Subprocess-per-request architecture forced CUDA + model load on every
/v1/tts call (~10-20s init, then 5-15s generation). Even though CUDA
is now actually being used (`-c 0` fix landed), 32s for "Verify."
proved per-request init was the bottleneck.
s2.cpp ships a built-in HTTP server (`--server -H -P`) that keeps the
model resident on the GPU. Refactor:
* entrypoint.sh — backgrounds `s2 --server -P 3030 -c 0 -m ... -t ...`,
waits for it to bind 3030, then foregrounds uvicorn. tini supervises
via `wait -n` so either child dying takes down the container.
* server.py — drops subprocess.run; instead httpx-POSTs Fish-shaped
/v1/tts JSON to s2's localhost:3030/generate (multipart form: text
+ optional prompt_text/prompt_audio for cloning). Model load + CUDA
init now happen once at container start, not per-request.
* Dockerfile — added httpx (shim dep), curl (entrypoint readiness
probe), and the entrypoint.sh COPY+chmod. CMD now invokes
entrypoint.sh instead of uvicorn directly.
* deploy-fish-cpp.yaml — uploads entrypoint.sh alongside server.py.
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