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zeekay
9631675dff
The lab runs amd64 (evo) and arm64 (spark), and this image was cut linux/amd64 only — so it deploys on half the fleet and an arm64 node cannot pull it at all. `docker build` + `docker push` cannot carry two platforms: the daemon's built-in builder builds one, and a manifest list cannot be loaded into a local daemon at all. So the publish runs on a docker-container builder and pushes straight to the registry, with binfmt for the emulated arm64 leg. Every native dependency in uv.lock has an aarch64 wheel, so the leg resolves rather than compiling from source. The two gates keep the daemon they had: both setups sit BELOW them, so the suites still build and run this arch natively instead of testing a QEMU translation of the binary.
speech
CPU speech for the Hanzo platform: STT (faster-whisper, CTranslate2 int8) and TTS (kokoro-onnx, 82M) behind one OpenAI-shaped surface.
Routes
POST /v1/audio/transcriptions— multipartfile+model(+language,response_format) →{"text": …}POST /v1/audio/speech—{model, input, voice, response_format}→ audio bytes (mp3 default, wav native)GET /v1/models,GET /healthz
Trust model
No auth of its own, in-cluster only. api.hanzo.ai fronts it through the ai
plane, which authenticates and meters every caller — a provider row pointing at
http://speech.hanzo.svc/v1 is the whole integration. Weights bake into the
image at build; the container boots without the network.
Run
uv sync && uv run uvicorn main:app --port 8000
Phase 2 is /v1/realtime: websocket full-duplex (silero-vad segmentation,
partial transcripts, kokoro frames, barge-in) plus camera-frame relay into the
multimodal models.