30-SECOND SUMMARY
What to take away
- Whisper requires a compatible Python environment and ffmpeg.
- Begin with a short public recording and a smaller model.
- Review names, numbers, overlaps, and noisy segments manually.
From audio to reviewed transcript
Begin with a short public clip.
- 01AUDIO
Consent and source
- 02FFMPEG
Compatible format
- 03WHISPER
Language-aware transcript
- 04REVIEW
Names and numbers
Prepare the environment
Check current Python requirements in the official repository, install ffmpeg, and use a virtual environment to isolate dependencies.
python -m pip install -U openai-whisperTranscribe a short file
Start with one or two minutes and specify the language. Larger models generally require more memory and processing time.
whisper sample.mp3 --model small --language KoreanReview critical errors
Mark proper nouns, numbers, overlapping speech, noise, and punctuation. A list of costly errors is more useful than one overall score.
Protect meeting data
Locate the source audio, converted files, transcript, temporary files, and backups. Recording consent and company policy still apply.
Frequently asked questions
Is a GPU required?
No. CPU works but can be slow.
Does Whisper identify speakers?
Base Whisper transcription is not a dedicated diarization pipeline.
Is there a Korean-only model?
Use a multilingual model and specify Korean; distinguish English-only `.en` variants.
Primary sources
Check the original documentation for version-specific details.