feat(stt): real Korean STT via persistent faster-whisper worker

Step 3 (귀): add WhisperSTT + whisper_worker, a warm out-of-venv worker
mirroring the MeloTTS shape (whisper312 venv, small/int8 on CPU). transcribe()
closes the voice round trip (MeloTTS wav -> whisper text); utterances() turns an
injected audio_source into Utterances (Discord voice feed pending). Wired into
factory as WSAI_STT=whisper.

Also address the arbiter's TTS follow-ups:
- melo worker error handling: capture stderr (drained in a bounded background
  task so the pipe can't fill), surface the real failure cause, and defend
  against an empty/invalid ready line instead of dying on JSONDecodeError.
- pipeline pre-warm: load slow backends (warmup()) at startup so the first
  utterance is answered warm; a warmup failure is logged, not fatal.

Verified: real TTS->STT round trip recovers the sentence near-perfectly;
warm transcribe ~1.2s (CPU). 12 tests pass.

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

requirements.txt

@@ -8,10 +8,13 @@
 # --- cloud eyes + brain (WSAI_VISION=claude / WSAI_BRAIN=claude) ---
 # anthropic
 
-# --- planned voice backends (not yet implemented) ---
-# faster-whisper      # STT (input = Discord voice reception, not a local mic)
-# (Opus decode for Discord voice audio, e.g. via the selfbot/ffmpeg path)
-# (a Korean TTS engine, e.g. MeloTTS)
+# --- voice backends (run in their OWN venvs; loaded as persistent workers) ---
+# STT: faster-whisper  (WSAI_STT=whisper) — installed in a dedicated venv, e.g.
+#   uv venv --python 3.12 /home/claude/jarvis-stt/whisper312
+#   uv pip install --python /home/claude/jarvis-stt/whisper312/bin/python faster-whisper
+#   (override interpreter/model via WSAI_WHISPER_PYTHON / WSAI_WHISPER_MODEL)
+# TTS: MeloTTS         (WSAI_TTS=melo)     — in /home/claude/jarvis-tts/melo311
+# (Opus decode for Discord voice audio, e.g. via the selfbot/ffmpeg path — pending)
 
 # --- dev ---
 # pytest