u/nestlyze

Been playing with on-device multilingual speech-to-text and hit something that seems unavoidable: dictate Korean or Japanese with English tech names in the sentence, and the model spells them out phonetically in the local script.

GitHub becomes 기터부 / ギットハブ. Docker becomes 도커 / ドッカー. React, Kafka, Postgres, all of it.

Makes sense, it's writing the sounds it hears, but it means any code-switched dictation needs a cleanup pass to turn those back into the real names. What I ended up doing was a correction dictionary keyed on the phonetic spelling. For Japanese I had to collapse the spaces it inserts mid-word first (ギット ハブ into ギットハブ) before matching. Korean was trickier because it uses real word spaces, so stripping them would fuse actual words.

Curious what everyone else does. Fine-tune on code-switched audio? Contextual/hotword biasing at decode time? A post-hoc dictionary like I did? Or just live with the phonetic version and read past it?

VocalCode is push-to-talk dictation aimed at people who talk to AI coding agents. Recognition runs entirely on-device (CPU). It ships a bilingual Chinese-English model plus Parakeet TDT v3 covering: Spanish, French, German, Italian, Portuguese, Dutch, Polish, Russian, Ukrainian, Czech, Romanian, Greek, Hungarian, Swedish, Danish, Norwegian, Finnish, Slovak, Croatian, Bulgarian, Lithuanian, Latvian, Estonian, Maltese.

I can only verify Chinese and English myself, so every other language is marked "preview" until a native speaker actually tests it. That is the test:

• ~10 minutes: 5 everyday sentences, 5 sentences mixing your language with English tech terms, 1 minute free speech
• Report what you said vs what it typed, plus your honest verdict
• Every completed test earns a permanent license - negative verdicts included. The first tester's "not usable yet" produced two same-day bug fixes; that's exactly what the program is for.

Requirements: native or daily-fluent speaker, Windows 10/11 or Apple Silicon Mac. The app has a 30-day full trial (no card), so testing costs nothing either way.

Full instructions: github.com/wudaming00/vocalcode-docs/blob/main/TESTERS.md

What it is: hold a key, describe a task, release - punctuated text lands in whatever has focus (terminal, editor, any field). A second binding sends it. Built for prompting coding agents: a good prompt is 4-6 sentences and speaking is ~3x faster than typing, so prompts stop getting trimmed.

Stack: Rust + sherpa-onnx. Speech recognition runs entirely on your own CPU - Parakeet TDT v3 for 25 European languages, a dedicated bilingual Chinese-English Paraformer model for code-switched speech. Works offline; no audio leaves the machine.

Details that took the longest: spelled acronyms collapsing ("m c p" -> MCP), a user dictionary for project jargon, and text insertion across OSes - Windows silently stops delivering low-level keyboard hooks when a Chromium window is focused, which cost a week.

Model: USD 4.99 one-time, 30-day full trial, no card, no account. Windows 10/11 + Apple Silicon macOS. I run a native-tester program: complete a 10-minute test in your language and you get a permanent license regardless of your verdict - the first Spanish tester's negative report produced two same-day bug fixes.

Site: vocalcode.app - and I keep honest comparison pages that tell Mac-only English speakers to try the free open-source options first.

I launched VocalCode (push-to-talk dictation for talking to AI coding agents - recognition runs on your own CPU) and spent the week promoting it everywhere I could reach. Sharing the unpolished numbers because I wished more launch retros did.

The numbers (first week)

• 975 licensing-service calls over 30 days, +213% the day after the first Reddit posts - the only marketing metric I fully trust, because every fresh install phones home exactly once for its trial receipt
• Single-digit sales. Week one, USD 4.99, not pretending otherwise
• 31 hand-researched cold emails to bloggers -> replies within hours, including one from a developer whose blog I'd read for years
• 13 honest comparison pages published against every competitor I could name, including the free ones that are genuinely good
• 5 permanent licenses given away to people who sent substantive feedback - two of their bug reports got fixed the same day

What got eaten (useful data)

Four posts were removed by Reddit's SITE-WIDE spam filter (not mods) across different subs - new domain + new-ish account = you are on thin ice everywhere. Check your posts logged-out, appeal politely by modmail, and put your instructions on GitHub because some users' networks flag small new domains.

What actually worked

1. Conceding first. My most-upvoted answer to "why not use the free alternative" started with "if you're Mac-only and English-only, use the free one." Trust converts better than pitching.
2. Giving licenses for feedback instead of discounts for hype. The feedback fixed real bugs; a discount would have bought nothing.
3. Answering every single comment with substance. The threads with real conversations are the ones that kept sending traffic.

Site is vocalcode.app if you want to see the comparison-page approach. Happy to share anything else - numbers, the exact appeal templates, whatever helps.