Update examples to use transcription events from context aggregators

examples/foundational/19-openai-realtime.py

@@ -15,14 +15,17 @@ from loguru import logger
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, LLMSetToolsFrame, TranscriptionMessage
+from pipecat.frames.frames import LLMRunFrame, LLMSetToolsFrame
 from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.transcript_processor import TranscriptProcessor
+from pipecat.processors.aggregators.llm_response_universal import (
+    AssistantTurnStoppedMessage,
+    LLMContextAggregatorPair,
+    UserTurnStoppedMessage,
+)
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.llm_service import FunctionCallParams
@@ -177,8 +180,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
     llm.register_function("get_news", get_news)
 
-    transcript = TranscriptProcessor()
-
     # Create a standard OpenAI LLM context object using the normal messages format. The
     # OpenAIRealtimeLLMService will convert this internally to messages that the
     # openai WebSocket API can understand.
@@ -189,15 +190,16 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
 
     context_aggregator = LLMContextAggregatorPair(context)
 
+    user_aggregator = context_aggregator.user()
+    assistant_aggregator = context_aggregator.assistant()
+
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
-            context_aggregator.user(),
-            transcript.user(),  # LLM pushes TranscriptionFrames upstream
+            user_aggregator,
             llm,  # LLM
             transport.output(),  # Transport bot output
-            transcript.assistant(),  # After the transcript output, to time with the audio output
-            context_aggregator.assistant(),
+            assistant_aggregator,
         ]
     )
 
@@ -238,14 +240,18 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    # Register event handler for transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        for msg in frame.messages:
-            if isinstance(msg, TranscriptionMessage):
-                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
-                line = f"{timestamp}{msg.role}: {msg.content}"
-                logger.info(f"Transcript: {line}")
+    # Log transcript updates
+    @user_aggregator.event_handler("on_user_turn_stopped")
+    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}user: {message.content}"
+        logger.info(f"Transcript: {line}")
+
+    @assistant_aggregator.event_handler("on_assistant_turn_stopped")
+    async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}assistant: {message.content}"
+        logger.info(f"Transcript: {line}")
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 

examples/foundational/19b-openai-realtime-beta-text.py

@@ -14,12 +14,11 @@ from loguru import logger
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
+from pipecat.frames.frames import LLMRunFrame
 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.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -157,8 +156,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
     llm.register_function("get_current_weather", fetch_weather_from_api)
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
 
-    transcript = TranscriptProcessor()
-
     # Create a standard OpenAI LLM context object using the normal messages format. The
     # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
     # openai WebSocket API can understand.
@@ -175,9 +172,7 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
             context_aggregator.user(),
             llm,  # LLM
             tts,  # TTS
-            transcript.user(),  # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
             transport.output(),  # Transport bot output
-            transcript.assistant(),  # After the transcript output, to time with the audio output
             context_aggregator.assistant(),
         ]
     )
@@ -202,15 +197,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    # Register event handler for transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        for msg in frame.messages:
-            if isinstance(msg, TranscriptionMessage):
-                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
-                line = f"{timestamp}{msg.role}: {msg.content}"
-                logger.info(f"Transcript: {line}")
-
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
     await runner.run(task)

examples/foundational/19b-openai-realtime-text.py

@@ -14,13 +14,12 @@ from loguru import logger
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
+from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -164,8 +163,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
     llm.register_function("get_current_weather", fetch_weather_from_api)
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
 
-    transcript = TranscriptProcessor()
-
     # Create a standard OpenAI LLM context object using the normal messages format. The
     # OpenAIRealtimeLLMService will convert this internally to messages that the
     # openai WebSocket API can understand.
@@ -180,11 +177,9 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         [
             transport.input(),  # Transport user input
             context_aggregator.user(),
-            transcript.user(),  # LLM pushes TranscriptionFrames upstream
             llm,  # LLM
             tts,  # TTS
             transport.output(),  # Transport bot output
-            transcript.assistant(),  # After the transcript output, to time with the audio output
             context_aggregator.assistant(),
         ]
     )
@@ -209,15 +204,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    # Register event handler for transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        for msg in frame.messages:
-            if isinstance(msg, TranscriptionMessage):
-                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
-                line = f"{timestamp}{msg.role}: {msg.content}"
-                logger.info(f"Transcript: {line}")
-
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
     await runner.run(task)

examples/foundational/40-aws-nova-sonic.py

@@ -21,7 +21,11 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.llm_response_universal import (
+    AssistantTurnStoppedMessage,
+    LLMContextAggregatorPair,
+    UserTurnStoppedMessage,
+)
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService
@@ -154,14 +158,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
     context_aggregator = LLMContextAggregatorPair(context)
 
+    user_aggregator = context_aggregator.user()
+    assistant_aggregator = context_aggregator.assistant()
+
     # Build the pipeline
     pipeline = Pipeline(
         [
             transport.input(),
-            context_aggregator.user(),
+            user_aggregator,
             llm,
             transport.output(),
-            context_aggregator.assistant(),
+            assistant_aggregator,
         ]
     )
 
@@ -192,6 +199,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client disconnected")
         await task.cancel()
 
+    @user_aggregator.event_handler("on_user_turn_stopped")
+    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}user: {message.content}"
+        logger.info(f"Transcript: {line}")
+
+    @assistant_aggregator.event_handler("on_assistant_turn_stopped")
+    async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}assistant: {message.content}"
+        logger.info(f"Transcript: {line}")
+
     # Run the pipeline
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
     await runner.run(task)

examples/foundational/42-interruption-config.py

@@ -13,6 +13,7 @@ from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnal
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import LLMRunFrame
+from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -21,7 +22,6 @@ from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
     LLMUserAggregatorParams,
 )
-from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -36,6 +36,7 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
 
 load_dotenv(override=True)
 
+
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets
 # selected.
@@ -70,8 +71,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
-    transcript = TranscriptProcessor()
-
     messages = [
         {
             "role": "system",
@@ -94,7 +93,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         [
             transport.input(),  # Transport user input
             stt,
-            transcript.user(),  # User transcripts
             context_aggregator.user(),  # User responses
             llm,  # LLM
             tts,  # TTS
@@ -110,6 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_usage_metrics=True,
         ),
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+        observers=[TranscriptionLogObserver()],
     )
 
     @transport.event_handler("on_client_connected")
@@ -124,12 +123,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    # Register event handler for transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        for message in frame.messages:
-            logger.info(f"Transcription [{message.role}]: {message.content}")
-
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
     await runner.run(task)

examples/foundational/51-grok-realtime.py

@@ -36,7 +36,7 @@ from pipecat.adapters.schemas.tools_schema import ToolsSchema
 
 # Note: Grok has built-in server-side VAD, so we don't need local VAD
 # from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
+from pipecat.frames.frames import LLMRunFrame
 from pipecat.observers.loggers.transcription_log_observer import (
     TranscriptionLogObserver,
 )
@@ -45,9 +45,10 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
+    AssistantTurnStoppedMessage,
     LLMContextAggregatorPair,
+    UserTurnStoppedMessage,
 )
-from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.grok.realtime.events import (
@@ -208,9 +209,6 @@ Always be helpful and proactive in offering assistance.""",
     llm.register_function("get_current_time", get_current_time)
     llm.register_function("get_restaurant_recommendation", get_restaurant_recommendation)
 
-    # Create transcript processor for logging
-    transcript = TranscriptProcessor()
-
     # Create context with initial message and tools
     context = LLMContext(
         [{"role": "user", "content": "Say hello and introduce yourself!"}],
@@ -219,18 +217,19 @@ Always be helpful and proactive in offering assistance.""",
 
     context_aggregator = LLMContextAggregatorPair(context)
 
+    user_aggregator = context_aggregator.user()
+    assistant_aggregator = context_aggregator.assistant()
+
     # Build the pipeline
     # Note: In realtime mode, transcription comes from Grok (upstream),
     # so transcript.user() goes BEFORE llm
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input (audio)
-            context_aggregator.user(),
-            transcript.user(),  # Transcription from Grok goes upstream
+            user_aggregator,
             llm,  # Grok Realtime LLM (handles STT + LLM + TTS)
             transport.output(),  # Transport bot output (audio)
-            transcript.assistant(),  # Log assistant speech
-            context_aggregator.assistant(),
+            assistant_aggregator,
         ]
     )
 
@@ -256,13 +255,17 @@ Always be helpful and proactive in offering assistance.""",
         await task.cancel()
 
     # Log transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        for msg in frame.messages:
-            if isinstance(msg, TranscriptionMessage):
-                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
-                line = f"{timestamp}{msg.role}: {msg.content}"
-                logger.info(f"Transcript: {line}")
+    @user_aggregator.event_handler("on_user_turn_stopped")
+    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}user: {message.content}"
+        logger.info(f"Transcript: {line}")
+
+    @assistant_aggregator.event_handler("on_assistant_turn_stopped")
+    async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}assistant: {message.content}"
+        logger.info(f"Transcript: {line}")
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)