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examples : update whisper.objc README.md #2916

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42 changes: 13 additions & 29 deletions examples/whisper.objc/README.md
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Expand Up @@ -11,39 +11,23 @@ https://user-images.githubusercontent.com/1991296/204126266-ce4177c6-6eca-4bd9-b

## Usage

This example uses the whisper.xcframework which needs to be built first using the following command:
```bash
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
./build_xcframework.sh
```

Make sure to build the project in `Release`:

<img width="947" alt="image" src="https://user-images.githubusercontent.com/1991296/197382607-9e1e6d1b-79fa-496f-9d16-b71dc1535701.png">

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:
A model is also required to be downloaded and can be done using the following command:
```bash
./models/download-ggml-model.sh base.en
```

<img width="1072" alt="image" src="https://user-images.githubusercontent.com/1991296/208511239-8d7cdbd1-aa48-41b5-becd-ca288d53cc07.png">
If you don't want to convert a Core ML model, you can skip this step by creating dummy model:
```bash
mkdir models/ggml-base.en-encoder.mlmodelc
```

## 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:

<img width="1072" alt="image" src="https://github.com/ggerganov/whisper.cpp/assets/3001525/103e8f57-6eb6-490d-a60c-f6cf6c319324">

Then follow the [`Core ML support` section of readme](../../README.md#core-ml-support) 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!
Follow the [`Core ML support` section of readme](../../README.md#core-ml-support) to convert the model.
That is all the needs to be done to use the Core ML model in the app. The converted model is a
resource in the project and will be used if it is available.
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