blumia
6c04071bb5
These were command-line only. Add them to the settings dialog so the whole feature set is reachable without arguments: stream mode, window limit, segment limit, prefix-rollback token count, cold-start chunk count, max tokens per step, thread count, verbose logging and the model directory. The streaming group is grouped and greyed out for streaming-ONNX models, which only use the commit granularity. The backend fingerprint now includes them, so changing one restarts the recogniser; appearance changes still apply live. Also add a test-only --settings-tab so a specific page can be screenshotted. 设置「高级」页补上原来只有命令行才有的选项：流式策略/滑窗上限/单段最长/前缀回滚 token 数/冷启动 chunk 数/单段最多生成 token/线程数/详细日志/模型目录。流式那一组 单独成组，选了流式 ONNX 模型时置灰（它们只用「更新粒度」）。这些值纳入「后端指纹」， 改动会重启识别后端；外观类改动仍然即时生效。另加测试用 --settings-tab 以便给单个 页签截图。
215 lines
8.7 KiB
C++
215 lines
8.7 KiB
C++
#pragma once
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#include <cstdio>
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#include <memory>
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#include <string>
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#include <QCommandLineOption>
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#include <QCommandLineParser>
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#include "iasr.h"
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#include "modelstore.h"
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#include "streamingasr.h"
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#include "qwen3asr.h"
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#if defined(LC_HAVE_SHERPA)
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#include "sherpaasr.h"
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#endif
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// 两个可执行程序（GUI / CLI）共用的命令行选项定义与解析。
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namespace lc::app {
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// 构造命令行选项。
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//
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// Qt 自己不会把默认值印进 --help（构造函数里传的默认值只作为 value() 的兜底），
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// 所以这里在给了默认值时把「（默认 X）」拼到说明后面。
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inline QCommandLineOption makeOption(const QString & name, const QString & description,
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const QString & valueName = QString(),
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const QString & defaultValue = QString())
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{
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QString desc = description;
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if (!defaultValue.isEmpty()) {
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desc += QStringLiteral("（默认 %1）").arg(defaultValue);
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}
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if (valueName.isEmpty()) return QCommandLineOption(name, desc);
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return QCommandLineOption(name, desc, valueName, defaultValue);
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}
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inline void addModelOptions(QCommandLineParser & p)
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{
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p.addOption(makeOption("model", "主模型 GGUF 路径（架构须为 qwen3vl）", "path"));
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p.addOption(makeOption("mmproj", "mmproj GGUF 路径（clip + qwen3a）", "path"));
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p.addOption(makeOption("threads", "推理线程数", "n", "8"));
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p.addOption(makeOption("max-tokens", "单段最多生成 token 数", "n", "256"));
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p.addOption(makeOption("verbose", "输出 llama.cpp/mtmd 的详细日志"));
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}
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inline void addStreamOptions(QCommandLineParser & p)
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{
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p.addOption(makeOption("stream-chunk", "流式 chunk / 提交粒度，单位秒", "sec", "2.0"));
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p.addOption(makeOption("stream-window", "滑窗上限，单位秒；仅 window 模式，0=不滑窗", "sec", "16"));
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p.addOption(makeOption("stream-segment", "单段最长时长，单位秒；0=不限", "sec", "30"));
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p.addOption(makeOption("unfixed-tokens", "前缀回滚丢弃的 token 数", "n", "5"));
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p.addOption(makeOption("unfixed-chunks", "冷启动不带前缀的 chunk 数", "n", "2"));
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p.addOption(makeOption("stream-mode", "流式后端: incremental / append / window", "mode",
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"incremental"));
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}
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inline AsrOptions asrOptions(const QCommandLineParser & p)
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{
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AsrOptions o;
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o.modelPath = p.value("model").toStdString();
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o.mmprojPath = p.value("mmproj").toStdString();
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o.threads = p.value("threads").toInt();
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o.maxTokens = p.value("max-tokens").toInt();
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o.verbose = p.isSet("verbose");
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return o;
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}
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inline StreamOptions streamOptions(const QCommandLineParser & p)
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{
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StreamOptions o;
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o.chunkSec = p.value("stream-chunk").toDouble();
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o.windowSec = p.value("stream-window").toDouble();
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o.maxUtteranceSec = p.value("stream-segment").toDouble();
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o.unfixedTokenNum = p.value("unfixed-tokens").toInt();
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o.unfixedChunkNum = p.value("unfixed-chunks").toInt();
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o.maxTokensPerStep = p.value("max-tokens").toInt();
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o.incremental = p.value("stream-mode") == QLatin1String("incremental");
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o.appendOnly = p.value("stream-mode") == QLatin1String("append");
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return o;
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}
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inline bool loadModel(Qwen3Asr & asr, const AsrOptions & opt)
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{
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if (opt.modelPath.empty() || opt.mmprojPath.empty()) {
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std::fprintf(stderr, "必须提供 --model 和 --mmproj\n");
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return false;
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}
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std::string err;
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if (!asr.load(opt, &err)) {
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std::fprintf(stderr, "加载失败: %s\n", err.c_str());
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return false;
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}
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return true;
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}
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// ---- 推理后端选择 ----
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//
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// llama 后端：Qwen3-ASR 的 GGUF 模型（--model / --mmproj）
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// sherpa 后端：流式 ONNX 模型，按注册表 id 从模型存储目录取（--asr-model <id>）
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inline void addBackendOptions(QCommandLineParser & p)
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{
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p.addOption(makeOption("asr-backend", "推理后端: llama（Qwen3-ASR GGUF）或 sherpa（流式 ONNX）",
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"kind", "llama"));
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p.addOption(makeOption("asr-model", "模型注册表里的 id（sherpa 后端用它定位文件）", "id"));
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p.addOption(makeOption("model-dir", "覆盖模型存储目录（默认 ~/.local/share/live-caption/models）", "dir"));
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p.addOption(makeOption("language", "语言提示，多语言模型用（zh / en / ja …）；留空=自动", "lang"));
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}
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// 按注册表里的模型直接构造后端（GUI 用；不依赖任何命令行参数）
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inline std::unique_ptr<IAsr> makeAsrForModel(const models::ModelInfo & m,
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const QString & language,
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int threads, int maxTokens, bool verbose,
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std::string * err)
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{
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QStringList missing;
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if (!models::ModelStore::isInstalled(m, &missing)) {
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if (err) {
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*err = "模型未安装，缺失: " + missing.join(QStringLiteral(", ")).toStdString();
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}
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return nullptr;
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}
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if (m.backend == models::Backend::LlamaCpp) {
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AsrOptions o;
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o.modelPath = m.filePath(QStringLiteral("model")).toStdString();
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o.mmprojPath = m.filePath(QStringLiteral("mmproj")).toStdString();
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o.threads = threads;
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if (maxTokens > 0) o.maxTokens = maxTokens;
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o.verbose = verbose;
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auto a = std::make_unique<Qwen3Asr>();
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if (!a->load(o, err)) return nullptr;
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return a;
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}
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#if defined(LC_HAVE_SHERPA)
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SherpaAsr::Options o;
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o.encoder = m.filePath(QStringLiteral("encoder")).toStdString();
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o.decoder = m.filePath(QStringLiteral("decoder")).toStdString();
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o.joiner = m.filePath(QStringLiteral("joiner")).toStdString();
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o.tokens = m.filePath(QStringLiteral("tokens")).toStdString();
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o.threads = 1; // 这类模型单线程最快
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if (m.languages.size() > 1 && !language.isEmpty()) o.language = language.toStdString();
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auto a = std::make_unique<SherpaAsr>();
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if (!a->load(o, err)) return nullptr;
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return a;
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#else
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if (err) *err = "编译时未找到 sherpa-onnx";
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return nullptr;
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#endif
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}
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// 按命令行选择并加载后端；失败时返回 nullptr 并把原因写进 err
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inline std::unique_ptr<IAsr> makeAsr(const QCommandLineParser & p,
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const AsrOptions & asrOpt, std::string * err)
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{
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const QString dir = p.value("model-dir");
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if (!dir.isEmpty()) models::ModelStore::setRootOverride(dir);
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const QString backend = p.value("asr-backend");
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const QString modelId = p.value("asr-model");
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const QString lang = p.value("language");
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const models::ModelInfo * info =
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modelId.isEmpty() ? nullptr : models::ModelStore::find(modelId);
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if (!modelId.isEmpty() && !info) {
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if (err) *err = "注册表里没有模型: " + modelId.toStdString();
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return nullptr;
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}
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const bool useSherpa = (backend == QLatin1String("sherpa"))
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|| (info && info->backend == models::Backend::SherpaOnline);
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if (useSherpa) {
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#if defined(LC_HAVE_SHERPA)
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if (!info) {
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if (err) *err = "sherpa 后端需要 --asr-model <id>（见 data/models.json）";
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return nullptr;
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}
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QStringList missing;
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if (!models::ModelStore::isInstalled(*info, &missing)) {
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if (err) {
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*err = "模型未安装，缺失: " + missing.join(QStringLiteral(", ")).toStdString()
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+ "\n 可运行: scripts/fetch-models.sh " + info->id.toStdString();
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}
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return nullptr;
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}
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SherpaAsr::Options o;
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o.encoder = info->filePath(QStringLiteral("encoder")).toStdString();
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o.decoder = info->filePath(QStringLiteral("decoder")).toStdString();
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o.joiner = info->filePath(QStringLiteral("joiner")).toStdString();
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o.tokens = info->filePath(QStringLiteral("tokens")).toStdString();
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o.threads = 1; // 这类模型单线程最快（多线程反而被同步开销拖慢）
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if (info->languages.size() > 1 && !lang.isEmpty()) {
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o.language = lang.toStdString(); // 多语言模型（Nemotron）需要语言提示
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}
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auto a = std::make_unique<SherpaAsr>();
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if (!a->load(o, err)) return nullptr;
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return a;
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#else
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if (err) *err = "编译时未找到 sherpa-onnx（-DSHERPA_ONNX_ROOT=...）";
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return nullptr;
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#endif
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}
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auto a = std::make_unique<Qwen3Asr>();
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if (asrOpt.modelPath.empty() || asrOpt.mmprojPath.empty()) {
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if (err) *err = "llama 后端需要 --model 和 --mmproj（或改用 --asr-backend sherpa --asr-model <id>）";
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return nullptr;
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}
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if (!a->load(asrOpt, err)) return nullptr;
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return a;
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}
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} // namespace lc::app
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