whisper-transcription — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited whisper-transcription (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Transcribe any audio or video to text using OpenAI's Whisper model - the same technology powering ChatGPT voice features.
| 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 |
pip install openai-whisper torch ffmpeg-python click
# Also requires ffmpeg installed on system
# macOS: brew install ffmpeg
# Ubuntu: sudo apt install ffmpegpython scripts/main.py transcribe audio.mp3 --model medium --output transcript.txt
python scripts/main.py transcribe video.mp4 --format srt --output subtitles.srtpython scripts/main.py batch ./recordings/ --format txt --output ./transcripts/python scripts/main.py translate foreign-audio.mp3 --to enpython scripts/main.py timestamps podcast.mp3 --format json# 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 Mac# Generate SRT for video upload
python scripts/main.py transcribe marketing-video.mp4 --format srt
# Output: marketing-video.srt
# Upload directly to YouTube/Vimeo# 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 | 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.
| 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 |
--languagecategory: automation
subcategory: audio-processing
dependencies: [openai-whisper, torch, ffmpeg-python]
difficulty: beginner
time_saved: 10+ hours/week~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.