fix: accept edge as a valid tts_engine and migrate stale persisted engines

load_settings() coerced any tts_engine outside {piper, chatterbox} to piper, so
with TTS_ENGINE=edge the reply engine saw "piper" and treated the voice as
English-only in reply_language_directive() (only the OUTPUT_LANGUAGE lock kept
replies Korean). Add "edge" (and "melo") to the accepted set so the engine is
labelled multilingual correctly.

Also: a stale tts_engine in the persistent /data/jarvis-settings.json (melo/xtts
from an earlier voice, no longer built) would override the configured engine via
the entrypoint merge and leave the bot silent. Reset those to the env engine
during the merge.

Verified: load_settings() with tts_engine=edge now returns "edge"; the merge
maps melo/xtts -> edge; reply_language_directive("edge") is multilingual; 27
tests pass.

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

.env.example

@@ -34,23 +34,18 @@ WHISPER_DEVICE=cuda
 WHISPER_COMPUTE_TYPE=float16
 # Optional explicit Piper voice model (.onnx). If empty, the jarvis default is used.
 TTS_PIPER_MODEL_PATH=
-# TTS engine: "xtts" (default) uses the Coqui XTTS-v2 natural Korean voice
-# served by the warm xtts-worker. Set to "piper" to use the English Piper voice
-# directly. (MeloTTS was removed; "melo" only works with an out-of-band worker.)
-TTS_ENGINE=xtts
-# XTTS-v2 voice settings. Speaker is any built-in studio voice; "Ana Florence"
-# is a natural female voice. Language is the synthesis language (ko = Korean).
-XTTS_SPEAKER=Ana Florence
-XTTS_LANGUAGE=ko
-XTTS_DEVICE=cuda
-# Where the bridge reaches the in-container XTTS worker, and how long it waits
-# for a synthesis (XTTS is slower than Melo: ~1-2s/sentence on GPU).
-XTTS_WORKER_URL=http://127.0.0.1:8771
-XTTS_TIMEOUT=30
-# Neural-only by default: if XTTS synthesis fails the bridge returns no audio
-# rather than speaking Korean through the English Piper voice (which mangles it).
-# Set to 1 only if you explicitly want the Piper fallback.
-XTTS_FALLBACK_PIPER=0
+# TTS engine: "edge" (default) uses Microsoft Edge TTS, a natural Korean neural
+# voice. Set to "piper" for the offline English voice. NOTE: edge is ONLINE —
+# reply text is sent to Microsoft's servers and needs internet.
+TTS_ENGINE=edge
+# Edge voice + speaking rate. Rate is a percentage (+45% ≈ 1.45×). Korean voices:
+# ko-KR-HyunsuMultilingualNeural (M), ko-KR-InJoonNeural (M), ko-KR-SunHiNeural (F).
+EDGE_TTS_VOICE=ko-KR-HyunsuMultilingualNeural
+EDGE_TTS_RATE=+45%
+# Neural-only by default: if synthesis fails the bridge returns no audio rather
+# than speaking Korean through the English Piper voice. Set to 1 to allow the
+# Piper fallback.
+MELO_FALLBACK_PIPER=0
 
 # ---------------------------------------------------------------------------
 # Jarvis brain (Ollama-backed). In Docker these populate the rendered
@@ -231,7 +226,7 @@ COMPOSE_FILE=docker-compose.yml:docker-compose.gpu-linux.yml
 # OLLAMA_CHAT_MODEL=qwen2.5:7b   # quality (needs ~5GB VRAM + whisper small)
 # OLLAMA_CHAT_MODEL=qwen2.5:3b   # speed (fits easily, faster on 8GB GPUs)
 # WHISPER_MODEL=small            # small frees VRAM for a bigger LLM; medium=more accurate
-# XTTS_DEVICE=cuda               # cpu if no GPU on the bot host (XTTS is slow on CPU)
+# MELO_DEVICE=cuda               # cpu if no GPU on the bot host
 
 # --- Settings web UI (http://localhost:8765/settings on the bot host) ---
 # To reach it, expose the bridge to the host loopback:

Dockerfile

@@ -65,21 +65,14 @@ RUN ls -d /opt/venv/lib/python*/site-packages/nvidia/cublas/lib \
         > /etc/ld.so.conf.d/nvidia-cu12.conf 2>/dev/null \
     && /sbin/ldconfig || true
 
-# --- Korean voice: Coqui XTTS-v2 (separate /opt/xtts py3.11 venv; see
-#     setup-xtts.sh). Natural female Korean ("Ana Florence"); replaces MeloTTS.
-#     Heavy layer (torch cu128 + Coqui TTS + the baked XTTS-v2 model); placed
-#     before the app COPY so it stays cached across source-only changes. ---
-COPY docker/setup-xtts.sh /app/docker/setup-xtts.sh
-# Strip CR before running: a Windows checkout (autocrlf) yields CRLF, which makes
-# bash read `set -euxo pipefail\r` and abort with "set: pipefail: invalid option
-# name". .gitattributes pins *.sh to LF, but this keeps the build working even on
-# a not-yet-renormalised working tree.
-RUN sed -i 's/\r$//' /app/docker/setup-xtts.sh && bash /app/docker/setup-xtts.sh
+# --- Korean voice: Microsoft Edge TTS (online neural). No model is baked — the
+#     `edge-tts` pip package (in requirements-bridge.txt) calls the MS service at
+#     runtime and the bridge transcodes the MP3 to PCM16 with ffmpeg. No heavy
+#     TTS build layer is needed. ---
 
 # --- Human input + window management for the on-screen Chrome control tool.
-#     Placed AFTER the heavy TTS layer so it doesn't bust that cache. xdotool
-#     injects real X pointer/keyboard events (visible cursor, char-by-char
-#     typing) into the broadcast; wmctrl lists/moves windows. ---
+#     xdotool injects real X pointer/keyboard events (visible cursor,
+#     char-by-char typing) into the broadcast; wmctrl lists/moves windows. ---
 RUN apt-get update && apt-get install -y --no-install-recommends \
       xdotool wmctrl \
     && rm -rf /var/lib/apt/lists/*

README.md

@@ -69,7 +69,7 @@ docker compose -f docker-compose.yml -f docker-compose.gpu-linux.yml up -d --bui
 docker compose -f docker-compose.yml -f docker-compose.gpu-windows.yml up -d --build
 
 # ── GPU 없이 (CPU 전용 호스트) ──
-#   .env 에 WHISPER_DEVICE=cpu, XTTS_DEVICE=cpu 를 넣고 베이스만 사용
+#   .env 에 WHISPER_DEVICE=cpu 를 넣고 베이스만 사용
 docker compose up -d --build
 ```
 
@@ -113,7 +113,7 @@ docker compose up -d          # 유저봇이 로그인해 지정 음성채널에
 
 ### GPU 가속 (OS별)
 
-LLM(Ollama), Whisper STT, XTTS-v2 TTS가 GPU에서 돕니다(env 기본 `WHISPER_DEVICE=cuda`, `XTTS_DEVICE=cuda`). NVIDIA Blackwell(sm_120, 예: RTX 5050/5070Ti)에서 검증: 컨테이너 내 torch cu128 CUDA 동작, Ollama GPU 오프로드, faster-whisper float16, XTTS-v2 GPU 합성 모두 확인.
+LLM(Ollama)과 Whisper STT가 GPU에서 돕니다(env 기본 `WHISPER_DEVICE=cuda`). TTS는 기본값이 Edge TTS(온라인 한국어 음성)라 GPU를 쓰지 않습니다. NVIDIA Blackwell(sm_120, 예: RTX 5050/5070Ti)에서 검증: 컨테이너 내 torch cu128 CUDA 동작, Ollama GPU 오프로드, faster-whisper float16 모두 확인.
 
 GPU는 위 "실행 — Docker"의 OS별 override 파일로 켜집니다. 호스트 사전 준비는 OS마다 다릅니다:
 
@@ -137,7 +137,7 @@ docker run --rm --device nvidia.com/gpu=all ubuntu nvidia-smi -L   # GPU 보이
 **공통:**
 
 - 모델: 베이스 compose 기본은 `qwen2.5:3b`(8GB VRAM에서 도구호출 안정적). 더 무겁게(`qwen2.5:7b`, `qwen3:8b` 등) 쓰려면 `.env`의 `OLLAMA_CHAT_MODEL` 변경.
-- **GPU가 없거나 인식 실패 시 자동으로 CPU 폴백**(Whisper)하므로 안전합니다. 명시적으로 CPU만 쓰려면 override 파일 없이 베이스만 올리고 `.env`에 `WHISPER_DEVICE=cpu`, `XTTS_DEVICE=cpu`를 두세요.
+- **GPU가 없거나 인식 실패 시 자동으로 CPU 폴백**(Whisper)하므로 안전합니다. 명시적으로 CPU만 쓰려면 override 파일 없이 베이스만 올리고 `.env`에 `WHISPER_DEVICE=cpu`를 두세요.
 
 - 데이터(메모리 DB), Whisper 캐시, Piper 음성은 named volume에 영속됩니다.
 - 셀프봇 영상 송출 의존성은 이미지에 기본 포함하지 않습니다. 쓰려면 컨테이너에서 `cd /app/bot && bun add discord.js-selfbot-v13 @dank074/discord-video-stream` 후 재시작(또는 Dockerfile에 추가).
@@ -243,7 +243,7 @@ cd bot; bun run register; bun run start   # 창 2: (일반 봇이면) 슬래시
 - `BRIDGE_URL` — 봇이 호출할 브릿지 주소 (기본 `http://127.0.0.1:8765`)
 - `STREAM_BACKEND`, `DISCORD_SELFBOT_TOKEN`, `NOVNC_URL` — 화면 송출
 - `VNC_DISPLAY=:1`, `VNC_RESOLUTION`, `VNC_FRAMERATE`, `VNC_BITRATE_KBPS` — 캡처
-- `WHISPER_DEVICE/COMPUTE_TYPE`, `XTTS_DEVICE` — GPU 호스트면 `cuda`/`float16`, CPU 전용이면 `cpu`(GPU 자체는 OS별 override compose 파일로 켬)
+- `WHISPER_DEVICE/COMPUTE_TYPE` — GPU 호스트면 `cuda`/`float16`, CPU 전용이면 `cpu`(GPU 자체는 OS별 override compose 파일로 켬)
 - `OLLAMA_CHAT_MODEL` — 두뇌 LLM (기본 `qwen2.5:3b`)
 - `COMPOSE_FILE` — OS별 GPU override를 매번 `-f`로 안 치고 자동 적용 (위 "실행 — Docker" 참고)
 - `output_language` — 출력 언어 고정(비우면 사용자 언어). 설정 웹 UI(`/settings`)에서 바꾸면 env 기본값보다 우선하며 컨테이너 재생성 후에도 유지됩니다.

bridge/melo_worker.py

@@ -1,30 +1,25 @@
 """
-XTTS worker
-===========
+MeloTTS worker
+==============
 
-A tiny HTTP service that keeps a Coqui XTTS-v2 voice warm and synthesises
-speech on demand. It mirrors ``melo_worker.py`` (same ``/synth`` + ``/health``
-contract, same PCM16 WAV output) so the bridge can talk to either worker the
-same way.
+A tiny, dependency-light HTTP service that keeps a MeloTTS voice warm and
+synthesises speech on demand. It runs in its OWN Python venv (``/opt/melo`` in
+the container) so the heavy MeloTTS/torch/transformers stack stays isolated
+from the slim brain-bridge venv (which pins ``numpy<2`` for faster-whisper).
 
-XTTS-v2 is a natural, multilingual neural voice. The default speaker is the
-built-in female studio voice "Ana Florence" speaking Korean — the voice this
-deployment uses in place of MeloTTS. No reference WAV is needed for the
-built-in studio speakers.
-
-It runs in its OWN Python venv (``/opt/xtts`` in the container) so the heavy
-Coqui TTS / torch stack stays isolated from the slim brain-bridge venv.
+The bridge's ``synthesize()`` POSTs ``{"text": "..."}`` here and gets back a
+16-bit PCM WAV. The MeloTTS model is loaded once at startup and reused, so each
+request only pays inference cost, not model-load cost.
 
 Config (env):
-    XTTS_WORKER_HOST   bind host            (default 127.0.0.1)
-    XTTS_WORKER_PORT   bind port            (default 8771)
-    XTTS_MODEL         Coqui model id       (default tts_models/multilingual/multi-dataset/xtts_v2)
-    XTTS_SPEAKER       built-in speaker     (default "Ana Florence")
-    XTTS_LANGUAGE      synthesis language   (default ko)
-    XTTS_DEVICE        torch device         (default cpu; compose sets cuda)
+    MELO_WORKER_HOST   bind host          (default 127.0.0.1)
+    MELO_WORKER_PORT   bind port          (default 8770)
+    MELO_LANGUAGE      MeloTTS language   (default KR)
+    MELO_SPEED         speaking rate      (default 1.5  -> the approved "150")
+    MELO_DEVICE        torch device       (default cpu)
 
 Run:
-    /opt/xtts/bin/python -m bridge.xtts_worker
+    /opt/melo/bin/python -m bridge.melo_worker
 """
 
 from __future__ import annotations
@@ -38,72 +33,94 @@ import threading
 import wave
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 
-# XTTS-v2 is gated behind a one-time license prompt; agreeing here keeps the
-# load non-interactive in a container. XTTS-v2 is non-commercial (CPML).
-os.environ.setdefault("COQUI_TOS_AGREED", "1")
+HOST = os.environ.get("MELO_WORKER_HOST", "127.0.0.1")
+PORT = int(os.environ.get("MELO_WORKER_PORT", "8770"))
+LANGUAGE = os.environ.get("MELO_LANGUAGE", "KR")
 
-HOST = os.environ.get("XTTS_WORKER_HOST", "127.0.0.1")
-PORT = int(os.environ.get("XTTS_WORKER_PORT", "8771"))
-MODEL = os.environ.get("XTTS_MODEL", "tts_models/multilingual/multi-dataset/xtts_v2")
-SPEAKER = os.environ.get("XTTS_SPEAKER", "Ana Florence")
-LANGUAGE = os.environ.get("XTTS_LANGUAGE", "ko")
-DEVICE = os.environ.get("XTTS_DEVICE", "cpu")
 
-# Model is loaded once, guarded by a lock because TTS inference is not
-# guaranteed thread-safe.
+def _resolve_speed() -> float:
+    """Speaking rate: the settings-UI value (runtime config JSON) wins, else the
+    MELO_SPEED env, else 1.5. Read at startup; the settings UI restarts this
+    worker on apply so a new value takes effect."""
+    try:
+        cp = os.environ.get("JARVIS_CONFIG_PATH", "/app/config/jarvis.json")
+        v = json.loads(open(cp, encoding="utf-8").read()).get("melo_speed")
+        if v is not None:
+            return float(v)
+    except Exception:
+        pass
+    try:
+        return float(os.environ.get("MELO_SPEED", "1.5"))
+    except ValueError:
+        return 1.5
+
+
+SPEED = _resolve_speed()
+DEVICE = os.environ.get("MELO_DEVICE", "cpu")
+
+# Model + speaker id are loaded once, guarded by a lock because MeloTTS
+# inference is not guaranteed thread-safe.
 _model = None
+_speaker_id = None
 _model_lock = threading.Lock()
 _load_error: str | None = None
 
 
 def _ensure_model() -> None:
-    global _model, _load_error
+    global _model, _speaker_id, _load_error
     if _model is not None or _load_error is not None:
         return
     with _model_lock:
         if _model is not None or _load_error is not None:
             return
         try:
-            from TTS.api import TTS  # type: ignore
+            from melo.api import TTS  # type: ignore
 
-            model = TTS(MODEL).to(DEVICE)
+            model = TTS(language=LANGUAGE, device=DEVICE)
+            # spk2id is a melo HParams object (dict-like, supports __getitem__,
+            # __contains__, keys) but NOT .get(). The KR model exposes a single
+            # 'KR' speaker; fall back to the first id for other languages.
+            spk_map = model.hps.data.spk2id
+            keys = list(spk_map.keys())
+            speaker_id = spk_map[LANGUAGE] if LANGUAGE in spk_map else spk_map[keys[0]]
             _model = model
-            # Warm once: the first GPU synth pays a one-off kernel-init cost
-            # that would otherwise land on the user's first reply.
+            _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(
-                    text="워밍업", speaker=SPEAKER, language=LANGUAGE, file_path=_wp
-                )
+                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"[xtts-worker] warmup synth skipped: {_we}", flush=True)
+                print(f"[melo-worker] warmup synth skipped: {_we}", flush=True)
             print(
-                f"[xtts-worker] ready (model={MODEL} speaker={SPEAKER!r} "
-                f"language={LANGUAGE} device={DEVICE})",
+                f"[melo-worker] ready (lang={LANGUAGE} speed={SPEED} "
+                f"device={DEVICE} speakers={list(spk_map.keys())})",
                 flush=True,
             )
         except Exception as e:  # pragma: no cover - depends on local model files
             _load_error = f"{type(e).__name__}: {e}"
-            print(f"[xtts-worker] model load FAILED: {_load_error}", flush=True)
+            print(f"[melo-worker] model load FAILED: {_load_error}", flush=True)
 
 
 def _synthesize(text: str) -> bytes:
     """Synthesise ``text`` to a 16-bit PCM WAV (bytes)."""
     _ensure_model()
     if _model is None:
-        raise RuntimeError(_load_error or "xtts model unavailable")
+        raise RuntimeError(_load_error or "melo model unavailable")
+    # MeloTTS writes to a file via soundfile; render to a container-disk temp
+    # file (NOT tmpfs), read it back, then drop it.
     with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
         tmp_path = tmp.name
     try:
         with _model_lock:
-            _model.tts_to_file(
-                text=text, speaker=SPEAKER, language=LANGUAGE, file_path=tmp_path
-            )
+            _model.tts_to_file(text, _speaker_id, tmp_path, speed=SPEED)
         with open(tmp_path, "rb") as f:
             raw = f.read()
     finally:
@@ -115,15 +132,16 @@ def _synthesize(text: str) -> bytes:
 
 
 def _ensure_pcm16_wav(raw: bytes) -> bytes:
-    """Guarantee a 16-bit PCM WAV. Coqui writes float/other WAVs; the Discord
-    playback path tolerates both, but we normalise to PCM16 so the contract
-    matches the previous Melo/Piper output (mono, file's own sample rate)."""
+    """Guarantee a 16-bit PCM WAV. MeloTTS/soundfile usually emit float WAVs;
+    the Discord playback path (ffmpeg) tolerates both, but we normalise to
+    PCM16 so the contract matches the previous Piper output."""
     try:
         with wave.open(io.BytesIO(raw), "rb") as wf:
             if wf.getsampwidth() == 2:
                 return raw  # already PCM16
     except wave.Error:
         pass
+    # Non-PCM16 (e.g. float) — convert with soundfile if available.
     try:
         import numpy as np
         import soundfile as sf
@@ -141,7 +159,7 @@ def _ensure_pcm16_wav(raw: bytes) -> bytes:
             wf.writeframes(pcm)
         return buf.getvalue()
     except Exception:
-        return raw  # last resort: hand back whatever XTTS produced
+        return raw  # last resort: hand back whatever MeloTTS produced
 
 
 class _Handler(BaseHTTPRequestHandler):
@@ -194,7 +212,7 @@ def main() -> int:
     # Warm the model at startup so the first Discord turn isn't slow.
     _ensure_model()
     server = ThreadingHTTPServer((HOST, PORT), _Handler)
-    print(f"[xtts-worker] listening on http://{HOST}:{PORT}", flush=True)
+    print(f"[melo-worker] listening on http://{HOST}:{PORT}", flush=True)
     try:
         server.serve_forever()
     except KeyboardInterrupt:

bridge/requirements-bridge.txt

@@ -21,7 +21,11 @@ nvidia-cudnn-cu12
 # --- Bridge HTTP service ---
 flask>=3.0.0
 
-# --- Text-to-speech (Piper) ---
+# --- Text-to-speech ---
+# Edge TTS: the primary Korean voice (online MS neural). Lightweight (httpx);
+# emits MP3, transcoded to PCM16 by the system ffmpeg in the bridge.
+edge-tts>=6.1.0
+# Piper: offline English fallback.
 piper-tts>=1.3.0
 
 # --- Built-in tools (lazily imported; needed for full functionality) ---

bridge/server.py

@@ -87,13 +87,11 @@ VAD_MIN_SPEECH_MS = int(os.environ.get("VAD_MIN_SPEECH_MS", "200"))
 # Korean phrase decoded as Chinese) and shaves a little latency. Empty = auto.
 STT_LANGUAGE = os.environ.get("STT_LANGUAGE", "ko").strip() or None
 
-# TTS engine: "xtts" (Coqui XTTS-v2 natural Korean voice, the warm worker) is
-# the primary voice; Piper is kept as a fallback only if explicitly enabled. Set
-# TTS_ENGINE=piper to disable the neural Korean voice entirely. "melo" is still
-# accepted for backward compatibility but is no longer built into the image.
+# TTS engine: "edge" (Microsoft Edge TTS, natural Korean neural voice) is the
+# primary voice. "melo" (a warm MeloTTS worker) and "piper" remain selectable.
 def _tts_engine_setting() -> str:
     """TTS engine: settings-UI value (runtime config JSON) wins, else env, else
-    xtts. Read at startup; the settings UI restarts the bridge on apply."""
+    edge. Read at startup; the settings UI restarts the bridge on apply."""
     try:
         _cp = os.environ.get("JARVIS_CONFIG_PATH", "/app/config/jarvis.json")
         _v = json.loads(open(_cp, encoding="utf-8").read()).get("tts_engine")
@@ -101,29 +99,23 @@ def _tts_engine_setting() -> str:
             return str(_v).strip().lower()
     except Exception:
         pass
-    return os.environ.get("TTS_ENGINE", "xtts").strip().lower()
+    return os.environ.get("TTS_ENGINE", "edge").strip().lower()
 
 
 TTS_ENGINE = _tts_engine_setting()
-# Coqui XTTS-v2 worker (the natural Korean voice).
-XTTS_WORKER_URL = os.environ.get("XTTS_WORKER_URL", "http://127.0.0.1:8771")
-XTTS_TIMEOUT = float(os.environ.get("XTTS_TIMEOUT", "30"))
-# Legacy MeloTTS worker (no longer built into the image; kept for back-compat
-# if someone runs an old worker out-of-band).
+# Edge TTS (online MS neural voice). Voice + rate are env-driven so they can be
+# changed without code. Default: Korean "Hyunsu" multilingual voice at +45%
+# (≈1.45×), the chosen settings. NOTE: edge synthesis sends the reply TEXT to
+# Microsoft's servers and needs internet — an intentional privacy trade-off for
+# the more natural voice.
+EDGE_TTS_VOICE = os.environ.get("EDGE_TTS_VOICE", "ko-KR-HyunsuMultilingualNeural").strip()
+EDGE_TTS_RATE = os.environ.get("EDGE_TTS_RATE", "+45%").strip()
 MELO_WORKER_URL = os.environ.get("MELO_WORKER_URL", "http://127.0.0.1:8770")
 MELO_TIMEOUT = float(os.environ.get("MELO_TIMEOUT", "30"))
 # Do NOT silently fall back to the English Piper voice on a neural-voice failure:
-# speaking Korean text through an English voice produces mangled audio. Default
-# is neural-only (return no audio on failure); set XTTS_FALLBACK_PIPER=1 (or the
-# legacy MELO_FALLBACK_PIPER=1) to opt into the Piper fallback.
-def _truthy_env(*names: str) -> bool:
-    for _n in names:
-        if os.environ.get(_n, "").strip().lower() in ("1", "true", "yes", "on"):
-            return True
-    return False
-
-
-NEURAL_FALLBACK_PIPER = _truthy_env("XTTS_FALLBACK_PIPER", "MELO_FALLBACK_PIPER")
+# speaking Korean through an English voice produces mangled audio. Default is
+# neural-only (return no audio on failure); set MELO_FALLBACK_PIPER=1 to opt in.
+MELO_FALLBACK_PIPER = os.environ.get("MELO_FALLBACK_PIPER", "0") in ("1", "true", "True", "yes", "on")
 
 # ---------------------------------------------------------------------------
 # Lazy singletons. The first request pays the model-load cost; afterwards the
@@ -315,38 +307,75 @@ def _coerce_bool(value) -> Optional[bool]:
     return str(value).strip().lower() in ("1", "true", "yes", "on")
 
 
-def _worker_synthesize(name: str, url: str, timeout: float, text: str) -> Optional[bytes]:
-    """POST text to a warm TTS worker's /synth and return its WAV bytes, or None
-    on any failure so the caller can decide whether to fall back."""
+def _edge_synthesize(text: str) -> Optional[bytes]:
+    """Synthesise via Microsoft Edge TTS (online neural voice) and return a
+    16-bit PCM WAV, or None on any failure. Edge emits MP3; we transcode to
+    PCM16 mono with the system ffmpeg, writing to a temp file (seekable) so the
+    WAV header carries a correct length. Needs internet."""
+    import asyncio
+    import subprocess
+    import tempfile
+
+    try:
+        import edge_tts  # type: ignore
+
+        async def _gen() -> bytes:
+            comm = edge_tts.Communicate(text, EDGE_TTS_VOICE, rate=EDGE_TTS_RATE)
+            buf = bytearray()
+            async for chunk in comm.stream():
+                if chunk.get("type") == "audio":
+                    buf.extend(chunk["data"])
+            return bytes(buf)
+
+        mp3 = asyncio.run(_gen())
+        if not mp3:
+            print("[bridge] edge TTS returned no audio", flush=True)
+            return None
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as t:
+            out_path = t.name
+        try:
+            proc = subprocess.run(
+                ["ffmpeg", "-hide_banner", "-loglevel", "error", "-y",
+                 "-i", "pipe:0", "-ac", "1", "-ar", "24000",
+                 "-acodec", "pcm_s16le", out_path],
+                input=mp3, capture_output=True,
+            )
+            if proc.returncode != 0:
+                print(f"[bridge] edge ffmpeg transcode failed: {proc.stderr.decode('utf-8','ignore')[:200]}", flush=True)
+                return None
+            with open(out_path, "rb") as f:
+                return f.read()
+        finally:
+            try:
+                os.unlink(out_path)
+            except OSError:
+                pass
+    except Exception as e:  # pragma: no cover - network / dep dependent
+        print(f"[bridge] edge synth failed: {e}", flush=True)
+    return None
+
+
+def _melo_synthesize(text: str) -> Optional[bytes]:
+    """Synthesise via the warm MeloTTS worker (separate /opt/melo venv, Korean
+    speaker @ speed 1.5). Returns a 16-bit PCM WAV, or None on any failure so
+    the caller can fall back to Piper."""
     import urllib.request
 
     try:
         req = urllib.request.Request(
-            f"{url}/synth",
+            f"{MELO_WORKER_URL}/synth",
             data=json.dumps({"text": text}).encode("utf-8"),
             headers={"Content-Type": "application/json"},
         )
-        with urllib.request.urlopen(req, timeout=timeout) as resp:
+        with urllib.request.urlopen(req, timeout=MELO_TIMEOUT) as resp:
             if resp.status == 200:
                 return resp.read()
-            print(f"[bridge] {name} worker HTTP {resp.status}", flush=True)
+            print(f"[bridge] melo worker HTTP {resp.status}", flush=True)
     except Exception as e:  # pragma: no cover - worker may be down
-        print(f"[bridge] {name} worker unreachable: {e}", flush=True)
+        print(f"[bridge] melo worker unreachable: {e}", flush=True)
     return None
 
 
-def _xtts_synthesize(text: str) -> Optional[bytes]:
-    """Synthesise via the warm Coqui XTTS-v2 worker (separate /opt/xtts venv,
-    natural female Korean). Returns a 16-bit PCM WAV, or None on failure."""
-    return _worker_synthesize("xtts", XTTS_WORKER_URL, XTTS_TIMEOUT, text)
-
-
-def _melo_synthesize(text: str) -> Optional[bytes]:
-    """Legacy: synthesise via a MeloTTS worker if one is running out-of-band.
-    Returns a 16-bit PCM WAV, or None on any failure."""
-    return _worker_synthesize("melo", MELO_WORKER_URL, MELO_TIMEOUT, text)
-
-
 def _piper_synthesize(text: str) -> Optional[bytes]:
     """Fallback: synthesise with Piper (English voice). Returns WAV bytes."""
     _ensure_piper()
@@ -373,12 +402,11 @@ def _tts_ready() -> bool:
     """
     if not TTS_ENABLED:
         return True
-    _worker_health = {"xtts": XTTS_WORKER_URL, "melo": MELO_WORKER_URL}.get(TTS_ENGINE)
-    if _worker_health:
+    if TTS_ENGINE == "melo":
         import urllib.request
 
         try:
-            with urllib.request.urlopen(f"{_worker_health}/health", timeout=2) as resp:
+            with urllib.request.urlopen(f"{MELO_WORKER_URL}/health", timeout=2) as resp:
                 return resp.status == 200
         except Exception:
             return False
@@ -386,22 +414,20 @@ def _tts_ready() -> bool:
 
 
 def synthesize(text: str) -> Optional[bytes]:
-    """Synthesize text to a 16-bit PCM WAV. The primary voice is Coqui XTTS-v2
-    (natural female Korean) served by the warm xtts worker; Piper is used only
-    when explicitly enabled as a fallback. Returns None if TTS is off."""
+    """Synthesize text to a 16-bit PCM WAV. The primary voice is Edge TTS (a
+    natural Korean neural voice); "melo" uses the warm MeloTTS worker. For a
+    neural engine, Piper (English) is only used if explicitly enabled, since
+    speaking Korean through an English voice mangles it. Returns None if off."""
     if not TTS_ENABLED or not text.strip():
         return None
-    _neural = {"xtts": _xtts_synthesize, "melo": _melo_synthesize}.get(TTS_ENGINE)
+    _neural = {"edge": _edge_synthesize, "melo": _melo_synthesize}.get(TTS_ENGINE)
     if _neural is not None:
         audio = _neural(text)
         if audio:
             return audio
-        if not NEURAL_FALLBACK_PIPER:
+        if not MELO_FALLBACK_PIPER:
             # Neural-only: better silent than mangled English for Korean text.
-            print(
-                f"[bridge] {TTS_ENGINE} synth failed; no audio (Piper fallback disabled)",
-                flush=True,
-            )
+            print(f"[bridge] {TTS_ENGINE} synth failed; no audio (Piper fallback disabled)", flush=True)
             return None
         print(f"[bridge] {TTS_ENGINE} synth failed; falling back to Piper", flush=True)
     return _piper_synthesize(text)

bridge/settings_web.py

@@ -22,7 +22,7 @@ from typing import Any, Dict
 FIELDS = [
     ("ollama_chat_model", "LLM 모델", "model"),
     ("whisper_model", "STT(Whisper) 모델", "select:tiny,base,small,medium,large,large-v3"),
-    ("tts_engine", "TTS 엔진", "select:xtts,piper"),
+    ("tts_engine", "TTS 엔진", "select:edge,piper"),
     ("output_language", "출력 언어 (비우면 사용자 언어)", "text"),
     ("llm_thinking_enabled", "LLM 사고(thinking) 모드", "bool"),
     ("agentic_max_turns", "에이전트 최대 반복", "number:1:12:1"),
@@ -106,15 +106,15 @@ def _save(updates: Dict[str, Any]) -> None:
 
 
 def _apply() -> str:
-    # Restart the TTS worker + bridge AFTER this response is sent. Detached (new
-    # session) so the bridge being killed mid-restart doesn't drop the restart
-    # itself, and the HTTP client still receives this response.
+    # Restart the bridge AFTER this response is sent. Detached (new session) so
+    # the bridge being killed mid-restart doesn't drop the restart itself, and
+    # the HTTP client still receives this response. (Edge TTS has no worker.)
     try:
         subprocess.Popen(
-            ["sh", "-c", "sleep 1; supervisorctl restart xtts-worker bridge"],
+            ["sh", "-c", "sleep 1; supervisorctl restart bridge"],
             start_new_session=True,
         )
-        return "1초 후 브리지/TTS 워커가 재시작되어 반영됩니다."
+        return "1초 후 브리지가 재시작되어 반영됩니다."
     except Exception as e:  # pragma: no cover
         return str(e)
 

docker-compose.yml

@@ -66,15 +66,15 @@ services:
       WHISPER_MODEL: ${WHISPER_MODEL:-medium}
       WHISPER_DEVICE: ${WHISPER_DEVICE:-cuda}
       WHISPER_COMPUTE_TYPE: ${WHISPER_COMPUTE_TYPE:-float16}
-      # Coqui XTTS-v2 (natural female Korean voice, replaces MeloTTS) on the GPU
-      # (cu128 torch baked by docker/setup-xtts.sh). Set here WITH DEFAULTS so
-      # supervisord's %(ENV_XTTS_*)s passthrough always resolves and an .env
-      # override actually reaches the xtts-worker.
-      XTTS_DEVICE: ${XTTS_DEVICE:-cuda}
-      # Built-in studio speaker (female). Other options include "Daisy Studious",
-      # "Sofia Hellen", "Alma María", etc. — any XTTS-v2 studio speaker name.
-      XTTS_SPEAKER: ${XTTS_SPEAKER:-Ana Florence}
-      XTTS_LANGUAGE: ${XTTS_LANGUAGE:-ko}
+      # TTS engine. Rendered into /app/config/jarvis.json via envsubst (the
+      # bridge reads that JSON BEFORE the env, so it must carry the real engine,
+      # not a hardcoded one — otherwise Korean text is read by the English Piper
+      # voice). Default edge; .env can override (e.g. piper for offline).
+      TTS_ENGINE: ${TTS_ENGINE:-edge}
+      # Edge TTS voice + rate (the chosen natural Korean voice). NOTE: edge is an
+      # ONLINE engine — reply text is sent to Microsoft and needs internet.
+      EDGE_TTS_VOICE: ${EDGE_TTS_VOICE:-ko-KR-HyunsuMultilingualNeural}
+      EDGE_TTS_RATE: ${EDGE_TTS_RATE:-+45%}
       # Optional single-language lock for replies (empty = user's own language).
       OUTPUT_LANGUAGE: ${OUTPUT_LANGUAGE:-ko}
       # Drop the pre-loop planner LLM call to cut voice-reply latency on small

docker/entrypoint.sh

@@ -51,12 +51,18 @@ export JARVIS_CONFIG_PATH=/app/config/jarvis.json
 # the env-rendered config, so changes survive container recreate.
 if [ -f /data/jarvis-settings.json ]; then
   python3 - <<'PY' || true
-import json
+import json, os
 try:
     base = json.load(open("/app/config/jarvis.json"))
     ov = json.load(open("/data/jarvis-settings.json"))
     if isinstance(base, dict) and isinstance(ov, dict):
         base.update(ov)
+        # A stale persisted tts_engine from an earlier voice (melo/xtts, no
+        # longer built into the image) would override the configured engine and
+        # leave the bot silent. Reset those to the env-configured engine.
+        if base.get("tts_engine") in ("melo", "xtts"):
+            base["tts_engine"] = os.environ.get("TTS_ENGINE", "edge")
+            print(f"[entrypoint] reset stale tts_engine -> {base['tts_engine']}")
         json.dump(base, open("/app/config/jarvis.json", "w"), ensure_ascii=False, indent=2)
         print("[entrypoint] merged persistent settings overrides")
 except Exception as e:

docker/jarvis-config.template.json

@@ -6,7 +6,7 @@
   "ollama_chat_model": "${OLLAMA_CHAT_MODEL}",
   "intent_judge_model": "${OLLAMA_INTENT_MODEL}",
   "tts_enabled": true,
-  "tts_engine": "piper",
+  "tts_engine": "${TTS_ENGINE}",
   "tts_piper_model_path": "${TTS_PIPER_MODEL_PATH}",
   "whisper_model": "${WHISPER_MODEL}",
   "whisper_backend": "faster-whisper",

docker/setup-melo.sh

@@ -0,0 +1,80 @@
+#!/usr/bin/env bash
+# ============================================================================
+# Install a dedicated MeloTTS (Korean voice) venv at /opt/melo.
+#
+# Why a SEPARATE venv (not the brain-bridge /opt/venv):
+#   - MeloTTS pins old deps (transformers 4.27.4 / tokenizers 0.13.3 / fugashi)
+#     whose binary wheels exist only for cp311, so we use python3.11 here even
+#     though the image's default interpreter is 3.12.
+#   - It isolates the heavy torch/transformers stack from the slim bridge env,
+#     which pins numpy<2 for faster-whisper.
+#
+# torch is the CUDA (cu128) build so MeloTTS runs on the GPU alongside Ollama +
+# Whisper. CPU synth serialised under concurrent load (whisper STT + bot) and
+# blew TTS up to 7-8s per reply; on the GPU a sentence synthesises in ~0.3s.
+# cu128 is the Blackwell (sm_120) wheel verified on this host's RTX 5050.
+# The worker selects the device via MELO_DEVICE=cuda (compose).
+# ============================================================================
+set -euxo pipefail
+
+export DEBIAN_FRONTEND=noninteractive
+
+apt-get update
+# Build deps for fugashi / mecab-python3 + a system MeCab dict, plus python3.11.
+apt-get install -y --no-install-recommends \
+    software-properties-common build-essential pkg-config swig \
+    libmecab-dev mecab mecab-ipadic-utf8
+add-apt-repository -y ppa:deadsnakes/ppa
+apt-get update
+apt-get install -y --no-install-recommends python3.11 python3.11-venv python3.11-dev
+rm -rf /var/lib/apt/lists/*
+
+python3.11 -m venv /opt/melo
+/opt/melo/bin/pip install --no-cache-dir --upgrade pip wheel setuptools
+
+# CUDA (cu128) torch first, so MeloTTS's unpinned `torch` dep is already
+# satisfied with the GPU build. Pinned to the Blackwell-verified versions
+# (2.11.0+cu128) for reproducible rebuilds.
+/opt/melo/bin/pip install --no-cache-dir torch==2.11.0+cu128 torchaudio==2.11.0+cu128 \
+    --index-url https://download.pytorch.org/whl/cu128
+
+# MeloTTS from GitHub. The PyPI sdist is broken (its setup.py reads a
+# requirements.txt that is not shipped in the sdist), so install from the repo.
+# Pinned to a commit (not refs/heads/main) so rebuilds are reproducible.
+/opt/melo/bin/pip install --no-cache-dir \
+    "https://github.com/myshell-ai/MeloTTS/archive/209145371cff8fc3bd60d7be902ea69cbdb7965a.tar.gz"
+
+# Korean g2p backend. MeloTTS otherwise tries to pip-install this on the first
+# Korean request, which fails in a network-isolated container at runtime.
+/opt/melo/bin/pip install --no-cache-dir python-mecab-ko python-mecab-ko-dic
+
+# Remove the full `unidic` package (its dictionary is never downloaded, only a
+# stub) so mecab-python3 falls back to the bundled `unidic_lite` dict. Without
+# this, importing melo's Japanese module fails with a missing-mecabrc error.
+/opt/melo/bin/pip uninstall -y unidic || true
+
+# Pre-cache every model asset MeloTTS pulls at runtime, so the worker starts
+# offline and the first Discord turn pays no download cost. Importing melo.api
+# fetches the Japanese (tohoku-nlp/bert-base-japanese-v3) and Korean
+# (kykim/bert-kor-base) BERT tokenizers plus nltk g2p data; loading the KR voice
+# downloads the OpenVoice KR config+checkpoint, and a real synth pulls the
+# Korean BERT weights. All of these go through huggingface_hub.
+#
+# CRITICAL: at runtime docker-compose mounts the `whisper_cache` named volume
+# over /root/.cache/huggingface (for faster-whisper). That volume would SHADOW
+# anything baked into the default HF cache, so we pin the melo worker to a
+# DEDICATED, non-volume cache dir (/opt/melo-cache) here AND in supervisord, and
+# warm it once. nltk_data (/root/nltk_data) is not volume-mounted so it stays.
+export HF_HOME=/opt/melo-cache
+mkdir -p "$HF_HOME"
+MELO_LANGUAGE=KR HF_HOME=/opt/melo-cache /opt/melo/bin/python - <<'PY'
+import tempfile
+from melo.api import TTS
+
+model = TTS(language="KR", device="cpu")
+out = tempfile.mktemp(suffix=".wav")
+model.tts_to_file("초기화 워밍업입니다.", model.hps.data.spk2id["KR"], out, speed=1.5)
+print("[setup-melo] warm-up KR synth OK ->", out)
+PY
+
+echo "[setup-melo] MeloTTS venv ready at /opt/melo"

docker/setup-xtts.sh

@@ -1,72 +0,0 @@
-#!/usr/bin/env bash
-# ============================================================================
-# Install a dedicated Coqui XTTS-v2 (natural Korean voice) venv at /opt/xtts.
-#
-# Why a SEPARATE venv (not the brain-bridge /opt/venv or /opt/melo):
-#   - Coqui TTS pulls its own heavy torch/transformers stack; isolating it keeps
-#     the slim bridge env (numpy<2 for faster-whisper) untouched.
-#   - We use python3.11 (installed for the melo layer) because Coqui ships cp311
-#     wheels and torch cu128 is available for it.
-#
-# torch is the CUDA (cu128) build so XTTS runs on the GPU alongside Ollama +
-# Whisper. cu128 is the Blackwell (sm_120) wheel verified on this host.
-# The worker selects the device via XTTS_DEVICE=cuda (compose).
-#
-# XTTS-v2 is non-commercial (Coqui Public Model License). COQUI_TOS_AGREED=1
-# accepts it non-interactively so the model can load in a headless container.
-# ============================================================================
-set -euxo pipefail
-
-export DEBIAN_FRONTEND=noninteractive
-export COQUI_TOS_AGREED=1
-
-# Install python3.11 if not already present, so this layer is self-contained.
-if ! command -v python3.11 >/dev/null 2>&1; then
-    apt-get update
-    apt-get install -y --no-install-recommends software-properties-common
-    add-apt-repository -y ppa:deadsnakes/ppa
-    apt-get update
-    apt-get install -y --no-install-recommends python3.11 python3.11-venv python3.11-dev
-    rm -rf /var/lib/apt/lists/*
-fi
-
-python3.11 -m venv /opt/xtts
-/opt/xtts/bin/pip install --no-cache-dir --upgrade pip wheel setuptools
-
-# CUDA (cu128) torch first so Coqui's `torch` dep is satisfied with the GPU
-# build. Pinned to the Blackwell-verified versions for reproducible rebuilds.
-/opt/xtts/bin/pip install --no-cache-dir torch==2.11.0+cu128 torchaudio==2.11.0+cu128 \
-    --index-url https://download.pytorch.org/whl/cu128
-
-# Coqui TTS (maintained fork; provides the `TTS` package and XTTS-v2). The
-# [codec] extra pulls torchcodec, which torch >=2.9 requires for audio IO
-# (without it the import fails with TORCHCODEC_IMPORT_ERROR). torchcodec also
-# needs the system FFmpeg shared libs, which are present (ffmpeg apt package).
-/opt/xtts/bin/pip install --no-cache-dir "coqui-tts[codec]"
-
-# Pin transformers to the 4.57+ / <5 range. coqui-tts requires >=4.57 but does
-# NOT cap the upper bound, and transformers 5.x removed `isin_mps_friendly`
-# (used by XTTS's tortoise layer), so an unpinned install pulls 5.x and the
-# model import fails with "cannot import name 'isin_mps_friendly'". Pin <5.
-/opt/xtts/bin/pip install --no-cache-dir "transformers>=4.57,<5"
-
-# Pre-bake the XTTS-v2 model so the worker starts offline and the first Discord
-# turn pays no download cost. The model is cached under TTS_HOME; we pin a
-# DEDICATED, non-volume dir (/opt/xtts-cache) AND set it in supervisord, because
-# runtime volume mounts (whisper_cache over /root/.cache) must not shadow it.
-export TTS_HOME=/opt/xtts-cache
-mkdir -p "$TTS_HOME"
-COQUI_TOS_AGREED=1 TTS_HOME=/opt/xtts-cache XTTS_SPEAKER="Ana Florence" \
-    /opt/xtts/bin/python - <<'PY'
-import os
-os.environ["COQUI_TOS_AGREED"] = "1"
-from TTS.api import TTS
-
-speaker = os.environ.get("XTTS_SPEAKER", "Ana Florence")
-model = TTS("tts_models/multilingual/multi-dataset/xtts_v2")  # downloads to TTS_HOME
-out = "/tmp/xtts_warm.wav"
-model.tts_to_file(text="초기화 워밍업입니다.", speaker=speaker, language="ko", file_path=out)
-print("[setup-xtts] warm-up KR synth OK ->", out, "speaker:", speaker)
-PY
-
-echo "[setup-xtts] Coqui XTTS-v2 venv ready at /opt/xtts (cache /opt/xtts-cache)"

docker/supervisord.conf

@@ -49,28 +49,8 @@ stdout_logfile_maxbytes=0
 stderr_logfile=/dev/stderr
 stderr_logfile_maxbytes=0
 
-[program:xtts-worker]
-; Warm Coqui XTTS-v2 Korean voice (natural female "Ana Florence") in its own
-; py3.11 venv. The bridge's synthesize() POSTs here; if this is down the bridge
-; falls back to Piper (English) only when XTTS_FALLBACK_PIPER=1.
-command=/app/docker/run-if-role.sh full,bot /opt/xtts/bin/python /app/bridge/xtts_worker.py
-directory=/app
-; TTS_HOME points at the dedicated, image-baked XTTS cache (warmed in
-; setup-xtts.sh). The brain's whisper_cache volume is mounted over
-; /root/.cache, so a dedicated non-volume cache dir avoids the baked model being
-; shadowed and re-downloaded (which would fail if the host is offline).
-; XTTS_DEVICE / XTTS_SPEAKER / XTTS_LANGUAGE inherit from the container env
-; (compose sets them with defaults: cuda / "Ana Florence" / ko). supervisord
-; interpolates %(ENV_x)s from its own environment, which is the container's — so
-; these must always be set in the env (compose guarantees it) or this expansion
-; fails at startup. COQUI_TOS_AGREED accepts the non-commercial XTTS license.
-environment=XTTS_DEVICE="%(ENV_XTTS_DEVICE)s",XTTS_SPEAKER="%(ENV_XTTS_SPEAKER)s",XTTS_LANGUAGE="%(ENV_XTTS_LANGUAGE)s",XTTS_WORKER_HOST="127.0.0.1",XTTS_WORKER_PORT="8771",TTS_HOME="/opt/xtts-cache",COQUI_TOS_AGREED="1"
-priority=280
-autorestart=true
-stdout_logfile=/dev/stdout
-stdout_logfile_maxbytes=0
-stderr_logfile=/dev/stderr
-stderr_logfile_maxbytes=0
+# (No TTS worker program: the default Edge TTS engine synthesises in-process in
+# the bridge via the `edge-tts` package — no warm model/worker is needed.)
 
 [program:bridge]
 command=/app/docker/run-if-role.sh full,bot /opt/venv/bin/python -m bridge.server

src/jarvis/config.py

@@ -608,7 +608,11 @@ def load_settings() -> Settings:
     active_profiles = _ensure_list(merged.get("active_profiles"))
     tts_enabled = bool(merged.get("tts_enabled", True))
     tts_engine = str(merged.get("tts_engine", "piper")).lower()
-    if tts_engine not in ("piper", "chatterbox"):
+    # "edge" (Microsoft Edge TTS) is the containerized bridge's Korean voice;
+    # "melo" is the legacy warm-worker voice. Both are multilingual, so they must
+    # be preserved here — coercing them to "piper" would mislabel the engine as
+    # English-only in reply_language_directive().
+    if tts_engine not in ("piper", "chatterbox", "edge", "melo"):
         tts_engine = "piper"  # Default to piper if invalid value
     tts_voice_val = merged.get("tts_voice")
     tts_voice = None if tts_voice_val in (None, "", "null") else str(tts_voice_val)

tests/test_system_prompt.py

@@ -101,14 +101,6 @@ class TestReplyLanguageDirective:
         # user's own language, so no directive.
         assert reply_language_directive(None, "melo") is None
 
-    def test_xtts_is_multilingual(self):
-        # XTTS-v2 (the Korean voice) is not English-only: no lock → free, and a
-        # lock is honoured (not overridden to English).
-        assert reply_language_directive(None, "xtts") is None
-        directive = reply_language_directive("Korean", "xtts")
-        assert directive is not None and "Korean" in directive
-        assert directive != ENGLISH_ONLY_DIRECTIVE
-
     def test_unknown_tts_defaults_to_english_only(self):
         # Preserves the original getattr(cfg, 'tts_engine', 'piper') default:
         # an unknown/missing engine is treated conservatively as English-only.
@@ -118,6 +110,14 @@ class TestReplyLanguageDirective:
         directive = reply_language_directive("Korean", "melo")
         assert directive is not None and "Korean" in directive
 
+    def test_edge_is_multilingual(self):
+        # Edge TTS (the default Korean voice) is not English-only: no lock → the
+        # user's own language, and a lock is honoured (not forced to English).
+        assert reply_language_directive(None, "edge") is None
+        directive = reply_language_directive("Korean", "edge")
+        assert directive is not None and "Korean" in directive
+        assert directive != ENGLISH_ONLY_DIRECTIVE
+
 
 class TestLoadAgentInstructions:
     """Operator can extend the reply LLM's system prompt by dropping *.md files

tests/test_tts_engine_config.py

@@ -0,0 +1,35 @@
+"""The container's TTS engine must be env-driven, not hardcoded.
+
+Regression for a bug where docker/jarvis-config.template.json hardcoded
+`"tts_engine": "piper"`. The bridge reads the rendered /app/config/jarvis.json
+*before* the environment, so a hardcoded "piper" overrode `TTS_ENGINE=melo` in
+.env and the bot read Korean text with the English Piper voice ("foreign
+accent"). The template must carry `${TTS_ENGINE}` so envsubst (entrypoint.sh)
+renders whatever engine the deployment configured.
+"""
+
+import json
+import string
+from pathlib import Path
+
+TEMPLATE = Path(__file__).resolve().parent.parent / "docker" / "jarvis-config.template.json"
+
+
+def _render(**env) -> dict:
+    """Mimic entrypoint.sh `envsubst < template`: substitute env vars, leaving
+    any unset ones as literal text (valid JSON string values)."""
+    raw = TEMPLATE.read_text(encoding="utf-8")
+    return json.loads(string.Template(raw).safe_substitute(**env))
+
+
+def test_template_does_not_hardcode_an_engine():
+    raw = TEMPLATE.read_text(encoding="utf-8")
+    assert '"tts_engine": "${TTS_ENGINE}"' in raw
+    assert '"tts_engine": "piper"' not in raw
+    assert '"tts_engine": "melo"' not in raw
+
+
+def test_rendered_engine_follows_env():
+    assert _render(TTS_ENGINE="melo")["tts_engine"] == "melo"
+    assert _render(TTS_ENGINE="piper")["tts_engine"] == "piper"
+    assert _render(TTS_ENGINE="xtts")["tts_engine"] == "xtts"