Revert "feat: replace MeloTTS with Coqui XTTS-v2 natural Korean voice"

This reverts commit 39a0944105.

.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
+# 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.
-XTTS_FALLBACK_PIPER=0
+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
@@ -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

@@ -65,19 +65,18 @@ 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
+# --- 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 `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$//' /app/docker/setup-xtts.sh && bash /app/docker/setup-xtts.sh
+# bash read line 18 as `set -euxo pipefail\r` and abort with
+# "set: pipefail: invalid option name". .gitattributes pins *.sh to LF, but this
+# keeps the build working even on a not-yet-renormalised working tree.
+RUN sed -i 's/\r$//' /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 TTS layer so it doesn't bust that cache. xdotool
+#     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. ---
 RUN apt-get update && apt-get install -y --no-install-recommends \

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, MELO_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), 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.
 
 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`, `MELO_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`, `MELO_DEVICE` — 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/server.py

@@ -87,13 +87,12 @@ 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: "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.
 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."""
+    melo. 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 +100,17 @@ 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", "melo").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).
 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")
+# 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.
+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 +302,27 @@ 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 _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 +349,11 @@ 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
@@ -386,24 +361,20 @@ 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 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."""
     if not TTS_ENABLED or not text.strip():
         return None
-    _neural = {"xtts": _xtts_synthesize, "melo": _melo_synthesize}.get(TTS_ENGINE)
-    if _neural is not None:
-        audio = _neural(text)
+    if TTS_ENGINE == "melo":
+        audio = _melo_synthesize(text)
         if audio:
             return audio
-        if not NEURAL_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,
-            )
+        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)
             return None
-        print(f"[bridge] {TTS_ENGINE} synth failed; falling back to Piper", flush=True)
+        print("[bridge] melo synth failed; falling back to Piper", flush=True)
     return _piper_synthesize(text)
 
 

bridge/settings_web.py

@@ -22,7 +22,8 @@ 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:melo,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"),
@@ -53,7 +54,9 @@ def _current() -> Dict[str, Any]:
     cfg = _read_config()
     out: Dict[str, Any] = {}
     for k in _KEYS:
-        if k == "output_language":
+        if k == "melo_speed":
+            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, "")
@@ -75,7 +78,12 @@ def _coerce(updates: Dict[str, Any]) -> Dict[str, Any]:
     for k, v in updates.items():
         if k not in _KEYS:
             continue
-        if k == "agentic_max_turns":
+        if k == "melo_speed":
+            try:
+                v = float(v)
+            except (TypeError, ValueError):
+                continue
+        elif k == "agentic_max_turns":
             try:
                 v = int(v)
             except (TypeError, ValueError):
@@ -106,12 +114,12 @@ 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 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.
     try:
         subprocess.Popen(
-            ["sh", "-c", "sleep 1; supervisorctl restart xtts-worker bridge"],
+            ["sh", "-c", "sleep 1; supervisorctl restart melo-worker bridge"],
             start_new_session=True,
         )
         return "1초 후 브리지/TTS 워커가 재시작되어 반영됩니다."

docker-compose.yml

@@ -66,15 +66,13 @@ 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}
+      # 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}
+      # Speaking rate for MeloTTS. Set here (with a default) so supervisord's
+      # %(ENV_MELO_SPEED)s passthrough always resolves and an .env override
+      # actually reaches the melo-worker. Lower it (e.g. 1.1) for a calmer pace.
+      MELO_SPEED: ${MELO_SPEED:-1.5}
       # 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/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

@@ -49,22 +49,25 @@ 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
+[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
-; 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"
+; 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

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.