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
• F16 support

Usage

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

$ make base.en

Downloading base.en (142 MB just once)
mkdir -p models
models/ggml-base.en.bin      100%[=================================>] 141.11M  7.50MB/s    in 19s

===============================================
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  = 782.00 MB
whisper_model_load: adding 1607 extra tokens
whisper_model_load: ggml ctx size = 186.26 MB
whisper_model_load: memory size =    45.66 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

 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 =    60.62 ms
main:      mel time =    38.69 ms
main:   sample time =     2.36 ms
main:   encode time =   875.63 ms / 145.94 ms per layer
main:   decode time =   103.17 ms
main:    total time =  1081.13 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 .en models as follows:

make tiny.en
make base.en
make small.en
make medium.en

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

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

Memory usage

Model	Mem
tiny.en	~600 MB
base.en	~800 MB
small.en	~1.6 GB
medium.en	~3.5 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

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