4bbb60efce
* Make models more "discoverable" * Clean up code block language identifiers * make 3 options clearer * undo Prettier formatter change * docs: `$` shell prompt, consistently * docs: minor changes
2.2 KiB
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!