사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill whisper-transcription시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
Whisper Transcription
Transcribe any audio or video to text using OpenAI's Whisper model - the same technology powering ChatGPT voice features.
When to Use This Skill
- Podcast repurposing - Convert episodes to blog posts, show notes, social snippets
- Video subtitles - Generate SRT/VTT files for YouTube, social media
- Interview extraction - Pull quotes and insights from recorded calls
- Content audit - Make audio/video libraries searchable
- Translation - Transcribe and translate foreign language content
What Claude Does vs What You Decide
| Claude Does | You Decide |
|---|---|
| Structures production workflow | Final creative direction |
| Suggests technical approaches | Equipment and tool choices |
| Creates templates and checklists | Quality standards |
| Identifies best practices | Brand/voice decisions |
| Generates script outlines | Final script approval |
Dependencies
bash
pip install openai-whisper torch ffmpeg-python click
# Also requires ffmpeg installed on system
# macOS: brew install ffmpeg
# Ubuntu: sudo apt install ffmpegCommands
Transcribe Single File
bash
python scripts/main.py transcribe audio.mp3 --model medium --output transcript.txt
python scripts/main.py transcribe video.mp4 --format srt --output subtitles.srtBatch Transcription
bash
python scripts/main.py batch ./recordings/ --format txt --output ./transcripts/Transcribe + Translate
bash
python scripts/main.py translate foreign-audio.mp3 --to enExtract Timestamps
bash
python scripts/main.py timestamps podcast.mp3 --format jsonExamples
Example 1: Podcast to Blog Post
bash
# Transcribe 1-hour podcast
python scripts/main.py transcribe episode-42.mp3 --model medium
# Output: episode-42.txt (full transcript with timestamps)
# Processing time: ~5 min for 1 hour audio on M1 MacExample 2: YouTube Subtitles
bash
# Generate SRT for video upload
python scripts/main.py transcribe marketing-video.mp4 --format srt
# Output: marketing-video.srt
# Upload directly to YouTube/VimeoExample 3: Batch Process Interview Library
bash
# Transcribe all recordings in folder
python scripts/main.py batch ./customer-interviews/ --model small --format txt
# Output: ./customer-interviews/*.txt (one per audio file)Model Selection Guide
| Model | Speed | Accuracy | VRAM | Best For |
|---|---|---|---|---|
tiny | Fastest | ~70% | 1GB | Quick drafts, short clips |
base | Fast | ~80% | 1GB | Social media clips |
small | Medium | ~85% | 2GB | Podcasts, interviews |
medium | Slow | ~90% | 5GB | Professional transcripts |
large | Slowest | ~95% | 10GB | Critical accuracy needs |
Recommendation: Start with small for most marketing content. Use medium for client deliverables.
Output Formats
| Format | Extension | Use Case |
|---|---|---|
txt | .txt | Blog posts, analysis |
srt | .srt | Video subtitles (YouTube) |
vtt | .vtt | Web video subtitles |
json | .json | Programmatic access |
tsv | .tsv | Spreadsheet analysis |
Performance Tips
- GPU acceleration - 10x faster with CUDA GPU
- Audio extraction - Script auto-extracts audio from video
- Chunking - Long files auto-split for memory efficiency
- Language detection - Automatic, or specify with
--language
Skill Boundaries
What This Skill Does Well
- Structuring audio production workflows
- Providing technical guidance
- Creating quality checklists
- Suggesting creative approaches
What This Skill Cannot Do
- Replace audio engineering expertise
- Make subjective creative decisions
- Access or edit audio files directly
- Guarantee commercial success
Related Skills
- video-processing - Extract audio from video
- youtube-downloader - Download videos to transcribe
- content-repurposer - Transform transcripts to content
- podcast-production - Create podcasts
Skill Metadata
- Mode: cyborg
yaml
category: automation
subcategory: audio-processing
dependencies: [openai-whisper, torch, ffmpeg-python]
difficulty: beginner
time_saved: 10+ hours/week
