Text-to-speech (TTS) integrations

Overview

Text-to-speech (TTS) models produce realtime synthetic speech from text input. In voice AI, this allows a text-based LLM to speak its response to the user.

Available providers

The agents framework includes plugins for the following TTS providers out-of-the-box. Choose a provider from the list for a step-by-step guide. You can also implement the TTS node to provide custom behavior or an alternative provider.

All TTS providers support high quality, low-latency, and lifelike multilingual voice synthesis. Support for other features is noted in the following table.

Provider	Prompt	Custom Voices	Pronunciation	Aligned Transcripts	Available in
Amazon Polly	—	—	✓ SSML	—	Python
Azure AI Speech	—	—	✓ SSML	—	Python
Azure OpenAI	✓	—	—	—	Python
Baseten	—	—	—	—	Python
Cartesia	—	✓	✓ SSML	✓	Python, Node.js
Deepgram	—	—	—	—	Python
ElevenLabs	—	✓	✓ SSML	✓	Python, Node.js
Gemini	—	—	✓ SSML	—	Python, Node.js
Google Cloud	—	—	✓ SSML	—	Python
Groq	—	—	—	—	Python
Hume	✓	✓	—	—	Python
Inworld	—	✓	—	—	Python
LMNT	—	✓	—	—	Python
Neuphonic	—	✓	—	—	Python
OpenAI	✓	—	—	—	Python, Node.js
PlayHT	—	✓	—	—	Python
Resemble AI	—	✓	✓ SSML	—	Python
Rime	—	—	✓ Custom	—	Python
Sarvam	—	—	—	—	Python
Speechify	—	✓	✓ SSML	—	Python
Spitch	—	—	—	—	Python

Have another provider in mind? LiveKit is open source and welcomes new plugin contributions.

How to use

The following sections describe high-level usage only.

For more detailed information about installing and using plugins, see the plugins overview.

Usage in AgentSession

Construct an AgentSession or Agent with a TTS instance created by your desired plugin:

from livekit.agents import AgentSession
from livekit.plugins import cartesia

session = AgentSession(
    tts=cartesia.TTS(model="sonic-english")
)

AgentSession automatically sends LLM responses to the TTS model, and also supports a say method for one-off responses.

Standalone usage

You can also use a TTS instance in a standalone fashion by creating a stream. You can use push_text to add text to the stream, and then consume a stream of SynthesizedAudio as to publish as realtime audio to another participant.

Here is an example of a standalone TTS app:

from livekit import agents, rtc
from livekit.agents.tts import SynthesizedAudio
from livekit.plugins import cartesia
from typing import AsyncIterable

async def entrypoint(ctx: agents.JobContext):
    text_stream: AsyncIterable[str] = ... # you need to provide a stream of text
    audio_source = rtc.AudioSource(44100, 1)

    track = rtc.LocalAudioTrack.create_audio_track("agent-audio", audio_source)
    await ctx.room.local_participant.publish_track(track)

    tts = cartesia.TTS(model="sonic-english")
    tts_stream = tts.stream()

    # create a task to consume and publish audio frames
    ctx.create_task(send_audio(tts_stream))

    # push text into the stream, TTS stream will emit audio frames along with events
    # indicating sentence (or segment) boundaries.
    async for text in text_stream:
        tts_stream.push_text(text)
    tts_stream.end_input()

    async def send_audio(audio_stream: AsyncIterable[SynthesizedAudio]):
        async for a in audio_stream:
            await audio_source.capture_frame(a.audio.frame)

if __name__ == "__main__":
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))

Further reading