singhai Sagar jain

[Shobhit Banga @shobhitbanga]

Drop 1/15: Introducing Joy, a dual-channel conversational dataset.
Real, unscripted conversations between two people, each on their own track. Built to teach voice models how people actually talk: when to listen, when to jump in, when to say “hmm”.

100,000 hours.
50,000 speakers.
10 languages.

[Shobhit Banga @shobhitbanga]

Introducing Diarization Bench, @voicearena_ai's benchmark for who spoke when.

The metric we measure is diarization error rate, or DER. It measures how much of a conversation a model attaches to the wrong person. Every second it misses, invents, or gives to another speaker counts against it.

Here's what we did. 139 conversations recorded for this board, in rooms and on calls, with people interrupting each other throughout. 12 systems scored under a single goal: testing models in the real-world conditions they are actually deployed in. @nvidia's Nemotron 3 leads at 14.72%. 🧵

[Voice Arena @voicearena_ai]

Introducing Monsoon ASR ⚡️ @voicearena_ai

Speech recognition does not have a model problem anymore. It has a data problem.

The best ASR systems are approaching human-level performance in English. But move into the long tail of the world's languages, especially real, conversational speech and error rates can still be 5-10X higher.

Today, we're releasing Monsoon ASR: a new generation of training data built specifically to close that gap.
50 languages. 100,000+ hours. Dense spontaneous speech.

And one goal: Single-digit WER across the world's languages.

[shash @gooner_joy]

AWS water supply straight from the CLOUD @peakbengaluru

[Harsh Gupta @hgupta947]

I think this is byfar your best work @Harshm2468.

Scenes at @joshskillsapp setting up for tomorrow's release.

[singhai Sagar jain @singhaiSagarja1]

@abhayjani4 @swiggy_in @whereisashwin 👋