Local CoreML Smart Turn

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Overview

LocalCoreMLSmartTurnAnalyzer runs Smart Turn inference directly on your Mac using Apple’s CoreML framework. This provides low-latency inference without external API dependencies, making it ideal for development and applications where network access is limited or latency is critical.

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Installation

pip install "pipecat-ai[local-smart-turn]"

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Requirements

• Apple Silicon Mac (M1/M2/M3 series)
• macOS 11.0 or later

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Local Model Setup

To use the LocalCoreMLSmartTurnAnalyzer, you need to set up the CoreML model locally:

1. Install Git LFS (Large File Storage):

   macOS

   Ubuntu/Debian

   brew install git-lfs
   

2. Initialize Git LFS

   git lfs install
   

3. Clone the Smart Turn model repository:

   git clone https://huggingface.co/pipecat-ai/smart-turn
   

4. Set the environment variable to the cloned repository path:

   # Add to your .env file or environment
   export LOCAL_SMART_TURN_MODEL_PATH=/path/to/smart-turn
   

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Configuration

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Constructor Parameters

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Example

import os
from pipecat.audio.turn.smart_turn.local_coreml_smart_turn import LocalCoreMLSmartTurnAnalyzer
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.transports.base_transport import TransportParams

# Get the path to the Smart Turn model
smart_turn_model_path = os.getenv("LOCAL_SMART_TURN_MODEL_PATH")

# Create transport with local Smart Turn detection
transport = SmallWebRTCTransport(
    webrtc_connection=webrtc_connection,
    params=TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_enabled=True,
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
        vad_audio_passthrough=True,
        turn_analyzer=LocalCoreMLSmartTurnAnalyzer(
            smart_turn_model_path=smart_turn_model_path,
            params=SmartTurnParams(
                stop_secs=2.0,  # Shorter stop time when using Smart Turn
                pre_speech_ms=0.0,
                max_duration_secs=8.0
            )
        ),
    ),
)

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Performance Considerations

• Latency: Very low latency since inference happens locally
• Resource Usage: Uses local CPU/GPU resources
• Reliability: No dependency on external services or network connectivity

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Notes

• Optimal for development environments and latency-sensitive applications
• The CoreML model is optimized for Apple Silicon but will work on Intel Macs with reduced performance
• First inference may be slower as the model is loaded and compiled