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* coreml : update endcoder header import path * coreml : force objc_arc in whisper-encoder.mm * whisper.objc : create coreml/ group link * whisper.objc : add coreml model link * whisper.objc : update readme * coreml : use -fobjc-arc for coreml/whisper-encoder.mm * ci: create dummy .mlmodelc for pass ios build * whisper.objc : update readme --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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| .. | ||
| whisper.objc | ||
| whisper.objc.xcodeproj | ||
| README.md | ||
README.md
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:
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.