Free Text-to-Speech Tool – No Signup, 40 Languages

Hacker News

I built a simple text-to-speech converter at texttospeech.site Free tier: 10 generations/day, standard voices, no account needed. Pro tier: Neural2 voices, 2000 chars, downloadable MP3s. Stack: Next.js, Google Cloud TTS API, Vercel. The $2 domain was an SEO experiment after my speechtotext.xyz satellite drove 22% of traffic to my main product. Curious if exact-match keyword domains still work for TTS searches. Feedback welcome — especially on voice quality and UX.

Visit product View on Hacker NewsJan 31, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: google · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

30%30% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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