feat: Add sentence aggregation and Whisker debugger to transcript processor

- Enhance TranscriptHandler to aggregate transcript fragments into complete sentences using match_endofsentence()
- Add Whisker debugger integration for real-time pipeline visualization
- Implement sentence buffering for both user and assistant messages
- Add finalize_partial_sentences() method to handle incomplete sentences on disconnect
- Improves transcript readability by reducing fragmented output

Changes:
- Import match_endofsentence utility for sentence boundary detection
- Add pipecat_whisker.WhiskerObserver for debugging capabilities
- Modify on_transcript_update() to accumulate and aggregate messages
- Create _save_sentence() helper method for complete sentence handling
- Update client disconnect handler to preserve partial sentences

examples/foundational/28-transcription-processor.py

@@ -29,6 +29,10 @@ from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.utils.string import match_endofsentence
+
+logger.info("Loading Whisker debugger...")
+from pipecat_whisker import WhiskerObserver
 
 load_dotenv(override=True)
 
@@ -52,6 +56,8 @@ class TranscriptHandler:
         """
         self.messages: List[TranscriptionMessage] = []
         self.output_file: Optional[str] = output_file
+        self._current_user_sentence = ""
+        self._current_assistant_sentence = ""
         logger.debug(
             f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}"
         )
@@ -78,11 +84,29 @@ class TranscriptHandler:
             except Exception as e:
                 logger.error(f"Error saving transcript message to file: {e}")
 
+    async def _save_sentence(self, role: str, content: str, timestamp: Optional[str] = None):
+        """Save a complete sentence as a transcript message.
+
+        Args:
+            role: The role (user/assistant)
+            content: The complete sentence content
+            timestamp: Optional timestamp
+        """
+        # Cast role to the appropriate literal type
+        message_role = "user" if role == "user" else "assistant"
+        sentence_message = TranscriptionMessage(
+            role=message_role, content=content.strip(), timestamp=timestamp
+        )
+        self.messages.append(sentence_message)
+        await self.save_message(sentence_message)
+
     async def on_transcript_update(
         self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
     ):
         """Handle new transcript messages.
 
+        Aggregates messages into complete sentences before saving them using match_endofsentence.
+
         Args:
             processor: The TranscriptProcessor that emitted the update
             frame: TranscriptionUpdateFrame containing new messages
@@ -90,8 +114,31 @@ class TranscriptHandler:
         logger.debug(f"Received transcript update with {len(frame.messages)} new messages")
 
         for msg in frame.messages:
-            self.messages.append(msg)
-            await self.save_message(msg)
+            # Accumulate text for the appropriate role
+            if msg.role == "user":
+                self._current_user_sentence += msg.content + " "
+                # Check if we have a complete sentence
+                if match_endofsentence(self._current_user_sentence):
+                    await self._save_sentence("user", self._current_user_sentence, msg.timestamp)
+                    self._current_user_sentence = ""
+            elif msg.role == "assistant":
+                self._current_assistant_sentence += msg.content + " "
+                # Check if we have a complete sentence
+                if match_endofsentence(self._current_assistant_sentence):
+                    await self._save_sentence(
+                        "assistant", self._current_assistant_sentence, msg.timestamp
+                    )
+                    self._current_assistant_sentence = ""
+
+    async def finalize_partial_sentences(self):
+        """Save any remaining partial sentences when the conversation ends."""
+        if self._current_user_sentence.strip():
+            await self._save_sentence("user", self._current_user_sentence)
+            self._current_user_sentence = ""
+
+        if self._current_assistant_sentence.strip():
+            await self._save_sentence("assistant", self._current_assistant_sentence)
+            self._current_assistant_sentence = ""
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -160,12 +207,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
+    # Create Whisker debugger observer
+    whisker = WhiskerObserver(pipeline)
+
     task = PipelineTask(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
+        observers=[whisker],
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
@@ -183,6 +234,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
+        # Finalize any partial sentences before canceling
+        await transcript_handler.finalize_partial_sentences()
         await task.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)