pipecat/examples/chatbot-audio-recording/bot.py

#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import asyncio
import datetime
import io
import os
import sys
import wave

import aiofiles
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport

load_dotenv(override=True)

logger.remove(0)
logger.add(sys.stderr, level="DEBUG")

# Create the recordings directory if it doesn't exist
os.makedirs("recordings", exist_ok=True)


async def save_audio(audio: bytes, sample_rate: int, num_channels: int, name: str):
    if len(audio) > 0:
        filename = os.path.join(
            "recordings",
            f"{name}_conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav",
        )
        with io.BytesIO() as buffer:
            with wave.open(buffer, "wb") as wf:
                wf.setsampwidth(2)
                wf.setnchannels(num_channels)
                wf.setframerate(sample_rate)
                wf.writeframes(audio)
            async with aiofiles.open(filename, "wb") as file:
                await file.write(buffer.getvalue())
        print(f"Merged audio saved to {filename}")
    else:
        print("No audio data to save")


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,
                video_out_enabled=False,
                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",
            #
            # Spanish
            #
            # model="eleven_multilingual_v2",
            # voice_id="gD1IexrzCvsXPHUuT0s3",
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))

        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.",
            },
        ]

        context = OpenAILLMContext(messages)
        context_aggregator = llm.create_context_aggregator(context)

        # NOTE: Watch out! This will save all the conversation in memory. You
        # can pass `buffer_size` to get periodic callbacks.
        audiobuffer = AudioBufferProcessor(enable_turn_audio=True)

        pipeline = Pipeline(
            [
                transport.input(),  # microphone
                context_aggregator.user(),
                llm,
                tts,
                transport.output(),
                audiobuffer,  # used to buffer the audio in the pipeline
                context_aggregator.assistant(),
            ]
        )

        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))

        @audiobuffer.event_handler("on_audio_data")
        async def on_audio_data(buffer, audio, sample_rate, num_channels):
            await save_audio(audio, sample_rate, num_channels, "full")

        @audiobuffer.event_handler("on_user_turn_audio_data")
        async def on_user_turn_audio_data(buffer, audio, sample_rate, num_channels):
            await save_audio(audio, sample_rate, num_channels, "user")

        @audiobuffer.event_handler("on_bot_turn_audio_data")
        async def on_bot_turn_audio_data(buffer, audio, sample_rate, num_channels):
            await save_audio(audio, sample_rate, num_channels, "bot")

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            await audiobuffer.start_recording()
            await transport.capture_participant_transcription(participant["id"])
            await task.queue_frames([context_aggregator.user().get_context_frame()])

        @transport.event_handler("on_participant_left")
        async def on_participant_left(transport, participant, reason):
            print(f"Participant left: {participant}")
            await task.cancel()

        runner = PipelineRunner()

        await runner.run(task)


if __name__ == "__main__":
    asyncio.run(main())