Remove vad

Retry chat completions for OpenAILLMService and its subclasses

.github/workflows/python-compatibility.yaml

@@ -9,14 +9,14 @@ on:
     paths: ['pyproject.toml']
 
 jobs:
-  test-dev-environment:
+  test-compatibility:
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
         python-version: ['3.10.18', '3.11.13', '3.12.11', '3.13.5']
 
-    name: Dev Environment - Python ${{ matrix.python-version }}
+    name: Python ${{ matrix.python-version }}
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -55,69 +55,7 @@ jobs:
             --no-extra moondream \
             --no-extra mlx-whisper
 
-      - name: Verify dev installation
+      - name: Verify installation
         run: |
           uv run python --version
-          uv run python -c "import pipecat; print('✅ Dev environment - Pipecat imports successfully')"
-
-  test-user-experience:
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ['3.10.18', '3.11.13', '3.12.11', '3.13.5']
-
-    name: User Experience - Python ${{ matrix.python-version }}
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Install system dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y \
-            portaudio19-dev \
-            libcairo2-dev \
-            libgirepository1.0-dev \
-            pkg-config
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-        with:
-          version: 'latest'
-
-      - name: Set up Python ${{ matrix.python-version }}
-        run: |
-          uv python install ${{ matrix.python-version }}
-
-      - name: Build local package
-        run: |
-          uv build
-
-      - name: Create test project
-        run: |
-          mkdir test-project
-          cd test-project
-          uv init --python ${{ matrix.python-version }}
-
-      - name: Test comprehensive extras with uv add (Python 3.10-3.12)
-        if: "!startsWith(matrix.python-version, '3.13.')"
-        run: |
-          cd test-project
-          # Use uv add with built wheel to leverage dependency management
-          uv add "../dist/pipecat_ai-"*".whl[anthropic,assemblyai,asyncai,aws,aws-nova-sonic,azure,cartesia,cerebras,deepseek,daily,deepgram,elevenlabs,fal,fireworks,fish,gladia,google,grok,groq,gstreamer,heygen,inworld,koala,langchain,livekit,lmnt,local,mcp,mem0,mlx-whisper,moondream,nim,neuphonic,noisereduce,openai,openpipe,openrouter,perplexity,playht,qwen,rime,riva,runner,sambanova,sentry,local-smart-turn,remote-smart-turn,silero,simli,soniox,soundfile,speechmatics,tavus,together,tracing,ultravox,webrtc,websocket,whisper]"
-
-      - name: Test Python 3.13 compatible extras with uv add
-        if: startsWith(matrix.python-version, '3.13.')
-        run: |
-          cd test-project
-          # Use uv add with built wheel and Python 3.13 compatible extras
-          uv add "../dist/pipecat_ai-"*".whl[anthropic,assemblyai,asyncai,aws,aws-nova-sonic,azure,cartesia,cerebras,deepseek,daily,deepgram,elevenlabs,fal,fireworks,fish,gladia,google,grok,groq,gstreamer,heygen,inworld,koala,langchain,livekit,lmnt,local,mcp,mem0,nim,neuphonic,noisereduce,openai,openpipe,openrouter,perplexity,playht,qwen,rime,riva,runner,sambanova,sentry,remote-smart-turn,silero,simli,soniox,soundfile,speechmatics,tavus,together,tracing,webrtc,websocket,whisper]"
-
-      - name: Verify user installation
-        run: |
-          cd test-project
-          uv run python --version
-          uv run python -c "import pipecat; print('✅ User experience - Pipecat imports successfully')"
-          # Test that basic functionality works
-          uv run python -c "from pipecat.pipeline.pipeline import Pipeline; print('✅ Pipeline import works')"
+          uv run python -c "import pipecat; print('✅ Pipecat imports successfully')"

.github/workflows/update-lockfile.yaml

@@ -1,42 +0,0 @@
-name: Update lockfile
-
-on:
-  push:
-    paths:
-      - 'pyproject.toml'
-    branches:
-      - main
-  workflow_dispatch: # Allows manual triggering from GitHub UI
-
-jobs:
-  update-lockfile:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          # This gives the workflow permission to push back to the repo
-          token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v1
-
-      - name: Update lockfile
-        run: uv lock
-
-      - name: Check for changes
-        id: verify-changed-files
-        run: |
-          if [ -n "$(git status --porcelain)" ]; then
-            echo "changed=true" >> $GITHUB_OUTPUT
-          else
-            echo "changed=false" >> $GITHUB_OUTPUT
-          fi
-
-      - name: Commit lockfile
-        if: steps.verify-changed-files.outputs.changed == 'true'
-        run: |
-          git config --local user.email "action@github.com"  
-          git config --local user.name "GitHub Action"
-          git add uv.lock
-          git commit -m "chore: update uv.lock after dependency changes"
-          git push

CHANGELOG.md

@@ -5,18 +5,109 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## Unreleased
+## [0.0.80] - 2025-08-13
+
+### Added
+
+- Added `GeminiTTSService` which uses Google Gemini to generate TTS output. The
+  Gemini model can be prompted to insert styled speech to control the TTS
+  output.
+
+- For `OpenAILLMService` and its subclasses, added the ability to retry
+  executing a chat completion after a timeout period. The new args are
+  `retry_timeout_secs` and `retry_on_timeout`. This feature is disabled by
+  default.
+
+- Added Exotel support to Pipecat's development runner. You can now connect
+  using the runner with `uv run bot.py -t exotel` and an ngrok connection to
+  HTTP port 7860.
+
+- Added `enable_direct_mode` argument to `FrameProcessor`. The direct mode is
+  for processors which require very little I/O or compute resources, that is
+  processors that can perform their task almost immediately. These type of
+  processors don't need any of the internal tasks and queues usually created by
+  frame processors which means overall application performance might be slightly
+  increased. Use with care.
+
+- Added TTFB metrics for `HeyGenVideoService` and `TavusVideoService`.
+
+- Added `endpoint_id` parameter to `AzureSTTService`. ([Custom EndpointId](https://docs.azure.cn/en-us/ai-services/speech-service/how-to-recognize-speech?pivots=programming-language-python#use-a-custom-endpoint))
 
 ### Changed
 
+- `WatchdogPriorityQueue` now requires the items to be inserted to always be
+  tuples and the size of the tuple needs to be specified in the constructor when
+  creating the queue with the `tuple_size` argument.
+
+- Updated Moondream to revision `2025-01-09`.
+
+- Updated `PlayHTHttpTTSService` to no longer use the `pyht` client to remove
+  compatibility issues with other packages. Now you can use the PlayHT HTTP
+  service with other services, like GoogleLLMService.
+
 - Updated `pyproject.toml` to once again pin `numba` to `>=0.61.2` in order to
   resolve package versioning issues.
 
+- Updated the `STTMuteFilter` to include `VADUserStartedSpeakingFrame` and
+  `VADUserStoppedSpeakingFrame` in the list of frames to filter when the
+  filtering is on.
+
+### Performance
+
+- Improving the latency of the `HeyGenVideoService`.
+
+- Improved some frame processors performance by using the new frame processor
+  direct mode. In direct mode a frame processor will process frames right away
+  avoiding the need for internal queues and tasks. This is useful for some
+  simple processors. For example, in processors that wrap other processors
+  (e.g. `Pipeline`, `ParallelPipeline`), we add one processor before and one
+  after the wrapped processors (internally, you will see them as sources and
+  sinks). These sources and sinks don't do any special processing and they
+  basically forward frames. So, for these simple processors we now enable the
+  new direct mode which avoids creating any internal tasks (and queues) and
+  therefore improves performance.
+
+### Fixed
+
+- Fixed an issue with the `BaseWhisperSTTService` where the language was
+  specified as an enum and not a string.
+
+- Fixed an issue where `SmallWebRTCTransport` ended before TTS finished.
+
+- Fixed an issue in `OpenAIRealtimeBetaLLMService` where specifying a `text`
+  `modalities` didn't result in text being outputted from the model.
+
+- Added SSML reserved character escaping to `AzureBaseTTSService` to properly
+  handle special characters in text sent to Azure TTS. This fixes an issue
+  where characters like `&`, `<`, `>`, `"`, and `'` in LLM-generated text would
+  cause TTS failures.
+
+- Fixed a `WatchdogPriorityQueue` issue that could cause an exception when
+  compating watchdog cancel sentinel items with other items in the queue.
+
+- Fixed an issue that would cause system frames to not be processed with higher
+  priority than other frames. This could cause slower interruption times.
+
+- Fixed an issue where retrying a websocket connection error would result in an
+  error.
+
 ### Other
 
-- Updated `15-switch-voices.py` and `15a-switch-languages.py` examples to show
-  how to enclose complex logic (e.g. `ParallelPipeline`) into a single processor
-  so the main pipeline becomes simpler.
+- Add foundation example `19b-openai-realtime-beta-text.py`, showing how to use
+  `OpenAIRealtimeBetaLLMService` to output text to a TTS service.
+
+- Add vision support to release evals so we can run the foundational examples 12
+  series.
+
+- Added foundational example `15a-switch-languages.py` to release evals. It is
+  able to detect if we switched the language properly.
+
+- Updated foundational examples to show how to enclose complex logic
+  (e.g. `ParallelPipeline`) into a single processor so the main pipeline becomes
+  simpler.
+
+- Added `07n-interruptible-gemini.py`, demonstrating how to use
+  `GeminiTTSService`.
 
 ## [0.0.79] - 2025-08-07
 

CONTRIBUTING.md

@@ -31,6 +31,23 @@ git push origin your-branch-name
 
 Our maintainers will review your PR, and once everything is good, your contributions will be merged!
 
+## Dependency Management
+
+This project uses [uv](https://docs.astral.sh/uv/) for dependency management. The `uv.lock` file is committed to ensure reproducible builds.
+
+### Adding or Updating Dependencies
+
+1. Edit `pyproject.toml` to add/update dependencies
+2. Run `uv lock` to update the lockfile with new dependency resolution
+3. Run `uv sync` to install the updated dependencies locally
+4. Always commit both files together:
+   ```bash
+   git add pyproject.toml uv.lock
+   git commit -m "feat: add new dependency for feature X"
+   ```
+
+**Important:** Never manually edit `uv.lock`. It's auto-generated by `uv lock`.
+
 ## Code Style and Documentation
 
 ### Python Code Style

README.md

@@ -114,7 +114,8 @@ You can get started with Pipecat running on your local machine, then move your a
 
 ### Prerequisites
 
-**Python Version:** 3.10+
+**Minimum Python Version:** 3.10
+**Recommended Python Version:** 3.11-3.12
 
 ### Setup Steps
 

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

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

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

@@ -0,0 +1,163 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""
+A conversational AI bot using Gemini for both LLM and TTS.
+
+This example demonstrates how to use Gemini's TTS capabilities with the new
+GeminiTTSService, which uses Gemini's TTS-specific models instead of Google Cloud TTS.
+
+Features showcased:
+- Gemini LLM for conversation
+- Gemini TTS with natural voice control
+- Support for different voice personalities
+- Style and tone control through natural language prompts
+
+Run with:
+    python examples/foundational/gemini-tts.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.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.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 GeminiTTSService
+from pipecat.transcriptions.language import Language
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
+
+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(),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot with Gemini TTS")
+
+    stt = GoogleSTTService(
+        params=GoogleSTTService.InputParams(languages=Language.EN_US),
+        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
+    )
+
+    tts = GeminiTTSService(
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        model="gemini-2.5-flash-preview-tts",  # TTS-specific model
+        voice_id="Charon",
+        params=GeminiTTSService.InputParams(language=Language.EN_US),
+    )
+
+    llm = GoogleLLMService(
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        model="gemini-2.5-flash",
+    )
+
+    # System message that instructs the AI on how to speak
+    messages = [
+        {
+            "role": "system",
+            "content": """You are a helpful AI assistant in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
+
+            IMPORTANT: Since you're using Gemini TTS which supports natural voice control, you can include speaking instructions in your responses. For example:
+            - "Say cheerfully: Welcome to our conversation!"
+            - "Read this in a calm, professional tone: Here are the details you requested."
+            - "Speak in an excited whisper: I have some great news to share!"
+            - "Say slowly and clearly: Let me explain this step by step."
+
+            Feel free to use natural language instructions to control your voice style, tone, pace, and emotion. The TTS system will interpret these instructions and adjust the speech accordingly.
+
+            Your output will be converted to audio, so avoid special characters in your answers. Respond to what the user said in a creative and helpful way.""",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(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": "Say cheerfully and warmly: Hello! I'm your AI assistant powered by Gemini's new TTS technology. I can speak with different voices, tones, and styles. How can I help you today?",
+            }
+        )
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @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/19-openai-realtime-beta.py

@@ -137,7 +137,7 @@ You have access to the following tools:
 - get_current_weather: Get the current weather for a given location.
 - get_restaurant_recommendation: Get a restaurant recommendation for a given location.
 
-Remember, your responses should be short. Just one or two sentences, usually.""",
+Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
     )
 
     llm = OpenAIRealtimeBetaLLMService(
@@ -158,16 +158,6 @@ Remember, your responses should be short. Just one or two sentences, usually."""
     # openai WebSocket API can understand.
     context = OpenAILLMContext(
         [{"role": "user", "content": "Say hello!"}],
-        # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
-        #     [
-        #         {
-        #             "role": "user",
-        #             "content": [
-        #                 {"type": "text", "text": "Say"},
-        #                 {"type": "text", "text": "yo what's up!"},
-        #             ],
-        #         }
-        #     ],
         tools,
     )
 

examples/foundational/19a-azure-realtime-beta.py

@@ -133,7 +133,7 @@ You have access to the following tools:
 - get_current_weather: Get the current weather for a given location.
 - get_restaurant_recommendation: Get a restaurant recommendation for a given location.
 
-Remember, your responses should be short. Just one or two sentences, usually.""",
+Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
     )
 
     llm = AzureRealtimeBetaLLMService(

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

@@ -0,0 +1,229 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import os
+from datetime import datetime
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import TranscriptionMessage
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.transcript_processor import TranscriptProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai_realtime_beta import (
+    InputAudioNoiseReduction,
+    InputAudioTranscription,
+    OpenAIRealtimeBetaLLMService,
+    SemanticTurnDetection,
+    SessionProperties,
+)
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
+
+load_dotenv(override=True)
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
+    await params.result_callback(
+        {
+            "conditions": "nice",
+            "temperature": temperature,
+            "format": params.arguments["format"],
+            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
+        }
+    )
+
+
+async def fetch_restaurant_recommendation(params: FunctionCallParams):
+    await params.result_callback({"name": "The Golden Dragon"})
+
+
+weather_function = FunctionSchema(
+    name="get_current_weather",
+    description="Get the current weather",
+    properties={
+        "location": {
+            "type": "string",
+            "description": "The city and state, e.g. San Francisco, CA",
+        },
+        "format": {
+            "type": "string",
+            "enum": ["celsius", "fahrenheit"],
+            "description": "The temperature unit to use. Infer this from the users location.",
+        },
+    },
+    required=["location", "format"],
+)
+
+restaurant_function = FunctionSchema(
+    name="get_restaurant_recommendation",
+    description="Get a restaurant recommendation",
+    properties={
+        "location": {
+            "type": "string",
+            "description": "The city and state, e.g. San Francisco, CA",
+        },
+    },
+    required=["location"],
+)
+
+# Create tools schema
+tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
+
+
+# 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(),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    session_properties = SessionProperties(
+        input_audio_transcription=InputAudioTranscription(),
+        modalities=["text"],
+        # Set openai TurnDetection parameters. Not setting this at all will turn it
+        # on by default
+        turn_detection=SemanticTurnDetection(),
+        # Or set to False to disable openai turn detection and use transport VAD
+        # turn_detection=False,
+        input_audio_noise_reduction=InputAudioNoiseReduction(type="near_field"),
+        # tools=tools,
+        instructions="""You are a helpful and friendly AI.
+
+Act like a human, but remember that you aren't a human and that you can't do human
+things in the real world. Your voice and personality should be warm and engaging, with a lively and
+playful tone.
+
+If interacting in a non-English language, start by using the standard accent or dialect familiar to
+the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
+even if you're asked about them.
+
+You are participating in a voice conversation. Keep your responses concise, short, and to the point
+unless specifically asked to elaborate on a topic.
+
+You have access to the following tools:
+- get_current_weather: Get the current weather for a given location.
+- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
+
+Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
+    )
+
+    llm = OpenAIRealtimeBetaLLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        session_properties=session_properties,
+        start_audio_paused=False,
+    )
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    # you can either register a single function for all function calls, or specific functions
+    # llm.register_function(None, fetch_weather_from_api)
+    llm.register_function("get_current_weather", fetch_weather_from_api)
+    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
+
+    transcript = TranscriptProcessor()
+
+    # Create a standard OpenAI LLM context object using the normal messages format. The
+    # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
+    # openai WebSocket API can understand.
+    context = OpenAILLMContext(
+        [{"role": "user", "content": "Say hello!"}],
+        tools,
+    )
+
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            context_aggregator.user(),
+            llm,  # LLM
+            tts,  # TTS
+            transcript.user(),  # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
+            transport.output(),  # Transport bot output
+            transcript.assistant(),  # After the transcript output, to time with the audio output
+            context_aggregator.assistant(),
+        ]
+    )
+
+    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.
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    # Register event handler for transcript updates
+    @transcript.event_handler("on_transcript_update")
+    async def on_transcript_update(processor, frame):
+        for msg in frame.messages:
+            if isinstance(msg, TranscriptionMessage):
+                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+                line = f"{timestamp}{msg.role}: {msg.content}"
+                logger.info(f"Transcript: {line}")
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+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/22-natural-conversation.py

@@ -25,7 +25,8 @@ from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.llm_service import LLMService
+from pipecat.services.openai.llm import OpenAIContextAggregatorPair, OpenAILLMService
 from pipecat.sync.event_notifier import EventNotifier
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
@@ -34,6 +35,76 @@ from pipecat.transports.services.daily import DailyParams
 load_dotenv(override=True)
 
 
+class TurnDetectionLLM(Pipeline):
+    def __init__(self, llm: LLMService, context_aggregator: OpenAIContextAggregatorPair):
+        # This is the LLM that will be used to detect if the user has finished a
+        # statement. This doesn't really need to be an LLM, we could use NLP
+        # libraries for that, but it was easier as an example because we
+        # leverage the context aggregators.
+        statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+        statement_messages = [
+            {
+                "role": "system",
+                "content": "Determine if the user's statement is a complete sentence or question, ending in a natural pause or punctuation. Return 'YES' if it is complete and 'NO' if it seems to leave a thought unfinished.",
+            },
+        ]
+
+        statement_context = OpenAILLMContext(statement_messages)
+        statement_context_aggregator = statement_llm.create_context_aggregator(statement_context)
+
+        # We have instructed the LLM to return 'YES' if it thinks the user
+        # completed a sentence. So, if it's 'YES' we will return true in this
+        # predicate which will wake up the notifier.
+        async def wake_check_filter(frame):
+            logger.debug(f"Completeness check frame: {frame}")
+            return frame.text == "YES"
+
+        # This is a notifier that we use to synchronize the two LLMs.
+        notifier = EventNotifier()
+
+        # This a filter that will wake up the notifier if the given predicate
+        # (wake_check_filter) returns true.
+        completness_check = WakeNotifierFilter(
+            notifier, types=(TextFrame,), filter=wake_check_filter
+        )
+
+        # This processor keeps the last context and will let it through once the
+        # notifier is woken up. We start with the gate open because we send an
+        # initial context frame to start the conversation.
+        gated_context_aggregator = GatedOpenAILLMContextAggregator(
+            notifier=notifier, start_open=True
+        )
+
+        # Notify if the user hasn't said anything.
+        async def user_idle_notifier(frame):
+            await notifier.notify()
+
+        # Sometimes the LLM will fail detecting if a user has completed a
+        # sentence, this will wake up the notifier if that happens.
+        user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=3.0)
+
+        # The ParallePipeline input are the user transcripts. We have two
+        # contexts. The first one will be used to determine if the user finished
+        # a statement and if so the notifier will be woken up. The second
+        # context is simply the regular context but it's gated waiting for the
+        # notifier to be woken up.
+        super().__init__(
+            [
+                ParallelPipeline(
+                    [
+                        statement_context_aggregator.user(),
+                        statement_llm,
+                        completness_check,
+                        NullFilter(),
+                    ],
+                    [context_aggregator.user(), gated_context_aggregator, llm],
+                ),
+                user_idle,
+            ]
+        )
+
+
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets
 # selected.
@@ -66,24 +137,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    # This is the LLM that will be used to detect if the user has finished a
-    # statement. This doesn't really need to be an LLM, we could use NLP
-    # libraries for that, but it was easier as an example because we
-    # leverage the context aggregators.
-    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    statement_messages = [
-        {
-            "role": "system",
-            "content": "Determine if the user's statement is a complete sentence or question, ending in a natural pause or punctuation. Return 'YES' if it is complete and 'NO' if it seems to leave a thought unfinished.",
-        },
-    ]
-
-    statement_context = OpenAILLMContext(statement_messages)
-    statement_context_aggregator = statement_llm.create_context_aggregator(statement_context)
-
     # This is the regular LLM.
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm_main = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {
@@ -93,53 +148,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     ]
 
     context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = llm_main.create_context_aggregator(context)
 
-    # We have instructed the LLM to return 'YES' if it thinks the user
-    # completed a sentence. So, if it's 'YES' we will return true in this
-    # predicate which will wake up the notifier.
-    async def wake_check_filter(frame):
-        return frame.text == "YES"
+    # LLM + turn detection (with an extra LLM as a judge)
+    llm = TurnDetectionLLM(llm_main, context_aggregator)
 
-    # This is a notifier that we use to synchronize the two LLMs.
-    notifier = EventNotifier()
-
-    # This a filter that will wake up the notifier if the given predicate
-    # (wake_check_filter) returns true.
-    completness_check = WakeNotifierFilter(notifier, types=(TextFrame,), filter=wake_check_filter)
-
-    # This processor keeps the last context and will let it through once the
-    # notifier is woken up. We start with the gate open because we send an
-    # initial context frame to start the conversation.
-    gated_context_aggregator = GatedOpenAILLMContextAggregator(notifier=notifier, start_open=True)
-
-    # Notify if the user hasn't said anything.
-    async def user_idle_notifier(frame):
-        await notifier.notify()
-
-    # Sometimes the LLM will fail detecting if a user has completed a
-    # sentence, this will wake up the notifier if that happens.
-    user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=3.0)
-
-    # The ParallePipeline input are the user transcripts. We have two
-    # contexts. The first one will be used to determine if the user finished
-    # a statement and if so the notifier will be woken up. The second
-    # context is simply the regular context but it's gated waiting for the
-    # notifier to be woken up.
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
-            stt,
-            ParallelPipeline(
-                [
-                    statement_context_aggregator.user(),
-                    statement_llm,
-                    completness_check,
-                    NullFilter(),
-                ],
-                [context_aggregator.user(), gated_context_aggregator, llm],
-            ),
-            user_idle,
+            stt,  # STT
+            llm,  # LLM with turn detection
             tts,  # TTS
             transport.output(),  # Transport bot output
             context_aggregator.assistant(),  # Assistant spoken responses

examples/foundational/22b-natural-conversation-proposal.py

@@ -6,7 +6,6 @@
 
 import asyncio
 import os
-import time
 
 from dotenv import load_dotenv
 from loguru import logger
@@ -44,13 +43,14 @@ from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.llm_service import FunctionCallParams, LLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.sync.base_notifier import BaseNotifier
 from pipecat.sync.event_notifier import EventNotifier
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
 from pipecat.transports.services.daily import DailyParams
+from pipecat.utils.time import time_now_iso8601
 
 load_dotenv(override=True)
 
@@ -192,6 +192,75 @@ async def fetch_weather_from_api(params: FunctionCallParams):
     await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
+class TurnDetectionLLM(Pipeline):
+    def __init__(self, llm: LLMService):
+        # This is the LLM that will be used to detect if the user has finished a
+        # statement. This doesn't really need to be an LLM, we could use NLP
+        # libraries for that, but we have the machinery to use an LLM, so we
+        # might as well!
+        statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+        # We have instructed the LLM to return 'YES' if it thinks the user
+        # completed a sentence. So, if it's 'YES' we will return true in this
+        # predicate which will wake up the notifier.
+        async def wake_check_filter(frame):
+            logger.debug(f"Completeness check frame: {frame}")
+            return frame.text == "YES"
+
+        # This is a notifier that we use to synchronize the two LLMs.
+        notifier = EventNotifier()
+
+        # This turns the LLM context into an inference request to classify the user's speech
+        # as complete or incomplete.
+        statement_judge_context_filter = StatementJudgeContextFilter()
+
+        # This sends a UserStoppedSpeakingFrame and triggers the notifier event
+        completeness_check = CompletenessCheck(notifier=notifier)
+
+        # # Notify if the user hasn't said anything.
+        async def user_idle_notifier(frame):
+            await notifier.notify()
+
+        # Sometimes the LLM will fail detecting if a user has completed a
+        # sentence, this will wake up the notifier if that happens.
+        user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
+
+        # We start with the gate open because we send an initial context frame
+        # to start the conversation.
+        bot_output_gate = OutputGate(notifier=notifier, start_open=True)
+
+        async def pass_only_llm_trigger_frames(frame):
+            return (
+                isinstance(frame, OpenAILLMContextFrame)
+                or isinstance(frame, StartInterruptionFrame)
+                or isinstance(frame, StopInterruptionFrame)
+                or isinstance(frame, FunctionCallInProgressFrame)
+                or isinstance(frame, FunctionCallResultFrame)
+            )
+
+        super().__init__(
+            [
+                ParallelPipeline(
+                    [
+                        # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
+                        # simplified context frame to the statement classifier LLM. The only frame this
+                        # sub-pipeline will output is a UserStoppedSpeakingFrame.
+                        statement_judge_context_filter,
+                        statement_llm,
+                        completeness_check,
+                    ],
+                    [
+                        # Block everything except frames that trigger LLM inference.
+                        FunctionFilter(filter=pass_only_llm_trigger_frames),
+                        llm,
+                        bot_output_gate,  # Buffer all llm/tts output until notified.
+                    ],
+                ),
+                user_idle,
+            ]
+        )
+
+
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets
 # selected.
@@ -224,18 +293,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    # This is the LLM that will be used to detect if the user has finished a
-    # statement. This doesn't really need to be an LLM, we could use NLP
-    # libraries for that, but we have the machinery to use an LLM, so we might as well!
-    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
     # This is the regular LLM.
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm_main = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     # You can also register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.
-    llm.register_function("get_current_weather", fetch_weather_from_api)
+    llm_main.register_function("get_current_weather", fetch_weather_from_api)
 
-    @llm.event_handler("on_function_calls_started")
+    @llm_main.event_handler("on_function_calls_started")
     async def on_function_calls_started(service, function_calls):
         await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
 
@@ -272,69 +336,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     ]
 
     context = OpenAILLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = llm_main.create_context_aggregator(context)
 
-    # We have instructed the LLM to return 'YES' if it thinks the user
-    # completed a sentence. So, if it's 'YES' we will return true in this
-    # predicate which will wake up the notifier.
-    async def wake_check_filter(frame):
-        logger.debug(f"Completeness check frame: {frame}")
-        return frame.text == "YES"
-
-    # This is a notifier that we use to synchronize the two LLMs.
-    notifier = EventNotifier()
-
-    # This turns the LLM context into an inference request to classify the user's speech
-    # as complete or incomplete.
-    statement_judge_context_filter = StatementJudgeContextFilter()
-
-    # This sends a UserStoppedSpeakingFrame and triggers the notifier event
-    completeness_check = CompletenessCheck(notifier=notifier)
-
-    # # Notify if the user hasn't said anything.
-    async def user_idle_notifier(frame):
-        await notifier.notify()
-
-    # Sometimes the LLM will fail detecting if a user has completed a
-    # sentence, this will wake up the notifier if that happens.
-    user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
-
-    # We start with the gate open because we send an initial context frame
-    # to start the conversation.
-    bot_output_gate = OutputGate(notifier=notifier, start_open=True)
-
-    async def pass_only_llm_trigger_frames(frame):
-        return (
-            isinstance(frame, OpenAILLMContextFrame)
-            or isinstance(frame, StartInterruptionFrame)
-            or isinstance(frame, StopInterruptionFrame)
-            or isinstance(frame, FunctionCallInProgressFrame)
-            or isinstance(frame, FunctionCallResultFrame)
-        )
+    # LLM + turn detection (with an extra LLM as a judge)
+    llm = TurnDetectionLLM(llm_main)
 
     pipeline = Pipeline(
         [
             transport.input(),
             stt,
             context_aggregator.user(),
-            ParallelPipeline(
-                [
-                    # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
-                    # simplified context frame to the statement classifier LLM. The only frame this
-                    # sub-pipeline will output is a UserStoppedSpeakingFrame.
-                    statement_judge_context_filter,
-                    statement_llm,
-                    completeness_check,
-                ],
-                [
-                    # Block everything except frames that trigger LLM inference.
-                    FunctionFilter(filter=pass_only_llm_trigger_frames),
-                    llm,
-                    bot_output_gate,  # Buffer all llm/tts output until notified.
-                ],
-            ),
+            llm,
             tts,
-            user_idle,
             transport.output(),
             context_aggregator.assistant(),
         ]
@@ -365,7 +378,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         await task.queue_frames(
             [
                 UserStartedSpeakingFrame(),
-                TranscriptionFrame(user_id="", timestamp=time.time(), text=message["message"]),
+                TranscriptionFrame(
+                    user_id="", timestamp=time_now_iso8601(), text=message["message"]
+                ),
                 UserStoppedSpeakingFrame(),
             ]
         )

examples/foundational/22c-natural-conversation-mixed-llms.py

@@ -6,7 +6,6 @@
 
 import asyncio
 import os
-import time
 
 from dotenv import load_dotenv
 from loguru import logger
@@ -45,13 +44,14 @@ from pipecat.runner.utils import create_transport
 from pipecat.services.anthropic.llm import AnthropicLLMService
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.llm_service import FunctionCallParams, LLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.sync.base_notifier import BaseNotifier
 from pipecat.sync.event_notifier import EventNotifier
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
 from pipecat.transports.services.daily import DailyParams
+from pipecat.utils.time import time_now_iso8601
 
 load_dotenv(override=True)
 
@@ -391,6 +391,75 @@ class OutputGate(FrameProcessor):
                 break
 
 
+class TurnDetectionLLM(Pipeline):
+    def __init__(self, llm: LLMService):
+        # This is the LLM that will be used to detect if the user has finished a
+        # statement. This doesn't really need to be an LLM, we could use NLP
+        # libraries for that, but we have the machinery to use an LLM, so we might as well!
+        statement_llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
+
+        # This is a notifier that we use to synchronize the two LLMs.
+        notifier = EventNotifier()
+
+        # This turns the LLM context into an inference request to classify the user's speech
+        # as complete or incomplete.
+        statement_judge_context_filter = StatementJudgeContextFilter()
+
+        # This sends a UserStoppedSpeakingFrame and triggers the notifier event
+        completeness_check = CompletenessCheck(notifier=notifier)
+
+        # # Notify if the user hasn't said anything.
+        async def user_idle_notifier(frame):
+            await notifier.notify()
+
+        # Sometimes the LLM will fail detecting if a user has completed a
+        # sentence, this will wake up the notifier if that happens.
+        user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
+
+        # We start with the gate open because we send an initial context frame
+        # to start the conversation.
+        bot_output_gate = OutputGate(notifier=notifier, start_open=True)
+
+        async def block_user_stopped_speaking(frame):
+            return not isinstance(frame, UserStoppedSpeakingFrame)
+
+        async def pass_only_llm_trigger_frames(frame):
+            return (
+                isinstance(frame, OpenAILLMContextFrame)
+                or isinstance(frame, StartInterruptionFrame)
+                or isinstance(frame, StopInterruptionFrame)
+                or isinstance(frame, FunctionCallInProgressFrame)
+                or isinstance(frame, FunctionCallResultFrame)
+            )
+
+        super().__init__(
+            [
+                ParallelPipeline(
+                    [
+                        # Pass everything except UserStoppedSpeaking to the elements after
+                        # this ParallelPipeline
+                        FunctionFilter(filter=block_user_stopped_speaking),
+                    ],
+                    [
+                        # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
+                        # simplified context frame to the statement classifier LLM. The only frame this
+                        # sub-pipeline will output is a UserStoppedSpeakingFrame.
+                        statement_judge_context_filter,
+                        statement_llm,
+                        completeness_check,
+                    ],
+                    [
+                        # Block everything except frames that trigger LLM inference.
+                        FunctionFilter(filter=pass_only_llm_trigger_frames),
+                        llm,
+                        bot_output_gate,  # Buffer all llm/tts output until notified.
+                    ],
+                ),
+                user_idle,
+            ]
+        )
+
+
 async def fetch_weather_from_api(params: FunctionCallParams):
     await params.result_callback({"conditions": "nice", "temperature": "75"})
 
@@ -427,18 +496,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    # This is the LLM that will be used to detect if the user has finished a
-    # statement. This doesn't really need to be an LLM, we could use NLP
-    # libraries for that, but we have the machinery to use an LLM, so we might as well!
-    statement_llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
-
     # This is the regular LLM.
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm_main = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     # Register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.
-    llm.register_function("get_current_weather", fetch_weather_from_api)
+    llm_main.register_function("get_current_weather", fetch_weather_from_api)
 
-    @llm.event_handler("on_function_calls_started")
+    @llm_main.event_handler("on_function_calls_started")
     async def on_function_calls_started(service, function_calls):
         await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
 
@@ -475,76 +539,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     ]
 
     context = OpenAILLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = llm_main.create_context_aggregator(context)
 
-    # We have instructed the LLM to return 'YES' if it thinks the user
-    # completed a sentence. So, if it's 'YES' we will return true in this
-    # predicate which will wake up the notifier.
-    async def wake_check_filter(frame):
-        return frame.text == "YES"
-
-    # This is a notifier that we use to synchronize the two LLMs.
-    notifier = EventNotifier()
-
-    # This turns the LLM context into an inference request to classify the user's speech
-    # as complete or incomplete.
-    statement_judge_context_filter = StatementJudgeContextFilter()
-
-    # This sends a UserStoppedSpeakingFrame and triggers the notifier event
-    completeness_check = CompletenessCheck(notifier=notifier)
-
-    # # Notify if the user hasn't said anything.
-    async def user_idle_notifier(frame):
-        await notifier.notify()
-
-    # Sometimes the LLM will fail detecting if a user has completed a
-    # sentence, this will wake up the notifier if that happens.
-    user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
-
-    # We start with the gate open because we send an initial context frame
-    # to start the conversation.
-    bot_output_gate = OutputGate(notifier=notifier, start_open=True)
-
-    async def block_user_stopped_speaking(frame):
-        return not isinstance(frame, UserStoppedSpeakingFrame)
-
-    async def pass_only_llm_trigger_frames(frame):
-        return (
-            isinstance(frame, OpenAILLMContextFrame)
-            or isinstance(frame, StartInterruptionFrame)
-            or isinstance(frame, StopInterruptionFrame)
-            or isinstance(frame, FunctionCallInProgressFrame)
-            or isinstance(frame, FunctionCallResultFrame)
-        )
+    # LLM + turn detection (with an extra LLM as a judge)
+    llm = TurnDetectionLLM(llm_main)
 
     pipeline = Pipeline(
         [
             transport.input(),
             stt,
             context_aggregator.user(),
-            ParallelPipeline(
-                [
-                    # Pass everything except UserStoppedSpeaking to the elements after
-                    # this ParallelPipeline
-                    FunctionFilter(filter=block_user_stopped_speaking),
-                ],
-                [
-                    # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
-                    # simplified context frame to the statement classifier LLM. The only frame this
-                    # sub-pipeline will output is a UserStoppedSpeakingFrame.
-                    statement_judge_context_filter,
-                    statement_llm,
-                    completeness_check,
-                ],
-                [
-                    # Block everything except frames that trigger LLM inference.
-                    FunctionFilter(filter=pass_only_llm_trigger_frames),
-                    llm,
-                    bot_output_gate,  # Buffer all llm/tts output until notified.
-                ],
-            ),
+            llm,
             tts,
-            user_idle,
             transport.output(),
             context_aggregator.assistant(),
         ]
@@ -580,7 +586,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         await task.queue_frames(
             [
                 UserStartedSpeakingFrame(),
-                TranscriptionFrame(user_id="", timestamp=time.time(), text=message["message"]),
+                TranscriptionFrame(
+                    user_id="", timestamp=time_now_iso8601(), text=message["message"]
+                ),
                 UserStoppedSpeakingFrame(),
             ]
         )

examples/foundational/22d-natural-conversation-gemini-audio.py

@@ -47,11 +47,13 @@ from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.google.llm import GoogleLLMContext, GoogleLLMService
+from pipecat.services.llm_service import LLMService
 from pipecat.sync.base_notifier import BaseNotifier
 from pipecat.sync.event_notifier import EventNotifier
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
 from pipecat.transports.services.daily import DailyParams
+from pipecat.utils.time import time_now_iso8601
 
 load_dotenv(override=True)
 
@@ -607,23 +609,90 @@ class OutputGate(FrameProcessor):
             self._gate_task = None
 
     async def _gate_task_handler(self):
-        while True:
-            try:
-                await self._notifier.wait()
+        await self._notifier.wait()
 
-                transcription = await self._transcription_buffer.wait_for_transcription() or "-"
-                self._context.add_message(Content(role="user", parts=[Part(text=transcription)]))
+        transcription = await self._transcription_buffer.wait_for_transcription() or "-"
+        self._context.add_message(Content(role="user", parts=[Part(text=transcription)]))
 
-                self.open_gate()
-                for frame, direction in self._frames_buffer:
-                    await self.push_frame(frame, direction)
-                self._frames_buffer = []
-            except asyncio.CancelledError:
-                break
-            except Exception as e:
-                logger.error(f"OutputGate error: {e}")
-                raise e
-                break
+        self.open_gate()
+        for frame, direction in self._frames_buffer:
+            await self.push_frame(frame, direction)
+        self._frames_buffer = []
+
+
+class TurnDetectionLLM(Pipeline):
+    def __init__(self, llm: LLMService, context: OpenAILLMContext):
+        # This is the LLM that will transcribe user speech.
+        tx_llm = GoogleLLMService(
+            name="Transcriber",
+            model=TRANSCRIBER_MODEL,
+            api_key=os.getenv("GOOGLE_API_KEY"),
+            temperature=0.0,
+            system_instruction=transcriber_system_instruction,
+        )
+
+        # This is the LLM that will classify user speech as complete or incomplete.
+        classifier_llm = GoogleLLMService(
+            name="Classifier",
+            model=CLASSIFIER_MODEL,
+            api_key=os.getenv("GOOGLE_API_KEY"),
+            temperature=0.0,
+            system_instruction=classifier_system_instruction,
+        )
+
+        # This is a notifier that we use to synchronize the two LLMs.
+        notifier = EventNotifier()
+
+        # This turns the LLM context into an inference request to classify the user's speech
+        # as complete or incomplete.
+        # statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
+
+        audio_accumulater = AudioAccumulator()
+        # This sends a UserStoppedSpeakingFrame and triggers the notifier event
+        completeness_check = CompletenessCheck(
+            notifier=notifier, audio_accumulator=audio_accumulater
+        )
+
+        async def block_user_stopped_speaking(frame):
+            return not isinstance(frame, UserStoppedSpeakingFrame)
+
+        conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)
+
+        llm_aggregator_buffer = LLMAggregatorBuffer()
+
+        bot_output_gate = OutputGate(
+            notifier=notifier, context=context, llm_transcription_buffer=llm_aggregator_buffer
+        )
+
+        super().__init__(
+            [
+                audio_accumulater,
+                ParallelPipeline(
+                    [
+                        # Pass everything except UserStoppedSpeaking to the elements after
+                        # this ParallelPipeline
+                        FunctionFilter(filter=block_user_stopped_speaking),
+                    ],
+                    [
+                        ParallelPipeline(
+                            [
+                                classifier_llm,
+                                completeness_check,
+                            ],
+                            [
+                                tx_llm,
+                                llm_aggregator_buffer,
+                            ],
+                        )
+                    ],
+                    [
+                        conversation_audio_context_assembler,
+                        llm,
+                        bot_output_gate,  # buffer output until notified, then flush frames and update context
+                    ],
+                ),
+            ]
+        )
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -656,24 +725,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    # This is the LLM that will transcribe user speech.
-    tx_llm = GoogleLLMService(
-        name="Transcriber",
-        model=TRANSCRIBER_MODEL,
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        temperature=0.0,
-        system_instruction=transcriber_system_instruction,
-    )
-
-    # This is the LLM that will classify user speech as complete or incomplete.
-    classifier_llm = GoogleLLMService(
-        name="Classifier",
-        model=CLASSIFIER_MODEL,
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        temperature=0.0,
-        system_instruction=classifier_system_instruction,
-    )
-
     # This is the regular LLM that responds conversationally.
     conversation_llm = GoogleLLMService(
         name="Conversation",
@@ -685,57 +736,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     context = OpenAILLMContext()
     context_aggregator = conversation_llm.create_context_aggregator(context)
 
-    # This is a notifier that we use to synchronize the two LLMs.
-    notifier = EventNotifier()
-
-    # This turns the LLM context into an inference request to classify the user's speech
-    # as complete or incomplete.
-    # statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
-
-    audio_accumulater = AudioAccumulator()
-    # This sends a UserStoppedSpeakingFrame and triggers the notifier event
-    completeness_check = CompletenessCheck(notifier=notifier, audio_accumulator=audio_accumulater)
-
-    async def block_user_stopped_speaking(frame):
-        return not isinstance(frame, UserStoppedSpeakingFrame)
-
-    conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)
-
-    llm_aggregator_buffer = LLMAggregatorBuffer()
-
-    bot_output_gate = OutputGate(
-        notifier=notifier, context=context, llm_transcription_buffer=llm_aggregator_buffer
-    )
+    llm = TurnDetectionLLM(conversation_llm, context)
 
     pipeline = Pipeline(
         [
             transport.input(),
-            audio_accumulater,
-            ParallelPipeline(
-                [
-                    # Pass everything except UserStoppedSpeaking to the elements after
-                    # this ParallelPipeline
-                    FunctionFilter(filter=block_user_stopped_speaking),
-                ],
-                [
-                    ParallelPipeline(
-                        [
-                            classifier_llm,
-                            completeness_check,
-                        ],
-                        [
-                            tx_llm,
-                            llm_aggregator_buffer,
-                        ],
-                    )
-                ],
-                [
-                    conversation_audio_context_assembler,
-                    conversation_llm,
-                    bot_output_gate,  # buffer output until notified, then flush frames and update context
-                    # TempPrinter(),
-                ],
-            ),
+            llm,
             tts,
             transport.output(),
             context_aggregator.assistant(),
@@ -766,7 +772,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         await task.queue_frames(
             [
                 UserStartedSpeakingFrame(),
-                TranscriptionFrame(user_id="", timestamp=time.time(), text=message["message"]),
+                TranscriptionFrame(
+                    user_id="", timestamp=time_now_iso8601(), text=message["message"]
+                ),
                 UserStoppedSpeakingFrame(),
             ]
         )

pyproject.toml

@@ -34,7 +34,7 @@ dependencies = [
     "resampy~=0.4.3",
     "soxr~=0.5.0",
     "openai>=1.74.0,<=1.99.1",
-    # Pinning numba (resampy dep) to resolve a package dependency
+    # Pinning numba to resolve package dependencies
     "numba==0.61.2",
 ]
 
@@ -74,7 +74,7 @@ local = [ "pyaudio~=0.2.14" ]
 mcp = [ "mcp[cli]~=1.9.4" ]
 mem0 = [ "mem0ai~=0.1.94" ]
 mlx-whisper = [ "mlx-whisper~=0.4.2" ]
-moondream = [ "einops~=0.8.0", "timm~=1.0.13", "transformers>=4.48.0" ]
+moondream = [ "accelerate~=1.10.0", "einops~=0.8.0", "pyvips[binary]~=3.0.0", "timm~=1.0.13", "transformers>=4.48.0" ]
 nim = []
 neuphonic = [ "websockets>=13.1,<15.0" ]
 noisereduce = [ "noisereduce~=3.0.3" ]
@@ -82,7 +82,7 @@ openai = [ "websockets>=13.1,<15.0" ]
 openpipe = [ "openpipe~=4.50.0" ]
 openrouter = []
 perplexity = []
-playht = [ "pyht>=0.1.6", "websockets>=13.1,<15.0" ]
+playht = [ "websockets>=13.1,<15.0" ]
 qwen = []
 rime = [ "websockets>=13.1,<15.0" ]
 riva = [ "nvidia-riva-client~=2.21.1" ]
@@ -124,7 +124,7 @@ dev = [
 docs = [
     "sphinx>=8.1.3",
     "sphinx-rtd-theme",
-    "sphinx-markdown-builder", 
+    "sphinx-markdown-builder",
     "sphinx-autodoc-typehints",
     "toml",
 ]

scripts/evals/eval.py

@@ -4,7 +4,6 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import argparse
 import asyncio
 import io
 import os
@@ -13,11 +12,12 @@ import time
 import wave
 from datetime import datetime
 from pathlib import Path
-from typing import List, Optional
+from typing import List, Optional, Tuple
 
 import aiofiles
 from deepgram import LiveOptions
 from loguru import logger
+from PIL.ImageFile import ImageFile
 from utils import (
     EvalResult,
     load_module_from_path,
@@ -30,7 +30,7 @@ from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import EndTaskFrame
+from pipecat.frames.frames import EndTaskFrame, OutputImageRawFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -49,6 +49,8 @@ SCRIPT_DIR = Path(__file__).resolve().parent
 PIPELINE_IDLE_TIMEOUT_SECS = 60
 EVAL_TIMEOUT_SECS = 90
 
+EvalPrompt = str | Tuple[str, ImageFile]
+
 
 class EvalRunner:
     def __init__(
@@ -87,7 +89,7 @@ class EvalRunner:
     async def assert_eval_false(self):
         await self._queue.put(False)
 
-    async def run_eval(self, example_file: str, prompt: str, eval: Optional[str] = None):
+    async def run_eval(self, example_file: str, prompt: EvalPrompt, eval: Optional[str] = None):
         if not re.match(self._pattern, example_file):
             return
 
@@ -178,6 +180,7 @@ async def run_example_pipeline(script_path: Path):
         DailyParams(
             audio_in_enabled=True,
             audio_out_enabled=True,
+            video_in_enabled=True,
             vad_analyzer=SileroVADAnalyzer(),
         ),
     )
@@ -189,7 +192,10 @@ async def run_example_pipeline(script_path: Path):
 
 
 async def run_eval_pipeline(
-    eval_runner: EvalRunner, example_file: str, prompt: str, eval: Optional[str]
+    eval_runner: EvalRunner,
+    example_file: str,
+    prompt: EvalPrompt,
+    eval: Optional[str],
 ):
     logger.info(f"Starting eval bot")
 
@@ -202,6 +208,7 @@ async def run_eval_pipeline(
         DailyParams(
             audio_in_enabled=True,
             audio_out_enabled=True,
+            video_out_enabled=True,
             vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=2.0)),
         ),
     )
@@ -210,7 +217,10 @@ async def run_eval_pipeline(
     # 5" (in audio) this can be converted to "32 is 5".
     stt = DeepgramSTTService(
         api_key=os.getenv("DEEPGRAM_API_KEY"),
-        live_options=LiveOptions(smart_format=False),
+        live_options=LiveOptions(
+            language="multi",
+            smart_format=False,
+        ),
     )
 
     tts = CartesiaTTSService(
@@ -239,6 +249,14 @@ async def run_eval_pipeline(
     )
     tools = ToolsSchema(standard_tools=[eval_function])
 
+    # Load example prompt depending on image.
+    example_prompt = ""
+    example_image: Optional[ImageFile] = None
+    if isinstance(prompt, str):
+        example_prompt = prompt
+    elif isinstance(prompt, tuple):
+        example_prompt, example_image = prompt
+
     # See if we need to include an eval prompt.
     eval_prompt = ""
     if eval:
@@ -247,7 +265,7 @@ async def run_eval_pipeline(
     messages = [
         {
             "role": "system",
-            "content": f"You are an LLM eval, be extremly brief. Your goal is to only ask one question: {prompt}. Call the eval function only if the user answers the question and check if the answer is correct (words as numbers are valid). {eval_prompt}",
+            "content": f"You are an LLM eval, be extremly brief. Your goal is to only ask one question: {example_prompt}. Call the eval function only if the user answers the question and check if the answer is correct (words as numbers are valid). {eval_prompt}",
         },
     ]
 
@@ -285,6 +303,14 @@ async def run_eval_pipeline(
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
+        if example_image:
+            await task.queue_frame(
+                OutputImageRawFrame(
+                    image=example_image.tobytes(),
+                    size=example_image.size,
+                    format="RGB",
+                )
+            )
         await audio_buffer.start_recording()
 
     @transport.event_handler("on_client_disconnected")

scripts/evals/run-release-evals.py

@@ -13,12 +13,15 @@ from pathlib import Path
 from dotenv import load_dotenv
 from eval import EvalRunner
 from loguru import logger
+from PIL import Image
 from utils import check_env_variables
 
 load_dotenv(override=True)
 
 SCRIPT_DIR = Path(__file__).resolve().parent
 
+ASSETS_DIR = SCRIPT_DIR / "assets"
+
 FOUNDATIONAL_DIR = SCRIPT_DIR.parent.parent / "examples" / "foundational"
 
 
@@ -35,6 +38,14 @@ EVAL_WEATHER = (
 PROMPT_ONLINE_SEARCH = "What's the date right now in London?"
 EVAL_ONLINE_SEARCH = f"Today is {datetime.now(timezone.utc).strftime('%B %d, %Y')}."
 
+# Switch language
+PROMPT_SWITCH_LANGUAGE = "Say something in Spanish."
+EVAL_SWITCH_LANGUAGE = "Check if the user is now talking in Spanish."
+
+# Vision
+PROMPT_VISION = ("What do you see?", Image.open(ASSETS_DIR / "cat.jpg"))
+EVAL_VISION = "A cat description."
+
 TESTS_07 = [
     # 07 series
     ("07-interruptible.py", PROMPT_SIMPLE_MATH, None),
@@ -57,6 +68,7 @@ TESTS_07 = [
     ("07k-interruptible-lmnt.py", PROMPT_SIMPLE_MATH, None),
     ("07l-interruptible-groq.py", PROMPT_SIMPLE_MATH, None),
     ("07m-interruptible-aws.py", PROMPT_SIMPLE_MATH, None),
+    ("07n-interruptible-gemini.py", PROMPT_SIMPLE_MATH, None),
     ("07n-interruptible-google.py", PROMPT_SIMPLE_MATH, None),
     ("07o-interruptible-assemblyai.py", PROMPT_SIMPLE_MATH, None),
     ("07q-interruptible-rime.py", PROMPT_SIMPLE_MATH, None),
@@ -77,6 +89,13 @@ TESTS_07 = [
     # ("07u-interruptible-ultravox.py", PROMPT_SIMPLE_MATH, None),
 ]
 
+TESTS_12 = [
+    ("12-describe-video.py", PROMPT_VISION, EVAL_VISION),
+    ("12a-describe-video-gemini-flash.py", PROMPT_VISION, EVAL_VISION),
+    ("12b-describe-video-gpt-4o.py", PROMPT_VISION, EVAL_VISION),
+    ("12c-describe-video-anthropic.py", PROMPT_VISION, EVAL_VISION),
+]
+
 TESTS_14 = [
     ("14-function-calling.py", PROMPT_WEATHER, EVAL_WEATHER),
     ("14a-function-calling-anthropic.py", PROMPT_WEATHER, EVAL_WEATHER),
@@ -101,9 +120,14 @@ TESTS_14 = [
     # ("14o-function-calling-gemini-openai-format.py", PROMPT_WEATHER, EVAL_WEATHER),
 ]
 
+TESTS_15 = [
+    ("15a-switch-languages.py", PROMPT_SWITCH_LANGUAGE, EVAL_SWITCH_LANGUAGE),
+]
+
 TESTS_19 = [
     ("19-openai-realtime-beta.py", PROMPT_WEATHER, EVAL_WEATHER),
     ("19a-azure-realtime-beta.py", PROMPT_WEATHER, EVAL_WEATHER),
+    ("19b-openai-realtime-beta-text.py", PROMPT_WEATHER, EVAL_WEATHER),
 ]
 
 TESTS_21 = [
@@ -134,7 +158,9 @@ TESTS_43 = [
 
 TESTS = [
     *TESTS_07,
+    *TESTS_12,
     *TESTS_14,
+    *TESTS_15,
     *TESTS_19,
     *TESTS_21,
     *TESTS_26,

src/pipecat/pipeline/parallel_pipeline.py

@@ -49,7 +49,7 @@ class ParallelPipelineSource(FrameProcessor):
             upstream_queue: Queue for collecting upstream frames from this branch.
             push_frame_func: Function to push frames to the parent parallel pipeline.
         """
-        super().__init__()
+        super().__init__(enable_direct_mode=True)
         self._up_queue = upstream_queue
         self._push_frame_func = push_frame_func
 
@@ -90,7 +90,7 @@ class ParallelPipelineSink(FrameProcessor):
             downstream_queue: Queue for collecting downstream frames from this branch.
             push_frame_func: Function to push frames to the parent parallel pipeline.
         """
-        super().__init__()
+        super().__init__(enable_direct_mode=True)
         self._down_queue = downstream_queue
         self._push_frame_func = push_frame_func
 

src/pipecat/pipeline/pipeline.py

@@ -32,7 +32,7 @@ class PipelineSource(FrameProcessor):
         Args:
             upstream_push_frame: Coroutine function to handle upstream frames.
         """
-        super().__init__()
+        super().__init__(enable_direct_mode=True)
         self._upstream_push_frame = upstream_push_frame
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -65,7 +65,7 @@ class PipelineSink(FrameProcessor):
         Args:
             downstream_push_frame: Coroutine function to handle downstream frames.
         """
-        super().__init__()
+        super().__init__(enable_direct_mode=True)
         self._downstream_push_frame = downstream_push_frame
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):

src/pipecat/pipeline/sync_parallel_pipeline.py

@@ -49,7 +49,7 @@ class SyncParallelPipelineSource(FrameProcessor):
         Args:
             upstream_queue: Queue for collecting upstream frames from the pipeline.
         """
-        super().__init__()
+        super().__init__(enable_direct_mode=True)
         self._up_queue = upstream_queue
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -81,7 +81,7 @@ class SyncParallelPipelineSink(FrameProcessor):
         Args:
             downstream_queue: Queue for collecting downstream frames from the pipeline.
         """
-        super().__init__()
+        super().__init__(enable_direct_mode=True)
         self._down_queue = downstream_queue
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):

src/pipecat/pipeline/task.py

@@ -110,14 +110,14 @@ class PipelineTaskSource(FrameProcessor):
     pipeline.
     """
 
-    def __init__(self, up_queue: asyncio.Queue, **kwargs):
+    def __init__(self, up_queue: asyncio.Queue):
         """Initialize the pipeline task source.
 
         Args:
             up_queue: Queue for upstream frame processing.
             **kwargs: Additional arguments passed to the parent class.
         """
-        super().__init__(**kwargs)
+        super().__init__(enable_direct_mode=True)
         self._up_queue = up_queue
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -144,14 +144,14 @@ class PipelineTaskSink(FrameProcessor):
     act on them, for example, waiting to receive an EndFrame.
     """
 
-    def __init__(self, down_queue: asyncio.Queue, **kwargs):
+    def __init__(self, down_queue: asyncio.Queue):
         """Initialize the pipeline task sink.
 
         Args:
             down_queue: Queue for downstream frame processing.
             **kwargs: Additional arguments passed to the parent class.
         """
-        super().__init__(**kwargs)
+        super().__init__(enable_direct_mode=True)
         self._down_queue = down_queue
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):

src/pipecat/processors/filters/stt_mute_filter.py

@@ -32,6 +32,8 @@ from pipecat.frames.frames import (
     TranscriptionFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
+    VADUserStartedSpeakingFrame,
+    VADUserStoppedSpeakingFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
@@ -205,6 +207,8 @@ class STTMuteFilter(FrameProcessor):
             (
                 StartInterruptionFrame,
                 StopInterruptionFrame,
+                VADUserStartedSpeakingFrame,
+                VADUserStoppedSpeakingFrame,
                 UserStartedSpeakingFrame,
                 UserStoppedSpeakingFrame,
                 InputAudioRawFrame,

src/pipecat/processors/frame_processor.py

@@ -14,7 +14,7 @@ management, and frame flow control mechanisms.
 import asyncio
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Awaitable, Callable, Coroutine, List, Optional, Sequence
+from typing import Any, Awaitable, Callable, Coroutine, List, Optional, Sequence, Tuple
 
 from loguru import logger
 
@@ -38,6 +38,10 @@ from pipecat.observers.base_observer import BaseObserver, FramePushed
 from pipecat.processors.metrics.frame_processor_metrics import FrameProcessorMetrics
 from pipecat.utils.asyncio.task_manager import BaseTaskManager
 from pipecat.utils.asyncio.watchdog_event import WatchdogEvent
+from pipecat.utils.asyncio.watchdog_priority_queue import (
+    WatchdogPriorityCancelSentinel,
+    WatchdogPriorityQueue,
+)
 from pipecat.utils.asyncio.watchdog_queue import WatchdogQueue
 from pipecat.utils.base_object import BaseObject
 
@@ -54,6 +58,9 @@ class FrameDirection(Enum):
     UPSTREAM = 2
 
 
+FrameCallback = Callable[["FrameProcessor", Frame, FrameDirection], Awaitable[None]]
+
+
 @dataclass
 class FrameProcessorSetup:
     """Configuration parameters for frame processor initialization.
@@ -71,23 +78,18 @@ class FrameProcessorSetup:
     watchdog_timers_enabled: bool = False
 
 
-class FrameProcessorQueue(WatchdogQueue):
+class FrameProcessorQueue(WatchdogPriorityQueue):
     """A priority queue for systems frames and other frames.
 
     This is a specialized queue for frame processors that separates and
-    prioritizes system frames over other frames.
-
-    This queue uses two internal `WatchdogQueue` instances:
-    - One for system-level frames (`SystemFrame`)
-    - One for regular frames
-
-    It ensures that `SystemFrame` objects are processed before any other
-    frames. Additionally, it uses an `asyncio.Event` to signal when new items
-    have been added to either queue, allowing consumers to wait efficiently when
-    the queue is empty.
+    prioritizes system frames over other frames. It ensures that `SystemFrame`
+    objects are processed before any other frames by using a priority queue.
 
     """
 
+    HIGH_PRIORITY = 1
+    LOW_PRIORITY = 2
+
     def __init__(self, manager: BaseTaskManager):
         """Initialize the FrameProcessorQueue.
 
@@ -95,26 +97,28 @@ class FrameProcessorQueue(WatchdogQueue):
             manager (BaseTaskManager): The task manager used by the internal watchdog queues.
 
         """
-        super().__init__(manager)
-        self.__event = WatchdogEvent(manager)
-        self.__main_queue = WatchdogQueue(manager)
-        self.__system_queue = WatchdogQueue(manager)
+        super().__init__(manager, tuple_size=3)
+        self.__high_counter = 0
+        self.__low_counter = 0
 
-    async def put(self, item: Any):
-        """Put an item into the appropriate queue.
+    async def put(self, item: Tuple[Frame, FrameDirection, FrameCallback]):
+        """Put an item into the priority queue.
 
-        System frames (`SystemFrame`) are placed into the system queue and all others
-        into the regular queue. Signals the event to wake up any waiting consumers.
+        System frames (`SystemFrame`) have higher priority than any other
+        frames. If a non-frame item (e.g. a watchdog cancellation sentinel) is
+        provided it will have the highest priority.
 
         Args:
             item (Any): The item to enqueue.
 
         """
-        if isinstance(item, SystemFrame):
-            await self.__system_queue.put(item)
+        frame, _, _ = item
+        if isinstance(frame, SystemFrame):
+            self.__high_counter += 1
+            await super().put((self.HIGH_PRIORITY, self.__high_counter, item))
         else:
-            await self.__main_queue.put(item)
-        self.__event.set()
+            self.__low_counter += 1
+            await super().put((self.LOW_PRIORITY, self.__low_counter, item))
 
     async def get(self) -> Any:
         """Retrieve the next item from the queue.
@@ -126,38 +130,9 @@ class FrameProcessorQueue(WatchdogQueue):
             Any: The next item from the system or main queue.
 
         """
-        # Wait for an item in any of the queues if they are empty.
-        if self.__main_queue.empty() and self.__system_queue.empty():
-            await self.__event.wait()
-
-        # Prioritize system frames.
-        if self.__system_queue.qsize() > 0:
-            item = await self.__system_queue.get()
-            self.__system_queue.task_done()
-        else:
-            item = await self.__main_queue.get()
-            self.__main_queue.task_done()
-
-        # Clear the event only if all queues are empty.
-        if self.__main_queue.empty() and self.__system_queue.empty():
-            self.__event.clear()
-
+        _, _, item = await super().get()
         return item
 
-    def cancel(self):
-        """Cancel both internal queues.
-
-        This method is used to stop processing and release any pending tasks
-        in both the system and main queues. Typically used during shutdown
-        or cleanup to prevent further processing of frames.
-
-        """
-        self.__main_queue.cancel()
-        self.__system_queue.cancel()
-
-
-FrameCallback = Callable[["FrameProcessor", Frame, FrameDirection], Awaitable[None]]
-
 
 class FrameProcessor(BaseObject):
     """Base class for all frame processors in the pipeline.
@@ -175,6 +150,7 @@ class FrameProcessor(BaseObject):
         self,
         *,
         name: Optional[str] = None,
+        enable_direct_mode: bool = False,
         enable_watchdog_logging: Optional[bool] = None,
         enable_watchdog_timers: Optional[bool] = None,
         metrics: Optional[FrameProcessorMetrics] = None,
@@ -185,6 +161,7 @@ class FrameProcessor(BaseObject):
 
         Args:
             name: Optional name for this processor instance.
+            enable_direct_mode: Whether to process frames immediately or use internal queues.
             enable_watchdog_logging: Whether to enable watchdog logging for tasks.
             enable_watchdog_timers: Whether to enable watchdog timers for tasks.
             metrics: Optional metrics collector for this processor.
@@ -196,6 +173,9 @@ class FrameProcessor(BaseObject):
         self._prev: Optional["FrameProcessor"] = None
         self._next: Optional["FrameProcessor"] = None
 
+        # Enable direct mode to skip queues and process frames right away.
+        self._enable_direct_mode = enable_direct_mode
+
         # Enable watchdog timers for all tasks created by this frame processor.
         self._enable_watchdog_timers = enable_watchdog_timers
 
@@ -254,9 +234,7 @@ class FrameProcessor(BaseObject):
         # called. To resume processing frames we need to call
         # `resume_processing_frames()` which will wake up the event.
         self.__should_block_frames = False
-        self.__process_event = None
         self.__process_frame_task: Optional[asyncio.Task] = None
-        self.__process_queue = None
 
     @property
     def id(self) -> int:
@@ -558,7 +536,10 @@ class FrameProcessor(BaseObject):
         if self._cancelling:
             return
 
-        await self.__input_queue.put((frame, direction, callback))
+        if self._enable_direct_mode:
+            await self.__process_frame(frame, direction, callback)
+        else:
+            await self.__input_queue.put((frame, direction, callback))
 
     async def pause_processing_frames(self):
         """Pause processing of queued frames."""
@@ -730,6 +711,9 @@ class FrameProcessor(BaseObject):
 
     def __create_input_task(self):
         """Create the frame input processing task."""
+        if self._enable_direct_mode:
+            return
+
         if not self.__input_frame_task:
             self.__input_queue = FrameProcessorQueue(self.task_manager)
             self.__input_frame_task = self.create_task(self.__input_frame_task_handler())
@@ -743,11 +727,12 @@ class FrameProcessor(BaseObject):
 
     def __create_process_task(self):
         """Create the non-system frame processing task."""
+        if self._enable_direct_mode:
+            return
+
         if not self.__process_frame_task:
             self.__should_block_frames = False
-            if not self.__process_event:
-                self.__process_event = WatchdogEvent(self.task_manager)
-            self.__process_event.clear()
+            self.__process_event = WatchdogEvent(self.task_manager)
             self.__process_queue = WatchdogQueue(self.task_manager)
             self.__process_frame_task = self.create_task(self.__process_frame_task_handler())
 
@@ -759,7 +744,7 @@ class FrameProcessor(BaseObject):
             self.__process_frame_task = None
 
     async def __process_frame(
-        self, frame: Frame, direction: FrameDirection, callback: FrameCallback
+        self, frame: Frame, direction: FrameDirection, callback: Optional[FrameCallback]
     ):
         try:
             # Process the frame.
@@ -790,10 +775,12 @@ class FrameProcessor(BaseObject):
                     f"{self}: __process_queue is None when processing frame {frame.name}"
                 )
 
+            self.__input_queue.task_done()
+
     async def __process_frame_task_handler(self):
         """Handle non-system frames from the process queue."""
         while True:
-            if self.__should_block_frames and self.__process_event:
+            if self.__should_block_frames:
                 logger.trace(f"{self}: frame processing paused")
                 await self.__process_event.wait()
                 self.__process_event.clear()
@@ -803,3 +790,5 @@ class FrameProcessor(BaseObject):
             (frame, direction, callback) = await self.__process_queue.get()
 
             await self.__process_frame(frame, direction, callback)
+
+            self.__process_queue.task_done()

src/pipecat/runner/run.py

@@ -53,7 +53,7 @@ Supported transports:
 
 - Daily - Creates rooms and tokens, runs bot as participant
 - WebRTC - Provides local WebRTC interface with prebuilt UI
-- Telephony - Handles webhook and WebSocket connections for Twilio, Telnyx, Plivo
+- Telephony - Handles webhook and WebSocket connections for Twilio, Telnyx, Plivo, Exotel
 
 To run locally:
 
@@ -62,6 +62,7 @@ To run locally:
 - Daily (server): `python bot.py -t daily`
 - Daily (direct, testing only): `python bot.py -d`
 - Telephony: `python bot.py -t twilio -x your_username.ngrok.io`
+- Exotel: `python bot.py -t exotel` (no proxy needed, but ngrok connection to HTTP 7860 is required)
 """
 
 import argparse
@@ -145,7 +146,6 @@ async def _run_telephony_bot(websocket: WebSocket):
 
     # Just pass the WebSocket - let the bot handle parsing
     runner_args = WebSocketRunnerArguments(websocket=websocket)
-    runner_args.handle_sigint = False
 
     await bot_module.bot(runner_args)
 
@@ -169,7 +169,7 @@ def _create_server_app(
         _setup_webrtc_routes(app, esp32_mode=esp32_mode, host=host)
     elif transport_type == "daily":
         _setup_daily_routes(app)
-    elif transport_type in ["twilio", "telnyx", "plivo"]:
+    elif transport_type in ["twilio", "telnyx", "plivo", "exotel"]:
         _setup_telephony_routes(app, transport_type, proxy)
     else:
         logger.warning(f"Unknown transport type: {transport_type}")
@@ -223,7 +223,6 @@ def _setup_webrtc_routes(app: FastAPI, esp32_mode: bool = False, host: str = "lo
 
             bot_module = _get_bot_module()
             runner_args = SmallWebRTCRunnerArguments(webrtc_connection=pipecat_connection)
-            runner_args.handle_sigint = False
             background_tasks.add_task(bot_module.bot, runner_args)
 
         answer = pipecat_connection.get_answer()
@@ -266,7 +265,6 @@ def _setup_daily_routes(app: FastAPI):
             # Start the bot in the background with empty body for GET requests
             bot_module = _get_bot_module()
             runner_args = DailyRunnerArguments(room_url=room_url, token=token)
-            runner_args.handle_sigint = False
             asyncio.create_task(bot_module.bot(runner_args))
             return RedirectResponse(room_url)
 
@@ -311,7 +309,6 @@ def _setup_daily_routes(app: FastAPI):
             # Start the bot in the background with extracted body data
             bot_module = _get_bot_module()
             runner_args = DailyRunnerArguments(room_url=room_url, token=token, body=bot_body)
-            runner_args.handle_sigint = False
             asyncio.create_task(bot_module.bot(runner_args))
             # Match PCC /start endpoint response format:
             return {"dailyRoom": room_url, "dailyToken": token}
@@ -337,7 +334,7 @@ def _setup_daily_routes(app: FastAPI):
 
 def _setup_telephony_routes(app: FastAPI, transport_type: str, proxy: str):
     """Set up telephony-specific routes."""
-    # XML response templates
+    # XML response templates (Exotel doesn't use XML webhooks)
     XML_TEMPLATES = {
         "twilio": f"""<?xml version="1.0" encoding="UTF-8"?>
 <Response>
@@ -362,9 +359,18 @@ def _setup_telephony_routes(app: FastAPI, transport_type: str, proxy: str):
     @app.post("/")
     async def start_call():
         """Handle telephony webhook and return XML response."""
-        logger.debug(f"POST {transport_type.upper()} XML")
-        xml_content = XML_TEMPLATES.get(transport_type, "<Response></Response>")
-        return HTMLResponse(content=xml_content, media_type="application/xml")
+        if transport_type == "exotel":
+            # Exotel doesn't use POST webhooks - redirect to proper documentation
+            logger.debug("POST Exotel endpoint - not used")
+            return {
+                "error": "Exotel doesn't use POST webhooks",
+                "websocket_url": f"wss://{proxy}/ws",
+                "note": "Configure the WebSocket URL above in your Exotel App Bazaar Voicebot Applet",
+            }
+        else:
+            logger.debug(f"POST {transport_type.upper()} XML")
+            xml_content = XML_TEMPLATES.get(transport_type, "<Response></Response>")
+            return HTMLResponse(content=xml_content, media_type="application/xml")
 
     @app.websocket("/ws")
     async def websocket_endpoint(websocket: WebSocket):
@@ -440,7 +446,7 @@ def main():
     Args:
         --host: Server host address (default: localhost)
         --port: Server port (default: 7860)
-        -t/--transport: Transport type (daily, webrtc, twilio, telnyx, plivo)
+        -t/--transport: Transport type (daily, webrtc, twilio, telnyx, plivo, exotel)
         -x/--proxy: Public proxy hostname for telephony webhooks
         --esp32: Enable SDP munging for ESP32 compatibility (requires --host with IP address)
         -d/--direct: Connect directly to Daily room (automatically sets transport to daily)
@@ -455,7 +461,7 @@ def main():
         "-t",
         "--transport",
         type=str,
-        choices=["daily", "webrtc", "twilio", "telnyx", "plivo"],
+        choices=["daily", "webrtc", "twilio", "telnyx", "plivo", "exotel"],
         default="webrtc",
         help="Transport type",
     )

src/pipecat/runner/types.py

@@ -25,7 +25,7 @@ class RunnerArguments:
     pipeline_idle_timeout_secs: int = field(init=False)
 
     def __post_init__(self):
-        self.handle_sigint = True
+        self.handle_sigint = False
         self.handle_sigterm = False
         self.pipeline_idle_timeout_secs = 300
 

src/pipecat/runner/utils.py

@@ -77,6 +77,17 @@ def _detect_transport_type_from_message(message_data: dict) -> str:
         logger.trace("Auto-detected: PLIVO")
         return "plivo"
 
+    # Exotel detection
+    if (
+        message_data.get("event") == "start"
+        and "start" in message_data
+        and "stream_sid" in message_data.get("start", {})
+        and "call_sid" in message_data.get("start", {})
+        and "account_sid" in message_data.get("start", {})
+    ):
+        logger.trace("Auto-detected: EXOTEL")
+        return "exotel"
+
     logger.trace("Auto-detection failed - unknown format")
     return "unknown"
 
@@ -91,6 +102,7 @@ async def parse_telephony_websocket(websocket: WebSocket):
         - Twilio: {"stream_id": str, "call_id": str}
         - Telnyx: {"stream_id": str, "call_control_id": str, "outbound_encoding": str}
         - Plivo: {"stream_id": str, "call_id": str}
+        - Exotel: {"stream_id": str, "call_id": str, "account_sid": str}
 
     Example usage::
 
@@ -160,6 +172,14 @@ async def parse_telephony_websocket(websocket: WebSocket):
                 "call_id": start_data.get("callId"),
             }
 
+        elif transport_type == "exotel":
+            start_data = call_data_raw.get("start", {})
+            call_data = {
+                "stream_id": start_data.get("stream_sid"),
+                "call_id": start_data.get("call_sid"),
+                "account_sid": start_data.get("account_sid"),
+            }
+
         else:
             call_data = {}
 
@@ -379,10 +399,17 @@ async def _create_telephony_transport(
             auth_id=os.getenv("PLIVO_AUTH_ID", ""),
             auth_token=os.getenv("PLIVO_AUTH_TOKEN", ""),
         )
+    elif transport_type == "exotel":
+        from pipecat.serializers.exotel import ExotelFrameSerializer
+
+        params.serializer = ExotelFrameSerializer(
+            stream_sid=call_data["stream_id"],
+            call_sid=call_data["call_id"],
+        )
     else:
         raise ValueError(
             f"Unsupported telephony provider: {transport_type}. "
-            f"Supported providers: twilio, telnyx, plivo"
+            f"Supported providers: twilio, telnyx, plivo, exotel"
         )
 
     return FastAPIWebsocketTransport(websocket=websocket, params=params)
@@ -399,7 +426,7 @@ async def create_transport(
     Args:
         runner_args: Arguments from the runner.
         transport_params: Dict mapping transport names to parameter factory functions.
-            Keys should be: "daily", "webrtc", "twilio", "telnyx", "plivo"
+            Keys should be: "daily", "webrtc", "twilio", "telnyx", "plivo", "exotel"
             Values should be functions that return transport parameters when called.
 
     Returns:
@@ -440,6 +467,12 @@ async def create_transport(
                 vad_analyzer=SileroVADAnalyzer(),
                 # add_wav_header and serializer will be set automatically
             ),
+            "exotel": lambda: FastAPIWebsocketParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                # add_wav_header and serializer will be set automatically
+            ),
         }
 
         transport = await create_transport(runner_args, transport_params)

src/pipecat/services/azure/stt.py

@@ -60,6 +60,7 @@ class AzureSTTService(STTService):
         region: str,
         language: Language = Language.EN_US,
         sample_rate: Optional[int] = None,
+        endpoint_id: Optional[str] = None,
         **kwargs,
     ):
         """Initialize the Azure STT service.
@@ -69,6 +70,7 @@ class AzureSTTService(STTService):
             region: Azure region for the Speech service (e.g., 'eastus').
             language: Language for speech recognition. Defaults to English (US).
             sample_rate: Audio sample rate in Hz. If None, uses service default.
+            endpoint_id: Custom model endpoint id.
             **kwargs: Additional arguments passed to parent STTService.
         """
         super().__init__(sample_rate=sample_rate, **kwargs)
@@ -79,6 +81,9 @@ class AzureSTTService(STTService):
             speech_recognition_language=language_to_azure_language(language),
         )
 
+        if endpoint_id:
+            self._speech_config.endpoint_id = endpoint_id
+
         self._audio_stream = None
         self._speech_recognizer = None
         self._settings = {

src/pipecat/services/azure/tts.py

@@ -68,6 +68,16 @@ class AzureBaseTTSService(TTSService):
     construction, voice configuration, and parameter management.
     """
 
+    # Define SSML escape mappings based on SSML reserved characters
+    # See - https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-synthesis-markup-structure
+    SSML_ESCAPE_CHARS = {
+        "&": "&",
+        "<": "&lt;",
+        ">": "&gt;",
+        '"': "&quot;",
+        "'": "&apos;",
+    }
+
     class InputParams(BaseModel):
         """Input parameters for Azure TTS voice configuration.
 
@@ -154,6 +164,10 @@ class AzureBaseTTSService(TTSService):
 
     def _construct_ssml(self, text: str) -> str:
         language = self._settings["language"]
+
+        # Escape special characters
+        escaped_text = self._escape_text(text)
+
         ssml = (
             f"<speak version='1.0' xml:lang='{language}' "
             "xmlns='http://www.w3.org/2001/10/synthesis' "
@@ -183,7 +197,7 @@ class AzureBaseTTSService(TTSService):
         if self._settings["emphasis"]:
             ssml += f"<emphasis level='{self._settings['emphasis']}'>"
 
-        ssml += text
+        ssml += escaped_text
 
         if self._settings["emphasis"]:
             ssml += "</emphasis>"
@@ -197,6 +211,27 @@ class AzureBaseTTSService(TTSService):
 
         return ssml
 
+    def _escape_text(self, text: str) -> str:
+        """Escapes XML/SSML reserved characters according to Microsoft documentation.
+
+        This method escapes the following characters:
+        - & becomes &amp;
+        - < becomes &lt;
+        - > becomes &gt;
+        - " becomes &quot;
+        - ' becomes &apos;
+
+        Args:
+            text: The text to escape.
+
+        Returns:
+            The escaped text.
+        """
+        escaped_text = text
+        for char, escape_code in AzureBaseTTSService.SSML_ESCAPE_CHARS.items():
+            escaped_text = escaped_text.replace(char, escape_code)
+        return escaped_text
+
 
 class AzureTTSService(AzureBaseTTSService):
     """Azure Cognitive Services streaming TTS service.

src/pipecat/services/cerebras/llm.py

@@ -9,8 +9,7 @@
 from typing import List
 
 from loguru import logger
-from openai import AsyncStream
-from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
+from openai.types.chat import ChatCompletionMessageParam
 
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai.llm import OpenAILLMService
@@ -55,20 +54,13 @@ class CerebrasLLMService(OpenAILLMService):
         logger.debug(f"Creating Cerebras client with api {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
 
-    async def get_chat_completions(
+    def build_chat_completion_params(
         self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> AsyncStream[ChatCompletionChunk]:
-        """Create a streaming chat completion using Cerebras's API.
+    ) -> dict:
+        """Build parameters for Cerebras chat completion request.
 
-        Args:
-            context: The context object containing tools configuration
-                and other settings for the chat completion.
-            messages: The list of messages comprising
-                the conversation history and current request.
-
-        Returns:
-            A streaming response of chat completion
-                chunks that can be processed asynchronously.
+        Cerebras supports a subset of OpenAI parameters, focusing on core
+        completion settings without advanced features like frequency/presence penalties.
         """
         params = {
             "model": self.model_name,
@@ -83,6 +75,4 @@ class CerebrasLLMService(OpenAILLMService):
         }
 
         params.update(self._settings["extra"])
-
-        chunks = await self._client.chat.completions.create(**params)
-        return chunks
+        return params

src/pipecat/services/deepseek/llm.py

@@ -9,8 +9,7 @@
 from typing import List
 
 from loguru import logger
-from openai import AsyncStream
-from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
+from openai.types.chat import ChatCompletionMessageParam
 
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai.llm import OpenAILLMService
@@ -55,20 +54,12 @@ class DeepSeekLLMService(OpenAILLMService):
         logger.debug(f"Creating DeepSeek client with api {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
 
-    async def get_chat_completions(
+    def _build_chat_completion_params(
         self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> AsyncStream[ChatCompletionChunk]:
-        """Create a streaming chat completion using DeepSeek's API.
+    ) -> dict:
+        """Build parameters for DeepSeek chat completion request.
 
-        Args:
-            context: The context object containing tools configuration
-                and other settings for the chat completion.
-            messages: The list of messages comprising the conversation
-                history and current request.
-
-        Returns:
-            A streaming response of chat completion chunks that can be
-            processed asynchronously.
+        DeepSeek doesn't support some OpenAI parameters like seed and max_completion_tokens.
         """
         params = {
             "model": self.model_name,
@@ -85,6 +76,4 @@ class DeepSeekLLMService(OpenAILLMService):
         }
 
         params.update(self._settings["extra"])
-
-        chunks = await self._client.chat.completions.create(**params)
-        return chunks
+        return params

src/pipecat/services/fireworks/llm.py

@@ -54,20 +54,13 @@ class FireworksLLMService(OpenAILLMService):
         logger.debug(f"Creating Fireworks client with api {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
 
-    async def get_chat_completions(
+    def build_chat_completion_params(
         self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ):
-        """Get chat completions from Fireworks API.
+    ) -> dict:
+        """Build parameters for Fireworks chat completion request.
 
-        Removes OpenAI-specific parameters not supported by Fireworks and
-        configures the request with Fireworks-compatible settings.
-
-        Args:
-            context: The OpenAI LLM context containing tools and settings.
-            messages: List of chat completion message parameters.
-
-        Returns:
-            Async generator yielding chat completion chunks from Fireworks API.
+        Fireworks doesn't support some OpenAI parameters like seed, max_completion_tokens,
+        and stream_options.
         """
         params = {
             "model": self.model_name,
@@ -83,6 +76,4 @@ class FireworksLLMService(OpenAILLMService):
         }
 
         params.update(self._settings["extra"])
-
-        chunks = await self._client.chat.completions.create(**params)
-        return chunks
+        return params

src/pipecat/services/gladia/stt.py

@@ -204,7 +204,7 @@ class GladiaSTTService(STTService):
         self,
         *,
         api_key: str,
-        region: Optional[Literal["us-west", "eu-west"]] = "eu-west",
+        region: Literal["us-west", "eu-west"] | None = None,
         url: str = "https://api.gladia.io/v2/live",
         confidence: float = 0.5,
         sample_rate: Optional[int] = None,
@@ -341,13 +341,6 @@ class GladiaSTTService(STTService):
 
         return settings
 
-    def _get_endpoint_url(self) -> str:
-        query_params = dict()
-        query_params["region"] = self._region or "eu-west"
-        query = urlencode(query_params)
-
-        return f"{self._url}?{query}"
-
     async def start(self, frame: StartFrame):
         """Start the Gladia STT websocket connection.
 
@@ -495,14 +488,16 @@ class GladiaSTTService(STTService):
 
     async def _setup_gladia(self, settings: Dict[str, Any]):
         async with aiohttp.ClientSession() as session:
+            params = {}
+            if self._region:
+                params["region"] = self._region
             async with session.post(
-                self._get_endpoint_url(),
-                headers={"X-Gladia-Key": self._api_key, "Content-Type": "application/json"},
+                self._url,
+                headers={"X-Gladia-Key": self._api_key},
                 json=settings,
+                params=params,
             ) as response:
                 if response.ok:
-                    response_text = await response.json()
-                    logger.error(f"Gladia response: {response_text}")
                     return await response.json()
                 else:
                     error_text = await response.text()

src/pipecat/services/google/tts.py

@@ -9,6 +9,9 @@
 This module provides integration with Google Cloud Text-to-Speech API,
 offering both HTTP-based synthesis with SSML support and streaming synthesis
 for real-time applications.
+
+It also includes GeminiTTSService which uses Gemini's TTS-specific models
+for natural voice control and multi-speaker conversations.
 """
 
 import json
@@ -19,7 +22,7 @@ from pipecat.utils.tracing.service_decorators import traced_tts
 # Suppress gRPC fork warnings
 os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
 
-from typing import AsyncGenerator, Literal, Optional
+from typing import AsyncGenerator, List, Literal, Optional
 
 from loguru import logger
 from pydantic import BaseModel
@@ -27,6 +30,7 @@ from pydantic import BaseModel
 from pipecat.frames.frames import (
     ErrorFrame,
     Frame,
+    StartFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
@@ -47,6 +51,15 @@ except ModuleNotFoundError as e:
     )
     raise Exception(f"Missing module: {e}")
 
+try:
+    from google import genai
+    from google.genai import types
+
+except ModuleNotFoundError as e:
+    logger.error(f"Exception: {e}")
+    logger.error("In order to use Gemini TTS, you need to `pip install pipecat-ai[google]`.")
+    raise Exception(f"Missing module: {e}")
+
 
 def language_to_google_tts_language(language: Language) -> Optional[str]:
     """Convert a Language enum to Google TTS language code.
@@ -642,3 +655,252 @@ class GoogleTTSService(TTSService):
             logger.exception(f"{self} error generating TTS: {e}")
             error_message = f"TTS generation error: {str(e)}"
             yield ErrorFrame(error=error_message)
+
+
+class GeminiTTSService(TTSService):
+    """Gemini Text-to-Speech service using Gemini TTS models.
+
+    Provides text-to-speech synthesis using Gemini's TTS-specific models
+    (gemini-2.5-flash-preview-tts and gemini-2.5-pro-preview-tts) with
+    support for natural voice control, multiple speakers, and voice styles.
+
+    Note:
+        Requires Google AI API key. This uses the Gemini API, not Google Cloud TTS.
+        Audio-out is currently a preview feature.
+
+    Example::
+
+        tts = GeminiTTSService(
+            api_key="your-google-ai-api-key",
+            model="gemini-2.5-flash-preview-tts",
+            voice_id="Kore",
+            params=GeminiTTSService.InputParams(
+                language=Language.EN_US,
+            )
+        )
+    """
+
+    GOOGLE_SAMPLE_RATE = 24000  # Google TTS always outputs at 24kHz
+
+    # List of available Gemini TTS voices
+    AVAILABLE_VOICES = [
+        "Zephyr",
+        "Puck",
+        "Charon",
+        "Kore",
+        "Fenrir",
+        "Leda",
+        "Orus",
+        "Aoede",
+        "Callirhoe",
+        "Autonoe",
+        "Enceladus",
+        "Iapetus",
+        "Umbriel",
+        "Algieba",
+        "Despina",
+        "Erinome",
+        "Algenib",
+        "Rasalgethi",
+        "Laomedeia",
+        "Achernar",
+        "Alnilam",
+        "Schedar",
+        "Gacrux",
+        "Pulcherrima",
+        "Achird",
+        "Zubenelgenubi",
+        "Vindemiatrix",
+        "Sadachbia",
+        "Sadaltager",
+        "Sulafar",
+    ]
+
+    class InputParams(BaseModel):
+        """Input parameters for Gemini TTS configuration.
+
+        Parameters:
+            language: Language for synthesis. Defaults to English.
+            multi_speaker: Whether to enable multi-speaker support.
+            speaker_configs: List of speaker configurations for multi-speaker mode.
+        """
+
+        language: Optional[Language] = Language.EN
+        multi_speaker: bool = False
+        speaker_configs: Optional[List[dict]] = None
+
+    def __init__(
+        self,
+        *,
+        api_key: str,
+        model: str = "gemini-2.5-flash-preview-tts",
+        voice_id: str = "Kore",
+        sample_rate: Optional[int] = None,
+        params: Optional[InputParams] = None,
+        **kwargs,
+    ):
+        """Initializes the Gemini TTS service.
+
+        Args:
+            api_key: Google AI API key for authentication.
+            model: Gemini TTS model to use. Must be a TTS model like
+                   "gemini-2.5-flash-preview-tts" or "gemini-2.5-pro-preview-tts".
+            voice_id: Voice name from the available Gemini voices.
+            sample_rate: Audio sample rate in Hz. If None, uses Google's default 24kHz.
+            params: TTS configuration parameters.
+            **kwargs: Additional arguments passed to parent TTSService.
+        """
+        if sample_rate and sample_rate != self.GOOGLE_SAMPLE_RATE:
+            logger.warning(
+                f"Google TTS only supports {self.GOOGLE_SAMPLE_RATE}Hz sample rate. "
+                f"Current rate of {sample_rate}Hz may cause issues."
+            )
+        super().__init__(sample_rate=sample_rate, **kwargs)
+
+        params = params or GeminiTTSService.InputParams()
+
+        if voice_id not in self.AVAILABLE_VOICES:
+            logger.warning(f"Voice '{voice_id}' not in known voices list. Using anyway.")
+
+        self._api_key = api_key
+        self._model = model
+        self._voice_id = voice_id
+        self._settings = {
+            "language": self.language_to_service_language(params.language)
+            if params.language
+            else "en-US",
+            "multi_speaker": params.multi_speaker,
+            "speaker_configs": params.speaker_configs,
+        }
+
+        self._client = genai.Client(api_key=api_key)
+
+    def can_generate_metrics(self) -> bool:
+        """Check if this service can generate processing metrics.
+
+        Returns:
+            True, as Gemini TTS service supports metrics generation.
+        """
+        return True
+
+    def language_to_service_language(self, language: Language) -> Optional[str]:
+        """Convert a Language enum to Gemini TTS language format.
+
+        Args:
+            language: The language to convert.
+
+        Returns:
+            The Gemini TTS-specific language code, or None if not supported.
+        """
+        return language_to_google_tts_language(language)
+
+    def set_voice(self, voice_id: str):
+        """Set the voice for TTS generation.
+
+        Args:
+            voice_id: Name of the voice to use from AVAILABLE_VOICES.
+        """
+        if voice_id not in self.AVAILABLE_VOICES:
+            logger.warning(f"Voice '{voice_id}' not in known voices list. Using anyway.")
+        self._voice_id = voice_id
+
+    async def start(self, frame: StartFrame):
+        """Start the Gemini TTS service.
+
+        Args:
+            frame: The start frame containing initialization parameters.
+        """
+        await super().start(frame)
+        if self.sample_rate != self.GOOGLE_SAMPLE_RATE:
+            logger.warning(
+                f"Google TTS requires {self.GOOGLE_SAMPLE_RATE}Hz sample rate. "
+                f"Current rate of {self.sample_rate}Hz may cause issues."
+            )
+
+    @traced_tts
+    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
+        """Generate speech from text using Gemini TTS models.
+
+        Args:
+            text: The text to synthesize into speech. Can include natural language
+                  instructions for style, tone, etc.
+
+        Yields:
+            Frame: Audio frames containing the synthesized speech.
+        """
+        logger.debug(f"{self}: Generating TTS [{text}]")
+
+        try:
+            await self.start_ttfb_metrics()
+
+            # Build the speech config
+            if self._settings["multi_speaker"] and self._settings["speaker_configs"]:
+                # Multi-speaker mode
+                speaker_voice_configs = []
+                for speaker_config in self._settings["speaker_configs"]:
+                    speaker_voice_configs.append(
+                        types.SpeakerVoiceConfig(
+                            speaker=speaker_config["speaker"],
+                            voice_config=types.VoiceConfig(
+                                prebuilt_voice_config=types.PrebuiltVoiceConfig(
+                                    voice_name=speaker_config.get("voice_id", self._voice_id)
+                                )
+                            ),
+                        )
+                    )
+
+                speech_config = types.SpeechConfig(
+                    multi_speaker_voice_config=types.MultiSpeakerVoiceConfig(
+                        speaker_voice_configs=speaker_voice_configs
+                    )
+                )
+            else:
+                # Single speaker mode
+                speech_config = types.SpeechConfig(
+                    voice_config=types.VoiceConfig(
+                        prebuilt_voice_config=types.PrebuiltVoiceConfig(voice_name=self._voice_id)
+                    )
+                )
+
+            # Create the generation config
+            generation_config = types.GenerateContentConfig(
+                response_modalities=["AUDIO"],
+                speech_config=speech_config,
+            )
+
+            # Generate the content
+            response = await self._client.aio.models.generate_content(
+                model=self._model,
+                contents=text,
+                config=generation_config,
+            )
+
+            await self.start_tts_usage_metrics(text)
+
+            yield TTSStartedFrame()
+
+            # Extract audio data from response
+            if response.candidates and len(response.candidates) > 0:
+                candidate = response.candidates[0]
+                if candidate.content and candidate.content.parts:
+                    for part in candidate.content.parts:
+                        if part.inline_data and part.inline_data.mime_type.startswith("audio/"):
+                            audio_data = part.inline_data.data
+                            await self.stop_ttfb_metrics()
+
+                            # Gemini TTS returns PCM audio data, chunk it appropriately
+                            CHUNK_SIZE = self.chunk_size
+
+                            for i in range(0, len(audio_data), CHUNK_SIZE):
+                                chunk = audio_data[i : i + CHUNK_SIZE]
+                                if not chunk:
+                                    break
+                                frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
+                                yield frame
+
+            yield TTSStoppedFrame()
+
+        except Exception as e:
+            logger.exception(f"{self} error generating TTS: {e}")
+            error_message = f"Gemini TTS generation error: {str(e)}"
+            yield ErrorFrame(error=error_message)

src/pipecat/services/heygen/client.py

@@ -362,7 +362,7 @@ class HeyGenClient:
         """Simulate audio playback timing with appropriate delays."""
         # Only sleep after we've sent the first second of audio
         # This appears to reduce the latency to receive the answer from HeyGen
-        if self._audio_seconds_sent < 1.0:
+        if self._audio_seconds_sent < 3.0:
             self._audio_seconds_sent += self._send_interval
             self._next_send_time = time.monotonic() + self._send_interval
             return

src/pipecat/services/heygen/video.py

@@ -20,6 +20,7 @@ from loguru import logger
 from pipecat.audio.utils import create_stream_resampler
 from pipecat.frames.frames import (
     AudioRawFrame,
+    BotStartedSpeakingFrame,
     CancelFrame,
     EndFrame,
     Frame,
@@ -30,6 +31,7 @@ from pipecat.frames.frames import (
     SpeechOutputAudioRawFrame,
     StartFrame,
     TTSAudioRawFrame,
+    TTSStartedFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
 )
@@ -232,9 +234,24 @@ class HeyGenVideoService(AIService):
             await self.push_frame(frame, direction)
         elif isinstance(frame, TTSAudioRawFrame):
             await self._handle_audio_frame(frame)
+        elif isinstance(frame, TTSStartedFrame):
+            await self.start_ttfb_metrics()
+        elif isinstance(frame, BotStartedSpeakingFrame):
+            # We constantly receive audio through WebRTC, but most of the time it is silence.
+            # As soon as we receive actual audio, the base output transport will create a
+            # BotStartedSpeakingFrame, which we can use as a signal for the TTFB metrics.
+            await self.stop_ttfb_metrics()
         else:
             await self.push_frame(frame, direction)
 
+    def can_generate_metrics(self) -> bool:
+        """Check if the service can generate metrics.
+
+        Returns:
+            True if metrics generation is supported.
+        """
+        return True
+
     async def _handle_user_started_speaking(self):
         """Handle the event when a user starts speaking.
 

src/pipecat/services/moondream/vision.py

@@ -62,7 +62,7 @@ class MoondreamService(VisionService):
     """
 
     def __init__(
-        self, *, model="vikhyatk/moondream2", revision="2024-08-26", use_cpu=False, **kwargs
+        self, *, model="vikhyatk/moondream2", revision="2025-01-09", use_cpu=False, **kwargs
     ):
         """Initialize the Moondream service.
 
@@ -82,14 +82,15 @@ class MoondreamService(VisionService):
             device = torch.device("cpu")
             dtype = torch.float32
 
-        self._tokenizer = AutoTokenizer.from_pretrained(model, revision=revision)
-
         logger.debug("Loading Moondream model...")
 
         self._model = AutoModelForCausalLM.from_pretrained(
-            model, trust_remote_code=True, revision=revision
-        ).to(device=device, dtype=dtype)
-        self._model.eval()
+            model,
+            trust_remote_code=True,
+            revision=revision,
+            device_map={"": device},
+            torch_dtype=dtype,
+        ).eval()
 
         logger.debug("Loaded Moondream model")
 
@@ -121,9 +122,7 @@ class MoondreamService(VisionService):
             """
             image = Image.frombytes(frame.format, frame.size, frame.image)
             image_embeds = self._model.encode_image(image)
-            description = self._model.answer_question(
-                image_embeds=image_embeds, question=frame.text, tokenizer=self._tokenizer
-            )
+            description = self._model.query(image_embeds, frame.text)["answer"]
             return description
 
         description = await asyncio.to_thread(get_image_description, frame)

src/pipecat/services/openai/base_llm.py

@@ -6,6 +6,7 @@
 
 """Base OpenAI LLM service implementation."""
 
+import asyncio
 import base64
 import json
 from typing import Any, Dict, List, Mapping, Optional
@@ -14,6 +15,7 @@ import httpx
 from loguru import logger
 from openai import (
     NOT_GIVEN,
+    APITimeoutError,
     AsyncOpenAI,
     AsyncStream,
     DefaultAsyncHttpxClient,
@@ -91,6 +93,8 @@ class BaseOpenAILLMService(LLMService):
         project=None,
         default_headers: Optional[Mapping[str, str]] = None,
         params: Optional[InputParams] = None,
+        retry_timeout_secs: Optional[float] = 5.0,
+        retry_on_timeout: Optional[bool] = False,
         **kwargs,
     ):
         """Initialize the BaseOpenAILLMService.
@@ -103,6 +107,8 @@ class BaseOpenAILLMService(LLMService):
             project: OpenAI project ID.
             default_headers: Additional HTTP headers to include in requests.
             params: Input parameters for model configuration and behavior.
+            retry_timeout_secs: Request timeout in seconds. Defaults to 5.0 seconds.
+            retry_on_timeout: Whether to retry the request once if it times out.
             **kwargs: Additional arguments passed to the parent LLMService.
         """
         super().__init__(**kwargs)
@@ -119,6 +125,8 @@ class BaseOpenAILLMService(LLMService):
             "max_completion_tokens": params.max_completion_tokens,
             "extra": params.extra if isinstance(params.extra, dict) else {},
         }
+        self._retry_timeout_secs = retry_timeout_secs
+        self._retry_on_timeout = retry_on_timeout
         self.set_model_name(model)
         self._client = self.create_client(
             api_key=api_key,
@@ -175,7 +183,7 @@ class BaseOpenAILLMService(LLMService):
     async def get_chat_completions(
         self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
     ) -> AsyncStream[ChatCompletionChunk]:
-        """Get streaming chat completions from OpenAI API.
+        """Get streaming chat completions from OpenAI API with optional timeout and retry.
 
         Args:
             context: The LLM context containing tools and configuration.
@@ -184,6 +192,37 @@ class BaseOpenAILLMService(LLMService):
         Returns:
             Async stream of chat completion chunks.
         """
+        params = self.build_chat_completion_params(context, messages)
+
+        if self._retry_on_timeout:
+            try:
+                chunks = await asyncio.wait_for(
+                    self._client.chat.completions.create(**params), timeout=self._retry_timeout_secs
+                )
+                return chunks
+            except (APITimeoutError, asyncio.TimeoutError):
+                # Retry, this time without a timeout so we get a response
+                logger.debug(f"{self}: Retrying chat completion due to timeout")
+                chunks = await self._client.chat.completions.create(**params)
+                return chunks
+        else:
+            chunks = await self._client.chat.completions.create(**params)
+            return chunks
+
+    def build_chat_completion_params(
+        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
+    ) -> dict:
+        """Build parameters for chat completion request.
+
+        Subclasses can override this to customize parameters for different providers.
+
+        Args:
+            context: The LLM context containing tools and configuration.
+            messages: List of chat completion messages to send.
+
+        Returns:
+            Dictionary of parameters for the chat completion request.
+        """
         params = {
             "model": self.model_name,
             "stream": True,
@@ -201,9 +240,7 @@ class BaseOpenAILLMService(LLMService):
         }
 
         params.update(self._settings["extra"])
-
-        chunks = await self._client.chat.completions.create(**params)
-        return chunks
+        return params
 
     async def _stream_chat_completions(
         self, context: OpenAILLMContext

src/pipecat/services/openai_realtime_beta/openai.py

@@ -171,6 +171,15 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         """
         self._audio_input_paused = paused
 
+    def _is_modality_enabled(self, modality: str) -> bool:
+        """Check if a specific modality is enabled, "text" or "audio"."""
+        modalities = self._session_properties.modalities or ["audio", "text"]
+        return modality in modalities
+
+    def _get_enabled_modalities(self) -> list[str]:
+        """Get the list of enabled modalities."""
+        return self._session_properties.modalities or ["audio", "text"]
+
     async def retrieve_conversation_item(self, item_id: str):
         """Retrieve a conversation item by ID from the server.
 
@@ -243,7 +252,9 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         await self.stop_all_metrics()
         if self._current_assistant_response:
             await self.push_frame(LLMFullResponseEndFrame())
-            await self.push_frame(TTSStoppedFrame())
+            # Only push TTSStoppedFrame if audio modality is enabled
+            if self._is_modality_enabled("audio"):
+                await self.push_frame(TTSStoppedFrame())
 
     async def _handle_user_started_speaking(self, frame):
         pass
@@ -469,6 +480,8 @@ class OpenAIRealtimeBetaLLMService(LLMService):
                 await self._handle_evt_speech_started(evt)
             elif evt.type == "input_audio_buffer.speech_stopped":
                 await self._handle_evt_speech_stopped(evt)
+            elif evt.type == "response.text.delta":
+                await self._handle_evt_text_delta(evt)
             elif evt.type == "response.audio_transcript.delta":
                 await self._handle_evt_audio_transcript_delta(evt)
             elif evt.type == "error":
@@ -617,6 +630,10 @@ class OpenAIRealtimeBetaLLMService(LLMService):
             # Response message without preceding user message. Add it to the context.
             await self._handle_assistant_output(evt.response.output)
 
+    async def _handle_evt_text_delta(self, evt):
+        if evt.delta:
+            await self.push_frame(LLMTextFrame(evt.delta))
+
     async def _handle_evt_audio_transcript_delta(self, evt):
         if evt.delta:
             await self.push_frame(LLMTextFrame(evt.delta))
@@ -723,7 +740,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         await self.start_ttfb_metrics()
         await self.send_client_event(
             events.ResponseCreateEvent(
-                response=events.ResponseProperties(modalities=["audio", "text"])
+                response=events.ResponseProperties(modalities=self._get_enabled_modalities())
             )
         )
 

src/pipecat/services/openpipe/llm.py

@@ -13,14 +13,13 @@ enabling integration with OpenPipe's fine-tuning and monitoring capabilities.
 from typing import Dict, List, Optional
 
 from loguru import logger
-from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
+from openai.types.chat import ChatCompletionMessageParam
 
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai.llm import OpenAILLMService
 
 try:
     from openpipe import AsyncOpenAI as OpenPipeAI
-    from openpipe import AsyncStream
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
     logger.error("In order to use OpenPipe, you need to `pip install pipecat-ai[openpipe]`.")
@@ -87,22 +86,27 @@ class OpenPipeLLMService(OpenAILLMService):
         )
         return client
 
-    async def get_chat_completions(
+    def build_chat_completion_params(
         self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> AsyncStream[ChatCompletionChunk]:
-        """Generate streaming chat completions with OpenPipe logging.
+    ) -> dict:
+        """Build parameters for OpenPipe chat completion request.
+
+        Adds OpenPipe-specific logging and tagging parameters.
 
         Args:
-            context: The OpenAI LLM context containing conversation state.
-            messages: List of chat completion message parameters.
+            context: The LLM context containing tools and configuration.
+            messages: List of chat completion messages to send.
 
         Returns:
-            Async stream of chat completion chunks.
+            Dictionary of parameters for the chat completion request.
         """
-        chunks = await self._client.chat.completions.create(
-            model=self.model_name,
-            stream=True,
-            messages=messages,
-            openpipe={"tags": self._tags, "log_request": True},
-        )
-        return chunks
+        # Start with base parameters
+        params = super().build_chat_completion_params(context, messages)
+
+        # Add OpenPipe-specific parameters
+        params["openpipe"] = {
+            "tags": self._tags,
+            "log_request": True,
+        }
+
+        return params

src/pipecat/services/perplexity/llm.py

@@ -13,8 +13,8 @@ reporting patterns while maintaining compatibility with the Pipecat framework.
 
 from typing import List
 
-from openai import NOT_GIVEN, AsyncStream
-from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
+from openai import NOT_GIVEN
+from openai.types.chat import ChatCompletionMessageParam
 
 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
@@ -53,17 +53,12 @@ class PerplexityLLMService(OpenAILLMService):
         self._has_reported_prompt_tokens = False
         self._is_processing = False
 
-    async def get_chat_completions(
+    def build_chat_completion_params(
         self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> AsyncStream[ChatCompletionChunk]:
-        """Get chat completions from Perplexity API using OpenAI-compatible parameters.
+    ) -> dict:
+        """Build parameters for Perplexity chat completion request.
 
-        Args:
-            context: The context containing conversation history and settings.
-            messages: The messages to send to the API.
-
-        Returns:
-            A stream of chat completion chunks from the Perplexity API.
+        Perplexity uses a subset of OpenAI parameters and doesn't support tools.
         """
         params = {
             "model": self.model_name,
@@ -83,8 +78,7 @@ class PerplexityLLMService(OpenAILLMService):
         if self._settings["max_tokens"] is not NOT_GIVEN:
             params["max_tokens"] = self._settings["max_tokens"]
 
-        chunks = await self._client.chat.completions.create(**params)
-        return chunks
+        return params
 
     async def _process_context(self, context: OpenAILLMContext):
         """Process a context through the LLM and accumulate token usage metrics.

src/pipecat/services/playht/tts.py

@@ -14,6 +14,7 @@ import io
 import json
 import struct
 import uuid
+import warnings
 from typing import AsyncGenerator, Optional
 
 import aiohttp
@@ -37,14 +38,11 @@ from pipecat.transcriptions.language import Language
 from pipecat.utils.tracing.service_decorators import traced_tts
 
 try:
-    from pyht.async_client import AsyncClient
-    from pyht.client import Format, TTSOptions
-    from pyht.client import Language as PlayHTLanguage
     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 PlayHT, you need to `pip install pipecat-ai[playht]`.")
+    logger.error("In order to use PlayHTTTSService, you need to `pip install pipecat-ai[playht]`.")
     raise Exception(f"Missing module: {e}")
 
 
@@ -429,7 +427,8 @@ class PlayHTHttpTTSService(TTSService):
         user_id: str,
         voice_url: str,
         voice_engine: str = "Play3.0-mini",
-        protocol: str = "http",  # Options: http, ws
+        protocol: Optional[str] = None,
+        output_format: str = "wav",
         sample_rate: Optional[int] = None,
         params: Optional[InputParams] = None,
         **kwargs,
@@ -441,40 +440,46 @@ class PlayHTHttpTTSService(TTSService):
             user_id: PlayHT user ID for authentication.
             voice_url: URL of the voice to use for synthesis.
             voice_engine: Voice engine to use. Defaults to "Play3.0-mini".
-            protocol: Protocol to use ("http" or "ws"). Defaults to "http".
+            protocol: Protocol to use ("http" or "ws").
+
+                .. deprecated:: 0.0.80
+                    This parameter no longer has any effect and will be removed in a future version.
+                    Use PlayHTTTSService for WebSocket or PlayHTHttpTTSService for HTTP.
+
+            output_format: Audio output format. Defaults to "wav".
             sample_rate: Audio sample rate. If None, uses default.
             params: Additional input parameters for voice customization.
             **kwargs: Additional arguments passed to parent TTSService.
         """
         super().__init__(sample_rate=sample_rate, **kwargs)
 
+        # Warn about deprecated protocol parameter if explicitly provided
+        if protocol:
+            warnings.warn(
+                "The 'protocol' parameter is deprecated and will be removed in a future version.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
         params = params or PlayHTHttpTTSService.InputParams()
 
         self._user_id = user_id
         self._api_key = api_key
 
-        self._client = AsyncClient(
-            user_id=self._user_id,
-            api_key=self._api_key,
-        )
-
         # Check if voice_engine contains protocol information (backward compatibility)
         if "-http" in voice_engine:
             # Extract the base engine name
             voice_engine = voice_engine.replace("-http", "")
-            protocol = "http"
         elif "-ws" in voice_engine:
             # Extract the base engine name
             voice_engine = voice_engine.replace("-ws", "")
-            protocol = "ws"
 
         self._settings = {
             "language": self.language_to_service_language(params.language)
             if params.language
             else "english",
-            "format": Format.FORMAT_WAV,
+            "output_format": output_format,
             "voice_engine": voice_engine,
-            "protocol": protocol,
             "speed": params.speed,
             "seed": params.seed,
         }
@@ -490,26 +495,6 @@ class PlayHTHttpTTSService(TTSService):
         await super().start(frame)
         self._settings["sample_rate"] = self.sample_rate
 
-    def _create_options(self) -> TTSOptions:
-        """Create TTSOptions object from current settings."""
-        language_str = self._settings["language"]
-        playht_language = None
-        if language_str:
-            # Convert string to PlayHT Language enum
-            for lang in PlayHTLanguage:
-                if lang.value == language_str:
-                    playht_language = lang
-                    break
-
-        return TTSOptions(
-            voice=self._voice_id,
-            language=playht_language,
-            sample_rate=self.sample_rate,
-            format=self._settings["format"],
-            speed=self._settings["speed"],
-            seed=self._settings["seed"],
-        )
-
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
 
@@ -542,41 +527,78 @@ class PlayHTHttpTTSService(TTSService):
         logger.debug(f"{self}: Generating TTS [{text}]")
 
         try:
-            options = self._create_options()
-
             await self.start_ttfb_metrics()
 
-            playht_gen = self._client.tts(
-                text,
-                voice_engine=self._settings["voice_engine"],
-                protocol=self._settings["protocol"],
-                options=options,
-            )
+            # Prepare the request payload
+            payload = {
+                "text": text,
+                "voice": self._voice_id,
+                "voice_engine": self._settings["voice_engine"],
+                "output_format": self._settings["output_format"],
+                "sample_rate": self.sample_rate,
+                "language": self._settings["language"],
+            }
+
+            # Add optional parameters if they exist
+            if self._settings["speed"] is not None:
+                payload["speed"] = self._settings["speed"]
+            if self._settings["seed"] is not None:
+                payload["seed"] = self._settings["seed"]
+
+            headers = {
+                "Authorization": f"Bearer {self._api_key}",
+                "X-User-Id": self._user_id,
+                "Content-Type": "application/json",
+                "Accept": "*/*",
+            }
 
             await self.start_tts_usage_metrics(text)
 
             yield TTSStartedFrame()
 
-            b = bytearray()
-            in_header = True
-            async for chunk in playht_gen:
-                # skip the RIFF header.
-                if in_header:
-                    b.extend(chunk)
-                    if len(b) <= 36:
-                        continue
-                    else:
-                        fh = io.BytesIO(b)
-                        fh.seek(36)
-                        (data, size) = struct.unpack("<4sI", fh.read(8))
-                        while data != b"data":
-                            fh.read(size)
-                            (data, size) = struct.unpack("<4sI", fh.read(8))
-                        in_header = False
-                elif len(chunk) > 0:
-                    await self.stop_ttfb_metrics()
-                    frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
-                    yield frame
+            async with aiohttp.ClientSession() as session:
+                async with session.post(
+                    "https://api.play.ht/api/v2/tts/stream",
+                    headers=headers,
+                    json=payload,
+                ) as response:
+                    if response.status not in (200, 201):
+                        error_text = await response.text()
+                        raise Exception(f"PlayHT API error {response.status}: {error_text}")
+
+                    in_header = True
+                    buffer = b""
+
+                    CHUNK_SIZE = self.chunk_size
+
+                    async for chunk in response.content.iter_chunked(CHUNK_SIZE):
+                        if len(chunk) == 0:
+                            continue
+
+                        # Skip the RIFF header
+                        if in_header:
+                            buffer += chunk
+                            if len(buffer) <= 36:
+                                continue
+                            else:
+                                fh = io.BytesIO(buffer)
+                                fh.seek(36)
+                                (data, size) = struct.unpack("<4sI", fh.read(8))
+                                while data != b"data":
+                                    fh.read(size)
+                                    (data, size) = struct.unpack("<4sI", fh.read(8))
+                                # Extract audio data after header
+                                audio_data = buffer[fh.tell() :]
+                                if len(audio_data) > 0:
+                                    await self.stop_ttfb_metrics()
+                                    frame = TTSAudioRawFrame(audio_data, self.sample_rate, 1)
+                                    yield frame
+                                in_header = False
+                        elif len(chunk) > 0:
+                            await self.stop_ttfb_metrics()
+                            frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
+                            yield frame
+
         except Exception as e:
             logger.error(f"{self} error generating TTS: {e}")
         finally:

src/pipecat/services/sambanova/llm.py

@@ -68,17 +68,20 @@ class SambaNovaLLMService(OpenAILLMService):  # type: ignore
         logger.debug(f"Creating SambaNova client with API {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
 
-    async def get_chat_completions(
+    def build_chat_completion_params(
         self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> Any:
-        """Get chat completions from SambaNova API endpoint.
+    ) -> dict:
+        """Build parameters for SambaNova chat completion request.
+
+        SambaNova doesn't support some OpenAI parameters like frequency_penalty,
+        presence_penalty, and seed.
 
         Args:
-            context: OpenAI LLM context containing tools and configuration.
-            messages: List of chat completion message parameters.
+            context: The LLM context containing tools and configuration.
+            messages: List of chat completion messages to send.
 
         Returns:
-            Chat completion response stream from SambaNova API.
+            Dictionary of parameters for the chat completion request.
         """
         params = {
             "model": self.model_name,
@@ -94,9 +97,7 @@ class SambaNovaLLMService(OpenAILLMService):  # type: ignore
         }
 
         params.update(self._settings["extra"])
-
-        chunks = await self._client.chat.completions.create(**params)
-        return chunks
+        return params
 
     @traced_llm  # type: ignore
     async def _process_context(self, context: OpenAILLMContext) -> AsyncStream[ChatCompletionChunk]:

src/pipecat/services/tavus/video.py

@@ -19,6 +19,7 @@ from loguru import logger
 
 from pipecat.audio.utils import create_stream_resampler
 from pipecat.frames.frames import (
+    BotStartedSpeakingFrame,
     CancelFrame,
     EndFrame,
     Frame,
@@ -29,6 +30,7 @@ from pipecat.frames.frames import (
     StartFrame,
     StartInterruptionFrame,
     TTSAudioRawFrame,
+    TTSStartedFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessorSetup
 from pipecat.services.ai_service import AIService
@@ -229,6 +231,13 @@ class TavusVideoService(AIService):
         elif isinstance(frame, OutputTransportReadyFrame):
             self._transport_ready = True
             await self.push_frame(frame, direction)
+        elif isinstance(frame, TTSStartedFrame):
+            await self.start_ttfb_metrics()
+        elif isinstance(frame, BotStartedSpeakingFrame):
+            # We constantly receive audio through WebRTC, but most of the time it is silence.
+            # As soon as we receive actual audio, the base output transport will create a
+            # BotStartedSpeakingFrame, which we can use as a signal for the TTFB metrics.
+            await self.stop_ttfb_metrics()
         else:
             await self.push_frame(frame, direction)
 

src/pipecat/services/websocket_service.py

@@ -12,6 +12,7 @@ from typing import Awaitable, Callable, Optional
 
 import websockets
 from loguru import logger
+from websockets.exceptions import ConnectionClosedOK
 from websockets.protocol import State
 
 from pipecat.frames.frames import ErrorFrame
@@ -82,12 +83,10 @@ class WebsocketService(ABC):
             try:
                 await self._receive_messages()
                 retry_count = 0  # Reset counter on successful message receive
-                if self._websocket and self._websocket.state is State.CLOSED:
-                    raise websockets.ConnectionClosedOK(
-                        self._websocket.close_rcvd,
-                        self._websocket.close_sent,
-                        self._websocket.close_rcvd_then_sent,
-                    )
+            except ConnectionClosedOK as e:
+                # Normal closure, don't retry
+                logger.debug(f"{self} connection closed normally: {e}")
+                break
             except Exception as e:
                 message = f"{self} error receiving messages: {e}"
                 logger.error(message)

src/pipecat/services/whisper/base_stt.py

@@ -186,7 +186,7 @@ class BaseWhisperSTTService(SegmentedSTTService):
             language: The Language enum value to use for transcription.
         """
         logger.info(f"Switching STT language to: [{language}]")
-        self._language = language
+        self._language = self.language_to_service_language(language)
 
     @traced_stt
     async def _handle_transcription(

src/pipecat/transports/base_output.py

@@ -12,7 +12,6 @@ output processing, including frame buffering, mixing, timing, and media streamin
 
 import asyncio
 import itertools
-import sys
 import time
 from concurrent.futures import ThreadPoolExecutor
 from typing import Any, AsyncGenerator, Dict, List, Mapping, Optional
@@ -429,7 +428,7 @@ class BaseOutputTransport(FrameProcessor):
                 frame: The end frame signaling sender shutdown.
             """
             # Let the sink tasks process the queue until they reach this EndFrame.
-            await self._clock_queue.put((sys.maxsize, frame.id, frame))
+            await self._clock_queue.put((float("inf"), frame.id, frame))
             await self._audio_queue.put(frame)
 
             # At this point we have enqueued an EndFrame and we need to wait for
@@ -828,7 +827,9 @@ class BaseOutputTransport(FrameProcessor):
         def _create_clock_task(self):
             """Create the clock/timing processing task."""
             if not self._clock_task:
-                self._clock_queue = WatchdogPriorityQueue(self._transport.task_manager)
+                self._clock_queue = WatchdogPriorityQueue(
+                    self._transport.task_manager, tuple_size=3
+                )
                 self._clock_task = self._transport.create_task(self._clock_task_handler())
 
         async def _cancel_clock_task(self):

src/pipecat/transports/network/small_webrtc.py

@@ -226,6 +226,7 @@ class SmallWebRTCClient:
         self._audio_in_channels = None
         self._in_sample_rate = None
         self._out_sample_rate = None
+        self._leave_counter = 0
 
         # We are always resampling it for 16000 if the sample_rate that we receive is bigger than that.
         # otherwise we face issues with Silero VAD
@@ -395,6 +396,7 @@ class SmallWebRTCClient:
         self._in_sample_rate = _params.audio_in_sample_rate or frame.audio_in_sample_rate
         self._out_sample_rate = _params.audio_out_sample_rate or frame.audio_out_sample_rate
         self._params = _params
+        self._leave_counter += 1
 
     async def connect(self):
         """Establish the WebRTC connection."""
@@ -407,6 +409,10 @@ class SmallWebRTCClient:
 
     async def disconnect(self):
         """Disconnect from the WebRTC peer."""
+        self._leave_counter -= 1
+        if self._leave_counter > 0:
+            return
+
         if self.is_connected and not self.is_closing:
             logger.info(f"Disconnecting to Small WebRTC")
             self._closing = True

src/pipecat/transports/services/daily.py

@@ -560,7 +560,7 @@ class DailyTransportClient(EventHandler):
         self._out_sample_rate = self._params.audio_out_sample_rate or frame.audio_out_sample_rate
 
         if self._params.audio_in_enabled:
-            if self._params.audio_in_user_tracks and not self._audio_task:
+            if self._params.audio_in_user_tracks and not self._audio_task and self._task_manager:
                 self._audio_queue = WatchdogQueue(self._task_manager)
                 self._audio_task = self._task_manager.create_task(
                     self._callback_task_handler(self._audio_queue),

src/pipecat/utils/asyncio/watchdog_priority_queue.py

@@ -20,23 +20,42 @@ from pipecat.utils.asyncio.task_manager import BaseTaskManager
 
 
 @dataclass
-class _WatchdogPriorityCancelSentinel:
-    def __lt__(self, other):
-        return True
+class WatchdogPriorityCancelSentinel:
+    """Sentinel object used in priority queues to force cancellation.
+
+    An instance of this class is typically inserted into a
+    `WatchdogPriorityQueue` to act as a high-priority marker asyncio task
+    cancellation.
+
+    """
+
+    pass
 
 
 class WatchdogPriorityQueue(asyncio.PriorityQueue):
-    """Watchdog-enabled asyncio PriorityQueue.
+    """Class for watchdog-enabled asyncio PriorityQueue.
 
     An asynchronous priority queue that resets the current task watchdog
     timer. This is necessary to avoid task watchdog timers to expire while we
     are waiting to get an item from the queue.
+
+    This queue expects items to be tuples, with the actual payload stored
+    in the last element. All preceding elements are treated as numeric
+    priority fields. For example:
+
+        (0, 1, "foo")
+
+    The tuple length must be specified at creation time so the queue can
+    correctly construct special items, such as the watchdog cancel sentinel,
+    with the proper tuple structure.
+
     """
 
     def __init__(
         self,
         manager: BaseTaskManager,
         *,
+        tuple_size: int,
         maxsize: int = 0,
         timeout: float = 2.0,
     ) -> None:
@@ -44,12 +63,14 @@ class WatchdogPriorityQueue(asyncio.PriorityQueue):
 
         Args:
             manager: The task manager for watchdog timer control.
+            tuple_size: The number of values in each inserted tuple.
             maxsize: Maximum queue size. 0 means unlimited.
             timeout: Timeout in seconds between watchdog resets while waiting.
         """
         super().__init__(maxsize)
         self._manager = manager
         self._timeout = timeout
+        self._tuple_size = tuple_size
 
     async def get(self):
         """Get an item from the queue with watchdog monitoring.
@@ -62,7 +83,10 @@ class WatchdogPriorityQueue(asyncio.PriorityQueue):
         else:
             get_result = await super().get()
 
-        if isinstance(get_result, _WatchdogPriorityCancelSentinel):
+        # Value is always at the end of the tuple.
+        item = get_result[-1]
+
+        if isinstance(item, WatchdogPriorityCancelSentinel):
             logger.trace(
                 "Received WatchdogPriorityCancelSentinel, throwing CancelledError to force cancelling"
             )
@@ -91,7 +115,10 @@ class WatchdogPriorityQueue(asyncio.PriorityQueue):
         forces the task to raise CancelledError when consumed, ensuring proper
         task termination.
         """
-        super().put_nowait(_WatchdogPriorityCancelSentinel())
+        item = [float("-inf")] * self._tuple_size
+        # Values go always at the end.
+        item[-1] = WatchdogPriorityCancelSentinel()
+        super().put_nowait(tuple(item))
 
     async def _watchdog_get(self):
         """Get item from queue while periodically resetting watchdog timer."""

src/pipecat/utils/asyncio/watchdog_queue.py

@@ -20,7 +20,14 @@ from pipecat.utils.asyncio.task_manager import BaseTaskManager
 
 
 @dataclass
-class _WatchdogQueueCancelSentinel:
+class WatchdogQueueCancelSentinel:
+    """Sentinel object used in queues to force cancellation.
+
+    An instance of this class is typically inserted into a `WatchdogQueue` to
+    act as a marker for asyncio task cancellation.
+
+    """
+
     pass
 
 
@@ -61,7 +68,7 @@ class WatchdogQueue(asyncio.Queue):
         else:
             get_result = await super().get()
 
-        if isinstance(get_result, _WatchdogQueueCancelSentinel):
+        if isinstance(get_result, WatchdogQueueCancelSentinel):
             logger.trace(
                 "Received WatchdogQueueCancelFrame, throwing CancelledError to force cancelling"
             )
@@ -90,7 +97,7 @@ class WatchdogQueue(asyncio.Queue):
         forces the task to raise CancelledError when consumed, ensuring proper
         task termination.
         """
-        super().put_nowait(_WatchdogQueueCancelSentinel())
+        super().put_nowait(WatchdogQueueCancelSentinel())
 
     async def _watchdog_get(self):
         """Get item from queue while periodically resetting watchdog timer."""

tests/test_stt_mute_filter.py

@@ -17,6 +17,8 @@ from pipecat.frames.frames import (
     TranscriptionFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
+    VADUserStartedSpeakingFrame,
+    VADUserStoppedSpeakingFrame,
 )
 from pipecat.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
 from pipecat.tests.utils import SleepFrame, run_test
@@ -28,15 +30,19 @@ class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase):
 
         frames_to_send = [
             BotStartedSpeakingFrame(),  # First bot speech starts
+            VADUserStartedSpeakingFrame(),  # Should be suppressed
             UserStartedSpeakingFrame(),  # Should be suppressed
             InputAudioRawFrame(
                 audio=b"", sample_rate=16000, num_channels=1
             ),  # Should be suppressed
+            VADUserStoppedSpeakingFrame(),  # Should be suppressed
             UserStoppedSpeakingFrame(),  # Should be suppressed
             BotStoppedSpeakingFrame(),  # First bot speech ends
             BotStartedSpeakingFrame(),  # Second bot speech
+            VADUserStartedSpeakingFrame(),  # Should pass through
             UserStartedSpeakingFrame(),  # Should pass through
             InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1),  # Should pass through
+            VADUserStoppedSpeakingFrame(),  # Should pass through
             UserStoppedSpeakingFrame(),  # Should pass through
             BotStoppedSpeakingFrame(),
         ]
@@ -47,8 +53,10 @@ class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase):
             BotStoppedSpeakingFrame,
             STTMuteFrame,  # mute=False
             BotStartedSpeakingFrame,
+            VADUserStartedSpeakingFrame,  # Now passes through
             UserStartedSpeakingFrame,  # Now passes through
             InputAudioRawFrame,  # Now passes through
+            VADUserStoppedSpeakingFrame,  # Now passes through
             UserStoppedSpeakingFrame,  # Now passes through
             BotStoppedSpeakingFrame,
         ]
@@ -64,20 +72,26 @@ class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase):
 
         frames_to_send = [
             BotStartedSpeakingFrame(),  # First speech starts
+            VADUserStartedSpeakingFrame(),  # Should be suppressed
             UserStartedSpeakingFrame(),  # Should be suppressed
             InputAudioRawFrame(
                 audio=b"", sample_rate=16000, num_channels=1
             ),  # Should be suppressed
+            VADUserStoppedSpeakingFrame(),  # Should be suppressed
             UserStoppedSpeakingFrame(),  # Should be suppressed
             BotStoppedSpeakingFrame(),  # First speech ends
+            VADUserStartedSpeakingFrame(),  # Should pass through
             UserStartedSpeakingFrame(),  # Should pass through
             InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1),  # Should pass through
+            VADUserStoppedSpeakingFrame(),  # Should pass through
             UserStoppedSpeakingFrame(),  # Should pass through
             BotStartedSpeakingFrame(),  # Second speech starts
+            VADUserStartedSpeakingFrame(),  # Should be suppressed again
             UserStartedSpeakingFrame(),  # Should be suppressed again
             InputAudioRawFrame(
                 audio=b"", sample_rate=16000, num_channels=1
             ),  # Should be suppressed again
+            VADUserStoppedSpeakingFrame(),  # Should be suppressed again
             UserStoppedSpeakingFrame(),  # Should be suppressed again
             BotStoppedSpeakingFrame(),  # Second speech ends
         ]
@@ -87,8 +101,10 @@ class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase):
             STTMuteFrame,  # mute=True
             BotStoppedSpeakingFrame,
             STTMuteFrame,  # mute=False
+            VADUserStartedSpeakingFrame,
             UserStartedSpeakingFrame,
             InputAudioRawFrame,
+            VADUserStoppedSpeakingFrame,
             UserStoppedSpeakingFrame,
             BotStartedSpeakingFrame,
             STTMuteFrame,  # mute=True
@@ -146,14 +162,18 @@ class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase):
     #     filter = STTMuteFilter(config=STTMuteConfig(strategies={STTMuteStrategy.FUNCTION_CALL}))
 
     #     frames_to_send = [
+    #         VADUserStartedSpeakingFrame(),  # Should pass through initially
     #         UserStartedSpeakingFrame(),  # Should pass through initially
+    #         VADUserStoppedSpeakingFrame(),
     #         UserStoppedSpeakingFrame(),
     #         FunctionCallInProgressFrame(
     #             function_name="get_weather",
     #             tool_call_id="call_123",
     #             arguments='{"location": "San Francisco"}',
     #         ),  # Start function call
+    #         VADUserStartedSpeakingFrame(),  # Should be suppressed
     #         UserStartedSpeakingFrame(),  # Should be suppressed
+    #         VADUserStoppedSpeakingFrame(),  # Should be suppressed
     #         UserStoppedSpeakingFrame(),  # Should be suppressed
     #         FunctionCallResultFrame(
     #             function_name="get_weather",
@@ -161,18 +181,24 @@ class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase):
     #             arguments='{"location": "San Francisco"}',
     #             result={"temperature": 22},
     #         ),  # End function call
+    #         VADUserStartedSpeakingFrame(),  # Should pass through again
     #         UserStartedSpeakingFrame(),  # Should pass through again
+    #         VADUserStoppedSpeakingFrame(),
     #         UserStoppedSpeakingFrame(),
     #     ]
 
     #     expected_returned_frames = [
+    #         VADUserStartedSpeakingFrame,
     #         UserStartedSpeakingFrame,
+    #         VADUserStoppedSpeakingFrame,
     #         UserStoppedSpeakingFrame,
     #         FunctionCallInProgressFrame,
     #         STTMuteFrame,  # mute=True
     #         FunctionCallResultFrame,
     #         STTMuteFrame,  # mute=False
+    #         VADUserStartedSpeakingFrame,
     #         UserStartedSpeakingFrame,
+    #         VADUserStoppedSpeakingFrame,
     #         UserStoppedSpeakingFrame,
     #     ]
 
@@ -188,24 +214,32 @@ class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase):
         )
 
         frames_to_send = [
+            VADUserStartedSpeakingFrame(),  # Should be suppressed (starts muted)
             UserStartedSpeakingFrame(),  # Should be suppressed (starts muted)
             InputAudioRawFrame(
                 audio=b"", sample_rate=16000, num_channels=1
             ),  # Should be suppressed
+            VADUserStoppedSpeakingFrame(),  # Should be suppressed
             UserStoppedSpeakingFrame(),  # Should be suppressed
             BotStartedSpeakingFrame(),  # First bot speech
+            VADUserStartedSpeakingFrame(),  # Should be suppressed
             UserStartedSpeakingFrame(),  # Should be suppressed
             InputAudioRawFrame(
                 audio=b"", sample_rate=16000, num_channels=1
             ),  # Should be suppressed
+            VADUserStoppedSpeakingFrame(),  # Should be suppressed
             UserStoppedSpeakingFrame(),  # Should be suppressed
             BotStoppedSpeakingFrame(),  # First speech ends, unmutes
+            VADUserStartedSpeakingFrame(),  # Should pass through
             UserStartedSpeakingFrame(),  # Should pass through
             InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1),  # Should pass through
+            VADUserStoppedSpeakingFrame(),  # Should pass through
             UserStoppedSpeakingFrame(),  # Should pass through
             BotStartedSpeakingFrame(),  # Second speech
+            VADUserStartedSpeakingFrame(),  # Should pass through
             UserStartedSpeakingFrame(),  # Should pass through
             InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1),  # Should pass through
+            VADUserStoppedSpeakingFrame(),  # Should pass through
             UserStoppedSpeakingFrame(),  # Should pass through
             BotStoppedSpeakingFrame(),
         ]
@@ -215,12 +249,16 @@ class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase):
             BotStartedSpeakingFrame,
             BotStoppedSpeakingFrame,
             STTMuteFrame,  # mute=False after first speech
+            VADUserStartedSpeakingFrame,
             UserStartedSpeakingFrame,
             InputAudioRawFrame,
+            VADUserStoppedSpeakingFrame,
             UserStoppedSpeakingFrame,
             BotStartedSpeakingFrame,
+            VADUserStartedSpeakingFrame,
             UserStartedSpeakingFrame,
             InputAudioRawFrame,
+            VADUserStoppedSpeakingFrame,
             UserStoppedSpeakingFrame,
             BotStoppedSpeakingFrame,
         ]
@@ -254,31 +292,41 @@ class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase):
         )
 
         frames_to_send = [
+            VADUserStartedSpeakingFrame(),  # Should pass through
             UserStartedSpeakingFrame(),  # Should pass through
             InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1),  # Should pass through
+            VADUserStoppedSpeakingFrame(),  # Should pass through
             UserStoppedSpeakingFrame(),  # Should pass through
             BotStartedSpeakingFrame(),  # Bot starts speaking
+            VADUserStartedSpeakingFrame(),  # Should be suppressed
             UserStartedSpeakingFrame(),  # Should be suppressed
             InputAudioRawFrame(
                 audio=b"", sample_rate=16000, num_channels=1
             ),  # Should be suppressed
+            VADUserStoppedSpeakingFrame(),  # Should be suppressed
             UserStoppedSpeakingFrame(),  # Should be suppressed
             BotStoppedSpeakingFrame(),  # Bot stops speaking
+            VADUserStartedSpeakingFrame(),  # Should pass through
             UserStartedSpeakingFrame(),  # Should pass through
             InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1),  # Should pass through
+            VADUserStoppedSpeakingFrame(),  # Should pass through
             UserStoppedSpeakingFrame(),  # Should pass through
         ]
 
         expected_returned_frames = [
+            VADUserStartedSpeakingFrame,
             UserStartedSpeakingFrame,
             InputAudioRawFrame,
+            VADUserStoppedSpeakingFrame,
             UserStoppedSpeakingFrame,
             BotStartedSpeakingFrame,
             STTMuteFrame,  # mute=True
             BotStoppedSpeakingFrame,
             STTMuteFrame,  # mute=False
+            VADUserStartedSpeakingFrame,
             UserStartedSpeakingFrame,
             InputAudioRawFrame,
+            VADUserStoppedSpeakingFrame,
             UserStoppedSpeakingFrame,
         ]
 

tests/test_watchdog_queue.py

@@ -0,0 +1,65 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import unittest
+
+from pipecat.utils.asyncio.task_manager import TaskManager
+from pipecat.utils.asyncio.watchdog_priority_queue import WatchdogPriorityQueue
+from pipecat.utils.asyncio.watchdog_queue import WatchdogQueue
+
+
+class TestWatchdogQueue(unittest.IsolatedAsyncioTestCase):
+    async def test_simple_item(self):
+        queue = WatchdogQueue(TaskManager())
+        await queue.put(1)
+        await queue.put(2)
+        await queue.put(3)
+        self.assertEqual(await queue.get(), 1)
+        queue.task_done()
+        self.assertEqual(await queue.get(), 2)
+        queue.task_done()
+        self.assertEqual(await queue.get(), 3)
+        queue.task_done()
+
+    async def test_watchdog_sentinel(self):
+        queue = WatchdogQueue(TaskManager())
+        await queue.put(1)
+        self.assertEqual(await queue.get(), 1)
+        queue.task_done()
+        # The get should throw an exception.
+        queue.cancel()
+        try:
+            await queue.get()
+            assert False
+        except asyncio.CancelledError:
+            assert True
+
+
+class TestWatchdogPriorityQueue(unittest.IsolatedAsyncioTestCase):
+    async def test_simple_item(self):
+        queue = WatchdogPriorityQueue(TaskManager(), tuple_size=2)
+        await queue.put((3, 1))
+        await queue.put((2, 1))
+        await queue.put((1, 1))
+        self.assertEqual(await queue.get(), (1, 1))
+        queue.task_done()
+        self.assertEqual(await queue.get(), (2, 1))
+        queue.task_done()
+        self.assertEqual(await queue.get(), (3, 1))
+        queue.task_done()
+
+    async def test_watchdog_sentinel(self):
+        queue = WatchdogPriorityQueue(TaskManager(), tuple_size=2)
+        await queue.put((0, 1))
+        # The get should throw an exception because the watchdog sentinel has
+        # higher priority.
+        queue.cancel()
+        try:
+            await queue.get()
+            assert False
+        except asyncio.CancelledError:
+            assert True

uv.lock

@@ -16,6 +16,24 @@ resolution-markers = [
     "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
+[[package]]
+name = "accelerate"
+version = "1.10.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f7/66/be171836d86dc5b8698b3a9bf4b9eb10cb53369729939f88bf650167588b/accelerate-1.10.0.tar.gz", hash = "sha256:8270568fda9036b5cccdc09703fef47872abccd56eb5f6d53b54ea5fb7581496", size = 392261, upload-time = "2025-08-07T10:54:51.664Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/30/dd/0107f0aa179869ee9f47ef5a2686abd5e022fdc82af901d535e52fe91ce1/accelerate-1.10.0-py3-none-any.whl", hash = "sha256:260a72b560e100e839b517a331ec85ed495b3889d12886e79d1913071993c5a3", size = 374718, upload-time = "2025-08-07T10:54:49.988Z" },
+]
+
 [[package]]
 name = "aenum"
 version = "3.1.16"
@@ -226,11 +244,11 @@ wheels = [
 
 [[package]]
 name = "airportsdata"
-version = "20250706"
+version = "20250811"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/83/e0/311f397752cb15d3772c3856924773eaaab905f922f24ef19739a1de67eb/airportsdata-20250706.tar.gz", hash = "sha256:66d7a03e825d592d85ed650f2c1d4b4302d1c04f8f37a15f1eda29a5e03d4af0", size = 903223, upload-time = "2025-07-06T22:19:23.1Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6b/61/3751adeb92d3197268ad5db00d6fb7ba9c2db72757f0d47462338c25dd0a/airportsdata-20250811.tar.gz", hash = "sha256:844b663c33e85c8f3b932c14c798ef314bec53bda3b14f5694eddc24c7ab4b25", size = 905387, upload-time = "2025-08-11T18:38:24.684Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/15/52/f229acde71b1b9c05f06e504d3dcfcc91bc0614946a35d68cbc36b746dfa/airportsdata-20250811-py3-none-any.whl", hash = "sha256:96f37fcf3f7732350b6dd606d2a8faac7e3b610d0c0b2c1637a3d2fab6b5bf39", size = 912696, upload-time = "2025-08-11T18:38:22.426Z" },
 ]
 
 [[package]]
@@ -2473,89 +2491,110 @@ wheels = [
 
 [[package]]
 name = "kiwisolver"
-version = "1.4.8"
+version = "1.4.9"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/82/59/7c91426a8ac292e1cdd53a63b6d9439abd573c875c3f92c146767dd33faf/kiwisolver-1.4.8.tar.gz", hash = "sha256:23d5f023bdc8c7e54eb65f03ca5d5bb25b601eac4d7f1a042888a1f45237987e", size = 97538, upload-time = "2024-12-24T18:30:51.519Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/5c/3c/85844f1b0feb11ee581ac23fe5fce65cd049a200c1446708cc1b7f922875/kiwisolver-1.4.9.tar.gz", hash = "sha256:c3b22c26c6fd6811b0ae8363b95ca8ce4ea3c202d3d0975b2914310ceb1bcc4d", size = 97564, upload-time = "2025-08-10T21:27:49.279Z" }
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