83b14c357c
* examples : use xcframework in whisper.objc example This commit updates the whisper.objc example to use the xcframework. The motivation for this to be consistent with the swift example and to also act as a reference for how to use the xcframework in an objc project. Resolves: https://github.com/ggerganov/whisper.cpp/issues/2881 * examples : setup audio session viewDidload This commit adds the setup of the audio session in the viewDidload method of the ViewController.m file. This is necessary to allow the app to record audio. The motivation for this is that without this it was not possible to caputue audio from the microphone. It was possible to click on the Capture button but nothing happened after that, and the button was not marked red indicating that the button could be clicked again to stop capturing. With this change it is possible to capture audio from the microphone and get it transcribed.
whisper.objc
Minimal Obj-C application for automatic offline speech recognition. The inference runs locally, on-device.
https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4
Real-time transcription demo:
https://user-images.githubusercontent.com/1991296/204126266-ce4177c6-6eca-4bd9-bca8-0e46d9da2364.mp4
Usage
git clone https://github.com/ggerganov/whisper.cpp
open whisper.cpp/examples/whisper.objc/whisper.objc.xcodeproj/
# if you don't want to convert a Core ML model, you can skip this step by create dummy model
mkdir models/ggml-base.en-encoder.mlmodelc
Make sure to build the project in Release:
Also, don't forget to add the -DGGML_USE_ACCELERATE compiler flag for ggml.c in Build Phases.
This can significantly improve the performance of the transcription:
Core ML
If you want to enable Core ML support, you can add the -DWHISPER_USE_COREML -DWHISPER_COREML_ALLOW_FALLBACK compiler flag for whisper.cpp in Build Phases:
Then follow the Core ML support section of readme for convert the model.
In this project, it also added -O3 -DNDEBUG to Other C Flags, but adding flags to app proj is not ideal in real world (applies to all C/C++ files), consider splitting xcodeproj in workspace in your own project.
Metal
You can also enable Metal to make the inference run on the GPU of your device. This might or might not be more efficient compared to Core ML depending on the model and device that you use.
To enable Metal, just add -DGGML_USE_METAL instead off the -DWHISPER_USE_COREML flag and you are ready.
This will make both the Encoder and the Decoder run on the GPU.
If you want to run the Encoder with Core ML and the Decoder with Metal then simply add both -DWHISPER_USE_COREML -DGGML_USE_METAL flags. That's all!