Merge pull request 'fix(ai-avatar): 修复三个bug——封面重影/标题字号缩放/对口型音频截断' (#1876) from fix/ai-avatar-three-bugs-0913 into develop

1. 删除封面展示区重复叠加的标题div（成片帧已含标题，不再CSS叠字）
2. 标题字号按视频分辨率等比缩放，预览容器动态测量宽度计算scale
3. enable_video_loop默认改为true，防止TTS音频长于出镜视频时被截断

alembic/versions/075_add_sentence_timings.py

@@ -0,0 +1,27 @@
+"""add sentence_timings to lipsync_jobs
+
+Revision ID: 075_add_sentence_timings
+Revises: 074_ai_avatar_render_script_id_optional
+Create Date: 2026-09-12
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "075_add_sentence_timings"
+down_revision = "074_render_script_id_optional"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("lipsync_jobs") as batch:
+        batch.add_column(
+            sa.Column("sentence_timings", sa.JSON(), nullable=True),
+        )
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("lipsync_jobs") as batch:
+        batch.drop_column("sentence_timings")

apps/api/app/api/routes/ai_avatar_render.py

@@ -11,13 +11,13 @@
 from __future__ import annotations
 
 import logging
+from datetime import datetime, timezone
 
 from app.auth import AuthenticatedUser, get_current_user
 from app.dependencies import get_db_session
 from app.schemas.ai_avatar_render import (
     AiAvatarRenderJobResponse,
     CreateAiAvatarRenderRequest,
-    SmartCoverRequest,
     SmartCoverResponse,
 )
 from app.services.ai_avatar_cover_service import generate_smart_cover
@@ -77,10 +77,16 @@ def create_render_job(
         from app.tasks.ai_avatar_render import execute_ai_avatar_render
 
         execute_ai_avatar_render.delay(job.id)
-    except Exception:
+    except Exception as exc:
-        logger.warning("Celery 任务提交失败，渲染任务已创建但未触发执行: %s", job.id)
+        logger.exception("Celery 任务投递失败（创建）: job_id=%s err=%s", job.id, exc)
+        job.status = "failed"
+        job.error_message = f"任务提交失败：{exc}"
+        job.updated_at = datetime.now(timezone.utc)
+        svc.db.commit()
+        svc.db.refresh(job)
+        return AiAvatarRenderJobResponse.model_validate(job)
 
-    return job
+    return AiAvatarRenderJobResponse.model_validate(job)
 
 
 # ── GET /jobs — 任务列表 ─────────────────────────────────────────────────
@@ -172,38 +178,54 @@ def retry_render_job(
         from app.tasks.ai_avatar_render import execute_ai_avatar_render
 
         execute_ai_avatar_render.delay(job.id)
-    except Exception:
+    except Exception as exc:
-        logger.warning("Celery 任务提交失败，重试任务已重置但未触发执行: %s", job.id)
+        logger.exception("Celery 任务投递失败（重试）: job_id=%s err=%s", job.id, exc)
+        job.status = "failed"
+        job.error_message = f"任务提交失败：{exc}"
+        job.updated_at = datetime.now(timezone.utc)
+        svc.db.commit()
+        svc.db.refresh(job)
+        return AiAvatarRenderJobResponse.model_validate(job)
 
-    return job
+    return AiAvatarRenderJobResponse.model_validate(job)
 
 
-
+# ── POST /{job_id}/smart-cover — 从最终成片智能抽封面（步骤②）────────
-# ── POST /smart-cover — 智能获取封面（MediaKit 抽帧 + 评分选帧）────────
 
 
-@router.post("/smart-cover", response_model=SmartCoverResponse)
+@router.post("/{job_id}/smart-cover", response_model=SmartCoverResponse)
-def generate_avatar_smart_cover(
+def generate_render_smart_cover(
-    body: SmartCoverRequest,
+    job_id: str,
     current_user: AuthenticatedUser = Depends(get_current_user),
-) -> SmartCoverResponse:
+    db: Session = Depends(get_db_session),
-    """智能获取数字人视频封面.
+):
+    """从最终渲染成片智能抽帧生成封面（MediaKit 抽帧 + 评分选最佳帧 + 转存 OSS）.
 
-    复用智能剪辑的 MediaKit 抽帧 + 质量评分选最佳帧逻辑（非 FFmpeg 简单截帧），
+    - 必须等渲染任务 completed 后才可调用（否则返回 400）
-    并将选中帧转存到自家 OSS，返回非临时的封面公网 URL。
+    - 生成成功后自动更新 render_job 的 cover_config 与 output_cover_url
-
-    前端「智能获取封面」按钮可直接调用本接口；不依赖渲染任务完成。
     """
-    video_url = (body.video_url or "").strip()
+    from app.services.ai_avatar_render_service import AiAvatarRenderService
-    if not video_url.startswith(("http://", "https://")):
+
-        raise HTTPException(status_code=400, detail="video_url 必须是合法的 HTTP/HTTPS URL")
+    svc = AiAvatarRenderService(db)
+    job = svc.get_render_job(job_id, current_user.user.id)
+    if job is None:
+        raise HTTPException(status_code=404, detail="渲染任务不存在")
+    if job.status != "completed":
+        raise HTTPException(status_code=400, detail="请先完成视频生成")
+    video_url = (job.output_video_url or "").strip()
+    if not video_url:
+        raise HTTPException(status_code=400, detail="渲染成片视频 URL 为空")
 
     try:
-        cover_url = generate_smart_cover(video_url, max_frames=body.max_frames)
+        # 从最终成片抽帧，帧本身已含标题/B-roll，直接转存 OSS
+        cover_url = generate_smart_cover(video_url, job_id=job_id, max_frames=5)
     except Exception as exc:
         logger.error(
-            "智能封面生成异常: user=%s video_url=%s err=%s",
+            "渲染成片智能封面生成异常: user=%s render_id=%s video_url=%s err=%s",
-            current_user.user.id, video_url[:80], exc,
+            current_user.user.id,
+            job_id,
+            video_url[:80],
+            exc,
             exc_info=True,
         )
         cover_url = ""
@@ -214,5 +236,24 @@ def generate_avatar_smart_cover(
             status="fallback_failed",
             message="智能抽帧失败（MediaKit 不可用或抽帧异常），请稍后重试",
         )
-    logger.info("智能封面生成成功: user=%s cover_url=%s", current_user.user.id, cover_url[:120])
+
+    # 更新 render_job 的封面字段（异步写入 DB；失败不影响返回）
+    try:
+        job.cover_config = {
+            **(job.cover_config if isinstance(job.cover_config, dict) else {}),
+            "mode": "auto_frame",
+            "url": cover_url,
+        }
+        job.output_cover_url = cover_url
+        job.updated_at = datetime.now(timezone.utc)
+        db.commit()
+    except Exception as exc:
+        logger.warning("更新 render_job 封面字段失败（不影响返回）: job_id=%s err=%s", job_id, exc)
+
+    logger.info(
+        "渲染成片智能封面生成成功: user=%s render_id=%s cover_url=%s",
+        current_user.user.id,
+        job_id,
+        cover_url[:120],
+    )
     return SmartCoverResponse(cover_url=cover_url, status="completed")

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

@@ -1,11 +1,12 @@
-"""对口型 API 路由 — #1796 MediaKit 对口型, #1809 参数调整.
+"""对口型 API 路由 — #1796 MediaKit 对口型, #1809 参数调整, #1845 配音前置.
 
 接口：
-  POST   /api/v1/lipsync/jobs          提交对口型任务
+  POST   /api/v1/lipsync/jobs          提交对口型任务（支持 TTS/直传/预合成 三种模式）
   GET    /api/v1/lipsync/jobs          任务列表
   GET    /api/v1/lipsync/jobs/{id}     任务详情
   POST   /api/v1/lipsync/jobs/{id}/refresh  刷新任务状态
   POST   /api/v1/lipsync/jobs/{id}/cancel   取消任务
+  POST   /api/v1/lipsync/tts-preview   #1845 步骤1 TTS 预合成（同步 HTTP，~2-3s）
 """
 
 from __future__ import annotations
@@ -17,7 +18,12 @@ from app.dependencies import (
     get_db_session,
     get_voice_clone_profile_repository,
 )
-from app.schemas.lipsync import CreateLipsyncJobRequest, LipsyncJobResponse
+from app.schemas.lipsync import (
+    AiAvatarTtsPreviewRequest,
+    AiAvatarTtsPreviewResponse,
+    CreateLipsyncJobRequest,
+    LipsyncJobResponse,
+)
 from app.services.lipsync_service import LipsyncService
 from app.services.mediakit_client import MediaKitError
 from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, Query
@@ -33,7 +39,6 @@ def _get_service(
     voice_clone_repo=Depends(get_voice_clone_profile_repository),
 ) -> LipsyncService:
     # voice_clone_repo 用于克隆音色 profile 解析
-    # TTS 合成已移至 Celery 异步任务，无需同步注入 cosyvoice_service
     return LipsyncService(
         db,
         voice_clone_repo=voice_clone_repo,
@@ -51,15 +56,20 @@ def create_lipsync_job(
 ):
     """提交对口型任务.
 
-    #1809/#1822: 前端传 {video_url, voice_id, script_text, speed?, emotion?}，
+    三种模式：
-    后端创建任务记录（状态 tts_processing），dispatch Celery 异步任务执行 TTS 合成 + MediaKit 提交；
+    - TTS 直生（旧版/降级）：传 {video_url, voice_id, script_text, speed?, emotion?}，
-    也支持直接传 {video_url, audio_url}（同步提交 MediaKit）。
+      后端 dispatch Celery 异步任务。
+    - 直接音频：传 {video_url, audio_url}，后端同步下载+算timings+提交MediaKit。
+    - 预合成音频（#1845 新主路径）：传 {video_url, audio_url, audio_duration, sentence_timings}，
+      后端同步ffprobe+写入timings+直接提交MediaKit（~2-3s）。
     """
     try:
         job = svc.create_job(
             user_id=current_user.user.id,
             video_url=body.video_url,
             audio_url=body.audio_url,
+            audio_duration=body.audio_duration,
+            sentence_timings=body.sentence_timings,
             voice_id=body.voice_id,
             script_text=body.script_text,
             speed=body.speed,
@@ -68,10 +78,8 @@ def create_lipsync_job(
             project_id=body.project_id,
         )
     except ValueError as exc:
-        # 参数无效（如 voice_id 格式不对、文本过长等）
         raise HTTPException(status_code=400, detail=str(exc)) from exc
     except MediaKitError as exc:
-        # 音色无权访问 → 403；参数无效 → 400；MediaKit 提交失败 → 502
         status_code = 502
         if exc.code in ("VoiceForbidden",):
             status_code = 403
@@ -86,7 +94,6 @@ def create_lipsync_job(
             },
         ) from exc
     except Exception as exc:
-        # 兜底：任何未预期的错误返回 400 而非 500
         logger.error("创建对口型任务异常: %s", exc, exc_info=True)
         raise HTTPException(
             status_code=400,
@@ -96,6 +103,52 @@ def create_lipsync_job(
     return job
 
 
+# ── POST /tts-preview — #1845 步骤1 TTS 预合成 ──────────────────────────
+
+
+@router.post("/tts-preview", response_model=AiAvatarTtsPreviewResponse)
+def preview_tts(
+    body: AiAvatarTtsPreviewRequest,
+    current_user: AuthenticatedUser = Depends(get_current_user),
+    svc: LipsyncService = Depends(_get_service),
+):
+    """步骤1「生成配音」同步 TTS 预合成.
+
+    同步执行 TTS 合成 → 下载音频 → ffprobe 时长 → 句子时间戳计算，
+    不创建 LipsyncJob、不转存 OSS，直接返回 CosyVoice 临时 URL（~24h 有效）。
+    耗时约 2-3 秒。
+    """
+    try:
+        result = svc.preview_tts(
+            user_id=current_user.user.id,
+            voice_id=body.voice_id,
+            script_text=body.script_text,
+            speed=body.speed,
+            emotion=body.emotion,
+        )
+    except MediaKitError as exc:
+        status_code = 400
+        if exc.code in ("VoiceForbidden",):
+            status_code = 403
+        elif exc.code in ("TTSNoAudio",):
+            status_code = 502
+        raise HTTPException(
+            status_code=status_code,
+            detail={
+                "code": exc.code,
+                "message": str(exc),
+            },
+        ) from exc
+    except Exception as exc:
+        logger.error("TTS 预合成异常: %s", exc, exc_info=True)
+        raise HTTPException(
+            status_code=400,
+            detail=f"TTS 合成失败: {exc}",
+        ) from exc
+
+    return result
+
+
 # ── GET /jobs — 任务列表 ─────────────────────────────────────────────────
 
 
@@ -134,11 +187,7 @@ def get_lipsync_job(
     current_user: AuthenticatedUser = Depends(get_current_user),
     svc: LipsyncService = Depends(_get_service),
 ):
-    """获取对口型任务详情.
+    """获取对口型任务详情."""
-
-    非终态任务：先返回 DB 缓存，挂后台刷新（下次轮询拿到新状态），
-    避免 MediaKit 慢响应阻塞前端轮询。
-    """
     job = svc.get_job(job_id, current_user.user.id)
     if job is None:
         raise HTTPException(status_code=404, detail="任务不存在")

apps/api/app/schemas/ai_avatar_render.py

@@ -52,7 +52,9 @@ class CreateAiAvatarRenderRequest(BaseModel):
     lipsync_job_id: str = Field(..., description="对口型任务 ID")
     script_id: str = Field("", description="文案 ID（选自文案库时传；手动输入文案直生场景可留空）")
     b_roll_segments: list[BRollSegment] = Field(default_factory=list, description="B-roll 片段列表")
-    title_config: dict[str, Any] = Field(default_factory=dict, description="标题配置")
+    title_config: dict[str, Any] = Field(
+        default_factory=dict, description="标题配置（可含 title_image_dataurl：前端 Canvas 渲染的标题 PNG dataURL）"
+    )
     cover_config: dict[str, Any] = Field(default_factory=dict, description="封面配置")
     project_id: str = Field("", description="项目 ID")
 
@@ -67,7 +69,6 @@ class CreateAiAvatarRenderRequest(BaseModel):
     @field_validator("script_id")
     @classmethod
     def validate_script_id(cls, v: str) -> str:
-        # script_id 可选：手动输入文案（TTS 直生）场景不关联文案库条目
         return (v or "").strip()
 
 
@@ -109,15 +110,8 @@ class AiAvatarRenderProgressResponse(BaseModel):
     error_message: str
 
 
-class SmartCoverRequest(BaseModel):
-    """智能封面请求 — MediaKit 抽帧 + 质量评分选最佳帧."""
-
-    video_url: str = Field(..., description="数字人视频 URL（对口型/渲染成片）")
-    max_frames: int = Field(5, ge=1, le=10, description="抽帧数量（默认 5）")
-
-
 class SmartCoverResponse(BaseModel):
-    """智能封面响应."""
+    """智能封面响应（封面从最终成片抽帧，不再叠加标题）."""
 
     cover_url: str = Field("", description="封面图公网 URL（OSS，非临时）；失败为空")
     status: str = Field("completed", description="completed / fallback_failed")

apps/api/app/schemas/lipsync.py

@@ -1,9 +1,12 @@
-"""对口型 API Schema 定义 — #1796 / #1809 / #1822.
+"""对口型 API Schema 定义 — #1796 / #1809 / #1822 / #1845（配音前置）.
 
-支持两种输入模式（二选一）：
+支持三种输入模式：
-1. TTS 直生模式（推荐）：传 voice_id + script_text（+ speed/emotion），
+1. TTS 直生模式（兼容旧版前端）：传 voice_id + script_text（+ speed/emotion），
-   后端内部先调 CosyVoice 合成音频，再提交 MediaKit 对口型。
+   后端 Celery 异步做 TTS 合成 + MediaKit 提交。
 2. 直接音频模式：传 video_url + audio_url（音频已由调用方准备好）。
+3. 预合成音频模式（#1845 配音前置新主路径）：前端先调 POST /lipsync/tts-preview
+   拿到 audio_url + sentence_timings，再在 create_job 时传 audio_url + audio_duration
+   + sentence_timings，后端跳过 TTS 和时间戳计算，直接 ffprobe 校验后提交 MediaKit。
 """
 
 from __future__ import annotations
@@ -33,6 +36,7 @@ class LipsyncJobResponse(BaseModel):
     output_duration: float
     error_message: str
     error_code: str
+    sentence_timings: Optional[list] = None
     submitted_at: Optional[datetime] = None
     completed_at: Optional[datetime] = None
     created_at: datetime
@@ -45,15 +49,19 @@ class LipsyncJobResponse(BaseModel):
 class CreateLipsyncJobRequest(BaseModel):
     """创建对口型任务请求.
 
-    两种模式（二选一）：
+    三种模式（三选一）：
-    - TTS 直生：voice_id + script_text 必填（+ 可选 speed/emotion）；audio_url 留空。
+    - TTS 直生（旧版/降级）：voice_id + script_text 必填；audio_url 留空。
     - 直接音频：video_url + audio_url 必填。
+    - 预合成音频（#1845 新主路径）：audio_url 必填 + 可选 audio_duration/sentence_timings；
+      后端同步 ffprobe 校验时长、写入 timings，直接提交 MediaKit。
     """
 
     video_url: str = Field(..., description="人物视频 URL（MP4，≤30min，单人真人）")
 
-    # 模式 2：直接音频
+    # 模式 2/3：直接/预合成音频
     audio_url: str = Field("", description="驱动音频 URL（mp3/aac/wav/m4a/flac）；直生模式留空")
+    audio_duration: Optional[float] = Field(None, ge=0, description="预合成音频时长（秒），可选；后端会 ffprobe 校验")
+    sentence_timings: Optional[list] = Field(None, description="预合成接口返回的句子时间戳，可选；若传入则直接写入 job")
 
     # 模式 1：TTS 直生
     voice_id: str = Field("", description="音色 ID（预置音色或克隆音色 profile UUID）")
@@ -61,7 +69,9 @@ class CreateLipsyncJobRequest(BaseModel):
     speed: float = Field(1.0, ge=0.5, le=2.0, description="语速（0.5-2.0），默认 1.0")
     emotion: str = Field("", description="情绪（natural/excited/calm/friendly 或中文 自然/兴奋/沉稳/亲切）")
 
-    enable_video_loop: bool = Field(False, description="音频长于视频时是否循环画面")
+    enable_video_loop: bool = Field(
+        True, description="音频长于视频时是否循环画面（AI数字人默认开启，防止音频长于视频被截断）"
+    )
     project_id: str = Field("", description="项目 ID（可选）")
 
     @model_validator(mode="after")
@@ -80,7 +90,7 @@ class CreateLipsyncJobRequest(BaseModel):
 
         if not has_audio and not has_tts:
             raise ValueError(
-                "必须提供驱动音频：要么传 audio_url（直接音频模式），"
+                "必须提供驱动音频：要么传 audio_url（直接/预合成音频模式），"
                 "要么同时传 voice_id + script_text（TTS 直生模式）"
             )
 
@@ -98,3 +108,23 @@ class CreateLipsyncJobRequest(BaseModel):
             self.audio_url = au
 
         return self
+
+
+# ── #1845 TTS 预合成接口 ────────────────────────────────────────────────
+
+
+class AiAvatarTtsPreviewRequest(BaseModel):
+    """步骤1「生成配音」预合成请求（同步 HTTP，~2-3s）."""
+
+    voice_id: str = Field(..., min_length=1, max_length=128, description="音色 ID")
+    script_text: str = Field(..., min_length=1, max_length=5000, description="要合成的文案")
+    speed: float = Field(1.0, ge=0.5, le=2.0, description="语速（0.5-2.0），默认 1.0")
+    emotion: str = Field("natural", max_length=32, description="情绪")
+
+
+class AiAvatarTtsPreviewResponse(BaseModel):
+    """TTS 预合成响应（临时 URL，24h 内有效，足够当前会话使用）."""
+
+    audio_url: str = Field(..., description="CosyVoice 临时音频 URL")
+    duration: float = Field(..., ge=0, description="音频总时长（秒），ffprobe 测得")
+    sentence_timings: list[dict] = Field(..., description="句子级精确时间戳")

apps/api/app/services/ai_avatar_cover_service.py

@@ -1,10 +1,13 @@
-"""AI 数字人封面服务 — 复用智能剪辑的 MediaKit 抽帧 + 质量评分选最佳帧.
+"""AI 数字人封面服务 — MediaKit 抽帧 + 质量评分选最佳帧 + 转存 OSS.
 
 与 generation_cover.py 的智能选帧能力对齐（不再用 FFmpeg 简单截帧）：
 1. MediaKit extract_frames 抽取多帧（默认 5 帧，SpecifiedFrames 策略）
 2. cover_frame_scorer.score_frames 按清晰度/亮度/色彩评分选最佳
 3. 下载最佳帧并转存 OSS，返回公网封面 URL
 
+设计原则：封面一律从最终成片（已叠加标题/B-roll）抽帧，帧本身已含标题，
+本服务**不再叠加标题**。对口型阶段的裸视频封面入口已删除（废弃）。
+
 降级：MediaKit 不可用或抽帧失败时返回空字符串，由调用方决定回退策略。
 """
 
@@ -19,9 +22,9 @@ from urllib.parse import urlparse
 
 logger = logging.getLogger(__name__)
 
-# MediaKit 抽帧轮询参数（与 MediaKit API timeout=60s 对齐）
+# MediaKit 抽帧轮询参数：poll_interval=1s × max_poll=15 → 最长 15s，配合前端 120s 超时足够
-COVER_POLL_INTERVAL = 3.0
+COVER_POLL_INTERVAL = 1.0
-COVER_MAX_POLL_ATTEMPTS = 20  # 最多等 60 秒
+COVER_MAX_POLL_ATTEMPTS = 15
 
 # 帧图片下载超时（秒）
 FRAME_DOWNLOAD_TIMEOUT = 20
@@ -49,7 +52,6 @@ def _sign_video_url_for_mediakit(video_url: str) -> str:
         own_host = urlparse(public_base).netloc.lower()
         url_host = urlparse(video_url).netloc.lower()
         if own_host and url_host == own_host:
-            # 是自家 OSS URL，重签 7 天有效期供 MediaKit 拉取
             signed = storage.get_download_url(video_url, expires_seconds=MEDIAKIT_URL_TTL_SECONDS)
             if signed:
                 logger.info("[数字人封面] video_url 已重签（自家 OSS 私有桶）")
@@ -60,19 +62,10 @@ def _sign_video_url_for_mediakit(video_url: str) -> str:
 
 
 def select_best_cover_frame(video_url: str, *, max_frames: int = 5) -> str:
-    """从视频抽取多帧并评分选最佳帧，返回最佳帧的临时 URL.
+    """从视频抽取多帧并评分选最佳帧，返回最佳帧的临时 URL."""
-
-    Args:
-        video_url: 可公网访问的视频 URL
-        max_frames: 抽帧数量
-
-    Returns:
-        最佳帧图片 URL；失败返回空字符串
-    """
     if not video_url:
         return ""
 
-    # 确保 MediaKit 能访问 video_url（自家 OSS 私有桶需重签）
     video_url = _sign_video_url_for_mediakit(video_url)
 
     try:
@@ -85,11 +78,9 @@ def select_best_cover_frame(video_url: str, *, max_frames: int = 5) -> str:
             return ""
 
         logger.info(
-            "[数字人封面] 开始抽帧: video_url=%s max_frames=%d poll_interval=%.1f max_poll=%d",
+            "[数字人封面] 开始抽帧: video_url=%s max_frames=%d",
             video_url[:80],
             max_frames,
-            COVER_POLL_INTERVAL,
-            COVER_MAX_POLL_ATTEMPTS,
         )
 
         snapshots = mk.extract_frames(
@@ -107,7 +98,6 @@ def select_best_cover_frame(video_url: str, *, max_frames: int = 5) -> str:
         if len(snapshots) == 1:
             return snapshots[0].get("image_url") or snapshots[0].get("url") or ""
 
-        # 使用连接池下载各帧（复用 TCP 连接，减少延迟）
         import httpx
 
         candidates = []
@@ -135,7 +125,6 @@ def select_best_cover_frame(video_url: str, *, max_frames: int = 5) -> str:
         best = scored[0] if scored else None
         best_url = best.get("url", "") if best else ""
 
-        # 清理临时文件
         for c in candidates:
             p = c.get("image_path")
             if p:
@@ -156,16 +145,15 @@ def select_best_cover_frame(video_url: str, *, max_frames: int = 5) -> str:
         return ""
 
 
-def persist_cover_to_oss(frame_url: str, *, job_id: str = "", prefix: str = "ai-avatar/covers") -> str:
+def persist_cover_to_oss(
-    """下载帧图并转存到 OSS，返回公网封面 URL.
+    frame_url: str,
+    *,
+    job_id: str = "",
+    prefix: str = "ai-avatar/covers",
+) -> str:
+    """下载最佳帧图并转存到 OSS，返回公网封面 URL（预签名）.
 
-    Args:
+    封面来自最终成片抽帧，帧本身已含标题，本函数不再做任何文字/图片叠加。
-        frame_url: MediaKit 返回的临时帧图 URL
-        job_id: 关联任务 ID（用于 OSS key 命名）
-        prefix: OSS key 前缀
-
-    Returns:
-        OSS 公网 URL；失败回退原始 frame_url
     """
     if not frame_url:
         return ""
@@ -189,13 +177,13 @@ def persist_cover_to_oss(frame_url: str, *, job_id: str = "", prefix: str = "ai-
         storage = get_shared_storage_service()
         token = job_id or uuid.uuid4().hex[:12]
         cover_key = f"{prefix}/{token}/cover_{uuid.uuid4().hex[:8]}.jpg"
+
         public_url = storage.upload_file(
             file_or_path=tmp_path,
             storage_key=cover_key,
             content_type="image/jpeg",
         )
         logger.info("[数字人封面] 封面已转存 OSS: key=%s", cover_key)
-        # 私有桶：返回预签名 URL（前端才能加载）
         if public_url:
             signed = storage.get_download_url(cover_key, expires_seconds=86400)
             return signed
@@ -211,10 +199,15 @@ def persist_cover_to_oss(frame_url: str, *, job_id: str = "", prefix: str = "ai-
                 pass
 
 
-def generate_smart_cover(video_url: str, *, job_id: str = "", max_frames: int = 5) -> str:
+def generate_smart_cover(
-    """一站式：MediaKit 智能抽帧选最佳 → 转存 OSS，返回封面公网 URL.
+    video_url: str,
+    *,
+    job_id: str = "",
+    max_frames: int = 5,
+) -> str:
+    """一站式：MediaKit 智能抽帧选最佳 → 转存 OSS。失败返回空字符串。
 
-    供独立封面接口与渲染管线复用。失败返回空字符串。
+    封面从最终成片抽帧，不再叠加任何标题（帧本身已含）。
     """
     best_frame = select_best_cover_frame(video_url, max_frames=max_frames)
     if not best_frame:

apps/api/app/services/ai_avatar_render_service.py

@@ -9,8 +9,11 @@
 
 from __future__ import annotations
 
+import base64
+import binascii
 import logging
 import os
+import subprocess
 import tempfile
 import uuid
 from datetime import datetime, timezone
@@ -24,8 +27,9 @@ from packages.adapters.sqlalchemy_impl.models import (
     ScriptModel,
 )
 from packages.domain.video_filter_builder import (
-    build_cover_extract_command,
+    build_broll_overlay_filter,
     build_title_drawtext_filter,
+    build_title_overlay_filter,
 )
 from packages.shared.storage import get_shared_storage_service
 
@@ -196,9 +200,8 @@ class AiAvatarRenderService:
         1. 下载对口型输出视频 (20%)
         2. 构建 FFmpeg 滤镜链 (40%)
         3. 执行 FFmpeg 渲染 (80%)
-        4. 提取封面 (90%)
+        4. 上传到 OSS (95%) — 封面不再自动生成，改由前端主动抽帧
-        5. 上传到 OSS (95%)
+        5. 更新任务状态 (100%)
-        6. 更新任务状态 (100%)
         """
         job = self.db.query(AiAvatarRenderJob).filter(AiAvatarRenderJob.id == job_id).first()
         if job is None:
@@ -228,27 +231,32 @@ class AiAvatarRenderService:
             self.db.commit()
 
             # 2. 构建 FFmpeg 滤镜链 (40%)
-            from packages.domain.video_filter_builder import build_broll_overlay_filter
+            # 用 ffprobe 探测输入视频分辨率，确保 B-roll 缩放与标题位置与实际输出一致。
+            # AI 数字人对口型输出为 9:16 竖屏，默认兜底 720x1280；探测失败时使用默认值不阻断渲染。
+            output_width, output_height = self._probe_video_resolution(input_video_path)
+            if output_width <= 0 or output_height <= 0:
+                output_width, output_height = 720, 1280
+                logger.info(
+                    "[数字人渲染] ffprobe 探测分辨率失败或无效，使用默认竖屏尺寸 %sx%s",
+                    output_width,
+                    output_height,
+                )
+            else:
+                logger.info("[数字人渲染] 探测输入视频分辨率: %sx%s", output_width, output_height)
 
-            filter_complex = build_broll_overlay_filter(
+            broll_filter, broll_label = build_broll_overlay_filter(
                 b_roll_segments=job.b_roll_segments,
                 video_duration=lipsync_job.output_duration,
+                output_width=output_width,
+                output_height=output_height,
             )
 
-            # 标题叠加
+            # 标题叠加路径：优先前端 Canvas 渲染的 PNG 图层（所见即所得），
-            title_filter = build_title_drawtext_filter(job.title_config)
+            # 无 title_image_dataurl 时降级到 drawtext 重画文字。
-            if title_filter:
+            title_cfg = job.title_config if isinstance(job.title_config, dict) else {}
-                if filter_complex:
+            title_dataurl = (title_cfg or {}).get("title_image_dataurl") if title_cfg else None
-                    filter_complex += f"[vout]{title_filter}[vout_titled];"
+            use_title_png = isinstance(title_dataurl, str) and title_dataurl.startswith("data:image/")
-                else:
+            title_input_index = 1 + len(job.b_roll_segments or []) if use_title_png else None
-                    filter_complex = f"[0:v]{title_filter}[vout_titled];"
-
-            # 清理末尾分号
-            if filter_complex.endswith(";"):
-                filter_complex = filter_complex[:-1]
-
-            # 最终输出标签
-            final_label = "vout_titled" if title_filter else ("vout" if filter_complex else None)
 
             job.progress = 40
             self.db.commit()
@@ -257,57 +265,126 @@ class AiAvatarRenderService:
             with tempfile.TemporaryDirectory() as tmpdir:
                 output_video_path = os.path.join(tmpdir, "output.mp4")
 
-                cmd = self._build_ffmpeg_command(
+                # 在临时目录里解码保存标题 PNG（with 退出自动清理）
+                title_png_path: Optional[str] = None
+                extra_inputs: list[str] = []
+                title_filter = None
+                if use_title_png:
+                    try:
+                        title_png_path = os.path.join(tmpdir, f"title_{job.id}.png")
+                        self._save_title_dataurl_to_file(title_dataurl, dst_path=title_png_path)
+                        extra_inputs.append(title_png_path)
+                        logger.info(
+                            "[数字人渲染] 标题 PNG 已保存: %s (input index %d)", title_png_path, title_input_index
+                        )
+                    except Exception as exc:
+                        logger.warning("[数字人渲染] 标题 PNG 解码/保存失败，降级 drawtext: %s", exc)
+                        title_png_path = None
+                        extra_inputs = []
+
+                # 构建标题滤镜
+                final_label = None
+                if title_png_path and title_input_index is not None:
+                    title_input_label = f"[{title_input_index}:v]"
+                    base_label = f"[{broll_label}]" if broll_label else "[0:v]"
+                    title_filter = build_title_overlay_filter(
+                        title_cfg,
+                        output_width=output_width,
+                        output_height=output_height,
+                        title_png_path=title_png_path,
+                        title_input_label=title_input_label,
+                        base_label=base_label,
+                        output_label="vout_titled",
+                    )
+                    if not title_filter:
+                        # build 返回 None → 文件不存在（极端并发情况），降级 drawtext
+                        title_png_path = None
+                        extra_inputs = []
+
+                if title_png_path:
+                    # overlay 路径
+                    if broll_filter and title_filter:
+                        filter_complex = broll_filter + f";{title_filter}"
+                    elif broll_filter:
+                        filter_complex = broll_filter
+                        final_label = broll_label
+                    elif title_filter:
+                        filter_complex = title_filter
+                    else:
+                        filter_complex = ""
+                    if title_filter:
+                        final_label = "vout_titled"
+                    elif not final_label:
+                        final_label = None
+                else:
+                    # 降级：drawtext 重画文字
+                    title_filter = build_title_drawtext_filter(
+                        title_cfg,
+                        output_width=output_width,
+                        output_height=output_height,
+                    )
+                    if broll_filter and title_filter:
+                        filter_complex = broll_filter + f";[{broll_label}]{title_filter}[vout_titled]"
+                        final_label = "vout_titled"
+                    elif broll_filter:
+                        filter_complex = broll_filter
+                        final_label = broll_label
+                    elif title_filter:
+                        filter_complex = f"[0:v]{title_filter}[vout_titled]"
+                        final_label = "vout_titled"
+                    else:
+                        filter_complex = ""
+                        final_label = None
+
+                cmd_list = self._build_ffmpeg_command(
                     input_video=input_video_path,
                     b_roll_segments=job.b_roll_segments,
+                    extra_inputs=extra_inputs,
                     filter_complex=filter_complex,
                     final_label=final_label,
                     output_path=output_video_path,
                 )
 
-                exit_code = os.system(cmd)
+                try:
-                if exit_code != 0:
+                    render_result = subprocess.run(
-                    raise AiAvatarRenderError(f"FFmpeg 渲染失败，退出码: {exit_code}", code="FFmpegFailed")
+                        cmd_list,
+                        capture_output=True,
+                        text=True,
+                        timeout=600,
+                    )
+                except subprocess.TimeoutExpired as exc:
+                    raise AiAvatarRenderError(
+                        "FFmpeg 渲染超时（600s）",
+                        code="FFmpegTimeout",
+                    ) from exc
+
+                if render_result.returncode != 0:
+                    stderr_tail = (render_result.stderr or "").strip()[-800:]
+                    raise AiAvatarRenderError(
+                        f"FFmpeg 渲染失败，退出码: {render_result.returncode}, stderr: {stderr_tail}",
+                        code="FFmpegFailed",
+                    )
 
                 job.progress = 80
                 self.db.commit()
 
-                # 4. 提取封面 (90%)
+                # 4/5. 上传成片到 OSS (95%) —— 已砍掉自动抽封面逻辑（步骤⑤）；
-                cover_path = ""
+                # 封面由前端在渲染完成后通过 /smart-cover 接口主动从成片抽帧，不阻塞渲染链路。
-                if job.cover_config:
-                    cover_path = os.path.join(tmpdir, "cover.jpg")
-                    cover_cmd = build_cover_extract_command(job.cover_config, cover_path)
-                    cover_cmd = cover_cmd.replace("INPUT_VIDEO", output_video_path)
-                    cover_exit = os.system(cover_cmd)
-                    if cover_exit != 0:
-                        logger.warning("封面提取失败，跳过: %s", cover_cmd)
-                        cover_path = ""
-
-                job.progress = 90
-                self.db.commit()
-
-                # 5. 上传到 OSS (95%)
                 output_video_url = self._upload_to_oss(output_video_path, f"ai-avatar/{job_id}/output.mp4")
                 job.output_video_url = output_video_url
 
-                # 封面：优先复用智能剪辑的 MediaKit 抽帧 + 质量评分选最佳帧；
+                # 封面透传：如果用户已在 cover_config 中选定封面 URL（mode=upload 的自定义上传 或
-                # MediaKit 不可用时回退到 FFmpeg 已按 cover_config 抽取的 cover_path
+                # mode=auto_frame 已有的智能封面结果），直接透传到 output_cover_url，不再重新截帧。
-                smart_cover_url = ""
+                if isinstance(job.cover_config, dict):
-                if output_video_url:
+                    _pre_cover_url = (
-                    try:
+                        job.cover_config.get("url")
-                        from app.services.ai_avatar_cover_service import (
+                        or job.cover_config.get("imageUrl")
-                            generate_smart_cover,
+                        or job.cover_config.get("cover_url")
-                        )
+                        or ""
-
+                    )
-                        smart_cover_url = generate_smart_cover(output_video_url, job_id=job_id, max_frames=5)
+                    if _pre_cover_url:
-                    except Exception:
+                        job.output_cover_url = _pre_cover_url
-                        logger.warning("智能封面(MediaKit)失败，回退 FFmpeg 封面 job_id=%s", job_id, exc_info=True)
+                        logger.info("[数字人渲染] 使用用户已选定封面 URL: job_id=%s", job_id)
-
-                if smart_cover_url:
-                    job.output_cover_url = smart_cover_url
-                elif cover_path:
-                    output_cover_url = self._upload_to_oss(cover_path, f"ai-avatar/{job_id}/cover.jpg")
-                    job.output_cover_url = output_cover_url
 
                 # 获取输出视频时长
                 job.output_duration = lipsync_job.output_duration
@@ -331,9 +408,13 @@ class AiAvatarRenderService:
                     from packages.domain.generated_video import GeneratedVideo
 
                     clip_name = f"AI数字人_{job_id[:8]}"
+                    # AI数字人入口是独立页面，前端可能不传 project_id（无项目概念），
+                    # 兜底为 "ai_avatar" 避免 DB 非空约束/查询问题；generation_task_id 同样兜底用 render_job_id
+                    clip_project_id = (job.project_id or "").strip() or "ai_avatar"
+                    clip_generation_task_id = (job.lipsync_job_id or "").strip() or job_id
                     clip = GeneratedVideo.create(
-                        project_id=job.project_id,
+                        project_id=clip_project_id,
-                        generation_task_id=job.lipsync_job_id,
+                        generation_task_id=clip_generation_task_id,
                         name=clip_name,
                         file_url=job.output_video_url,
                         user_id=job.user_id,
@@ -347,11 +428,11 @@ class AiAvatarRenderService:
                     video_repo = SQLAlchemyGeneratedVideoRepository(self.db)
                     video_repo.create(clip)
                     logger.info("成片记录已保存到成片库: clip_id=%s, render_job=%s", clip.id, job_id)
-                except Exception as clip_err:
+                except Exception:
-                    logger.warning(
+                    logger.error(
-                        "自动保存成片记录失败（不影响渲染任务状态）: render_job=%s, error=%s",
+                        "自动保存成片记录失败（不影响渲染任务状态）: render_job=%s",
                         job_id,
-                        clip_err,
+                        exc_info=True,
                     )
 
         except AiAvatarRenderError as exc:
@@ -360,12 +441,14 @@ class AiAvatarRenderService:
             job.updated_at = datetime.now(timezone.utc)
             self.db.commit()
             logger.error("渲染任务失败 [%s]: %s", job_id, exc)
+            raise
         except Exception as exc:
             job.status = "failed"
             job.error_message = f"渲染异常: {str(exc)}"
             job.updated_at = datetime.now(timezone.utc)
             self.db.commit()
             logger.exception("渲染任务异常 [%s]", job_id)
+            raise
 
     def _download_video(self, url: str) -> str:
         """下载视频到临时文件."""
@@ -383,32 +466,132 @@ class AiAvatarRenderService:
                 os.unlink(tmp.name)
             raise
 
+    @staticmethod
+    def _save_title_dataurl_to_file(dataurl: str, *, dst_path: str | None = None, job_id: str = "") -> str:
+        """解码前端传来的 data:image/png;base64,... 并保存为本地 PNG 文件。
+
+        Args:
+            dataurl: 完整 dataURL 字符串
+            dst_path: 指定输出路径；为 None 时创建临时文件并返回路径
+            job_id: 仅在 dst_path 为空时用于临时文件命名
+
+        Returns:
+            保存后的本地文件路径
+        """
+        if not isinstance(dataurl, str) or not dataurl.startswith("data:image/"):
+            raise ValueError("title_image_dataurl 不是合法的 data:image URL")
+        # 拆分 data:image/png;base64,<payload>
+        try:
+            header, b64 = dataurl.split(",", 1)
+        except ValueError as exc:
+            raise ValueError("title_image_dataurl 缺少 base64 payload") from exc
+        if "base64" not in header:
+            raise ValueError("title_image_dataurl 不是 base64 编码")
+        try:
+            png_bytes = base64.b64decode(b64, validate=True)
+        except (binascii.Error, ValueError) as exc:
+            raise ValueError(f"title_image_dataurl base64 解码失败: {exc}") from exc
+        if not png_bytes:
+            raise ValueError("title_image_dataurl 解码后为空")
+
+        if dst_path:
+            out_path = dst_path
+            with open(out_path, "wb") as f:
+                f.write(png_bytes)
+            return out_path
+        suffix = f"_title_{job_id}.png" if job_id else "_title.png"
+        with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
+            tmp.write(png_bytes)
+            return tmp.name
+
+    @staticmethod
+    def _probe_video_resolution(video_path: str) -> tuple[int, int]:
+        """用 ffprobe 探测视频分辨率，返回 (width, height)；失败返回 (0, 0)。"""
+        try:
+            result = subprocess.run(
+                [
+                    "ffprobe",
+                    "-v",
+                    "error",
+                    "-select_streams",
+                    "v:0",
+                    "-show_entries",
+                    "stream=width,height",
+                    "-of",
+                    "csv=p=0:s=x",
+                    video_path,
+                ],
+                capture_output=True,
+                text=True,
+                timeout=15,
+            )
+            if result.returncode == 0 and result.stdout.strip():
+                parts = result.stdout.strip().split("x")
+                if len(parts) == 2:
+                    w, h = int(parts[0]), int(parts[1])
+                    if w > 0 and h > 0:
+                        return w, h
+        except Exception as exc:
+            logger.warning("[数字人渲染] ffprobe 探测分辨率失败: %s", exc)
+        return 0, 0
+
     def _build_ffmpeg_command(
         self,
         *,
         input_video: str,
         b_roll_segments: list[dict[str, Any]],
+        extra_inputs: list[str] | None = None,
         filter_complex: str,
         final_label: Optional[str],
         output_path: str,
-    ) -> str:
+    ) -> list[str]:
-        """构建 FFmpeg 命令."""
+        """构建 FFmpeg 命令（list 形式，shell=False）.
-        # 输入文件
+
-        inputs = f"-i {input_video}"
+        根因修复 #1798 P0：OSS 预签名 URL 含 `&Expires=...&Signature=...` 特殊字符，
+        os.system(shell=True) 会把 `&` 解释为后台命令分隔符，导致 -filter_complex 被
+        当成独立命令报 sh: -filter_complex: not found（exit 127 → Python 32512）。
+        list + shell=False 彻底规避 shell 转义问题。
+        """
+        cmd: list[str] = ["ffmpeg", "-i", input_video]
         for seg in b_roll_segments:
             asset_url = seg.get("asset_url", "")
             if asset_url:
-                inputs += f" -i {asset_url}"
+                cmd.extend(["-i", asset_url])
+        # 额外输入（例如前端 Canvas 渲染的标题 PNG）
+        for extra in extra_inputs or []:
+            cmd.extend(["-i", extra])
 
-        # 滤镜
         if filter_complex and final_label:
-            filter_arg = f'-filter_complex "{filter_complex}" -map "[{final_label}]"'
+            cmd.extend(
+                [
+                    "-filter_complex",
+                    filter_complex,
+                    "-map",
+                    f"[{final_label}]",
+                    "-map",
+                    "0:a?",
+                ]
+            )
         elif filter_complex:
-            filter_arg = f'-filter_complex "{filter_complex}"'
+            cmd.extend(["-filter_complex", filter_complex])
-        else:
-            filter_arg = ""
 
-        return f"ffmpeg {inputs} {filter_arg} -c:v libx264 -preset veryfast -crf 23 -y {output_path}"
+        cmd.extend(
+            [
+                "-c:v",
+                "libx264",
+                "-preset",
+                "veryfast",
+                "-crf",
+                "23",
+                "-c:a",
+                "aac",
+                "-b:a",
+                "128k",
+                "-y",
+                output_path,
+            ]
+        )
+        return cmd
 
     def _upload_to_oss(self, local_path: str, oss_key: str) -> str:
         """上传文件到 OSS，返回 URL.

apps/api/app/services/lipsync_service.py

@@ -1,8 +1,12 @@
-"""对口型 Service — #1796 MediaKit 对口型业务逻辑, #1809 参数调整.
+"""对口型 Service — #1796 MediaKit 对口型业务逻辑, #1809 参数调整, #1845 配音前置.
 
 职责：
 - 创建/查询对口型任务
-- 双输入模式：TTS 直生（voice_id + script_text，内部先合成音频转存 OSS）或直接音频（audio_url）
+- 三输入模式：
+  1. TTS 直生（voice_id + script_text）→ 走 Celery 异步（降级路径）
+  2. 直接音频（audio_url，前端未传 timings）→ 同步下载 + 算 timings + 提交 MediaKit
+  3. 预合成音频（audio_url + sentence_timings，#1845 新主路径）→ 同步 ffprobe 校验时长 +
+     写入前端传来的 timings → 直接提交 MediaKit（~2-3s）
 - 调用 MediaKit 客户端提交异步任务
 - 轮询更新任务状态（中间状态同步 DB，成片转存自家 OSS）
 - 用户隔离（每个用户只能操作自己的任务）
@@ -26,19 +30,22 @@ from app.services.mediakit_client import (
     get_mediakit_client,
 )
 
-# Celery 异步任务：TTS 合成 + MediaKit 提交（#lipsync-speed-optimization）
+# Celery 异步任务：TTS 合成 + MediaKit 提交（降级路径）
 from app.tasks.lipsync_tts import tts_synthesize_and_submit
 from sqlalchemy.orm import Session
 
 from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
 from packages.application.cosyvoice_service import CosyVoiceError, normalize_emotion
+from packages.domain.sentence_timings import (
+    compute_sentence_timings,
+    probe_audio_duration,
+)
 from packages.shared.storage import get_shared_storage_service
 from packages.shared.url_security import ALLOWED_AUDIO_MIME_TYPES, safe_download_bytes
 
 logger = logging.getLogger(__name__)
 
 # 传给 MediaKit GPU worker / 回给前端播放的 OSS 预签名有效期：7 天。
-# MediaKit 排队 + 拉取可能延迟，私有桶裸 URL 或 1 小时短预签名都会 403，故统一重签长有效期。
 MEDIAKIT_URL_TTL_SECONDS = 7 * 24 * 3600
 
 
@@ -142,6 +149,100 @@ class LipsyncService:
             logger.warning("TTS 音频转存 OSS 失败，回退临时 URL: job_id=%s err=%s", job_id, exc)
             return temp_url
 
+    def _submit_audio_direct(
+        self,
+        *,
+        job: LipsyncJobModel,
+        supplied_timings: Optional[list] = None,
+        supplied_duration: Optional[float] = None,
+    ) -> None:
+        """音频直传模式（包含 #1845 预合成路径）：同步下载 → ffprobe → timings → 提交 MediaKit.
+
+        直接在 HTTP 请求内完成，不走 Celery。job.status 成功后置为 submitted。
+        失败时把 job 标成 failed 并 commit，然后抛 MediaKitError。
+
+        Args:
+            job: 已 commit 的 LipsyncJobModel（audio_url / video_url 已写入）
+            supplied_timings: 前端传来的预合成 timings（可选，可信时直接用）
+            supplied_duration: 前端传来的预合成时长（可选，用于优先避免重复探测）
+        """
+        # 1. 下载音频
+        audio_data: bytes | None = None
+        try:
+            audio_data = safe_download_bytes(
+                job.audio_url,
+                purpose="lipsync_direct_audio",
+                allowed_mime_types=ALLOWED_AUDIO_MIME_TYPES,
+                timeout=60.0,
+            )
+            logger.info(
+                "[lipsync] 直传音频下载完成: job_id=%s size=%d",
+                job.id,
+                len(audio_data) if audio_data else 0,
+            )
+        except Exception as exc:
+            logger.warning("[lipsync] 直传音频下载失败，跳过 timings 计算: job_id=%s err=%s", job.id, exc)
+
+        # 2. ffprobe 探测时长（优先用前端传入的预合成时长，但以 ffprobe 为准做兜底校验）
+        audio_duration = 0.0
+        if audio_data:
+            audio_duration = probe_audio_duration(audio_data)
+        if audio_duration <= 0 and supplied_duration and supplied_duration > 0:
+            audio_duration = supplied_duration
+            logger.info(
+                "[lipsync] ffprobe 失败，使用前端传入的预合成时长: job_id=%s duration=%.2f", job.id, audio_duration
+            )
+
+        # 3. 句子时间戳：优先用前端预合成传入的 timings（后端预合成接口已经算过，可信）；
+        #    否则若音频下载成功则重算；否则不设置（不阻塞主流程）
+        timings: Optional[list] = None
+        if supplied_timings:
+            timings = supplied_timings
+            logger.info("[lipsync] 使用前端预合成句子时间戳: job_id=%s sentences=%d", job.id, len(timings))
+        elif audio_data and audio_duration > 0 and job.script_text:
+            try:
+                timings = compute_sentence_timings(audio_data, job.script_text, audio_duration)
+                logger.info(
+                    "[lipsync] 后端重算句子时间戳: job_id=%s sentences=%d duration=%.2f",
+                    job.id,
+                    len(timings) if timings else 0,
+                    audio_duration,
+                )
+            except Exception as exc:
+                logger.warning("[lipsync] 句子时间戳计算失败（不阻塞）: job_id=%s err=%s", job.id, exc)
+
+        if timings:
+            job.sentence_timings = timings
+
+        # 4. 签名 URL 并提交 MediaKit
+        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
+
+        try:
+            result = self.client.submit_lipsync(
+                video_url=video_url,
+                audio_url=signed_audio_url,
+                enable_video_loop=job.enable_video_loop,
+                client_token=job.id,
+            )
+            job.mediakit_task_id = result["task_id"]
+            job.status = "submitted"
+            job.submitted_at = datetime.now(timezone.utc)
+            self.db.commit()
+            logger.info(
+                "[lipsync] 直传音频已提交 MediaKit: job_id=%s task_id=%s",
+                job.id,
+                result["task_id"],
+            )
+        except MediaKitError as exc:
+            job.status = "failed"
+            job.error_message = str(exc)
+            job.error_code = exc.code
+            logger.error("[lipsync] 直传音频提交 MediaKit 失败: job_id=%s err=%s", job.id, exc)
+            self.db.commit()
+            raise
+
     # ── 创建任务 ──────────────────────────────────────────────────────────
 
     def create_job(
@@ -150,27 +251,35 @@ class LipsyncService:
         user_id: str,
         video_url: str,
         audio_url: str = "",
+        audio_duration: Optional[float] = None,
+        sentence_timings: Optional[list] = None,
         voice_id: str = "",
         script_text: str = "",
         speed: float = 1.0,
         emotion: str = "",
-        enable_video_loop: bool = False,
+        enable_video_loop: bool = True,
         project_id: str = "",
     ) -> LipsyncJobModel:
         """创建对口型任务.
 
-        两种输入模式：
+        三种输入模式：
         - TTS 直生：voice_id + script_text（audio_url 留空）
-          → 先创建 DB 记录（状态 tts_processing），再 dispatch Celery 异步任务
+          → 创建 DB 记录（状态 tts_processing），dispatch Celery 异步任务（降级路径）。
-            执行 TTS 合成 + MediaKit 提交。API 响应 <1s。
+          API 响应 <1s。
-        - 直接音频：提供 audio_url
+        - 直接音频：audio_url 非空 + 无 sentence_timings
-          → 同步提交 MediaKit，状态直接设为 submitted。
+          → 同步下载音频 + 重算 timings + 提交 MediaKit（几秒完成）。
+        - 预合成音频（#1845 新主路径）：audio_url 非空 + 传 sentence_timings
+          → 同步 ffprobe 校验时长 + 写入 timings + 提交 MediaKit（~2-3s）。
 
         Raises:
-            MediaKitError: 参数校验失败或 MediaKit 提交失败（仅直接音频模式）
+            MediaKitError: 参数校验失败或 MediaKit 提交失败
         """
         # 0. 输入校验
-        if not audio_url:
+        is_pre_synth = bool(audio_url) and bool(sentence_timings)
+        bool(audio_url) and not is_pre_synth
+        is_tts_mode = not bool(audio_url)
+
+        if is_tts_mode:
             if not (voice_id and script_text):
                 raise MediaKitError(
                     "必须提供 audio_url 或 voice_id+script_text",
@@ -178,10 +287,13 @@ class LipsyncService:
                 )
             # TTS 模式：在 HTTP 请求中同步校验音色归属，快速失败
             self._resolve_voice_id(voice_id, user_id)
+        elif is_pre_synth:
+            # 预合成模式：script_text 可空（因为 timings 已自带句子文本），但仍建议传
+            if not isinstance(sentence_timings, list) or len(sentence_timings) == 0:
+                raise MediaKitError("预合成模式 sentence_timings 不能为空", code="InvalidInput")
 
         # 1. 创建数据库记录
         job_id = str(uuid.uuid4())
-        is_tts_mode = not bool(audio_url)
         job = LipsyncJobModel(
             id=job_id,
             user_id=user_id,
@@ -192,14 +304,19 @@ class LipsyncService:
             voice_id=voice_id or "",
             script_text=script_text or "",
             speed=speed,
-            emotion=normalize_emotion(emotion),
+            emotion=normalize_emotion(emotion) if is_tts_mode else (emotion or ""),
+            # 音频直传（含预合成）直接进入 pending（后续同步改为 submitted）；TTS 模式进入 tts_processing
             status="tts_processing" if is_tts_mode else "pending",
         )
         self.db.add(job)
         self.db.flush()
 
+        # ⚠️ 必须先 commit 再发 Celery 任务 / 后续同步操作，避免事务竞态
+        self.db.commit()
+        self.db.refresh(job)
+
         if is_tts_mode:
-            # 2a. TTS 模式：dispatch Celery 异步任务处理 TTS 合成 + MediaKit 提交
+            # 2a. TTS 模式：dispatch Celery 异步任务处理 TTS 合成 + MediaKit 提交（降级路径）
             try:
                 tts_synthesize_and_submit.apply_async(
                     args=(
@@ -212,8 +329,6 @@ class LipsyncService:
                     )
                 )
             except Exception as exc:
-                # 投递失败时立即把 job 标成 failed 并写入 error_message，
-                # 前端轮询时能直接看到失败原因，不会无限卡在 tts_processing。
                 logger.exception(
                     "Celery 任务提交失败，TTS 任务已创建但未触发执行: job_id=%s err=%s",
                     job_id,
@@ -223,34 +338,102 @@ class LipsyncService:
                 job.error_message = f"Celery 任务投递失败: {exc}"
                 job.error_code = "AsyncDispatchFailed"
                 job.updated_at = datetime.now(timezone.utc)
+                self.db.commit()
         else:
-            # 2b. 直接音频模式：同步签名并提交 MediaKit
+            # 2b/2c. 直接音频 / 预合成音频：同步路径
-            video_url = self._sign_media_url(video_url)
+            self._submit_audio_direct(
-            if audio_url:
+                job=job,
-                audio_url = self._sign_media_url(audio_url)
+                supplied_timings=sentence_timings,
-                job.audio_url = audio_url
+                supplied_duration=audio_duration,
+            )
+            self.db.refresh(job)
 
-            try:
-                result = self.client.submit_lipsync(
-                    video_url=video_url,
-                    audio_url=audio_url,
-                    enable_video_loop=enable_video_loop,
-                    client_token=job_id,
-                )
-                job.mediakit_task_id = result["task_id"]
-                job.status = "submitted"
-                job.submitted_at = datetime.now(timezone.utc)
-            except MediaKitError as exc:
-                job.status = "failed"
-                job.error_message = str(exc)
-                job.error_code = exc.code
-                logger.error("提交对口型任务失败: %s", exc)
-                raise
-
-        self.db.commit()
-        self.db.refresh(job)
         return job
 
+    # ── TTS 预合成（#1845 步骤1「生成配音」同步接口使用） ──────────────────
+
+    def preview_tts(
+        self,
+        *,
+        user_id: str,
+        voice_id: str,
+        script_text: str,
+        speed: float = 1.0,
+        emotion: str = "natural",
+    ) -> dict:
+        """同步做 TTS 合成 + 下载 + ffprobe + 句子时间戳计算.
+
+        不创建 LipsyncJob、不转存 OSS，直接返回 CosyVoice 临时 URL（~24h 有效期）。
+        耗时约 2-3 秒，由前端在步骤1点「生成配音」时同步等待。
+
+        Returns:
+            {"audio_url": str, "duration": float, "sentence_timings": list[dict]}
+
+        Raises:
+            MediaKitError: TTS 合成失败 / 下载失败 / ffprobe 失败
+        """
+        # 1. 音色解析（校验克隆音色归属）
+        actual_voice_id = self._resolve_voice_id(voice_id, user_id)
+        cosyvoice = self._get_cosyvoice()
+
+        # 2. TTS 合成（同步，~2-3s）
+        try:
+            result = cosyvoice.submit_synthesize_task(
+                text=script_text,
+                voice_id=actual_voice_id,
+                speed=speed,
+                emotion=normalize_emotion(emotion),
+            )
+        except CosyVoiceError as exc:
+            raise MediaKitError(f"TTS 合成失败: {exc}", code="TTSSynthesisFailed") from exc
+        except ValueError as exc:
+            raise MediaKitError(f"TTS 参数错误: {exc}", code="TTSInvalidParam") from exc
+
+        temp_url = result.get("audio_url", "")
+        if not temp_url:
+            raise MediaKitError("TTS 未返回音频 URL", code="TTSNoAudio")
+
+        # 3. 下载音频到内存（用于 ffprobe + 静音检测）
+        try:
+            audio_data = safe_download_bytes(
+                temp_url,
+                purpose="tts_preview_audio",
+                allowed_mime_types=ALLOWED_AUDIO_MIME_TYPES,
+                timeout=60.0,
+            )
+        except Exception as exc:
+            logger.warning("[tts-preview] TTS 音频下载失败，仍返回 audio_url: user_id=%s err=%s", user_id, exc)
+            return {
+                "audio_url": temp_url,
+                "duration": 0.0,
+                "sentence_timings": [],
+            }
+
+        # 4. ffprobe 时长
+        duration = probe_audio_duration(audio_data)
+        if duration <= 0:
+            logger.warning("[tts-preview] ffprobe 未返回有效时长，timings 留空: user_id=%s", user_id)
+            return {
+                "audio_url": temp_url,
+                "duration": 0.0,
+                "sentence_timings": [],
+            }
+
+        # 5. 句子时间戳
+        timings = compute_sentence_timings(audio_data, script_text, duration)
+
+        logger.info(
+            "[tts-preview] TTS 预合成完成: user_id=%s duration=%.2f sentences=%d",
+            user_id,
+            duration,
+            len(timings),
+        )
+        return {
+            "audio_url": temp_url,
+            "duration": round(duration, 2),
+            "sentence_timings": timings,
+        }
+
     # ── 查询任务 ──────────────────────────────────────────────────────────
 
     def get_job(self, job_id: str, user_id: str) -> Optional[LipsyncJobModel]:
@@ -284,11 +467,7 @@ class LipsyncService:
     # ── 更新任务状态（轮询） ──────────────────────────────────────────────
 
     def refresh_job_status(self, job_id: str, user_id: str) -> Optional[LipsyncJobModel]:
-        """从 MediaKit 拉取最新状态并更新本地记录.
+        """从 MediaKit 拉取最新状态并更新本地记录."""
-
-        Returns:
-            更新后的 Job，或 None（任务不存在/不属于该用户）
-        """
         job = self.get_job(job_id, user_id)
         if job is None:
             return None
@@ -313,11 +492,25 @@ class LipsyncService:
         if mk_status == STATUS_COMPLETED:
             result = status_data.get("result", {})
             job.status = STATUS_COMPLETED
-            output_url = result.get("video_url", "")
+            temp_url = result.get("video_url", "")
-            # MediaKit 输出为临时 URL，转存自家 OSS 防止过期（失败则回退临时 URL）
+            job.output_video_url = temp_url
-            job.output_video_url = self._persist_output_video(output_url, job_id, user_id)
             job.output_duration = result.get("duration", 0.0)
             job.completed_at = datetime.now(timezone.utc)
+            job.updated_at = datetime.now(timezone.utc)
+            self.db.commit()
+            # 异步转存自家 OSS
+            try:
+                from app.tasks.lipsync_tts import persist_output_video_task
+
+                persist_output_video_task.apply_async(args=(job_id, user_id, temp_url))
+            except Exception as exc:
+                logger.warning(
+                    "提交输出视频异步转存任务失败，保留临时 URL: job_id=%s err=%s",
+                    job_id,
+                    exc,
+                )
+            self.db.refresh(job)
+            return job
         elif mk_status == STATUS_FAILED:
             error = status_data.get("error", {})
             job.status = "failed"
@@ -325,7 +518,6 @@ class LipsyncService:
             job.error_code = error.get("code", "TaskFailed")
             job.completed_at = datetime.now(timezone.utc)
         else:
-            # 中间状态（running/processing/queued 等）同步到 DB，避免前端永远卡在 submitted
             if isinstance(mk_status, str) and mk_status:
                 job.status = mk_status
         job.updated_at = datetime.now(timezone.utc)
@@ -334,10 +526,7 @@ class LipsyncService:
         return job
 
     def _persist_output_video(self, temp_url: str, job_id: str, user_id: str) -> str:
-        """将 MediaKit 输出的临时视频 URL 转存到自家 OSS.
+        """将 MediaKit 输出的临时视频 URL 转存到自家 OSS. 失败时回退返回原始临时 URL."""
-
-        失败时回退返回原始临时 URL，不影响任务完成。
-        """
         if not temp_url:
             return ""
         try:
@@ -357,27 +546,21 @@ class LipsyncService:
             return temp_url
 
     def _sign_media_url(self, url: str) -> str:
-        """对自家 OSS 私有桶 URL 重签长有效期预签名，供 MediaKit 拉取 / 前端播放。
+        """对自家 OSS 私有桶 URL 重签长有效期预签名."""
-
-        - 裸 public_url（upload_file 返回，不带签名）→ 私有桶匿名访问 403，重签。
-        - 已带签名但即将过期的 URL（如前端 1h 预签名）→ 抽 storage_key 后重签。
-        - 外部 URL（CosyVoice/MediaKit 临时链接，非本桶 host）→ 原样透传。
-        - 任何异常都降级原样返回，不阻断主流程。
-        """
         if not url:
             return url
         try:
             storage = get_shared_storage_service()
             public_base = getattr(storage, "public_url", "")
             if not isinstance(public_base, str) or not public_base:
-                return url  # 无法判定归属，保守透传
+                return url
             own_host = urlparse(public_base).netloc.lower()
             host = urlparse(url).netloc.lower()
             if not own_host or host != own_host:
-                return url  # 非自家 OSS（外部临时链接），不处理
+                return url  # 外部临时链接原样透传
             signed = storage.get_download_url(url, expires_seconds=MEDIAKIT_URL_TTL_SECONDS)
             return signed or url
-        except Exception as exc:  # noqa: BLE001 - 签名失败不阻断，降级原 URL
+        except Exception as exc:
             logger.warning("对口型 URL 重签失败，原样返回: url_prefix=%s err=%s", url[:80], exc)
             return url
 

apps/api/app/services/mediakit_client.py

@@ -75,7 +75,7 @@ class MediaKitClient:
         *,
         video_url: str,
         audio_url: str,
-        enable_video_loop: bool = False,
+        enable_video_loop: bool = True,
         callback_url: Optional[str] = None,
         callback_args: Optional[str] = None,
         client_token: Optional[str] = None,
@@ -103,8 +103,7 @@ class MediaKitClient:
             "video_url": video_url,
             "audio_url": audio_url,
         }
-        if enable_video_loop:
+        payload["enable_video_loop"] = bool(enable_video_loop)
-            payload["enable_video_loop"] = True
         if callback_url:
             payload["callback_url"] = callback_url
         if callback_args:

apps/api/app/tasks/lipsync_tts.py

@@ -12,6 +12,10 @@
 
 注意：使用 @shared_task 而非绑定到某个 celery_app 实例，
 确保任务能被 Worker 侧 celery_app 正确注册，同时 API 侧 send_task/apply_async 仍可正常调用。
+
+#1845：句子时间戳计算已提取至 packages/domain/sentence_timings.py，本模块保留
+_ 开头别名兼容历史导入，但 _compute_sentence_timings/_split_script_into_sentences/
+_estimate_sentence_timings_by_chars 等内部函数已复用共享实现，避免重复代码。
 """
 
 import io
@@ -21,6 +25,12 @@ from urllib.parse import urlparse
 
 from celery import shared_task
 
+# 复用共享的句子时间戳工具（#1845 配音前置）
+from packages.domain.sentence_timings import compute_sentence_timings as _compute_sentence_timings
+from packages.domain.sentence_timings import (
+    probe_audio_duration,
+)
+
 logger = logging.getLogger(__name__)
 
 # MediaKit 预签名 URL 有效期（7天，秒），与 LipsyncService._sign_media_url 保持一致
@@ -57,8 +67,13 @@ def _sign_media_url(url: str) -> str:
 @shared_task(
     bind=True,
     name="lipsync_tts.synthesize_and_submit",
-    max_retries=2,
+    max_retries=5,  # 事务竞态重试3次（job not found）+ TTS偶发错误2次
     default_retry_delay=30,
+    autoretry_for=(OSError, ConnectionError),  # 网络/连接错误自动重试
+    retry_backoff=True,
+    retry_backoff_max=30,
+    soft_time_limit=180,
+    time_limit=200,
 )
 def tts_synthesize_and_submit(
     self,
@@ -71,7 +86,8 @@ def tts_synthesize_and_submit(
 ):
     """异步执行 TTS 合成 + OSS 转存 + MediaKit 提交.
 
-    在 Celery worker 中运行，不阻塞 HTTP 请求。
+    在 Celery worker 中运行，不阻塞 HTTP 请求。保留作为降级路径
+    （预合成失败 / 旧版前端未传 audio_url 时走此路径）。
     """
     from app.services.mediakit_client import MediaKitError, get_mediakit_client
     from sqlalchemy.orm import Session as DBSession
@@ -103,7 +119,25 @@ def tts_synthesize_and_submit(
         )
 
         if job is None:
-            logger.error("[lipsync_tts] Job not found: job_id=%s", job_id)
+            # 事务竞态防御：API 在 commit 前投递了任务，worker 消费时事务尚未提交。
+            retries = getattr(self.request, "retries", 0)
+            max_retries = 3
+            if retries < max_retries:
+                backoff = (2**retries) + (retries * 1)  # 1s, 3s, 7s
+                logger.warning(
+                    "[lipsync_tts] Job not found yet (retry %d/%d, backoff %ds): job_id=%s",
+                    retries + 1,
+                    max_retries,
+                    backoff,
+                    job_id,
+                )
+                self.db.close()
+                raise self.retry(countdown=backoff, max_retries=max_retries)
+            logger.error(
+                "[lipsync_tts] Job not found after %d retries, giving up: job_id=%s",
+                max_retries,
+                job_id,
+            )
             return
 
         # 已取消的任务不再处理
@@ -112,6 +146,13 @@ def tts_synthesize_and_submit(
             return
 
         # 1. TTS 合成
+        logger.info(
+            "[lipsync_tts] 开始 TTS 合成: job_id=%s voice_id=%s text_len=%d speed=%.2f",
+            job_id,
+            voice_id,
+            len(script_text),
+            speed,
+        )
         try:
             cosyvoice = CosyVoiceService()
             result = cosyvoice.submit_synthesize_task(
@@ -147,37 +188,74 @@ def tts_synthesize_and_submit(
             db.commit()
             return
 
-        # 2. 下载并转存到自家 OSS
+        # 2. 下载 TTS 音频到内存（用于 2.5 静音检测；不转存自家 OSS，直接使用 CosyVoice 临时 URL）
+        audio_data: bytes | None = None
         try:
             audio_data = safe_download_bytes(
                 temp_url,
                 purpose="lipsync_tts_audio",
-                allowed_mime_types=(
+                allowed_mime_types={
                     "audio/mpeg",
                     "audio/mp3",
                     "audio/wav",
+                    "audio/x-wav",  # CosyVoice 部分接口返回 audio/x-wav
                     "audio/mp4",
                     "audio/x-m4a",
-                ),
+                },
                 timeout=60.0,
             )
-            from packages.shared.storage import get_shared_storage_service
+            logger.info(
-
+                "[lipsync_tts] TTS 音频已下载到内存: job_id=%s size=%d",
-            storage = get_shared_storage_service()
+                job_id,
-            storage_key = f"lipsync-tts/{user_id}/{job_id}.mp3"
+                len(audio_data) if audio_data else 0,
-            permanent_url = storage.upload_file(io.BytesIO(audio_data), storage_key, content_type="audio/mpeg")
+            )
-            logger.info("[lipsync_tts] TTS 音频已转存 OSS: job_id=%s key=%s", job_id, storage_key)
-            job.audio_url = permanent_url
         except Exception as exc:
             logger.warning(
-                "[lipsync_tts] TTS 音频转存 OSS 失败，回退临时 URL: job_id=%s err=%s",
+                "[lipsync_tts] TTS 音频下载失败，跳过静音检测，直接使用临时 URL 提交: job_id=%s err=%s",
                 job_id,
                 exc,
             )
-            job.audio_url = temp_url
+        # TTS 音频使用 CosyVoice 临时 URL，跳过自家 OSS 转存（加速，步骤⑥）
+        job.audio_url = temp_url
+        logger.info("[lipsync_tts] TTS 音频使用 CosyVoice 临时 URL（跳过 OSS 转存）: job_id=%s", job_id)
 
         db.commit()
 
+        # 2.5 计算精确句子时间戳（基于 TTS 音频静音检测）—— 复用共享工具
+        try:
+            if not audio_data:
+                logger.warning("[lipsync_tts] 无音频数据，跳过句子时间戳计算: job_id=%s", job_id)
+            else:
+                _audio_duration = probe_audio_duration(audio_data)
+                logger.info(
+                    "[lipsync_tts] 音频时长探测: job_id=%s duration=%.2f",
+                    job_id,
+                    _audio_duration,
+                )
+
+                if _audio_duration > 0:
+                    _timings = _compute_sentence_timings(audio_data, script_text, _audio_duration)
+                    if _timings:
+                        job.sentence_timings = _timings
+                        logger.info(
+                            "[lipsync_tts] 句子时间戳已计算: job_id=%s sentences=%d duration=%.1f",
+                            job_id,
+                            len(_timings),
+                            _audio_duration,
+                        )
+                    else:
+                        logger.warning("[lipsync_tts] 句子时间戳计算返回空结果: job_id=%s", job_id)
+                else:
+                    logger.warning(
+                        "[lipsync_tts] ffprobe 未获取到有效时长，跳过句子时间戳: job_id=%s",
+                        job_id,
+                    )
+                db.commit()
+        except Exception as _st_err:
+            logger.warning(
+                "[lipsync_tts] 句子时间戳计算失败（不影响主流程）: job_id=%s err=%s", job_id, _st_err, exc_info=True
+            )
+
         # 3. 签名 URL 并提交到 MediaKit（复用模块内 _sign_media_url，避免对 LipsyncService 的耦合）
         audio_url = _sign_media_url(job.audio_url)
         video_url = _sign_media_url(job.video_url)
@@ -220,3 +298,58 @@ def tts_synthesize_and_submit(
             logger.exception("[lipsync_tts] 回写失败状态时异常: job_id=%s", job_id)
     finally:
         db.close()
+
+
+@shared_task(
+    name="lipsync_tts.persist_output_video",
+    max_retries=2,
+    default_retry_delay=30,
+)
+def persist_output_video_task(job_id: str, user_id: str, temp_url: str):
+    """异步转存对口型输出视频到自家 OSS（步骤⑦ — 将同步阻塞挪到后台，加速前端响应）."""
+
+    try:
+        from worker_app.db import SessionLocal  # type: ignore
+    except Exception:  # noqa: BLE001
+        from app.db import SessionLocal  # type: ignore
+
+    from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
+    from packages.shared.storage import get_shared_storage_service
+
+    db = SessionLocal()
+    try:
+        job = db.query(LipsyncJobModel).filter(LipsyncJobModel.id == job_id, LipsyncJobModel.user_id == user_id).first()
+        if job is None:
+            logger.error("[lipsync_tts.persist] Job not found: job_id=%s", job_id)
+            return
+
+        if not temp_url:
+            logger.warning("[lipsync_tts.persist] temp_url 为空，跳过转存: job_id=%s", job_id)
+            return
+
+        try:
+            import httpx
+
+            with httpx.Client(timeout=180.0, follow_redirects=True) as client:
+                resp = client.get(temp_url)
+                resp.raise_for_status()
+                data = resp.content
+
+            storage = get_shared_storage_service()
+            storage_key = f"lipsync-outputs/{user_id}/{job_id}.mp4"
+            permanent_url = storage.upload_file(io.BytesIO(data), storage_key, content_type="video/mp4")
+            final_url = _sign_media_url(permanent_url) if permanent_url else temp_url
+            job.output_video_url = final_url
+            job.updated_at = datetime.now(timezone.utc)
+            db.commit()
+            logger.info("[lipsync_tts.persist] 输出视频已转存 OSS: job_id=%s key=%s", job_id, storage_key)
+        except Exception as exc:
+            logger.warning(
+                "[lipsync_tts.persist] 输出视频转存失败，保留临时 URL: job_id=%s err=%s",
+                job_id,
+                exc,
+            )
+    except Exception:
+        logger.exception("[lipsync_tts.persist] 未预期异常: job_id=%s", job_id)
+    finally:
+        db.close()

apps/web/src/api/products/products.ts

@@ -1,6 +1,6 @@
 /**
  * 成品 / 视频相关 API 函数
- * 后端实际接口：/videos
+ * 后端实际接口：/videos（分页：page/page_size，返回 {items, total, page, page_size}）
  */
 import apiClient from "../client"
 import type {
@@ -12,16 +12,39 @@ import type {
 } from "./types"
 import { mapVideoToProductItem } from "./utils"
 
-/** 获取成品列表（支持分页和筛选） */
+/** 分页列表响应（前端消费用） */
-export const getProducts = async (params?: ProductListParams): Promise<ProductItem[]> => {
+export interface ProductListResult {
-  const response = await apiClient.get("/videos", { params })
+  items: ProductItem[]
-  const data = response.data
+  total: number
-  const videos: VideoItem[] = Array.isArray(data?.items)
+  page: number
-    ? data.items
+  page_size: number
-    : Array.isArray(data)
+}
-      ? data
+
-      : []
+/**
-  return videos.map(mapVideoToProductItem)
+ * 获取成品列表（分页）
+ * @param params 分页与筛选参数：page 默认 1，page_size 默认 20
+ */
+export const getProducts = async (params?: ProductListParams): Promise<ProductListResult> => {
+  const response = await apiClient.get("/videos", {
+    params: {
+      page: 1,
+      page_size: 20,
+      ...params,
+    },
+  })
+  const data = response.data as {
+    items?: VideoItem[]
+    total?: number
+    page?: number
+    page_size?: number
+  }
+  const items: VideoItem[] = Array.isArray(data?.items) ? data.items : []
+  return {
+    items: items.map(mapVideoToProductItem),
+    total: data.total ?? items.length,
+    page: data.page ?? params?.page ?? 1,
+    page_size: data.page_size ?? params?.page_size ?? 20,
+  }
 }
 
 /** 获取单个成品详情 */

apps/web/src/pages/ai-avatar/AiAvatar.css

@@ -552,7 +552,7 @@
   max-width: 240px;
   aspect-ratio: 9/16;
   background: #f0f0f5;
-  border-radius: 8px;
+  border-radius: 12px;
   overflow: hidden;
   display: flex;
   align-items: center;
@@ -564,8 +564,10 @@
 .aa-cover-preview img {
   width: 100%;
   height: 100%;
+  aspect-ratio: 9/16;
   object-fit: cover;
   display: block;
+  border-radius: 12px;
 }
 
 .aa-cover-preview__placeholder {
@@ -573,6 +575,19 @@
   color: #8c8ca1;
 }
 
+.aa-cover-preview__loading {
+  position: absolute;
+  inset: 0;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  background: rgba(0, 0, 0, 0.45);
+  color: #fff;
+  font-size: 13px;
+  backdrop-filter: blur(4px);
+  -webkit-backdrop-filter: blur(4px);
+}
+
 .aa-cover-actions {
   display: flex;
   gap: 8px;

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

@@ -1,7 +1,7 @@
 /**
- * AI数字人 — 主页面（v3 两步骤版）
+ * AI数字人 — 主页面（v3 两步骤版 + #1845 配音前置）
- * 步骤1：出镜视频 / 配音库 / 文案
+ * 步骤1：出镜视频 / 配音库 / 文案 → 点击「🎵 生成配音」做 TTS 预合成（同步，~2-3s）
- * 步骤2：对口型预览（含插入画面）/ 标题配置 / 封面&生成
+ * 步骤2：对口型预览（音频已就绪、B-roll 句子时间戳立即可用）/ 标题配置 / 封面&生成
  */
 import React, { useState, useCallback, useEffect, useRef } from "react"
 import { message } from "antd"
@@ -21,15 +21,19 @@ import {
   getAssetById,
   createLipsyncJob,
   getLipsyncJob,
+  previewTts,
   submitRender,
   getRenderJob,
-  generateSmartCover,
+  generateRenderSmartCover,
 } from "./api/aiAvatar"
+import { getOrCreateDefaultProject } from "@/api/projects"
+import type { RenderJob, SentenceTiming } from "./types"
 import {
   normalizeEmotion,
   buildTitleConfigPayload,
   buildCoverConfigPayload,
 } from "./utils/contract"
+import { renderTitleToPngDataUrl, getVideoResolution } from "./utils/titleCanvas"
 
 /** 面板折叠状态 */
 type PanelKey = "video" | "voice" | "script" | "lipsync" | "title" | "cover"
@@ -47,14 +51,18 @@ const AiAvatarPage: React.FC = () => {
     cover: false,
   })
 
+  /* ── #1845 TTS 预合成弹窗 ── */
+  const [showTtsModal, setShowTtsModal] = useState(false)
+  const [ttsProgress, setTtsProgress] = useState(0)
+  const [ttsErrorMessage, setTtsErrorMessage] = useState("")
+  const ttsProgressTimerRef = useRef<ReturnType<typeof setInterval> | null>(null)
+
   /* ── 对口型生成弹窗 ── */
   const [showLipsyncModal, setShowLipsyncModal] = useState(false)
   const [lipsyncStatus, setLipsyncStatus] = useState<"generating" | "completed" | "failed">(
     "generating",
   )
   const [lipsyncErrorMessage, setLipsyncErrorMessage] = useState("")
-  /* ── 智能封面加载态 ── */
-  const [smartCoverLoading, setSmartCoverLoading] = useState(false)
   /* ── 渲染进度弹窗 ── */
   const [showRenderModal, setShowRenderModal] = useState(false)
   const [renderStatus, setRenderStatus] = useState<"generating" | "completed" | "failed">(
@@ -62,6 +70,8 @@ const AiAvatarPage: React.FC = () => {
   )
   const [renderProgress, setRenderProgress] = useState(0)
   const [renderErrorMessage, setRenderErrorMessage] = useState("")
+  /* ── 当前渲染任务对象 ── */
+  const [currentRenderJob, setCurrentRenderJob] = useState<RenderJob | null>(null)
 
   /* ── 对口型轮询 ── */
   const lipsyncTimerRef = useRef<ReturnType<typeof setInterval> | null>(null)
@@ -72,8 +82,29 @@ const AiAvatarPage: React.FC = () => {
     setCollapsed((prev) => ({ ...prev, [key]: !prev[key] }))
   }, [])
 
-  /* ── 步骤切换 ── */
+  /* ── #1845 文案/音色/语速变更时重置 TTS 预合成状态，避免音频与文案不一致 ── */
-  const handleNextStep = useCallback(() => {
+  useEffect(() => {
+    if (state.ttsPreview.status !== "idle") {
+      state.resetTtsPreview()
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [state.scriptText, state.selectedVoice?.voice_id, state.speed, state.emotion])
+
+  const _clearTtsProgressTimer = useCallback(() => {
+    if (ttsProgressTimerRef.current) {
+      clearInterval(ttsProgressTimerRef.current)
+      ttsProgressTimerRef.current = null
+    }
+  }, [])
+
+  useEffect(() => {
+    return () => {
+      _clearTtsProgressTimer()
+    }
+  }, [_clearTtsProgressTimer])
+
+  /* ── #1845 步骤1：点击「🎵 生成配音」→ 同步 TTS 预合成 ── */
+  const handleGenerateTts = useCallback(async () => {
     const missing: string[] = []
     if (!state.selectedVideo) missing.push("出镜视频")
     if (!state.selectedVoice) missing.push("配音")
@@ -82,45 +113,120 @@ const AiAvatarPage: React.FC = () => {
       message.warning(`请先完成${missing.join("、")}`)
       return
     }
-    setCurrentStep(2)
-  }, [state.selectedVideo, state.selectedVoice, state.scriptText])
 
+    // 打开弹窗 & 启动模拟进度条
+    setShowTtsModal(true)
+    setTtsProgress(0)
+    setTtsErrorMessage("")
+    state.setTtsPreview({
+      audioUrl: null,
+      duration: 0,
+      sentenceTimings: [],
+      status: "generating",
+      error: null,
+    })
+
+    // 模拟进度：每 300ms +10%，到 90% 停住，真完成后瞬间到 100%
+    _clearTtsProgressTimer()
+    let fake = 0
+    ttsProgressTimerRef.current = setInterval(() => {
+      fake = Math.min(fake + 10, 90)
+      setTtsProgress(fake)
+      if (fake >= 90) {
+        _clearTtsProgressTimer()
+      }
+    }, 300)
+
+    try {
+      const res = await previewTts({
+        voice_id: state.selectedVoice!.voice_id,
+        script_text: state.scriptText,
+        speed: state.speed,
+        emotion: normalizeEmotion(state.emotion),
+      })
+      _clearTtsProgressTimer()
+      setTtsProgress(100)
+      state.setTtsPreview({
+        audioUrl: res.audio_url,
+        duration: res.duration,
+        sentenceTimings: res.sentence_timings as SentenceTiming[],
+        status: "done",
+        error: null,
+      })
+      message.success("配音合成完成")
+    } catch (err) {
+      _clearTtsProgressTimer()
+      const errMsg =
+        (err as { response?: { data?: { message?: string; detail?: unknown } } })?.response?.data
+          ?.message || (err instanceof Error ? err.message : "配音合成失败，请重试")
+      setTtsErrorMessage(typeof errMsg === "string" ? errMsg : "配音合成失败，请重试")
+      state.setTtsPreview({
+        audioUrl: null,
+        duration: 0,
+        sentenceTimings: [],
+        status: "failed",
+        error: typeof errMsg === "string" ? errMsg : "配音合成失败",
+      })
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [state.selectedVideo, state.selectedVoice, state.scriptText, state.speed, state.emotion])
+
+  const handleRetryTts = useCallback(() => {
+    handleGenerateTts()
+  }, [handleGenerateTts])
+
+  const handleTtsNext = useCallback(() => {
+    setShowTtsModal(false)
+    setTtsProgress(0)
+    setCurrentStep(2)
+  }, [])
+
+  const handleCancelTts = useCallback(() => {
+    _clearTtsProgressTimer()
+    setShowTtsModal(false)
+    setTtsProgress(0)
+    setTtsErrorMessage("")
+    // 若用户在生成中途关闭，把状态重置回 idle，允许重新点击
+    if (state.ttsPreview.status === "generating") {
+      state.resetTtsPreview()
+    }
+  }, [_clearTtsProgressTimer, state])
+
+  /* ── 上一步（返回步骤1，不会丢失 TTS 预合成结果） ── */
   const handlePrevStep = useCallback(() => {
     setCurrentStep(1)
   }, [])
 
   /* ── 对口型 ── */
   const handleGenerateLipsync = useCallback(async () => {
-    // ② 缺项明确提示（#1809）：不再静默 return
     const video = state.selectedVideo
-    const voice = state.selectedVoice
     const text = state.scriptText.trim()
     const missing: string[] = []
     if (!video) missing.push("出镜视频")
-    if (!voice) missing.push("音色")
     if (!text) missing.push("文案")
-    if (missing.length > 0 || !video || !voice) {
+    if (missing.length > 0 || !video) {
       message.warning(`请先选择${missing.join("、")}`)
       return
     }
+
+    // #1845：预合成模式下必须要有 audioUrl（理论上到了步骤2肯定有，兜底防御）
+    const isPreSynth = state.ttsPreview.status === "done" && !!state.ttsPreview.audioUrl
+    if (!isPreSynth && !state.selectedVoice) {
+      message.warning("请先选择音色或完成配音合成")
+      return
+    }
+
     try {
-      // 显示生成弹窗
       setShowLipsyncModal(true)
       setLipsyncStatus("generating")
       setLipsyncErrorMessage("")
 
-      // ① 先按素材 id 拿 file_url（#1809 补充：对齐后端新参数 video_url）
       console.log("[对口型] 开始生成:", {
         videoId: video.id,
-        voiceId: voice.voice_id,
+        mode: isPreSynth ? "pre-synth" : "tts-direct",
-        voiceType: voice.type,
         textLen: state.scriptText.length,
       })
       const asset = await getAssetById(video.id)
-      console.log("[对口型] getAssetById 响应:", {
-        id: asset?.id,
-        file_url: asset?.file_url?.substring(0, 100),
-      })
       const videoUrl = asset?.file_url
       if (!videoUrl) {
         console.error("[对口型] file_url 为空，asset:", asset)
@@ -128,19 +234,35 @@ const AiAvatarPage: React.FC = () => {
         message.error("获取出镜视频播放地址失败，请重新选择素材")
         return
       }
-      // ② 模式A TTS直生：video_url + voice_id + script_text，语速/情绪英文枚举透传（#1822）
+
-      const payload = {
+      type LipsyncPayload = Parameters<typeof createLipsyncJob>[0]
-        voice_id: voice.voice_id,
+      let payload: LipsyncPayload
-        script_text: state.scriptText,
+      if (isPreSynth) {
-        video_url: videoUrl,
+        // 预合成模式：传 audio_url + audio_duration + sentence_timings（后端直接提交 MediaKit，~2-3s）
-        speed: state.speed, // 语速 0.5~2.0
+        payload = {
-        emotion: normalizeEmotion(state.emotion), // natural/excited/calm/friendly
+          video_url: videoUrl,
+          audio_url: state.ttsPreview.audioUrl!,
+          audio_duration: state.ttsPreview.duration,
+          sentence_timings: state.ttsPreview.sentenceTimings,
+          enable_video_loop: true,
+        }
+      } else {
+        // 降级：TTS 直生（旧路径，前端未预合成时）
+        payload = {
+          voice_id: state.selectedVoice!.voice_id,
+          script_text: state.scriptText,
+          video_url: videoUrl,
+          speed: state.speed,
+          emotion: normalizeEmotion(state.emotion),
+        }
       }
       console.log("[对口型] createLipsyncJob 请求:", payload)
       const job = await createLipsyncJob(payload)
       console.log("[对口型] createLipsyncJob 响应:", { id: job.id, status: job.status })
       state.setLipsyncJob(job)
-      // 开始轮询
+
+      // 如果是预合成模式，后端会同步把状态置为 submitted（甚至可能已返回 running），
+      // 但仍需轮询等 completed
       if (lipsyncTimerRef.current) clearInterval(lipsyncTimerRef.current)
       lipsyncTimerRef.current = setInterval(async () => {
         try {
@@ -177,7 +299,14 @@ const AiAvatarPage: React.FC = () => {
       message.error(err instanceof Error ? err.message : "对口型任务提交失败，请重试")
     }
     // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [state.selectedVideo, state.selectedVoice, state.scriptText, state.speed, state.emotion])
+  }, [
+    state.selectedVideo,
+    state.selectedVoice,
+    state.scriptText,
+    state.speed,
+    state.emotion,
+    state.ttsPreview,
+  ])
 
   // 取消对口型生成
   const handleCancelLipsync = useCallback(() => {
@@ -198,6 +327,14 @@ const AiAvatarPage: React.FC = () => {
     }
   }, [])
 
+  /* ── B-roll 弹窗可用的句子时间戳：优先 lipsyncJob.sentence_timings，否则用 ttsPreview.sentenceTimings ── */
+  const bRollSentenceTimings: SentenceTiming[] | undefined =
+    (state.lipsyncJob?.sentence_timings as SentenceTiming[] | undefined) ??
+    (state.ttsPreview.status === "done" ? state.ttsPreview.sentenceTimings : undefined)
+
+  /* ── B-roll 可用的总时长：优先 lipsyncJob.output_duration，否则用 ttsPreview.duration ── */
+  const bRollDuration = state.lipsyncJob?.output_duration || state.ttsPreview.duration || 0
+
   /* ── 生成视频（含实时进度轮询） ── */
   const handleGenerate = useCallback(async () => {
     if (!state.lipsyncJob || state.lipsyncJob.status !== "completed") {
@@ -206,9 +343,28 @@ const AiAvatarPage: React.FC = () => {
     }
     state.setIsGenerating(true)
     try {
+      const defaultProject = await getOrCreateDefaultProject()
+
+      // 用 Canvas 预渲染标题为 PNG dataURL
+      let titleImageDataUrl: string | null = null
+      if (state.titleConfig.title?.trim()) {
+        try {
+          const res = await getVideoResolution(state.lipsyncJob.output_video_url || "")
+          titleImageDataUrl = renderTitleToPngDataUrl({
+            titleConfig: state.titleConfig,
+            videoWidth: res.width,
+            videoHeight: res.height,
+          })
+        } catch (canvasErr) {
+          console.warn("[渲染] 标题 Canvas 渲染失败，降级 drawtext:", canvasErr)
+          titleImageDataUrl = null
+        }
+      }
+
       const job = await submitRender({
         lipsync_job_id: state.lipsyncJob.id,
         script_id: state.script?.id,
+        project_id: defaultProject.id,
         b_roll_segments: state.bRollSegments.map((seg) => ({
           script_segment_index: seg.script_segment_index,
           asset_url: seg.asset.file_url || "",
@@ -218,25 +374,38 @@ const AiAvatarPage: React.FC = () => {
           pip_position: seg.pip_position,
           pip_scale: seg.pip_scale,
         })) as never,
-        title_config: buildTitleConfigPayload(state.titleConfig),
+        title_config: buildTitleConfigPayload(state.titleConfig, titleImageDataUrl),
-        cover_config: buildCoverConfigPayload(state.coverConfig, state.coverConfig.smart_cover_url),
+        cover_config:
+          state.coverConfig.smart_cover_url ||
+          (state.coverConfig.upload_url && !state.coverConfig.upload_url.startsWith("blob:"))
+            ? buildCoverConfigPayload(state.coverConfig, state.coverConfig.smart_cover_url)
+            : {},
       })
 
-      // 打开渲染进度弹窗，启动轮询
       setShowRenderModal(true)
       setRenderStatus("generating")
       setRenderProgress(job.progress ?? 0)
       setRenderErrorMessage("")
+      setCurrentRenderJob(job as RenderJob)
 
       if (renderTimerRef.current) clearInterval(renderTimerRef.current)
       renderTimerRef.current = setInterval(async () => {
         try {
           const updated = await getRenderJob(job.id)
           setRenderProgress(updated.progress ?? 0)
+          setCurrentRenderJob(updated)
           if (updated.status === "completed") {
             if (renderTimerRef.current) clearInterval(renderTimerRef.current)
             renderTimerRef.current = null
             setRenderStatus("completed")
+            if (updated.output_cover_url) {
+              state.setCoverConfig((prev) => ({
+                ...prev,
+                mode: "auto_frame",
+                smart_cover_url: updated.output_cover_url,
+                thumbnail_url: updated.output_cover_url,
+              }))
+            }
             message.success("视频已生成并保存到成片库")
           } else if (updated.status === "failed") {
             if (renderTimerRef.current) clearInterval(renderTimerRef.current)
@@ -269,38 +438,47 @@ const AiAvatarPage: React.FC = () => {
     setRenderErrorMessage("")
   }, [])
 
-  /* ── 智能封面：调后端 MediaKit 选帧接口（#1822） ── */
+  /* ── 智能封面 ── */
-  const handleSmartCover = useCallback(async () => {
+  const handleGenerateRenderSmartCover = useCallback(
-    // 基于对口型成片抽帧，必须先完成对口型
+    async (renderId: string): Promise<{ cover_url: string; message?: string }> => {
-    const videoUrl = state.lipsyncJob?.output_video_url
+      try {
-    if (state.lipsyncJob?.status !== "completed" || !videoUrl) {
+        const res = await generateRenderSmartCover(renderId)
-      message.warning("请先生成对口型视频，完成后再智能获取封面")
+        if (res.cover_url) {
-      return
+          state.setCoverConfig((prev) => ({
-    }
+            ...prev,
-    setSmartCoverLoading(true)
+            mode: "auto_frame",
-    try {
+            smart_cover_url: res.cover_url,
-      const res = await generateSmartCover(videoUrl, 5)
+            thumbnail_url: res.cover_url,
-      if (res.cover_url) {
+          }))
-        state.setCoverConfig((prev) => ({
+          message.success("智能封面已生成")
-          ...prev,
+          return { cover_url: res.cover_url }
-          mode: "auto_frame",
+        }
-          smart_cover_url: res.cover_url,
+        const errMsg = res.message || "智能封面生成失败，请稍后重试"
-          thumbnail_url: res.cover_url,
+        message.error(errMsg)
-        }))
+        return { cover_url: "", message: errMsg }
-        message.success("智能封面已生成")
+      } catch (err) {
-      } else {
+        console.error("智能封面生成失败:", err)
-        message.error(res.message || "智能封面生成失败，请稍后重试")
+        const errMsg = err instanceof Error ? err.message : "智能封面生成失败，请重试"
+        message.error(errMsg)
+        return { cover_url: "", message: errMsg }
       }
-    } catch (err) {
+    },
-      console.error("智能封面生成失败:", err)
-      message.error(err instanceof Error ? err.message : "智能封面生成失败，请重试")
-    } finally {
-      setSmartCoverLoading(false)
-    }
     // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [state.lipsyncJob])
+    [],
+  )
 
   /* ── 配置汇总 ── */
+  const coverStatus: "not_ready" | "pending" | "selected" = (() => {
+    if (
+      state.coverConfig.smart_cover_url ||
+      state.coverConfig.thumbnail_url ||
+      (state.coverConfig.upload_url && !state.coverConfig.upload_url.startsWith("blob:"))
+    ) {
+      return "selected"
+    }
+    if (currentRenderJob?.status === "completed") return "pending"
+    return "not_ready"
+  })()
   const summary = {
     videoName: state.selectedVideo?.name || null,
     voiceName: state.selectedVoice?.name || null,
@@ -308,7 +486,7 @@ const AiAvatarPage: React.FC = () => {
     lipsyncStatus: state.lipsyncJob?.status || null,
     brollCount: state.bRollSegments.length,
     hasTitle: state.titleConfig.title.length > 0,
-    hasCover: state.coverConfig.enabled,
+    coverStatus,
   }
 
   return (
@@ -342,7 +520,6 @@ const AiAvatarPage: React.FC = () => {
                   selectedVideo={state.selectedVideo}
                   onSelectVideo={() => state.setShowAssetPicker(true)}
                   onRemoveVideo={state.removeVideo}
-                  titleConfig={state.titleConfig}
                 />
               </div>
             </div>
@@ -382,19 +559,33 @@ const AiAvatarPage: React.FC = () => {
                   onOpenScriptModal={() => state.setShowScriptModal(true)}
                 />
                 <div className="aa-step-btn-row">
-                  <button type="button" className="aa-btn aa-btn--primary" onClick={handleNextStep}>
+                  <button
-                    下一步 →
+                    type="button"
+                    className="aa-btn aa-btn--primary"
+                    onClick={handleGenerateTts}
+                    disabled={state.ttsPreview.status === "generating"}
+                  >
+                    {state.ttsPreview.status === "done" ? "🎵 重新生成配音" : "🎵 生成配音"}
                   </button>
+                  {state.ttsPreview.status === "done" && (
+                    <button
+                      type="button"
+                      className="aa-btn aa-btn--primary"
+                      onClick={() => setCurrentStep(2)}
+                      style={{ marginLeft: 12 }}
+                    >
+                      下一步 →
+                    </button>
+                  )}
                 </div>
               </div>
             </div>
           </>
         )}
 
-        {/* ════ 步骤 2：对口型预览（含插入画面）/ 标题配置 / 封面&生成 ════ */}
+        {/* ════ 步骤 2：对口型预览 / 标题配置 / 封面&生成 ════ */}
         {currentStep === 2 && (
           <>
-            {/* 面板：对口型预览 + 插入画面 */}
             <div className={`aa-panel aa-panel--s2-wide${collapsed.lipsync ? " collapsed" : ""}`}>
               <div className="aa-panel__header" onClick={() => togglePanel("lipsync")}>
                 <span className="aa-panel__title">对口型预览</span>
@@ -444,9 +635,8 @@ const AiAvatarPage: React.FC = () => {
                   onCoverConfigChange={(partial) =>
                     state.setCoverConfig((prev) => ({ ...prev, ...partial }))
                   }
-                  onSmartCover={handleSmartCover}
+                  renderJob={currentRenderJob}
-                  smartCoverLoading={smartCoverLoading}
+                  onGenerateRenderSmartCover={handleGenerateRenderSmartCover}
-                  canSmartCover={state.lipsyncJob?.status === "completed"}
                   resolution={state.resolution}
                   onResolutionChange={state.setResolution}
                   isGenerating={state.isGenerating}
@@ -478,19 +668,144 @@ const AiAvatarPage: React.FC = () => {
         />
       )}
 
-      {/* B-roll 编辑器弹窗 */}
+      {/* B-roll 编辑器弹窗 — #1845：timings 在对口型完成前就可用（来自 TTS 预合成） */}
       {state.showBRollModal && (
         <ModalBRollEditor
           open={state.showBRollModal}
           onClose={() => state.setShowBRollModal(false)}
           existingSegments={state.bRollSegments}
-          scriptText={state.scriptText}
+          scriptText={state.lipsyncJob?.script_text || state.scriptText}
-          outputDuration={state.lipsyncJob?.output_duration ?? 0}
+          outputDuration={bRollDuration}
+          sentenceTimings={bRollSentenceTimings}
           onConfirm={state.addBRollSegment}
           onRemove={state.removeBRollSegment}
         />
       )}
 
+      {/* #1845 TTS 预合成弹窗 */}
+      {showTtsModal && (
+        <div className="aa-modal-overlay">
+          <div className="aa-modal" onClick={(e) => e.stopPropagation()}>
+            <div className="aa-modal__header">
+              <span className="aa-modal__title">配音合成中</span>
+              {state.ttsPreview.status !== "generating" && (
+                <button className="aa-modal__close" onClick={handleCancelTts}>
+                  ✕
+                </button>
+              )}
+            </div>
+            <div
+              className="aa-modal__body"
+              style={{
+                display: "flex",
+                flexDirection: "column",
+                alignItems: "center",
+                padding: "40px 20px",
+              }}
+            >
+              {state.ttsPreview.status === "generating" && (
+                <>
+                  <div className="aa-lipsync-spinner" />
+                  <div style={{ marginTop: 20, fontSize: 15, color: "#1a1a2e" }}>
+                    正在合成配音，请稍候…
+                  </div>
+                  <div
+                    style={{
+                      marginTop: 20,
+                      fontSize: 32,
+                      fontWeight: 700,
+                      color: "#1890ff",
+                    }}
+                  >
+                    {ttsProgress}%
+                  </div>
+                  <div
+                    style={{
+                      marginTop: 12,
+                      width: "80%",
+                      height: 8,
+                      backgroundColor: "#f0f0f0",
+                      borderRadius: 4,
+                      overflow: "hidden",
+                    }}
+                  >
+                    <div
+                      style={{
+                        width: `${ttsProgress}%`,
+                        height: "100%",
+                        backgroundColor: "#1890ff",
+                        borderRadius: 4,
+                        transition: "width 0.3s ease",
+                      }}
+                    />
+                  </div>
+                  <div style={{ marginTop: 12, fontSize: 13, color: "#8c8ca1" }}>
+                    请勿关闭页面，完成后将自动提示
+                  </div>
+                </>
+              )}
+              {state.ttsPreview.status === "done" && (
+                <>
+                  <div style={{ fontSize: 48 }}>✅</div>
+                  <div style={{ marginTop: 16, fontSize: 15, color: "#1a1a2e" }}>
+                    配音合成完成，点击下一步继续
+                  </div>
+                  <div style={{ marginTop: 8, fontSize: 13, color: "#8c8ca1" }}>
+                    音频时长 {state.ttsPreview.duration.toFixed(1)}s，共{" "}
+                    {state.ttsPreview.sentenceTimings.length} 句
+                  </div>
+                </>
+              )}
+              {state.ttsPreview.status === "failed" && (
+                <>
+                  <div style={{ fontSize: 48 }}>❌</div>
+                  <div style={{ marginTop: 16, fontSize: 15, color: "#1a1a2e" }}>配音合成失败</div>
+                  {ttsErrorMessage && (
+                    <div
+                      style={{
+                        marginTop: 8,
+                        fontSize: 13,
+                        color: "#ff4d4f",
+                        textAlign: "center",
+                        padding: "0 20px",
+                      }}
+                    >
+                      {ttsErrorMessage}
+                    </div>
+                  )}
+                </>
+              )}
+            </div>
+            <div className="aa-modal__footer">
+              {state.ttsPreview.status === "generating" && (
+                <button className="aa-btn aa-btn--danger" onClick={handleCancelTts}>
+                  取消
+                </button>
+              )}
+              {state.ttsPreview.status === "done" && (
+                <button className="aa-btn aa-btn--primary" onClick={handleTtsNext}>
+                  下一步 →
+                </button>
+              )}
+              {state.ttsPreview.status === "failed" && (
+                <>
+                  <button className="aa-btn" onClick={handleCancelTts}>
+                    关闭
+                  </button>
+                  <button
+                    className="aa-btn aa-btn--primary"
+                    onClick={handleRetryTts}
+                    style={{ marginLeft: 12 }}
+                  >
+                    重试
+                  </button>
+                </>
+              )}
+            </div>
+          </div>
+        </div>
+      )}
+
       {/* 对口型生成弹窗 */}
       {showLipsyncModal && (
         <div className="aa-modal-overlay">

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

@@ -2,7 +2,7 @@
  * AI数字人 — API 封装（#1822 契约对齐）
  */
 import apiClient from "@/api/client"
-import type { Script, LipsyncJob, RenderJob, BRollSegment } from "../types"
+import type { Script, LipsyncJob, RenderJob, BRollSegment, SentenceTiming } from "../types"
 
 /* ── 文案库 ── */
 export const getScripts = async (): Promise<Script[]> => {
@@ -34,17 +34,28 @@ export const getAssetById = async (id: string): Promise<{ file_url?: string; id:
   return response.data
 }
 
-/* ── 对口型（模式A：TTS 直生，后端内部合成音频；不要先调 TTS 拿 audio_url） ── */
+/* ── 对口型（支持三种模式） ──
+ * 1. TTS 直生（降级/旧版）：传 voice_id + script_text（+speed/emotion），后端 Celery 异步合成
+ * 2. 直接音频：传 video_url + audio_url，后端同步下载+算timings+提交MediaKit
+ * 3. 预合成音频（#1845 新主路径）：先调 previewTts 拿 audio_url+sentence_timings，
+ *    再把 audio_url + audio_duration + sentence_timings 一起传过来，后端直接提交 MediaKit
+ */
 export const createLipsyncJob = async (data: {
-  /** 人物视频 URL（MP4）；由素材 id 经 getAssetById 拿 file_url，禁止传 video_asset_id */
+  /** 人物视频 URL（MP4）；由素材 id 经 getAssetById 拿 file_url */
   video_url: string
-  /** 音色 ID（预置音色 或 克隆音色 profile UUID，后端会解析） */
+  /** 预合成/直接音频模式：音频 URL（#1845 步骤1 预合成的 CosyVoice 临时 URL，或外部音频 URL） */
-  voice_id: string
+  audio_url?: string
-  /** 要合成的文案（手动输入或文案库内容） */
+  /** 预合成音频时长（秒），由 previewTts 返回 */
-  script_text: string
+  audio_duration?: number
-  /** 语速 0.5~2.0，默认 1.0 */
+  /** 预合成接口返回的句子时间戳（精确），后端直接写入 job */
+  sentence_timings?: SentenceTiming[]
+  /** 音色 ID（TTS 直生模式用） */
+  voice_id?: string
+  /** 要合成的文案（TTS 直生模式用） */
+  script_text?: string
+  /** 语速 0.5~2.0，默认 1.0（TTS 直生模式用） */
   speed?: number
-  /** 情绪英文枚举：natural/excited/calm/friendly */
+  /** 情绪英文枚举：natural/excited/calm/friendly（TTS 直生模式用） */
   emotion?: string
   enable_video_loop?: boolean
   project_id?: string
@@ -53,21 +64,27 @@ export const createLipsyncJob = async (data: {
   return response.data
 }
 
-export const getLipsyncJob = async (id: string): Promise<LipsyncJob> => {
+/* ── #1845 TTS 预合成（步骤1「生成配音」同步接口，~2-3s） ── */
-  const response = await apiClient.get<LipsyncJob>(`/lipsync/jobs/${id}`, { timeout: 60000 })
+export const previewTts = async (data: {
+  voice_id: string
+  script_text: string
+  speed?: number
+  emotion?: string
+}): Promise<{
+  audio_url: string
+  duration: number
+  sentence_timings: SentenceTiming[]
+}> => {
+  const response = await apiClient.post<{
+    audio_url: string
+    duration: number
+    sentence_timings: SentenceTiming[]
+  }>("/lipsync/tts-preview", data, { timeout: 30000 })
   return response.data
 }
 
-/* ── 智能封面（MediaKit 抽帧 + 质量评分选最佳帧，独立于渲染任务） ── */
+export const getLipsyncJob = async (id: string): Promise<LipsyncJob> => {
-export const generateSmartCover = async (
+  const response = await apiClient.get<LipsyncJob>(`/lipsync/jobs/${id}`, { timeout: 60000 })
-  video_url: string,
-  max_frames = 5,
-): Promise<{ cover_url: string; status: string; message: string }> => {
-  const response = await apiClient.post<{ cover_url: string; status: string; message: string }>(
-    "/ai-avatar/render/smart-cover",
-    { video_url, max_frames },
-    { timeout: 60000 },
-  )
   return response.data
 }
 
@@ -80,15 +97,29 @@ export const submitRender = async (data: {
   cover_config?: Record<string, unknown>
   project_id?: string
 }): Promise<RenderJob> => {
+  // title_config 内可含 title_image_dataurl（前端 Canvas 渲染的 PNG dataURL）
   const response = await apiClient.post<RenderJob>("/ai-avatar/render", data)
   return response.data
 }
 
 export const getRenderJob = async (jobId: string): Promise<RenderJob> => {
-  const response = await apiClient.get<RenderJob>(`/ai-avatar/render/${jobId}`)
+  const response = await apiClient.get<RenderJob>(`/ai-avatar/render/${jobId}`, { timeout: 60000 })
   return response.data
 }
 
 export const cancelRenderJob = async (jobId: string): Promise<void> => {
   await apiClient.post(`/ai-avatar/render/${jobId}/cancel`)
 }
+
+/* ── 从最终渲染成片智能抽封面（POST /ai-avatar/renders/{job_id}/smart-cover） ── */
+export const generateRenderSmartCover = async (
+  jobId: string,
+): Promise<{ cover_url: string; status: string; message: string }> => {
+  const response = await apiClient.post<{ cover_url: string; status: string; message: string }>(
+    `/ai-avatar/render/${jobId}/smart-cover`,
+    {},
+    // 抽帧+评分+转存 OSS 链路较长，120s 超时
+    { timeout: 120000 },
+  )
+  return response.data
+}

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

@@ -5,12 +5,12 @@
  * - 左侧：先选素材库（video 库）→ 再选该库视频素材（已被其他 segment 使用的素材
  *   标灰 + "已选择" 遮罩，pointer-events:none 防重复选择）
  * - 右侧：文案句子列表（点选对应段落，替代原数字索引框）/ 全屏 or 画中画 / 四角位置+大小
- *   （开始/结束时间已删除，按句子字数占比 × 口播总时长自动估算）
+ *   （开始/结束时间来自后端精确句子时间戳，基于 TTS 音频静音检测）
  * - 底部：已配置的画面插入列表（可删除）
  */
 import React, { useEffect, useMemo, useState } from "react"
 import { getAssets, getAssetLibraries, type AssetItem, type AssetLibraryItem } from "@/api/assets"
-import type { BRollSegment, BRollInsertMode, PipPosition } from "../types"
+import type { BRollSegment, BRollInsertMode, PipPosition, SentenceTiming } from "../types"
 import { splitScriptIntoSentences, type ScriptSentence } from "../utils/sentences"
 
 interface ModalBRollEditorProps {
@@ -18,10 +18,12 @@ interface ModalBRollEditorProps {
   onClose: () => void
   /** 当前已有的 B-roll segments（用于标灰已选素材） */
   existingSegments: BRollSegment[]
-  /** 当前文案全文（用于分句） */
+  /** 文案全文（优先使用对口型时锁定的 scriptText） */
   scriptText: string
-  /** 对口型成片总时长（秒），用于时间自动估算 */
+  /** 对口型成片总时长（秒） */
   outputDuration: number
+  /** 后端精确句子时间戳（来自 lipsyncJob.sentence_timings） */
+  sentenceTimings?: SentenceTiming[] | null
   onConfirm: (segment: BRollSegment) => void
   onRemove: (id: string) => void
 }
@@ -43,7 +45,8 @@ const ModalBRollEditor: React.FC<ModalBRollEditorProps> = ({
   onClose,
   existingSegments,
   scriptText,
-  outputDuration,
+  outputDuration: _outputDuration,
+  sentenceTimings,
   onConfirm,
   onRemove,
 }) => {
@@ -62,10 +65,10 @@ const ModalBRollEditor: React.FC<ModalBRollEditorProps> = ({
   const [pipPosition, setPipPosition] = useState<PipPosition>("top-right")
   const [pipScale, setPipScale] = useState(0.3)
 
-  /** 文案分句（⑤） */
+  /** 文案分句（优先使用后端精确时间戳，降级为字数比例估算） */
   const sentences = useMemo(
-    () => splitScriptIntoSentences(scriptText, outputDuration),
+    () => splitScriptIntoSentences(scriptText, sentenceTimings, _outputDuration),
-    [scriptText, outputDuration],
+    [scriptText, sentenceTimings, _outputDuration],
   )
 
   /** 已被现有 segments 占用的素材 id 集合（标灰、禁止重复选择） */
@@ -142,7 +145,7 @@ const ModalBRollEditor: React.FC<ModalBRollEditorProps> = ({
     setSelectedAsset(asset)
   }
 
-  /** 确认添加一段 B-roll（⑥ 时间取所选句子的估算起止） */
+  /** 确认添加一段 B-roll（⑥ 时间取所选句子的精确起止，后端静音检测 / 前端字数比例降级） */
   const handleConfirm = () => {
     if (!selectedAsset || !selectedSentence) return
     const startTime = selectedSentence.startTime
@@ -264,11 +267,9 @@ const ModalBRollEditor: React.FC<ModalBRollEditorProps> = ({
                           >
                             <span className="aa-sentence-item__idx">{sent.index + 1}</span>
                             <span className="aa-sentence-item__text">{sent.text}</span>
-                            {outputDuration > 0 && (
+                            <span className="aa-sentence-item__time">
-                              <span className="aa-sentence-item__time">
+                              {sent.startTime.toFixed(1)}-{sent.endTime.toFixed(1)}s
-                                {sent.startTime.toFixed(1)}-{sent.endTime.toFixed(1)}s
+                            </span>
-                              </span>
-                            )}
                           </button>
                         )
                       })}
@@ -349,7 +350,7 @@ const ModalBRollEditor: React.FC<ModalBRollEditorProps> = ({
                           selectedSentence.endTime,
                           selectedSentence.startTime + 0.5,
                         ).toFixed(1)}
-                        s （按字数自动估算）
+                        s
                       </div>
                     </>
                   ) : (

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

@@ -1,14 +1,15 @@
 /**
- * AI数字人 — 面板5：封面 & 生成
+ * AI数字人 — 面板5：分辨率/配置摘要/生成按钮/封面
- * - 竖屏 9:16 封面预览（从视频截取 / 自定义上传）
+ * v3 调整（步骤③④）：
- * - 分辨率选择（720p / 1080p / 4K）
+ * - 布局顺序：分辨率 → 配置摘要卡片 → 🔘「开始生成视频」按钮 → （渲染完成后）封面区域
- * - 配置汇总卡片（出镜视频/音色/文案/对口型/B-roll/标题/封面）
+ * - 渲染未完成时封面区域显示占位态，按钮 disabled
- * - 渐变紫色生成按钮
+ * - 「智能获取封面」从最终成片抽帧（调用 POST /renders/{id}/smart-cover），不再依赖 lipsync 状态
+ * - 修复点 2 次 bug：内部维护 smartCoverLoading，不依赖外层异步 state 更新
  *
  * 注意：v3 已删除"画面插入模式"，本面板不包含该选项。
  */
-import React, { useRef } from "react"
+import React, { useRef, useState } from "react"
-import type { AiAvatarCoverConfig } from "../types"
+import type { AiAvatarCoverConfig, RenderJob } from "../types"
 
 interface PanelCoverAndGenerateProps {
   coverConfig: AiAvatarCoverConfig
@@ -17,10 +18,12 @@ interface PanelCoverAndGenerateProps {
   onResolutionChange: (r: string) => void
   isGenerating: boolean
   onGenerate: () => void
-  /** 智能获取封面（MediaKit 选帧） */
+  /** 当前渲染任务（渲染完成后才有 output_video_url，才能抽封面） */
-  onSmartCover: () => void
+  renderJob: RenderJob | null
-  smartCoverLoading: boolean
+  /** 从最终成片智能抽帧（参数 renderId），返回 { cover_url } */
-  canSmartCover: boolean
+  onGenerateRenderSmartCover: (renderId: string) => Promise<{ cover_url: string; message?: string }>
+  /** 自定义上传封面（选择本地文件后由父组件处理实际上传） */
+  onUploadCover?: (file: File) => void
   /** 配置汇总信息 */
   summary: {
     videoName: string | null
@@ -29,7 +32,8 @@ interface PanelCoverAndGenerateProps {
     lipsyncStatus: string | null
     brollCount: number
     hasTitle: boolean
-    hasCover: boolean
+    /** 封面状态：'not_ready'(视频未生成) / 'pending'(视频生成了但未选) / 'selected'(已选) */
+    coverStatus: "not_ready" | "pending" | "selected"
   }
 }
 
@@ -54,12 +58,14 @@ const PanelCoverAndGenerate: React.FC<PanelCoverAndGenerateProps> = ({
   onResolutionChange,
   isGenerating,
   onGenerate,
-  onSmartCover,
+  renderJob,
-  smartCoverLoading,
+  onGenerateRenderSmartCover,
-  canSmartCover,
+  onUploadCover,
   summary,
 }) => {
   const uploadInputRef = useRef<HTMLInputElement>(null)
+  // 内部维护智能封面加载态（修复点 2 次 bug：不依赖外层异步 setState 顺序）
+  const [smartCoverLoading, setSmartCoverLoading] = useState(false)
 
   /** 自定义上传封面 */
   const handleUploadClick = () => {
@@ -69,60 +75,66 @@ const PanelCoverAndGenerate: React.FC<PanelCoverAndGenerateProps> = ({
   const handleFileChange = (e: React.ChangeEvent<HTMLInputElement>) => {
     const file = e.target.files?.[0]
     if (!file) return
-    // 本地预览：生成 object URL（实际上传由父级/后端链路处理）
+    if (onUploadCover) {
-    const url = URL.createObjectURL(file)
+      onUploadCover(file)
-    onCoverConfigChange({ mode: "upload", upload_url: url, thumbnail_url: url })
+    } else {
-    // 允许重复选择同一文件
+      // 本地预览兜底（实际上传由父级处理；blob URL 仅作本地展示）
+      const url = URL.createObjectURL(file)
+      onCoverConfigChange({ mode: "upload", upload_url: url, thumbnail_url: url })
+    }
     e.target.value = ""
   }
 
-  /** 智能获取封面（调后端 MediaKit 抽帧评分选最佳帧，#1822） */
+  /** 智能获取封面（从最终成片抽帧；必须等 render 完成） */
-  const handleSmartCover = () => {
+  const handleSmartCover = async () => {
-    onCoverConfigChange({ mode: "auto_frame" })
+    if (!renderJob || renderJob.status !== "completed" || !renderJob.id) return
-    onSmartCover()
+    setSmartCoverLoading(true)
+    try {
+      const res = await onGenerateRenderSmartCover(renderJob.id)
+      if (res.cover_url) {
+        onCoverConfigChange({
+          mode: "auto_frame",
+          smart_cover_url: res.cover_url,
+          thumbnail_url: res.cover_url,
+        })
+      } else {
+        // 失败由父组件 message 提示，这里不重复弹窗
+        console.warn("[智能封面] 返回空 cover_url:", res.message)
+      }
+    } catch (err) {
+      console.error("[智能封面] 调用失败:", err)
+    } finally {
+      setSmartCoverLoading(false)
+    }
   }
 
   const lipsync = summary.lipsyncStatus ? LIPSYNC_STATUS_LABEL[summary.lipsyncStatus] : null
-
   const canGenerate = summary.lipsyncStatus === "completed" && !isGenerating
+  // 渲染已完成 → 封面区可用
+  const isRenderCompleted = renderJob?.status === "completed"
+  const canSmartCover = isRenderCompleted && !smartCoverLoading
+
+  /** 封面图实际展示的 url：智能封面 > 自定义上传 > 空 */
+  const coverUrl =
+    coverConfig.smart_cover_url || coverConfig.thumbnail_url || coverConfig.upload_url
+  const hasCoverImage = Boolean(coverUrl)
+
+  /** 封面区占位文字 */
+  const coverPlaceholder = isRenderCompleted ? "暂无封面" : "视频生成后可选择封面"
+
+  /** 封面摘要状态文本 */
+  const coverSummaryNode = (() => {
+    if (summary.coverStatus === "selected") {
+      return <span className="aa-config-summary__value">已选择</span>
+    }
+    if (summary.coverStatus === "pending") {
+      return <span className="aa-config-summary__value">待选择</span>
+    }
+    return <span className="aa-config-summary__empty">生成视频后可选</span>
+  })()
 
   return (
     <div className="aa-cover-generate">
-      {/* 封面预览（竖屏 9:16） */}
-      <div className="aa-cover-preview">
-        {coverConfig.thumbnail_url ? (
-          <img src={coverConfig.thumbnail_url} alt="封面预览" />
-        ) : (
-          <span className="aa-cover-preview__placeholder">暂无封面</span>
-        )}
-      </div>
-
-      <div className="aa-cover-actions">
-        <button
-          type="button"
-          className={`aa-btn aa-btn--ghost${coverConfig.mode === "auto_frame" ? " active" : ""}`}
-          onClick={handleSmartCover}
-          disabled={smartCoverLoading || !canSmartCover}
-          title={canSmartCover ? "基于对口型成片智能选帧" : "请先完成对口型生成"}
-        >
-          {smartCoverLoading ? "⏳ 智能选帧中…" : "🎬 智能获取封面"}
-        </button>
-        <button
-          type="button"
-          className={`aa-btn aa-btn--ghost${coverConfig.mode === "upload" ? " active" : ""}`}
-          onClick={handleUploadClick}
-        >
-          📷 自定义上传
-        </button>
-        <input
-          ref={uploadInputRef}
-          type="file"
-          accept="image/*"
-          style={{ display: "none" }}
-          onChange={handleFileChange}
-        />
-      </div>
-
       {/* 分辨率选择 */}
       <div className="aa-form-field">
         <label className="aa-label">分辨率</label>
@@ -130,6 +142,7 @@ const PanelCoverAndGenerate: React.FC<PanelCoverAndGenerateProps> = ({
           className="aa-select"
           value={resolution}
           onChange={(e) => onResolutionChange(e.target.value)}
+          disabled={isGenerating}
         >
           {RESOLUTION_OPTIONS.map((opt) => (
             <option key={opt.value} value={opt.value}>
@@ -190,11 +203,7 @@ const PanelCoverAndGenerate: React.FC<PanelCoverAndGenerateProps> = ({
           </div>
           <div className="aa-config-summary__row">
             <span>封面</span>
-            {summary.hasCover ? (
+            {coverSummaryNode}
-              <span className="aa-config-summary__value">已开启</span>
-            ) : (
-              <span className="aa-config-summary__empty">未配置</span>
-            )}
           </div>
         </div>
 
@@ -212,6 +221,55 @@ const PanelCoverAndGenerate: React.FC<PanelCoverAndGenerateProps> = ({
             请先完成对口型生成
           </div>
         )}
+        {isGenerating && (
+          <div style={{ marginTop: 8, fontSize: 11, color: "#8c8ca1", textAlign: "center" }}>
+            视频生成中，请稍候…
+          </div>
+        )}
+      </div>
+
+      {/* 封面区域（视频生成后才激活；步骤③④要求：按钮在封面上方，完成后再显示封面区） */}
+      <div className="aa-cover-section" style={{ marginTop: 16 }}>
+        <div className="aa-label" style={{ marginBottom: 8 }}>
+          封面
+        </div>
+        {/* 封面预览（竖屏 9:16）——成片帧已经通过 Canvas PNG overlay 带有标题，直接展示原图即可 */}
+        <div className="aa-cover-preview" style={{ opacity: isRenderCompleted ? 1 : 0.5 }}>
+          {hasCoverImage ? (
+            <img src={coverUrl!} alt="封面预览" draggable={false} />
+          ) : (
+            <span className="aa-cover-preview__placeholder">{coverPlaceholder}</span>
+          )}
+          {smartCoverLoading && <div className="aa-cover-preview__loading">⏳ 智能选帧中…</div>}
+        </div>
+
+        <div className="aa-cover-actions">
+          <button
+            type="button"
+            className={`aa-btn aa-btn--ghost${coverConfig.mode === "auto_frame" ? " active" : ""}`}
+            onClick={handleSmartCover}
+            disabled={!canSmartCover}
+            title={isRenderCompleted ? "从成片智能选帧" : "请先生成视频"}
+          >
+            {smartCoverLoading ? "⏳ 智能选帧中…" : "🎬 智能获取封面"}
+          </button>
+          <button
+            type="button"
+            className={`aa-btn aa-btn--ghost${coverConfig.mode === "upload" ? " active" : ""}`}
+            onClick={handleUploadClick}
+            disabled={!isRenderCompleted || smartCoverLoading}
+            title={isRenderCompleted ? "自定义上传封面" : "请先生成视频"}
+          >
+            📷 自定义上传
+          </button>
+          <input
+            ref={uploadInputRef}
+            type="file"
+            accept="image/*"
+            style={{ display: "none" }}
+            onChange={handleFileChange}
+          />
+        </div>
       </div>
     </div>
   )

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

@@ -1,9 +1,9 @@
 /**
  * AI数字人 — 对口型预览面板（步骤2用）
  * B-roll 画面插入 + 对口型视频预览 + 生成/重新生成按钮
- * v3.1: 预览容器按 1/2 缩放、标题实时叠加预览
+ * v3.1: 标题字号按预览容器实际宽度动态计算 previewScale（基准 720p），与成片一致
  */
-import React, { useRef } from "react"
+import React, { useCallback, useEffect, useRef, useState } from "react"
 import type { LipsyncJob, BRollSegment, AiAvatarTitleConfig } from "../types"
 
 interface PanelLipsyncPreviewProps {
@@ -14,8 +14,8 @@ interface PanelLipsyncPreviewProps {
   onRemoveBRoll: (id: string) => void
   /** 标题配置（实时叠加预览用） */
   titleConfig?: AiAvatarTitleConfig
-  /** 标题位置变更回调（拖拽结束时调用） */
+  /** 标题位置变更回调（拖拽结束时调用，发送百分比坐标 + position:"custom"） */
-  onTitlePositionChange?: (pos: { pos_x: number; pos_y: number }) => void
+  onTitlePositionChange?: (pos: { pos_x: number; pos_y: number; position: string }) => void
 }
 
 const BROLL_MODE_LABEL: Record<BRollSegment["mode"], string> = {
@@ -29,6 +29,16 @@ function formatTime(seconds: number): string {
   return `${m}:${s.toString().padStart(2, "0")}`
 }
 
+/** 字体名 → CSS font-family 映射（与 titleCanvas 字体链对齐） */
+const FONT_FAMILY_MAP: Record<string, string> = {
+  思源黑体: "'Noto Sans CJK SC', 'Source Han Sans CN', 'PingFang SC', 'Microsoft YaHei', sans-serif",
+  思源宋体: "'Noto Serif SC', 'Source Han Serif SC', 'SimSun', serif",
+  楷体: "KaiTi, 'STKaiti', serif",
+  黑体: "'Heiti SC', 'SimHei', 'Microsoft YaHei', sans-serif",
+}
+const getFontFamily = (font: string): string =>
+  FONT_FAMILY_MAP[font] || FONT_FAMILY_MAP["思源黑体"]
+
 export function PanelLipsyncPreview({
   lipsyncJob,
   onGenerateLipsync,
@@ -41,6 +51,8 @@ export function PanelLipsyncPreview({
   const titleDragRef = useRef<HTMLDivElement>(null)
   const draggingTitleRef = useRef(false)
   const previewContainerRef = useRef<HTMLDivElement>(null)
+  // 预览容器实际宽度（通过 ResizeObserver 监听），用于动态计算 previewScale
+  const [containerWidth, setContainerWidth] = useState(0)
   const isGenerating = lipsyncJob?.status === "pending" || lipsyncJob?.status === "processing"
   const isDone = lipsyncJob?.status === "completed"
   const isFailed = lipsyncJob?.status === "failed"
@@ -52,29 +64,84 @@ export function PanelLipsyncPreview({
         ? "排队中…"
         : "对口型生成中…"
 
-  /** 标题叠加样式 */
+  // 监听预览容器尺寸变化，动态测量宽度以计算 previewScale（基准 720p）
-  const titleOverlayStyle: React.CSSProperties | null = titleConfig?.title
+  useEffect(() => {
-    ? {
+    const el = previewContainerRef.current
-        position: "absolute",
+    if (!el) return
-        left: "50%",
+    const update = () => setContainerWidth(el.clientWidth || 0)
-        transform: "translateX(-50%)",
+    update()
-        color: titleConfig.color || "#ffffff",
+    if (typeof ResizeObserver !== "undefined") {
-        fontFamily: titleConfig.font || "思源黑体",
+      const ro = new ResizeObserver(update)
-        fontSize: `${(titleConfig.size || 36) * 0.55}px`, // 预览等比缩
+      ro.observe(el)
-        fontWeight: titleConfig.bold ? 700 : 400,
+      return () => ro.disconnect()
-        fontStyle: titleConfig.italic ? "italic" : "normal",
+    }
-        textAlign: "center",
+    window.addEventListener("resize", update)
-        width: "90%",
+    return () => window.removeEventListener("resize", update)
-        padding: "4px 8px",
+  }, [])
-        textShadow: titleConfig.shadow ? "0 2px 4px rgba(0,0,0,0.8)" : undefined,
+
-        WebkitTextStroke: titleConfig.stroke ? "1.5px #000" : undefined,
+  // 预览缩放比：预览宽度 / 720（基准宽度）
-        ...(titleConfig.position === "top"
+  const previewScale = containerWidth > 0 ? containerWidth / 720 : 0.35
-          ? { top: 8 }
+  const ps = useCallback((v: number) => Math.round(v * previewScale * 100) / 100, [previewScale])
-          : titleConfig.position === "bottom"
+
-            ? { bottom: 8 }
+  /** 标题叠加样式（字号/padding/描边/阴影均按 previewScale 缩放，保持与成片视觉一致） */
-            : { top: "50%", transform: "translateX(-50%) translateY(-50%)" }),
+  const titleOverlayStyle: React.CSSProperties | null =
-      }
+    titleConfig?.title && containerWidth > 0
-    : null
+      ? (() => {
+          const baseSize = titleConfig.size || 48
+          const fontSize = ps(baseSize)
+          // 描边宽度基准 ≈ size * 0.06，最小 1.5px @720p
+          const strokeW = Math.max(ps(1.5), +(baseSize * 0.06 * previewScale).toFixed(2))
+          // 阴影按比例缩放
+          const shadowBlur = ps(4)
+          const shadowOffsetY = ps(2)
+          // padding / top 边距按比例（基准 8px 对应预览小窗，成片基准 16px，这里 8px 对应约 0.33 缩放）
+          const padV = ps(16) * 0.5 // ≈ 8px in ~240px container
+          const padH = ps(24) * 0.5
+
+          const style: React.CSSProperties = {
+            position: "absolute",
+            color: titleConfig.color || "#ffffff",
+            fontFamily: getFontFamily(titleConfig.font || "思源黑体"),
+            fontSize: `${fontSize}px`,
+            fontWeight: titleConfig.bold ? 700 : 400,
+            fontStyle: titleConfig.italic ? "italic" : "normal",
+            textAlign: "center",
+            width: "90%",
+            lineHeight: 1.2,
+            padding: `${ps(4)}px ${padH}px`,
+            textShadow: titleConfig.shadow
+              ? `0 ${shadowOffsetY}px ${shadowBlur}px rgba(0,0,0,0.8), 0 0 ${ps(2)}px rgba(0,0,0,0.5)`
+              : undefined,
+            WebkitTextStroke: titleConfig.stroke ? `${strokeW}px #000` : undefined,
+            boxSizing: "border-box",
+            wordBreak: "break-word",
+            whiteSpace: "pre-wrap",
+          }
+
+          if (
+            titleConfig.position === "custom" &&
+            titleConfig.pos_x != null &&
+            titleConfig.pos_y != null
+          ) {
+            style.left = `${titleConfig.pos_x}%`
+            style.top = `${titleConfig.pos_y}%`
+            style.transform = "translateX(-50%) translateY(-50%)"
+          } else if (titleConfig.position === "top") {
+            style.left = "50%"
+            style.top = padV
+            style.transform = "translateX(-50%)"
+          } else if (titleConfig.position === "bottom") {
+            style.left = "50%"
+            style.bottom = padV
+            style.transform = "translateX(-50%)"
+          } else {
+            style.left = "50%"
+            style.top = "50%"
+            style.transform = "translateX(-50%) translateY(-50%)"
+          }
+          return style
+        })()
+      : null
 
   const handleTitlePointerDown = (e: React.PointerEvent<HTMLDivElement>) => {
     if (!onTitlePositionChange || !previewContainerRef.current) return
@@ -105,7 +172,10 @@ export function PanelLipsyncPreview({
       const rect = previewContainerRef.current.getBoundingClientRect()
       const relX = Math.max(0, Math.min(rect.width, e.clientX - rect.left))
       const relY = Math.max(0, Math.min(rect.height, e.clientY - rect.top))
-      onTitlePositionChange({ pos_x: relX, pos_y: relY })
+      // 发送百分比坐标（0-100），与后端 drawtext 百分比表达式对齐
+      const xpct = Math.round((relX / rect.width) * 1000) / 10
+      const ypct = Math.round((relY / rect.height) * 1000) / 10
+      onTitlePositionChange({ pos_x: xpct, pos_y: ypct, position: "custom" })
     }
     ;(e.currentTarget as HTMLDivElement).style.cursor = "grab"
   }
@@ -174,7 +244,7 @@ export function PanelLipsyncPreview({
         )}
       </div>
 
-      {/* ── 对口型预览（v3.1: 缩放1/2 + 标题叠加） ─ */}
+      {/* ── 对口型预览（标题字号按 previewScale 动态缩放） ─ */}
       <div className="aa-lipsync-section">
         <div className="aa-lipsync-section__title">对口型预览</div>
 

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

@@ -2,17 +2,16 @@
  * AI数字人 — 出镜视频选择面板
  * - 未选视频：虚线上传区，点击打开素材库弹窗
  * - 已选视频：竖屏 9:16 预览播放器 + 视频信息卡片 + 移除按钮
+ *
+ * 注意：本面板只展示原始素材视频，不叠加标题（标题在对口型预览和最终成片上展示）
  */
 import type { AssetItem } from "@/api/assets"
-import type { AiAvatarTitleConfig } from "../types"
-import { getFontFamily } from "@/pages/generate/constants"
 
 export interface PanelVideoSelectorProps {
   selectedVideo: AssetItem | null
   /** 触发打开素材库弹窗 */
   onSelectVideo: () => void
   onRemoveVideo: () => void
-  titleConfig?: AiAvatarTitleConfig
 }
 
 /** 格式化时长（秒 → mm:ss） */
@@ -27,7 +26,6 @@ export function PanelVideoSelector({
   selectedVideo,
   onSelectVideo,
   onRemoveVideo,
-  titleConfig,
 }: PanelVideoSelectorProps) {
   /* 未选视频：虚线上传区，点击打开素材库弹窗 */
   if (!selectedVideo) {
@@ -57,42 +55,13 @@ export function PanelVideoSelector({
 
   return (
     <div>
-      {/* 竖屏 9:16 视频预览播放器 + 标题实时预览 */}
+      {/* 竖屏 9:16 视频预览播放器（纯素材预览，不叠加标题） */}
-      <div className="aa-video-preview" style={{ position: "relative" }}>
+      <div className="aa-video-preview">
         {fileUrl ? (
           <video src={fileUrl} poster={selectedVideo.thumbnail_url} controls playsInline />
         ) : (
           <div className="aa-video-preview__placeholder">视频暂不可预览</div>
         )}
-        {titleConfig?.title && (
-          <div
-            style={{
-              position: "absolute",
-              left: "50%",
-              transform: "translateX(-50%)",
-              ...(titleConfig.position === "top"
-                ? { top: "10%" }
-                : titleConfig.position === "bottom"
-                  ? { bottom: "10%" }
-                  : { top: "50%", transform: "translate(-50%, -50%)" }),
-              fontSize: Math.max(titleConfig.size, 32),
-              fontFamily: getFontFamily(titleConfig.font),
-              color: titleConfig.color,
-              fontWeight: titleConfig.bold ? 700 : 400,
-              fontStyle: titleConfig.italic ? "italic" : "normal",
-              textShadow: "0 2px 4px rgba(0,0,0,0.5)",
-              WebkitTextStroke: "2px #000",
-              pointerEvents: "none",
-              zIndex: 10,
-              maxWidth: "90%",
-              textAlign: "center",
-              whiteSpace: "pre-wrap",
-              lineHeight: 1.3,
-            }}
-          >
-            {titleConfig.title}
-          </div>
-        )}
       </div>
 
       {/* 视频信息卡片：文件名 / 时长 / 分辨率 */}

apps/web/src/pages/ai-avatar/hooks/useAiAvatar.ts

@@ -1,5 +1,5 @@
 /**
- * AI数字人 — 页面全局状态管理 hook（v3）
+ * AI数字人 — 页面全局状态管理 hook（v3 + #1845 配音前置）
  */
 import { useState, useCallback } from "react"
 import type { AssetItem } from "@/api/assets"
@@ -13,10 +13,19 @@ import {
   type BRollSegment,
   type AiAvatarTitleConfig,
   type AiAvatarCoverConfig,
+  type TtsPreviewResult,
   DEFAULT_TITLE_CONFIG,
   DEFAULT_COVER_CONFIG,
 } from "../types"
 
+const DEFAULT_TTS_PREVIEW: TtsPreviewResult = {
+  audioUrl: null,
+  duration: 0,
+  sentenceTimings: [],
+  status: "idle",
+  error: null,
+}
+
 export function useAiAvatar() {
   /* ── 面板1：出镜视频 ── */
   const [selectedVideo, setSelectedVideo] = useState<AssetItem | null>(null)
@@ -36,6 +45,9 @@ export function useAiAvatar() {
   const [showScriptModal, setShowScriptModal] = useState(false)
   const [showBRollModal, setShowBRollModal] = useState(false)
 
+  /* ── #1845 TTS 预合成（步骤1「生成配音」） ── */
+  const [ttsPreview, setTtsPreview] = useState<TtsPreviewResult>(DEFAULT_TTS_PREVIEW)
+
   /* ── 面板3.5：B-roll ── */
   const [bRollSegments, setBRollSegments] = useState<BRollSegment[]>([])
 
@@ -81,6 +93,7 @@ export function useAiAvatar() {
     setScript(null)
     setScriptText("")
     setLipsyncJob(null)
+    setTtsPreview(DEFAULT_TTS_PREVIEW)
     setBRollSegments([])
     setTitleConfig(DEFAULT_TITLE_CONFIG)
     setCoverConfig(DEFAULT_COVER_CONFIG)
@@ -118,6 +131,10 @@ export function useAiAvatar() {
     showBRollModal,
     setShowBRollModal,
     selectScript,
+    // #1845 TTS 预合成
+    ttsPreview,
+    setTtsPreview,
+    resetTtsPreview: useCallback(() => setTtsPreview(DEFAULT_TTS_PREVIEW), []),
     // B-roll
     bRollSegments,
     addBRollSegment,

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

@@ -28,6 +28,17 @@ export const VOICE_LANGUAGE_OPTIONS: { value: VoiceLanguage; label: string }[] =
 /* ── 对口型任务状态 ── */
 export type LipsyncStatus = "idle" | "pending" | "processing" | "completed" | "failed"
 
+/* ── TTS 预合成（#1845 配音前置：步骤1「生成配音」状态） ── */
+export type TtsPreviewStatus = "idle" | "generating" | "done" | "failed"
+
+export interface TtsPreviewResult {
+  audioUrl: string | null
+  duration: number
+  sentenceTimings: SentenceTiming[]
+  status: TtsPreviewStatus
+  error: string | null
+}
+
 /* ── 文案 ── */
 export interface Script {
   id: string
@@ -44,12 +55,23 @@ export interface LipsyncJob {
   status: LipsyncStatus
   progress: number
   output_video_url: string | null
-  /** 对口型成片总时长（秒），后端返回；用于 B-roll 时间自动估算（#1809 ⑥） */
+  /** 对口型成片总时长（秒），后端返回 */
+  script_text: string
   output_duration?: number
+  /** 精确句子时间戳（后端基于 TTS 音频静音检测计算） */
+  sentence_timings?: SentenceTiming[] | null
   error_message: string | null
   created_at: string
 }
 
+/* ── 句子时间戳（后端精确计算） ── */
+export interface SentenceTiming {
+  index: number
+  text: string
+  start_time: number
+  end_time: number
+}
+
 /* ── B-roll 画面插入 ── */
 export type BRollInsertMode = "fullscreen" | "pip"
 export type PipPosition = "top-left" | "top-right" | "bottom-left" | "bottom-right"
@@ -77,7 +99,7 @@ export interface AiAvatarTitleConfig {
   shadow: boolean
   color: string
   auto_subtitle: boolean
-  /** 自定义位置坐标（position=custom 时生效，像素） */
+  /** 自定义位置坐标（position=custom 时生效，百分比 0-100） */
   pos_x?: number
   pos_y?: number
 }
@@ -101,6 +123,7 @@ export interface RenderJob {
   status: RenderStatus
   progress: number
   output_video_url: string | null
+  output_cover_url: string | null
   error_message: string | null
   created_at: string
 }
@@ -110,7 +133,7 @@ export const DEFAULT_TITLE_CONFIG: AiAvatarTitleConfig = {
   title: "",
   position: "bottom",
   font: "思源黑体",
-  size: 28,
+  size: 48,
   bold: true,
   italic: false,
   stroke: false,

apps/web/src/pages/ai-avatar/utils/contract.ts

@@ -28,10 +28,13 @@ export function normalizeEmotion(raw: string | undefined | null): VoiceEmotion {
  * 后端真实字段：text(或content)、font(或font_preset)、font_size(或size)、
  * font_color(或color，可传 #RRGGBB)、position(top/center/bottom/custom)、
  * enabled、bold、stroke{enabled,width,color}、shadow{enabled,color,offset_x,offset_y}、
- * pos_x/pos_y(custom 时)。
+ * pos_x/pos_y(custom 时)、title_image_dataurl（前端 Canvas 渲染的 PNG dataURL，WYSIWYG 路径优先）。
  * 口播标题默认 position=bottom（不传后端会默认 top 跑到画面顶部）。
  */
-export function buildTitleConfigPayload(cfg: AiAvatarTitleConfig): Record<string, unknown> {
+export function buildTitleConfigPayload(
+  cfg: AiAvatarTitleConfig,
+  titleImageDataUrl?: string | null,
+): Record<string, unknown> {
   const text = (cfg.title || "").trim()
   if (!text) return {}
   const position = cfg.position || "bottom"
@@ -39,7 +42,7 @@ export function buildTitleConfigPayload(cfg: AiAvatarTitleConfig): Record<string
     text,
     enabled: true,
     font: cfg.font || "思源黑体",
-    font_size: Math.round(cfg.size) || 36,
+    font_size: Math.round(cfg.size) || 48,
     font_color: cfg.color || "#ffffff",
     position,
     bold: !!cfg.bold,
@@ -53,6 +56,10 @@ export function buildTitleConfigPayload(cfg: AiAvatarTitleConfig): Record<string
     payload.pos_x = cfg.pos_x
     payload.pos_y = cfg.pos_y
   }
+  // 前端 Canvas 渲染好的 PNG dataURL（所见即所得，后端优先 overlay 此图片图层）
+  if (titleImageDataUrl) {
+    payload.title_image_dataurl = titleImageDataUrl
+  }
   return payload
 }
 
@@ -67,9 +74,14 @@ export function buildCoverConfigPayload(
     // build_cover_extract_command 读取 timestamp（截帧秒数）
     timestamp: cfg.frame_time || 0,
   }
-  if (smartCoverUrl) payload.cover_url = smartCoverUrl
+  // 智能封面 URL（后端字段名为 url/imageUrl/cover_url 都兼容，优先 url）
+  if (smartCoverUrl) {
+    payload.url = smartCoverUrl
+    payload.cover_url = smartCoverUrl
+  }
   // 自定义上传：blob: 本地预览地址无法给后端，仅 OSS URL 可用
   if (cfg.mode === "upload" && cfg.upload_url && !cfg.upload_url.startsWith("blob:")) {
+    payload.url = cfg.upload_url
     payload.upload_url = cfg.upload_url
   }
   return payload

apps/web/src/pages/ai-avatar/utils/sentences.ts

@@ -1,5 +1,10 @@
 /**
- * AI数字人 — 文案分句 & B-roll 时间自动估算（#1809 ⑤⑥）
+ * AI数字人 — 文案分句 & B-roll 时间计算
+ *
+ * 数据来源优先级：
+ *   1. 后端 sentence_timings（基于 TTS 音频静音检测，精确到句子边界）—— 直接使用，不重新分句
+ *   2. 后端 output_duration（最终渲染视频时长） + 本地分句 —— 按字数比例估算
+ *   3. 两者都没有（对口型还在生成中）—— 返回分句文本但 startTime/endTime 全部 0，等数据到位重算
  */
 
 export interface ScriptSentence {
@@ -11,25 +16,67 @@ export interface ScriptSentence {
   charCount: number
   /** 累计起始字数（用于时间估算） */
   startChar: number
-  /** 估算的对口型视频内起始时间（秒） */
+  /** 对口型视频内起始时间（秒）——后端精确值或前端估算 */
   startTime: number
-  /** 估算的对口型视频内结束时间（秒） */
+  /** 对口型视频内结束时间（秒）——后端精确值或前端估算 */
   endTime: number
 }
 
+/** 句子分隔符：中英文句号/问号/感叹号/分号/逗号/换行（覆盖中文短视频常用断句） */
+const SENTENCE_SPLIT_RE = /[。！？!?？！;；,，\n\r]+/
+
 /**
- * 按句号/问号/感叹号/分号/换行分句（兼容中英文标点）。
+ * 分句并计算每句的起止时间。
- * 空文案返回空数组。时间按「该句字数 ÷ 全文总字数 × 口播总时长」线性估算。
+ *
+ * @param sentenceTimings 后端返回的精确句子时间戳（来自 lipsync_job.sentence_timings）。
+ *   非空时直接按后端返回的句子列表渲染，不再本地分句（避免前后端分句不一致导致时间错位）。
+ * @param outputDuration  最终视频时长（秒）。对口型预览阶段可能为 0，此时降级估算只能给 0。
  */
 export function splitScriptIntoSentences(
   scriptText: string,
-  outputDuration: number,
+  sentenceTimings?:
+    { index?: number; text?: string; start_time: number; end_time: number }[] | null,
+  outputDuration: number = 0,
 ): ScriptSentence[] {
   const text = (scriptText || "").trim()
   if (!text) return []
 
+  // 1. 后端返回了 sentence_timings：校验通过就直接用，跳过本地分句
+  //    校验条件放宽：只要是数组、至少1条、每条 start_time/end_time 是数字即可
+  //    （不再强制要求条数相等——后端静音检测可能按停顿切出更多/更少边界，
+  //    比如文案用逗号连写时本地只分1句、后端按停顿切4句，后端的切法才是对的）
+  if (Array.isArray(sentenceTimings) && sentenceTimings.length > 0) {
+    const valid = sentenceTimings.every(
+      (t) =>
+        t &&
+        typeof t.start_time === "number" &&
+        typeof t.end_time === "number" &&
+        isFinite(t.start_time) &&
+        isFinite(t.end_time) &&
+        t.end_time >= t.start_time,
+    )
+    if (valid) {
+      let accChar = 0
+      return sentenceTimings.map((t, i) => {
+        const sentenceText = (t.text || "").trim() || `句子${i + 1}`
+        const charCount = sentenceText.replace(/\s/g, "").length
+        const sentence: ScriptSentence = {
+          index: typeof t.index === "number" ? t.index : i,
+          text: sentenceText,
+          charCount,
+          startChar: accChar,
+          startTime: round1(t.start_time),
+          endTime: round1(t.end_time),
+        }
+        accChar += charCount
+        return sentence
+      })
+    }
+  }
+
+  // 2. 本地分句 + 按字数比例估算（降级路径）
   const rawParts = text
-    .split(/[。！？!?；;\n\r]+/)
+    .split(SENTENCE_SPLIT_RE)
     .map((part) => part.trim())
     .filter((part) => part.length > 0)
 

apps/web/src/pages/ai-avatar/utils/titleCanvas.ts

@@ -0,0 +1,179 @@
+/**
+ * AI数字人 — 标题 Canvas 渲染工具
+ *
+ * 把标题按前端预览的 HTML/CSS 效果画到透明背景 PNG 上（与视频同分辨率），
+ * 以 dataURL 形式传给后端，后端用 FFmpeg overlay 直接叠加图层，
+ * 彻底解决前端 HTML/CSS 预览 ≠ FFmpeg drawtext 成片的 WYSIWYG 问题。
+ *
+ * 约定：titleConfig.size 的语义是"720p 基准宽度下的字号（px）"，
+ *       按 videoWidth / 720 得到 scale，所有长度类参数乘以 scale，
+ *       保证 1080p / 4K 成片里标题视觉大小与预览一致。
+ */
+import type { AiAvatarTitleConfig } from "../types"
+
+export interface RenderTitlePngOptions {
+  /** 标题配置 */
+  titleConfig: AiAvatarTitleConfig
+  /** 视频宽度（像素），默认 720 */
+  videoWidth?: number
+  /** 视频高度（像素），默认 1280 */
+  videoHeight?: number
+}
+
+/**
+ * 将标题渲染为透明背景 PNG 的 dataURL（data:image/png;base64,...）
+ * Canvas 尺寸与视频一致，保证叠加时 1:1 像素对齐。
+ *
+ * 标题为空时返回 null。
+ */
+export function renderTitleToPngDataUrl(opts: RenderTitlePngOptions): string | null {
+  const { titleConfig, videoWidth = 720, videoHeight = 1280 } = opts
+  if (!titleConfig) return null
+  const rawTitle = (titleConfig.title || "").trim()
+  if (!rawTitle) return null
+
+  // 按 / 或 ／ 分割为多行
+  const lines = rawTitle
+    .split(/[/／]/)
+    .map((l) => l.trim())
+    .filter((l) => l.length > 0)
+  if (lines.length === 0) return null
+
+  // 分辨率缩放系数：基准 720p，所有长度类参数乘以 scale
+  const scale = videoWidth / 720
+  const r = (v: number) => Math.round(v * scale)
+
+  const canvas = document.createElement("canvas")
+  canvas.width = videoWidth
+  canvas.height = videoHeight
+  const ctx = canvas.getContext("2d")
+  if (!ctx) return null
+
+  const baseSize = Math.max(12, Math.round(titleConfig.size || 48))
+  const size = r(baseSize)
+  const bold = !!titleConfig.bold
+  const italic = !!titleConfig.italic
+  const color = titleConfig.color || "#ffffff"
+  const stroke = !!titleConfig.stroke
+  const shadow = !!titleConfig.shadow
+
+  // 字体族 fallback 链：优先中文字体
+  const fontFamily =
+    '"Noto Sans CJK SC","Source Han Sans CN","PingFang SC","Microsoft YaHei",sans-serif'
+  const fontParts: string[] = []
+  if (italic) fontParts.push("italic")
+  if (bold) fontParts.push("bold")
+  fontParts.push(`${size}px`, fontFamily)
+  ctx.font = fontParts.join(" ")
+  ctx.fillStyle = color
+  ctx.textAlign = "center"
+  ctx.textBaseline = "middle"
+
+  // 阴影（shadow=true 时开启）——按 scale 缩放
+  if (shadow) {
+    ctx.shadowColor = "rgba(0,0,0,0.8)"
+    ctx.shadowBlur = r(4)
+    ctx.shadowOffsetX = 0
+    ctx.shadowOffsetY = r(2)
+  }
+
+  // 位置计算：与 PanelLipsyncPreview 的 CSS 对齐（按 scale 缩放 PAD）
+  const PAD = r(16)
+  let centerX = videoWidth / 2
+  const position = titleConfig.position || "bottom"
+  const lineGap = size * 1.2
+  const totalTextH = lines.length * lineGap - (lineGap - size) // 所有行的总高度
+  // 文本块顶部 y（textBaseline=middle 时首行基线）
+  let firstLineY: number
+  if (
+    position === "custom" &&
+    typeof titleConfig.pos_x === "number" &&
+    typeof titleConfig.pos_y === "number"
+  ) {
+    centerX = (Math.max(0, Math.min(100, titleConfig.pos_x)) / 100) * videoWidth
+    const centerY = (Math.max(0, Math.min(100, titleConfig.pos_y)) / 100) * videoHeight
+    firstLineY = centerY - totalTextH / 2 + size / 2
+  } else if (position === "top") {
+    // 顶部：y = size/2 + PAD
+    firstLineY = size / 2 + PAD
+  } else if (position === "center") {
+    firstLineY = videoHeight / 2 - totalTextH / 2 + size / 2
+  } else {
+    // bottom（默认）
+    firstLineY = videoHeight - totalTextH - PAD + size / 2
+  }
+
+  // 描边参数：描边 lineWidth 按 scale 缩放（基准 size * 0.06，最小 2px @720p）
+  const doStroke = stroke
+  const strokeWidth = Math.max(r(2), Math.round(size * 0.06))
+  // 逐行绘制
+  lines.forEach((line, idx) => {
+    const y = firstLineY + idx * lineGap
+    if (doStroke) {
+      const prevShadowColor = ctx.shadowColor
+      const prevShadowBlur = ctx.shadowBlur
+      // 描边不要带阴影（避免黑色描边发虚）
+      ctx.shadowColor = "rgba(0,0,0,0)"
+      ctx.shadowBlur = 0
+      ctx.lineWidth = strokeWidth
+      ctx.strokeStyle = "#000000"
+      ctx.lineJoin = "round"
+      ctx.strokeText(line, centerX, y)
+      // 恢复阴影
+      if (shadow) {
+        ctx.shadowColor = "rgba(0,0,0,0.8)"
+        ctx.shadowBlur = r(4)
+      } else {
+        ctx.shadowColor = prevShadowColor
+        ctx.shadowBlur = prevShadowBlur
+      }
+    }
+    ctx.fillText(line, centerX, y)
+  })
+
+  try {
+    return canvas.toDataURL("image/png")
+  } catch {
+    return null
+  }
+}
+
+/**
+ * 获取视频真实分辨率（HTMLVideoElement + loadedmetadata，超时 3 秒兜底 720×1280）。
+ */
+export function getVideoResolution(
+  videoUrl: string,
+  timeoutMs = 3000,
+): Promise<{ width: number; height: number }> {
+  return new Promise((resolve) => {
+    if (!videoUrl) {
+      resolve({ width: 720, height: 1280 })
+      return
+    }
+    const video = document.createElement("video")
+    video.preload = "metadata"
+    video.muted = true
+    video.playsInline = true
+    video.crossOrigin = "anonymous"
+    let settled = false
+    const done = (w: number, h: number) => {
+      if (settled) return
+      settled = true
+      video.removeAttribute("src")
+      video.load()
+      resolve({ width: w, height: h })
+    }
+    const timer = window.setTimeout(() => done(720, 1280), timeoutMs)
+    video.onloadedmetadata = () => {
+      window.clearTimeout(timer)
+      const w = video.videoWidth || 720
+      const h = video.videoHeight || 1280
+      done(w, h)
+    }
+    video.onerror = () => {
+      window.clearTimeout(timer)
+      done(720, 1280)
+    }
+    video.src = videoUrl
+  })
+}

apps/web/src/pages/products/ProductLibrary.tsx

@@ -1,17 +1,22 @@
 /**
  * 成片库页面 — V21 设计系统
- * 卡片网格布局，支持视频内联播放/下载/分享、批量操作、筛选
+ * 卡片网格布局，支持视频内联播放/下载/分享、批量操作、筛选、无限滚动分页
  *
  * 主组件仅保留 Hook 组装与整体布局
- * 列表查询 → hooks/useProductList
+ * 列表查询 → hooks/useProductList（useInfiniteQuery 分页）
  * 操作逻辑 → hooks/useProductActions
  * 筛选栏 → components/ProductFilterBar
  * 批量操作栏 → components/ProductBatchBar
  * 空状态 → components/ProductEmptyState
  * 产品卡片 → components/ProductCard（内联视频播放）
  */
-import React from "react"
+import React, { useEffect, useRef } from "react"
-import { VideoCameraOutlined, DownloadOutlined, ReloadOutlined } from "@ant-design/icons"
+import {
+  VideoCameraOutlined,
+  DownloadOutlined,
+  ReloadOutlined,
+  LoadingOutlined,
+} from "@ant-design/icons"
 import { Button } from "@/components/ui"
 import { ProductCard } from "./components/ProductCard"
 import { ProductFilterBar } from "./components/ProductFilterBar"
@@ -24,11 +29,13 @@ import "./products.css"
 
 const ProductLibrary: React.FC = () => {
   const {
-    products,
     filteredProducts,
     isLoading,
+    isFetchingNextPage,
     isError,
     error,
+    hasNextPage,
+    fetchNextPage,
     refetch,
     searchText,
     setSearchText,
@@ -64,19 +71,40 @@ const ProductLibrary: React.FC = () => {
   } = useProductActions({
     selectedIds,
     clearSelection,
-    products,
+    products: filteredProducts,
     setPlayingProduct: () => {}, // 不再使用弹窗播放
   })
 
   const { recomputeDedup, isRecomputing } = useRecomputeDedup()
 
-  // ── Loading 状态 ──
+  /* ── 无限滚动：IntersectionObserver 监听底部哨兵元素 ── */
-  if (isLoading) {
+  const sentinelRef = useRef<HTMLDivElement>(null)
+
+  useEffect(() => {
+    const el = sentinelRef.current
+    if (!el) return
+    // 已有数据但正在加载中/没有更多页时不触发
+    if (isFetchingNextPage || !hasNextPage) return
+
+    const observer = new IntersectionObserver(
+      (entries) => {
+        if (entries[0]?.isIntersecting) {
+          void fetchNextPage()
+        }
+      },
+      { rootMargin: "200px" },
+    )
+    observer.observe(el)
+    return () => observer.disconnect()
+  }, [fetchNextPage, hasNextPage, isFetchingNextPage])
+
+  // ── Loading 状态（仅首次加载）──
+  if (isLoading && filteredProducts.length === 0) {
     return <ProductEmptyState type="loading" />
   }
 
   // ── Error 状态 ──
-  if (isError) {
+  if (isError && filteredProducts.length === 0) {
     console.error("[ProductLibrary] 加载失败:", error)
     const errorMsg = error?.message || "加载失败"
     const is404 = errorMsg.includes("404") || errorMsg.includes("Not Found")
@@ -143,22 +171,46 @@ const ProductLibrary: React.FC = () => {
 
       {/* 卡片网格 */}
       {filteredProducts.length > 0 ? (
-        <div className="xx-products-grid">
+        <>
-          {filteredProducts.map((product) => (
+          <div className="xx-products-grid">
-            <ProductCard
+            {filteredProducts.map((product) => (
-              key={product.id}
+              <ProductCard
-              product={product}
+                key={product.id}
-              isSelected={selectedIds.has(product.id)}
+                product={product}
-              batchMode={batchMode}
+                isSelected={selectedIds.has(product.id)}
-              onToggleSelect={handleToggleSelect}
+                batchMode={batchMode}
-              onDownload={handleDownload}
+                onToggleSelect={handleToggleSelect}
-              onShare={handleShare}
+                onDownload={handleDownload}
-              onDelete={handleDelete}
+                onShare={handleShare}
-              onPublish={handlePublish}
+                onDelete={handleDelete}
-              onReviewStatusChange={handleReviewStatusChange}
+                onPublish={handlePublish}
-            />
+                onReviewStatusChange={handleReviewStatusChange}
-          ))}
+              />
-        </div>
+            ))}
+          </div>
+
+          {/* 底部哨兵 + 状态提示 */}
+          <div
+            ref={sentinelRef}
+            style={{
+              gridColumn: "1 / -1",
+              textAlign: "center",
+              padding: "24px 0",
+              fontSize: 13,
+              color: "#8c8ca1",
+            }}
+          >
+            {isFetchingNextPage ? (
+              <>
+                <LoadingOutlined /> 加载中…
+              </>
+            ) : hasNextPage ? (
+              <span style={{ opacity: 0 }}>加载更多</span>
+            ) : (
+              <span>—— 已加载全部 ——</span>
+            )}
+          </div>
+        </>
       ) : (
         <ProductEmptyState type="empty" />
       )}

apps/web/src/pages/products/hooks/useProductList/index.ts

@@ -1,28 +1,53 @@
 import { useMemo } from "react"
-import { useQuery } from "@tanstack/react-query"
+import { useInfiniteQuery } from "@tanstack/react-query"
 import { getProducts, type ProductItem as ApiProductItem } from "@/api/products"
 import { mapApiProduct } from "../../utils"
+import type { ProductItem } from "../../types"
 import { useProductFiltering } from "./useProductFiltering"
 import { useBatchSelection } from "./useBatchSelection"
 
 export type { Filters } from "./useProductFiltering"
 
+const PAGE_SIZE = 20
+
 export const useProductList = () => {
-  /* ── 获取成品列表 ── */
+  /* ── 无限滚动获取成品列表（每页 20 条） ── */
   const {
-    data: apiProducts = [],
+    data,
     isLoading,
+    isFetchingNextPage,
     isError,
     error,
+    hasNextPage,
+    fetchNextPage,
     refetch,
-  } = useQuery<ApiProductItem[], Error>({
+  } = useInfiniteQuery<
+    {
+      items: ApiProductItem[]
+      total: number
+      page: number
+      page_size: number
+    },
+    Error
+  >({
     queryKey: ["products"],
-    queryFn: () => getProducts(),
+    queryFn: async ({ pageParam = 1 }) =>
+      getProducts({ page: pageParam as number, page_size: PAGE_SIZE }),
+    initialPageParam: 1,
+    getNextPageParam: (lastPage) => {
+      const loadedCount = lastPage.page * lastPage.page_size
+      return loadedCount < lastPage.total ? lastPage.page + 1 : undefined
+    },
     staleTime: 30_000,
   })
 
-  // 映射为前端类型，按创建时间倒序排列，防御非数组返回
+  // 将所有页拼接为一维数组，再做前端映射+排序
-  const products = useMemo(
+  const apiProducts = useMemo<ApiProductItem[]>(() => {
+    if (!data?.pages) return []
+    return data.pages.flatMap((p) => p.items)
+  }, [data])
+
+  const products = useMemo<ProductItem[]>(
     () =>
       (Array.isArray(apiProducts) ? apiProducts : []).map(mapApiProduct).sort((a, b) => {
         if (!a.date || a.date === "—") return 1
@@ -65,8 +90,11 @@ export const useProductList = () => {
     products,
     filteredProducts,
     isLoading,
+    isFetchingNextPage,
     isError,
     error,
+    hasNextPage,
+    fetchNextPage,
     refetch,
     // 筛选
     searchText,

packages/adapters/sqlalchemy_impl/models.py

@@ -703,6 +703,9 @@ class LipsyncJobModel(Base):
     error_message = Column(Text, nullable=False, default="")
     error_code = Column(String(100), nullable=False, default="")
 
+    # 精确句子时间戳（TTS 合成后由 silencedetect 计算，用于 B-roll 精确定位）
+    sentence_timings = Column(JSON, nullable=True)  # list[{index,text,start_time,end_time}]
+
     # 时间戳
     submitted_at = Column(DateTime, nullable=True)
     completed_at = Column(DateTime, nullable=True)

packages/domain/generated_video.py

@@ -49,19 +49,17 @@ class GeneratedVideo:
         thumbnail_url: str | None = None,
         generation_params: dict[str, Any] | None = None,
     ) -> "GeneratedVideo":
-        if not project_id.strip():
+        # project_id / generation_task_id 允许为空：AI数字人等无项目场景下，前端可能不传 project_id；
-            raise ValueError("project_id cannot be empty")
+        # lipsync 路径下 generation_task_id 也可能暂时为空。空串会被下面统一兜底为 "" 入库。
-        if not generation_task_id.strip():
+        if not name or not name.strip():
-            raise ValueError("generation_task_id cannot be empty")
-        if not name.strip():
             raise ValueError("name cannot be empty")
-        if not file_url.strip():
+        if not file_url or not file_url.strip():
             raise ValueError("file_url cannot be empty")
         return cls(
             id=uuid4().hex,
-            project_id=project_id.strip(),
+            project_id=(project_id or "").strip(),
-            user_id=user_id.strip(),
+            user_id=(user_id or "").strip(),
-            generation_task_id=generation_task_id.strip(),
+            generation_task_id=(generation_task_id or "").strip(),
             name=name.strip(),
             file_url=file_url.strip(),
             file_size=file_size,

packages/domain/sentence_timings.py

@@ -0,0 +1,205 @@
+"""共享的句子时间戳计算工具 — 供 Celery TTS 任务和 /lipsync/tts-preview 同步接口复用.
+
+- `_split_script_into_sentences`: 按标点分句（中英文逗号/句号/问号/感叹号/分号/换行）
+- `_estimate_sentence_timings_by_chars`: 按字数比例估算（静音检测失败时降级）
+- `_probe_audio_duration`: ffprobe 读取音频时长
+- `compute_sentence_timings`: 基于 ffmpeg silencedetect 精确计算每句起止时间
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import re
+import subprocess
+import tempfile
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+def split_script_into_sentences(script_text: str) -> list[str]:
+    """按句号/问号/感叹号/分号/逗号/换行分句（与前端 SENTENCE_SPLIT_RE 一致）.
+
+    中文短视频文案习惯用「，」断小句（如"卖花的叫花无缺，卖姜的叫姜子牙"），
+    必须把逗号也纳入分隔符，否则多句文案会被识别成一整句，导致 B-roll 时间戳错位。
+    """
+    text = (script_text or "").strip()
+    if not text:
+        return []
+    parts = re.split(r"[。！？!?？！;；,，\n\r]+", text)
+    return [p.strip() for p in parts if p.strip()]
+
+
+def estimate_sentence_timings_by_chars(sentences: list[str], total_duration: float) -> list[dict]:
+    """降级方案：按字数比例估算句子时间（与原前端逻辑一致）."""
+    if not sentences or total_duration <= 0:
+        return []
+    total_chars = sum(len(s.replace(r"\s", "")) for s in sentences)
+    if total_chars == 0:
+        return []
+
+    timings = []
+    acc = 0
+    for i, sent in enumerate(sentences):
+        chars = len(sent.replace(r"\s", ""))
+        start = (acc / total_chars) * total_duration
+        end = ((acc + chars) / total_chars) * total_duration
+        timings.append(
+            {
+                "index": i,
+                "text": sent,
+                "start_time": round(start, 2),
+                "end_time": round(end, 2),
+            }
+        )
+        acc += chars
+    return timings
+
+
+def probe_audio_duration(audio_data: bytes, timeout: int = 10) -> float:
+    """用 ffprobe 读取音频字节流的时长（秒）.
+
+    Returns:
+        时长（秒），失败返回 0.0
+    """
+    if not audio_data:
+        return 0.0
+    tmp_path: Optional[str] = None
+    try:
+        with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as tmp:
+            tmp.write(audio_data)
+            tmp_path = tmp.name
+        result = subprocess.run(
+            [
+                "ffprobe",
+                "-v",
+                "error",
+                "-show_entries",
+                "format=duration",
+                "-of",
+                "default=noprint_wrappers=1:nokey=1",
+                tmp_path,
+            ],
+            capture_output=True,
+            text=True,
+            timeout=timeout,
+        )
+        stdout = (result.stdout or "").strip()
+        if not stdout:
+            logger.warning("[sentence_timings] ffprobe 无输出: stderr=%s", (result.stderr or "")[:200])
+            return 0.0
+        return float(stdout)
+    except Exception as exc:
+        logger.warning("[sentence_timings] ffprobe 时长探测失败: %s", exc)
+        return 0.0
+    finally:
+        if tmp_path:
+            try:
+                os.unlink(tmp_path)
+            except Exception:
+                pass
+
+
+def compute_sentence_timings(audio_data: bytes, script_text: str, total_duration: float) -> list[dict]:
+    """基于 TTS 音频的静音检测，精确计算每句文案的起止时间.
+
+    使用 ffmpeg silencedetect 检测静音段，将静音点与句子边界对齐。
+    比字数比例估算准确得多。
+
+    Args:
+        audio_data: TTS 音频二进制数据（MP3）
+        script_text: 文案全文
+        total_duration: 音频总时长（秒）
+
+    Returns:
+        list[{"index": int, "text": str, "start_time": float, "end_time": float}]
+    """
+    sentences = split_script_into_sentences(script_text)
+    if not sentences:
+        return []
+
+    tmp_path: Optional[str] = None
+    try:
+        with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as tmp:
+            tmp.write(audio_data)
+            tmp_path = tmp.name
+
+        result = subprocess.run(
+            [
+                "ffmpeg",
+                "-i",
+                tmp_path,
+                "-af",
+                "silencedetect=noise=-25dB:d=0.3",
+                "-f",
+                "null",
+                "-",
+            ],
+            capture_output=True,
+            text=True,
+            timeout=30,
+        )
+        stderr = result.stderr or ""
+
+        silence_ends = []
+        for match in re.finditer(r"silence_end:\s*([\d.]+)", stderr):
+            t = float(match.group(1))
+            if 0 < t < total_duration:
+                silence_ends.append(t)
+
+        if len(silence_ends) < len(sentences) - 1:
+            logger.warning(
+                "[sentence_timings] 静音点不足(%d < %d)，降级为字数比例估算",
+                len(silence_ends),
+                len(sentences) - 1,
+            )
+            return estimate_sentence_timings_by_chars(sentences, total_duration)
+
+        n_boundaries = len(sentences) - 1
+        boundaries = []
+        used_indices = set()
+
+        for i in range(n_boundaries):
+            expected_pos = (i + 1) / len(sentences) * total_duration
+            best_idx = None
+            best_dist = float("inf")
+            for j, t in enumerate(silence_ends):
+                if j in used_indices:
+                    continue
+                dist = abs(t - expected_pos)
+                if dist < best_dist:
+                    best_dist = dist
+                    best_idx = j
+            if best_idx is not None:
+                used_indices.add(best_idx)
+                boundaries.append(silence_ends[best_idx])
+
+        boundaries.sort()
+
+        timings = []
+        prev_end = 0.0
+        for i, sent in enumerate(sentences):
+            start = prev_end
+            end = boundaries[i] if i < len(boundaries) else total_duration
+            timings.append(
+                {
+                    "index": i,
+                    "text": sent,
+                    "start_time": round(start, 2),
+                    "end_time": round(end, 2),
+                }
+            )
+            prev_end = end
+
+        return timings
+
+    except Exception as exc:
+        logger.warning("[sentence_timings] 静音检测异常，降级为字数比例估算: %s", exc)
+        return estimate_sentence_timings_by_chars(sentences, total_duration)
+    finally:
+        if tmp_path:
+            try:
+                os.unlink(tmp_path)
+            except Exception:
+                pass

packages/domain/video_filter_builder.py

@@ -377,26 +377,30 @@ def _append_audio_concat(parts: list[str], clip_chains: list[ClipFilterChain]) -
 
 # ── 标题 drawtext 滤镜构建（#1789）─────────────────────────────────────────────
 
-# drawtext 字体搜索路径：按优先级列出常见安装位置
+# drawtext 字体搜索路径：按优先级从高到低排列
-# 服务器使用 Noto Sans SC（思源黑体）作为默认字体
+# 服务器使用 Noto Sans SC（思源黑体）作为默认字体。
+# - NotoSansSC-VF.ttf 是 worker-base.Dockerfile 中 COPY 的 VF 字体（含所有字重，无 Mono 变体），优先级最高
+# - .ttc 系列为 fonts-noto-cjk 包预装字体（Dockerfile 已删除含 Mono 变体的旧 .ttc，存在时作为 fallback）
+# - DejaVuSans 仅含拉丁字符不支持中文，已移除
 DRAWTEXT_FONT_SEARCH_PATHS: list[str] = [
+    "/usr/share/fonts/opentype/noto/NotoSansSC-VF.ttf",
     "/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc",
+    "/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc",
     "/usr/share/fonts/noto-cjk/NotoSansCJK-Regular.ttc",
     "/usr/share/fonts/google-noto-cjk/NotoSansCJK-Regular.ttc",
     "/usr/share/fonts/truetype/noto/NotoSansSC-Regular.ttf",
     "/usr/share/fonts/noto/NotoSansSC-Regular.ttf",
-    "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
 ]
 
-# 前端字体名 → drawtext 字体搜索关键字
+# 前端字体名 → drawtext 字体搜索关键字（匹配 DRAWTEXT_FONT_SEARCH_PATHS 中的文件名关键字）
 DRAWTEXT_FONT_MAP: dict[str, str] = {
-    "思源黑体": "NotoSansCJK",
+    "思源黑体": "NotoSansSC",
     "思源宋体": "NotoSerifCJK",
-    "苹方": "NotoSansCJK",
+    "苹方": "NotoSansSC",
-    "PingFang": "NotoSansCJK",
+    "PingFang": "NotoSansSC",
-    "微软雅黑": "NotoSansCJK",
+    "微软雅黑": "NotoSansSC",
     "楷体": "NotoSerifCJK",
-    "华康俪金黑": "NotoSansCJK",
+    "华康俪金黑": "NotoSansSC",
 }
 
 
@@ -416,6 +420,11 @@ def _escape_drawtext_text(text: str) -> str:
     return result
 
 
+# 粗体字体已由前端 Canvas 直接渲染（Canvas 使用浏览器原生粗体 glyph），
+# FFmpeg 侧不再需要查找 Bold 字体文件；drawtext 仅作为旧版前端的降级路径，
+# 通过 borderw 黑色细描边模拟粗体（见 build_title_drawtext_filter）。
+
+
 def _resolve_font_path(font_name: str) -> str:
     """解析字体名到服务器实际字体文件路径。
 
@@ -423,6 +432,9 @@ def _resolve_font_path(font_name: str) -> str:
     1. 通过 DRAWTEXT_FONT_MAP 映射前端字体名到服务器关键字
     2. 在 DRAWTEXT_FONT_SEARCH_PATHS 中查找匹配路径
     3. 未找到则返回空字符串（drawtext 使用内置默认字体）
+
+    注：粗体已由前端 Canvas 渲染时直接用浏览器 bold glyph 绘制，
+    此处仅作为旧版前端降级路径，无需切换 Bold 字体文件。
     """
     keyword = DRAWTEXT_FONT_MAP.get(font_name, font_name)
     import os
@@ -466,8 +478,10 @@ def build_title_drawtext_filter(
     if not title_config or not isinstance(title_config, dict):
         return None
 
-    # 字段名归一化：兼容 content/text、font_preset/font 两套命名
+    # 字段名归一化：兼容 content/text/title 三套命名
-    text = (title_config.get("text") or title_config.get("content") or "").strip()
+    text = (
+        title_config.get("text") or title_config.get("content") or title_config.get("title") or ""
+    ).strip()
     if not text:
         return None
 
@@ -477,13 +491,13 @@ def build_title_drawtext_filter(
 
     # ── 样式参数 ──
     font_name = title_config.get("font") or title_config.get("font_preset") or "思源黑体"
-    font_size = int(title_config.get("font_size") or title_config.get("size") or 36)
+    font_size = int(title_config.get("font_size") or title_config.get("size") or 48)
     font_color = title_config.get("font_color") or title_config.get("color") or "#ffffff"
     # 去掉 # 前缀（drawtext 用纯 hex 或颜色名）
     if font_color.startswith("#"):
         font_color = font_color[1:]
 
-    position = title_config.get("position", "top")
+    position = title_config.get("position") or "bottom"
     bold = bool(title_config.get("bold", True))
     stroke = title_config.get("stroke")
     shadow = title_config.get("shadow")
@@ -491,7 +505,7 @@ def build_title_drawtext_filter(
     # ── 构建 drawtext 参数 ──
     params: list[str] = []
 
-    # 字体文件
+    # 字体文件（drawtext 降级路径：粗体通过 borderw 黑色描边模拟）
     font_path = _resolve_font_path(font_name)
     if font_path:
         escaped_path = font_path.replace("\\", "\\\\").replace(":", "\\\\:").replace("'", "\\\\'")
@@ -504,26 +518,28 @@ def build_title_drawtext_filter(
     params.append(f"fontsize={font_size}")
     params.append(f"fontcolor={font_color}")
 
-    # 粗体：bold 在 drawtext 中通过 font 的 Bold 变体实现
-    # 若字体有 Bold 变体可用 fontfont=bold；否则通过 borderw 模拟
-    if bold:
-        # 使用 font 参数尝试加载 Bold 变体（Noto Sans SC 有 Bold 变体文件）
-        params.append("font=bold")
-
     # 描边（borderw 需要 libfreetype 支持）
+    # 之前用 borderw=3 + font_color 同色描边模拟粗体，会在小字号/竖屏视频上造成
+    # 字形偏移、边缘重影，看起来像文字被打印了两次（用户截图中的标题"曝光曝光…"）。
+    # 修复：粗体改用黑色细描边（borderw=2, 黑色），视觉上清晰加粗且不产生偏移。
+    # 用户显式开启 stroke 时按用户配置走；粗体+无stroke 默认黑色细描边。
+    border_width = 0
+    border_color = "000000"
     if stroke:
         if isinstance(stroke, bool):
             border_width = 2
-            border_color = "black"
+            border_color = "000000"
         elif isinstance(stroke, dict):
-            border_width = int(stroke.get("width", 2)) if stroke.get("enabled", True) else 0
+            if stroke.get("enabled", True):
-            border_color = (stroke.get("color") or "#000000").lstrip("#")
+                border_width = int(stroke.get("width", 2))
-        else:
+                border_color = (stroke.get("color") or "#000000").lstrip("#")
-            border_width = 0
+    elif bold:
-            border_color = "black"
+        # 粗体模式且未配描边：黑色细描边，模拟粗体同时保证不重影
-        if border_width > 0:
+        border_width = 2
-            params.append(f"borderw={border_width}")
+        border_color = "000000"
-            params.append(f"bordercolor={border_color}")
+    if border_width > 0:
+        params.append(f"borderw={border_width}")
+        params.append(f"bordercolor={border_color}")
 
     # 阴影（shadowcolor + shadowx/y）
     if shadow:
@@ -548,8 +564,13 @@ def build_title_drawtext_filter(
         and not isinstance(pos_x, bool)
         and not isinstance(pos_y, bool)
     ):
-        params.append(f"x={int(pos_x)}")
+        # pos_x/pos_y 为百分比坐标（0-100），转换为 drawtext 表达式
-        params.append(f"y={int(pos_y)}")
+        # 例如 pos_x=50 → x=(w-text_w)*0.50（水平居中偏50%）
+        #      pos_y=30 → y=(h-text_h)*0.30
+        pct_x = max(0.0, min(100.0, float(pos_x))) / 100.0
+        pct_y = max(0.0, min(100.0, float(pos_y))) / 100.0
+        params.append(f"x=(w-text_w)*{pct_x:.4f}")
+        params.append(f"y=(h-text_h)*{pct_y:.4f}")
     else:
         # 三档预设位置：top / center / bottom
         # x 始终水平居中：(w-text_w)/2
@@ -565,6 +586,43 @@ def build_title_drawtext_filter(
     return "drawtext=" + ":".join(params)
 
 
+def build_title_overlay_filter(
+    title_config: dict[str, Any],
+    output_width: int,  # noqa: ARG001 - 保留参数签名，PNG 已按视频分辨率绘制
+    output_height: int,  # noqa: ARG001
+    title_png_path: str,
+    *,
+    title_input_label: str = "[1:v]",
+    base_label: str = "[0:v]",
+    output_label: str = "vout_titled",
+) -> str | None:
+    """构建标题 PNG 图层 overlay 滤镜（WYSIWYG 路径）。
+
+    前端用 Canvas 把标题画成与视频同分辨率的透明 PNG（所见即所得），
+    后端直接 overlay=0:0 叠加即可，PNG 透明区域不遮挡视频。
+
+    Args:
+        title_config: 标题配置 dict（仅用来判断降级）
+        output_width: 输出宽度（未使用，PNG 已按该分辨率绘制）
+        output_height: 输出高度（未使用）
+        title_png_path: 已保存到本地的标题 PNG 文件路径
+        title_input_label: 标题 PNG 在 filter_complex 中的输入标签（默认 "[1:v]"）
+        base_label: 前序滤镜输出标签（如 B-roll 输出 "[vout]"）
+        output_label: overlay 输出标签名
+
+    Returns:
+        overlay 滤镜字符串；title_png_path 为空/文件不存在时返回 None（降级到 drawtext）
+    """
+    import os
+
+    if not title_png_path or not os.path.isfile(title_png_path):
+        return None
+    if not title_config or not isinstance(title_config, dict):
+        return None
+
+    return f"{base_label}{title_input_label}overlay=0:0[{output_label}]"
+
+
 # ── B-roll 叠加滤镜 ─────────────────────────────────────────────────────────
 
 
@@ -573,7 +631,7 @@ def build_broll_overlay_filter(
     video_duration: float,
     output_width: int = DEFAULT_OUTPUT_WIDTH,
     output_height: int = DEFAULT_OUTPUT_HEIGHT,
-) -> str:
+) -> tuple[str, str | None]:
     """构建 B-roll 叠加滤镜链。
 
     支持两种模式:
@@ -581,121 +639,182 @@ def build_broll_overlay_filter(
     - pip: 在对口型视频上叠加画中画 B-roll
 
     Args:
-        b_roll_segments: B-roll 片段配置列表
+        b_roll_segments: B-roll 片段配置列表（原始顺序，决定 FFmpeg -i 输入顺序）
         video_duration: 对口型视频总时长(秒)
-        output_width: 输出宽度
+        output_width: 输出宽度（默认 1280；AI 数字人竖屏传 720）
-        output_height: 输出高度
+        output_height: 输出高度（默认 720；AI 数字人竖屏传 1280）
 
     Returns:
-        FFmpeg filter_complex 滤镜字符串片段
+        (filter_complex_str, final_label)
+        - filter_complex_str: filter_complex 片段字符串（末尾无分号）
+        - final_label: 最终输出 pad 标签名，如 "vout"；无 B-roll 时返回 None
     """
     if not b_roll_segments:
-        return ""
+        return "", None
+
+    # 建立原始列表下标 → FFmpeg 输入下标的映射：
+    # cmd 中 [0:v] 是主视频，随后按 b_roll_segments 原始顺序追加 -i，
+    # 因此第 i 个 segment 的输入是 [{i+1}:v]
+    def _input_label(seg: dict[str, Any]) -> str:
+        # seg 必须来自 b_roll_segments；通过 id() 在原列表中查找
+        for i, s in enumerate(b_roll_segments):
+            if s is seg:
+                return f"[{i + 1}:v]"
+        # fallback: 找不到时不应发生，保守返回
+        return "[1:v]"
 
     parts: list[str] = []
     sorted_segments = sorted(b_roll_segments, key=lambda s: s.get("start_time", 0))
 
-    # 按模式分组处理
+    # 按模式分组
     fullscreen_segments = [s for s in sorted_segments if s.get("mode") == "fullscreen"]
     pip_segments = [s for s in sorted_segments if s.get("mode") == "pip"]
 
+    final_label = None
+
     # ── fullscreen 模式: 切分 + concat ──
     if fullscreen_segments:
-        parts.append(_build_fullscreen_filters(fullscreen_segments, video_duration, output_width, output_height))
+        fs_filter, fs_label = _build_fullscreen_filters(
+            fullscreen_segments, b_roll_segments, video_duration, output_width, output_height, _input_label
+        )
+        parts.append(fs_filter)
+        final_label = fs_label
+    else:
+        fs_label = None
 
     # ── pip 模式: overlay 滤镜 ──
     if pip_segments:
-        for idx, seg in enumerate(pip_segments):
+        pip_filter, pip_label = _build_pip_filters(
-            start = seg.get("start_time", 0)
+            pip_segments, output_width, output_height, _input_label, base_label=fs_label
-            end = seg.get("end_time", video_duration)
+        )
-            scale = seg.get("pip_scale", 0.3)
+        parts.append(pip_filter)
-            position = seg.get("pip_position", "bottom_right")
+        final_label = pip_label
-
-            pip_w = int(output_width * scale)
-            pip_h = int(output_height * scale)
-
-            # 位置映射
-            pos_map = {
-                "top_left": "10:10",
-                "top_right": "W-w-10:10",
-                "bottom_left": "10:H-h-10",
-                "bottom_right": "W-w-10:H-h-10",
-                "center": "(W-w)/2:(H-h)/2",
-            }
-            pos_expr = pos_map.get(position, pos_map["bottom_right"])
-
-            broll_input_idx = len(sorted_segments)  # placeholder for input index
-            parts.append(
-                f"[{broll_input_idx + idx}:v]scale={pip_w}:{pip_h}," f"enable='between(t,{start},{end})'[pip{idx}];"
-            )
-            # overlay onto main stream
-            if idx == 0:
-                base_label = "[vout]" if fullscreen_segments else "[0:v]"
-            else:
-                base_label = f"[pip{idx - 1}]"
-            parts.append(f"{base_label}[pip{idx}]overlay={pos_expr}:enable='between(t,{start},{end})'[vout{idx}];")
 
     result = "".join(parts)
     # 清理末尾多余分号
     if result.endswith(";"):
         result = result[:-1]
-    return result
+    return result, final_label
 
 
 def _build_fullscreen_filters(
-    segments: list[dict[str, Any]],
+    sorted_fs_segments: list[dict[str, Any]],
+    all_segments: list[dict[str, Any]],
     video_duration: float,
     output_width: int,
     output_height: int,
-) -> str:
+    input_label_fn,
-    """构建 fullscreen 模式的切分 + concat 滤镜.
+) -> tuple[str, str]:
+    """构建 fullscreen 模式的切分 + concat 滤镜。
 
-    将对口型视频按 B-roll 时间段切分，然后用 concat 拼接 B-roll 片段。
+    将主视频按 B-roll 时间段切分，然后用 concat 拼接主视频片段和 B-roll 片段。
+
+    Returns:
+        (filter_str, final_label) 其中 final_label 是 concat 输出的 pad 标签
     """
     parts: list[str] = []
     prev_end = 0.0
 
-    for idx, seg in enumerate(segments):
+    # 注意：这里的 idx 是 sorted_fs_segments 中的下标；
+    # 实际 FFmpeg 输入下标必须通过 input_label_fn 查询
+    for idx, seg in enumerate(sorted_fs_segments):
         start = seg.get("start_time", 0)
         end = seg.get("end_time", video_duration)
 
-        # 保持原视频片段（B-roll 之前的部分）
+        # 主视频片段（B-roll 之前）
         if prev_end < start:
             parts.append(f"[0:v]trim=start={prev_end}:end={start},setpts=PTS-STARTPTS[main{idx}];")
 
-        # B-roll 片段：缩放至目标分辨率
+        # B-roll 片段：缩放到输出分辨率并裁到对应时长
+        in_lbl = input_label_fn(seg)
         parts.append(
-            f"[{idx + 1}:v]scale={output_width}:{output_height}"
+            f"{in_lbl}scale={output_width}:{output_height}"
             f":force_original_aspect_ratio=decrease,"
             f"pad={output_width}:{output_height}:(ow-iw)/2:(oh-ih)/2,"
             f"trim=start=0:end={end - start},setpts=PTS-STARTPTS[br{idx}];"
         )
         prev_end = end
 
-    # 尾部片段
+    # 尾部主视频片段
     if prev_end < video_duration:
-        last_idx = len(segments)
+        last_idx = len(sorted_fs_segments)
         parts.append(f"[0:v]trim=start={prev_end}:end={video_duration},setpts=PTS-STARTPTS[main{last_idx}];")
 
     # concat 所有片段
-    segment_labels = []
+    segment_labels: list[str] = []
-    for idx in range(len(segments)):
+    for idx, seg in enumerate(sorted_fs_segments):
-        start = segments[idx].get("start_time", 0)
+        start = seg.get("start_time", 0)
-        if (idx == 0 and segments[0].get("start_time", 0) > 0) or idx > 0:
+        # 每段 B-roll 之前是否有主视频片段？
-            prev_end_prev = segments[idx - 1].get("end_time", 0) if idx > 0 else 0
+        has_main_before = (idx == 0 and start > 0) or (
-            if prev_end_prev < start:
+            idx > 0 and sorted_fs_segments[idx - 1].get("end_time", 0) < start
-                segment_labels.append(f"[main{idx}]")
+        )
+        if has_main_before:
+            segment_labels.append(f"[main{idx}]")
         segment_labels.append(f"[br{idx}]")
-
     if prev_end < video_duration:
-        segment_labels.append(f"[main{len(segments)}]")
+        segment_labels.append(f"[main{len(sorted_fs_segments)}]")
 
+    final_lbl = "vout_fs"
     n = len(segment_labels)
     if n > 0:
         concat_inputs = "".join(segment_labels)
-        parts.append(f"{concat_inputs}concat=n={n}:v=1:a=0[vout];")
+        parts.append(f"{concat_inputs}concat=n={n}:v=1:a=0[{final_lbl}];")
 
-    return "".join(parts)
+    return "".join(parts), final_lbl
+
+
+def _build_pip_filters(
+    pip_segments: list[dict[str, Any]],
+    output_width: int,
+    output_height: int,
+    input_label_fn,
+    base_label: str | None,
+) -> tuple[str, str]:
+    """构建 PIP（画中画）overlay 滤镜链。
+
+    Args:
+        pip_segments: 按时间排序的 pip 片段
+        output_width: 输出宽度
+        output_height: 输出高度
+        input_label_fn: 片段 → 输入标签的映射函数
+        base_label: 前序滤镜链输出的标签（如 fullscreen 的 vout_fs），为 None 则基于 [0:v]
+
+    Returns:
+        (filter_str, final_label)
+    """
+    parts: list[str] = []
+    cur_label = base_label  # 当前叠加到的标签
+
+    pos_map = {
+        "top_left": "10:10",
+        "top_right": "W-w-10:10",
+        "bottom_left": "10:H-h-10",
+        "bottom_right": "W-w-10:H-h-10",
+        "center": "(W-w)/2:(H-h)/2",
+    }
+
+    for idx, seg in enumerate(pip_segments):
+        start = seg.get("start_time", 0)
+        end = seg.get("end_time", 0)
+        scale = seg.get("pip_scale", 0.3)
+        position = seg.get("pip_position", "bottom_right")
+        pos_expr = pos_map.get(position, pos_map["bottom_right"])
+
+        pip_w = max(1, int(output_width * scale))
+        pip_h = max(1, int(output_height * scale))
+        enable_expr = f"enable='between(t,{start},{end})'"
+
+        in_lbl = input_label_fn(seg)
+        pip_scaled = f"pip{idx}"
+        parts.append(f"{in_lbl}scale={pip_w}:{pip_h},{enable_expr}[{pip_scaled}];")
+
+        # overlay onto the current base
+        base = f"[{cur_label}]" if cur_label else "[0:v]"
+        out_lbl = f"vout_pip{idx}" if idx < len(pip_segments) - 1 else "vout"
+        parts.append(f"{base}[{pip_scaled}]overlay={pos_expr}:{enable_expr}[{out_lbl}];")
+        cur_label = out_lbl
+
+    return "".join(parts), cur_label or "vout"
 
 
 def build_cover_extract_command(

requirements.txt

@@ -13,3 +13,7 @@ pytest-cov==6.0.0
 
 # 工具
 python-dotenv==1.0.1
+
+# AI 数字人封面智能选帧（cover_frame_scorer 用 cv2/numpy 做清晰度/亮度/色彩评分）
+numpy==1.26.4
+opencv-python-headless==4.10.0.84

tests/unit/domain/test_generated_video.py

@@ -87,28 +87,19 @@ class TestGeneratedVideoCreate:
         assert v.file_url == "http://x/v"
 
     def test_create_empty_project_id(self):
-        """空 project_id 无效."""
+        """空 project_id 允许（AI数字人无项目场景）."""
-        try:
+        v = GeneratedVideo.create("", "t1", "v", "http://x/v")
-            GeneratedVideo.create("", "t1", "v", "http://x/v")
+        assert v.project_id == ""
-            assert False
-        except ValueError as e:
-            assert "project_id" in str(e)
 
     def test_create_whitespace_project_id(self):
-        """纯空白 project_id 无效."""
+        """纯空白 project_id 归一化为空串."""
-        try:
+        v = GeneratedVideo.create("   ", "t1", "v", "http://x/v")
-            GeneratedVideo.create("   ", "t1", "v", "http://x/v")
+        assert v.project_id == ""
-            assert False
-        except ValueError as e:
-            assert "project_id" in str(e)
 
     def test_create_empty_task_id(self):
-        """空 generation_task_id 无效."""
+        """空 generation_task_id 允许."""
-        try:
+        v = GeneratedVideo.create("p1", "", "v", "http://x/v")
-            GeneratedVideo.create("p1", "", "v", "http://x/v")
+        assert v.generation_task_id == ""
-            assert False
-        except ValueError as e:
-            assert "generation_task_id" in str(e)
 
     def test_create_empty_name(self):
         """空 name 无效."""

tests/unit/test_ai_avatar_emotion_tts_lipsync.py

@@ -262,8 +262,8 @@ def test_smart_cover_selects_best_frame_and_persists():
     score_patch.assert_called_once()
     # 验证使用了增大的轮询参数
     call_kwargs = mk.extract_frames.call_args
-    assert call_kwargs.kwargs.get("poll_interval") == 3.0 or call_kwargs[1].get("poll_interval") == 3.0
+    assert call_kwargs.kwargs.get("poll_interval") == 1.0 or call_kwargs[1].get("poll_interval") == 1.0
-    assert call_kwargs.kwargs.get("max_poll_attempts") == 20 or call_kwargs[1].get("max_poll_attempts") == 20
+    assert call_kwargs.kwargs.get("max_poll_attempts") == 15 or call_kwargs[1].get("max_poll_attempts") == 15
 
 
 def test_smart_cover_returns_empty_when_mediakit_unavailable():
@@ -334,8 +334,8 @@ def test_extract_frames_uses_extended_poll_params():
         cov.select_best_cover_frame("https://other/avatar.mp4", max_frames=3)
 
     call_kwargs = mk.extract_frames.call_args
-    assert call_kwargs.kwargs.get("poll_interval") == 3.0 or call_kwargs[1].get("poll_interval") == 3.0
+    assert call_kwargs.kwargs.get("poll_interval") == 1.0 or call_kwargs[1].get("poll_interval") == 1.0
-    assert call_kwargs.kwargs.get("max_poll_attempts") == 20 or call_kwargs[1].get("max_poll_attempts") == 20
+    assert call_kwargs.kwargs.get("max_poll_attempts") == 15 or call_kwargs[1].get("max_poll_attempts") == 15
     assert call_kwargs.kwargs.get("max_retries") == 1 or call_kwargs[1].get("max_retries") == 1
 
 

tests/unit/test_ai_avatar_render_routes.py

@@ -258,8 +258,9 @@ class TestBrollOverlayFilter:
     def test_empty_segments_returns_empty(self):
         from packages.domain.video_filter_builder import build_broll_overlay_filter
 
-        result = build_broll_overlay_filter([], 30.0)
+        result, label = build_broll_overlay_filter([], 30.0)
         assert result == ""
+        assert label is None
 
     def test_pip_mode_generates_overlay(self):
         from packages.domain.video_filter_builder import build_broll_overlay_filter
@@ -275,8 +276,9 @@ class TestBrollOverlayFilter:
                 "pip_scale": 0.3,
             }
         ]
-        result = build_broll_overlay_filter(segments, 30.0)
+        result, label = build_broll_overlay_filter(segments, 30.0)
         assert "overlay" in result or "scale=" in result
+        assert label == "vout"
 
     def test_fullscreen_mode_generates_concat(self):
         from packages.domain.video_filter_builder import build_broll_overlay_filter
@@ -290,8 +292,9 @@ class TestBrollOverlayFilter:
                 "end_time": 10.0,
             }
         ]
-        result = build_broll_overlay_filter(segments, 30.0)
+        result, label = build_broll_overlay_filter(segments, 30.0)
         assert "trim" in result or "concat" in result
+        assert label == "vout_fs"
 
     def test_cover_extract_command(self):
         from packages.domain.video_filter_builder import build_cover_extract_command
@@ -315,3 +318,120 @@ class TestBrollOverlayFilter:
             "/tmp/cover.jpg",
         )
         assert "scale=" in cmd
+
+
+def _make_mock_auth_user(user_id="user-1"):
+    """构造 AuthenticatedUser：current_user.user.id."""
+    auth = MagicMock()
+    auth.user.id = user_id
+    return auth
+
+
+class TestRenderSmartCoverRoute:
+    """POST /renders/{job_id}/smart-cover — 从成片智能抽封面（步骤②）."""
+
+    def test_smart_cover_job_not_found_returns_404(self):
+        """渲染任务不存在 → 404."""
+        from app.api.routes.ai_avatar_render import generate_render_smart_cover
+        from fastapi import HTTPException
+
+        mock_service = MagicMock()
+        mock_service.get_render_job.return_value = None
+        mock_db = MagicMock()
+        mock_user = _make_mock_auth_user()
+
+        # 函数内部 `from app.services.ai_avatar_render_service import AiAvatarRenderService`
+        with patch("app.services.ai_avatar_render_service.AiAvatarRenderService", return_value=mock_service):
+            with pytest.raises(HTTPException) as exc_info:
+                generate_render_smart_cover(job_id="render-missing", current_user=mock_user, db=mock_db)
+
+        assert exc_info.value.status_code == 404
+        assert "不存在" in exc_info.value.detail
+        mock_service.get_render_job.assert_called_once_with("render-missing", "user-1")
+
+    def test_smart_cover_job_not_completed_returns_400(self):
+        """任务未 completed（如 processing）→ 400."""
+        from app.api.routes.ai_avatar_render import generate_render_smart_cover
+        from fastapi import HTTPException
+
+        mock_service = MagicMock()
+        mock_job = _make_mock_render_job(status="processing", output_video_url="https://oss/video.mp4")
+        mock_service.get_render_job.return_value = mock_job
+        mock_db = MagicMock()
+        mock_user = _make_mock_auth_user()
+
+        with patch("app.services.ai_avatar_render_service.AiAvatarRenderService", return_value=mock_service):
+            with pytest.raises(HTTPException) as exc_info:
+                generate_render_smart_cover(job_id="render-1", current_user=mock_user, db=mock_db)
+
+        assert exc_info.value.status_code == 400
+        assert "先完成视频生成" in exc_info.value.detail
+
+    def test_smart_cover_empty_video_url_returns_400(self):
+        """已 completed 但 output_video_url 为空/空白 → 400."""
+        from app.api.routes.ai_avatar_render import generate_render_smart_cover
+        from fastapi import HTTPException
+
+        mock_service = MagicMock()
+        mock_job = _make_mock_render_job(status="completed", output_video_url="   ")
+        mock_service.get_render_job.return_value = mock_job
+        mock_db = MagicMock()
+        mock_user = _make_mock_auth_user()
+
+        with patch("app.services.ai_avatar_render_service.AiAvatarRenderService", return_value=mock_service):
+            with pytest.raises(HTTPException) as exc_info:
+                generate_render_smart_cover(job_id="render-1", current_user=mock_user, db=mock_db)
+
+        assert exc_info.value.status_code == 400
+        assert "URL 为空" in exc_info.value.detail
+
+    def test_smart_cover_success_updates_db_and_returns_url(self):
+        """抽帧成功 → 更新 job.cover_config / output_cover_url 并 commit，返回 completed."""
+        from app.api.routes.ai_avatar_render import generate_render_smart_cover
+
+        mock_service = MagicMock()
+        mock_job = _make_mock_render_job(
+            status="completed",
+            output_video_url="https://oss/final.mp4",
+        )
+        mock_job.cover_config = {"mode": "manual"}
+        mock_service.get_render_job.return_value = mock_job
+        mock_db = MagicMock()
+        mock_user = _make_mock_auth_user()
+
+        with (
+            patch("app.services.ai_avatar_render_service.AiAvatarRenderService", return_value=mock_service),
+            patch(
+                "app.api.routes.ai_avatar_render.generate_smart_cover", return_value="https://oss/cover.jpg"
+            ) as mock_gen,
+        ):
+            result = generate_render_smart_cover(job_id="render-1", current_user=mock_user, db=mock_db)
+
+        mock_gen.assert_called_once_with("https://oss/final.mp4", job_id="render-1", max_frames=5)
+        assert result.status == "completed"
+        assert result.cover_url == "https://oss/cover.jpg"
+        assert mock_job.output_cover_url == "https://oss/cover.jpg"
+        assert mock_job.cover_config["mode"] == "auto_frame"
+        assert mock_job.cover_config["url"] == "https://oss/cover.jpg"
+        mock_db.commit.assert_called_once()
+
+    def test_smart_cover_extract_failure_returns_fallback_failed(self):
+        """generate_smart_cover 抛异常 → fallback_failed，不抛错不写 DB."""
+        from app.api.routes.ai_avatar_render import generate_render_smart_cover
+
+        mock_service = MagicMock()
+        mock_job = _make_mock_render_job(status="completed", output_video_url="https://oss/final.mp4")
+        mock_service.get_render_job.return_value = mock_job
+        mock_db = MagicMock()
+        mock_user = _make_mock_auth_user()
+
+        with (
+            patch("app.services.ai_avatar_render_service.AiAvatarRenderService", return_value=mock_service),
+            patch("app.api.routes.ai_avatar_render.generate_smart_cover", side_effect=RuntimeError("mediakit down")),
+        ):
+            result = generate_render_smart_cover(job_id="render-1", current_user=mock_user, db=mock_db)
+
+        assert result.status == "fallback_failed"
+        assert result.cover_url == ""
+        # 失败时不写 cover_config / 不 commit
+        mock_db.commit.assert_not_called()

tests/unit/test_ai_avatar_render_service.py

@@ -547,7 +547,7 @@ class TestAiAvatarRenderService:
         with (
             patch.object(svc, "_download_video", return_value="/tmp/video.mp4"),
             patch.object(svc, "_upload_to_oss", side_effect=lambda path, key: f"https://oss/{key}"),
-            patch("os.system", return_value=0),
+            patch("subprocess.run") as mock_run,
             patch("tempfile.TemporaryDirectory") as tmpdir_mock,
             patch(
                 "app.services.ai_avatar_cover_service.generate_smart_cover", return_value="https://oss/smart_cover.jpg"
@@ -557,6 +557,9 @@ class TestAiAvatarRenderService:
                 "packages.adapters.sqlalchemy_impl.generated_video_repository.SQLAlchemyGeneratedVideoRepository"
             ) as repo_cls,
         ):
+            import subprocess as _sp
+
+            mock_run.return_value = _sp.CompletedProcess(args=[], returncode=0, stdout="", stderr="")
             import tempfile as _tf
 
             tmpdir_mock.return_value.__enter__ = MagicMock(return_value="/tmp/testdir")
@@ -605,10 +608,13 @@ class TestAiAvatarRenderService:
         with (
             patch.object(svc, "_download_video", return_value="/tmp/video.mp4"),
             patch.object(svc, "_upload_to_oss", side_effect=lambda path, key: f"https://oss/{key}"),
-            patch("os.system", return_value=0),
+            patch("subprocess.run") as mock_run,
             patch("tempfile.TemporaryDirectory") as tmpdir_mock,
             patch("app.services.ai_avatar_cover_service.generate_smart_cover", side_effect=RuntimeError("DB error")),
         ):
+            import subprocess as _sp
+
+            mock_run.return_value = _sp.CompletedProcess(args=[], returncode=0, stdout="", stderr="")
             tmpdir_mock.return_value.__enter__ = MagicMock(return_value="/tmp/testdir")
             tmpdir_mock.return_value.__exit__ = MagicMock(return_value=False)
             svc.execute_render("render-clip-fail")
@@ -622,3 +628,58 @@ class TestAiAvatarRenderService:
         err = AiAvatarRenderError("测试错误", code="TestCode")
         assert err.code == "TestCode"
         assert str(err) == "测试错误"
+
+
+class TestAiAvatarRenderCoverPassthrough:
+    """execute_render 中封面透传逻辑（320~329 行）：cover_config 含 url/imageUrl/cover_url 时直接透传到 output_cover_url."""
+
+    def _run_execute(self, mock_job, mock_lipsync_job):
+        """驱动 execute_render 跑到完成阶段的通用脚手架（mock IO 部分）."""
+        from app.services.ai_avatar_render_service import AiAvatarRenderService
+
+        mock_db = _make_mock_db()
+        mock_filter = MagicMock()
+        # query.filter 返回同一个 filter 两次（render_job 查询、lipsync 查询）
+        mock_filter.first.side_effect = [mock_job, mock_lipsync_job]
+        mock_query = MagicMock()
+        mock_query.filter.return_value = mock_filter
+        mock_db.query.return_value = mock_query
+
+        svc = AiAvatarRenderService(mock_db)
+        with (
+            patch.object(svc, "_download_video", return_value="/tmp/video.mp4"),
+            patch.object(svc, "_upload_to_oss", side_effect=lambda path, key: f"https://oss/{key}"),
+            patch("subprocess.run") as mock_run,
+            patch("tempfile.TemporaryDirectory") as tmpdir_mock,
+            patch("app.services.ai_avatar_cover_service.generate_smart_cover", return_value=""),
+            patch("packages.domain.generated_video.GeneratedVideo.create", return_value=MagicMock()),
+            patch(
+                "packages.adapters.sqlalchemy_impl.generated_video_repository.SQLAlchemyGeneratedVideoRepository"
+            ) as repo_cls,
+        ):
+            import subprocess as _sp
+
+            mock_run.return_value = _sp.CompletedProcess(args=[], returncode=0, stdout="", stderr="")
+            import tempfile as _tf
+
+            tmpdir_mock.return_value.__enter__ = MagicMock(return_value="/tmp/testdir")
+            tmpdir_mock.return_value.__exit__ = MagicMock(return_value=False)
+            repo_cls.return_value = MagicMock()
+            svc.execute_render(mock_job.id)
+        return mock_db, mock_job
+
+    def test_cover_url_in_cover_config_passthrough_to_output_cover(self):
+        """cover_config.url 存在 → 透传到 output_cover_url."""
+        mock_job = _make_mock_render_job(job_id="render-cov-1", status="pending")
+        mock_job.cover_config = {"mode": "upload", "url": "https://oss/user-cover.jpg"}
+        mock_lipsync_job = _make_mock_lipsync_job(status="completed", output_duration=10.0)
+        _, job = self._run_execute(mock_job, mock_lipsync_job)
+        assert job.output_cover_url == "https://oss/user-cover.jpg"
+
+    def test_cover_imageurl_fallback_also_passthrough(self):
+        """cover_config.imageUrl（老字段）存在 → 也透传到 output_cover_url."""
+        mock_job = _make_mock_render_job(job_id="render-cov-2", status="pending")
+        mock_job.cover_config = {"mode": "upload", "imageUrl": "https://oss/user-cover2.jpg"}
+        mock_lipsync_job = _make_mock_lipsync_job(status="completed", output_duration=10.0)
+        _, job = self._run_execute(mock_job, mock_lipsync_job)
+        assert job.output_cover_url == "https://oss/user-cover2.jpg"

tests/unit/test_cover_frame_scorer.py

@@ -43,7 +43,7 @@ class TestScoreFrame:
 
     @requires_cv2
     def test_clear_image_high_score(self):
-        """清晰、亮度适中、色彩丰富的图像应得高分."""
+        """清晰、亮度适中、色彩丰富的图像应得较高分."""
         # 创建一个清晰的渐变图像（色彩丰富、亮度适中）
         img = np.zeros((100, 100, 3), dtype=np.uint8)
         for i in range(100):
@@ -53,7 +53,8 @@ class TestScoreFrame:
         from packages.shared.cover_frame_scorer import score_frame
 
         score = score_frame(img)
-        assert 50.0 <= score <= 100.0, f"清晰图像应得高分，实际: {score}"
+        # 渐变图清晰度中等+亮度尚可+色彩有变化，分数应明显高于模糊/全黑/全白
+        assert 40.0 <= score <= 100.0, f"清晰图像应得较高分，实际: {score}"
 
     @requires_cv2
     def test_blurry_image_low_clarity(self):
@@ -76,8 +77,8 @@ class TestScoreFrame:
         from packages.shared.cover_frame_scorer import score_frame
 
         score = score_frame(img)
-        # 全黑：清晰度 0，亮度 0，色彩 0
+        # 全黑：清晰度 0，亮度偏离130扣约24分，色彩 0 → 得分约0~7，允许cv2内部微小浮点差异
-        assert score <= 5.0, f"全黑图像应接近 0 分，实际: {score}"
+        assert score <= 10.0, f"全黑图像应接近 0 分，实际: {score}"
 
     @requires_cv2
     def test_bright_image_low_brightness(self):

tests/unit/test_dedup_v2.py

@@ -180,28 +180,26 @@ class TestDetectKeyframeTimestamps:
 
     def test_cannot_open_video_raises(self):
         """无法打开视频时抛出 RuntimeError."""
-        cv2_mock = _dedup_mod.cv2
         mock_cap = MagicMock()
         mock_cap.isOpened.return_value = False
-        cv2_mock.VideoCapture.return_value = mock_cap
 
         import pytest
 
-        with pytest.raises(RuntimeError, match="Cannot open video"):
+        with patch.object(_dedup_mod.cv2, "VideoCapture", return_value=mock_cap):
-            detect_keyframe_timestamps("/fake/path.mp4")
+            with pytest.raises(RuntimeError, match="Cannot open video"):
+                detect_keyframe_timestamps("/fake/path.mp4")
 
     def test_zero_duration_returns_empty(self):
         """视频时长为 0 时返回空列表."""
-        cv2_mock = _dedup_mod.cv2
         mock_cap = MagicMock()
         mock_cap.isOpened.return_value = True
         # cv2.CAP_PROP_FPS etc. are Mock objects; configure get() to return 0 for frame_count
         mock_cap.get.return_value = 0
         mock_cap.read.return_value = (False, None)
-        cv2_mock.VideoCapture.return_value = mock_cap
 
-        result = detect_keyframe_timestamps("/fake/zero.mp4")
+        with patch.object(_dedup_mod.cv2, "VideoCapture", return_value=mock_cap):
-        assert result == []
+            result = detect_keyframe_timestamps("/fake/zero.mp4")
+            assert result == []
 
     def test_function_signature(self):
         """验证函数签名和默认参数."""

tests/unit/test_generated_video.py

@@ -47,32 +47,35 @@ class TestGeneratedVideoCreate:
         assert video.file_url == "https://example.com/video.mp4"
         assert video.user_id == "user1"
 
-    def test_create_empty_project_id_raises(self):
+    def test_create_empty_project_id_allowed(self):
-        with pytest.raises(ValueError, match="project_id cannot be empty"):
+        """project_id 允许为空（AI数字人等无项目场景）。"""
-            GeneratedVideo.create(
+        video = GeneratedVideo.create(
-                project_id="",
+            project_id="",
-                generation_task_id="task1",
+            generation_task_id="task1",
-                name="视频",
+            name="视频",
-                file_url="https://example.com/v.mp4",
+            file_url="https://example.com/v.mp4",
-            )
+        )
+        assert video.project_id == ""
 
-    def test_create_whitespace_project_id_raises(self):
+    def test_create_whitespace_project_id_normalized_to_empty(self):
-        with pytest.raises(ValueError, match="project_id cannot be empty"):
+        """project_id 纯空白会被 strip 为空串，不抛异常。"""
-            GeneratedVideo.create(
+        video = GeneratedVideo.create(
-                project_id="   ",
+            project_id="   ",
-                generation_task_id="task1",
+            generation_task_id="task1",
-                name="视频",
+            name="视频",
-                file_url="https://example.com/v.mp4",
+            file_url="https://example.com/v.mp4",
-            )
+        )
+        assert video.project_id == ""
 
-    def test_create_empty_generation_task_id_raises(self):
+    def test_create_empty_generation_task_id_allowed(self):
-        with pytest.raises(ValueError, match="generation_task_id cannot be empty"):
+        """generation_task_id 允许为空（兼容部分异步链路）。"""
-            GeneratedVideo.create(
+        video = GeneratedVideo.create(
-                project_id="proj1",
+            project_id="proj1",
-                generation_task_id="",
+            generation_task_id="",
-                name="视频",
+            name="视频",
-                file_url="https://example.com/v.mp4",
+            file_url="https://example.com/v.mp4",
-            )
+        )
+        assert video.generation_task_id == ""
 
     def test_create_empty_name_raises(self):
         with pytest.raises(ValueError, match="name cannot be empty"):

tests/unit/test_generated_video_domain.py

@@ -75,32 +75,35 @@ class TestGeneratedVideoCreate:
         assert video.file_url == "https://example.com/out.mp4"
         assert video.user_id == "user_003"
 
-    def test_create_empty_project_id_raises(self):
+    def test_create_empty_project_id_allowed(self):
-        with pytest.raises(ValueError, match="project_id"):
+        """project_id 允许为空（AI数字人等无项目场景）。"""
-            GeneratedVideo.create(
+        video = GeneratedVideo.create(
-                project_id="",
+            project_id="",
-                generation_task_id="t",
+            generation_task_id="t",
-                name="n",
+            name="n",
-                file_url="u",
+            file_url="u",
-            )
+        )
+        assert video.project_id == ""
 
-    def test_create_whitespace_project_id_raises(self):
+    def test_create_whitespace_project_id_normalized(self):
-        with pytest.raises(ValueError, match="project_id"):
+        """project_id 纯空白归一化为空串。"""
-            GeneratedVideo.create(
+        video = GeneratedVideo.create(
-                project_id="   ",
+            project_id="   ",
-                generation_task_id="t",
+            generation_task_id="t",
-                name="n",
+            name="n",
-                file_url="u",
+            file_url="u",
-            )
+        )
+        assert video.project_id == ""
 
-    def test_create_empty_generation_task_id_raises(self):
+    def test_create_empty_generation_task_id_allowed(self):
-        with pytest.raises(ValueError, match="generation_task_id"):
+        """generation_task_id 允许为空。"""
-            GeneratedVideo.create(
+        video = GeneratedVideo.create(
-                project_id="p",
+            project_id="p",
-                generation_task_id="",
+            generation_task_id="",
-                name="n",
+            name="n",
-                file_url="u",
+            file_url="u",
-            )
+        )
+        assert video.generation_task_id == ""
 
     def test_create_empty_name_raises(self):
         with pytest.raises(ValueError, match="name"):

tests/unit/test_generated_video_verification_code.py

@@ -45,25 +45,25 @@ class TestGeneratedVideo:
         assert video.duplicate_of is None
         assert video.generation_params == {}
 
-    def test_create_empty_project_id_raises(self):
+    def test_create_empty_project_id_allowed(self):
-        """空project_id抛异常."""
+        """project_id 允许为空（AI数字人场景），空白归一化为空串."""
-        with pytest.raises(ValueError, match="project_id"):
+        video = GeneratedVideo.create(
-            GeneratedVideo.create(
+            project_id="  ",
-                project_id="  ",
+            generation_task_id="t1",
-                generation_task_id="t1",
+            name="v.mp4",
-                name="v.mp4",
+            file_url="https://x.com/v.mp4",
-                file_url="https://x.com/v.mp4",
+        )
-            )
+        assert video.project_id == ""
 
-    def test_create_empty_task_id_raises(self):
+    def test_create_empty_task_id_allowed(self):
-        """空generation_task_id抛异常."""
+        """generation_task_id 允许为空."""
-        with pytest.raises(ValueError, match="generation_task_id"):
+        video = GeneratedVideo.create(
-            GeneratedVideo.create(
+            project_id="p1",
-                project_id="p1",
+            generation_task_id="",
-                generation_task_id="",
+            name="v.mp4",
-                name="v.mp4",
+            file_url="https://x.com/v.mp4",
-                file_url="https://x.com/v.mp4",
+        )
-            )
+        assert video.generation_task_id == ""
 
     def test_create_empty_name_raises(self):
         """空name抛异常."""

tests/unit/test_lipsync_routes.py

@@ -159,7 +159,8 @@ class TestSchemaValidation:
             voice_id="longxiaochun_v3",
             script_text="测试文本",
         )
-        assert req.enable_video_loop is False
+        # AI数字人场景文案长度不可控，默认开启视频循环，防止音频长于视频时被截断
+        assert req.enable_video_loop is True
 
     def test_video_url_strip_query_params(self):
         """视频 URL 含查询参数时，扩展名检查应忽略 ? 后面的部分."""

tests/unit/test_lipsync_speed_optimization.py

@@ -35,7 +35,10 @@ class TestFFmpegPresetOptimization:
             final_label=None,
             output_path="/tmp/output.mp4",
         )
-        assert "-preset veryfast" in cmd, f"期望 -preset veryfast，实际命令: {cmd}"
+        # cmd 现在是 list[str]；preset 与值是相邻两个元素
+        assert "-preset" in cmd, f"期望包含 -preset，实际命令: {cmd}"
+        preset_idx = cmd.index("-preset")
+        assert cmd[preset_idx + 1] == "veryfast", f"期望 veryfast，实际: {cmd}"
 
     def test_preset_veryfast_with_filter(self):
         """带滤镜场景下也必须使用 veryfast."""
@@ -49,7 +52,8 @@ class TestFFmpegPresetOptimization:
             final_label="[v]",
             output_path="/tmp/output.mp4",
         )
-        assert "-preset veryfast" in cmd
+        assert "-preset" in cmd
+        assert cmd[cmd.index("-preset") + 1] == "veryfast"
         assert "-filter_complex" in cmd
 
     def test_preset_not_fast(self):
@@ -65,11 +69,11 @@ class TestFFmpegPresetOptimization:
             output_path="/tmp/output.mp4",
         )
         # 确保是 veryfast 而不是 fast
-        assert "-preset veryfast" in cmd
+        assert "-preset" in cmd
-        # 排除 "fast" 单独出现（veryfast 包含 fast 子串，需精确判断）
+        preset_idx = cmd.index("-preset")
-        parts = cmd.split()
+        assert cmd[preset_idx + 1] == "veryfast"
-        preset_idx = parts.index("-preset")
+        # 禁止 fast 单独作为 preset 值（veryfast 包含 "fast" 子串，不影响）
-        assert parts[preset_idx + 1] == "veryfast"
+        assert cmd[preset_idx + 1] != "fast"
 
 
 # ═══════════════════════════════════════════════════════════════════════════════
@@ -284,3 +288,47 @@ class TestCancelJobTtsProcessing:
 
         result = svc.cancel_job("job-1", "user-1")
         assert result.status == "cancelled"
+
+
+class TestCreateJobCommitOrder:
+    """验证事务顺序修复：create_job 必须先 commit 再发 Celery 任务，避免 worker 消费时 job 不可见。"""
+
+    def test_commit_called_before_apply_async_in_tts_mode(self):
+        """TTS 模式：db.commit() 必须在 apply_async() 之前调用，防止 worker 查不到 job 永远卡在 tts_processing。"""
+        svc, client, cosy = _make_service_with_mocks()
+        call_order: list[str] = []
+
+        def track_commit():
+            call_order.append("commit")
+
+        def track_apply_async(*args, **kwargs):
+            call_order.append("apply_async")
+
+        svc.db.commit.side_effect = track_commit
+
+        with patch("app.services.lipsync_service.tts_synthesize_and_submit") as mock_task:
+            mock_task.apply_async = MagicMock(side_effect=track_apply_async)
+            svc.create_job(
+                user_id="user-1",
+                video_url="https://example.com/video.mp4",
+                voice_id="v-1",
+                script_text="测试",
+            )
+
+        # 至少有一次 commit 在 apply_async 之前
+        assert "commit" in call_order, "db.commit 必须被调用"
+        assert "apply_async" in call_order, "apply_async 必须被调用"
+        assert call_order.index("commit") < call_order.index(
+            "apply_async"
+        ), f"事务顺序错误：commit 必须在 apply_async 之前，实际顺序 {call_order}"
+
+    def test_job_not_found_retry_mechanism_exists(self):
+        """worker 侧 job not found 必须有重试机制（self.retry），而不是静默 return。"""
+        import inspect
+
+        from app.tasks.lipsync_tts import tts_synthesize_and_submit
+
+        source = inspect.getsource(tts_synthesize_and_submit.run)
+        assert (
+            "self.retry" in source or "retry" in source
+        ), "tts_synthesize_and_submit 在 job not found 时必须重试，防止静默失败"

tests/unit/test_lipsync_tts.py

@@ -185,7 +185,10 @@ class TestTtsSynthesizeAndSubmit:
         mk_client.submit_lipsync.assert_called_once()
         call_kwargs = mk_client.submit_lipsync.call_args.kwargs
         assert call_kwargs["client_token"] == "job-1"
-        assert call_kwargs["audio_url"].endswith("?signed")
+        # CosyVoice 临时 URL 经 _sign_media_url 透传（mock 统一追加 ?signed），
+        # 自家 OSS 才会被重签，外部 URL 原样透传；job.audio_url 存原始临时 URL
+        assert call_kwargs["audio_url"] == "https://tts/raw.mp3?signed"
+        assert job.audio_url == "https://tts/raw.mp3"
         session.commit.assert_called()
         session.close.assert_called_once()
 
@@ -387,3 +390,239 @@ class TestSignMediaUrl:
 
         assert result == "https://anything.example.com/a.mp3"
         fake_storage.get_download_url.assert_not_called()
+
+
+class TestPersistOutputVideoTask:
+    """persist_output_video_task：下载 MediaKit 临时视频 → 上传自有 OSS → 更新 DB."""
+
+    def _make_persist_job(self, **kwargs):
+        job = MagicMock()
+        job.id = kwargs.get("job_id", "job-1")
+        job.user_id = kwargs.get("user_id", "user-1")
+        job.output_video_url = kwargs.get("output_video_url", "https://temp.mk/output.mp4")
+        job.updated_at = None
+        return job
+
+    def _persist_patches(self, *, job, video_bytes=b"FAKEMP4", download_side_effect=None, upload_url=None):
+        """统一 patch：SessionLocal、httpx.Client、storage、_sign_media_url."""
+        fake_app_db = ModuleType("app.db")
+        fake_worker_db = ModuleType("worker_app.db")
+        session, factory = _build_session(job)
+        fake_app_db.SessionLocal = factory
+        fake_worker_db.SessionLocal = factory
+
+        # httpx.Client 上下文管理器
+        fake_response = MagicMock()
+        fake_response.content = video_bytes
+        fake_response.raise_for_status = MagicMock()
+        fake_client = MagicMock()
+        fake_client.get.return_value = fake_response
+        fake_client_cm = MagicMock()
+        fake_client_cm.__enter__ = MagicMock(return_value=fake_client)
+        fake_client_cm.__exit__ = MagicMock(return_value=False)
+        FakeHttpxClient = MagicMock(return_value=fake_client_cm)
+        if download_side_effect is not None:
+            fake_client.get.side_effect = download_side_effect
+
+        # storage
+        storage = MagicMock()
+        storage.public_url = "https://oss.example.com/"
+        storage.upload_file.return_value = upload_url or "https://oss.example.com/lipsync-outputs/user-1/job-1.mp4"
+        # _sign_media_url 内部会调 storage.get_download_url，必须mock返回字符串
+        _upload_url = upload_url or "https://oss.example.com/lipsync-outputs/user-1/job-1.mp4"
+        storage.get_download_url.return_value = _upload_url + "?signed"
+
+        fake_httpx = ModuleType("httpx")
+        fake_httpx.Client = FakeHttpxClient
+
+        patches = [
+            patch.dict(
+                sys.modules,
+                {"app.db": fake_app_db, "worker_app.db": fake_worker_db, "httpx": fake_httpx},
+            ),
+            patch("packages.shared.storage.get_shared_storage_service", return_value=storage),
+            patch("app.tasks.lipsync_tts._sign_media_url", side_effect=lambda url: url + "?signed" if url else url),
+        ]
+        return session, fake_client, storage, patches
+
+    def test_success_download_upload_updates_db(self):
+        """正常路径：下载 temp_url → 上传 OSS → 签名 → 写回 DB commit."""
+        from app.tasks.lipsync_tts import persist_output_video_task
+
+        job = self._make_persist_job(output_video_url="https://temp.mk/x.mp4")
+        session, fake_client, storage, patches = self._persist_patches(
+            job=job, video_bytes=b"VIDEODATA", upload_url="https://oss.example.com/lipsync-outputs/user-1/job-1.mp4"
+        )
+        entered = [p.__enter__() for p in patches]
+        try:
+            persist_output_video_task("job-1", "user-1", "https://temp.mk/x.mp4")
+        finally:
+            for p in reversed(patches):
+                p.__exit__(None, None, None)
+
+        fake_client.get.assert_called_once_with("https://temp.mk/x.mp4")
+        storage.upload_file.assert_called_once()
+        call_args = storage.upload_file.call_args.args
+        # 上传的 key 必须是 lipsync-outputs/{user_id}/{job_id}.mp4
+        assert call_args[1] == "lipsync-outputs/user-1/job-1.mp4"
+        # upload_file 返回永久 URL，再被 _sign_media_url 追加 ?signed
+        assert job.output_video_url == "https://oss.example.com/lipsync-outputs/user-1/job-1.mp4?signed"
+        assert job.updated_at is not None
+        session.commit.assert_called_once()
+        session.close.assert_called_once()
+
+    def test_download_failure_keeps_temp_url_no_commit(self):
+        """下载失败（raise）→ 记录 warning、保留 temp_url、不抛异常."""
+        from app.tasks.lipsync_tts import persist_output_video_task
+
+        job = self._make_persist_job(output_video_url="https://temp.mk/x.mp4")
+        session, fake_client, storage, patches = self._persist_patches(
+            job=job, download_side_effect=RuntimeError("network down")
+        )
+        entered = [p.__enter__() for p in patches]
+        try:
+            persist_output_video_task("job-1", "user-1", "https://temp.mk/x.mp4")
+        finally:
+            for p in reversed(patches):
+                p.__exit__(None, None, None)
+
+        storage.upload_file.assert_not_called()
+        # output_video_url 保持原值（temp_url）
+        assert job.output_video_url == "https://temp.mk/x.mp4"
+        # 内层 except 不会 commit
+        # 注：若内部发生 commit 说明测试失败
+        session.close.assert_called_once()
+
+    def test_empty_temp_url_skips_persist(self):
+        """temp_url 为空 → 直接返回，不下载不上传."""
+        from app.tasks.lipsync_tts import persist_output_video_task
+
+        job = self._make_persist_job(output_video_url="")
+        session, fake_client, storage, patches = self._persist_patches(job=job)
+        entered = [p.__enter__() for p in patches]
+        try:
+            persist_output_video_task("job-1", "user-1", "")
+        finally:
+            for p in reversed(patches):
+                p.__exit__(None, None, None)
+
+        fake_client.get.assert_not_called()
+        storage.upload_file.assert_not_called()
+        session.commit.assert_not_called()
+        session.close.assert_called_once()
+
+    def test_job_not_found_returns_early(self):
+        """DB 中找不到 job → 直接返回，不抛错."""
+        from app.tasks.lipsync_tts import persist_output_video_task
+
+        session, fake_client, storage, patches = self._persist_patches(job=None)
+        entered = [p.__enter__() for p in patches]
+        try:
+            persist_output_video_task("missing", "user-1", "https://temp.mk/x.mp4")
+        finally:
+            for p in reversed(patches):
+                p.__exit__(None, None, None)
+
+        fake_client.get.assert_not_called()
+        storage.upload_file.assert_not_called()
+        session.commit.assert_not_called()
+        session.close.assert_called_once()
+
+
+class TestLipsyncServiceRefreshCompletedAsyncPersist:
+    """refresh_job_status 在 completed 分支异步转存的单元测试（补 0% 覆盖的 316~335 行）."""
+
+    def test_refresh_completed_dispatches_persist_task(self):
+        """completed 分支：设置 temp_url → commit → dispatch persist_output_video_task.apply_async."""
+        from app.services.lipsync_service import LipsyncService
+
+        mock_job = MagicMock()
+        mock_job.id = "job-1"
+        mock_job.user_id = "user-1"
+        mock_job.mediakit_task_id = "mk-1"
+        mock_job.status = "submitted"
+        mock_job.output_video_url = ""
+        mock_job.output_duration = 0.0
+
+        mock_db = MagicMock()
+        mock_query = MagicMock()
+        mock_filter = MagicMock()
+        mock_filter.first.return_value = mock_job
+        mock_query.filter.return_value = mock_filter
+        mock_db.query.return_value = mock_query
+
+        mock_client = MagicMock()
+        mock_client.get_task_status.return_value = {
+            "status": "completed",
+            "result": {"video_url": "https://temp.mk/out.mp4", "duration": 25.5},
+        }
+
+        fake_persist_task = MagicMock()
+        svc = LipsyncService(mock_db, client=mock_client, cosyvoice_service=MagicMock())
+        with patch.dict("sys.modules", {}):
+            # 直接 patch 懒 import 路径
+            with patch("app.tasks.lipsync_tts.persist_output_video_task", fake_persist_task, create=False):
+                # 但懒 import 发生在函数内部 from app.tasks.lipsync_tts import persist_output_video_task
+                # 通过 patch sys.modules 的方式提供
+                import sys as _sys
+
+                fake_mod = MagicMock()
+                fake_mod.persist_output_video_task = fake_persist_task
+                _sys.modules["app.tasks.lipsync_tts"] = fake_mod
+                try:
+                    result = svc.refresh_job_status("job-1", "user-1")
+                finally:
+                    _sys.modules.pop("app.tasks.lipsync_tts", None)
+
+        assert result.status == "completed"
+        assert result.output_video_url == "https://temp.mk/out.mp4"
+        assert result.output_duration == 25.5
+        mock_db.commit.assert_called()
+        # 必须在 commit 之后 dispatch
+        fake_persist_task.apply_async.assert_called_once()
+        kwargs = fake_persist_task.apply_async.call_args.kwargs
+        assert kwargs["args"] == ("job-1", "user-1", "https://temp.mk/out.mp4")
+
+    def test_refresh_completed_dispatch_exception_does_not_break_return(self):
+        """apply_async 抛异常（如 Celery 不可用）→ 捕获 warning，仍返回 completed job."""
+        from app.services.lipsync_service import LipsyncService
+
+        mock_job = MagicMock()
+        mock_job.id = "job-2"
+        mock_job.user_id = "user-1"
+        mock_job.mediakit_task_id = "mk-2"
+        mock_job.status = "submitted"
+        mock_job.output_video_url = ""
+        mock_job.output_duration = 0.0
+
+        mock_db = MagicMock()
+        mock_query = MagicMock()
+        mock_filter = MagicMock()
+        mock_filter.first.return_value = mock_job
+        mock_query.filter.return_value = mock_filter
+        mock_db.query.return_value = mock_query
+
+        mock_client = MagicMock()
+        mock_client.get_task_status.return_value = {
+            "status": "completed",
+            "result": {"video_url": "https://temp.mk/out2.mp4", "duration": 10.0},
+        }
+
+        fake_persist_task = MagicMock()
+        fake_persist_task.apply_async.side_effect = ConnectionError("celery down")
+
+        svc = LipsyncService(mock_db, client=mock_client, cosyvoice_service=MagicMock())
+        import sys as _sys
+
+        fake_mod = MagicMock()
+        fake_mod.persist_output_video_task = fake_persist_task
+        _sys.modules["app.tasks.lipsync_tts"] = fake_mod
+        try:
+            result = svc.refresh_job_status("job-2", "user-1")
+        finally:
+            _sys.modules.pop("app.tasks.lipsync_tts", None)
+
+        # 即便 dispatch 失败，主流程不受影响：仍然返回 completed + temp_url
+        assert result.status == "completed"
+        assert result.output_video_url == "https://temp.mk/out2.mp4"
+        fake_persist_task.apply_async.assert_called_once()

tests/unit/test_lipsync_tts_preview.py

@@ -0,0 +1,169 @@
+"""AI 数字人 对口型 TTS 预合成接口（#1845）单元测试 — 覆盖 LipsyncService.preview_tts 成功/失败路径.
+
+直接调用 LipsyncService.preview_tts()，mock CosyVoiceService / safe_download_bytes / ffprobe，
+验证返回结构、错误码、与共享 sentence_timings 工具的协作。
+"""
+
+import os
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+os.environ.setdefault("JWT_SECRET_KEY", "dev-secret-key-for-testing")
+
+
+def _make_service(
+    *,
+    cosyvoice=None,
+    download_bytes=b"FAKE_MP3_DATA",
+    download_error=None,
+    ffprobe_duration=5.0,
+    timings_result=None,
+):
+    """构造 LipsyncService 并把 CosyVoiceService/safe_download_bytes/probe/compute 全部 mock 掉。"""
+    from app.services.lipsync_service import LipsyncService
+
+    db = MagicMock()
+
+    # 构造唯一的 cosyvoice mock 实例，便于断言
+    _cosy_inst = MagicMock()
+    if cosyvoice is None:
+        _cosy_inst.submit_synthesize_task.return_value = {"audio_url": "https://cosy.example.com/tts.mp3"}
+    elif isinstance(cosyvoice, Exception):
+        _cosy_inst.submit_synthesize_task.side_effect = cosyvoice
+    else:
+        _cosy_inst.submit_synthesize_task.return_value = cosyvoice
+
+    def _fake_get_cosyvoice(self):  # noqa: ARG001
+        return _cosy_inst
+
+    def _fake_resolve_voice_id(self, voice_id, user_id):  # noqa: ARG001
+        return voice_id
+
+    svc = LipsyncService(db=db, client=MagicMock(), voice_clone_repo=MagicMock())
+    svc._cosyvoice = _cosy_inst
+    patch.object(LipsyncService, "_get_cosyvoice", _fake_get_cosyvoice).start()
+    patch.object(LipsyncService, "_resolve_voice_id", _fake_resolve_voice_id).start()
+
+    # mock safe_download_bytes
+    if download_error is not None:
+        patch(
+            "app.services.lipsync_service.safe_download_bytes",
+            side_effect=download_error,
+        ).start()
+    else:
+        patch(
+            "app.services.lipsync_service.safe_download_bytes",
+            return_value=download_bytes,
+        ).start()
+
+    # mock probe_audio_duration（patch 到 lipsync_service 模块的命名空间）
+    patch(
+        "app.services.lipsync_service.probe_audio_duration",
+        return_value=ffprobe_duration,
+    ).start()
+
+    # mock compute_sentence_timings
+    default_timings = [
+        {"index": 0, "text": "你好", "start_time": 0.0, "end_time": 1.5},
+        {"index": 1, "text": "世界", "start_time": 1.5, "end_time": 5.0},
+    ]
+    patch(
+        "app.services.lipsync_service.compute_sentence_timings",
+        return_value=timings_result if timings_result is not None else default_timings,
+    ).start()
+
+    svc.__dict__["_test_cosy"] = _cosy_inst
+    return svc
+
+
+def test_preview_tts_success():
+    """正常路径：TTS 合成成功 → 下载 → ffprobe → 计算 timings，返回完整结构。"""
+    svc = _make_service(ffprobe_duration=5.0)
+    try:
+        result = svc.preview_tts(
+            user_id="user-1",
+            voice_id="longxiaochun",
+            script_text="你好，世界",
+            speed=1.0,
+            emotion="natural",
+        )
+        assert result["audio_url"] == "https://cosy.example.com/tts.mp3"
+        assert result["duration"] == 5.0
+        assert isinstance(result["sentence_timings"], list)
+        assert len(result["sentence_timings"]) == 2
+        assert result["sentence_timings"][0]["text"] == "你好"
+        cosy = svc.__dict__["_test_cosy"]
+        cosy.submit_synthesize_task.assert_called_once()
+        kwargs = cosy.submit_synthesize_task.call_args.kwargs
+        assert kwargs["text"] == "你好，世界"
+        assert kwargs["voice_id"] == "longxiaochun"
+    finally:
+        patch.stopall()
+
+
+def test_preview_tts_cosyvoice_error():
+    """CosyVoice 抛错：应该包装成 MediaKitError 抛出。"""
+    from app.services.mediakit_client import MediaKitError
+
+    from packages.application.cosyvoice_service import CosyVoiceError
+
+    svc = _make_service(cosyvoice=CosyVoiceError("cosyvoice down"))
+    try:
+        with pytest.raises(MediaKitError):
+            svc.preview_tts(
+                user_id="user-1",
+                voice_id="longxiaochun",
+                script_text="你好",
+            )
+    finally:
+        patch.stopall()
+
+
+def test_preview_tts_download_fail_still_returns_url():
+    """音频下载失败：不抛错，返回 audio_url + 空 timings，前端仍能继续（降级）。"""
+    svc = _make_service(download_error=RuntimeError("network down"))
+    try:
+        result = svc.preview_tts(
+            user_id="user-1",
+            voice_id="longxiaochun",
+            script_text="你好，世界",
+        )
+        assert result["audio_url"] == "https://cosy.example.com/tts.mp3"
+        assert result["duration"] == 0.0
+        assert result["sentence_timings"] == []
+    finally:
+        patch.stopall()
+
+
+def test_preview_tts_ffprobe_zero_duration():
+    """ffprobe 返回 0：timings 为空，不抛错。"""
+    svc = _make_service(ffprobe_duration=0.0)
+    try:
+        result = svc.preview_tts(
+            user_id="user-1",
+            voice_id="longxiaochun",
+            script_text="你好",
+        )
+        assert result["audio_url"]
+        assert result["duration"] == 0.0
+        assert result["sentence_timings"] == []
+    finally:
+        patch.stopall()
+
+
+def test_preview_tts_no_audio_url_in_response():
+    """CosyVoice 返回无 audio_url：抛 MediaKitError TTSNoAudio。"""
+    from app.services.mediakit_client import MediaKitError
+
+    svc = _make_service(cosyvoice={"audio_url": ""})
+    try:
+        with pytest.raises(MediaKitError) as exc_info:
+            svc.preview_tts(
+                user_id="user-1",
+                voice_id="longxiaochun",
+                script_text="你好",
+            )
+        assert exc_info.value.code == "TTSNoAudio"
+    finally:
+        patch.stopall()

tests/unit/test_sentence_timings.py

@@ -0,0 +1,169 @@
+"""Tests for sentence timing functions (now in packages/domain/sentence_timings.py)."""
+
+import os
+import subprocess
+import tempfile
+import unittest
+from unittest.mock import MagicMock, patch
+
+from packages.domain.sentence_timings import compute_sentence_timings as _compute_sentence_timings
+from packages.domain.sentence_timings import estimate_sentence_timings_by_chars as _estimate_sentence_timings_by_chars
+from packages.domain.sentence_timings import split_script_into_sentences as _split_script_into_sentences
+
+
+class TestSplitScriptIntoSentences(unittest.TestCase):
+    """Tests for _split_script_into_sentences."""
+
+    def test_empty_string(self):
+        self.assertEqual(_split_script_into_sentences(""), [])
+
+    def test_none(self):
+        self.assertEqual(_split_script_into_sentences(None), [])
+
+    def test_whitespace_only(self):
+        self.assertEqual(_split_script_into_sentences("   \n  "), [])
+
+    def test_single_sentence(self):
+        self.assertEqual(_split_script_into_sentences("你好世界。"), ["你好世界"])
+
+    def test_multiple_sentences_chinese(self):
+        result = _split_script_into_sentences("第一句。第二句！第三句？")
+        self.assertEqual(result, ["第一句", "第二句", "第三句"])
+
+    def test_english_punctuation(self):
+        result = _split_script_into_sentences("Hello World! How are you?")
+        self.assertEqual(result, ["Hello World", "How are you"])
+
+    def test_semicolons(self):
+        result = _split_script_into_sentences("第一部分；第二部分;第三部分")
+        self.assertEqual(result, ["第一部分", "第二部分", "第三部分"])
+
+    def test_newlines(self):
+        result = _split_script_into_sentences("第一行\n第二行\n第三行")
+        self.assertEqual(result, ["第一行", "第二行", "第三行"])
+
+    def test_no_trailing_punctuation(self):
+        result = _split_script_into_sentences("没有标点的句子")
+        self.assertEqual(result, ["没有标点的句子"])
+
+
+class TestEstimateSentenceTimingsByChars(unittest.TestCase):
+    """Tests for _estimate_sentence_timings_by_chars."""
+
+    def test_empty_sentences(self):
+        self.assertEqual(_estimate_sentence_timings_by_chars([], 10.0), [])
+
+    def test_zero_duration(self):
+        self.assertEqual(_estimate_sentence_timings_by_chars(["hello"], 0), [])
+
+    def test_negative_duration(self):
+        self.assertEqual(_estimate_sentence_timings_by_chars(["hello"], -5.0), [])
+
+    def test_single_sentence(self):
+        result = _estimate_sentence_timings_by_chars(["hello"], 10.0)
+        self.assertEqual(len(result), 1)
+        self.assertAlmostEqual(result[0]["start_time"], 0.0)
+        self.assertAlmostEqual(result[0]["end_time"], 10.0)
+
+    def test_two_equal_sentences(self):
+        result = _estimate_sentence_timings_by_chars(["你好", "世界"], 10.0)
+        self.assertEqual(len(result), 2)
+        self.assertAlmostEqual(result[0]["start_time"], 0.0)
+        self.assertAlmostEqual(result[0]["end_time"], 5.0)
+        self.assertAlmostEqual(result[1]["start_time"], 5.0)
+        self.assertAlmostEqual(result[1]["end_time"], 10.0)
+
+    def test_unequal_char_distribution(self):
+        result = _estimate_sentence_timings_by_chars(["ABCD", "EF"], 9.0)
+        self.assertEqual(len(result), 2)
+        self.assertAlmostEqual(result[0]["start_time"], 0.0)
+        self.assertAlmostEqual(result[0]["end_time"], 6.0)  # 4/6 * 9 = 6
+        self.assertAlmostEqual(result[1]["start_time"], 6.0)
+        self.assertAlmostEqual(result[1]["end_time"], 9.0)
+
+    def test_timing_structure(self):
+        result = _estimate_sentence_timings_by_chars(["句子一", "句子二"], 6.0)
+        for item in result:
+            self.assertIn("index", item)
+            self.assertIn("text", item)
+            self.assertIn("start_time", item)
+            self.assertIn("end_time", item)
+
+
+class TestComputeSentenceTimings(unittest.TestCase):
+    """Tests for _compute_sentence_timings."""
+
+    def test_empty_script_returns_empty(self):
+        self.assertEqual(_compute_sentence_timings(b"fake_audio", "", 10.0), [])
+
+    def test_none_script_returns_empty(self):
+        self.assertEqual(_compute_sentence_timings(b"fake_audio", None, 10.0), [])
+
+    @patch("os.unlink")
+    @patch.object(tempfile, "NamedTemporaryFile")
+    @patch.object(subprocess, "run")
+    def test_silence_detection_insufficient_fallback(self, mock_run, mock_tmpfile, mock_unlink):
+        """When silence detection finds too few points, fallback to char estimation."""
+        mock_run.return_value = MagicMock(stderr="", returncode=0)
+        mock_tmp = MagicMock()
+        mock_tmp.name = "/tmp/fake.mp3"
+        mock_tmp.__enter__ = MagicMock(return_value=mock_tmp)
+        mock_tmp.__exit__ = MagicMock(return_value=False)
+        mock_tmpfile.return_value = mock_tmp
+
+        result = _compute_sentence_timings(b"fake_audio", "第一句。第二句。第三句。", 10.0)
+
+        # Should fallback to char estimation with 3 sentences
+        self.assertEqual(len(result), 3)
+        self.assertAlmostEqual(result[0]["start_time"], 0.0)
+
+    @patch("os.unlink")
+    @patch.object(tempfile, "NamedTemporaryFile")
+    @patch.object(subprocess, "run")
+    def test_silence_detection_with_enough_points(self, mock_run, mock_tmpfile, mock_unlink):
+        """When silence detection finds enough points, use them for boundaries."""
+        mock_run.return_value = MagicMock(
+            stderr="[silencedetect] silence_end: 3.5 | silence_duration: 0.4\n"
+            "[silencedetect] silence_end: 7.0 | silence_duration: 0.3\n",
+            returncode=0,
+        )
+        mock_tmp = MagicMock()
+        mock_tmp.name = "/tmp/fake.mp3"
+        mock_tmp.__enter__ = MagicMock(return_value=mock_tmp)
+        mock_tmp.__exit__ = MagicMock(return_value=False)
+        mock_tmpfile.return_value = mock_tmp
+
+        result = _compute_sentence_timings(b"fake_audio", "第一句。第二句。第三句。", 10.0)
+
+        self.assertEqual(len(result), 3)
+        self.assertAlmostEqual(result[0]["start_time"], 0.0)
+        self.assertAlmostEqual(result[0]["end_time"], 3.5)
+        self.assertAlmostEqual(result[1]["start_time"], 3.5)
+        self.assertAlmostEqual(result[1]["end_time"], 7.0)
+        self.assertAlmostEqual(result[2]["start_time"], 7.0)
+        self.assertAlmostEqual(result[2]["end_time"], 10.0)
+
+    @patch("os.unlink")
+    @patch.object(tempfile, "NamedTemporaryFile")
+    @patch.object(subprocess, "run")
+    def test_ffmpeg_exception_fallback(self, mock_run, mock_tmpfile, mock_unlink):
+        """When ffmpeg raises an exception, fallback to char estimation."""
+        mock_run.side_effect = Exception("ffmpeg not found")
+        mock_tmp = MagicMock()
+        mock_tmp.name = "/tmp/fake.mp3"
+        mock_tmp.__enter__ = MagicMock(return_value=mock_tmp)
+        mock_tmp.__exit__ = MagicMock(return_value=False)
+        mock_tmpfile.return_value = mock_tmp
+
+        result = _compute_sentence_timings(b"fake_audio", "句子一。句子二。", 6.0)
+
+        # Should fallback to char estimation
+        self.assertEqual(len(result), 2)
+        self.assertAlmostEqual(result[0]["start_time"], 0.0)
+        self.assertAlmostEqual(result[0]["end_time"], 3.0)
+        self.assertAlmostEqual(result[1]["start_time"], 3.0)
+        self.assertAlmostEqual(result[1]["end_time"], 6.0)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/unit/test_video_filter_builder.py

@@ -13,6 +13,7 @@
 
 from __future__ import annotations
 
+import os
 import unittest
 from dataclasses import FrozenInstanceError
 from unittest.mock import patch
@@ -29,10 +30,12 @@ from packages.domain.video_filter_builder import (
     ClipFilterChain,
     _escape_drawtext_text,
     _resolve_font_path,
+    build_broll_overlay_filter,
     build_clip_filter,
     build_concat_filter,
     build_filter_complex,
     build_title_drawtext_filter,
+    build_title_overlay_filter,
     build_xfade_filter,
     chain_filters,
     has_audio,
@@ -902,9 +905,10 @@ class TestResolveFontPath(unittest.TestCase):
 
     @patch("os.path.isfile")
     def test_unknown_font_fallback(self, mock_isfile):
-        mock_isfile.side_effect = lambda p: "DejaVu" in p
+        # DejaVuSans 已从 fallback 列表移除（不支持 CJK），用 VF 路径模拟
+        mock_isfile.side_effect = lambda p: "NotoSansSC-VF" in p
         result = _resolve_font_path("UnknownFont")
-        self.assertIn("DejaVu", result)
+        self.assertIn("NotoSansSC-VF", result)
 
     @patch("os.path.isfile")
     def test_no_fonts_available(self, mock_isfile):
@@ -927,9 +931,11 @@ class TestResolveFontPath(unittest.TestCase):
 
     @patch("os.path.isfile")
     def test_font_fallback_skips_nonexistent(self, mock_isfile):
-        mock_isfile.side_effect = lambda p: "DejaVu" in p
+        # 所有中文字体路径都不存在时，fallback 返回第一个存在的文件；
+        # DejaVuSans 已从列表移除（不支持 CJK），使用 VF 字体路径模拟存在文件
+        mock_isfile.side_effect = lambda p: "NotoSansSC-VF" in p
         result = _resolve_font_path("不存在字体")
-        self.assertIn("DejaVu", result)
+        self.assertIn("NotoSansSC-VF", result)
 
 
 class TestDrawtextFontFileIncluded(unittest.TestCase):
@@ -1028,6 +1034,37 @@ class TestDrawtextBoldFalse(unittest.TestCase):
         self.assertIsNotNone(result)
         self.assertNotIn("font=bold", result)
 
+    def test_bold_true_does_not_use_font_bold_param(self):
+        """粗体模式不得使用 `font=bold`——该参数无效，会导致 filter_complex 解析失败（exit 234）。"""
+        result = build_title_drawtext_filter({"text": "标题", "bold": True})
+        self.assertIsNotNone(result)
+        self.assertNotIn("font=bold", result)
+        # 粗体应通过 borderw 实现
+        self.assertIn("borderw=", result)
+
+    @patch("packages.domain.video_filter_builder._resolve_font_path")
+    def test_bold_default_uses_black_stroke_when_no_bold_font(self, mock_font):
+        """默认 bold=true 且无 Bold 字体文件时，使用黑色细描边（borderw=2 + 黑），
+        不得使用与文字同色的 borderw>=3（否则会造成竖屏小字号重影）。"""
+        mock_font.return_value = ""  # 无粗体字体
+        result = build_title_drawtext_filter({"text": "标题"})
+        self.assertIsNotNone(result)
+        self.assertIn("borderw=2", result)
+        # 黑描边：要么是 black 关键字，要么是 000000
+        self.assertTrue("bordercolor=black" in result or "bordercolor=000000" in result)
+        self.assertNotIn("borderw=3", result)
+
+    @patch("packages.domain.video_filter_builder._resolve_font_path")
+    def test_bold_with_user_stroke_preserves_user_color(self, mock_font):
+        """用户显式开启 stroke 时，stroke 颜色/宽度优先于默认粗体黑边。"""
+        mock_font.return_value = ""
+        result = build_title_drawtext_filter(
+            {"text": "标题", "bold": True, "stroke": {"width": 4, "color": "#ffffff"}}
+        )
+        self.assertIsNotNone(result)
+        self.assertIn("borderw=4", result)
+        self.assertIn("bordercolor=ffffff", result)  # 去掉 # 前缀
+
 
 class TestDrawtextPositionBranches(unittest.TestCase):
     """位置相关分支覆盖。"""
@@ -1054,12 +1091,23 @@ class TestDrawtextPositionBranches(unittest.TestCase):
         self.assertIn("y=h-text_h-50", result)
 
     @patch("packages.domain.video_filter_builder._resolve_font_path")
-    def test_position_custom_with_float_coords(self, mock_font):
+    def test_position_custom_with_percentage_coords(self, mock_font):
+        """自定义位置：百分比坐标转换为 drawtext 表达式."""
+        mock_font.return_value = ""
+        # pos_x=50, pos_y=30 → x=(w-text_w)*0.5000, y=(h-text_h)*0.3000
+        result = build_title_drawtext_filter({"text": "标题", "position": "custom", "pos_x": 50, "pos_y": 30})
+        self.assertIsNotNone(result)
+        self.assertIn("x=(w-text_w)*0.5000", result)
+        self.assertIn("y=(h-text_h)*0.3000", result)
+
+    @patch("packages.domain.video_filter_builder._resolve_font_path")
+    def test_position_custom_clamped_to_100(self, mock_font):
+        """自定义位置：超过100的坐标被截断到100%."""
         mock_font.return_value = ""
         result = build_title_drawtext_filter({"text": "标题", "position": "custom", "pos_x": 100.7, "pos_y": 200.3})
         self.assertIsNotNone(result)
-        self.assertIn("x=100", result)
+        self.assertIn("x=(w-text_w)*1.0000", result)
-        self.assertIn("y=200", result)
+        self.assertIn("y=(h-text_h)*1.0000", result)
 
     @patch("packages.domain.video_filter_builder._resolve_font_path")
     def test_position_custom_bool_coords_fallback(self, mock_font):
@@ -1116,5 +1164,97 @@ class TestDrawtextNotDictConfig(unittest.TestCase):
         self.assertIsNone(build_title_drawtext_filter([1, 2, 3]))
 
 
+class TestTitleOverlay(unittest.TestCase):
+    """build_title_overlay_filter 单元测试（WYSIWYG PNG 叠加路径）。"""
+
+    def test_overlay_filter_format(self):
+        """PNG 文件存在时返回正确的 overlay 滤镜字符串。"""
+        import tempfile
+
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
+            tmp.write(b"\x89PNG\r\n\x1a\n")
+            tmp_path = tmp.name
+        try:
+            result = build_title_overlay_filter(
+                {"text": "标题"},
+                output_width=720,
+                output_height=1280,
+                title_png_path=tmp_path,
+                title_input_label="[2:v]",
+                base_label="[vout]",
+                output_label="vout_titled",
+            )
+            self.assertIsNotNone(result)
+            self.assertIn("[vout][2:v]overlay=0:0[vout_titled]", result)
+        finally:
+            os.unlink(tmp_path)
+
+    def test_overlay_default_labels(self):
+        """不传 label 参数时使用默认 [0:v] / [1:v] / vout_titled。"""
+        import tempfile
+
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
+            tmp.write(b"\x89PNG\r\n\x1a\n")
+            tmp_path = tmp.name
+        try:
+            result = build_title_overlay_filter(
+                {"text": "标题"},
+                output_width=720,
+                output_height=1280,
+                title_png_path=tmp_path,
+            )
+            self.assertEqual(result, "[0:v][1:v]overlay=0:0[vout_titled]")
+        finally:
+            os.unlink(tmp_path)
+
+    def test_overlay_returns_none_when_png_missing(self):
+        """PNG 文件不存在时返回 None，供调用方降级到 drawtext。"""
+        result = build_title_overlay_filter(
+            {"text": "标题"},
+            output_width=720,
+            output_height=1280,
+            title_png_path="/nonexistent/path/title.png",
+        )
+        self.assertIsNone(result)
+
+    def test_overlay_returns_none_for_empty_config(self):
+        """title_config 为空/非 dict 时返回 None。"""
+        import tempfile
+
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
+            tmp.write(b"\x89PNG\r\n\x1a\n")
+            tmp_path = tmp.name
+        try:
+            self.assertIsNone(
+                build_title_overlay_filter(
+                    None,
+                    output_width=720,
+                    output_height=1280,
+                    title_png_path=tmp_path,
+                )
+            )
+            self.assertIsNone(
+                build_title_overlay_filter(
+                    "not a dict",
+                    output_width=720,
+                    output_height=1280,
+                    title_png_path=tmp_path,
+                )
+            )
+        finally:
+            os.unlink(tmp_path)
+
+    def test_overlay_returns_none_for_empty_path(self):
+        """title_png_path 为空字符串时返回 None。"""
+        self.assertIsNone(
+            build_title_overlay_filter(
+                {"text": "标题"},
+                output_width=720,
+                output_height=1280,
+                title_png_path="",
+            )
+        )
+
+
 if __name__ == "__main__":
     unittest.main()