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

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

category: automation
subcategory: audio-processing
dependencies: [openai-whisper, torch, ffmpeg-python]
difficulty: beginner
time_saved: 10+ hours/week