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Overview

Smart Turn Detection uses an advanced machine learning model to determine when a user has finished speaking and your bot should respond. Unlike basic Voice Activity Detection (VAD) which only detects speech vs. non-speech, Smart Turn Detection recognizes natural conversational cues like intonation patterns and linguistic signals for more natural conversations.

On Pipecat Cloud, Smart Turn Detection is powered by Fal.ai’s hosted smart-turn model, providing scalable inference without any setup required.

Smart Turn Model

Try the model in Fal’s interactive playground

GitHub Repository

Open source model for conversational turn detection

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Key Benefits

  • Natural conversations: More human-like turn-taking patterns
  • Zero setup: A Fal API key is automatically provisioned for you
  • Free to use: Included at no additional cost
  • Scalable: Powered by Fal.ai’s cloud infrastructure

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Quick Start

To enable Smart Turn Detection in your Pipecat Cloud bot, add the FalSmartTurnAnalyzer to your transport configuration.

Use an environment variable of FAL_API_KEY which automatically receives a Fal API key at runtime when deployed to Pipecat Cloud.

import os
import aiohttp
from pipecat.audio.turn.smart_turn.fal_smart_turn import FalSmartTurnAnalyzer
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.transports.services.daily import DailyParams, DailyTransport

async def main(room_url: str, token: str):
    async with aiohttp.ClientSession() as session:
        transport = DailyTransport(
            room_url,
            token,
            "Voice AI Bot",
            DailyParams(
                audio_in_enabled=True,
                audio_out_enabled=True,
                # Set VAD to 0.2 seconds for optimal Smart Turn performance
                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
                # Enable Smart Turn Detection using FAL_API_KEY, which is automatically provisioned
                turn_analyzer=FalSmartTurnAnalyzer(
                    api_key=os.getenv("FAL_API_KEY"), aiohttp_session=session
                ),
            ),
        )

        # Continue with your pipeline setup...

Smart Turn Detection requires VAD to be enabled with stop_secs=0.2. This value mimics the training data and allows Smart Turn to dynamically adjust timing based on the model’s predictions.

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How It Works

  1. Audio Analysis: The system continuously analyzes incoming audio for speech patterns
  2. VAD Processing: Voice Activity Detection segments audio into speech and silence
  3. Turn Classification: When VAD detects a pause, the ML model analyzes the speech segment for natural completion cues
  4. Smart Response: The model determines if the turn is complete or if the user is likely to continue speaking

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Training Data Collection

The smart-turn model is trained on real conversational data collected through these applications. Help us improve the model by contributing your own data or classifying existing data:

Data Collector

Contribute conversational data to improve the model

Data Classifier

Help classify turn completion patterns in conversations

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Deployment Requirements

  • Automatic API key provisioning: When deployed to Pipecat Cloud, a Fal API key is automatically provided via the FAL_API_KEY environment variable
  • Manual setup for other deployments: You can use Smart Turn Detection locally or on other platforms by obtaining your own Fal API key from fal.ai
  • Internet connectivity: Requires connection to Fal.ai’s inference servers

On Pipecat Cloud, the FAL_API_KEY environment variable is automatically provided at no cost. For local development or other deployment platforms, you’ll need to sign up for your own Fal account and API key.

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Performance Notes

  • The model is optimized for English conversations
  • Network latency may vary based on geographic location
  • Fallback VAD-based turn detection if the service is unavailable
  • Best results with clear audio input and minimal background noise