Emotion tags now steer delivery rather than being read aloud. parse_segments()
splits a reply on [감정] tags: a recognised emotion word switches the pitch and
speed of the text that follows (and is dropped), while a non-emotion bracket
(e.g. [1번]) keeps its inner words as spoken content. Emotions can change
mid-reply, so a single turn is synthesised as several pitch-shifted segments and
concatenated in the melo worker (librosa pitch_shift, warmed at startup).
The emotion vocabulary is grounded in Azure Neural TTS speaking styles plus
Ekman's basic emotions, with Korean synonyms. The brain persona is updated to
emit inline tags from that set.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Claude replies with markdown/backticks by default; MeloTTS's Korean text
normaliser has no entry for '`' and dies with KeyError: '`', so any reply
mentioning a command/code block crashed the whole voice turn (500 on
/api/voice-turn). Fix at the shared synth() choke point with
normalize_for_speech(), which flattens code fences/inline code/links/markdown
and guarantees no backtick reaches the worker — covering both the dashboard
voice turn and the Discord speak() bridge. Also add a PERSONA line asking the
model to avoid markdown (belt-and-suspenders; the code strip is the real fix).
errors_total never moved for turn-level failures: it was only bumped by
log("error") events, and the dashboard voice path calls turn.finish(error=...)
without logging. Emit one error-level log event from Turn.finish() when a turn
ends in error, so both the server counter and the browser SSE mirror stay
consistent, guarded to count at most once. Drop the now-redundant pipeline
log("error") to avoid double counting and remove the dead _publish stub.
Verified: raw backtick -> worker KeyError '`' reproduced; after fix real
MeloTTS synth of a backtick+fenced reply succeeds; /api/voice-turn returns 200
with a wav body on a backtick reply and errors_total stays 0, and an induced
synth failure returns 500 with errors_total incrementing to exactly 1. Full
suite 18 passed.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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>
Add a stdlib-only observability site so you can open a browser and watch,
step by step: whether it is listening, what it heard, what the brain thought
and answered, how long each stage took, and whether anything errored.
- wsai/monitor.py: thread-safe telemetry hub (per-turn timed steps, status
header, error log) with a pub/sub for live push.
- wsai/dashboard.py: stdlib http.server serving a self-contained page plus an
SSE (/events) live stream; /api/state snapshot fallback.
- Pipeline emits step-by-step turn telemetry (화면 맥락 → 두뇌 → 응답) and
listening/running status; optional monitor, so existing paths are untouched.
- `python -m wsai --dashboard` starts the site (0.0.0.0:8787, WSAI_DASHBOARD_PORT)
and loops the mock voice demo so there is always live activity to watch.
- Tests cover turn recording, per-step timing, error marking, and live push.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Pipeline.run() used asyncio.gather, so if one loop raised, the failing
coroutine propagated while the sibling loops kept running detached; aclose()
in the finally then closed a source/stt out from under a still-live loop.
Switch to asyncio.TaskGroup so a failing loop cancels+awaits the siblings
before teardown. Add a regression test asserting an error in the conversation
loop cancels the perception loop and still closes every source.
Make source/vision optional so the conversation loop runs with no screen
capture. Add Settings.voice() preset and `python -m wsai --voice` demo.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Modular async pipeline: FrameSource->Vision->context and STT/text->Brain->TTS.
All stages are Protocols; mock backends run end-to-end with no deps/keys.
Real backends included: mss screen capture, Claude vision+brain (guarded imports).
