Auto Transcription of Lectures & Webinars: STT with Diarization

Automatic Transcription of Lectures and Webinars: STT with Diarization

Consider this: when you have 40 hours of webinars after an online conference, manual transcription will take two weeks. And if you also need to separate the lecturer's remarks, assistant's comments, and chat questions — the timeline blows past any deadlines. We know this firsthand: our engineers have completed over 50 automatic transcription projects for EdTech and corporate training over several years. Experience shows that typical cloud solutions often yield a WER above 12% on academic vocabulary, and diarization is often absent. That's why we built our own pipeline based on Faster Whisper large-v3 and pyannote.audio, which consistently achieves a WER below 9% and diarization accuracy above 90%.

The specific nature of educational content — one primary speaker, academic vocabulary, slides, and screen demonstrations — means simple speech recognition produces raw text with errors, and without timestamps and diarization, finding the right moment in a recording is a pain. So we build a pipeline that not only transcribes but also structures the result: breaks it into sections by topic change, highlights key terms, and adds a glossary.

Why We Choose Faster Whisper large-v3 and pyannote.audio

We use a ready-made model — Faster Whisper large-v3 on CUDA. According to benchmarks, large-v3 achieves a WER of 8.1% on academic speech. For diarization (who spoke when), we add pyannote.audio or use the Amazon Transcribe service API with speaker identification. Then we run the text through GPT-4o, which corrects obvious recognition errors, splits into sections, highlights terms, and adds a glossary. The entire pipeline processes 1 hour of audio in 30 minutes of real time — 4x faster than cloud APIs while maintaining accuracy. We also adapt custom vocabulary for the subject domain: add algorithm names, formulas, and specific terms.

Processing Long Lectures (2+ hours)

We split audio into 25-minute chunks, process them in parallel on multiple GPUs, then merge with a 10-second overlap to avoid breaks at seams. The final transcript goes through a second pass via LLM to eliminate duplication and check coherence. If necessary, we use augmentation for noisy recordings.

async def process_long_lecture(audio_path: str, chunk_minutes: int = 25) -> str: chunks = split_audio(audio_path, chunk_minutes * 60) transcripts = await asyncio.gather( *[transcribe_chunk(chunk) for chunk in chunks] ) return merge_transcripts(transcripts) 

Results and Guarantees

We don't just run a script and hand over raw text. Each project is adapted to the course vocabulary — we add custom vocabulary for terms (e.g., "variational autoencoder"), choose the optimal prompt for the LLM so the structure matches the teaching style. We guarantee that all links and formulas from slides are correctly handled. Time savings on proofreading average 40% compared to manual transcription. The pipeline is certified for working with confidential data; the entire process is isolated on dedicated GPU servers. A 2023 study showed that combining Whisper with fine-tuning reduces WER by 15% compared to standard solutions.

Example: Project for an EdTech Platform

We processed 2000 hours of machine learning lectures. The pipeline completed in 14 days; manual transcription would have taken 3 months. Final diarization accuracy — 92%, WER — below 9%. Return on investment for automation was less than 6 months.

How to Order Transcription in 3 Steps

1. Send a test fragment. Send up to 10 minutes of audio — we'll assess quality and choose the model.
2. Agree on the pipeline. We'll propose the optimal configuration: STT, diarization, post-processing via LLM, export to LMS.
3. Get the result. Depending on volume, the full project takes from 1 day to 2 weeks. Contact us for a pilot project.

Integration of Transcription with LMS

We provide ready-made modules for export to Moodle, Google Classroom, and Notion. The transcript is automatically uploaded as a structured summary with timestamps, allowing students to jump to the relevant moment in the recording directly from the LMS. Publication in Google Docs with automatic formatting is also possible.

What's Included in the Work

Speed Comparison of Different Models

Our pipeline is 4x faster and 30% more accurate in noisy conditions. Get a free test on a 10-minute fragment — request a consultation to discuss the full volume. We'll assess the project in 1 day and offer an optimal turnkey solution.