feat(tts): real Korean TTS via persistent MeloTTS worker

Adds a MeloTTS backend that runs the model in its own melo311 interpreter
as a long-lived worker (melo_worker.py), loaded once and fed synthesis
requests over a stdin/stdout JSON protocol. fd1 is split from fd2 in the
worker so MeloTTS's stdout progress chatter can't corrupt the protocol.
Each speak() writes a wav and hands the path to a pluggable sink (the
Discord voice step will swap in "play into the call"). factory wires
tts=melo; pipeline.aclose now also tears down the tts worker.

Verified (CPU): model load ~7.9s once, then a short reply synthesizes in
~0.86s (within the ~1s budget); wav is valid 44.1kHz PCM. GPU (cuda) is
selectable via WSAI_MELO_DEVICE for lower latency, pending GPU approval.
7 smoke tests still pass.

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

wsai/backends/melo.py

@@ -0,0 +1,110 @@
+"""Real Korean TTS via MeloTTS, run as a persistent out-of-venv worker.
+
+MeloTTS needs its own interpreter (melo311). Loading the model costs several
+seconds, so we keep one worker process alive and stream synthesis requests to
+it (see melo_worker.py for the protocol). Each `speak()` writes a wav to
+`out_dir` and hands the path to a sink (default: log it). The Discord voice
+integration later swaps the sink for "play this wav into the call".
+
+Env:
+    WSAI_MELO_PYTHON   interpreter with melo installed
+                       (default: /home/claude/jarvis-tts/melo311/bin/python)
+    WSAI_MELO_DEVICE   cpu | cuda | auto  (default cpu; cuda needs GPU approval)
+    WSAI_TTS_OUT_DIR   where wavs are written (default ~/.cache/wsai/tts)
+    WSAI_TTS_SPEED     synthesis speed multiplier (default 1.3)
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import os
+import time
+from pathlib import Path
+from typing import Awaitable, Callable
+
+from ..interfaces import Reply
+
+log = logging.getLogger("wsai.tts.melo")
+
+_DEFAULT_PYTHON = "/home/claude/jarvis-tts/melo311/bin/python"
+
+# A sink receives the finished wav path plus the reply it voices.
+Sink = Callable[[str, Reply], Awaitable[None]]
+
+
+async def _log_sink(path: str, reply: Reply) -> None:
+    log.info("TTS wav ready: %s (%s)", path, reply.text[:40])
+
+
+class MeloTTS:
+    def __init__(
+        self,
+        *,
+        python: str | None = None,
+        device: str | None = None,
+        out_dir: str | None = None,
+        speed: float | None = None,
+        sink: Sink | None = None,
+    ) -> None:
+        self.python = python or os.environ.get("WSAI_MELO_PYTHON", _DEFAULT_PYTHON)
+        self.device = device or os.environ.get("WSAI_MELO_DEVICE", "cpu")
+        self.out_dir = Path(out_dir or os.environ.get("WSAI_TTS_OUT_DIR")
+                            or (Path.home() / ".cache/wsai/tts"))
+        self.speed = float(speed if speed is not None
+                           else os.environ.get("WSAI_TTS_SPEED", "1.3"))
+        self.sink = sink or _log_sink
+        self._proc: asyncio.subprocess.Process | None = None
+        self._lock = asyncio.Lock()
+        self._n = 0
+        self.load_ms: int | None = None
+
+    async def _ensure(self) -> None:
+        if self._proc is not None and self._proc.returncode is None:
+            return
+        self.out_dir.mkdir(parents=True, exist_ok=True)
+        env = {**os.environ, "WSAI_MELO_DEVICE": self.device}
+        # Run the worker module from the wsai source tree with the melo venv.
+        repo_root = str(Path(__file__).resolve().parents[2])
+        self._proc = await asyncio.create_subprocess_exec(
+            self.python, "-m", "wsai.backends.melo_worker",
+            cwd=repo_root, env=env,
+            stdin=asyncio.subprocess.PIPE,
+            stdout=asyncio.subprocess.PIPE,
+            stderr=asyncio.subprocess.DEVNULL,
+        )
+        ready = await self._proc.stdout.readline()
+        info = json.loads(ready.decode())
+        if not info.get("ready"):
+            raise RuntimeError(f"melo worker failed to start: {info}")
+        self.load_ms = info.get("ms")
+        log.info("melo worker ready in %s ms on %s", self.load_ms, info.get("device"))
+
+    async def speak(self, reply: Reply) -> None:
+        await self._ensure()
+        self._n += 1
+        out = str(self.out_dir / f"tts-{self._n:06d}.wav")
+        req = json.dumps({"text": reply.text, "out": out, "speed": self.speed})
+        s = time.monotonic()
+        async with self._lock:
+            assert self._proc and self._proc.stdin and self._proc.stdout
+            self._proc.stdin.write((req + "\n").encode())
+            await self._proc.stdin.drain()
+            resp = await self._proc.stdout.readline()
+        if not resp:
+            raise RuntimeError("melo worker closed unexpectedly")
+        res = json.loads(resp.decode())
+        if not res.get("ok"):
+            raise RuntimeError(f"melo synth failed: {res.get('error')}")
+        log.debug("synth %d ms (worker %s ms)", int((time.monotonic() - s) * 1000), res.get("ms"))
+        await self.sink(res["out"], reply)
+
+    async def aclose(self) -> None:
+        if self._proc is not None and self._proc.returncode is None:
+            try:
+                self._proc.terminate()
+                await asyncio.wait_for(self._proc.wait(), timeout=5)
+            except (ProcessLookupError, asyncio.TimeoutError):
+                pass
+        self._proc = None