From research paper to podcast: how my automated pipeline works
The short answer
My pipeline connects paper selection, text preparation, speech synthesis and RSS publishing. It produces stored outputs in separate stages. For me, its value is making AI research more accessible and gaining practical experience integrating models into a complete workflow.
Why I built the pipeline
AI research moves faster than I can read every interesting paper in detail. That problem led to a personal learning and development project: turn selected publications into a conversation between two AI hosts and make it available as a podcast.
The result is Weekly AI Paper Spotlight. A newsletter and Substack complement the podcast. This is my own project; I do not infer client outcomes or measured productivity gains from it.
Weekly AI Paper Spotlight · Spotify · Dietrich AI · Substack
The implemented workflow
The technical foundation consists of separate steps and stored intermediate outputs:
- Selection: a weekly overview from Hugging Face or a previously prepared paper list supplies candidates. One selected entry is processed further.
- Source: a PDF source for the paper is resolved, downloaded and cached.
- Script: the content is prepared as a dialogue between two speakers. A structural check examines the transcript.
- Audio: text-to-speech produces the episode. The transcript, audio file and metadata are separate outputs.
- Publishing: episode metadata is used to build an RSS feed pointing to the audio files. Podcast platforms can retrieve this feed.
Why intermediate outputs matter
A media pipeline has several potential failure points: the PDF source is unavailable, the script is incomplete or speech synthesis fails. If only a finished audio file remains, it is difficult to tell where something went wrong.
The project therefore brings together the paper reference, transcript, audio and duration in episode metadata. This separation makes failures easier to understand. It does not promise that every stage can be rerun automatically without additional work.
What the technical checks establish — and what remains open
The generator contains checks for transcript structure and episode duration. These help detect some incomplete or formally unsuitable outputs. They do not establish whether an explanation accurately represents a paper’s methods and findings.
For a subject-matter review, I would compare key statements with the paper: has a correlation become a causal claim? Have the study’s limitations been omitted? Are numbers and the claimed scope correct? These are recommended editorial checks, not a claimed approval process for every episode produced so far.
What transfers to other AI projects
The transferable part is the clear division of work: obtain sources, process content, check results and publish. Model-assisted steps sit alongside conventional software logic such as file management, error handling and scheduling.
For a business application, I would additionally define clear content ownership, targeted subject-matter approval and verifiable retry rules. The scope depends on the consequences of an incorrect result. A listenable podcast is not evidence of an accurate interpretation.
Limits and scope
Community popularity is not a mark of scientific quality. AI-generated explanations can be wrong or incomplete and do not replace the original paper.
This description is based on my project and a review of the existing generator and feed code. No new episode was generated for this article, and no scientific review of existing episodes is claimed.