Agent skill · creative production · davila7
whisper
OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
Why this skill is useful
Provides specific commands and options for using the Whisper model that enhance the AI's ability to handle speech recognition tasks effectively.
What it needs
Requires ffmpeg, openai-whisper installed locally. About 4k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
Whisper - Robust Speech Recognition OpenAI's multilingual speech recognition model. When to use Whisper Use when: Speech-to-text transcription (99 languages) Podcast/video transcription Meeting notes automation Translation to English Noisy audio transcription Multilingual audio processing Metrics: 72,900+ GitHub stars 99 languages supported Trained on 680,000 hours of audio MIT License Use alternatives instead: AssemblyAI: Managed API, speaker diarization Deepgram: Real-time streaming ASR Google Speech-to-Text: Cloud-based Quick start Installation Basic transcription Model sizes Model Parameters English-only Multilingual Speed VRAM ------- ------------ -------------- -------------- ------- ------ tiny 39M ✓ ✓ ~32x ~1 GB base 74M ✓ ✓ ~16x ~1 GB small 244M ✓ ✓ ~6x ~2 GB medium 769M ✓ ✓ ~2x ~5 GB large 1550M ✗ ✓ 1x ~10 GB turbo 809M ✗ ✓ ~8x ~6 GB Recommendation: Use turbo for best speed/quality, base for prototyping Transcription options Language specification Task selection Initial prompt Timestamps Temperature fallback Command line usage Batch processing Real-time transcription GPU acceleration Integration with other tools Subtitle generation With LangChain Extract audio from video Best practices 1. Use turbo model - Best speed/quality for English 2. Specify language - Faster than auto-detect 3. Add initial prompt - Improves technical terms 4. Use GPU - 10-20× faster 5. Batch process - More efficient 6. Convert to WAV - Better compatibility 7. Split long audio - <30 min chunks 8. Check language support - Quality varies by language 9. Use faster-whisper - 4× faster than openai-whisper 10. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/multimodal-whisper