Remove logs

FEAT: Example of muting LLMMessages to LLM.

examples/simple-chatbot/server/bot-openai.py

@@ -29,18 +29,30 @@ from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
+    BotSpeakingFrame,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     Frame,
+    InterimTranscriptionFrame,
+    LLMMessagesFrame,
     OutputImageRawFrame,
     SpriteFrame,
+    STTMuteFrame,
+    TranscriptionFrame,
 )
 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.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.processors.frameworks.rtvi import (
+    RTVIConfig,
+    RTVIObserver,
+    RTVIProcessor,
+    RTVIServerMessageFrame,
+)
+from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -49,6 +61,51 @@ load_dotenv(override=True)
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
+
+class TranscriptionMuteProcessor(FrameProcessor):
+    """Takes in STTMuteFrame and mutes TranscriptionFrame based on its content."""
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._is_muted = False
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        """Process incoming frames and mute TranscriptionFrame based on STTMuteFrame content.
+
+        Args:
+            frame: The incoming frame to process
+            direction: The direction of frame flow in the pipeline
+        """
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, STTMuteFrame):
+            self._is_muted = frame.mute
+
+            frame = RTVIServerMessageFrame(
+                data={"type": "user-muted-event", "payload": {"is_muted": self._is_muted}}
+            )
+
+            self.push_frame(frame)
+
+        if isinstance(
+            frame,
+            (
+                TranscriptionFrame,
+                InterimTranscriptionFrame,
+                LLMMessagesFrame,
+            ),
+        ):
+            # Only pass frames when not muted
+            if not self._is_muted:
+                await self.push_frame(frame, direction)
+            else:
+                logger.trace(
+                    f"{frame.__class__.__name__} suppressed - Transcription STT currently muted"
+                )
+        else:
+            await self.push_frame(frame, direction)
+
+
 sprites = []
 script_dir = os.path.dirname(__file__)
 
@@ -128,7 +185,8 @@ async def main():
                 camera_out_height=576,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
-                transcription_enabled=True,
+                vad_audio_passthrough=True,
+                # transcription_enabled=True,
                 #
                 # Spanish
                 #
@@ -183,9 +241,20 @@ async def main():
         #
         rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
 
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        stt_mute_processor = STTMuteFilter(
+            config=STTMuteConfig(strategies={STTMuteStrategy.ALWAYS}),
+        )
+
+        transcription_mute_processor = TranscriptionMuteProcessor()
+
         pipeline = Pipeline(
             [
                 transport.input(),
+                stt,
+                stt_mute_processor,
+                transcription_mute_processor,
                 rtvi,
                 context_aggregator.user(),
                 llm,
@@ -213,7 +282,7 @@ async def main():
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            # await transport.capture_participant_transcription(participant["id"])
             await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_participant_left")