Audio to Markdown for Podcasters — Show Notes & SEO

Every podcast episode you publish is a gold mine of searchable text — if you bother to transcribe it. Most shows don't, because show-note writing is a 90-minute slog per episode. Upload the audio to mdisbetter.com and you walk away with a structured Markdown transcript: speakers labelled, topic shifts auto-cut into H2 sections, timestamps inline. From that one file you ship show notes, an SEO blog post, social pull-quotes, and chapter markers — same afternoon the episode drops.

Why this is hard without the right tool

Manual show notes take hours per episode
Audio content is invisible to Google search
Can't easily repurpose audio as text
No SEO traffic from podcast episodes

Recommended workflow

Open /convert/audio-to-markdown in your browser
Upload the episode's MP3 or WAV (or whatever your DAW exports)
Click Convert — wait a few minutes depending on episode length
Download the .md file: **Host:**/**Guest:** labels, ## Topic H2s, [12:34] timestamps
Paste into your CMS for the show-notes page; pipe the same Markdown to ChatGPT/Claude with "draft a 600-word blog post" and "extract 5 tweet-length quotes"
Re-run for each new episode — the workflow stays under 10 minutes of human time per drop

Why structured Markdown beats a flat text transcript

Tools like Otter and Descript give you a wall of words. That's fine for ctrl-F search, useless for show notes. The H2-per-topic structure mdisbetter outputs is what makes the file directly usable: each H2 maps to a chapter, each chapter maps to a paragraph in your show notes, each chapter maps to a timestamp marker in your podcast host. One conversion, four downstream artefacts.

SEO play: index every episode

Google doesn't index audio. It indexes the transcript page you publish next to the audio. Episodes published with full Markdown transcripts (rendered as HTML on your site) pull long-tail search traffic for years — every guest name, product name, and topic mentioned becomes a potential ranking term. Compare to publishing audio-only, where the only indexable surface is your title and 200-word summary.

Repurposing pipeline

From one Markdown transcript: (1) show-notes page on your podcast site, (2) a 600-1000 word blog post derived by AI from the structured transcript, (3) 5-10 tweet-length pull-quotes for social, (4) a YouTube description with chapter timestamps if you also publish video. All four come from the same source file, generated in under an hour of editorial time per episode.

For batch podcast back-catalogues, go OSS

If you have 200 back-catalogue episodes to transcribe in one go, mdisbetter's web UI is the wrong tool — it's one-upload-at-a-time. Run openai-whisper or faster-whisper locally on your archive (free, runs on CPU or GPU, MIT-licensed). Use mdisbetter's web tool for every new episode going forward where you want clean structured output without setting up a local pipeline.

Frequently asked questions

How do I get show notes from a one-hour episode?

Upload the MP3 to <a href="/convert/audio-to-markdown">/convert/audio-to-markdown</a>, wait a few minutes, download the structured Markdown. Each H2 in the output is roughly a topic shift — that's your show-notes outline. Either publish the full transcript with topic anchors, or paste the Markdown into ChatGPT with "summarise each H2 section in one sentence and add timestamps" for a tight 200-300 word notes block. Total human time: 10-15 minutes vs the 90 minutes manual transcription used to take.

Will Google actually rank my podcast transcript pages?

Yes, when published as readable HTML alongside the audio player. Google treats the transcript as the indexable text content of the episode page — every topic discussed, every guest name, every product mentioned becomes a long-tail ranking signal. A podcast with 100 episodes and full transcripts published has 100 long-form indexable pages; an audio-only podcast has 100 thin pages with a title and summary. The SEO delta compounds over time.

Can I get YouTube chapter timestamps from the transcript?

Yes — the timestamps in the Markdown output (<code>[12:34]</code> format) map directly to YouTube's chapter format if you also publish a video version. Either copy them manually into your YouTube description as <code>0:00 Intro</code> / <code>12:34 Topic name</code>, or paste the Markdown into ChatGPT with "convert these H2 timestamps to YouTube chapter format" for an instant copy-paste-ready chapter list.

How do I handle multi-host or guest interview episodes?

Diarisation auto-labels Speaker 1 / Speaker 2 / Speaker 3 in the output. After download, find-and-replace those labels with actual names (your DAW knows who was on each track if you record multitrack — match by talking time). For two-person shows the labels are usually correct after one pass; for round-table episodes with 4+ voices, expect to clean up a few mis-attributions.

My back-catalogue has 200 episodes — what's the best path?

For one-time bulk back-fill, run <a href="https://github.com/SYSTRAN/faster-whisper">faster-whisper</a> on your local machine — it processes hundreds of hours of audio overnight on a single GPU, MIT-licensed, free. For new episodes going forward, use the mdisbetter web tool per episode for the cleaner structured Markdown output (speakers + H2s + timestamps), since you're only doing one upload per release cycle.

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