How Deepgram optimized voice AI at scale with AWS Trainium | Amazon Web Services
As voice becomes a first-class modality for AI, Deepgram’s models span speech-to-text, text-to-speech, and voice agents. They now process quadrillions of audio tokens a year for more than 200,000 developers and 1,400 organizations. Customers use them through the Deepgram API or run them in their own environments, including on Amazon SageMaker AI.
For Deepgram, serving efficiency determines how much audio the system can process for a given infrastructure budget. High-volume batch workloads put heavy pressure on that budget, so the more efficiently each accelerator hour is used, the more audio Deepgram can serve on the same fleet. That made inference efficiency a natural target and Deepgram turned to AWS AI chips to improve it. They worked with AWS to bring its Nova-3 speech-to-text model to AWS Trainium2 for offline batch workloads such as contact-center analytics and medical transcription.
The engagement began with Deepgram delivering Nova-3 as standard PyTorch code. Over...
Copyright of this story solely belongs to aws.amazon.com. To see the full text click HERE
