Moving the environment variables to inside the demo.

examples/foundational/38-smart-turn.py

@@ -29,6 +29,8 @@ load_dotenv(override=True)
 async def run_bot(webrtc_connection: SmallWebRTCConnection):
     logger.info(f"Starting bot")
 
+    remote_smart_turn_url = os.getenv("REMOTE_SMART_TURN_URL")
+
     transport = SmallWebRTCTransport(
         webrtc_connection=webrtc_connection,
         params=TransportParams(
@@ -37,7 +39,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             vad_enabled=True,
             vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
             vad_audio_passthrough=True,
-            end_of_turn_analyzer=SmartTurnAnalyzer(),
+            end_of_turn_analyzer=SmartTurnAnalyzer(url=remote_smart_turn_url),
         ),
     )
 

examples/foundational/38a-local-smart-turn.py

@@ -30,6 +30,23 @@ load_dotenv(override=True)
 async def run_bot(webrtc_connection: SmallWebRTCConnection):
     logger.info(f"Starting bot")
 
+    # To use this locally, set the environment variable LOCAL_SMART_TURN_MODEL_PATH
+    # to the path where the smart-turn repo is cloned.
+    #
+    # Example setup:
+    #
+    #   # Git LFS (Large File Storage)
+    #   brew install git-lfs
+    #   # Hugging Face uses LFS to store large model files, including .mlpackage
+    #   git lfs install
+    #   # Clone the repo with the smart_turn_classifier.mlpackage
+    #   git clone https://huggingface.co/pipecat-ai/smart-turn
+    #
+    # Then set the env variable:
+    #   export LOCAL_SMART_TURN_MODEL_PATH=./smart-turn
+    # or add it to your .env file
+    smart_turn_model_path = os.getenv("LOCAL_SMART_TURN_MODEL_PATH")
+
     transport = SmallWebRTCTransport(
         webrtc_connection=webrtc_connection,
         params=TransportParams(
@@ -38,7 +55,9 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             vad_enabled=True,
             vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
             vad_audio_passthrough=True,
-            end_of_turn_analyzer=LocalCoreMLSmartTurnAnalyzer(params=SmartTurnParams(stop_secs=5)),
+            end_of_turn_analyzer=LocalCoreMLSmartTurnAnalyzer(
+                smart_turn_model_path=smart_turn_model_path, params=SmartTurnParams(stop_secs=5)
+            ),
         ),
     )
 

src/pipecat/audio/turn/local_smart_turn.py

@@ -26,28 +26,12 @@ except ModuleNotFoundError as e:
 
 
 class LocalCoreMLSmartTurnAnalyzer(BaseSmartTurn):
-    def __init__(self, **kwargs):
+    def __init__(self, smart_turn_model_path: str, **kwargs):
         super().__init__(**kwargs)
-        # To use this locally, set the environment variable LOCAL_SMART_TURN_MODEL_PATH
-        # to the path where the smart-turn repo is cloned.
-        #
-        # Example setup:
-        #
-        #   # Git LFS (Large File Storage)
-        #   brew install git-lfs
-        #   # Hugging Face uses LFS to store large model files, including .mlpackage
-        #   git lfs install
-        #   # Clone the repo with the smart_turn_classifier.mlpackage
-        #   git clone https://huggingface.co/pipecat-ai/smart-turn
-        #
-        # Then set the env variable:
-        #   export LOCAL_SMART_TURN_MODEL_PATH=./smart-turn
-        # or add it to your .env file
-        smart_turn_model_path = os.getenv("LOCAL_SMART_TURN_MODEL_PATH")
 
         if not smart_turn_model_path:
-            logger.error("LOCAL_SMART_TURN_MODEL_PATH is not set.")
+            logger.error("smart_turn_model_path is not set.")
-            raise Exception("LOCAL_SMART_TURN_MODEL_PATH environment variable must be provided.")
+            raise Exception("smart_turn_model_path must be provided.")
 
         core_ml_model_path = f"{smart_turn_model_path}/coreml/smart_turn_classifier.mlpackage"
 

src/pipecat/audio/turn/smart_turn.py

@@ -17,13 +17,13 @@ from pipecat.audio.turn.base_smart_turn import BaseSmartTurn
 
 
 class SmartTurnAnalyzer(BaseSmartTurn):
-    def __init__(self, **kwargs):
+    def __init__(self, url: str, **kwargs):
         super().__init__(**kwargs)
-        self.remote_smart_turn_url = os.getenv("REMOTE_SMART_TURN_URL")
+        self.remote_smart_turn_url = url
 
         if not self.remote_smart_turn_url:
-            logger.error("REMOTE_SMART_TURN_URL is not set.")
+            logger.error("remote_smart_turn_url is not set.")
-            raise Exception("REMOTE_SMART_TURN_URL environment variable must be provided.")
+            raise Exception("remote_smart_turn_url must be provided.")
 
         # Use a session to reuse connections (keep-alive)
         self.session = requests.Session()