在OpenHarmony上，使用tensorflow lite推理Yamnet实现语音分类

# TensorFlow Lite C++ minimal example This example shows how you can build a simple TensorFlow Lite application. #### Step 1. Install CMake tool It requires CMake 3.16 or higher. On Ubuntu, you can simply run the following command. ```sh sudo apt-get install cmake ``` Or you can follow [the official cmake installation guide](https://cmake.org/install/) #### Step 2. Clone TensorFlow repository ```sh git clone https://github.com/tensorflow/tensorflow.git tensorflow git checkout origin/r2.10 ``` #### Step 3. Create CMake build directory and run CMake tool ```sh # move current directory to tensorflow_root/tensorflow/lite/examples mkdir build_yamnet cd build_yamnet cmake -DCMAKE_TOOLCHAIN_FILE=/home/yons/data/ohos-sdk/linux/native/build/cmake/ohos.toolchain.cmake -DTFLITE_ENABLE_XNNPACK=OFF ../tensorflow/lite/examples/yamnet # enter tensorflow root path, patch different cd ../ patch -p0 build_yamnet/flatbuffers/include/flatbuffers/util.h flatbuffers_util_h.diff patch -p0 build_yamnet/flatbuffers/src/util.cpp flatbuffers_util_cpp.diff patch -p0 build_yamnet/cpuinfo/CMakeLists.txt cpuinfo.diff # re cmake configuration and build cd build_yamnet cmake -DCMAKE_TOOLCHAIN_FILE=/home/yons/data/ohos-sdk/linux/native/build/cmake/ohos.toolchain.cmake -DTFLITE_ENABLE_XNNPACK=OFF ../tensorflow/lite/examples/yamnet cmake --build ./ -j20 ``` #### Step 4. copy binary file and related file to openHarmony device through device * make sure that connect to device. ``` hdc list targets hdc shell ``` ```sh # In the build_yamnet directory hdc file send yamnet /data hdc file send yamnet_class_map.txt /data hdc file send miaow_16k.wav /data hdc file send golden_whistle.wav /data hdc file send speech_whistling2.wav /data # copy libc++_shared.so from ohos-sdk cross compile toolchain hdc file send libc++_shared.so /data ``` #### Step 5. execute binary programation in OHOS ``` hdc shell cd data export LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./yamnet wav_filepath # 从上面三个文件选一 golden_whistle.wav miaow_16k.wav speech_whistling2.wav ``` #### Step 5. result