CPU MeloTTS serialised under concurrent load (whisper STT + bot) and blew
voice-reply TTS to 7-8s. Install the Blackwell-verified cu128 torch in the
melo venv, select the GPU via MELO_DEVICE=cuda, and do a throwaway synth at
worker startup so the one-off CUDA kernel-init (~5s) doesn't land on the
user's first reply. Measured: ~0.3s/sentence on GPU vs ~1.2-2.6s on CPU.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds a dedicated MeloTTS Korean voice (speed 1.5) as the primary TTS engine,
served by a long-lived in-container worker so each Discord turn pays only
inference cost, not model-load cost.
- bridge/melo_worker.py: tiny HTTP service in its own /opt/melo py3.11 venv,
keeps the KR model warm, returns PCM16 WAV on POST /synth.
- bridge/server.py: synthesize() routes to the melo worker first; Piper stays
as an opt-in fallback (MELO_FALLBACK_PIPER, default off so Korean is never
mangled through the English voice). /health reports tts_engine.
- docker/setup-melo.sh: builds the isolated venv (pinned torch 2.12.0 /
torchaudio 2.11.0 CPU, MeloTTS pinned to a commit for reproducible rebuilds),
pre-fetches mecab-ko, and warms a dedicated HF cache (/opt/melo-cache) with a
real KR synth so all BERT + KR checkpoint assets are baked into the image.
- docker/supervisord.conf: runs melo-worker before the bridge with
HF_HOME=/opt/melo-cache (the whisper_cache volume shadows the default HF
cache) plus HF_HUB_OFFLINE/TRANSFORMERS_OFFLINE so it reads the baked cache
and never retries the network on load.
- Dockerfile/.env.example: wire the melo build layer and config knobs.
Verified: offline synth passes with --network none and the prod volume mounted;
prod container recreated, all supervisord services up, bot logged in, and an
end-to-end /tts call returns a 44.1kHz mono PCM16 WAV.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
