The LiveKit Python SDK provides a convenient interface for integrating LiveKit's real-time video and audio capabilities into your Python applications. With it, developers can easily leverage LiveKit's WebRTC functionalities, allowing them to focus on building their AI models or other application logic without worrying about the complexities of WebRTC.
This repo contains two packages
- livekit: Real-time SDK for connecting to LiveKit as a participant
- livekit-api: Access token generation and server APIs
$ pip install livekit-apifrom livekit import api
import os
# will automatically use the LIVEKIT_API_KEY and LIVEKIT_API_SECRET env vars
token = api.AccessToken() \
.with_identity("python-bot") \
.with_name("Python Bot") \
.with_grants(api.VideoGrants(
room_join=True,
room="my-room",
)).to_jwt()RoomService uses asyncio and aiohttp to make API calls. It needs to be used with an event loop.
from livekit import api
import asyncio
async def main():
lkapi = api.LiveKitAPI(
'http://localhost:7880',
)
room_info = await lkapi.room.create_room(
api.CreateRoomRequest(name="my-room"),
)
print(room_info)
results = await lkapi.room.list_rooms(api.ListRoomsRequest())
print(results)
await lkapi.aclose()
asyncio.get_event_loop().run_until_complete(main())$ pip install livekitfrom livekit import rtc
async def main():
room = rtc.Room()
@room.on("participant_connected")
def on_participant_connected(participant: rtc.RemoteParticipant):
logging.info(
"participant connected: %s %s", participant.sid, participant.identity)
async def receive_frames(stream: rtc.VideoStream):
async for frame in video_stream:
# received a video frame from the track, process it here
pass
# track_subscribed is emitted whenever the local participant is subscribed to a new track
@room.on("track_subscribed")
def on_track_subscribed(track: rtc.Track, publication: rtc.RemoteTrackPublication, participant: rtc.RemoteParticipant):
logging.info("track subscribed: %s", publication.sid)
if track.kind == rtc.TrackKind.KIND_VIDEO:
video_stream = rtc.VideoStream(track)
asyncio.ensure_future(receive_frames(video_stream))
# By default, autosubscribe is enabled. The participant will be subscribed to
# all published tracks in the room
await room.connect(URL, TOKEN)
logging.info("connected to room %s", room.name)
# participants and tracks that are already available in the room
# participant_connected and track_published events will *not* be emitted for them
for participant in room.participants.items():
for publication in participant.tracks.items():
print("track publication: %s", publication.sid)- Facelandmark: Use mediapipe to detect face landmarks (eyes, nose ...)
- Whisper: Transcribe an audio track using OpenAI whisper
- Basic room: Connect to a room
- Publish hue: Publish a rainbow video track
- Publish wave: Publish a sine wave
Please join us on Slack to get help from our devs / community members. We welcome your contributions(PRs) and details can be discussed there.
| LiveKit Ecosystem | |
|---|---|
| Client SDKs | Components · JavaScript · iOS/macOS · Android · Flutter · React Native · Rust · Python · Unity (web) · Unity (beta) |
| Server SDKs | Node.js · Golang · Ruby · Java/Kotlin · PHP (community) · Python (community) |
| Services | Livekit server · Egress · Ingress |
| Resources | Docs · Example apps · Cloud · Self-hosting · CLI |