Automate Podcast Show Notes with AI on GPU

What You’ll Build

In about 45 minutes, you will have a show notes pipeline that takes a raw podcast audio file, transcribes it with speaker diarisation, extracts key discussion topics with timestamps, generates a structured summary, pulls out notable quotes, and formats everything into publish-ready show notes. A one-hour episode processes in under four minutes on a dedicated GPU server, and your audio never leaves your infrastructure.

Podcast producers spend 2-3 hours per episode writing show notes manually — listening back, noting timestamps, and drafting summaries. For daily or multi-show networks, this becomes a full-time role. GPU-accelerated transcription with Whisper and summarisation with open-source LLMs eliminates the bottleneck while producing more thorough notes than manual efforts.

Architecture Overview

The pipeline has three stages: transcription, analysis, and formatting. Whisper large-v3 transcribes the audio with word-level timestamps and speaker diarisation, producing a full transcript tagged by speaker and time. An LLM through vLLM analyses the transcript to identify topic segments, extract key points from each segment, select notable quotes, and generate an episode summary.

The formatting stage assembles structured show notes: episode title suggestion, summary paragraph, timestamped topic list, guest bio extraction, mentioned resources and links, and a selection of quotable moments. Output formats include HTML for your website, markdown for your CMS, and JSON for programmatic publishing.

GPU Requirements

Whisper large-v3 uses roughly 10GB VRAM, leaving room for the LLM on the same card. For podcast networks processing multiple episodes daily, the larger card handles concurrent transcription and summarisation. See our self-hosted LLM guide for model pairing recommendations.

Step-by-Step Build

Deploy Whisper and vLLM on your GPU server. Set up an ingestion endpoint via your API layer that accepts audio uploads or RSS feed URLs. Build the transcription-to-notes pipeline.

import whisper, json

# Stage 1: Transcribe with timestamps
model = whisper.load_model("large-v3", device="cuda")
result = model.transcribe("episode.mp3", word_timestamps=True)

# Stage 2: LLM analysis prompt
SHOWNOTES_PROMPT = """Analyse this podcast transcript and generate show notes.
Transcript with timestamps:
{transcript}

Return JSON:
{title_suggestion: string,
 summary: "2-3 paragraph episode summary",
 topics: [{timestamp: "MM:SS", title: string, key_points: [string]}],
 guest_info: {name, title, organisation},
 quotes: [{timestamp: "MM:SS", speaker: string, text: string}],
 mentioned_resources: [{name, url_hint}],
 tags: [string]}"""

# Stage 3: Format output
def format_html(notes_json):
    html = f"
{notes_json['title_suggestion']}
"
    html += f"
{notes_json['summary']}
"
    html += "
Topics
"
    for t in notes_json["topics"]:
        html += f"
{t['timestamp']} - {t['title']}
"
    html += "
"
    return html

For multi-speaker episodes, add a diarisation step before LLM analysis so the model can attribute quotes accurately. The vLLM production guide covers batch processing configuration for networks publishing multiple episodes per day.

Scaling for Podcast Networks

A single GPU server handles 20-30 episodes per day with room to spare. For larger networks, batch overnight processing queues episodes as they arrive and delivers show notes by morning. Integrate with your publishing CMS to auto-draft posts that editors review and publish.

Extend the pipeline with text-to-speech to generate audio summaries or teaser clips from the extracted quotes. Pair with social media formatting to produce tweet threads, LinkedIn posts, and newsletter snippets from a single episode analysis. The same infrastructure powers transcription for video content with minimal changes.

Deploy Your Show Notes Pipeline

Automated show notes save hours per episode while producing more detailed, timestamped notes than manual efforts. Process all audio on your own hardware with zero per-minute transcription fees. Launch on GigaGPU dedicated GPU hosting and streamline your podcast production. Explore more automation use cases and integration tutorials in our library.