Faster Whisper

Faster Whisper

Local speech-to-text using faster-whisper — a CTranslate2 reimplementation of OpenAI's Whisper that runs 4-6x faster with identical accuracy. With GPU acceleration, expect ~20x realtime transcription (a 10-minute audio file in ~30 seconds).

When to Use

Use this skill when you need to:

• Transcribe audio/video files — meetings, interviews, podcasts, lectures, YouTube videos
• Convert speech to text locally — no API costs, works offline (after model download)
• Batch process multiple audio files — efficient for large collections
• Generate subtitles/captions — word-level timestamps available
• Multilingual transcription — supports 99+ languages with auto-detection

Trigger phrases: "transcribe this audio", "convert speech to text", "what did they say", "make a transcript", "audio to text", "subtitle this video"

When NOT to use:

• Real-time/streaming transcription (use streaming-optimized tools instead)
• Cloud-only environments without local compute
• Files <10 seconds where API call latency doesn't matter

Quick Reference

Model Selection

Choose the right model for your needs:

digraph model_selection {
    rankdir=LR;
    node [shape=box, style=rounded];