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
pip install openai-whisper torch ffmpeg-python click
# Also requires ffmpeg installed on system
# macOS: brew install ffmpeg
# Ubuntu: sudo apt install ffmpeg
Commands
Transcribe Single File
python scripts/main.py transcribe audio.mp3 --model medium --output transcript.txt
python scripts/main.py transcribe video.mp4 --format srt --output subtitles.srt
Batch Transcription
python scripts/main.py batch ./recordings/ --format txt --output ./transcripts/
Transcribe + Translate
python scripts/main.py translate foreign-audio.mp3 --to en
Extract Timestamps
python scripts/main.py timestamps podcast.mp3 --format json
Examples
Example 1: Podcast to Blog Post
# 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
Example 2: YouTube Subtitles
# Generate SRT for video upload
python scripts/main.py transcribe marketing-video.mp4 --format srt
# Output: marketing-video.srt
# Upload directly to YouTube/Vimeo
Example 3: Batch Process Interview Library
# 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
Skill Metadata
category: automation
subcategory: audio-processing
dependencies: [openai-whisper, torch, ffmpeg-python]
difficulty: beginner
time_saved: 10+ hours/week
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