prosolis
8410b6315b
Deploy plumbing so Petal can run on the public VPS behind the Traefik already on that box, with vLLM reached over headscale. - Dockerfile: node build -> go build -> alpine runtime. CGO stays off (modernc SQLite is pure Go), so the runtime layer exists only for ffmpeg (read-aloud transcodes Piper's WAV) and tzdata (the companion's bedtime nag and night mode read the local clock). Runs as uid 10001 with /data as the single writable mount. - docker-compose.yml: Traefik labels following this host's convention (external `traefik` network, `web-secure` entrypoint, `default` cert resolver). Petal publishes no host port. ./data is a bind mount, not a named volume, so the nightly backup and a restore are reachable from the host. - Piper runs as two sibling containers rather than host systemd units. The plan assumed Piper was already installed on the VPS; it is not, the host has no lingering user session to keep user units alive, and containers keep the TTS ports on an internal network unreachable from anywhere but Petal. One image, voice chosen per service, model cached in a shared volume -- so the pt-PT voice is a new service, not a new image. - db.Backup + a `-backup` flag: VACUUM INTO, not a file copy. Petal runs in WAL mode, so the newest committed pages may live in petal.db-wal; copying the three files separately can capture a torn mid-checkpoint state. VACUUM INTO reads one coherent snapshot without taking a write lock, and emits a single file with no -wal/-shm companions. Refuses an existing destination so a failed run can't destroy the last good backup. - deploy/backup-petal.sh: nightly snapshot, compress, push to millenia over headscale with a post-transfer size check, prune both sides. - deploy/petal.env.example: LLM_TIMEOUT raised 30s -> 90s for the WAN+VPN round trip, since the voice and collocation passes send a whole document and the timeout is a hard deadline on Complete.
38 lines
1.4 KiB
Docker
38 lines
1.4 KiB
Docker
# Piper neural-TTS HTTP server — the read-aloud backend Petal proxies to.
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#
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# One image, any voice: the model is named by PIPER_VOICE at runtime and
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# downloaded into the shared /voices volume on first start. Each Piper server
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# loads exactly one voice, so a new language is a new service in
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# docker-compose.yml, not a new image (English and Chinese today; pt-PT lands
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# with the Portuguese pair).
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#
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# python:3.12 rather than 3.13 — piper-tts pulls onnxruntime, whose wheel
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# coverage for 3.13 still lags.
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FROM python:3.12-slim
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RUN pip install --no-cache-dir "piper-tts[http]" \
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&& useradd -m -u 10002 piper
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ENV PIPER_VOICE=en_US-amy-medium \
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PIPER_DATA_DIR=/voices \
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PIPER_PORT=5000
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RUN mkdir -p /voices && chown piper:piper /voices
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VOLUME ["/voices"]
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COPY entrypoint.sh /usr/local/bin/entrypoint.sh
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RUN chmod +x /usr/local/bin/entrypoint.sh
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USER piper
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EXPOSE 5000
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# The server has no dedicated health route, so synthesizing a single word is
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# the honest check: it proves the model loaded, not just that a port is open.
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HEALTHCHECK --interval=60s --timeout=20s --start-period=180s --retries=3 \
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CMD python -c "import os,urllib.request,json; \
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urllib.request.urlopen(urllib.request.Request('http://127.0.0.1:'+os.environ['PIPER_PORT']+'/', \
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data=json.dumps({'text':'ok','voice':os.environ['PIPER_VOICE']}).encode(), \
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headers={'Content-Type':'application/json'}), timeout=15).read(1)"
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ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
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