speech-to-text

To transcribe video to text with timestamps locally, use this Skill's script to process media files via a local Whisper model, generating timestamped plaintext or JSON transcripts without external APIs.

You can transcribe audio offline without external API keys by running a local Whisper model. This Skill processes standalone media files entirely locally, ensuring privacy for tutorials, lectures, or meeting recordings.

Yes, local Whisper transcription requires a compatible Python environment and a local Whisper installation. The Skill operates offline using model sizes like tiny, base, small, or medium.

For privacy-preserving meeting recordings, offline transcription using a local Whisper model is ideal. It processes audio locally without external API keys, generating timestamped text suitable for searchable notes.

Local Whisper transcription outputs either plain text with timestamps or JSON transcripts. These formats are suitable for creating subtitles, searchable transcripts, or reference notes from instructional videos.

Yes, offline transcription supports optional Whisper model sizes including tiny, base, small, and medium. Choose a model based on your accuracy requirements and available local compute resources.