Transcribe audio and video files to text using OpenAI Whisper. Use when: converting podcasts to blog posts; creating video subtitles; extracting quotes from interviews; repurposing video content to text; building searchable audio archives
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
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 ffmpeg
python scripts/main.py transcribe audio.mp3 --model medium --output transcript.txt
python scripts/main.py transcribe video.mp4 --format srt --output subtitles.srt
python scripts/main.py batch ./recordings/ --format txt --output ./transcripts/
python scripts/main.py translate foreign-audio.mp3 --to en
python 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
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Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
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Assess customer account health using product usage, support sentiment, payment status, and relationship signals
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Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
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Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
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Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
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