Remove extra code

.github/workflows/publish.yaml

@@ -5,25 +5,25 @@ on:
     inputs:
       gitref:
         type: string
-        description: 'what git tag to build (e.g. v0.0.74)'
+        description: "what git tag to build (e.g. v0.0.74)"
         required: true
 
 jobs:
   build:
-    name: 'Build and upload wheels'
+    name: "Build and upload wheels"
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
         uses: actions/checkout@v4
         with:
           ref: ${{ github.event.inputs.gitref }}
-
+      
       - name: Install uv
         uses: astral-sh/setup-uv@v3
         with:
-          version: 'latest'
+          version: "latest"
       - name: Set up Python
-        run: uv python install 3.12
+        run: uv python install 3.10
       - name: Install development dependencies
         run: uv sync --group dev
       - name: Build project
@@ -35,9 +35,9 @@ jobs:
           path: ./dist
 
   publish-to-pypi:
-    name: 'Publish to PyPI'
+    name: "Publish to PyPI"
     runs-on: ubuntu-latest
-    needs: [build]
+    needs: [ build ]
     environment:
       name: pypi
       url: https://pypi.org/p/pipecat-ai
@@ -56,12 +56,12 @@ jobs:
           print-hash: true
 
   publish-to-test-pypi:
-    name: 'Publish to Test PyPI'
+    name: "Publish to Test PyPI"
     runs-on: ubuntu-latest
-    needs: [build]
+    needs: [ build ]
     environment:
       name: testpypi
-      url: https://test.pypi.org/p/pipecat-ai
+      url: https://pypi.org/p/pipecat-ai
     permissions:
       id-token: write
     steps:
@@ -70,7 +70,7 @@ jobs:
         with:
           name: wheels
           path: ./dist
-      - name: Publish to Test PyPI
+      - name: Publish to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           verbose: true

.github/workflows/publish_test.yaml

@@ -4,7 +4,7 @@ on: workflow_dispatch
 
 jobs:
   build:
-    name: 'Build and upload wheels'
+    name: "Build and upload wheels"
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
@@ -15,9 +15,9 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v3
         with:
-          version: 'latest'
+          version: "latest"
       - name: Set up Python
-        run: uv python install 3.12
+        run: uv python install 3.10
       - name: Install development dependencies
         run: uv sync --group dev
       - name: Build project
@@ -29,12 +29,12 @@ jobs:
           path: ./dist
 
   publish-to-test-pypi:
-    name: 'Publish to Test PyPI'
+    name: "Publish to Test PyPI"
     runs-on: ubuntu-latest
     needs: [build]
     environment:
       name: testpypi
-      url: https://test.pypi.org/p/pipecat-ai
+      url: https://pypi.org/p/pipecat-ai
     permissions:
       id-token: write
     steps:
@@ -43,7 +43,7 @@ jobs:
         with:
           name: wheels
           path: ./dist
-      - name: Publish to Test PyPI
+      - name: Publish to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           verbose: true

CHANGELOG.md

@@ -5,61 +5,10 @@ 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).
 
-## [0.0.89] - 2025-10-07
-
-### Fixed
-
-- Reverted a change introduced in 0.0.88 that was causing pipelines to be frozen
-  when using interruption strategies and processors that block interruption
-  frames (e.g. `STTMuteFilter`).
-
-## [0.0.88] - 2025-10-07
+## [Unreleased]
 
 ### Added
 
-- Added support for Nano Banana models to `GoogleLLMService`. For example, you
-  can now use the `gemini-2.5-flash-image` model to generate images.
-
-- Added `HumeTTSService` for text-to-speech synthesis using Hume AI's expressive
-  voice models. Provides high-quality, emotionally expressive speech synthesis
-  with support for various voice models. Includes example in
-  `examples/foundational/07ad-interruptible-hume.py`. Use with `uv pip install
-  pipecat-ai[hume]`.
-
-### Changed
-
-- Updated default `GoogleLLMService` model to `gemini-2.5-flash`.
-
-### Deprecated
-
-- PlayHT is shutting down their API on December 31st, 2025. As a result,
-  `PlayHTTTSService` and `PlayHTHttpTTSService` are deprecated and will be
-  removed in a future version.
-
-### Fixed
-
-- Fixed an issue with `AWSNovaSonicLLMService` where the client wouldn't
-  connect due to a breaking change in the AWS dependency chain.
-
-- `PermissionError` is now caught if NLTK's `punkt_tab` can't be downloaded.
-
-- Fixed an issue that would cause wrong user/assistant context ordering when
-  using interruption strategies.
-
-- Fixed RTVI incoming message handling, broken in 0.0.87.
-
-## [0.0.87] - 2025-10-02
-
-### Added
-
-- Added `WebsocketSTTService` base class for websocket-based STT services.
-  Combines STT functionality with websocket connectivity, providing automatic
-  error handling and reconnection capabilities with exponential backoff.
-
-- Added `DeepgramFluxSTTService` for real-time speech recognition using
-  Deepgram's Flux WebSocket API. Flux understands conversational flow and
-  automatically handles turn-taking.
-
 - Added RTVI messages for user/bot audio levels and system logs.
 
 - Include OpenAI-based LLM services cached tokens to `MetricsFrame`.
@@ -71,21 +20,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Deprecated
 
-- `DailyTransportMessageFrame` and `DailyTransportMessageUrgentFrame` are
-  deprecated, use `DailyOutputTransportMessageFrame` and
-  `DailyOutputTransportMessageUrgentFrame` respectively instead.
-
-- `LiveKitTransportMessageFrame` and `LiveKitTransportMessageUrgentFrame` are
-  deprecated, use `LiveKitOutputTransportMessageFrame` and
-  `LiveKitOutputTransportMessageUrgentFrame` respectively instead.
-
-- `TransportMessageFrame` and `TransportMessageUrgentFrame` are deprecated, use
-  `OutputTransportMessageFrame` and `OutputTransportMessageUrgentFrame`
-  respectively instead.
-
-- `InputTransportMessageUrgentFrame` is deprecated, use
-  `InputTransportMessageFrame` instead.
-
 - `DailyUpdateRemoteParticipantsFrame` is deprecated and will be removed in a
   future version. Instead, create your own custom frame and handle it in the
   `@transport.output().event_handler("on_after_push_frame")` event handler or a
@@ -93,9 +27,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Fixed
 
-- Fixed an issue in `AWSBedrockLLMService` where timeout exceptions weren't
-  being detected.
-
 - Fixed a `PipelineTask` issue that could prevent the application to exit if
   `task.cancel()` was called when the task was already finished.
 

COMMUNITY_INTEGRATIONS.md

@@ -1,336 +0,0 @@
-# Community Integrations Guide
-
-Pipecat welcomes community-maintained integrations! As our ecosystem grows, we've established a process for any developer to create and maintain their own service integrations while ensuring discoverability for the Pipecat community.
-
-## Overview
-
-**What we support:** Community-maintained integrations that live in separate repositories and are maintained by their authors.
-
-**What we don't do:** The Pipecat team does not code review, test, or maintain community integrations. We provide guidance and list approved integrations for discoverability.
-
-**Why this approach:** This allows the community to move quickly while keeping the Pipecat core team focused on maintaining the framework itself.
-
-## Submitting your Integration
-
-To be listed as an official community integration, follow these steps:
-
-### Step 1: Build Your Integration
-
-Create your integration following the patterns and examples shown in the "Integration Patterns and Examples" section below.
-
-### Step 2: Set Up Your Repository
-
-Your repository must contain these components:
-
-- **Source code** - Complete implementation following Pipecat patterns
-- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational))
-- **README.md** - Must include:
-
-  - Introduction and explanation of your integration
-  - Installation instructions
-  - Usage instructions with Pipecat Pipeline
-  - How to run your example
-  - Pipecat version compatibility (e.g., "Tested with Pipecat v0.0.86")
-  - Company attribution: If you work for the company providing the service, please mention this in your README. This helps build confidence that the integration will be actively maintained.
-
-- **LICENSE** - Permissive license (BSD-2 like Pipecat, or equivalent open source terms)
-- **Code documentation** - Source code with docstrings (we recommend following [Pipecat's docstring conventions](https://github.com/pipecat-ai/pipecat/blob/main/CONTRIBUTING.md#docstring-conventions))
-- **Changelog** - Maintain a changelog for version updates
-
-### Step 3: Join Discord
-
-Join our Discord: https://discord.gg/pipecat
-
-### Step 4: Submit for Listing
-
-Submit a pull request to add your integration to our [Community Integrations documentation page](https://docs.pipecat.ai/server/services/community-integrations).
-
-**To submit:**
-
-1. Fork the [Pipecat docs repository](https://github.com/pipecat-ai/docs)
-2. Edit the file `server/services/community-integrations.mdx`
-3. Add your integration to the appropriate service category table with:
-   - Service name
-   - Link to your repository
-   - Maintainer GitHub username(s)
-4. Include a link to your demo video (approx 30-60 seconds) in your PR description showing:
-   - Core functionality of your integration
-   - Handling of an interruption (if applicable to service type)
-5. Submit your pull request
-
-Once your PR is submitted, post in the `#community-integrations` Discord channel to let us know.
-
-## Integration Patterns and Examples
-
-### STT (Speech-to-Text) Services
-
-#### Websocket-based Services
-
-**Base class:** `STTService`
-
-**Examples:**
-
-- [DeepgramSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/deepgram/stt.py)
-- [SpeechmaticsSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/speechmatics/stt.py)
-
-#### File-based Services
-
-**Base class:** `SegmentedSTTService`
-
-**Examples:**
-
-- [RivaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/riva/stt.py)
-- [FalSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/stt.py)
-
-#### Key requirements:
-
-- STT services should push `InterimTranscriptionFrames` and `TranscriptionFrames`
-- If confidence values are available, filter for values >50% confidence
-
-### LLM (Large Language Model) Services
-
-#### OpenAI-Compatible Services
-
-**Base class:** `OpenAILLMService`
-
-**Examples:**
-
-- [AzureLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/azure/llm.py)
-- [GrokLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/grok/llm.py) - Shows overriding the base class where needed
-
-#### Non-OpenAI Compatible Services
-
-**Requires:** Full implementation
-
-**Examples:**
-
-- [AnthropicLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/anthropic/llm.py)
-- [GoogleLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/llm.py)
-
-#### Key requirements:
-
-- **Frame sequence:** Output must follow this frame sequence pattern:
-
-  - `LLMFullResponseStartFrame` - Signals the start of an LLM response
-  - `LLMTextFrame` - Contains LLM content, typically streamed as tokens
-  - `LLMFullResponseEndFrame` - Signals the end of an LLM response
-
-- **Context aggregation:** Implement context aggregation to collect user and assistant content:
-  - Aggregators come in pairs with a `user()` instance and `assistant()` instance
-  - Context must adhere to the `LLMContext` universal format
-  - Aggregators should handle adding messages, function calls, and images to the context
-
-### TTS (Text-to-Speech) Services
-
-#### AudioContextWordTTSService
-
-**Use for:** Websocket-based services supporting word/timestamp alignment
-
-**Example:**
-
-- [CartesiaTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/tts.py)
-
-#### InterruptibleTTSService
-
-**Use for:** Websocket-based services without word/timestamp alignment, requiring disconnection on interruption
-
-**Example:**
-
-- [SarvamTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/sarvam/tts.py)
-
-#### WordTTSService
-
-**Use for:** HTTP-based services supporting word/timestamp alignment
-
-**Example:**
-
-- [ElevenLabsHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
-
-#### TTSService
-
-**Use for:** HTTP-based services without word/timestamp alignment
-
-**Example:**
-
-- [GoogleHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/tts.py)
-
-#### Key requirements:
-
-- For websocket services, use asyncio WebSocket implementation (required for v13+ support)
-- Handle idle service timeouts with keepalives
-- TTSServices push both audio (`TTSRawAudioFrame`) and text (`TTSTextFrame`) frames
-
-### Telephony Serializers
-
-Pipecat supports telephony provider integration using websocket connections to exchange MediaStreams. These services use a FrameSerializer to serialize and deserialize inputs from the FastAPIWebsocketTransport.
-
-**Examples:**
-
-- [Twilio](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/serializers/twilio.py)
-- [Telnyx](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/serializers/telnyx.py)
-
-#### Key requirements:
-
-- Include hang-up functionality using the provider's native API, ideally using `aiohttp`
-- Support DTMF (dual-tone multi-frequency) events if the provider supports them:
-  - Deserialize DTMF events from the provider's protocol to `InputDTMFFrame`
-  - Use `KeypadEntry` enum for valid keypad entries (0-9, \*, #, A-D)
-  - Handle invalid DTMF digits gracefully by returning `None`
-
-### Image Generation Services
-
-**Base class:** `ImageGenService`
-
-**Examples:**
-
-- [FalImageGenService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/image.py)
-- [GoogleImageGenService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/image.py)
-
-#### Key requirements:
-
-- Must implement `run_image_gen` method returning an `AsyncGenerator`
-
-### Vision Services
-
-Vision services process images and provide analysis such as descriptions, object detection, or visual question answering.
-
-**Base class:** `VisionService`
-
-**Example:**
-
-- [MoondreamVisionService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/moondream/vision.py)
-
-#### Key requirements:
-
-- Must implement `run_vision` method that takes an `LLMContext` and returns an `AsyncGenerator[Frame, None]`
-- The method processes the latest image in the context and yields frames with analysis results
-- Typically yields `TextFrame` objects containing descriptions or answers
-
-## Implementation Guidelines
-
-### Naming Conventions
-
-- **STT:** `VendorSTTService`
-- **LLM:** `VendorLLMService`
-- **TTS:**
-  - Websocket: `VendorTTSService`
-  - HTTP: `VendorHttpTTSService`
-- **Image:** `VendorImageGenService`
-- **Vision:** `VendorVisionService`
-- **Telephony:** `VendorFrameSerializer`
-
-### Metrics Support
-
-Enable metrics in your service:
-
-```python
-def can_generate_metrics(self) -> bool:
-    """Check if this service can generate processing metrics.
-
-    Returns:
-        True, as this service supports metrics.
-    """
-    return True
-```
-
-### Dynamic Settings Updates
-
-STT, LLM, and TTS services support `ServiceUpdateSettingsFrame` for dynamic configuration changes. The base STTService has an `_update_settings()` method that handles settings, and the private `_settings` `Dict` is used to store settings and provide access to the subclass.
-
-```python
-async def set_language(self, language: Language):
-    """Set the recognition language and reconnect.
-
-    Args:
-        language: The language to use for speech recognition.
-    """
-    logger.info(f"Switching STT language to: [{language}]")
-    self._settings["language"] = language
-    await self._disconnect()
-    await self._connect()
-```
-
-Note that, in this example, Deepgram requires the websocket connection be disconnected and reconnected to reinitialize the service with the new value. Consider if your service requires reconnection.
-
-### Sample Rate Handling
-
-Sample rates are set via PipelineParams and passed to each frame processor at initialization. The pattern is to _not_ set the sample rate value in the constructor of a given service. Instead, use the `start()` method to initialize sample rates from the frame:
-
-```python
-async def start(self, frame: StartFrame):
-    """Start the service."""
-    await super().start(frame)
-    self._settings["output_format"]["sample_rate"] = self.sample_rate
-    await self._connect()
-```
-
-Note that `self.sample_rate` is a `@property` set in the TTSService base class, which provides access to the private sample rate value obtained from the StartFrame.
-
-### Tracing Decorators
-
-Use Pipecat's tracing decorators:
-
-- **STT:** `@traced_stt` - decorate a function that handles `transcript`, `is_final`, `language` as args
-- **LLM:** `@traced_llm` - decorate the `_process_context()` method
-- **TTS:** `@traced_tts` - decorate the `run_tts()` method
-
-## Best Practices
-
-### Packaging and Distribution
-
-- Use [uv](https://docs.astral.sh/uv/) for packaging (encouraged)
-- Consider releasing to PyPI for easier installation
-- Follow semantic versioning principles
-- Maintain a changelog
-
-### HTTP Communication
-
-For REST-based communication, use aiohttp. Pipecat includes this as a required dependency, so using it prevents adding an additional dependency to your integration.
-
-### Error Handling
-
-- Wrap API calls in appropriate try/catch blocks
-- Handle rate limits and network failures gracefully
-- Provide meaningful error messages
-- When errors occur, raise exceptions AND push `ErrorFrame`s to notify the pipeline:
-
-```python
-from pipecat.frames.frames import ErrorFrame
-
-try:
-    # Your API call
-    result = await self._make_api_call()
-except Exception as e:
-    # Push error frame to pipeline
-    await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
-    # Raise or handle as appropriate
-    raise
-```
-
-### Testing
-
-- Your foundational example serves as a valuable integration-level test
-- Unit tests are nice to have. As the Pipecat teams provides better guidance, we will encourage unit testing more
-
-## Disclaimer
-
-Community integrations are community-maintained and not officially supported by the Pipecat team. Users should evaluate these integrations independently. The Pipecat team reserves the right to remove listings that become unmaintained or problematic.
-
-## Staying Up to Date
-
-Pipecat evolves rapidly to support the latest AI technologies and patterns. While we strive to minimize breaking changes, they do occur as the framework matures.
-
-**We strongly recommend:**
-
-- Join our Discord at https://discord.gg/pipecat and monitor the `#announcements` channel for release notifications
-- Follow our changelog: https://github.com/pipecat-ai/pipecat/blob/main/CHANGELOG.md
-- Test your integration against new Pipecat releases promptly
-- Update your README with the last tested Pipecat version
-
-This helps ensure your integration remains compatible and your users have clear expectations about version support.
-
-## Questions?
-
-Join our Discord community at https://discord.gg/pipecat and post in the `#community-integrations` channel for guidance and support.
-
-For additional questions, you can also reach out to us at pipecat-ai@daily.co.

CONTRIBUTING.md

@@ -1,9 +1,5 @@
 ## Contributing to Pipecat
 
-**Want to add a new service integration?**
-We encourage community-maintained integrations! Please see our [Community Integration Guide](COMMUNITY_INTEGRATIONS.md) for the process and requirements.
-
-**Want to contribute to Pipecat core?**
 We welcome contributions of all kinds! Your help is appreciated. Follow these steps to get involved:
 
 1. **Fork this repository**: Start by forking the Pipecat Documentation repository to your GitHub account.

README.md

@@ -19,6 +19,10 @@
 - **Business Agents** – customer intake, support bots, guided flows
 - **Complex Dialog Systems** – design logic with structured conversations
 
+🧭 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
+
+🔍 Looking for help debugging your pipeline and processors? Check out [Whisker](https://github.com/pipecat-ai/whisker), a real-time Pipecat debugger.
+
 ## 🧠 Why Pipecat?
 
 - **Voice-first**: Integrates speech recognition, text-to-speech, and conversation handling
@@ -26,30 +30,40 @@
 - **Composable Pipelines**: Build complex behavior from modular components
 - **Real-Time**: Ultra-low latency interaction with different transports (e.g. WebSockets or WebRTC)
 
-## 🌐 Pipecat Ecosystem
+## 📱 Client SDKs
 
-### 📱 Client SDKs
+You can connect to Pipecat from any platform using our official SDKs:
 
-Building client applications? You can connect to Pipecat from any platform using our official SDKs:
-
-<a href="https://docs.pipecat.ai/client/js/introduction">JavaScript</a> | <a href="https://docs.pipecat.ai/client/react/introduction">React</a> | <a href="https://docs.pipecat.ai/client/react-native/introduction">React Native</a> |
-<a href="https://docs.pipecat.ai/client/ios/introduction">Swift</a> | <a href="https://docs.pipecat.ai/client/android/introduction">Kotlin</a> | <a href="https://docs.pipecat.ai/client/c++/introduction">C++</a> | <a href="https://github.com/pipecat-ai/pipecat-esp32">ESP32</a>
-
-### 🧭 Structured conversations
-
-Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
-
-### 🪄 Beautiful UIs
-
-Want to build beautiful and engaging experiences? Checkout the [Voice UI Kit](https://github.com/pipecat-ai/voice-ui-kit), a collection of components, hooks and templates for building voice AI applications quickly.
-
-### 🔍 Debugging
-
-Looking for help debugging your pipeline and processors? Check out [Whisker](https://github.com/pipecat-ai/whisker), a real-time Pipecat debugger.
-
-### 🖥️ Terminal
-
-Love terminal applications? Check out [Tail](https://github.com/pipecat-ai/tail), a terminal dashboard for Pipecat.
+<table>
+  <tr>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/javascript/javascript-original.svg" width="40" height="40" alt="JavaScript"/>
+      <a href="https://docs.pipecat.ai/client/js/introduction">JavaScript</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/react/react-original.svg" width="40" height="40" alt="React"/>
+      <a href="https://docs.pipecat.ai/client/react/introduction">React</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/react/react-original.svg" width="40" height="40" alt="React Native"/>
+      <a href="https://docs.pipecat.ai/client/react-native/introduction">React Native</a>
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/swift/swift-original.svg" width="40" height="40" alt="Swift"/>
+      <a href="https://docs.pipecat.ai/client/ios/introduction">Swift</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/kotlin/kotlin-original.svg" width="40" height="40" alt="Kotlin"/>
+      <a href="https://docs.pipecat.ai/client/android/introduction">Kotlin</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/cplusplus/cplusplus-original.svg" width="40" height="40" alt="JavaScript"/>
+      <a href="https://docs.pipecat.ai/client/c++/introduction">C++</a>
+    </td>
+  </tr>
+</table>
 
 ## 🎬 See it in action
 
@@ -67,7 +81,7 @@ Love terminal applications? Check out [Tail](https://github.com/pipecat-ai/tail)
 | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                    |
 | LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                          |
-| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
+| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
 | Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
 | Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
 | Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
@@ -170,6 +184,54 @@ Run a specific test suite:
 uv run pytest tests/test_name.py
 ```
 
+### Setting up your editor
+
+This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting via [Ruff](https://github.com/astral-sh/ruff).
+
+#### Emacs
+
+You can use [use-package](https://github.com/jwiegley/use-package) to install [emacs-lazy-ruff](https://github.com/christophermadsen/emacs-lazy-ruff) package and configure `ruff` arguments:
+
+```elisp
+(use-package lazy-ruff
+  :ensure t
+  :hook ((python-mode . lazy-ruff-mode))
+  :config
+  (setq lazy-ruff-format-command "ruff format")
+  (setq lazy-ruff-check-command "ruff check --select I"))
+```
+
+`ruff` was installed in the `venv` environment described before, so you should be able to use [pyvenv-auto](https://github.com/ryotaro612/pyvenv-auto) to automatically load that environment inside Emacs.
+
+```elisp
+(use-package pyvenv-auto
+  :ensure t
+  :defer t
+  :hook ((python-mode . pyvenv-auto-run)))
+```
+
+#### Visual Studio Code
+
+Install the
+[Ruff](https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff) extension. Then edit the user settings (_Ctrl-Shift-P_ `Open User Settings (JSON)`) and set it as the default Python formatter, and enable formatting on save:
+
+```json
+"[python]": {
+    "editor.defaultFormatter": "charliermarsh.ruff",
+    "editor.formatOnSave": true
+}
+```
+
+#### PyCharm
+
+`ruff` was installed in the `venv` environment described before, now to enable autoformatting on save, go to `File` -> `Settings` -> `Tools` -> `File Watchers` and add a new watcher with the following settings:
+
+1. **Name**: `Ruff formatter`
+2. **File type**: `Python`
+3. **Working directory**: `$ContentRoot$`
+4. **Arguments**: `format $FilePath$`
+5. **Program**: `$PyInterpreterDirectory$/ruff`
+
 ## 🤝 Contributing
 
 We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:

SECURITY.md

@@ -1,5 +0,0 @@
-# Security Policy
-
-## Reporting a Vulnerability
-
-Please email `disclosures@daily.co`.

env.example

@@ -58,9 +58,6 @@ GOOGLE_CLOUD_PROJECT_ID=...
 GOOGLE_TEST_CREDENTIALS=...
 GOOGLE_VERTEX_TEST_CREDENTIALS=...
 
-# Hume
-HUME_API_KEY=...
-
 # LMNT
 LMNT_API_KEY=...
 LMNT_VOICE_ID=...
@@ -158,9 +155,3 @@ NVIDIA_API_KEY=...
 
 # Qwen
 QWEN_API_KEY=...
-
-# WhatsApp
-WHATSAPP_TOKEN=
-WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=
-WHATSAPP_PHONE_NUMBER_ID=
-WHATSAPP_APP_SECRET=

examples/foundational/04a-transports-daily.py

@@ -25,7 +25,7 @@ from pipecat.processors.aggregators.llm_response_universal import LLMContextAggr
 from pipecat.runner.daily import configure
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.daily.transport import DailyParams, DailyTransport
+from pipecat.transports.daily.transport import DailyLogLevel, DailyParams, DailyTransport
 
 load_dotenv(override=True)
 
@@ -49,6 +49,7 @@ async def main():
                 turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
             ),
         )
+        transport.set_log_level(DailyLogLevel.Info)
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/07-interruptible.py

@@ -5,6 +5,7 @@
 #
 
 import os
+import wave
 
 from dotenv import load_dotenv
 from loguru import logger
@@ -13,7 +14,14 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
+from pipecat.frames.frames import (
+    LLMFullResponseEndFrame,
+    LLMFullResponseStartFrame,
+    LLMRunFrame,
+    LLMTextFrame,
+    OutputAudioRawFrame,
+    TextFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -103,7 +111,27 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+
+        audio_file_path = os.path.join(os.path.dirname(__file__), "assets", "pre-recorded.wav")
+
+        with wave.open(audio_file_path, "rb") as wav_file:
+            llm_text_frame = TextFrame(text="This is a pre-recorded message.")
+            llm_text_frame.skip_tts = True
+
+            audio_data = wav_file.readframes(wav_file.getnframes())
+            output_audio_raw_frame = OutputAudioRawFrame(
+                audio=audio_data, sample_rate=44100, num_channels=1
+            )
+
+            await task.queue_frames(
+                [
+                    LLMRunFrame(),
+                    LLMFullResponseStartFrame(),
+                    llm_text_frame,
+                    output_audio_raw_frame,
+                    LLMFullResponseEndFrame(),
+                ]
+            )
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/foundational/07ae-interruptible-hume.py

@@ -1,138 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.hume.tts import HUME_SAMPLE_RATE, HumeTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = HumeTTSService(
-        api_key=os.getenv("HUME_API_KEY"),
-        # Replace with your Hume voice ID
-        voice_id="f898a92e-685f-43fa-985b-a46920f0650b",
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. 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.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            rtvi,
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            audio_out_sample_rate=HUME_SAMPLE_RATE,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        observers=[RTVIObserver(rtvi)],
-    )
-
-    @rtvi.event_handler("on_client_ready")
-    async def on_client_ready(rtvi):
-        await rtvi.set_bot_ready()
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/07c-interruptible-deepgram-flux.py

@@ -1,118 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContext,
-    LLMContextAggregatorPair,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.flux.stt import DeepgramFluxSTTService
-from pipecat.services.deepgram.tts import DeepgramTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramFluxSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. 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.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -23,6 +23,7 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
+from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.elevenlabs.stt import ElevenLabsSTTService
 from pipecat.services.elevenlabs.tts import ElevenLabsHttpTTSService
 from pipecat.services.openai.llm import OpenAILLMService

examples/foundational/07n-interruptible-gemini-image.py

@@ -1,151 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""
-A conversational AI bot using Gemini for both LLM, STT and TTS.
-
-This example demonstrates how to use Gemini's image generation capabilities.
-
-Features showcased:
-- Gemini LLM for conversation and image generation
-- Google TTS and STT
-
-Run with:
-    python examples/foundational/07n-interruptible-gemini-image.py
-
-Make sure to set your environment variables:
-    export GOOGLE_API_KEY=your_api_key_here
-"""
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.google.stt import GoogleSTTService
-from pipecat.services.google.tts import GoogleTTSService
-from pipecat.transcriptions.language import Language
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = GoogleSTTService(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US),
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-    )
-
-    tts = GoogleTTSService(
-        voice_id="en-US-Chirp3-HD-Charon",
-        params=GoogleTTSService.InputParams(language=Language.EN_US),
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-    )
-
-    llm = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        model="gemini-2.5-flash-image",
-    )
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. 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.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # Gemini TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation with a styled introduction
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/16-gpu-container-local-bot.py

@@ -26,11 +26,7 @@ from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.deepgram.tts import DeepgramTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import (
-    DailyOutputTransportMessageFrame,
-    DailyOutputTransportMessageUrgentFrame,
-    DailyParams,
-)
+from pipecat.transports.daily.transport import DailyParams, DailyTransportMessageFrame
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 
 load_dotenv(override=True)
@@ -132,14 +128,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 logger.debug(f"Received latency ping app message: {message}")
                 ts = message["latency-ping"]["ts"]
                 # Send immediately
-                await task.queue_frame(
-                    DailyOutputTransportMessageUrgentFrame(
+                transport.output().send_message(
+                    DailyTransportMessageFrame(
                         message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
                     )
                 )
                 # And push to the pipeline for the Daily transport.output to send
                 await task.queue_frame(
-                    DailyOutputTransportMessageFrame(
+                    DailyTransportMessageFrame(
                         message={"latency-pong-pipeline-delivery": {"ts": ts}},
                         participant_id=sender,
                     )

pyproject.toml

@@ -50,7 +50,7 @@ anthropic = [ "anthropic~=0.49.0" ]
 assemblyai = [ "pipecat-ai[websockets-base]" ]
 asyncai = [ "pipecat-ai[websockets-base]" ]
 aws = [ "aioboto3~=15.0.0", "pipecat-ai[websockets-base]" ]
-aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.1.0; python_version>='3.12'" ]
+aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.0.2; python_version>='3.12'" ]
 azure = [ "azure-cognitiveservices-speech~=1.42.0"]
 cartesia = [ "cartesia~=2.0.3", "pipecat-ai[websockets-base]" ]
 cerebras = []
@@ -62,12 +62,11 @@ fal = [ "fal-client~=0.5.9" ]
 fireworks = []
 fish = [ "ormsgpack~=1.7.0", "pipecat-ai[websockets-base]" ]
 gladia = [ "pipecat-ai[websockets-base]" ]
-google = [ "google-cloud-speech>=2.33.0,<3", "google-cloud-texttospeech>=2.31.0,<3", "google-genai>=1.41.0,<2", "pipecat-ai[websockets-base]" ]
+google = [ "google-cloud-speech~=2.32.0", "google-cloud-texttospeech~=2.26.0", "google-genai~=1.24.0", "pipecat-ai[websockets-base]" ]
 grok = []
 groq = [ "groq~=0.23.0" ]
 gstreamer = [ "pygobject~=3.50.0" ]
 heygen = [ "livekit>=1.0.13", "pipecat-ai[websockets-base]" ]
-hume = [ "hume>=0.11.2" ]
 inworld = []
 krisp = [ "pipecat-ai-krisp~=0.4.0" ]
 koala = [ "pvkoala~=2.0.3" ]

scripts/evals/run-release-evals.py

@@ -67,7 +67,6 @@ TESTS_07 = [
     ("07ac-interruptible-asyncai-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07b-interruptible-langchain.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07c-interruptible-deepgram.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
-    ("07c-interruptible-deepgram-flux.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07d-interruptible-elevenlabs.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     (
         "07d-interruptible-elevenlabs-http.py",
@@ -75,6 +74,8 @@ TESTS_07 = [
         EVAL_SIMPLE_MATH,
         BOT_SPEAKS_FIRST,
     ),
+    ("07e-interruptible-playht.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
+    ("07e-interruptible-playht-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07f-interruptible-azure.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07g-interruptible-openai.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07h-interruptible-openpipe.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
@@ -101,7 +102,6 @@ TESTS_07 = [
     ("07w-interruptible-fal.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07y-interruptible-minimax.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07z-interruptible-sarvam.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
-    ("07ae-interruptible-hume.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     # Needs a local XTTS docker instance running.
     # ("07i-interruptible-xtts.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     # Needs a Krisp license.

src/pipecat/frames/frames.py

@@ -672,7 +672,7 @@ class TTSSpeakFrame(DataFrame):
 
 
 @dataclass
-class OutputTransportMessageFrame(DataFrame):
+class TransportMessageFrame(DataFrame):
     """Frame containing transport-specific message data.
 
     Parameters:
@@ -685,32 +685,6 @@ class OutputTransportMessageFrame(DataFrame):
         return f"{self.name}(message: {self.message})"
 
 
-@dataclass
-class TransportMessageFrame(OutputTransportMessageFrame):
-    """Frame containing transport-specific message data.
-
-    .. deprecated:: 0.0.87
-        This frame is deprecated and will be removed in a future version.
-        Instead, use `OutputTransportMessageFrame`.
-
-    Parameters:
-        message: The transport message payload.
-    """
-
-    def __post_init__(self):
-        super().__post_init__()
-        import warnings
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("always")
-            warnings.warn(
-                "TransportMessageFrame is deprecated and will be removed in a future version. "
-                "Instead, use OutputTransportMessageFrame.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
-
 @dataclass
 class DTMFFrame:
     """Base class for DTMF (Dual-Tone Multi-Frequency) keypad frames.
@@ -1118,8 +1092,8 @@ class STTMuteFrame(SystemFrame):
 
 
 @dataclass
-class InputTransportMessageFrame(SystemFrame):
-    """Frame for transport messages received from external sources.
+class TransportMessageUrgentFrame(SystemFrame):
+    """Frame for urgent transport messages that need immediate processing.
 
     Parameters:
         message: The urgent transport message payload.
@@ -1132,69 +1106,20 @@ class InputTransportMessageFrame(SystemFrame):
 
 
 @dataclass
-class InputTransportMessageUrgentFrame(InputTransportMessageFrame):
+class InputTransportMessageUrgentFrame(TransportMessageUrgentFrame):
     """Frame for transport messages received from external sources.
 
-    .. deprecated:: 0.0.87
-        This frame is deprecated and will be removed in a future version.
-        Instead, use `InputTransportMessageFrame`.
+    This frame wraps incoming transport messages to distinguish them from outgoing
+    urgent transport messages (TransportMessageUrgentFrame), preventing infinite
+    message loops in the transport layer. It inherits the message payload from
+    TransportMessageFrame while marking the message as having been received
+    rather than generated locally.
 
-    Parameters:
-        message: The urgent transport message payload.
+    Used by transport implementations to properly handle bidirectional message
+    flow without creating feedback loops.
     """
 
-    def __post_init__(self):
-        super().__post_init__()
-        import warnings
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("always")
-            warnings.warn(
-                "InputTransportMessageUrgentFrame is deprecated and will be removed in a future version. "
-                "Instead, use InputTransportMessageFrame.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
-
-@dataclass
-class OutputTransportMessageUrgentFrame(SystemFrame):
-    """Frame for urgent transport messages that need to be sent immediately.
-
-    Parameters:
-        message: The urgent transport message payload.
-    """
-
-    message: Any
-
-    def __str__(self):
-        return f"{self.name}(message: {self.message})"
-
-
-@dataclass
-class TransportMessageUrgentFrame(OutputTransportMessageUrgentFrame):
-    """Frame for urgent transport messages that need to be sent immediately.
-
-    .. deprecated:: 0.0.87
-        This frame is deprecated and will be removed in a future version.
-        Instead, use `OutputTransportMessageUrgentFrame`.
-
-    Parameters:
-        message: The urgent transport message payload.
-    """
-
-    def __post_init__(self):
-        super().__post_init__()
-        import warnings
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("always")
-            warnings.warn(
-                "TransportMessageUrgentFrame is deprecated and will be removed in a future version. "
-                "Instead, use OutputTransportMessageFrame.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
+    pass
 
 
 @dataclass

src/pipecat/pipeline/task.py

@@ -130,11 +130,9 @@ class PipelineTask(BasePipelineTask):
 
     - on_pipeline_finished: Called after the pipeline has reached any terminal state.
           This includes:
-
               - StopFrame: pipeline was stopped (processors keep connections open)
               - EndFrame: pipeline ended normally
               - CancelFrame: pipeline was cancelled
-
           Use this event for cleanup, logging, or post-processing tasks. Users can inspect
           the frame if they need to handle specific cases.
 

src/pipecat/processors/frameworks/rtvi.py

@@ -42,7 +42,6 @@ from pipecat.frames.frames import (
     Frame,
     FunctionCallResultFrame,
     InputAudioRawFrame,
-    InputTransportMessageFrame,
     InterimTranscriptionFrame,
     LLMConfigureOutputFrame,
     LLMContextFrame,
@@ -51,10 +50,10 @@ from pipecat.frames.frames import (
     LLMMessagesAppendFrame,
     LLMTextFrame,
     MetricsFrame,
-    OutputTransportMessageUrgentFrame,
     StartFrame,
     SystemFrame,
     TranscriptionFrame,
+    TransportMessageUrgentFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
@@ -920,7 +919,7 @@ class RTVIObserverParams:
     user_audio_level_enabled: bool = False
     metrics_enabled: bool = True
     system_logs_enabled: bool = False
-    errors_enabled: Optional[bool] = None
+    errors_enabled: bool = True
     audio_level_period_secs: float = 0.15
 
 
@@ -963,7 +962,7 @@ class RTVIObserver(BaseObserver):
         if self._params.system_logs_enabled:
             self._system_logger_id = logger.add(self._logger_sink)
 
-        if self._params.errors_enabled is not None:
+        if self._params.errors_enabled:
             import warnings
 
             with warnings.catch_warnings():
@@ -1210,10 +1209,11 @@ class RTVIObserver(BaseObserver):
 
     async def _send_error_response(self, frame: RTVIServerResponseFrame):
         """Send a response to the client for a specific request."""
-        message = RTVIErrorResponse(
-            id=str(frame.client_msg.msg_id), data=RTVIErrorResponseData(error=frame.error)
-        )
-        await self.send_rtvi_message(message)
+        if self._params.errors_enabled:
+            message = RTVIErrorResponse(
+                id=str(frame.client_msg.msg_id), data=RTVIErrorResponseData(error=frame.error)
+            )
+            await self.send_rtvi_message(message)
 
 
 class RTVIProcessor(FrameProcessor):
@@ -1346,9 +1346,7 @@ class RTVIProcessor(FrameProcessor):
 
     async def push_transport_message(self, model: BaseModel, exclude_none: bool = True):
         """Push a transport message frame."""
-        frame = OutputTransportMessageUrgentFrame(
-            message=model.model_dump(exclude_none=exclude_none)
-        )
+        frame = TransportMessageUrgentFrame(message=model.model_dump(exclude_none=exclude_none))
         await self.push_frame(frame)
 
     async def handle_message(self, message: RTVIMessage):
@@ -1421,7 +1419,7 @@ class RTVIProcessor(FrameProcessor):
         elif isinstance(frame, ErrorFrame):
             await self._send_error_frame(frame)
             await self.push_frame(frame, direction)
-        elif isinstance(frame, InputTransportMessageFrame):
+        elif isinstance(frame, TransportMessageUrgentFrame):
             await self._handle_transport_message(frame)
         # All other system frames
         elif isinstance(frame, SystemFrame):
@@ -1484,7 +1482,7 @@ class RTVIProcessor(FrameProcessor):
             await self._handle_message(message)
             self._message_queue.task_done()
 
-    async def _handle_transport_message(self, frame: InputTransportMessageFrame):
+    async def _handle_transport_message(self, frame: TransportMessageUrgentFrame):
         """Handle an incoming transport message frame."""
         try:
             transport_message = frame.message

src/pipecat/processors/logger.py

@@ -15,7 +15,7 @@ from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
     OutputAudioRawFrame,
-    UserSpeakingFrame,
+    TransportMessageFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
@@ -36,9 +36,9 @@ class FrameLogger(FrameProcessor):
         color: Optional[str] = None,
         ignored_frame_types: Tuple[Type[Frame], ...] = (
             BotSpeakingFrame,
-            UserSpeakingFrame,
             InputAudioRawFrame,
             OutputAudioRawFrame,
+            TransportMessageFrame,
         ),
     ):
         """Initialize the frame logger.

src/pipecat/runner/run.py

@@ -70,9 +70,7 @@ import asyncio
 import os
 import sys
 from contextlib import asynccontextmanager
-from typing import Optional
 
-import aiohttp
 from loguru import logger
 
 from pipecat.runner.types import (
@@ -84,7 +82,7 @@ from pipecat.runner.types import (
 try:
     import uvicorn
     from dotenv import load_dotenv
-    from fastapi import BackgroundTasks, FastAPI, Header, HTTPException, Request, WebSocket
+    from fastapi import BackgroundTasks, FastAPI, Request, WebSocket
     from fastapi.middleware.cors import CORSMiddleware
     from fastapi.responses import HTMLResponse, RedirectResponse
 except ImportError as e:
@@ -168,7 +166,6 @@ def _create_server_app(
     # Set up transport-specific routes
     if transport_type == "webrtc":
         _setup_webrtc_routes(app, esp32_mode=esp32_mode, host=host)
-        _setup_whatsapp_routes(app)
     elif transport_type == "daily":
         _setup_daily_routes(app)
     elif transport_type in ["twilio", "telnyx", "plivo", "exotel"]:
@@ -224,184 +221,12 @@ def _setup_webrtc_routes(app: FastAPI, esp32_mode: bool = False, host: str = "lo
         return answer
 
     @asynccontextmanager
-    async def smallwebrtc_lifespan(app: FastAPI):
+    async def lifespan(app: FastAPI):
         """Manage FastAPI application lifecycle and cleanup connections."""
         yield
         await small_webrtc_handler.close()
 
-    # Add the SmallWebRTC lifespan to the app
-    _add_lifespan_to_app(app, smallwebrtc_lifespan)
-
-
-def _add_lifespan_to_app(app: FastAPI, new_lifespan):
-    """Add a new lifespan context manager to the app, combining with existing if present.
-
-    Args:
-        app: The FastAPI application instance
-        new_lifespan: The new lifespan context manager to add
-    """
-    if hasattr(app.router, "lifespan_context") and app.router.lifespan_context is not None:
-        # If there's already a lifespan context, combine them
-        existing_lifespan = app.router.lifespan_context
-
-        @asynccontextmanager
-        async def combined_lifespan(app: FastAPI):
-            async with existing_lifespan(app):
-                async with new_lifespan(app):
-                    yield
-
-        app.router.lifespan_context = combined_lifespan
-    else:
-        # No existing lifespan, use the new one
-        app.router.lifespan_context = new_lifespan
-
-
-def _setup_whatsapp_routes(app: FastAPI):
-    """Set up WebRTC-specific routes."""
-    try:
-        from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
-
-        from pipecat.transports.smallwebrtc.connection import SmallWebRTCConnection
-        from pipecat.transports.smallwebrtc.request_handler import (
-            SmallWebRTCRequest,
-            SmallWebRTCRequestHandler,
-        )
-        from pipecat.transports.whatsapp.api import WhatsAppWebhookRequest
-        from pipecat.transports.whatsapp.client import WhatsAppClient
-    except ImportError as e:
-        logger.error(f"WebRTC transport dependencies not installed: {e}")
-        return
-
-    WHATSAPP_TOKEN = os.getenv("WHATSAPP_TOKEN")
-    WHATSAPP_PHONE_NUMBER_ID = os.getenv("WHATSAPP_PHONE_NUMBER_ID")
-    WHATSAPP_WEBHOOK_VERIFICATION_TOKEN = os.getenv("WHATSAPP_WEBHOOK_VERIFICATION_TOKEN")
-    WHATSAPP_APP_SECRET = os.getenv("WHATSAPP_APP_SECRET")
-
-    if not all(
-        [
-            WHATSAPP_TOKEN,
-            WHATSAPP_PHONE_NUMBER_ID,
-            WHATSAPP_WEBHOOK_VERIFICATION_TOKEN,
-        ]
-    ):
-        logger.debug(
-            "Missing required environment variables for WhatsApp transport. Keeping it disabled."
-        )
-        return
-
-    # Global WhatsApp client instance
-    whatsapp_client: Optional[WhatsAppClient] = None
-
-    @app.get(
-        "/whatsapp",
-        summary="Verify WhatsApp webhook",
-        description="Handles WhatsApp webhook verification requests from Meta",
-    )
-    async def verify_webhook(request: Request):
-        """Verify WhatsApp webhook endpoint.
-
-        This endpoint is called by Meta's WhatsApp Business API to verify
-        the webhook URL during setup. It validates the verification token
-        and returns the challenge parameter if successful.
-        """
-        if whatsapp_client is None:
-            logger.error("WhatsApp client is not initialized")
-            raise HTTPException(status_code=503, detail="Service unavailable")
-
-        params = dict(request.query_params)
-        logger.debug(f"Webhook verification request received with params: {list(params.keys())}")
-
-        try:
-            result = await whatsapp_client.handle_verify_webhook_request(
-                params=params, expected_verification_token=WHATSAPP_WEBHOOK_VERIFICATION_TOKEN
-            )
-            logger.info("Webhook verification successful")
-            return result
-        except ValueError as e:
-            logger.warning(f"Webhook verification failed: {e}")
-            raise HTTPException(status_code=403, detail="Verification failed")
-
-    @app.post(
-        "/whatsapp",
-        summary="Handle WhatsApp webhook events",
-        description="Processes incoming WhatsApp messages and call events",
-    )
-    async def whatsapp_webhook(
-        body: WhatsAppWebhookRequest,
-        background_tasks: BackgroundTasks,
-        request: Request,
-        x_hub_signature_256: str = Header(None),
-    ):
-        """Handle incoming WhatsApp webhook events.
-
-        For call events, establishes WebRTC connections and spawns bot instances
-        in the background to handle real-time communication.
-        """
-        if whatsapp_client is None:
-            logger.error("WhatsApp client is not initialized")
-            raise HTTPException(status_code=503, detail="Service unavailable")
-
-        # Validate webhook object type
-        if body.object != "whatsapp_business_account":
-            logger.warning(f"Invalid webhook object type: {body.object}")
-            raise HTTPException(status_code=400, detail="Invalid object type")
-
-        logger.debug(f"Processing WhatsApp webhook: {body.model_dump()}")
-
-        async def connection_callback(connection: SmallWebRTCConnection):
-            """Handle new WebRTC connections from WhatsApp calls.
-
-            Called when a WebRTC connection is established for a WhatsApp call.
-            Spawns a bot instance to handle the conversation.
-
-            Args:
-                connection: The established WebRTC connection
-            """
-            bot_module = _get_bot_module()
-            runner_args = SmallWebRTCRunnerArguments(webrtc_connection=connection)
-            background_tasks.add_task(bot_module.bot, runner_args)
-
-        try:
-            # Process the webhook request
-            raw_body = await request.body()
-            result = await whatsapp_client.handle_webhook_request(
-                body, connection_callback, sha256_signature=x_hub_signature_256, raw_body=raw_body
-            )
-            logger.debug(f"Webhook processed successfully: {result}")
-            return {"status": "success", "message": "Webhook processed successfully"}
-        except ValueError as ve:
-            logger.warning(f"Invalid webhook request format: {ve}")
-            raise HTTPException(status_code=400, detail=f"Invalid request: {str(ve)}")
-        except Exception as e:
-            logger.error(f"Internal error processing webhook: {e}")
-            raise HTTPException(status_code=500, detail="Internal server error processing webhook")
-
-    @asynccontextmanager
-    async def whatsapp_lifespan(app: FastAPI):
-        """Manage WhatsApp client lifecycle and cleanup connections."""
-        nonlocal whatsapp_client
-
-        # Initialize WhatsApp client with persistent HTTP session
-        async with aiohttp.ClientSession() as session:
-            whatsapp_client = WhatsAppClient(
-                whatsapp_token=WHATSAPP_TOKEN,
-                whatsapp_secret=WHATSAPP_APP_SECRET,
-                phone_number_id=WHATSAPP_PHONE_NUMBER_ID,
-                session=session,
-            )
-            logger.info("WhatsApp client initialized successfully")
-
-            try:
-                yield  # Run the application
-            finally:
-                # Cleanup all active calls on shutdown
-                logger.info("Cleaning up WhatsApp client resources...")
-                if whatsapp_client:
-                    await whatsapp_client.terminate_all_calls()
-                logger.info("WhatsApp cleanup completed")
-
-    # Add the WhatsApp lifespan to the app
-    _add_lifespan_to_app(app, whatsapp_lifespan)
+    app.router.lifespan_context = lifespan
 
 
 def _setup_daily_routes(app: FastAPI):

src/pipecat/runner/utils.py

@@ -99,41 +99,29 @@ async def parse_telephony_websocket(websocket: WebSocket):
         tuple: (transport_type: str, call_data: dict)
 
         call_data contains provider-specific fields:
-
-        - Twilio::
-
-            {
-                "stream_id": str,
-                "call_id": str,
-                "body": dict
-            }
-
-        - Telnyx::
-
-            {
-                "stream_id": str,
-                "call_control_id": str,
-                "outbound_encoding": str,
-                "from": str,
-                "to": str,
-            }
-
-        - Plivo::
-
-            {
-                "stream_id": str,
-                "call_id": str,
-            }
-
-        - Exotel::
-
-            {
-                "stream_id": str,
-                "call_id": str,
-                "account_sid": str,
-                "from": str,
-                "to": str,
-            }
+        - Twilio: {
+            "stream_id": str,
+            "call_id": str,
+            "body": dict
+        }
+        - Telnyx: {
+            "stream_id": str,
+            "call_control_id": str,
+            "outbound_encoding": str,
+            "from": str,
+            "to": str,
+        }
+        - Plivo: {
+            "stream_id": str,
+            "call_id": str,
+        }
+        - Exotel: {
+            "stream_id": str,
+            "call_id": str,
+            "account_sid": str,
+            "from": str,
+            "to": str,
+        }
 
     Example usage::
 
@@ -313,7 +301,6 @@ def _smallwebrtc_sdp_cleanup_ice_candidates(text: str, pattern: str) -> str:
     Returns:
         Cleaned SDP text with filtered ICE candidates.
     """
-    logger.debug("Removing unsupported ICE candidates from SDP")
     result = []
     lines = text.splitlines()
     for line in lines:
@@ -322,7 +309,7 @@ def _smallwebrtc_sdp_cleanup_ice_candidates(text: str, pattern: str) -> str:
                 result.append(line)
         else:
             result.append(line)
-    return "\r\n".join(result) + "\r\n"
+    return "\r\n".join(result)
 
 
 def _smallwebrtc_sdp_cleanup_fingerprints(text: str) -> str:
@@ -334,16 +321,15 @@ def _smallwebrtc_sdp_cleanup_fingerprints(text: str) -> str:
     Returns:
         SDP text with sha-384 and sha-512 fingerprints removed.
     """
-    logger.debug("Removing unsupported fingerprints from SDP")
     result = []
     lines = text.splitlines()
     for line in lines:
         if not re.search("sha-384", line) and not re.search("sha-512", line):
             result.append(line)
-    return "\r\n".join(result) + "\r\n"
+    return "\r\n".join(result)
 
 
-def smallwebrtc_sdp_munging(sdp: str, host: Optional[str]) -> str:
+def smallwebrtc_sdp_munging(sdp: str, host: str) -> str:
     """Apply SDP modifications for SmallWebRTC compatibility.
 
     Args:
@@ -354,8 +340,7 @@ def smallwebrtc_sdp_munging(sdp: str, host: Optional[str]) -> str:
         Modified SDP string with fingerprint and ICE candidate cleanup.
     """
     sdp = _smallwebrtc_sdp_cleanup_fingerprints(sdp)
-    if host:
-        sdp = _smallwebrtc_sdp_cleanup_ice_candidates(sdp, host)
+    sdp = _smallwebrtc_sdp_cleanup_ice_candidates(sdp, host)
     return sdp
 
 

src/pipecat/serializers/exotel.py

@@ -21,9 +21,9 @@ from pipecat.frames.frames import (
     InputAudioRawFrame,
     InputDTMFFrame,
     InterruptionFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     StartFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
 )
 from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializerType
 
@@ -121,7 +121,7 @@ class ExotelFrameSerializer(FrameSerializer):
             }
 
             return json.dumps(answer)
-        elif isinstance(frame, (OutputTransportMessageFrame, OutputTransportMessageUrgentFrame)):
+        elif isinstance(frame, (TransportMessageFrame, TransportMessageUrgentFrame)):
             return json.dumps(frame.message)
 
         return None

src/pipecat/serializers/plivo.py

@@ -23,9 +23,9 @@ from pipecat.frames.frames import (
     InputAudioRawFrame,
     InputDTMFFrame,
     InterruptionFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     StartFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
 )
 from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializerType
 
@@ -148,7 +148,7 @@ class PlivoFrameSerializer(FrameSerializer):
             }
 
             return json.dumps(answer)
-        elif isinstance(frame, (OutputTransportMessageFrame, OutputTransportMessageUrgentFrame)):
+        elif isinstance(frame, (TransportMessageFrame, TransportMessageUrgentFrame)):
             return json.dumps(frame.message)
 
         # Return None for unhandled frames

src/pipecat/serializers/protobuf.py

@@ -15,12 +15,11 @@ import pipecat.frames.protobufs.frames_pb2 as frame_protos
 from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
-    InputTransportMessageFrame,
     OutputAudioRawFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     TextFrame,
     TranscriptionFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
 )
 from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializerType
 
@@ -83,7 +82,7 @@ class ProtobufFrameSerializer(FrameSerializer):
             Serialized frame as bytes, or None if frame type is not serializable.
         """
         # Wrapping this messages as a JSONFrame to send
-        if isinstance(frame, (OutputTransportMessageFrame, OutputTransportMessageUrgentFrame)):
+        if isinstance(frame, (TransportMessageFrame, TransportMessageUrgentFrame)):
             frame = MessageFrame(
                 data=json.dumps(frame.message),
             )
@@ -135,11 +134,11 @@ class ProtobufFrameSerializer(FrameSerializer):
         if "pts" in args_dict:
             del args_dict["pts"]
 
-        # Special handling for MessageFrame -> OutputTransportMessageUrgentFrame
+        # Special handling for MessageFrame -> TransportMessageUrgentFrame
         if class_name == MessageFrame:
             try:
                 msg = json.loads(args_dict["data"])
-                instance = InputTransportMessageFrame(message=msg)
+                instance = TransportMessageUrgentFrame(message=msg)
                 logger.debug(f"ProtobufFrameSerializer: Transport message {instance}")
             except Exception as e:
                 logger.error(f"Error parsing MessageFrame data: {e}")

src/pipecat/serializers/twilio.py

@@ -23,9 +23,9 @@ from pipecat.frames.frames import (
     InputAudioRawFrame,
     InputDTMFFrame,
     InterruptionFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     StartFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
 )
 from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializerType
 
@@ -175,7 +175,7 @@ class TwilioFrameSerializer(FrameSerializer):
             }
 
             return json.dumps(answer)
-        elif isinstance(frame, (OutputTransportMessageFrame, OutputTransportMessageUrgentFrame)):
+        elif isinstance(frame, (TransportMessageFrame, TransportMessageUrgentFrame)):
             return json.dumps(frame.message)
 
         # Return None for unhandled frames

src/pipecat/services/aws/llm.py

@@ -61,6 +61,7 @@ from pipecat.utils.tracing.service_decorators import traced_llm
 
 try:
     import aioboto3
+    import httpx
     from botocore.config import Config
     from botocore.exceptions import ReadTimeoutError
 except ModuleNotFoundError as e:
@@ -1116,7 +1117,7 @@ class AWSBedrockLLMService(LLMService):
             # also get cancelled.
             use_completion_tokens_estimate = True
             raise
-        except (ReadTimeoutError, asyncio.TimeoutError):
+        except httpx.TimeoutException:
             await self._call_event_handler("on_completion_timeout")
         except Exception as e:
             logger.exception(f"{self} exception: {e}")

src/pipecat/services/aws_nova_sonic/aws.py

@@ -70,7 +70,7 @@ try:
         BedrockRuntimeClient,
         InvokeModelWithBidirectionalStreamOperationInput,
     )
-    from aws_sdk_bedrock_runtime.config import Config
+    from aws_sdk_bedrock_runtime.config import Config, HTTPAuthSchemeResolver, SigV4AuthScheme
     from aws_sdk_bedrock_runtime.models import (
         BidirectionalInputPayloadPart,
         InvokeModelWithBidirectionalStreamInput,
@@ -78,8 +78,8 @@ try:
         InvokeModelWithBidirectionalStreamOperationOutput,
         InvokeModelWithBidirectionalStreamOutput,
     )
-    from smithy_aws_core.auth.sigv4 import SigV4AuthScheme
-    from smithy_aws_core.identity.static import StaticCredentialsResolver
+    from smithy_aws_core.credentials_resolvers.static import StaticCredentialsResolver
+    from smithy_aws_core.identity import AWSCredentialsIdentity
     from smithy_core.aio.eventstream import DuplexEventStream
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
@@ -528,11 +528,15 @@ class AWSNovaSonicLLMService(LLMService):
         config = Config(
             endpoint_uri=f"https://bedrock-runtime.{self._region}.amazonaws.com",
             region=self._region,
-            aws_access_key_id=self._access_key_id,
-            aws_secret_access_key=self._secret_access_key,
-            aws_session_token=self._session_token,
-            aws_credentials_identity_resolver=StaticCredentialsResolver(),
-            auth_schemes={"aws.auth#sigv4": SigV4AuthScheme(service="bedrock")},
+            aws_credentials_identity_resolver=StaticCredentialsResolver(
+                credentials=AWSCredentialsIdentity(
+                    access_key_id=self._access_key_id,
+                    secret_access_key=self._secret_access_key,
+                    session_token=self._session_token,
+                )
+            ),
+            http_auth_scheme_resolver=HTTPAuthSchemeResolver(),
+            http_auth_schemes={"aws.auth#sigv4": SigV4AuthScheme()},
         )
         return BedrockRuntimeClient(config=config)
 

src/pipecat/services/deepgram/__init__.py

@@ -8,7 +8,6 @@ import sys
 
 from pipecat.services import DeprecatedModuleProxy
 
-from .flux import *
 from .stt import *
 from .tts import *
 

src/pipecat/services/deepgram/flux/stt.py

@@ -1,636 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Deepgram Flux speech-to-text service implementation."""
-
-import json
-from enum import Enum
-from typing import Any, AsyncGenerator, Dict, Optional
-
-from loguru import logger
-from pydantic import BaseModel
-
-from pipecat.frames.frames import (
-    CancelFrame,
-    EndFrame,
-    ErrorFrame,
-    Frame,
-    InterimTranscriptionFrame,
-    StartFrame,
-    TranscriptionFrame,
-    UserStartedSpeakingFrame,
-    UserStoppedSpeakingFrame,
-)
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.stt_service import WebsocketSTTService
-from pipecat.transcriptions.language import Language
-from pipecat.utils.time import time_now_iso8601
-from pipecat.utils.tracing.service_decorators import traced_stt
-
-try:
-    from websockets.asyncio.client import connect as websocket_connect
-    from websockets.protocol import State
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error("In order to use Deepgram Flux, you need to `pip install pipecat-ai[deepgram]`.")
-    raise Exception(f"Missing module: {e}")
-
-
-class FluxMessageType(str, Enum):
-    """Deepgram Flux WebSocket message types.
-
-    These are the top-level message types that can be received from the
-    Deepgram Flux WebSocket connection.
-    """
-
-    RECEIVE_CONNECTED = "Connected"
-    RECEIVE_FATAL_ERROR = "Error"
-    TURN_INFO = "TurnInfo"
-
-
-class FluxEventType(str, Enum):
-    """Deepgram Flux TurnInfo event types.
-
-    These events are contained within TurnInfo messages and indicate
-    different stages of speech processing and turn detection.
-    """
-
-    START_OF_TURN = "StartOfTurn"
-    TURN_RESUMED = "TurnResumed"
-    END_OF_TURN = "EndOfTurn"
-    EAGER_END_OF_TURN = "EagerEndOfTurn"
-    UPDATE = "Update"
-
-
-class DeepgramFluxSTTService(WebsocketSTTService):
-    """Deepgram Flux speech-to-text service.
-
-    Provides real-time speech recognition using Deepgram's WebSocket API with Flux capabilities.
-    Supports configurable models, VAD events, and various audio processing options
-    including advanced turn detection and EagerEndOfTurn events for improved conversational AI performance.
-    """
-
-    class InputParams(BaseModel):
-        """Configuration parameters for Deepgram Flux API.
-
-        This class defines all available connection parameters for the Deepgram Flux API
-        based on the official documentation.
-
-        Parameters:
-            eager_eot_threshold: Optional. EagerEndOfTurn/TurnResumed are off by default.
-                You can turn them on by setting eager_eot_threshold to a valid value.
-                Lower values = more aggressive EagerEndOfTurning (faster response, more LLM calls).
-                Higher values = more conservative EagerEndOfTurning (slower response, fewer LLM calls).
-            eot_threshold: Optional. End-of-turn confidence required to finish a turn (default 0.7).
-                Lower values = turns end sooner (more interruptions, faster responses).
-                Higher values = turns end later (fewer interruptions, more complete utterances).
-            eot_timeout_ms: Optional. Time in milliseconds after speech to finish a turn
-                regardless of EOT confidence (default 5000).
-            keyterm: List of keyterms to boost recognition accuracy for specialized terminology.
-            mip_opt_out: Optional. Opts out requests from the Deepgram Model Improvement Program
-                (default False).
-            tag: List of tags to label requests for identification during usage reporting.
-        """
-
-        eager_eot_threshold: Optional[float] = None
-        eot_threshold: Optional[float] = None
-        eot_timeout_ms: Optional[int] = None
-        keyterm: list = []
-        mip_opt_out: Optional[bool] = None
-        tag: list = []
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        url: str = "wss://api.deepgram.com/v2/listen",
-        sample_rate: Optional[int] = None,
-        model: str = "flux-general-en",
-        flux_encoding: str = "linear16",
-        params: Optional[InputParams] = None,
-        **kwargs,
-    ):
-        """Initialize the Deepgram Flux STT service.
-
-        Args:
-            api_key: Deepgram API key for authentication. Required for API access.
-            url: WebSocket URL for the Deepgram Flux API. Defaults to the preview endpoint.
-            sample_rate: Audio sample rate in Hz. If None, uses the rate from params or 16000.
-            model: Deepgram Flux model to use for transcription. Currently only supports "flux-general-en".
-            flux_encoding: Audio encoding format required by Flux API. Must be "linear16".
-                Raw signed little-endian 16-bit PCM encoding.
-            params: InputParams instance containing detailed API configuration options.
-                If None, default parameters will be used.
-            **kwargs: Additional arguments passed to the parent WebsocketSTTService class.
-
-        Examples:
-            Basic usage with default parameters::
-
-                stt = DeepgramFluxSTTService(api_key="your-api-key")
-
-            Advanced usage with custom parameters::
-
-                params = DeepgramFluxSTTService.InputParams(
-                    eager_eot_threshold=0.5,
-                    eot_threshold=0.8,
-                    keyterm=["AI", "machine learning", "neural network"],
-                    tag=["production", "voice-agent"]
-                )
-                stt = DeepgramFluxSTTService(
-                    api_key="your-api-key",
-                    model="flux-general-en",
-                    params=params
-                )
-        """
-        super().__init__(sample_rate=sample_rate, **kwargs)
-
-        self._api_key = api_key
-        self._url = url
-        self._model = model
-        self._params = params or DeepgramFluxSTTService.InputParams()
-        self._flux_encoding = flux_encoding
-        # This is the currently only supported language
-        self._language = Language.EN
-        self._websocket_url = None
-        self._receive_task = None
-
-    async def _connect(self):
-        """Connect to WebSocket and start background tasks.
-
-        Establishes the WebSocket connection to the Deepgram Flux API and starts
-        the background task for receiving transcription results.
-        """
-        await self._connect_websocket()
-
-        if self._websocket and not self._receive_task:
-            self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
-
-    async def _disconnect(self):
-        """Disconnect from WebSocket and clean up tasks.
-
-        Gracefully disconnects from the Deepgram Flux API, cancels background tasks,
-        and cleans up resources to prevent memory leaks.
-        """
-        try:
-            # Cancel background tasks BEFORE closing websocket
-            if self._receive_task:
-                await self.cancel_task(self._receive_task, timeout=2.0)
-                self._receive_task = None
-
-            # Now close the websocket
-            await self._disconnect_websocket()
-
-        except Exception as e:
-            logger.error(f"Error during disconnect: {e}")
-        finally:
-            # Reset state only after everything is cleaned up
-            self._websocket = None
-
-    async def _connect_websocket(self):
-        """Establish WebSocket connection to API.
-
-        Creates a WebSocket connection to the Deepgram Flux API using the configured
-        URL and authentication headers. Handles connection errors and reports them
-        through the event handler system.
-        """
-        try:
-            if self._websocket and self._websocket.state is State.OPEN:
-                return
-
-            self._websocket = await websocket_connect(
-                self._websocket_url,
-                additional_headers={"Authorization": f"Token {self._api_key}"},
-            )
-            logger.debug("Connected to Deepgram Flux Websocket")
-        except Exception as e:
-            logger.error(f"{self} initialization error: {e}")
-            self._websocket = None
-            await self._call_event_handler("on_connection_error", f"{e}")
-
-    async def _disconnect_websocket(self):
-        """Close WebSocket connection and clean up state.
-
-        Closes the WebSocket connection to the Deepgram Flux API and stops all
-        metrics collection. Handles disconnection errors gracefully.
-        """
-        try:
-            await self.stop_all_metrics()
-
-            if self._websocket:
-                await self._send_close_stream()
-                logger.debug("Disconnecting from Deepgram Flux Websocket")
-                await self._websocket.close()
-        except Exception as e:
-            logger.error(f"{self} error closing websocket: {e}")
-
-    async def _send_close_stream(self) -> None:
-        """Sends a CloseStream control message to the Deepgram Flux WebSocket API.
-
-        This signals to the server that no more audio data will be sent.
-        """
-        if self._websocket:
-            logger.debug("Sending CloseStream message to Deepgram Flux")
-            message = {"type": "CloseStream"}
-            await self._websocket.send(json.dumps(message))
-
-    def can_generate_metrics(self) -> bool:
-        """Check if this service can generate processing metrics.
-
-        Returns:
-            True, as Deepgram service supports metrics generation.
-        """
-        return True
-
-    async def start(self, frame: StartFrame):
-        """Start the Deepgram Flux STT service.
-
-        Initializes the service by constructing the WebSocket URL with all configured
-        parameters and establishing the connection to begin transcription processing.
-
-        Args:
-            frame: The start frame containing initialization parameters and metadata.
-        """
-        await super().start(frame)
-
-        url_params = [
-            f"model={self._model}",
-            f"sample_rate={self.sample_rate}",
-            f"encoding={self._flux_encoding}",
-        ]
-
-        if self._params.eager_eot_threshold is not None:
-            url_params.append(f"eager_eot_threshold={self._params.eager_eot_threshold}")
-
-        if self._params.eot_threshold is not None:
-            url_params.append(f"eot_threshold={self._params.eot_threshold}")
-
-        if self._params.eot_timeout_ms is not None:
-            url_params.append(f"eot_timeout_ms={self._params.eot_timeout_ms}")
-
-        if self._params.mip_opt_out is not None:
-            url_params.append(f"mip_opt_out={str(self._params.mip_opt_out).lower()}")
-
-        # Add keyterm parameters (can have multiple)
-        for keyterm in self._params.keyterm:
-            url_params.append(f"keyterm={keyterm}")
-
-        # Add tag parameters (can have multiple)
-        for tag_value in self._params.tag:
-            url_params.append(f"tag={tag_value}")
-
-        self._websocket_url = f"{self._url}?{'&'.join(url_params)}"
-        await self._connect()
-
-    async def stop(self, frame: EndFrame):
-        """Stop the Deepgram Flux STT service.
-
-        Args:
-            frame: The end frame.
-        """
-        await super().stop(frame)
-        await self._disconnect()
-
-    async def cancel(self, frame: CancelFrame):
-        """Cancel the Deepgram Flux STT service.
-
-        Args:
-            frame: The cancel frame.
-        """
-        await super().cancel(frame)
-        await self._disconnect()
-
-    async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
-        """Send audio data to Deepgram Flux for transcription.
-
-        Transmits raw audio bytes to the Deepgram Flux API for real-time speech
-        recognition. Transcription results are received asynchronously through
-        WebSocket callbacks and processed in the background.
-
-        Args:
-            audio: Raw audio bytes in linear16 format (signed little-endian 16-bit PCM).
-
-        Yields:
-            Frame: None (transcription results are delivered via WebSocket callbacks
-                rather than as return values from this method).
-
-        Raises:
-            Exception: If the WebSocket connection is not established or if there
-                are issues sending the audio data.
-        """
-        if not self._websocket:
-            logger.error("Not connected to Deepgram Flux.")
-            yield ErrorFrame("Not connected to Deepgram Flux.", fatal=True)
-            return
-
-        try:
-            await self._websocket.send(audio)
-        except Exception as e:
-            logger.error(f"Failed to send audio to Flux: {e}")
-            yield ErrorFrame(f"Failed to send audio to Flux:  {e}")
-            return
-
-        yield None
-
-    async def start_metrics(self):
-        """Start TTFB and processing metrics collection."""
-        # TTFB (Time To First Byte) metrics are currently disabled for Deepgram Flux.
-        # Ideally, TTFB should measure the time from when a user starts speaking
-        # until we receive the first transcript. However, Deepgram Flux delivers
-        # both the "user started speaking" event and the first transcript simultaneously,
-        # making this timing measurement meaningless in this context.
-        # await self.start_ttfb_metrics()
-        await self.start_processing_metrics()
-
-    @traced_stt
-    async def _handle_transcription(
-        self, transcript: str, is_final: bool, language: Optional[Language] = None
-    ):
-        """Handle a transcription result with tracing."""
-        pass
-
-    def _get_websocket(self):
-        """Get the current WebSocket connection.
-
-        Returns the active WebSocket connection instance, raising an exception
-        if no connection is currently established.
-
-        Returns:
-            The active WebSocket connection instance.
-
-        Raises:
-            Exception: If no WebSocket connection is currently active.
-        """
-        if self._websocket:
-            return self._websocket
-        raise Exception("Websocket not connected")
-
-    def _validate_message(self, data: Dict[str, Any]) -> bool:
-        """Validate basic message structure from Deepgram Flux.
-
-        Ensures the received message has the expected structure before processing.
-
-        Args:
-            data: The parsed JSON message data to validate.
-
-        Returns:
-            True if the message structure is valid, False otherwise.
-        """
-        if not isinstance(data, dict):
-            logger.warning("Message is not a dictionary")
-            return False
-
-        if "type" not in data:
-            logger.warning("Message missing 'type' field")
-            return False
-
-        return True
-
-    async def _receive_messages(self):
-        """Receive and process messages from WebSocket.
-
-        Continuously receives messages from the Deepgram Flux WebSocket connection
-        and processes various message types including connection status, transcription
-        results, turn information, and error conditions. Handles different event types
-        such as StartOfTurn, EndOfTurn, EagerEndOfTurn, and Update events.
-        """
-        async for message in self._get_websocket():
-            if isinstance(message, str):
-                try:
-                    data = json.loads(message)
-                    await self._handle_message(data)
-                except json.JSONDecodeError as e:
-                    logger.error(f"Failed to decode JSON message: {e}")
-                    # Skip malformed messages
-                    continue
-                except Exception as e:
-                    logger.error(f"Error processing message: {e}")
-                    # Error will be handled inside WebsocketService->_receive_task_handler
-                    raise
-            else:
-                logger.warning(f"Received non-string message: {type(message)}")
-
-    async def _handle_message(self, data: Dict[str, Any]):
-        """Handle a parsed WebSocket message from Deepgram Flux.
-
-        Routes messages to appropriate handlers based on their type. Validates
-        message structure before processing.
-
-        Args:
-            data: The parsed JSON message data from the WebSocket.
-        """
-        if not self._validate_message(data):
-            return
-
-        message_type = data.get("type")
-
-        try:
-            flux_message_type = FluxMessageType(message_type)
-        except ValueError:
-            logger.debug(f"Unhandled message type: {message_type or 'unknown'}")
-            return
-
-        match flux_message_type:
-            case FluxMessageType.RECEIVE_CONNECTED:
-                await self._handle_connection_established()
-            case FluxMessageType.RECEIVE_FATAL_ERROR:
-                await self._handle_fatal_error(data)
-            case FluxMessageType.TURN_INFO:
-                await self._handle_turn_info(data)
-
-    async def _handle_connection_established(self):
-        """Handle successful connection establishment to Deepgram Flux.
-
-        This event is fired when the WebSocket connection to Deepgram Flux
-        is successfully established and ready to receive audio data for
-        transcription processing.
-        """
-        logger.info("Connected to Flux - ready to stream audio")
-
-    async def _handle_fatal_error(self, data: Dict[str, Any]):
-        """Handle fatal error messages from Deepgram Flux.
-
-        Fatal errors indicate unrecoverable issues with the connection or
-        configuration that require intervention. These errors will cause
-        the connection to be terminated.
-
-        Args:
-            data: The error message data containing error details.
-
-        Raises:
-            Exception: Always raises to trigger error handling in the parent service.
-        """
-        error_msg = data.get("error", "Unknown error")
-        deepgram_error = f"Fatal error: {error_msg}"
-        logger.error(deepgram_error)
-        # Error will be handled inside WebsocketService->_receive_task_handler
-        raise Exception(deepgram_error)
-
-    async def _handle_turn_info(self, data: Dict[str, Any]):
-        """Handle TurnInfo events from Deepgram Flux.
-
-        TurnInfo messages contain various turn-based events that indicate
-        the state of speech processing, including turn boundaries, interim
-        results, and turn finalization events.
-
-        Args:
-            data: The TurnInfo message data containing event type, transcript and some extra metadata.
-        """
-        event = data.get("event")
-        transcript = data.get("transcript", "")
-
-        try:
-            flux_event_type = FluxEventType(event)
-        except ValueError:
-            logger.debug(f"Unhandled TurnInfo event: {event}")
-            return
-
-        match flux_event_type:
-            case FluxEventType.START_OF_TURN:
-                await self._handle_start_of_turn(transcript)
-            case FluxEventType.TURN_RESUMED:
-                await self._handle_turn_resumed(event)
-            case FluxEventType.END_OF_TURN:
-                await self._handle_end_of_turn(transcript, data)
-            case FluxEventType.EAGER_END_OF_TURN:
-                await self._handle_eager_end_of_turn(transcript, data)
-            case FluxEventType.UPDATE:
-                await self._handle_update(transcript)
-
-    async def _handle_start_of_turn(self, transcript: str):
-        """Handle StartOfTurn events from Deepgram Flux.
-
-        StartOfTurn events are fired when Deepgram Flux detects the beginning
-        of a new speaking turn. This triggers bot interruption to stop any
-        ongoing speech synthesis and signals the start of user speech detection.
-
-        The service will:
-        - Send a BotInterruptionFrame upstream to stop bot speech
-        - Send a UserStartedSpeakingFrame downstream to notify other components
-        - Start metrics collection for measuring response times
-
-        Args:
-            transcript: maybe the first few words of the turn.
-        """
-        logger.debug("User started speaking")
-        await self.push_interruption_task_frame_and_wait()
-        await self.push_frame(UserStartedSpeakingFrame(), FrameDirection.DOWNSTREAM)
-        await self.push_frame(UserStartedSpeakingFrame(), FrameDirection.UPSTREAM)
-        await self.start_metrics()
-        if transcript:
-            logger.trace(f"Start of turn transcript: {transcript}")
-
-    async def _handle_turn_resumed(self, event: str):
-        """Handle TurnResumed events from Deepgram Flux.
-
-        TurnResumed events indicate that speech has resumed after a brief pause
-        within the same turn. This is primarily used for logging and debugging
-        purposes and doesn't trigger any significant processing changes.
-
-        Args:
-            event: The event type string for logging purposes.
-        """
-        logger.trace(f"Received event TurnResumed: {event}")
-
-    async def _handle_end_of_turn(self, transcript: str, data: Dict[str, Any]):
-        """Handle EndOfTurn events from Deepgram Flux.
-
-        EndOfTurn events are fired when Deepgram Flux determines that a speaking
-        turn has concluded, either due to sufficient silence or end-of-turn
-        confidence thresholds being met. This provides the final transcript
-        for the completed turn.
-
-        The service will:
-        - Create and send a final TranscriptionFrame with the complete transcript
-        - Trigger transcription handling with tracing for metrics
-        - Stop processing metrics collection
-        - Send a UserStoppedSpeakingFrame to signal turn completion
-
-        Args:
-            transcript: The final transcript text for the completed turn.
-            data: The TurnInfo message data containing event type, transcript and some extra metadata.
-        """
-        logger.debug("User stopped speaking")
-
-        await self.push_frame(
-            TranscriptionFrame(
-                transcript,
-                self._user_id,
-                time_now_iso8601(),
-                self._language,
-                result=data,
-            )
-        )
-        await self._handle_transcription(transcript, True, self._language)
-        await self.stop_processing_metrics()
-        await self.push_frame(UserStoppedSpeakingFrame(), FrameDirection.DOWNSTREAM)
-        await self.push_frame(UserStoppedSpeakingFrame(), FrameDirection.UPSTREAM)
-
-    async def _handle_eager_end_of_turn(self, transcript: str, data: Dict[str, Any]):
-        """Handle EagerEndOfTurn events from Deepgram Flux.
-
-        EagerEndOfTurn events are fired when the end-of-turn confidence reaches the
-        EagerEndOfTurn threshold but hasn't yet reached the full end-of-turn threshold.
-        These provide interim transcripts that can be used for faster response
-        generation while still allowing the user to continue speaking.
-
-        EagerEndOfTurn events enable more responsive conversational AI by allowing
-        the LLM to start processing likely final transcripts before the turn
-        is definitively ended.
-
-        Args:
-            transcript: The interim transcript text that triggered the EagerEndOfTurn event.
-            data: The TurnInfo message data containing event type, transcript and some extra metadata.
-        """
-        logger.trace(f"EagerEndOfTurn - {transcript}")
-        # Deepgram's EagerEndOfTurn feature enables lower-latency voice agents by sending
-        # medium-confidence transcripts before EndOfTurn certainty, allowing LLM processing to
-        # begin early.
-        #
-        # However, if speech resumes or the transcripts differ from the final EndOfTurn, the
-        # EagerEndOfTurn response should be cancelled to avoid incorrect or partial responses.
-        #
-        # Pipecat doesn't yet provide built-in Gate/control mechanisms to:
-        # 1. Start LLM/TTS processing early on EagerEndOfTurn events
-        # 2. Cancel in-flight processing when TurnResumed occurs
-        #
-        # By pushing EagerEndOfTurn transcripts as InterimTranscriptionFrame, we enable
-        # developers to implement custom EagerEndOfTurn handling in their applications while
-        # maintaining compatibility with existing interim transcription workflows.
-        #
-        # TODO: Implement proper EagerEndOfTurn support with cancellable processing pipeline
-        # that can start response generation on EagerEndOfTurn and cancel or confirm it.
-        await self.push_frame(
-            InterimTranscriptionFrame(
-                transcript,
-                self._user_id,
-                time_now_iso8601(),
-                self._language,
-                result=data,
-            )
-        )
-
-    async def _handle_update(self, transcript: str):
-        """Handle Update events from Deepgram Flux.
-
-        Update events provide incremental transcript updates during an ongoing
-        turn. These events allow for real-time display of transcription progress
-        and can be used to provide visual feedback to users about what's being
-        recognized.
-
-        The service stops TTFB (Time To First Byte) metrics when the first
-        substantial update is received, indicating successful processing start.
-
-        Args:
-            transcript: The current partial transcript text for the ongoing turn.
-        """
-        if transcript:
-            logger.trace(f"Update event: {transcript}")
-            # TTFB (Time To First Byte) metrics are currently disabled for Deepgram Flux.
-            # Ideally, TTFB should measure the time from when a user starts speaking
-            # until we receive the first transcript. However, Deepgram Flux delivers
-            # both the "user started speaking" event and the first transcript simultaneously,
-            # making this timing measurement meaningless in this context.
-            # await self.stop_ttfb_metrics()

src/pipecat/services/elevenlabs/__init__.py

@@ -8,7 +8,6 @@ import sys
 
 from pipecat.services import DeprecatedModuleProxy
 
-from .stt import *
 from .tts import *
 
-sys.modules[__name__] = DeprecatedModuleProxy(globals(), "elevenlabs", "elevenlabs.[stt,tts]")
+sys.modules[__name__] = DeprecatedModuleProxy(globals(), "elevenlabs", "elevenlabs.tts")

src/pipecat/services/google/llm.py

@@ -35,7 +35,6 @@ from pipecat.frames.frames import (
     LLMMessagesFrame,
     LLMTextFrame,
     LLMUpdateSettingsFrame,
-    OutputImageRawFrame,
     UserImageRawFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage
@@ -73,9 +72,6 @@ try:
         HttpOptions,
         Part,
     )
-
-    # Temporary hack to be able to process Nano Banana returned images.
-    genai._api_client.READ_BUFFER_SIZE = 5 * 1024 * 1024
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
     logger.error("In order to use Google AI, you need to `pip install pipecat-ai[google]`.")
@@ -686,7 +682,7 @@ class GoogleLLMService(LLMService):
         self,
         *,
         api_key: str,
-        model: str = "gemini-2.5-flash",
+        model: str = "gemini-2.0-flash",
         params: Optional[InputParams] = None,
         system_instruction: Optional[str] = None,
         tools: Optional[List[Dict[str, Any]]] = None,
@@ -714,7 +710,6 @@ class GoogleLLMService(LLMService):
         self._api_key = api_key
         self._system_instruction = system_instruction
         self._http_options = http_options
-
         self._create_client(api_key, http_options)
         self._settings = {
             "max_tokens": params.max_tokens,
@@ -793,9 +788,6 @@ class GoogleLLMService(LLMService):
             # and can be configured to turn it off.
             if not self._model_name.startswith("gemini-2.5-flash"):
                 return
-            # If we have an image model, we don't use a budget either.
-            if "image" in self._model_name:
-                return
             # If thinking_config is already set, don't override it.
             if "thinking_config" in generation_params:
                 return
@@ -935,12 +927,6 @@ class GoogleLLMService(LLMService):
                                         arguments=function_call.args or {},
                                     )
                                 )
-                            elif part.inline_data and part.inline_data.data:
-                                image = Image.open(io.BytesIO(part.inline_data.data))
-                                frame = OutputImageRawFrame(
-                                    image=image.tobytes(), size=image.size, format="RGB"
-                                )
-                                await self.push_frame(frame)
 
                     if (
                         candidate.grounding_metadata

src/pipecat/services/hume/__init__.py

@@ -1,5 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#

src/pipecat/services/hume/tts.py

@@ -1,220 +0,0 @@
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-
-"""Hume Text-to-Speech service implementation."""
-
-import base64
-import os
-from typing import Any, AsyncGenerator, Optional
-
-from loguru import logger
-from pydantic import BaseModel
-
-from pipecat.frames.frames import (
-    ErrorFrame,
-    Frame,
-    StartFrame,
-    TTSAudioRawFrame,
-    TTSStartedFrame,
-    TTSStoppedFrame,
-)
-from pipecat.services.tts_service import TTSService
-from pipecat.utils.tracing.service_decorators import traced_tts
-
-try:
-    from hume import AsyncHumeClient
-    from hume.tts import (
-        FormatPcm,
-        PostedUtterance,
-        PostedUtteranceVoiceWithId,
-    )
-except ModuleNotFoundError as e:  # pragma: no cover - import-time guidance
-    logger.error(f"Exception: {e}")
-    logger.error("In order to use Hume, you need to `pip install pipecat-ai[hume]`.")
-    raise Exception(f"Missing module: {e}")
-
-
-HUME_SAMPLE_RATE = 48_000  # Hume TTS streams at 48 kHz
-
-
-class HumeTTSService(TTSService):
-    """Hume Octave Text-to-Speech service.
-
-    Streams PCM audio via Hume's HTTP output streaming (JSON chunks) endpoint
-    using the Python SDK and emits `TTSAudioRawFrame`s suitable for Pipecat transports.
-
-    Supported features:
-
-    - Generates speech from text using Hume TTS.
-    - Streams PCM audio.
-    - Supports dynamic updates of voice and synthesis parameters at runtime.
-    - Provides metrics for Time To First Byte (TTFB) and TTS usage.
-    """
-
-    class InputParams(BaseModel):
-        """Optional synthesis parameters for Hume TTS.
-
-        Parameters:
-            description: Natural-language acting directions (up to 100 characters).
-            speed: Speaking-rate multiplier (0.5-2.0).
-            trailing_silence: Seconds of silence to append at the end (0-5).
-        """
-
-        description: Optional[str] = None
-        speed: Optional[float] = None
-        trailing_silence: Optional[float] = None
-
-    def __init__(
-        self,
-        *,
-        api_key: Optional[str] = None,
-        voice_id: str,
-        params: Optional[InputParams] = None,
-        sample_rate: Optional[int] = HUME_SAMPLE_RATE,
-        **kwargs,
-    ) -> None:
-        """Initialize the HumeTTSService.
-
-        Args:
-            api_key: Hume API key. If omitted, reads the ``HUME_API_KEY`` environment variable.
-            voice_id: ID of the voice to use (ID-only; names are not supported here).
-            params: Optional synthesis controls (acting instructions, speed, trailing silence).
-            sample_rate: Output sample rate for emitted PCM frames. Defaults to 48_000 (Hume).
-            **kwargs: Additional arguments passed to the parent class.
-        """
-        api_key = api_key or os.getenv("HUME_API_KEY")
-        if not api_key:
-            raise ValueError("HumeTTSService requires an API key (env HUME_API_KEY or api_key=)")
-
-        if sample_rate != HUME_SAMPLE_RATE:
-            logger.warning(
-                f"Hume TTS streams at {HUME_SAMPLE_RATE} Hz; configured sample_rate={sample_rate}"
-            )
-
-        super().__init__(sample_rate=sample_rate, **kwargs)
-
-        self._client = AsyncHumeClient(api_key=api_key)
-        self._params = params or HumeTTSService.InputParams()
-
-        # Store voice in the base class (mirrors other services)
-        self.set_voice(voice_id)
-
-        self._audio_bytes = b""
-
-    def can_generate_metrics(self) -> bool:
-        """Can generate metrics.
-
-        Returns:
-            True if metrics can be generated, False otherwise.
-        """
-        return True
-
-    async def start(self, frame: StartFrame) -> None:
-        """Start the service.
-
-        Args:
-            frame: The start frame.
-        """
-        await super().start(frame)
-
-    async def update_setting(self, key: str, value: Any) -> None:
-        """Runtime updates via `TTSUpdateSettingsFrame`.
-
-        Args:
-            key: The name of the setting to update. Recognized keys are:
-                - "voice_id"
-                - "description"
-                - "speed"
-                - "trailing_silence"
-            value: The new value for the setting.
-        """
-        key_l = (key or "").lower()
-
-        if key_l == "voice_id":
-            self.set_voice(str(value))
-            logger.info(f"HumeTTSService voice_id set to: {self.voice}")
-        elif key_l == "description":
-            self._params.description = None if value is None else str(value)
-        elif key_l == "speed":
-            self._params.speed = None if value is None else float(value)
-        elif key_l == "trailing_silence":
-            self._params.trailing_silence = None if value is None else float(value)
-        else:
-            # Defer unknown keys to the base class
-            await super().update_setting(key, value)
-
-    @traced_tts
-    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        """Generate speech from text using Hume TTS.
-
-        Args:
-            text: The text to be synthesized.
-
-        Returns:
-            An async generator that yields `Frame` objects, including
-            `TTSStartedFrame`, `TTSAudioRawFrame`, `ErrorFrame`, and
-            `TTSStoppedFrame`.
-        """
-        logger.debug(f"{self}: Generating Hume TTS: [{text}]")
-
-        # Build the request payload
-        utterance_kwargs: dict[str, Any] = {
-            "text": text,
-            "voice": PostedUtteranceVoiceWithId(id=self._voice_id),
-        }
-        if self._params.description is not None:
-            utterance_kwargs["description"] = self._params.description
-        if self._params.speed is not None:
-            utterance_kwargs["speed"] = self._params.speed
-        if self._params.trailing_silence is not None:
-            utterance_kwargs["trailing_silence"] = self._params.trailing_silence
-
-        utterance = PostedUtterance(**utterance_kwargs)
-
-        # Request raw PCM chunks in the streaming JSON
-        pcm_fmt = FormatPcm(type="pcm")
-
-        await self.start_ttfb_metrics()
-        await self.start_tts_usage_metrics(text)
-        yield TTSStartedFrame()
-
-        try:
-            # Instant mode is always enabled here (not user-configurable)
-            # Hume emits mono PCM at 48 kHz; downstream can resample if needed.
-            # We buffer audio bytes before sending to prevent glitches.
-            self._audio_bytes = b""
-            async for chunk in self._client.tts.synthesize_json_streaming(
-                utterances=[utterance],
-                format=pcm_fmt,
-                instant_mode=True,
-                version="2",
-            ):
-                audio_b64 = getattr(chunk, "audio", None)
-                if not audio_b64:
-                    continue
-
-                pcm_bytes = base64.b64decode(audio_b64)
-                self._audio_bytes += pcm_bytes
-
-                # Buffer audio until we have enough to avoid glitches
-                if len(self._audio_bytes) < self.chunk_size:
-                    continue
-
-                frame = TTSAudioRawFrame(
-                    audio=self._audio_bytes,
-                    sample_rate=self.sample_rate,
-                    num_channels=1,
-                )
-
-                yield frame
-
-                self._audio_bytes = b""
-
-        except Exception as e:
-            logger.exception(f"{self} error generating TTS: {e}")
-            await self.push_error(ErrorFrame(f"Error generating TTS: {e}"))
-        finally:
-            # Ensure TTFB timer is stopped even on early failures
-            await self.stop_ttfb_metrics()
-            yield TTSStoppedFrame()

src/pipecat/services/openai_realtime/frames.py

@@ -12,7 +12,7 @@ from typing import TYPE_CHECKING
 from pipecat.frames.frames import DataFrame, FunctionCallResultFrame
 
 if TYPE_CHECKING:
-    from pipecat.services.openai_realtime.context import OpenAIRealtimeLLMContext
+    from pipecat.services.openai_realtime_beta.context import OpenAIRealtimeLLMContext
 
 
 @dataclass

src/pipecat/services/playht/tts.py

@@ -14,7 +14,6 @@ import io
 import json
 import struct
 import uuid
-import warnings
 from typing import AsyncGenerator, Optional
 
 import aiohttp
@@ -111,11 +110,6 @@ def language_to_playht_language(language: Language) -> Optional[str]:
 class PlayHTTTSService(InterruptibleTTSService):
     """PlayHT WebSocket-based text-to-speech service.
 
-    .. deprecated:: 0.0.88
-
-        This class is deprecated and will be removed in a future version.
-        PlayHT is shutting down their API on December 31st, 2025.
-
     Provides real-time text-to-speech synthesis using PlayHT's WebSocket API.
     Supports streaming audio generation with configurable voice engines and
     language settings.
@@ -164,15 +158,6 @@ class PlayHTTTSService(InterruptibleTTSService):
             **kwargs,
         )
 
-        with warnings.catch_warnings():
-            warnings.simplefilter("always")
-            warnings.warn(
-                "PlayHT is shutting down their API on December 31st, 2025. "
-                "'PlayHTTTSService' is deprecated and will be removed in a future version.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
         params = params or PlayHTTTSService.InputParams()
 
         self._api_key = api_key
@@ -416,11 +401,6 @@ class PlayHTTTSService(InterruptibleTTSService):
 class PlayHTHttpTTSService(TTSService):
     """PlayHT HTTP-based text-to-speech service.
 
-    .. deprecated:: 0.0.88
-
-        This class is deprecated and will be removed in a future version.
-        PlayHT is shutting down their API on December 31st, 2025.
-
     Provides text-to-speech synthesis using PlayHT's HTTP API for simpler,
     non-streaming synthesis. Suitable for use cases where streaming is not
     required and simpler integration is preferred.
@@ -474,6 +454,8 @@ class PlayHTHttpTTSService(TTSService):
 
         # Warn about deprecated protocol parameter if explicitly provided
         if protocol:
+            import warnings
+
             with warnings.catch_warnings():
                 warnings.simplefilter("always")
                 warnings.warn(
@@ -482,15 +464,6 @@ class PlayHTHttpTTSService(TTSService):
                     stacklevel=2,
                 )
 
-        with warnings.catch_warnings():
-            warnings.simplefilter("always")
-            warnings.warn(
-                "PlayHT is shutting down their API on December 31st, 2025. "
-                "'PlayHTHttpTTSService' is deprecated and will be removed in a future version.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
         params = params or PlayHTHttpTTSService.InputParams()
 
         self._user_id = user_id

src/pipecat/services/stt_service.py

@@ -15,7 +15,6 @@ from loguru import logger
 
 from pipecat.frames.frames import (
     AudioRawFrame,
-    ErrorFrame,
     Frame,
     StartFrame,
     STTMuteFrame,
@@ -25,7 +24,6 @@ from pipecat.frames.frames import (
 )
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.ai_service import AIService
-from pipecat.services.websocket_service import WebsocketService
 from pipecat.transcriptions.language import Language
 
 
@@ -285,35 +283,3 @@ class SegmentedSTTService(STTService):
         if not self._user_speaking and len(self._audio_buffer) > self._audio_buffer_size_1s:
             discarded = len(self._audio_buffer) - self._audio_buffer_size_1s
             self._audio_buffer = self._audio_buffer[discarded:]
-
-
-class WebsocketSTTService(STTService, WebsocketService):
-    """Base class for websocket-based STT services.
-
-    Combines STT functionality with websocket connectivity, providing automatic
-    error handling and reconnection capabilities.
-
-    Event handlers:
-        on_connection_error: Called when a websocket connection error occurs.
-
-    Example::
-
-        @stt.event_handler("on_connection_error")
-        async def on_connection_error(stt: STTService, error: str):
-            logger.error(f"STT connection error: {error}")
-    """
-
-    def __init__(self, *, reconnect_on_error: bool = True, **kwargs):
-        """Initialize the Websocket STT service.
-
-        Args:
-            reconnect_on_error: Whether to automatically reconnect on websocket errors.
-            **kwargs: Additional arguments passed to parent classes.
-        """
-        STTService.__init__(self, **kwargs)
-        WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs)
-        self._register_event_handler("on_connection_error")
-
-    async def _report_error(self, error: ErrorFrame):
-        await self._call_event_handler("on_connection_error", error.error)
-        await self.push_error(error)

src/pipecat/transports/base_output.py

@@ -29,19 +29,20 @@ from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
     Frame,
+    InputTransportMessageUrgentFrame,
     InterruptionFrame,
     MixerControlFrame,
     OutputAudioRawFrame,
     OutputDTMFFrame,
     OutputDTMFUrgentFrame,
     OutputImageRawFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     OutputTransportReadyFrame,
     SpeechOutputAudioRawFrame,
     SpriteFrame,
     StartFrame,
     SystemFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
     TTSAudioRawFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
@@ -177,9 +178,7 @@ class BaseOutputTransport(FrameProcessor):
         # Sending a frame indicating that the output transport is ready and able to receive frames.
         await self.push_frame(OutputTransportReadyFrame(), FrameDirection.UPSTREAM)
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send a transport message.
 
         Args:
@@ -308,7 +307,9 @@ class BaseOutputTransport(FrameProcessor):
         elif isinstance(frame, InterruptionFrame):
             await self.push_frame(frame, direction)
             await self._handle_frame(frame)
-        elif isinstance(frame, OutputTransportMessageUrgentFrame):
+        elif isinstance(frame, TransportMessageUrgentFrame) and not isinstance(
+            frame, InputTransportMessageUrgentFrame
+        ):
             await self.send_message(frame)
         elif isinstance(frame, OutputDTMFUrgentFrame):
             await self.write_dtmf(frame)
@@ -645,7 +646,7 @@ class BaseOutputTransport(FrameProcessor):
                 await self._set_video_image(frame)
             elif isinstance(frame, SpriteFrame):
                 await self._set_video_images(frame.images)
-            elif isinstance(frame, OutputTransportMessageFrame):
+            elif isinstance(frame, TransportMessageFrame):
                 await self._transport.send_message(frame)
             elif isinstance(frame, OutputDTMFFrame):
                 await self._transport.write_dtmf(frame)

src/pipecat/transports/daily/transport.py

@@ -30,15 +30,15 @@ from pipecat.frames.frames import (
     ErrorFrame,
     Frame,
     InputAudioRawFrame,
-    InputTransportMessageFrame,
+    InputTransportMessageUrgentFrame,
     InterimTranscriptionFrame,
     OutputAudioRawFrame,
     OutputImageRawFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     SpriteFrame,
     StartFrame,
     TranscriptionFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
     UserAudioRawFrame,
     UserImageRawFrame,
     UserImageRequestFrame,
@@ -74,7 +74,7 @@ VAD_RESET_PERIOD_MS = 2000
 
 
 @dataclass
-class DailyOutputTransportMessageFrame(OutputTransportMessageFrame):
+class DailyTransportMessageFrame(TransportMessageFrame):
     """Frame for transport messages in Daily calls.
 
     Parameters:
@@ -85,7 +85,7 @@ class DailyOutputTransportMessageFrame(OutputTransportMessageFrame):
 
 
 @dataclass
-class DailyOutputTransportMessageUrgentFrame(OutputTransportMessageUrgentFrame):
+class DailyTransportMessageUrgentFrame(TransportMessageUrgentFrame):
     """Frame for urgent transport messages in Daily calls.
 
     Parameters:
@@ -96,59 +96,7 @@ class DailyOutputTransportMessageUrgentFrame(OutputTransportMessageUrgentFrame):
 
 
 @dataclass
-class DailyTransportMessageFrame(DailyOutputTransportMessageFrame):
-    """Frame for transport messages in Daily calls.
-
-    .. deprecated:: 0.0.87
-        This frame is deprecated and will be removed in a future version.
-        Instead, use `DailyOutputTransportMessageFrame`.
-
-    Parameters:
-        participant_id: Optional ID of the participant this message is for/from.
-    """
-
-    def __post_init__(self):
-        super().__post_init__()
-        import warnings
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("always")
-            warnings.warn(
-                "DailyTransportMessageFrame is deprecated and will be removed in a future version. "
-                "Instead, use DailyOutputTransportMessageFrame.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
-
-@dataclass
-class DailyTransportMessageUrgentFrame(DailyOutputTransportMessageUrgentFrame):
-    """Frame for urgent transport messages in Daily calls.
-
-    .. deprecated:: 0.0.87
-        This frame is deprecated and will be removed in a future version.
-        Instead, use `DailyOutputTransportMessageUrgentFrame`.
-
-    Parameters:
-        participant_id: Optional ID of the participant this message is for/from.
-    """
-
-    def __post_init__(self):
-        super().__post_init__()
-        import warnings
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("always")
-            warnings.warn(
-                "DailyTransportMessageUrgentFrame is deprecated and will be removed in a future version. "
-                "Instead, use DailyOutputTransportMessageUrgentFrame.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
-
-@dataclass
-class DailyInputTransportMessageFrame(InputTransportMessageFrame):
+class DailyInputTransportMessageUrgentFrame(InputTransportMessageUrgentFrame):
     """Frame for input urgent transport messages in Daily calls.
 
     Parameters:
@@ -158,31 +106,6 @@ class DailyInputTransportMessageFrame(InputTransportMessageFrame):
     participant_id: Optional[str] = None
 
 
-class DailyInputTransportMessageUrgentFrame(DailyInputTransportMessageFrame):
-    """Frame for input urgent transport messages in Daily calls.
-
-    .. deprecated:: 0.0.87
-        This frame is deprecated and will be removed in a future version.
-        Instead, use `DailyInputTransportMessageFrame`.
-
-    Parameters:
-        participant_id: Optional ID of the participant this message is for/from.
-    """
-
-    def __post_init__(self):
-        super().__post_init__()
-        import warnings
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("always")
-            warnings.warn(
-                "DailyInputTransportMessageUrgentFrame is deprecated and will be removed in a future version. "
-                "Instead, use DailyInputTransportMessageFrame.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
-
 @dataclass
 class DailyUpdateRemoteParticipantsFrame(ControlFrame):
     """Frame to update remote participants in Daily calls.
@@ -551,9 +474,7 @@ class DailyTransportClient(EventHandler):
         """
         return self._out_sample_rate
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send an application message to participants.
 
         Args:
@@ -563,9 +484,7 @@ class DailyTransportClient(EventHandler):
             return
 
         participant_id = None
-        if isinstance(
-            frame, (DailyOutputTransportMessageFrame, DailyOutputTransportMessageUrgentFrame)
-        ):
+        if isinstance(frame, (DailyTransportMessageFrame, DailyTransportMessageUrgentFrame)):
             participant_id = frame.participant_id
 
         future = self._get_event_loop().create_future()
@@ -1702,7 +1621,7 @@ class DailyInputTransport(BaseInputTransport):
             message: The message data to send.
             sender: ID of the message sender.
         """
-        frame = DailyInputTransportMessageFrame(message=message, participant_id=sender)
+        frame = DailyInputTransportMessageUrgentFrame(message=message, participant_id=sender)
         await self.push_frame(frame)
 
     #
@@ -1924,9 +1843,7 @@ class DailyOutputTransport(BaseOutputTransport):
         if isinstance(frame, DailyUpdateRemoteParticipantsFrame):
             await self._client.update_remote_participants(frame.remote_participants)
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send a transport message to participants.
 
         Args:

src/pipecat/transports/livekit/transport.py

@@ -29,9 +29,9 @@ from pipecat.frames.frames import (
     OutputAudioRawFrame,
     OutputDTMFFrame,
     OutputDTMFUrgentFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     StartFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
     UserAudioRawFrame,
     UserImageRawFrame,
 )
@@ -68,7 +68,7 @@ DTMF_CODE_MAP = {
 
 
 @dataclass
-class LiveKitOutputTransportMessageFrame(OutputTransportMessageFrame):
+class LiveKitTransportMessageFrame(TransportMessageFrame):
     """Frame for transport messages in LiveKit rooms.
 
     Parameters:
@@ -79,7 +79,7 @@ class LiveKitOutputTransportMessageFrame(OutputTransportMessageFrame):
 
 
 @dataclass
-class LiveKitOutputTransportMessageUrgentFrame(OutputTransportMessageUrgentFrame):
+class LiveKitTransportMessageUrgentFrame(TransportMessageUrgentFrame):
     """Frame for urgent transport messages in LiveKit rooms.
 
     Parameters:
@@ -89,50 +89,6 @@ class LiveKitOutputTransportMessageUrgentFrame(OutputTransportMessageUrgentFrame
     participant_id: Optional[str] = None
 
 
-@dataclass
-class LiveKitTransportMessageFrame(LiveKitOutputTransportMessageFrame):
-    """Frame for transport messages in LiveKit rooms.
-
-    Parameters:
-        participant_id: Optional ID of the participant this message is for/from.
-    """
-
-    def __post_init__(self):
-        super().__post_init__()
-        import warnings
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("always")
-            warnings.warn(
-                "LiveKitTransportMessageFrame is deprecated and will be removed in a future version. "
-                "Instead, use LiveKitOutputTransportMessageFrame.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
-
-@dataclass
-class LiveKitTransportMessageUrgentFrame(LiveKitOutputTransportMessageUrgentFrame):
-    """Frame for urgent transport messages in LiveKit rooms.
-
-    Parameters:
-        participant_id: Optional ID of the participant this message is for/from.
-    """
-
-    def __post_init__(self):
-        super().__post_init__()
-        import warnings
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("always")
-            warnings.warn(
-                "LiveKitTransportMessageUrgentFrame is deprecated and will be removed in a future version. "
-                "Instead, use LiveKitOutputTransportMessageUrgentFrame.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
-
 class LiveKitParams(TransportParams):
     """Configuration parameters for LiveKit transport.
 
@@ -354,10 +310,10 @@ class LiveKitTransportClient:
             logger.error(f"Error sending data: {e}")
 
     async def send_dtmf(self, digit: str):
-        r"""Send DTMF tone to the room.
+        """Send DTMF tone to the room.
 
         Args:
-            digit: The DTMF digit to send (0-9, \*, #).
+            digit: The DTMF digit to send (0-9, *, #).
         """
         if not self._connected:
             return
@@ -721,7 +677,7 @@ class LiveKitInputTransport(BaseInputTransport):
             message: The message data to send.
             sender: ID of the message sender.
         """
-        frame = LiveKitOutputTransportMessageUrgentFrame(message=message, participant_id=sender)
+        frame = LiveKitTransportMessageUrgentFrame(message=message, participant_id=sender)
         await self.push_frame(frame)
 
     async def _audio_in_task_handler(self):
@@ -880,9 +836,7 @@ class LiveKitOutputTransport(BaseOutputTransport):
         await super().cleanup()
         await self._transport.cleanup()
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send a transport message to participants.
 
         Args:
@@ -892,9 +846,7 @@ class LiveKitOutputTransport(BaseOutputTransport):
         if isinstance(message, dict):
             # fix message encoding for dict-like messages, e.g. RTVI messages.
             message = json.dumps(message, ensure_ascii=False)
-        if isinstance(
-            frame, (LiveKitOutputTransportMessageFrame, LiveKitOutputTransportMessageUrgentFrame)
-        ):
+        if isinstance(frame, (LiveKitTransportMessageFrame, LiveKitTransportMessageUrgentFrame)):
             await self._client.send_data(message.encode(), frame.participant_id)
         else:
             await self._client.send_data(message.encode())
@@ -1153,9 +1105,7 @@ class LiveKitTransport(BaseTransport):
             participant_id: Optional specific participant to send to.
         """
         if self._output:
-            frame = LiveKitOutputTransportMessageFrame(
-                message=message, participant_id=participant_id
-            )
+            frame = LiveKitTransportMessageFrame(message=message, participant_id=participant_id)
             await self._output.send_message(frame)
 
     async def send_message_urgent(self, message: str, participant_id: Optional[str] = None):
@@ -1166,7 +1116,7 @@ class LiveKitTransport(BaseTransport):
             participant_id: Optional specific participant to send to.
         """
         if self._output:
-            frame = LiveKitOutputTransportMessageUrgentFrame(
+            frame = LiveKitTransportMessageUrgentFrame(
                 message=message, participant_id=participant_id
             )
             await self._output.send_message(frame)

src/pipecat/transports/smallwebrtc/connection.py

@@ -283,6 +283,7 @@ class SmallWebRTCConnection(BaseObject):
         self._data_channel = None
         self._renegotiation_in_progress = False
         self._last_received_time = None
+        self._message_queue = []
         self._pending_app_messages = []
         self._connecting_timeout_task = None
 
@@ -296,7 +297,10 @@ class SmallWebRTCConnection(BaseObject):
             # Flush queued messages once the data channel is open
             @channel.on("open")
             async def on_open():
-                logger.debug("Data channel is open!")
+                logger.debug("Data channel is open, flushing queued messages")
+                while self._message_queue:
+                    message = self._message_queue.pop(0)
+                    self._data_channel.send(message)
 
             @channel.on("message")
             async def on_message(message):
@@ -499,6 +503,7 @@ class SmallWebRTCConnection(BaseObject):
         self._track_map.clear()
         if self._pc:
             await self._pc.close()
+        self._message_queue.clear()
         self._pending_app_messages.clear()
         self._track_map = {}
         self._cancel_monitoring_connecting_state()
@@ -664,8 +669,8 @@ class SmallWebRTCConnection(BaseObject):
         if self._data_channel and self._data_channel.readyState == "open":
             self._data_channel.send(json_message)
         else:
-            # The client might choose never to create a data channel.
-            logger.trace("Data channel not ready, discarding message!")
+            logger.debug("Data channel not ready, queuing message")
+            self._message_queue.append(json_message)
 
     def ask_to_renegotiate(self):
         """Request renegotiation of the WebRTC connection."""

src/pipecat/transports/smallwebrtc/request_handler.py

@@ -116,10 +116,6 @@ class SmallWebRTCRequestHandler:
                 detail="Cannot create new connection with existing connection active",
             )
 
-    def update_ice_servers(self, ice_servers: Optional[List[IceServer]] = None):
-        """Update the list of ICE servers used for WebRTC connections."""
-        self._ice_servers = ice_servers
-
     async def handle_web_request(
         self,
         request: SmallWebRTCRequest,
@@ -184,7 +180,7 @@ class SmallWebRTCRequestHandler:
 
             answer = pipecat_connection.get_answer()
 
-            if self._esp32_mode:
+            if self._esp32_mode and self._host and self._host != "localhost":
                 from pipecat.runner.utils import smallwebrtc_sdp_munging
 
                 answer["sdp"] = smallwebrtc_sdp_munging(answer["sdp"], self._host)

src/pipecat/transports/smallwebrtc/transport.py

@@ -26,13 +26,13 @@ from pipecat.frames.frames import (
     EndFrame,
     Frame,
     InputAudioRawFrame,
-    InputTransportMessageFrame,
+    InputTransportMessageUrgentFrame,
     OutputAudioRawFrame,
     OutputImageRawFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     SpriteFrame,
     StartFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
     UserImageRawFrame,
     UserImageRequestFrame,
 )
@@ -461,9 +461,7 @@ class SmallWebRTCClient:
             await self._webrtc_connection.disconnect()
             await self._handle_peer_disconnected()
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send an application message through the WebRTC connection.
 
         Args:
@@ -685,7 +683,7 @@ class SmallWebRTCInputTransport(BaseInputTransport):
             message: The application message to process.
         """
         logger.debug(f"Received app message inside SmallWebRTCInputTransport  {message}")
-        frame = InputTransportMessageFrame(message=message)
+        frame = InputTransportMessageUrgentFrame(message=message)
         await self.push_frame(frame)
 
     # Add this method similar to DailyInputTransport.request_participant_image
@@ -822,9 +820,7 @@ class SmallWebRTCOutputTransport(BaseOutputTransport):
         await super().cancel(frame)
         await self._client.disconnect()
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send a transport message through the WebRTC connection.
 
         Args:

src/pipecat/transports/tavus/transport.py

@@ -27,9 +27,9 @@ from pipecat.frames.frames import (
     InputAudioRawFrame,
     InterruptionFrame,
     OutputAudioRawFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     StartFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor, FrameProcessorSetup
 from pipecat.transports.base_input import BaseInputTransport
@@ -345,9 +345,7 @@ class TavusTransportClient:
             participant_id, callback, audio_source, sample_rate, callback_interval_ms
         )
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send a message to participants.
 
         Args:
@@ -375,7 +373,7 @@ class TavusTransportClient:
 
     async def send_interrupt_message(self) -> None:
         """Send an interrupt message to the conversation."""
-        transport_frame = OutputTransportMessageUrgentFrame(
+        transport_frame = TransportMessageUrgentFrame(
             message={
                 "message_type": "conversation",
                 "event_type": "conversation.interrupt",
@@ -607,9 +605,7 @@ class TavusOutputTransport(BaseOutputTransport):
         await super().cancel(frame)
         await self._client.stop()
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send a message to participants.
 
         Args:

src/pipecat/transports/websocket/client.py

@@ -28,9 +28,9 @@ from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
     OutputAudioRawFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     StartFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
 )
 from pipecat.processors.frame_processor import FrameProcessorSetup
 from pipecat.serializers.base_serializer import FrameSerializer
@@ -385,9 +385,7 @@ class WebsocketClientOutputTransport(BaseOutputTransport):
         await super().cleanup()
         await self._transport.cleanup()
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send a transport message through the WebSocket.
 
         Args:

src/pipecat/transports/websocket/fastapi.py

@@ -28,9 +28,9 @@ from pipecat.frames.frames import (
     InputAudioRawFrame,
     InterruptionFrame,
     OutputAudioRawFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     StartFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializerType
@@ -402,9 +402,7 @@ class FastAPIWebsocketOutputTransport(BaseOutputTransport):
             await self._write_frame(frame)
             self._next_send_time = 0
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send a transport message frame.
 
         Args:

src/pipecat/transports/websocket/server.py

@@ -27,9 +27,9 @@ from pipecat.frames.frames import (
     InputAudioRawFrame,
     InterruptionFrame,
     OutputAudioRawFrame,
-    OutputTransportMessageFrame,
-    OutputTransportMessageUrgentFrame,
     StartFrame,
+    TransportMessageFrame,
+    TransportMessageUrgentFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.serializers.base_serializer import FrameSerializer
@@ -338,9 +338,7 @@ class WebsocketServerOutputTransport(BaseOutputTransport):
             await self._write_frame(frame)
             self._next_send_time = 0
 
-    async def send_message(
-        self, frame: OutputTransportMessageFrame | OutputTransportMessageUrgentFrame
-    ):
+    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         """Send a transport message frame to the client.
 
         Args:

src/pipecat/transports/whatsapp/api.py

@@ -241,14 +241,6 @@ class WhatsAppApi:
         self._whatsapp_url = f"{self.BASE_URL}{phone_number_id}/calls"
         self._whatsapp_token = whatsapp_token
 
-    def update_whatsapp_token(self, whatsapp_token: str):
-        """Update the WhatsApp access token for authentication."""
-        self._whatsapp_token = whatsapp_token
-
-    def update_whatsapp_phone_number_id(self, phone_number_id: str):
-        """Update the WhatsApp phone number ID for authentication."""
-        self._phone_number_id = phone_number_id
-
     async def answer_call_to_whatsapp(self, call_id: str, action: str, sdp: str, from_: str):
         """Answer an incoming WhatsApp call.
 

src/pipecat/transports/whatsapp/client.py

@@ -12,8 +12,6 @@ WhatsApp call events.
 """
 
 import asyncio
-import hashlib
-import hmac
 from typing import Awaitable, Callable, Dict, List, Optional
 
 import aiohttp
@@ -49,7 +47,6 @@ class WhatsAppClient:
         phone_number_id: str,
         session: aiohttp.ClientSession,
         ice_servers: Optional[List[IceServer]] = None,
-        whatsapp_secret: Optional[str] = None,
     ) -> None:
         """Initialize the WhatsApp client.
 
@@ -59,12 +56,10 @@ class WhatsAppClient:
             session: aiohttp session for making HTTP requests
             ice_servers: List of ICE servers for WebRTC connections. If None,
                         defaults to Google's public STUN server
-            whatsapp_secret: WhatsApp APP secret for validating that the webhook request came from WhatsApp.
         """
         self._whatsapp_api = WhatsAppApi(
             whatsapp_token=whatsapp_token, phone_number_id=phone_number_id, session=session
         )
-        self._whatsapp_secret = whatsapp_secret
         self._ongoing_calls_map: Dict[str, SmallWebRTCConnection] = {}
 
         # Set default ICE servers if none provided
@@ -73,22 +68,6 @@ class WhatsAppClient:
         else:
             self._ice_servers = ice_servers
 
-    def update_ice_servers(self, ice_servers: Optional[List[IceServer]] = None):
-        """Update the list of ICE servers used for WebRTC connections."""
-        self._ice_servers = ice_servers
-
-    def update_whatsapp_secret(self, whatsapp_secret: Optional[str] = None):
-        """Update the WhatsApp APP secret for validating that the webhook request came from WhatsApp."""
-        self._whatsapp_secret = whatsapp_secret
-
-    def update_whatsapp_token(self, whatsapp_token: str):
-        """Update the WhatsApp API access token."""
-        self._whatsapp_api.update_whatsapp_token(whatsapp_token)
-
-    def update_whatsapp_phone_number_id(self, phone_number_id: str):
-        """Update the WhatsApp phone number ID for authentication."""
-        self._whatsapp_api.update_whatsapp_phone_number_id(phone_number_id)
-
     async def terminate_all_calls(self) -> None:
         """Terminate all ongoing WhatsApp calls.
 
@@ -154,32 +133,10 @@ class WhatsAppClient:
 
         return int(challenge)
 
-    async def _validate_whatsapp_webhook_request(self, raw_body: bytes, sha256_signature: str):
-        """Common handler for both /start and /connect endpoints."""
-        # Compute HMAC SHA256 using your App Secret
-        expected_signature = hmac.new(
-            key=self._whatsapp_secret.encode("utf-8"),
-            msg=raw_body,
-            digestmod=hashlib.sha256,
-        ).hexdigest()
-
-        # Extract signature from header (strip 'sha256=' prefix)
-        if not sha256_signature:
-            raise Exception("Missing X-Hub-Signature-256 header")
-        received_signature = sha256_signature.split("sha256=")[-1]
-
-        # Compare signatures securely
-        if not hmac.compare_digest(expected_signature, received_signature):
-            raise Exception("Invalid webhook signature")
-
-        logger.debug(f"Webhook signature verified!")
-
     async def handle_webhook_request(
         self,
         request: WhatsAppWebhookRequest,
         connection_callback: Optional[Callable[[SmallWebRTCConnection], Awaitable[None]]] = None,
-        raw_body: Optional[bytes] = None,
-        sha256_signature: Optional[str] = None,
     ) -> bool:
         """Handle a webhook request from WhatsApp.
 
@@ -193,8 +150,6 @@ class WhatsAppClient:
             connection_callback: Optional callback function to invoke when a new
                                WebRTC connection is established. The callback
                                receives the SmallWebRTCConnection instance.
-            raw_body: Optional bytes containing the raw request body.
-            sha256_signature: Optional X-Hub-Signature-256 header value from the request.
 
         Returns:
             bool: True if the webhook request was handled successfully, False otherwise
@@ -204,8 +159,6 @@ class WhatsAppClient:
             Exception: If connection establishment or API calls fail
         """
         try:
-            if self._whatsapp_secret:
-                await self._validate_whatsapp_webhook_request(raw_body, sha256_signature)
             for entry in request.entry:
                 for change in entry.changes:
                     # Handle connect events

src/pipecat/utils/base_object.py

@@ -29,7 +29,7 @@ class EventHandler:
     This data class stores the event name, a list of handlers to run for this
     event, and whether these handlers will be executed in a task.
 
-    Parameters:
+    Attributes:
         name (str): The name of the event handler.
         handlers (List[Any]): A list of functions to be called when this event is triggered.
         is_sync (bool): Indicates whether the functions are executed in a task.

src/pipecat/utils/string.py

@@ -21,24 +21,13 @@ import re
 from typing import FrozenSet, Optional, Sequence, Tuple
 
 import nltk
-from loguru import logger
 from nltk.tokenize import sent_tokenize
 
 # Ensure punkt_tab tokenizer data is available
 try:
     nltk.data.find("tokenizers/punkt_tab")
 except LookupError:
-    try:
-        nltk.download("punkt_tab", quiet=True)
-    except (OSError, PermissionError) as e:
-        logger.error(
-            f"Failed to download NLTK 'punkt_tab' tokenizer data: {e}. "
-            "This data is required for sentence tokenization features. "
-            "The download failed due to filesystem permissions. "
-            "To resolve: pre-install the data in a location with appropriate read permissions, "
-            "or set the NLTK_DATA environment variable to point to a writable directory. "
-            "See https://www.nltk.org/data.html for more information."
-        )
+    nltk.download("punkt_tab", quiet=True)
 
 SENTENCE_ENDING_PUNCTUATION: FrozenSet[str] = frozenset(
     {

tests/test_frame_processor.py

@@ -11,8 +11,8 @@ from pipecat.frames.frames import (
     EndFrame,
     Frame,
     InterruptionFrame,
-    OutputTransportMessageUrgentFrame,
     TextFrame,
+    TransportMessageUrgentFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.processors.filters.identity_filter import IdentityFilter
@@ -81,7 +81,7 @@ class TestFrameProcessor(unittest.IsolatedAsyncioTestCase):
 
                 if isinstance(frame, TextFrame):
                     await self.push_interruption_task_frame_and_wait()
-                    await self.push_frame(OutputTransportMessageUrgentFrame(message=frame.text))
+                    await self.push_frame(TransportMessageUrgentFrame(message=frame.text))
                 else:
                     await self.push_frame(frame, direction)
 
@@ -101,7 +101,7 @@ class TestFrameProcessor(unittest.IsolatedAsyncioTestCase):
         expected_down_frames = [
             InterruptionFrame,
             InterruptionFrame,
-            OutputTransportMessageUrgentFrame,
+            TransportMessageUrgentFrame,
             EndFrame,
         ]
         await run_test(