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
50 lines
2.2 KiB
Markdown
50 lines
2.2 KiB
Markdown
# whisper.objc
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Minimal Obj-C application for automatic offline speech recognition.
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The inference runs locally, on-device.
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https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4
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Real-time transcription demo:
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https://user-images.githubusercontent.com/1991296/204126266-ce4177c6-6eca-4bd9-bca8-0e46d9da2364.mp4
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## Usage
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```bash
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git clone https://github.com/ggerganov/whisper.cpp
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open whisper.cpp/examples/whisper.objc/whisper.objc.xcodeproj/
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# if you don't want to convert a Core ML model, you can skip this step by create dummy model
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mkdir models/ggml-base.en-encoder.mlmodelc
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```
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Make sure to build the project in `Release`:
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<img width="947" alt="image" src="https://user-images.githubusercontent.com/1991296/197382607-9e1e6d1b-79fa-496f-9d16-b71dc1535701.png">
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Also, don't forget to add the `-DGGML_USE_ACCELERATE` compiler flag for `ggml.c` in Build Phases.
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This can significantly improve the performance of the transcription:
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<img width="1072" alt="image" src="https://user-images.githubusercontent.com/1991296/208511239-8d7cdbd1-aa48-41b5-becd-ca288d53cc07.png">
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## Core ML
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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:
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<img width="1072" alt="image" src="https://github.com/ggerganov/whisper.cpp/assets/3001525/103e8f57-6eb6-490d-a60c-f6cf6c319324">
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Then follow the [`Core ML support` section of readme](../../README.md#core-ml-support) for convert the model.
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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.
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## Metal
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You can also enable Metal to make the inference run on the GPU of your device. This might or might not be more efficient
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compared to Core ML depending on the model and device that you use.
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To enable Metal, just add `-DGGML_USE_METAL` instead off the `-DWHISPER_USE_COREML` flag and you are ready.
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This will make both the Encoder and the Decoder run on the GPU.
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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!
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