Notes & feedback

Jake

The bigger problem isn't really the format — it's that you're working across three different environments and manually moving files between them. That's the part that should go away.

We need to crack open your sanitize skill: if it's hardcoded to a specific output folder, we pull that out — either let it save where you're working, or have it ask.

Better yet: collapse the whole thing into one workflow. Hand Claude Code a raw transcript and have it sanitize, convert format, and generate audio in one step. No file shuffling.

The actual format answer: there's really only one format this project knows — [hh:mm:ss] Speaker: text. "Studio format" comes from a different ElevenLabs product. They exist because they serve different tools.

The parser uses speaker name + text body. Continuation lines fold into the previous segment. The timestamp itself is ignored — output uses fixed gap rules (250ms / 500ms), not your numbers. So timestamp accuracy doesn't matter.

Jake

Quick rundown:

• v3 — our expressive model. Good emotional range, less natural in long form.
• v4 — newest model (not publicly released). Same architecture as v3, retrained to sound more natural. This is why your output sounded so good.
• v4_hq — same as v4 but higher quality output. Bigger files. Worth it for finished deliverables, not for iteration.

The samples page runs the same interview through all the models — listen to it. That's the best way to internalize the difference.

Voice settings being silently ignored on v4 — yeah, mostly. You can simplify voices.config.json to the short string format. No harm leaving them in either.

And to your meta-question: yes, you should have experimented. Always experiment. That's the answer to a lot of these.

String vs object voice config: think of it like ordering coffee. Short form ("Tanner": "voice-id") is "I'll have a coffee" — default everything. Long form ({ voiceId, voiceSettings }) is "medium roast, oat milk, two sugars."

On v4 those settings mostly don't do anything — the model decides. On v3 they matter. For v4, short form is cleaner. And really, you shouldn't be hand-editing this file anyway — tell Claude what voices and model you want, and it picks the right form.

Jake

You shouldn't be doing any of this find-and-replace by hand. That's exactly the kind of thing Claude should swap on the fly.

This belongs in the workflow from #1. When we build (or rebuild) the skill, it can:

• Ask upfront: "What names should the speakers be?"
• Auto-detect from the transcript and confirm with you
• Read names from a config — your choice

If the script has speaker names hardcoded anywhere, that's a bug we just fix. Manual conversion is never the answer when Claude can do it deterministically.

Jake

There isn't really a science. It's mostly:

• Some voices are optimized for expressiveness, some aren't — depends on how the voice was created
• Some voices pair better with older models, some with newer
• The voice library has descriptions worth reading ("works better than X for Y")

Trial and error is the move. Your two voices worked — the contrast between monotone interviewer and enthusiastic interviewee was a smart choice. If you want to level up, A/B a couple of alternates next time and pick the one that lands better.

Jake

The page Claude built you is fine — it got the job done. But "good enough" and "polished" are different bars, and next session I'll show you how to make polished pages pretty easily.

Important reframe: GitHub isn't really a hosting platform. It's a great place to store code and an okay intermediary, but for actually hosting and showing off, we use Vercel. That's the production move.

GitHub Pages works for a basic landing page like our index.html, but it's not where polished projects live. Next session we'll cover Vercel and the accessibility/performance side of things.

Jake

This is the biggest gap, and it's not a "you" problem — it's that your computer doesn't have a workflow or organization system in place yet. Files land wherever they land. There's no clear pattern.

I want to make this the centerpiece of next session:

• Consolidate what's on your machine into a sane structure
• Set habits for spinning up new projects so this doesn't sprawl again
• Deep-dive on infrastructure — local vs GitHub vs hosting
• Build the mental model — what's in git, what isn't, what gets deployed

The fact that you can articulate the confusion this clearly tells me you're already 80% there — you just need the framework hung on it.

While we're here, one concrete answer: the out/ folder is gitignored generally, but .gitignore has explicit exceptions for your three training clips so Pages can serve them. New clips would need the same exception (Claude handles that — just say "add this clip to the gitignore exception list"). Everything else stays local.

Jake

The reason it didn't ask is that I hardcoded v4 into the script. Not a Claude thing — a me thing. You can always ask Claude to swap it, or change it yourself.

Important detail buried in your notes: the 1,500 character limit you hit is a dialogue-mode constraint. Dialogue mode sends everything in one API call. Segment mode generates each speaker's lines separately and stitches them — no character limit, sounds better in testing.

You were in dialogue mode by accident — that's why you had to manually split that 2,268-char block. Segment mode would've absorbed it.

On the three files: using -elevenlabs-upload.txt was correct. The full picture needs us to look at the skill together. Parking-lotted.

Pulling on the framing note above: you don't need to memorize that v4 is hardcoded, or the dialogue character limit, or which sanitize file maps to what. Know what you want — clean two-speaker audio, this long, this voice — and tell Claude. If you hit a limit, Claude swaps modes. If a default is wrong, Claude changes it. Your job is knowing what good looks like. Claude's job is wiring up the path.

Jake

You're spotting a real future problem. Three candidates sounding identical is fine for three clips, less fine when there are thirty.

Yes — use different voice IDs per candidate. My suggestion:

• Pick 2–3 good male voices and 2–3 good female voices you like
• Rotate them across candidates so each interview has a distinct sound
• Voice differentiation matters more than name differentiation for audio listeners

Practically, Claude will create a different voice config per interview (or one config that maps candidate → voice). That's the fun part — when scale changes, the structure changes with it. You can do basically anything you can imagine here.