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A sample Android app using whisper.cpp to do voice-to-text transcriptions.

To use:

  1. Select a model from the whisper.cpp repository.1
  2. Copy the model to the "app/src/main/assets/models" folder.
  3. Select a sample audio file (for example, jfk.wav).
  4. Copy the sample to the "app/src/main/assets/samples" folder.
  5. Select the "release" active build variant, and use Android Studio to run and deploy to your device.

(PS: Do not move this android project folder individually to other folders, because this android project folder depends on the files of the whole project.)

image

CLBlast

Note

  • OpenCL does not have the same level of support as CUDA or Metal.
  • Turning on CLBlast may degrade OpenCL performance if your device isn't already tuned. See tuning.md for a list of devices that are already tuned and what to do if yours is missing.

Build CLBlast.

# In path/to/CLBlast (we assume OpenCL-Headers relative location)
$ANDROID_SDK_PATH/cmake/3.22.1/bin/cmake .. \
    -DCMAKE_SYSTEM_NAME=Android \
    -DCMAKE_SYSTEM_VERSION=33 \
    -DCMAKE_ANDROID_ARCH_ABI=arm64-v8a \
    -DCMAKE_ANDROID_NDK=$ANDROID_NDK_PATH \
    -DCMAKE_ANDROID_STL_TYPE=c++_static \
    -DOPENCL_ROOT=$(readlink -f ../../OpenCL-Headers) \
    -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=BOTH \
    -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH

# Build libclblast.so
make -j4

Pull libGLES_mali.so to libOpenCL.so.

# In path/to/whisper.android
mkdir lib/src/main/jniLibs/arm64-v8a
adb pull /system/vendor/lib64/egl/libGLES_mali.so lib/src/main/jniLibs/arm64-v8a/libOpenCL.so

In gradle.properties, set GGML_HOME to the location of GGML, as well as required options for turning on CLBlast.

GGML_HOME=/path/to/ggml
GGML_CLBLAST=ON
CLBLAST_HOME=/path/to/CLBlast
OPENCL_LIB=/path/to/libOpenCL.so
OPENCL_ROOT=/path/to/OpenCL-Headers

Footnotes

  1. I recommend the tiny or base models for running on an Android device. ↩