Fish Audio S2

Product Hunt

Real Expressive AI Voices

Fish Audio is the most expressive and emotionally rich text-to-speech model. It generates lifelike voices that capture emotion, rhythm, and nuance with remarkable realism. Fish Audio Voice Clone recreates a natural voice from just 10 seconds of audio—preserving accent, tone, and speaking habits. Proudly built by the open-source team behind So-VITS-SVC and Bert-VITS2, giving a soul to every voice.

Visit product View on Product HuntMar 10, 2026Open Source Artificial Intelligence GitHub

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Actual performance

328upvotes

63comments

Made the leaderboard

Traction signals

Makers1

Launch Intel predictions

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

BetaListSuited for BetaList early-adopters · Strong signals: audio · Missing: web3, chat, crypto

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

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

31%31% 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

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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