Add transports examples to foundational examples
This commit is contained in:
Mark Backman
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# Changelog
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All notable changes to **Pipecat** will be documented in this file.
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@@ -45,6 +44,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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- Fixed an issue where the `SmartTurnMetricsData` was reporting 0ms for
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inference and processing time when using the `FalSmartTurnAnalyzer`.
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### Other
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- Added 04 foundational examples for client/server transports. Also, renamed
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`29-livekit-audio-chat.py` to `04b-transports-livekit.py`.
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## [0.0.65] - 2025-04-23 "Sant Jordi's release" 🌹📕
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https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
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107
examples/foundational/04-transports-small-webrtc.py
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107
examples/foundational/04-transports-small-webrtc.py
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import argparse
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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load_dotenv(override=True)
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async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
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logger.info(f"Starting bot")
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transport = SmallWebRTCTransport(
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webrtc_connection=webrtc_connection,
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params=TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt,
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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report_only_initial_ttfb=True,
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),
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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@transport.event_handler("on_client_closed")
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async def on_client_closed(transport, client):
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logger.info(f"Client closed connection")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=False)
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await runner.run(task)
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if __name__ == "__main__":
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from run import main
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main()
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@@ -1,86 +0,0 @@
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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#
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# This example broken on latest pipecat and needs updating.
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#
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import asyncio
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import os
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import sys
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import aiohttp
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from daily_runner import configure
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.frames.frames import EndPipeFrame, LLMMessagesFrame, TextFrame
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from pipecat.pipeline.merge_pipeline import SequentialMergePipeline
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.task import PipelineTask
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from pipecat.services.azure import AzureLLMService, AzureTTSService
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.transport_services import TransportServiceOutput
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from pipecat.services.transports.daily_transport import DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, _) = await configure(session)
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transport = DailyTransport(room_url, None, "Static And Dynamic Speech")
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meeting = TransportServiceOutput(transport, mic_enabled=True)
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llm = AzureLLMService(
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api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
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endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
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model=os.getenv("AZURE_CHATGPT_MODEL"),
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)
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azure_tts = AzureTTSService(
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api_key=os.getenv("AZURE_SPEECH_API_KEY"),
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region=os.getenv("AZURE_SPEECH_REGION"),
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)
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elevenlabs_tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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)
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messages = [{"role": "system", "content": "tell the user a joke about llamas"}]
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# Start a task to run the LLM to create a joke, and convert the LLM
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# output to audio frames. This task will run in parallel with generating
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# and speaking the audio for static text, so there's no delay to speak
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# the LLM response.
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llm_pipeline = Pipeline([llm, elevenlabs_tts])
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llm_task = PipelineTask(llm_pipeline)
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await llm_task.queue_frames([LLMMessagesFrame(messages), EndPipeFrame()])
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simple_tts_pipeline = Pipeline([azure_tts])
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await simple_tts_pipeline.queue_frames(
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[
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TextFrame("My friend the LLM is going to tell a joke about llamas."),
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EndPipeFrame(),
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]
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)
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merge_pipeline = SequentialMergePipeline([simple_tts_pipeline, llm_pipeline])
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await asyncio.gather(
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transport.run(merge_pipeline),
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simple_tts_pipeline.run_pipeline(),
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llm_pipeline.run_pipeline(),
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)
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if __name__ == "__main__":
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asyncio.run(main())
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102
examples/foundational/04a-transports-daily.py
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102
examples/foundational/04a-transports-daily.py
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import os
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import sys
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import aiohttp
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from daily_runner import configure
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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report_only_initial_ttfb=True,
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),
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)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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await task.cancel()
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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