Roadmap | F.A.Q.

[ggerganov]

Updated roadmap as Github Project

https://github.com/users/ggerganov/projects/7

Roadmap (old)

In decreasing priority:

• Decoding strategies
  Try to achieve at least parity with the OpenAI implementation
  Target release: v1.1.0

• Memory usage reduction
  This will allow wider application on low-memory devices. Should be possible to cut memory usage in half with a few simple changes in ggml
  Target release: v1.2.0

• Core ML support
  This will allow to utilize the Apple Neural Engine for very efficient inference of the model
  Target release: v1.3.0

• Q4 / Q5 / Q8 integer quantization
  Added via Integer quantisation support #540
  Target release: v1.4.0

• GPU support
  Partial CUDA support via cuBLAS
  Target release: v1.4.0

• Documentation of ggml
  Hopefully this leads to more contributions

• Diarization
  Very wanted feature, but very difficult to achieve. Interesting to explore and experiment

• Low-power mode
  This has the potential for some performance improvements too

F.A.Q.

• Is whisper.cpp faster or slower than PyTorch CPU?

  The performance should be comparable. At the time of writing, the performance on Apple Silicon when using whisper.cpp is better since we utilise FP16 + Accelerate framework, while PyTorch not yet. But this will soon change.

  In general, it is not very easy to make a proper benchmark between the 2 implementations. For more information, read the following comment: Benchmark results #89 (comment)

• Should I use whisper.cpp in my project?

  whisper.cpp is a hobby project. It does not strive to provide a production ready implementation. The main goals of the implementation is to be educational, minimalistic, portable, hackable and performant. There are no guarantees that the implementation is correct and bug-free and stuff can break at any point in the future. Support and updates will depend mostly on contributions, since with time I will move on and won't dedicate too much time on the project.

  If you plan to use whisper.cpp in your own project, keep in mind the above.
  My advice is to not put all your eggs into the whisper.cpp basket.

• How can I contribute?

  • Add tests
  • Add bindings
  • Add examples
  • Add benchmarks
  • Good first issues
  • Help wanted issues
  • Experiment
  • Make cool demos

• Will ggml / whisper.cpp support CUDA / GPU?

  One of the main goals of this implementation is to be very minimalistic and be able to run it on a large spectrum of hardware. The existing CPU-only implementation achieves this goal - it is bloat-free and very simple. I think it also has some educational value. Of course, not taking advantage of modern GPU hardware is a huge drawback in terms of performance. However, adding a dependency on a certain GPU framework will tie the project with the corresponding hardware and will introduce some extra complexity.

  With that said, adding GPU support to the project is low priority.

  In any case, it would not be too difficult to add initial support. The main thing that needs to be offloaded to the GPU is the GGML_OP_MUL_MAT operator:
  

  whisper.cpp/ggml.c

  Lines 6231 to 6234 in c71363f

  	case GGML_OP_MUL_MAT:
  	{
  	ggml_compute_forward_mul_mat(params, tensor->src0, tensor->src1, tensor);
  	} break;

  
  This is where more than 90% of the computation time is currently spent. Also, I don't think it's necessary to offload the entire model to the GPU. For example, the 2 convolution layers at the start of the Encoder can easily remain on the CPU as they are not very computationally heavy. Not uploading the full model to VRAM will make it require less memory and thus make it compatible with more video cards.

  Another candidate GPU framework that will likely be supported in the future is Apple's Metal Performance Shaders (MPS). Currently, ggml supports Apple's Accelerate framework and I really like how seamlessly it integrates in the project - both on MacOS and iOS. It does not feel like a third-party dependency at all and that is why I think MPS support can be added in a similar way. In theory, it will help to utilize the GPU on Apple devices and could potentially lead to some additional performance improvement. Also, the unified memory model of modern Apple Silicon devices allows to seamlessly share the model weights and the data embeddings between the CPU and the GPU, which is not the case for CUDA. So far, my initial experiments haven't shown any benefit of using MPS for the transformer inference, but maybe some more work is needed.

  Edit: Sample Metal support has been demonstrated here: Metal support #127

  Edit2: There is a promising CUDA support in the works through NVBLAS: Experiments with GPU CUDA acceleration...sort of #220

  Edit3: CUDA support via cuBLAS has been added: Add CUDA support via cuBLAS #834

Notable whisper.cpp discussions

• Original announcement
• Reddit /r/MachineLearning
• Hackaday
• Hacker News (Show HN)
• The New Yorker
• Changelog podcast

[rpikus27]

I saw the demo with command recognition. How do you trigger events after a command? Is there an intent handler model built in?

[ggerganov]

The command example only demonstrates how to receive voice commands. Handling the commands is up to you.
You can either extend the C++ example with a handler or you can pipe / parse the output from an external app, etc.
There is no built-in intent model available.

I am planning to soon demonstrate how to extend the command example for the purpose of making moves on a chess board with voice. Updates will be posted in #428 if you are interested.

[rpikus27]

I am interested what you choose to parse the text. Whisper is so good, for my purposes, a switch statement would do the trick, but a mass market app will probably need a model.

[ruban22]

Hi,

Im intersted in your solution for real time streaming transcription/translation in our Video confrencing, can you offer your service to implement it in our enviroment?

Thanks

[katurov]

Hi! I've built version for M1 as in instruction but after all I have my stream translated from original language to English. And I cannot switch it off. Is here some ideas how to?

[SonOfLilit]

Found the solution here:

#526

./main --language auto ...

[fromparis]

can you make an example of the word time stamp in swiftUI ?? I'm totally lost in c++

[rexendevar]

Is there any current or planned method of setting up a file to use as an additional dictionary?