Build production voice AI agents with streaming STT, LLM, and TTS.
npx skills add https://github.com/ScientiaCapital/skills --skill voice-ai
Please help me install this Agent Skill. Skill: voice-ai Source: https://github.com/ScientiaCapital/skills/tree/main/active/voice-ai-skill Command: npx skills add https://github.com/ScientiaCapital/skills --skill voice-ai
Production-ready voice AI pipelines enable real-time, end-to-end voice conversations in telephony contexts with sub-500ms latency, bridging STT, LLM, and TTS.
Run the quick-start to deploy a production voice pipeline with streaming STT, LLM, and TTS.
High-intent search queries and answers about installing and using this skill.
To build a voice AI agent with sub-500ms latency, you need a streaming pipeline combining Deepgram for STT, Groq for LLM generation, Cartesia for TTS, and Twilio for real-time media transport. This achieves production-grade, real-time voice conversations.
The best way to integrate real-time STT and TTS for IVR call centers is using a streaming pipeline with Deepgram Nova-3 for transcription and Cartesia Sonic for voice synthesis, ensuring near real-time responses with bilingual support.
Yes, you can use Groq with Twilio Media Streams for low-latency phone bots. Groq's llama-3.1-8b-instant provides fast LLM responses, while Twilio handles real-time audio transport to maintain sub-500ms end-to-end latency.
Yes, the voice AI pipeline supports bilingual English and Spanish interactions. It uses Cartesia Sonic for streaming TTS with emotion controls, enabling bilingual real-time voice responses for telephony and call center use cases.
Avoiding OpenAI for low-latency real-time voice agents is necessary because Groq provides faster LLM generation with llama-3.1-8b-instant. This streaming approach ensures the pipeline maintains sub-500ms end-to-end latency for telephony.
Yes, you need Deepgram and Cartesia for streaming voice AI transcription and synthesis. Deepgram Nova-3 handles near real-time STT, while Cartesia Sonic manages streaming TTS with emotion controls to achieve sub-500ms latency.
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