# whisper.cpp [![Actions Status](https://github.com/ggerganov/whisper.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/whisper.cpp/actions) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) 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), Windows (MSVC and MinGW), 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) -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 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 half a second 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 or from here: https://ggml.ggerganov.com For more details, see the conversion script [convert-pt-to-ggml.py](convert-pt-to-ggml.py) or the README in [models](models). ## Bindings - [X] Rust: [tazz4843/whisper-rs](https://github.com/tazz4843/whisper-rs) - [ ] Python: - [ ] Obj-C: - [ ] Java: