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This guide provides a quickstart for integrating the ai-coustics filter into your Pipecat applications.

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Prerequisites

Before you start, make sure you have a valid SDK key from the developer portal.

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Installation

To use AICFilter, you need to install the aic extra for pipecat-ai (not needed when using uv): 
pip install pipecat-ai[aic,local,webrtc] loguru pyaudio fastapi uvicorn dotenv

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Usage

The AICFilter can be easily integrated into a Pipecat pipeline between an audio input transport (e.g., microphone) and an audio output transport (e.g., speaker). Here’s a complete example of a simple Pipecat application that uses the AICFilter. 
# /// script
# requires-python = ">=3.10,<3.14"
# dependencies = [
#     "pipecat-ai[aic,local,webrtc]",
#     "loguru",
#     "llvmlite",
#     "pyaudio",
#     "fastapi",
#     "uvicorn",
#     "dotenv",
#     "pipecat-ai-small-webrtc-prebuilt",
# ]
# ///
import os

from loguru import logger

from pipecat.audio.filters.aic_filter import AICFilter
from pipecat.frames.frames import Frame, InputAudioRawFrame, OutputAudioRawFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.transports.base_transport import BaseTransport, TransportParams


# Loopback Processor
class AudioFrameConverter(FrameProcessor):
    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

        if isinstance(frame, InputAudioRawFrame):
            output_frame = OutputAudioRawFrame(
                audio=frame.audio,
                sample_rate=frame.sample_rate,
                num_channels=frame.num_channels,
            )
            await self.push_frame(output_frame, direction)
        else:
            await self.push_frame(frame, direction)


# Bot Logic
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    logger.info("Bot starting: Direct Audio Loopback with AIC Filter")

    converter = AudioFrameConverter()
    pipeline = Pipeline(
        [
            transport.input(),
            converter,
            transport.output(),
        ]
    )
    task = PipelineTask(
        pipeline,
        params=PipelineParams(),
    )

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info("WebRTC Client Connected")

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info("WebRTC Client Disconnected")
        await task.cancel()

    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
    await runner.run(task)


async def bot(runner_args: RunnerArguments):
    # initialize aic filter
    aic_filter = AICFilter(
        license_key=os.getenv("AIC_LICENSE_KEY", ""),
        model_id="quail-vf-l-16khz",  # or "quail-l-16khz", "quail-s-8khz", etc.
    )
    transport_params = {
        "webrtc": lambda: TransportParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            audio_in_filter=aic_filter,
        )
    }
    transport = await create_transport(runner_args, transport_params)
    await run_bot(transport, runner_args)


if __name__ == "__main__":
    from pipecat.runner.run import main

    main()

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Running the Example

1

Save the code

Save the code above as bot.py.
2

Set Environment Variables

Set the necessary environment variable in your terminal:
export AIC_SDK_LICENSE="YOUR_AIC_LICENSE_KEY"
Replace the placeholder value with your actual SDK key.
3

Run the Application

Execute the script from your terminal:
python bot.py
Or use uv:
uv run bot.py
You can now navigate to http://localhost:7860 and click the green ‘Connect’ button in the top right corner. Please note that you will hear the filtered microphone output processed by Quail STT models; because these models are optimized specifically for human-to-machine interaction, the audio is tuned for speech-to-text accuracy rather than human listening comfort, and may sound distorted or unusual to your ears.More information on AICFilter is provided in the Pipecat Documentation.