Self-restart worker process when model loading fails

Discovered vibevoice sitting at 3.2GB resident GPU memory with
loaded_models: [] and no idle-unload ever firing for it again. Root cause:
a load attempt had OOM'd partway through (competing with an unrelated
ollama process on the same GPU), so _MODEL_CACHE was never populated —
the idle-unload loop only clears that cache, so it had nothing to act on,
even though the partially-constructed model had already left memory
allocated. gc.collect()+empty_cache() don't reliably reclaim memory from
an interrupted from_pretrained() call.

Confirmed a plain process restart does fully reclaim it, so each worker
now treats any load failure as fatal: log it and os._exit(1), letting
supervisord's autorestart=true respawn a clean process immediately.
Verified with a bogus model id — worker exits, respawns, and passes the
smoke test right after.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

app/tts/workers/xtts_worker.py

@@ -83,7 +83,17 @@ def _load_model() -> Any:
     from TTS.api import TTS
 
     _log(f"loading model {MODEL_NAME}")
-    tts = TTS(MODEL_NAME).to(_device())
+    try:
+        tts = TTS(MODEL_NAME).to(_device())
+    except Exception as e:
+        # A load interrupted partway (e.g. OOM) can leave CUDA memory
+        # fragmented/leaked in ways gc.collect()+empty_cache() don't reliably
+        # reclaim, and since _MODEL_CACHE never got populated the idle-unload
+        # loop has nothing to clean up either. Restarting the whole process
+        # is the only guaranteed way to get that memory back — supervisord's
+        # autorestart=true respawns it immediately.
+        _log(f"model load failed, restarting process to reclaim GPU memory: {type(e).__name__}: {e}")
+        os._exit(1)
     _MODEL_CACHE[MODEL_NAME] = tts
     _log("model loaded")
     return tts