Georgi Gerganov 5877c3578e

Can be turned off with "-nt" argument.
Performance has also improved.

2022-09-29 23:09:39 +03:00

9 KiB

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whisper.cpp

C/C++ port of OpenAI's Whisper speech-to-text model

Plain C/C++ implementation without dependencies
ARM_NEON and AVX intrinsics support
Mixed F16 / F32 support
Low memory usage (Flash Attention + Flash Forward)
Zero memory allocations at runtime

Usage

To build the main program, run make. You can then transcribe a .wav file like this:

$ ./main -f input.wav

Before running the program, make sure to download one of the ggml Whisper models. For example:

bash ./download-ggml-model.sh base.en

For a quick demo, simply run make base.en:

$ make base.en

gcc -pthread -O3 -mavx -mavx2 -mfma -mf16c -c ggml.c
g++ -pthread -O3 -std=c++11 -c main.cpp
g++ -pthread -o main ggml.o main.o
./main -h

usage: ./main [options]

options:
  -h,       --help           show this help message and exit
  -s SEED,  --seed SEED      RNG seed (default: -1)
  -t N,     --threads N      number of threads to use during computation (default: 4)
  -v,       --verbose        verbose output
            --translate      translate from source language to english
  -ps,      --print_special  print special tokens
  -nt,      --no_timestamps  do not print timestamps
  -l LANG,  --language LANG  spoken language (default: en)
  -m FNAME, --model FNAME    model path (default: models/ggml-base.en.bin)
  -f FNAME, --file FNAME     input WAV file path (default: samples/jfk.wav)

bash ./download-ggml-model.sh base.en
Downloading ggml model base.en ...
Model base.en already exists. Skipping download.

===============================================
Running base.en on all samples in ./samples ...
===============================================

----------------------------------------------
[+] Running base.en on samples/jfk.wav ... (run 'ffplay samples/jfk.wav' to listen)
----------------------------------------------

whisper_model_load: loading model from 'models/ggml-base.en.bin'
whisper_model_load: n_vocab       = 51864
whisper_model_load: n_audio_ctx   = 1500
whisper_model_load: n_audio_state = 512
whisper_model_load: n_audio_head  = 8
whisper_model_load: n_audio_layer = 6
whisper_model_load: n_text_ctx    = 448
whisper_model_load: n_text_state  = 512
whisper_model_load: n_text_head   = 8
whisper_model_load: n_text_layer  = 6
whisper_model_load: n_mels        = 80
whisper_model_load: f16           = 1
whisper_model_load: type          = 2
whisper_model_load: mem_required  = 611.00 MB
whisper_model_load: adding 1607 extra tokens
whisper_model_load: ggml ctx size = 163.43 MB
whisper_model_load: memory size =    22.83 MB
whisper_model_load: model size  =   140.54 MB
log_mel_spectrogram: n_sample = 176000, n_len = 1100
log_mel_spectrogram: recording length: 11.000000 s

main: processing 176000 samples (11.0 sec), 4 threads, lang = english, task = transcribe, timestamps = 1 ...

[00:00.000 --> 00:11.000]   And so my fellow Americans ask not what your country can do for you. Ask what you can do for your country.


main:     load time =    61.78 ms
main:      mel time =    41.74 ms
main:   sample time =     2.10 ms
main:   encode time =   718.60 ms / 119.77 ms per layer
main:   decode time =    83.55 ms
main:    total time =   908.15 ms

The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples.

If you want some extra audio samples to play with, simply run:

make samples

This will download a few more audio files from Wikipedia and convert them to 16-bit WAV format via ffmpeg.

You can download and run the other models as follows:

make tiny.en
make tiny
make base.en
make base
make small.en
make small
make medium.en
make medium
make large

For detailed usage instructions, run: ./main -h

Note that whisper.cpp runs only with 16-bit WAV files, so make sure to convert your input before running the tool. For example, you can use ffmpeg like this:

ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav

Here is another example of transcribing a 3:24 min speech in less than a minute, using medium.en model:

$ ./main -m models/ggml-medium.en.bin -f samples/gb1.wav -t 8
whisper_model_load: loading model from 'models/ggml-medium.en.bin'
whisper_model_load: n_vocab       = 51864
whisper_model_load: n_audio_ctx   = 1500
whisper_model_load: n_audio_state = 1024
whisper_model_load: n_audio_head  = 16
whisper_model_load: n_audio_layer = 24
whisper_model_load: n_text_ctx    = 448
whisper_model_load: n_text_state  = 1024
whisper_model_load: n_text_head   = 16
whisper_model_load: n_text_layer  = 24
whisper_model_load: n_mels        = 80
whisper_model_load: f16           = 1
whisper_model_load: type          = 4
whisper_model_load: mem_required  = 2786.00 MB
whisper_model_load: adding 1607 extra tokens
whisper_model_load: ggml ctx size = 1644.97 MB
whisper_model_load: memory size =   182.62 MB
whisper_model_load: model size  =  1462.12 MB
log_mel_spectrogram: n_sample = 3179750, n_len = 19873
log_mel_spectrogram: recording length: 198.734375 s

main: processing 3179750 samples (198.7 sec), 8 threads, lang = english, task = transcribe, timestamps = 1 ...

[00:00.000 --> 00:08.000]   My fellow Americans, this day has brought terrible news and great sadness to our country.
[00:08.000 --> 00:17.000]   At 9 o'clock this morning, Mission Control in Houston lost contact with our Space Shuttle Columbia.
[00:17.000 --> 00:24.000]   A short time later, debris was seen falling from the skies above Texas.
[00:24.000 --> 00:29.000]   The Columbia's lost. There are no survivors.
[00:29.000 --> 00:32.000]   On board was a crew of seven.
[00:32.000 --> 00:43.000]   Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark, Captain David Brown, Commander William McCool,
[00:43.000 --> 00:52.000]   Dr. Kultner Aschavla, and Elon Ramon, a Colonel in the Israeli Air Force.
[00:52.000 --> 00:58.000]   These men and women assumed great risk in the service to all humanity.
[00:58.000 --> 01:06.000]   In an age when space flight has come to seem almost routine, it is easy to overlook the dangers of travel by rocket
[01:06.000 --> 01:12.000]   and the difficulties of navigating the fierce outer atmosphere of the Earth.
[01:12.000 --> 01:22.000]   These astronauts knew the dangers, and they faced them willingly, knowing they had a high and noble purpose in life.
[01:22.000 --> 01:30.000]   Because of their courage, endearing, and idealism, we will miss them all the more.
[01:30.000 --> 01:40.000]   All Americans today are thinking as well of the families of these men and women who have been given this sudden shock and grief.
[01:40.000 --> 01:45.000]   You're not alone. Our entire nation agrees with you.
[01:45.000 --> 01:52.000]   And those you love will always have the respect and gratitude of this country.
[01:52.000 --> 01:56.000]   The cause in which they died will continue.
[01:56.000 --> 02:07.000]   Mankind is led into the darkness beyond our world by the inspiration of discovery and the longing to understand.
[02:07.000 --> 02:11.000]   Our journey into space will go on.
[02:11.000 --> 02:16.000]   In the skies today, we saw destruction and tragedy.
[02:16.000 --> 02:22.000]   Yet farther than we can see, there is comfort and hope.
[02:22.000 --> 02:31.000]   In the words of the prophet Isaiah, "Lift your eyes and look to the heavens who created all these.
[02:31.000 --> 02:39.000]   He who brings out the starry hosts one by one and calls them each by name."
[02:39.000 --> 02:46.000]   Because of his great power and mighty strength, not one of them is missing.
[02:46.000 --> 02:55.000]   The same creator who names the stars also knows the names of the seven souls we mourn today.
[02:55.000 --> 03:05.000]   The crew of the shuttle Columbia did not return safely to Earth, yet we can pray that all are safely home.
[03:05.000 --> 03:14.000]   May God bless the grieving families and may God continue to bless America.
[03:14.000 --> 03:24.000]   [Music]


main:     load time =   438.55 ms
main:      mel time =   440.22 ms
main:   sample time =    32.23 ms
main:   encode time = 42329.63 ms / 1763.73 ms per layer
main:   decode time = 15190.00 ms
main:    total time = 58444.63 ms

Limitations

Very basic greedy sampling scheme - always pick up the top token
Inference only
Runs on the CPU
Only mono-channel 16-bit WAV is supported

Memory usage

Model	Disk	Mem
tiny	75 MB	~460 MB
base	142 MB	~620 MB
small	466 MB	~1.3 GB
medium	1.5 GB	~2.8 GB
large	2.9 GB	~4.9 GB

ggml format

The original models are converted to a custom binary format. This allows to pack everything needed into a single file:

model parameters
mel filters
vocabulary
weights

You can download the converted models using the download-ggml-model.sh script.

For more details, see the conversion script convert-pt-to-ggml.py