adding a frame processor with the ability to save a conversation to a buffer and another frame processor to upload audio to Canonical for evaluation and metrics collection. Examples included

examples/chatbot-audio-recording/bot.py

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+#
+# Copyright (c) 2024, Daily
+#
+# 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.frames.frames import EndFrame, LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.processors.audio_buffer_processor import AudioBufferProcessor
+from pipecat.processors.user_marker_processor import UserMarkerProcessor
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Chatbot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_in_enabled=True,
+                camera_out_enabled=False,
+                vad_enabled=True,
+                vad_audio_passthrough=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
+                #
+                # Spanish
+                #
+                # transcription_settings=DailyTranscriptionSettings(
+                #     language="es",
+                #     tier="nova",
+                #     model="2-general"
+                # )
+            )
+        )
+
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+
+            #
+            # English
+            #
+            voice_id="cgSgspJ2msm6clMCkdW9",
+            aiohttp_session=session,
+
+            #
+            # Spanish
+            #
+            # model="eleven_multilingual_v2",
+            # voice_id="gD1IexrzCvsXPHUuT0s3",
+        )
+
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                #
+                # English
+                #
+                "content": "You are Chatbot, a friendly, helpful robot. 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, but keep your responses brief. Start by introducing yourself. Keep all your response to 12 words or fewer.",
+
+                #
+                # Spanish
+                #
+                # "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
+            },
+        ]
+
+        user_response = LLMUserResponseAggregator()
+        assistant_response = LLMAssistantResponseAggregator()
+
+        audiobuffer = AudioBufferProcessor()
+        usermarker = UserMarkerProcessor()
+        pipeline = Pipeline([
+            transport.input(),  # microphone
+            usermarker,  # used to mark the user's audio in the pipeline
+            user_response,
+            llm,
+            tts,
+            audiobuffer,  # used to buffer the audio in the pipeline
+            transport.output(),
+            assistant_response,
+        ])
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            print(f"Participant left: {participant}")
+            await task.queue_frame(EndFrame())
+
+        @transport.event_handler("on_call_state_updated")
+        async def on_call_state_updated(transport, state):
+            if state == "left":
+                await task.queue_frame(EndFrame())
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())