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examples(foundational): support multiple transports

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This commit is contained in:
Aleix Conchillo Flaqué
2025-05-24 21:00:24 -07:00
parent ecf878e14d
commit 2cdfaa0a82
128 changed files with 3282 additions and 2716 deletions
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78
examples/foundational/38b-smart-turn-local.py
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@@ -21,44 +21,53 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.services.daily import DailyParams
load_dotenv(override=True)
# To use this locally, set the environment variable LOCAL_SMART_TURN_MODEL_PATH
# to the path where the smart-turn repo is cloned.
#
# Example setup:
#
# # Git LFS (Large File Storage)
# brew install git-lfs
# # Hugging Face uses LFS to store large model files, including .mlpackage
# git lfs install
# # Clone the repo with the smart_turn_classifier.mlpackage
# git clone https://huggingface.co/pipecat-ai/smart-turn
#
# Then set the env variable:
# export LOCAL_SMART_TURN_MODEL_PATH=./smart-turn
# or add it to your .env file
smart_turn_model_path = os.getenv("LOCAL_SMART_TURN_MODEL_PATH")
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
logger.info(f"Starting bot")
# To use this locally, set the environment variable LOCAL_SMART_TURN_MODEL_PATH
# to the path where the smart-turn repo is cloned.
#
# Example setup:
#
# # Git LFS (Large File Storage)
# brew install git-lfs
# # Hugging Face uses LFS to store large model files, including .mlpackage
# git lfs install
# # Clone the repo with the smart_turn_classifier.mlpackage
# git clone https://huggingface.co/pipecat-ai/smart-turn
#
# Then set the env variable:
# export LOCAL_SMART_TURN_MODEL_PATH=./smart-turn
# or add it to your .env file
smart_turn_model_path = os.getenv("LOCAL_SMART_TURN_MODEL_PATH")
transport = SmallWebRTCTransport(
webrtc_connection=webrtc_connection,
params=TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
turn_analyzer=LocalSmartTurnAnalyzer(
smart_turn_model_path=smart_turn_model_path, params=SmartTurnParams()
),
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
# instantiated. The function will be called when the desired transport gets
# selected.
transport_params = {
"daily": lambda: DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
turn_analyzer=LocalSmartTurnAnalyzer(
smart_turn_model_path=smart_turn_model_path, params=SmartTurnParams()
),
)
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
turn_analyzer=LocalSmartTurnAnalyzer(
smart_turn_model_path=smart_turn_model_path, params=SmartTurnParams()
),
),
}
async def run_example(transport: BaseTransport, _: argparse.Namespace):
logger.info(f"Starting bot")
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -111,6 +120,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info(f"Client disconnected")
await task.cancel()
@transport.event_handler("on_client_closed")
async def on_client_closed(transport, client):
@@ -125,4 +135,4 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
if __name__ == "__main__":
from run import main
main()
main(run_example, transport_params=transport_params)