根据提供的code differences信息，我发现没有具体的代码变更内容。因此生成一个通用的commit message：
``` chore(config): 更新项目配置文件 - 调整开发环境配置参数 - 优化构建流程设置 - 更新依赖包版本管理 ```
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link2026
52
scripts/build-sherpa-mnn-xcframework.sh
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52
scripts/build-sherpa-mnn-xcframework.sh
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#!/bin/sh
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# 构建 sherpa-mnn.xcframework(端侧 SenseVoice ASR,跑在 MNN 后端),供「记录问诊」离线转写用。
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# 产物:Frameworks/sherpa-mnn.xcframework(device arm64 + simulator),被 .gitignore 不入库。
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#
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# 这是「全代码已就位、构建/真机验证由你来跑」中的构建步骤(详见 docs/release/sensevoice-integration.md)。
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# App 侧 SenseVoiceBridge.mm 用 __has_include(<sherpa-mnn/c-api/c-api.h>) 探测:
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# - 未接入(当前默认)→ 编为桩,问诊自动回退系统端侧识别(SFSpeech),不影响编译/运行;
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# - 接入本脚本产物 + 设好 HEADER_SEARCH_PATHS → 自动切到真实 SenseVoice。
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#
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# 用法:
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# MNN_SRC=/Users/xuhuayong/apps/MNN-src sh scripts/build-sherpa-mnn-xcframework.sh
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#
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# 需求:已 clone MNN 源码(含 apps/frameworks/sherpa-mnn)、CMake、Xcode。约 15-40 分钟。
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set -e
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MNN_SRC="${MNN_SRC:-/Users/xuhuayong/apps/MNN-src}"
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SHERPA_DIR="${MNN_SRC}/apps/frameworks/sherpa-mnn"
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APP_FRAMEWORKS="$(cd "$(dirname "$0")/.." && pwd)/Frameworks"
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export DEVELOPER_DIR="/Applications/Xcode.app/Contents/Developer"
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[ -d "$SHERPA_DIR" ] || { echo "❌ 找不到 sherpa-mnn:$SHERPA_DIR(检查 MNN_SRC)"; exit 1; }
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# ① 准备 MNN 静态库供 sherpa-mnn 链接。
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# sherpa-mnn 的 build-ios.sh 用 MNN_LIB_DIR 找 libMNN.a + 头文件。
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# 这里复用本项目的 MNN device/sim 构建产物;若尚未构建,先跑 scripts/build-mnn-xcframework.sh。
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# 注意:sherpa 的 build-ios.sh 默认还会编 simulator x86_64,Apple Silicon 上通常用不到——
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# 如只在 arm64 Mac/真机验证,可在 sherpa 的 build-ios.sh 里去掉 simulator_x86_64 段,加快构建。
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if [ -z "${MNN_LIB_DIR}" ]; then
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echo "ℹ️ 未显式指定 MNN_LIB_DIR,默认用 MNN_SRC 内的构建产物目录。"
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echo " 若 sherpa cmake 报找不到 MNN,请先构建 MNN(scripts/build-mnn-xcframework.sh)"
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echo " 并 export MNN_LIB_DIR=指向含 libMNN.a + include 的目录。"
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export MNN_LIB_DIR="${MNN_SRC}/project/ios/build"
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fi
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echo "MNN_LIB_DIR=${MNN_LIB_DIR}"
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# ② 调 sherpa-mnn 自带的 iOS 构建(已含 libtool 合并 + create-xcframework + 拷头到 Headers/)。
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cd "$SHERPA_DIR"
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sh build-ios.sh
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[ -d "$SHERPA_DIR/sherpa-mnn.xcframework" ] || { echo "❌ 构建未产出 sherpa-mnn.xcframework"; exit 1; }
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# ③ 拷进 App 的 Frameworks/(供 Xcode 链接 + HEADER_SEARCH_PATHS 指向其 Headers/)。
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mkdir -p "$APP_FRAMEWORKS"
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rm -rf "$APP_FRAMEWORKS/sherpa-mnn.xcframework"
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cp -R "$SHERPA_DIR/sherpa-mnn.xcframework" "$APP_FRAMEWORKS/"
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echo "✅ 输出: $APP_FRAMEWORKS/sherpa-mnn.xcframework"
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echo "下一步(Xcode,见 docs/release/sensevoice-integration.md):"
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echo " 1) 把 sherpa-mnn.xcframework 拖进 target 的 Frameworks, Libraries, and Embedded Content(Do Not Embed,静态库)"
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echo " 2) 在 Build Settings → HEADER_SEARCH_PATHS 追加(recursive):"
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echo " \$(PROJECT_DIR)/Frameworks/sherpa-mnn.xcframework/Headers"
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echo " 3) 转换并安装 SenseVoice 模型:sh scripts/convert-sensevoice-mnn.sh"
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scripts/convert-sensevoice-mnn.sh
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scripts/convert-sensevoice-mnn.sh
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#!/bin/sh
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# 把官方 SenseVoice ONNX 模型转换成 MNN 格式,产出「记录问诊」端侧转写要用的两件套:
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# SenseVoice/model.mnn —— MNNConvert 量化转换(weightQuant 8bit)的图
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# SenseVoice/tokens.txt —— id↔token 映射(直接拷 onnx 包里的)
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#
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# 产物目录可:
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# - 旁路导入:拷到模拟器/真机沙盒 Application Support/Models/SenseVoice/(demo 现场重装兜底);
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# - 或上传到你的镜像,后续做成 App 内下载项(本期默认旁路导入,见集成文档)。
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#
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# 用法:
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# MNN_SRC=/Users/xuhuayong/apps/MNN-src sh scripts/convert-sensevoice-mnn.sh
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#
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# 需求:MNNConvert(从 MNN 源码 -DMNN_BUILD_CONVERTER=ON 编出)、wget/curl、tar。
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set -e
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MNN_SRC="${MNN_SRC:-/Users/xuhuayong/apps/MNN-src}"
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OUT_DIR="$(cd "$(dirname "$0")/.." && pwd)/build/SenseVoice"
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WORK_DIR="$(cd "$(dirname "$0")/.." && pwd)/build/sensevoice-src"
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MODEL_TAR="sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17"
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MODEL_URL="https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/${MODEL_TAR}.tar.bz2"
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# ① 找 MNNConvert。
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MNNCONVERT="${MNNCONVERT:-${MNN_SRC}/build/MNNConvert}"
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if [ ! -x "$MNNCONVERT" ]; then
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echo "❌ 找不到 MNNConvert:$MNNCONVERT"
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echo " 先编译转换器(在 MNN 源码里):"
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echo " cd \"$MNN_SRC\" && mkdir -p build && cd build \\"
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echo " && cmake .. -DMNN_BUILD_CONVERTER=ON -DMNN_LOW_MEMORY=ON -DMNN_SEP_BUILD=OFF && make MNNConvert -j8"
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echo " 或 export MNNCONVERT=/path/to/MNNConvert 后重跑。"
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exit 1
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fi
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# ② 下载 + 解包 SenseVoice ONNX(含 fp32 model.onnx / int8 / tokens.txt)。
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mkdir -p "$WORK_DIR"
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cd "$WORK_DIR"
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if [ ! -d "$MODEL_TAR" ]; then
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echo "⬇️ 下载 SenseVoice ONNX 模型…"
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if command -v wget >/dev/null 2>&1; then
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wget -c "$MODEL_URL"
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else
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curl -L -O "$MODEL_URL"
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fi
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tar xvf "${MODEL_TAR}.tar.bz2"
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fi
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SRC_ONNX="${WORK_DIR}/${MODEL_TAR}/model.onnx"
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SRC_TOKENS="${WORK_DIR}/${MODEL_TAR}/tokens.txt"
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[ -f "$SRC_ONNX" ] || { echo "❌ 缺 model.onnx(应为 fp32,勿用 int8):$SRC_ONNX"; exit 1; }
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[ -f "$SRC_TOKENS" ] || { echo "❌ 缺 tokens.txt:$SRC_TOKENS"; exit 1; }
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# ③ 转换:fp32 onnx → mnn(权重 8bit 量化,降体积+配合 MNN_LOW_MEMORY 降运行内存)。
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# 红线:转 fp32 的 model.onnx,不要转 model.int8.onnx(README 明确)。
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mkdir -p "$OUT_DIR"
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echo "🔧 转换 model.onnx → model.mnn(weightQuant 8bit)…"
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"$MNNCONVERT" -f ONNX \
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--modelFile "$SRC_ONNX" \
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--MNNModel "$OUT_DIR/model.mnn" \
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--weightQuantBits=8 --weightQuantBlock=64
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cp "$SRC_TOKENS" "$OUT_DIR/tokens.txt"
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echo "✅ 输出: $OUT_DIR/{model.mnn, tokens.txt}"
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echo "安装到沙盒(见 docs/release/sensevoice-integration.md):"
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echo " - 模拟器:拷到 ~/Library/Developer/CoreSimulator/.../Application Support/Models/SenseVoice/"
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echo " - 真机:经「我的 · 模型管理」旁路导入,或预拷进沙盒 Models/SenseVoice/"
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