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New Cartesia Text-to-Speech Model

Hacker News

New Cartesia Text-to-Speech Model

Hi HN - team behind Cartesia here! We just announced our $27M seed today ( https://techcrunch.com/2024/12/12/cartesia-claims-its-ai-is-... ) + some major product updates we are excited to share with the developer community. Our latest releases: sonic-preview - a new model building on a new architecture that includes fundamental improvements over common TTS architectures which can struggle to follow complex, repetitive transcripts – you can find a fun example here ( https://youtu.be/JTAmu8_qY4E )! Try this new model in the playground under sonic-preview and it will be available via the API over the coming weeks. Multilingual Improvements - We launched a new multilingual model with enhanced number, date, and time recognition, as well as improved volume stability. Try it out in the playground or via our API! Why Developers Are Building With Cartesia Today: - We have the lowest latency at 90ms time to first audio, enabling truly real-time conversation - We have the highest quality, ultra-realistic voices as determined by respected third-party evals like Labelbox: https://labelbox.com/guides/evaluating-leading-text-to-speec... . - We can clone voices instantly, using only 5s of speech, and don’t cap the number of voices you can create - We can fine-tune professional voice clones using ~30 min of speech - We support 15 different languages, offer emotion and speed control, localization to different accents, and voice changer capabilities. - We offer all this and more at a more affordable price than competitors due to our innovative SSM architecture perfected for voice. SSMs offer clear advantages over transformers as they scale linearly with sequence length and enable cheap, high-throughput inference. Our founding team authored the widely cited Mamba SSM paper and our team has since built our models to be highly efficient, with better long-term memory, lower latency, and the ability to run locally on any device. - If you’re an early-stage startup building with voice, check out our startup grant ( https://cartesia.ai/startup ) or YC deal ( https://deals.ycombinator.com/deals/2877 ) and qualify for 4 months on our scale tier (8M char/mo). You can try all of these features on our playground yourself: play.cartesia.ai, or if you have questions for the team or are interested in an enterprise plan with us, email support@cartesia.ai.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
94%94% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, lua, ide · Missing: https docs, just released, exist
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
47%47% 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
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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