0463028bc2
* whisper : check state->ctx_metal not null
* whisper : add whisper_context_params { use_gpu }
* whisper : new API with params & deprecate old API
* examples : use no-gpu param && whisper_init_from_file_with_params
* whisper.objc : enable metal & disable on simulator
* whisper.swiftui, metal : enable metal & support load default.metallib
* whisper.android : use new API
* bindings : use new API
* addon.node : fix build & test
* bindings : updata java binding
* bindings : add missing whisper_context_default_params_by_ref WHISPER_API for java
* metal : use SWIFTPM_MODULE_BUNDLE for GGML_SWIFT and reuse library load
* metal : move bundle var into block
* metal : use SWIFT_PACKAGE instead of GGML_SWIFT
* style : minor updates
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
|
||
|---|---|---|
| .. | ||
| whisper.objc | ||
| whisper.objc.xcodeproj | ||
| 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:
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!