@Nuts99618279@0801_PoTaTo agreed. i don’t think tts fully captures the range of expression in real speech yet, like hesitation, emotion, or laughing mid-sentence. cleaner audio doesn’t necessarily mean more representative training data.
Even though this week feels like the GPT-6 Astra week, there were also many updates on the RealTime AI front! 🎉
On the STT side, two big companies released updates: Meta with Muse Voice Transcribe and Microsoft with MAI-Transcribe-2. Competition is heating up here! On the TTS side, Inworld’s Realtime TTS-2 went GA and topped the Artificial Analysis TTS arena. For turn detection, we have a new contender in Sesame's TurnBench, with Tavus’ Sparrow-2 taking the #1 spot.
LiveKit also dropped multiple updates this week, including Connectors as an alternative to SIP integrations, a staging environment for agents, and a new release of their Agents framework.
On the product side, Google keeps adding voice features, including additions to Google Suite and improvements to Google Translate on Android. Additionally, Tesla upgraded its voice interactions to the latest Grok model, and Genesys partnered with ElevenLabs on agentic voice for contact centers.
More news and updates are in the newsletter 👇. Have a great week!
Today marks a major step for Mistral: we’re announcing a €3B Series D, the largest equity round ever raised by a European tech company, just three years after launch.
JUST IN: NVIDIA's $12,930,300,000 acquisition of Hugging Face contains an easter egg. The number 129,303 is the decimal conversion of Unicode point U+1F917.
The 🤗 emoji.
Cutting the six hour lag down to real-time using satellite data sounds like a massive improvement. Can’t wait to try this - also would’ve been super helpful during my PPL training lol
Today, @GoogleDeepMind and @GoogleResearch are introducing WeatherNext 3, our most advanced and accurate global weather AI model to date. It uses real-time satellite data to generate hourly high-resolution forecasts, precise precipitation forecasting, and clean energy variables.
Most AI weather models are trained on data from numerical weather prediction (NWP) models, which come with a six-hour data lag. But by training on real-world observations, WeatherNext 3 is able to bypass these traditional constraints — meaning more people can get more accurate predictions to help them plan ahead.
Starting today, WeatherNext 3 will power forecasts within Search, @GeminiApp, @GoogleMaps, Google Maps Platform Weather API and Google Earth Engine.