Remove logs

[bug]TypeError: object of type 'NoneType' has no len()

.github/workflows/tests.yaml

@@ -1,4 +1,4 @@
-name: test
+name: tests
 
 on:
   workflow_dispatch:
@@ -49,4 +49,4 @@ jobs:
       - name: Test with pytest
         run: |
           source .venv/bin/activate
-          pytest --ignore-glob="*to_be_updated*" --ignore-glob=*pipeline_source* src tests
+          pytest

CHANGELOG.md

@@ -5,12 +5,48 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Added
+
+- It is now possible to specify the period of the `PipelineTask` heartbeat
+  frames with `heartbeats_period_secs`.
+
+### Changed
+
+- Modified `TranscriptProcessor` to use TTS text frames for more accurate assistant
+  transcripts. Assistant messages are now aggregated based on bot speaking boundaries
+  rather than LLM context, providing better handling of interruptions and partial
+  utterances.
+
+- Updated foundational examples `28a-transcription-processor-openai.py`,
+  `28b-transcript-processor-anthropic.py`, and
+  `28c-transcription-processor-gemini.py` to use the updated
+  `TranscriptProcessor`.
+
+### Fixed
+
+- Fixed a type error when using `voice_settings` in `ElevenLabsHttpTTSService`.
+
+- Fixed an issue where `OpenAIRealtimeBetaLLMService` function calling resulted
+  in an error.
+
+### Performance
+
+- Replaced audio resampling library `resampy` with `soxr`. Resampling a 2:21s
+  audio file from 24KHz to 16KHz took 1.41s with `resampy` and 0.031s with
+  `soxr` with similar audio quality.
+
+### Other
+
+- Added initial unit test infrastructure.
+
 ## [0.0.53] - 2025-01-18
 
 ### Added
 
-- Added `ElevenLabsHttpTTSService` and the
+- Added `ElevenLabsHttpTTSService` which uses EleveLabs' HTTP API instead of the
-  `07d-interruptible-elevenlabs-http.py` foundational example.
+  websocket one.
 
 - Introduced pipeline frame observers. Observers can view all the frames that go
   through the pipeline without the need to inject processors in the

README.md

@@ -2,7 +2,7 @@
  <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
 </div></h1>
 
-[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
+[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
 
 Pipecat is an open source Python framework for building voice and multimodal conversational agents. It handles the complex orchestration of AI services, network transport, audio processing, and multimodal interactions, letting you focus on creating engaging experiences.
 
@@ -53,12 +53,6 @@ To keep things lightweight, only the core framework is included by default. If y
 pip install "pipecat-ai[option,...]"
 ```
 
-Or you can install all of them with:
-
-```shell
-pip install "pipecat-ai[all]"
-```
-
 Available options include:
 
 | Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | Install Command Example                 |
@@ -195,7 +189,7 @@ pip install "path_to_this_repo[option,...]"
 From the root directory, run:
 
 ```shell
-pytest --doctest-modules --ignore-glob="*to_be_updated*" --ignore-glob=*pipeline_source* src tests
+pytest
 ```
 
 ## Setting up your editor

dev-requirements.txt

@@ -4,6 +4,7 @@ pip-tools~=7.4.1
 pre-commit~=4.0.1
 pyright~=1.1.392
 pytest~=8.3.4
+pytest-asyncio~=0.25.2
 ruff~=0.9.1
 setuptools~=75.8.0
 setuptools_scm~=8.1.0

examples/README.md

@@ -42,7 +42,7 @@ Next, follow the steps in the README for each demo.
 | [Dialin Chatbot](dialin-chatbot)             | A chatbot that connects to an incoming phone call from Daily or Twilio.                                                                    | Deepgram, ElevenLabs, OpenAI, Daily, Twilio                       |
 | [Twilio Chatbot](twilio-chatbot)             | A chatbot that connects to an incoming phone call from Twilio.                                                                             | Deepgram, ElevenLabs, OpenAI, Daily, Twilio                       |
 | [studypal](studypal)                         | A chatbot to have a conversation about any article on the web                                                                              |                                                                   |
-| [WebSocket Chatbot Server](websocket-server) | A real-time websocket server that handles audio streaming and bot interactions with speech-to-text and text-to-speech capabilities | `python-websockets`, `openai`, `deepgram`, `silero-tts`, `numpy` |
+| [WebSocket Chatbot Server](websocket-server) | A real-time websocket server that handles audio streaming and bot interactions with speech-to-text and text-to-speech capabilities. | Cartesia, Deepgram, OpenAI, Websockets |
 
 > [!IMPORTANT]
 > These example projects use Daily as a WebRTC transport and can be joined using their hosted Prebuilt UI.

examples/dialin-chatbot/README.md

@@ -37,7 +37,16 @@ Run `bot_runner.py` to handle incoming HTTP requests:
 
 Then target the following URL:
 
-`POST /daily_start_bot`
+```bash
+curl -X POST 'http://localhost:7860/daily_start_bot' \
+  -H 'Content-Type: application/json' \
+  -d '{
+      "callId": "callId-from-call",
+      "callDomain": "callDomain-from-call"
+  }'
+```
+
+Use [this guide](https://docs.pipecat.ai/guides/telephony/daily-webrtc) to connect a phone number purchased from Daily to the bot.
 
 For more configuration options, please consult Daily's API documentation.
 

examples/dialin-chatbot/bot_daily.py

@@ -1,3 +1,8 @@
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import argparse
 import asyncio
 import os
@@ -5,13 +10,16 @@ import sys
 
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import EndFrame, TextFrame
 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.services.ai_services import LLMService
+from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
@@ -55,16 +63,62 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
 
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
+    content = f"""
+You are a delivery service customer support specialist supporting customers with their orders. 
+Begin with: "Hello, this is Hailey from customer support. What can I help you with today?"
+    """
+
     messages = [
         {
             "role": "system",
-            "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying 'Oh, hello! Who dares dial me at this hour?!'.",
+            "content": content,
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    tools = [
+        ChatCompletionToolParam(
+            type="function",
+            function={
+                "name": "transfer_call",
+                "description": "Transfer the call to a person. This function is used to connect the call to a real person. Examples of real people are: managers, supervisors, or other customer support specialists. Any person is okay as long as they are not a bot.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "call_id": {
+                            "type": "string",
+                            "description": "This is always {callId}.",
+                        },
+                        "summary": {
+                            "type": "string",
+                            "description": """
+Provide a concise summary in 3-5 sentences. Highlight any  important details or unusual aspects of the conversation.
+                            """,
+                        },
+                    },
+                },
+            },
+        )
+    ]
+
+    context = OpenAILLMContext(messages, tools)
     context_aggregator = llm.create_context_aggregator(context)
 
+    async def default_transfer_call(
+        function_name, tool_call_id, args, llm: LLMService, context, result_callback
+    ):
+        logger.debug(f"default_transfer_call: {function_name} {tool_call_id} {args}")
+        await result_callback(
+            {
+                "transfer_call": False,
+                "reason": "To transfer call calls, please dial in to the room using a phone or a SIP client.",
+            }
+        )
+
+    llm.register_function(
+        function_name="transfer_call",
+        callback=default_transfer_call,
+    )
+
     pipeline = Pipeline(
         [
             transport.input(),
@@ -87,6 +141,44 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
     async def on_participant_left(transport, participant, reason):
         await task.queue_frame(EndFrame())
 
+    @transport.event_handler("on_dialin_ready")
+    async def on_dialin_ready(_, sip_endpoint):
+        logger.info(f"on_dialin_ready: {sip_endpoint}")
+
+    @transport.event_handler("on_dialin_connected")
+    async def on_dialin_connected(transport, event):
+        logger.info(f"on_dialin_connected: {event}")
+        sip_session_id = event["sessionId"]
+
+        async def transfer_call(
+            function_name, tool_call_id, args, llm: LLMService, context, result_callback
+        ):
+            logger.debug(f"transfer_call: {function_name} {tool_call_id} {args}")
+
+            # sip_url = "sip:your_user_name@sip.linphone.org"
+
+            sip_url = (
+                f"sip:your_username@dailyco.sip.twilio.com?x-daily_id={room_url.split('/')[-1]}"
+            )
+
+            try:
+                await transport.sip_refer(
+                    settings={
+                        "sessionId": sip_session_id,
+                        "toEndPoint": sip_url,
+                    }
+                )
+            except Exception as e:
+                logger.error(f"An error occurred during SIP refer: {e}")
+                await result_callback({"transfer_call": False})
+
+            await result_callback({"transfer_call": True})
+
+        llm.register_function(
+            function_name="transfer_call",
+            callback=transfer_call,
+        )
+
     runner = PipelineRunner()
 
     await runner.run(task)

examples/foundational/28a-transcription-processor-openai.py

@@ -7,7 +7,7 @@
 import asyncio
 import os
 import sys
-from typing import List
+from typing import List, Optional
 
 import aiohttp
 from dotenv import load_dotenv
@@ -15,7 +15,11 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame, TranscriptionMessage, TranscriptionUpdateFrame
+from pipecat.frames.frames import (
+    CancelFrame,
+    TranscriptionMessage,
+    TranscriptionUpdateFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -33,13 +37,49 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class TranscriptHandler:
-    """Simple handler to demonstrate transcript processing.
+    """Handles real-time transcript processing and output.
 
-    Maintains a list of conversation messages and logs them with timestamps.
+    Maintains a list of conversation messages and outputs them either to a log
+    or to a file as they are received. Each message includes its timestamp and role.
+
+    Attributes:
+        messages: List of all processed transcript messages
+        output_file: Optional path to file where transcript is saved. If None, outputs to log only.
     """
 
-    def __init__(self):
+    def __init__(self, output_file: Optional[str] = None):
+        """Initialize handler with optional file output.
+
+        Args:
+            output_file: Path to output file. If None, outputs to log only.
+        """
         self.messages: List[TranscriptionMessage] = []
+        self.output_file: Optional[str] = output_file
+        logger.debug(
+            f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}"
+        )
+
+    async def save_message(self, message: TranscriptionMessage):
+        """Save a single transcript message.
+
+        Outputs the message to the log and optionally to a file.
+
+        Args:
+            message: The message to save
+        """
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}{message.role}: {message.content}"
+
+        # Always log the message
+        logger.info(f"Transcript: {line}")
+
+        # Optionally write to file
+        if self.output_file:
+            try:
+                with open(self.output_file, "a", encoding="utf-8") as f:
+                    f.write(line + "\n")
+            except Exception as e:
+                logger.error(f"Error saving transcript message to file: {e}")
 
     async def on_transcript_update(
         self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
@@ -50,13 +90,11 @@ class TranscriptHandler:
             processor: The TranscriptProcessor that emitted the update
             frame: TranscriptionUpdateFrame containing new messages
         """
-        self.messages.extend(frame.messages)
+        logger.debug(f"Received transcript update with {len(frame.messages)} new messages")
 
-        # Log the new messages
-        logger.info("New transcript messages:")
         for msg in frame.messages:
-            timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+            self.messages.append(msg)
-            logger.info(f"{timestamp}{msg.role}: {msg.content}")
+            await self.save_message(msg)
 
 
 async def main():
@@ -99,7 +137,8 @@ async def main():
 
         # Create transcript processor and handler
         transcript = TranscriptProcessor()
-        transcript_handler = TranscriptHandler()
+        transcript_handler = TranscriptHandler()  # Output to log only
+        # transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log
 
         pipeline = Pipeline(
             [
@@ -110,8 +149,8 @@ async def main():
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
                 transcript.assistant(),  # Assistant transcripts
+                context_aggregator.assistant(),  # Assistant spoken responses
             ]
         )
 
@@ -130,7 +169,8 @@ async def main():
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            # Stop the pipeline immediately when the participant leaves
+            await task.queue_frame(CancelFrame())
 
         runner = PipelineRunner()
 

examples/foundational/28b-transcript-processor-anthropic.py

@@ -7,7 +7,7 @@
 import asyncio
 import os
 import sys
-from typing import List
+from typing import List, Optional
 
 import aiohttp
 from dotenv import load_dotenv
@@ -15,7 +15,11 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame, TranscriptionMessage, TranscriptionUpdateFrame
+from pipecat.frames.frames import (
+    CancelFrame,
+    TranscriptionMessage,
+    TranscriptionUpdateFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -33,13 +37,49 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class TranscriptHandler:
-    """Simple handler to demonstrate transcript processing.
+    """Handles real-time transcript processing and output.
 
-    Maintains a list of conversation messages and logs them with timestamps.
+    Maintains a list of conversation messages and outputs them either to a log
+    or to a file as they are received. Each message includes its timestamp and role.
+
+    Attributes:
+        messages: List of all processed transcript messages
+        output_file: Optional path to file where transcript is saved. If None, outputs to log only.
     """
 
-    def __init__(self):
+    def __init__(self, output_file: Optional[str] = None):
+        """Initialize handler with optional file output.
+
+        Args:
+            output_file: Path to output file. If None, outputs to log only.
+        """
         self.messages: List[TranscriptionMessage] = []
+        self.output_file: Optional[str] = output_file
+        logger.debug(
+            f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}"
+        )
+
+    async def save_message(self, message: TranscriptionMessage):
+        """Save a single transcript message.
+
+        Outputs the message to the log and optionally to a file.
+
+        Args:
+            message: The message to save
+        """
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}{message.role}: {message.content}"
+
+        # Always log the message
+        logger.info(f"Transcript: {line}")
+
+        # Optionally write to file
+        if self.output_file:
+            try:
+                with open(self.output_file, "a", encoding="utf-8") as f:
+                    f.write(line + "\n")
+            except Exception as e:
+                logger.error(f"Error saving transcript message to file: {e}")
 
     async def on_transcript_update(
         self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
@@ -50,13 +90,11 @@ class TranscriptHandler:
             processor: The TranscriptProcessor that emitted the update
             frame: TranscriptionUpdateFrame containing new messages
         """
-        self.messages.extend(frame.messages)
+        logger.debug(f"Received transcript update with {len(frame.messages)} new messages")
 
-        # Log the new messages
-        logger.info("New transcript messages:")
         for msg in frame.messages:
-            timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+            self.messages.append(msg)
-            logger.info(f"{timestamp}{msg.role}: {msg.content}")
+            await self.save_message(msg)
 
 
 async def main():
@@ -99,7 +137,8 @@ async def main():
 
         # Create transcript processor and handler
         transcript = TranscriptProcessor()
-        transcript_handler = TranscriptHandler()
+        transcript_handler = TranscriptHandler()  # Output to log only
+        # transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log
 
         pipeline = Pipeline(
             [
@@ -110,8 +149,8 @@ async def main():
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
                 transcript.assistant(),  # Assistant transcripts
+                context_aggregator.assistant(),  # Assistant spoken responses
             ]
         )
 
@@ -130,7 +169,8 @@ async def main():
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            # Stop the pipeline immediately when the participant leaves
+            await task.queue_frame(CancelFrame())
 
         runner = PipelineRunner()
 

examples/foundational/28c-transcription-processor-gemini.py

@@ -7,7 +7,7 @@
 import asyncio
 import os
 import sys
-from typing import List
+from typing import List, Optional
 
 import aiohttp
 from dotenv import load_dotenv
@@ -15,7 +15,11 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame, TranscriptionMessage, TranscriptionUpdateFrame
+from pipecat.frames.frames import (
+    CancelFrame,
+    TranscriptionMessage,
+    TranscriptionUpdateFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -34,13 +38,49 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class TranscriptHandler:
-    """Simple handler to demonstrate transcript processing.
+    """Handles real-time transcript processing and output.
 
-    Maintains a list of conversation messages and logs them with timestamps.
+    Maintains a list of conversation messages and outputs them either to a log
+    or to a file as they are received. Each message includes its timestamp and role.
+
+    Attributes:
+        messages: List of all processed transcript messages
+        output_file: Optional path to file where transcript is saved. If None, outputs to log only.
     """
 
-    def __init__(self):
+    def __init__(self, output_file: Optional[str] = None):
+        """Initialize handler with optional file output.
+
+        Args:
+            output_file: Path to output file. If None, outputs to log only.
+        """
         self.messages: List[TranscriptionMessage] = []
+        self.output_file: Optional[str] = output_file
+        logger.debug(
+            f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}"
+        )
+
+    async def save_message(self, message: TranscriptionMessage):
+        """Save a single transcript message.
+
+        Outputs the message to the log and optionally to a file.
+
+        Args:
+            message: The message to save
+        """
+        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
+        line = f"{timestamp}{message.role}: {message.content}"
+
+        # Always log the message
+        logger.info(f"Transcript: {line}")
+
+        # Optionally write to file
+        if self.output_file:
+            try:
+                with open(self.output_file, "a", encoding="utf-8") as f:
+                    f.write(line + "\n")
+            except Exception as e:
+                logger.error(f"Error saving transcript message to file: {e}")
 
     async def on_transcript_update(
         self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
@@ -51,13 +91,11 @@ class TranscriptHandler:
             processor: The TranscriptProcessor that emitted the update
             frame: TranscriptionUpdateFrame containing new messages
         """
-        self.messages.extend(frame.messages)
+        logger.debug(f"Received transcript update with {len(frame.messages)} new messages")
 
-        # Log the new messages
-        logger.info("New transcript messages:")
         for msg in frame.messages:
-            timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+            self.messages.append(msg)
-            logger.info(f"{timestamp}{msg.role}: {msg.content}")
+            await self.save_message(msg)
 
 
 async def main():
@@ -102,7 +140,8 @@ async def main():
 
         # Create transcript processor and handler
         transcript = TranscriptProcessor()
-        transcript_handler = TranscriptHandler()
+        transcript_handler = TranscriptHandler()  # Output to log only
+        # transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log
 
         pipeline = Pipeline(
             [
@@ -113,8 +152,8 @@ async def main():
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
                 transcript.assistant(),  # Assistant transcripts
+                context_aggregator.assistant(),  # Assistant spoken responses
             ]
         )
 
@@ -140,7 +179,8 @@ async def main():
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            # Stop the pipeline immediately when the participant leaves
+            await task.queue_frame(CancelFrame())
 
         runner = PipelineRunner()
 

examples/moondream-chatbot/bot.py

@@ -67,8 +67,8 @@ talking_frame = SpriteFrame(images=sprites)
 
 
 class TalkingAnimation(FrameProcessor):
-    """This class starts a talking animation when it receives an first AudioFrame,
+    """This class starts a talking animation when it receives an first BotStartedSpeakingFrame,
-    and then returns to a "quiet" sprite when it sees a TTSStoppedFrame.
+    and then returns to a "quiet" sprite when it sees a BotStoppedSpeakingFrame.
     """
 
     def __init__(self):

examples/storytelling-chatbot/README.md

@@ -66,7 +66,7 @@ The build UI files can be found in `frontend/out`
 
 Start the API / bot manager:
 
-`python src/bot_runner.py`
+`python src/bot_runner.py --host localhost`
 
 If you'd like to run a custom domain or port:
 

examples/storytelling-chatbot/env.example

@@ -4,6 +4,7 @@ ELEVENLABS_API_KEY=
 ELEVENLABS_VOICE_ID=
 FAL_KEY=
 OPENAI_API_KEY=
+GOOGLE_API_KEY=
 
 ENV= # dev | production
 RUN_AS_VM= # Set this if you want to run bots on process (not launch a new VM)

examples/storytelling-chatbot/frontend/components/Story.tsx

@@ -1,4 +1,4 @@
-import React, { useState } from "react";
+import React, { useState, useEffect } from "react";
 import {
   useDaily,
   useParticipantIds,
@@ -33,7 +33,9 @@ const Story: React.FC<StoryProps> = ({ handleLeave }) => {
         setTimeout(() => daily.setLocalAudio(true), 500);
         setStoryState("user");
       } else {
-        daily.setLocalAudio(false);
+        // Uncomment the next line to mute the mic while the 
+        // assistant it talking. Leave it commented to allow for interruptions
+        // daily.setLocalAudio(false);
         setStoryState("assistant");
       }
     },
@@ -58,7 +60,7 @@ const Story: React.FC<StoryProps> = ({ handleLeave }) => {
         {participantIds.length >= 1 ? (
           <VideoTile
             sessionId={participantIds[0]}
-            inactive={storyState === "user"}
+            inactive={false}
           />
         ) : (
           <span className="p-3 rounded-full bg-gray-900/60 animate-pulse">
@@ -71,7 +73,7 @@ const Story: React.FC<StoryProps> = ({ handleLeave }) => {
         )}
         <DailyAudio />
       </div>
-      <UserInputIndicator active={storyState === "user"} />
+      <UserInputIndicator active={true} />
     </div>
   );
 };

examples/storytelling-chatbot/frontend/components/UserInputIndicator/UserInputIndicator.module.css

@@ -43,25 +43,8 @@
     transition: opacity 0.5s ease; 
 }
 
-
-@keyframes pulse {
-    0% {
-        outline-width: 6px;
-        @apply  outline-teal-500/10; 
-    }
-    50% {
-        outline-width: 24px;
-        @apply  outline-teal-500/50; 
-    }
-    100% {
-        outline-width: 6px;
-        @apply  outline-teal-500/10; 
-    }
-}
-
 .micIconActive{
     @apply bg-teal-950 border-teal-500 outline-teal-500/20;
-    animation: pulse 2s infinite ease-in-out;
 }
 
 .micIconActive svg{

examples/storytelling-chatbot/frontend/components/UserInputIndicator/index.tsx

@@ -1,4 +1,4 @@
-import React, { useState, useEffect } from "react";
+import React, { useState, useEffect, useRef } from "react";
 
 import { useAppMessage } from "@daily-co/daily-react";
 import { DailyEventObjectAppMessage } from "@daily-co/daily-js";
@@ -13,12 +13,31 @@ interface Props {
 
 export default function UserInputIndicator({ active }: Props) {
   const [transcription, setTranscription] = useState<string[]>([]);
+  const timeoutRef = useRef<NodeJS.Timeout>();
+
+  const resetTimeout = () => {
+    if (timeoutRef.current) {
+      clearTimeout(timeoutRef.current);
+    }
+    timeoutRef.current = setTimeout(() => {
+      setTranscription([]);
+    }, 5000);
+  };
+
+  useEffect(() => {
+    return () => {
+      if (timeoutRef.current) {
+        clearTimeout(timeoutRef.current);
+      }
+    };
+  }, []);
 
   useAppMessage({
     onAppMessage: (e: DailyEventObjectAppMessage<any>) => {
       if (e.fromId && e.fromId === "transcription") {
         if (e.data.user_id === "" && e.data.is_final) {
           setTranscription((t) => [...t, ...e.data.text.split(" ")]);
+          resetTimeout();
         }
       }
     },

examples/storytelling-chatbot/requirements.txt

@@ -2,4 +2,4 @@ async_timeout
 fastapi
 uvicorn
 python-dotenv
-pipecat-ai[daily,elevenlabs,openai,fal]
+pipecat-ai[daily,openai,fal,google,cartesia]

examples/storytelling-chatbot/src/bot.py

@@ -13,16 +13,23 @@ import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 from processors import StoryImageProcessor, StoryProcessor
-from prompts import CUE_USER_TURN, LLM_BASE_PROMPT, LLM_INTRO_PROMPT
+from prompts import CUE_USER_TURN, LLM_BASE_PROMPT
 from utils.helpers import load_images, load_sounds
 
-from pipecat.frames.frames import EndFrame, LLMMessagesFrame, StopTaskFrame
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import EndFrame, StopTaskFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+    OpenAILLMContextFrame,
+)
+from pipecat.processors.logger import FrameLogger
+from pipecat.services.cartesia import CartesiaHttpTTSService, CartesiaTTSService
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.fal import FalImageGenService
+from pipecat.services.google import GoogleLLMService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import (
     DailyParams,
@@ -53,6 +60,7 @@ async def main(room_url, token=None):
                 camera_out_width=768,
                 camera_out_height=768,
                 transcription_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
                 vad_enabled=True,
             ),
         )
@@ -61,11 +69,10 @@ async def main(room_url, token=None):
 
         # -------------- Services --------------- #
 
-        llm_service = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm_service = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
 
         tts_service = ElevenLabsTTSService(
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id=os.getenv("ELEVENLABS_VOICE_ID")
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
         )
 
         fal_service_params = FalImageGenService.InputParams(
@@ -74,7 +81,7 @@ async def main(room_url, token=None):
 
         fal_service = FalImageGenService(
             aiohttp_session=session,
-            model="fal-ai/fast-lightning-sdxl",
+            model="fal-ai/stable-diffusion-v35-medium",
             params=fal_service_params,
             key=os.getenv("FAL_KEY"),
         )
@@ -97,35 +104,8 @@ async def main(room_url, token=None):
 
         runner = PipelineRunner()
 
-        # The intro pipeline is used to start
-        # the story (as per LLM_INTRO_PROMPT)
-        intro_pipeline = Pipeline([llm_service, tts_service, transport.output()])
-
-        intro_task = PipelineTask(intro_pipeline)
-
         logger.debug("Waiting for participant...")
 
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            logger.debug("Participant joined, storytime commence!")
-            await transport.capture_participant_transcription(participant["id"])
-            await intro_task.queue_frames(
-                [
-                    images["book1"],
-                    LLMMessagesFrame([LLM_INTRO_PROMPT]),
-                    DailyTransportMessageFrame(CUE_USER_TURN),
-                    sounds["listening"],
-                    images["book2"],
-                    StopTaskFrame(),
-                ]
-            )
-
-        # We run the intro pipeline. This will start the transport. The intro
-        # task will exit after StopTaskFrame is processed.
-        await runner.run(intro_task)
-
-        # The main story pipeline is used to continue the story based on user
-        # input.
         main_pipeline = Pipeline(
             [
                 transport.input(),
@@ -139,11 +119,32 @@ async def main(room_url, token=None):
             ]
         )
 
-        main_task = PipelineTask(main_pipeline)
+        main_task = PipelineTask(
+            main_pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            logger.debug("Participant joined, storytime commence!")
+            await transport.capture_participant_transcription(participant["id"])
+            await main_task.queue_frames(
+                [
+                    images["book1"],
+                    context_aggregator.user().get_context_frame(),
+                    DailyTransportMessageFrame(CUE_USER_TURN),
+                    # sounds["listening"],
+                    images["book2"],
+                ]
+            )
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):
-            await intro_task.queue_frame(EndFrame())
             await main_task.queue_frame(EndFrame())
 
         @transport.event_handler("on_call_state_updated")

examples/storytelling-chatbot/src/bot_runner.py

@@ -114,7 +114,7 @@ async def start_bot(request: Request) -> JSONResponse:
     else:
         try:
             subprocess.Popen(
-                [f"python3 -m bot -u {room.url} -t {token}"],
+                [f"python -m bot -u {room.url} -t {token}"],
                 shell=True,
                 bufsize=1,
                 cwd=os.path.dirname(os.path.abspath(__file__)),
@@ -175,7 +175,7 @@ async def virtualize_bot(room_url: str, token: str):
             image = data[0]["config"]["image"]
 
         # Machine configuration
-        cmd = f"python3 src/bot.py -u {room_url} -t {token}"
+        cmd = f"python src/bot.py -u {room_url} -t {token}"
         cmd = cmd.split()
         worker_props = {
             "config": {
@@ -215,7 +215,7 @@ async def virtualize_bot(room_url: str, token: str):
 if __name__ == "__main__":
     # Check environment variables
     required_env_vars = [
-        "OPENAI_API_KEY",
+        "GOOGLE_API_KEY",
         "DAILY_API_KEY",
         "FAL_KEY",
         "ELEVENLABS_VOICE_ID",

examples/storytelling-chatbot/src/processors.py

@@ -37,8 +37,7 @@ class StoryPromptFrame(TextFrame):
 
 
 class StoryImageProcessor(FrameProcessor):
-    """
+    """Processor for image prompt frames that will be sent to the FAL service.
-    Processor for image prompt frames that will be sent to the FAL service.
 
     This processor is responsible for consuming frames of type `StoryImageFrame`.
     It processes them by passing it to the FAL service.
@@ -68,8 +67,7 @@ class StoryImageProcessor(FrameProcessor):
 
 
 class StoryProcessor(FrameProcessor):
-    """
+    """Primary frame processor. It takes the frames generated by the LLM
-    Primary frame processor. It takes the frames generated by the LLM
     and processes them into image prompts and story pages (sentences).
     For a clearer picture of how this works, reference prompts.py
 
@@ -97,44 +95,10 @@ class StoryProcessor(FrameProcessor):
             await self.push_frame(sounds["talking"])
 
         elif isinstance(frame, TextFrame):
-            # We want to look for sentence breaks in the text
+            # Add new text to the buffer
-            # but since TextFrames are streamed from the LLM
-            # we need to keep a buffer of the text we've seen so far
             self._text += frame.text
-
+            # Process any complete patterns in the order they appear
-            # IMAGE PROMPT
+            await self.process_text_content()
-            # Looking for: < [image prompt] > in the LLM response
-            # We prompted our LLM to add an image prompt in the response
-            # so we use regex matching to find it and yield a StoryImageFrame
-            if re.search(r"<.*?>", self._text):
-                if not re.search(r"<.*?>.*?>", self._text):
-                    # Pass any frames until we have a closing bracket
-                    # otherwise the image prompt will be passed to TTS
-                    pass
-                # Extract the image prompt from the text using regex
-                image_prompt = re.search(r"<(.*?)>", self._text).group(1)
-                # Remove the image prompt from the text
-                self._text = re.sub(r"<.*?>", "", self._text, count=1)
-                # Process the image prompt frame
-                await self.push_frame(StoryImageFrame(image_prompt))
-
-            # STORY PAGE
-            # Looking for: [break] in the LLM response
-            # We prompted our LLM to add a [break] after each sentence
-            # so we use regex matching to find it in the LLM response
-            if re.search(r".*\[[bB]reak\].*", self._text):
-                # Remove the [break] token from the text
-                # so it isn't spoken out loud by the TTS
-                self._text = re.sub(r"\[[bB]reak\]", "", self._text, flags=re.IGNORECASE)
-                self._text = self._text.replace("\n", " ")
-                if len(self._text) > 2:
-                    # Append the sentence to the story
-                    self._story.append(self._text)
-                    await self.push_frame(StoryPageFrame(self._text))
-                    # Assert that it's the LLMs turn, until we're finished
-                    await self.push_frame(DailyTransportMessageFrame(CUE_ASSISTANT_TURN))
-                # Clear the buffer
-                self._text = ""
 
         # End of a full LLM response
         # Driven by the prompt, the LLM should have asked the user for input
@@ -150,3 +114,38 @@ class StoryProcessor(FrameProcessor):
         # Anything that is not a TextFrame pass through
         else:
             await self.push_frame(frame)
+
+    async def process_text_content(self):
+        """Process text content in order of appearance, handling both image prompts and story breaks."""
+        while True:
+            # Find the first occurrence of each pattern
+            image_match = re.search(r"<(.*?)>", self._text)
+            break_match = re.search(r"\[[bB]reak\]", self._text)
+
+            # If neither pattern is found, we're done processing
+            if not image_match and not break_match:
+                break
+
+            # Find which pattern comes first in the text
+            image_pos = image_match.start() if image_match else float("inf")
+            break_pos = break_match.start() if break_match else float("inf")
+
+            if image_pos < break_pos:
+                # Process image prompt first
+                image_prompt = image_match.group(1)
+                # Remove the image prompt from the text
+                self._text = self._text[: image_match.start()] + self._text[image_match.end() :]
+                await self.push_frame(StoryImageFrame(image_prompt))
+            else:
+                # Process story break first
+                parts = re.split(r"\[[bB]reak\]", self._text, flags=re.IGNORECASE, maxsplit=1)
+                before_break = parts[0].replace("\n", " ").strip()
+
+                if len(before_break) > 2:
+                    self._story.append(before_break)
+                    await self.push_frame(StoryPageFrame(before_break))
+                    # await self.push_frame(sounds["ding"])
+                    await self.push_frame(DailyTransportMessageFrame(CUE_ASSISTANT_TURN))
+
+                # Keep the remainder (if any) in the buffer
+                self._text = parts[1].strip() if len(parts) > 1 else ""

examples/storytelling-chatbot/src/prompts.py

@@ -1,31 +1,34 @@
-LLM_INTRO_PROMPT = {
-    "role": "system",
-    "content": "You are a creative storyteller who loves to tell whimsical, fantastical stories. \
-        Your goal is to craft an engaging and fun story. \
-        Start by asking the user what kind of story they'd like to hear. Don't provide any examples. \
-        Keep your response to only a few sentences.",
-}
-
-
 LLM_BASE_PROMPT = {
     "role": "system",
-    "content": "You are a creative storyteller who loves tell whimsical, fantastical stories. \
+    "content": """You are a creative storyteller who loves tell whimsical, fantastical stories.
-        Your goal is to craft an engaging and fun story. \
+        Your goal is to craft an engaging and fun story.
-        Keep all responses short and no more than a few sentences. Include [break] after each sentence of the story. \
+        Keep all responses short and no more than a few sentences.
-        \
+        Start by asking the user what kind of story they'd like to hear. Don't provide any examples.
-        Start each sentence with an image prompt, wrapped in triangle braces, that I can use to generate an illustration representing the upcoming scene. \
+        After they've answered the question, start telling the story. Include [break] after each sentence of the story.
-        Image prompts should always be wrapped in triangle braces, like this: <image prompt goes here>. \
+        
-        You should provide as much descriptive detail in your image prompt as you can to help recreate the current scene depicted by the sentence. \
+        Start each sentence with an image prompt, wrapped in triangle braces, that I can use to generate an illustration representing the upcoming scene.
-        For any recurring characters, you should provide a description of them in the image prompt each time, for example: <a brown fluffy dog ...>. \
+        Image prompts should always be wrapped in triangle braces, like this: <image prompt goes here>.
-        Please do not include any character names in the image prompts, just their descriptions. \
+        You should provide as much descriptive detail in your image prompt as you can to help recreate the current scene depicted by the sentence.
-        Image prompts should focus on key visual attributes of all characters each time, for example <a brown fluffy dog and the tiny red cat ...>. \
+        For any recurring characters, you should provide a description of them in the image prompt each time, for example: <a brown fluffy dog ...>.
-        Please use the following structure for your image prompts: characters, setting, action, and mood. \
+        Please do not include any character names in the image prompts, just their descriptions.
-        Image prompts should be less than 150-200 characters and start in lowercase. \
+        Image prompts should focus on key visual attributes of all characters each time, for example <a brown fluffy dog and the tiny red cat ...>.
-        \
+        Please use the following structure for your image prompts: characters, setting, action, and mood.
-        Responses should use the format: <...> story sentence [break] <...> story sentence [break] ... \
+        Image prompts should be less than 150-200 characters and start in lowercase.
-        After each response, ask me how I'd like the story to continue and wait for my input. \
+
-        Please ensure your responses are less than 3-4 sentences long. \
+        STORY SENTENCE OUTPUT FORMAT:
-        Please refrain from using any explicit language or content. Do not tell scary stories.",
+        <image description 1>
+        story sentence 1 [break]
+        <image description 2>
+        story sentence 2 [break]
+        <image description 3>
+        story sentence 3 [break]
+        How would you like the story to continue?
+        END OF EXAMPLE OUTPUT
+
+        Generate three story sentences, then ask what should happen next and wait for my input. You can propose an idea for how the story should proceed, but make sure to tell me I can suggest whatever I want. \
+        Please ensure your responses are less than 5 sentences long. \
+        Please refrain from using any explicit language or content. Do not tell scary stories.
+        Once you've started telling the story, EVERY RESPONSE should follow the story sentence output format. It is VERY IMPORTANT that you continue to include <image descriptions> and [break] between story sentences. DO NOT RESPOND without image descriptions and break tags.""",
 }
 
 

pyproject.toml

@@ -32,7 +32,7 @@ dependencies = [
     "protobuf~=5.29.3",
     "pydantic~=2.10.5",
     "pyloudnorm~=0.1.1",
-    "resampy~=0.4.3"
+    "soxr~=0.5.0"
 ]
 
 [project.urls]
@@ -85,7 +85,13 @@ openrouter = [ "openai~=1.59.6" ]
 where = ["src"]
 
 [tool.pytest.ini_options]
+addopts = "--verbose"
+testpaths = ["tests"]
 pythonpath = ["src"]
+asyncio_default_fixture_loop_scope = "function"
+filterwarnings = [
+    "ignore:'audioop' is deprecated:DeprecationWarning",
+]
 
 [tool.setuptools_scm]
 local_scheme = "no-local-version"

src/pipecat/audio/utils.py

@@ -8,14 +8,14 @@ import audioop
 
 import numpy as np
 import pyloudnorm as pyln
-import resampy
+import soxr
 
 
 def resample_audio(audio: bytes, original_rate: int, target_rate: int) -> bytes:
     if original_rate == target_rate:
         return audio
     audio_data = np.frombuffer(audio, dtype=np.int16)
-    resampled_audio = resampy.resample(audio_data, original_rate, target_rate)
+    resampled_audio = soxr.resample(audio_data, original_rate, target_rate)
     return resampled_audio.astype(np.int16).tobytes()
 
 

src/pipecat/pipeline/base_task.py

@@ -0,0 +1,56 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from abc import ABC, abstractmethod
+from typing import AsyncIterable, Iterable
+
+from pipecat.frames.frames import Frame
+
+
+class BaseTask(ABC):
+    @abstractmethod
+    def has_finished(self) -> bool:
+        """Indicates whether the tasks has finished. That is, all processors
+        have stopped.
+
+        """
+        pass
+
+    @abstractmethod
+    async def stop_when_done(self):
+        """This is a helper function that sends an EndFrame to the pipeline in
+        order to stop the task after everything in it has been processed.
+
+        """
+        pass
+
+    @abstractmethod
+    async def cancel(self):
+        """
+        Stops the running pipeline immediately.
+        """
+        pass
+
+    @abstractmethod
+    async def run(self):
+        """
+        Starts running the given pipeline.
+        """
+        pass
+
+    @abstractmethod
+    async def queue_frame(self, frame: Frame):
+        """
+        Queue a frame to be pushed down the pipeline.
+        """
+        pass
+
+    @abstractmethod
+    async def queue_frames(self, frames: Iterable[Frame] | AsyncIterable[Frame]):
+        """
+        Queues multiple frames to be pushed down the pipeline.
+        """
+        pass

src/pipecat/pipeline/task.py

@@ -27,6 +27,7 @@ from pipecat.frames.frames import (
 from pipecat.metrics.metrics import ProcessingMetricsData, TTFBMetricsData
 from pipecat.observers.base_observer import BaseObserver
 from pipecat.pipeline.base_pipeline import BasePipeline
+from pipecat.pipeline.base_task import BaseTask
 from pipecat.pipeline.task_observer import TaskObserver
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.utils.utils import obj_count, obj_id
@@ -45,6 +46,7 @@ class PipelineParams(BaseModel):
     send_initial_empty_metrics: bool = True
     report_only_initial_ttfb: bool = False
     observers: List[BaseObserver] = []
+    heartbeats_period_secs: float = HEARTBEAT_SECONDS
 
 
 class Source(FrameProcessor):
@@ -85,7 +87,7 @@ class Sink(FrameProcessor):
         await self._down_queue.put(frame)
 
 
-class PipelineTask:
+class PipelineTask(BaseTask):
     def __init__(
         self,
         pipeline: BasePipeline,
@@ -121,7 +123,7 @@ class PipelineTask:
 
         self._observer = TaskObserver(params.observers)
 
-    def has_finished(self):
+    def has_finished(self) -> bool:
         """Indicates whether the tasks has finished. That is, all processors
         have stopped.
 
@@ -315,7 +317,7 @@ class PipelineTask:
 
     async def _heartbeat_push_handler(self):
         """
-        This tasks pushes a heartbeat frame every HEARTBEAT_SECONDS.
+        This tasks pushes a heartbeat frame every heartbeat period.
         """
         while True:
             try:
@@ -323,7 +325,7 @@ class PipelineTask:
                 # task will just stop waiting for the pipeline to finish not
                 # allowing more frames to be pushed.
                 await self._source.queue_frame(HeartbeatFrame(timestamp=self._clock.get_time()))
-                await asyncio.sleep(HEARTBEAT_SECONDS)
+                await asyncio.sleep(self._params.heartbeats_period_secs)
             except asyncio.CancelledError:
                 break
 

src/pipecat/processors/filters/frame_filter.py

@@ -11,7 +11,7 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 
 class FrameFilter(FrameProcessor):
-    def __init__(self, types: Tuple[Type[Frame]]):
+    def __init__(self, types: Tuple[Type[Frame], ...]):
         super().__init__()
         self._types = types
 

src/pipecat/processors/frame_processor.py

@@ -186,7 +186,7 @@ class FrameProcessor:
         self.__should_block_frames = True
 
     async def resume_processing_frames(self):
-        logger.trace("f{self}: resuming frame processing")
+        logger.trace(f"{self}: resuming frame processing")
         self.__input_event.set()
         self.__should_block_frames = False
 
@@ -293,8 +293,7 @@ class FrameProcessor:
         await self.__input_frame_task
 
     async def __input_frame_task_handler(self):
-        running = True
+        while True:
-        while running:
             try:
                 if self.__should_block_frames:
                     logger.trace(f"{self}: frame processing paused")
@@ -311,8 +310,6 @@ class FrameProcessor:
                 if callback:
                     await callback(self, frame, direction)
 
-                running = not isinstance(frame, EndFrame)
-
                 self.__input_queue.task_done()
             except asyncio.CancelledError:
                 logger.trace(f"{self}: cancelled input task")
@@ -330,12 +327,10 @@ class FrameProcessor:
         await self.__push_frame_task
 
     async def __push_frame_task_handler(self):
-        running = True
+        while True:
-        while running:
             try:
                 (frame, direction) = await self.__push_queue.get()
                 await self.__internal_push_frame(frame, direction)
-                running = not isinstance(frame, EndFrame)
                 self.__push_queue.task_done()
             except asyncio.CancelledError:
                 logger.trace(f"{self}: cancelled push task")

src/pipecat/processors/frameworks/rtvi.py

@@ -62,6 +62,9 @@ from pipecat.utils.string import match_endofsentence
 
 RTVI_PROTOCOL_VERSION = "0.3.0"
 
+RTVI_MESSAGE_LABEL = "rtvi-ai"
+RTVIMessageLiteral = Literal["rtvi-ai"]
+
 ActionResult = Union[bool, int, float, str, list, dict]
 
 
@@ -154,7 +157,7 @@ class RTVIActionFrame(DataFrame):
 
 
 class RTVIMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: str
     id: str
     data: Optional[Dict[str, Any]] = None
@@ -170,7 +173,7 @@ class RTVIErrorResponseData(BaseModel):
 
 
 class RTVIErrorResponse(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["error-response"] = "error-response"
     id: str
     data: RTVIErrorResponseData
@@ -182,7 +185,7 @@ class RTVIErrorData(BaseModel):
 
 
 class RTVIError(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["error"] = "error"
     data: RTVIErrorData
 
@@ -192,7 +195,7 @@ class RTVIDescribeConfigData(BaseModel):
 
 
 class RTVIDescribeConfig(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["config-available"] = "config-available"
     id: str
     data: RTVIDescribeConfigData
@@ -203,14 +206,14 @@ class RTVIDescribeActionsData(BaseModel):
 
 
 class RTVIDescribeActions(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["actions-available"] = "actions-available"
     id: str
     data: RTVIDescribeActionsData
 
 
 class RTVIConfigResponse(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["config"] = "config"
     id: str
     data: RTVIConfig
@@ -221,7 +224,7 @@ class RTVIActionResponseData(BaseModel):
 
 
 class RTVIActionResponse(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["action-response"] = "action-response"
     id: str
     data: RTVIActionResponseData
@@ -233,7 +236,7 @@ class RTVIBotReadyData(BaseModel):
 
 
 class RTVIBotReady(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-ready"] = "bot-ready"
     id: str
     data: RTVIBotReadyData
@@ -246,7 +249,7 @@ class RTVILLMFunctionCallMessageData(BaseModel):
 
 
 class RTVILLMFunctionCallMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["llm-function-call"] = "llm-function-call"
     data: RTVILLMFunctionCallMessageData
 
@@ -256,7 +259,7 @@ class RTVILLMFunctionCallStartMessageData(BaseModel):
 
 
 class RTVILLMFunctionCallStartMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["llm-function-call-start"] = "llm-function-call-start"
     data: RTVILLMFunctionCallStartMessageData
 
@@ -269,22 +272,22 @@ class RTVILLMFunctionCallResultData(BaseModel):
 
 
 class RTVIBotLLMStartedMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-llm-started"] = "bot-llm-started"
 
 
 class RTVIBotLLMStoppedMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-llm-stopped"] = "bot-llm-stopped"
 
 
 class RTVIBotTTSStartedMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-tts-started"] = "bot-tts-started"
 
 
 class RTVIBotTTSStoppedMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-tts-stopped"] = "bot-tts-stopped"
 
 
@@ -293,19 +296,19 @@ class RTVITextMessageData(BaseModel):
 
 
 class RTVIBotTranscriptionMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-transcription"] = "bot-transcription"
     data: RTVITextMessageData
 
 
 class RTVIBotLLMTextMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-llm-text"] = "bot-llm-text"
     data: RTVITextMessageData
 
 
 class RTVIBotTTSTextMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-tts-text"] = "bot-tts-text"
     data: RTVITextMessageData
 
@@ -317,7 +320,7 @@ class RTVIAudioMessageData(BaseModel):
 
 
 class RTVIBotTTSAudioMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-tts-audio"] = "bot-tts-audio"
     data: RTVIAudioMessageData
 
@@ -330,39 +333,39 @@ class RTVIUserTranscriptionMessageData(BaseModel):
 
 
 class RTVIUserTranscriptionMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["user-transcription"] = "user-transcription"
     data: RTVIUserTranscriptionMessageData
 
 
 class RTVIUserLLMTextMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["user-llm-text"] = "user-llm-text"
     data: RTVITextMessageData
 
 
 class RTVIUserStartedSpeakingMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["user-started-speaking"] = "user-started-speaking"
 
 
 class RTVIUserStoppedSpeakingMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["user-stopped-speaking"] = "user-stopped-speaking"
 
 
 class RTVIBotStartedSpeakingMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-started-speaking"] = "bot-started-speaking"
 
 
 class RTVIBotStoppedSpeakingMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["bot-stopped-speaking"] = "bot-stopped-speaking"
 
 
 class RTVIMetricsMessage(BaseModel):
-    label: Literal["rtvi-ai"] = "rtvi-ai"
+    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
     type: Literal["metrics"] = "metrics"
     data: Mapping[str, Any]
 
@@ -875,7 +878,11 @@ class RTVIProcessor(FrameProcessor):
 
     async def _handle_transport_message(self, frame: TransportMessageUrgentFrame):
         try:
-            message = RTVIMessage.model_validate(frame.message)
+            transport_message = frame.message
+            if transport_message.get("label") != RTVI_MESSAGE_LABEL:
+                logger.warning(f"Ignoring not RTVI message: {transport_message}")
+                return
+            message = RTVIMessage.model_validate(transport_message)
             await self._message_queue.put(message)
         except ValidationError as e:
             await self.send_error(f"Invalid RTVI transport message: {e}")

src/pipecat/processors/transcript_processor.py

@@ -4,17 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-from typing import List
+from typing import List, Optional
+
+from loguru import logger
 
 from pipecat.frames.frames import (
+    BotStoppedSpeakingFrame,
+    CancelFrame,
+    EndFrame,
     Frame,
-    OpenAILLMContextAssistantTimestampFrame,
+    StartInterruptionFrame,
     TranscriptionFrame,
     TranscriptionMessage,
     TranscriptionUpdateFrame,
+    TTSTextFrame,
 )
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.utils.time import time_now_iso8601
 
 
 class BaseTranscriptProcessor(FrameProcessor):
@@ -64,89 +70,74 @@ class UserTranscriptProcessor(BaseTranscriptProcessor):
 
 
 class AssistantTranscriptProcessor(BaseTranscriptProcessor):
-    """Processes assistant LLM context frames into timestamped conversation messages."""
+    """Processes assistant TTS text frames into timestamped conversation messages.
+
+    This processor aggregates TTS text frames into complete utterances and emits them as
+    transcript messages. Utterances are completed when:
+    - The bot stops speaking (BotStoppedSpeakingFrame)
+    - The bot is interrupted (StartInterruptionFrame)
+    - The pipeline ends (EndFrame)
+
+    Attributes:
+        _current_text_parts: List of text fragments being aggregated for current utterance
+        _aggregation_start_time: Timestamp when the current utterance began
+    """
 
     def __init__(self, **kwargs):
-        """Initialize processor with empty message stores."""
+        """Initialize processor with aggregation state."""
         super().__init__(**kwargs)
-        self._pending_assistant_messages: List[TranscriptionMessage] = []
+        self._current_text_parts: List[str] = []
+        self._aggregation_start_time: Optional[str] | None = None
 
-    def _extract_messages(self, messages: List[dict]) -> List[TranscriptionMessage]:
+    async def _emit_aggregated_text(self):
-        """Extract assistant messages from the OpenAI standard message format.
+        """Emit aggregated text as a transcript message."""
+        if self._current_text_parts and self._aggregation_start_time:
+            content = " ".join(self._current_text_parts).strip()
+            if content:
+                logger.debug(f"Emitting aggregated assistant message: {content}")
+                message = TranscriptionMessage(
+                    role="assistant",
+                    content=content,
+                    timestamp=self._aggregation_start_time,
+                )
+                await self._emit_update([message])
+            else:
+                logger.debug("No content to emit after stripping whitespace")
 
-        Args:
+            # Reset aggregation state
-            messages: List of messages in OpenAI format, which can be either:
+            self._current_text_parts = []
-                - Simple format: {"role": "user", "content": "Hello"}
+            self._aggregation_start_time = None
-                - Content list: {"role": "user", "content": [{"type": "text", "text": "Hello"}]}
-
-        Returns:
-            List[TranscriptionMessage]: Normalized conversation messages
-        """
-        result = []
-        for msg in messages:
-            if msg["role"] != "assistant":
-                continue
-
-            content = msg.get("content")
-            if isinstance(content, str):
-                if content:
-                    result.append(TranscriptionMessage(role="assistant", content=content))
-            elif isinstance(content, list):
-                text_parts = []
-                for part in content:
-                    if isinstance(part, dict) and part.get("type") == "text":
-                        text_parts.append(part["text"])
-
-                if text_parts:
-                    result.append(
-                        TranscriptionMessage(role="assistant", content=" ".join(text_parts))
-                    )
-
-        return result
-
-    def _find_new_messages(self, current: List[TranscriptionMessage]) -> List[TranscriptionMessage]:
-        """Find unprocessed messages from current list.
-
-        Args:
-            current: List of current messages
-
-        Returns:
-            List[TranscriptionMessage]: New messages not yet processed
-        """
-        if not self._processed_messages:
-            return current
-
-        processed_len = len(self._processed_messages)
-        if len(current) <= processed_len:
-            return []
-
-        return current[processed_len:]
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         """Process frames into assistant conversation messages.
 
+        Handles different frame types:
+        - TTSTextFrame: Aggregates text for current utterance
+        - BotStoppedSpeakingFrame: Completes current utterance
+        - StartInterruptionFrame: Completes current utterance due to interruption
+        - EndFrame: Completes current utterance at pipeline end
+        - CancelFrame: Completes current utterance due to cancellation
+
         Args:
             frame: Input frame to process
             direction: Frame processing direction
         """
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, OpenAILLMContextFrame):
+        if isinstance(frame, TTSTextFrame):
-            standard_messages = []
+            # Start timestamp on first text part
-            for msg in frame.context.messages:
+            if not self._aggregation_start_time:
-                converted = frame.context.to_standard_messages(msg)
+                self._aggregation_start_time = time_now_iso8601()
-                standard_messages.extend(converted)
 
-            current_messages = self._extract_messages(standard_messages)
+            self._current_text_parts.append(frame.text)
-            new_messages = self._find_new_messages(current_messages)
-            self._pending_assistant_messages.extend(new_messages)
 
-        elif isinstance(frame, OpenAILLMContextAssistantTimestampFrame):
+        elif isinstance(frame, (BotStoppedSpeakingFrame, StartInterruptionFrame, CancelFrame)):
-            if self._pending_assistant_messages:
+            # Emit accumulated text when bot finishes speaking or is interrupted
-                for msg in self._pending_assistant_messages:
+            await self._emit_aggregated_text()
-                    msg.timestamp = frame.timestamp
+
-                await self._emit_update(self._pending_assistant_messages)
+        elif isinstance(frame, EndFrame):
-                self._pending_assistant_messages = []
+            # Emit any remaining text when pipeline ends
+            await self._emit_aggregated_text()
 
         await self.push_frame(frame, direction)
 
@@ -170,8 +161,8 @@ class TranscriptProcessor:
                 llm,
                 tts,
                 transport.output(),
+                transcript.assistant_tts(),     # Assistant transcripts
                 context_aggregator.assistant(),
-                transcript.assistant(),         # Assistant transcripts
             ]
         )
 

src/pipecat/serializers/protobuf.py

@@ -93,11 +93,11 @@ class ProtobufFrameSerializer(FrameSerializer):
         id = getattr(args, "id", None)
         name = getattr(args, "name", None)
         pts = getattr(args, "pts", None)
-        if not id and "id" in args_dict:
+        if "id" in args_dict:
             del args_dict["id"]
-        if not name and "name" in args_dict:
+        if "name" in args_dict:
             del args_dict["name"]
-        if not pts and "pts" in args_dict:
+        if "pts" in args_dict:
             del args_dict["pts"]
 
         # Create the instance
@@ -105,10 +105,10 @@ class ProtobufFrameSerializer(FrameSerializer):
 
         # Set special fields
         if id:
-            setattr(instance, "id", getattr(args, "id", None))
+            setattr(instance, "id", id)
         if name:
-            setattr(instance, "name", getattr(args, "name", None))
+            setattr(instance, "name", name)
         if pts:
-            setattr(instance, "pts", getattr(args, "pts", None))
+            setattr(instance, "pts", pts)
 
         return instance

src/pipecat/services/canonical.py

@@ -18,6 +18,7 @@ from pipecat.frames.frames import CancelFrame, EndFrame, Frame
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
 from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.ai_services import AIService
 
 try:
     import aiofiles

src/pipecat/services/elevenlabs.py

@@ -524,13 +524,13 @@ class ElevenLabsHttpTTSService(TTSService):
 
         url = f"{self._base_url}/v1/text-to-speech/{self._voice_id}/stream"
 
-        payload = {
+        payload: Dict[str, Union[str, Dict[str, Union[float, bool]]]] = {
             "text": text,
             "model_id": self._model_name,
         }
 
         if self._voice_settings:
-            payload["voice_settings"] = json.dumps(self._voice_settings)
+            payload["voice_settings"] = self._voice_settings
 
         if self._settings["language"]:
             payload["language_code"] = self._settings["language"]

src/pipecat/services/gemini_multimodal_live/gemini.py

@@ -288,6 +288,10 @@ class GeminiMultimodalLiveLLMService(LLMService):
         )
 
     async def _handle_transcribe_model_audio(self, audio, context):
+        # Early return if modalities are not set to audio.
+        if self._settings["modalities"] != GeminiMultimodalModalities.AUDIO:
+            return
+
         text = await self._transcribe_audio(audio, context)
         logger.debug(f"[Transcription:model] {text}")
         # We add user messages directly to the context. We don't do that for assistant messages,

src/pipecat/services/openai.py

@@ -221,7 +221,7 @@ class BaseOpenAILLMService(LLMService):
                 )
                 await self.start_llm_usage_metrics(tokens)
 
-            if len(chunk.choices) == 0:
+            if chunk.choices is None or len(chunk.choices) == 0:
                 continue
 
             await self.stop_ttfb_metrics()

src/pipecat/services/openai_realtime_beta/context.py

@@ -6,10 +6,16 @@
 
 import copy
 import json
+from typing import Optional
 
 from loguru import logger
 
-from pipecat.frames.frames import Frame, LLMMessagesUpdateFrame, LLMSetToolsFrame
+from pipecat.frames.frames import (
+    Frame,
+    FunctionCallResultProperties,
+    LLMMessagesUpdateFrame,
+    LLMSetToolsFrame,
+)
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
     OpenAILLMContextFrame,
@@ -174,10 +180,13 @@ class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator)
         if not self._function_call_result:
             return
 
+        properties: Optional[FunctionCallResultProperties] = None
+
         self._reset()
         try:
             run_llm = True
             frame = self._function_call_result
+            properties = frame.properties
             self._function_call_result = None
             if frame.result:
                 # The "tool_call" message from the LLM that triggered the function call
@@ -211,11 +220,20 @@ class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator)
                 await self._user_context_aggregator.push_frame(
                     RealtimeFunctionCallResultFrame(result_frame=frame)
                 )
-                run_llm = frame.run_llm
+                if properties and properties.run_llm is not None:
+                    # If the tool call result has a run_llm property, use it
+                    run_llm = properties.run_llm
+                else:
+                    # Default behavior is to run the LLM if there are no function calls in progress
+                    run_llm = not bool(self._function_calls_in_progress)
 
             if run_llm:
                 await self._user_context_aggregator.push_context_frame()
 
+            # Emit the on_context_updated callback once the function call result is added to the context
+            if properties and properties.on_context_updated is not None:
+                await properties.on_context_updated()
+
             frame = OpenAILLMContextFrame(self._context)
             await self.push_frame(frame)
 

test-requirements.txt

@@ -22,8 +22,8 @@ pydantic~=2.8.2
 pyloudnorm~=0.1.1
 pyht~=0.1.4
 python-dotenv~=1.0.1
-resampy~=0.4.3
 silero-vad~=5.1
+soxr~=0.5.0
 together~=1.2.7
 transformers~=4.44.0
 websockets~=13.1

tests/test_aggregators.py

@@ -1,122 +1,70 @@
-import asyncio
+#
-import doctest
+# Copyright (c) 2024-2025 Daily
-import functools
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import unittest
 
 from pipecat.frames.frames import (
-    AudioRawFrame,
-    EndFrame,
-    Frame,
     ImageRawFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
+    OutputAudioRawFrame,
+    OutputImageRawFrame,
     TextFrame,
 )
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
 from pipecat.processors.aggregators.gated import GatedAggregator
 from pipecat.processors.aggregators.sentence import SentenceAggregator
-from pipecat.processors.text_transformer import StatelessTextTransformer
+from tests.utils import run_test
 
 
-class TestDailyFrameAggregators(unittest.IsolatedAsyncioTestCase):
+class TestSentenceAggregator(unittest.IsolatedAsyncioTestCase):
-    @unittest.skip("FIXME: This test is failing")
     async def test_sentence_aggregator(self):
-        sentence = "Hello, world. How are you? I am fine"
-        expected_sentences = ["Hello, world.", " How are you?", " I am fine "]
         aggregator = SentenceAggregator()
+
+        sentence = "Hello, world. How are you? I am fine!"
+
+        frames_to_send = []
         for word in sentence.split(" "):
-            async for sentence in aggregator.process_frame(TextFrame(word + " ")):
+            frames_to_send.append(TextFrame(text=word + " "))
-                self.assertIsInstance(sentence, TextFrame)
-                if isinstance(sentence, TextFrame):
-                    self.assertEqual(sentence.text, expected_sentences.pop(0))
 
-        async for sentence in aggregator.process_frame(EndFrame()):
+        expected_returned_frames = [TextFrame, TextFrame, TextFrame]
-            if len(expected_sentences):
-                self.assertIsInstance(sentence, TextFrame)
-                if isinstance(sentence, TextFrame):
-                    self.assertEqual(sentence.text, expected_sentences.pop(0))
-            else:
-                self.assertIsInstance(sentence, EndFrame)
 
-        self.assertEqual(expected_sentences, [])
+        (received_down, _) = await run_test(aggregator, frames_to_send, expected_returned_frames)
+        assert received_down[-3].text == "Hello, world. "
+        assert received_down[-2].text == "How are you? "
+        assert received_down[-1].text == "I am fine! "
 
-    @unittest.skip("FIXME: This test is failing")
+
-    async def test_gated_accumulator(self):
+class TestGatedAggregator(unittest.IsolatedAsyncioTestCase):
+    async def test_gated_aggregator(self):
         gated_aggregator = GatedAggregator(
             gate_open_fn=lambda frame: isinstance(frame, ImageRawFrame),
             gate_close_fn=lambda frame: isinstance(frame, LLMFullResponseStartFrame),
             start_open=False,
         )
 
-        frames = [
+        frames_to_send = [
             LLMFullResponseStartFrame(),
             TextFrame("Hello, "),
             TextFrame("world."),
-            AudioRawFrame(b"hello"),
+            OutputAudioRawFrame(audio=b"hello", sample_rate=16000, num_channels=1),
-            ImageRawFrame(b"image", (0, 0)),
+            OutputImageRawFrame(image=b"image", size=(0, 0), format="RGB"),
-            AudioRawFrame(b"world"),
+            OutputAudioRawFrame(audio=b"world", sample_rate=16000, num_channels=1),
             LLMFullResponseEndFrame(),
         ]
 
-        expected_output_frames = [
+        expected_returned_frames = [
-            ImageRawFrame(b"image", (0, 0)),
+            OutputImageRawFrame,
-            LLMFullResponseStartFrame(),
+            LLMFullResponseStartFrame,
-            TextFrame("Hello, "),
+            TextFrame,
-            TextFrame("world."),
+            TextFrame,
-            AudioRawFrame(b"hello"),
+            OutputAudioRawFrame,
-            AudioRawFrame(b"world"),
+            OutputAudioRawFrame,
-            LLMFullResponseEndFrame(),
+            LLMFullResponseEndFrame,
         ]
-        for frame in frames:
-            async for out_frame in gated_aggregator.process_frame(frame):
-                self.assertEqual(out_frame, expected_output_frames.pop(0))
-        self.assertEqual(expected_output_frames, [])
 
-    @unittest.skip("FIXME: This test is failing")
+        (received_down, _) = await run_test(
-    async def test_parallel_pipeline(self):
+            gated_aggregator, frames_to_send, expected_returned_frames
-        async def slow_add(sleep_time: float, name: str, x: str):
-            await asyncio.sleep(sleep_time)
-            return ":".join([x, name])
-
-        pipe1_annotation = StatelessTextTransformer(functools.partial(slow_add, 0.1, "pipe1"))
-        pipe2_annotation = StatelessTextTransformer(functools.partial(slow_add, 0.2, "pipe2"))
-        sentence_aggregator = SentenceAggregator()
-        add_dots = StatelessTextTransformer(lambda x: x + ".")
-
-        source = asyncio.Queue()
-        sink = asyncio.Queue()
-        pipeline = Pipeline(
-            [
-                ParallelPipeline([[pipe1_annotation], [sentence_aggregator, pipe2_annotation]]),
-                add_dots,
-            ],
-            source,
-            sink,
         )
-
-        frames = [TextFrame("Hello, "), TextFrame("world."), EndFrame()]
-
-        expected_output_frames: list[Frame] = [
-            TextFrame(text="Hello, :pipe1."),
-            TextFrame(text="world.:pipe1."),
-            TextFrame(text="Hello, world.:pipe2."),
-            EndFrame(),
-        ]
-
-        for frame in frames:
-            await source.put(frame)
-
-        await pipeline.run_pipeline()
-
-        while not sink.empty():
-            frame = await sink.get()
-            self.assertEqual(frame, expected_output_frames.pop(0))
-
-
-def load_tests(loader, tests, ignore):
-    """Run doctests on the aggregators module."""
-    from pipecat.processors import aggregators
-
-    tests.addTests(doctest.DocTestSuite(aggregators))
-    return tests

tests/test_daily_transport_service.py

@@ -1,3 +1,9 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import unittest
 
 

tests/test_filters.py

@@ -0,0 +1,94 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import unittest
+
+from pipecat.frames.frames import (
+    EndFrame,
+    Frame,
+    TextFrame,
+    TranscriptionFrame,
+    UserStartedSpeakingFrame,
+    UserStoppedSpeakingFrame,
+)
+from pipecat.processors.filters.frame_filter import FrameFilter
+from pipecat.processors.filters.function_filter import FunctionFilter
+from pipecat.processors.filters.identity_filter import IdentityFilter
+from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
+from tests.utils import EndTestFrame, run_test
+
+
+class TestIdentifyFilter(unittest.IsolatedAsyncioTestCase):
+    async def test_identity(self):
+        filter = IdentityFilter()
+        frames_to_send = [UserStartedSpeakingFrame(), UserStoppedSpeakingFrame()]
+        expected_returned_frames = [UserStartedSpeakingFrame, UserStoppedSpeakingFrame]
+        await run_test(filter, frames_to_send, expected_returned_frames)
+
+
+class TestFrameFilter(unittest.IsolatedAsyncioTestCase):
+    async def test_text_frame(self):
+        filter = FrameFilter(types=(TextFrame, EndTestFrame))
+        frames_to_send = [TextFrame(text="Hello Pipecat!")]
+        expected_returned_frames = [TextFrame]
+        await run_test(filter, frames_to_send, expected_returned_frames)
+
+    async def test_end_frame(self):
+        filter = FrameFilter(types=(EndFrame, EndTestFrame))
+        frames_to_send = [EndFrame()]
+        expected_returned_frames = [EndFrame]
+        await run_test(filter, frames_to_send, expected_returned_frames)
+
+    async def test_system_frame(self):
+        filter = FrameFilter(types=(EndTestFrame,))
+        frames_to_send = [UserStartedSpeakingFrame()]
+        expected_returned_frames = [UserStartedSpeakingFrame]
+        await run_test(filter, frames_to_send, expected_returned_frames)
+
+
+class TestFunctionFilter(unittest.IsolatedAsyncioTestCase):
+    async def test_passthrough(self):
+        async def passthrough(frame: Frame):
+            return True
+
+        filter = FunctionFilter(filter=passthrough)
+        frames_to_send = [TextFrame(text="Hello Pipecat!")]
+        expected_returned_frames = [TextFrame]
+        await run_test(filter, frames_to_send, expected_returned_frames)
+
+    async def test_no_passthrough(self):
+        async def no_passthrough(frame: Frame):
+            return False
+
+        filter = FunctionFilter(filter=no_passthrough)
+        frames_to_send = [TextFrame(text="Hello Pipecat!")]
+        expected_returned_frames = [TextFrame]
+        try:
+            await asyncio.wait_for(
+                run_test(filter, frames_to_send, expected_returned_frames), timeout=0.5
+            )
+            assert False
+        except asyncio.TimeoutError:
+            pass
+
+
+class TestWakeCheckFilter(unittest.IsolatedAsyncioTestCase):
+    async def test_no_wake_word(self):
+        filter = WakeCheckFilter(wake_phrases=["Hey, Pipecat"])
+        frames_to_send = [TranscriptionFrame(user_id="test", text="Phrase 1", timestamp="")]
+        expected_returned_frames = []
+        await run_test(filter, frames_to_send, expected_returned_frames)
+
+    async def test_wake_word(self):
+        filter = WakeCheckFilter(wake_phrases=["Hey, Pipecat"])
+        frames_to_send = [
+            TranscriptionFrame(user_id="test", text="Hey, Pipecat", timestamp=""),
+            TranscriptionFrame(user_id="test", text="Phrase 1", timestamp=""),
+        ]
+        expected_returned_frames = [TranscriptionFrame, TranscriptionFrame]
+        (received_down, _) = await run_test(filter, frames_to_send, expected_returned_frames)
+        assert received_down[-1].text == "Phrase 1"

tests/test_langchain.py

@@ -93,7 +93,3 @@ class TestLangchain(unittest.IsolatedAsyncioTestCase):
         # This next one would fail with:
         # AssertionError: ' H e l l o   d e a r   h u m a n' != 'Hello dear human'
         # self.assertEqual(tma_out.messages[-1]["content"], self.expected_response)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_openai_tts.py

@@ -1,37 +0,0 @@
-import asyncio
-import unittest
-
-import openai
-import pyaudio
-from dotenv import load_dotenv
-
-from pipecat.frames.frames import AudioRawFrame, ErrorFrame
-from pipecat.services.openai import OpenAITTSService
-
-load_dotenv()
-
-
-class TestWhisperOpenAIService(unittest.IsolatedAsyncioTestCase):
-    @unittest.skip("FIXME: This test is failing")
-    async def test_whisper_tts(self):
-        pa = pyaudio.PyAudio()
-        stream = pa.open(format=pyaudio.paInt16, channels=1, rate=24_000, output=True)
-
-        tts = OpenAITTSService(voice="nova")
-
-        async for frame in tts.run_tts("Hello, there. Nice to meet you, seems to work well"):
-            self.assertIsInstance(frame, AudioRawFrame)
-            stream.write(frame.audio)
-
-        await asyncio.sleep(0.5)
-        stream.stop_stream()
-        pa.terminate()
-
-        tts = OpenAITTSService(voice="invalid_voice")
-        with self.assertRaises(openai.BadRequestError):
-            async for frame in tts.run_tts("wont work"):
-                self.assertIsInstance(frame, ErrorFrame)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_pipeline.py

@@ -1,116 +1,92 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import asyncio
 import unittest
-from unittest.mock import Mock
 
-from pipecat.frames.frames import EndFrame, TextFrame
+from pipecat.frames.frames import EndFrame, HeartbeatFrame, TextFrame
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
-from pipecat.processors.aggregators.sentence import SentenceAggregator
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.filters.identity_filter import IdentityFilter
 from pipecat.processors.frame_processor import FrameProcessor
-from pipecat.processors.text_transformer import StatelessTextTransformer
+from tests.utils import HeartbeatsObserver, run_test
 
 
-class TestDailyPipeline(unittest.IsolatedAsyncioTestCase):
+class TestPipeline(unittest.IsolatedAsyncioTestCase):
-    @unittest.skip("FIXME: This test is failing")
+    async def test_pipeline_single(self):
-    async def test_pipeline_simple(self):
+        pipeline = Pipeline([IdentityFilter()])
-        aggregator = SentenceAggregator()
 
-        outgoing_queue = asyncio.Queue()
+        frames_to_send = [TextFrame(text="Hello from Pipecat!")]
-        incoming_queue = asyncio.Queue()
+        expected_returned_frames = [TextFrame]
-        pipeline = Pipeline([aggregator], incoming_queue, outgoing_queue)
+        await run_test(pipeline, frames_to_send, expected_returned_frames)
 
-        await incoming_queue.put(TextFrame("Hello, "))
+    async def test_pipeline_multiple(self):
-        await incoming_queue.put(TextFrame("world."))
+        identity1 = IdentityFilter()
-        await incoming_queue.put(EndFrame())
+        identity2 = IdentityFilter()
+        identity3 = IdentityFilter()
 
-        await pipeline.run_pipeline()
+        pipeline = Pipeline([identity1, identity2, identity3])
 
-        self.assertEqual(await outgoing_queue.get(), TextFrame("Hello, world."))
+        frames_to_send = [TextFrame(text="Hello from Pipecat!")]
-        self.assertIsInstance(await outgoing_queue.get(), EndFrame)
+        expected_returned_frames = [TextFrame]
+        await run_test(pipeline, frames_to_send, expected_returned_frames)
 
-    @unittest.skip("FIXME: This test is failing")
-    async def test_pipeline_multiple_stages(self):
-        sentence_aggregator = SentenceAggregator()
-        to_upper = StatelessTextTransformer(lambda x: x.upper())
-        add_space = StatelessTextTransformer(lambda x: x + " ")
 
-        outgoing_queue = asyncio.Queue()
+class TestParallelPipeline(unittest.IsolatedAsyncioTestCase):
-        incoming_queue = asyncio.Queue()
+    async def test_parallel_single(self):
-        pipeline = Pipeline(
+        pipeline = ParallelPipeline([IdentityFilter()])
-            [add_space, sentence_aggregator, to_upper], incoming_queue, outgoing_queue
+
+        frames_to_send = [TextFrame(text="Hello from Pipecat!")]
+        expected_returned_frames = [TextFrame]
+        await run_test(pipeline, frames_to_send, expected_returned_frames)
+
+    async def test_parallel_multiple(self):
+        """Should only passthrough one instance of TextFrame."""
+        pipeline = ParallelPipeline([IdentityFilter()], [IdentityFilter()])
+
+        frames_to_send = [TextFrame(text="Hello from Pipecat!")]
+        expected_returned_frames = [TextFrame]
+        await run_test(pipeline, frames_to_send, expected_returned_frames)
+
+
+class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
+    async def test_task_single(self):
+        pipeline = Pipeline([IdentityFilter()])
+        task = PipelineTask(pipeline)
+
+        await task.queue_frame(TextFrame(text="Hello!"))
+        await task.queue_frames([TextFrame(text="Bye!"), EndFrame()])
+        await task.run()
+        assert task.has_finished()
+
+    async def test_task_heartbeats(self):
+        heartbeats_counter = 0
+
+        async def heartbeat_received(processor: FrameProcessor, heartbeat: HeartbeatFrame):
+            nonlocal heartbeats_counter
+            heartbeats_counter += 1
+
+        identity = IdentityFilter()
+        pipeline = Pipeline([identity])
+        heartbeats_observer = HeartbeatsObserver(
+            target=identity, heartbeat_callback=heartbeat_received
+        )
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                enable_heartbeats=True, heartbeats_period_secs=0.2, observers=[heartbeats_observer]
+            ),
         )
 
-        sentence = "Hello, world. It's me, a pipeline."
+        expected_heartbeats = 1.0 / 0.2
-        for c in sentence:
-            await incoming_queue.put(TextFrame(c))
-        await incoming_queue.put(EndFrame())
 
-        await pipeline.run_pipeline()
+        await task.queue_frame(TextFrame(text="Hello!"))
-
+        try:
-        self.assertEqual(await outgoing_queue.get(), TextFrame("H E L L O ,   W O R L D ."))
+            await asyncio.wait_for(task.run(), timeout=1.0)
-        self.assertEqual(
+        except asyncio.TimeoutError:
-            await outgoing_queue.get(),
+            pass
-            TextFrame("   I T ' S   M E ,   A   P I P E L I N E ."),
+        assert heartbeats_counter == expected_heartbeats
-        )
-        # leftover little bit because of the spacing
-        self.assertEqual(
-            await outgoing_queue.get(),
-            TextFrame(" "),
-        )
-        self.assertIsInstance(await outgoing_queue.get(), EndFrame)
-
-
-class TestLogFrame(unittest.TestCase):
-    class MockProcessor(FrameProcessor):
-        def __init__(self, name):
-            self.name = name
-
-        def __str__(self):
-            return self.name
-
-    def setUp(self):
-        self.processor1 = self.MockProcessor("processor1")
-        self.processor2 = self.MockProcessor("processor2")
-        self.pipeline = Pipeline(processors=[self.processor1, self.processor2])
-        self.pipeline._name = "MyClass"
-        self.pipeline._logger = Mock()
-
-    @unittest.skip("FIXME: This test is failing")
-    def test_log_frame_from_source(self):
-        frame = Mock(__class__=Mock(__name__="MyFrame"))
-        self.pipeline._log_frame(frame, depth=1)
-        self.pipeline._logger.debug.assert_called_once_with(
-            "MyClass  source -> MyFrame -> processor1"
-        )
-
-    @unittest.skip("FIXME: This test is failing")
-    def test_log_frame_to_sink(self):
-        frame = Mock(__class__=Mock(__name__="MyFrame"))
-        self.pipeline._log_frame(frame, depth=3)
-        self.pipeline._logger.debug.assert_called_once_with(
-            "MyClass      processor2 -> MyFrame -> sink"
-        )
-
-    @unittest.skip("FIXME: This test is failing")
-    def test_log_frame_repeated_log(self):
-        frame = Mock(__class__=Mock(__name__="MyFrame"))
-        self.pipeline._log_frame(frame, depth=2)
-        self.pipeline._logger.debug.assert_called_once_with(
-            "MyClass    processor1 -> MyFrame -> processor2"
-        )
-        self.pipeline._log_frame(frame, depth=2)
-        self.pipeline._logger.debug.assert_called_with("MyClass    ... repeated")
-
-    @unittest.skip("FIXME: This test is failing")
-    def test_log_frame_reset_repeated_log(self):
-        frame1 = Mock(__class__=Mock(__name__="MyFrame1"))
-        frame2 = Mock(__class__=Mock(__name__="MyFrame2"))
-        self.pipeline._log_frame(frame1, depth=2)
-        self.pipeline._logger.debug.assert_called_once_with(
-            "MyClass    processor1 -> MyFrame1 -> processor2"
-        )
-        self.pipeline._log_frame(frame1, depth=2)
-        self.pipeline._logger.debug.assert_called_with("MyClass    ... repeated")
-        self.pipeline._log_frame(frame2, depth=2)
-        self.pipeline._logger.debug.assert_called_with(
-            "MyClass    processor1 -> MyFrame2 -> processor2"
-        )

tests/test_protobuf_serializer.py

@@ -1,6 +1,16 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import unittest
 
-from pipecat.frames.frames import AudioRawFrame, TextFrame, TranscriptionFrame
+from pipecat.frames.frames import (
+    OutputAudioRawFrame,
+    TextFrame,
+    TranscriptionFrame,
+)
 from pipecat.serializers.protobuf import ProtobufFrameSerializer
 
 
@@ -8,22 +18,19 @@ class TestProtobufFrameSerializer(unittest.IsolatedAsyncioTestCase):
     def setUp(self):
         self.serializer = ProtobufFrameSerializer()
 
-    @unittest.skip("FIXME: This test is failing")
     async def test_roundtrip(self):
         text_frame = TextFrame(text="hello world")
         frame = self.serializer.deserialize(self.serializer.serialize(text_frame))
-        self.assertEqual(frame, TextFrame(text="hello world"))
+        self.assertEqual(text_frame, frame)
 
         transcription_frame = TranscriptionFrame(
-            text="Hello there!", participantId="123", timestamp="2021-01-01"
+            text="Hello there!", user_id="123", timestamp="2021-01-01"
         )
         frame = self.serializer.deserialize(self.serializer.serialize(transcription_frame))
         self.assertEqual(frame, transcription_frame)
 
-        audio_frame = AudioRawFrame(data=b"1234567890")
+        audio_frame = OutputAudioRawFrame(audio=b"1234567890", sample_rate=16000, num_channels=1)
         frame = self.serializer.deserialize(self.serializer.serialize(audio_frame))
-        self.assertEqual(frame, audio_frame)
+        self.assertEqual(frame.audio, audio_frame.audio)
-
+        self.assertEqual(frame.sample_rate, audio_frame.sample_rate)
-
+        self.assertEqual(frame.num_channels, audio_frame.num_channels)
-if __name__ == "__main__":
-    unittest.main()

tests/test_utils_string.py

@@ -1,28 +1,15 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import unittest
-from typing import AsyncGenerator
 
-from pipecat.frames.frames import EndFrame, Frame, TextFrame
+from pipecat.utils.string import match_endofsentence
-from pipecat.services.ai_services import AIService, match_endofsentence
 
 
-class SimpleAIService(AIService):
+class TestUtilsString(unittest.IsolatedAsyncioTestCase):
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        yield frame
-
-
-class TestBaseAIService(unittest.IsolatedAsyncioTestCase):
-    async def test_simple_processing(self):
-        service = SimpleAIService()
-
-        input_frames = [TextFrame("hello"), EndFrame()]
-
-        output_frames = []
-        for input_frame in input_frames:
-            async for output_frame in service.process_frame(input_frame):
-                output_frames.append(output_frame)
-
-        self.assertEqual(input_frames, output_frames)
-
     async def test_endofsentence(self):
         assert match_endofsentence("This is a sentence.")
         assert match_endofsentence("This is a sentence! ")
@@ -51,7 +38,3 @@ class TestBaseAIService(unittest.IsolatedAsyncioTestCase):
         for i in chinese_sentences:
             assert match_endofsentence(i)
         assert not match_endofsentence("你好，")
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_websocket_transport.py

@@ -1,3 +1,9 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 # import asyncio
 # import unittest
 # from unittest.mock import AsyncMock, patch, Mock

tests/utils.py

@@ -0,0 +1,120 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+from dataclasses import dataclass
+from typing import Awaitable, Callable, Sequence, Tuple
+
+from pipecat.clocks.system_clock import SystemClock
+from pipecat.frames.frames import (
+    ControlFrame,
+    Frame,
+    HeartbeatFrame,
+    StartFrame,
+)
+from pipecat.observers.base_observer import BaseObserver
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+
+
+@dataclass
+class EndTestFrame(ControlFrame):
+    pass
+
+
+class HeartbeatsObserver(BaseObserver):
+    def __init__(
+        self,
+        *,
+        target: FrameProcessor,
+        heartbeat_callback: Callable[[FrameProcessor, HeartbeatFrame], Awaitable[None]],
+    ):
+        self._target = target
+        self._callback = heartbeat_callback
+
+    async def on_push_frame(
+        self,
+        src: FrameProcessor,
+        dst: FrameProcessor,
+        frame: Frame,
+        direction: FrameDirection,
+        timestamp: int,
+    ):
+        if src == self._target and isinstance(frame, HeartbeatFrame):
+            await self._callback(self._target, frame)
+
+
+class QueuedFrameProcessor(FrameProcessor):
+    def __init__(self, queue: asyncio.Queue, ignore_start: bool = True):
+        super().__init__()
+        self._queue = queue
+        self._ignore_start = ignore_start
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+        if self._ignore_start and isinstance(frame, StartFrame):
+            return
+        await self._queue.put(frame)
+
+
+async def run_test(
+    processor: FrameProcessor,
+    frames_to_send: Sequence[Frame],
+    expected_down_frames: Sequence[type],
+    expected_up_frames: Sequence[type] = [],
+) -> Tuple[Sequence[Frame], Sequence[Frame]]:
+    received_up = asyncio.Queue()
+    received_down = asyncio.Queue()
+    up_processor = QueuedFrameProcessor(received_up)
+    down_processor = QueuedFrameProcessor(received_down)
+
+    up_processor.link(processor)
+    processor.link(down_processor)
+
+    await processor.queue_frame(StartFrame(clock=SystemClock()))
+
+    for frame in frames_to_send:
+        await processor.process_frame(frame, FrameDirection.DOWNSTREAM)
+
+    await processor.queue_frame(EndTestFrame())
+    await processor.queue_frame(EndTestFrame(), FrameDirection.UPSTREAM)
+
+    #
+    # Down frames
+    #
+    received_down_frames: Sequence[Frame] = []
+    running = True
+    while running:
+        frame = await received_down.get()
+        running = not isinstance(frame, EndTestFrame)
+        if running:
+            received_down_frames.append(frame)
+
+    print("received DOWN frames =", received_down_frames)
+
+    assert len(received_down_frames) == len(expected_down_frames)
+
+    for real, expected in zip(received_down_frames, expected_down_frames):
+        assert isinstance(real, expected)
+
+    #
+    # Up frames
+    #
+    received_up_frames: Sequence[Frame] = []
+    running = True
+    while running:
+        frame = await received_up.get()
+        running = not isinstance(frame, EndTestFrame)
+        if running:
+            received_up_frames.append(frame)
+
+    print("received UP frames =", received_up_frames)
+
+    assert len(received_up_frames) == len(expected_up_frames)
+
+    for real, expected in zip(received_up_frames, expected_up_frames):
+        assert isinstance(real, expected)
+
+    return (received_down_frames, received_up_frames)