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| # 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 | |
| This example uses the whisper.xcframework which needs to be built first using the following command: | |
| ```bash | |
| ./build-xcframework.sh | |
| ``` | |
| A model is also required to be downloaded and can be done using the following command: | |
| ```bash | |
| ./models/download-ggml-model.sh base.en | |
| ``` | |
| 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 support | |
| 1. Follow all the steps in the `Usage` section, including adding the ggml model file. | |
| The ggml model file is required as the Core ML model is only used for the encoder. The | |
| decoder which is in the ggml model is still required. | |
| 2. Follow the [`Core ML support` section of readme](../../README.md#core-ml-support) to convert the | |
| model. | |
| 3. Add the Core ML model (`models/ggml-base.en-encoder.mlmodelc/`) to `whisper.swiftui.demo/Resources/models` **via Xcode**. | |
| When the example starts running you should now see that it is using the Core ML model: | |
| ```console | |
| whisper_init_state: loading Core ML model from '/Library/Developer/CoreSimulator/Devices/25E8C27D-0253-4281-AF17-C3F2A4D1D8F4/data/Containers/Bundle/Application/3ADA7D59-7B9C-43B4-A7E1-A87183FC546A/whisper.swiftui.app/models/ggml-base.en-encoder.mlmodelc' | |
| whisper_init_state: first run on a device may take a while ... | |
| whisper_init_state: Core ML model loaded | |
| ``` | |