Practical guide for transcribing audio and video files in Audio Note, with the right first-pass choices for Whisper, realtime models, and batch workflows.
File Transcription
Transcription
File transcription parameters screenshot
What This Page Solves
File Transcription is the most reliable starting point in Audio Note. It works best when you need:
- audio or video files transcribed after the fact
- better accuracy than a live workflow usually provides
- exports, review, and repeatable team workflows
If you are not sure where to begin, begin here.
When To Use It
Prefer File Transcription when
- accuracy matters more than instant text
- you want to process long-form meetings, interviews, lectures, or replay media
- you need batch work, exports, or a review step before sharing
Do not start here when
- you need live microphone text: use Realtime Microphone Transcription
- the source is still on a website: use Link Transcription
- you want folders to queue automatically: use Watch Folder
Recommended Workflow
- Start with one real sample file.
- Choose Whisper or a realtime model, then lock language and export target.
- Validate the result before launching a full batch.
- Move accepted results into Note for cleanup.
- Use AI Chat only after the transcript is trustworthy enough for semantic work.
The goal is not to process everything immediately. The goal is to prove that one parameter set works for this content type.
Key Decisions
1. Whisper or realtime model first
| Situation | First choice | Why |
|---|---|---|
| archive review, long-form meetings, final-quality output | Whisper | stronger quality ceiling and better fit for editorial review |
| lower-power device, fast draft, lighter workloads | Realtime model | steadier throughput and friendlier device requirements |
| strong GPU and still quality-first | Whisper | often worth validating first when hardware is strong |
2. What to lock first
For a first production pass, lock these four items before touching advanced parameters:
- model route
- language
- GPU on or off
- export target
Only move on to Advanced Parameter Transcription once the baseline is already close to usable.
3. When GPU is worth it
GPU is usually worth testing when you have:
- long files
- Medium or Large Whisper models
- repeatable batch work
- a stable GPU runtime on your machine
If you mainly use realtime models, GPU is usually not the first optimization to care about.
Common Mistakes And Troubleshooting
- Mixing unrelated languages into one batch Split by language, content type, or downstream use.
- Launching a full batch without a sample run Validate one or two files first.
- Treating first export as final output Normalize names, numbers, and terminology in Note before sharing.
- Changing model, GPU, and parameters at the same time Change one variable at a time so the result stays explainable.
If a task stalls, check in this order:
- model download completeness
- disk space and cache path health
- language and model fit
- GPU/runtime stability
- source file integrity or container issues
Read Next
- URL-based media workflows: Link Transcription
- Automatic folder ingestion: Watch Folder
- Post-transcription editing: Note
- Fine-tuning after the baseline works: Advanced Parameter Transcription