Voice AI Engine Development

Voice AI Engine Development

Overview

This skill guides you through building production-ready voice AI engines with real-time conversation capabilities. Voice AI engines enable natural, bidirectional conversations between users and AI agents through streaming audio processing, speech-to-text transcription, LLM-powered responses, and text-to-speech synthesis.

The core architecture uses an async queue-based worker pipeline where each component runs independently and communicates via asyncio.Queue objects, enabling concurrent processing, interrupt handling, and real-time streaming at every stage.

When to Use This Skill

Use this skill when:

• Building real-time voice conversation systems
• Implementing voice assistants or chatbots
• Creating voice-enabled customer service agents
• Developing voice AI applications with interrupt capabilities
• Integrating multiple transcription, LLM, or TTS providers
• Working with streaming audio processing pipelines
• The user mentions Vocode, voice engines, or conversational AI

Core Architecture Principles

The Worker Pipeline Pattern

Every voice AI engine follows this pipeline:

Audio In → Transcriber → Agent → Synthesizer → Audio Out
           (Worker 1)   (Worker 2)  (Worker 3)

Key Benefits:

• Decoupling: Workers only know about their input/output queues
• Concurrency: All workers run simultaneously via asyncio
• Backpressure: Queues automatically handle rate differences
• Interruptibility: Everything can be stopped mid-stream

Base Worker Pattern

Every worker follows this pattern:

class BaseWorker:
    def __init__(self, input_queue, output_queue):
        self.input_queue = input_queue   # asyncio.Queue to consume from
        self.output_queue = output_queue # asyncio.Queue to produce to
        self.active = False
    
    def start(self):
        """Start the worker's processing loop"""
        self.active = True
        asyncio.create_task(self._run_loop())
    
    async def _run_loop(self):
        """Main processing loop - runs forever until terminated"""
        while self.active:
            item = await self.input_queue.get()  # Block until item arrives
            await self.process(item)              # Process the item
    
    async def process(self, item):
        """Override this - does the actual work"""
        raise NotImplementedError
    
    def terminate(self):
        """Stop the worker"""
        self.active = False

Component Implementation Guide

1. Transcriber (Audio → Text)

Purpose: Converts incoming audio chunks to text transcriptions

Interface Requirements:

class BaseTranscriber:
    def __init__(self, transcriber_config):
        self.input_queue = asyncio.Queue()   # Audio chunks (bytes)
        self.output_queue = asyncio.Queue()  # Transcriptions
        self.is_muted = False
    
    def send_audio(self, chunk: bytes):
        """Client calls this to send audio"""
        if not self.is_muted:
            self.input_queue.put_nowait(chunk)
        else:
            # Send silence instead (prevents echo during bot speech)
            self.input_queue.put_nowait(self.create_silent_chunk(len(chunk)))
    
    def mute(self):
        """Called when bot starts speaking (prevents echo)"""
        self.is_muted = True
    
    def unmute(self):
        """Called when bot stops speaking"""
        self.is_muted = False

Output Format:

class Transcription:
    message: str          # "Hello, how are you?"
    confidence: float     # 0.95
    is_final: bool        # True = complete sentence, False = partial
    is_interrupt: bool    # Set by TranscriptionsWorker

Supported Providers:

• Deepgram - Fast, accurate, streaming
• AssemblyAI - High accuracy, good for accents
• Azure Speech - Enterprise-grade
• Google Cloud Speech - Multi-language support

Critical Implementation Details:

• Use WebSocket for bidirectional streaming
• Run sender and receiver tasks concurrently with asyncio.gather()
• Mute transcriber when bot speaks to prevent echo/feedback loops
• Handle both final and partial transcriptions

2. Agent (Text → Response)

Purpose: Processes user input and generates conversational responses

Interface Requirements:

class BaseAgent:
    def __init__(self, agent_config):
        self.input_queue = asyncio.Queue()   # TranscriptionAgentInput
        self.output_queue = asyncio.Queue()  # AgentResponse
        self.transcript = None               # Conversation history
    
    async def generate_response(self, human_input, is_interrupt, conversation_id):
        """Override this - returns AsyncGenerator of responses"""
        raise NotImplementedError

Why Streaming Responses?

• Lower latency: Start speaking as soon as first sentence is ready
• Better interrupts: Can stop mid-response
• Sentence-by-sentence: More natural conversation flow

**Supported Pro