Remove vad

examples/foundational/07d-interruptible-elevenlabs.py

@@ -5,16 +5,27 @@
 #
 
 
+import asyncio
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import (
+    BotStartedSpeakingFrame,
+    Frame,
+    LLMFullResponseStartFrame,
+    LLMTextFrame,
+    TranscriptionFrame,
+    TTSSpeakFrame,
+)
 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.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -49,6 +60,65 @@ transport_params = {
 }
 
 
+class TranscriptionLogger(FrameProcessor):
+    """Custom processor that logs transcription frames."""
+
+    async def process_frame(self, frame, direction):
+        await super().process_frame(frame, direction)
+
+        # Only log TranscriptionFrame objects
+        if isinstance(frame, TranscriptionFrame):
+            logger.info(f"[TRANSCRIPTION]: {frame.text}")
+
+        # Always pass the frame through to maintain pipeline flow
+        await self.push_frame(frame, direction)
+
+
+class InterventionProcessor(FrameProcessor):
+    """Custom processor that logs LLM response frames."""
+
+    def __init__(self):
+        super().__init__()
+        self._timer_task = None
+
+    async def process_frame(self, frame, direction):
+        await super().process_frame(frame, direction)
+
+        # Log LLM response start frames
+        if isinstance(frame, LLMFullResponseStartFrame):
+            logger.info(f"[LLM_START]: Starting LLM response")
+
+            # Cancel any existing timer
+            if self._timer_task and not self._timer_task.done():
+                self._timer_task.cancel()
+
+            # Start a new 500ms timer
+            self._timer_task = asyncio.create_task(self._log_after_delay())
+
+        # Cancel timer if bot started speaking before 500ms
+        elif isinstance(frame, BotStartedSpeakingFrame):
+            logger.info(f"[BOT_SPEAKING]: Bot started speaking, canceling intervention timer")
+            if self._timer_task and not self._timer_task.done():
+                self._timer_task.cancel()
+
+        # Log LLM text frames
+        elif isinstance(frame, LLMTextFrame):
+            logger.info(f"[LLM_TEXT]: {frame.text}")
+
+        # Always pass the frame through to maintain pipeline flow
+        await self.push_frame(frame, direction)
+
+    async def _log_after_delay(self):
+        """Log a message after 500ms delay."""
+        try:
+            await asyncio.sleep(0.5)  # 500ms
+            logger.info(f"500ms passed since LLMFullResponseStartFrame")
+            await self.queue_frame(TTSSpeakFrame("um..."))
+        except asyncio.CancelledError:
+            # Timer was cancelled, which is fine
+            pass
+
+
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
@@ -71,13 +141,21 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     context = OpenAILLMContext(messages)
     context_aggregator = llm.create_context_aggregator(context)
 
+    # Create transcription logger instance
+    transcription_logger = TranscriptionLogger()
+
+    # Create LLM logger instance
+    intervention = InterventionProcessor()
+
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
             stt,
+            transcription_logger,  # Log transcription frames
             context_aggregator.user(),  # User responses
             llm,  # LLM
             tts,  # TTS
+            intervention,  # Log LLM response frames
             transport.output(),  # Transport bot output
             context_aggregator.assistant(),  # Assistant spoken responses
         ]