soubarnak
ea7be5f111
The fork takes its own name everywhere a user can see it: binaries (voculinux, voculinux-gui), desktop entry, autostart, tray, D-Bus namespace (com.voculinux.Voculinux, no alias for the old name), config/data/cache dirs, single-instance lock, IBus engine identity, tray icons, resource prefixes, VOCULINUX_* environment names, and the update-checker user agent. The internal Python module stays vocalinux and the distribution metadata keeps the upstream URLs as provenance. On first run a previous Vocalinux install's config.json, custom dictionary, and portal restore token are copied once into the new locations (new config always wins, no merge, no write-back); models re-resolve through the pinned-checksum download path. An offline naming-sweep test proves no old-name strings remain on user-visible surfaces outside the allowlist. Refs #18
81 lines
4.1 KiB
Markdown
81 lines
4.1 KiB
Markdown
# Installed-build performance baseline protocol (ticket #19)
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Locked measurement protocol for the Voculinux port's deferred performance
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numbers. The numbers themselves are recorded as evidence on the ticket,
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never here: until measured they are protocol, not results.
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## Machine class
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Void Linux (glibc, x86_64), niri Wayland session, NVIDIA RTX 2050 4 GB,
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no CUDA toolkit installed (Vulkan then CPU backend order).
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## Prerequisites
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- Installed build from `install.sh` (venv or system install), default
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whisper.cpp engine, debug logging available (`--debug`).
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- Ladder models on disk with pinned checksums verified against
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`src/vocalinux/utils/model_checksums.txt`: `large-v3-turbo-q8_0`
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(default rung), `large-v3-turbo-q5_0` (first fallback), `small` and
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`small.en` (floor, by language).
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- Isolated XDG directories so the pass never touches a live install:
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point `XDG_CONFIG_HOME`, `XDG_DATA_HOME`, `XDG_STATE_HOME` at scratch
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roots and link the verified models under `<data>/voculinux/models/`.
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- Seed `config.json` from the app's own `DEFAULT_CONFIG`: engine
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`whisper_cpp`, the rung under test, keep-alive enabled with the
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minimum 60 s timeout, `first_run` false, update checks off.
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## The three metrics
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All commands are `scripts/measure_perf_baseline.py` subcommands run
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with the installed build's interpreter.
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1. **Idle tray memory, both states.** Start with `--debug
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--start-minimized`, sample RSS with `sample-rss --pid`, and read
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VRAM with `nvidia-smi`. Loaded state is right after startup; the
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unloaded state follows the keep-alive timeout (watch the log for
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`reason=idle_keepalive`). RSS does not reflect the unload (freed
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native pages stay mapped); VRAM-used is the state signal.
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2. **Wake latency, toggle to capture start.** Drive starts over D-Bus
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(`busctl --user call com.voculinux.Voculinux
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/com/voculinux/Voculinux com.voculinux.Voculinux Toggle`; the
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`--toggle` client also works but spawns the full app stack).
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Parse repeated runs with `parse-log --log`: each `Toggle requested
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in state IDLE` pairs with the next `Opened capture stream`. Report
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steady-state spread separately from the first-capture cost (model
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reload after an idle unload lands on the first toggle).
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3. **Transcription real-time factor, fixed sample.** Regenerate the
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sample with `make-sample --out scripts/perf_baseline_sample.wav
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--seed 19` (30 s speech-like babble, 16 kHz mono 16-bit, committed
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next to the script so reruns are bit-identical), then `transcribe
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--model-file … --backend vulkan|cpu --wav … --runs 3`. Report the
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first (cold: shader/pipeline build, one-time) run separately from
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warm runs. CUDA is measured only where a complete toolkit exists;
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otherwise its absence is recorded, not worked around.
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## Same-pass extras
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- **Per-rung load footprint:** `probe-load --model-file … --backend …`
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reports load wall time, child RSS, and VRAM used while the model is
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held. Free versus total memory is distinguished via `nvidia-smi`
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before/during/after. CPU loads touch no VRAM; their RSS is the
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footprint.
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- **Injection latency:** `inject-probe` records the RemoteDesktop
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version-probe result and timing, then a session-establishment attempt
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(fast failure versus hang matters more than the version answer), plus
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the resolved first rung and a no-emission `wtype ""` spawn cost as a
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lower bound. End-to-end key-to-screen latency is deliberately not
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taken here: emitting keystrokes into a live desktop from a background
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run would disturb the session. It belongs to the hardware checklist
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with a dedicated target window.
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- **Thresholds for the model ladder:** derive free-memory cutoffs from
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the maximum observed per-rung footprint (transcribe peak, not just
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load) plus explicit headroom for the compositor and other GPU
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clients, and record the derivation next to the numbers.
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## Report
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`render_markdown_report` (and the JSON the subcommands print) carries
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the protocol version, full host facts, and observed numbers only.
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`tests/test_perf_baseline_protocol.py` covers the parsing, statistics,
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threshold-derivation, and sample-determinism helpers offline.
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