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zeekayandHanzo Dev
005298be18
Neither earlier form published 0.1.1. A bare `uv` depends on how the runner image happens to be built; `docker run -v "$PWD":/w` depends on the runner sharing a filesystem with the docker daemon, and under docker-in-docker that path resolves on the HOST — the container mounts an empty directory and the suite cannot even find pyproject.toml. `docker build --target test` has neither dependency, and building an image is the one thing this lane is already known to do. The stage sits ABOVE the weight bake: the suite stubs both model calls, so it needs no weights, and a contract break is now caught before 350MB is downloaded rather than after. Verified locally — 18 passed in 1.83s, 4.5s of build. Co-authored-by: Hanzo Dev <[email protected]>
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.