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
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) |
language_options |
[] |
Restrict auto-detection to these codes, e.g. ["en", "de"]. More reliable than "auto" on short telephony utterances. Requires Whisper runtime v0.5.0+ |
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",
)
Qwen3 (STT + TTS)
Baseten also hosts Qwen3-ASR and Qwen3-TTS. They speak different wire
protocols from the Whisper/Orpheus trusses, so select them with model — the
same baseten.STT / baseten.TTS classes handle both.
from livekit.agents import AgentSession
from livekit.plugins import baseten
session = AgentSession(
stt=baseten.STT(model="qwen3-asr", model_id="your-qwen3-asr-model-id"),
tts=baseten.TTS(model="qwen3-tts", model_id="your-qwen3-tts-model-id",
voice="your-voice"),
# llm=...
)
model="whisper" / "orpheus" (default) |
model="qwen3-asr" / "qwen3-tts" |
|
|---|---|---|
| STT audio | raw binary PCM | base64 input_audio_buffer.append |
| STT results | message_type / transcript |
type: "transcription" / segments[].text |
| TTS transport | HTTP, or WS with an __END__ sentinel |
session.config → input.text → input.done |
| TTS voices | preset names (tara) |
registered voice clones |
Several defaults follow the selected model, matching each deployment's own:
| Parameter | whisper / orpheus |
qwen3-asr / qwen3-tts |
|---|---|---|
language |
en |
auto (STT) / Auto (TTS) |
partial_transcript_interval_s |
1.0 |
0.5 |
vad_min_silence_duration_ms |
300 |
500 |
vad_speech_pad_ms |
30 |
100 |
show_word_timestamps |
on | off (needs STREAM_ALIGNER=mms on the deployment) |
voice |
tara |
required — a registered clone |
Qwen3 TTS voices
Qwen3-TTS Base ships no built-in speakers — there is no tara equivalent.
Register a clone from 10–20s of clean speech, then pass its name as voice:
from livekit.plugins.baseten import list_voices, register_voice
await register_voice(
model_endpoint="wss://model-{model_id}.api.baseten.co/environments/production/websocket",
name="my_voice",
ref_audio_path="./reference.wav",
ref_text="Transcript of the reference audio.",
)
await list_voices(model_endpoint=...) # {"voices": [...], "uploaded_voices": [...]}
The server stores uploaded voices on the container's local disk, so a voice
registered at runtime lives on one replica and is lost when that container
restarts. For anything beyond single-replica testing, bake the reference audio
into the deployment (REQUIRED_VOICES) so every replica starts with it, or pass
ref_audio/ref_text to clone inline on each session.
Qwen3-only options
task_type (Base cloning, or CustomVoice/VoiceDesign deployments),
instructions, max_new_tokens, initial_codec_chunk_frames,
x_vector_only_mode, ref_audio/ref_text, and extra_config (merged into
session.config for server fields newer than this plugin) apply to
model="qwen3-tts" only.
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
Metadata
Release files for livekit-plugins-baseten 1.8.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| livekit_plugins_baseten-1.8.5.tar.gz | 27.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| livekit_plugins_baseten-1.8.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 63.6 kB
Release files / livekit_plugins_baseten-1.8.5.tar.gz
| Download URL | livekit_plugins_baseten-1.8.5.tar.gz |
|---|---|
| Size | 27.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
b80a97df0a9f7d196150d110fcf4b84f432610d7d20b1e4c2a7b1c9326e9d6c0
|
|
BLAKE2b-256 checksum How to use checksums |
ae623e82cff7f29a311a1b2e0c9fc63d64ec181ae262b5bd4bdf5238ce20f010
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 6, 2026.
Transparency logRelease files / livekit_plugins_baseten-1.8.5-py3-none-any.whl
| Download URL | livekit_plugins_baseten-1.8.5-py3-none-any.whl |
|---|---|
| Size | 35.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
e822c589f041e43cfbad8f40542c9d53864509881e2cc90871240446ac5bcf4c
|
|
BLAKE2b-256 checksum How to use checksums |
805642ad10cff6bbcabada81899ab472975d6a356a7a49229baa0cb73ae3b41f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 6, 2026.
Transparency log
