Accessibility: Lecture Transcription on GPU

The Challenge: 4,200 Lectures and a Legal Obligation

A post-92 university records approximately 4,200 lectures per term across its five faculties. Under the Equality Act 2010 and the university’s own accessibility commitments, every recorded lecture must be available with accurate captions for deaf and hard-of-hearing students, students with auditory processing difficulties, and international students for whom English is a second language. Currently, 1,800 registered students have declared additional learning needs that benefit from transcriptions. The university outsources transcription to a captioning service charging £1.80 per minute. With average lecture lengths of 55 minutes, that costs £415,800 per term — nearly £1.25 million per academic year. Budget pressure means only 40% of lectures are currently captioned, leaving 2,520 lectures per term without accessible alternatives.

Cloud-based speech-to-text APIs reduce cost but require sending lecture audio — which may contain student voices, Q&A discussions, and references to identifiable individuals — to external servers. The university’s data protection officer has flagged this as a GDPR compliance risk, particularly for recordings from counselling, health, and law programmes where sensitive topics arise.

AI Solution: Whisper-Based Transcription Pipeline

OpenAI’s Whisper model, running self-hosted on a dedicated GPU server, delivers transcription accuracy that rivals professional human captioners — particularly Whisper Large V3, which achieves word error rates below 5% on clear lecture audio. The pipeline ingests lecture recordings from the university’s Panopto or Mediasite platform, transcribes the audio, generates timestamped SRT/VTT subtitle files, and pushes them back to the lecture capture system within 30 minutes of upload.

The system handles the full range of lecture audio conditions: single-speaker lectures, panel discussions, Q&A sessions with distant microphones, and lectures with heavy domain-specific terminology (medical, legal, engineering). Fine-tuning Whisper on a sample of manually transcribed lectures from each faculty improves accuracy on discipline-specific vocabulary.

GPU Requirements

Whisper Large V3 requires approximately 6 GB of VRAM and processes audio at varying speeds depending on GPU compute capability. The university needs to process 4,200 lectures (each ~55 minutes) within the term window, ideally within days of recording rather than weeks.

A single RTX 6000 Pro processes the full term’s lectures in under 9 days of continuous processing. Running two GPUs cuts this to 4.5 days, easily keeping pace with weekly recording volumes. All processing stays on UK infrastructure through private AI hosting.

Recommended Stack

• Whisper Large V3 via faster-whisper (CTranslate2 backend) for optimised transcription throughput.
• WhisperX for word-level timestamp alignment, producing accurate subtitle timing.
• FFmpeg for audio extraction and preprocessing from video recordings.
• Celery with Redis for managing the transcription job queue, handling concurrent uploads.
• LTI integration with the university’s VLE (Moodle, Canvas) for automated workflow.

For generating lecture summaries and searchable indexes, add an LLM via vLLM to produce chapter markers and topic summaries from transcriptions. Integrate document AI to process lecture slide PDFs alongside audio for enriched, multimodal study resources.

Cost Analysis

Outsourced captioning at £1.80 per minute costs £1.25 million per academic year for full coverage. Cloud speech-to-text APIs cost approximately £0.02 per minute, totalling £27,700 annually — far cheaper, but with GDPR concerns. Self-hosting Whisper on a dedicated GPU eliminates per-minute charges entirely, providing unlimited transcription capacity at a fixed monthly server cost that represents a fraction of even the API pricing.

The savings are transformative for accessibility coverage. Instead of captioning 40% of lectures, the university can now caption 100% — meeting its legal obligations fully and providing all 25,000 students (not just those with declared needs) with searchable, text-based lecture content that enhances revision and exam preparation.

Getting Started

Upload 200 sample lectures spanning all faculties to test base Whisper accuracy. Evaluate word error rates using a small set of manually verified transcriptions. If domain-specific terms (medical nomenclature, legal citations, engineering formulae) cause consistent errors, fine-tune Whisper on 50 hours of faculty-specific audio with corrected transcriptions. Integrate with your lecture capture platform’s API for automated processing.

GigaGPU provides UK-based dedicated GPU servers optimised for transcription workloads with Whisper pre-configured. Add an AI chatbot for student queries against lecture transcription archives.