whisper.cpp/examples/stream
litong 707507ff6d
Examples: Add save audio to file option in stream.cpp (#1310)
* save the recorded audio to a file

* Alignment -help

* Save the correct audio

* chage to a consistent coding style

* Correct typo

* Update examples/stream/stream.cpp

* Update examples/stream/stream.cpp

* Correct variable misuse

* Update examples/stream/stream.cpp

* Update examples/stream/stream.cpp

* Update examples/stream/stream.cpp

* Update examples/stream/stream.cpp

---------

Co-authored-by: bobqianic <129547291+bobqianic@users.noreply.github.com>
2023-09-22 23:43:21 +08:00
..
CMakeLists.txt whisper : add GPU support via cuBLAS (#834) 2023-04-30 12:14:33 +03:00
README.md stream : update README.md + comments 2022-12-16 18:04:19 +02:00
stream.cpp Examples: Add save audio to file option in stream.cpp (#1310) 2023-09-22 23:43:21 +08:00

stream

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.

./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

Sliding window mode with VAD

Setting the --step argument to 0 enables the sliding window mode:

 ./stream -m ./models/ggml-small.en.bin -t 6 --step 0 --length 30000 -vth 0.6

In this mode, the tool will transcribe only after some speech activity is detected. A very basic VAD detector is used, but in theory a more sophisticated approach can be added. The -vth argument determines the VAD threshold - higher values will make it detect silence more often. It's best to tune it to the specific use case, but a value around 0.6 should be OK in general. When silence is detected, it will transcribe the last --length milliseconds of audio and output a transcription block that is suitable for parsing.

Building

The stream tool depends on SDL2 library to capture audio from the microphone. You can build it like this:

# Install SDL2 on Linux
sudo apt-get install libsdl2-dev

# Install SDL2 on Mac OS
brew install sdl2

make stream

Web version

This tool can also run in the browser: examples/stream.wasm