Updating foundation examples to use SmallWebRTCTransport and pipecat-ai-small-webrtc-prebuilt (#1534)

Co-authored-by: Filipi Fuchter <filipi@daily.co>
This commit is contained in:
Mark Backman
2025-04-11 19:44:16 -04:00
committed by GitHub
parent 8186219879
commit f6accbd510
120 changed files with 7989 additions and 7179 deletions

View File

@@ -4,14 +4,10 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -20,7 +16,9 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.ultravox.stt import UltravoxSTTService
from pipecat.transports.services.daily import DailyParams, DailyTransport
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
@@ -28,8 +26,6 @@ load_dotenv(override=True)
# The Ultravox model is compute-intensive and performs best with GPU acceleration.
# This can be deployed on cloud GPU providers like Cerebrium.ai for optimal performance.
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
# Want to initialize the ultravox processor since it takes time to load the model and dont
# want to load it every time the pipeline is run
@@ -39,53 +35,61 @@ ultravox_processor = UltravoxSTTService(
)
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
async def run_bot(webrtc_connection: SmallWebRTCConnection):
logger.info(f"Starting bot")
transport = DailyTransport(
room_url,
token,
"Respond bot",
DailyParams(
audio_out_enabled=True,
transcription_enabled=False,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
vad_audio_passthrough=True,
),
)
transport = SmallWebRTCTransport(
webrtc_connection=webrtc_connection,
params=TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
vad_audio_passthrough=True,
),
)
tts = CartesiaTTSService(
api_key=os.environ.get("CARTESIA_API_KEY"),
voice_id="97f4b8fb-f2fe-444b-bb9a-c109783a857a",
)
tts = CartesiaTTSService(
api_key=os.environ.get("CARTESIA_API_KEY"),
voice_id="97f4b8fb-f2fe-444b-bb9a-c109783a857a",
)
pipeline = Pipeline(
[
transport.input(), # Transport user input
ultravox_processor,
tts, # TTS
transport.output(), # Transport bot output
]
)
pipeline = Pipeline(
[
transport.input(), # Transport user input
ultravox_processor,
tts, # TTS
transport.output(), # Transport bot output
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
allow_interruptions=True,
enable_metrics=True,
),
)
task = PipelineTask(
pipeline,
params=PipelineParams(
allow_interruptions=True,
enable_metrics=True,
),
)
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.cancel()
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info(f"Client connected")
runner = PipelineRunner()
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info(f"Client disconnected")
await runner.run(task)
@transport.event_handler("on_client_closed")
async def on_client_closed(transport, client):
logger.info(f"Client closed connection")
await task.cancel()
runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())
from run import main
main()