Newsletter-to-Podcast Auto-Generation Pipeline
Automatically convert your written newsletter into a listenable podcast using TTS and structured audio logic
This pipeline reuses the logic that already drives a newsletter — what triggers a send, what content qualifies, how far back to look for missed items — and adds a text-to-speech layer that converts each qualifying issue into a podcast episode. The method goes well beyond simple TTS playback: it assigns voice roles (primary voices per article, secondary voices for block quotes), handles formatting edge cases (code snippets, subheads, hyperlinks), normalizes audio volume across segments, and inserts chapter markers. The result is an automatically published podcast that meets basic production quality standards with no human recording time per episode.
- Reuse existing publication logic rather than building a parallel system from scratch
- Deterministic TTS engines are more reliable than LLM-based voice generation for verbatim reading
- Voice differentiation (body vs quote vs article rotation) compensates for the absence of a human narrator
- Every text formatting convention needs a corresponding audio convention
- Volume normalization is non-negotiable for a listenable product
- Test with real audience members before public launch
- Start with your existing newsletter publication logic as the code baseIdentify the script or rules that currently govern when a newsletter is sent — what content qualifies, how far back to look for missed items, and what gets included. Use this as the foundation for the podcast script so both products stay in sync.Pro tipIf you do not have a newsletter script, document your manual publication decisions first. The podcast pipeline inherits whatever logic your newsletter uses.WarningBuilding the podcast as a completely independent system creates a maintenance burden and drift between what the newsletter and podcast include.
- Choose a deterministic TTS API that reads all words exactly as givenSelect a TTS engine from a provider like OpenAI that uses rule-based speech generation rather than an LLM-based approach. LLM-based TTS voices may rephrase, skip, or paraphrase content unpredictably. Determinism is non-negotiable for verbatim publishing.Pro tipThe older OpenAI TTS API (non-GPT-4o) is deterministic and reliable for word-for-word reading. Its voices are slightly less natural but will read every word you provide.WarningTest any TTS API candidate with edge-case content — terminal commands, unusual punctuation, block quotes, footnotes — before committing to it.
- Assign voice roles for different content typesChoose at least two high-quality voices: use primary voices for body text, rotating them between articles (e.g., alternating male and female voices per story), and assign one or more distinct secondary voices exclusively for block quotes so in-line citations are audibly differentiated from author voice.Pro tipPick secondary voices for block quotes from your provider's next tier — slightly less natural voices are fine for shorter quote segments and the contrast still works.
- Build audio handling rules for every text formatting typeAudit your newsletter for every formatting convention — hyperlinks, code blocks, subheadings, footnotes, inline images — and define an explicit audio rule for each: strip hyperlinks, read footnotes in place with 'begin footnote' and 'end footnote' markers, map code symbols to spoken words (backslash becomes 'backslash'), insert silence padding before and after subheadings.Pro tipUse a code mode flag: when content is in code font, switch to a symbol-to-word mapping table rather than standard TTS. The result sounds like a human reading a terminal command aloud.WarningHyperlinks read aloud as URLs break listening flow completely. Strip them or substitute the domain name only.
- Break content into small text segments and normalize audio volumeSplit the text into small chunks (a few paragraphs each) to stay within TTS API payload limits. After generating audio for each chunk, run a volume normalization pass across all chunks so loud and quiet segments play back at a consistent level.WarningSkipping volume normalization produces a jarring listening experience — different paragraphs at wildly different volumes — that will cause listeners to abandon the episode.
- Stitch audio segments with chapter markers and silence gapsConcatenate the normalized audio chunks in article order, inserting chapter markers at each article boundary and adding silence gaps of appropriate length between sections so the episode feels structured rather than a continuous stream.Pro tipListen to the stitched output at 1.5x speed — the way many podcast listeners consume audio. Problems with pacing and gaps are more obvious at accelerated speed.
- Test prototype episodes with real audience members before launchShare two or three prototype episodes with a small group of existing subscribers and ask specifically whether the audio is listenable at normal podcast consumption speed. Gather feedback on voice quality, pacing, and edge-case failures before committing to a public feed.Pro tipAsk testers to listen during a normal podcast context — commute, workout, household tasks — not at a desk. The use case is ambient listening, not careful reading.WarningDo not skip audience testing. Edge cases that seem minor in text (unusual formatting, long code snippets) can make entire episodes unlistenable.
- Automate publishing and build an edge-case monitoring processDeploy the pipeline on the same schedule as your newsletter and configure it to post automatically to your podcast feed. Establish a lightweight monitoring process — spot-checking recent episodes and tracking listener feedback — to catch new edge cases as your content evolves.Pro tipLog every episode the pipeline produces along with any error states. When a listener reports a bad episode, the log tells you exactly which content triggered the issue.
Jason Snell built a Python script that mirrors the trigger logic of his Six Colors member newsletter — only firing when substantive content qualifies — and routes qualifying issues through the OpenAI deterministic TTS API. He assigns alternating male and female primary voices per article, uses three secondary voices on rotation for block quotes, strips hyperlinks, reads footnotes in place with verbal markers, maps code-font content to a spoken-symbol mode, normalizes volume across all chunks, and adds chapter markers between articles. The podcast publishes automatically at newsletter time with no manual recording.
When a contributor published a how-to article containing terminal commands, the initial pipeline read the code blocks as prose — producing incomprehensible output. Jason worked with an AI coding assistant to define a code mode: when content is wrapped in code font tags, the TTS segment switches to a symbol-to-word mapping table so 'backslash period' is spoken as 'backslash period' rather than interpreted as punctuation.
The initial plan was to clone the actual voices of Six Colors contributors using ElevenLabs and have the podcast sound like the authors reading their own work. After testing, the cloned voices sounded uncanny — recognizable enough to be unsettling but inaccurate enough to undermine trust. Jason rejected cloning entirely and chose high-quality synthetic voices instead, deliberately selecting a female voice as the lead to make clear the audio is a produced artifact, not an impersonation.
Extracted from Mac Power Users, developed by Jason Snell for his Six Colors membership newsletter over several months of iteration with AI coding assistance.