faster-whisper

To transcribe audio locally with GPU acceleration, use faster-whisper to process meetings, lectures, and podcasts offline at 4-6x speeds. It requires a Python environment and ffmpeg, leveraging CUDA-enabled PyTorch for GPU processing.

Yes, ffmpeg is required to run speech-to-text transcription locally with faster-whisper. You also need a Python environment, and optionally CUDA-enabled PyTorch to leverage GPU acceleration for high-speed offline processing.

Yes, you can generate word-level timestamps for subtitles from audio files. The local speech-to-text processing supports optional word-level timestamps, enabling precise transcripts and subtitle alignment for meetings and video content.

Yes, faster-whisper supports multilingual offline transcription for podcasts. It processes audio locally without internet connectivity, handling multilingual input for meetings, lectures, and video content using CPU-to-GPU acceleration.

The best way to run speech-to-text without sending audio to the cloud is local offline processing using faster-whisper. It enables fast, private transcription on your machine via included scripts, utilizing GPU acceleration for high-speed results.

To set up a Python environment for local transcription with faster-whisper, install the faster-whisper dependency and ffmpeg. For high-speed GPU acceleration, ensure you have CUDA-enabled PyTorch installed, then load models via the included scripts.