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TTS Accents – 1000 voice combinations

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TTS Accents – 1000 voice combinations

Most TTS models today have a fixed set of voices that are tied to a fixed set of languages. We built a trained TTS model with the concept of accents like we have in real life. You can have a Chinese person speaking English or a German person speaking French. Hints of their accents from their original language can be heard when they speak another language. We took this idea and the understanding of how one-shot voice cloning works and trained a model on 100+ voices & languages, giving you 1000+ unique voice combinations. One interesting use case is customer support to have a voice sound similar to where the person is calling from, making them feel more at home! It's still pretty new, and there's more to come, but we do have users using it in production, open to any feedback and ideas :) Try it for free: https://jigsawstack.com/text-to-speech

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real life · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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