Further improve the speed of Whisper.cpp (might need a dependency)

[haikyuu]

I came across Faster Whisper which is 5x faster than whisper.cpp with comparable memory footprint. It's built in python and uses this C++ library (Ctranslate2)

Just bringing this to your attention as this is very compelling ... 5x faster whisper.cpp (plus models are 4x smaller on disk) would unlock so much more use cases

[guillaumekln]

Hey, thanks for sharing this here! I'm the author of faster-whisper and CTranslate2.

I was a bit surprised by the benchmark results on this CPU. I attribute most of the gains to our integration of Intel oneMKL which basically offers unmatched performance for matrix multiplication. Also oneMKL can make use of AVX512 instructions, which are supported by this processor.

I just relaunched the whisper.cpp fp16 run to make sure I did not make a mistake. For reference, below is the command that I used and the output logs (with commit 3b010f9):

$ /usr/bin/time -v ./main -t 8 -bs 5 -l auto -m models/ggml-small.bin -f output.wav
whisper_init_from_file: loading model from 'models/ggml-small.bin'
whisper_model_load: loading model
whisper_model_load: n_vocab       = 51865
whisper_model_load: n_audio_ctx   = 1500
whisper_model_load: n_audio_state = 768
whisper_model_load: n_audio_head  = 12
whisper_model_load: n_audio_layer = 12
whisper_model_load: n_text_ctx    = 448
whisper_model_load: n_text_state  = 768
whisper_model_load: n_text_head   = 12
whisper_model_load: n_text_layer  = 12
whisper_model_load: n_mels        = 80
whisper_model_load: f16           = 1
whisper_model_load: type          = 3
whisper_model_load: mem required  =  603.00 MB (+   16.00 MB per decoder)
whisper_model_load: kv self size  =   15.75 MB
whisper_model_load: kv cross size =   52.73 MB
whisper_model_load: adding 1608 extra tokens
whisper_model_load: model ctx     =  464.56 MB
whisper_model_load: model size    =  464.44 MB

system_info: n_threads = 8 / 15 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 |

main: processing 'output.wav' (12786951 samples, 799.2 sec), 8 threads, 1 processors, lang = auto, task = transcribe, timestamps = 1 ...

whisper_full: auto-detected language: fr (p = 0.987908)

[...]

whisper_print_timings:     fallbacks =   0 p /   0 h
whisper_print_timings:     load time =   647.20 ms
whisper_print_timings:      mel time =  3202.05 ms
whisper_print_timings:   sample time = 166569.81 ms / 18858 runs (    8.83 ms per run)
whisper_print_timings:   encode time = 131752.80 ms /    32 runs ( 4117.27 ms per run)
whisper_print_timings:   decode time = 437354.44 ms / 18735 runs (   23.34 ms per run)
whisper_print_timings:    total time = 740297.12 ms
        Command being timed: "./main -t 8 -bs 5 -l auto -m models/ggml-small.bin -f output.wav"
        User time (seconds): 4618.27
        System time (seconds): 20.27
        Percent of CPU this job got: 626%
        Elapsed (wall clock) time (h:mm:ss or m:ss): 12:20.37
        Average shared text size (kbytes): 0
        Average unshared data size (kbytes): 0
        Average stack size (kbytes): 0
        Average total size (kbytes): 0
        Maximum resident set size (kbytes): 873572
        Average resident set size (kbytes): 0
        Major (requiring I/O) page faults: 0
        Minor (reclaiming a frame) page faults: 336643
        Voluntary context switches: 18972
        Involuntary context switches: 57161
        Swaps: 0
        File system inputs: 0
        File system outputs: 0
        Socket messages sent: 0
        Socket messages received: 0
        Signals delivered: 0
        Page size (bytes): 4096
        Exit status: 0

Let me know if you have any questions. I would be happy to help making whisper.cpp faster!

[guillaumekln]

Unfortunately I don't have access to an Apple machine at the moment. However, we do support OpenBLAS. I will run a more extensive comparison with oneMKL vs OpenBLAS and AVX2 vs AVX512.

[ggerganov]

One suggestion - I think it would make more sense to compare the Encoder time per run and Decoder time per run since due to the auto-regressive nature of the model the 2 code bases could branch into different transcription results and effectively perform different number of inferences for the same audio input. In the end, there is no optimal decoding strategy, so one is mostly interested in having an optimal inference of the model per run.

[guillaumekln]

FYI, here are additional data points comparing AVX2/AVX512 and oneMKL/OpenBLAS for a fixed code base.

It seems that AVX512 does not have a big impact, at least for the small model and the CPU used in this test. However, oneMKL is noticeably faster than OpenBLAS.

Implementation	Backend	ISA	Precision	Beam size	Time for 13m of audio
faster-whisper	OpenBLAS	AVX2	fp32	1	3m06s
faster-whisper	OpenBLAS	AVX512	fp32	1	2m54s
faster-whisper	oneMKL	AVX	fp32	1	2m24s
faster-whisper	oneMKL	AVX2	fp32	1	1m50s
faster-whisper	oneMKL	AVX512	fp32	1	1m50s
faster-whisper	oneMKL	AVX2	int8	1	1m25s
faster-whisper	oneMKL	AVX512	int8	1	1m17s

[mshcherbyna99]

Hello, @guillaumekln and @ggerganov.

I want to share my search info with you guys about a repository that I found online.

I haven't tested it out yet and haven't looked into any explanations, what exactly did a performance speed up for faster-whisper and insanely-fast-whisper, but here is the repository link:
Vaibhavs10/insanely-fast-whisper

Thank you in advance and happy coding

[robomotic]

Love this thread, how easy will be to add a compile flag in Whisper.cpp to switch between current implementation vs CTranslate2 library? @ggerganov

[wenchaoliu-93]

I came across Faster Whisper which is 5x faster than whisper.cpp with comparable memory footprint. It's built in python and uses this C++ library (Ctranslate2)

Just bringing this to your attention as this is very compelling ... 5x faster whisper.cpp (plus models are 4x smaller on disk) would unlock so much more use cases

Why are the two Whisper.app times much slower?
https://github.com/SYSTRAN/faster-whisper