The Ledger
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Major cloud providers are too big to ship good developer products
“I'm surprised the big cloud companies, and apologies if anyone here works there, I'm surprised they can't ship, you know, better developer products, but it's, I think they're maybe just too big at this point.”
Most speech AI value comes from downstream workflows, not just transcription
“Where a lot of value is created is you're taking the transcription, and then you're using it as an input to do something else.”
Fox: AssemblyAI delays exploratory model investment until developers prove value
“Some of these other things that are more exploratory, like, we're not gonna put a ton of effort into those until we see that our customers the developers that use our API are actually able to find value and create value with those.”
Most commercial speech models historically trained on roughly 50,000 hours of audio
“And our models prior, and most commercial speech recognition models trained on like, 50,000 hours”
AssemblyAI is training its next speech model on ~4 million hours of audio
“We're actually training conformer two or what might call it 1.5, but whatever this accessory will be is training right now. And that's something around four million hours of labeled audio data.”
AssemblyAI processes over 100 million audio files monthly via API
“We've processed, ah, yeah, it's like over a hundred million audio files a month that are flowing through the API, and that's growing pretty quickly.”
State-of-the-art speech recognition models still carry a 15% error rate
“State-of-the-art automatic speech recognition still has, like, a 15% error rate on a lot of data sets”
AssemblyAI has processed almost two billion audio files to date
“We've processed almost two billion audio files through our system.”
Fox: AssemblyAI serves over 1,000 customers and tens of thousands of monthly developers
“We've got over a thousand customers tens of thousands a month of developers that are building with the API.”
AssemblyAI trained Conformer-1 on 650,000 hours of labeled audio data
“So we trained it on, like, 60 terabytes of audio data, like, labeled audio data. So it was, I think, something like 650,000 hours of audio data.”
AssemblyAI's JAX contribution sped up Whisper model training by 10x
“We actually, I think, published like, made a contribution to Jax to make it, like, 10 times faster to train Whisper.”