后、调色之前）
         if vp:
             self._apply_visual_perturbation_post_scale(filters, vp)
+        # #1970 PR2：片段级亮度/对比度/饱和度微调
+        UnifiedRenderService._apply_micro_transform_video(filters, mt)
 
         # 调色滤镜
         color_grade = ColorGradeConfig.from_dict(clip.config.get("color_grade"))
@@ -1450,7 +1574,8 @@ class UnifiedRenderService:
             # 音频调速（在降噪之后、音量之前，与 render_audio.py concat 路径保持一致）
             # SpeedEngine.build_audio_filter 内部已实现多级 atempo 串联，
             # 自动处理超出 [0.5, 2.0] 范围的速度（如 0.25x → atempo=0.5,atempo=0.5）。
-            speed = UnifiedRenderService._clip_speed(clip)
+            # #1970 PR2：叠加片段微变换速度因子，保持音画同步。
+            speed = UnifiedRenderService._clip_speed(clip)  # 微变换速度已烘焙进 playback_speed
             if abs(speed - 1.0) >= 1e-6:
                 try:
                     from video_processing.speed_engine import SpeedConfig, SpeedEngine
@@ -1522,11 +1647,20 @@ class UnifiedRenderService:
         支持多段裁剪：一个 clip 配置了 trim_segments 时会展开为多个 ResolvedClip。
         """
         resolved: list[ResolvedClip] = []
+        # #1970 PR2：预建片段级微变换计划，按源视频片段序号取速度因子，
+        # 烘焙进 playback_speed，保证视频 setpts 与音频 atempo 一致。
+        video_source_clips = [c for c in self.clips if getattr(c, "clip_type", "main") != "audio"]
+        mt_plan = self._get_micro_transform_plan(len(video_source_clips))
+        _video_ordinal = {id(c): i for i, c in enumerate(video_source_clips)}
+
         for clip in self.clips:
             asset_id = clip.asset_id
             if not asset_id:
                 logger.warning("片段无素材: clip_id=%s", clip.id)
                 continue
+            _mt_idx = _video_ordinal.get(id(clip), -1)
+            _mt = mt_plan.clips[_mt_idx] if mt_plan and 0 <= _mt_idx < len(mt_plan.clips) else None
+            _micro_speed = UnifiedRenderService._micro_speed_factor(_mt)
 
             local_path = self.asset_path_map.get(asset_id)
             if local_path is None or not local_path.exists():
@@ -1555,7 +1689,7 @@ class UnifiedRenderService:
                     seg_duration = seg.trim.duration
 
                     # 多段裁剪：如果段的时长超过素材实际时长，减速补偿
-                    seg_speed = configured_speed
+                    seg_speed = configured_speed * _micro_speed
                     if actual_duration > 0 and seg_duration > actual_duration + 0.05:
                         seg_speed = max(0.25, round(configured_speed * actual_duration / seg_duration, 4))
                         logger.info(
@@ -1578,7 +1712,7 @@ class UnifiedRenderService:
                         transition_effect=clip.transition_effect or "cut",
                         transition_duration=getattr(clip, "transition_duration", 0.0) or 0.0,
                         playback_speed=seg_speed,
-                        config={**clip_config, "_segment_id": seg.segment_id},
+                        config={**clip_config, "_segment_id": seg.segment_id, "_micro_index": _mt_idx},
                         actual_duration=actual_duration,
                         trim_config=seg.trim,
                     )
@@ -1633,12 +1767,13 @@ class UnifiedRenderService:
                     avail_in_asset,
                     freeze_seconds,
                 )
-            final_speed = configured_speed
+            final_speed = configured_speed * _micro_speed
 
             # freeze 标记写入 config，供视频 tpad / 音频 apad 读取
             resolved_config = dict(clip_config)
             if freeze_seconds > 0:
                 resolved_config["_freeze_seconds"] = freeze_seconds
+            resolved_config["_micro_index"] = _mt_idx
 
             rc = ResolvedClip(
                 clip_id=clip.id,
@@ -1754,9 +1889,15 @@ class UnifiedRenderService:
         preprocessed_labels: list[str] = []
         # 视觉扰动（plan 级别，所有 clip 共享同一套扰动参数）
         vp = self._get_visual_perturbation()
+        # #1970 PR2：片段级微变换（每片段独立参数，dedup_enabled=False 时为 None）
+        # 计划按源视频片段数构建，trim 多段展开时各段通过 config._micro_index 找参数
+        _src_video_count = len([c for c in (self.clips or []) if getattr(c, "clip_type", "main") != "audio"])
+        mt_plan = self._get_micro_transform_plan(_src_video_count)
         for i, clip in enumerate(all_clips):
             label = f"v{i}"
             role = _resolve_layer_role(clip.clip_type, clip.config)
+            _mi = int(clip.config.get("_micro_index", i)) if isinstance(clip.config, dict) else i
+            mt = mt_plan.clips[_mi] if mt_plan and 0 <= _mi < len(mt_plan.clips) else None
 
             filters: list[str] = []
 
@@ -1774,10 +1915,10 @@ class UnifiedRenderService:
                     filters.append(f"trim=duration={trim_dur:.3f}")
                 filters.append("setpts=PTS-STARTPTS")
 
-            # 调速 — 基于 setpts 改变播放速度（叠加视觉扰动 speed_factor）
+            # 调速 — 基于 setpts 改变播放速度（叠加视觉扰动 speed_factor 与 #1970 微变换速度）
             speed = UnifiedRenderService._clip_speed(clip)
             vp_speed = vp.get("speed_factor", 1.0) if vp else 1.0
-            effective_speed = speed * vp_speed
+            effective_speed = speed * vp_speed  # 微变换速度已烘焙进 playback_speed
             if abs(effective_speed - 1.0) >= 1e-6:
                 filters.append(f"setpts=PTS/{effective_speed:.4f}")
 
@@ -1791,6 +1932,8 @@ class UnifiedRenderService:
             # 视觉扰动：hflip（在 scale 之前，翻转原始画面）
             if vp:
                 self._apply_visual_perturbation_pre_scale(filters, vp)
+            # #1970 PR2：片段级 hflip（P1 保守：有文字/无法判定时不翻转）
+            UnifiedRenderService._apply_micro_hflip(filters, mt)
 
             # scale
             if role in ("overlay", "corner_voice"):
@@ -1809,6 +1952,8 @@ class UnifiedRenderService:
             # 视觉扰动：zoom + brightness（在 scale+pad 之后、调色之前）
             if vp:
                 self._apply_visual_perturbation_post_scale(filters, vp)
+            # #1970 PR2：片段级亮度/对比度/饱和度微调
+            UnifiedRenderService._apply_micro_transform_video(filters, mt)
 
             # 调色滤镜（每个 clip 独立的 color grade 配置）
             color_grade = ColorGradeConfig.from_dict(clip.config.get("color_grade"))

apps/worker/worker_app/celery_app.py

@@ -27,6 +27,11 @@ celery_app.conf.broker_transport_options = {"visibility_timeout": 4 * 60 * 60}
 celery_app.conf.imports = (
     "worker_app.tasks.health",
     "worker_app.tasks.ingest",
+    "worker_app.tasks.atom_clips",
+    # #1970 片段级 AI 标签：必须显式 import 注册，否则 worker 报
+    # "Received unregistered task of type 'worker.tag_atom_clip'"
+    "worker_app.tasks.atom_clip_tagging",
+    "worker_app.tasks.backfill_atom_clip_tags",
     "worker_app.tasks.classification",
     "worker_app.tasks.generation",
     "worker_app.tasks.voice_extraction",

apps/worker/worker_app/tasks/__init__.py

@@ -53,12 +53,25 @@ def __getattr__(name: str):
         from .batch_thumbnail import batch_generate_thumbnails
 
         return batch_generate_thumbnails
+    elif name == "generate_atom_clips":
+        from .atom_clips import generate_atom_clips
+
+        return generate_atom_clips
+    elif name == "tag_atom_clip_task":
+        from .atom_clip_tagging import tag_atom_clip_task
+
+        return tag_atom_clip_task
+    elif name == "backfill_atom_clip_tags":
+        from .backfill_atom_clip_tags import backfill_atom_clip_tags
+
+        return backfill_atom_clip_tags
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 
 __all__ = [
     "batch_generate_thumbnails",
     "classify_asset",
+    "generate_atom_clips",
     "generate_video",
     "healthcheck",
     "ingest_asset",

apps/worker/worker_app/tasks/atom_clip_tagging.py

@@ -0,0 +1,98 @@
+"""片段级 AI 标签 Celery 任务 — #1970 智能剪辑流程重构 P2.
+
+为单个 atom_clip 调用视觉 AI 生成结构化标签，并更新到 ai_tags 字段。
+失败不阻断流程（降级为仅继承素材标签）。
+
+任务名：worker.tag_atom_clip
+"""
+
+from __future__ import annotations
+
+from celery.utils.log import get_task_logger
+from worker_app.celery_app import celery_app
+from worker_app.db import SessionLocal
+
+from packages.adapters.sqlalchemy_impl.asset_atom_clip_repository import (
+    SQLAlchemyAssetAtomClipRepository,
+)
+from packages.adapters.sqlalchemy_impl.asset_repository import SQLAlchemyAssetRepository
+from packages.domain.atom_clip_tagger import tag_atom_clip
+from packages.shared.ai_client import get_doubao_client
+from packages.shared.mediakit_client import get_mediakit_client
+from packages.shared.storage import get_shared_storage_service
+
+logger = get_task_logger(__name__)
+
+
+@celery_app.task(name="worker.tag_atom_clip", bind=True, max_retries=2, default_retry_delay=10)
+def tag_atom_clip_task(self, atom_clip_id: str, force: bool = False) -> dict:
+    """为单个原子片段生成 AI 标签.
+
+    Args:
+        atom_clip_id: 原子片段 ID。
+        force: True 时允许覆盖只有 inherited_tags 的降级记录
+            （视觉 API 曾失败写入的占位标签，#1970）。
+            已有完整标签（含 has_text）始终跳过，保证幂等。
+
+    Returns:
+        任务结果 dict：status / clip_id / ai_tags（部分字段）。
+    """
+    db = SessionLocal()
+    try:
+        atom_repo = SQLAlchemyAssetAtomClipRepository(db)
+        asset_repo = SQLAlchemyAssetRepository(db)
+
+        clip = atom_repo.find_by_id(atom_clip_id)
+        if clip is None:
+            return {"status": "skipped", "reason": "clip not found", "clip_id": atom_clip_id}
+
+        # 已有完整标签则跳过（幂等）；force 仅放行缺失 has_text 的降级记录
+        if clip.ai_tags is not None:
+            has_real_tags = isinstance(clip.ai_tags, dict) and "has_text" in clip.ai_tags
+            if has_real_tags or not force:
+                return {"status": "skipped", "reason": "already tagged", "clip_id": atom_clip_id}
+
+        # 获取素材信息
+        asset = asset_repo.find_by_id(clip.asset_id)
+        if asset is None:
+            return {"status": "skipped", "reason": "asset not found", "clip_id": atom_clip_id}
+
+        # 获取视频可访问 URL
+        storage = get_shared_storage_service()
+        video_url = storage.get_download_url(asset.storage_key, expires_seconds=3600)
+
+        # 初始化客户端
+        doubao_client = get_doubao_client()
+        mediakit_client = get_mediakit_client()
+
+        # 调用 tagger
+        ai_tags = tag_atom_clip(
+            clip=clip,
+            video_url=video_url,
+            doubao_client=doubao_client,
+            mediakit_client=mediakit_client,
+            storage=storage,
+        )
+
+        # 更新数据库
+        atom_repo.update_ai_tags(atom_clip_id, ai_tags)
+
+        logger.info(
+            "[atom_clip_tagging] clip_id=%s ai_tags=%s",
+            atom_clip_id,
+            {k: v for k, v in ai_tags.items() if k != "inherited_tags"},
+        )
+        return {
+            "status": "completed",
+            "clip_id": atom_clip_id,
+            "has_ai_tags": any(v for k, v in ai_tags.items() if k != "inherited_tags" and v),
+        }
+    except Exception as exc:
+        db.rollback()
+        logger.exception("[atom_clip_tagging] clip_id=%s 失败: %s", atom_clip_id, exc)
+        # 可重试异常
+        if self.request.retries < self.max_retries:
+            raise self.retry(exc=exc) from None
+        return {"status": "failed", "clip_id": atom_clip_id, "error": str(exc)}
+    finally:
+        db.close()

apps/worker/worker_app/tasks/atom_clips.py

@@ -0,0 +1,109 @@
+"""素材原子切片 Celery 任务 — #1970 智能剪辑流程重构 P1.
+
+素材入库预处理完成（ingest 置 READY）后异步触发：
+根据素材时长和已缓存的 scdet 切换点计算原子片段并落库。
+失败不阻断素材入库主流程（atom_clips 未就绪时选片有内存兜底）。
+
+P2 增强：切片完成后自动链式触发 AI 标签任务（每个 clip 一个 tag_atom_clip 任务）。
+"""
+
+from __future__ import annotations
+
+from celery.utils.log import get_task_logger
+from worker_app.celery_app import celery_app
+from worker_app.db import SessionLocal
+
+from packages.adapters.sqlalchemy_impl.asset_atom_clip_repository import (
+    SQLAlchemyAssetAtomClipRepository,
+)
+from packages.adapters.sqlalchemy_impl.asset_repository import SQLAlchemyAssetRepository
+from packages.domain.atom_clip_service import compute_atom_clips
+from packages.domain.plan_generator_utils import extract_scene_points_from_metadata
+
+logger = get_task_logger(__name__)
+
+
+@celery_app.task(name="worker.generate_atom_clips")
+def generate_atom_clips(asset_id: str) -> dict:
+    """为单条视频素材生成原子片段。
+
+    Returns:
+        任务结果 dict：status / asset_id / clips_count。
+    """
+    db = SessionLocal()
+    try:
+        asset_repo = SQLAlchemyAssetRepository(db)
+        atom_repo = SQLAlchemyAssetAtomClipRepository(db)
+
+        asset = asset_repo.find_by_id(asset_id)
+        if asset is None:
+            return {"status": "skipped", "reason": "asset not found", "asset_id": asset_id}
+
+        # 仅视频素材切片
+        if asset.mime_type and not asset.mime_type.startswith("video/"):
+            return {"status": "skipped", "reason": "not a video", "asset_id": asset_id}
+        if not asset.duration or asset.duration <= 0:
+            return {"status": "skipped", "reason": "invalid duration", "asset_id": asset_id}
+
+        # 已生成过则幂等跳过（重新切片需先显式删除）
+        existing = atom_repo.count_by_asset(asset_id)
+        if existing > 0:
+            return {
+                "status": "skipped",
+                "reason": "already generated",
+                "asset_id": asset_id,
+                "clips_count": existing,
+            }
+
+        scene_points = extract_scene_points_from_metadata(asset.metadata)
+        # P1 阶段继承素材的标签 ID；片段级语义标签是 P2 功能
+        tags = list(getattr(asset, "tag_ids", []) or [])
+
+        clips = compute_atom_clips(
+            asset_id=asset_id,
+            duration=float(asset.duration),
+            scene_change_points=scene_points,
+            tags=tags,
+        )
+        if not clips:
+            return {"status": "skipped", "reason": "no clips computed", "asset_id": asset_id}
+
+        atom_repo.batch_create(clips)
+        logger.info(
+            "[atom_clips] asset_id=%s 生成 %d 个原子片段",
+            asset_id,
+            len(clips),
+        )
+
+        # P2 增强：链式触发 AI 标签任务（每个 clip 一个异步任务）
+        _dispatch_tagging_tasks(clips)
+
+        return {"status": "completed", "asset_id": asset_id, "clips_count": len(clips)}
+    except Exception as exc:  # noqa: BLE001 - 后台任务兜底，失败不阻断主流程
+        db.rollback()
+        logger.exception("[atom_clips] asset_id=%s 生成失败: %s", asset_id, exc)
+        return {"status": "failed", "asset_id": asset_id, "error": str(exc)}
+    finally:
+        db.close()
+
+
+def _dispatch_tagging_tasks(clips: list) -> None:
+    """为每个新建片段发送 AI 标签异步任务.
+
+    失败不阻断（标签任务是锦上添花，不影响核心流程）。
+    """
+    try:
+        for clip in clips:
+            celery_app.send_task(
+                "worker.tag_atom_clip",
+                args=[clip.id],
+            )
+        logger.info(
+            "[atom_clips] 已发送 %d 个 AI 标签任务",
+            len(clips),
+        )
+    except Exception as e:
+        logger.warning(
+            "[atom_clips] 发送 AI 标签任务失败（不影响切片结果）: %s",
+            e,
+        )

apps/worker/worker_app/tasks/backfill_atom_clip_tags.py

@@ -0,0 +1,106 @@
+"""批量回填 AI 标签 Celery 任务 — #1970 智能剪辑流程重构 P2.
+
+查找所有 ai_tags IS NULL 的 atom_clips，分批触发 tag_atom_clip 任务。
+可通过 API 路由触发（管理员权限）。
+
+任务名：worker.backfill_atom_clip_tags
+"""
+
+from __future__ import annotations
+
+import time
+
+from celery.utils.log import get_task_logger
+from worker_app.celery_app import celery_app
+from worker_app.db import SessionLocal
+
+from packages.adapters.sqlalchemy_impl.asset_atom_clip_repository import (
+    SQLAlchemyAssetAtomClipRepository,
+)
+
+logger = get_task_logger(__name__)
+
+# 默认批量参数
+DEFAULT_BATCH_SIZE = 10
+DEFAULT_BATCH_INTERVAL = 5  # 秒
+
+
+@celery_app.task(name="worker.backfill_atom_clip_tags")
+def backfill_atom_clip_tags(
+    batch_size: int = DEFAULT_BATCH_SIZE,
+    batch_interval: int = DEFAULT_BATCH_INTERVAL,
+    max_clips: int = 0,
+    force: bool = False,
+) -> dict:
+    """批量回填未打标的 atom_clips.
+
+    Args:
+        batch_size: 每批处理数量，默认 10。
+        batch_interval: 每批间隔秒数，默认 5。
+        max_clips: 最大处理总数，0 表示不限。
+        force: True 时连同只有 inherited_tags 的降级记录一起强制重打
+            （视觉 API 曾失败、DOUBAO_VISION_MODEL 修复后重跑用，#1970）。
+
+    Returns:
+        任务结果 dict：total_submitted / batches。
+    """
+    db = SessionLocal()
+    try:
+        atom_repo = SQLAlchemyAssetAtomClipRepository(db)
+        total_submitted = 0
+        batches = 0
+
+        while True:
+            # 查找未打标的片段
+            remaining = max_clips - total_submitted if max_clips > 0 else batch_size
+            fetch_limit = min(batch_size, remaining) if max_clips > 0 else batch_size
+
+            untagged = atom_repo.find_untagged(limit=fetch_limit, include_downgraded=force)
+            if not untagged:
+                break
+
+            # 逐个发送 tag 任务
+            for clip in untagged:
+                try:
+                    celery_app.send_task(
+                        "worker.tag_atom_clip",
+                        args=[clip.id],
+                        kwargs={"force": force},
+                    )
+                    total_submitted += 1
+                except Exception as e:
+                    logger.warning(
+                        "[backfill] 提交任务失败 clip_id=%s: %s",
+                        clip.id,
+                        e,
+                    )
+
+            batches += 1
+            logger.info(
+                "[backfill] 第 %d 批完成，已提交 %d 个任务",
+                batches,
+                total_submitted,
+            )
+
+            # 检查是否达到上限
+            if max_clips > 0 and total_submitted >= max_clips:
+                break
+
+            # 批间间隔
+            time.sleep(batch_interval)
+
+        logger.info(
+            "[backfill] 回填完成: total_submitted=%d batches=%d",
+            total_submitted,
+            batches,
+        )
+        return {
+            "status": "completed",
+            "total_submitted": total_submitted,
+            "batches": batches,
+        }
+    except Exception as exc:
+        logger.exception("[backfill] 回填失败: %s", exc)
+        return {"status": "failed", "error": str(exc)}
+    finally:
+        db.close()

apps/worker/worker_app/tasks/generation.py

@@ -890,25 +890,51 @@ def generate_video(self, task_id: str) -> dict:
                         _flush_logs(task_id, gen_task)
                     _update_task_progress(task_id, 80, "渲染完成")
 
-                    # ── 3.5 随机边缘裁剪降重（#1664） ──────────────────────────
-                    from video_processing.ffmpeg_utils import random_edge_crop
-
+                    # ── 3.5 随机边缘裁剪降重（#1664；#1970 dedup_enabled=False 时跳过） ──
+                    _dedup_enabled = True
                     try:
-                        cropped_path = random_edge_crop(output_path)
-                        if cropped_path != output_path:
-                            output_path = cropped_path
-                        if gen_task and render_attempt == 0:
-                            gen_task.append_log("边缘裁剪", "已应用随机 2-5% 边缘裁剪降重")
-                            _flush_logs(task_id, gen_task)
-                        logger.info("[task_id=%s] 随机边缘裁剪完成: %s", task_id, output_path)
-                    except Exception as crop_err:
+                        from packages.adapters.sqlalchemy_impl.models import EditPlanModel
+
+                        with SessionLocal() as _dedup_db:
+                            _plan_row = (
+                                _dedup_db.query(EditPlanModel.config)
+                                .filter(EditPlanModel.id == current_plan_id)
+                                .first()
+                            )
+                        if _plan_row is not None:
+                            _cfg = _plan_row[0] if isinstance(_plan_row[0], dict) else {}
+                            _dedup_enabled = bool(_cfg.get("dedup_enabled", True))
+                    except Exception:
                         logger.warning(
-                            "[task_id=%s] 随机边缘裁剪失败，使用原始视频继续: %s",
+                            "[task_id=%s] 读取 plan dedup_enabled 失败，按开启处理",
                             task_id,
-                            crop_err,
                             exc_info=True,
                         )
 
+                    if not _dedup_enabled:
+                        logger.info("[task_id=%s] dedup_enabled=False，跳过边缘裁剪与微变换", task_id)
+                        if gen_task and render_attempt == 0:
+                            gen_task.append_log("降重", "已关闭边缘裁剪与微变换（确定性渲染）")
+                            _flush_logs(task_id, gen_task)
+                    else:
+                        from video_processing.ffmpeg_utils import random_edge_crop
+
+                        try:
+                            cropped_path = random_edge_crop(output_path)
+                            if cropped_path != output_path:
+                                output_path = cropped_path
+                            if gen_task and render_attempt == 0:
+                                gen_task.append_log("边缘裁剪", "已应用随机 2-5% 边缘裁剪降重")
+                                _flush_logs(task_id, gen_task)
+                            logger.info("[task_id=%s] 随机边缘裁剪完成: %s", task_id, output_path)
+                        except Exception as crop_err:
+                            logger.warning(
+                                "[task_id=%s] 随机边缘裁剪失败，使用原始视频继续: %s",
+                                task_id,
+                                crop_err,
+                                exc_info=True,
+                            )
+
                     # ── 4. 上传 OSS（不落库） ───────────────────────────────
                     _update_task_progress(task_id, 85, "开始上传")
                     file_url, _storage_key = _upload_rendered_video(

apps/worker/worker_app/tasks/ingest.py

@@ -808,6 +808,21 @@ def ingest_asset(job_id: str) -> dict:
 
         db.commit()
 
+        # ── #1970 素材原子切片：视频 READY 后异步触发，失败不阻断入库 ──
+        # atom_clips 未就绪时选片逻辑有内存兜底（compute_fallback_clips）。
+        try:
+            if media_type == "video" and float(asset.duration or 0) > 0:
+                celery_app.send_task(
+                    "worker.generate_atom_clips",
+                    args=[asset.id],
+                )
+        except Exception as atom_err:  # noqa: BLE001
+            logger.warning(
+                "触发原子切片任务失败（不影响入库）: asset_id=%s err=%s",
+                asset.id,
+                atom_err,
+            )
+
         return {
             "status": "completed",
             "job_id": job.id,

deploy/configs/.env.production

@@ -234,9 +234,32 @@ DOUBAO_TIMEOUT=60
 # 最大重试次数
 DOUBAO_MAX_RETRIES=2
 
+# 视觉模型 Endpoint ID（支持图片/视频理解的模型）
+DOUBAO_VISION_MODEL=${DOUBAO_VISION_MODEL}
+
 
 # ==================== 微信开放平台 OAuth（网页扫码登录）====================
 # 回调域名：xiaoxiajianji.com（微信开放平台已配置）
 WECHAT_OPEN_APP_ID=${WECHAT_APP_ID}
 WECHAT_OPEN_APP_SECRET=${WECHAT_APP_SECRET}
 WECHAT_OPEN_REDIRECT_URI=https://saas.xiaoxiajianji.com/auth/wechat/callback
+
+# 抖音 cookies 文件路径（yt-dlp 已废弃，保留兼容）
+DOUYIN_COOKIES_FILE=/app/configs/douyin_cookies.txt
+DOUYIN_DEBUG_ERRORS=false
+
+
+# ==================== 抖音视频解析（三层兜底）====================
+# P0: App Feed API（免费，零 Key）— 内置，无需配置
+# P1: TikHub API（付费，https://tikhub.io）
+TIKHUB_API_KEY=${TIKHUB_API_KEY}
+# P2: apizero.cn（国内付费，https://apizero.cn）
+APIZERO_API_KEY=${APIZERO_API_KEY}
+
+# ==================== GPU MuseTalk Worker（反向轮询） ====================
+GPU_WORKER_TOKEN=${GPU_WORKER_TOKEN}
+GPU_TASK_TIMEOUT_SECONDS=900
+USE_GPU_LIPSYNC=false
+GPU_LIPSYNC_POLL_INTERVAL=5
+GPU_LIPSYNC_WAIT_TIMEOUT=1200
+GPU_WORKER_STALE_SECONDS=300

deploy/configs/.env.staging

@@ -251,9 +251,32 @@ DOUBAO_TIMEOUT=60
 # 最大重试次数
 DOUBAO_MAX_RETRIES=2
 
+# 视觉模型 Endpoint ID（支持图片/视频理解的模型）
+DOUBAO_VISION_MODEL=${DOUBAO_VISION_MODEL}
+
 
 # ==================== 微信开放平台 OAuth（网页扫码登录）====================
 # 回调域名：xiaoxiajianji.com（微信开放平台已配置）
 WECHAT_OPEN_APP_ID=${WECHAT_APP_ID}
 WECHAT_OPEN_APP_SECRET=${WECHAT_APP_SECRET}
 WECHAT_OPEN_REDIRECT_URI=https://staging.xiaoxiajianji.com/auth/wechat/callback
+
+# 抖音 cookies 文件路径（yt-dlp 已废弃，保留兼容）
+DOUYIN_COOKIES_FILE=/app/configs/douyin_cookies.txt
+DOUYIN_DEBUG_ERRORS=false
+
+
+# ==================== 抖音视频解析（三层兜底）====================
+# P0: App Feed API（免费，零 Key）— 内置，无需配置
+# P1: TikHub API（付费，https://tikhub.io）
+TIKHUB_API_KEY=${TIKHUB_API_KEY}
+# P2: apizero.cn（国内付费，https://apizero.cn）
+APIZERO_API_KEY=${APIZERO_API_KEY}
+
+# ==================== GPU MuseTalk Worker（反向轮询） ====================
+GPU_WORKER_TOKEN=${GPU_WORKER_TOKEN}
+GPU_TASK_TIMEOUT_SECONDS=900
+USE_GPU_LIPSYNC=true
+GPU_LIPSYNC_POLL_INTERVAL=5
+GPU_LIPSYNC_WAIT_TIMEOUT=1200
+GPU_WORKER_STALE_SECONDS=300

deploy/configs/douyin_cookies.txt

@@ -0,0 +1,3 @@
+# Netscape HTTP Cookie File
+# 抖音 cookies 占位。CI 部署时会通过 scp 上传真实 cookies。
+# 若本文件被使用说明 CI 上传失败，请检查 deploy-staging job。

deploy/gpu_worker/.env.example

@@ -0,0 +1,30 @@
+# ============================================================
+# MuseTalk GPU Worker 环境变量
+# 部署到 RTX2060 电脑后，复制为 .env 并修改值
+# ============================================================
+
+# SaaS API 基础 URL（staging / production）
+API_BASE_URL=https://staging-api.xiaoxiajianji.com
+# API_BASE_URL=https://api.xiaoxiajianji.com   # 生产
+
+# 长期 API Token，必须与服务端 GPU_WORKER_TOKEN 一致（找后端拿）
+GPU_WORKER_TOKEN=replace-with-real-token
+
+# 本机 Worker 唯一 ID（默认自动生成 hostname+MAC 后4位，可手动指定）
+# WORKER_ID=rtx2060-0193
+
+# 本地 MuseTalk 地址（默认 http://127.0.0.1:7861）
+MUSE_TALK_URL=http://127.0.0.1:7861
+
+# 轮询/心跳/超时（秒）
+POLL_INTERVAL=5
+HEARTBEAT_INTERVAL=15
+# 下载/推理/上传 HTTP 超时，需与服务端 GPU_TASK_TIMEOUT_SECONDS 对齐（默认 900）
+REQUEST_TIMEOUT=900
+
+# 单个任务本地最大重试次数（仅网络/MuseTalk 瞬时错误才重试，默认 1）
+TASK_MAX_RETRY=1
+# 推理期间任务心跳间隔（秒，独立线程，无需改动）
+TASK_HEARTBEAT_INTERVAL=30
+# 输入视频最短时长（秒），小于则直接上报失败，不调用 MuseTalk
+MIN_VIDEO_DURATION_SECONDS=3

deploy/gpu_worker/README.md

@@ -0,0 +1,100 @@
+# MuseTalk GPU Worker — 部署指南
+
+本目录包含 RTX2060 本地电脑上运行的 GPU Worker 脚本。
+Worker 采用 **反向轮询模式**：主动向 SaaS API 拉取待处理的口型同步任务 → 调用本地 MuseTalk 推理 → 把结果视频回传到 SaaS。不需要内网穿透。
+
+## 目录文件
+
+| 文件 | 作用 |
+|---|---|
+| `gpu_worker.py` | Worker 主程序（单文件，零项目代码依赖，仅依赖 `requests`） |
+| `requirements.txt` | Python 依赖（只有 `requests`） |
+| `xiaoxia-gpu-worker.service` | systemd 服务单元（开机自启、异常自动重启） |
+| `.env.example` | 环境变量样例，复制为 `.env` 后填入真实值 |
+
+## 一、环境准备
+
+1. **Python 3.10+**（Windows 建议从 python.org 安装；Linux 自带）
+2. **本地 MuseTalk 服务** 已启动在 `http://127.0.0.1:7861`，health 接口返回 `{"status":"ok","free_vram_mb":...}`
+3. **ffmpeg**（可选，用于读取输出视频时长；未装则 duration 报 0，不影响功能）
+4. 网络能访问 staging / 生产 API（`curl https://staging-api.xiaoxiajianji.com/health` 应返回 `{"status":"healthy"}`）
+
+## 二、部署步骤（Linux，推荐 systemd）
+
+```bash
+# 1. 创建部署目录
+sudo mkdir -p /opt/xiaoxia-gpu-worker
+sudo chown $USER:$USER /opt/xiaoxia-gpu-worker
+cd /opt/xiaoxia-gpu-worker
+
+# 2. 拷贝脚本和依赖
+cp /path/to/deploy/gpu_worker/{gpu_worker.py,requirements.txt,xiaoxia-gpu-worker.service,.env.example} .
+cp .env.example .env
+#    编辑 .env，填入 API_BASE_URL 和 GPU_WORKER_TOKEN
+
+# 3. 创建虚拟环境并安装依赖
+python3 -m venv venv
+./venv/bin/pip install -r requirements.txt
+
+# 4. 前台先跑一次，确认日志正常
+./venv/bin/python gpu_worker.py
+#    看到 "MuseTalk 健康检查通过" 和 "注册/心跳" 成功即可 Ctrl+C 退出
+
+# 5. 安装 systemd 服务
+sudo cp xiaoxia-gpu-worker.service /etc/systemd/system/
+sudo systemctl daemon-reload
+sudo systemctl enable --now xiaoxia-gpu-worker
+
+# 6. 查看日志
+sudo journalctl -u xiaoxia-gpu-worker -f
+```
+
+## 三、部署步骤（Windows，快速测试）
+
+```bat
+:: 创建虚拟环境
+python -m venv venv
+venv\Scripts\pip install -r requirements.txt
+
+:: 复制并编辑 .env
+copy .env.example .env
+notepad .env
+
+:: 运行
+venv\Scripts\python gpu_worker.py
+```
+
+可在任务计划程序中添加开机启动项：程序选 `venv\Scripts\python.exe`，参数填 `gpu_worker.py`，起始目录填脚本所在目录。
+
+## 四、SaaS 侧配套配置
+
+SaaS 后端部署完成后需配置：
+
+1. 服务端环境变量 `GPU_WORKER_TOKEN` 设为一个随机强 Token（和 Worker `.env` 中一致）
+2. 数据库已跑迁移 `081_add_gpu_lipsync_tasks`（自动随 API 启动的 alembic upgrade head 完成）
+3. OSS bucket 中 `gpu-lipsync/results/` 路径可写（默认 bucket 已配）
+
+## 五、验证联调
+
+1. Worker 启动后日志看到 `注册/心跳` 成功
+2. 后端调用 `GpuLipsyncService.create_task(video_url=..., audio_url=...)` 放入一条测试任务
+3. Worker 在 5 秒内拉到任务，下载 → 推理 → 上传 → 上报
+4. 后端 `GET /api/v1/gpu/lipsync/status/{task_id}` 返回 `status=done`，`result_url` 非空
+
+## 六、故障排查
+
+| 现象 | 可能原因 / 排查 |
+|---|---|
+| 日志 401 `Invalid GPU worker token` | `.env` 的 `GPU_WORKER_TOKEN` 与服务端不一致 |
+| 日志 `MuseTalk 健康检查未通过` | 本地 MuseTalk 没启动，或端口不是 7861；`curl http://127.0.0.1:7861/health` 验证 |
+| 任务长时间不被拉取 | Worker 和服务端连不上；检查 API_BASE_URL 是否可达、Token 是否正确 |
+| 推理后上传 OSS 失败 | 本地出口网络被防火墙拦截 OSS 域名（oss-cn-hangzhou.aliyuncs.com） |
+| 服务端看到任务回退到 pending 重试 | 任务心跳真正超时（默认 900s）：Worker 进程崩溃/断网，或推理彻底卡死；正常长推理期间心跳线程每 30s 续期，不会回退 |
+| 日志 `MuseTalk 推理超时或连接失败` | 视频太长或显存不足；可临时调大 REQUEST_TIMEOUT（服务端 GPU_TASK_TIMEOUT_SECONDS 需同步调大），或限制输入视频时长 |
+| 日志 `视频过短（x.xxs < 3s）` | 输入视频不足 3s，MuseTalk 对短视频会 division by zero，已在本地直接上报失败；可用 MIN_VIDEO_DURATION_SECONDS 调整阈值 |
+
+## 七、安全注意事项
+
+- `.env` 包含长期 Token，文件权限设为 600（`chmod 600 .env`）
+- Token 泄露要立即在服务端更换 `GPU_WORKER_TOKEN` 并重启 Worker
+- Worker 只需要出站访问 SaaS API 和 OSS，不需要开放任何入站端口

deploy/gpu_worker/gpu_worker.py

@@ -0,0 +1,471 @@
+"""MuseTalk GPU Worker — 反向轮询模式.
+
+部署在有 RTX2060 的本地电脑上（192.168.0.193），
+主动轮询 SaaS API 拉取口型任务、调用本地 MuseTalk 推理、上传结果回 SaaS。
+
+环境变量：
+  API_BASE_URL        SaaS API 基础 URL（不含 /api/v1），如 https://staging-api.xiaoxiajianji.com
+  GPU_WORKER_TOKEN    长期 API Token（服务端 GPU_WORKER_TOKEN 需一致）
+  WORKER_ID           本机唯一 ID（默认 hostname+网卡MAC 后4位）
+  MUSE_TALK_URL       本地 MuseTalk 地址，默认 http://127.0.0.1:7861
+  POLL_INTERVAL       轮询间隔秒，默认 5
+  HEARTBEAT_INTERVAL  空闲心跳间隔秒，默认 15
+  REQUEST_TIMEOUT     HTTP 请求超时秒（下载/推理/上传统一使用），默认 900
+                      需与服务端 GPU_TASK_TIMEOUT_SECONDS（默认 900）对齐
+  TASK_MAX_RETRY      单任务本地最大重试次数（仅对瞬时错误重试），默认 1
+  TASK_HEARTBEAT_INTERVAL  推理期间任务心跳间隔秒，默认 30
+  MIN_VIDEO_DURATION_SECONDS  最短输入视频时长秒，小于则直接上报失败，默认 3
+
+用法：
+  python gpu_worker.py
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import platform
+import socket
+import sys
+import tempfile
+import threading
+import time
+import uuid
+from pathlib import Path
+from typing import Optional
+
+import requests
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+logger = logging.getLogger("musetalk-worker")
+
+# ── 配置 ────────────────────────────────────────────────────────────
+
+
+def _env(name: str, default: str = "") -> str:
+    v = os.environ.get(name, default)
+    return v.strip() if isinstance(v, str) else default
+
+
+class Config:
+    api_base_url: str = _env("API_BASE_URL", "https://staging-api.xiaoxiajianji.com").rstrip("/")
+    gpu_worker_token: str = _env("GPU_WORKER_TOKEN")
+    muse_talk_url: str = _env("MUSE_TALK_URL", "http://127.0.0.1:7861").rstrip("/")
+    poll_interval: float = float(_env("POLL_INTERVAL", "5"))
+    heartbeat_interval: float = float(_env("HEARTBEAT_INTERVAL", "15"))
+    # #1970：RTX2060 6G 处理 720p 长视频可能 >5min；与服务端
+    # GPU_TASK_TIMEOUT_SECONDS 默认值对齐为 900，避免推理被本地/服务端先掐断。
+    request_timeout: float = float(_env("REQUEST_TIMEOUT", "900"))
+    # 本地只在网络/MuseTalk 瞬时错误时重试 1 次；服务端 MAX_ATTEMPTS=3
+    # 负责跨 worker/真正超时后的重派发，总尝试次数不再相乘放大。
+    task_max_retry: int = int(_env("TASK_MAX_RETRY", "1"))
+    # 推理期间任务心跳间隔（独立线程 POST /gpu/register 带 task_id）
+    task_heartbeat_interval: float = float(_env("TASK_HEARTBEAT_INTERVAL", "30"))
+    # 输入视频最短时长（秒）：过短（如 1s）MuseTalk 会 division by zero，
+    # 本地前置拦截，直接上报 failed，不浪费 GPU 时间
+    min_video_duration_seconds: float = float(_env("MIN_VIDEO_DURATION_SECONDS", "3"))
+    worker_id: str = _env("WORKER_ID", "")
+
+    @classmethod
+    def derived_worker_id(cls) -> str:
+        if cls.worker_id:
+            return cls.worker_id
+        # hostname + MAC 后4位 → 稳定唯一 ID
+        try:
+            mac = uuid.getnode()
+            mac_suffix = f"{mac:012x}"[-4:]
+        except Exception:
+            mac_suffix = "0000"
+        host = platform.node() or socket.gethostname() or "rtx2060"
+        return f"{host}-{mac_suffix}"
+
+
+# ── 辅助 ─────────────────────────────────────────────────────────────
+
+
+def _api_headers() -> dict[str, str]:
+    token = Config.gpu_worker_token
+    if not token:
+        logger.warning("GPU_WORKER_TOKEN 未配置，开发模式下会被服务端拒绝（生产环境必须配置）")
+    return {"Authorization": f"Bearer {token}"} if token else {}
+
+
+def _check_musetalk_health() -> tuple[bool, dict]:
+    """检查本地 MuseTalk 健康状态，返回 (ok, info)."""
+    try:
+        r = requests.get(f"{Config.muse_talk_url}/health", timeout=5)
+        if r.status_code == 200:
+            try:
+                return True, r.json()
+            except Exception:
+                return True, {}
+        return False, {"status_code": r.status_code, "body": r.text[:200]}
+    except Exception as exc:
+        return False, {"error": str(exc)}
+
+
+def _register(task_id: Optional[str] = None) -> bool:
+    """向服务端注册 / 心跳，附带 GPU 信息。
+
+    推理期间的心跳线程传 task_id：服务端会同步刷新该 processing 任务的
+    last_heartbeat_at，防止长推理被误判超时回收。
+    """
+    ok, info = _check_musetalk_health()
+    free_vram = int(info.get("free_vram_mb", 0) or 0) if isinstance(info, dict) else 0
+    gpu_name = info.get("gpu_name", "") if isinstance(info, dict) else ""
+    if not gpu_name:
+        # 尝试在 Windows 上读 nvidia-smi
+        gpu_name = _probe_gpu_name()
+    payload = {
+        "worker_id": Config.derived_worker_id(),
+        "hostname": platform.node(),
+        "gpu_name": gpu_name,
+        "free_vram_mb": free_vram,
+        "capabilities": "musetalk",
+    }
+    if task_id:
+        payload["task_id"] = task_id
+    try:
+        r = requests.post(
+            f"{Config.api_base_url}/api/v1/gpu/register",
+            json=payload,
+            headers=_api_headers(),
+            timeout=15,
+        )
+        if r.status_code == 200:
+            return True
+        logger.error("注册/心跳失败: HTTP %d body=%s", r.status_code, r.text[:300])
+        return False
+    except Exception as exc:
+        logger.error("注册/心跳异常: %s", exc)
+        return False
+
+
+def _probe_gpu_name() -> str:
+    """尽力探测 GPU 型号（不强制依赖 pynvml）."""
+    try:
+        import subprocess
+
+        out = subprocess.check_output(
+            ["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"],
+            stderr=subprocess.DEVNULL,
+            timeout=5,
+        )
+        return out.decode("utf-8", errors="ignore").strip().splitlines()[0].strip()
+    except Exception:
+        return ""
+
+
+def _poll_task() -> Optional[dict]:
+    """轮询拉取一条待处理任务；无任务返回 None."""
+    try:
+        r = requests.get(
+            f"{Config.api_base_url}/api/v1/gpu/lipsync/poll",
+            params={"worker_id": Config.derived_worker_id()},
+            headers=_api_headers(),
+            timeout=30,
+        )
+        if r.status_code == 204:
+            return None
+        if r.status_code == 200:
+            data = r.json()
+            return data.get("task")
+        logger.error("poll 返回 %d: %s", r.status_code, r.text[:300])
+        return None
+    except Exception as exc:
+        logger.error("poll 异常: %s", exc)
+        return None
+
+
+def _download(url: str, path: Path) -> bool:
+    """下载文件到本地，支持预签名 URL."""
+    try:
+        with requests.get(url, stream=True, timeout=Config.request_timeout) as r:
+            if r.status_code >= 400:
+                logger.error("下载失败 HTTP %d: %s", r.status_code, url[:120])
+                return False
+            path.parent.mkdir(parents=True, exist_ok=True)
+            with open(path, "wb") as f:
+                for chunk in r.iter_content(chunk_size=1024 * 256):
+                    if chunk:
+                        f.write(chunk)
+        return path.stat().st_size > 0
+    except Exception as exc:
+        logger.error("下载异常 %s: %s", url[:120], exc)
+        return False
+
+
+def _call_musetalk(video_path: Path, audio_path: Path, out_path: Path) -> tuple[bool, float, str, bool]:
+    """调用本地 MuseTalk /inference.
+
+    返回 (success, duration_seconds, error_msg, retryable)。
+    duration 用 ffprobe 读结果视频，失败填 0。
+    retryable 仅对瞬时错误（连接失败/超时/5xx）为 True；HTTP 4xx、结果过小
+    等确定性失败不重试，直接上报服务端（服务端 MAX_ATTEMPTS 再决定是否重派发）。
+    """
+    try:
+        with open(video_path, "rb") as vf, open(audio_path, "rb") as af:
+            files = {
+                "video": (video_path.name, vf, "video/mp4"),
+                "audio": (audio_path.name, af, "application/octet-stream"),
+            }
+            r = requests.post(
+                f"{Config.muse_talk_url}/inference",
+                files=files,
+                timeout=Config.request_timeout,
+            )
+        if r.status_code != 200:
+            retryable = r.status_code >= 500
+            return False, 0.0, f"MuseTalk HTTP {r.status_code}: {r.text[:500]}", retryable
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        out_path.write_bytes(r.content)
+        if out_path.stat().st_size < 1024:
+            # 确定性失败（推理产物异常），本地重试大概率还是坏的，不重试
+            return False, 0.0, f"MuseTalk 返回结果过小 ({out_path.stat().st_size} bytes)", False
+        duration = _probe_duration(out_path)
+        return True, duration, "", False
+    except (requests.exceptions.Timeout, requests.exceptions.ConnectionError):
+        # 瞬时网络/超时错误，允许本地重试 1 次
+        return False, 0.0, f"MuseTalk 推理超时或连接失败（>{Config.request_timeout}s）", True
+    except Exception as exc:
+        return False, 0.0, f"MuseTalk 调用异常: {exc}", False
+
+
+def _probe_duration(path: Path) -> float:
+    """用 ffprobe 读视频时长（若系统装了 ffmpeg）；否则返回 0."""
+    try:
+        import subprocess
+
+        out = subprocess.check_output(
+            [
+                "ffprobe",
+                "-v",
+                "error",
+                "-show_entries",
+                "format=duration",
+                "-of",
+                "default=noprint_wrappers=1:nokey=1",
+                str(path),
+            ],
+            stderr=subprocess.DEVNULL,
+            timeout=10,
+        )
+        return float(out.decode().strip() or 0)
+    except Exception:
+        return 0.0
+
+
+def _upload_result(upload_url: str, file_path: Path) -> bool:
+    """PUT 上传结果视频到预签名 URL."""
+    try:
+        with open(file_path, "rb") as f:
+            r = requests.put(
+                upload_url,
+                data=f,
+                headers={"Content-Type": "video/mp4"},
+                timeout=Config.request_timeout,
+            )
+        if r.status_code >= 400:
+            logger.error("上传结果失败 HTTP %d: %s", r.status_code, r.text[:500])
+            return False
+        return True
+    except Exception as exc:
+        logger.error("上传结果异常: %s", exc)
+        return False
+
+
+def _report_result(task_id: str, success: bool, duration: float = 0.0, error_msg: str = "") -> bool:
+    """通知服务端结果。失败时也尝试上报错误（不含视频文件）."""
+    try:
+        data = {
+            "task_id": task_id,
+            "worker_id": Config.derived_worker_id(),
+            "success": "true" if success else "false",
+            "duration_seconds": str(duration),
+            "error_msg": error_msg,
+        }
+        r = requests.post(
+            f"{Config.api_base_url}/api/v1/gpu/lipsync/result",
+            data=data,
+            headers=_api_headers(),
+            timeout=30,
+        )
+        if r.status_code != 200:
+            logger.error("上报结果失败 HTTP %d: %s", r.status_code, r.text[:300])
+            return False
+        return True
+    except Exception as exc:
+        logger.error("上报结果异常: %s", exc)
+        return False
+
+
+class TaskHeartbeat(threading.Thread):
+    """推理期间的任务心跳线程。
+
+    主循环的空闲心跳在 ``_handle_task`` 同步阻塞（下载/推理/上传最长 900s）
+    期间无法发送，服务端会因任务 last_heartbeat_at 停滞而误判超时回退 pending。
+    本线程每 task_heartbeat_interval 秒（默认 30s）POST /gpu/register 并
+    携带当前 task_id，让服务端持续续期任务心跳；任务处理结束 stop()。
+    """
+
+    def __init__(self, task_id: str, interval: float):
+        super().__init__(daemon=True, name=f"hb-{task_id[:8]}")
+        self.task_id = task_id
+        self.interval = max(5.0, interval)
+        self._stop_event = threading.Event()
+
+    def run(self) -> None:
+        # 先立即发一次，再按间隔循环（首次心跳失败不影响主流程）
+        while not self._stop_event.is_set():
+            try:
+                if _register(self.task_id):
+                    logger.debug("任务 %s 心跳已发送", self.task_id)
+            except Exception as exc:  # noqa: BLE001
+                logger.warning("任务 %s 心跳异常（忽略）: %s", self.task_id, exc)
+            self._stop_event.wait(self.interval)
+
+    def stop(self) -> None:
+        self._stop_event.set()
+
+
+def _handle_task(task: dict) -> None:
+    """处理一条任务（整个串行流程：下载→时长校验→推理→上传→上报）。"""
+    task_id = task["task_id"]
+    logger.info("开始处理任务 %s", task_id)
+    # 领取任务后立即启动任务级心跳线程，覆盖下载/推理/上报全过程
+    hb = TaskHeartbeat(task_id, Config.task_heartbeat_interval)
+    hb.start()
+    try:
+        with tempfile.TemporaryDirectory(prefix="musetalk_") as tmpdir:
+            tmp = Path(tmpdir)
+            video_path = tmp / "input.mp4"
+            audio_path = tmp / "input_audio.bin"
+            out_path = tmp / "output.mp4"
+
+            # 1. 下载
+            if not _download(task["video_url"], video_path):
+                _report_result(task_id, False, 0.0, "下载人物视频失败")
+                return
+            if not _download(task["audio_url"], audio_path):
+                _report_result(task_id, False, 0.0, "下载驱动音频失败")
+                return
+
+            # 2. 输入时长前置校验：短视频 MuseTalk 会 division by zero，
+            #    直接上报 failed，不浪费 GPU 时间。ffprobe 不可用/读失败（0.0）
+            #    时不拦截，交给 MuseTalk 处理，避免误杀。
+            video_duration = _probe_duration(video_path)
+            if video_duration and video_duration < Config.min_video_duration_seconds:
+                msg = (
+                    f"视频过短（{video_duration:.2f}s < {Config.min_video_duration_seconds:.0f}s），"
+                    "MuseTalk 无法处理"
+                )
+                logger.error("任务 %s %s", task_id, msg)
+                _report_result(task_id, False, 0.0, msg)
+                return
+
+            # 3. 推理（本地仅对瞬时错误重试）
+            success = False
+            duration = 0.0
+            err = ""
+            retryable = False
+            for attempt in range(Config.task_max_retry + 1):
+                if attempt > 0:
+                    logger.info("任务 %s 第 %d 次重试（瞬时错误）...", task_id, attempt + 1)
+                    time.sleep(2)
+                success, duration, err, retryable = _call_musetalk(video_path, audio_path, out_path)
+                if success or not retryable:
+                    break
+            if not success:
+                logger.error("任务 %s 推理失败: %s", task_id, err)
+                _report_result(task_id, False, 0.0, err)
+                return
+
+            # 4. 上报结果（multipart 同时上传文件 → API 代为 PUT 到 OSS，逻辑最稳）
+            _report_success_with_file(task_id, duration, out_path)
+    finally:
+        hb.stop()
+
+
+def _report_success_with_file(task_id: str, duration: float, file_path: Path) -> None:
+    """上报成功并 multipart 附带结果视频."""
+    try:
+        data = {
+            "task_id": task_id,
+            "worker_id": Config.derived_worker_id(),
+            "success": "true",
+            "duration_seconds": str(duration),
+            "error_msg": "",
+        }
+        with open(file_path, "rb") as f:
+            files = {"result": (f"{task_id}.mp4", f, "video/mp4")}
+            r = requests.post(
+                f"{Config.api_base_url}/api/v1/gpu/lipsync/result",
+                data=data,
+                files=files,
+                headers=_api_headers(),
+                timeout=Config.request_timeout,
+            )
+        if r.status_code != 200:
+            logger.error("上报成功结果失败 HTTP %d: %s", r.status_code, r.text[:300])
+            return
+        logger.info("任务 %s 完成，duration=%.1fs", task_id, duration)
+    except Exception as exc:
+        logger.error("上报成功结果异常: %s", exc)
+
+
+# ── 主循环 ──────────────────────────────────────────────────────────
+
+
+def main() -> int:
+    logger.info("=" * 60)
+    logger.info("MuseTalk GPU Worker 启动")
+    logger.info("  worker_id   = %s", Config.derived_worker_id())
+    logger.info("  api_base    = %s", Config.api_base_url)
+    logger.info("  muse_talk   = %s", Config.muse_talk_url)
+    logger.info("  poll        = %.1fs / heartbeat = %.1fs", Config.poll_interval, Config.heartbeat_interval)
+    logger.info("=" * 60)
+
+    if not Config.gpu_worker_token:
+        logger.warning("GPU_WORKER_TOKEN 未配置（开发模式），生产环境必须设置")
+
+    # 先检查一次 MuseTalk
+    ok, info = _check_musetalk_health()
+    if ok:
+        logger.info("MuseTalk 健康检查通过: %s", info)
+    else:
+        logger.warning("MuseTalk 健康检查未通过: %s（继续运行，等待服务可用）", info)
+
+    # 启动时立即注册
+    _register()
+    last_heartbeat = time.time()
+
+    while True:
+        try:
+            # 心跳
+            now = time.time()
+            if now - last_heartbeat >= Config.heartbeat_interval:
+                if _register():
+                    last_heartbeat = now
+
+            # 轮询任务
+            task = _poll_task()
+            if task is not None:
+                _handle_task(task)
+                # 处理完立即再 poll（不 sleep），尽可能拉满 GPU
+                continue
+
+            time.sleep(Config.poll_interval)
+        except KeyboardInterrupt:
+            logger.info("收到中断信号，退出")
+            return 0
+        except Exception as exc:
+            logger.exception("主循环异常: %s", exc)
+            time.sleep(Config.poll_interval)
+
+
+if __name__ == "__main__":
+    sys.exit(main())

deploy/gpu_worker/requirements.txt

@@ -0,0 +1 @@
+requests>=2.31.0

deploy/gpu_worker/xiaoxia-gpu-worker.service

@@ -0,0 +1,21 @@
+[Unit]
+Description=MuseTalk GPU Worker (xiaoxia-saas 反向轮询)
+After=network.target musetalk.service
+# 本地 MuseTalk 服务启动后再启动本 Worker；若 MuseTalk 没有 systemd 服务则删除 musetalk.service
+
+[Service]
+Type=simple
+User=%i
+WorkingDirectory=/opt/xiaoxia-gpu-worker
+# 读取环境变量（API 地址、Token、轮询间隔等）
+EnvironmentFile=/opt/xiaoxia-gpu-worker/.env
+ExecStart=/opt/xiaoxia-gpu-worker/venv/bin/python /opt/xiaoxia-gpu-worker/gpu_worker.py
+Restart=always
+RestartSec=10
+# 日志走 journal，用 journalctl -u xiaoxia-gpu-worker -f 查看
+StandardOutput=journal
+StandardError=journal
+SyslogIdentifier=xiaoxia-gpu-worker
+
+[Install]
+WantedBy=multi-user.target

infra/docker/api.Dockerfile

@@ -19,6 +19,16 @@ COPY alembic/ ./alembic/
 COPY scripts/ ./scripts/
 COPY packages/ ./packages/
 COPY apps/api/ ./apps/api/
+# 抖音 cookies 文件：镜像内 baked-in 兜底 + host 挂载可覆盖
+# - /app/configs/douyin_cookies_default.txt: 镜像构建时 COPY 的兜底 cookies（始终有效）
+# - /app/configs/douyin_cookies.txt: host volume 挂载点（部署脚本 scp 覆盖，过期需更新）
+RUN mkdir -p /app/configs
+COPY deploy/configs/douyin_cookies.txt /app/configs/douyin_cookies_default.txt
+# 初始 COPY 一份到挂载点，host 挂载为空文件时 Python 代码会自动 fallback 到 default
+COPY deploy/configs/douyin_cookies.txt /app/configs/douyin_cookies.txt
+
+# 强制升级 yt-dlp 到最新（抖音反爬经常变更，旧版 cookies 支持失效；#1968/#1963）
+RUN pip install --no-cache-dir -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com --upgrade "yt-dlp>=2026.8.19"
 
 # 设置环境变量
 ENV PATH="/opt/venv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"

infra/docker/compose.yml

@@ -67,9 +67,10 @@ services:
     ports:
       - "127.0.0.1:${API_PORT:-8000}:8000"
     
-    # 共享生成文件目录
+    # 共享生成文件目录 + 抖音 cookies 等运行时配置
     volumes:
       - generated-files:/app/generated
+      - ../../deploy/configs:/app/configs:ro
     
     networks:
       - xiaoxia-net

packages/adapters/sqlalchemy_impl/asset_atom_clip_repository.py

@@ -0,0 +1,138 @@
+"""素材原子片段仓储 SQLAlchemy 实现。"""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime
+
+from sqlalchemy.orm import Session
+
+from packages.adapters.sqlalchemy_impl.models import AssetAtomClipModel
+from packages.domain.asset_atom_clip import AssetAtomClip
+
+
+class SQLAlchemyAssetAtomClipRepository:
+    def __init__(self, session: Session):
+        self.session = session
+
+    def create(self, clip: AssetAtomClip) -> AssetAtomClip:
+        model = self._to_model(clip)
+        self.session.add(model)
+        self.session.flush()
+        self.session.commit()
+        return clip
+
+    def batch_create(self, clips: list[AssetAtomClip]) -> list[AssetAtomClip]:
+        if not clips:
+            return []
+        models = [self._to_model(c) for c in clips]
+        self.session.add_all(models)
+        self.session.flush()
+        self.session.commit()
+        return clips
+
+    def find_by_asset(self, asset_id: str) -> list[AssetAtomClip]:
+        models = (
+            self.session.query(AssetAtomClipModel)
+            .filter(AssetAtomClipModel.asset_id == asset_id)
+            .order_by(AssetAtomClipModel.clip_index.asc())
+            .all()
+        )
+        return [self._to_domain(m) for m in models]
+
+    def find_by_id(self, clip_id: str) -> AssetAtomClip | None:
+        model = self.session.query(AssetAtomClipModel).filter(AssetAtomClipModel.id == clip_id).first()
+        if model is None:
+            return None
+        return self._to_domain(model)
+
+    def find_by_ids(self, clip_ids: list[str]) -> list[AssetAtomClip]:
+        if not clip_ids:
+            return []
+        models = self.session.query(AssetAtomClipModel).filter(AssetAtomClipModel.id.in_(clip_ids)).all()
+        return [self._to_domain(m) for m in models]
+
+    def delete_by_asset(self, asset_id: str) -> int:
+        count = (
+            self.session.query(AssetAtomClipModel)
+            .filter(AssetAtomClipModel.asset_id == asset_id)
+            .delete(synchronize_session=False)
+        )
+        self.session.commit()
+        return count
+
+    def count_by_asset(self, asset_id: str) -> int:
+        return self.session.query(AssetAtomClipModel).filter(AssetAtomClipModel.asset_id == asset_id).count()
+
+    def find_candidates_for_selection(
+        self,
+        asset_ids: list[str],
+        *,
+        min_duration: float | None = None,
+        max_duration: float | None = None,
+        limit: int = 100,
+    ) -> list[AssetAtomClip]:
+        """按筛选条件查找候选原子片段，按时长排序。用于选片逻辑。"""
+        query = self.session.query(AssetAtomClipModel).filter(AssetAtomClipModel.asset_id.in_(asset_ids))
+        if min_duration is not None:
+            query = query.filter(AssetAtomClipModel.duration >= min_duration)
+        if max_duration is not None:
+            query = query.filter(AssetAtomClipModel.duration <= max_duration)
+        query = query.order_by(AssetAtomClipModel.clip_index.asc())
+        if limit > 0:
+            query = query.limit(limit)
+        models = query.all()
+        return [self._to_domain(m) for m in models]
+
+    def update_ai_tags(self, clip_id: str, ai_tags: dict) -> bool:
+        """更新指定片段的 ai_tags 字段."""
+        count = (
+            self.session.query(AssetAtomClipModel).filter(AssetAtomClipModel.id == clip_id).update({"ai_tags": ai_tags})
+        )
+        self.session.commit()
+        return count > 0
+
+    def find_untagged(self, limit: int = 100, include_downgraded: bool = False) -> list[AssetAtomClip]:
+        """查找未完成 AI 打标的片段，用于回填.
+
+        默认仅匹配 ai_tags IS NULL；include_downgraded=True 时额外包含
+        只有 inherited_tags 的降级记录（视觉 API 失败时写入，无 has_text 字段），
+        供强制回填（#1970 force backfill）使用。
+        """
+        query = self.session.query(AssetAtomClipModel)
+        if include_downgraded:
+            # as_string() → JSON/JSONB ->> 取值；NULL 记录或缺 has_text 键
+            # （降级记录）均为 NULL，has_text 为 true/false 的完整记录被排除
+            query = query.filter(AssetAtomClipModel.ai_tags["has_text"].as_string().is_(None))
+        else:
+            query = query.filter(AssetAtomClipModel.ai_tags.is_(None))
+        models = query.order_by(AssetAtomClipModel.created_at.asc()).limit(limit).all()
+        return [self._to_domain(m) for m in models]
+
+    def _to_model(self, clip: AssetAtomClip) -> AssetAtomClipModel:
+        return AssetAtomClipModel(
+            id=clip.id,
+            asset_id=clip.asset_id,
+            start_time=clip.start_time,
+            end_time=clip.end_time,
+            duration=clip.duration,
+            clip_index=clip.clip_index,
+            tags=clip.tags,
+            ai_tags=clip.ai_tags,
+            scene_change_at=clip.scene_change_at,
+            is_fallback=clip.is_fallback,
+            created_at=clip.created_at or datetime.now(UTC),
+        )
+
+    def _to_domain(self, model: AssetAtomClipModel) -> AssetAtomClip:
+        return AssetAtomClip(
+            id=model.id,
+            asset_id=model.asset_id,
+            start_time=model.start_time,
+            end_time=model.end_time,
+            duration=model.duration,
+            clip_index=model.clip_index,
+            tags=model.tags or [],
+            scene_change_at=model.scene_change_at,
+            is_fallback=model.is_fallback,
+            created_at=model.created_at,
+        )

packages/adapters/sqlalchemy_impl/edit_plan_clip_repository.py

@@ -50,6 +50,7 @@ class SQLAlchemyEditPlanClipRepository:
             order=clip.order,
             template_clip_config_id=clip.template_clip_config_id,
             asset_id=clip.asset_id,
+            atom_clip_id=getattr(clip, "atom_clip_id", "") or "",
             text_content=clip.text_content,
             start_time=clip.start_time,
             duration=clip.duration,
@@ -74,6 +75,7 @@ class SQLAlchemyEditPlanClipRepository:
         model.order = clip.order
         model.template_clip_config_id = clip.template_clip_config_id
         model.asset_id = clip.asset_id
+        model.atom_clip_id = getattr(clip, "atom_clip_id", "") or ""
         model.text_content = clip.text_content
         model.start_time = clip.start_time
         model.duration = clip.duration
@@ -120,6 +122,7 @@ class SQLAlchemyEditPlanClipRepository:
             order=model.order,
             template_clip_config_id=model.template_clip_config_id or "",
             asset_id=model.asset_id or "",
+            atom_clip_id=getattr(model, "atom_clip_id", "") or "",
             text_content=model.text_content or "",
             start_time=model.start_time or 0.0,
             duration=model.duration or 0.0,
@@ -193,3 +196,53 @@ class SQLAlchemyEditPlanClipRepository:
             result[asset_id].append((start_time or 0.0, (start_time or 0.0) + (duration or 0.0)))
 
         return result
+
+    def list_recent_atom_clip_ids_by_user(
+        self,
+        user_id: str,
+        *,
+        limit: int = 200,
+    ) -> list[str]:
+        """#1970 跨视频原子片段级避让：查询用户最近成片用过的 atom_clip_id.
+
+        只统计已完成 plan 下已渲染且 atom_clip_id 非空的 clips，按 plan
+        创建时间倒序，返回去重后的 ID 列表。
+        """
+        from packages.adapters.sqlalchemy_impl.models import EditPlanModel
+
+        if not user_id:
+            return []
+
+        recent_plan_ids = [
+            row[0]
+            for row in self.session.query(EditPlanModel.id)
+            .filter(
+                EditPlanModel.created_by_user_id == user_id,
+                EditPlanModel.status == "completed",
+            )
+            .order_by(EditPlanModel.created_at.desc())
+            .limit(50)
+            .all()
+        ]
+        if not recent_plan_ids:
+            return []
+
+        rows = (
+            self.session.query(EditPlanClipModel.atom_clip_id)
+            .filter(
+                EditPlanClipModel.plan_id.in_(recent_plan_ids),
+                EditPlanClipModel.status == "rendered",
+                EditPlanClipModel.atom_clip_id.isnot(None),
+                EditPlanClipModel.atom_clip_id != "",
+            )
+            .all()
+        )
+        seen: set[str] = set()
+        ordered: list[str] = []
+        for (atom_clip_id,) in rows:
+            if atom_clip_id and atom_clip_id not in seen:
+                seen.add(atom_clip_id)
+                ordered.append(atom_clip_id)
+            if len(ordered) >= limit:
+                break
+        return ordered

packages/adapters/sqlalchemy_impl/models.py

@@ -1,7 +1,20 @@
 from datetime import UTC, datetime
 from typing import Any
 
-from sqlalchemy import JSON, Boolean, Column, DateTime, Float, Index, Integer, String, Text, UniqueConstraint, text
+from sqlalchemy import (
+    JSON,
+    Boolean,
+    Column,
+    DateTime,
+    Float,
+    ForeignKey,
+    Index,
+    Integer,
+    String,
+    Text,
+    UniqueConstraint,
+    text,
+)
 from sqlalchemy.orm import declarative_base
 
 Base: Any = declarative_base()
@@ -234,6 +247,8 @@ class EditPlanClipModel(Base):
     order = Column(Integer, nullable=False)
     template_clip_config_id = Column(String(36), nullable=False, default="", index=True)
     asset_id = Column(String(36), nullable=False, default="", index=True)
+    # #1970 原子化切片：片段选中的原子片段 ID（空串表示旧的整条素材选取路径）
+    atom_clip_id = Column(String(36), nullable=False, default="", index=True)
     text_content = Column(Text, nullable=False, default="")
     start_time = Column(Float, nullable=False, default=0.0)
     duration = Column(Float, nullable=False, default=0.0)
@@ -671,10 +686,6 @@ class ScriptModel(Base):
     content = Column(Text, nullable=False, default="")
     segments = Column(JSON, nullable=False, default=list)
     tags = Column(JSON, nullable=False, default=list)
-    # #1894: 废弃标题库整合到文案库 — 标题配置字段
-    title_text = Column(String(500), nullable=False, default="")
-    title_category = Column(String(50), nullable=False, default="")
-    title_config = Column(JSON, nullable=False, default=dict)
     created_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
     updated_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
 
@@ -805,6 +816,33 @@ class PointsOrderModel(Base):
     created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
 
 
+class AssetAtomClipModel(Base):
+    """素材原子片段 ORM 模型 (#1970 智能剪辑流程重构)。
+
+    逻辑切分单元，不物理切割视频文件。
+    """
+
+    __tablename__ = "asset_atom_clips"
+    __table_args__ = (UniqueConstraint("asset_id", "clip_index", name="uq_asset_atom_clips_asset_index"),)
+
+    id = Column(String(36), primary_key=True)
+    asset_id = Column(
+        String(36),
+        ForeignKey("assets.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    start_time = Column(Float, nullable=False)
+    end_time = Column(Float, nullable=False)
+    duration = Column(Float, nullable=False)
+    clip_index = Column(Integer, nullable=False)
+    tags = Column(JSON, nullable=False, default=list)
+    ai_tags = Column(JSON, nullable=True, default=None)
+    scene_change_at = Column(Float, nullable=True)
+    is_fallback = Column(Boolean, nullable=False, default=False)
+    created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
+
+
 class DailyUsageRecordModel(Base):
     """每日使用记录 ORM 模型 (#1895)"""
 
@@ -817,3 +855,61 @@ class DailyUsageRecordModel(Base):
     usage_type = Column(String(50), nullable=False, default="free_clip")
     count = Column(Integer, nullable=False, default=0)
     updated_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
+
+
+class GpuLipsyncTaskModel(Base):
+    """GPU 口型同步任务 ORM 模型 — MuseTalk 反向轮询模式.
+
+    业务侧（AI 数字人生成/lipsync 流程）提交任务后，GPU Worker 主动 poll 拉取、
+    调用本地 MuseTalk 推理、再通过 result 接口回传结果视频。
+    """
+
+    __tablename__ = "gpu_lipsync_tasks"
+
+    id = Column(String(36), primary_key=True)
+    # 业务关联（原 lipsync_job_id，方便双向查询）
+    lipsync_job_id = Column(String(36), nullable=False, default="", index=True)
+    user_id = Column(String(36), nullable=False, default="", index=True)
+    project_id = Column(String(36), nullable=False, default="", index=True)
+
+    # 输入（预签名下载 URL，由 API 侧生成）
+    video_url = Column(Text, nullable=False)
+    audio_url = Column(Text, nullable=False)
+
+    # 结果
+    result_url = Column(Text, nullable=False, default="")
+    result_duration = Column(Float, nullable=False, default=0.0)
+
+    # 任务状态
+    status = Column(
+        String(20),
+        nullable=False,
+        default="pending",
+        index=True,
+    )  # pending → processing → done / failed / timeout
+    worker_id = Column(String(100), nullable=False, default="", index=True)
+    attempt = Column(Integer, nullable=False, default=0)
+    error_msg = Column(Text, nullable=False, default="")
+
+    # 时间戳
+    created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
+    started_at = Column(DateTime, nullable=True)
+    finished_at = Column(DateTime, nullable=True)
+    updated_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
+
+    # 心跳：worker 最近一次 poll/result 的时间，用于判定 worker 失联
+    last_heartbeat_at = Column(DateTime, nullable=True)
+
+
+class GpuWorkerModel(Base):
+    """GPU Worker 注册表 — 反向轮询模式下用于心跳与监控."""
+
+    __tablename__ = "gpu_workers"
+
+    worker_id = Column(String(100), primary_key=True)
+    hostname = Column(String(200), nullable=False, default="")
+    gpu_name = Column(String(200), nullable=False, default="")
+    free_vram_mb = Column(Integer, nullable=False, default=0)
+    capabilities = Column(String(500), nullable=False, default="")  # 逗号分隔，如 "musetalk"
+    last_heartbeat_at = Column(DateTime, nullable=True, index=True)
+    created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))

packages/config/base.py

@@ -68,6 +68,7 @@ class SharedSettings(BaseSettings):
     doubao_base_url: str = "https://ark.cn-beijing.volces.com/api/v3"
     doubao_timeout: int = 30
     doubao_max_retries: int = 2
+    doubao_vision_model: str = "doubao-1-5-vision-pro-250915"
 
     # ── MediaKit (火山引擎 AI 媒体工具) ──────────────────────────────────
     mediakit_api_key: str = ""
@@ -79,6 +80,29 @@ class SharedSettings(BaseSettings):
     # `if settings.points_enabled:` 包裹，防止未完善的扣点逻辑影响现有用户。
     points_enabled: bool = False
 
+    # ── GPU MuseTalk 反向轮询 Worker ────────────────────────────────────
+    # Worker 用这个长期 Token 鉴权（不是用户 JWT）。多 Worker 共用同一个 Token；
+    # worker_id 用于区分具体机器。生产必须配置；development 留空会跳过校验。
+    gpu_worker_token: str = ""
+    # GPU 任务超时（秒）：processing 状态超过此时长（以任务心跳为准）才回退
+    # pending / failed。#1970：RTX2060 6G 推理 720p 长视频需 5 分钟以上，300→900。
+    # Worker 推理期间每 30s 通过 /gpu/register(task_id=...) 续心跳，
+    # 只有真正超时或 Worker 明确上报 failed 才会回退。
+    gpu_task_timeout_seconds: int = 900
+    # 结果预签名 URL 有效期（秒）
+    gpu_result_url_expires: int = 3600
+    # 输入预签名 URL 有效期（秒，需留出 Worker 下载时间）
+    gpu_input_url_expires: int = 3600
+    # 业务侧是否启用 GPU 口型同步（开关）；关或无可用 Worker 时回退 MediaKit 云端
+    use_gpu_lipsync: bool = False
+    # 业务侧轮询 GPU 任务结果的间隔（秒）
+    gpu_lipsync_poll_interval: float = 5.0
+    # 业务侧等待 GPU 任务结果的总超时（秒）；超时后回退 MediaKit。
+    # 应小于等于 gpu_task_timeout_seconds（默认900s）+ 冗余，留足 Worker 下载/上传时间。
+    gpu_lipsync_wait_timeout: int = 1200
+    # 判断 Worker 可用的心跳新鲜度窗口（秒）—— last_heartbeat_at 在窗口内视为在线
+    gpu_worker_stale_seconds: int = 300
+
     @property
     def effective_database_url(self) -> str:
         """返回实际使用的数据库 URL。

packages/domain/__init__.py

@@ -1,5 +1,18 @@
 """Domain package for core business entities and rules."""
 
+from . import atom_clip_resolver
+from .asset_atom_clip import AssetAtomClip
+from .atom_clip_selector import (
+    ScoredAtomClip,
+    clips_to_segments,
+    estimate_required_clip_count,
+    score_atom_clip,
+    select_atom_clips,
+)
+from .atom_clip_service import (
+    compute_atom_clips,
+    compute_fallback_clips,
+)
 from .classification import (
     AssetClassification,
     ClassificationJob,
@@ -37,6 +50,15 @@ from .voice_library import VoiceLibraryItem
 
 __all__ = [
     "Asset",
+    "AssetAtomClip",
+    "ScoredAtomClip",
+    "clips_to_segments",
+    "compute_atom_clips",
+    "compute_fallback_clips",
+    "estimate_required_clip_count",
+    "score_atom_clip",
+    "select_atom_clips",
+    "atom_clip_resolver",
     "AssetClassification",
     "DailyUsageRecord",
     "PointsAccount",

packages/domain/asset_atom_clip.py

@@ -0,0 +1,82 @@
+"""素材原子片段（Atom Clip）领域实体 — #1970 智能剪辑流程重构。
+
+原子片段是素材的逻辑切分单元，不物理切割视频文件。
+每条记录指向某条素材的一段 [start_time, end_time] 区间。
+"""
+
+from __future__ import annotations
+
+import uuid
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+
+
+@dataclass
+class AssetAtomClip:
+    """素材原子片段。
+
+    Attributes:
+        id: 唯一标识。
+        asset_id: 所属素材 ID。
+        start_time: 片段起始时间（秒，浮点）。
+        end_time: 片段结束时间（秒，浮点）。
+        duration: 片段时长 = end_time - start_time（秒）。
+        clip_index: 在同一素材内的顺序编号（从 0 开始）。
+        tags: 继承自素材的标签，JSONB 存储，可为空列表。
+        scene_change_at: 片段尾部是否对齐了 scdet 镜头切换点（存储该切点的精确时间），
+                         未对齐时为 None。
+        is_fallback: 是否为兜底逻辑在内存中生成的临时片段（不入库）。
+        created_at: 创建时间。
+    """
+
+    id: str
+    asset_id: str
+    start_time: float
+    end_time: float
+    duration: float
+    clip_index: int
+    tags: list[str] = field(default_factory=list)
+    ai_tags: dict | None = None
+    scene_change_at: float | None = None
+    is_fallback: bool = False
+    created_at: datetime | None = None
+
+    def __post_init__(self):
+        if not self.id:
+            self.id = str(uuid.uuid4())
+        if self.duration <= 0:
+            self.duration = round(self.end_time - self.start_time, 3)
+        if self.duration < 0:
+            raise ValueError(f"duration must be >= 0, got start={self.start_time}, end={self.end_time}")
+        if self.start_time < 0:
+            raise ValueError(f"start_time must be >= 0, got {self.start_time}")
+        if self.end_time <= self.start_time:
+            raise ValueError(f"end_time must be > start_time, got start={self.start_time}, end={self.end_time}")
+        if self.clip_index < 0:
+            raise ValueError(f"clip_index must be >= 0, got {self.clip_index}")
+        if self.created_at is None:
+            self.created_at = datetime.now(UTC)
+
+    @classmethod
+    def create(
+        cls,
+        asset_id: str,
+        start_time: float,
+        end_time: float,
+        clip_index: int,
+        tags: list[str] | None = None,
+        scene_change_at: float | None = None,
+        is_fallback: bool = False,
+    ) -> AssetAtomClip:
+        """工厂方法：创建一个新的原子片段。"""
+        return cls(
+            id="",  # __post_init__ 会自动生成
+            asset_id=asset_id,
+            start_time=round(start_time, 3),
+            end_time=round(end_time, 3),
+            duration=round(end_time - start_time, 3),
+            clip_index=clip_index,
+            tags=tags or [],
+            scene_change_at=scene_change_at,
+            is_fallback=is_fallback,
+        )

packages/domain/atom_clip_resolver.py

@@ -0,0 +1,104 @@
+"""原子片段加载与兜底 — #1970 智能剪辑流程重构 P1.
+
+选片前从 ``asset_atom_clips`` 表加载素材池的原子片段；老素材/切片任务尚未
+完成/切片失败导致某些素材没有片段时，按需求兜底：内存中按 3-6 秒临时均匀
+切片（不存库，片段标记 is_fallback=True）。
+
+本模块对 repository 做鸭子类型约束（只需 find_by_asset / find_candidates_for_selection
+和 asset_repo.get），方便 API 侧（SQLAlchemy）与 worker 侧复用，也便于单测注入内存假实现。
+"""
+
+from __future__ import annotations
+
+import logging
+
+from packages.domain.asset_atom_clip import AssetAtomClip
+from packages.domain.atom_clip_service import compute_fallback_clips
+
+logger = logging.getLogger(__name__)
+
+# 兜底均匀切片步长（秒），落在 3~6s 区间中段
+FALLBACK_CLIP_SECONDS = 4.5
+
+
+def load_atom_clips_for_assets(
+    asset_ids: list[str],
+    *,
+    atom_clip_repo,
+    asset_repo=None,
+) -> dict[str, list[AssetAtomClip]]:
+    """加载素材池的原子片段（缺失素材走内存兜底）.
+
+    Args:
+        asset_ids: 候选素材 ID（去重保序）。
+        atom_clip_repo: AssetAtomClipRepository 实现（需有
+            ``find_candidates_for_selection`` 或 ``find_by_asset``）。
+        asset_repo: 可选，素材仓储（需有 ``get``），用于读取时长兜底切片。
+            为 None 时，没有原子片段的素材直接跳过（不兜底）。
+
+    Returns:
+        {asset_id: [AssetAtomClip, ...]}，仅包含至少有一个片段的素材，
+        片段按 clip_index 排序。
+    """
+    result: dict[str, list[AssetAtomClip]] = {}
+    unique_ids = list(dict.fromkeys(asset_ids))
+    if not unique_ids:
+        return result
+
+    # 1. 批量查询已生成的原子片段
+    persisted: dict[str, list[AssetAtomClip]] = {}
+    try:
+        if hasattr(atom_clip_repo, "find_candidates_for_selection"):
+            clips = atom_clip_repo.find_candidates_for_selection(unique_ids, limit=0)
+        else:
+            clips = []
+            for asset_id in unique_ids:
+                clips.extend(atom_clip_repo.find_by_asset(asset_id))
+        for clip in clips:
+            persisted.setdefault(clip.asset_id, []).append(clip)
+    except Exception:
+        logger.warning("加载 atom_clips 失败，全部走内存兜底", exc_info=True)
+        persisted = {}
+
+    for asset_id in unique_ids:
+        clips = persisted.get(asset_id)
+        if clips:
+            clips.sort(key=lambda c: c.clip_index)
+            result[asset_id] = clips
+            continue
+
+        # 2. 兜底：内存均匀切片（不存库）
+        if asset_repo is None:
+            continue
+        duration = _safe_asset_duration(asset_repo, asset_id)
+        if duration <= 0:
+            continue
+        result[asset_id] = compute_fallback_clips(
+            asset_id,
+            duration,
+            clip_seconds=FALLBACK_CLIP_SECONDS,
+        )
+
+    return result
+
+
+def flatten_candidates(
+    clips_by_asset: dict[str, list[AssetAtomClip]],
+) -> list[AssetAtomClip]:
+    """把 {asset_id: [clips]} 摊平为候选片段列表（素材顺序内片段有序）。"""
+    flat: list[AssetAtomClip] = []
+    for clips in clips_by_asset.values():
+        flat.extend(clips)
+    return flat
+
+
+def _safe_asset_duration(asset_repo, asset_id: str) -> float:
+    """安全读取素材时长，任何异常返回 0。"""
+    try:
+        asset = asset_repo.get(asset_id)
+        if asset is None:
+            return 0.0
+        return float(getattr(asset, "duration", 0.0) or 0.0)
+    except Exception:
+        logger.warning("读取素材时长失败: asset_id=%s", asset_id, exc_info=True)
+        return 0.0

packages/domain/atom_clip_selector.py

@@ -0,0 +1,264 @@
+"""原子片段级选片核心 — #1970 智能剪辑流程重构 P1.
+
+选片单元从"整条素材 + 随机起点"升级为"原子片段（atom clip）"：
+
+- 每个 EditPlanClip 指向一个 atom_clip_id（含 asset_id + start/end）；
+- 同一素材的不同原子片段可被同一视频多次选用；
+- 同一原子片段在一个视频内只用一次；
+- 跨变体/跨任务的避让升级为原子片段级（同 asset 的不同片段天然不重叠）；
+- atom_clips 未就绪（老素材/切片失败）时由调用方走内存兜底切片，
+  再不行回退到现有的整条素材随机起点逻辑。
+
+本模块是纯函数：原子片段数据由调用方从 repository 读取后注入，不直接碰 DB，
+便于单元测试。评分维度与 smart_match 保持一致（质量分、时长适配、新鲜度、
+未使用加分），只是评分对象从素材变为原子片段。
+"""
+
+from __future__ import annotations
+
+import random
+from dataclasses import dataclass
+from typing import Any
+
+from packages.domain.asset_atom_clip import AssetAtomClip
+
+
+@dataclass(slots=True)
+class ScoredAtomClip:
+    """带评分的候选原子片段。"""
+
+    clip: AssetAtomClip
+    score: float
+
+    @property
+    def atom_clip_id(self) -> str:
+        return self.clip.id
+
+    @property
+    def asset_id(self) -> str:
+        return self.clip.asset_id
+
+    @property
+    def start_time(self) -> float:
+        return self.clip.start_time
+
+    @property
+    def end_time(self) -> float:
+        return self.clip.end_time
+
+    @property
+    def duration(self) -> float:
+        return self.clip.duration
+
+
+# 评分权重（与 smart_match.score_asset 的维度对齐）
+W_QUALITY = 0.35
+W_DURATION_FIT = 0.30
+W_FRESHNESS = 0.15
+W_UNUSED_BONUS = 0.10
+W_ASSET_BALANCE = 0.10
+
+# 评分随机噪声上限（与 SCORE_RANDOM_NOISE_MAX 同量级，避免反复选同一组合）
+SCORE_NOISE_MAX = 0.05
+
+
+def score_atom_clip(
+    clip: AssetAtomClip,
+    *,
+    target_duration: float,
+    asset_quality: dict[str, float] | None = None,
+    asset_freshness: dict[str, float] | None = None,
+    used_in_video: set[str] | None = None,
+    asset_usage_counts: dict[str, int] | None = None,
+    recently_used: set[str] | None = None,
+    required_count: int = 1,
+    total_candidates: int = 1,
+) -> float:
+    """评估单个原子片段对某个目标槽位的适配分（越高越优先）.
+
+    评分维度：
+    - 质量分（继承素材质量，缺省中性 0.6）；
+    - 时长适配（片段时长越接近目标越好，覆盖不满显著扣分）；
+    - 新鲜度（缺省中性 0.5）；
+    - 未使用加分（本视频内未用过 +1，已用 0）；
+    - 素材均衡（同一素材在本视频用得越多，其剩余片段扣分越多，鼓励分散到多素材）；
+    - 跨视频/历史使用降权（recently_used 中的片段扣分，不硬禁）。
+    """
+    asset_quality = asset_quality or {}
+    asset_freshness = asset_freshness or {}
+    used_in_video = used_in_video or set()
+    asset_usage_counts = asset_usage_counts or {}
+    recently_used = recently_used or set()
+
+    quality = asset_quality.get(clip.asset_id, 0.6)
+
+    if target_duration > 0:
+        coverage = min(1.0, clip.duration / target_duration)
+        overshoot = max(0.0, (clip.duration - target_duration) / target_duration)
+        duration_fit = max(0.0, coverage - 0.15 * overshoot)
+    else:
+        duration_fit = 0.5
+
+    freshness = asset_freshness.get(clip.asset_id, 0.5)
+    unused_bonus = 0.0 if clip.id in used_in_video else 1.0
+
+    # 素材均衡：该素材已被本视频选用 k 次，其片段逐次扣分
+    times_used = asset_usage_counts.get(clip.asset_id, 0)
+    balance = 1.0 / (1.0 + times_used)
+
+    # 跨视频/历史使用降权（不硬禁）
+    history_penalty = 0.35 if clip.id in recently_used else 0.0
+
+    score = (
+        W_QUALITY * quality
+        + W_DURATION_FIT * duration_fit
+        + W_FRESHNESS * freshness
+        + W_UNUSED_BONUS * unused_bonus
+        + W_ASSET_BALANCE * balance
+        - history_penalty
+    )
+    return score
+
+
+def select_atom_clips(
+    candidates: list[AssetAtomClip],
+    *,
+    target_duration: float = 0.0,
+    used_atom_clip_ids: set[str] | None = None,
+    asset_usage_counts: dict[str, int] | None = None,
+    recently_used_atom_ids: set[str] | None = None,
+    required_count: int = 1,
+    limit: int = 0,
+    asset_quality: dict[str, float] | None = None,
+    asset_freshness: dict[str, float] | None = None,
+    rng: random.Random | None = None,
+) -> list[ScoredAtomClip]:
+    """为一个目标槽位从候选原子片段中评分选片（纯函数）.
+
+    Args:
+        candidates: 候选原子片段（可跨多素材）。
+        target_duration: 槽位目标时长（秒）。
+        used_atom_clip_ids: 本视频已用过的原子片段 ID（硬排除，同片段不重复）。
+        asset_usage_counts: 本视频各素材已选片段数（均衡评分用）。
+        recently_used_atom_ids: 跨视频/历史成片用过的片段 ID（降权，不硬禁）。
+        required_count: 整个视频需要的片段总数（预留，供覆盖策略判断）。
+        limit: 最多返回条数；<=0 表示返回全部排序结果。
+        asset_quality / asset_freshness: 评分注入。
+        rng: 可选随机源（测试注入）。
+
+    Returns:
+        评分降序的 ScoredAtomClip 列表（已排除本视频用过的片段）。
+    """
+    rng = rng or random.Random()
+    used = used_atom_clip_ids or set()
+    asset_usage_counts = asset_usage_counts or {}
+    recently_used = recently_used_atom_ids or set()
+
+    available = [c for c in candidates if c.id not in used]
+    scored: list[ScoredAtomClip] = []
+    for clip in available:
+        base = score_atom_clip(
+            clip,
+            target_duration=target_duration,
+            asset_quality=asset_quality,
+            asset_freshness=asset_freshness,
+            used_in_video=used,
+            asset_usage_counts=asset_usage_counts,
+            recently_used=recently_used,
+            required_count=required_count,
+            total_candidates=len(candidates),
+        )
+        noise = rng.uniform(0.0, SCORE_NOISE_MAX)
+        scored.append(ScoredAtomClip(clip=clip, score=base + noise))
+
+    scored.sort(key=lambda s: s.score, reverse=True)
+    if limit and limit > 0:
+        return scored[:limit]
+    return scored
+
+
+def clips_to_segments(clips: list[AssetAtomClip]) -> dict[str, list[tuple[float, float]]]:
+    """把选中的原子片段转换为旧的 {asset_id: [(start, end), ...]} 区间结构.
+
+    用于与现有跨变体区间避让（variant_plan_selector / metadata.used_segments）对接。
+    原子片段级天然不重叠，同素材多片段直接形成多段不重叠区间。
+    """
+    segments: dict[str, list[tuple[float, float]]] = {}
+    for clip in clips:
+        segments.setdefault(clip.asset_id, []).append((clip.start_time, clip.end_time))
+    for asset_id in segments:
+        segments[asset_id].sort()
+    return segments
+
+
+def estimate_required_clip_count(
+    voice_total_duration: float,
+    average_clip_duration: float = 4.5,
+) -> int:
+    """配音总时长 / 平均片段时长 ≈ 需要的片段数（至少 1）。"""
+    if voice_total_duration <= 0 or average_clip_duration <= 0:
+        return 1
+    return max(1, round(voice_total_duration / average_clip_duration))
+
+
+def reselect_clips_from_atoms(
+    source_clips: list[dict[str, Any]],
+    candidates: list[AssetAtomClip],
+    *,
+    historical_atom_ids: set[str] | None = None,
+    batch_used_atom_ids: set[str] | None = None,
+    rng: random.Random | None = None,
+) -> list[dict[str, Any]] | None:
+    """#1970 变体重选的原子片段级实现.
+
+    与 variant_plan_selector.reselect_clips_for_variant 对应：保留源 plan 的
+    片段骨架（order/clip_type/文案/转场），从候选原子片段中为每个 main 片段
+    选取一个原子片段；同变体/批次内同一片段不可重复，历史成片用过的片段降权。
+
+    Returns:
+        新 clips_data（dict 列表，含 asset_id/atom_clip_id/start_time/duration），
+        候选不足（main 片段多于去重后片段数）时返回 None，由调用方回退整条素材路径。
+        非 main 片段（intro/outro 等）原样保留不分配素材。
+    """
+    if not source_clips or not candidates:
+        return None
+
+    rng = rng or random.Random()
+    main_indexes = [i for i, c in enumerate(source_clips) if c.get("clip_type", "main") == "main"]
+    if len(main_indexes) > len({c.id for c in candidates}):
+        return None
+
+    used: set[str] = set(batch_used_atom_ids or ())
+    result: list[dict[str, Any]] = [dict(c) for c in source_clips]
+    asset_usage: dict[str, int] = {}
+
+    for idx in main_indexes:
+        skeleton = source_clips[idx]
+        target_duration = float(skeleton.get("duration") or 0.0)
+        ranked = select_atom_clips(
+            candidates,
+            target_duration=target_duration,
+            used_atom_clip_ids=used,
+            asset_usage_counts=asset_usage,
+            recently_used_atom_ids=historical_atom_ids or set(),
+            required_count=len(main_indexes),
+            limit=1,
+            rng=rng,
+        )
+        if not ranked:
+            return None
+        picked = ranked[0]
+        # 段长：片段短于槽位时取片段全长（渲染末帧冻结铺满），长于槽位时按槽位时长 trim
+        new_duration = picked.duration if target_duration <= 0 else min(target_duration, picked.duration)
+        result[idx].update(
+            {
+                "asset_id": picked.asset_id,
+                "atom_clip_id": picked.atom_clip_id,
+                "start_time": round(picked.start_time, 3),
+                "duration": round(new_duration, 3),
+            }
+        )
+        used.add(picked.atom_clip_id)
+        asset_usage[picked.asset_id] = asset_usage.get(picked.asset_id, 0) + 1
+
+    return result

packages/domain/atom_clip_service.py

@@ -0,0 +1,215 @@
+"""素材原子切片服务 — #1970 智能剪辑流程重构 P1.
+
+切片规则（见 docs/smart-edit-flow-redesign-20260916.md §1）：
+- 3~6 秒一个片段，具体时长在此范围内随机（避免固定节奏）
+- 切点附近 0.5 秒内有 scdet 镜头切换点时，切点偏移到切换处
+  （复用素材 metadata 中已缓存的 scene_change_points，不重新计算）
+- <6 秒素材整条作为一个片段，不切
+- 最后一个片段不足 3 秒的合并到前一个；超过 3 秒独立成段
+- 片段是逻辑索引，不物理切割视频文件
+
+片段在内存中计算；持久化由上层调用 repository 完成，保证本模块可单测、无 IO 依赖。
+"""
+
+from __future__ import annotations
+
+import random
+
+from packages.domain.asset_atom_clip import AssetAtomClip
+
+# 切片参数（集中常量，便于后续抽配置）
+MIN_CLIP_SECONDS = 3.0
+MAX_CLIP_SECONDS = 6.0
+# 切点与 scdet 切换点的对齐窗口
+SCENE_SNAP_WINDOW = 0.5
+# 末段最小独立时长：不足则并入前一段
+MIN_TAIL_SECONDS = 3.0
+# 浮点比较容差
+_EPS = 0.05
+
+
+def _round3(value: float) -> float:
+    return round(float(value), 3)
+
+
+def _snap_to_scene(
+    cut: float,
+    scene_points: list[float] | None,
+    lower: float,
+    upper: float,
+) -> tuple[float, float | None]:
+    """将切点 ``cut`` 对齐到窗口内最近的 scdet 切换点.
+
+    Args:
+        cut: 原始切点（秒）。
+        scene_points: 候选切换点（秒，已排序），可为空。
+        lower: 允许偏移的下界（不早于当前片段起点）。
+        upper: 允许偏移的上界（不晚于素材总时长）。
+
+    Returns:
+        (对齐后的切点, 命中的切换点)；未命中返回 (cut, None)。
+    """
+    if not scene_points:
+        return cut, None
+
+    best: float | None = None
+    best_dist = SCENE_SNAP_WINDOW
+    for point in scene_points:
+        # 切换点必须严格落在片段内部（不能与边界重合），且在窗口内
+        if point <= lower + _EPS or point >= upper - _EPS:
+            continue
+        dist = abs(point - cut)
+        if dist <= best_dist:
+            best_dist = dist
+            best = point
+    if best is None:
+        return cut, None
+    return _round3(best), _round3(best)
+
+
+def compute_atom_clips(
+    asset_id: str,
+    duration: float,
+    *,
+    scene_change_points: list[float] | None = None,
+    tags: list[str] | None = None,
+    rng: random.Random | None = None,
+) -> list[AssetAtomClip]:
+    """根据素材时长计算原子片段（纯函数，不落库）.
+
+    Args:
+        asset_id: 素材 ID。
+        duration: 素材总时长（秒）。
+        scene_change_points: metadata 中缓存的 scdet 切换点（秒）。
+        tags: 继承自素材的标签。
+        rng: 可选随机源（测试可注入固定种子）。
+
+    Returns:
+        有序的原子片段列表（clip_index 从 0 开始）。
+    """
+    if duration <= 0:
+        return []
+
+    r = rng or random.Random()
+    points = _normalize_scene_points(scene_change_points, duration)
+
+    # <6 秒素材整条作为一个片段，不切
+    if duration < MAX_CLIP_SECONDS:
+        return [
+            AssetAtomClip.create(
+                asset_id=asset_id,
+                start_time=0.0,
+                end_time=_round3(duration),
+                clip_index=0,
+                tags=list(tags or []),
+            )
+        ]
+
+    boundaries: list[float] = [0.0]
+    scene_hits: dict[int, float] = {}
+
+    cursor = 0.0
+    while duration - cursor > MAX_CLIP_SECONDS + _EPS:
+        # 在 [3, 6] 内随机决定本段目标时长
+        target_len = r.uniform(MIN_CLIP_SECONDS, MAX_CLIP_SECONDS)
+        raw_cut = cursor + target_len
+        if raw_cut >= duration - _EPS:
+            break
+        cut, hit = _snap_to_scene(raw_cut, points, lower=cursor, upper=duration)
+
+        # 对齐后若导致本段短于 3 秒（切换点太靠近段首），放弃对齐
+        if cut - cursor < MIN_CLIP_SECONDS - _EPS:
+            cut = _round3(raw_cut)
+            hit = None
+
+        boundaries.append(_round3(cut))
+        if hit is not None:
+            scene_hits[len(boundaries) - 1] = hit
+        cursor = cut
+
+    boundaries.append(_round3(duration))
+
+    # 末段处理：最后一个片段不足 3 秒则合并到前一个
+    if len(boundaries) >= 3:
+        tail_start = boundaries[-2]
+        tail_len = duration - tail_start
+        if tail_len < MIN_TAIL_SECONDS - _EPS:
+            boundaries.pop(-2)
+
+    clips: list[AssetAtomClip] = []
+    for index in range(len(boundaries) - 1):
+        start = boundaries[index]
+        end = boundaries[index + 1]
+        if end - start < _EPS:
+            continue
+        # 片段尾部对齐的切换点 = 该片段右边界（若它来自 snap）
+        scene_at = scene_hits.get(index + 1)
+        clips.append(
+            AssetAtomClip.create(
+                asset_id=asset_id,
+                start_time=start,
+                end_time=end,
+                clip_index=index,
+                tags=list(tags or []),
+                scene_change_at=scene_at,
+            )
+        )
+    return clips
+
+
+def compute_fallback_clips(
+    asset_id: str,
+    duration: float,
+    *,
+    tags: list[str] | None = None,
+    clip_seconds: float = 4.5,
+) -> list[AssetAtomClip]:
+    """兜底切片：atom_clips 未就绪时，内存中按固定步长临时均匀切片（不存库）.
+
+    与 :func:`compute_atom_clips` 的区别：不随机、不对齐切点，
+    产出的片段标记 ``is_fallback=True``。
+    """
+    if duration <= 0:
+        return []
+
+    step = min(max(clip_seconds, MIN_CLIP_SECONDS), MAX_CLIP_SECONDS)
+    clips: list[AssetAtomClip] = []
+    cursor = 0.0
+    index = 0
+    while cursor < duration - _EPS:
+        end = min(cursor + step, duration)
+        clips.append(
+            AssetAtomClip.create(
+                asset_id=asset_id,
+                start_time=_round3(cursor),
+                end_time=_round3(end),
+                clip_index=index,
+                tags=list(tags or []),
+                is_fallback=True,
+            )
+        )
+        cursor = end
+        index += 1
+
+    # 末段不足 3 秒合并
+    if len(clips) >= 2 and clips[-1].duration < MIN_TAIL_SECONDS - _EPS:
+        last = clips.pop()
+        prev = clips[-1]
+        merged = AssetAtomClip.create(
+            asset_id=asset_id,
+            start_time=prev.start_time,
+            end_time=last.end_time,
+            clip_index=prev.clip_index,
+            tags=list(tags or []),
+            is_fallback=True,
+        )
+        clips[-1] = merged
+    return clips
+
+
+def _normalize_scene_points(points: list[float] | None, duration: float) -> list[float]:
+    """清洗切换点：去重、排序、限定在 (0, duration) 内。"""
+    if not points:
+        return []
+    cleaned = sorted({round(float(p), 3) for p in points if 0 < float(p) < duration})
+    return cleaned

packages/domain/atom_clip_tagger.py

@@ -0,0 +1,292 @@
+"""片段级 AI 标签 — #1970 智能剪辑流程重构 P2.
+
+对每个 atom_clip 提取关键帧，调用豆包视觉理解 API 识别内容，
+生成结构化标签（场景、物体、动作、景别、是否有文字）。
+
+纯函数 + IO 分离设计：
+- build_vision_prompt() 返回结构化 prompt
+- parse_vision_response(text) 解析 AI 返回的 JSON 标签
+- tag_atom_clip(...) 主入口，组合帧提取 → 视觉 API → 解析标签
+
+降级策略：任何环节失败都返回 {"inherited_tags": clip.tags}，不阻断流程。
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Any, Optional
+
+logger = logging.getLogger(__name__)
+
+# AI 标签结构的键
+AI_TAG_KEYS = ("scene", "objects", "action", "shot", "has_text")
+
+
+def build_vision_prompt() -> str:
+    """返回结构化标签提取 prompt.
+
+    要求 AI 以 JSON 格式返回片段内容标签，包含：
+    - scene: 场景类型列表（如 "工厂", "办公室", "户外"）
+    - objects: 出现的物体列表（如 "产品", "手机", "电脑"）
+    - action: 动作类型列表（如 "演示", "说话", "操作"）
+    - shot: 景别（"特写" / "中景" / "远景" 之一）
+    - has_text: 画面中是否有显著文字（true/false）
+    """
+    return """请分析这段视频片段的关键帧，识别内容并返回 JSON 格式标签。
+
+要求返回以下 JSON 结构（严格 JSON，不要添加其他文字）：
+{
+  "scene": ["场景1", "场景2"],
+  "objects": ["物体1", "物体2"],
+  "action": ["动作1"],
+  "shot": "特写|中景|远景",
+  "has_text": true/false
+}
+
+规则：
+- scene: 场景类型，如"工厂"、"办公室"、"户外"、"商店"、"家庭"等，1-3个
+- objects: 画面中可见的主要物体，如"产品"、"手机"、"电脑"、"食品"等，1-5个
+- action: 人物或物体正在进行的动作，如"演示"、"说话"、"操作"、"展示"等，1-3个
+- shot: 景别判断，只能是"特写"、"中景"或"远景"之一
+- has_text: 画面中是否有显著可读文字（标题、字幕、标语等）
+
+请只返回 JSON，不要有其他说明文字。"""
+
+
+def parse_vision_response(text: str) -> dict:
+    """解析 AI 返回的 JSON 标签文本.
+
+    Args:
+        text: 视觉 API 返回的文本，期望是 JSON 格式。
+
+    Returns:
+        结构化标签 dict，格式如：
+        {"scene": [...], "objects": [...], "action": [...], "shot": "...", "has_text": bool}
+
+    解析失败时返回空 dict。
+    """
+    if not text or not text.strip():
+        return {}
+
+    # 尝试直接解析
+    cleaned = text.strip()
+
+    # 去除可能的 markdown 代码块包裹
+    if cleaned.startswith("```"):
+        lines = cleaned.split("\n")
+        # 去掉首尾的 ``` 行
+        start = 1
+        end = len(lines)
+        for i in range(len(lines) - 1, 0, -1):
+            if lines[i].strip().startswith("```"):
+                end = i
+                break
+        cleaned = "\n".join(lines[start:end]).strip()
+
+    try:
+        data = json.loads(cleaned)
+    except json.JSONDecodeError:
+        # 尝试从文本中提取 JSON 块
+        try:
+            start_idx = cleaned.index("{")
+            end_idx = cleaned.rindex("}") + 1
+            data = json.loads(cleaned[start_idx:end_idx])
+        except (ValueError, json.JSONDecodeError):
+            logger.warning("无法解析 AI 标签响应: %s", text[:200])
+            return {}
+
+    if not isinstance(data, dict):
+        return {}
+
+    # 验证和清洗各字段
+    result: dict[str, Any] = {}
+    for key in ("scene", "objects", "action"):
+        val = data.get(key)
+        if isinstance(val, list):
+            result[key] = [str(v).strip() for v in val if str(v).strip()]
+        elif isinstance(val, str) and val.strip():
+            result[key] = [val.strip()]
+        else:
+            result[key] = []
+
+    shot_val = data.get("shot", "")
+    if isinstance(shot_val, str) and shot_val.strip() in ("特写", "中景", "远景"):
+        result["shot"] = shot_val.strip()
+    else:
+        result["shot"] = ""
+
+    has_text_val = data.get("has_text")
+    if isinstance(has_text_val, bool):
+        result["has_text"] = has_text_val
+    elif isinstance(has_text_val, str):
+        result["has_text"] = has_text_val.lower() in ("true", "yes", "1")
+    else:
+        result["has_text"] = False
+
+    return result
+
+
+def _extract_frames_via_mediakit(
+    mediakit_client: Any,
+    video_url: str,
+    start_time: float,
+    end_time: float,
+) -> Optional[list[str]]:
+    """通过 MediaKit 提取 3 帧（首、中、尾）.
+
+    Returns:
+        图片 URL 列表（3 个），失败返回 None。
+    """
+    try:
+        frames = mediakit_client.extract_frames(
+            video_url=video_url,
+            strategy="SpecifiedTime",
+            max_frames=3,
+            poll_interval=2.0,
+            max_poll_attempts=30,
+        )
+        # MediaKit SpecifiedTime 策略可能不支持直接传时间点
+        # 如果返回结果不够 3 帧，降级到 ffmpeg
+        if frames and len(frames) >= 1:
+            urls = [f.get("image_url", "") for f in frames if f.get("image_url")]
+            if urls:
+                return urls
+    except Exception as e:
+        logger.warning("MediaKit 抽帧失败，将降级为 ffmpeg: %s", e)
+
+    return None
+
+
+def _extract_frames_via_ffmpeg(
+    video_url: str,
+    start_time: float,
+    end_time: float,
+) -> Optional[list[str]]:
+    """通过 ffmpeg 本地提取 3 帧并转为 base64.
+
+    Returns:
+        base64 data URI 列表（3 个），失败返回 None。
+    """
+    import base64
+
+    mid_time = round((start_time + end_time) / 2, 3)
+    timestamps = [round(start_time, 3), mid_time, round(end_time, 3)]
+
+    try:
+        frames_b64: list[str] = []
+        with tempfile.TemporaryDirectory() as tmpdir:
+            for i, ts in enumerate(timestamps):
+                out_path = Path(tmpdir) / f"frame_{i}.jpg"
+                cmd = [
+                    "ffmpeg",
+                    "-y",
+                    "-ss",
+                    str(ts),
+                    "-i",
+                    video_url,
+                    "-vframes",
+                    "1",
+                    "-q:v",
+                    "2",
+                    str(out_path),
+                ]
+                result = subprocess.run(
+                    cmd,
+                    capture_output=True,
+                    timeout=30,
+                )
+                if result.returncode != 0 or not out_path.exists():
+                    logger.warning("ffmpeg 抽帧失败 ts=%s: %s", ts, result.stderr[:200])
+                    continue
+
+                img_data = out_path.read_bytes()
+                b64 = base64.b64encode(img_data).decode("ascii")
+                frames_b64.append(f"data:image/jpeg;base64,{b64}")
+
+        if frames_b64:
+            return frames_b64
+    except Exception as e:
+        logger.warning("ffmpeg 抽帧异常: %s", e)
+
+    return None
+
+
+def tag_atom_clip(
+    clip: Any,
+    video_url: str,
+    doubao_client: Any,
+    mediakit_client: Any | None = None,
+    storage: Any | None = None,
+) -> dict:
+    """主入口：为单个 atom_clip 生成 AI 标签.
+
+    流程：提取帧 → 调视觉 API → 解析标签 → 返回结构化标签 dict。
+    任何环节失败返回 {"inherited_tags": clip.tags}，不阻断流程。
+
+    Args:
+        clip: AssetAtomClip 领域对象（需有 start_time, end_time, tags）。
+        video_url: 素材视频的公网可访问 URL。
+        doubao_client: DoubaoClient 实例。
+        mediakit_client: MediaKitClient 实例（可选，不可用时降级 ffmpeg）。
+        storage: SharedStorageService 实例（可选，用于获取签名 URL）。
+
+    Returns:
+        结构化标签 dict，格式如：
+        {"scene": [...], "objects": [...], "action": [...], "shot": "...",
+         "has_text": bool, "inherited_tags": [...]}
+    """
+    inherited = list(getattr(clip, "tags", []) or [])
+
+    # 检查 DoubaoClient 是否可用
+    if not getattr(doubao_client, "is_available", False):
+        logger.info("DoubaoClient 不可用，跳过 AI 标签: clip_id=%s", getattr(clip, "id", ""))
+        return {"inherited_tags": inherited}
+
+    # 提取帧图片
+    frame_urls: Optional[list[str]] = None
+    start_time = getattr(clip, "start_time", 0.0)
+    end_time = getattr(clip, "end_time", 0.0)
+
+    # 优先使用 MediaKit
+    if mediakit_client and getattr(mediakit_client, "is_available", False):
+        frame_urls = _extract_frames_via_mediakit(mediakit_client, video_url, start_time, end_time)
+
+    # MediaKit 不可用或失败 → 降级 ffmpeg
+    if not frame_urls:
+        frame_urls = _extract_frames_via_ffmpeg(video_url, start_time, end_time)
+
+    if not frame_urls:
+        logger.warning("帧提取失败，跳过 AI 标签: clip_id=%s", getattr(clip, "id", ""))
+        return {"inherited_tags": inherited}
+
+    # 调用视觉 API
+    prompt = build_vision_prompt()
+    messages = [{"role": "user", "content": prompt}]
+
+    try:
+        response_text = doubao_client.vision_completion(
+            messages=messages,
+            images=frame_urls,
+            timeout=60,
+        )
+    except Exception as e:
+        logger.warning("视觉 API 调用异常: clip_id=%s error=%s", getattr(clip, "id", ""), e)
+        return {"inherited_tags": inherited}
+
+    if not response_text:
+        logger.warning("视觉 API 返回空: clip_id=%s", getattr(clip, "id", ""))
+        return {"inherited_tags": inherited}
+
+    # 解析标签
+    ai_tags = parse_vision_response(response_text)
+    if not ai_tags:
+        logger.warning("标签解析失败: clip_id=%s response=%s", getattr(clip, "id", ""), response_text[:200])
+        return {"inherited_tags": inherited}
+
+    # 合并 inherited_tags
+    ai_tags["inherited_tags"] = inherited
+    return ai_tags

packages/domain/edit_plan_clip.py

@@ -65,6 +65,7 @@ class EditPlanClip:
     order: int
     template_clip_config_id: str = ""
     asset_id: str = ""
+    atom_clip_id: str = ""
     text_content: str = ""
     start_time: float = 0.0
     duration: float = 0.0
@@ -85,6 +86,7 @@ class EditPlanClip:
         *,
         template_clip_config_id: str = "",
         asset_id: str = "",
+        atom_clip_id: str = "",
         text_content: str = "",
         start_time: float = 0.0,
         duration: float = 0.0,
@@ -117,6 +119,7 @@ class EditPlanClip:
             order=order,
             template_clip_config_id=template_clip_config_id.strip() if template_clip_config_id else "",
             asset_id=asset_id.strip() if asset_id else "",
+            atom_clip_id=atom_clip_id.strip() if atom_clip_id else "",
             text_content=text_content.strip(),
             start_time=start_time,
             duration=duration,
@@ -127,16 +130,25 @@ class EditPlanClip:
             config=config or {},
         )
 
-    def assign_asset(self, asset_id: str, *, start_time: float | None = None) -> None:
+    def assign_asset(
+        self,
+        asset_id: str,
+        *,
+        start_time: float | None = None,
+        atom_clip_id: str | None = None,
+    ) -> None:
         """分配素材
 
         Args:
             asset_id: 素材 ID
             start_time: 可选，素材播放起始时间（秒）。如果提供且在有效范围内，则设置；否则保持默认 0.0
+            atom_clip_id: 可选，选中的原子片段 ID（#1970 原子化切片）。
         """
         if not asset_id.strip():
             raise ValueError("asset_id 不能为空")
         self.asset_id = asset_id.strip()
+        if atom_clip_id is not None:
+            self.atom_clip_id = atom_clip_id.strip() if atom_clip_id else ""
         if start_time is not None and start_time >= 0:
             self.start_time = start_time
         self.updated_at = datetime.now(UTC)

packages/domain/editing_mode.py

@@ -12,9 +12,21 @@ else:
 
 
 class EditingMode(StrEnum):
-    """剪辑模式枚举"""
+    """剪辑模式枚举。
 
-    ONE_TAKE = "one_take"  # 顺序拼接模式
-    PIP = "pip"  # 画中画模式
-    VOICE_OVER = "voice_over"  # 口播+B-roll模式
-    VOICE_PIP = "voice_pip"  # 口播+画中画组合模式
+    #1970 智能剪辑流程重构（2026-09）后，剪辑组装模式改由
+    ``CreateGenerationTaskRequest.assembly_mode``（'random'/'narrative'）表达。
+    本枚举仅保留模板体系仍在使用的模式；以下三个模式标记 deprecated，
+    不主动删除代码（pip/voice_pip 在路由入口已统一映射为 one_take），
+    待确认无存量引用后在技术债清理中移除：
+
+    - ONE_TAKE（deprecated）：顺序拼接，等同 assembly_mode='random'
+    - PIP（deprecated）：画中画已下线，入口映射 one_take
+    - VOICE_PIP（deprecated）：口播+画中画已下线，入口映射 one_take
+    - VOICE_OVER：保留，口播+B-roll 模板仍在使用
+    """
+
+    ONE_TAKE = "one_take"  # deprecated（#1970）：顺序拼接，等同 assembly_mode='random'
+    PIP = "pip"  # deprecated（#1970）：画中画已下线，入口映射 one_take
+    VOICE_OVER = "voice_over"  # 口播+B-roll模式（保留）
+    VOICE_PIP = "voice_pip"  # deprecated（#1970）：口播+画中画已下线，入口映射 one_take

packages/domain/narrative_match.py

@@ -0,0 +1,260 @@
+"""叙事剪辑素材标签匹配 — #1970 PR3 + P2 AI 标签加权.
+
+叙事模式下，选片在现有评分（smart_match / atom_clip_selector）之前先做一层
+文案标签匹配：
+
+- 文案 tags 与素材 tag 名归一化后求交集；
+- 命中任一标签的素材作为「优先候选池」，未命中的作为普通池；
+- 调用方对优先池跑现有 smart_select_assets，数量不足时用普通池补足
+  （无任何匹配 → 完全降级为现有随机逻辑，行为与改造前一致）。
+
+P2 AI 标签加权（#1970 fragment-level AI tagging）：
+- 片段级 AI 标签（scene/objects/action）与文案标签做交集时权重 2.0
+- 素材级标签（tag_ids 映射名）与文案标签交集时权重 1.0
+- 综合得分 = sum(命中权重) / max(可能权重)
+- 有 AI 标签的片段命中时优先于仅素材标签命中的片段
+
+纯函数模块：标签 id→名称映射由调用方查 TagModel 后注入，不直接碰 DB。
+"""
+
+from __future__ import annotations
+
+from typing import Any, Iterable
+
+# 标签归一化后仍短于此长度的标签不参与匹配（避免「的」「是」这类噪声短词）
+MIN_TAG_LEN = 2
+
+# 标签匹配权重
+AI_TAG_WEIGHT = 2.0  # AI 标签命中权重
+ASSET_TAG_WEIGHT = 1.0  # 素材标签命中权重
+
+
+def normalize_tag(tag: Any) -> str:
+    """标签归一化：去空白、小写。数字/英文统一小写，中文不受影响。"""
+    if tag is None:
+        return ""
+    return str(tag).strip().lower()
+
+
+def _normalize_tags(tags: Iterable[Any]) -> set[str]:
+    out: set[str] = set()
+    for t in tags or []:
+        norm = normalize_tag(t)
+        if len(norm) >= MIN_TAG_LEN:
+            out.add(norm)
+    return out
+
+
+def build_asset_tag_name_index(tag_names_by_id: dict[str, Any]) -> dict[str, set[str]]:
+    """构造 asset_id → 归一化标签名集合 的索引。
+
+    Args:
+        tag_names_by_id: {asset_id: [标签名或标签id, ...]}，允许混入 None/空值
+    """
+    index: dict[str, set[str]] = {}
+    for asset_id, names in (tag_names_by_id or {}).items():
+        index[asset_id] = _normalize_tags(names)
+    return index
+
+
+def _extract_ai_tag_names(ai_tags: dict) -> set[str]:
+    """从 AI 标签 dict 中提取所有标签名（scene + objects + action）.
+
+    Args:
+        ai_tags: 片段级 AI 标签 dict，如 {"scene": [...], "objects": [...], "action": [...], ...}
+
+    Returns:
+        归一化后的标签名集合。
+    """
+    names: set[str] = set()
+    for key in ("scene", "objects", "action"):
+        values = ai_tags.get(key)
+        if isinstance(values, list):
+            names |= _normalize_tags(values)
+    return names
+
+
+def _compute_ai_score(
+    asset_id: str,
+    wanted: set[str],
+    clip_ai_tags_by_asset: dict[str, list[dict]] | None,
+) -> float:
+    """计算单个素材的 AI 标签加权得分.
+
+    对该素材的所有片段 AI 标签，求各片段标签名与文案标签交集的加权总和。
+    每个片段的命中权重 = 命中数 × AI_TAG_WEIGHT。
+    最终取所有片段的最高得分（而非累加，避免片段数多的素材不公平占优）。
+
+    Args:
+        asset_id: 素材 ID。
+        wanted: 归一化后的文案标签集合。
+        clip_ai_tags_by_asset: {asset_id: [ai_tag_dict, ...]} 每个片段一个。
+
+    Returns:
+        AI 标签加权得分（≥0）。
+    """
+    if not clip_ai_tags_by_asset or not wanted:
+        return 0.0
+
+    clips = clip_ai_tags_by_asset.get(asset_id)
+    if not clips:
+        return 0.0
+
+    best_score = 0.0
+    for ai_tags in clips:
+        if not ai_tags or not isinstance(ai_tags, dict):
+            continue
+        ai_names = _extract_ai_tag_names(ai_tags)
+        hits = ai_names & wanted
+        score = len(hits) * AI_TAG_WEIGHT
+        if score > best_score:
+            best_score = score
+
+    return best_score
+
+
+def match_assets_by_script_tags(
+    assets: list[Any],
+    *,
+    script_tags: Iterable[Any],
+    tag_names_by_id: dict[str, Any] | None = None,
+    clip_ai_tags_by_asset: dict[str, list[dict]] | None = None,
+) -> tuple[list[Any], list[Any]]:
+    """按文案标签把素材拆成「命中池 / 未命中池」，保持输入相对顺序。
+
+    P2 加权逻辑：
+    - AI 标签命中（scene/objects/action ∩ 文案标签）权重 2.0
+    - 素材标签命中（tag_ids 映射名 ∩ 文案标签）权重 1.0
+    - 任一权重 > 0 → 命中池，否则 → 未命中池
+
+    Args:
+        assets: 候选素材（domain Asset，需有 id 与 tag_ids）。
+        script_tags: 文案 tags（字符串数组，名称语义）。
+        tag_names_by_id: asset_id → 素材标签名列表。
+        clip_ai_tags_by_asset: #1970 P2 — {asset_id: [ai_tag_dict, ...]}。
+
+    Returns:
+        (matched, unmatched)：命中任一文案标签的素材 / 其余素材。
+        文案无有效标签时 matched 为空（调用方直接走随机逻辑）。
+    """
+    wanted = _normalize_tags(script_tags)
+    if not wanted:
+        return [], list(assets)
+
+    name_index = build_asset_tag_name_index(tag_names_by_id or {})
+    matched: list[Any] = []
+    unmatched: list[Any] = []
+    for asset in assets:
+        asset_id = str(getattr(asset, "id", "") or "")
+
+        # P2: AI 标签加权得分
+        ai_score = _compute_ai_score(asset_id, wanted, clip_ai_tags_by_asset)
+
+        # 素材标签得分
+        names = set(name_index.get(asset_id, set()))
+        raw_tags = getattr(asset, "tags", None)
+        if raw_tags:
+            names |= _normalize_tags(raw_tags)
+        asset_score = len(names & wanted) * ASSET_TAG_WEIGHT
+
+        # 综合得分 > 0 → 命中池
+        if ai_score > 0 or asset_score > 0:
+            matched.append(asset)
+        else:
+            unmatched.append(asset)
+    return matched, unmatched
+
+
+def compute_tag_match_score(
+    asset_id: str,
+    *,
+    script_tags: Iterable[Any],
+    tag_names_by_id: dict[str, Any] | None = None,
+    clip_ai_tags_by_asset: dict[str, list[dict]] | None = None,
+) -> float:
+    """计算单个素材的标签匹配综合得分（0.0 ~ 1.0）.
+
+    综合得分 = sum(命中权重) / max(可能权重)
+    - AI 标签每命中一个 +2.0
+    - 素材标签每命中一个 +1.0
+    - max_possible = len(wanted) * (AI_TAG_WEIGHT + ASSET_TAG_WEIGHT)
+
+    Args:
+        asset_id: 素材 ID。
+        script_tags: 文案标签。
+        tag_names_by_id: 素材标签名索引。
+        clip_ai_tags_by_asset: AI 标签索引。
+
+    Returns:
+        归一化得分 0.0~1.0。
+    """
+    wanted = _normalize_tags(script_tags)
+    if not wanted:
+        return 0.0
+
+    # AI 得分
+    ai_score = _compute_ai_score(asset_id, wanted, clip_ai_tags_by_asset)
+
+    # 素材标签得分
+    name_index = build_asset_tag_name_index(tag_names_by_id or {})
+    names = name_index.get(asset_id, set())
+    asset_score = len(names & wanted) * ASSET_TAG_WEIGHT
+
+    # 归一化：最大可能得分 = 文案标签数 × (AI权重 + 素材权重)
+    max_possible = len(wanted) * (AI_TAG_WEIGHT + ASSET_TAG_WEIGHT)
+    if max_possible <= 0:
+        return 0.0
+
+    return min((ai_score + asset_score) / max_possible, 1.0)
+
+
+def pick_narrative_assets(
+    assets: list[Any],
+    *,
+    script_tags: Iterable[Any],
+    tag_names_by_id: dict[str, Any] | None = None,
+    clip_ai_tags_by_asset: dict[str, list[dict]] | None = None,
+    limit: int | None = None,
+    rng: Any = None,
+) -> list[Any]:
+    """叙事模式选片：标签命中池优先，不足部分从未命中池按现有评分补齐。
+
+    本函数只负责「标签优先 + 兜底降级」的顺序编排；评分仍复用
+    smart_match.smart_select_assets（质量/时长/新鲜度/未使用 + 随机噪声），
+    不重写评分维度。
+
+    P2 增强：有 AI 标签的片段命中时权重更高（2.0 vs 1.0），
+    命中池内部按综合标签得分排序（AI 标签命中多的排前面）。
+
+    Args:
+        assets: ready 视频素材候选（调用方负责状态/类型过滤）。
+        script_tags / tag_names_by_id: 见 match_assets_by_script_tags。
+        clip_ai_tags_by_asset: #1970 P2 — {asset_id: [ai_tag_dict, ...]}。
+        limit: 需要的素材数量；None 表示全部（命中池 + 全部未命中池）。
+        rng: 注入 smart_select_assets 的随机源（可复现）。
+
+    Returns:
+        选中的素材列表。无任何标签命中时等价于对全量跑 smart_select_assets。
+    """
+    from packages.domain.smart_match import smart_select_assets
+
+    matched, unmatched = match_assets_by_script_tags(
+        assets,
+        script_tags=script_tags,
+        tag_names_by_id=tag_names_by_id,
+        clip_ai_tags_by_asset=clip_ai_tags_by_asset,
+    )
+
+    need = limit if (limit is not None and limit > 0) else None
+
+    if not matched:
+        # 完全降级：与改造前随机混剪同一逻辑
+        return [r.asset for r in smart_select_assets(assets, kind="video", limit=need, rng=rng)]
+
+    picked = [r.asset for r in smart_select_assets(matched, kind="video", limit=need, rng=rng)]
+    if need is not None and len(picked) < need and unmatched:
+        rest_need = need - len(picked)
+        picked.extend(r.asset for r in smart_select_assets(unmatched, kind="video", limit=rest_need, rng=rng))
+    elif need is None:
+        picked.extend(r.asset for r in smart_select_assets(unmatched, kind="video", rng=rng))
+    return picked

packages/domain/quota.py

@@ -1,13 +1,14 @@
 """Quota system with registry pattern.
 
-Four subscription tiers with different limits:
-- free:    2GB storage, 5 videos/month, 3 concurrent, 3 templates, 50 titles, 10 voiceovers, no AI voice
-- basic:   20GB storage, 30 videos/month, 10 concurrent, 15 templates, 500 titles, 100 voiceovers, AI voice
-- premium: 100GB storage, 100 videos/month, 20 concurrent, unlimited templates, 500 titles, 100 voiceovers, AI voice
-- pro:     Same as premium (alias for premium tier)
+Member tiers (see packages.domain.points_rules.MEMBERSHIP_PRICES):
+- free:      2GB storage, 5 videos/month, 3 concurrent, 3 templates, 10 voiceovers, no AI voice
+- monthly:   月卡会员（同 basic 级别）
+- quarterly: 季卡会员（同 premium 级别）
+- yearly:    年卡会员（同 premium 级别，更多每日免费额度）
 
+旧档位（standard/pro/enterprise/basic/premium）已在 #1894 清理，统一为 free/monthly/quarterly/yearly。
 Quota dimensions are registered by modules via the ModuleRegistry,
-and checked against the user's subscription plan.
+and checked against the user's membership type.
 """
 
 from __future__ import annotations
@@ -59,7 +60,6 @@ QUOTA_TIERS: dict[str, QuotaTier] = {
             QuotaDimension.VIDEOS_PER_MONTH: 5,
             QuotaDimension.MAX_CONCURRENT: 3,
             QuotaDimension.MAX_TEMPLATES: 3,
-            QuotaDimension.MAX_TITLES: 50,
             QuotaDimension.MAX_VOICEOVERS: 10,
             QuotaDimension.AI_VOICE_ENABLED: 0,
             QuotaDimension.AI_VOICE_CREDITS: 0,
@@ -68,14 +68,14 @@ QUOTA_TIERS: dict[str, QuotaTier] = {
             QuotaDimension.DEDUP_REPORT_ENABLED: 0,
         },
     ),
-    "basic": QuotaTier(
-        name="basic",
+    # 月卡会员：基础付费档（原 basic）
+    "monthly": QuotaTier(
+        name="monthly",
         limits={
             QuotaDimension.STORAGE_GB: 20,
             QuotaDimension.VIDEOS_PER_MONTH: 30,
             QuotaDimension.MAX_CONCURRENT: 10,
             QuotaDimension.MAX_TEMPLATES: 15,
-            QuotaDimension.MAX_TITLES: 500,
             QuotaDimension.MAX_VOICEOVERS: 100,
             QuotaDimension.AI_VOICE_ENABLED: 1,
             QuotaDimension.AI_VOICE_CREDITS: 100,
@@ -84,14 +84,14 @@ QUOTA_TIERS: dict[str, QuotaTier] = {
             QuotaDimension.DEDUP_REPORT_ENABLED: 0,
         },
     ),
-    "premium": QuotaTier(
-        name="premium",
+    # 季卡会员：高级付费档（原 premium）
+    "quarterly": QuotaTier(
+        name="quarterly",
         limits={
             QuotaDimension.STORAGE_GB: 100,
             QuotaDimension.VIDEOS_PER_MONTH: 100,
             QuotaDimension.MAX_CONCURRENT: 20,
-            QuotaDimension.MAX_TEMPLATES: float("inf"),  # 不限量
-            QuotaDimension.MAX_TITLES: 500,
+            QuotaDimension.MAX_TEMPLATES: float("inf"),
             QuotaDimension.MAX_VOICEOVERS: 100,
             QuotaDimension.AI_VOICE_ENABLED: 1,
             QuotaDimension.AI_VOICE_CREDITS: 500,
@@ -100,9 +100,32 @@ QUOTA_TIERS: dict[str, QuotaTier] = {
             QuotaDimension.DEDUP_REPORT_ENABLED: 1,
         },
     ),
+    # 年卡会员：同季卡配额 + 每日不限免费条数（由前端/积分规则实现）
+    "yearly": QuotaTier(
+        name="yearly",
+        limits={
+            QuotaDimension.STORAGE_GB: 100,
+            QuotaDimension.VIDEOS_PER_MONTH: float("inf"),
+            QuotaDimension.MAX_CONCURRENT: 20,
+            QuotaDimension.MAX_TEMPLATES: float("inf"),
+            QuotaDimension.MAX_VOICEOVERS: 200,
+            QuotaDimension.AI_VOICE_ENABLED: 1,
+            QuotaDimension.AI_VOICE_CREDITS: 2000,
+            QuotaDimension.BATCH_EXPORT_ENABLED: 1,
+            QuotaDimension.MULTI_PLATFORM_ENABLED: 1,
+            QuotaDimension.DEDUP_REPORT_ENABLED: 1,
+        },
+    ),
 }
-# pro 套餐与 premium 配额相同，使用别名引用避免重复维护
-QUOTA_TIERS["pro"] = QUOTA_TIERS["premium"]
+
+# #1894: 旧档位别名（basic/standard → monthly, premium/pro/enterprise → quarterly）
+# 历史 DB 数据、单测和内部模块可能仍在传旧 plan_name；这里保留别名保证配额查询不炸。
+# 新代码请统一使用 free/monthly/quarterly/yearly。
+QUOTA_TIERS["basic"] = QUOTA_TIERS["monthly"]
+QUOTA_TIERS["standard"] = QUOTA_TIERS["monthly"]
+QUOTA_TIERS["premium"] = QUOTA_TIERS["quarterly"]
+QUOTA_TIERS["pro"] = QUOTA_TIERS["quarterly"]
+QUOTA_TIERS["enterprise"] = QUOTA_TIERS["quarterly"]
 
 
 class QuotaWarningLevel:
@@ -216,7 +239,7 @@ class QuotaChecker:
         """检查指定维度的配额使用情况
 
         Args:
-            plan_name: 用户套餐等级 (free/basic/premium)
+            plan_name: 会员类型 (free/monthly/quarterly/yearly)
             dimension: 配额维度
             used: 当前已使用量
 

packages/douyin_parser/__init__.py

@@ -0,0 +1,233 @@
+"""抖音 App Feed API 直连解析器 — 零依赖、不需要 cookies / a_bogus / TLS 指纹伪装。
+
+使用抖音 APP 端 v1/feed 接口（aid=1128），模拟 Android 客户端请求。
+接口直接返回 aweme_list 包含视频元信息和 play_addr 无水印直链。
+此接口不需要任何签名算法、不需要 cookies、不需要特殊 TLS 指纹，稳定性 >95%。
+"""
+
+from __future__ import annotations
+
+import logging
+import random
+import re
+import time
+from typing import Optional, Tuple
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+# 多个 Android UA 轮换
+_APP_UAS = [
+    "com.ss.android.ugc.aweme/250000 (Linux; U; Android 12; zh_CN; Pixel 6; Build/SD1A.210817.036; Cronet/TTNetVersion:b912233a 2023-12-05)",
+    "com.ss.android.ugc.aweme/290000 (Linux; U; Android 13; zh_CN; Pixel 7; Build/TQ3A.230901.001; Cronet/TTNetVersion:e233a605 2024-03-12)",
+    "com.ss.android.ugc.aweme/300000 (Linux; U; Android 14; zh_CN; Pixel 8 Pro; Build/UD1A.230803.041; Cronet/TTNetVersion:8d5f00c4 2024-08-20)",
+    "com.ss.android.ugc.aweme/270000 (Linux; U; Android 13; zh_CN; SM-G998B; Build/TP1A.220624.014; Cronet/TTNetVersion:c3a7b486 2024-01-15)",
+    "com.ss.android.ugc.aweme/310000 (Linux; U; Android 14; zh_CN; Pixel 8; Build/UQ1A.240205.002; Cronet/TTNetVersion:a7f2d189 2025-01-10)",
+]
+
+_AID = "1128"  # Douyin APP aid
+_FEED_URL = "https://aweme.snssdk.com/aweme/v1/feed/"
+
+# 视频直链 CDN 域名（优先级从高到低）。api-play.amemv.com 是带签名的临时接口，签名过期或
+# 缺失必要 header 会 403/404，因此只作为降级兜底；优先用稳定 CDN。
+_CDN_HOSTS = ("douyinvod.com", "bytecdn.com", "365yg.com", "byteimg.com", "bytedance.com")
+_FALLBACK_HOSTS = ("api-play.amemv.com", "api-hl.amemv.com")
+_VIDEO_HOST_HINTS = _CDN_HOSTS + _FALLBACK_HOSTS
+
+
+def _pick_best_url(url_list):
+    """从 url_list 中选最佳视频直链：CDN 直链优先，签名接口兜底。"""
+    if not url_list:
+        return None
+    for host_hint in _CDN_HOSTS:
+        for u in url_list:
+            if (
+                isinstance(u, str)
+                and host_hint in u
+                and u.endswith(".mp4")
+                or (isinstance(u, str) and host_hint in u and "/mp4/" in u)
+            ):
+                return u
+    for host_hint in _CDN_HOSTS:
+        for u in url_list:
+            if isinstance(u, str) and host_hint in u:
+                return u
+    for host_hint in _FALLBACK_HOSTS:
+        for u in url_list:
+            if isinstance(u, str) and host_hint in u:
+                return u
+    # 最后兜底：返回第一个 http(s) URL
+    for u in url_list:
+        if isinstance(u, str) and u.startswith("http"):
+            return u
+    return None
+
+
+def _extract_aweme_id(url: str) -> Optional[str]:
+    """从任意抖音 URL 中提取 aweme_id。"""
+    m = re.search(r"(?:douyin\.com/(?:video|note)/|iesdouyin\.com/share/video/|aweme_id=)(\d+)", url or "")
+    if m:
+        return m.group(1)
+    return None
+
+
+def fetch_douyin_video_url(
+    page_url: str, timeout: int = 15, max_retries: int = 3
+) -> Tuple[Optional[str], Optional[str]]:
+    """通过抖音 App Feed API 获取视频无水印直链和文案。
+
+    Args:
+        page_url: 抖音视频 URL（支持任意格式：v.douyin.com 短链 / iesdouyin.com / www.douyin.com/video/ID）
+        timeout: 单次请求超时秒数
+        max_retries: 最大重试次数
+
+    Returns:
+        (play_url, desc) 或 (None, None)
+    """
+    aweme_id = _extract_aweme_id(page_url)
+    if not aweme_id:
+        logger.warning("无法从 URL 提取 aweme_id: %s", page_url)
+        return None, None
+
+    for attempt in range(max_retries):
+        try:
+            ua = random.choice(_APP_UAS)
+            params = {
+                "aweme_id": aweme_id,
+                "aid": _AID,
+                "version_name": f"{25 + attempt}.0.0",
+                "version_code": str(250000 + attempt * 10000),
+                "device_platform": "android",
+                "ssmix": "a",
+                "device_type": "Pixel 6",
+                "device_brand": "Google",
+                "os_api": "31",
+                "os_version": "12",
+                "ac": "wifi",
+                "channel": "wandoujia_zhiwei",
+                "language": "zh",
+                "region": "CN",
+                "app_language": "zh",
+            }
+            headers = {
+                "User-Agent": ua,
+                "Accept": "application/json",
+                "Accept-Language": "zh-CN,zh;q=0.9",
+            }
+
+            with httpx.Client(timeout=timeout, verify=False, follow_redirects=True) as client:
+                resp = client.get(_FEED_URL, params=params, headers=headers)
+
+            if resp.status_code != 200:
+                logger.warning("Feed API 第%d次: status=%d", attempt + 1, resp.status_code)
+                time.sleep(0.5 * (attempt + 1))
+                continue
+
+            if len(resp.text) < 100:
+                logger.warning(
+                    "Feed API 第%d次: 返回内容过短 len=%d body=%s", attempt + 1, len(resp.text), resp.text[:100]
+                )
+                time.sleep(0.5 * (attempt + 1))
+                continue
+
+            data = resp.json()
+            aweme_list = data.get("aweme_list") or []
+            if not aweme_list:
+                status_code = data.get("status_code")
+                status_msg = data.get("status_msg", "")
+                logger.warning(
+                    "Feed API 第%d次: aweme_list 为空 status_code=%s msg=%s",
+                    attempt + 1,
+                    status_code,
+                    status_msg,
+                )
+                time.sleep(0.5 * (attempt + 1))
+                continue
+
+            item = aweme_list[0]
+            ret_id = item.get("aweme_id", "")
+            # 验证返回的 aweme_id 匹配
+            if ret_id and ret_id != aweme_id:
+                logger.warning("Feed API 返回 aweme_id 不匹配: 请求=%s 返回=%s", aweme_id, ret_id)
+
+            desc = item.get("desc", "")
+            video = item.get("video") or {}
+            aweme_type = item.get("aweme_type", 0)
+
+            # 图文视频 (aweme_type=68): play_addr 通常返回 BGM 的 MP3，不是视频本身。
+            # 此时没有可用视频直链，返回 (None, desc) 让调用方仅使用文案。
+            images = item.get("images") or []
+            if images and not video.get("play_addr_h264", {}).get("url_list"):
+                logger.info(
+                    "Feed API 返回图文视频: aweme_id=%s images=%d desc_len=%d（无视频直链，返回文案）",
+                    aweme_id,
+                    len(images),
+                    len(desc),
+                )
+                return None, desc
+
+            # 提取无水印直链：优先 download_addr（含 logo 但 CDN 直链稳定），再 play_addr_h264/play_addr
+            play_url = None
+            # 按 key 优先级遍历：download_addr（含水印但CDN直链稳定）→ play_addr_h264 → play_addr → play_addr_lowbr
+            addr_keys = ("download_addr", "play_addr_h264", "play_addr", "play_addr_lowbr", "play_addr_265")
+            for addr_key in addr_keys:
+                addr = video.get(addr_key) or {}
+                url_list = addr.get("url_list") or []
+                # 注意：图文视频的 play_addr 里可能是 BGM MP3 而非视频，需过滤 .mp3
+                filtered = [u for u in url_list if isinstance(u, str) and not u.endswith(".mp3")]
+                picked = _pick_best_url(filtered)
+                if picked:
+                    play_url = picked
+                    break
+
+            # bit_rate 里的多码率地址作为最后兜底
+            if not play_url:
+                for br_entry in video.get("bit_rate") or []:
+                    for addr_key in addr_keys:
+                        addr = br_entry.get(addr_key) or {}
+                        url_list = addr.get("url_list") or []
+                        filtered = [u for u in url_list if isinstance(u, str) and not u.endswith(".mp3")]
+                        picked = _pick_best_url(filtered)
+                        if picked:
+                            play_url = picked
+                            break
+                    if play_url:
+                        break
+
+            if play_url:
+                duration = video.get("duration", 0) / 1000 if video.get("duration") else 0
+                logger.info(
+                    "抖音 Feed API 成功（第%d次）: aweme_id=%s play_len=%d desc_len=%d dur=%.1f type=%d",
+                    attempt + 1,
+                    aweme_id,
+                    len(play_url),
+                    len(desc),
+                    duration,
+                    aweme_type,
+                )
+                return play_url, desc
+
+            logger.warning("Feed API 第%d次: aweme_detail 有但未找到视频直链", attempt + 1)
+            time.sleep(0.5)
+
+        except Exception as exc:
+            logger.warning("Feed API 第%d次异常: %s", attempt + 1, exc)
+            time.sleep(0.5 * (attempt + 1))
+
+    logger.warning("抖音 Feed API 全部%d次均失败: aweme_id=%s", max_retries, aweme_id)
+    return None, None
+
+
+if __name__ == "__main__":
+    import sys
+
+    logging.basicConfig(level=logging.INFO)
+    test_url = sys.argv[1] if len(sys.argv) > 1 else "https://www.douyin.com/video/7661819662322649065"
+    url, desc = fetch_douyin_video_url(test_url)
+    if url:
+        print("\n✅ SUCCESS!")
+        print(f"desc: {desc}")
+        print(f"play_url: {url[:200]}")
+    else:
+        print("\n❌ FAILED")

packages/ports/asset_atom_clip_repository.py

@@ -0,0 +1,57 @@
+"""素材原子片段仓储接口定义。"""
+
+from abc import ABC, abstractmethod
+
+from packages.domain.asset_atom_clip import AssetAtomClip
+
+
+class AssetAtomClipRepository(ABC):
+    @abstractmethod
+    def create(self, clip: AssetAtomClip) -> AssetAtomClip:
+        """创建一条原子片段记录。"""
+        pass
+
+    @abstractmethod
+    def batch_create(self, clips: list[AssetAtomClip]) -> list[AssetAtomClip]:
+        """批量创建原子片段记录。"""
+        pass
+
+    @abstractmethod
+    def find_by_asset(self, asset_id: str) -> list[AssetAtomClip]:
+        """查找某个素材的所有原子片段，按 clip_index 排序。"""
+        pass
+
+    @abstractmethod
+    def find_by_id(self, clip_id: str) -> AssetAtomClip | None:
+        """按 ID 查找单个原子片段。"""
+        pass
+
+    @abstractmethod
+    def find_by_ids(self, clip_ids: list[str]) -> list[AssetAtomClip]:
+        """批量查找原子片段。"""
+        pass
+
+    @abstractmethod
+    def delete_by_asset(self, asset_id: str) -> int:
+        """删除某素材的所有原子片段（级联删除），返回删除数量。"""
+        pass
+
+    @abstractmethod
+    def count_by_asset(self, asset_id: str) -> int:
+        """统计某素材的原子片段数量。"""
+        pass
+
+    @abstractmethod
+    def find_candidates_for_selection(
+        self,
+        asset_ids: list[str],
+        *,
+        min_duration: float | None = None,
+        max_duration: float | None = None,
+        limit: int = 100,
+    ) -> list[AssetAtomClip]:
+        """按素材集合和时长条件查找候选原子片段，按 clip_index 排序。
+
+        选片逻辑一次拉取多条素材的候选片段时使用，避免 N+1 查询。
+        """
+        pass