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Overview

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

Deepgram TTS API Reference

Pipecat’s API methods for Deepgram TTS integration

Example Implementation

Complete example with Silero VAD

Deepgram Documentation

Official Deepgram Aura TTS API documentation

Voice Models

Browse available Aura voice models

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Installation

To use Deepgram services, install the required dependencies: 
pip install "pipecat-ai[deepgram]"

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Prerequisites

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Deepgram Account Setup

Before using Deepgram TTS services, you need:
  1. Deepgram Account: Sign up at Deepgram Console
  2. API Key: Generate an API key from your project dashboard
  3. Voice Selection: Choose from available Aura voice models

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Required Environment Variables

  • DEEPGRAM_API_KEY: Your Deepgram API key for authentication

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Configuration

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DeepgramTTSService

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api_key
str
required
Deepgram API key for authentication.
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voice
str
default:"aura-2-helena-en"
Voice model to use for synthesis.
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base_url
str
default:"wss://api.deepgram.com"
WebSocket base URL for Deepgram API.
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sample_rate
int
default:"None"
Output audio sample rate in Hz. When None, uses the pipeline’s configured sample rate.
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encoding
str
default:"linear16"
Audio encoding format. Must be one of: "linear16", "mulaw", "alaw".

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DeepgramHttpTTSService

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api_key
str
required
Deepgram API key for authentication.
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voice
str
default:"aura-2-helena-en"
Voice model to use for synthesis.
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aiohttp_session
aiohttp.ClientSession
required
An aiohttp session for HTTP requests. You must create and manage this yourself.
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base_url
str
default:"https://api.deepgram.com"
HTTP API base URL.
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sample_rate
int
default:"None"
Output audio sample rate in Hz.
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encoding
str
default:"linear16"
Audio encoding format.

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Usage

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Basic Setup

from pipecat.services.deepgram import DeepgramTTSService

tts = DeepgramTTSService(
    api_key=os.getenv("DEEPGRAM_API_KEY"),
    voice="aura-2-helena-en",
)

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HTTP Service

import aiohttp
from pipecat.services.deepgram import DeepgramHttpTTSService

async with aiohttp.ClientSession() as session:
    tts = DeepgramHttpTTSService(
        api_key=os.getenv("DEEPGRAM_API_KEY"),
        voice="aura-2-helena-en",
        aiohttp_session=session,
    )

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Notes

  • WebSocket vs HTTP: The WebSocket service (DeepgramTTSService) supports real-time streaming with interruption handling via the Clear message, making it suitable for interactive conversations. The HTTP service is simpler but processes each request as a batch.
  • Flush behavior: The WebSocket service automatically flushes pending text when it receives an LLMFullResponseEndFrame or EndFrame, forcing Deepgram to generate audio for any remaining buffered text.
  • Encoding validation: The WebSocket service validates the encoding parameter at initialization and raises a ValueError for unsupported formats.

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Event Handlers

Deepgram TTS supports the standard service connection events:
EventDescription
on_connectedConnected to Deepgram WebSocket
on_disconnectedDisconnected from Deepgram WebSocket
on_connection_errorWebSocket connection error occurred
@tts.event_handler("on_connected")
async def on_connected(service):
    print("Connected to Deepgram")