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README.md
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@@ -34,10 +34,8 @@ Supported platforms:
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- [x] Windows ([MSVC](https://github.com/ggerganov/whisper.cpp/blob/master/.github/workflows/build.yml#L117-L144) and [MinGW](https://github.com/ggerganov/whisper.cpp/issues/168)]
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- [x] [Raspberry Pi](https://github.com/ggerganov/whisper.cpp/discussions/166)
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The entire implementation of the model is contained in
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- Tensor operations: [ggml.h](ggml.h) / [ggml.c](ggml.c)
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- Transformer inference: [whisper.h](whisper.h) / [whisper.cpp](whisper.cpp)
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Having such a lightweight implementation of the model allows to easily integrate it in different platforms and applications.
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As an example, here is a video of running the model on an iPhone 13 device - fully offline, on-device: [whisper.objc](examples/whisper.objc)
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- [x] Windows ([MSVC](https://github.com/ggerganov/whisper.cpp/blob/master/.github/workflows/build.yml#L117-L144) and [MinGW](https://github.com/ggerganov/whisper.cpp/issues/168)]
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- [x] [Raspberry Pi](https://github.com/ggerganov/whisper.cpp/discussions/166)
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The entire high-level implementation of the model is contained in [whisper.h](whisper.h) and [whisper.cpp](whisper.cpp).
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The rest of the code is part of the [ggml](https://github.com/ggerganov/ggml) machine learning library.
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Having such a lightweight implementation of the model allows to easily integrate it in different platforms and applications.
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As an example, here is a video of running the model on an iPhone 13 device - fully offline, on-device: [whisper.objc](examples/whisper.objc)
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