examples(websocket-server): use VAD analyzer from transport
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@@ -1,39 +0,0 @@
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#
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# Copyright (c) 2024, 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 sys
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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 PipelineTask
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from pipecat.transports.network.websocket_server import WebsocketServerTransport
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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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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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transport = WebsocketServerTransport()
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pipeline = Pipeline([transport.input(), transport.output()])
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task = PipelineTask(pipeline)
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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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@@ -173,6 +173,7 @@
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}
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function stopAudio(closeWebsocket) {
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playTime = 0;
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isPlaying = false;
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startBtn.disabled = false;
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stopBtn.disabled = true;
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@@ -12,13 +12,16 @@ import sys
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from pipecat.frames.frames import LLMMessagesFrame
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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 PipelineTask
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from pipecat.processors.aggregators.llm_response import LLMAssistantResponseAggregator, LLMUserResponseAggregator
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_response import (
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LLMAssistantResponseAggregator,
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LLMUserResponseAggregator
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)
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.services.whisper import WhisperSTTService
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from pipecat.transports.network.websocket_server import WebsocketServerParams, WebsocketServerTransport
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from pipecat.vad.silero import SileroVAD
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from pipecat.vad.silero import SileroVADAnalyzer
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from loguru import logger
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@@ -33,13 +36,15 @@ async def main():
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async with aiohttp.ClientSession() as session:
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transport = WebsocketServerTransport(
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params=WebsocketServerParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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add_wav_header=True
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add_wav_header=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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vad = SileroVAD(audio_passthrough=True)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o")
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@@ -64,7 +69,6 @@ async def main():
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pipeline = Pipeline([
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transport.input(), # Websocket input from client
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vad, # VAD to detect user speech
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stt, # Speech-To-Text
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tma_in, # User responses
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llm, # LLM
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