livekit-plugins-baseten 1.6.1

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, inference
from livekit.plugins import baseten, openai, noise_cancellation
from livekit.agents.inference import TurnDetector

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=inference.VAD(),
        turn_detection=TurnDetector(),
    )

    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