Build Your First Voice Agent using Pipecat

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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.frames.frames import LLMRunFrame
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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_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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    LLMContextAggregatorPair,
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)
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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.sarvam.stt import SarvamSTTService
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from pipecat.services.sarvam.tts import SarvamTTSService
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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.daily.transport import DailyParams
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load_dotenv(override=True)
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async def bot(runner_args: RunnerArguments):
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    """Main bot entry point."""
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    # Create transport (supports both Daily and WebRTC)
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    transport = await create_transport(
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        runner_args,
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        {
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            "daily": lambda: DailyParams(audio_in_enabled=True, audio_out_enabled=True),
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            "webrtc": lambda: TransportParams(
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                audio_in_enabled=True, audio_out_enabled=True
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            ),
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        },
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    )
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    # Initialize AI services
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    stt = SarvamSTTService(api_key=os.getenv("SARVAM_API_KEY"))
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    tts = SarvamTTSService(api_key=os.getenv("SARVAM_API_KEY"))
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    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
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    # Set up conversation context
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    messages = [
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        {
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            "role": "system",
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            "content": "You are a friendly AI assistant. Keep your responses brief and conversational.",
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        },
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    ]
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    context = LLMContext(messages)
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    context_aggregator = LLMContextAggregatorPair(context)
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    # Build pipeline
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    pipeline = Pipeline(
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        [
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            transport.input(),
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            stt,
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            context_aggregator.user(),
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            llm,
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            tts,
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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(pipeline)
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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("Client connected")
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        messages.append(
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            {"role": "system", "content": "Say hello and briefly introduce yourself."}
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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("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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if __name__ == "__main__":
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    from pipecat.runner.run import main
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    main()

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# Initialize AI services with Hindi support
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stt = SarvamSTTService(
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    api_key=os.getenv("SARVAM_API_KEY"),
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    language="hi-IN",  # Hindi
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    model="saaras:v3",
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    mode="transcribe"
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)
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tts = SarvamTTSService(
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    api_key=os.getenv("SARVAM_API_KEY"),
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    target_language_code="hi-IN",
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    model="bulbul:v3",
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    speaker="simran"  # Or: priya, ishita, kavya, aditya, anand, rohan
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")

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stt = SarvamSTTService(
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    api_key=os.getenv("SARVAM_API_KEY"),
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    language="ta-IN",
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    model="saaras:v3",
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    mode="transcribe"
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)
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tts = SarvamTTSService(
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    api_key=os.getenv("SARVAM_API_KEY"),
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    target_language_code="ta-IN",
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    model="bulbul:v3",
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    speaker="shubh"
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")

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# Auto-detect the user's language
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stt = SarvamSTTService(
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    api_key=os.getenv("SARVAM_API_KEY"),
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    language="unknown",  # Auto-detects language
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    model="saaras:v3",
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    mode="transcribe"
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)
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tts = SarvamTTSService(
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    api_key=os.getenv("SARVAM_API_KEY"),
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    target_language_code="en-IN",
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    model="bulbul:v3",
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    speaker="anand"
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")

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# User speaks Hindi → Saaras converts to English → LLM processes → Responds in English
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stt = SarvamSTTService(
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    api_key=os.getenv("SARVAM_API_KEY"),
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    model="saaras:v3",
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    mode="translate"  # Speech-to-English translation
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)
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tts = SarvamTTSService(
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    api_key=os.getenv("SARVAM_API_KEY"),
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    target_language_code="en-IN",
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    model="bulbul:v3",
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    speaker="aditya"
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")

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tts = SarvamTTSService(
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    api_key=os.getenv("SARVAM_API_KEY"),
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    target_language_code="en-IN",
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    model="bulbul:v3",
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    speaker="shubh",
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    pace=1.0,            # Range: 0.5 to 2.0
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    speech_sample_rate=24000  # 8000, 16000, 22050, 24000 Hz (default). v3 REST API also supports 32000, 44100, 48000 Hz
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)