feat: use Edge TTS (Korean Hyunsu voice @ +45%) as the default voice

The user chose Microsoft Edge TTS, voice ko-KR-HyunsuMultilingualNeural at rate
+45% (~1.45x), as the natural Korean voice. Wire it into the bridge and make it
the default engine.

- bridge/server.py: _edge_synthesize() calls edge-tts and transcodes the MP3 to
  PCM16 mono WAV with the system ffmpeg (temp file for a correct header);
  TTS_ENGINE default -> edge; EDGE_TTS_VOICE / EDGE_TTS_RATE env-driven
- requirements-bridge.txt: add edge-tts (lightweight; httpx)
- compose/.env.example/README: TTS_ENGINE=edge + EDGE_TTS_* knobs; note the
  online/privacy trade-off (reply text is sent to Microsoft, needs internet)
- drop the now-unused MeloTTS build layer (Dockerfile) and melo-worker
  (supervisord) — edge synthesises in-process, no model/worker baked, slimmer
  and faster image; settings UI engine list -> edge/piper, restart only bridge

Verified on host: edge-tts -> ffmpeg yields a valid 16-bit mono 24kHz WAV;
envsubst renders tts_engine=edge; docker build --check + 26 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
+# TTS engine: "edge" (default) uses Microsoft Edge TTS, a natural Korean neural
-# melo-worker (Korean speaker, speed 1.5). Set to "piper" to use Piper directly.
+# voice. Set to "piper" for the offline English voice. NOTE: edge is ONLINE —
-TTS_ENGINE=melo
+# reply text is sent to Microsoft's servers and needs internet.
-# Melo-only by default: if MeloTTS synthesis fails the bridge returns no audio
+TTS_ENGINE=edge
-# rather than speaking Korean through the English Piper voice (which mangles it).
+# Edge voice + speaking rate. Rate is a percentage (+45% ≈ 1.45×). Korean voices:
-# Set to 1 only if you explicitly want the Piper fallback.
+# 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).
+# --- Korean voice: Microsoft Edge TTS (online neural). No model is baked — the
-#     Heavy layer (torch CPU + transformers + MeCab); placed before the app
+#     `edge-tts` pip package (in requirements-bridge.txt) calls the MS service at
-#     COPY so it stays cached across source-only changes. ---
+#     runtime and the bridge transcodes the MP3 to PCM16 with ffmpeg. No heavy
-COPY docker/setup-melo.sh /app/docker/setup-melo.sh
+#     TTS build layer is needed. ---
-# 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
 
 # --- 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
+#     xdotool injects real X pointer/keyboard events (visible cursor,
-#     injects real X pointer/keyboard events (visible cursor, char-by-char
+#     char-by-char typing) into the broadcast; wmctrl lists/moves windows. ---
-#     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
+# TTS engine: "edge" (Microsoft Edge TTS, natural Korean neural voice) is the
-# voice; Piper is kept as a fallback if the worker is unreachable. Set
+# primary voice. "melo" (a warm MeloTTS worker) and "piper" remain selectable.
-# TTS_ENGINE=piper to disable MeloTTS entirely.
 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
+# Do NOT silently fall back to the English Piper voice on a neural-voice failure:
-# voice on failure: speaking Korean text through an English voice produces
+# speaking Korean through an English voice produces mangled audio. Default is
-# mangled audio. Default is melo-only (return no audio on failure); set
+# neural-only (return no audio on failure); set MELO_FALLBACK_PIPER=1 to opt in.
-# MELO_FALLBACK_PIPER=1 to opt into the Piper fallback.
 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
+    """Synthesize text to a 16-bit PCM WAV. The primary voice is Edge TTS (a
-    (Korean speaker, speed 1.5) served by the warm melo worker; Piper is a
+    natural Korean neural voice); "melo" uses the warm MeloTTS worker. For a
-    fallback if the worker is unavailable. Returns None if TTS is off."""
+    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":
+    _neural = {"edge": _edge_synthesize, "melo": _melo_synthesize}.get(TTS_ENGINE)
-        audio = _melo_synthesize(text)
+    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.
+            # Neural-only: better silent than mangled English for Korean text.
-            print("[bridge] melo 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("[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"),
+    ("tts_engine", "TTS 엔진", "select:edge,piper"),
-    ("melo_speed", "TTS 속도 (MeloTTS)", "number:0.5:2.5:0.1"),
     ("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":
+        if k == "output_language":
-            out[k] = cfg.get("melo_speed", os.environ.get("MELO_SPEED", "1.5"))
-        elif 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":
+        if k == "agentic_max_turns":
-            try:
-                v = float(v)
-            except (TypeError, ValueError):
-                continue
-        elif 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)
+    # Restart the bridge AFTER this response is sent. Detached (new session) so
-    # so the bridge being killed mid-restart doesn't drop the restart itself,
+    # the bridge being killed mid-restart doesn't drop the restart itself, and
-    # and the HTTP client still receives this response.
+    # 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

@@ -66,13 +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
+      # TTS engine. Rendered into /app/config/jarvis.json via envsubst (the
-      # serialised under load and pushed TTS to 7-8s; GPU does ~0.3s/sentence.
+      # bridge reads that JSON BEFORE the env, so it must carry the real engine,
-      MELO_DEVICE: ${MELO_DEVICE:-cuda}
+      # not a hardcoded one — otherwise Korean text is read by the English Piper
-      # Speaking rate for MeloTTS. Set here (with a default) so supervisord's
+      # voice). Default edge; .env can override (e.g. piper for offline).
-      # %(ENV_MELO_SPEED)s passthrough always resolves and an .env override
+      TTS_ENGINE: ${TTS_ENGINE:-edge}
-      # actually reaches the melo-worker. Lower it (e.g. 1.1) for a calmer pace.
+      # Edge TTS voice + rate (the chosen natural Korean voice). NOTE: edge is an
-      MELO_SPEED: ${MELO_SPEED:-1.5}
+      # 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/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

@@ -49,31 +49,8 @@ stdout_logfile_maxbytes=0
 stderr_logfile=/dev/stderr
 stderr_logfile_maxbytes=0
 
-[program:melo-worker]
+# (No TTS worker program: the default Edge TTS engine synthesises in-process in
-; Warm MeloTTS Korean voice (speed 1.5) in its own py3.11 venv. The bridge's
+# the bridge via the `edge-tts` package — no warm model/worker is needed.)
-; 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 and MELO_SPEED inherit from the container env (compose sets both
-; with defaults: cuda / 1.5) so the worker runs MeloTTS on the GPU at the
-; configured rate. supervisord interpolates %(ENV_x)s from its own environment,
-; which is the container's — so MELO_SPEED must always be set in the env
-; (compose guarantees it) or this expansion fails at startup. Hardcoding 1.5
-; here previously shadowed the .env value, so lowering MELO_SPEED had no effect.
-environment=MELO_LANGUAGE="KR",MELO_SPEED="%(ENV_MELO_SPEED)s",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
 
 [program:bridge]
 command=/app/docker/run-if-role.sh full,bot /opt/venv/bin/python -m bridge.server

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"