Podcast AI: Editing & Chapters on GPU

The Challenge: 45 Episodes Per Week and a Post-Production Bottleneck

A UK-based podcast network manages 15 shows across true crime, business, technology, and culture genres, producing a combined 45 episodes per week. Each episode requires extensive post-production work: manual transcription for SEO and accessibility (90 minutes per episode), chapter marker creation (30 minutes), show notes writing (45 minutes), social media clip identification and description (60 minutes), and audiogram creation (30 minutes). With a production team of six, post-production consumes 270 hours per week — the team’s entire capacity — leaving no bandwidth for growing the network or improving production quality. The network has turned down three new show pitches because they cannot absorb additional post-production load.

Episodes feature guest interviews containing commercially sensitive pre-announcements, personal stories shared in confidence, and unreleased content. Sending raw episode audio to external transcription or AI services risks leaking content before publication — a competitive issue in the podcast industry where exclusivity drives audience growth.

AI Solution: End-to-End Podcast Post-Production Pipeline

Whisper running on a dedicated GPU server transcribes each episode with speaker diarisation, identifying who said what. The transcription feeds into an open-source LLM via vLLM that generates: chapter markers with timestamps and titles, a 200-word show notes summary, five social media post drafts with timestamps for the best quotable moments, SEO-optimised episode descriptions, and keyword tags for discoverability.

The pipeline processes a 60-minute episode end-to-end in approximately 15 minutes, running entirely on private infrastructure. The production team reviews and approves AI output rather than creating everything from scratch — a 45-minute review process versus 6 hours of manual work.

GPU Requirements

The pipeline runs Whisper for transcription and an LLM for content generation sequentially per episode. Processing 45 episodes per week with a maximum 24-hour turnaround from recording to publication-ready assets requires sufficient throughput.

An RTX 5090 processes the entire weekly output within a single overnight batch. For networks needing faster individual episode turnaround, the RTX 6000 Pro completes each episode in 11 minutes. Private AI hosting ensures pre-release content never leaves controlled infrastructure.

Recommended Stack

• Whisper Large V3 via faster-whisper for high-accuracy transcription with speaker diarisation.
• pyannote.audio for speaker identification and labelling.
• vLLM serving Mistral 7B or LLaMA 3 8B fine-tuned on the network’s existing show notes and episode metadata.
• FFmpeg for extracting audio clips at identified highlight timestamps.
• Celery job queue for automated processing as episodes are uploaded.

For networks wanting AI-generated audiograms or video clips, add a vision model for selecting the most visually engaging frames from video episodes. Integrate document AI to process guest research materials and pre-interview briefs.

Cost Analysis

Manual post-production at 6 hours per episode costs the network approximately £65 per episode in production staff time, totalling £152,100 annually for 45 weekly episodes. AI-assisted post-production reduces this to approximately £10 per episode (45 minutes of review), saving £128,700 annually. The freed production capacity enables the network to onboard three new shows without additional hiring — representing potential revenue of £180,000 per year in additional advertising and sponsorship.

Transcription quality also improves: Whisper produces more consistent, accurate transcriptions than the human transcribers the network previously used, reducing listener complaints about inaccurate show notes by an estimated 80%.

Getting Started

Upload 50 recent episodes to test base Whisper accuracy across your show formats (single-host, interview, panel discussion). Fine-tune the LLM on your existing show notes, chapter markers, and social media posts to match each show’s voice. Run the pipeline in parallel with your manual process for two weeks, comparing output quality before switching to AI-first production.

GigaGPU provides UK-based dedicated GPU servers for media production workloads. Add an AI chatbot for listener engagement, or scale infrastructure for live event coverage and rapid turnaround episodes.