Merge pull request #3512 from pipecat-ai/changelog-0.0.100

Release 0.0.100 - Changelog Update

.github/workflows/coverage.yaml

@@ -33,7 +33,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain --extra websocket
+          uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain --extra livekit --extra websocket
 
       - name: Run tests with coverage
         run: |

.github/workflows/tests.yaml

@@ -37,7 +37,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain --extra websocket
+          uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain --extra livekit --extra websocket
 
       - name: Test with pytest
         run: |

CHANGELOG.md

@@ -7,6 +7,129 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 <!-- towncrier release notes start -->
 
+## [0.0.100] - 2026-01-20
+
+### Added
+
+- Added Hathora service to support Hathora-hosted TTS and STT models (only
+  non-streaming)
+  (PR [#3169](https://github.com/pipecat-ai/pipecat/pull/3169))
+
+- Added `CambTTSService`, using Camb.ai's TTS integration with MARS models
+  (mars-flash, mars-pro, mars-instruct) for high-quality text-to-speech
+  synthesis.
+  (PR [#3349](https://github.com/pipecat-ai/pipecat/pull/3349))
+
+- Added the `additional_headers` param to `WebsocketClientParams`, allowing
+  `WebsocketClientTransport` to send custom headers on connect, for cases such
+  as authentication.
+  (PR [#3461](https://github.com/pipecat-ai/pipecat/pull/3461))
+
+- Added `UserIdleController` for detecting user idle state, integrated into
+  `LLMUserAggregator` and `UserTurnProcessor` via optional `user_idle_timeout`
+  parameter. Emits `on_user_turn_idle` event for application-level handling.
+  Deprecated `UserIdleProcessor` in favor of the new compositional approach.
+  (PR [#3482](https://github.com/pipecat-ai/pipecat/pull/3482))
+
+- Added `on_user_mute_started` and `on_user_mute_stopped` event handlers to
+  `LLMUserAggregator` for tracking user mute state changes.
+  (PR [#3490](https://github.com/pipecat-ai/pipecat/pull/3490))
+
+### Changed
+
+- Enhanced interruption handling in `AsyncAITTSService` by supporting
+  multi-context WebSocket sessions for more robust context management.
+  (PR [#3287](https://github.com/pipecat-ai/pipecat/pull/3287))
+
+- Throttle `UserSpeakingFrame` to broadcast at most every 200ms instead of on
+  every audio chunk, reducing frame processing overhead during user speech.
+  (PR [#3483](https://github.com/pipecat-ai/pipecat/pull/3483))
+
+### Deprecated
+
+- For consistency with other package names, we just deprecated
+  `pipecat.turns.mute` (introduced in Pipecat 0.0.99) in favor of
+  `pipecat.turns.user_mute`.
+  (PR [#3479](https://github.com/pipecat-ai/pipecat/pull/3479))
+
+### Fixed
+
+- Corrected TTFB metric calculation in `AsyncAIHttpTTSService`.
+  (PR [#3287](https://github.com/pipecat-ai/pipecat/pull/3287))
+
+- Fixed an issue where the "bot-llm-text" RTVI event would not fire for
+  realtime (speech-to-speech) services:
+
+    - `AWSNovaSonicLLMService`
+    - `GeminiLiveLLMService`
+    - `OpenAIRealtimeLLMService`
+    - `GrokRealtimeLLMService`
+
+  The issue was that these services weren't pushing `LLMTextFrame`s. Now
+  they  do.
+  (PR [#3446](https://github.com/pipecat-ai/pipecat/pull/3446))
+
+- Fixed an issue where `on_user_turn_stop_timeout` could fire while a user is
+  talking when using `ExternalUserTurnStrategies`.
+  (PR [#3454](https://github.com/pipecat-ai/pipecat/pull/3454))
+
+- Fixed an issue where user turn start strategies were not being reset after a
+  user turn started, causing incorrect strategy behavior.
+  (PR [#3455](https://github.com/pipecat-ai/pipecat/pull/3455))
+
+- Fixed `MinWordsUserTurnStartStrategy` to not aggregate transcriptions,
+  preventing incorrect turn starts when words are spoken with pauses between
+  them.
+  (PR [#3462](https://github.com/pipecat-ai/pipecat/pull/3462))
+
+- Fixed an issue where Grok Realtime would error out when running with
+  SmallWebRTC transport.
+  (PR [#3480](https://github.com/pipecat-ai/pipecat/pull/3480))
+
+- Fixed a `Mem0MemoryService` issue where passing `async_mode: true` was
+  causing an error. See
+  https://docs.mem0.ai/platform/features/async-mode-default-change.
+  (PR [#3484](https://github.com/pipecat-ai/pipecat/pull/3484))
+
+- Fixed `AWSNovaSonicLLMService.reset_conversation()`, which would previously
+  error out. Now it successfully reconnects and "rehydrates" from the context
+  object.
+  (PR [#3486](https://github.com/pipecat-ai/pipecat/pull/3486))
+
+- Fixed `AzureTTSService` transcript formatting issues:
+    - Punctuation now appears without extra spaces (e.g., "Hello!" instead of
+      "Hello !")
+    - CJK languages (Chinese, Japanese, Korean) no longer have unwanted spaces
+      between characters
+  (PR [#3489](https://github.com/pipecat-ai/pipecat/pull/3489))
+
+- Fixed an issue where `UninterruptibleFrame` frames would not be preserved in
+  some cases.
+  (PR [#3494](https://github.com/pipecat-ai/pipecat/pull/3494))
+
+- Fixed memory leak in `LiveKitTransport` when `video_in_enabled` is `False`.
+  (PR [#3499](https://github.com/pipecat-ai/pipecat/pull/3499))
+
+- Fixed an issue in `AIService` where unhandled exceptions in `start()`,
+  `stop()`, or `cancel()` implementations would prevent `process_frame()` to
+  continue and therefore `StartFrame`, `EndFrame`, or `CancelFrame` from being
+  pushed downstream, causing the pipeline to not start or stop properly.
+  (PR [#3503](https://github.com/pipecat-ai/pipecat/pull/3503))
+
+- Moved `NVIDIATTSService` and `NVIDIASTTService` client initialization from
+  constructor to `start()` for better error handling.
+  (PR [#3504](https://github.com/pipecat-ai/pipecat/pull/3504))
+
+- Optimized `NVIDIATTSService` to process incoming audio frames immediately.
+  (PR [#3509](https://github.com/pipecat-ai/pipecat/pull/3509))
+
+- Optimized `NVIDIASTTService` by removing unnecessary queue and task.
+  (PR [#3509](https://github.com/pipecat-ai/pipecat/pull/3509))
+
+- Fixed a `CambTTSService` issue where client was being initialized in the
+  constructor which wouldn't allow for proper Pipeline error handling.
+  (PR [#3511](https://github.com/pipecat-ai/pipecat/pull/3511))
+
 ## [0.0.99] - 2026-01-13
 
 ### Added

changelog/3169.added.md

@@ -1 +0,0 @@
-- Added Hathora service to support Hathora-hosted TTS and STT models (only non-streaming)

changelog/3287.changed.md

@@ -1 +0,0 @@
-- Enhanced interruption handling in `AsyncAITTSService` by supporting multi-context WebSocket sessions for more robust context management.

changelog/3287.fixed.md

@@ -1 +0,0 @@
-- Corrected TTFB metric calculation in `AsyncAIHttpTTSService`.

changelog/3349.added.md

@@ -1 +0,0 @@
-- Added `CambTTSService`, using Camb.ai's TTS integration with MARS models (mars-flash, mars-pro, mars-instruct) for high-quality text-to-speech synthesis.

changelog/3446.fixed.md

@@ -1,8 +0,0 @@
-- Fixed an issue where the "bot-llm-text" RTVI event would not fire for realtime (speech-to-speech) services:
-
-  - `AWSNovaSonicLLMService`
-  - `GeminiLiveLLMService`
-  - `OpenAIRealtimeLLMService`
-  - `GrokRealtimeLLMService`
-
-  The issue was that these services weren't pushing `LLMTextFrame`s. Now they do.

changelog/3454.fixed.md

@@ -1 +0,0 @@
-- Fixed an issue where `on_user_turn_stop_timeout` could fire while a user is talking when using `ExternalUserTurnStrategies`.

changelog/3455.fixed.md

@@ -1 +0,0 @@
-- Fixed an issue where user turn start strategies were not being reset after a user turn started, causing incorrect strategy behavior.

changelog/3461.added.md

@@ -1 +0,0 @@
-- Added the `additional_headers` param to `WebsocketClientParams`, allowing `WebsocketClientTransport` to send custom headers on connect, for cases such as authentication.

changelog/3462.fixed.md

@@ -1 +0,0 @@
-- Fixed `MinWordsUserTurnStartStrategy` to not aggregate transcriptions, preventing incorrect turn starts when words are spoken with pauses between them.

changelog/3479.deprecated.md

@@ -1 +0,0 @@
-- For consistency with other package names, we just deprecated `pipecat.turns.mute` (introduced in Pipecat 0.0.99) in favor of `pipecat.turns.user_mute`.

changelog/3480.fixed.md

@@ -1 +0,0 @@
-- Fixed an issue where Grok Realtime would error out when running with SmallWebRTC transport.

changelog/3482.added.md

@@ -1 +0,0 @@
-- Added `UserIdleController` for detecting user idle state, integrated into `LLMUserAggregator` and `UserTurnProcessor` via optional `user_idle_timeout` parameter. Emits `on_user_turn_idle` event for application-level handling. Deprecated `UserIdleProcessor` in favor of the new compositional approach.

changelog/3483.changed.md

@@ -1 +0,0 @@
-- Throttle `UserSpeakingFrame` to broadcast at most every 200ms instead of on every audio chunk, reducing frame processing overhead during user speech.

changelog/3484.fixed.md

@@ -1 +0,0 @@
-- Fixed a `Mem0MemoryService` issue where passing `async_mode: true` was causing an error. See https://docs.mem0.ai/platform/features/async-mode-default-change.

changelog/3489.fixed.md

@@ -1,3 +0,0 @@
-- Fixed `AzureTTSService` transcript formatting issues:
-  - Punctuation now appears without extra spaces (e.g., "Hello!" instead of "Hello !")
-  - CJK languages (Chinese, Japanese, Korean) no longer have unwanted spaces between characters

changelog/3490.added.md

@@ -1 +0,0 @@
-- Added `on_user_mute_started` and `on_user_mute_stopped` event handlers to `LLMUserAggregator` for tracking user mute state changes.

examples/foundational/07k-interruptible-lmnt.py

@@ -10,7 +10,6 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams

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

@@ -45,7 +45,6 @@ from pipecat.services.google.tts import GoogleTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
 from pipecat.turns.user_turn_strategies import UserTurnStrategies
 

examples/foundational/07p-interruptible-krisp-viva.py

@@ -28,7 +28,7 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.filters.krisp_viva_filter import KrispVivaFilter
-from pipecat.audio.turn.krisp_viva_turn import KrispTurnParams, KrispVivaTurn
+from pipecat.audio.turn.krisp_viva_turn import KrispVivaTurn
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import LLMRunFrame

examples/foundational/11-sound-effects.py

@@ -22,7 +22,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,

examples/foundational/20d-persistent-context-gemini.py

@@ -17,7 +17,7 @@ from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
+from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask

examples/foundational/20e-persistent-context-aws-nova-sonic.py

@@ -22,7 +22,6 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService
@@ -114,6 +113,14 @@ async def load_conversation(params: FunctionCallParams):
                 #         "content": f"{AWSNovaSonicLLMService.AWAIT_TRIGGER_ASSISTANT_RESPONSE_INSTRUCTION}",
                 #     }
                 # )
+                # If the last message isn't from the user, add a message asking for a recap
+                if messages and messages[-1].get("role") != "user":
+                    messages.append(
+                        {
+                            "role": "user",
+                            "content": "Can you catch me up on what we were talking about?",
+                        }
+                    )
                 params.context.set_messages(messages)
                 await params.llm.reset_conversation()
                 # await params.llm.trigger_assistant_response()

examples/foundational/39c-multiple-mcp.py

@@ -9,7 +9,6 @@ import asyncio
 import io
 import json
 import os
-import re
 import shutil
 
 import aiohttp

examples/foundational/49d-thinking-functions-google.py

@@ -13,7 +13,7 @@ from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, ThoughtTranscriptionMessage, TranscriptionMessage
+from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask

examples/foundational/51-grok-realtime.py

@@ -53,8 +53,6 @@ from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.grok.realtime.events import (
     SessionProperties,
-    WebSearchTool,
-    XSearchTool,
 )
 from pipecat.services.grok.realtime.llm import GrokRealtimeLLMService
 from pipecat.services.llm_service import FunctionCallParams

examples/quickstart/pcc-deploy.toml

@@ -1,11 +1,11 @@
-agent_name = "quickstart"
-image = "your_username/quickstart:0.1"
-secret_set = "quickstart-secrets"
+agent_name = "quickstart-test"
+image = "markatdaily/quickstart-test:latest"
+secret_set = "quickstart-test-secrets"
 agent_profile = "agent-1x"
 
 # RECOMMENDED: Set an image pull secret:
 # https://docs.pipecat.ai/deployment/pipecat-cloud/fundamentals/secrets#image-pull-secrets
-# image_credentials = "your_image_pull_secret"
+image_credentials = "dockerhub-access"
 
 [scaling]
 	min_agents = 1

scripts/evals/eval.py

@@ -293,12 +293,13 @@ async def run_eval_pipeline(
         "You should only call the eval function if:\n"
         "- The user explicitly attempts to answer the question, AND\n"
         f"- Their answer can be cleanly evaluated using: {eval_config.eval}\n"
-        "Ignore greetings, comments, non-answers, or requests for clarification."
+        "Ignore greetings, comments, non-answers, or requests for clarification.\n"
+        "Numerical word answers are allowed (e.g., 'five' is the same as '5').\n"
     )
     if eval_config.eval_speaks_first:
-        system_prompt = f"You are an evaluation agent, be extremly brief. Numerical word answers are allowed. You will start the conversation by saying: '{example_prompt}'. {common_system_prompt}"
+        system_prompt = f"You are an evaluation agent, be extremly brief. You will start the conversation by saying: '{example_prompt}'. {common_system_prompt}"
     else:
-        system_prompt = f"You are an evaluation agent, be extremly brief. Numerical word answers are allowed. First, ask one question: {example_prompt}. {common_system_prompt}"
+        system_prompt = f"You are an evaluation agent, be extremly brief. First, ask one question: {example_prompt}. {common_system_prompt}"
 
     messages = [
         {

scripts/evals/run-release-evals.py

@@ -137,6 +137,7 @@ TESTS_07 = [
     # ("07zd-interruptible-aicoustics.py", EVAL_SIMPLE_MATH),
     ("07ze-interruptible-hume.py", EVAL_SIMPLE_MATH),
     ("07zf-interruptible-gradium.py", EVAL_SIMPLE_MATH),
+    ("07zg-interruptible-camb.py", EVAL_SIMPLE_MATH),
     ("07zh-interruptible-hathora.py", EVAL_SIMPLE_MATH),
     # Needs a local XTTS docker instance running.
     # ("07i-interruptible-xtts.py", EVAL_SIMPLE_MATH),

scripts/krisp/test_krisp_viva_filter_audiofile.py

@@ -22,7 +22,7 @@ from pathlib import Path
 
 try:
     import numpy as np
-    import soundfile as sf
+    import soundfile as sf  # noqa: F401
     from audio_file_utils import calculate_audio_stats, read_audio_file, write_audio_file
 except ImportError as e:
     print(f"Error: Missing required dependencies: {e}")

scripts/krisp/test_krisp_viva_turn_audiofile.py

@@ -23,7 +23,7 @@ from pathlib import Path
 
 try:
     import numpy as np
-    import soundfile as sf
+    import soundfile as sf  # noqa: F401
     from audio_file_utils import read_audio_file
 except ImportError as e:
     print(f"Error: Missing required dependencies: {e}")

src/pipecat/adapters/services/bedrock_adapter.py

@@ -10,7 +10,7 @@ import base64
 import copy
 import json
 from dataclasses import dataclass
-from typing import Any, Dict, List, Literal, Optional, TypedDict
+from typing import Any, Dict, List, Optional, TypedDict
 
 from loguru import logger
 

src/pipecat/adapters/services/gemini_adapter.py

@@ -9,7 +9,7 @@
 import base64
 import json
 from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Tuple, TypedDict
+from typing import Any, Dict, List, Optional, TypedDict
 
 from loguru import logger
 from openai import NotGiven

src/pipecat/adapters/services/open_ai_adapter.py

@@ -7,10 +7,8 @@
 """OpenAI LLM adapter for Pipecat."""
 
 import copy
-import json
 from typing import Any, Dict, List, TypedDict
 
-from openai._types import NOT_GIVEN as OPEN_AI_NOT_GIVEN
 from openai._types import NotGiven as OpenAINotGiven
 from openai.types.chat import (
     ChatCompletionMessageParam,

src/pipecat/audio/filters/krisp_viva_filter.py

@@ -9,7 +9,6 @@
 This module provides an audio filter implementation using Krisp VIVA SDK.
 """
 
-import asyncio
 import os
 
 import numpy as np

src/pipecat/processors/aggregators/openai_llm_context.py

@@ -34,7 +34,6 @@ from PIL import Image
 from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.frames.frames import AudioRawFrame, Frame
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 # JSON custom encoder to handle bytes arrays so that we can log contexts
 # with images to the console.

src/pipecat/processors/filters/wake_check_filter.py

@@ -18,7 +18,7 @@ from typing import List
 
 from loguru import logger
 
-from pipecat.frames.frames import ErrorFrame, Frame, TranscriptionFrame
+from pipecat.frames.frames import Frame, TranscriptionFrame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 

src/pipecat/processors/frame_processor.py

@@ -950,7 +950,8 @@ class FrameProcessor(BaseObject):
         # Process current queue and keep UninterruptibleFrame frames.
         while not self.__process_queue.empty():
             item = self.__process_queue.get_nowait()
-            if isinstance(item, UninterruptibleFrame):
+            frame = item[0]
+            if isinstance(frame, UninterruptibleFrame):
                 new_queue.put_nowait(item)
             self.__process_queue.task_done()
 

src/pipecat/runner/run.py

@@ -263,7 +263,7 @@ def _setup_webrtc_routes(
         """Handle WebRTC offer requests via SmallWebRTCRequestHandler."""
 
         # Prepare runner arguments with the callback to run your bot
-        async def webrtc_connection_callback(connection):
+        async def webrtc_connection_callback(connection: SmallWebRTCConnection):
             bot_module = _get_bot_module()
 
             runner_args = SmallWebRTCRunnerArguments(
@@ -406,13 +406,7 @@ def _setup_whatsapp_routes(app: FastAPI):
         return
 
     try:
-        from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
-
         from pipecat.transports.smallwebrtc.connection import SmallWebRTCConnection
-        from pipecat.transports.smallwebrtc.request_handler import (
-            SmallWebRTCRequest,
-            SmallWebRTCRequestHandler,
-        )
         from pipecat.transports.whatsapp.api import WhatsAppWebhookRequest
         from pipecat.transports.whatsapp.client import WhatsAppClient
     except ImportError as e:

src/pipecat/services/ai_service.py

@@ -148,11 +148,11 @@ class AIService(FrameProcessor):
         await super().process_frame(frame, direction)
 
         if isinstance(frame, StartFrame):
-            await self.start(frame)
-        elif isinstance(frame, CancelFrame):
-            await self.cancel(frame)
+            await self._start(frame)
         elif isinstance(frame, EndFrame):
-            await self.stop(frame)
+            await self._stop(frame)
+        elif isinstance(frame, CancelFrame):
+            await self._cancel(frame)
 
     async def process_generator(self, generator: AsyncGenerator[Frame | None, None]):
         """Process frames from an async generator.
@@ -169,3 +169,21 @@ class AIService(FrameProcessor):
                     await self.push_error_frame(f)
                 else:
                     await self.push_frame(f)
+
+    async def _start(self, frame: StartFrame):
+        try:
+            await self.start(frame)
+        except Exception as e:
+            logger.error(f"{self}: exception processing {frame}: {e}")
+
+    async def _stop(self, frame: EndFrame):
+        try:
+            await self.stop(frame)
+        except Exception as e:
+            logger.error(f"{self}: exception processing {frame}: {e}")
+
+    async def _cancel(self, frame: CancelFrame):
+        try:
+            await self.cancel(frame)
+        except Exception as e:
+            logger.error(f"{self}: exception processing {frame}: {e}")

src/pipecat/services/aws/nova_sonic/llm.py

@@ -296,6 +296,7 @@ class AWSNovaSonicLLMService(LLMService):
         self._user_text_buffer = ""
         self._assistant_text_buffer = ""
         self._completed_tool_calls = set()
+        self._audio_input_started = False
 
         file_path = files("pipecat.services.aws.nova_sonic").joinpath("ready.wav")
         with wave.open(file_path.open("rb"), "rb") as wav_file:
@@ -532,14 +533,30 @@ class AWSNovaSonicLLMService(LLMService):
         if system_instruction:
             await self._send_text_event(text=system_instruction, role=Role.SYSTEM)
 
-        # Send conversation history
-        for message in llm_connection_params["messages"]:
+        # Send conversation history (except for the last message if it's from the
+        # user, which we'll send as interactive after starting audio input)
+        messages = llm_connection_params["messages"]
+        last_user_message = None
+        for i, message in enumerate(messages):
             # logger.debug(f"Seeding conversation history with message: {message}")
-            await self._send_text_event(text=message.text, role=message.role)
+            is_last_message = i == len(messages) - 1
+            if is_last_message and message.role == Role.USER:
+                # Save for sending after audio input starts
+                last_user_message = message
+            else:
+                await self._send_text_event(text=message.text, role=message.role)
 
         # Start audio input
         await self._send_audio_input_start_event()
 
+        # Now send the last user message as interactive to trigger bot response
+        if last_user_message:
+            # logger.debug(
+            #     f"Sending last user message as interactive to trigger bot response: {last_user_message}")
+            await self._send_text_event(
+                text=last_user_message.text, role=last_user_message.role, interactive=True
+            )
+
         # Start receiving events
         self._receive_task = self.create_task(self._receive_task_handler())
 
@@ -602,6 +619,7 @@ class AWSNovaSonicLLMService(LLMService):
             self._user_text_buffer = ""
             self._assistant_text_buffer = ""
             self._completed_tool_calls = set()
+            self._audio_input_started = False
 
             logger.info("Finished disconnecting")
         except Exception as e:
@@ -727,8 +745,18 @@ class AWSNovaSonicLLMService(LLMService):
         }}
         '''
         await self._send_client_event(audio_content_start)
+        self._audio_input_started = True
 
-    async def _send_text_event(self, text: str, role: Role):
+    async def _send_text_event(self, text: str, role: Role, interactive: bool = False):
+        """Send a text event to the LLM.
+
+        Args:
+            text: The text content to send.
+            role: The role associated with the text (e.g., USER, ASSISTANT, SYSTEM).
+            interactive: Whether the content is interactive. Defaults to False.
+                False: conversation history or system instruction, sent prior to interactive audio
+                True: text input sent during (or at the start of) interactive audio
+        """
         if not self._stream or not self._prompt_name or not text:
             return
 
@@ -741,7 +769,7 @@ class AWSNovaSonicLLMService(LLMService):
                     "promptName": "{self._prompt_name}",
                     "contentName": "{content_name}",
                     "type": "TEXT",
-                    "interactive": true,
+                    "interactive": {json.dumps(interactive)},
                     "role": "{role.value}",
                     "textInputConfiguration": {{
                         "mediaType": "text/plain"
@@ -779,7 +807,7 @@ class AWSNovaSonicLLMService(LLMService):
         await self._send_client_event(text_content_end)
 
     async def _send_user_audio_event(self, audio: bytes):
-        if not self._stream:
+        if not self._stream or not self._audio_input_started:
             return
 
         blob = base64.b64encode(audio)

src/pipecat/services/aws/stt.py

@@ -10,7 +10,6 @@ This module provides a WebSocket-based connection to AWS Transcribe for real-tim
 speech-to-text transcription with support for multiple languages and audio formats.
 """
 
-import asyncio
 import json
 import os
 import random

src/pipecat/services/aws/tts.py

@@ -10,7 +10,6 @@ This module provides integration with Amazon Polly for text-to-speech synthesis,
 supporting multiple languages, voices, and SSML features.
 """
 
-import asyncio
 import os
 from typing import AsyncGenerator, List, Optional
 

src/pipecat/services/aws_nova_sonic/__init__.py

@@ -17,3 +17,8 @@ with warnings.catch_warnings():
         DeprecationWarning,
         stacklevel=2,
     )
+
+__all__ = [
+    "AWSNovaSonicLLMService",
+    "Params",
+]

src/pipecat/services/azure/common.py

@@ -8,8 +8,6 @@
 
 from typing import Optional
 
-from loguru import logger
-
 from pipecat.transcriptions.language import Language, resolve_language
 
 

src/pipecat/services/azure/image.py

@@ -15,7 +15,6 @@ import io
 from typing import AsyncGenerator
 
 import aiohttp
-from loguru import logger
 from PIL import Image
 
 from pipecat.frames.frames import ErrorFrame, Frame, URLImageRawFrame

src/pipecat/services/camb/tts.py

@@ -199,9 +199,10 @@ class CambTTSService(TTSService):
         """
         super().__init__(sample_rate=sample_rate, **kwargs)
 
-        params = params or CambTTSService.InputParams()
+        self._api_key = api_key
+        self._timeout = timeout
 
-        self._client = AsyncCambAI(api_key=api_key, timeout=timeout)
+        params = params or CambTTSService.InputParams()
 
         # Warn if sample rate doesn't match model's supported rate
         if sample_rate and sample_rate != MODEL_SAMPLE_RATES.get(model):
@@ -222,6 +223,8 @@ class CambTTSService(TTSService):
         self.set_voice(str(voice_id))
         self._voice_id = voice_id
 
+        self._client = None
+
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
 
@@ -249,6 +252,8 @@ class CambTTSService(TTSService):
         """
         await super().start(frame)
 
+        self._client = AsyncCambAI(api_key=self._api_key, timeout=self._timeout)
+
         # Use model-specific sample rate if not explicitly specified
         if not self._init_sample_rate:
             self._sample_rate = MODEL_SAMPLE_RATES.get(self.model_name, 22050)
@@ -289,6 +294,8 @@ class CambTTSService(TTSService):
             await self.start_tts_usage_metrics(text)
             yield TTSStartedFrame()
 
+            assert self._client is not None, "Camb.ai TTS service not initialized"
+
             # Buffer for aligning chunks to 2-byte boundaries (16-bit PCM)
             audio_buffer = b""
 

src/pipecat/services/cerebras/llm.py

@@ -6,8 +6,6 @@
 
 """Cerebras LLM service implementation using OpenAI-compatible interface."""
 
-from typing import List
-
 from loguru import logger
 
 from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams

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

@@ -27,7 +27,6 @@ from pipecat.frames.frames import (
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
 )
-from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.stt_service import WebsocketSTTService
 from pipecat.transcriptions.language import Language
 from pipecat.utils.time import time_now_iso8601

src/pipecat/services/deepseek/llm.py

@@ -6,8 +6,6 @@
 
 """DeepSeek LLM service implementation using OpenAI-compatible interface."""
 
-from typing import List
-
 from loguru import logger
 
 from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams

src/pipecat/services/fireworks/llm.py

@@ -6,8 +6,6 @@
 
 """Fireworks AI service implementation using OpenAI-compatible interface."""
 
-from typing import List
-
 from loguru import logger
 
 from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams

src/pipecat/services/gemini_multimodal_live/__init__.py

@@ -1,2 +1,7 @@
 from .file_api import GeminiFileAPI
 from .gemini import GeminiMultimodalLiveLLMService
+
+__all__ = [
+    "GeminiFileAPI",
+    "GeminiMultimodalLiveLLMService",
+]

src/pipecat/services/google/gemini_live/__init__.py

@@ -1,3 +1,9 @@
 from .file_api import GeminiFileAPI
 from .llm import GeminiLiveLLMService
 from .llm_vertex import GeminiLiveVertexLLMService
+
+__all__ = [
+    "GeminiFileAPI",
+    "GeminiLiveLLMService",
+    "GeminiLiveVertexLLMService",
+]

src/pipecat/services/google/llm.py

@@ -40,7 +40,6 @@ from pipecat.frames.frames import (
     LLMThoughtStartFrame,
     LLMThoughtTextFrame,
     LLMUpdateSettingsFrame,
-    OutputImageRawFrame,
     UserImageRawFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage

src/pipecat/services/google/rtvi.py

@@ -15,9 +15,7 @@ from typing import List, Literal, Optional
 
 from pydantic import BaseModel
 
-from pipecat.frames.frames import Frame
 from pipecat.observers.base_observer import FramePushed
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.processors.frameworks.rtvi import RTVIObserver, RTVIProcessor
 from pipecat.services.google.frames import LLMSearchOrigin, LLMSearchResponseFrame
 

src/pipecat/services/google/stt.py

@@ -29,7 +29,6 @@ from pydantic import BaseModel, Field, field_validator
 from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
-    ErrorFrame,
     Frame,
     InterimTranscriptionFrame,
     StartFrame,

src/pipecat/services/gradium/tts.py

@@ -6,7 +6,6 @@
 
 import base64
 import json
-import uuid
 from typing import Any, AsyncGenerator, Mapping, Optional
 
 from loguru import logger

src/pipecat/services/hume/tts.py

@@ -16,7 +16,6 @@ from pipecat import version as pipecat_version
 from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
-    ErrorFrame,
     Frame,
     InterruptionFrame,
     StartFrame,

src/pipecat/services/mem0/memory.py

@@ -16,7 +16,7 @@ from typing import Any, Dict, List, Optional
 from loguru import logger
 from pydantic import BaseModel, Field
 
-from pipecat.frames.frames import ErrorFrame, Frame, LLMContextFrame, LLMMessagesFrame
+from pipecat.frames.frames import Frame, LLMContextFrame, LLMMessagesFrame
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,

src/pipecat/services/mistral/llm.py

@@ -9,12 +9,10 @@
 from typing import List, Sequence
 
 from loguru import logger
-from openai import AsyncStream
-from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
+from openai.types.chat import ChatCompletionMessageParam
 
 from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
 from pipecat.frames.frames import FunctionCallFromLLM
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai.llm import OpenAILLMService
 
 

src/pipecat/services/moondream/vision.py

@@ -46,7 +46,7 @@ def detect_device():
                and dtype is the recommended torch data type for that device.
     """
     try:
-        import intel_extension_for_pytorch
+        import intel_extension_for_pytorch  # noqa: F401
 
         if torch.xpu.is_available():
             return torch.device("xpu"), torch.float32

src/pipecat/services/nvidia/stt.py

@@ -134,6 +134,7 @@ class NvidiaSTTService(STTService):
 
         params = params or NvidiaSTTService.InputParams()
 
+        self._server = server
         self._api_key = api_key
         self._use_ssl = use_ssl
         self._profanity_filter = False
@@ -162,18 +163,53 @@ class NvidiaSTTService(STTService):
 
         self.set_model_name(model_function_map.get("model_name"))
 
-        metadata = [
-            ["function-id", self._function_id],
-            ["authorization", f"Bearer {api_key}"],
-        ]
-        auth = riva.client.Auth(None, self._use_ssl, server, metadata)
-
-        self._asr_service = riva.client.ASRService(auth)
-
+        self._asr_service = None
         self._queue = None
         self._config = None
         self._thread_task = None
-        self._response_task = None
+
+    def _initialize_client(self):
+        metadata = [
+            ["function-id", self._function_id],
+            ["authorization", f"Bearer {self._api_key}"],
+        ]
+        auth = riva.client.Auth(None, self._use_ssl, self._server, metadata)
+
+        self._asr_service = riva.client.ASRService(auth)
+
+    def _create_recognition_config(self):
+        """Create the NVIDIA Riva ASR recognition configuration."""
+        config = riva.client.StreamingRecognitionConfig(
+            config=riva.client.RecognitionConfig(
+                encoding=riva.client.AudioEncoding.LINEAR_PCM,
+                language_code=self._language_code,
+                model="",
+                max_alternatives=1,
+                profanity_filter=self._profanity_filter,
+                enable_automatic_punctuation=self._automatic_punctuation,
+                verbatim_transcripts=not self._no_verbatim_transcripts,
+                sample_rate_hertz=self.sample_rate,
+                audio_channel_count=1,
+            ),
+            interim_results=True,
+        )
+
+        riva.client.add_word_boosting_to_config(
+            config, self._boosted_lm_words, self._boosted_lm_score
+        )
+
+        riva.client.add_endpoint_parameters_to_config(
+            config,
+            self._start_history,
+            self._start_threshold,
+            self._stop_history,
+            self._stop_history_eou,
+            self._stop_threshold,
+            self._stop_threshold_eou,
+        )
+        riva.client.add_custom_configuration_to_config(config, self._custom_configuration)
+
+        return config
 
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
@@ -206,49 +242,15 @@ class NvidiaSTTService(STTService):
             frame: StartFrame indicating pipeline start.
         """
         await super().start(frame)
+        self._initialize_client()
+        self._config = self._create_recognition_config()
 
-        if self._config:
-            return
-
-        config = riva.client.StreamingRecognitionConfig(
-            config=riva.client.RecognitionConfig(
-                encoding=riva.client.AudioEncoding.LINEAR_PCM,
-                language_code=self._language_code,
-                model="",
-                max_alternatives=1,
-                profanity_filter=self._profanity_filter,
-                enable_automatic_punctuation=self._automatic_punctuation,
-                verbatim_transcripts=not self._no_verbatim_transcripts,
-                sample_rate_hertz=self.sample_rate,
-                audio_channel_count=1,
-            ),
-            interim_results=True,
-        )
-
-        riva.client.add_word_boosting_to_config(
-            config, self._boosted_lm_words, self._boosted_lm_score
-        )
-
-        riva.client.add_endpoint_parameters_to_config(
-            config,
-            self._start_history,
-            self._start_threshold,
-            self._stop_history,
-            self._stop_history_eou,
-            self._stop_threshold,
-            self._stop_threshold_eou,
-        )
-        riva.client.add_custom_configuration_to_config(config, self._custom_configuration)
-
-        self._config = config
         self._queue = asyncio.Queue()
 
         if not self._thread_task:
             self._thread_task = self.create_task(self._thread_task_handler())
 
-        if not self._response_task:
-            self._response_queue = asyncio.Queue()
-            self._response_task = self.create_task(self._response_task_handler())
+        logger.debug(f"Initialized NvidiaSTTService with model: {self.model_name}")
 
     async def stop(self, frame: EndFrame):
         """Stop the NVIDIA Riva STT service and clean up resources.
@@ -273,10 +275,6 @@ class NvidiaSTTService(STTService):
             await self.cancel_task(self._thread_task)
             self._thread_task = None
 
-        if self._response_task:
-            await self.cancel_task(self._response_task)
-            self._response_task = None
-
     def _response_handler(self):
         responses = self._asr_service.streaming_response_generator(
             audio_chunks=self,
@@ -285,9 +283,7 @@ class NvidiaSTTService(STTService):
         for response in responses:
             if not response.results:
                 continue
-            asyncio.run_coroutine_threadsafe(
-                self._response_queue.put(response), self.get_event_loop()
-            )
+            asyncio.run_coroutine_threadsafe(self._handle_response(response), self.get_event_loop())
 
     async def _thread_task_handler(self):
         try:
@@ -339,12 +335,6 @@ class NvidiaSTTService(STTService):
                         )
                     )
 
-    async def _response_task_handler(self):
-        while True:
-            response = await self._response_queue.get()
-            await self._handle_response(response)
-            self._response_queue.task_done()
-
     async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
         """Process audio data for speech-to-text transcription.
 
@@ -503,8 +493,6 @@ class NvidiaSegmentedSTTService(SegmentedSTTService):
         auth = riva.client.Auth(None, self._use_ssl, self._server, metadata)
         self._asr_service = riva.client.ASRService(auth)
 
-        logger.info(f"Initialized NvidiaSegmentedSTTService with model: {self.model_name}")
-
     def _create_recognition_config(self):
         """Create the NVIDIA Riva ASR recognition configuration."""
         # Create base configuration
@@ -572,6 +560,7 @@ class NvidiaSegmentedSTTService(SegmentedSTTService):
         await super().start(frame)
         self._initialize_client()
         self._config = self._create_recognition_config()
+        logger.debug(f"Initialized NvidiaSegmentedSTTService with model: {self.model_name}")
 
     async def set_language(self, language: Language):
         """Set the language for the STT service.
@@ -605,21 +594,12 @@ class NvidiaSegmentedSTTService(SegmentedSTTService):
             Frame: TranscriptionFrame containing the transcribed text.
         """
         try:
-            await self.start_processing_metrics()
-            await self.start_ttfb_metrics()
-
-            # Make sure the client is initialized
-            if self._asr_service is None:
-                self._initialize_client()
-
-            # Make sure the config is created
-            if self._config is None:
-                self._config = self._create_recognition_config()
-
-            # Type assertion to satisfy the IDE
             assert self._asr_service is not None, "ASR service not initialized"
             assert self._config is not None, "Recognition config not created"
 
+            await self.start_processing_metrics()
+            await self.start_ttfb_metrics()
+
             # Process audio with NVIDIA Riva ASR - explicitly request non-future response
             raw_response = self._asr_service.offline_recognize(audio, self._config, future=False)
 
@@ -627,43 +607,40 @@ class NvidiaSegmentedSTTService(SegmentedSTTService):
             await self.stop_processing_metrics()
 
             # Process the response - handle different possible return types
-            try:
-                # If it's a future-like object, get the result
-                if hasattr(raw_response, "result"):
-                    response = raw_response.result()
-                else:
-                    response = raw_response
+            # If it's a future-like object, get the result
+            if hasattr(raw_response, "result"):
+                response = raw_response.result()
+            else:
+                response = raw_response
 
-                # Process transcription results
-                transcription_found = False
+            # Process transcription results
+            transcription_found = False
 
-                # Now we can safely check results
-                # Type hint for the IDE
-                results = getattr(response, "results", [])
+            # Now we can safely check results
+            # Type hint for the IDE
+            results = getattr(response, "results", [])
 
-                for result in results:
-                    alternatives = getattr(result, "alternatives", [])
-                    if alternatives:
-                        text = alternatives[0].transcript.strip()
-                        if text:
-                            logger.debug(f"Transcription: [{text}]")
-                            yield TranscriptionFrame(
-                                text,
-                                self._user_id,
-                                time_now_iso8601(),
-                                self._language_enum,
-                            )
-                            transcription_found = True
+            for result in results:
+                alternatives = getattr(result, "alternatives", [])
+                if alternatives:
+                    text = alternatives[0].transcript.strip()
+                    if text:
+                        logger.debug(f"Transcription: [{text}]")
+                        yield TranscriptionFrame(
+                            text,
+                            self._user_id,
+                            time_now_iso8601(),
+                            self._language_enum,
+                        )
+                        transcription_found = True
 
-                            await self._handle_transcription(text, True, self._language_enum)
-
-                if not transcription_found:
-                    logger.debug("No transcription results found in NVIDIA Riva response")
-
-            except AttributeError as ae:
-                logger.error(f"Unexpected response structure from NVIDIA Riva: {ae}")
-                yield ErrorFrame(f"Unexpected NVIDIA Riva response format: {str(ae)}")
+                        await self._handle_transcription(text, True, self._language_enum)
 
+            if not transcription_found:
+                logger.debug(f"{self}: No transcription results found in NVIDIA Riva response")
+        except AttributeError as ae:
+            logger.error(f"{self}: Unexpected response structure from NVIDIA Riva: {ae}")
+            yield ErrorFrame(f"{self}: Unexpected NVIDIA Riva response format: {str(ae)}")
         except Exception as e:
             logger.error(f"{self} exception: {e}")
             yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/nvidia/tts.py

@@ -12,7 +12,7 @@ gRPC API for high-quality speech synthesis.
 
 import asyncio
 import os
-from typing import AsyncGenerator, Mapping, Optional
+from typing import AsyncGenerator, AsyncIterable, Generator, Mapping, Optional
 
 from pipecat.utils.tracing.service_decorators import traced_tts
 
@@ -25,6 +25,7 @@ from pydantic import BaseModel
 from pipecat.frames.frames import (
     ErrorFrame,
     Frame,
+    StartFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
@@ -34,14 +35,12 @@ from pipecat.transcriptions.language import Language
 
 try:
     import riva.client
-
+    import riva.client.proto.riva_tts_pb2 as rtts
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
     logger.error("In order to use NVIDIA Riva TTS, you need to `pip install pipecat-ai[nvidia]`.")
     raise Exception(f"Missing module: {e}")
 
-NVIDIA_TTS_TIMEOUT_SECS = 5
-
 
 class NvidiaTTSService(TTSService):
     """NVIDIA Riva text-to-speech service.
@@ -93,6 +92,7 @@ class NvidiaTTSService(TTSService):
 
         params = params or NvidiaTTSService.InputParams()
 
+        self._server = server
         self._api_key = api_key
         self._voice_id = voice_id
         self._language_code = params.language
@@ -102,18 +102,8 @@ class NvidiaTTSService(TTSService):
         self.set_model_name(model_function_map.get("model_name"))
         self.set_voice(voice_id)
 
-        metadata = [
-            ["function-id", self._function_id],
-            ["authorization", f"Bearer {api_key}"],
-        ]
-        auth = riva.client.Auth(None, self._use_ssl, server, metadata)
-
-        self._service = riva.client.SpeechSynthesisService(auth)
-
-        # warm up the service
-        config_response = self._service.stub.GetRivaSynthesisConfig(
-            riva.client.proto.riva_tts_pb2.RivaSynthesisConfigRequest()
-        )
+        self._service = None
+        self._config = None
 
     async def set_model(self, model: str):
         """Attempt to set the TTS model.
@@ -129,6 +119,39 @@ class NvidiaTTSService(TTSService):
             f"{self.__class__.__name__}(api_key=<api_key>, model_function_map={example})"
         )
 
+    def _initialize_client(self):
+        if self._service is not None:
+            return
+
+        metadata = [
+            ["function-id", self._function_id],
+            ["authorization", f"Bearer {self._api_key}"],
+        ]
+        auth = riva.client.Auth(None, self._use_ssl, self._server, metadata)
+
+        self._service = riva.client.SpeechSynthesisService(auth)
+
+    def _create_synthesis_config(self):
+        if not self._service:
+            return
+
+        # warm up the service
+        config = self._service.stub.GetRivaSynthesisConfig(
+            riva.client.proto.riva_tts_pb2.RivaSynthesisConfigRequest()
+        )
+        return config
+
+    async def start(self, frame: StartFrame):
+        """Start the Cartesia TTS service.
+
+        Args:
+            frame: The start frame containing initialization parameters.
+        """
+        await super().start(frame)
+        self._initialize_client()
+        self._config = self._create_synthesis_config()
+        logger.debug(f"Initialized NvidiaTTSService with model: {self.model_name}")
+
     @traced_tts
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
         """Generate speech from text using NVIDIA Riva TTS.
@@ -140,39 +163,43 @@ class NvidiaTTSService(TTSService):
             Frame: Audio frames containing the synthesized speech data.
         """
 
-        def read_audio_responses(queue: asyncio.Queue):
-            def add_response(r):
-                asyncio.run_coroutine_threadsafe(queue.put(r), self.get_event_loop())
+        def read_audio_responses() -> Generator[rtts.SynthesizeSpeechResponse, None, None]:
+            responses = self._service.synthesize_online(
+                text,
+                self._voice_id,
+                self._language_code,
+                sample_rate_hz=self.sample_rate,
+                zero_shot_audio_prompt_file=None,
+                zero_shot_quality=self._quality,
+                custom_dictionary={},
+            )
+            return responses
 
+        def async_next(it):
             try:
-                responses = self._service.synthesize_online(
-                    text,
-                    self._voice_id,
-                    self._language_code,
-                    sample_rate_hz=self.sample_rate,
-                    zero_shot_audio_prompt_file=None,
-                    zero_shot_quality=self._quality,
-                    custom_dictionary={},
-                )
-                for r in responses:
-                    add_response(r)
-                add_response(None)
-            except Exception as e:
-                logger.error(f"{self} exception: {e}")
-                add_response(None)
+                return next(it)
+            except StopIteration:
+                return None
 
-        await self.start_ttfb_metrics()
-        yield TTSStartedFrame()
-
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        async def async_iterator(iterator) -> AsyncIterable[rtts.SynthesizeSpeechResponse]:
+            while True:
+                item = await asyncio.to_thread(async_next, iterator)
+                if item is None:
+                    return
+                yield item
 
         try:
-            queue = asyncio.Queue()
-            await asyncio.to_thread(read_audio_responses, queue)
+            assert self._service is not None, "TTS service not initialized"
+            assert self._config is not None, "Synthesis configuration not created"
 
-            # Wait for the thread to start.
-            resp = await asyncio.wait_for(queue.get(), timeout=NVIDIA_TTS_TIMEOUT_SECS)
-            while resp:
+            await self.start_ttfb_metrics()
+            yield TTSStartedFrame()
+
+            logger.debug(f"{self}: Generating TTS [{text}]")
+
+            responses = await asyncio.to_thread(read_audio_responses)
+
+            async for resp in async_iterator(responses):
                 await self.stop_ttfb_metrics()
                 frame = TTSAudioRawFrame(
                     audio=resp.audio,
@@ -180,10 +207,12 @@ class NvidiaTTSService(TTSService):
                     num_channels=1,
                 )
                 yield frame
-                resp = await asyncio.wait_for(queue.get(), timeout=NVIDIA_TTS_TIMEOUT_SECS)
+
+            await self.start_tts_usage_metrics(text)
+            yield TTSStoppedFrame()
         except asyncio.TimeoutError:
             logger.error(f"{self} timeout waiting for audio response")
             yield ErrorFrame(error=f"{self} error: {e}")
-
-        await self.start_tts_usage_metrics(text)
-        yield TTSStoppedFrame()
+        except Exception as e:
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/openai_realtime/__init__.py

@@ -25,3 +25,13 @@ with warnings.catch_warnings():
         DeprecationWarning,
         stacklevel=2,
     )
+
+__all__ = [
+    "AzureRealtimeLLMService",
+    "InputAudioNoiseReduction",
+    "InputAudioTranscription",
+    "SemanticTurnDetection",
+    "SessionProperties",
+    "TurnDetection",
+    "OpenAIRealtimeLLMService",
+]

src/pipecat/services/openai_realtime_beta/__init__.py

@@ -7,3 +7,13 @@ from .events import (
     TurnDetection,
 )
 from .openai import OpenAIRealtimeBetaLLMService
+
+__all__ = [
+    "AzureRealtimeBetaLLMService",
+    "InputAudioNoiseReduction",
+    "InputAudioTranscription",
+    "SemanticTurnDetection",
+    "SessionProperties",
+    "TurnDetection",
+    "OpenAIRealtimeBetaLLMService",
+]

src/pipecat/services/openpipe/llm.py

@@ -10,7 +10,7 @@ This module provides an OpenPipe-specific implementation of the OpenAI LLM servi
 enabling integration with OpenPipe's fine-tuning and monitoring capabilities.
 """
 
-from typing import Dict, List, Optional
+from typing import Dict, Optional
 
 from loguru import logger
 

src/pipecat/services/ultravox/llm.py

@@ -19,7 +19,6 @@ from typing import Any, Dict, List, Literal, Optional, Union
 
 import aiohttp
 from loguru import logger
-from openai.types import chat as openai_chat_types
 from pydantic import BaseModel, Field
 
 from pipecat.adapters.schemas.tools_schema import ToolsSchema

src/pipecat/services/whisper/stt.py

@@ -33,7 +33,7 @@ if TYPE_CHECKING:
         raise Exception(f"Missing module: {e}")
 
     try:
-        import mlx_whisper
+        import mlx_whisper  # noqa: F401
     except ModuleNotFoundError as e:
         logger.error(f"Exception: {e}")
         logger.error("In order to use Whisper, you need to `pip install pipecat-ai[mlx-whisper]`.")

src/pipecat/sync/base_notifier.py

@@ -8,8 +8,6 @@
 
 import warnings
 
-from pipecat.utils.sync.base_notifier import BaseNotifier
-
 with warnings.catch_warnings():
     warnings.simplefilter("always")
     warnings.warn(

src/pipecat/sync/event_notifier.py

@@ -8,8 +8,6 @@
 
 import warnings
 
-from pipecat.utils.sync.event_notifier import EventNotifier
-
 with warnings.catch_warnings():
     warnings.simplefilter("always")
     warnings.warn(

src/pipecat/transports/daily/transport.py

@@ -759,7 +759,11 @@ class DailyTransportClient(EventHandler):
             # Increment leave counter if we successfully joined.
             self._leave_counter += 1
 
-            logger.info(f"Joined {self._room_url}")
+            participant_id = data.get("participants", {}).get("local", {}).get("id")
+            meeting_id = data.get("meetingSession", {}).get("id")
+            logger.info(
+                f"Joined {self._room_url}. Participant ID: {participant_id}, Meeting ID: {meeting_id}"
+            )
 
             await self._callbacks.on_joined(data)
 

src/pipecat/transports/livekit/transport.py

@@ -539,11 +539,14 @@ class LiveKitTransportClient:
         elif track.kind == rtc.TrackKind.KIND_VIDEO:
             logger.info(f"Video track subscribed: {track.sid} from participant {participant.sid}")
             self._video_tracks[participant.sid] = track
-            video_stream = rtc.VideoStream(track)
-            self._task_manager.create_task(
-                self._process_video_stream(video_stream, participant.sid),
-                f"{self}::_process_video_stream",
-            )
+            # Only process video stream if video input is enabled to prevent
+            # unbounded queue growth when there is no consumer for video frames.
+            if self._params.video_in_enabled:
+                video_stream = rtc.VideoStream(track)
+                self._task_manager.create_task(
+                    self._process_video_stream(video_stream, participant.sid),
+                    f"{self}::_process_video_stream",
+                )
             await self._callbacks.on_video_track_subscribed(participant.sid)
 
     async def _async_on_track_unsubscribed(

src/pipecat/transports/smallwebrtc/connection.py

@@ -14,6 +14,7 @@ for real-time communication applications.
 import asyncio
 import json
 import time
+import uuid
 from typing import Any, List, Literal, Optional, Union
 
 from loguru import logger
@@ -23,7 +24,6 @@ from pipecat.utils.base_object import BaseObject
 
 try:
     from aiortc import (
-        MediaStreamTrack,
         RTCConfiguration,
         RTCIceServer,
         RTCPeerConnection,
@@ -278,7 +278,7 @@ class SmallWebRTCConnection(BaseObject):
 
         self._answer: Optional[RTCSessionDescription] = None
         self._pc = RTCPeerConnection(rtc_config)
-        self._pc_id = self.name
+        self._pc_id = f"{self.name}-{uuid.uuid4().hex}"
         self._setup_listeners()
         self._data_channel = None
         self._renegotiation_in_progress = False

src/pipecat/turns/mute/__init__.py

@@ -22,3 +22,11 @@ with warnings.catch_warnings():
         DeprecationWarning,
         stacklevel=2,
     )
+
+__all__ = [
+    "AlwaysUserMuteStrategy",
+    "BaseUserMuteStrategy",
+    "FirstSpeechUserMuteStrategy",
+    "FunctionCallUserMuteStrategy",
+    "MuteUntilFirstBotCompleteUserMuteStrategy",
+]

src/pipecat/turns/user_stop/turn_analyzer_user_turn_stop_strategy.py

@@ -10,7 +10,6 @@ import asyncio
 from typing import Optional
 
 from pipecat.audio.turn.base_turn_analyzer import BaseTurnAnalyzer, EndOfTurnState
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,

src/pipecat/utils/text/skip_tags_aggregator.py

@@ -14,7 +14,7 @@ as a unit regardless of internal punctuation.
 from typing import AsyncIterator, Optional, Sequence
 
 from pipecat.utils.string import StartEndTags, parse_start_end_tags
-from pipecat.utils.text.base_text_aggregator import Aggregation, AggregationType
+from pipecat.utils.text.base_text_aggregator import Aggregation
 from pipecat.utils.text.simple_text_aggregator import SimpleTextAggregator
 
 

tests/integration/test_integration_unified_function_calling.py

@@ -5,7 +5,6 @@
 #
 
 import os
-from unittest.mock import AsyncMock
 
 import pytest
 from dotenv import load_dotenv

tests/test_get_llm_invocation_params.py

@@ -43,7 +43,6 @@ For AWS Bedrock adapter:
 import unittest
 
 from google.genai.types import Content, Part
-from openai.types.chat import ChatCompletionMessage
 
 from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter
 from pipecat.adapters.services.bedrock_adapter import AWSBedrockLLMAdapter
@@ -51,7 +50,6 @@ from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
 from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
 from pipecat.processors.aggregators.llm_context import (
     LLMContext,
-    LLMSpecificMessage,
     LLMStandardMessage,
 )
 

tests/test_livekit_transport.py

@@ -0,0 +1,124 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Tests for LiveKit transport video stream handling.
+
+Regression tests for issue #3116: Memory leak when video_in_enabled=False
+but video tracks are subscribed. The fix ensures video stream processing
+only starts when there is a consumer for the frames.
+"""
+
+import unittest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+try:
+    from livekit import rtc
+
+    from pipecat.transports.livekit.transport import (
+        LiveKitCallbacks,
+        LiveKitParams,
+        LiveKitTransportClient,
+    )
+
+    LIVEKIT_AVAILABLE = True
+except ImportError:
+    LIVEKIT_AVAILABLE = False
+
+
+@unittest.skipUnless(LIVEKIT_AVAILABLE, "livekit package not installed")
+class TestLiveKitVideoStreamMemoryLeak(unittest.IsolatedAsyncioTestCase):
+    """Regression tests for video queue memory leak (#3116).
+
+    The bug: When video_in_enabled=False, subscribing to a video track would
+    start a producer that fills _video_queue, but no consumer would drain it,
+    causing unbounded memory growth (~3GB/min).
+
+    The fix: Only start video stream processing when video_in_enabled=True.
+    """
+
+    def _create_client(self, video_in_enabled: bool) -> LiveKitTransportClient:
+        """Create a client with the specified video input setting."""
+        params = LiveKitParams(video_in_enabled=video_in_enabled)
+        callbacks = LiveKitCallbacks(
+            on_connected=AsyncMock(),
+            on_disconnected=AsyncMock(),
+            on_before_disconnect=AsyncMock(),
+            on_participant_connected=AsyncMock(),
+            on_participant_disconnected=AsyncMock(),
+            on_audio_track_subscribed=AsyncMock(),
+            on_audio_track_unsubscribed=AsyncMock(),
+            on_video_track_subscribed=AsyncMock(),
+            on_video_track_unsubscribed=AsyncMock(),
+            on_data_received=AsyncMock(),
+            on_first_participant_joined=AsyncMock(),
+        )
+        client = LiveKitTransportClient(
+            url="wss://test.livekit.cloud",
+            token="test-token",
+            room_name="test-room",
+            params=params,
+            callbacks=callbacks,
+            transport_name="test-transport",
+        )
+        client._task_manager = MagicMock()
+        return client
+
+    def _create_mock_video_track(self):
+        """Create a mock video track subscription event."""
+        track = MagicMock()
+        track.kind = rtc.TrackKind.KIND_VIDEO
+        track.sid = "video-track-123"
+        publication = MagicMock()
+        participant = MagicMock()
+        participant.sid = "participant-456"
+        return track, publication, participant
+
+    async def test_disabled_video_input_does_not_start_queue_producer(self):
+        """When video input is disabled, no producer should fill the queue.
+
+        This prevents the memory leak where frames accumulate with no consumer.
+        """
+        client = self._create_client(video_in_enabled=False)
+        track, publication, participant = self._create_mock_video_track()
+
+        await client._async_on_track_subscribed(track, publication, participant)
+
+        # Verify no video processing task was started
+        task_names = [call[0][1] for call in client._task_manager.create_task.call_args_list]
+        video_tasks = [name for name in task_names if "video" in name.lower()]
+        self.assertEqual(video_tasks, [], "No video processing task should be started")
+
+        # Queue should remain empty
+        self.assertEqual(client._video_queue.qsize(), 0)
+
+        # Track metadata should still be recorded
+        self.assertIn(participant.sid, client._video_tracks)
+
+        # Callback should still fire for user code
+        client._callbacks.on_video_track_subscribed.assert_called_once()
+
+    async def test_enabled_video_input_starts_queue_producer(self):
+        """When video input is enabled, the producer should start."""
+        client = self._create_client(video_in_enabled=True)
+        track, publication, participant = self._create_mock_video_track()
+
+        with patch.object(rtc, "VideoStream"):
+            await client._async_on_track_subscribed(track, publication, participant)
+
+        # Verify video processing task was started
+        task_names = [call[0][1] for call in client._task_manager.create_task.call_args_list]
+        video_tasks = [name for name in task_names if "video" in name.lower()]
+        self.assertEqual(len(video_tasks), 1, "Video processing task should be started")
+
+        # Track metadata should be recorded
+        self.assertIn(participant.sid, client._video_tracks)
+
+        # Callback should fire
+        client._callbacks.on_video_track_subscribed.assert_called_once()
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_rnnoise_filter.py

@@ -5,7 +5,7 @@
 #
 
 import unittest
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import AsyncMock
 
 import numpy as np
 

tests/test_rnnoise_resampling.py

@@ -4,7 +4,6 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import asyncio
 import sys
 import unittest
 from unittest.mock import MagicMock, patch

tests/test_stt_mute_filter.py

@@ -10,7 +10,6 @@ from pipecat.frames.frames import (
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     FunctionCallFromLLM,
-    FunctionCallInProgressFrame,
     FunctionCallResultFrame,
     FunctionCallsStartedFrame,
     InputAudioRawFrame,