Add foundational example 35
This commit is contained in:
Mark Backman
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examples/foundational/35-voice-switching.py
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192
examples/foundational/35-voice-switching.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 dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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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 import CartesiaTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator
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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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# Define voice IDs
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VOICE_IDS = {
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"narrator": "c45bc5ec-dc68-4feb-8829-6e6b2748095d", # Narrator voice
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"female": "71a7ad14-091c-4e8e-a314-022ece01c121", # Female character voice
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"male": "7cf0e2b1-8daf-4fe4-89ad-f6039398f359", # Male character voice
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}
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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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"Storytelling 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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# Initialize TTS with narrator voice as default
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id=VOICE_IDS["narrator"],
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)
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# Create pattern pair aggregator for voice switching
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pattern_aggregator = PatternPairAggregator()
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# Add pattern for voice switching
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pattern_aggregator.add_pattern_pair(
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pattern_id="voice_tag",
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start_pattern="<voice>",
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end_pattern="</voice>",
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remove_match=True,
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)
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# Register handler for voice switching
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def on_voice_tag(match: PatternMatch):
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voice_name = match.content.strip().lower()
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if voice_name in VOICE_IDS:
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voice_id = VOICE_IDS[voice_name]
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tts.set_voice(voice_id)
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logger.info(f"Switched to {voice_name} voice")
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else:
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logger.warning(f"Unknown voice: {voice_name}")
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pattern_aggregator.on_pattern_match("voice_tag", on_voice_tag)
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# Set the pattern aggregator on the TTS service
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tts._text_aggregator = pattern_aggregator
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# Initialize LLM
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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# System prompt for storytelling with voice switching
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system_prompt = """You are an engaging storyteller that uses different voices to bring stories to life.
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You have three voices to use, but each has a specific purpose:
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<voice>narrator</voice>
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This is the default narrator voice. Use this for all narration, descriptions, and non-dialogue text.
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<voice>female</voice>
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Use this ONLY for direct speech by female characters (just the quoted text).
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<voice>male</voice>
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Use this ONLY for direct speech by male characters (just the quoted text).
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IMPORTANT: Switch back to narrator voice immediately after character dialogue.
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Here's an EXAMPLE of correct voice usage:
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<voice>narrator</voice>
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Sarah spotted her old friend across the café. She couldn't believe her eyes.
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<voice>female</voice>
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"Jacob! It's been so long!"
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<voice>narrator</voice>
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Sarah exclaimed, jumping up from her seat with a radiant smile.
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<voice>male</voice>
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"Sarah, is it really you? I can't believe it!"
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<voice>narrator</voice>
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Jacob replied, grinning widely as he walked over to her. The two friends embraced warmly, as if trying to make up for all the years spent apart.
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<voice>female</voice>
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"What are you doing in town? Last I heard you were in Seattle."
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<voice>narrator</voice>
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She asked, gesturing for him to join her at the table.
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FOLLOW THESE RULES:
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1. Always begin with the narrator voice
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2. Only use character voices for the EXACT words they speak (in quotes)
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3. SWITCH BACK to narrator voice for speech tags and all other text
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4. Begin by asking what kind of story the user would like to hear
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5. Create engaging dialogue with distinct characters
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Remember: Use narrator voice for EVERYTHING except the actual quoted dialogue."""
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# Set up LLM context
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messages = [
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{
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"role": "system",
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"content": system_prompt,
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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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# Create pipeline
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pipeline = Pipeline(
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[
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transport.input(),
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context_aggregator.user(),
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llm,
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tts, # TTS with pattern aggregator
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transport.output(),
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context_aggregator.assistant(),
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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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logger.info(f"First participant joined: {participant['id']}")
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await transport.capture_participant_transcription(participant["id"])
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# Start conversation - empty prompt to let LLM follow system instructions
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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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logger.info(f"Participant left: {participant['id']}")
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await task.cancel()
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logger.info(f"Starting storytelling bot at: {room_url}")
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logger.info("Join the room to interact with the bot!")
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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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