whisper.cpp/examples/whisper.objc/README.md

# 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

```java
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
```

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:

<img width="1072" alt="image" src="https://user-images.githubusercontent.com/1991296/208511239-8d7cdbd1-aa48-41b5-becd-ca288d53cc07.png">

## 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!
Create README.md 2022-10-23 10:36:36 +02:00			`# whisper.objc`

			`Minimal Obj-C application for automatic offline speech recognition.`
			`The inference runs locally, on-device.`

Update README.md 2022-10-23 11:51:09 +02:00			`https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4`
Create README.md 2022-10-23 10:36:36 +02:00
whisper.objc : fix context + broken readme links 2022-11-27 09:48:59 +01:00			`Real-time transcription demo:`

			`https://user-images.githubusercontent.com/1991296/204126266-ce4177c6-6eca-4bd9-bca8-0e46d9da2364.mp4`

Create README.md 2022-10-23 10:36:36 +02:00			`## Usage`

			```java
			`git clone https://github.com/ggerganov/whisper.cpp`
			`open whisper.cpp/examples/whisper.objc/whisper.objc.xcodeproj/`
whisper.objc : enable Core ML in example & fix segmentation fault (#910) * coreml : update endcoder header import path * coreml : force objc_arc in whisper-encoder.mm * whisper.objc : create coreml/ group link * whisper.objc : add coreml model link * whisper.objc : update readme * coreml : use -fobjc-arc for coreml/whisper-encoder.mm * ci: create dummy .mlmodelc for pass ios build * whisper.objc : update readme --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> 2023-05-14 08:47:02 +02:00
			`// 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`
Create README.md 2022-10-23 10:36:36 +02:00			```

			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">`
Update README.md 2022-12-19 21:09:21 +01:00
whisper.objc : enable Core ML in example & fix segmentation fault (#910) * coreml : update endcoder header import path * coreml : force objc_arc in whisper-encoder.mm * whisper.objc : create coreml/ group link * whisper.objc : add coreml model link * whisper.objc : update readme * coreml : use -fobjc-arc for coreml/whisper-encoder.mm * ci: create dummy .mlmodelc for pass ios build * whisper.objc : update readme --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> 2023-05-14 08:47:02 +02:00			Also, don't forget to add the `-DGGML_USE_ACCELERATE` compiler flag for `ggml.c` in Build Phases.
Update README.md 2022-12-19 21:09:21 +01:00			`This can significantly improve the performance of the transcription:`

			`<img width="1072" alt="image" src="https://user-images.githubusercontent.com/1991296/208511239-8d7cdbd1-aa48-41b5-becd-ca288d53cc07.png">`
whisper.objc : add `-O3 -DNDEBUG` in release mode (#640) 2023-03-22 21:16:04 +01:00
whisper : Metal and ggml-alloc support (#1270) * metal : init * whisper : factor out graph builds * whisper : allocate encoder and decoder using ggml-alloc * whisper : ggml-alloc is now supported * whisper : CoreML support ggml-alloc * build : fix ggml-alloc * ios : update submodule * extra : update sync-ggml.sh script to also sync ggml-alloc * ci : see if this is causing the crash * whisper : refactor ggml-alloc init * whisper.android : try to fix build * whisper : initial Metal version * ci : try to debug vmem issue * metal : decoder works on GPU! * metal : add multi-decoder support * ggml : fix ggml_nbytes (probably temp solution) * metal : run "cross" step on the GPU * whisper : remove ggml_repeat in the encoder * whisper : offload the Encoder to Metal * ggml : use simpler ggml_bytes() implementation * ggml-alloc : try to make CI happy by reducing vram to 128GB * whisper : add whisper_allocr to wrap ggml_allocr * whisper : factor out alloc init in a function * cmake : update to support Metal build * whisper : add <functional> header * objc : fix build (no Metal yet) * ios : add Metal support * swiftui : fix build * metal : speed-up KQ multiplication * metal : sync latest llama.cpp kernels * readme : add Metal info * ios : update submodule * coreml : add code to toggle Core ML config (CPU, ANE, GPU) * bench : fix timings by running a pre-heat * bench : start benching the decoder * whisper : add ggml_mul_mat_pad * bench : fix uninitialized vars * whisper : add comment for disabling mul-mat padding * whisper : add description of ggml_mul_mat_pad * whisper : clean-up ggml_mul_mat_pad * metal : remove the "concurrent" flag * bench : variable n_past * ios : update SPM package 2023-09-15 11:18:18 +02:00			`## Core ML`

whisper.objc : enable Core ML in example & fix segmentation fault (#910) * coreml : update endcoder header import path * coreml : force objc_arc in whisper-encoder.mm * whisper.objc : create coreml/ group link * whisper.objc : add coreml model link * whisper.objc : update readme * coreml : use -fobjc-arc for coreml/whisper-encoder.mm * ci: create dummy .mlmodelc for pass ios build * whisper.objc : update readme --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> 2023-05-14 08:47:02 +02:00			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.

whisper.objc : add `-O3 -DNDEBUG` in release mode (#640) 2023-03-22 21:16:04 +01:00			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.
whisper : Metal and ggml-alloc support (#1270) * metal : init * whisper : factor out graph builds * whisper : allocate encoder and decoder using ggml-alloc * whisper : ggml-alloc is now supported * whisper : CoreML support ggml-alloc * build : fix ggml-alloc * ios : update submodule * extra : update sync-ggml.sh script to also sync ggml-alloc * ci : see if this is causing the crash * whisper : refactor ggml-alloc init * whisper.android : try to fix build * whisper : initial Metal version * ci : try to debug vmem issue * metal : decoder works on GPU! * metal : add multi-decoder support * ggml : fix ggml_nbytes (probably temp solution) * metal : run "cross" step on the GPU * whisper : remove ggml_repeat in the encoder * whisper : offload the Encoder to Metal * ggml : use simpler ggml_bytes() implementation * ggml-alloc : try to make CI happy by reducing vram to 128GB * whisper : add whisper_allocr to wrap ggml_allocr * whisper : factor out alloc init in a function * cmake : update to support Metal build * whisper : add <functional> header * objc : fix build (no Metal yet) * ios : add Metal support * swiftui : fix build * metal : speed-up KQ multiplication * metal : sync latest llama.cpp kernels * readme : add Metal info * ios : update submodule * coreml : add code to toggle Core ML config (CPU, ANE, GPU) * bench : fix timings by running a pre-heat * bench : start benching the decoder * whisper : add ggml_mul_mat_pad * bench : fix uninitialized vars * whisper : add comment for disabling mul-mat padding * whisper : add description of ggml_mul_mat_pad * whisper : clean-up ggml_mul_mat_pad * metal : remove the "concurrent" flag * bench : variable n_past * ios : update SPM package 2023-09-15 11:18:18 +02:00
			`## 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!