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>

docker/supervisord.conf

@@ -49,31 +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 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
+# (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