​
Overview

Deepgram’s Aura API provides high-quality text-to-speech synthesis with streaming capabilities and ultra-low latency. The service offers various voice models optimized for conversational AI applications with efficient audio streaming.

API Reference

Complete API documentation and method details

Deepgram TTS Docs

Official Deepgram text-to-speech API documentation

Example Code

Working example with Silero VAD

​
Installation

To use Deepgram services, install the required dependencies: 
pip install "pipecat-ai[deepgram]"
You’ll also need to set up your Deepgram API key as an environment variable: DEEPGRAM_API_KEY.
Get your API key from the Deepgram Console.

​
Frames

​
Input

  • TextFrame - Text content to synthesize into speech
  • TTSSpeakFrame - Text that should be spoken immediately
  • TTSUpdateSettingsFrame - Runtime configuration updates
  • LLMFullResponseStartFrame / LLMFullResponseEndFrame - LLM response boundaries

​
Output

  • TTSStartedFrame - Signals start of synthesis
  • TTSAudioRawFrame - Generated audio data chunks (streaming)
  • TTSStoppedFrame - Signals completion of synthesis
  • ErrorFrame - API or processing errors

​
Voice Models

Deepgram offers various Aura voice models optimized for different use cases. Here are some highlights:
Voice ModelDescriptionLanguage
aura-2-helena-enNatural female voiceEnglish
aura-2-andromeda-enExpressive female voiceEnglish
aura-helios-enWarm male voiceEnglish
aura-luna-enConversational female voiceEnglish
aura-stella-enProfessional female voiceEnglish
aura-zeus-enAuthoritative male voiceEnglish
Deepgram regularly adds new voice models. Check the official documentation for the latest available voices.

​
Supported Sample Rates

  • 8000 Hz - Phone quality
  • 16000 Hz - Standard quality
  • 24000 Hz - High quality (default)
  • 44100 Hz - CD quality
  • 48000 Hz - Professional quality

​
Integration with VAD

Deepgram TTS works seamlessly with Voice Activity Detection: 
from pipecat.audio.vad.silero import SileroVADAnalyzer

transport_params = DailyParams(
    audio_in_enabled=True,
    audio_out_enabled=True,
    vad_analyzer=SileroVADAnalyzer()
)

​
Using Deepgram’s Built-in VAD Events

from deepgram import LiveOptions

stt = DeepgramSTTService(
    api_key=os.getenv("DEEPGRAM_API_KEY"),
    live_options=LiveOptions(
        vad_events=True,
        utterance_end_ms="1000"
    )
)

@stt.event_handler("on_speech_started")
async def on_speech_started(stt, *args, **kwargs):
    await task.queue_frames([BotInterruptionFrame()])

@stt.event_handler("on_utterance_end")
async def on_utterance_end(stt, *args, **kwargs):
    await task.queue_frames([StopInterruptionFrame()])

​
Usage Example

​
Basic Configuration

Initialize the DeepgramTTSService with your API key and use it in your pipeline: 
from pipecat.services.deepgram.tts import DeepgramTTSService
import os

# Configure service
tts = DeepgramTTSService(
    api_key=os.getenv("DEEPGRAM_API_KEY"),
    voice="aura-2-andromeda-en",
    sample_rate=24000,
    encoding="linear16"
)

# Use in pipeline
pipeline = Pipeline([
    transport.input(),
    stt,
    context_aggregator.user(),
    llm,
    tts,
    transport.output(),
    context_aggregator.assistant()
])

​
Custom Configuration

# Advanced configuration with custom settings
tts = DeepgramTTSService(
    api_key=os.getenv("DEEPGRAM_API_KEY"),
    voice="aura-helios-en",        # Male voice
    base_url="https://api.deepgram.com",  # Custom endpoint
    sample_rate=48000,             # High-quality audio
    encoding="linear16"
)

​
Dynamic Configuration

Make settings updates by pushing a TTSUpdateSettingsFrame for the DeepgramTTSService: 
from pipecat.frames.frames import TTSUpdateSettingsFrame

await task.queue_frame(TTSUpdateSettingsFrame(
    voice="aura-luna-en",  # Change to a different voice
  )
)

​
Metrics

The service provides comprehensive metrics:
  • Time to First Byte (TTFB) - Latency from text input to first audio
  • Processing Duration - Total synthesis time
  • Character Usage - Text processed for billing
Learn how to enable Metrics in your Pipeline.

​
Additional Notes

  • Streaming Audio: Service streams audio in chunks for low-latency playback
  • Voice Selection: Choose voices based on your application’s tone and audience
  • Sample Rate Matching: Ensure sample rate matches your pipeline’s audio output sample rate