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>

wsai/config.py

@@ -18,7 +18,7 @@ from dataclasses import dataclass
 class Settings:
     source: str | None = "mock"  # mock | mss | None (eyes-free)
     vision: str | None = "mock"  # mock | claude | None (eyes-free)
-    stt: str | None = "mock"     # mock | (whisper) | None
+    stt: str | None = "mock"     # mock | whisper   | None
     tts: str | None = "mock"     # mock | melo      | None
     brain: str = "mock"          # mock | claude
     text: str | None = None      # None | (discord)