Reorganize examples into topic-based subfolders
Move 304 examples from a flat numbered directory into 14 descriptive subfolders: getting-started, services (speech + function-calling), transcription, vision, realtime, persistent-context, context-summarization, update-settings (stt/tts/llm), turn-management, thinking-and-mcp, transports, video-avatar, video-processing, and features. Strip numbered prefixes from filenames (e.g. 07c-interruptible-deepgram.py becomes services/speech/deepgram.py) since the folder context makes them redundant. Keep numbered prefixes only in getting-started/ where ordering matters. Update eval script paths and README to match the new structure.
This commit is contained in:
Mark Backman
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examples/features/switch-voices.py
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examples/features/switch-voices.py
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#
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# Copyright (c) 2024-2026, 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 os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import Frame, LLMRunFrame
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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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.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.processors.filters.function_filter import FunctionFilter
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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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.llm_service import FunctionCallParams
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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class SwitchVoices(ParallelPipeline):
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def __init__(self):
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self._current_voice = "News Lady"
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news_lady = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaTTSService.Settings(
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voice="bf991597-6c13-47e4-8411-91ec2de5c466", # Newslady
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),
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)
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british_lady = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaTTSService.Settings(
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voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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),
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)
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barbershop_man = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaTTSService.Settings(
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voice="a0e99841-438c-4a64-b679-ae501e7d6091", # Barbershop Man
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),
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)
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super().__init__(
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# News Lady voice
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[FunctionFilter(self.news_lady_filter), news_lady],
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# British Reading Lady voice
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[FunctionFilter(self.british_lady_filter), british_lady],
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# Barbershop Man voice
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[FunctionFilter(self.barbershop_man_filter), barbershop_man],
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)
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@property
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def current_voice(self):
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return self._current_voice
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async def switch_voice(self, params: FunctionCallParams):
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self._current_voice = params.arguments["voice"]
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await params.result_callback(
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{
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"voice": f"You are now using your {self.current_voice} voice. Your responses should now be as if you were a {self.current_voice}."
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}
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)
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async def news_lady_filter(self, _: Frame) -> bool:
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return self.current_voice == "News Lady"
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async def british_lady_filter(self, _: Frame) -> bool:
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return self.current_voice == "British Lady"
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async def barbershop_man_filter(self, _: Frame) -> bool:
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return self.current_voice == "Barbershop Man"
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = SwitchVoices()
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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settings=OpenAILLMService.Settings(
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system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative and helpful way. You can do the following voices: 'News Lady', 'British Lady' and 'Barbershop Man'.",
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),
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)
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llm.register_function("switch_voice", tts.switch_voice)
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switch_voice_function = FunctionSchema(
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name="switch_voice",
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description="Switch your voice only when the user asks you to",
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properties={
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"voice": {
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"type": "string",
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"description": "The voice the user wants you to use",
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},
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},
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required=["voice"],
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)
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tools = ToolsSchema(standard_tools=[switch_voice_function])
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context = LLMContext(tools=tools)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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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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user_aggregator, # User responses
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llm, # LLM
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tts, # TTS with switch voice functionality
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transport.output(), # Transport bot output
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assistant_aggregator, # 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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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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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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context.add_message(
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{
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"role": "developer",
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"content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {tts.current_voice}.",
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}
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)
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await task.queue_frames([LLMRunFrame()])
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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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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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