The first CUDA inference pays a large lazy cost (kernel autotune/cudnn) —
~10s for a cold TTS synth — which would blow the voice loop's ~1s budget on
the very first reply. Each worker now runs one dummy inference (TTS: a short
phrase; STT: 1s of silence) after model load and before emitting "ready", so
"ready" means "hot". Warmup failures are logged and never block startup.
Verified: first real call after startup is now TTS ~238ms / STT ~189ms
(was ~11s cold for TTS). 12 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Both voice backends defaulted to CPU. Fix the "CUDA unavailable" gaps so
everything that benefits from the RTX 5050 uses it:
- MeloTTS venv had CPU-only torch (2.12.0+cpu) -> installed Blackwell-capable
torch/torchaudio 2.11.0+cu128 (sm_120 verified with a real GPU matmul).
- faster-whisper CUDA loaded but transcribe() died with "libcublas.so.12 not
found": installed nvidia-cublas-cu12 + nvidia-cudnn-cu12 into the whisper
venv and inject those nvidia/*/lib dirs into the worker's LD_LIBRARY_PATH at
spawn (the loader only honours it at exec).
- WSAI_WHISPER_DEVICE / WSAI_MELO_DEVICE now default to "auto": pick CUDA when
present, else CPU, and each worker falls back to CPU if a CUDA load fails so
the voice loop never dies on a GPU-less host.
Verified end-to-end through the real backend classes: both workers report
"ready on cuda"; steady-state STT ~170ms (was ~1350ms CPU), TTS ~4s first call
vs ~23s CPU. All 12 tests pass.
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>
Adds a MeloTTS backend that runs the model in its own melo311 interpreter
as a long-lived worker (melo_worker.py), loaded once and fed synthesis
requests over a stdin/stdout JSON protocol. fd1 is split from fd2 in the
worker so MeloTTS's stdout progress chatter can't corrupt the protocol.
Each speak() writes a wav and hands the path to a pluggable sink (the
Discord voice step will swap in "play into the call"). factory wires
tts=melo; pipeline.aclose now also tears down the tts worker.
Verified (CPU): model load ~7.9s once, then a short reply synthesizes in
~0.86s (within the ~1s budget); wav is valid 44.1kHz PCM. GPU (cuda) is
selectable via WSAI_MELO_DEVICE for lower latency, pending GPU approval.
7 smoke tests still pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Wires the deployment's Claude Max login (OAuth token from
$CLAUDE_CREDENTIALS_PATH) into ClaudeBrain/ClaudeVision. OAuth tokens
authenticate as Bearer (auth_token=), not x-api-key, and only answer
when the first system block is the Claude Code identity string, so the
real persona moves to a second system block. Token is re-read per
request so a host-side refresh is picked up without a restart. Falls
back to ANTHROPIC_API_KEY when set.
Verified: WSAI_BRAIN=claude returns a real Korean reply through the
factory; 7 smoke tests still 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>
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).
