AI Campus • Practical resources
Audio Lecture Transcriber & Note Generator
Transcribe a consented recording locally, correct the transcript and create notes with traceable timestamps.
Build guide and prerequisites
Follow this roadmap to implement and validate your own application, then record your observed results.
- Basic terminal use and Python environment setup.
- A short recording you own or have permission to transcribe; sufficient local storage.
Required tools and free options
- Python
- Open-source Whisper
- FFmpeg
- Optional local summarization model
Cost: Open-source local transcription requires no hosted speech API. Initial model downloads and local compute are required; CPU transcription may be slow. A paid Whisper API is not required.
Build it step by step
1. Prepare a known recording
Record a one-minute sample with three facts and one technical term. Keep a handwritten reference transcript. Avoid uploading other people’s recordings without permission.
2. Install the local transcription prerequisites
Follow the official Whisper repository instructions for a compatible Python environment and FFmpeg. Choose the smallest model suitable for your language/hardware first and download its weights before an offline run.
3. Generate timestamped artifacts
Run the local Whisper command on the sample and keep plain text plus SRT or JSON output. Store the language, model and runtime next to those files. Do not treat generated punctuation or speaker identity as verified metadata.
4. Review transcription accuracy
Listen around each technical term and compare the three known facts. Correct mistakes while retaining the original output for comparison. If adding speaker labels later, identify that diarization is a separate capability.
5. Create notes and flashcards
Start manually: write three notes and three question/answer cards, each tied to a transcript timestamp. Optionally use a local model to draft them, then reject unsupported claims and show the associated segment in the UI.
6. Add file processing states
Build queued, transcribing, reviewing and completed states, plus a recoverable failure state. Test unsupported files, silent audio and cancellation. Export the corrected transcript, notes and source-linked flashcards.
Expected output and validation
A local timestamped transcript and reviewed study artifacts. Speaker diarization, live transcription and a polished upload service are separate extensions.
- Each card can be traced to a recorded segment.
- Transcription failures do not appear as successful empty notes.
- Runtime and observed errors are documented for the chosen model and hardware.
Official references
Free to read. Software setup, model access and hosted services have their own requirements. References checked 3 November 2026.