The bot (dave/bot.mjs) previously only joined the channel and counted audio
frames — it never fed STT or spoke back. Wire the real loop:
- Node bot: buffer each speaker's Opus->PCM utterance until AfterSilence,
wrap as WAV, POST to the Python voice-turn endpoint, then play the returned
reply wav into the channel via an AudioPlayer (ffmpeg->Opus). Skips its own
audio, dedupes overlapping subscriptions, and ignores sub-0.35s noise.
- Python: new `python -m wsai --voice-server` serves /api/voice-turn — decode
the uploaded utterance, GPU faster-whisper STT, produce a reply (echo of what
was heard for now), GPU MeloTTS synth, return the reply wav (recognised/reply
text ride along as X-Heard/X-Reply headers). Both engines pre-warmed; turns
show in the dashboard feed. MeloTTS.synth() extracted for direct wav reuse.
Echo mode verifies listening+speaking+GPU recognition entirely in Discord; the
Claude brain is the next slice. Verified the endpoint round-trip: utterance wav
-> correct Korean X-Heard/X-Reply + a WAVE reply on device=cuda. 12 tests pass,
node --check clean.
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>
