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

@@ -34,18 +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: "melo" (default) uses the MeloTTS Korean voice served by the warm
-# melo-worker (Korean speaker, speed 1.5). Set to "piper" to use Piper directly.
-TTS_ENGINE=melo
-# Melo-only by default: if MeloTTS 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.
+# 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
-# Where the bridge reaches the in-container MeloTTS worker, and how long it
-# waits for a synthesis. Speaking rate is set on the worker via MELO_SPEED.
-MELO_WORKER_URL=http://127.0.0.1:8770
-MELO_TIMEOUT=30
-MELO_SPEED=1.5
 
 # ---------------------------------------------------------------------------
 # Jarvis brain (Ollama-backed). In Docker these populate the rendered

Dockerfile

@@ -65,20 +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
 
-# --- MeloTTS Korean voice (separate /opt/melo py3.11 venv; see setup-melo.sh).
-#     Heavy layer (torch CPU + transformers + MeCab); placed before the app
-#     COPY so it stays cached across source-only changes. ---
-COPY docker/setup-melo.sh /app/docker/setup-melo.sh
-# Strip CR before running: a Windows checkout (autocrlf) yields CRLF, which makes
-# bash read line 18 as `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-melo.sh && bash /app/docker/setup-melo.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 melo 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, MELO_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, MeloTTS가 GPU에서 돕니다(env 기본 `WHISPER_DEVICE=cuda`, `MELO_DEVICE=cuda`). NVIDIA Blackwell(sm_120, 예: RTX 5050/5070Ti)에서 검증: 컨테이너 내 torch cu128 CUDA 동작, Ollama GPU 오프로드, faster-whisper float16, MeloTTS 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`, `MELO_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`, `MELO_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/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

@@ -87,12 +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: "melo" (MeloTTS Korean speaker, the warm worker) is the primary
-# voice; Piper is kept as a fallback if the worker is unreachable. Set
-# TTS_ENGINE=piper to disable MeloTTS entirely.
+# 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
-    melo. 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")
@@ -100,16 +99,22 @@ def _tts_engine_setting() -> str:
             return str(_v).strip().lower()
     except Exception:
         pass
-    return os.environ.get("TTS_ENGINE", "melo").strip().lower()
+    return os.environ.get("TTS_ENGINE", "edge").strip().lower()
 
 
 TTS_ENGINE = _tts_engine_setting()
+# 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"))
-# When MeloTTS is the engine, do NOT silently fall back to the English Piper
-# voice on failure: speaking Korean text through an English voice produces
-# mangled audio. Default is melo-only (return no audio on failure); set
-# MELO_FALLBACK_PIPER=1 to opt into the Piper fallback.
+# Do NOT silently fall back to the English Piper voice on a neural-voice failure:
+# 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")
 
 # ---------------------------------------------------------------------------
@@ -302,6 +307,54 @@ def _coerce_bool(value) -> Optional[bool]:
     return str(value).strip().lower() in ("1", "true", "yes", "on")
 
 
+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
@@ -361,20 +414,22 @@ def _tts_ready() -> bool:
 
 
 def synthesize(text: str) -> Optional[bytes]:
-    """Synthesize text to a 16-bit PCM WAV. The primary voice is MeloTTS
-    (Korean speaker, speed 1.5) served by the warm melo worker; Piper is a
-    fallback if the worker is unavailable. 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
-    if TTS_ENGINE == "melo":
-        audio = _melo_synthesize(text)
+    _neural = {"edge": _edge_synthesize, "melo": _melo_synthesize}.get(TTS_ENGINE)
+    if _neural is not None:
+        audio = _neural(text)
         if audio:
             return audio
         if not MELO_FALLBACK_PIPER:
-            # Melo-only: better silent than mangled English for Korean text.
-            print("[bridge] melo synth failed; no audio (Piper fallback disabled)", flush=True)
+            # Neural-only: better silent than mangled English for Korean text.
+            print(f"[bridge] {TTS_ENGINE} synth failed; no audio (Piper fallback disabled)", flush=True)
             return None
-        print("[bridge] melo synth failed; falling back to Piper", flush=True)
+        print(f"[bridge] {TTS_ENGINE} synth failed; falling back to Piper", flush=True)
     return _piper_synthesize(text)
 
 

bridge/settings_web.py

@@ -22,8 +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:melo,piper"),
-    ("melo_speed", "TTS 속도 (MeloTTS)", "number:0.5:2.5:0.1"),
+    ("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"),
@@ -54,9 +53,7 @@ def _current() -> Dict[str, Any]:
     cfg = _read_config()
     out: Dict[str, Any] = {}
     for k in _KEYS:
-        if k == "melo_speed":
-            out[k] = cfg.get("melo_speed", os.environ.get("MELO_SPEED", "1.5"))
-        elif k == "output_language":
+        if k == "output_language":
             out[k] = cfg.get("output_language", os.environ.get("OUTPUT_LANGUAGE", ""))
         else:
             out[k] = cfg.get(k, "")
@@ -78,12 +75,7 @@ def _coerce(updates: Dict[str, Any]) -> Dict[str, Any]:
     for k, v in updates.items():
         if k not in _KEYS:
             continue
-        if k == "melo_speed":
-            try:
-                v = float(v)
-            except (TypeError, ValueError):
-                continue
-        elif k == "agentic_max_turns":
+        if k == "agentic_max_turns":
             try:
                 v = int(v)
             except (TypeError, ValueError):
@@ -114,15 +106,15 @@ def _save(updates: Dict[str, Any]) -> None:
 
 
 def _apply() -> str:
-    # Restart melo + 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 melo-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

@@ -66,9 +66,15 @@ services:
       WHISPER_MODEL: ${WHISPER_MODEL:-medium}
       WHISPER_DEVICE: ${WHISPER_DEVICE:-cuda}
       WHISPER_COMPUTE_TYPE: ${WHISPER_COMPUTE_TYPE:-float16}
-      # MeloTTS on the GPU (cu128 torch baked by docker/setup-melo.sh). CPU synth
-      # serialised under load and pushed TTS to 7-8s; GPU does ~0.3s/sentence.
-      MELO_DEVICE: ${MELO_DEVICE:-cuda}
+      # 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/supervisord.conf

@@ -49,28 +49,8 @@ stdout_logfile_maxbytes=0
 stderr_logfile=/dev/stderr
 stderr_logfile_maxbytes=0
 
-[program:melo-worker]
-; Warm MeloTTS Korean voice (speed 1.5) in its own py3.11 venv. The bridge's
-; synthesize() POSTs here; if this is down the bridge falls back to Piper.
-command=/app/docker/run-if-role.sh full,bot /opt/melo/bin/python /app/bridge/melo_worker.py
-directory=/app
-; HF_HOME points at the dedicated, image-baked melo cache (warmed in
-; setup-melo.sh). The brain's whisper_cache volume is mounted over
-; /root/.cache/huggingface, so without this the pre-cached BERT + KR checkpoint
-; would be shadowed and re-downloaded (and would fail if the host is offline).
-; HF_HUB_OFFLINE/TRANSFORMERS_OFFLINE force pure-cache reads: the pinned old
-; transformers/huggingface_hub otherwise retry the network on every load and
-; error out instead of falling back to the (complete) baked cache.
-; MELO_DEVICE inherits from the container env (compose sets it; default cuda)
-; so the worker runs MeloTTS on the GPU. supervisord interpolates %(ENV_x)s
-; from its own environment, which is the container's.
-environment=MELO_LANGUAGE="KR",MELO_SPEED="1.5",MELO_DEVICE="%(ENV_MELO_DEVICE)s",MELO_WORKER_HOST="127.0.0.1",MELO_WORKER_PORT="8770",HF_HOME="/opt/melo-cache",HF_HUB_OFFLINE="1",TRANSFORMERS_OFFLINE="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

@@ -110,6 +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"