Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support
Scanned 2/12/2026
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Skills Directoryopenskills install sickn33/antigravity-awesome-skills---
name: voice-ai-engine-development
description: "Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support"
---
# 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:
```python
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**:
```python
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**:
```python
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**:
```python
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 Providers**:
- **OpenAI** (GPT-4, GPT-3.5) - High quality, fast
- **Google Gemini** - Multimodal, cost-effective
- **Anthropic Claude** - Long context, nuanced responses
**Critical Implementation Details**:
- Maintain conversation history in `Transcript` object
- Stream responses using `AsyncGenerator`
- **IMPORTANT**: Buffer entire LLM response before yielding to synthesizer (prevents audio jumping)
- Handle interrupts by canceling current generation task
- Update conversation history with partial messages on interrupt
### 3. Synthesizer (Text → Audio)
**Purpose**: Converts agent text responses to speech audio
**Interface Requirements**:
```python
class BaseSynthesizer:
async def create_speech(self, message: BaseMessage, chunk_size: int) -> SynthesisResult:
"""
Returns a SynthesisResult containing:
- chunk_generator: AsyncGenerator that yields audio chunks
- get_message_up_to: Function to get partial text (for interrupts)
"""
raise NotImplementedError
```
**SynthesisResult Structure**:
```python
class SynthesisResult:
chunk_generator: AsyncGenerator[ChunkResult, None]
get_message_up_to: Callable[[float], str] # seconds → partial text
class ChunkResult:
chunk: bytes # Raw PCM audio
is_last_chunk: bool
```
**Supported Providers**:
- **ElevenLabs** - Most natural voices, streaming
- **Azure TTS** - Enterprise-grade, many languages
- **Google Cloud TTS** - Cost-effective, good quality
- **Amazon Polly** - AWS integration
- **Play.ht** - Voice cloning
**Critical Implementation Details**:
- Stream audio chunks as they're generated
- Convert audio to LINEAR16 PCM format (16kHz sample rate)
- Implement `get_message_up_to()` for interrupt handling
- Handle audio format conversion (MP3 → PCM)
### 4. Output Device (Audio → Client)
**Purpose**: Sends synthesized audio back to the client
**CRITICAL: Rate Limiting for Interrupts**
```python
async def send_speech_to_output(self, message, synthesis_result,
stop_event, seconds_per_chunk):
chunk_idx = 0
async for chunk_result in synthesis_result.chunk_generator:
# Check for interrupt
if stop_event.is_set():
logger.debug(f"Interrupted after {chunk_idx} chunks")
message_sent = synthesis_result.get_message_up_to(
chunk_idx * seconds_per_chunk
)
return message_sent, True # cut_off = True
start_time = time.time()
# Send chunk to output device
self.output_device.consume_nonblocking(chunk_result.chunk)
# CRITICAL: Wait for chunk to play before sending next one
# This is what makes interrupts work!
speech_length = seconds_per_chunk
processing_time = time.time() - start_time
await asyncio.sleep(max(speech_length - processing_time, 0))
chunk_idx += 1
return message, False # cut_off = False
```
**Why Rate Limiting?**
Without rate limiting, all audio chunks would be sent immediately, which would:
- Buffer entire message on client side
- Make interrupts impossible (all audio already sent)
- Cause timing issues
By sending one chunk every N seconds:
- Real-time playback is maintained
- Interrupts can stop mid-sentence
- Natural conversation flow is preserved
## The Interrupt System
The interrupt system is critical for natural conversations.
### How Interrupts Work
**Scenario**: Bot is saying "I think the weather will be nice today and tomorrow and—" when user interrupts with "Stop".
**Step 1: User starts speaking**
```python
# TranscriptionsWorker detects new transcription while bot speaking
async def process(self, transcription):
if not self.conversation.is_human_speaking: # Bot was speaking!
# Broadcast interrupt to all in-flight events
interrupted = self.conversation.broadcast_interrupt()
transcription.is_interrupt = interrupted
```
**Step 2: broadcast_interrupt() stops everything**
```python
def broadcast_interrupt(self):
num_interrupts = 0
# Interrupt all queued events
while True:
try:
interruptible_event = self.interruptible_events.get_nowait()
if interruptible_event.interrupt(): # Sets interruption_event
num_interrupts += 1
except queue.Empty:
break
# Cancel current tasks
self.agent.cancel_current_task() # Stop generating text
self.agent_responses_worker.cancel_current_task() # Stop synthesizing
return num_interrupts > 0
```
**Step 3: SynthesisResultsWorker detects interrupt**
```python
async def send_speech_to_output(self, synthesis_result, stop_event, ...):
async for chunk_result in synthesis_result.chunk_generator:
# Check stop_event (this is the interruption_event)
if stop_event.is_set():
logger.debug("Interrupted! Stopping speech.")
# Calculate what was actually spoken
seconds_spoken = chunk_idx * seconds_per_chunk
partial_message = synthesis_result.get_message_up_to(seconds_spoken)
# e.g., "I think the weather will be nice today"
return partial_message, True # cut_off = True
```
**Step 4: Agent updates history**
```python
if cut_off:
# Update conversation history with partial message
self.agent.update_last_bot_message_on_cut_off(message_sent)
# History now shows:
# Bot: "I think the weather will be nice today" (incomplete)
```
### InterruptibleEvent Pattern
Every event in the pipeline is wrapped in an `InterruptibleEvent`:
```python
class InterruptibleEvent:
def __init__(self, payload, is_interruptible=True):
self.payload = payload
self.is_interruptible = is_interruptible
self.interruption_event = threading.Event() # Initially not set
self.interrupted = False
def interrupt(self) -> bool:
"""Interrupt this event"""
if not self.is_interruptible:
return False
if not self.interrupted:
self.interruption_event.set() # Signal to stop!
self.interrupted = True
return True
return False
def is_interrupted(self) -> bool:
return self.interruption_event.is_set()
```
## Multi-Provider Factory Pattern
Support multiple providers with a factory pattern:
```python
class VoiceHandler:
"""Multi-provider factory for voice components"""
def create_transcriber(self, agent_config: Dict):
"""Create transcriber based on transcriberProvider"""
provider = agent_config.get("transcriberProvider", "deepgram")
if provider == "deepgram":
return self._create_deepgram_transcriber(agent_config)
elif provider == "assemblyai":
return self._create_assemblyai_transcriber(agent_config)
elif provider == "azure":
return self._create_azure_transcriber(agent_config)
elif provider == "google":
return self._create_google_transcriber(agent_config)
else:
raise ValueError(f"Unknown transcriber provider: {provider}")
def create_agent(self, agent_config: Dict):
"""Create LLM agent based on llmProvider"""
provider = agent_config.get("llmProvider", "openai")
if provider == "openai":
return self._create_openai_agent(agent_config)
elif provider == "gemini":
return self._create_gemini_agent(agent_config)
else:
raise ValueError(f"Unknown LLM provider: {provider}")
def create_synthesizer(self, agent_config: Dict):
"""Create voice synthesizer based on voiceProvider"""
provider = agent_config.get("voiceProvider", "elevenlabs")
if provider == "elevenlabs":
return self._create_elevenlabs_synthesizer(agent_config)
elif provider == "azure":
return self._create_azure_synthesizer(agent_config)
elif provider == "google":
return self._create_google_synthesizer(agent_config)
elif provider == "polly":
return self._create_polly_synthesizer(agent_config)
elif provider == "playht":
return self._create_playht_synthesizer(agent_config)
else:
raise ValueError(f"Unknown voice provider: {provider}")
```
## WebSocket Integration
Voice AI engines typically use WebSocket for bidirectional audio streaming:
```python
@app.websocket("/conversation")
async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
# Create voice components
voice_handler = VoiceHandler()
transcriber = voice_handler.create_transcriber(agent_config)
agent = voice_handler.create_agent(agent_config)
synthesizer = voice_handler.create_synthesizer(agent_config)
# Create output device
output_device = WebsocketOutputDevice(
ws=websocket,
sampling_rate=16000,
audio_encoding=AudioEncoding.LINEAR16
)
# Create conversation orchestrator
conversation = StreamingConversation(
output_device=output_device,
transcriber=transcriber,
agent=agent,
synthesizer=synthesizer
)
# Start all workers
await conversation.start()
try:
# Receive audio from client
async for message in websocket.iter_bytes():
conversation.receive_audio(message)
except WebSocketDisconnect:
logger.info("Client disconnected")
finally:
await conversation.terminate()
```
## Common Pitfalls and Solutions
### 1. Audio Jumping/Cutting Off
**Problem**: Bot's audio jumps or cuts off mid-response.
**Cause**: Sending text to synthesizer in small chunks causes multiple TTS calls.
**Solution**: Buffer the entire LLM response before sending to synthesizer:
```python
# ❌ Bad: Yields sentence-by-sentence
async for sentence in llm_stream:
yield GeneratedResponse(message=BaseMessage(text=sentence))
# ✅ Good: Buffer entire response
full_response = ""
async for chunk in llm_stream:
full_response += chunk
yield GeneratedResponse(message=BaseMessage(text=full_response))
```
### 2. Echo/Feedback Loop
**Problem**: Bot hears itself speaking and responds to its own audio.
**Cause**: Transcriber not muted during bot speech.
**Solution**: Mute transcriber when bot starts speaking:
```python
# Before sending audio to output
self.transcriber.mute()
# After audio playback complete
self.transcriber.unmute()
```
### 3. Interrupts Not Working
**Problem**: User can't interrupt bot mid-sentence.
**Cause**: All audio chunks sent at once instead of rate-limited.
**Solution**: Rate-limit audio chunks to match real-time playback:
```python
async for chunk in synthesis_result.chunk_generator:
start_time = time.time()
# Send chunk
output_device.consume_nonblocking(chunk)
# Wait for chunk duration before sending next
processing_time = time.time() - start_time
await asyncio.sleep(max(seconds_per_chunk - processing_time, 0))
```
### 4. Memory Leaks from Unclosed Streams
**Problem**: Memory usage grows over time.
**Cause**: WebSocket connections or API streams not properly closed.
**Solution**: Always use context managers and cleanup:
```python
try:
async with websockets.connect(url) as ws:
# Use websocket
pass
finally:
# Cleanup
await conversation.terminate()
await transcriber.terminate()
```
## Production Considerations
### 1. Error Handling
```python
async def _run_loop(self):
while self.active:
try:
item = await self.input_queue.get()
await self.process(item)
except Exception as e:
logger.error(f"Worker error: {e}", exc_info=True)
# Don't crash the worker, continue processing
```
### 2. Graceful Shutdown
```python
async def terminate(self):
"""Gracefully shut down all workers"""
self.active = False
# Stop all workers
self.transcriber.terminate()
self.agent.terminate()
self.synthesizer.terminate()
# Wait for queues to drain
await asyncio.sleep(0.5)
# Close connections
if self.websocket:
await self.websocket.close()
```
### 3. Monitoring and Logging
```python
# Log key events
logger.info(f"🎤 [TRANSCRIBER] Received: '{transcription.message}'")
logger.info(f"🤖 [AGENT] Generating response...")
logger.info(f"🔊 [SYNTHESIZER] Synthesizing {len(text)} characters")
logger.info(f"⚠️ [INTERRUPT] User interrupted bot")
# Track metrics
metrics.increment("transcriptions.count")
metrics.timing("agent.response_time", duration)
metrics.gauge("active_conversations", count)
```
### 4. Rate Limiting and Quotas
```python
# Implement rate limiting for API calls
from aiolimiter import AsyncLimiter
rate_limiter = AsyncLimiter(max_rate=10, time_period=1) # 10 calls/second
async def call_api(self, data):
async with rate_limiter:
return await self.client.post(data)
```
## Key Design Patterns
### 1. Producer-Consumer with Queues
```python
# Producer
async def producer(queue):
while True:
item = await generate_item()
queue.put_nowait(item)
# Consumer
async def consumer(queue):
while True:
item = await queue.get()
await process_item(item)
```
### 2. Streaming Generators
Instead of returning complete results:
```python
# ❌ Bad: Wait for entire response
async def generate_response(prompt):
response = await openai.complete(prompt) # 5 seconds
return response
# ✅ Good: Stream chunks as they arrive
async def generate_response(prompt):
async for chunk in openai.complete(prompt, stream=True):
yield chunk # Yield after 0.1s, 0.2s, etc.
```
### 3. Conversation State Management
Maintain conversation history for context:
```python
class Transcript:
event_logs: List[Message] = []
def add_human_message(self, text):
self.event_logs.append(Message(sender=Sender.HUMAN, text=text))
def add_bot_message(self, text):
self.event_logs.append(Message(sender=Sender.BOT, text=text))
def to_openai_messages(self):
return [
{"role": "user" if msg.sender == Sender.HUMAN else "assistant",
"content": msg.text}
for msg in self.event_logs
]
```
## Testing Strategies
### 1. Unit Test Workers in Isolation
```python
async def test_transcriber():
transcriber = DeepgramTranscriber(config)
# Mock audio input
audio_chunk = b'\x00\x01\x02...'
transcriber.send_audio(audio_chunk)
# Check output
transcription = await transcriber.output_queue.get()
assert transcription.message == "expected text"
```
### 2. Integration Test Pipeline
```python
async def test_full_pipeline():
# Create all components
conversation = create_test_conversation()
# Send test audio
conversation.receive_audio(test_audio_chunk)
# Wait for response
response = await wait_for_audio_output(timeout=5)
assert response is not None
```
### 3. Test Interrupts
```python
async def test_interrupt():
conversation = create_test_conversation()
# Start bot speaking
await conversation.agent.generate_response("Tell me a long story")
# Interrupt mid-response
await asyncio.sleep(1) # Let it speak for 1 second
conversation.broadcast_interrupt()
# Verify partial message in transcript
last_message = conversation.transcript.event_logs[-1]
assert last_message.text != full_expected_message
```
## Implementation Workflow
When implementing a voice AI engine:
1. **Start with Base Workers**: Implement the base worker pattern first
2. **Add Transcriber**: Choose a provider and implement streaming transcription
3. **Add Agent**: Implement LLM integration with streaming responses
4. **Add Synthesizer**: Implement TTS with audio streaming
5. **Connect Pipeline**: Wire all workers together with queues
6. **Add Interrupts**: Implement the interrupt system
7. **Add WebSocket**: Create WebSocket endpoint for client communication
8. **Test Components**: Unit test each worker in isolation
9. **Test Integration**: Test the full pipeline end-to-end
10. **Add Error Handling**: Implement robust error handling and logging
11. **Optimize**: Add rate limiting, monitoring, and performance optimizations
## Related Skills
- `@websocket-patterns` - For WebSocket implementation details
- `@async-python` - For asyncio and async patterns
- `@streaming-apis` - For streaming API integration
- `@audio-processing` - For audio format conversion and processing
- `@systematic-debugging` - For debugging complex async pipelines
## Resources
**Libraries**:
- `asyncio` - Async programming
- `websockets` - WebSocket client/server
- `FastAPI` - WebSocket server framework
- `pydub` - Audio manipulation
- `numpy` - Audio data processing
**API Providers**:
- Transcription: Deepgram, AssemblyAI, Azure Speech, Google Cloud Speech
- LLM: OpenAI, Google Gemini, Anthropic Claude
- TTS: ElevenLabs, Azure TTS, Google Cloud TTS, Amazon Polly, Play.ht
## Summary
Building a voice AI engine requires:
- ✅ Async worker pipeline for concurrent processing
- ✅ Queue-based communication between components
- ✅ Streaming at every stage (transcription, LLM, synthesis)
- ✅ Interrupt system for natural conversations
- ✅ Rate limiting for real-time audio playback
- ✅ Multi-provider support for flexibility
- ✅ Proper error handling and graceful shutdown
**The key insight**: Everything must stream and everything must be interruptible for natural, real-time conversations.
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