Accepting the settings dialog quit the app: with a tray-resident program
"last window closed" must not mean "quit", but QApplication's default
made it so, and on first run the dialog is the only window. Disable
quitOnLastWindowClosed. Also fix listOutputDevices hanging forever
(its callbacks never signalled the PulseAudio main loop), enlarge the
caption window's close button, and add capture-device selection
(enumerated sinks, settings combo, --list-devices/--device) plus model
integrity verification (size + sha256 in the registry, --verify-model and
a "校验完整性" button).
点「确定」后程序自己退出：本程序常驻托盘，"最后一个窗口关闭"不该等于退出，
但 QApplication 默认如此，而首次启动时设置对话框正是唯一窗口。已关闭该默认
行为。同时修复 listOutputDevices 永久卡死（回调没有 signal 主循环）、放大
字幕窗关闭按钮，并新增采集设备选择与模型完整性校验（注册表含 size/sha256，
提供 --verify-model 与设置里的「校验完整性」按钮）。
The GUI no longer needs command line arguments: on first run it opens a
settings dialog that lists the registry, downloads models in-app (mirror
base, HTTP proxy, resume), and picks the active model. The caption window
gains a tray icon (show/hide, settings, quit); its close button hides to
the tray so the window can always be brought back, and the language pill
opens the settings. Appearance (font, opacity, line counts, position) is
persisted. CLI flags remain for automated testing and override settings.
GUI 不再依赖命令行参数：首次启动打开设置对话框，可在应用内下载模型
（下载源/代理/断点续传）、选择当前模型与识别语言；字幕窗增加托盘（显示
隐藏/设置/退出），关闭按钮改为隐藏到托盘以免关掉后无法再打开，语言标签
点击进设置；字号/透明度/行数/位置都会持久化。命令行参数保留作自动化测试。
Add a second backend (sherpa-onnx) alongside llama.cpp, so native
streaming models can be selected by ID from a JSON registry. All such
models share one adapter: only the model files and a few options differ.
Models now live under the app data directory and are fetched by
scripts/fetch-models.sh. Adding a model is a data change, not code.
Measured on the same audio: the streaming backend is ~35x cheaper in CPU
than Qwen3-ASR (0.10-0.12 vs 4.27 core-seconds per audio second), shows
the first caption at 0.48-1.44s, and matches its accuracy for zh/en.
新增 sherpa-onnx 流式后端，与 llama.cpp 并存；模型按注册表 id 从应用
数据目录加载。同族模型共用一个适配器，新增模型只改 data/models.json。
实测流式后端 CPU 约为 Qwen3-ASR 的 1/35，首个字幕 0.48~1.44s，中英
准确率持平。注意预编译库是旧 C++ ABI，必须走其 C API。
Move all business logic into liblivecaption_core (no Qt dependency) and
the widgets into liblivecaption_ui, leaving two executables: the caption
window and the CLI, which no longer links Qt Widgets. Abstract the ASR
backend behind IAsr so the streaming policy and the live pipeline can be
tested with a scripted fake backend instead of a real model.
把全部业务逻辑下沉到不依赖 Qt 的 liblivecaption_core，界面独立成
liblivecaption_ui，拆出悬浮窗与命令行两个可执行程序（CLI 不再链接 Qt
Widgets）；并用 IAsr 抽象推理后端，使流式策略与实时管线可脱离真模型
用假实现做单元测试。测试还抓到一个真 bug：段恰好结束在 chunk 边界时
只提交了稳定部分，最后几个词被丢掉，已修。
Add a frameless always-on-top caption window wired to the live capture
pipeline. Silence-based segmentation gates the audio so the model never
sees silence, and the panel background is painted in paintEvent with a
configurable opacity instead of a style sheet.
新增悬浮字幕窗（无边框、置顶、可折叠/展开），接入实时采集与流式转写。
分段由静音检测驱动，静音期间不送音频，避免模型对静音产生幻觉。底板
改为在 paintEvent 中绘制，不透明度可调。
Add native streaming ASR: 8s-aligned audio slices are cached in the KV
cache, only new audio is encoded, and the previous text is fed back as
an assistant prefix with token rollback. Text matches offline results.
Streaming keeps each step within the 2s budget for segments up to 20s.
新增原生流式解码：8s 对齐的音频切片存入 KV 复用，只编码新音频，并把
上一轮文本作为 assistant 前缀回喂（带 token 回滚）。转写结果与离线
一致；20s 以内的段落每步计算都在 2s 预算内。
Add a C++/Qt6/CMake CLI prototype that captures the default sink
monitor through libpulse and transcribes it in-process with Qwen3-ASR
via libllama/libmtmd. Add phase 0/1 verification docs.
新增实时字幕命令行原型：libpulse 采集默认输出 monitor，进程内用
libllama/libmtmd 跑 Qwen3-ASR 推理。附阶段 0/1 验证文档。
blumia