Standalone executables of OpenAI's Whisper & Faster-Whisper for those who don't want to bother with Python.
Faster-Whisper executables are x86-64 compatible with Windows 7, Linux v5.4, macOS v10.15 and above.
Meant to be used in command-line interface or in programs like Subtitle Edit, Tero Subtitler, FFAStrans.
Faster-Whisper is much faster & better than OpenAI's Whisper, and it requires less RAM/VRAM.
whisper-faster.exe "D:\videofile.mkv" --language English --model medium --output_dir sourcewhisper-faster.exe "D:\videofile.mkv" -l English -m medium -o source --sentencewhisper-faster.exe "D:\videofile.mkv" -l Japanese -m medium --task translate --standardwhisper-faster.exe --help
Executables & libs can be downloaded from Releases. [at the right side of this page]
Don't copy programs to the Windows' folders! [run as Administrator if you did]
Programs automatically will choose to work on GPU if CUDA is detected.
For decent transcription use not smaller than medium model.
Guide how to run the command line programs: https://www.youtube.com/watch?v=A3nwRCV-bTU
Examples how to do batch processing on the multiple files: Purfview#29
Some defaults are tweaked for movies transcriptions and to make it portable.
Features various new experimental settings.
Shows the progress bar in the title bar of command-line interface. [or it can be printed with -pp]
By default it looks for models in the same folder, in path like this -> _models\faster-whisper-medium.
Models are downloaded automatically or can be downloaded manually from: https://huggingface.co/Systran
beam_size=1: can speed-up transcription twice. [ in my tests it had insignificant impact on accuracy ]
compute_type: test different types to find fastest for your hardware. [--verbose=true to see all supported types]
To reduce memory usage try incrementally: --best_of=1, beam_size=1, --temperature_increment_on_fallback=None.
