voice-ai-development

Implement real-time voice agents with STT/TTS pipelines and WebRTC audio.

Updated Aug 27, 2026

Search Tags:#agent development #real-time #latency #voice ai #tts #stt #webrtc

Ready to run

npx skills add https://github.com/sharkitect-solutions/sharkitect-claude-toolkit --skill voice-ai-development

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: voice-ai-development
Source: https://github.com/sharkitect-solutions/sharkitect-claude-toolkit/tree/main/skills/voice-ai-development
Command: npx skills add https://github.com/sharkitect-solutions/sharkitect-claude-toolkit --skill voice-ai-development

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive guide and set of tools for implementing real-time voice AI features, addressing challenges like latency, provider integration, and audio quality.

Core Features & Use Cases

Real-time Voice Agents: Implement STT/TTS pipelines, WebRTC audio, and voice provider integration.
Latency Optimization: Techniques and frameworks for minimizing voice interaction delays.
Provider Integration: Guidance on using services like OpenAI Realtime, Deepgram, ElevenLabs, Vapi, and LiveKit.
Use Case: Develop a customer service voice bot that can handle live conversations with low latency, understand user interruptions, and provide natural-sounding responses.

Quick Start

Use the voice-ai-development skill to implement barge-in handling for a voice agent.

Frequently Asked Questions about voice-ai-development

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a real-time voice AI agent with low latency?▼

To build a real-time voice AI agent, you must implement STT/TTS pipelines, WebRTC audio streaming, and optimize interaction delays. This involves integrating provider APIs, tuning Voice Activity Detection (VAD), and handling barge-in detection for natural conversations.

How does barge-in detection work for voice AI agents?▼

Barge-in detection allows a voice AI agent to process user interruptions during speech generation. It requires continuous audio monitoring and immediate cancellation of Text-to-Speech output to handle live conversation dynamics and ensure natural interactions.

Can I use Deepgram and ElevenLabs together in a voice AI pipeline?▼

Yes, you can integrate Deepgram for Speech-to-Text and ElevenLabs for Text-to-Speech within your voice AI pipeline. Provider integration requires understanding their specific APIs, managing audio formats, and synchronizing the STT/TTS handoff for real-time responses.

What's the best way to optimize latency for WebRTC voice interactions?▼

Optimizing WebRTC voice latency involves tuning audio formats, configuring Voice Activity Detection (VAD), and implementing voice-specific error handling. Minimizing interaction delays ensures your voice agent can handle live conversations without noticeable lag.

Do I need to tune Voice Activity Detection for voice provider integration?▼

Yes, tuning Voice Activity Detection (VAD) is essential for voice provider integration. Proper VAD configuration ensures accurate speech endpoint detection, which directly impacts barge-in handling and the responsiveness of STT/TTS pipelines.

Why does my voice AI agent struggle with user interruptions?▼

User interruptions fail when barge-in detection is not properly configured within your STT/TTS pipeline. Addressing this requires implementing voice-specific error handling and continuous audio stream monitoring to immediately halt ongoing TTS playback.