whisper.cpp/README.md

# whisper.cpp

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High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:

- Plain C/C++ implementation without dependencies
- ARM_NEON and AVX intrinsics support
- Mixed F16 / F32 precision
- Low memory usage (Flash Attention + Flash Forward)
- Zero memory allocations at runtime
- Runs on the CPU
- [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h)
- Supported platforms: Linux, Mac OS (Intel and Arm), Raspberry Pi, Android

## Usage

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

```bash
$ ./main -f input.wav
```

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

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

---

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

```java
$ make base.en
cc  -O3 -std=c11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread   -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
./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)
  -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

bash ./download-ggml-model.sh base.en
Downloading ggml model base.en ...
models/ggml-base.en.bin            100%[===================================>] 141.11M  6.49MB/s    in 23s
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  = 377.00 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 =    77.48 ms
whisper_print_timings:      mel time =    26.10 ms
whisper_print_timings:   sample time =     2.19 ms
whisper_print_timings:   encode time =   632.95 ms / 105.49 ms per layer
whisper_print_timings:   decode time =    85.11 ms / 14.18 ms per layer
whisper_print_timings:    total time =   824.14 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:

```java
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 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
```

## Another example

Here is another example of transcribing a [3:24 min speech](https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg)
in less than a minute on a MacBook M1 Pro, using `medium.en` model:

```java
$ ./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  = 2502.00 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 =   522.18 ms
main:      mel time =   423.43 ms
main:   sample time =    31.42 ms
main:   encode time = 41518.51 ms / 1729.94 ms per layer
main:   decode time = 14907.22 ms
main:    total time = 57416.63 ms
```

## 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 3 seconds and runs the transcription continously. More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).

```java
$ ./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

## Implementation details

- The core tensor operations are implemented in C ([ggml.h](ggml.h) / [ggml.c](ggml.c))
- The high-level C-style API is implemented in C++ ([whisper.h](whisper.h) / [whisper.cpp](whisper.cpp))
- Simple usage is demonstrated in [main.cpp](main.cpp)
- Sample real-time audio transcription from the microphone is demonstrated in [stream.cpp](stream.cpp)

## Limitations

- Very basic greedy sampling scheme - always pick up the top token. You can implement your own strategy
- Inference only
- No GPU support

## Memory usage

| Model  | Disk   | Mem     |
| ---    | ---    | ---     |
| tiny   |  75 MB | ~240 MB |
| base   | 142 MB | ~380 MB |
| small  | 466 MB | ~970 MB |
| medium | 1.5 GB | ~2.5 GB |
| large  | 2.9 GB | ~4.6 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](download-ggml-model.sh) script.

For more details, see the conversion script [convert-pt-to-ggml.py](convert-pt-to-ggml.py) or the README in [models](models).