Adding support for RemoteSmartTurn

dot-env.template

@@ -94,5 +94,6 @@ OPENROUTER_API_KEY=...
 # Piper
 PIPER_BASE_URL=...
 
-# Local Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=
+# Smart turn
+LOCAL_SMART_TURN_MODEL_PATH=
+REMOTE_SMART_TURN_URL=

examples/foundational/38-smart-turn.py

@@ -0,0 +1,109 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.turn.remote_smart_turn import RemoteSmartTurnAnalyzer
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+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.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection):
+    logger.info(f"Starting bot")
+
+    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,
+            end_of_turn_analyzer=RemoteSmartTurnAnalyzer(),
+        ),
+    )
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful LLM in a WebRTC call. 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.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+            report_only_initial_ttfb=True,
+        ),
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    from run import main
+
+    main()

src/pipecat/audio/turn/base_turn_analyzer.py

@@ -154,4 +154,15 @@ class BaseEndOfTurnAnalyzer(ABC):
 
     @abstractmethod
     def _predict_endpoint(self, buffer: np.ndarray) -> Dict[str, any]:
+        """
+        Predict whether an audio segment is complete (turn ended) or incomplete.
+
+        Args:
+            audio_array: Numpy array containing audio samples at 16kHz
+
+        Returns:
+            Dictionary containing prediction results:
+            - prediction: 1 for complete, 0 for incomplete
+            - probability: Probability of completion class
+        """
         pass

src/pipecat/audio/turn/local_smart_turn.py

@@ -60,18 +60,6 @@ class LocalSmartTurnAnalyzer(BaseEndOfTurnAnalyzer):
         logger.debug("Loaded Local Smart Turn")
 
     def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, any]:
-        """
-        Predict whether an audio segment is complete (turn ended) or incomplete.
-
-        Args:
-            audio_array: Numpy array containing audio samples at 16kHz
-
-        Returns:
-            Dictionary containing prediction results:
-            - prediction: 1 for complete, 0 for incomplete
-            - probability: Probability of completion class
-        """
-
         inputs = self._turn_processor(
             audio_array,
             sampling_rate=16000,

src/pipecat/audio/turn/remote_smart_turn.py

@@ -0,0 +1,73 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import io
+import os
+from typing import Dict
+
+import numpy as np
+import requests
+from loguru import logger
+
+from pipecat.audio.turn.base_turn_analyzer import (
+    BaseEndOfTurnAnalyzer,
+)
+
+
+class RemoteSmartTurnAnalyzer(BaseEndOfTurnAnalyzer):
+    def __init__(self):
+        super().__init__()
+        self.remote_smart_turn_url = os.getenv("REMOTE_SMART_TURN_URL")
+
+        if not self.remote_smart_turn_url:
+            logger.error("REMOTE_SMART_TURN_URL is not set.")
+            raise Exception("REMOTE_SMART_TURN_URL environment variable must be provided.")
+
+    def _serialize_array(self, audio_array: np.ndarray) -> bytes:
+        """Serializes a NumPy array into bytes using np.save."""
+        logger.debug("Serializing NumPy array to bytes...")
+        buffer = io.BytesIO()
+        np.save(buffer, audio_array)  # Saves in npy format
+        serialized_bytes = buffer.getvalue()
+        logger.debug(f"Serialized size: {len(serialized_bytes)} bytes")
+        return serialized_bytes
+
+    def _send_raw_request(self, data_bytes: bytes):
+        """Sends the bytes as the raw request body."""
+        headers = {"Content-Type": "application/octet-stream"}
+        logger.debug(
+            f"Sending {len(data_bytes)} bytes as raw body to {self.remote_smart_turn_url}..."
+        )
+        try:
+            response = requests.post(
+                self.remote_smart_turn_url, data=data_bytes, headers=headers, timeout=60
+            )  # Added timeout
+
+            logger.debug("\n--- Response ---")
+            logger.debug(f"Status Code: {response.status_code}")
+
+            # Try to logger.debug JSON if successful, otherwise logger.debug text
+            if response.ok:
+                try:
+                    logger.debug("Response JSON:")
+                    logger.debug(response.json())
+                    return response.json()
+                except requests.exceptions.JSONDecodeError:
+                    logger.debug("Response Content (non-JSON):")
+                    logger.debug(response.text)
+            else:
+                logger.debug("Response Content (Error):")
+                logger.debug(response.text)
+                response.raise_for_status()  # Raise an exception for bad status codes
+
+        except requests.exceptions.RequestException as e:
+            logger.debug(f"Failed to send raw request to Daily Smart Turn: {e}")
+            raise Exception("Failed to send raw request to Daily Smart Turn.")
+
+    def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, any]:
+        serialized_array = self._serialize_array(audio_array)
+        return self._send_raw_request(serialized_array)