whisper.cpp

Roadmap | F.A.Q.

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)
• iOS
• Android
• Linux / FreeBSD
• WebAssembly
• Windows (MSVC and MinGW]
• Raspberry Pi

The entire implementation of the model is contained in 2 source files:

• Tensor operations: ggml.h / ggml.c
• Transformer inference: whisper.h / whisper.cpp

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.objc

whisper-iphone-13-mini-2.mp4

You can also easily make your own offline voice assistant application: command

command-0.mp4

Or you can even run it straight in the browser: talk.wasm

Implementation details

• The core tensor operations are implemented in C (ggml.h / ggml.c)
• The transformer model and the high-level C-style API are implemented in C++ (whisper.h / whisper.cpp)
• Sample usage is demonstrated in main.cpp
• Sample real-time audio transcription from the microphone is demonstrated in stream.cpp
• Various other examples are available in the examples folder

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.

Quick start

First, download one of the Whisper models converted in ggml format. For example:

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

Now build the main example and transcribe an audio file like this:

# build the main example
make
# transcribe an audio file
./main -f input.wav

---

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

$makebase.encc -I. -O3 -std=c11 -pthread -DGGML_USE_ACCELERATE -cggml.c -oggml.oc++ -I. -I./examples -O3 -std=c++11 -pthread -cwhisper.cpp -owhisper.oc++ -I. -I./examples -O3 -std=c++11 -pthreadexamples/main/main.cppwhisper.oggml.o -omain -frameworkAccelerate
./main -husage: ./main [options] file0.wavfile1.wav ...
options:
-h, --help [default] showthishelpmessageandexit
-tN, --threadsN [4 ] numberofthreadstouseduringcomputation
-pN, --processorsN [1 ] numberofprocessorstouseduringcomputation
-otN, --offset-tN [0 ] timeoffsetinmilliseconds
-onN, --offset-nN [0 ] segmentindexoffset
-dN, --durationN [0 ] durationofaudiotoprocessinmilliseconds
-mcN, --max-contextN [-1 ] maximumnumberoftextcontexttokenstostore
-mlN, --max-lenN [0 ] maximumsegmentlengthincharacters
-wtN, --word-tholdN [0.01 ] wordtimestampprobabilitythreshold
-su, --speed-up [false ] speedupaudiobyx2 (reducedaccuracy)
-tr, --translate [false ] translatefromsourcelanguagetoenglish
-otxt, --output-txt [false ] outputresultinatextfile
-ovtt, --output-vtt [false ] outputresultinavttfile
-osrt, --output-srt [false ] outputresultinasrtfile
-owts, --output-words [false ] outputscriptforgeneratingkaraokevideo
-ps, --print-special [false ] printspecialtokens
-pc, --print-colors [false ] printcolors
-nt, --no-timestamps [true ] donotprinttimestamps
-lLANG, --languageLANG [en ] spokenlanguage
-mFNAME, --modelFNAME [models/ggml-base.en.bin] modelpath
-fFNAME, --fileFNAME [ ] inputWAVfilepathbash ./models/download-ggml-model.shbase.enDownloadingggmlmodelbase.en ...
ggml-base.en.bin100%[========================>] 141.11M6.34MB/sin24sDone! Model'base.en'savedin'models/ggml-base.en.bin'Youcannowuseitlikethis:
$ ./main -mmodels/ggml-base.en.bin -fsamples/jfk.wav
===============================================
Runningbase.enonallsamplesin ./samples ...
===============================================
----------------------------------------------
[+] Runningbase.enonsamples/jfk.wav ... (run'ffplay samples/jfk.wav'tolisten)
----------------------------------------------
whisper_model_load: loadingmodelfrom'models/ggml-base.en.bin'whisper_model_load: n_vocab = 51864whisper_model_load: n_audio_ctx = 1500whisper_model_load: n_audio_state = 512whisper_model_load: n_audio_head = 8whisper_model_load: n_audio_layer = 6whisper_model_load: n_text_ctx = 448whisper_model_load: n_text_state = 512whisper_model_load: n_text_head = 8whisper_model_load: n_text_layer = 6whisper_model_load: n_mels = 80whisper_model_load: f16 = 1whisper_model_load: type = 2whisper_model_load: adding1607extratokenswhisper_model_load: mem_required = 506.00MBwhisper_model_load: ggmlctxsize = 140.60MBwhisper_model_load: memorysize = 22.83MBwhisper_model_load: modelsize = 140.54MBsystem_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 |
main: processing'samples/jfk.wav' (176000samples, 11.0sec), 4threads, 1processors, lang = en, task = transcribe, timestamps = 1 ...
[00:00:00.000 --> 00:00:11.000] AndsomyfellowAmericans, asknotwhatyourcountrycandoforyou, askwhatyoucandoforyourcountry.
whisper_print_timings: loadtime = 105.91mswhisper_print_timings: meltime = 24.62mswhisper_print_timings: sampletime = 3.63mswhisper_print_timings: encodetime = 324.71ms / 54.12msperlayerwhisper_print_timings: decodetime = 83.58ms / 13.93msperlayerwhisper_print_timings: totaltime = 542.81ms

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 the main example 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 -iinput.mp3 -ar16000 -ac1 -c:apcm_s16leoutput.wav

More audio 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-v1
make large

Memory usage

Model	Disk	Mem	SHA
tiny	75 MB	~390 MB	bd577a113a864445d4c299885e0cb97d4ba92b5f
base	142 MB	~500 MB	465707469ff3a37a2b9b8d8f89f2f99de7299dac
small	466 MB	~1.0 GB	55356645c2b361a969dfd0ef2c5a50d530afd8d5
medium	1.5 GB	~2.6 GB	fd9727b6e1217c2f614f9b698455c4ffd82463b4
large	2.9 GB	~4.7 GB	0f4c8e34f21cf1a914c59d8b3ce882345ad349d6

Limitations

• 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.

Another example

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:

Expand to see the result

$ ./main -mmodels/ggml-medium.en.bin -fsamples/gb1.wav -t8whisper_model_load: loadingmodelfrom'models/ggml-medium.en.bin'whisper_model_load: n_vocab = 51864whisper_model_load: n_audio_ctx = 1500whisper_model_load: n_audio_state = 1024whisper_model_load: n_audio_head = 16whisper_model_load: n_audio_layer = 24whisper_model_load: n_text_ctx = 448whisper_model_load: n_text_state = 1024whisper_model_load: n_text_head = 16whisper_model_load: n_text_layer = 24whisper_model_load: n_mels = 80whisper_model_load: f16 = 1whisper_model_load: type = 4whisper_model_load: mem_required = 2610.00MBwhisper_model_load: adding1607extratokenswhisper_model_load: ggmlctxsize = 1644.97MBwhisper_model_load: memorysize = 182.62MBwhisper_model_load: modelsize = 1462.12MBmain: processing'samples/gb1.wav' (3179750samples, 198.7sec), 8threads, lang = en, task = transcribe, timestamps = 1 ...
[00:00.000 --> 00:08.000] MyfellowAmericans, thisdayhasbroughtterriblenewsandgreatsadnesstoourcountry.
[00:08.000 --> 00:17.000] Atnineo'clock this morning, Mission Control in Houston lost contact with our Space Shuttle Columbia.
[00:17.000 --> 00:23.000] Ashorttimelater, debriswasseenfallingfromtheskiesaboveTexas.
[00:23.000 --> 00:29.000] TheColumbia's lost. There are no survivors.
[00:29.000 --> 00:32.000] Onboardwasacrewofseven.
[00:32.000 --> 00:39.000] ColonelRickHusband, LieutenantColonelMichaelAnderson, CommanderLaurelClark,
[00:39.000 --> 00:48.000] CaptainDavidBrown, CommanderWilliamMcCool, Dr. KultnaShavla, andIlanRamon,
[00:48.000 --> 00:52.000] acolonelintheIsraeliAirForce.
[00:52.000 --> 00:58.000] Thesemenandwomenassumedgreatriskintheservicetoallhumanity.
[00:58.000 --> 01:03.000] Inanagewhenspaceflighthascometoseemalmostroutine,
[01:03.000 --> 01:07.000] itiseasytooverlookthedangersoftravelbyrocket
[01:07.000 --> 01:12.000] andthedifficultiesofnavigatingthefierceouteratmosphereoftheEarth.
[01:12.000 --> 01:18.000] Theseastronautsknewthedangers, andtheyfacedthemwillingly,
[01:18.000 --> 01:23.000] knowingtheyhadahighandnoblepurposeinlife.
[01:23.000 --> 01:31.000] Becauseoftheircourageanddaringandidealism, wewillmissthemallthemore.
[01:31.000 --> 01:36.000] AllAmericanstodayarethinkingaswellofthefamiliesofthesemenandwomen
[01:36.000 --> 01:40.000] whohavebeengiventhissuddenshockandgrief.
[01:40.000 --> 01:45.000] You're not alone. Our entire nation grieves with you,
[01:45.000 --> 01:52.000] andthoseyoulovewillalwayshavetherespectandgratitudeofthiscountry.
[01:52.000 --> 01:56.000] Thecauseinwhichtheydiedwillcontinue.
[01:56.000 --> 02:04.000] Mankindisledintothedarknessbeyondourworldbytheinspirationofdiscovery
[02:04.000 --> 02:11.000] andthelongingtounderstand. Ourjourneyintospacewillgoon.
[02:11.000 --> 02:16.000] Intheskiestoday, wesawdestructionandtragedy.
[02:16.000 --> 02:22.000] Yetfartherthanwecansee, thereiscomfortandhope.
[02:22.000 --> 02:29.000] InthewordsoftheprophetIsaiah, "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] BecauseofHisgreatpowerandmightystrength, notoneofthemismissing.
[02:46.000 --> 02:55.000] ThesameCreatorwhonamesthestarsalsoknowsthenamesofthesevensoulswemourntoday.
[02:55.000 --> 03:01.000] ThecrewoftheshuttleColumbiadidnotreturnsafelytoearth,
[03:01.000 --> 03:05.000] yetwecanpraythatallaresafelyhome.
[03:05.000 --> 03:13.000] MayGodblessthegrievingfamilies, andmayGodcontinuetoblessAmerica.
[03:13.000 --> 03:41.000] Audiowhisper_print_timings: loadtime = 575.92mswhisper_print_timings: meltime = 230.60mswhisper_print_timings: sampletime = 73.19mswhisper_print_timings: encodetime = 19552.61ms / 814.69msperlayerwhisper_print_timings: decodetime = 13249.96ms / 552.08msperlayerwhisper_print_timings: totaltime = 33686.27ms

Real-time audio input example

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 -t8 --step500 --length5000

rt_esl_csgo_2.mp4

Confidence color-coding

Adding the --print-colors argument will print the transcribed text using an experimental color coding strategy to highlight words with high or low confidence:

Controlling the length of the generated text segments (experimental)

For example, to limit the line length to a maximum of 16 characters, simply add -ml 16:

./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml16whisper_model_load: loadingmodelfrom'./models/ggml-base.en.bin'
...
system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | main: processing'./samples/jfk.wav' (176000samples, 11.0sec), 4threads, 1processors, lang = en, task = transcribe, timestamps = 1 ...
[00:00:00.000 --> 00:00:00.850] Andsomy
[00:00:00.850 --> 00:00:01.590] fellow
[00:00:01.590 --> 00:00:04.140] Americans, ask
[00:00:04.140 --> 00:00:05.660] notwhatyour
[00:00:05.660 --> 00:00:06.840] countrycando
[00:00:06.840 --> 00:00:08.430] foryou, ask
[00:00:08.430 --> 00:00:09.440] whatyoucando
[00:00:09.440 --> 00:00:10.020] foryour
[00:00:10.020 --> 00:00:11.000] country.

Word-level timestamp

The --max-len argument can be used to obtain word-level timestamps. Simply use -ml 1:

./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml1whisper_model_load: loadingmodelfrom'./models/ggml-base.en.bin'
...
system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | main: processing'./samples/jfk.wav' (176000samples, 11.0sec), 4threads, 1processors, lang = en, task = transcribe, timestamps = 1 ...
[00:00:00.000 --> 00:00:00.320] [00:00:00.320 --> 00:00:00.370] And
[00:00:00.370 --> 00:00:00.690] so
[00:00:00.690 --> 00:00:00.850] my
[00:00:00.850 --> 00:00:01.590] fellow
[00:00:01.590 --> 00:00:02.850] Americans
[00:00:02.850 --> 00:00:03.300] ,
[00:00:03.300 --> 00:00:04.140] ask
[00:00:04.140 --> 00:00:04.990] not
[00:00:04.990 --> 00:00:05.410] what
[00:00:05.410 --> 00:00:05.660] your
[00:00:05.660 --> 00:00:06.260] country
[00:00:06.260 --> 00:00:06.600] can
[00:00:06.600 --> 00:00:06.840] do
[00:00:06.840 --> 00:00:07.010] for
[00:00:07.010 --> 00:00:08.170] you
[00:00:08.170 --> 00:00:08.190] ,
[00:00:08.190 --> 00:00:08.430] ask
[00:00:08.430 --> 00:00:08.910] what
[00:00:08.910 --> 00:00:09.040] you
[00:00:09.040 --> 00:00:09.320] can
[00:00:09.320 --> 00:00:09.440] do
[00:00:09.440 --> 00:00:09.760] for
[00:00:09.760 --> 00:00:10.020] your
[00:00:10.020 --> 00:00:10.510] country
[00:00:10.510 --> 00:00:11.000] .

Karaoke-style movie generation (experimental)

The main example provides support for output of karaoke-style movies, where the currently pronounced word is highlighted. Use the -wts argument and run the generated bash script. This requires to have ffmpeg installed.

Here are a few "typical" examples:

./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -owtssource ./samples/jfk.wav.wtsffplay ./samples/jfk.wav.mp4

jfk.wav.mp4

---

./main -m ./models/ggml-base.en.bin -f ./samples/mm0.wav -owtssource ./samples/mm0.wav.wtsffplay ./samples/mm0.wav.mp4

mm0.wav.mp4

---

./main -m ./models/ggml-base.en.bin -f ./samples/gb0.wav -owtssource ./samples/gb0.wav.wtsffplay ./samples/gb0.wav.mp4

gb0.wav.mp4

---

Benchmarks

In order to have an objective comparison of the performance of the inference across different system configurations, use the bench tool. The tool simply runs the Encoder part of the model and prints how much time it took to execute it. The results are summarized in the following Github issue:

Benchmark results

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 models/download-ggml-model.sh script or manually from here:

• https://huggingface.co/datasets/ggerganov/whisper.cpp
• https://ggml.ggerganov.com

For more details, see the conversion script models/convert-pt-to-ggml.py or the README in models.

Bindings

• Rust: tazz4843/whisper-rs | #310
• Javascript: bindings/javascript | #309
• Go: bindings/go | #312
• Objective-C / Swift: ggerganov/whisper.spm | #313
• Python: soon | WIP

Examples

There are various examples of using the library for different projects in the examples folder. Some of the examples are even ported to run in the browser using WebAssembly. Check them out!

Example	Web	Description
main	whisper.wasm	Tool for translating and transcribing audio using Whisper
bench	bench.wasm	Benchmark the performance of Whisper on your machine
stream	stream.wasm	Real-time transcription of raw microphone capture
command	command.wasm	Basic voice assistant example for receiving voice commands from the mic
talk	talk.wasm	Talk with a GPT-2 bot
whisper.objc		iOS mobile application using whisper.cpp
whisper.swiftui		SwiftUI iOS / macOS application using whisper.cpp
whisper.android		Android mobile application using whisper.cpp
whisper.nvim		Speech-to-text plugin for Neovim
generate-karaoke.sh		Helper script to easily generate a karaoke video of raw audio capture
livestream.sh		Livestream audio transcription
yt-wsp.sh		Download + transcribe and/or translate any VOD (original)

Discussions

If you have any kind of feedback about this project feel free to use the Discussions section and open a new topic. You can use the Show and tell category to share your own projects that use whisper.cpp. If you have a question, make sure to check the Frequently asked questions (#126) discussion.