Remove extra

[Rime] - Update url for Websockets API

CHANGELOG.md

@@ -9,6 +9,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Added `RTVIObserverParams` which allows you to configure what RTVI messages
+  are sent to the clients.
+
+- Added a `context_window_compression` InputParam to
+  `GeminiMultimodalLiveLLMService` which allows you to enable a sliding context
+  window for the session as well as set the token limit of the sliding window.
+
 - Updated `SmallWebRTCConnection` to support `ice_servers` with credentials.
 
 - Added `VADUserStartedSpeakingFrame` and `VADUserStoppedSpeakingFrame`,
@@ -25,10 +32,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `MCPClient`; a way to connect to MCP servers and use the MCP servers'
   tools.
 
-- Added `Mem0 OSS`, along with Mem0 cloud support now the OSS version is also available.
+- Added `Mem0 OSS`, along with Mem0 cloud support now the OSS version is also
+  available.
 
 ### Changed
 
+- The `STTMuteFilter` now mutes `InterimTranscriptionFrame` and
+  `TranscriptionFrame` which allows the `STTMuteFilter` to be used in
+  conjunction with transports that generate transcripts, e.g. `DailyTransport`.
+
 - Function calls now receive a single parameter `FunctionCallParams` instead of
   `(function_name, tool_call_id, args, llm, context, result_callback)` which is
   now deprecated.
@@ -75,6 +87,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- Fixed an issue with `GeminiMultimodalLiveLLMService` where the context
+  contained tokens instead of words.
+
 - Fixed an issue with HTTP Smart Turn handling, where the service returns a 500
   error. Previously, this would cause an unhandled exception. Now, a 500 error
   is treated as an incomplete response.
@@ -196,8 +211,9 @@ https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
 - Fixed an issue in `SmallWebRTCTransport` where an error was thrown if the
   client did not create a video transceiver.
 
-- Fixed an issue where LLM input parameters were not working and applied correctly in `GoogleVertexLLMService`, causing
-  unexpected behavior during inference.
+- Fixed an issue where LLM input parameters were not working and applied
+  correctly in `GoogleVertexLLMService`, causing unexpected behavior during
+  inference.
 
 ### Other
 

docs/api/conf.py

@@ -50,7 +50,6 @@ autodoc_mock_imports = [
     "pyht.protos",
     "pyht.protos.api_pb2",
     "pipecat_ai_playht",  # PlayHT wrapper
-    "vllm",
     "aiortc",
     "aiortc.mediastreams",
     "cv2",
@@ -76,7 +75,6 @@ autodoc_mock_imports = [
     "openpipe",
     "simli",
     "soundfile",
-    # Existing mocks
     "pipecat_ai_krisp",
     "pyaudio",
     "_tkinter",
@@ -87,6 +85,66 @@ autodoc_mock_imports = [
     "pydantic.Field",
     "pydantic._internal._model_construction",
     "pydantic._internal._fields",
+    # Moondream dependencies
+    "torch",
+    "transformers",
+    "intel_extension_for_pytorch",
+    # Ultravox dependencies
+    "huggingface_hub",
+    "vllm",
+    "vllm.engine.arg_utils",
+    "transformers.AutoTokenizer",
+    # Langchain dependencies
+    "langchain_core",
+    "langchain_core.messages",
+    "langchain_core.runnables",
+    "langchain_core.messages.AIMessageChunk",
+    "langchain_core.runnables.Runnable",
+    # LiveKit dependencies
+    "livekit",
+    "livekit.rtc",
+    "livekit_api",
+    "livekit_protocol",
+    "tenacity",
+    "tenacity.retry",
+    "tenacity.stop_after_attempt",
+    "tenacity.wait_exponential",
+    "rtc",
+    "rtc.Room",
+    "rtc.RoomOptions",
+    "rtc.AudioSource",
+    "rtc.LocalAudioTrack",
+    "rtc.TrackPublishOptions",
+    "rtc.TrackSource",
+    "rtc.AudioStream",
+    "rtc.AudioFrameEvent",
+    "rtc.AudioFrame",
+    "rtc.Track",
+    "rtc.TrackKind",
+    "rtc.RemoteParticipant",
+    "rtc.RemoteTrackPublication",
+    "rtc.DataPacket",
+    # Riva dependencies
+    "riva",
+    "riva.client",
+    "riva.client.Auth",
+    "riva.client.ASRService",
+    "riva.client.StreamingRecognitionConfig",
+    "riva.client.RecognitionConfig",
+    "riva.client.AudioEncoding",
+    "riva.client.proto.riva_tts_pb2",
+    "riva.client.SpeechSynthesisService",
+    # Local CoreML Smart Turn dependencies
+    "coremltools",
+    "coremltools.models",
+    "coremltools.models.MLModel",
+    "torch",
+    "torch.nn",
+    "torch.nn.functional",
+    "transformers",
+    "transformers.AutoFeatureExtractor",
+    # Also add specific classes that are imported
+    "AutoFeatureExtractor",
 ]
 
 # HTML output settings
@@ -118,12 +176,25 @@ def verify_modules():
         },
     }
 
+    # Skip importing modules that are in autodoc_mock_imports
+    skipped_modules = set(autodoc_mock_imports)
+
     missing = []
     for category, modules in required_modules.items():
         if isinstance(modules, dict):
             # Handle nested structure
             for subcategory, submodules in modules.items():
                 for module in submodules:
+                    # Check if module is in autodoc_mock_imports
+                    if (
+                        f"pipecat.{category}.{subcategory}.{module}" in skipped_modules
+                        or module in skipped_modules
+                    ):
+                        logger.info(
+                            f"Skipping import of mocked module: pipecat.{category}.{subcategory}.{module}"
+                        )
+                        continue
+
                     try:
                         __import__(f"pipecat.{category}.{subcategory}.{module}")
                         logger.info(
@@ -137,6 +208,11 @@ def verify_modules():
         else:
             # Handle flat structure
             for module in modules:
+                # Check if module is in autodoc_mock_imports
+                if f"pipecat.{category}.{module}" in skipped_modules or module in skipped_modules:
+                    logger.info(f"Skipping import of mocked module: pipecat.{category}.{module}")
+                    continue
+
                 try:
                     __import__(f"pipecat.{category}.{module}")
                     logger.info(f"Successfully imported pipecat.{category}.{module}")

docs/api/requirements.txt

@@ -26,20 +26,23 @@ pipecat-ai[grok]
 pipecat-ai[groq]
 # pipecat-ai[krisp] # Mocked
 pipecat-ai[koala]
-pipecat-ai[langchain]
-pipecat-ai[livekit]
+# pipecat-ai[langchain] # Mocked
+# pipecat-ai[livekit] # Mocked
 pipecat-ai[lmnt]
 pipecat-ai[local]
+# pipecat-ai[local-smart-turn] # Mocked
 # pipecat-ai[mem0] # Mocked
 # pipecat-ai[mlx-whisper] # Mocked
-pipecat-ai[moondream]
+# pipecat-ai[moondream] # Mocked
 pipecat-ai[nim]
 # pipecat-ai[neuphonic] # Mocked
 pipecat-ai[noisereduce]
 pipecat-ai[openai]
 # pipecat-ai[openpipe]
 # pipecat-ai[playht] # Mocked due to grpcio conflict with riva
-pipecat-ai[riva]
+pipecat-ai[qwen]
+pipecat-ai[remote-smart-turn]
+# pipecat-ai[riva] # Mocked
 pipecat-ai[silero]
 pipecat-ai[simli]
 pipecat-ai[soundfile]

examples/foundational/26b-gemini-multimodal-live-function-calling.py

@@ -89,6 +89,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     llm = GeminiMultimodalLiveLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
         system_instruction=system_instruction,
+        transcribe_user_audio=True,
         tools=tools,
     )
 

examples/simple-chatbot/client/react/src/App.tsx

@@ -1,17 +1,18 @@
 import {
   RTVIClientAudio,
   RTVIClientVideo,
+  useRTVIClient,
   useRTVIClientTransportState,
-} from '@pipecat-ai/client-react';
-import { RTVIProvider } from './providers/RTVIProvider';
-import { ConnectButton } from './components/ConnectButton';
-import { StatusDisplay } from './components/StatusDisplay';
-import { DebugDisplay } from './components/DebugDisplay';
-import './App.css';
+} from "@pipecat-ai/client-react";
+import { RTVIProvider } from "./providers/RTVIProvider";
+import { ConnectButton } from "./components/ConnectButton";
+import { StatusDisplay } from "./components/StatusDisplay";
+import { DebugDisplay } from "./components/DebugDisplay";
+import "./App.css";
 
 function BotVideo() {
   const transportState = useRTVIClientTransportState();
-  const isConnected = transportState !== 'disconnected';
+  const isConnected = transportState !== "disconnected";
 
   return (
     <div className="bot-container">
@@ -23,11 +24,31 @@ function BotVideo() {
 }
 
 function AppContent() {
+  const client = useRTVIClient();
   return (
     <div className="app">
       <div className="status-bar">
         <StatusDisplay />
         <ConnectButton />
+        <div
+          className="controls"
+          onClick={async () => {
+            if (!client) {
+              console.error("RTVI client is not initialized");
+              return;
+            }
+            client.action({
+              service: "tts",
+              action: "say",
+              arguments: [
+                { name: "text", value: "Hello, world!" },
+                { name: "interrupt", value: false },
+              ],
+            });
+          }}
+        >
+          <button className="connect-btn">Say something</button>
+        </div>
       </div>
 
       <div className="main-content">

examples/simple-chatbot/server/bot-openai.py

@@ -20,6 +20,7 @@ the conversation flow.
 import asyncio
 import os
 import sys
+from typing import Any, Dict
 
 import aiohttp
 from dotenv import load_dotenv
@@ -32,15 +33,24 @@ from pipecat.frames.frames import (
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     Frame,
+    LLMMessagesAppendFrame,
     OutputImageRawFrame,
     SpriteFrame,
+    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.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.processors.frameworks.rtvi import (
+    ActionResult,
+    RTVIAction,
+    RTVIActionArgument,
+    RTVIObserver,
+    RTVIProcessor,
+    RTVIService,
+)
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -181,8 +191,57 @@ async def main():
         #
         # RTVI events for Pipecat client UI
         #
+
         rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
 
+        rtvi_tts = RTVIService(
+            name="tts",
+            options=[],
+        )
+
+        async def action_tts_say_handler(
+            rtvi: RTVIProcessor, service: str, arguments: Dict[str, Any]
+        ) -> ActionResult:
+            if "interrupt" in arguments and arguments["interrupt"]:
+                # interrupting breaks function handling
+                await rtvi.interrupt_bot()
+            if "text" in arguments:
+                save = arguments["save"] if "save" in arguments else False
+                frame = TTSSpeakFrame(text=arguments["text"])
+                await rtvi.push_frame(frame)
+                if save:
+                    llm_frame = LLMMessagesAppendFrame(
+                        messages=[{"role": "assistant", "content": arguments["text"]}]
+                    )
+                    await rtvi.push_frame(llm_frame)
+
+            return True
+
+        action_tts_say = RTVIAction(
+            service="tts",
+            action="say",
+            result="bool",
+            arguments=[
+                RTVIActionArgument(name="text", type="string"),
+                RTVIActionArgument(name="save_in_context", type="bool"),
+            ],
+            handler=action_tts_say_handler,
+        )
+
+        async def action_tts_interrupt_handler(
+            rtvi: RTVIProcessor, service: str, arguments: Dict[str, Any]
+        ) -> ActionResult:
+            await rtvi.interrupt_bot()
+            return True
+
+        action_tts_interrupt = RTVIAction(
+            service="tts", action="interrupt", result="bool", handler=action_tts_interrupt_handler
+        )
+
+        rtvi.register_service(rtvi_tts)
+        rtvi.register_action(action_tts_say)
+        rtvi.register_action(action_tts_interrupt)
+
         pipeline = Pipeline(
             [
                 transport.input(),

src/pipecat/processors/filters/stt_mute_filter.py

@@ -24,10 +24,12 @@ from pipecat.frames.frames import (
     FunctionCallInProgressFrame,
     FunctionCallResultFrame,
     InputAudioRawFrame,
+    InterimTranscriptionFrame,
     StartFrame,
     StartInterruptionFrame,
     StopInterruptionFrame,
     STTMuteFrame,
+    TranscriptionFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
 )
@@ -175,6 +177,8 @@ class STTMuteFilter(FrameProcessor):
                 UserStartedSpeakingFrame,
                 UserStoppedSpeakingFrame,
                 InputAudioRawFrame,
+                InterimTranscriptionFrame,
+                TranscriptionFrame,
             ),
         ):
             # Only pass VAD-related frames when not muted

src/pipecat/processors/frameworks/rtvi.py

@@ -61,6 +61,9 @@ from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContextFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.llm_service import (
+    FunctionCallParams,  # TODO(aleix): we shouldn't import `services` from `processors`
+)
 from pipecat.transports.base_input import BaseInputTransport
 from pipecat.transports.base_output import BaseOutputTransport
 from pipecat.transports.base_transport import BaseTransport
@@ -392,6 +395,32 @@ class RTVIServerMessageFrame(SystemFrame):
         return f"{self.name}(data: {self.data})"
 
 
+@dataclass
+class RTVIObserverParams:
+    """
+    Parameters for configuring RTVI Observer behavior.
+
+    Attributes:
+        bot_llm_enabled (bool): Indicates if the bot's LLM messages should be sent.
+        bot_tts_enabled (bool): Indicates if the bot's TTS messages should be sent.
+        bot_speaking_enabled (bool): Indicates if the bot's started/stopped speaking messages should be sent.
+        user_llm_enabled (bool): Indicates if the user's LLM input messages should be sent.
+        user_speaking_enabled (bool): Indicates if the user's started/stopped speaking messages should be sent.
+        user_transcription_enabled (bool): Indicates if user's transcription messages should be sent.
+        metrics_enabled (bool): Indicates if metrics messages should be sent.
+        errors_enabled (bool): Indicates if errors messages should be sent.
+    """
+
+    bot_llm_enabled: bool = True
+    bot_tts_enabled: bool = True
+    bot_speaking_enabled: bool = True
+    user_llm_enabled: bool = True
+    user_speaking_enabled: bool = True
+    user_transcription_enabled: bool = True
+    metrics_enabled: bool = True
+    errors_enabled: bool = True
+
+
 class RTVIObserver(BaseObserver):
     """Pipeline frame observer for RTVI server message handling.
 
@@ -404,14 +433,17 @@ class RTVIObserver(BaseObserver):
         are handled by the RTVIProcessor.
 
     Args:
-        rtvi (FrameProcessor): The RTVI processor to push frames to.
+        rtvi (RTVIProcessor): The RTVI processor to push frames to.
+        params (RTVIObserverParams): Settings to enable/disable specific messages.
     """
 
-    def __init__(self, rtvi: FrameProcessor):
+    def __init__(self, rtvi: "RTVIProcessor", *, params: RTVIObserverParams = RTVIObserverParams()):
         super().__init__()
         self._rtvi = rtvi
+        self._params = params
         self._bot_transcription = ""
         self._frames_seen = set()
+        rtvi.set_errors_enabled(self._params.errors_enabled)
 
     async def on_push_frame(
         self,
@@ -438,35 +470,41 @@ class RTVIObserver(BaseObserver):
         # again the next time we see the frame.
         mark_as_seen = True
 
-        if isinstance(frame, (UserStartedSpeakingFrame, UserStoppedSpeakingFrame)):
+        if (
+            isinstance(frame, (UserStartedSpeakingFrame, UserStoppedSpeakingFrame))
+            and self._params.user_speaking_enabled
+        ):
             await self._handle_interruptions(frame)
-        elif isinstance(frame, (BotStartedSpeakingFrame, BotStoppedSpeakingFrame)) and (
-            direction == FrameDirection.UPSTREAM
+        elif (
+            isinstance(frame, (BotStartedSpeakingFrame, BotStoppedSpeakingFrame))
+            and (direction == FrameDirection.UPSTREAM)
+            and self._params.bot_speaking_enabled
         ):
             await self._handle_bot_speaking(frame)
-        elif isinstance(frame, (TranscriptionFrame, InterimTranscriptionFrame)):
+        elif (
+            isinstance(frame, (TranscriptionFrame, InterimTranscriptionFrame))
+            and self._params.user_transcription_enabled
+        ):
             await self._handle_user_transcriptions(frame)
-        elif isinstance(frame, OpenAILLMContextFrame):
+        elif isinstance(frame, OpenAILLMContextFrame) and self._params.user_llm_enabled:
             await self._handle_context(frame)
-        elif isinstance(frame, UserStartedSpeakingFrame):
-            await self._push_bot_transcription()
-        elif isinstance(frame, LLMFullResponseStartFrame):
+        elif isinstance(frame, LLMFullResponseStartFrame) and self._params.bot_llm_enabled:
             await self.push_transport_message_urgent(RTVIBotLLMStartedMessage())
-        elif isinstance(frame, LLMFullResponseEndFrame):
+        elif isinstance(frame, LLMFullResponseEndFrame) and self._params.bot_llm_enabled:
             await self.push_transport_message_urgent(RTVIBotLLMStoppedMessage())
-        elif isinstance(frame, LLMTextFrame):
+        elif isinstance(frame, LLMTextFrame) and self._params.bot_llm_enabled:
             await self._handle_llm_text_frame(frame)
-        elif isinstance(frame, TTSStartedFrame):
+        elif isinstance(frame, TTSStartedFrame) and self._params.bot_tts_enabled:
             await self.push_transport_message_urgent(RTVIBotTTSStartedMessage())
-        elif isinstance(frame, TTSStoppedFrame):
+        elif isinstance(frame, TTSStoppedFrame) and self._params.bot_tts_enabled:
             await self.push_transport_message_urgent(RTVIBotTTSStoppedMessage())
-        elif isinstance(frame, TTSTextFrame):
+        elif isinstance(frame, TTSTextFrame) and self._params.bot_tts_enabled:
             if isinstance(src, BaseOutputTransport):
                 message = RTVIBotTTSTextMessage(data=RTVITextMessageData(text=frame.text))
                 await self.push_transport_message_urgent(message)
             else:
                 mark_as_seen = False
-        elif isinstance(frame, MetricsFrame):
+        elif isinstance(frame, MetricsFrame) and self._params.metrics_enabled:
             await self._handle_metrics(frame)
         elif isinstance(frame, RTVIServerMessageFrame):
             message = RTVIServerMessage(data=frame.data)
@@ -609,6 +647,7 @@ class RTVIProcessor(FrameProcessor):
         self._bot_ready = False
         self._client_ready = False
         self._client_ready_id = ""
+        self._errors_enabled = True
 
         self._registered_actions: Dict[str, RTVIAction] = {}
         self._registered_services: Dict[str, RTVIService] = {}
@@ -648,26 +687,23 @@ class RTVIProcessor(FrameProcessor):
         await self._update_config(self._config, False)
         await self._send_bot_ready()
 
+    def set_errors_enabled(self, enabled: bool):
+        self._errors_enabled = enabled
+
     async def interrupt_bot(self):
         await self.push_frame(BotInterruptionFrame(), FrameDirection.UPSTREAM)
 
     async def send_error(self, error: str):
-        message = RTVIError(data=RTVIErrorData(error=error, fatal=False))
-        await self._push_transport_message(message)
+        await self._send_error_frame(ErrorFrame(error=error))
 
     async def handle_message(self, message: RTVIMessage):
         await self._message_queue.put(message)
 
-    async def handle_function_call(
-        self,
-        function_name: str,
-        tool_call_id: str,
-        arguments: Mapping[str, Any],
-    ):
+    async def handle_function_call(self, params: FunctionCallParams):
         fn = RTVILLMFunctionCallMessageData(
-            function_name=function_name,
-            tool_call_id=tool_call_id,
-            arguments=arguments,
+            function_name=params.function_name,
+            tool_call_id=params.tool_call_id,
+            arguments=params.arguments,
         )
         message = RTVILLMFunctionCallMessage(data=fn)
         await self._push_transport_message(message, exclude_none=False)
@@ -917,12 +953,14 @@ class RTVIProcessor(FrameProcessor):
         await self._push_transport_message(message)
 
     async def _send_error_frame(self, frame: ErrorFrame):
-        message = RTVIError(data=RTVIErrorData(error=frame.error, fatal=frame.fatal))
-        await self._push_transport_message(message)
+        if self._errors_enabled:
+            message = RTVIError(data=RTVIErrorData(error=frame.error, fatal=frame.fatal))
+            await self._push_transport_message(message)
 
     async def _send_error_response(self, id: str, error: str):
-        message = RTVIErrorResponse(id=id, data=RTVIErrorResponseData(error=error))
-        await self._push_transport_message(message)
+        if self._errors_enabled:
+            message = RTVIErrorResponse(id=id, data=RTVIErrorResponseData(error=error))
+            await self._push_transport_message(message)
 
     def _action_id(self, service: str, action: str) -> str:
         return f"{service}:{action}"

src/pipecat/processors/transcript_processor.py

@@ -93,49 +93,55 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
         """Aggregates and emits text fragments as a transcript message.
 
         This method uses a heuristic to automatically detect whether text fragments
-        use pre-spacing (spaces at the beginning of fragments) or not, and applies
-        the appropriate joining strategy. It handles fragments from different TTS
-        services with different formatting patterns.
+        contain embedded spacing (spaces at the beginning or end of fragments) or not,
+        and applies the appropriate joining strategy. It handles fragments from different
+        TTS services with different formatting patterns.
 
         Examples:
-            Pre-spaced fragments (concatenated):
+            Fragments with embedded spacing (concatenated):
                 ```
                 TTSTextFrame: ["Hello"]
-                TTSTextFrame: [" there"]
+                TTSTextFrame: [" there"]  # Leading space
                 TTSTextFrame: ["!"]
-                TTSTextFrame: [" How"]
+                TTSTextFrame: [" How"]    # Leading space
                 TTSTextFrame: ["'s"]
-                TTSTextFrame: [" it"]
-                TTSTextFrame: [" going"]
-                TTSTextFrame: ["?"]
+                TTSTextFrame: [" it"]     # Leading space
                 ```
-                Result: "Hello there! How's it going?"
+                Result: "Hello there! How's it"
 
-            Word-by-word fragments (joined with spaces):
+            Fragments with trailing spaces (concatenated):
+                ```
+                TTSTextFrame: ["Hel"]
+                TTSTextFrame: ["lo "]     # Trailing space
+                TTSTextFrame: ["to "]     # Trailing space
+                TTSTextFrame: ["you"]
+                ```
+                Result: "Hello to you"
+
+            Word-by-word fragments without spacing (joined with spaces):
                 ```
                 TTSTextFrame: ["Hello"]
-                TTSTextFrame: ["there!"]
-                TTSTextFrame: ["How"]
-                TTSTextFrame: ["is"]
-                TTSTextFrame: ["it"]
-                TTSTextFrame: ["going?"]
+                TTSTextFrame: ["there"]
+                TTSTextFrame: ["how"]
+                TTSTextFrame: ["are"]
+                TTSTextFrame: ["you"]
                 ```
-                Result: "Hello there! How is it going?"
+                Result: "Hello there how are you"
         """
         if self._current_text_parts and self._aggregation_start_time:
-            # Heuristic to detect pre-spaced fragments
-            uses_prespacing = False
-            if len(self._current_text_parts) > 1:
-                # Check if any fragment after the first one starts with whitespace
-                has_spaced_parts = any(
-                    part and part[0].isspace() for part in self._current_text_parts[1:]
-                )
-                if has_spaced_parts:
-                    uses_prespacing = True
+            has_leading_spaces = any(
+                part and part[0].isspace() for part in self._current_text_parts[1:]
+            )
+            has_trailing_spaces = any(
+                part and part[-1].isspace() for part in self._current_text_parts[:-1]
+            )
 
-            # Apply appropriate joining method
-            if uses_prespacing:
-                # Pre-spaced fragments - just concatenate
+            # If there are embedded spaces in the fragments, use direct concatenation
+            contains_spacing_between_fragments = has_leading_spaces or has_trailing_spaces
+
+            # Apply corresponding joining method
+            if contains_spacing_between_fragments:
+                # Fragments already have spacing - just concatenate
                 content = "".join(self._current_text_parts)
             else:
                 # Word-by-word fragments - join with spaces

src/pipecat/services/gemini_multimodal_live/events.py

@@ -193,3 +193,10 @@ def parse_server_event(str):
     except Exception as e:
         print(f"Error parsing server event: {e}")
         return None
+
+
+class ContextWindowCompressionConfig(BaseModel):
+    """Configuration for context window compression."""
+
+    sliding_window: Optional[bool] = Field(default=True)
+    trigger_tokens: Optional[int] = Field(default=None)

src/pipecat/services/gemini_multimodal_live/gemini.py

@@ -223,6 +223,16 @@ class GeminiMultimodalLiveUserContextAggregator(OpenAIUserContextAggregator):
 
 
 class GeminiMultimodalLiveAssistantContextAggregator(OpenAIAssistantContextAggregator):
+    # The LLMAssistantContextAggregator uses TextFrames to aggregate the LLM output,
+    # but the GeminiMultimodalLiveAssistantContextAggregator pushes LLMTextFrames and TTSTextFrames. We
+    # need to override this proces_frame for LLMTextFrame, so that only the TTSTextFrames
+    # are process. This ensures that the context gets only one set of messages.
+    # GeminiMultimodalLiveLLMService also pushes TranscriptionFrames, so we need to
+    # ignore pushing those as well, as they're also TextFrames.
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        if not isinstance(frame, (LLMTextFrame, TranscriptionFrame)):
+            await super().process_frame(frame, direction)
+
     async def handle_user_image_frame(self, frame: UserImageRawFrame):
         # We don't want to store any images in the context. Revisit this later
         # when the API evolves.
@@ -265,6 +275,15 @@ class GeminiVADParams(BaseModel):
     silence_duration_ms: Optional[int] = Field(default=None)
 
 
+class ContextWindowCompressionParams(BaseModel):
+    """Parameters for context window compression."""
+
+    enabled: bool = Field(default=False)
+    trigger_tokens: Optional[int] = Field(
+        default=None
+    )  # None = use default (80% of context window)
+
+
 class InputParams(BaseModel):
     frequency_penalty: Optional[float] = Field(default=None, ge=0.0, le=2.0)
     max_tokens: Optional[int] = Field(default=4096, ge=1)
@@ -280,6 +299,7 @@ class InputParams(BaseModel):
         default=GeminiMediaResolution.UNSPECIFIED
     )
     vad: Optional[GeminiVADParams] = Field(default=None)
+    context_window_compression: Optional[ContextWindowCompressionParams] = Field(default=None)
     extra: Optional[Dict[str, Any]] = Field(default_factory=dict)
 
 
@@ -334,7 +354,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
         self._bot_is_speaking = False
         self._user_audio_buffer = bytearray()
         self._bot_audio_buffer = bytearray()
-        self._bot_text_buffer = ""
 
         self._sample_rate = 24000
 
@@ -355,6 +374,9 @@ class GeminiMultimodalLiveLLMService(LLMService):
             "language": self._language_code,
             "media_resolution": params.media_resolution,
             "vad": params.vad,
+            "context_window_compression": params.context_window_compression.model_dump()
+            if params.context_window_compression
+            else {},
             "extra": params.extra if isinstance(params.extra, dict) else {},
         }
 
@@ -414,7 +436,9 @@ class GeminiMultimodalLiveLLMService(LLMService):
     #
 
     async def _handle_interruption(self):
-        pass
+        self._bot_is_speaking = False
+        await self.push_frame(TTSStoppedFrame())
+        await self.push_frame(LLMFullResponseEndFrame())
 
     async def _handle_user_started_speaking(self, frame):
         self._user_is_speaking = True
@@ -437,10 +461,12 @@ class GeminiMultimodalLiveLLMService(LLMService):
         text = await self._transcribe_audio(audio, context)
         if not text:
             return
-        logger.debug(f"[Transcription:user] {text}")
-        context.add_message({"role": "user", "content": [{"type": "text", "text": text}]})
+        # Sometimes the transcription contains newlines; we want to remove them.
+        cleaned_text = text.rstrip("\n")
+        logger.debug(f"[Transcription:user] {cleaned_text}")
+        context.add_message({"role": "user", "content": [{"type": "text", "text": cleaned_text}]})
         await self.push_frame(
-            TranscriptionFrame(text=text, user_id="user", timestamp=time_now_iso8601())
+            TranscriptionFrame(text=cleaned_text, user_id="user", timestamp=time_now_iso8601())
         )
 
     async def _transcribe_audio(self, audio, context):
@@ -561,6 +587,21 @@ class GeminiMultimodalLiveLLMService(LLMService):
                 }
             }
 
+            # Add context window compression if enabled
+            if self._settings.get("context_window_compression", {}).get("enabled", False):
+                compression_config = {}
+                # Add sliding window (always true if compression is enabled)
+                compression_config["sliding_window"] = {}
+
+                # Add trigger_tokens if specified
+                trigger_tokens = self._settings.get("context_window_compression", {}).get(
+                    "trigger_tokens"
+                )
+                if trigger_tokens is not None:
+                    compression_config["trigger_tokens"] = trigger_tokens
+
+                config_data["setup"]["context_window_compression"] = compression_config
+
             # Add VAD configuration if provided
             if self._settings.get("vad"):
                 vad_config = {}
@@ -811,14 +852,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
         if not part:
             return
 
-        text = part.text
-        if text:
-            if not self._bot_text_buffer:
-                await self.push_frame(LLMFullResponseStartFrame())
-
-            self._bot_text_buffer += text
-            await self.push_frame(LLMTextFrame(text=text))
-
         inline_data = part.inlineData
         if not inline_data:
             return
@@ -833,6 +866,7 @@ class GeminiMultimodalLiveLLMService(LLMService):
         if not self._bot_is_speaking:
             self._bot_is_speaking = True
             await self.push_frame(TTSStartedFrame())
+            await self.push_frame(LLMFullResponseStartFrame())
 
         self._bot_audio_buffer.extend(audio)
         frame = TTSAudioRawFrame(
@@ -858,24 +892,20 @@ class GeminiMultimodalLiveLLMService(LLMService):
 
     async def _handle_evt_turn_complete(self, evt):
         self._bot_is_speaking = False
-        text = self._bot_text_buffer
-        self._bot_text_buffer = ""
-
-        if text:
-            await self.push_frame(LLMFullResponseEndFrame())
-
         await self.push_frame(TTSStoppedFrame())
+        await self.push_frame(LLMFullResponseEndFrame())
 
     async def _handle_evt_output_transcription(self, evt):
         if not evt.serverContent.outputTranscription:
             return
 
         text = evt.serverContent.outputTranscription.text
-        if text:
-            await self.push_frame(LLMFullResponseStartFrame())
-            await self.push_frame(LLMTextFrame(text=text))
-            await self.push_frame(TTSTextFrame(text=text))
-            await self.push_frame(LLMFullResponseEndFrame())
+
+        if not text:
+            return
+
+        await self.push_frame(LLMTextFrame(text=text))
+        await self.push_frame(TTSTextFrame(text=text))
 
     def create_context_aggregator(
         self,
@@ -906,6 +936,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
         GeminiMultimodalLiveContext.upgrade(context)
         user = GeminiMultimodalLiveUserContextAggregator(context, params=user_params)
 
-        assistant_params.expect_stripped_words = True
+        assistant_params.expect_stripped_words = False
         assistant = GeminiMultimodalLiveAssistantContextAggregator(context, params=assistant_params)
         return GeminiMultimodalLiveContextAggregatorPair(_user=user, _assistant=assistant)

src/pipecat/services/openai_realtime_beta/context.py

@@ -14,6 +14,7 @@ from pipecat.frames.frames import (
     FunctionCallResultFrame,
     LLMMessagesUpdateFrame,
     LLMSetToolsFrame,
+    LLMTextFrame,
 )
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection
@@ -170,6 +171,14 @@ class OpenAIRealtimeUserContextAggregator(OpenAIUserContextAggregator):
 
 
 class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator):
+    # The LLMAssistantContextAggregator uses TextFrames to aggregate the LLM output,
+    # but the OpenAIRealtimeLLMService pushes LLMTextFrames and TTSTextFrames. We
+    # need to override this proces_frame for LLMTextFrame, so that only the TTSTextFrames
+    # are process. This ensures that the context gets only one set of messages.
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        if not isinstance(frame, LLMTextFrame):
+            await super().process_frame(frame, direction)
+
     async def handle_function_call_result(self, frame: FunctionCallResultFrame):
         await super().handle_function_call_result(frame)
 

src/pipecat/services/rime/tts.py

@@ -74,7 +74,7 @@ class RimeTTSService(AudioContextWordTTSService):
         *,
         api_key: str,
         voice_id: str,
-        url: str = "wss://users-ws.rime.ai/ws2",
+        url: str = "wss://users.rime.ai/ws2",
         model: str = "mistv2",
         sample_rate: Optional[int] = None,
         params: InputParams = InputParams(),

tests/test_stt_mute_filter.py

@@ -12,7 +12,9 @@ from pipecat.frames.frames import (
     FunctionCallInProgressFrame,
     FunctionCallResultFrame,
     InputAudioRawFrame,
+    InterimTranscriptionFrame,
     STTMuteFrame,
+    TranscriptionFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
 )
@@ -100,6 +102,45 @@ class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase):
             expected_down_frames=expected_returned_frames,
         )
 
+    async def test_transcription_frames_with_always_strategy(self):
+        filter = STTMuteFilter(config=STTMuteConfig(strategies={STTMuteStrategy.ALWAYS}))
+
+        frames_to_send = [
+            # Bot speaking - should mute
+            BotStartedSpeakingFrame(),
+            SleepFrame(sleep=0.1),  # Wait for StartedSpeaking to process
+            InterimTranscriptionFrame(
+                user_id="user1", text="This should be suppressed", timestamp="1234567890"
+            ),
+            TranscriptionFrame(
+                user_id="user1", text="This should be suppressed", timestamp="1234567890"
+            ),
+            SleepFrame(sleep=0.1),  # Wait for transcription frames to queue
+            BotStoppedSpeakingFrame(),
+            # Bot not speaking - should pass through
+            InterimTranscriptionFrame(
+                user_id="user1", text="This should pass", timestamp="1234567891"
+            ),
+            TranscriptionFrame(
+                user_id="user1", text="This should pass through", timestamp="1234567891"
+            ),
+        ]
+
+        expected_returned_frames = [
+            BotStartedSpeakingFrame,
+            STTMuteFrame,  # mute=True
+            BotStoppedSpeakingFrame,
+            STTMuteFrame,  # mute=False
+            InterimTranscriptionFrame,  # Only passes through after bot stops speaking
+            TranscriptionFrame,  # Only passes through after bot stops speaking
+        ]
+
+        await run_test(
+            filter,
+            frames_to_send=frames_to_send,
+            expected_down_frames=expected_returned_frames,
+        )
+
     # TODO: Revisit once we figure out how to test SystemFrames and DataFrames
     # async def test_function_call_strategy(self):
     #     filter = STTMuteFilter(config=STTMuteConfig(strategies={STTMuteStrategy.FUNCTION_CALL}))