Awesome

whisper.cpp

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, Accelerate framework, Metal and Core ML
AVX intrinsics support for x86 architectures
VSX intrinsics support for POWER architectures
Mixed F16 / F32 precision
Integer quantization support
Zero memory allocations at runtime
Vulkan support
Support for CPU-only inference
Efficient GPU support for NVIDIA
OpenVINO Support
Ascend NPU Support
C-style API

Supported platforms:

The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library.

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

https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4

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

https://user-images.githubusercontent.com/1991296/204038393-2f846eae-c255-4099-a76d-5735c25c49da.mp4

On Apple Silicon, the inference runs fully on the GPU via Metal:

https://github.com/ggerganov/whisper.cpp/assets/1991296/c82e8f86-60dc-49f2-b048-d2fdbd6b5225

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 intrinsics 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 clone the repository:

git clone https://github.com/ggerganov/whisper.cpp.git

Navigate into the directory:

cd whisper.cpp

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

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

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

# build the main example
make -j

# transcribe an audio file
./main -f samples/jfk.wav

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

$ make -j base.en

cc  -I.              -O3 -std=c11   -pthread -DGGML_USE_ACCELERATE   -c ggml.c -o ggml.o
c++ -I. -I./examples -O3 -std=c++11 -pthread -c whisper.cpp -o whisper.o
c++ -I. -I./examples -O3 -std=c++11 -pthread examples/main/main.cpp whisper.o ggml.o -o main  -framework Accelerate
./main -h

usage: ./main [options] file0.wav file1.wav ...

options:
  -h,        --help              [default] show this help message and exit
  -t N,      --threads N         [4      ] number of threads to use during computation
  -p N,      --processors N      [1      ] number of processors to use during computation
  -ot N,     --offset-t N        [0      ] time offset in milliseconds
  -on N,     --offset-n N        [0      ] segment index offset
  -d  N,     --duration N        [0      ] duration of audio to process in milliseconds
  -mc N,     --max-context N     [-1     ] maximum number of text context tokens to store
  -ml N,     --max-len N         [0      ] maximum segment length in characters
  -sow,      --split-on-word     [false  ] split on word rather than on token
  -bo N,     --best-of N         [5      ] number of best candidates to keep
  -bs N,     --beam-size N       [5      ] beam size for beam search
  -wt N,     --word-thold N      [0.01   ] word timestamp probability threshold
  -et N,     --entropy-thold N   [2.40   ] entropy threshold for decoder fail
  -lpt N,    --logprob-thold N   [-1.00  ] log probability threshold for decoder fail
  -debug,    --debug-mode        [false  ] enable debug mode (eg. dump log_mel)
  -tr,       --translate         [false  ] translate from source language to english
  -di,       --diarize           [false  ] stereo audio diarization
  -tdrz,     --tinydiarize       [false  ] enable tinydiarize (requires a tdrz model)
  -nf,       --no-fallback       [false  ] do not use temperature fallback while decoding
  -otxt,     --output-txt        [false  ] output result in a text file
  -ovtt,     --output-vtt        [false  ] output result in a vtt file
  -osrt,     --output-srt        [false  ] output result in a srt file
  -olrc,     --output-lrc        [false  ] output result in a lrc file
  -owts,     --output-words      [false  ] output script for generating karaoke video
  -fp,       --font-path         [/System/Library/Fonts/Supplemental/Courier New Bold.ttf] path to a monospace font for karaoke video
  -ocsv,     --output-csv        [false  ] output result in a CSV file
  -oj,       --output-json       [false  ] output result in a JSON file
  -ojf,      --output-json-full  [false  ] include more information in the JSON file
  -of FNAME, --output-file FNAME [       ] output file path (without file extension)
  -ps,       --print-special     [false  ] print special tokens
  -pc,       --print-colors      [false  ] print colors
  -pp,       --print-progress    [false  ] print progress
  -nt,       --no-timestamps     [false  ] do not print timestamps
  -l LANG,   --language LANG     [en     ] spoken language ('auto' for auto-detect)
  -dl,       --detect-language   [false  ] exit after automatically detecting language
             --prompt PROMPT     [       ] initial prompt
  -m FNAME,  --model FNAME       [models/ggml-base.en.bin] model path
  -f FNAME,  --file FNAME        [       ] input WAV file path
  -oved D,   --ov-e-device DNAME [CPU    ] the OpenVINO device used for encode inference
  -ls,       --log-score         [false  ] log best decoder scores of tokens
  -ng,       --no-gpu            [false  ] disable GPU


sh ./models/download-ggml-model.sh base.en
Downloading ggml model base.en ...
ggml-base.en.bin               100%[========================>] 141.11M  6.34MB/s    in 24s
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_init_from_file: loading model from 'models/ggml-base.en.bin'
whisper_model_load: loading model
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  =  215.00 MB (+    6.00 MB per decoder)
whisper_model_load: kv self size  =    5.25 MB
whisper_model_load: kv cross size =   17.58 MB
whisper_model_load: adding 1607 extra tokens
whisper_model_load: model ctx     =  140.60 MB
whisper_model_load: model size    =  140.54 MB

system_info: n_threads = 4 / 10 | 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 |

main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...


[00:00:00.000 --> 00:00:11.000]   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:     fallbacks =   0 p /   0 h
whisper_print_timings:     load time =   113.81 ms
whisper_print_timings:      mel time =    15.40 ms
whisper_print_timings:   sample time =    11.58 ms /    27 runs (    0.43 ms per run)
whisper_print_timings:   encode time =   266.60 ms /     1 runs (  266.60 ms per run)
whisper_print_timings:   decode time =    66.11 ms /    27 runs (    2.45 ms per run)
whisper_print_timings:    total time =   476.31 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 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 -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav

More audio samples

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

make -j 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 -j tiny.en
make -j tiny
make -j base.en
make -j base
make -j small.en
make -j small
make -j medium.en
make -j medium
make -j large-v1
make -j large-v2
make -j large-v3
make -j large-v3-turbo

Memory usage

Model	Disk	Mem
tiny	75 MiB	~273 MB
base	142 MiB	~388 MB
small	466 MiB	~852 MB
medium	1.5 GiB	~2.1 GB
large	2.9 GiB	~3.9 GB

Quantization

whisper.cpp supports integer quantization of the Whisper ggml models. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently.

Here are the steps for creating and using a quantized model:

# quantize a model with Q5_0 method
make -j quantize
./quantize models/ggml-base.en.bin models/ggml-base.en-q5_0.bin q5_0

# run the examples as usual, specifying the quantized model file
./main -m models/ggml-base.en-q5_0.bin ./samples/gb0.wav

Core ML support

On Apple Silicon devices, the Encoder inference can be executed on the Apple Neural Engine (ANE) via Core ML. This can result in significant speed-up - more than x3 faster compared with CPU-only execution. Here are the instructions for generating a Core ML model and using it with whisper.cpp:

Install Python dependencies needed for the creation of the Core ML model:
```
pip install ane_transformers
pip install openai-whisper
pip install coremltools
```
- To ensure coremltools operates correctly, please confirm that Xcode is installed and execute xcode-select --install to install the command-line tools.
- Python 3.10 is recommended.
- MacOS Sonoma (version 14) or newer is recommended, as older versions of MacOS might experience issues with transcription hallucination.
- [OPTIONAL] It is recommended to utilize a Python version management system, such as Miniconda for this step:
  - To create an environment, use: conda create -n py310-whisper python=3.10 -y
  - To activate the environment, use: conda activate py310-whisper
Generate a Core ML model. For example, to generate a base.en model, use:
```
./models/generate-coreml-model.sh base.en
```
This will generate the folder models/ggml-base.en-encoder.mlmodelc

Build whisper.cpp with Core ML support:

# using Makefile
make clean
WHISPER_COREML=1 make -j

# using CMake
cmake -B build -DWHISPER_COREML=1
cmake --build build -j --config Release

Run the examples as usual. For example:

$ ./main -m models/ggml-base.en.bin -f samples/jfk.wav

...

whisper_init_state: loading Core ML model from 'models/ggml-base.en-encoder.mlmodelc'
whisper_init_state: first run on a device may take a while ...
whisper_init_state: Core ML model loaded

system_info: n_threads = 4 / 10 | 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 | COREML = 1 |

...

The first run on a device is slow, since the ANE service compiles the Core ML model to some device-specific format. Next runs are faster.

For more information about the Core ML implementation please refer to PR #566.

OpenVINO support

On platforms that support OpenVINO, the Encoder inference can be executed on OpenVINO-supported devices including x86 CPUs and Intel GPUs (integrated & discrete).

This can result in significant speedup in encoder performance. Here are the instructions for generating the OpenVINO model and using it with whisper.cpp:

First, setup python virtual env. and install python dependencies. Python 3.10 is recommended.

Windows:

cd models
python -m venv openvino_conv_env
openvino_conv_env\Scripts\activate
python -m pip install --upgrade pip
pip install -r requirements-openvino.txt

Linux and macOS:

cd models
python3 -m venv openvino_conv_env
source openvino_conv_env/bin/activate
python -m pip install --upgrade pip
pip install -r requirements-openvino.txt

Generate an OpenVINO encoder model. For example, to generate a base.en model, use:
```
python convert-whisper-to-openvino.py --model base.en
```
This will produce ggml-base.en-encoder-openvino.xml/.bin IR model files. It's recommended to relocate these to the same folder as ggml models, as that is the default location that the OpenVINO extension will search at runtime.
Build whisper.cpp with OpenVINO support:

Download OpenVINO package from release page. The recommended version to use is 2023.0.0.

After downloading & extracting package onto your development system, set up required environment by sourcing setupvars script. For example:

Linux:
```
source /path/to/l_openvino_toolkit_ubuntu22_2023.0.0.10926.b4452d56304_x86_64/setupvars.sh
```
Windows (cmd):
```
C:\Path\To\w_openvino_toolkit_windows_2023.0.0.10926.b4452d56304_x86_64\setupvars.bat
```
And then build the project using cmake:
```
cmake -B build -DWHISPER_OPENVINO=1
cmake --build build -j --config Release
```

Run the examples as usual. For example:

$ ./main -m models/ggml-base.en.bin -f samples/jfk.wav

...

whisper_ctx_init_openvino_encoder: loading OpenVINO model from 'models/ggml-base.en-encoder-openvino.xml'
whisper_ctx_init_openvino_encoder: first run on a device may take a while ...
whisper_openvino_init: path_model = models/ggml-base.en-encoder-openvino.xml, device = GPU, cache_dir = models/ggml-base.en-encoder-openvino-cache
whisper_ctx_init_openvino_encoder: OpenVINO model loaded

system_info: n_threads = 4 / 8 | 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 | COREML = 0 | OPENVINO = 1 |

...

The first time run on an OpenVINO device is slow, since the OpenVINO framework will compile the IR (Intermediate Representation) model to a device-specific 'blob'. This device-specific blob will get cached for the next run.

For more information about the Core ML implementation please refer to PR #1037.

NVIDIA GPU support

With NVIDIA cards the processing of the models is done efficiently on the GPU via cuBLAS and custom CUDA kernels. First, make sure you have installed cuda: https://developer.nvidia.com/cuda-downloads

Now build whisper.cpp with CUDA support:

make clean
GGML_CUDA=1 make -j

Vulkan GPU support

Cross-vendor solution which allows you to accelerate workload on your GPU. First, make sure your graphics card driver provides support for Vulkan API.

Now build whisper.cpp with Vulkan support:

make clean
make GGML_VULKAN=1 -j

BLAS CPU support via OpenBLAS

Encoder processing can be accelerated on the CPU via OpenBLAS. First, make sure you have installed openblas: https://www.openblas.net/

Now build whisper.cpp with OpenBLAS support:

make clean
GGML_OPENBLAS=1 make -j

BLAS CPU support via Intel MKL

Encoder processing can be accelerated on the CPU via the BLAS compatible interface of Intel's Math Kernel Library. First, make sure you have installed Intel's MKL runtime and development packages: https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-download.html

Now build whisper.cpp with Intel MKL BLAS support:

source /opt/intel/oneapi/setvars.sh
mkdir build
cd build
cmake -DWHISPER_MKL=ON ..
WHISPER_MKL=1 make -j

Ascend NPU support

Ascend NPU provides inference acceleration via CANN and AI cores.

First, check if your Ascend NPU device is supported:

Verified devices

Ascend NPU	Status
Atlas 300T A2	Support

Then, make sure you have installed CANN toolkit . The lasted version of CANN is recommanded.

Now build whisper.cpp with CANN support:

mkdir build
cd build
cmake .. -D GGML_CANN=on
make -j

Run the inference examples as usual, for example:

./build/bin/main -f samples/jfk.wav -m models/ggml-base.en.bin -t 8

Notes:

If you have trouble with Ascend NPU device, please create a issue with [CANN] prefix/tag.
If you run successfully with your Ascend NPU device, please help update the table Verified devices.

Docker

Prerequisites

Docker must be installed and running on your system.
Create a folder to store big models & intermediate files (ex. /whisper/models)

Images

We have two Docker images available for this project:

ghcr.io/ggerganov/whisper.cpp:main: This image includes the main executable file as well as curl and ffmpeg. (platforms: linux/amd64, linux/arm64)
ghcr.io/ggerganov/whisper.cpp:main-cuda: Same as main but compiled with CUDA support. (platforms: linux/amd64)

Usage

# download model and persist it in a local folder
docker run -it --rm \
  -v path/to/models:/models \
  whisper.cpp:main "./models/download-ggml-model.sh base /models"
# transcribe an audio file
docker run -it --rm \
  -v path/to/models:/models \
  -v path/to/audios:/audios \
  whisper.cpp:main "./main -m /models/ggml-base.bin -f /audios/jfk.wav"
# transcribe an audio file in samples folder
docker run -it --rm \
  -v path/to/models:/models \
  whisper.cpp:main "./main -m /models/ggml-base.bin -f ./samples/jfk.wav"

Installing with Conan

You can install pre-built binaries for whisper.cpp or build it from source using Conan. Use the following command:

conan install --requires="whisper-cpp/[*]" --build=missing

For detailed instructions on how to use Conan, please refer to the Conan documentation.

Limitations

Inference only

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:

<details> <summary>Expand to see the result</summary>

$ ./main -m models/ggml-medium.en.bin -f samples/gb1.wav -t 8

whisper_init_from_file: loading model from 'models/ggml-medium.en.bin'
whisper_model_load: loading model
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  = 1720.00 MB (+   43.00 MB per decoder)
whisper_model_load: kv self size  =   42.00 MB
whisper_model_load: kv cross size =  140.62 MB
whisper_model_load: adding 1607 extra tokens
whisper_model_load: model ctx     = 1462.35 MB
whisper_model_load: model size    = 1462.12 MB

system_info: n_threads = 8 / 10 | 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 |

main: processing 'samples/gb1.wav' (3179750 samples, 198.7 sec), 8 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...


[00:00:00.000 --> 00:00:08.000]   My fellow Americans, this day has brought terrible news and great sadness to our country.
[00:00:08.000 --> 00:00:17.000]   At nine o'clock this morning, Mission Control in Houston lost contact with our Space Shuttle Columbia.
[00:00:17.000 --> 00:00:23.000]   A short time later, debris was seen falling from the skies above Texas.
[00:00:23.000 --> 00:00:29.000]   The Columbia's lost. There are no survivors.
[00:00:29.000 --> 00:00:32.000]   On board was a crew of seven.
[00:00:32.000 --> 00:00:39.000]   Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark,
[00:00:39.000 --> 00:00:48.000]   Captain David Brown, Commander William McCool, Dr. Kultna Shavla, and Ilan Ramon,
[00:00:48.000 --> 00:00:52.000]   a colonel in the Israeli Air Force.
[00:00:52.000 --> 00:00:58.000]   These men and women assumed great risk in the service to all humanity.
[00:00:58.000 --> 00:01:03.000]   In an age when space flight has come to seem almost routine,
[00:01:03.000 --> 00:01:07.000]   it is easy to overlook the dangers of travel by rocket
[00:01:07.000 --> 00:01:12.000]   and the difficulties of navigating the fierce outer atmosphere of the Earth.
[00:01:12.000 --> 00:01:18.000]   These astronauts knew the dangers, and they faced them willingly,
[00:01:18.000 --> 00:01:23.000]   knowing they had a high and noble purpose in life.
[00:01:23.000 --> 00:01:31.000]   Because of their courage and daring and idealism, we will miss them all the more.
[00:01:31.000 --> 00:01:36.000]   All Americans today are thinking as well of the families of these men and women
[00:01:36.000 --> 00:01:40.000]   who have been given this sudden shock and grief.
[00:01:40.000 --> 00:01:45.000]   You're not alone. Our entire nation grieves with you,
[00:01:45.000 --> 00:01:52.000]   and those you love will always have the respect and gratitude of this country.
[00:01:52.000 --> 00:01:56.000]   The cause in which they died will continue.
[00:01:56.000 --> 00:02:04.000]   Mankind is led into the darkness beyond our world by the inspiration of discovery
[00:02:04.000 --> 00:02:11.000]   and the longing to understand. Our journey into space will go on.
[00:02:11.000 --> 00:02:16.000]   In the skies today, we saw destruction and tragedy.
[00:02:16.000 --> 00:02:22.000]   Yet farther than we can see, there is comfort and hope.
[00:02:22.000 --> 00:02:29.000]   In the words of the prophet Isaiah, "Lift your eyes and look to the heavens
[00:02:29.000 --> 00:02:35.000]   who created all these. He who brings out the starry hosts one by one
[00:02:35.000 --> 00:02:39.000]   and calls them each by name."
[00:02:39.000 --> 00:02:46.000]   Because of His great power and mighty strength, not one of them is missing.
[00:02:46.000 --> 00:02:55.000]   The same Creator who names the stars also knows the names of the seven souls we mourn today.
[00:02:55.000 --> 00:03:01.000]   The crew of the shuttle Columbia did not return safely to earth,
[00:03:01.000 --> 00:03:05.000]   yet we can pray that all are safely home.
[00:03:05.000 --> 00:03:13.000]   May God bless the grieving families, and may God continue to bless America.
[00:03:13.000 --> 00:03:19.000]   [Silence]


whisper_print_timings:     fallbacks =   1 p /   0 h
whisper_print_timings:     load time =   569.03 ms
whisper_print_timings:      mel time =   146.85 ms
whisper_print_timings:   sample time =   238.66 ms /   553 runs (    0.43 ms per run)
whisper_print_timings:   encode time = 18665.10 ms /     9 runs ( 2073.90 ms per run)
whisper_print_timings:   decode time = 13090.93 ms /   549 runs (   23.85 ms per run)
whisper_print_timings:    total time = 32733.52 ms

</details>

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 continuously. More info is available in issue #10.

make stream -j
./stream -m ./models/ggml-base.en.bin -t 8 --step 500 --length 5000

https://user-images.githubusercontent.com/1991296/194935793-76afede7-cfa8-48d8-a80f-28ba83be7d09.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:

./main -m models/ggml-base.en.bin -f samples/gb0.wav --print-colors

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 -ml 16

whisper_model_load: loading model from './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' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...

[00:00:00.000 --> 00:00:00.850]   And so my
[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]   not what your
[00:00:05.660 --> 00:00:06.840]   country can do
[00:00:06.840 --> 00:00:08.430]   for you, ask
[00:00:08.430 --> 00:00:09.440]   what you can do
[00:00:09.440 --> 00:00:10.020]   for your
[00:00:10.020 --> 00:00:11.000]   country.

Word-level timestamp (experimental)

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 -ml 1

whisper_model_load: loading model from './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' (176000 samples, 11.0 sec), 4 threads, 1 processors, 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]  .

Speaker segmentation via tinydiarize (experimental)

More information about this approach is available here: https://github.com/ggerganov/whisper.cpp/pull/1058

Sample usage:

# download a tinydiarize compatible model
./models/download-ggml-model.sh small.en-tdrz

# run as usual, adding the "-tdrz" command-line argument
./main -f ./samples/a13.wav -m ./models/ggml-small.en-tdrz.bin -tdrz
...
main: processing './samples/a13.wav' (480000 samples, 30.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, tdrz = 1, timestamps = 1 ...
...
[00:00:00.000 --> 00:00:03.800]   Okay Houston, we've had a problem here. [SPEAKER_TURN]
[00:00:03.800 --> 00:00:06.200]   This is Houston. Say again please. [SPEAKER_TURN]
[00:00:06.200 --> 00:00:08.260]   Uh Houston we've had a problem.
[00:00:08.260 --> 00:00:11.320]   We've had a main beam up on a volt. [SPEAKER_TURN]
[00:00:11.320 --> 00:00:13.820]   Roger main beam interval. [SPEAKER_TURN]
[00:00:13.820 --> 00:00:15.100]   Uh uh [SPEAKER_TURN]
[00:00:15.100 --> 00:00:18.020]   So okay stand, by thirteen we're looking at it. [SPEAKER_TURN]
[00:00:18.020 --> 00:00:25.740]   Okay uh right now uh Houston the uh voltage is uh is looking good um.
[00:00:27.620 --> 00:00:29.940]   And we had a a pretty large bank or so.

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 -owts
source ./samples/jfk.wav.wts
ffplay ./samples/jfk.wav.mp4

https://user-images.githubusercontent.com/1991296/199337465-dbee4b5e-9aeb-48a3-b1c6-323ac4db5b2c.mp4

./main -m ./models/ggml-base.en.bin -f ./samples/mm0.wav -owts
source ./samples/mm0.wav.wts
ffplay ./samples/mm0.wav.mp4

https://user-images.githubusercontent.com/1991296/199337504-cc8fd233-0cb7-4920-95f9-4227de3570aa.mp4

./main -m ./models/ggml-base.en.bin -f ./samples/gb0.wav -owts
source ./samples/gb0.wav.wts
ffplay ./samples/gb0.wav.mp4

https://user-images.githubusercontent.com/1991296/199337538-b7b0c7a3-2753-4a88-a0cd-f28a317987ba.mp4

Video comparison of different models

Use the scripts/bench-wts.sh script to generate a video in the following format:

./scripts/bench-wts.sh samples/jfk.wav
ffplay ./samples/jfk.wav.all.mp4

https://user-images.githubusercontent.com/1991296/223206245-2d36d903-cf8e-4f09-8c3b-eb9f9c39d6fc.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

Additionally a script to run whisper.cpp with different models and audio files is provided bench.py.

You can run it with the following command, by default it will run against any standard model in the models folder.

python3 scripts/bench.py -f samples/jfk.wav -t 2,4,8 -p 1,2

It is written in python with the intention of being easy to modify and extend for your benchmarking use case.

It outputs a csv file with the results of the benchmarking.

`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:

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

Bindings

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
wchess	wchess.wasm	Voice-controlled chess
talk	talk.wasm	Talk with a GPT-2 bot
talk-llama		Talk with a LLaMA 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)
server		HTTP transcription server with OAI-like API

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.