High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model:
- Plain C/C++ implementation without dependencies
- Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
- AVX intrinsics support for x86 architectures
- Mixed F16 / F32 precision
- Low memory usage (Flash Attention + Flash Forward)
- Zero memory allocations at runtime
- Runs on the CPU
- C-style API
Supported platforms:
- Mac OS (Intel and Arm)
- Linux
- Windows (MSVC and MinGW)
- WebAssembly
- Raspberry Pi
- Android
- iOS
Having such a lightweight implementation of the model allows to easily integrate it in different platforms and applications. As an example, here is a video of running the model on an iPhone 13 device - fully offline, on-device:
whisper-iphone-13-mini.mp4
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
cc -O3 -std=c11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread -DGGML_USE_ACCELERATE -c ggml.c
c++ -O3 -std=c++11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread -c whisper.cpp
c++ -O3 -std=c++11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread main.cpp whisper.o ggml.o -o main -framework Accelerate
./main -h
usage: ./main [options] file0.wav file1.wav ...
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)
-o N, --offset N offset in milliseconds (default: 0)
-v, --verbose verbose output
--translate translate from source language to english
-otxt, --output-txt output result in a text file
-ovtt, --output-vtt output result in a vtt file
-osrt, --output-srt output result in a srt file
-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
bash ./download-ggml-model.sh base.en
Downloading ggml model base.en ...
models/ggml-base.en.bin 100%[=============================================>] 141.11M 3.13MB/s in 79s
Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
You can now use it like this:
$ ./main -m models/ggml-base.en.bin -f samples/jfk.wav
===============================================
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 = 505.00 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
main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, lang = en, 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.
whisper_print_timings: load time = 87.21 ms
whisper_print_timings: mel time = 24.26 ms
whisper_print_timings: sample time = 3.87 ms
whisper_print_timings: encode time = 323.67 ms / 53.94 ms per layer
whisper_print_timings: decode time = 83.25 ms / 13.87 ms per layer
whisper_print_timings: total time = 522.66 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
.
For detailed usage instructions, run: ./main -h
Note that whisper.cpp
currently 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
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
Here is another example of transcribing a 3:24 min speech
in about half a minute on a MacBook M1 Pro, 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 = 2610.00 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
main: processing 'samples/gb1.wav' (3179750 samples, 198.7 sec), 8 threads, lang = en, 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 nine o'clock this morning, Mission Control in Houston lost contact with our Space Shuttle Columbia.
[00:17.000 --> 00:23.000] A short time later, debris was seen falling from the skies above Texas.
[00:23.000 --> 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 --> 00:39.000] Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark,
[00:39.000 --> 00:48.000] Captain David Brown, Commander William McCool, Dr. Kultna Shavla, and Ilan Ramon,
[00:48.000 --> 00:52.000] 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 --> 01:03.000] In an age when space flight has come to seem almost routine,
[01:03.000 --> 01:07.000] it is easy to overlook the dangers of travel by rocket
[01:07.000 --> 01:12.000] and the difficulties of navigating the fierce outer atmosphere of the Earth.
[01:12.000 --> 01:18.000] These astronauts knew the dangers, and they faced them willingly,
[01:18.000 --> 01:23.000] knowing they had a high and noble purpose in life.
[01:23.000 --> 01:31.000] Because of their courage and daring and idealism, we will miss them all the more.
[01:31.000 --> 01:36.000] All Americans today are thinking as well of the families of these men and women
[01:36.000 --> 01:40.000] who have been given this sudden shock and grief.
[01:40.000 --> 01:45.000] You're not alone. Our entire nation grieves 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 --> 02:04.000] Mankind is led into the darkness beyond our world by the inspiration of discovery
[02:04.000 --> 02:11.000] and the longing to understand. 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 --> 02:29.000] In the words of the prophet Isaiah, "Lift your eyes and look to the heavens
[02:29.000 --> 02:35.000] who created all these. He who brings out the starry hosts one by one
[02:35.000 --> 02:39.000] 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 --> 03:01.000] The crew of the shuttle Columbia did not return safely to earth,
[03:01.000 --> 03:05.000] yet we can pray that all are safely home.
[03:05.000 --> 03:13.000] May God bless the grieving families, and may God continue to bless America.
[03:13.000 --> 03:41.000] Audio
whisper_print_timings: load time = 575.92 ms
whisper_print_timings: mel time = 230.60 ms
whisper_print_timings: sample time = 73.19 ms
whisper_print_timings: encode time = 19552.61 ms / 814.69 ms per layer
whisper_print_timings: decode time = 13249.96 ms / 552.08 ms per layer
whisper_print_timings: total time = 33686.27 ms
This is a naive example of performing real-time inference on audio from your microphone.
The stream
tool samples the audio every half a second and runs the transcription continously.
More info is available in issue #10.
./stream -m ./models/ggml-base.en.bin -t 8 --step 500 --length 5000
rt_esl_csgo_2.mp4
The stream
tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
# Install SDL2 on Linux
sudo apt-get install libsdl2-dev
# Install SDL2 on Mac OS
brew install sdl2
make stream
Adding the --print-colors
argument will print the transcribed text using an experimental color coding strategy
to highlight words with high or low confidence:
- The core tensor operations are implemented in C (ggml.h / ggml.c)
- The high-level C-style API is implemented in C++ (whisper.h / whisper.cpp)
- Simple usage is demonstrated in main.cpp
- Sample real-time audio transcription from the microphone is demonstrated in stream.cpp
The tensor operators are optimized heavily for Apple silicon CPUs. Depending on the computation size, Arm Neon SIMD instrisics or CBLAS Accelerate framework routines are used. The latter are especially effective for bigger sizes since the Accelerate framework utilizes the special-purpose AMX coprocessor available in modern Apple products.
-
Inference only
-
No GPU support
-
Very basic greedy sampling scheme - always pick up the token with highest probability. This should be similar to the GreedyDecoder from the original python implementation, so in order to make a fair comparison between the 2 implementations, make sure to run the python code with the following parameters:
whisper --best_of None --beam_size None ...
In the future,
whisper.cpp
will support more sampling strategies.
Model | Disk | Mem |
---|---|---|
tiny | 75 MB | ~280 MB |
base | 142 MB | ~430 MB |
small | 466 MB | ~1.0 GB |
medium | 1.5 GB | ~2.6 GB |
large | 2.9 GB | ~4.7 GB |
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 or from here:
For more details, see the conversion script models/convert-pt-to-ggml.py or the README in models.
- Rust: tazz4843/whisper-rs
- Python:
- Obj-C:
- Java: