Podcast Content Pipeline
End-to-end AI pipeline that transforms podcast audio into clips, show notes, trailers, and thumbnails. Processed 70 episodes with 283 clips identified.
TL;DR: TL;DR: I built an end-to-end AI pipeline that transforms raw podcast audio into publishable content: transcriptions, clips, show notes, trailers, and thumbnails. Processed 70 episodes with 283 clips identified and 6,049 semantic segments indexed for RAG search.
The Problem
Running a podcast means creating one long-form piece of content that needs to be repurposed across multiple platforms. For each episode of Funds & Founders, I needed to:
- Transcribe the full episode with speaker attribution
- Identify 3-5 compelling clips (4-10 minutes each) for social media
- Write detailed show notes with timestamps
- Generate promotional trailers
- Create eye-catching thumbnails
Doing this manually took 4-6 hours per episode. With 70+ episodes, that's 280-420 hours of content work alone.
My Approach
I built a modular pipeline where each stage feeds into the next:
- Transcription Layer: AssemblyAI processes the audio and returns speaker-diarized text
- Clip Detection: Gemini analyzes the transcript to find compelling segments (actionable advice, emotional stories, contrarian insights)
- Content Generation: Gemini generates show notes, metadata, and descriptions
- Video Processing: FFmpeg extracts clips at precise timestamps
- RAG System: Content is chunked and indexed in Gemini File Search for semantic querying
The key insight was using composite clips - segments that combine multiple non-contiguous parts of the episode into a single coherent narrative.
Architecture
Podcast Content Pipeline - Architecture Diagram
Key Features
- Multi-type Clip Detection: Identifies actionable advice, emotional stories, and contrarian insights
- Composite Clips: Combines non-contiguous segments into cohesive narratives
- Batch Processing: Process multiple episodes in parallel
- Quality Validation: Checks transcript completeness and clip coherence
- RAG-Ready Output: All content searchable via natural language
Results & Metrics
- 70 episodes processed with 100% completion rate
- 283 clips identified (average 4.0 per episode)
- 69 episodes with complete show notes
- 6,049 semantic segments indexed for RAG search
- Processing time: ~15 minutes per episode (vs 4-6 hours manual)
- Cost: ~$2-3 per episode (API costs)
What I Learned
The biggest challenge was clip boundary detection. Initial versions would cut mid-sentence or miss the emotional peak of a story. I solved this by:
- Adding "buffer zones" of 2-3 seconds on each side of detected segments
- Using Gemini to validate that clips start and end at natural break points
- Implementing a scoring system that penalizes clips with abrupt endings
If I were starting over, I'd invest more upfront in the transcript quality layer. Bad transcriptions cascade through the entire pipeline, making every downstream task harder.
Frequently Asked Questions
What problem does the Podcast Content Pipeline solve?
It automates the labor-intensive process of repurposing podcast content. Instead of spending 4-6 hours per episode on transcription, clip identification, and show notes, the pipeline handles it in ~15 minutes with AI.
What technologies power this project?
Python for orchestration, AssemblyAI for transcription with speaker diarization, Google Gemini for content analysis and generation, FFmpeg for video processing, and Gemini File Search for RAG indexing.
How accurate is the clip detection?
The pipeline identifies clips with about 85% accuracy for "good" segments. I manually review and adjust about 15% of clips for timing or content refinement. The composite clip feature is particularly valuable for stitching together related insights from different parts of an episode.
Frequently Asked Questions
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View allBuilt by Abhinav Sinha
AI-First Product Manager who builds production-grade tools. Passionate about turning complex problems into elegant solutions using AI, automation, and modern web technologies.