livekit-plugins-baseten 1.5.19rc1

Agent Framework plugin for Baseten

Baseten plugin for LiveKit Agents

Support for Baseten-hosted models in LiveKit Agents, including STT (Speech-to-Text), TTS (Text-to-Speech), and LLM (Large Language Model) integrations.

Installation

pip install livekit-plugins-baseten

Pre-requisites

You'll need an API key from Baseten. It can be set as an environment variable: BASETEN_API_KEY

You also need to deploy a model to Baseten and will need your model endpoint to configure the plugin.

STT (Speech-to-Text)

The STT plugin connects to Baseten's Whisper Streaming WebSocket endpoint for real-time transcription. It works with both truss and chain deployments.

Recommended model

Whisper v3 Turbo – WebSocket

Endpoint URL formats

Deployment type	URL pattern
Truss	wss://model-{model_id}.api.baseten.co/environments/production/websocket
Chain	wss://chain-{chain_id}.api.baseten.co/environments/production/websocket

Basic usage

You can specify the endpoint in three ways:

from livekit.plugins import baseten

# 1. Using a truss model ID (recommended for truss deployments)
stt = baseten.STT(
    api_key="your-baseten-api-key",  # or set BASETEN_API_KEY env var
    model_id="your-model-id",
    language="en",
)

# 2. Using a chain ID (recommended for chain deployments)
stt = baseten.STT(
    api_key="your-baseten-api-key",
    chain_id="your-chain-id",
    language="en",
)

# 3. Using a full endpoint URL (for custom routing or deployment URLs)
stt = baseten.STT(
    api_key="your-baseten-api-key",
    model_endpoint="wss://model-{model_id}.api.baseten.co/environments/production/websocket",
    language="en",
)

Configuration options

Parameter	Default	Description
api_key	BASETEN_API_KEY env var	Baseten API key
model_endpoint	BASETEN_MODEL_ENDPOINT env var	Full WebSocket URL (takes priority over model_id/chain_id)
model_id	—	Baseten truss model ID; auto-constructs the endpoint URL
chain_id	—	Baseten chain ID; auto-constructs the endpoint URL
language	"en"	BCP-47 language code (use "auto" for auto-detection)
encoding	"pcm_s16le"	Audio encoding (pcm_s16le or pcm_mulaw)
sample_rate	16000	Audio sample rate in Hz
enable_partial_transcripts	True	Emit interim transcripts while the speaker is talking
partial_transcript_interval_s	1.0	Interval (seconds) between partial transcript updates
final_transcript_max_duration_s	30	Max seconds of audio before forcing a final transcript
show_word_timestamps	True	Include word-level timestamps in results
vad_threshold	0.5	Server-side VAD speech probability threshold (0.0–1.0)
vad_min_silence_duration_ms	300	Minimum silence (ms) to mark end of speech
vad_speech_pad_ms	30	Padding (ms) added around detected speech

Full voice pipeline example

import os
from livekit import agents
from livekit.agents import AgentSession, Agent, RoomInputOptions
from livekit.plugins import baseten, openai, noise_cancellation, silero
from livekit.plugins.turn_detector.multilingual import MultilingualModel

BASETEN_API_KEY = os.getenv("BASETEN_API_KEY")
whisper_model_id = "your-whisper-model-id"  # or use chain_id for chain deployments
orpheus_model_id = "your-orpheus-model-id"


class Assistant(Agent):
    def __init__(self) -> None:
        super().__init__(instructions="You are a helpful voice AI assistant.")


async def entrypoint(ctx: agents.JobContext):
    session = AgentSession(
        stt=baseten.STT(
            api_key=BASETEN_API_KEY,
            model_id=whisper_model_id,  # or chain_id="your-chain-id"
            language="en",
            enable_partial_transcripts=True,
        ),
        llm=openai.LLM(
            api_key=BASETEN_API_KEY,
            base_url="https://inference.baseten.co/v1",
            model="openai/gpt-oss-120b",
        ),
        tts=baseten.TTS(
            api_key=BASETEN_API_KEY,
            model_endpoint=(
                f"https://model-{orpheus_model_id}"
                ".api.baseten.co/environments/production/predict"
            ),
        ),
        vad=silero.VAD.load(),
        turn_detection=MultilingualModel(),
    )

    await session.start(
        room=ctx.room,
        agent=Assistant(),
        room_input_options=RoomInputOptions(
            noise_cancellation=noise_cancellation.BVC(),
        ),
    )

    await session.generate_reply(
        instructions="Greet the user and offer your assistance."
    )


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

TTS (Text-to-Speech)

The TTS plugin calls Baseten-hosted TTS models (e.g. Orpheus 3B) over HTTP.

tts = baseten.TTS(
    api_key="your-baseten-api-key",
    model_endpoint="https://model-{model_id}.api.baseten.co/environments/production/predict",
    voice="tara",
    language="en",
)

LLM (Large Language Model)

The LLM plugin wraps Baseten's OpenAI-compatible inference endpoint.

llm = baseten.LLM(
    api_key="your-baseten-api-key",
    model="openai/gpt-oss-120b",
)

Documentation

• LiveKit STT integration guide
• LiveKit TTS integration guide
• Baseten Whisper Streaming docs
• Baseten Model Library