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Excited to share ZONOS2 updates: • Demo showcasing unique features: expressive speech, multilingual output, and mid-sentence code-switching • Updated local inference code • Full tech report • New pricing and higher concurrency Try ZONOS2 now on 🧵

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Zyphra

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18,847 просмотров • 25 дней назад •via X (Twitter)

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We have officially launched Fun-ASR1.5, a major update to our end-to-end speech recognition model. This release focuses on three core pillars: broader language coverage, language switching, and production-ready text output. Key Features: • Multilingual Support: Supports high-accuracy recognition for 30 languages across Asia, Europe, and the Middle East within a single model. • Language Switching: Handles mixed-language speech (Code-Switching) natively, automatically detecting and transcribing language shifts without the need for manual tagging. • Professional Text Output: Delivers "ready-to-use" text with smart punctuation and automatic formatting for dates, numbers, and currencies. Fun-ASR1.5 bridges the gap between raw audio and professional documentation, providing a reliable engine for global communication.
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We have officially launched Fun-ASR1.5, a major update to our end-to-end speech recognition model. This release focuses on three core pillars: broader language coverage, language switching, and production-ready text output. Key Features: • Multilingual Support: Supports high-accuracy recognition for 30 languages across Asia, Europe, and the Middle East within a single model. • Language Switching: Handles mixed-language speech (Code-Switching) natively, automatically detecting and transcribing language shifts without the need for manual tagging. • Professional Text Output: Delivers "ready-to-use" text with smart punctuation and automatic formatting for dates, numbers, and currencies. Fun-ASR1.5 bridges the gap between raw audio and professional documentation, providing a reliable engine for global communication.

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Many people have asked me which library I use for my simulations, and I didn't have an answer until now. Excited to share that I'm releasing algovivo, a JS + WASM library to simulate soft-bodied virtual creatures. demo: code: 🧵

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Excited to introduce Fish Speech 1.4 - now open-source and more powerful than ever! 🎉 Our mission is to make cutting-edge voice tech accessible to everyone. What's new: - Trained on 700k hours of multilingual data (up from 200k) - Now supports 8 languages: English, Chinese, German, Japanese, French, Spanish, Korean, and Arabic - Fully open-source, empowering developers and researchers worldwide Key features: - Lightning-fast TTS with ultra-low latency - Instant voice cloning - Self-host or use our cloud service - Simple, flat-rate pricing Try it out: - Playground: - GitHub: - HuggingFace Model: - Demo: - Product Hunt: We can't wait to see what you'll create with Fish Audio. Happy voice building! 🎧🐠
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StealthSDK has just implemented a game changing feature! Powered by SOTA reasoning models, you can now use Stealth to generate dynamic Lua snippets which are executed at runtime. This means our AI agents now have full control over the Roblox game code and players can even recreate entire Roblox games without writing a single line of code. The possibilities this feature brings are endless, and we are excited to see what you build with it. See our demo showcasing this functionality 👇

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Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from Joelle Pineau. This work is another important step towards our goal of achieving Advanced Machine Intelligence (AMI). What we’re releasing: • Meta Spirit LM: An open source language model for seamless speech and text integration. • Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. Plus a new developer suite to make it easier for developers to build with SAM 2. • Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance. • SALSA: New code to enable researchers to benchmark AI-based attacks in support of validating security for post-quantum cryptography. • Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale. • Meta Open Materials: New open source models and the largest dataset of its kind to accelerate AI-driven discovery of new inorganic materials. • MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder with coverage across 80 languages. • Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations. Access to state-of-the-art AI creates opportunities for everyone. We’re excited to share this work and look forward to seeing the community innovation that results from it. Details and access to everything released by FAIR today ➡️

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I’ve been exploring Gemini 2.0’s new native audio output capability, which is available for early testers. I’m a developer at Google Creative Lab, and wanted to share one of my favorite experiments so far called ✨ VoiceCursor (🔊 sound on for video) Unlike traditional TTS, native audio lets you prompt the model with expressive styles, ie “Say this like a disgruntled pirate…” So I made ✨VoiceCursor… it lets you rapidly try different prompts. Just type, highlight your phrase, then hear it spoken in different ways! My code is open-sourced here: Here’s a thread 🧵

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I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

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I'm building a Figma plugin to generate design system variables/tokens 🛠️ After sharing the first demo, it grew too big, making it hard to manage and add new features. So, I restarted the project and cleaned up the code to make it easier to handle. Here’s detailed demo - It generate variable from preset and all alow to customize. Now, Just working on UI before final release I’ve already shared test version, who signed up. Let' me know if you wanna test.

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"I am a Claude Code maximalist. I try to live in it all day if I can." Carl Vellotti 🥞 has gone deeper on Claude Code than any PM I know. In my new episode, he showed me how to connect Claude Code to: → Google Workspace: Prep for meetings → Linear: Create tickets from PRDs → Slack: Share status updates → Reddit: Monitor subreddit discussions He does all of the above using skills and MCPs in his personal Claude Code OS. Some quotes from Carl: "Connecting Claude Code to the apps that you use at work makes it way more powerful." "Why would you need to go to all these websites if you can just get the info right here?" "I use this consult-the-council skill to get Claude, ChatGPT, and Grok to all review my work in one shot." 📌 Watch now: Thanks to our sponsors: Granola - The best AI meeting notes app I've ever used: Replit ⠕ - Create beautiful prototypes and full stack apps:

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ANTHROPIC 🔥: Early look at Claude Science, a new app designed for scientific research. > "Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect." Claude Science was named "Operon" during development and is now being distributed as a desktop app. Interestingly, the app acts as a local web server, and its UI runs in your browser. Some unique features include the ability to create dedicated "Categories" in Memory and custom agents called "Specialists". We need this on consumer version too 👀

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INVITE YOUR FRIENDS TO FOVUE You can now invite your friends to Find your invite code on FOVUE app. You can share with 10 friends. Choose wisely. Every referral matters. We're excited to announce our first public beta! Test our platform's premium features for free. Simply visit our site, fill out the form with your connected wallet address, and waiting to be selected!

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Introducing Lightning V3 - it beats every model we tested against. ElevenLabs, Cartesia, OpenAI. Lightning sets a new SOTA with V3 in conversational text-to-speech. → Highest MOS score for conversational TTS at 3.9 → ~76% win rate vs gpt-4o-mini-tts on naturalness → 15 languages with mid-sentence code-switching → Built from scratch for voice agents, not read-aloud Every TTS model sounds clean in a demo. You type a sentence and you get beautiful audio. Voice agents don't work that way. They stream. They're generating audio in real-time chunks with half the context missing. That's where everything breaks. A great reading voice and a great conversational voice are fundamentally different things. A conversational voice has to sound like it's thinking - with the pauses, the rhythm shifts, the reactions. It has to handle the way real people actually talk, including switching languages mid-sentence. That's what V3 does. V3.1 also ships voice cloning. 5 to 15 seconds of audio, no fine-tuning, production-grade clone across 15 languages. Blog link in the comments.

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