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Const-me
Whisper
C++2023–2024
Const-me / Whisper

Whisper

High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model

AI & Machine Learning
Explore on GitHubRead Hacker News discussions
7Distinct people
30Mentions
0Explicit recommenders
10.7KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 12:45:45 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month30 dated mentions

Observed mentions: 2023-01-16 15:43:29 UTC to 2024-08-12 20:29:22 UTC.

02468102023-01-01 00:00:00 UTC to 2023-02-01 00:00:00 UTC (exclusive): 9 mentions; 1 submissions, 8 commentsJan 20232023-02-01 00:00:00 UTC to 2023-03-01 00:00:00 UTC (exclusive): 7 mentions; 0 submissions, 7 comments2023-03-01 00:00:00 UTC to 2023-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-04-01 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-01 00:00:00 UTC to 2023-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-06-01 00:00:00 UTC to 2023-07-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2023-07-01 00:00:00 UTC to 2023-08-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2023-08-01 00:00:00 UTC to 2023-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-09-01 00:00:00 UTC to 2023-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-10-01 00:00:00 UTC to 2023-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 20232023-11-01 00:00:00 UTC to 2023-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsAug 2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jan 20232023-01-01 00:00:00 UTC2023-02-01 00:00:00 UTC918
Feb 20232023-02-01 00:00:00 UTC2023-03-01 00:00:00 UTC707
Mar 20232023-03-01 00:00:00 UTC2023-04-01 00:00:00 UTC101
Apr 20232023-04-01 00:00:00 UTC2023-05-01 00:00:00 UTC000
May 20232023-05-01 00:00:00 UTC2023-06-01 00:00:00 UTC101
Jun 20232023-06-01 00:00:00 UTC2023-07-01 00:00:00 UTC110
Jul 20232023-07-01 00:00:00 UTC2023-08-01 00:00:00 UTC303
Aug 20232023-08-01 00:00:00 UTC2023-09-01 00:00:00 UTC000
Sep 20232023-09-01 00:00:00 UTC2023-10-01 00:00:00 UTC101
Oct 20232023-10-01 00:00:00 UTC2023-11-01 00:00:00 UTC000
Nov 20232023-11-01 00:00:00 UTC2023-12-01 00:00:00 UTC000
Dec 20232023-12-01 00:00:00 UTC2024-01-01 00:00:00 UTC303
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC000
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC000
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC000
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC101
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC101
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC101
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC000
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC101

What Hacker News says

9 sources
Const-me·Jan 22, 2023
> not only are more interesting libraries available, there seem to be generally high quality versions I wonder what's an equivalent of Eigen? https://eigen.tuxfamily.org/index.php?title=Main_Page > if you’re on windows, you also don’t have to worry about whether the target person has the right c++ runtime library version installed It's usually easy to build such binaries for Windows. That's a single combobox in project settings. Here's an example, just running the .exe works: https://github.com/Const-me/Whisper
In GNU Octave
Const-me·Jan 20, 2023
> I have no idea how GPU stats influence neural network benchmarks I don’t have any idea either, I don’t do ML stuff professionally. On my day job I’m using the same tech (C++, SSE and AVX SIMD, DirectCompute) for a CAM/CAE application. > would slapping a 1080ti alongside my 3060ti gain me anything In the current version of my library, you’ll gain very little. You’ll probably get the same performance as on my computer. I think it should be technically possible to split the work to multiple GPUs. The most expensive compute shaders in that library, by far, are computing matrix*matrix products. When each GPU has enough VRAM to fit both input matrices, the problem is parallelizable. However, that’s a lot of work, not something I’m willing to do within the scope of that project. Also, if you have multiple input streams to transcribe, you’ll ge
Read the rest
t better overall throughput processing these streams in parallel on different GPUs. > I'm open to the idea of buying an additional old gen GPU that nails a good price/VRAM ratio Based on my observations from the tests https://github.com/Const-me/Whisper/blob/master/SampleClips/... and also this thread about 3060Ti, it looks like the library is indeed bound by VRAM, not compute. I have another data point, that commit https://github.com/Const-me/Whisper/commit/062d01a9701a11468... Same AMD iGPU, the only difference is BIOS setup, I switched the memory from the default DDR4-2400T into the faster XMP-3332 mode. If you can, try on Radeon RX 6700 XT, or better ones from that table: https://en.wikipedia.org/wiki/Radeon_RX_6000_series#Desktop The figure for VRAM bandwidth is “only” 384 GB/sec, but the GPU has 96 MB L3 cache, which might make a difference for these compute shaders. That’s pure theory though, I haven’t tested on such GPUs. If you do that, make sure to play with the comboboxes on the “Advanced GPU Settings” dialog in the desktop example.
In Show HN: Windows port of OpenAI's Whisper automatic speech recognition model
Const-me·Jan 17, 2023
> What does this mean? It probably means you flipped the combobox on the first screen. In the build on github, the only included model implementation is GPU. The other two implementations are disabled with macros, there: https://github.com/Const-me/Whisper/blob/1.1.0/Whisper/stdaf... These implementations are lacking some UX features like callbacks and cancellation, and I haven't tested them for a while, but they might still work. > does this use both GPU and CPU simultaneously? No, it's sequential. There's a data dependency between these two stages. The encode function computes some buffers (probably called "cross attention" but I'm not sure, not an ML expert), and then the decode function needs that data to generate the output text.
In Show HN: Windows port of OpenAI's Whisper automatic speech recognition model
Const-me·Jan 16, 2023
That latency is in my code, not in some Windows component. I’m accumulating several seconds of the audio before running the model to transcribe the buffer with these audio samples. The logic is in that method: https://github.com/Const-me/Whisper/blob/15aea5bc/Whisper/Wh... How long is “several seconds” controlled by these user-adjustable parameters: https://github.com/Const-me/Whisper/blob/8648d1d5/Whisper/AP...
In Show HN: Windows port of OpenAI's Whisper automatic speech recognition model
Const-me·Jan 16, 2023Submission

Show HN: Windows port of OpenAI's Whisper automatic speech recognition model

This project is a Windows port of the whisper.cpp implementation: https://github.com/ggerganov/whisper.cpp Which in turn is a C++ port of OpenAI's Whisper automatic speech recognition (ASR) model: https://github.com/openai/whisper The implementation has no dependencies, usually much faster than realtime, and should hopefully work on most Windows computers in the world.
43 points · Open discussion

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