extract routes from main.py into domain routers (Phase 4)

Split the 2,578-line main.py (90 routes) into 12 domain-specific router
modules under routes/. main.py is now a 45-line entry point.

New structure:
- app.py: FastAPI instance, CORS, startup/shutdown, safe_content_disposition
- routes/: health, profiles, channels, generations, history, transcription,
  stories, effects, audio, models, tasks, cuda
- services/cuda.py: moved from cuda_download.py

Also includes Phase 5 database/ package (from parallel agent):
- database/__init__.py re-exports all symbols for backward compat
- database/models.py, session.py, migrations.py, seed.py

All 90 routes verified registered and app imports cleanly.

backend/routes/transcription.py

@@ -0,0 +1,78 @@
+"""Transcription endpoints."""
+
+import asyncio
+import tempfile
+from pathlib import Path
+
+from fastapi import APIRouter, File, Form, HTTPException, UploadFile
+
+from .. import models, transcribe
+from ..services.task_queue import create_background_task
+from ..utils.tasks import get_task_manager
+
+router = APIRouter()
+
+
+@router.post("/transcribe", response_model=models.TranscriptionResponse)
+async def transcribe_audio(
+    file: UploadFile = File(...),
+    language: str | None = Form(None),
+):
+    """Transcribe audio file to text."""
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        content = await file.read()
+        tmp.write(content)
+        tmp_path = tmp.name
+
+    try:
+        from ..utils.audio import load_audio
+
+        audio, sr = await asyncio.to_thread(load_audio, tmp_path)
+        duration = len(audio) / sr
+
+        whisper_model = transcribe.get_whisper_model()
+
+        model_size = whisper_model.model_size
+        whisper_hf_repos = {
+            "large": "openai/whisper-large-v3",
+            "turbo": "openai/whisper-large-v3-turbo",
+        }
+        model_name = whisper_hf_repos.get(model_size, f"openai/whisper-{model_size}")
+
+        from huggingface_hub import constants as hf_constants
+
+        repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + model_name.replace("/", "--"))
+        if not repo_cache.exists():
+            progress_model_name = f"whisper-{model_size}"
+
+            async def download_whisper_background():
+                try:
+                    await whisper_model.load_model_async(model_size)
+                except Exception as e:
+                    get_task_manager().error_download(progress_model_name, str(e))
+
+            get_task_manager().start_download(progress_model_name)
+            create_background_task(download_whisper_background())
+
+            raise HTTPException(
+                status_code=202,
+                detail={
+                    "message": f"Whisper model {model_size} is being downloaded. Please wait and try again.",
+                    "model_name": progress_model_name,
+                    "downloading": True,
+                },
+            )
+
+        text = await whisper_model.transcribe(tmp_path, language)
+
+        return models.TranscriptionResponse(
+            text=text,
+            duration=duration,
+        )
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+    finally:
+        Path(tmp_path).unlink(missing_ok=True)