⚡ Deepgram Aura: Ultra-Fast TTS

Industry-leading speed with ~75ms latency. Purpose-built for real-time phone calls, live chat, and interactive applications where every millisecond matters.

Quick Setup

1

Get API Key

  1. Visit Deepgram Console and create an account
  2. Navigate to API Keys in the dashboard
  3. Create a new API key with TTS permissions
  4. Copy your API key
2

Configure in Burki

  1. Go to AI ConfigurationTTS tab
  2. Select Deepgram as provider
  3. Paste your API key in the TTS API Key field
3

Select Aura Model

Choose Aura-2 for best quality or Aura for proven stability

Available Models

🚀 Aura-2

~75ms latencyNext-generation model with improved qualityBest for: Production phone calls, live applications Status: Latest and recommended

⚖️ Aura

~85ms latencyProven stable model with consistent qualityBest for: Stable production environments Status: Battle-tested, reliable

Available Voices

Deepgram Aura Voices

Phone Call Optimization

📞 Twilio Integration

Deepgram Aura is specifically optimized for phone systems with built-in Twilio compatibility.

Audio Format Settings

Recommended for Twilio
{
  "encoding": "mulaw",
  "sample_rate": 8000
}
  • Format: G.711 µ-law
  • Sample Rate: 8kHz
  • Best for: Phone calls, VoIP systems
  • Quality: Optimized for voice clarity over networks

Performance Metrics

⚡ Latency

~75ms end-to-endFrom text input to first audio chunk3x faster than most competitors

🎯 Accuracy

99.9% uptimeEnterprise-grade reliabilityProduction-ready stability

📊 Throughput

High concurrencyScales automatically with demandNo rate limit bottlenecks

API Integration

import asyncio
import websockets
import json
import base64

async def stream_tts():
    uri = "wss://api.deepgram.com/v1/speak?model=aura-asteria-en&encoding=mulaw&sample_rate=8000"
    
    headers = {
        "Authorization": "Token YOUR_DEEPGRAM_API_KEY"
    }
    
    async with websockets.connect(uri, extra_headers=headers) as websocket:
        # Send text
        await websocket.send(json.dumps({
            "type": "speak",
            "text": "Hello from Deepgram Aura!"
        }))
        
        # Receive audio chunks
        async for message in websocket:
            data = json.loads(message)
            if data.get("type") == "audio":
                audio_data = base64.b64decode(data["data"])
                # Process audio data
                yield audio_data

Configuration Examples

Optimal Settings for Phone Systems
{
  "model": "aura-2-asteria-en",
  "encoding": "mulaw",
  "sample_rate": 8000,
  "text": "Thank you for calling. How can I help you today?"
}
  • Ultra-low latency for real-time conversation
  • Phone-compatible audio format
  • Clear, professional voice

Best Practices

🚀 Optimization Tips

Maximize Deepgram’s Speed Advantage

Pricing

💰 Simple, Predictable Pricing

Pay-per-character with volume discounts. No hidden fees or subscription tiers.
Usage TierPrice per CharacterBest For
First 10M chars/month$0.0135Small to medium businesses
Next 90M chars/month$0.0108Growing applications
Next 400M chars/month$0.0081Enterprise usage
500M+ chars/monthCustom pricingLarge-scale deployments

Language Support

🇺🇸 English Optimizations

Specialized for English-language applications
  • Native English training data
  • Optimized for American English pronunciation
  • Best-in-class quality for English content
  • Perfect for US-based business applications

Troubleshooting

Migration from Other Providers

Key Differences:
  • 3x faster latency (75ms vs 250ms)
  • English-only vs multilingual
  • WebSocket-only vs REST+WebSocket
  • Different voice ID format
Migration Tips:
  • Map ElevenLabs voices to Deepgram equivalents
  • Update API calls to WebSocket format
  • Adjust audio encoding for your use case

⚡ Ready for Ultra-Fast TTS?

Configure Deepgram Aura in your assistant settings and experience the speed difference in your phone calls!