Pavel Shtykovskiy retweeted
Realtime TTS-2 is GA today, now the #1 model on Artificial Analysis and fastest TTS in the world of its class.
The research preview got far more usage than expected. That experience went back into research and today's GA model represents the biggest leap in Inworld's history.
Pavel Shtykovskiy retweeted
Many roughly know how a transformer works
To REALLY understand modern neural LMs—MoEs, GPU tiling, kernels, RLHF, data—you need CS336
By @tatsu_hashimoto, @percyliang
The 2026 edition appears on yt with ~2 weeks delay
youtube.com/playlist?list=PL…
Materials
cs336.stanford.edu/
Pavel Shtykovskiy retweeted
Introducing Realtime TTS-2, a new generation of voice model built for realtime conversation.
It is the first voice model that hears the conversation, takes natural-language voice direction, holds one voice identity across over 100 languages, and speaks like a person who is paying attention.
The result is voice AI that feels as good as it sounds.
Try it out: tinyurl.com/RealtimeAI
Learn More: tinyurl.com/TTS-2Blog
Pavel Shtykovskiy retweeted
Inworld TTS-1.5 releases today. The #1 TTS on Artificial Analysis now offers realtime latency under 250ms and optimized expression and stability for user engagement, and costs half a cent per minute.
Some voice models are fast, some are expressive, some are affordable. We outperform them all across the board.
Production-grade realtime latency: <250ms latency for Max model, <130ms for Mini (P90 first audio) - 4x faster than before. Voice agents now respond before users notice any delay.
Engagement-optimized quality: 30% more expressive to serve a wider range of personalities and 40% lower word error rates for fewer hallucinations, word cutoffs, and audio artifacts.
Built for consumer-scale: Radically affordable with enhanced multilingual support (15 languages including Hindi) and enhanced voice cloning, now via API. On-prem options now available for enterprises.
Pavel Shtykovskiy retweeted
Our TTS Max model just debuted at #1 on the @ArtificialAnlys leaderboard.
And at $10/million characters, it’s also the most cost-efficient commercial TTS model available.
Excited to keep making state-of-the-art voice more accessible.
Check it out at inworld.ai/tts or through our partners @pipecat_ai and @livekit.
Inworld TTS 1 Max is the new leader on the Artificial Analysis Speech Arena Leaderboard, surpassing MiniMax’s Speech-02 series and OpenAI’s TTS-1 series
The Artificial Analysis Speech Arena ranks leading Text to Speech models based on human preferences. In the arena, users compare two pieces of generated speech side by side and select their preferred output without knowing which models created them. The speech arena includes prompts across four real-world categories of prompts: Customer Service, Knowledge Sharing, Digital Assistants, and Entertainment.
Inworld TTS 1 Max and Inworld TTS 1 both support 12 languages including English, Spanish, French, Korean, and Chinese, and voice cloning from 2-15 seconds of audio. Inworld TTS 1 processes ~153 characters per second of generation time on average, with the larger model, Inworld TTS 1 Max processing ~69 characters on average. Both models also support voice tags, allowing users to add emotion, delivery style, and non-verbal sounds, such as “whispering”, “cough”, and “surprised”.
Both TTS-1 and TTS-1-Max are transformer-based, autoregressive models employing LLaMA-3.2-1B and LLaMA-3.1-8B respectively as their SpeechLM backbones.
See the leading models in the Speech Arena, and listen to sample clips below 🎧
Pavel Shtykovskiy retweeted
Tired to go back to the original papers again and again? Our monograph: a systematic and fundamental recipe you can rely on!
📘 We’re excited to release 《The Principles of Diffusion Models》— with @DrYangSong, @gimdong58085414, @mittu1204, and @StefanoErmon.
It traces the core ideas that shaped diffusion modeling and explains how today’s models work, why they work, and where they’re heading.
🧵You’ll find the link and a few highlights in the thread.
We’d love to hear your thoughts and join some discussions!
⚡ Stay tuned for our markdown version, where you can drop your comments!
Pavel Shtykovskiy retweeted
Just signed a book deal for The RLHF Book, excited to make improvements to it this fall and get physical copies in your hands soon :)
(rlhfbook dot com)
Pavel Shtykovskiy retweeted
1 43.6 Grok-4-Wiz-AI-Cha died in The Dungeons of Doom on level 1. Killed by a housecat.
LLMs acing math olympiads? Cute.
But BALROG is where agents fight dragons (and actual Balrogs)🐉😈
And today, Grok-4 (@grok) takes the gold 🥇
Welcome to the podium, champion!
Pavel Shtykovskiy retweeted
I am happy to announce that the first draft of my RL tutorial is now available.
arxiv.org/abs/2412.05265
Pavel Shtykovskiy retweeted
Earlier, we with @framrus developed a humor generation method that gives human-level results on blind tests.
Now, we with @SaveTheRbtz are launching HUMOR-ARENA (humor.ph34r.me/), generated humor labeling site with the models ranking, and the top of generated jokes. Blog-post: altsoph.medium.com/humor-are…
Pavel Shtykovskiy retweeted
Want to learn about meta-learning & few-shot learning?
All of the latest lecture videos for Stanford CS330 are now online!
youtube.com/playlist?list=PL…
New topics in Fall '22 include:
- self-supervised pre-training
- large scale meta-optimization
- domain adaptation & generalization
Pavel Shtykovskiy retweeted
You can now watch the recorded material from #NeurIPS2022 online without registration at:
slideslive.com/neurips-2022
Pavel Shtykovskiy retweeted
Our 2021 CS330 (cs330.stanford.edu/fall2021) lectures are online: youtube.com/playlist?list=PL…
It was a pleasure to co-teach this class with @chelseabfinn. Topics incl. meta-learning, MTL, few-shot learning, deep RL (incl. multi-task, meta, goal-conditioned, hierarchical and offline RL)
Pavel Shtykovskiy retweeted
The video of my talk @EPFL_en today on Transformers and how to make sense of them is online!
youtube.com/watch?v=brmidghO…
Pavel Shtykovskiy retweeted
Fun read on why MLOps is still somewhat broken -- the engineers who build them are not users.
In ML Frameworks, the authors were ML scientists -- (Py)Torch, Theano, Caffe, MXNet, Keras, Chainer, TF, etc. and that helped in design requirements accurately being in your head.
Bananas and ML infrastructure: I've asked around about cloud workflows, and most of the feedback had unhappiness with cloud tooling. This prompted a discussion in @chipro's MLops community -- why are MLops frameworks so bad? (1/9)
Pavel Shtykovskiy retweeted
Bananas and ML infrastructure: I've asked around about cloud workflows, and most of the feedback had unhappiness with cloud tooling. This prompted a discussion in @chipro's MLops community -- why are MLops frameworks so bad? (1/9)
Pavel Shtykovskiy retweeted
Today the videos that I made to accompany my book Linear Algebra Done Right surpassed two million minutes of total viewing on YouTube. Those videos are freely available from the links at linear.axler.net/LADRvideos.….
#LinearAlgebra
Pavel Shtykovskiy retweeted
Just watched an incredible talk by @AlexGDimakis at the Simons Institute, highly recommended. Their Iterative Layer Optimization technique to solve inverse problems with GANs make a LOT of sense!
The empirical results on the famous blurred Obama face speak for themselves!
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