Incredibly slow on Windows with CPU having AVX support

[polkovnikov]

I've compiled your command and main example projects with latest 16-th Clang on Windows.

I have CPU Intel i7-2630QM @ 2.00GHz, which has 4 cores (8 hardware threads). And CPU has AVX support.

Used -O3 -march=native option, so it means full optimization and using all CPU features.

When I use Command program to say a short phrase, after it prints Speech detected! Processing ... it takes 50-60 seconds to output resulting transcription.

Same is with Main program, if I provide it with WAV having short phrase, it takes also 50-60 seconds to process it and output recognized text.

Note. I did my own compilation of your program from Command Line, I didn't use your Make or CMake files. It could be the reason why it slowed down. But if I provide -O3 -march=native to Clang then I see no reason why should be there any problem then. I need to build it from command line only because I'm integrating Whisper.Cpp into my own project that has its own C++ build system.

[prusnak]

What does the system_info line of the output say?

Find a line that looks like this:

system_info: n_threads = 4 / 8 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | 

[polkovnikov]

@prusnak Just AVX + SSE3. Also, I don't have even F16C, I checked that in cpuinfo.

system_info: n_threads = 8 / 8 | AVX = 1 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 |

[misutoneko]

Have you tried with, say, 4 threads if it still behaves the same? I think I saw a graph somewhere that you don't get that much benefit from extra threads anyway. EDIT: Yup, take a look at issue #200.

Also, is it possible that you've run out of memory? Idk, just something to check.

[polkovnikov]

@misutoneko I've tried on both laptops that I have. Both of them are bought around 2009-2012 year.

Another one with 2 cores (2 hardware threads) also gives very slow result, above 50 seconds. It has no AVX, just SSE3.

When I reduce threads from 8 to 4 or even to 1, things get even slower, especially with one thread.

Also, I have a question, should I look at encode or decode time? See console output below:

whisper_print_timings:   encode time = 184388.62 ms /     2 runs (92194.31 ms per run)
whisper_print_timings:   decode time =  3506.90 ms /    17 runs (  206.29 ms per run)

What here encode and decode mean? Seems to me that encode is 1000 times slower than decode. Is it alright?

---

Also I've tried python package of whisper, which is

python -m pip install git+https://github.com/openai/whisper.git

It allows you to run a command like this

whisper --model base.en --language en test.wav

This command takes only 5-10 seconds to recognize, unlike Whisper.Cpp which took 50 and more seconds.

But as I saw in code Python version uses PyTorch package and model. Hence it is much more optimized than whisper.cpp, it could be the reason of great speedup.

[misutoneko]

OK I guess it's unlikely to be a memory issue with base.en. Did you set build type to "Release"?
I think the default is Debug and that one is slow.
EDIT: The build type thing is CMake related (issue #33), and I see you're not using CMake. So scratch that.

[ulatekh]

I find Whisper is incredibly slow unless CUDA support is enabled.