perf: run MeloTTS on the GPU (cu128 torch) + warm CUDA at startup

CPU MeloTTS serialised under concurrent load (whisper STT + bot) and blew
voice-reply TTS to 7-8s. Install the Blackwell-verified cu128 torch in the
melo venv, select the GPU via MELO_DEVICE=cuda, and do a throwaway synth at
worker startup so the one-off CUDA kernel-init (~5s) doesn't land on the
user's first reply. Measured: ~0.3s/sentence on GPU vs ~1.2-2.6s on CPU.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

bridge/melo_worker.py

@@ -66,6 +66,20 @@ def _ensure_model() -> None:
             speaker_id = spk_map[LANGUAGE] if LANGUAGE in spk_map else spk_map[keys[0]]
             _model = model
             _speaker_id = speaker_id
+            # Warm the GPU once at load: the first CUDA synth pays a one-off
+            # kernel-init cost (~5s) that would otherwise land on the user's
+            # first reply. A throwaway synth here moves it to startup. No-op
+            # cost on CPU.
+            try:
+                with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as _wt:
+                    _wp = _wt.name
+                model.tts_to_file("워밍업", speaker_id, _wp, speed=SPEED)
+                try:
+                    os.unlink(_wp)
+                except OSError:
+                    pass
+            except Exception as _we:  # pragma: no cover
+                print(f"[melo-worker] warmup synth skipped: {_we}", flush=True)
             print(
                 f"[melo-worker] ready (lang={LANGUAGE} speed={SPEED} "
                 f"device={DEVICE} speakers={list(spk_map.keys())})",