Albert Gu ✓

@_albertgu · joined 21 Dec 2018

assistant prof @mldcmu. chief scientist @cartesia. leading the ssm revolution.

21 726Followers

78Following

577Posts total

237.6KViews on collected posts

Latest posts

Albert Gu ✓@_albertgu31 Aug

misleading paper title, and even the phrasing in the tweet is still a misnomer. this is not about "post-training" but "retrofitting" (cross-architecture distillation) fwiw i've been bearish on distilling Transformers to recurrent models for a while, they're too different; 78.9K views · 659 likes · 24 reposts · 17 replies Open on X →

Albert Gu ✓@_albertgu27 Aug

sonic-3.6 is out, with a large improvement over (the already #1) sonic-3.5 in just a few months! the research team's focus on fundamentals is accelerating progress at the frontier of architectures and audio 13.2K views · 84 likes · 2 reposts · 2 replies Open on X →

Albert Gu ✓@_albertgu17 Aug

the team continues cooking 👩‍🍳 this is now an unprecedented gap on both the super competitive Provider Voices leaderboard as well as the newer Controlled Voices leaderboard (a stronger benchmark that can't be benchmaxxed, requiring truly better algorithms) Cartesia's TTS model i

11.8K views · 112 likes · 10 reposts · 5 replies Open on X →

Albert Gu ✓@_albertgu17 Aug

Cartesia's Sonic 3.6 takes the #1 spot on both the Provider Voice and Controlled Voice Artificial Analysis Speech Arena leaderboards, surpassing Speechify AI's Simba 3.2 and Alibaba's Qwen-Audio-3.0-TTS-Plus, with Sonic 3.5 holding #2 on Controlled Voice Sonic 3.6 is the latest

GIF

66.3K views · 493 likes · 51 reposts · 27 replies Open on X →

Albert Gu ✓@_albertgu28 Jul

Evaluations are difficult and vague for all generative models, and benchmarks only capture a small slice. Our blog post dives into the nuances for TTS 11.9K views · 55 likes · 4 reposts · 3 replies Open on X →

Albert Gu ✓@_albertgu28 Jul

"Is this TTS model good?" gets harder to answer as models improve. "Good" is at least five axes: correctness, naturalness, contextual correctness, robustness, and most evals only capture the first. We wrote up the failure modes that make TTS eval hard: https://t.co/ILbJM3zXyL 19.9K views · 65 likes · 18 reposts · 5 replies Open on X →

Albert Gu ✓@_albertgu8 Jul

Cartesia is hosting an ICML party tomorrow (Thursday) night! it'll go late, but come early or i may have to bounce you again https://t.co/6LySuzxpNy 35.6K views · 140 likes · 10 reposts · 10 replies Open on X →

Against accounts of the same size

7 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views19 889this account993median for 10K–100K

Reach, %91.54%this account3.63%median for 10K–100K

Engagement, %0.67%this account1.92%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	19 889	993	20.0×
Reach (views ÷ followers)	91.54%	3.63%	25.2×
Engagement rate	0.67%	1.92%	0.35×

Others in this range → Compare with another account → How these benchmarks are built →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

35.6K8 Jul

19.9K28 Jul

11.9K

66.3K17 Aug

11.8K

13.2K27 Aug

78.9K31 Aug

Last 7 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.46%8 Jul

0.47%28 Jul

0.52%

0.88%17 Aug

1.08%

0.67%27 Aug

0.89%31 Aug

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.92%.

What the audience does

Likes78.3%1 608 in total

Reposts5.8%119 in total

Replies3.4%69 in total

Quotes1.3%27 in total

Bookmarks11.2%230 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.