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readme : update comment about source code

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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 2 source files:
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-
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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)