Voice AI Engine Development
Goal: Build low-latency, conversational Voice AI agents capable of full-duplex communication.
- The Voice Pipeline (Latency is King)
The total loop Latency (Voice-to-Ear) should be < 1000ms (Ideal < 500ms).
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Transport: WebRTC (preferred for browser) or WebSocket (server-server).
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VAD (Voice Activity Detection): Detect when user starts/stops speaking.
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Tools: Silero VAD, WebRTC VAD.
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STT (Speech-to-Text): Transcribe audio to text.
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Tools: Deepgram (fastest), Whisper (high accuracy but slower), AssemblyAI.
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LLM (Brain): Process text and generate response.
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Tools: Groq (Llama 3), GPT-4o, Claude 3.5 Sonnet.
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TTS (Text-to-Speech): Convert response to audio.
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Tools: ElevenLabs (Quality), Cartesia (Speed), OpenAI TTS.
- Architecture Patterns
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Streaming Pipeline: DO NOT wait for full transcription or full generation. Stream everything.
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User Audio Stream -> VAD -> STT Stream -> LLM Stream -> TTS Stream -> Audio Output.
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Interruption Handling (Barge-in):
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If VAD detects user speech while AI is talking -> Immediately CUT text generation and audio playback. Clear buffers.
- Implementation Stack
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Backend: Python (FastAPI) or Node.js. Python ecosystem is stronger for audio processing (numpy/scipy).
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Frameworks:
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Pipecat: Open source framework for building voice agents.
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LiveKit: WebRTC infrastructure for real-time audio/video.
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Twilio: For telephony integration.
- Optimization Techniques
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Optimistic VAD: Tune VAD to be sensitive to start, but careful with "silence" timeout (usually 500ms-800ms) to detect end of turn.
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Prompt Engineering: Instruct LLM to be concise and conversational.
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System Prompt: "You are a helpful voice assistant. Keep responses short (1-2 sentences). Do not use markdown or emojis."
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Audio Formats: Use OPUS or PCM (16khz/24khz/48khz) for transmission. Avoid MP3 transcoding latency.
- Debugging & Metrics
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WER (Word Error Rate): For STT accuracy.
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TTFT (Time to First Token): LLM speed.
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TTA (Time to Audio): The critical metric. Time from user silence to first AI sound.
Common Pitfalls:
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Echo cancellation issues (User hears themselves). Use WebRTC's built-in AEC.
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Hallucination in STT (Whisper transcribing silence).
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Race conditions during interruptions.