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300 lines
12 KiB
300 lines
12 KiB
# whisper.cpp
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[](https://github.com/ggerganov/whisper.cpp/actions)
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[](https://opensource.org/licenses/MIT)
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High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
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- Plain C/C++ implementation without dependencies
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- Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
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- AVX intrinsics support for x86 architectures
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- Mixed F16 / F32 precision
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- Low memory usage (Flash Attention + Flash Forward)
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- Zero memory allocations at runtime
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- Runs on the CPU
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- [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h)
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- Supported platforms: Linux, Mac OS (Intel and Arm), Windows (MSVC and MinGW), Raspberry Pi, Android
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## Usage
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To build the main program, run `make`. You can then transcribe a `.wav` file like this:
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```bash
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./main -f input.wav
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```
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Before running the program, make sure to download one of the ggml Whisper models. For example:
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```bash
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bash ./download-ggml-model.sh base.en
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```
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---
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For a quick demo, simply run `make base.en`:
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```java
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$ make base.en
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cc -O3 -std=c11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread -DGGML_USE_ACCELERATE -c ggml.c
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c++ -O3 -std=c++11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread -c whisper.cpp
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c++ -O3 -std=c++11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread main.cpp whisper.o ggml.o -o main -framework Accelerate
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./main -h
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usage: ./main [options] file0.wav file1.wav ...
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options:
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-h, --help show this help message and exit
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-s SEED, --seed SEED RNG seed (default: -1)
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-t N, --threads N number of threads to use during computation (default: 4)
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-o N, --offset N offset in milliseconds (default: 0)
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-v, --verbose verbose output
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--translate translate from source language to english
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-otxt, --output-txt output result in a text file
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-ovtt, --output-vtt output result in a vtt file
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-osrt, --output-srt output result in a srt file
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-ps, --print_special print special tokens
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-nt, --no_timestamps do not print timestamps
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-l LANG, --language LANG spoken language (default: en)
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-m FNAME, --model FNAME model path (default: models/ggml-base.en.bin)
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-f FNAME, --file FNAME input WAV file path
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bash ./download-ggml-model.sh base.en
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Downloading ggml model base.en ...
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models/ggml-base.en.bin 100%[=============================================>] 141.11M 3.13MB/s in 79s
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Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
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You can now use it like this:
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$ ./main -m models/ggml-base.en.bin -f samples/jfk.wav
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===============================================
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Running base.en on all samples in ./samples ...
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===============================================
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----------------------------------------------
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[+] Running base.en on samples/jfk.wav ... (run 'ffplay samples/jfk.wav' to listen)
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----------------------------------------------
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whisper_model_load: loading model from 'models/ggml-base.en.bin'
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whisper_model_load: n_vocab = 51864
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whisper_model_load: n_audio_ctx = 1500
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whisper_model_load: n_audio_state = 512
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whisper_model_load: n_audio_head = 8
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whisper_model_load: n_audio_layer = 6
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whisper_model_load: n_text_ctx = 448
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whisper_model_load: n_text_state = 512
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whisper_model_load: n_text_head = 8
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whisper_model_load: n_text_layer = 6
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whisper_model_load: n_mels = 80
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whisper_model_load: f16 = 1
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whisper_model_load: type = 2
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whisper_model_load: mem_required = 505.00 MB
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whisper_model_load: adding 1607 extra tokens
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whisper_model_load: ggml ctx size = 163.43 MB
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whisper_model_load: memory size = 22.83 MB
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whisper_model_load: model size = 140.54 MB
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main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, lang = en, task = transcribe, timestamps = 1 ...
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[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.
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whisper_print_timings: load time = 87.21 ms
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whisper_print_timings: mel time = 24.26 ms
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whisper_print_timings: sample time = 3.87 ms
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whisper_print_timings: encode time = 323.67 ms / 53.94 ms per layer
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whisper_print_timings: decode time = 83.25 ms / 13.87 ms per layer
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whisper_print_timings: total time = 522.66 ms
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```
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The command downloads the `base.en` model converted to custom `ggml` format and runs the inference on all `.wav` samples in the folder `samples`.
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For detailed usage instructions, run: `./main -h`
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Note that `whisper.cpp` currently runs only with 16-bit WAV files, so make sure to convert your input before running the tool.
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For example, you can use `ffmpeg` like this:
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```java
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ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav
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```
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## More audio samples
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If you want some extra audio samples to play with, simply run:
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```
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make samples
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```
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This will download a few more audio files from Wikipedia and convert them to 16-bit WAV format via `ffmpeg`.
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You can download and run the other models as follows:
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```
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make tiny.en
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make tiny
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make base.en
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make base
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make small.en
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make small
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make medium.en
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make medium
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make large
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```
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## Another example
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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)
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in about half a minute on a MacBook M1 Pro, using `medium.en` model:
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```java
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$ ./main -m models/ggml-medium.en.bin -f samples/gb1.wav -t 8
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whisper_model_load: loading model from 'models/ggml-medium.en.bin'
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whisper_model_load: n_vocab = 51864
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whisper_model_load: n_audio_ctx = 1500
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whisper_model_load: n_audio_state = 1024
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whisper_model_load: n_audio_head = 16
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whisper_model_load: n_audio_layer = 24
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whisper_model_load: n_text_ctx = 448
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whisper_model_load: n_text_state = 1024
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whisper_model_load: n_text_head = 16
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whisper_model_load: n_text_layer = 24
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whisper_model_load: n_mels = 80
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whisper_model_load: f16 = 1
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whisper_model_load: type = 4
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whisper_model_load: mem_required = 2610.00 MB
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whisper_model_load: adding 1607 extra tokens
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whisper_model_load: ggml ctx size = 1644.97 MB
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whisper_model_load: memory size = 182.62 MB
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whisper_model_load: model size = 1462.12 MB
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main: processing 'samples/gb1.wav' (3179750 samples, 198.7 sec), 8 threads, lang = en, task = transcribe, timestamps = 1 ...
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[00:00.000 --> 00:08.000] My fellow Americans, this day has brought terrible news and great sadness to our country.
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[00:08.000 --> 00:17.000] At nine o'clock this morning, Mission Control in Houston lost contact with our Space Shuttle Columbia.
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[00:17.000 --> 00:23.000] A short time later, debris was seen falling from the skies above Texas.
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[00:23.000 --> 00:29.000] The Columbia's lost. There are no survivors.
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[00:29.000 --> 00:32.000] On board was a crew of seven.
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[00:32.000 --> 00:39.000] Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark,
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[00:39.000 --> 00:48.000] Captain David Brown, Commander William McCool, Dr. Kultna Shavla, and Ilan Ramon,
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[00:48.000 --> 00:52.000] a colonel in the Israeli Air Force.
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[00:52.000 --> 00:58.000] These men and women assumed great risk in the service to all humanity.
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[00:58.000 --> 01:03.000] In an age when space flight has come to seem almost routine,
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[01:03.000 --> 01:07.000] it is easy to overlook the dangers of travel by rocket
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[01:07.000 --> 01:12.000] and the difficulties of navigating the fierce outer atmosphere of the Earth.
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[01:12.000 --> 01:18.000] These astronauts knew the dangers, and they faced them willingly,
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[01:18.000 --> 01:23.000] knowing they had a high and noble purpose in life.
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[01:23.000 --> 01:31.000] Because of their courage and daring and idealism, we will miss them all the more.
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[01:31.000 --> 01:36.000] All Americans today are thinking as well of the families of these men and women
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[01:36.000 --> 01:40.000] who have been given this sudden shock and grief.
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[01:40.000 --> 01:45.000] You're not alone. Our entire nation grieves with you,
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[01:45.000 --> 01:52.000] and those you love will always have the respect and gratitude of this country.
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[01:52.000 --> 01:56.000] The cause in which they died will continue.
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[01:56.000 --> 02:04.000] Mankind is led into the darkness beyond our world by the inspiration of discovery
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[02:04.000 --> 02:11.000] and the longing to understand. Our journey into space will go on.
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[02:11.000 --> 02:16.000] In the skies today, we saw destruction and tragedy.
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[02:16.000 --> 02:22.000] Yet farther than we can see, there is comfort and hope.
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[02:22.000 --> 02:29.000] In the words of the prophet Isaiah, "Lift your eyes and look to the heavens
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[02:29.000 --> 02:35.000] who created all these. He who brings out the starry hosts one by one
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[02:35.000 --> 02:39.000] and calls them each by name."
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[02:39.000 --> 02:46.000] Because of His great power and mighty strength, not one of them is missing.
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[02:46.000 --> 02:55.000] The same Creator who names the stars also knows the names of the seven souls we mourn today.
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[02:55.000 --> 03:01.000] The crew of the shuttle Columbia did not return safely to earth,
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[03:01.000 --> 03:05.000] yet we can pray that all are safely home.
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[03:05.000 --> 03:13.000] May God bless the grieving families, and may God continue to bless America.
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[03:13.000 --> 03:41.000] Audio
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whisper_print_timings: load time = 575.92 ms
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whisper_print_timings: mel time = 230.60 ms
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whisper_print_timings: sample time = 73.19 ms
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whisper_print_timings: encode time = 19552.61 ms / 814.69 ms per layer
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whisper_print_timings: decode time = 13249.96 ms / 552.08 ms per layer
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whisper_print_timings: total time = 33686.27 ms
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```
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## Real-time audio input example
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This is a naive example of performing real-time inference on audio from your microphone.
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The `stream` tool samples the audio every half a second and runs the transcription continously.
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More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
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```java
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./stream -m ./models/ggml-base.en.bin -t 8 --step 500 --length 5000
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```
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https://user-images.githubusercontent.com/1991296/194935793-76afede7-cfa8-48d8-a80f-28ba83be7d09.mp4
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The `stream` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
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```bash
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# Install SDL2 on Linux
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sudo apt-get install libsdl2-dev
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# Install SDL2 on Mac OS
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brew install sdl2
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make stream
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```
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## Implementation details
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- The core tensor operations are implemented in C ([ggml.h](ggml.h) / [ggml.c](ggml.c))
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- The high-level C-style API is implemented in C++ ([whisper.h](whisper.h) / [whisper.cpp](whisper.cpp))
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- Simple usage is demonstrated in [main.cpp](main.cpp)
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- Sample real-time audio transcription from the microphone is demonstrated in [stream.cpp](stream.cpp)
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The tensor operators are optimized heavily for Apple silicon CPUs. Depending on the computation size, Arm Neon SIMD
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instrisics or CBLAS Accelerate framework routines are used. The latter are especially effective for bigger sizes since
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the Accelerate framework utilizes the special-purpose AMX coprocessor available in modern Apple products.
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## Limitations
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- Inference only
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- No GPU support
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- Very basic greedy sampling scheme - always pick up the token with highest probability.
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This should be similar to the [GreedyDecoder](https://github.com/openai/whisper/blob/main/whisper/decoding.py#L249-L274)
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from the original python implementation, so in order to make a fair comparison between the 2 implementations, make sure
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to run the python code with the following parameters:
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```
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whisper --best_of None --beam_size None ...
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```
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In the future, `whisper.cpp` will support more sampling strategies.
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## Memory usage
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| Model | Disk | Mem |
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| tiny | 75 MB | ~280 MB |
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| base | 142 MB | ~430 MB |
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| small | 466 MB | ~1.0 GB |
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| medium | 1.5 GB | ~2.6 GB |
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| large | 2.9 GB | ~4.7 GB |
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## ggml format
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The original models are converted to a custom binary format. This allows to pack everything needed into a single file:
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- model parameters
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- mel filters
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- vocabulary
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- weights
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You can download the converted models using the [download-ggml-model.sh](download-ggml-model.sh) script or from here:
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https://ggml.ggerganov.com
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For more details, see the conversion script [models/convert-pt-to-ggml.py](models/convert-pt-to-ggml.py) or the README in [models](models).
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## Bindings
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- [X] Rust: [tazz4843/whisper-rs](https://github.com/tazz4843/whisper-rs)
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- [ ] Python:
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- [ ] Obj-C:
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- [ ] Java:
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