whisper.cpp

C/C++ port of OpenAI's Whisper speech-to-text model

• Plain C/C++ implementation without dependencies
• ARM_NEON and AVX intrinsics support
• Mixed F16 / F32 support
• Low memory usage (Flash Attention + Flash Forward)

Usage

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

$ ./main -f input.wav

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

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

---

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

$ make base.en

gcc -pthread -O3 -mavx -mavx2 -mfma -mf16c -c ggml.c
g++ -pthread -O3 -std=c++11 -c main.cpp
g++ -o main ggml.o main.o
./main -h

usage: ./main [options]

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)
  -T N,     --tokens N       maximum number of tokens to generate per iteration (default: 64)
  -v,       --verbose        verbose output
            --translate      translate from source language to english
  -ps,      --print_special  print special tokens
  -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 (default: samples/jfk.wav)

bash ./download-ggml-model.sh base.en
Downloading ggml model base.en ...
models/ggml-base.en.bin         100%[=====================================>] 141.11M  7.84MB/s    in 18s
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  = 611.00 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
log_mel_spectrogram: n_sample = 176000, n_len = 1100
log_mel_spectrogram: recording length: 11.000000 s

main: processing 176000 samples (11.0 sec), 4 threads, lang = english, task = transcribe ...

 And so my fellow Americans ask not what your country can do for you. Ask what you can do for your country.

main:     load time =    71.89 ms
main:      mel time =    36.95 ms
main:   sample time =     2.10 ms
main:   encode time =   700.94 ms / 116.82 ms per layer
main:   decode time =    86.14 ms
main:    total time =   898.72 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.

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

For detailed usage instructions, run: ./main -h

Note that whisper.cpp 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

Limitations

• Very basic greedy sampling scheme - always pick up the top token
• No timestamps
• Inference only
• Runs on the CPU
• Only mono-channel 16-bit WAV is supported

Memory usage

Model	Disk	Mem
tiny	75 MB	~460 MB
base	142 MB	~620 MB
small	466 MB	~1.3 GB
medium	1.5 GB	~2.8 GB
large	2.9 GB	~4.9 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 script.

For more details, see the conversion script convert-pt-to-ggml.py