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

MeloTTS's single Korean speaker sounded non-native ("foreign accent"). Swap it
for Coqui XTTS-v2 with the built-in female studio speaker "Ana Florence"
(language ko), the natural voice used in earlier local runs.

- bridge/xtts_worker.py: new warm HTTP worker (own /opt/xtts venv), same
  /synth + /health contract and PCM16 output as the old melo worker
- docker/setup-xtts.sh: builds the venv with cu128 torch (Blackwell) + Coqui
  TTS and bakes the XTTS-v2 model offline. Pins transformers>=4.57,<5 (5.x
  removed isin_mps_friendly, breaking XTTS) and installs the [codec] extra
  (torch>=2.9 needs torchcodec) — both verified by a real host synth
- Dockerfile: replace the melo build layer with the xtts layer
- supervisord.conf: melo-worker -> xtts-worker, env passthrough for
  XTTS_DEVICE/SPEAKER/LANGUAGE (always set via compose defaults)
- bridge/server.py: default TTS_ENGINE=xtts, route to the xtts worker, generic
  worker-synth helper, neural-only fallback flag (XTTS_FALLBACK_PIPER)
- settings UI: engine dropdown xtts/piper, drop the dead melo_speed field, fix
  the supervisorctl restart target to xtts-worker
- compose/.env.example/README: XTTS_* vars, speaker/language knobs, remove melo
- remove bridge/melo_worker.py and docker/setup-melo.sh
- tests: xtts treated as multilingual (not English-only)

Verified on host: coqui-tts loads XTTS-v2 and synthesises Korean as
"Ana Florence" to a 16-bit mono 24kHz WAV.

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

docker/setup-melo.sh

@@ -1,80 +0,0 @@
-#!/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"