Aura-2 Text to Speech features
Unlike entertainment-focused TTS models, Aura-2 offers text-to-speech engineered to meet the rigorous, real-time, and scalable demands of enterprise environments.
Domain-tuned pronunciation
Ensures accurate pronunciation for industry-specific terminology in healthcare, finance, legal, and beyond.
Authentic, Natural Voices
Features 40+ English voices with localized accents, delivering natural, business-appropriate speech for professional settings.
Context-aware delivery
Adjusts pacing, tone, and expression to ensure smooth, coherent communication in any context.
Real-time performance
Delivers sub-200ms latency for ultra-responsive interactions, while efficiently handling thousands of concurrent requests.
Cost-effectiveness at scale
Achieves enterprise-grade speech at $0.030 per 1,000 characters—no hidden fees, with volume discounts for large deployments.
Flexible deployment options
Supports public, private cloud, and on-premises deployments, ensuring compliance and security.
Enterprise-ready AI voices
You need more than voices that sound good—you need voices that communicate precisely and reliably in professional contexts. With a diverse catalog of 40+ AI voices and distinct persona profiles, Aura-2 balances realism with clarity, pacing, and consistency to deliver enterprise-optimized voice experiences.
Scalable infrastructure for Text to Speech
Powered by the Deepgram Enterprise Runtime, Aura-2 delivers real-time text-to-speech using the same infrastructure that powers our trusted speech-to-text and speech-to-speech capabilities, providing enterprises with the control, adaptability, and performance needed to deploy and scale production-grade voice AI.
Speech to Text leadership enhances Text to Speech
With Deepgram’s unified architecture, improvements in speech recognition automatically enhance Aura-2's text-to-speech capabilities via the shared runtime. This cross-model learning allows the platform to adapt to industry terminology and user interactions, ensuring consistent pronunciation, reduced latency, and real-time model customization.
