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>

tests/test_whisper_stt.py

@@ -0,0 +1,60 @@
+"""Unit tests for the WhisperSTT source plumbing.
+
+These do NOT load a model or spawn the worker (that needs the whisper312 venv and
+is exercised by the manual TTS->STT round trip). They pin the SpeechToText
+contract: how `utterances()` turns an audio source into Utterances, and how it
+behaves with no source wired yet.
+"""
+
+import asyncio
+from typing import AsyncIterator
+
+from wsai.backends.whisper import WhisperSTT
+from wsai.interfaces import SpeechToText, Utterance
+
+
+def _collect(stt: WhisperSTT) -> list[Utterance]:
+    async def run():
+        return [u async for u in stt.utterances()]
+
+    return asyncio.run(asyncio.wait_for(run(), timeout=5))
+
+
+async def _paths(items) -> AsyncIterator[str]:
+    for it in items:
+        yield it
+
+
+def test_is_speech_to_text():
+    assert isinstance(WhisperSTT(), SpeechToText)
+
+
+def test_no_audio_source_yields_nothing():
+    # Discord voice receiver not wired yet -> the loop idles and returns.
+    assert _collect(WhisperSTT(audio_source=None)) == []
+
+
+def test_utterances_transcribes_each_chunk(monkeypatch):
+    stt = WhisperSTT(audio_source=_paths(["a.wav", "b.wav"]))
+
+    async def fake_transcribe(wav_path, *, language=None):
+        return f"text::{wav_path}"
+
+    monkeypatch.setattr(stt, "transcribe", fake_transcribe)
+
+    utts = _collect(stt)
+    assert [u.text for u in utts] == ["text::a.wav", "text::b.wav"]
+    assert all(u.source == "voice" for u in utts)
+
+
+def test_empty_transcript_is_skipped(monkeypatch):
+    # Silence / VAD-filtered audio transcribes to "" and must not become a turn.
+    stt = WhisperSTT(audio_source=_paths(["silent.wav", "real.wav"]))
+
+    async def fake_transcribe(wav_path, *, language=None):
+        return "" if wav_path == "silent.wav" else "안녕"
+
+    monkeypatch.setattr(stt, "transcribe", fake_transcribe)
+
+    utts = _collect(stt)
+    assert [u.text for u in utts] == ["안녕"]